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Identification of Native 20S Proteasomal Substrates in the Haloarchaeon Haloferax volcanii through Degradomic and Phosph...

Permanent Link: http://ufdc.ufl.edu/UFE0021641/00001

Material Information

Title: Identification of Native 20S Proteasomal Substrates in the Haloarchaeon Haloferax volcanii through Degradomic and Phosphoproteomic Analysis
Physical Description: 1 online resource (236 p.)
Language: english
Creator: Kirkland, Phillip Aaro
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: archaea, degradomic, haloferax, halophilic, proteasome, proteomic
Microbiology and Cell Science -- Dissertations, Academic -- UF
Genre: Microbiology and Cell Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: There exists a considerable void in our understanding of the archaeal domain. These remarkable organisms, dwelling in some of the most extreme and, otherwise uninhabitable environments on earth, may possess, within their unique physiology and biochemistry, answers to many difficult questions about the evolution of life on earth and the connection between single-cellular life and the human race. The subject of our research, Haloferax volcanii, thrives in saline environments around the world, at salt concentrations that exclude most other life forms. Because of its unusual adaptation to harsh extracellular conditions, it may prove to be valuable in an assortment of industrial process. Additionally, H. volcanii and other archaeal species exhibit many common traits with plant or animal cells, making them valuable models for many dynamic cellular processes that occur within the mammalian system. This heightens their value to the research community as potential tools for understanding and controlling various neurodegenerative and autoimmune diseases as well as cancer.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Phillip Aaro Kirkland.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Maupin, Julie A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021641:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021641/00001

Material Information

Title: Identification of Native 20S Proteasomal Substrates in the Haloarchaeon Haloferax volcanii through Degradomic and Phosphoproteomic Analysis
Physical Description: 1 online resource (236 p.)
Language: english
Creator: Kirkland, Phillip Aaro
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: archaea, degradomic, haloferax, halophilic, proteasome, proteomic
Microbiology and Cell Science -- Dissertations, Academic -- UF
Genre: Microbiology and Cell Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: There exists a considerable void in our understanding of the archaeal domain. These remarkable organisms, dwelling in some of the most extreme and, otherwise uninhabitable environments on earth, may possess, within their unique physiology and biochemistry, answers to many difficult questions about the evolution of life on earth and the connection between single-cellular life and the human race. The subject of our research, Haloferax volcanii, thrives in saline environments around the world, at salt concentrations that exclude most other life forms. Because of its unusual adaptation to harsh extracellular conditions, it may prove to be valuable in an assortment of industrial process. Additionally, H. volcanii and other archaeal species exhibit many common traits with plant or animal cells, making them valuable models for many dynamic cellular processes that occur within the mammalian system. This heightens their value to the research community as potential tools for understanding and controlling various neurodegenerative and autoimmune diseases as well as cancer.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Phillip Aaro Kirkland.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Maupin, Julie A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021641:00001


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IDENTIFICATION OF NATIVE 20S PROTEASOMAL SUBSTRATES IN THE
HALOARCHAEON Haloferax volcanii THROUGH DEGRADOMIC AND
PHOSPHOPROTEOMIC ANALYSIS





















By

PHILLIP A. KIRKLAND


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007































O 2007 Phillip Kirkland






































To my mother, Kelly Kirkland; my grandmother Judy Jansen; and to the loving memory of Mr.
and Mrs. L.V. Stevens.









ACKNOWLEDGMENTS

I would like to express my deepest gratitude to Dr. Julie Maupin for her patience and

guidance. Many thanks go to the Maupin lab staff (Matt Humbard, Wei Li, and Guanguin Zhou)

and to former graduates Dr. Chris Reuter and Gosia Gil for all of their help and support. My

sincerest thanks go to my supervisory committee (Dr. James Preston, Dr. Madeline Rasche, Dr.

Richard Lamont, Dr. Peter Kima and Dr. Eric Triplett) for their insight. I owe a debt of gratitude

to John Moore, Phi Do, Dr. Franz St. John, Youngnyun Kim, John Rice and the entire

Microbiology and Cell Science Department. I am also extremely grateful to all of those who

have offered help and support with instrumentation, bioinformatics and general proteomic

endeavors including Dr. Stan Stevens, Scott McClung, Dr. Marjorie Chow and Dr. Alfred Chung

and the entire ICBR staff as well as Dr. Jennifer Busby and Valerie Cavett at Scripps Florida.

Finally, I am eternally grateful for the support and encouragement of my family and friends: my

mom, Kelly Kirkland, my grandmother Judy Jansen, my fiance Shelley Cooper, Jeremy and Matt

Jones, William Knapp, and the Smith, Bright, Blair, Stevens and Jansen families and my great

grandparents, the late Lavert V. and Una B. Stevens.












TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. ...............4.....


LI ST OF T ABLE S ................. ...............8................


LI ST OF FIGURE S .............. ...............9.....


LI ST OF AB BREVIAT IONS ................. ................. 10......... ....


AB S TRAC T ............._. .......... ..............._ 15...


CHAPTER


1 LITERATURE REVIEW .............. ...............17....


Introducti on ............... ... ......_. ._ ...............17......
Proteasome Structure and Function ................. ...............17................
Core Particle Structure and Arrangement............... ..............1
Associated ATPase Regulatory Components ................. ............_ ........ 20.........
The 19S regulatory particle ........._.._ ..... ._._ ...............20...
Proteasome-activating nucleotidases............... .............2
Bacterial ARC ATPase............... ...............22
Other proteasome-associated complexes .............. ...............22....
Protein Degradation ............ _....... ._ ...............23....
Protein Degradation Signals ............ ..... .__ ...............24...
Ubiquitination and other PTM's .............. ...............25....
The N-end rule .............. ...............28....
PE ST sequences ................ .......... ........ ........
Oxidative damage and hydrophobic patch exposure ................. .......................32
Known Substrates and Cellular Influences of the Proteasome ................. ................. .34
Cell cycle............... ....... .............3
Transcription and translation............... ..............3
Cellular metabolism and stress response ................. ...............38........... ...
Proteomics and Biological Mass Spectrometry ................. ...............41........... ...
The Current State of Bottom-Up Proteomics .................... ...............4
Protein/peptide separation ................. .......... ...............43.......
Post-translational modification analysis............... .. ........ ........4
Shotgun proteomics and quantitative, high-throughput strategies ...........................49
Mass spectrometry and bioinformatics .............. ...............53....
Special Considerations for Extreme Proteomes .............. ...............60....
Proteomes with extreme pl value bias............... ...............60..
Hydrophobic proteomes and membrane proteomics............... ...............6
Proj ect Rationale and Design ................. ...............64........... ...











2 METHODS AND MATERIALS................ ...............6


Chemical s and Reagents ........._._...........__. ...............67....
Strains, Plasmids and Culture Conditions .............. ...............67....
Construction of panA Knockout Mutant GG102............... ...............69.
Protein Preparation and Quantification .............. ...............70....
Standard Protein Extraction............... ...............7
Trizol-Mediated Protein Extraction ........._.. ...._._..... ...............71...
Protein M odification................. .... ..............7
Protein Reduction, Alkylation and Tryptic Digestion............... ...............7
Peptide Methyl Esterification ............_ ..... ..__ ...............73...
Phosphoprotein Enrichment and Purification ................. ...............74................
Immobilized Metal Affinity Chromatography .............. ...............74....
Titanium Dioxide Phosphopeptide Enrichment .............. ...............74....
Nickel Purification of Polyhi stadine-Tagged Proteins ................. ......__. ........._...74
2D PAGE Analysis and Imaging .........._.... .....___ ...............75...
Liquid Chromatography and Mass Spectrometry .................. ................ ... .......... ....... ........7
Reversed Phase HPLC Coupled with Nano-ESI-QTOF (QSTAR) MS/MS.............._._.76
Three-Dimensional LCQ Deca lon Trap MS .............. ...............77....
MS Data and Protein Identity Analyses............... ......... .........7
Radioactive 35S Pulse-Chase Labeling of H. volcanii Proteins ............... ...................7
Immunoprecipitation of Radiolabeled H. volcanii Proteins ........._._ ...... ..............79

3 OPTIMIZING ISOELECTRIC FOCUSING OF HALOPHILIC PROTEINS THROUGH
A TRIZOL-BASED SAMPLE PREPARATION METHOD .................... ...............8


Introducti on ..... .................. ...............86.......
Results and Discussion .............. ...............87....


4 EFFECT OF CLASTO-LACTACYSTINT BETA LACTONE ON THE PROTEOME OF

Haloferax volcanii .............. ...............95....

Introducti on ........._._ ...... .. ...............95...
Re sults........._._ ...... .. ...............96...
Discussion ........._..... ...... ...............101...
Ribosomal Proteins............... ...............10

Elongation Factor 1A .............. ...............102....
D J-1/Thi J/Pfpl Superfamily .............. ...............103....
Cell Division............... ...............10
2-Oxoacid Dehydrogenase .............. ...............104....
Dihydroxyacetone Kinase .............. ...............105....
Al dehyde Dehydrogenase ................. ...............105................
Fe-S Cluster Assembly ................. ...............106...............
Divalent M etal Transport .............. ...............106....
Conclusions............... ..............10











5 PHOSPHOPROTEOME ANALYSIS OF PROTEASOME-ACTIVATING
NUCLEOTIDASE A MUTANT OF Haloferax volcanii ......___ ........... ........._....112

Introducti on ................. ...............112................
Results and Discussion ................... .... .............. ........... ...............114
Construction of H. volcanii PanAPPPPP~~~~~~~PPPPPP Mutant GG102 ........._.._... .. .. ......... ........._.._.....114
The 20S Proteasome and PanB Protein Levels Are Not Altered by the panA
M utation ..........._. ... ... ....._.. ........._.. ... .. .... ... .............11
Growth Phenotype of panA Mutant GG102 Compared to Its Parental and
Complemented Strains ........._...... ... ....._.. .. ... .... ..._.. ...........11
Comparative 2-DE Analysis of the panA Mutant to Its Parent ..........._. .........._......116
Phosphoprotein and Phosphopeptide Enrichments ................. ............ ................11 7
Categorization of 'Unique' Proteins into Clusters of Orthologous Groups ..................1 19
Phosphopeptide Identification ................. ....._._ ...............123......
Conclusions............... ..............12


6 ESTABLISHMENT OF A BASELINE PROTEOME OF Haloferax volcanii....................140

Introducti on ........._._ ...... .. ...............140...
Results and Discussion .........._.... ........_._ .......... ............14
Statistical Analysis of the H. volcanii Proteome Map ........._._ ...... .. ..............140
Reannotation of High-Scoring Hypotheticals ................. ........... .....................142
Comparative COG Analysis of the H. volcanii Proteome Map .........._... ..............144
Identification of Paralogs in H. volcanii ...._. ......_._._ .......__. .........14
Conclusion ........._._ ...... __ ...............146...

7 SUMMARY OF RESULTS AND CONTINUING INVESTIGATIONS ................... ...........152


Summary of Results............... ...............15
Continuing Investigations ................. ...............157..............

APPENDIX

A CLASTO LACTACYSTIN BETA LACTONE-TREATED PROTEOME SPOT DATA ....160

B PHOSPHOPEPTIDE TANDEM MASS SPECTRA ................. ...._.. ................ ...175


C COMPLETE Haloferax volcanii PROTEOME MAPPING DATA ................. ................. 183

LI ST OF REFERENCE S ................. ...............2.. 13......... ...

BIOGRAPHICAL SKETCH .............. ...............236....










LIST OF TABLES


Table page

3-1. Advantages of the Trizol extraction method .............. ...............94....

4-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs.
ab sence of the proteasome inhibitor cla~sto-l actacy stin-p -lactone ................ ................. 11 1

5 -1. Phospho-enriched proteins uniquely identified in each strain of H volcanii ................... ...13 3

5-2. Proteins with a minimum 2-fold abundance, comparatively, as determined by spectral
counting .........._. ..... ._ __ ...............137......

5-3. Putative phosphosites identified by MS/MS .............. ...............139....

6-1. Number of proteins mapped in H. volcanii, categorized by experiment ................... .......... 148

6-2. Total protein identifications categorized by contig of origin .............. .....................149

6-3. Division of identified H. volcanii proteins by functional category ........._..... ........_.....150

A-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs.
ab sence of the proteasome inhibitor cla~sto-l actacy stin-p -lactone. ................ .................1 69

C-1. A complete listing of H. volcanii proteins included in current proteome map ........._._......183










LIST OF FIGURES


Figure page


1-1. Proteasomal core particle arrangement .............. ...............65....

1-2. Assorted composition and association combinations for the 20S proteasomal core
particle and the PAN regulatory complexes of Haloferax volcanii. .............. ...................66

3-1i. Two-dimensional PAGE maps of H. volcanii total cell lysate ................. ............. .......90

3 -2. Magnified regions of 2D maps of H volcanii cell lysate ................. .......... ...............9 1

3-3. Magnified regions of Gaussian images of 2D PAGE maps of H. volcanii total lysate.........92

3-4. Gaussian images of 2D PAGE maps of H. volcanii total cell lysate ................. ................93

4-1. Cellular response of H volcanii to proteasomal inhibition. ................ ...................0

4-2. Modified Gaussian 2-DE images ofH. volcanii proteomes isolated from cells grown in
the presence and absence of proteasome inhibitor cLPL.. ....._.__._ .... ....___ ..............109

4-3. Magnified regions of 2-DE proteome maps of H volcanii cells grown in the absence
and presence of cLP L ........._...... ...............110.__... .....

5-1. Western blot analysis of proteasomal subunits a1 and a2 and proteasome-associating
regulatory particles PanA and PanB.. ............ ...............128.....

5-2. A growth curve plotting proliferation of wild type and complemented panA knockout
mutant in H. volcanii ........... ..... .___ ...............129...

5-3. ProQ Diamond phosphoprotein stained 2D PAGE gels of total cell lysate extracted
from H. volcanii. .............. ............... 13 0...

5-4. Proteins identified by mass spectrometry grouped according to COG (clusters of
orthologous groups) database. ............. ...............131....

5-5. Organization of genes linked to PHO regulon which encode proteins altered by or are
related in primary sequence to those altered by the GG102 panA mutation. ..................132

6-1. Comparative COG profiles ofH. volcanii and representatives from other domains. .........151

7-1. 35S pulse-chase and immunoprecipitation of H volcanii PCNA. ............ ....................159

A-1. Magnified regions of 2-DE proteome maps ofH. volcanii cells grown in the absence
and presence of proteasome inhibitor cla~sto-l actacy stin-p -lactone ................. ................1 60

B-1. MS/MS spectra of phosphorylated peptides identified in H. volcanii .............. ..... ........._.175









LIST OF ABBREVIATIONS

16-BAC 1 6-b enzyl di methyl -n-hexadecyl amm onium chl ori de

2DE Two-dimensional electrophoresis

AAA+ Atpase Associated with Various Cellular Activities

AAEE Acryloylaminoethoxyethanol

ABC Atp binding cassette

ACN Acetonitrile

ALDH Aldehyde dehydrogenase

Amp Ampicillin

AP Activator protein

AQUA Absolute quantification

ARC AAA+ atpase Forming Ring-Shaped Complexes

ATCC American type culture collection

ATP Adenosine triphosphate

BN-PAGE Blue native polyacrylamide gel electrophoresis

Cdc Cell division control

Cdk Cyclin-dependent kinase

CE Capillary electrophoresis

CHAPS 3 -Cholamidopropyl dimethylammonio-1 -propanesulfonate

CID Collision-induced dissociation

CILAT Cleavable isobaric labeled affinity tag

cL[3L Cla sto-Lactacy stin-b eta-l actone

CN-PAGE Colorless native polyacrylamide gel electrophoresis
COG Clusters of Orthologous Groups









CP Core particle

CSN Cop9 signalosome

CTAB Cetyl trimethyl ammonium bromide

DHA Dihydroxy acetone

DMA Dimethylacrylamide

DMSO Dimethyl sulfoxide

DTT Dithiothreitol

ER Endoplasmic reticulum

ERAD Endoplasmic reticulum associated degradation

Fc Antibody constant domain

FT-ICR Fourier transform ion cyclotron resonance

Fv Antibody variable domain

GAPDH Glyceraldehyde-3 -phosphate dehydrogenase

HMG CoA Hydroxymethylglutaryl coenzyme a

HPLC High performance liquid chromatography

Hsp Heat shock protein

ICAT Isotope-coded affinity tag

IDA Information-dependent acquisition

IEF Isoelectric focusing

IMAC Immobilized metal affinity chromatography

IPG Immobilized ph Gradient

ISC Iron-sulfur cluster

Kan Kanamycin









LB Luria-bertani media

LC Liquid chromatography

m/z Mass-to-charge ratio

MALDI Matrix-assisted laser desorption ionization

MCM Mini-chromosome maintenance

Mev Mevinolin

MOAC Metal oxide affinity chromatography

mODC Mouse Ornithine Decarboxylase

MS Mass spectrometry

MS/MS Tandem mass spectrometry

MudPIT Multidimensional protein identificaiton technology

MW Molecular weight

NEPHGE Non-Equilibrium ph Gradient Electrophoresis

NHS N-hydroxysuccinimidyl ester

Nov Novabiocin

OADH 2-oxoacid dehydrogenase

OD Optical density

ORC Origin recognition complex

ORF Open reading frame

PAGE Polyacrylamide gel electrophoresis

PAN Proteasome-activating nucleotidase

PCNA Proliferating cell nuclear antigen

PCR Polymerase chain reaction










PDDAC Polydiallyldimethylammonium chloride

PEST Proline, Glutamic acid, Serine and Threonine

pl Isoelectric point

PTM Post-translational modification

PTS Phosphotransferase system

Quadrupole

QIT Quadrupole ion trap

QTOF Quadrupole-Time of Flight

RC Replication complex

ROS Reactive oxygen species

RP Regulatory particle

RP Reversed phase

RPA Replication protein a

Rpn Regulatory particle non-atpase

Rpt Regulatory particle triple-A atpase type I

SCX Strong cation exchange

SDS Sodium dodecal sulfate (sodium laurel sulfate)

SILAC Stable Isotope Labeling by Amino Acids in Cell Culture

TEA Triethanolamine

TFA Trifluoro acetic acid

TNF Tumor necrosis factor

TOF Time of Flight

ULS Universal linkage system










UPR Unfolded protein response
Ultraviolet

VCP Valosin-containing protein

YPC Yeast peptone caseamino acid media

ZE-FFE Zone electrophoresis-free flow electrophoresis









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

IDENTIFICATION OF NATIVE 20S PROTEASOMAL SUBSTRATES IN THE
HALOARCHAEON Haloferax volcanii THROUGH DEGRADOMIC AND
PHOSPHOPROTEOMIC ANALYSIS

By

Phillip A. Kirkland

December 2007

Chair: Julie A. Maupin-Furlow
Major: Microbiology and Cell Science

The fact that protein levels in the archaeal cell frequently do not correlate with transcript

levels suggests the importance of understanding the details of post-transcriptional regulation. In

addition, there is a widespread distribution of proteasome machinery among the archaea and a

noticeable absence of other energy-dependent proteases. This suggests that archaeal

proteasomes are key factors in controlling the quality and quantity of proteins within the cell and

are a driving force behind protein turnover. At present, the details of how proteins are

recognized for degradation by the proteasome in prokaryotes remain poorly understood. The

lack of an ubiquitination system in archaea complicates the identification of native substrates;

however, recent developments in degradomics (comparative proteomics of protease mutants)

reveal a promising future in the investigation of phosphorylated proteins as potential native

substrates. With at least 30 unique phosphoproteins (detected from a total proteome through the

use of a phospho-specifie dye) that accumulate within the proteome of a proteasome-activating

nucleotidase (PanA) -deficient mutant, evidence is mounting that supports our hypothesis that

protein phosphorylation plays a key role in mediating substrate recognition and degradation by

the PanA/proteasome complex.











Our efforts have focused on high-throughput degradomic and phosphoproteomic analysis of the

H. volcanii proteome in order to identify multiple candidates for native degradation targets of the

20S proteasome and the assembly of a list of the ten most supported putative substrates based on

overlapping proteomic data and literary support beyond the archaeal domain. To date, we have

identified a wide consortium of proteins with likely involvement in the 20S proteasomal

degradation pathway. These candidates represent a myriad of cellular functions ranging from

cell division, translational and transcriptional control to supplementary proteolysis, circadian

rhythm and central metabolism.











CHAPTER 1
LITERATURE REVIEW

Introduction

The structure and function of the proteasomal machinery across eukaryotic, bacterial and

archaeal domains of life will be presented in this chapter. In addition, the modes of protein

substrate recognition and degradation by these cellular components and other commonly

encountered cellular proteases will be summarized. This review of current literature in the

disciplines of microbiology, molecular biology, proteomics and bioinformatics will also outline

various post-translational modifications made to proteins and how these influence protein

stability and degradation. The most current technical approaches for analyzing total cell

proteomes and phosphoproteomes in a high-throughput capacity will also be reviewed.

Proteasome Structure and Function

Proteasomes are energy-dependent barrel-shaped proteases that can be found across

eukaryotic and archaeal domains and, in a more limited capacity, in the eubacterial domain

(existing primarily in the actinomycetes) (Volker and Lupas, 2002; Wolf and Hilt, 2004; De Mot,

2007). They are key quality control factors in the cell, assuming the responsibility of degrading

damaged or misfolded proteins (Kostova and Wolf, 2003). Additionally, the proteasome exerts a

regulatory role in a myriad of essential cellular functions as evidenced by the mediation of the

turnover of proteins functioning in translation, oxidative damage repair, cell division, DNA

replication, metabolism and antigen presentation (Guo et al., 1995; Gillette et al., 2001; Liu et

al., 2002; Xu-Welliver and Pegg, 2002; Blanchard et al., 2002; Yamamoto et al., 2004; Baugh

and Pilipenko, 2004; Rock et al., 2004; Takahashi et al., 2005; Harada et al., 2005; Chuang and

Yew, 2005; Hershko, 2005; Gallego et al., 2006; Perales et al., 2006). In higher eukaryotic

systems, the proteasome has also been shown to play a role in signal transduction, apoptosis and









the development of several neurodegenerative diseases such as ALS, Alzheimer' s and

Parkinson's as well as the generation of and therapy for numerous forms of cancer (Kabashi and

Durham, 2006; Hung et al., 2006; van Leeuwen et al., 2006; Olanow and McNaught, 2006;

Zavrski et al., 2007; Meyer et al., 2007; Stapnes et al., 2007).

Core Particle Structure and Arrangement

The proteasome is often defined by a characteristic barrel-shaped 20S core particle (CP)

and an associated ATPase regulatory component (Maupin-Furlow et al., 2006). The core particle

is a series of 4 stacked heptameric rings each consisting entirely of either alpha-type or beta-type

subunits (Fig. 1-1). In the yeast 26S proteasome, these rings are heteroheptamers, composed of

seven different protein subunits while those characterized in prokaryotes have been found to be

homoheptameric, consisting of seven identical subunits (Zuhl et al., 1997b; Kaczowka and

Maupin-Furlow, 2003; Groll and Huber, 2005). Proteasomes of the archaeal domain are often

composed of a single alpha-type and one or two beta-type subunits (Zuhl et al., 1997a). There

are some exceptions within the haloarchaeal clade, one of which being Haloferax volcanii, which

possesses two distinct alpha-type subunits and a single beta-type subunit (De Mot et al., 1999;

Kaczowka and Maupin-Furlow, 2003) (Fig. 1-2). Proteasomes of the higher eukaryotes are

notably more complex due, not only to their mixed subunit arrangements but also to their

tendency to produce additional, sub-specialized proteasomal complexes such as the

immunoproteasome in mammals (Groettrup et al., 2001; Ahtoniemi et al., 2007). Coincidentally,

some archaea also produce subspecialized proteomes given a demonstrated shift in proteasome

subunit and regulatory particle isoforms, dependent on growth phase, as is the case with H.

volcanii (Kaczowka and Maupin-Furlow, 2003).

The stacked rings of the proteasome core particle are generally arranged with the beta-

type rings paired together forming the central proteolytic core. This beta ring core is flanked on









both ends by alpha-type rings that form gates. The cylinder measures approximately 15 nm in

height and 12 nm in width, according to crystallographic analysis of both yeast and

Thermopla~sma acidophihtm CPs (Lowe et al., 1995; Groll and Huber, 2005). Once assembled

into the 20S complex, a cross-sectional view reveals the formation of three distinct chambers

within the proteasome. Two ante-chambers are formed at the interface between the alpha and

beta rings. These ante-chambers are entered through restrictively narrow, gated axial pores (~1.3

nm) which ensure exclusion of folded proteins and create the necessity for a substrate

recognition system which provides unfoldase and ATP-dependent translocation activities making

protein entrance into the barrel possible (Groll et al., 2000; Benaroudj et al., 2001; Kohler et al.,

2001a). The N-termini of the alpha subunits are thought to guard the entrance to the 20S

proteasome and require an associated regulatory particle (RP) for ATP-dependent opening of the

pore (Kohler et al., 2001b). Past studies have shown that deletion of the N-terminal region of the

alpha subunits notably reduces the selectivity of the proteasome and creates an uncontrolled

opening for folded substrate passage (Kohler et al., 2001a; Groll and Huber, 2005).

Furthermore, N-terminal acetylation of the alpha subunits has been suggested to enhance the

alpha ring's ability to restrict unregulated substrate entrance into the proteasome aperture

(Kimura et al., 2000; Humbard et al., 2006).

The central chamber formed at the interface of the beta rings represents the catalytic

center of the proteasome. It houses the N-terminal threonine residues of each beta subunit,

which, after exposure due to an autocatalytic processing step, mediate the nucleophilic attack of

substrate peptide backbones and provides the proteasome with its tryptic, chymotryptic and post-

acidic cleavage capabilities (Lowe et al., 1995; Brannigan et al., 1995). This combination of

proteolytic activities results in the release of degradation products of 3 to 24 amino acids in










length which can, in turn, be recycled to amino acids or smaller products by other cellular

peptidases (Akopian et al., 1997; Kisseley et al., 2006).

Associated ATPase Regulatory Components

Proteasomes are energy-dependent proteases that require ATP-hydrolyzing accessory

components for the capture, unfolding and translocation of protein substrates into their catalytic

core. The specific function and composition of each associated ATPase is variable among the

domains and physiological subsystems; however, some degree of relatedness is evident when

considering the general arrangement of these complexes and their roles across all three domains

(Fenton and Horwich, 1997; Bukau and Horwich, 1998; Glickman et al., 1999). Categorically,

these accessory complexes belong to the AAA+ superfamily of proteins (ATPases associated

with diverse cellular activities) which can also serve the cell by mediating any number of non-

proteolytic tasks ranging from control of the cell cycle to vesicle-mediated transport (Ogura and

Wilkinson, 2001; Lupas and Martin, 2002).

The 19S regulatory particle

The ATP-dependent regulatory ATPase known as the 19S cap (or PA700), found in

eukaryotic 26S proteasomal complexes, is one of the more complex examples of an AAA+

regulatory particle. It consists of 17 subunits which compose two substructures: the "lid" and the

"base" (Finley et al., 1998; Glickman et al., 1998). Of these 17 structural components, 6 of them

are classified as regulatory particle triple-A type I (Rpt) proteins and are of the AAA+

superfamily (Glickman et al., 1998). These Rpt subunits collectively compose the "base"

structure of the cap and are responsible for the unfolding of globular proteins for degradation by

the proteasome (Glickman et al., 1998). The "base" structure also possesses three regulatory

particle non-ATPase (Rpn) subunits Rpn1, Rpn2 and Rpnl0; the latter of which is thought to

form a hinge or common anchor point for both substructures (Glickman et al., 1998). The "lid"









structure is required for ubiquitin-tagged substrate binding and degradation (Glickman et al.,

1998). This "lid" is composed of the remainder of the 17 subunits of the 19S cap and consists of

a heterogeneous combination of Rpn proteins (Rpn3, Rpn5-9, Rpnl1 and Rpnl2) (Glickman et

al., 1998). This full 19S cap structure in association with the 20S core particle yields a 30S

structure in which the catalytic cylinder is capped at both ends or, alternatively, a 26S structure

in which only a single CP terminus is cap-associated (Hendil and Hartmann-Petersen, 2004).

Proteasome-activating nucleotidases

Some members of the archaeal domain also possess ATPase regulatory particles that are

very similar in structure and function to the base structure of the 19S cap in eukaryotes (Zwickl

et al., 1999; Wilson et al., 2000). These RPs are known as PAN (Proteasome-Activating

Nucleotidases) and are composed of subunits that are homologous to the eukaryotic Rpt proteins.

Generally, archaea encoding PAN proteins produce at least one isoform, however, in some cases,

such as Haloferax volcanii, multiple subtypes have been identified (Reuter et al., 2004). It is

evident that PAN proteins are able to both associate with the proteasome and unfold/translocate

proteins into the catalytic chamber without the aid of other components, therefore it is believed

that PAN is capable of recognizing and binding select substrates (Goldberg, 1990). In actuality,

it is the binding of the substrate to PAN that stimulates ATP hydrolysis, unfoldase activity and

subsequent movement of the polypeptide into the proteolytic center of the proteasome

(Benaroudj et al., 2003).

Characterization of both M~ethanococcus jnannaschii and Haloferax volcanii PAN

complexes reveal seemingly conserved features among proteins of this type in the archaeal

domain. Both PAN isoforms possess well-conserved Walker A and Walker B motifs, each of

which are predicted to function in magnesium binding and ATP hydrolysis (Wilson et al., 2000;

Reuter et al., 2004). In the M. janna~schii model, PAN also appears to assemble into









dodecameric complexes which, in turn, associate with the 20S proteasome (Zwickl et al., 1999;

Wilson et al., 2000).

While there are examples of proteins with ubiquitin-like folds or ubiquitin-associated

domains in prokaryotes, there has yet to be a true ubiquitin protein found to be encoded in any

prokaryotic genome; therefore the signal influencing substrate binding by PAN is unclear

(Bienkowska et al., 2003; Spreter et al., 2005). There are, however, several theoretical

possibilities that will be discussed in future sections.

Bacterial ARC ATPase

The Bacteria domain is no exception with regards to having a proteasome as well as an

ATPase regulatory component. An assembly known as ARC (AAA ATPase forming Ring-

shaped Complexes) is found in multiple gram-positive bacteria such as Rhodococcus and

Streptomyces species (Nagy et al., 1998; Wolf et al., 1998; De Mot, 2007; De Mot et al., 2007).

It was shown that ARC forms homohexameric rings that associate with the 20S proteasome with

general similarity in structure and function to Lon proteases as well as ClpA and ClpX ATPases

(Wolf et al., 1998). Interestingly, it was also noted that ARC is a divergent relative of the AAA

ATPase family which deviates from the bacterial branch of proteasome/proteasome-associated

proteins much earlier than most other, more familiar AAA representatives of the eubacterial

domain, such as the FtsH homologs (Wolf et al., 1998). This indicates a closer relationship,

evolutionarily, between ARC and archaeal AAA+ ATPases such as PAN compared to other

bacterial counterparts.

Other proteasome-associated complexes

There is an assortment of other ATPase and non-ATPase complexes that associate with

the proteasome to modulate function in some capacity. One such complex is the COP9

signalosome (CSN) found in higher eukaryotes. CSN is an eight-membered complex (Csn1-8)









which possesses domains that indicate its relationship to other eukaryotic regulatory components

such as translation initiation factor 3 (elF3) and the lid of the 19S proteasome cap (Wei and

Deng, 2003; Harari-Steinberg and Chamovitz, 2004). From a functional standpoint, CSN plays

vital roles in both plant and insect development and may act as a "switchboard" that is

responsible for the coordination of protein kinases and kinase substrates to ubiquitin ligase

proteins, linking protein phosphorylation to the poly-ubiquitination-proteasomal degradation

pathway (Harari-Steinberg and Chamovitz, 2004).

Another notable group of proteasome-related proteins are the cell division control 48

(Cdc48) homologs, which can be found across all three domains. In eukaryotes, these AAA

ATPases function in multiple ways, including endoplasmic reticulum-associated protein

degradation (ERAD) and nuclear envelope formation (Rabinovich et al., 2002; Bar-Nun, 2005).

Eukaryotic Cdc48/VCP/p97 has also been shown to exhibit segregase activity by removing

ubiquitinated substrates from larger complexes possessing unmarked subunits and unfolding

them for proteasomal degradation (Golbik et al., 1999; Jentsch and Rumpf, 2007). In the

archaea, Cdc48 homologs may operate in the proteasomal substrate recognition and unfolding

aspect where PAN complexes are absent (Maupin-Furlow et al., 2004). Cdc48 homologs such as

valosine-containing protein (VCP), valosine-containing protein-like ATPase from

Thermoplasma. acidophihtm (VAT) and p97 all form stacked hexameric ring structures that are

somewhat similar to other ring-shaped nucleotidases of the AAA superfamily (Frohlich et al.,

1995; Rockel et al., 1999).

Protein Degradation

In the past two decades, considerable insight has been gained into how energy-dependent

proteolysis works throughout all domains. Much of the machinery has been identified and

significant progress has been made towards resolving details of assembly and mechanistics.









More attention is now turning towards gaining a clearer understanding of the "what" and "why"

aspects of proteolysis. Questions still remain about the structural motifs or modifications that

make proteins recognizable to these degradation systems, which proteins are under control of

these systems and what effect does the degradation of these proteins have on global cell

physiology. The following sections provide some insight into what is known on these topics to

date.

Protein Degradation Signals

The Eukarya, Bacteria and Archaea domains have adapted an array of mechanisms

designed to ear-mark protein substrates for degradation by a diverse collection of proteases

within the cell. Often these protein substrates are designated by some form of post-translational

modification; such is the case with eukaryotic polyubiquitin-tagging systems. Other times,

intrinsic signals are used to promote recognition of proteins destined to be destroyed or

processed. These signals include internal sequences such as PEST and conserved degrons as

with the N-end rule in bacteria. Yet another strategy for promoting substrate degradation is the

use of molecular adapters to tether substrate proteins to their appropriate protease complex

(Chien et al., 2007). This has been demonstrated in Escherichia coli where RssB and SspB serve

as adapters mediating interaction and degradation between ClpX and substrate proteins such as

sigma factors (Levehenko et al., 2005; Chien et al., 2007). Development of these regulatory

systems may be necessary for controlled regulation of certain cellular subsystems through the

careful selection of proteins to be overturned. Such regulatory discipline is demonstrated in the

controlled degradation of specific targets involved in cell division of higher plants and animals or

the degradative processing of proteins for presentation by maj or histocompatability complexes in

the immune system (Rock et al., 2004; Hershko, 2005). In many cases, the signaling system for

degradation is less regulated and complex. Many proteins are targeted for destruction simply









through the act of becoming misfolded or damaged. Oxidation and improper exposure of

internal, hydrophobic residues may provide the cue for proteolysis (Grune et al., 2003). This

section will discuss known mechanisms for introducing substrate into a proteolytic system as

well as specific proteins and functional categories that are targeted by the proteasome and the

influence these events have on the cell.

Ubiquitination and other PTM's

Post-translational modification is used by the cell to toggle between functional states and

to create sub-populations of proteins. One of the most commonly studied sub-proteomes in the

eukaryotic cell is that which is designated for proteasomal degradation by ubiquitination.

Ubiquitin is a 76-residue polypeptide that is present in virtually all eukaryotic cells (Ciechanover

and Schwartz, 1994). The ubiquitination of proteins is primarily managed by a system of

activating, carrier and ligase components collectively referred to as the ubiquitin cascade. The

process of ubiquitination starts with the activation of the C-terminal glycine of the ubiquitin

polypeptide by the first member of the cascade system, El (Ciechanover and Schwartz, 1994).

The El or ubiquitin-activating enzyme converts the glycine residue into a high-energy thiol-ester

intermediate in an ATP-dependent manner (Ciechanover and Schwartz, 1994). The next step in

the cascade involves the ubiquitin carrier protein E2 which removes the activated unit from El

and delivers it to an ubiquitin ligase complex known as E3 (Ciechanover and Schwartz, 1994).

Subsequently, the E3 complex carries out the last step in the ubiquitin ligation process; addition

of ubiquitin to the amino side chains of lysine residues within the target protein or continued

addition of ubiquitin subunits to a growing polyubiquitin chain (Ciechanover and Schwartz,

1994; Gilon et al., 1998). In some cases, ubiquitin is transferred to the substrate directly by E2,

however, this variation of the ligation process usually involves monoubiquitination and does not









usually serve a role in proteolysis but rather in functional modification (Ciechanover and

Schwartz, 1994).

There are numerous examples in bacteria, yeast and higher eukaryotes of structural

signals that impart instability of proteins and direct them into a degradation pathway (Gilon et

al., 1998). An interesting example of sequence and/or structural signals that influence

ubiquitination and degradation is the destruction box (D-box) found in mitotic cyclins. This 9-

amino acid sequence found in the N-terminal domain has been shown to destabilize proteins and

lead directly to ubiquitin ligation and degradation by the proteasome in mammalian cells (King

et al., 1996). Similarly, in Saccharomyces cerevisiae, the C-terminal domain SINNDAKSS of

the alpha-factor pheromone receptor Ste2p has been determined to be necessary for

ubiquitination and internalization into the endomembrane system where vacuolar degradation

follows (Hicke et al., 1998). Destabilizing motifs are not always constitutively available for

ubiquitination as demonstrated by the Unfolded Protein Response (UPR)-associated transcription

factor Haclp, which undergoes alternative splicing at the mRNA level to preferentially expose

either a stabilizing C-terminus or a C-terminal domain which leads to degradation (Cox and

Walter, 1996).

While intrinsic destabilizing signals in both eukaryotes and bacteria appear to be

commonplace, they are not the only aiding forces at work in the cells effort to recognize and

overturn protein substrates. There are numerous examples of both protein phosphorylation and

acetylation serving as modulators of ubiquitination and degradation in eukaryotes. G protein-

coupled receptors such as the Ste2p mentioned previously have been demonstrated to be

hyperphosphorylated at a C-terminal domain; an event that appears to be necessary for ubiquitin

ligation and subsequent degradation (Hicke et al., 1998). A much more common example of the









cooperative phosphorylation-ubiquitination relationship is found in the circadian regulatory

systems of plants and higher eukaryotes. In Arabidopsis, the phosphorylated form of the CK2

regulatory subunit CKB4 in its light-dependent biological clock system is preferentially

ubiquitinated and degraded in a proteasome-dependent manner (Perales et al., 2006).

Interestingly, phosphorylation has also been shown to have the opposite effect on protein

stability when considering its role in stabilizing the small G protein RhoA from ubiquitination

and proteasomal degradation through modification of serine 188 in a vascular muscle cell model

(Rolli-Derkinderen et al., 2005). The interplay between protein phosphorylation and

ubiquitination/degradation is even more evident when considering the COP9 signalosome (CSN)

and its coordinated association with a multitude of kinases such as CK2 and PKD, which are

known to phosphorylate the p53 tumor suppressor and enhance interaction with the Mdm2

ubiquitin ligase (Harari-Steinberg and Chamovitz, 2004). In contrast, the CSN also coordinates

kinases for the modification of c-Jun; a subunit of the AP-1 transcription complex (Harari-

Steinberg and Chamovitz, 2004). This event serves to stabilize c-Jun against ubiquitination and

degradation by the proteasome (Harari-Steinberg and Chamovitz, 2004).

In addition to protein phosphorylation, acetylation also affects the stability of certain

proteins in the presence of an ubiquitin-proteasome degradation system. In 1989 it was

discovered that N-terminal acetylation of proteins in reticulocyte lysate interacted directly with

ubiquitin activating enzymes and served as a beacon for ubiquitination and subsequent

proteasomal degradation (Mayer et al., 1989). Several years later, it was also discovered that N-

terminally acetylated proteins required a separate factor for interaction with the

ubiquitin/proteasome machinery (Gonen et al., 1994). This factor was determined to be the

alpha subunit of the translation elongation factor EF-1 and was ultimately determined to be an










isopeptidase that presumably functions to "trim" the N-terminally blocked proteins into more

manageable or recognizable fragments for degradation by the 26S proteasome (Gonen et al.,

1996). N-terminal acetylated substrates of the proteasome may also be recognized by an entirely

separate group of ubiquitin ligases which recognize internal sequences downstream of the

modified N-terminus (Bartel et al., 1990).

The N-end rule

It has been acknowledged for a number of years that there exists a real relationship

between the N-terminal amino acid residue of a given protein and its stability in the presence of

proteolytic machinery (Varshavsky, 1996). This phenomenon is known as the N-end rule and

has been demonstrated in yeast, mammals, plants and even bacteria (Varshavsky, 1996). The N-

end method of bacterial substrate recognition is well conserved among eukaryotes, despite the

major differences in molecular components that comprise each system (Mogk et al., 2007). As a

whole, the N-end protein destabilization/degradation pathway is surprisingly extensive and

meticulously regulated.

The eukaryotic N-end rule pathway naturally represents the more complicated version

compared to bacteria as it serves as an intermediary substrate recognition system for the

exclusively eukaryotic ubiquitin/proteasome degradation system. It consists of a hierarchical N-

terminal degradation signal (N-degron) and a specialized subset of ubiquitin ligases (N-recognin)

in conjunction with 26S proteasomal machinery (Varshavsky, 1996; Mogk et al., 2007).

Destabilizing residues for mammalian and yeast cells have been categorized as either type one

(Arg, Lys and His) or type two (Phe, Leu, Trp, Ile and Tyr), which corresponds to one of two

ubiquitin ligase (N-recognin) binding sites (Varshavsky, 1996). Degradation signals in

eukaryotic systems are typically accompanied by internal lysine residues to accommodate










ubiquitin chain formation (Mogk et al., 2007). Additionally, plant systems expand their list of

destabilizing residues to include basic and aromatic amino acids as well (Mogk et al., 2007).

Co-translational, methionine aminopeptidase-mediated removal of the N-terminal and

naturally stable methionine residues (or formyl-methionine in bacteria) from newly synthesized

proteins occurs only in the presence of a stable penultimate residue (Gly, Cys, Ala and Ser)

(Mogk et al., 2007). There are, however, other methods employed by the cell to regulate the

stability of certain proteins based on terminal residue exposure. One alternative method includes

the endoproteolytic cleavage of a protein to expose an otherwise internal degron (Mogk et al.,

2007). A variation of this mechanism has been observed in the processing of viral polyproteins

to generate destabilized products such as the HIV-1 integrase or the Sindbis virus RNA

polymerase (de Groot et al., 1991; Mulder and Muesing, 2000).

In eukaryotes, the canon ofN-degron signals is a three-tiered hierarchy. The first tier,

consisting of primary destabilizing residues, requires no modification other than exposure

through proteolytic cleavage or methionine removal and binding to N-recognin or E3

(Varshavsky, 1996). The second tier, termed the secondary destabilizing residues include Asp

and Glu and, in the case of mammalian cells, Cys (Varshavsky, 1996). These residues become

destabilizing only after modification by an arginyl-tRNA-protein transferase (R-transferase)

(Varshavsky, 1996). This category gives way to a third tier known as tertiary destabilizing

residues which include Asn and Gin (Varshavsky, 1996). Like the secondary destabilizers, these

residues also require exposure to a modifying enzyme in order to become destabilizing

themselves. This process is mediated by the enzyme N-terminal amidohydrolase (Varshavsky,

1996).









Interestingly, the N-end rule is rather well conserved between bacteria and eukaryotes

despite the absence of an ubiquitin system or, in most cases, an actual proteasomal degradation

system in bacteria. Regulated proteolysis in bacteria is often regulated by the Hspl100 category

of proteolytic components such as ClpA and S (Mogk et al., 2007). These components either

interact directly with selected substrates or serve as a "platform" for adapter molecules which

intermediate the binding of destabilized protein substrates (Mogk et al., 2007). Some differences

are present, however, when considering the details of the N-terminal degradation signal itself. In

bacteria, the destabilizing amino acids differ from those of eukaryotes and only include Phe, Leu,

Trp and Tyr (Varshavsky, 1996; Mogk et al., 2007). In addition, the tiered system of substrate

destabilization is also not as extensive when compared to its eukaryotic counterpart and varies in

terms of amino acids included in each group. In bacteria, secondary destabilizers include Arg

and Lys and are converted to a destabilizing form by way of the leucine/phenylalanyl t-RNA-

protein transferase enzyme (Varshavsky, 1996). In contrast, tertiary destabilizing residues have

not been identified at this point in the prokaryotic division, creating a less extensive system for

proteolytic substrate selection and destabilization.

PEST sequences

Since the mid-1980's, protein modifications and N-terminal composition have been

recognized as proven mechanisms for proteolytic substrate recognition; however, as the

collection of energy-dependent proteolytic substrates grew, it became evident that alternative

mechanisms for making stable proteins vulnerable to degradation must exist. Collective primary

sequence analysis of important cellular components such as transcription factors, signal

transduction proteins and kinases have revealed a pattern of reoccurring sequences of definite

order and composition that have come to be known as PEST sequences. Examination of key

factors such as p53, Jun and Fos transcription proteins has revealed consistent regions that are









rich in proline (P), glutamate (E), serine (S) and threonine (T) (Rechsteiner and Rogers, 1996).

These PEST sequences are uninterrupted by positively charged amino acids and typically appear

in the form of 12 or more hydrophilic residues (Rechsteiner and Rogers, 1996). They invariably

contain at least one proline and at least one glutamic or aspartic acid as well as at least one serine

or threonine (Rechsteiner and Rogers, 1996). These hydrophilic stretches are also typically

flanked by histadine, lysine or arginine residues (Rechsteiner and Rogers, 1996).

While PEST sequence composition is well-conserved, there is no apparent conservation

of secondary structure among the sequences (Rechsteiner and Rogers, 1996). In fact, educated

predictions have been made that, given their dominantly hydrophilic nature, these sequences

would likely form soluble protrusions or tails as opposed to compact structural organization

(Rechsteiner and Rogers, 1996). Because the PEST sequence is ever-present within the protein,

it was concluded that covalent modification of this sequence was the determining factor in

destabilization of a protein substrate (Garcia-Alai et al., 2006). PEST motifs were discovered to

be finely tunable by phosphorylation of the internal serine and threonine residues and mutational

analysis and NMR spectroscopy of substrate proteins revealed that entry into a proteolytic

pathway correlated to thermodynamic instability provided by phospho-perturbation rather than

the phosphate moiety itself (Garcia-Alai et al., 2006).

The PEST sequence motif has been demonstrated to operate both in the presence and

absence of a ubiquitin-tagging system (Kornitzer et al., 1994; Yaglom et al., 1995; Rechsteiner

and Rogers, 1996). In yeast, both the transcriptional activator Gen4 and the Cln3 cyclin protein

are degraded by the ubiquitin-proteasome degradation pathway as a result of the presence and

phosphorylation of internal PEST sequences (Kornitzer et al., 1994; Yaglom et al., 1995). E2

proteins from papillomavirus particles also contain a PEST sequence within a flexible loop










region of its structure, which directly influences its half-life in the presence of the ubiquitin-

proteasome pathway (Garcia-Alai et al., 2006). The influence of PEST sequences on protein

stability was established through both mutational analysis and transposition of PEST tags to

otherwise stable proteins. A favorite model of PEST-tagged substrate degradation is the mouse

ornithine decarboxylase (mODC). Removal of the mODC C-terminal PEST sequence increased

its half-life dramatically and transfer of the mODC PEST sequence to the more stable

Trypanosoma ODC imparted extreme instability in the presence of 26S proteasomes (Ghoda et

al., 1989; Ghoda et al., 1990). Mammalian ornithine decarboxylase, however, differs from other

eukaryotic PEST substrates in that it does require proteasomal degradation machinery and

antizyme but its degradation is not mediated by ubiquitin (Murakami et al., 1992; Murakami et

al., 1999). The transcription factor Jun, in similarity, also does not require ubiquitin for its

PEST-dependent turnover in the cell (Rechsteiner and Rogers, 1996).

Oxidative damage and hydrophobic patch exposure

The intrinsic properties that make proteins susceptible to proteolysis have been a subj ect

of interest for more than 25 years and over time it has become apparent that not all degradation-

promoting modifications made to proteins are deliberate. Over time, an aerobic cell generates

oxidative free radicals or becomes exposed to chemical oxidants which can damage and contort

proteins and necessitate their degradation (Grune et al., 2003). It is observed that proteins

experiencing oxidative stress undergo a biphasic mode of degradation whereby initial exposure

of hydrophobic residues prompt an increasing susceptibility to proteasomal degradation

consistent with an increase in oxidant concentration (Grune et al., 2003). After an apex in

degradation is reached, exposed hydrophobic patches begin to facilitate protein aggregation and

conceal the recognition motifs from proteases (Grune et al., 2003). This leads to a return to basal

levels of degradation (Grune et al., 2003). The specific motifs which make proteins vulnerable









to proteasomal degradation are unclear but represent a long-standing question in the study of

protease substrate degradation. There is considerable evidence that exposure and/or oxidation of

individual amino acids can be a maj or determining factor for degradation of a particular protein.

Popular examples include the degradation of glutamine synthetase after oxidation of methionine

residues and the relationship between tyrosine oxidation and degradation of RNase A (Levine et

al., 1996; Lasch et al., 2001). Evidence also strongly supports changes in secondary and tertiary

structure as cues for recognition by degradative machinery. Lasch and colleagues were able to

establish, through work with RNase A, that oxidative modification of certain domains caused as

much as 50% unfolding which was observed to significantly increase proteolytic susceptibility

(Lasch et al., 2001). As supporting evidence for the relationship between protein unfolding and

degradation, a correlation between the proteolytic susceptibility and overall hydrophobicity of

multiple proteins has been shown through primary sequence analysis, hydrophobic interaction

chromatography and fluorescent detection of hydrophobic surface regions (Pacifici et al., 1993;

Giulivi et al., 1994; Levine et al., 1996; Levine et al., 2000). This proposed mode of substrate

recognition is further supported by the observation that the proteasome gives preference to

binding hydrophobic and aromatic amino acids within substrate protein sequences (Hough et al.,

1987).

In addition to intrinsic sequence characteristics which lead proteins into a proteolytic

system, other means by which hydrophobicity can modulate protein stability have been

discovered. The most notable example of a mechanism of this type is the ssrA tagging system in

E. coli. Robert Sauer and colleagues observed that transcripts which lacked terminal translation

stop sequences were modified with a terminal sequence coding for an 11-residue tag that is

predominantly hydrophobic in nature (AANDENYALAA) (Keiler et al., 1996). They










hypothesized that this system substituted for a termination sequence and made provisions to rid

the cell of the deleterious effects of a mis-transcribed gene by marking its product for

degradation in the pre-translational stage. This system is likened to another terminal tagging

system; the Tsp protease-dependent degradation of substrates possessing the non-polar C-

terminal recognition sequence WVAAA (Keiler et al., 1995).

Known Substrates and Cellular Influences of the Proteasome

There is a broad spectrum of proteins, representing a multitude of cellular subsystems

that are degraded by proteasomes. Proteasomes and accessory ATPase components control the

"how" and "when" of cell division, help to modulate the appropriate cell response to changing

metabolic conditions and serve as a maj or influence in the ability of the cell to express certain

crucial genes. This section will discuss, in greater detail, the role of the 26S proteasomal

degradation system in manipulating three essential and well-characterized functions of the

eukaryotic cell: division, transcription/translation and metabolism.

Cell cycle

One aspect of eukaryotic cell division, DNA replication, has been studied in great detail

over the years. It has become evident that the process of cell division and the assembly of the

necessary pre-replication complex (pre-RC) is very well conserved in the eukaryotic domain,

from budding yeast to human cell lines (Chuang and Yew, 2005). It has also been observed that

the 26S proteasome system is a vital part of DNA replication as well as other phases of cell

division. The assembly of the pre-replication complex involves the assembly of 6 origin

recognition complex proteins (ORC), cell division control protein 6 (Cdc6) and other

components such as mini-chromosome maintenance (MCM) proteins (Blanchard et al., 2002). A

key component of the pre-RC is Cdc6, which has been found to be a target of the proteasomal

degradation system in both yeast and mammalian cells (Blanchard et al., 2002; Luo et al., 2003).









This pre-replication element is primarily responsible for coupling DNA replication to mitosis by

helping to assemble pre-initiation complexes in late mitosis and working to maintain these

complexes until replication begins in S phase (Blanchard et al., 2002). The depletion of Cdc6 or

the addition of geminin, an origin licensing inhibitor, causes uncoupling of mitosis and DNA

replication and has been demonstrated to cause unwarranted mitotic behavior in Xenopus egg

extract nuclei (Hekmat-Nejad et al., 2000; Michael et al., 2000; Blanchard et al., 2002). The

degradation of Cdc6 in both Saccharomyces cerevisiae and Arabidopsis thalianaiiii~~~~~~iiiiii has been shown

to be mediated by both caspases and proteasomes in response to apoptosis-inducing factors such

as TNF-a and cycloheximide (Blanchard et al., 2002).

Another essential component of the eukaryotic DNA replication system is the

proliferating cell nuclear antigen (PCNA). PCNA is a sliding clamp protein which functions to

keep DNA polymerase in contact with the DNA helix and to enhance processivity of DNA

synthesis (Yamamoto et al., 2004). It has also been found to play pivotal roles in DNA repair,

apoptosis and cytosine methylation (Chuang et al., 1997). Recent evidence reveals that a

homolog of PCNA (OsPCNA) found in rice plants (Oryza sativa) interacts with the Rpt6 ATPase

subunit of the proteasomal 19S cap (Yamamoto et al., 2004). Furthermore, it was determined

that OsPCNA was degraded by the ubiquitin/proteasome system over an eight-hour time course

in vitro and in vivo and that the addition of proteasome-specific inhibitor MGl32 consistently

stabilized OsPCNA levels in the cell (Yamamoto et al., 2004). More recent analysis of PCNA in

Xenopus egg extracts has revealed that PCNA is vital to the temporal regulation of DNA

replication through binding to the cyclin-dependent kinase (CDK) inhibitor Xicl and promoting

its degradation by the ubiquitin/proteasome system (Chuang and Yew, 2005).









Perhaps the most studied and arguably the most important proteasomal substrate

discoveries to date are the cyclin proteins, considering that their discovery as substrates directly

associates proteasomal function to cancer development. Cyclin proteins from the A, B, D and E

categories as well as their regulatory cyclin-dependent kinases such as Cdc2 have been shown to

be targeted for destruction by the ubiquitin-proteasome pathway in yeast and mammals as a

means of controlling progression through the G1 phase (Cyclin D, Cdk4, Cdk6), S phase

transition (Cyclin E, Cdk2), S phase (CyclinA, Cdk2) and G2 or Mitotic phases (Cyclin A and B

and Cdkl) (Casanovas et al., 2004; Fung et al., 2005; Lahne et al., 2006; Tanaka et al., 2006;

Aggarwal et al., 2007). Proteasomal inhibitory drugs such as the newly developed Velcade

(bortezomib) are designed with the purpose of limiting the proteasome itself as a means of

stabilizing cyclins and cyclin-dependent kinases in an effort to inhibit rampant cell division and

may prove to be an invaluable tool in the treatment of many types of cancer (Ishii et al., 2007).

Transcription and translation

DNA transcription and protein synthesis are also maj or targets for proteasomal regulation

in eukaryotes. Regulation of these functions by the ubiquitin/proteasome pathway has been

demonstrated through the degradation of transcription factors, translation elongation and

specifically initiation factors, ribosomes and RNA polymerase II (Baugh and Pilipenko, 2004;

Takahashi et al., 2005; Ribar et al., 2006; Carle et al., 2007; Lam et al., 2007). Additionally,

interference with proteasome degradation has also been demonstrated to induce the accumulation

of 90S preribosomes, alter the dynamic properties of a number of processing factors, slow the

release of mature rRNA from the nucleolus, and lead to the depletion of 18S and 28S rRNAs

(Stavreva et al., 2006).

A popular characterization of the relationship between the ubiquitin-proteasome system

and transcriptional control is the degradation of activator protein 1 (AP-1)-family pre-oncogene









transcription factors c-Jun and c-Fos. It has been demonstrated in recent years that these and

other members of the AP-1 family are ubiquitinated and degraded by proteasomes in mammalian

cells (Ito et al., 2005; Matsumoto et al., 2005; Carle et al., 2007). These components, along with

a host of other AP-1 family proteins have been demonstrated to exhibit control over such things

as cell differentiation, apoptosis and tumorgenesis, often in response to inflammatory conditions

(Matsumoto et al., 2005).

The proteasome regulates overall protein levels not only through degradation of damaged

or unwanted proteins but also through selective degradation of protein synthesis components.

An interesting example of this regulatory duty is the demonstrated proteolysis of translation

initiation factors elF4F and elF3 as a means of affecting pre-initiation complex formation on

various cellular and viral mRNAs which results in an overall decrease in translation (Baugh and

Pilipenko, 2004). Other examples of translational control carried out by the ubiquitin-

proteasome system include degradation of ribosomal proteins themselves within the nucleus of

the cell (Lam et al., 2007). This process is thought to balance the ribosomal protein over-

expression in the nucleoplasm which prevents ribosomal protein availability from becoming the

limiting factor in initiation complex formation (Lam et al., 2007). Proteasomes are not simply

destructive in this respect, however, considering the fact that they have also been indicated as

key factors in multiple steps in the ribosome biogenesis process and ultimately, the assembly of

the 90S pre-ribosome (Stavreva et al., 2006).

Finally, a proteasomal effect on transcription/translation not to be overlooked is the

ubiquitin-dependent degradation of RNA polymerase II, most notably in the yeast

Saccharomyces cerevisiae (Ribar et al., 2006). In cells exposed to UV light or DNA-damaging

chemical agents, polyubiquitination of Rpb l, the largest subunit of RNA pol II, leads to the









subsequent proteasomal degradation of the RNA pol II complex and serves as an obvious control

point for preventing transcription of damaged genes (Ribar et al., 2006). In this capacity, the

ubiquitin-proteasome system is integral to adaptive response to acute environmental stresses and

can confer flexibility to the cell in a harsh or changing environment.

Cellular metabolism and stress response

While the regulatory capabilities of the 26S proteasomal system are often thought of as

being most commonly exploited at the control points of the cell, such as cell division or

transcription, ubiquitin conjugation and proteasomal degradation also control metabolism and

conditional responses. Those who study the 26S proteasome in a capacity other than cell cycle

control or transcription factor degradation have discovered a whole new level of involvement of

the proteasome in processes like light-dependent circadian rhythmicity, lipid and cholesterol

metabolism, carbon metabolism and metabolic switching in response to changing cellular

conditions like oxidative stress.

Arguably the most studied function of 26S proteasomes, as it relates to metabolic

regulation, is the turnover of light-dependent circadian regulatory elements for the establishment

of an oscillatory pattern. This topic has been explored in a wide array of model systems

including photosynthetic algae, yeast, plants, mice and humans. In Arabidopsis thaliana, it was

determined that, in contrast to previous notions about the circadian system in this organism as a

transcriptional feedback loop, the oscillatory pattern of its biorhythmic system was the result of

post-translational modification and nuclear localization of the regulatory CKB4 protein through

phosphorylation and subsequent ubiquitination and degradation by proteasomes (Perales et al.,

2006). This concept is very much in line with current models of how mammalian circadian

systems appear to function. While mammalian circadian rhythms do appear to function in a

negative transcriptional feedback loop, maj or commonalities do exist between plant and algal









systems and higher eukaryotes. One such similarity is exemplified through analysis of mouse

CRY2 proteins and PER2 proteins in Xenopus egg extract and the fact that they both require

phosphorylation for effective localization in the nucleus (Harada et al., 2005; Gallego et al.,

2006). In all cases, the phosphorylation/localization process also appears to destabilize the

proteins in the presence of the ubiquitin/proteasome system and thus serves to establish and

maintain a tightly controlled 24-hour internal clock (Harada et al., 2005; Gallego et al., 2006;

Perales et al., 2006).

Specific pathways for lipid and carbon metabolism in eukaryotes have also been shown

to be regulated by 26S proteasomes. The rate-determining enzyme for cholesterol biosynthesis,

hydroxyl-methyl-glutaryl coenzyme A (HMG CoA) reductase, is degraded by the ER-based

ubiquitin/proteasome pathway in mammalian cells (McGee et al., 1996; Song and Debose-Boyd,

2006). Further work in this area has identified the membrane-bound ubiquitin ligase, gp71 and

its necessary cofactor, Ufd1, responsible for the endoplasmic reticulum-associated degradation

(ERAD) of this essential cholesterol synthesis enzyme; a target of great interest in the

development of new therapies for high cholesterol conditions and heart disease (Cao et al.,

2007).

Eukaryotic 26S proteasomes have been indicated in limited regulation of various carbon

metabolism pathways in the cell. One such case is the known degradation of glycolytic enzyme

glyceraldehyde-3 -phosphate dehydrogenase (GAPDH) by proteasomes (Sukhanov et al., 2006).

The proteasomal destruction of this enzyme is also reported to be significantly enhanced by the

presence of reactive oxygen species (RO S), exemplifying the role of the ubiquitin/proteasome

system in protein turnover as a means of regulating central carbohydrate metabolism as well as

helping to coordinate a response to deteriorating cellular conditions (Sukhanov et al., 2006).









Additional modes of proteasomal regulation of primary metabolism have been suggested in

which metabolic sensors such as the Arabidopsis hexokinase-1 glucose sensor associate with

regulatory components of the 26S proteasomal system (namely the Rpt~b subunit of the 19S cap)

and work in concert with other regulatory elements to control the transcription of specific carbon

metabolism genes (Cho et al., 2006).

In addition to its various roles in assorted metabolic pathways, transcription, protein

synthesis and cell cycle progression, one vitally important role of the ubiquitin/proteasome

function is its response to environmental challenges such as oxidative stress, osmotic or

temperature shock and UV exposure. Multiple comparative proteomic studies have been

conducted on cells grown under stressing conditions and have consistently revealed the increased

appearance of structural and regulatory proteins of the proteasome, including instances where the

proteasome itself is inhibited through chemical modification (Zang and Komatsu, 2007; Zhang et

al., 2007a). Focused investigation into the precise mechanisms for stress adaptation has revealed

transcriptional upregulation of 26S proteasomal machinery during heat shock, enhanced

proteasomal activities during prolonged cold shock (4oC) and proteasomal degradation of

oxidatively damaged proteins within the cytosol, nucleus and endoplasmic reticulum (Davies,

2001; Perepechaeva et al., 2006; Szustakowski et al., 2007). Exposure to UV radiation or DNA-

damaging chemical agents has also been an inducing factor in proteasome-mediated damage

control. As observed in the degradation of DNA polymerase delta subunit pl2, the proteasome

is proactive in slowing replication where DNA damage has occurred (Zhang et al., 2007b).

Interestingly, eukaryotic proteasomes have also been identified as key figures in cell aging.

Experiments in Caenorhabditis elegan2s have shown that post-transcriptional changes in the

levels of forkhead transcription factor DAF-16, mediated by the proteasome, affect cellular









lifespan by a distinguishable margin (Li et al., 2007c). Complementary experiments also

indicate that over expression of the DAF-16 protein or disruption of the RLE-1 ubiquitin ligase

(E3) that is responsible for marking DAF-16 for destruction, has a prolonging affect on the

lifespan of cells (Li et al., 2007c). Work done by other groups indicates that FoxO transcription

factors and mammalian orthologs of the C. elegan2s DAF-16 are also controlled by the

ubiquitin/proteasome pathway. Furthermore, FoxO controls such vital cellular processes as

apoptosis, cell cycle control, stress response, DNA damage repair and cell differentiation (Huang

and Tindall, 2007).

Proteomics and Biological Mass Spectrometry

As the age of genomics continues, we are accruing overwhelming amounts of

information about the coding capacities of organisms across the full spectrum of life on earth.

With complementing transcriptome technology available, we can now start to understand which

genes are constitutive and which are regulated in response to varying conditions encountered by

the cell, however, this is only a portion of the information required for comprehensive

understanding of the cell. The fact that transcription of a gene does not equal expression is often

overlooked but the fact remains that a large portion of the work carried out within a cell or by a

cell is facilitated by its "toolbox" of proteins. When considering this, the need for high-

throughput characterization of protein expression profiles becomes apparent. Basic proteomic

techniques have opened the door to this type of information and continue to expand and evolve

to meet the needs of the biology community and to answer the questions that arise from

transcriptional analysis. With the advent of advanced mass spectrometric devices and protein

resolution techniques, we are able to delve deeper into our curiosity about changing protein

levels, adapting metabolic pathways and, perhaps most importantly, the added complexity

afforded by endless post-translational modification possibilities. This section outlines the current









state of proteomics and explores the documented application of many of these modern

techniques across all domains of life.

The Current State of Bottom-Up Proteomics

Bottom-up proteomic strategies are most commonly associated with typical large-scale,

high-throughput analyses. They involve enzymatic digestion of protein prior to mobilization and

analysis by mass spectrometry. These methods are becoming increasingly important as dramatic

advances in genomics and systems biology are made. A bottom-up approach allows

investigators to compare expression profies, analyze post-translational modification sites and

identify disease biomarkers with increasing sensitivity. Bottom-up proteomics is not without its

drawbacks, however. One maj or limitation is the fact that protease-mediated digestion coupled

with MS/MS analysis rarely, if ever, produces full coverage of a protein. Also, peptides that are

generated through enzymatic digestion may be biased in one direction or another, with respect to

ionization and MS flight (e.g. due to post-translational modification, size or native charge).

These features represent inherent limitations to this approach. While the alternative top-down

approach (focused MS analysis of whole proteins) satisfies some of these bottom-up

shortcomings, its primary drawback is the continual struggle to mobilize fully intact proteins for

MS fragmentation. Experts in the Hields of proteomics and bioinformatics feel as though both

categories of proteomic analysis are gradually moving towards a hybrid strategy in which

specific protein domains or structural features are cleaved and analyzed in an "abbreviated" top-

down analysis which may rely upon classical bottom-up pre-MS methodologies combined with

conventional top-down MS/MS analysis with optimized desorption and ionization steps. The

following sections explore standard techniques and recent advancements in the field of bottom-

up proteomics and comparative expression profile analysis.









Protein/peptide separation

The success of any complex proteomic analysis relies, in large part, on the ability to

effectively separate proteins and peptides based on specific characteristics such as isoelectric

point or covalent modification. In mass spectrometry, peptide scans are improved through sub-

fractionation of complex mixtures as a means of eliminating signal masking and bias in

ionization and detection of dominant peptides. From its genesis in 1975 (O'Farrell, 1975)

through the modern era of proteomics, two-dimensional polyacrylamide gel electrophoresis or

2D PAGE has been a consistent and reliable method for effective separation of proteins in

complex mixtures and has served as the first link in a chain of proteomic evolution that continues

today (Arrigoni et al., 2006a). Two-dimensional PAGE traditionally separates proteins based on

isoelectric point (pl) and molecular weight (MW) and has the ability to resolve as many as 5,000

proteins simultaneously with pl resolution as fine as 0.001 units (Gorg et al., 2004).

Additionally, with the advent of advanced pre-fractionation methods and a collection of reliable

ultra-high sensitivity fluorescent protein stains such as Krypton, Deep Purple, Flamingo and

Sypro Ruby, minimal protein detection limits have been reported at less than 1 ng of protein

(Gorg et al., 2004; Harris et al., 2007). In fact, with the development of ultrazoom focusing

methods for the analysis of proteins with extreme pl shifts, a minimal detection limit of 300

protein copies per cell has been demonstrated in B-lymphoma cell lines (Hoving et al., 2000).

As in-gel enzymatic digestion procedures have become routine in proteomics, this method of

protein separation has become amenable to downstream mass spectrometric analysis and has

reaffirmed its role as the workhorse of modern proteomics.

With the obvious limitations of in-gel proteomic separation such as protein insolubility,

sub-detectable abundance and failure to resolve within a given set of mass and pl parameters,

attention in recent years has diverted to the development of practical, high-throughput liquid









separation methods that would eliminate these problems and provide a more reliable and

unrestricted view of full proteomes. One general approach to proteomic pre-fractionation that

has become standard protocol for many proteomic investigators is multidimensional protein

identification technology or MudPIT. This approach to pre-MS separation requires little sample

manipulation and therefore limits opportunity for sample loss or contamination. The process is

generally performed with fully digested protein samples which are loaded onto a fused silica

microcolumn (50-100 Cpm inner diameter) packed with multiple matrix materials in tandem that

are non-redundant with regards to the peptide selection properties (Delahunty and Yates, III,

2005). In theory, MudPIT can be carried out over an infinite linkage of complementary

enrichment matrices but current applications typically utilize a biphasic or triphasic

chromatography scheme with strong cation exchange (SCX) as a higher-capacity first phase,

followed by either C4 or C18 reversed phase zones and, in some instances, materials used for

specialized selection such as iron or gallium (for phosphopeptide enrichment) may be added to

the sequence in place of or in addition to the basic SCX/reversed phase pairing (Delahunty and

Yates, III, 2005; Florens and Washburn, 2006). A low-voltage current is applied to the

micocolumn to facilitate nano-electrospray elution into an MS/MS system through a pulled

proximal-end tip with a diameter of 2-5 Cpm (Delahunty and Yates, III, 2005). Primary

limitations of MudPIT-based MS/MS experiments are that it can be a lengthy, time-consuming

process with runs that last from 6 to 24 hours followed by as much as 8 hours of database

searching (Delahunty and Yates, III, 2005). A second drawback is the sheer amount of data

generated by single complex-sample MudPIT experiments. As many as 70,000 mass spectra can

be generated in a single run, leading to overwhelming amounts of data analysis and organization









and placing pressure on bioinformaticists for improved data management technology (Delahunty

and Yates, III, 2005).

Other, less common methods for enriching and simplifying complex peptide mixtures are

making their way to the forefront of proteomic technology. Among these methods is an altered

approach to capillary electrophoresis (CE). In the past, capillary electrophoresis has been less

than desirable for large-scale proteomic applications due to its relatively limited abilities in

effective separation of peptides and proteins; however, recent adaptations involving addition of

high-concentration poly(diallyldimethylammonium chloride) (PDDAC) have allowed

simultaneous resolution of both cationic and anionic peptide species over a pl range of 4.7 to

11.1 and a mass range of 6.5 kDa to 198 kDa by serving as an ion-pairing agent for the

separation of anionic proteins and a coating agent for separation of cationic proteins {Lin, 2007

481 /id}. This innovation to capillary electrophoresis creates a much broader dynamic range in

separation of peptides and may represent a powerful new tool for proteomic analysis.

Finally, a considerable amount of thought and innovation has been invested in the

development of enrichment methods for post-translational modification such as phosphorylation.

Currently, there are two maj or categories of non-immunoaffinity phosphoprotein enrichment:

metal-mediated chromatography and isoelectric separation. In the former category, iron and

gallium-based immobilized metal affinity chromatography (IMAC) have traditionally been the

most commonly used in the enrichment of low-abundance phosphoproteins from complex

samples; however, these materials are being used less frequently due to their deficiencies in

phosphoprotein/peptide selectivity and the discovery of higher-affinity metal oxides (Imanishi et

al., 2007). Materials such as titanium dioxide and zirconium dioxide are now the standards for

phospho-enrichment due to their superior ability to bind phosphate moieties specifically and to









reduce non-specifie binding of acidic peptides (Thingholm et al., 2006). The effectiveness of

metal oxide separation has also been enhanced through the use of 2,5-dihydroxybenzoic acid in

coordination with low pH contributed by trifluoroacetic acid (TFA) in mobile phase, which has

been demonstrated to restrict binding of non-phosphorylated components to titanium or

zirconium dioxide nanoparticles with greater efficiency than traditional TFA/acetonitrile (ACN)

gradients alone (Thingholm et al., 2006; Zhou et al., 2007). The improved phospho-enrichment

characteristics of metal oxide chromatography have been demonstrated recently in the analysis of

mouse liver lysate which was digested and enriched with zirconium dioxide nanoparticles prior

to LC/MS/MS (Zhou et al., 2007). This experiment yielded the detection of femtomole levels of

low-abundance phosphopeptides and facilitated the identification of over 240 phosphosites

(Zhou et al., 2007).

Isoelectric separation of proteins based on pl values has served as an alternate approach

to phosphoenrichment, as phosphorylated proteins possess a discernable acid shift compared to

their non-phosphorylated forms. Recent advancements in this particular separation method make

use of protein methyl esterifieation, creating a greater difference in average isoelectric point

values of phosphorylated and non-phosphorylated proteins and making it feasible to effectively

separate modified proteins in the acidic and neutral ranges of an IEF Hield while non-

phosphorylated methyl esterifications are excluded from the Hield due to the fact that they exceed

the upper limits of the pl range (Xu et al., 2007). While this method is not optimal for proteomes

with extreme acidic bias in average pl value, it has been proven as a viable method for phospho-

enrichment in more neutral proteomes.

Post-translational modification analysis

Bottom-up proteomic identification of post-translationally modified peptides from a

complex protein digest pool is fraught with obstacles, the most significant of which are their









naturally low abundance, their transient nature or their "invisibility" with respect to purification

based on distinguishing features or identification through mass spectrometry. One of the best

characterized and most frequently studied modifications is protein phosphorylation. As a result,

it also has the most extensive collection of enrichment and identification methods of any PTM.

Most phosphoanalyses begin with one of a few enrichment strategies discussed in the previous

section; however, the effective identification of a phospho-modification site often requires more

sophisticated analysis. One approach to phospho-enrichment and modification site identification

involved the beta elimination of the phosphate moieties on pSer and pThr residues and replacing

them with ethanedithiol to facilitate the addition of any one of a variety of thiol-reactive affinity

tags such as commercially available maleamide-PEO2-biotin (Oda et al., 2001; Kalume et al.,

2003). This labeling method allows for specific affinity-based enrichment of phosphoproteins

and is reasonably efficient; however, it is not without its disadvantages. The beta elimination

and Michael addition chemistry does not distinguish between phosphorylations and other O-

modificaitons such as O-glycosylation (Oda et al., 2001). The thiol-reactive affinity tags also

have been found to bind to cysteins, negating their selective nature (Oda et al., 2001). Another

maj or drawback to this technique is that it requires large amounts of material, thereby masking

any analysis of lower abundance phosphopeptides (Kalume et al., 2003). Other methods of

phosphoanalysis based on beta elimination/Michael addition chemistry involve pSer and pThr

derivitizaiton with ethylpyridyl to promote better mobilization and thus, more efficient detection

of modified peptides using MALDI MS, however, similar complications persist (Arrigoni et al.,

2006b).

An alternative approach to phosphopeptide identification which also relies on beta

elimination and Michael addition chemistry has found a way to side-step the usual problems









associated with an affinity tagging type of analysis through targeted conversion of modification

sites at pSer and pThr to dehydroalanine or P-methyldehydroalanine, respectively (Knight et al.,

2003; Stevens, Jr. et al., 2005). The beta-eliminated residues act as Michael acceptors for

cystamine, creating aminoethylcysteine or P-methylaminoethylcystamine for modified serine or

threonine residues, respectively, and become isoteric with lysine and thus recognizable for

cleavage by trypsin, Lys-C or any other lysine-specific peptidase (Knight et al., 2003). Simple

digestion and analysis by LC-MS/MS or MALDI MS yields a unique yl ion representing the

modified site (Knight et al., 2003). This method has been tested for effectiveness in

phosphopeptide enrichment from complex mixtures and has been shown to be successful for

such applications (Knight et al., 2003; Stevens, Jr. et al., 2005). Disadvantages of this approach,

however, have been identified as an obvious inability to label large ranges of proteins or peptides

efficiently due to their physiochemical variability (Waugh, 2005) as well as variability (or

transient nature) of particular modifications used as the basis for labeling.

Protein glycosylation is also a maj or post-translational modification within the cell. The

realization that the types and quantity of carbohydrate modification made to proteins can be

directly related to certain disease states has given rise to the field of glycoproteomics (Sparbier et

al., 2005). The challenge of selective enrichment and analysis of glycosylated proteins or

peptides is being met with some interesting solutions. One approach to the selective enrichment

of O-GlcNAc-modified proteins from complex mixtures involves the use of a genetically

modified galactosyltransferase (Y289L GalT mutant) which possesses the ability to selectively

label proteins containing an O-GlcNAc moiety with a ketone-biotin tag to facilitate avidin

enrichment and subsequent ESI-MS (Khidekel et al., 2004). This method has been employed to

effectively analyze the glycoproteomic profile of the mammalian brain (Khidekel et al., 2004).









Other glycoprotein analysis methods are more straight-forward and include affinity

chromatography-based separation utilizing functionalized magnetic beads. These beads are

covalently coated with glyco-affinity chemicals such as Concanavalin A or diboronic acid,

depending on the type of glycosylation being enriched (high mannose, hybrid or complex)

(Sparbier et al., 2005). Samples are analyzed through MALDI-TOF and enrichment,

identification and mapping of glyco-modified peptides of various types has been demonstrated

using this latter approach, as was the specifieity of this technique in the presence of competing

free mannose concentrations, proving it to be an effective tool in glycoproteomic analysis

(Sparbier et al., 2005).

Other functionally important post-translational modifications such as methylation and

acetylation are proving to be much more difficult to enrich for and analyze due to the fact that

they are not amenable to conventional affinity tagging or labeling techniques pioneered by

phosphoproteomics and glycoproteomics (Leitner and Lindner, 2006). Studies focused on these

types of post-translational modification, especially the characterization of histone modification,

have relied almost exclusively on analytical mass spectrometry for confirmation of modification

and mapping of modification sites (Karanam et al., 2006; Wisniewski et al., 2007). These mass

spectrometry-based approaches, including ion trapping and tandem MS, will be discussed in

future sections.

Shotgun proteomics and quantitative, high-throughput strategies

The power and versatility of proteomic analysis extends well beyond protein

identification and analysis of post-translational modification. Technical advances of the past

decade have permitted us to explore such aspects as relative and absolute protein quantification,

protein-protein interaction and subcellular localization en route to mapping changing expression

profiles of cells and identifying better disease biomarkers. This section will explore several









modern developments that have extended our ability to analyze large, complex mixtures of

proteins in a comparative and/or quantitative capacity.

Array-style experiments have revolutionized the fields of genomic and transcriptional

analysis as well as biochemical characterization. Similar array-based procedures are now being

applied to large-scale proteomic analysis through the use of antibody arrays. Currently, antibody

microarrays are being designed to probe progressively larger divisions of the proteome through

the use of human recombinant single-chain variable domain (Fv) antibody fragments (scFv)

designed to be adapted to solid support attachment for microarray applications and selected from

a substantial phage display library (Ingvarsson et al., 2007). This recombinant approach allows

for flexible design and expanded range of specificity. Methods for quantifying protein

microarray results are also being expanded and improved upon. Current methods for antibody

microarray analysis include direct labeling of protein samples using muliplex fluorescent dyes;

however, this method has given way to much more sensitive indirect labeling approaches

(Kusnezow et al., 2007). Presently, N-hydroxysuccinimidyl ester (NHS) and Universal Linkage

System (ULS)-based labels using fluorescein or biotin are being used for immunoaffinity or

extravidin detection, respectively, with several dozen-fold improvement over direct labeling

techniques (Kusnezow et al., 2007).

While antibody microarray technology is promising with regards to its universal

application to proteomic analysis and its ability to be customized to specific sub-proteomic

analyses, its current cost and as-of-yet unresolved technical restrictions force investigators to

turn attention to other methods for comparative quantification of complex protein samples.

Among the alternatives is the Isotope-Coded Affinity Tagging (ICAT) method. This technique

has become standard for comparative analysis since its inception in 1999 (Gygi et al., 1999).









This method employs thiol-reactive labels possessing isotopic components, an affinity

purification element such as biotin and in many cases, a linking region capable of acid- or photo-

cleavability (Gygi et al., 1999; Bottari et al., 2004; Chiang et al., 2007). Separate conditions are

analyzed, pair-wise, through differential labeling with light and heavy isotope-coded tags. MS

and MS/MS analysis of sample mixtures can simultaneously determine relative quantities and

identities of specific peptides within complex mixtures (Gygi et al., 1999). While ICAT has

immense value in its high throughput and high sensitivity features, its negatives are that it

adversely complicates resulting spectra, particularly in instances where multiple cysteines are

labeled (Li and Zeng, 2007). Also, peptides lacking cysteines are not available for ICAT

labeling, representing a severe limitation with this method (Li and Zeng, 2007).

Another method of large-scale quantitative comparison in frequent use today involves

sample labeling with isobaric tags. This approach has been made popular through Applied

Biosystem' s introduction of the iTRAQ system. This system is composed of four (or available

up to 8) different primary amine-reactive isobaric tags that are used to label as many different

samples. Labeled peptide samples are then pooled and separated through LC-MS/MS where

fragmentation (CID) produces signature mass peaks at 114.1, 115.1, 116.1 and 117.1 m/z which

are distinctive for each tag (Aggarwal et al., 2006). This approach shows a vast improvement

over conventional gel-based analysis or even isotope-coded affinity tagging (ICAT) methods due

to the fact that it allows for multiplex analysis and enables quantification of zero protein levels;

a feature not afforded by conventional ICAT experiments due to its requirement for observed

mass shifts (Aggarwal et al., 2006). Additionally, because the tags are isobaric in nature,

resulting spectra are simplified by revealing single additive peaks resulting from elimination of

the tag (Aggarwal et al., 2006). Isobaric tagging does, however, possess some drawbacks in that









iTRAQ experiments are relatively costly and there exists some variability in peptide labeling

between comparable samples. Clevable Isobaric Labeled Affinity Tags (CILAT) are also

making their way into large-scale quantitative proteomics, offering the benefits of both ICAT

and iTRAQ labels with none of the apparent drawbacks (Li and Zeng, 2007). CILAT combines

the multiplexing capabilities of an isobaric tagging procedure with the utility and convenience of

an affinity purification option and cleavable linking region (Li and Zeng, 2007).

Mass spectrometry-based approaches to comparative quantification are, perhaps, the

most commonly relied upon, offering a large number of labeling and separation tools with ever-

increasing sensitivity and reproducibility. One method, however, has proven to be of equal value

and effectiveness of any discussed here, but without the complication of tagging inefficiencies

and convoluted spectra. This method, known as AQUA or the Absolute Quantification method,

utilizes synthesized internal standard peptides as the basis for quantification in actual molar

quantities as opposed to relative quantity ratios (Kirkpatrick et al., 2005). Precise absolute

quantification of a specific peptide within a complex mixture is achieved through Selected

Reaction Monitoring (SRM) of a peptide of a defined mass and comparing to a known quantity

of stable isotope-labeled internal peptide standard (Kirkpatrick et al., 2005). This allows for

actual quantities of a given protein to be determined and is also used as a reliable method for

quantifying certain post-translational modification occurrences within complex samples

(Kirkpatrick et al., 2005).

Finally, a method very similar to AQUA in both simplicity and power has been used for

quite some time for straight-forward comparative analysis of multiple complex protein mixtures.

This technique is known as Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)

(Ong et al., 2002; Mann, 2006). SILAC is a simplified version of other differential isotope









labeling methods which uses metabolic labeling of total protein in cell culture or during tryptic

digestion via heavy and light isotopic amino acids (often 15N/14N or 18O/160 coded) (Ong et al.,

2002; Mann, 2006). Sample analysis and the style of data generated is similar to that of ICAT

with similar complications in that it too produces somewhat convoluted spectra (Ong et al., 2002;

Mann, 2006). However, the advantages of SILAC are plenty, considering the simplicity of

labeling and how much less time-consuming this process is compared to other methods utilizing

an external tag (Ong et al., 2002; Mann, 2006). SILAC is also advantageous in that it allows for

total proteome comparison in contrast to a much more restricted type of analysis exhibited by

AQUA. Finally, SILAC also incorporates differential label completely without restriction, as

opposed to ICAT or other affinity labeling methods.

Mass spectrometry and bioinformatics

Refinement of mass spectrometric tools over the past 30 years has ushered its migration

from the physics and chemistry labs of academia to the forefront of biology and applied health

sciences. In fact, innovative development of new mass analysis and ionization instruments, data

analysis methods and assembly of hybrid MS configurations have transformed mass

spectrometry and bioinformatics into independent divisions of science. This subsection will

discuss, in general terms, the advantages and disadvantages of the most recent innovations in

biological mass spectrometry as well as the most heavily-relied upon methods, instrumentation

and software and their inherent features that made them so popular.

Two basic types of mass spectrometer currently exist: ion-trapping instruments and

beam-type instruments. Each type of MS technology has evolved independently but has crossed

paths with the other, particularly where hybrid instrumentation is concerned. These categories of

MS differ fundamentally in how they operate with respect to tandem function. Beam-type

instruments typically work to separate and analyze fragment ions spatially (a "tandem-in-space"










approach) as opposed to ion-trapping devices which work by scanning and re-scanning ion pools

on a time scale (a "tandem-in-time" approach). For this primary reason, ion traps hold several

advantages over beam instruments when it comes to tandem mass analysis. One obvious

advantage is that separation and recurrent mass analysis in space is limited by space, cost and ion

intensity. Considering the case of peptide analysis through tandem mass spectrometry, after

ionization and fragmentation, the number of successive mass analyses to be performed depends

on how many analyzers one can physically configure. To compete with a theoretical MSn

instrument (such as those in the ion trap category) one would require unrealistic (or likely

impossible) amounts of space and resources. Additionally, such analysis would require

impractical levels of starting material and unattainable efficiency for continued tandem analysis.

In the case of an ion trap MS, fragment ions are scanned, trapped and re-scanned through time,

using the same mass analyzer. This configuration allows for a theoretically infinite sequence of

dissociation and analysis limited only by the size and quantity of peptide to be analyzed. A

common practical limitation to any type of ion trap in use today, however, is that 100%

efficiency in product ion trapping is rarely, if ever, achieved. This presents the issue of "additive

loss" whereby the combined effects of inefficiency between each MS scan ultimately reaches a

point where product ion levels diminish below detectability. This effect also exists in beam-type

instruments as well and has been demonstrated to be much more severe as this type of MS/MS

instrument possesses a level of efficiency at least one order of magnitude lower than its ion trap

counterparts.

There are two maj or types of ion trapping instruments in use today. These are the

quadrupole ion trap mass spectrometer (QIT-MS) and the Fourier transform MS, of which the

lon Cyclotron Resonance MS (FT-ICR) is the most recognized. The quadrupole configuration is









of the simplest design and is also the most widely used among biological MS laboratories. Its

widespread use is a result of several factors, including considerably lower cost and space

commitment compared to its FT-MS alternatives. Its relative simplicity also translates to less

maintenance and ease of operation. Besides these factors, QIT-MS designs are considered a

workhorse for biological MS applications due to its speed and adaptability to most any

commonly-used ionization methods as well as its considerable resolution (106 Spectra per m/z)

and mass range of about 70,000 Da/charge.

Comparatively, Fourier transform instruments such as the FT-ICR require much more

commitment in terms of space and monetary resources. These instruments often occupy entire

laboratories and can exceed the one million dollar mark for instrumentation and hardware alone.

This instrument is also costly in terms of maintenance and required expertise for operation.

These negatives are offset, however, by the fact that the Fourier Transform lon Cyclotron

Resonance MS instrument is widely considered to be the superior device for biological and small

molecule analysis. Its mass resolution and mass accuracy capabilities are unsurpassed by any

instrument in current use. Its mass resolution routinely reaches the upper limits of any QIT-MS

device (106 Spectra per m/z) and its mass range exceeds 105 Da/charge, making it appealing for

top-down and MS-based structural experiments. Its mass accuracy can also reach sub-ppm

range, compared to QIT-MS devices which are bound by a 1-2 ppm limit.

While ion trap instruments have a number of advantages, beam instruments such as the

triple quadrupole (Q3) configuration are in heavy use in biological MS facilities, particularly

those interested in post-translational modification. Instruments of this design are used for an

assortment of PTM experiments such as precursor ion scanning, product ion scanning and neutral

loss scanning. This configuration lends itself to tandem processes in three sectors starting with









an initial scan of peptides in the first quadrupole (Q1) and a final, diagnostic scan in the third

quadrupole (Q3) with Q2 typically serving as a collision cell for fragmentation of input peptides

(collision-induced dissociation or CID). In precursor ion scanning experiments, peptides in Q1

are scanned over a pre-determined m/z range, all of which are subj ected to fragmentation in Q2.

Mass analysis by the third quadrupole selects for a specific CID product, often possessing some

form of post-translational modification. Neutral loss scanning processes are identical to

precursor ion scans with the exception that Q3 scans are broad range scans that match the initial

Q1 scan and facilitate the isolation of peptide fragments that have lost a signature mass

indicative of a particular PTM. Product ion scans, in contrast, start with the selection of a given

m/z value prior to CID and subsequent broad-range scanning of resulting fragment ions. This

approach allows for efficient PTM characterization of a single m/z.

Hybrid MS/MS devices merging desirable features of both ion trapping and beam-type

devices are also commonplace, not only for mass fingerprinting and PTM analysis but also for

metabolomic, toxicological and drug discovery applications. Two hybrid configurations are of

particular importance to these fields. These are the quadrupole/time-of-flight (QTOF) MS/MS

and the triple quadrupole/linear ion trap (QTrap) MS/MS hybrid instruments. The QTOF

arrangement is one of the original hybrid MS/MS devices. It contains an initial mass filter (Q1)

and a collision cell (Q2) with a time-of-flight (TOF) mass analyzer in the distal position. This

combination of quadrupole ion trapping technology and TOF mass analysis is advantageous for a

variety of reasons. One positive aspect of this instrument is that it is modular with respect to ion

sources and is designed to interface with either a pulsed ion source such as matrix-assisted laser

desorption ionization (MALDI) or a continuous ion source such as electrospray ionization (ESI)

and because of its TOF back-end arrangement, it is insensitive to changes in ionization source.









Perhaps the greatest advantage of a QTOF hybrid instrument is that the TOF mass analyzer can

measure a full daughter ion spectrum and thus has unparalleled sensitivity. The TOF component

is also theoretically limitless with respect to mass range; however, in practice, a mass limit is

determined by the ion source and quadrupole mass filters in the proximal position. While the

time-of-flight mass analyzer is the maj or asset to a hybrid instrument of this type, it is also the

major drawback. Because it is strictly a mass analysis tool and has no scanning capabilities, the

TOF component of the QTOF hybrid MS/MS limits the instruments capabilities to a scan-and-

detect function and prevents typical PTM analyses, such as precursor ion scanning, from being

performed.

Another hybrid device that is, arguably, the most versatile combination MS/MS

instrument is known commercially as the QTrap. It is a quadrupole/linear ion trap hybrid

machine arranged in a triple quadrupole format. This instrument combines the PTM analysis

capabilities of a triple quadrupole with the enhanced ion trapping efficiency of a linear ion trap to

provide structural/qualitative data and quantification simultaneously. The key component of the

QTrap, the linear ion trap, is the primary reason for its sensitivity and versatility. The enhanced

design of a linear ion trap as opposed to other quadrupole or three-dimensional ion traps is that

fragment ion ej section from the trap during scanning occurs bilaterally through exit slits that

either lead to separate mass analyzers or channel all fragment ions to a third quadrupole for

analysis. This configuration translates to a theoretical 100% rate of efficiency with respect to

fragment ions detected from the trap as opposed to conventional ion traps such as a quadrupole

arrangement in which fragment ion expulsion is an elastic event, leading to significant loss of

material through the proximal (ion source) end of the trap.









Finally, an aspect of mass spectrometry not to be overlooked is data acquisition and

analysis, which has become the rate-limiting step in the high-throughput proteomic workflow.

Advances in mass scanning and detection have pushed MS/MS to the limits of what we are able

to manage given the software technology currently coupled to this process. Additionally, the

staggering amounts of spectral data that can be generated often become too much to manage

efficiently. On-going development of new software and MS/MS data search algorithms as well

as frequent improvements on existing algorithms is helping to keep data analysis and

management up to speed with increasingly rapid data production by modern MS systems.

From the computational aspect of mass spectrometry, database searching algorithms have

received the most attention over the past several years as peptide mass fingerprinting (PMF) has

become routine. Such popular algorithms as ChemApplex, PeptIdent or the more recently

developed Aldente all operate on slightly different principles, accounting for such factors as peak

intensity, mass accuracy and matching hit frequency; however, the most widely used and most

recognizable PMF search algorithms are MOWSE (Mascot and MS-FIT) and XCorr

(SEQUEST). Both MOWSE and SEQUEST are first-generation algorithms widely used for

large-scale database searching and MS/MS peptide matching but each operates in a distinctively

different way.

The MOWSE algorithm is a probability-based search tool which assigns a score to each

matching MS peptide based primarily on database size and governed by user-defined parameters

which set acceptable margins for mass difference values and PTM occurrence. MOWSE scoring

also uses signal intensities and consecutive fragment heuristics as additional guidelines for

peptide matching.









The most widely used alternative to the MOWSE algorithm is SEQUEST. This program

differs from Mascot or MS-FIT programs in that it uses a cross-correlation (XCorr) approach to

assigning probable peptide hits to database members. This is primarily accomplished through

comparative analysis of m/z and intensity values of actual peptide spectra with the top 500

candidate peptides predicted from the database. A Einal identification and ultimate confidence

score is then assigned based on cross-correlation of the top two hits of a given protein.

SEQUEST also has some similarity to the MOWSE algorithm in that it gives consideration to

signal intensity and does not make assignments exclusively based on m/z values.

Both SEQUEST and MOWSE programs have gained enormous popularity in the

proteomic field but unfortunately, their popularity is not entirely attributed to accuracy. Perhaps

the main selling point for each of these algorithms is that they produce a user-friendly matching

report with an easy-to-interpret confidence score. They also have the capacity for customization

where fixed and variable modifications, mass tolerances and charge states can all be defined by

the user. These and many other MS/MS search algorithms tend to fall short where common

PMF pitfalls exist. These primarily include instances where detected peptide masses do not

match appropriately with database members for a variety of reasons. These reasons include (but

are not limited to) errors within the database, sequence polymorphism, non-specific cleavage of

peptides and, of particular importance, failure to match due to PTM. These represent some of

the greatest obstacles to overcome in developing an accurate and dependable tandem MS search

algorithm, along with the inclusion of an uncomplicated user interface. Efforts to alleviate these

complications have included more advanced algorithms such as Aldente, which matches peptides

to database members based on two-stage scoring at both the protein and peptide level or

assumptive algorithms such as FindPept which can account for mass deviations resulting from









non-specific cleavage, common contaminants and a wide range of PTMs annotated in the Swiss-

Prot feature tables.

Special Considerations for Extreme Proteomes

Inherent properties of certain proteomes often prevent conventional analysis with

standard tools and methods. In instances where proteomes are seemingly resistant to routine

manipulation, alternative approaches have been adapted. This section will briefly discuss two

common examples of extreme proteomes and measures that have been taken to enhance the

efficiency with which they can be explored. These examples include proteomes with extreme pl

value bias (highly acidic or basic proteomes) and membrane-associated protein components.

Proteomes with extreme pl value bias

In silico analysis of several select prokaryotic proteomes has revealed a bimodal pattern

in their migration with respect to isoelectric point value (Lamberti et al., 2007). In fact, the

maj ority of proteomes subj ected to this analysis clustered in pl ranges of 4-7 and 9-11 (Lamberti

et al., 2007). Similar pl bias has been demonstrated in the archaea and in certain cellular sub-

proteomes of eukaryotes (Corton et al., 2004; Joo and Kim, 2005; Kirkland et al., 2006; Lamberti

et al., 2007). Furthermore, highly modified sections of a proteome such as those possessing

numerous phosphorylations or glycosylations can also exhibit poor resolution during charge-

based separations due to impartation of strong net negative charges. Methods have been

developed in these special cases that allow for enhanced resolution and thus more complete

analysis of these proteins, particularly where gel-based proteomic strategies are employed

(Molloy et al., 2002; Kirkland et al., 2006).

The issue of protein acidity and its complication of the proteome has had a notable

impact on proteomic investigations of prokaryotes. Average pl values have been determined to

be remarkably low among haloarchaeal species like Haloferax volcanii (4.5) and Halobacterium









NRC-1 (4.9) and even organisms of importance in food and industrial microbiology such as

Lactococcus lactis with a large portion of its proteome resolving near 5.0 (Kennedy et al., 2001;

Guillot et al., 2003; Joo and Kim, 2005). Common methods for improving resolution of acidic

proteomes have included the use of ultrazoom ranges for IEF separation. Commercially

available immobilized pH gradients (IPG) with ranges as narrow as 3.5-4.5 or 3.9-5.1

(Amersham Biosciences and Bio-Rad) are in common use while custom IPG ranges can be

purchased or synthesized that encompass even narrower stretches of the pl scale. The use of

narrow-range carrier ampholytes (amphoteric electrolytes) has been demonstrated to have some

effect on improving resolution during focusing as well. Perhaps the simplest measure for

separation of clustered proteomes, however, is the use of larger IPG/gel formats, with 11 cm

being a standard improvement over the mini-gel (7 cm) format and 18 and 24 cm formats being

commercially available as well. All of these improvements have been implemented together

with vast improvements in resolving power of acidic proteome clusters using gel-based

proteomic methods.

Extremely basic (alkaline) proteomes have also posed similar problems with proteomic

surveys and like extreme proteome acidity, alkalinity places serious restrictions on our ability to

thoroughly investigate the proteomes of many common, well-studied organisms. In fact,

proteins with alkaline pl values (> 7.5) have been shown to constitute significantly large portions

of a wide variety of organisms such as E. coli (3 8%), M\~ethanococcus j~annaschii (49%) and

Helicobacter pylori (62%). Additionally, in eukaryotes, the average pl distribution tends to be

trimodal with clustering at 5, 7 and 9, leaving hundreds of proteins out of reach. Earlier attempts

to effectively resolve proteins within the alkaline range involved the use of Non-Equilibrium pH

Gradient Electrophoresis (NEPHGE) but resulted in a significant resolution deficit when










compared to steady-state IPG separation. Attempts were also made to adapt conventional IPG

focusing methods for basic proteins; however, this required a highly basic buffer system for

establishing the pH gradient and overcoming the buffering power of water. This approach

eventually let to reverse endo-osmotic flow of water towards the anode during focusing and

ultimately interfered with steady-state focusing conditions. In addition, extreme pH buffers were

found to have a degenerative effect on acrylamide IPG support matricies, causing hydrolysis and

preventing normal IEF. Current efforts towards resolving the basic proteome have involved

combined developments in commercial and custom alkaline-range ultrazoom IPGs (typically in

focused regions between 8 and 1 1) and the use of immobilized pH support material alternatives

such as dimethylacrylamide (DMA) and acryloylaminoethoxyethanol (AAEE).

Hydrophobic proteomes and membrane proteomics

As much as one-third of the genes in a given organism encode for membrane-bound

proteins (Hopkins and Groom, 2002; Wu et al., 2003). Additionally, as much as two-thirds of all

medications being prescribed today target membrane-associated components (Hopkins and

Groom, 2002). When considering these statistics, it is evident that there is a definite need for in-

depth proteomic analysis of this considerable subset of proteins. Unfortunately, these proteins

come with an inherent set of difficulties that include their hydrophobic nature and thus a

tendency to aggregate and to resist solubilizaiton. These properties make membrane proteomics

one of the most challenging endeavors in biology.

In recent years, simple approaches have been taken to adapt in-gel proteomics to

membrane protein analysis. Most notably, modified two-dimensional PAGE techniques have

been devised which rely upon electrophoretic separation of proteins in the first dimension as

opposed to IEF and which use strong cationic detergents such as 1 6-benzyldimethyl-n-

hexadecylammonium chloride (16-BAC) and cetyl trimethyl ammonium bromide (CTAB) or









strong anionic detergents like SDS (Braun et al., 2007). Because two-dimensional SDS-PAGE

(SDS/SDS-PAGE) results in a diagonal migration pattern due to separation by protein mass in

both dimensions, proteins can be resolved individually by deviation from the diagonal which is

achieved through varying the buffer composition in each dimension (Braun et al., 2007). An

additional adaptation to the 2DE format has been the use of native gel electrophoresis in the first

dimension and denaturing SDS-PAGE in the second. Native gel electrophoresis has typically

been used for determination of membrane complexes or for in-gel activity assays but further

modification of this process has given rise to colorless native gel electrophoresis (CN-PAGE)

and blue native gel electrophoresis (BN-PAGE) (Braun et al., 2007; Heinemeyer et al., 2007).

These processes differ only by the pl range in which they resolve hydrophobic proteins natively.

Those proteins with pl values less than 7 have been found to resolve reasonably well in colorless

gels, however, it was discovered that the addition of the negatively charged protein binding dye

Coomassie Brilliant Blue G-250 to samples higher on the pl scale could provide a charge shift to

the proteins and allow for better resolution (Braun et al., 2007; Heinemeyer et al., 2007).

Finally, gel-free methods have also been explored for enhanced fractionation and analysis of

membrane proteins. In particular, zone electrophoresis using a free-flow apparatus (ZE-FFE) has

been of interest in this Hield primarily for its greater sample loading capacity and simplicity in

comparison to in-gel hydrophobic protein analysis (Zischka et al., 2006; Braun et al., 2007).

Zone electrophoresis operates by separating membranes, organelles, proteins and even entire

cells based on their charge-to-mass ratio where samples are placed into an electrophoretic buffer

Hield of uniform conductivity and pH oriented perpendicular to the buffer stream. The speed and

versatility of this method in membrane proteomics has made it an appealing method for large and

insoluble components of any proteome.










Project Rationale and Design

The goals of this study are focused on identifying native substrates of the proteasomal

degradation system in the haloarchaeal model organism Haloferax volcanii and determining

potential signals for degradation/destabilization of substrate proteins in an ubiquitin-free system.

Established genetic tools for detailed analysis of Haloferax volcanii have allowed proteasome-

defieient mutants to be created through deletion or disruption of the primary regulatory particles

of the proteasome. Development of these mutants, in addition to chemical inhibition of the

proteasome core particle, has facilitated a degradomic approach to be used to identify proteins in

the absence of proteasomal activity levels which are altered and/or isoform conformations. The

potential for these proteins as proteasomal substrates was also studied. Extensive high-

throughput comparative proteomic analysis has led to the establishment of a putative substrate

list of 10 proteins for which polyclonal antibodies have been produced and subsequent pulse-

chase and immunoprecipitation methods have been applied to further investigate their status as

substrates of the proteasome. These analyses have also revealed significant differences in the

levels of accumulating phosphorylated proteins between wild-type cells and those lacking full

proteasome-activating nucleotidase activity; a potential indicator of a substrate recognition

mechanism in the prokaryotic system. Identification of substrate proteins in this study represent

the first confirmed proteasomal substrate identifications in any archaeon and among the first in

prokaryotes. Additionally, these proteins may serve as a starting point for the detailed

characterization of a degradation signaling system comparable to that of the ubiquitin cascade in

eukaryotes.















20S Core Particle


Figure 1-1. Proteasomal core particle arranged in typical alpha-beta-beta-alpha ring order. Beta
ring structures possess N-terminal threonine residues which act as catalytic sites for
protease activity.


ac ring


p ring
























PanA




,





..


.

~**


atl/(


at2/(


Figure 1-2. Assorted composition and association combinations for the 20S proteasomal core
particle and the PAN regulatory complexes of Haloferax volcanii. The ability to
produce two separate alpha subunit types as well as two distinctly different PAN
protein subunits suggests the assembly of multiple sub-specialized proteasomal
complexes. The pattern of association between the various PAN complexes and the
assortment of core particle arrangements is undetermined, however. All proteasome
variations presented here have been confirmed with the exception of that which
possesses homogenous alpha rings of different types.


~51 ~51 ~51


acl/ac2 (hetero)/(


acl/ac2 (homo)/(S









CHAPTER 2
METHODS AND MATERIALS

Chemicals and Reagents

Immobilized metal ion affinity chromatography (IMAC) and metal oxide affinity

chromatography (MOAC) materials were supplied by Qiagen (Valencia, CA) and Perkin-

Elmer (Waltham, MA), respectively. Trizol reagent and Pro-Q Diamond phosphoprotein

stain were purchased from Invitrogen (Carlsbad, CA). Mevinolin was from Merck

(Whitehouse Station, NJ). Two-dimensional gel electrophoresis (2-DE) materials were

supplied by BioRad (Hercules, CA). Reversed phase C18 spin columns and trypsin were

respectively purchased from The Nest Group, Inc. (Southborough, MA) and Promega

(Madison, WI). Pre-packed HPLC C4 and C18 reversed phase columns were supplied by

LC Packings, San Francisco, CA). Restriction enzymes and other DNA modifying

enzymes were from New England BioLabs (Ipswitch, MA). Hi-Lo DNA and Precision

Plus protein molecular mass standards were from Minnesota Molecular, Inc.

(Minneapolis, MN) and BioRad, respectively. All other chemicals and reagents were

supplied by Sigma-Aldrich (St. Louis, MO) and Fisher Scientifie (Pittsburgh, PA).

Strains, Plasmids and Culture Conditions

Strains and plasmids used and/or constructed through the course of this work are

listed in Table 2. 1. All Haloferax volcanii cultures (with the exception of those used for

genetic studies, described in the next section) were grown in ATCC 974 medium or

defined high-salt medium (Allers et al., 2004) at 42oC with orbital shaking at 200 rpm

(see Table 2-2 for culture conditions of specific experiments). Growth was monitored at

an optical density using a wavelength of 600 nanometers (OD 600 nm) using a 50-2000

CIl, 220-1600 nm Uvette (Eppendorf) and SmartSpec 3000 spectrophotometer (BioRad).









To determine growth rates of proteasome inhibitor-treated cells, triplicate cultures of 6 ml

were inoculated at 0.33% (v/v) with log-phase cells (OD 600 nm of 0.426) grown in

liquid culture from freshly isolated colonies. Proteasome inhibitor cla~sto-lactacystin

beta-lactone (cLPL; 0, 20, and 30 CIM) with 0.5% (v/v) dimethyl sulfoxide (DMSO) was

added at an OD 600 nm of 0.20 (15 h). Growth was monitored at 12 separate intervals

over the course of 55 h. For preparation of proteins for two-dimensional PAGE analysis

of inhibitor-treated samples, cells were grown similar to above (see Table 2-2 for specific

conditions). Log phase cells (OD 600 nm of 0.55, 1 ml in 13 x 100 mm tubes) from

freshly isolated colonies were used as a 0.33% (v/v) inoculum for triplicate cultures (25

ml in 125 ml Erlenmeyer flasks). Group 1 cultures were grown to an OD 600 nm of 0. 15,

supplemented with 0.5% (v/v) DMSO & 20 pLM cLPL, and harvested after 18 to 24 h of

growth (final OD 600 nm of 1.7 to 1.9). Group 2 cultures were grown to an OD 600 nm

of 0.2, supplemented with 0.5% (v/v) DMSO & 30 CLM cLPL, and harvested at an OD 600

nm of 0.8-1.0.

Escherichia coli DH5a and GM2163 strains (NE BioLabs) were respectively used

for routine cloning and purification of plasmid DNA for transformation of H. volcanii

(Cline et al., 1989). E. coli strains were grown at 37oC (200 rpm) in Luria Broth (LB)

supplemented with 100 mg ampicillin, 50 mg kanamycin and/or 30 mg of

chloramphenicol per liter as needed.

For the analysis of proteins through Immobilized Metal Affinity Chromatography

(IMAC) or Metal Oxide Affinity Chromatography (MOAC), cells were grown as

indicated in Table 2-2. Cells were harvested at a final OD 600 nm of 1.0-1.2 for both

analyses and were collected by centrifugation at 10,000 to 14,000 x g at 4oC. Cell pellets









that were not immediately subj ected to protein extraction were flash frozen in liquid

nitrogen and stored at -80oC for up to 6 months.

Construction of panzA Knockout Mutant GG102

For genetic analysis, H. volcanii strains were grown at 42oC in YPC medium as

described by Allers et. al. (2004). Medium was supplemented with 4 5 mg mevinolin

per ml and/or 0.1 mg novobiocin per liter as needed. Haloferax volcanii DS70

(Wendoloski et al., 2001) served as the parent strain for the generation of the panA

mutant strain GG102. The chromosomal copy of the GG102 panA has a 187-bp deletion

and an insertion of a modified Haloarcula hispanica hmgA gene (hmgA*") encoding 3-

hydroxy-3 -methylglutaryl coenzyme A (HMG-CoA) reductase which renders H. volcanii

cells resistant to mevinolin (Wendoloski et al., 2001). The following approach was used

to generate GG102 (through the efforts of G. Gil). Vent DNA polymerase was used for

polymerase chain reaction (PCR) amplification of the panA gene from H. volcanii

genomic DNA using primers (Primer 1: 5'-CATATGATGACCGATACTGTGGAC-3'

and Pri mer 2: 5'- GAAT TCAAAAC GAAAT CGAAG GAC -3') (Ndel and EcoRI site s i n

bold). Haloferax volcanii genomic DNA was prepared for PCR from colonies of cells

freshly grown on YPC plates. In brief, cells were transferred into 30 Cl1 deionized H20

using a toothpick, boiled (10 min), chilled on ice (10 min), and centrifuged (10 min,

14,000 x g). The supernatant (10 Cl) was used as the template for PCR. The 1.28-kb

PCR fragment was cloned into pCR-BluntlI-TOPO (Invitrogen) to generate plasmid

pJAM636. The fidelity of this insert was confirmed by Sanger dideoxy DNA sequencing

at the University of Florida Interdisciplinary Center for Biotechnology Research. The

1.5-kb NotI fragment of plasmid pMDS99 which carries hmgA*" (Wendoloski et al.,

2001) was inserted into the BbvCI to Nrul sites of pan2A carried on pJAM636 by blunt-









end ligation to generate plasmid pJAM906. This suicide plasmid (pJAM906), which

carries hnagA*" and panA in the same orientation as determined by restriction mapping

using KpnI and Agel, was transformed into H. volcanii DS70, and recombinants were

selected on YPC solid medium supplemented with mevinolin (YPC + Mev). Isolated

colonies were re-streaked for isolation on fresh YPC + Mev and screened by PCR using

primers which annealed outside the panA region of suicide plasmid pJAM906 (Primer 3:

5' -TACGATAAGGACTCGGCGTCGCAGC-3 and Primer 4: 5'-TACGTC

GCGTTCGC GGCGTAGTCAC-3'). Clones, which generated the appropriate PCR

product, were further screened by Western Blot using anti-PanA, -PanB, -al, -a2 and -P

antibodies as previously described (Reuter et al., 2004). For complementation studies, H.

volcanii-E. coli shuttle plasmids carrying panA and panB genes were transformed into

mutant strain GG102. These plasmids, pJAM650 and pJAM1012, encode C-terminal

poly-histidine tagged PanA and PanB (PanA-His6 and PanB-His6), respectively, under

control of the Halobacterium cutirubrunt rRNA P2 promoter and were constructed by

C.J. Reuter and J.A. Maupin-Furlow as follows. The 1.2-kb Ndel-HindIII fragment of

pJAM642 and the 1.24-kb Ndel-Xhol fragment of pJAM1006 (Reuter et al., 2004) were

separately blunt-end ligated into the Ndel-Blpl site of pJAM202 (Kaczowka and Maupin-

Furlow, 2003) to replace psnzB-his6 with panA-his6 (pJAM650) and panB-his6

(pJAM1012), respectively.

Protein Preparation and Quantification

Protein was extracted using either a standard method (Karadzic and Maupin-

Furlow, 2005) or Trizol-mediated method optimized from the standard method (Kirkland

et al., 2006) of protein extraction as described below. Samples were dried through

vacuum centrifugation and stored at -80oC. Protein concentration was determined by









Bradford Protein Assay using bovine serum albumin as a standard according to supplier

(BioRad).

Standard Protein Extraction

The standard protein extraction method described by Karadzic and Maupin-

Furlow (2005) was used at the onset of this study and was performed as follows.

Haloferax volcanii cells were grown as indicated in Table 2-2 and harvested by

centrifugation at 10,000 x g for 15 min at 40C. Cell pellets were washed two times with

300 ml of 1.5 M Tris-HCI (pH 7.1i), and the final pellet was resuspended in 2 ml of 50

mM Tris-HCI (pH 7. 1) containing 0.8% (w/v) sodium dodecyl sulfate (SDS) and

supplemented with phosphatase and protease inhibitor cocktails (Sigma) according to the

supplier' s instructions. Resuspended cells were passed through a French pressure cell at

10,000 psi. Extract was clarifed by centrifugation at 15,600 x g for 20 min at 40C. Cell

lysate was boiled for 5 min, chilled on ice for 5 min, and incubated with 300 U of

Benzonase for 30 min at room temperature. Final cell extract was obtained by

centrifugation at 15,600 x g for 30 min. Cell extract was precipitated by 50% (v/v) cold

acetone, followed by centrifugation at 14,000 x g for 10 min. The pellets were

resuspended in 250 mM glycerol supplemented with 10 mM triethanolamine (TEA).

Sample was diluted (80-100-fold) and stored in isoelectric focusing (IEF) rehydration

buffer (250 mM glycerol, 10 mM triethanolamine (TEA), and 4% (w/v) 3-

cholamidopropyl dimethylammonio-1 -propanesulfonate (CHAPS) detergent).

Trizol-Mediated Protein Extraction

Haloferax volcanii cells were grown as indicated in Table 2-2 and were collected

by centrifugation at 12,000 x g for 10 min at 4 OC. The resulting pellet was resuspended

in 1 ml of Trizol per 100 mg cells (wet weight). The sample was incubated at 65-700C









for 15-20 min to fully homogenize the cells. The mixture was incubated at room

temperature for 5 min to allow debris to settle. After the addition of 0.2 ml chloroform

(Sigma) per 1 ml of starting Trizol volume, the sample was mixed by vortexing for 10-15

s and incubated at room temperature for 5 min to allow phase separation. Once separated,

the sample was centrifuged at 12,000 x g for 15 min at 40C. The upper aqueous layer

containing extracted microarray-quality RNA was removed, and the lower phenol layer

was combined with 0.3 ml of 100% ethanol per 1ml of initial Trizol volume to precipitate

DNA. This Trizol-containing solution was mixed by inversion and incubated at room

temperature for 2-3 min to allow nonproteinaceous material to settle. The sample was

centrifuged at 2000 x g for 5 min at 40C to pellet remaining nucleic acid. The

phenol/ethanol supernatant was removed from the DNA pellet and placed into a new

tube, where the protein was precipitated by the addition of 1.5 ml of 100% isopropanol

per 1 ml of Trizol initially used. The sample was incubated at room temperature for 15

min to allow for complete protein precipitation before collecting the protein by

centrifugation at 12,000 x g for 10-15 min at 40C. The resulting protein pellet was

mechanically disrupted, with the aid of a sterile pipette tip or ethanol-cleansed spatula, in

2 ml of 300 mM guanidine HCI (Sigma) in 95% ethanol to remove residual phenol. The

mixture was incubated for 20 min at room temperature prior to centrifugation at 12,000 x

g for 5 min at 40C. This step was repeated four to six times to thoroughly remove phenol

from the sample. The resulting protein pellet was washed twice with 2 ml of cold 100%

acetone to remove any residual salt (12,000 x g for 5 min at 40C) and air-dried to rid the

sample of acetone. The dry pellet was resuspended in an IEF-compatible buffer

(described above). Appropriate quantities of this resuspended sample (50-250 Cpg protein)









were added to IEF rehydration buffer (7 M urea, 2 M thiourea, 4% [w/v] CHAPS, 2 mM

tributylphosphine, 0.2% [v/v] ampholyte 3-5 and 3-10 mixture [2:1], and 0.001% [w/v]

bromophenol blue) and prepared for rehydration and subsequent focusing. Remaining

samples, including unused dry protein pellet as well as resuspended sample, were frozen

in liquid nitrogen and stored at -700C for up to 1 year.

Protein Modification

Protein Reduction, Alkylation and Tryptic Digestion

Dried protein samples (300 Cpg) from Trizol preparations were resuspended in 100

Cl1 of 50 mM NH4HCO3 (pH 7.5). Samples were reduced by the addition of 5 Cl1 of 200

mM dithiothreitol (DTT solution) (1 h, room temperature or 21oC). Samples were

alkylated by the addition of 4 Cl1 of IM iodoacetamide (1 h, 21oC). Alkylation was

stopped by the addition of 20 Cl1 of DTT solution (1 h, 21oC). Samples were digested

with a 1:20 microgram ratio of trypsin to protein for 18-24 h at 37oC. Digested peptides

were purified using 300 Cl1 C18 spin columns and dried under vacuum centrifugation. In-

gel proteins were reduced, alkylated, and digested with trypsin using an automated

platform for protein digestion (ProGest, Genomics Solutions, Ann Arbor, MI).

Peptide Methyl Esterification

2 N methanolic acid was generated by adding 60 Cl1 of 99% (v/v) acetyl chloride

dropwise to 300 Cl1 of 100% anhydrous methanol in a glass tube. The mixture was sealed

and incubated (10-15 min, 21oC). Dried tryptic peptides were mixed with 30 Cl~ of the

methanolic acid reagent and incubated in a sealed jar containing desiccate material for 90

min at 21oC. Methyl esterified samples were dried under vacuum centrifugation and

reconstituted in mobile phase B [0.1% (v/v) acetic acid, 0.01% (v/v) trifluoroacetic acid

and 95% (v/v) acetonitrile] for MS/MS analysis.









Phosphoprotein Enrichment and Purification

Immobilized Metal Affinity Chromatography

Phosphoprotein enrichment by immobilized metal affinity chromatography

(IMAC) was performed using a Phosphopurification System according to supplier's

instructions (Qiagen) with the following modifications: protein (2.5 mg) prepared via the

standard method of extraction (Karadzic and Maupin-Furlow, 2005) was resuspended in

25 ml of supplied lysis buffer to a final concentration of 0. 1 mg protein per ml. Six 500

Cl1 fractions per sample, as opposed to the suggested four, were collected for analysis.

Titanium Dioxide Phosphopeptide Enrichment

Metal oxide affinity chromatography (MOAC) ofH. volcanii DS70 and GG102

tryptic peptides (with and without methyl esterification) was performed using the Phos-

Trap Phosphopeptide Enrichment Kit (Perkin Elmer, cat. no. PRT301001KT) with the

following modifications: samples were agitated in the presence of 40 Cl1 of elution buffer

as opposed to the recommended 20 Cl~. Samples were incubated with the TiO2 TOSin foT

10 min instead of 5 min and were incubated with elution buffer for 15 min instead of 10

min. Resulting samples were dried under vacuum centrifugation at 42oC for 30 min and

reconstituted in mobile phase B for MS/MS analysis.

Nickel Purification of Polyhistadine-Tagged Proteins

Escherichia coli BL21(DE3) (Table 2-1) cells in culture at 37oC were induced to

synthesize polyhistidine-tagged proteins through the addition of 1 mM isopropyl-beta-D-

thiogalactopyranoside (IPTG). Cells were incubated at 30oC (200 rpm) for 12 h post-

induction. Cultures (12-15 h) were collected through centrifugation (10,000 x g) and

resuspended in 30 ml of lysis buffer (20 mM Tris-HC1, pH 7.2 and 150 mM NaC1) prior

to mechanical lysis using a French pressure cell (SLM-Aminco Cat. No. FA-030)









operated at 20,000 psi. Lysate was clarified through centrifugation at 10,000 x g and

tagged proteins were purified as follows. A 5 ml HisTrap HP Ni Sepharose column

(Amersham-GE Biosciences, Cat. No. 17-5248-01) was equilibrated with 10 ml of buffer

A (20 mM Tris-HC1, pH 7.2 with 150 mM NaCl and 5 mM imidazole). The 30 ml

sample was applied to the column at a flowrate of approximately 2 ml per min. The

column was washed with 20 ml of buffer A and 20 ml of buffer B (20 mM Tris-HC1, pH

7.2 with 150 mM NaCl and 60 mM imidazole). The polyhistadine-tagged sample was

eluted into 10-1 ml fractions with 10 ml of buffer C (20 mM Tris-HC1, pH 7.2 with 150

mM NaCl and 500 mM imidazole). Fractions were analyzed by 12% SDS-PAGE and

Coomassie staining.

2D PAGE Analysis and Imaging

Isoelectric focusing was performed using 11-cm immobilized pH gradient (IPG)

strips (Bio-Rad) with a pl range of 3.9-5.1. The strips were loaded with 150 Cpg of

protein in rehydration buffer (as described above) at 20 OC for 18-24 h. Rehydrated IPG

strips were placed in an 11-cm focusing tray (Bio-Rad) and covered with 2-3 ml of

mineral oil. The proteins were focused at a maximum of 8,000 V for 35,000 volt-hours

(V-h) at 200C. Once complete, the strips were removed from the mineral oil, rinsed with

proteomics grade water, and equilibrated for 10 min in 2 ml of equilibration buffer A

(375 mM Tris-HC1, pH 8.8 with 6 M urea, 2% [w/v] SDS, 20% [v/v] glycerol, and 2%

[w/v] dithiothreitol [DTT]) and again for 10 min in 2 ml equilibration buffer B (375 mM

Tris-HC1, pH 8.8 with 6 M urea, 2% [w/v] SDS, 20% [v/v] glycerol, and 2.5% [w/v]

iodoacetamide). The equilibrated IPG strips were placed in the upper well of an 11l-cm

Criterion precast gel (Bio-Rad) and set in place with a 0.5% (w/v) agarose overlay (Bio-

Rad). The second dimension was run at 200 V for 55 min at 160C. The completed gel









was removed from the cassette and stained overnight in 150 ml of SYPRO Ruby

fluorescent protein stain (Bio-Rad) or ProQ Diamond phosphoprotien stain (Molecular

Probes) and destined according to the supplier' s instructions. The gels were imaged

with a Molecular Imager FX Scanner (Bio-Rad) with a 532-nm excitation laser and a

555-nm LP emissions filter. Acquired images were analyzed with PDQuest (version

7.0.1) software (Bio-Rad).

Liquid Chromatography and Mass Spectrometry

Reversed Phase HPLC Coupled with Nano-ESI-QTOF (QSTAR) MS/MS

Capillary RP HPLC separation of tryptic peptides with and without methyl

esterification (desalted with a PepMap C18 cartridge) was performed using a PepMap

C18 column (15 cm x 75 Cpm i.d.) and Ultimate Capillary HPLC System (LC Packings,

San Francisco, CA). A linear gradient of 5% to 40% (v/v) acetonitrile for 25 min at 200

nl-min was used for separation. MS/MS analysis was performed online using a hybrid

quadrupole time-of-flight instrument (QSTAR XL hybrid LC/MS/MS) equipped with a

nanoelectrospray source (Applied Biosystems, Foster City, CA) and operated with the

Analyst QS v1.1 data acquisition software. Information dependent acquisition (IDA) was

employed in which each cycle consisted of a full scan from m/z 400-1500 (1 sec)

followed by MS/MS (3 sec) of the two ions that exhibited the highest signal intensity. In

the full scan acquisition mode, ions were focused through the first quadrupole by

focusing and declustering potentials of 275 V and 55 V, respectively, and guided to the

TOF region via two quadrupole filters operated in rf-only mode. Ions were orthogonally

extracted, accelerated through the flight tube (plate, grid, and offset voltages were 340,

380, and -15 V, respectively), and refocused to a 4-anode microchannel plate detector via

an ion mirror held at 990 V. The same parameters were utilized with MS/MS mode of









operation; however, the second quadrupole was employed to filter a specific ion of

interest while the third quadrupole operated as a collision cell. Nitrogen was used as the

collision gas and collision energy values were optimized automatically using the rolling

collision energy function based on m/z and the charge state of the peptide ion.

Three-Dimensional LCQ Deca lon Trap MS

A portion of the IMAC-enriched samples separated by 1D SDS PAGE and were

analyzed with a Thermo LCQ Deca quadrupole ion trap MS (LCQ Deca MS) in line with

a 5 cm x 75 Cpm inner diameter Pepmaptm C18 5 Cpm/ 300 A+ capillary column (LC

Packings). The RP HPLC C18 column operating upstream of the MS system was run

with a 60-min gradient from 5% to 50% mobile phase B with a flow rate of 12 Cl-minl

MS parent ion scans were followed by four data-dependent MS/MS scans.

MS Data and Protein Identity Analyses

Spectra from all experiments were converted to DTA files and merged to

facilitate database searching using the Mascot search algorithm v2. 1 (Matrix Science,

Boston, MA) against the deduced H. volcanii proteome (05/26/06 assembly;

http://archaea.ucsc. edu/). Search parameters included trypsin as the cleavage enzyme.

Carbamidomethylation was defined as the only Eixed modification in the search while

methionine oxidation, pyro-glu from glutamine or glutamic acid, acetylation, and

phosphorylation of serine, threonine and tyrosine residues were set as variable

modifications. Mass tolerances for all LCQ analyses were 2 Da for MS and 1 Da for

MS/MS. Mass tolerances for all QSTAR analyses were 0.3 Da for both MS and MS/MS.

Protein identifications for which a probability-based MOWSE score average of 30 or

above was not assigned were excluded. Proteins were only considered unique to GG102

panA or DS70 if protein identities were exclusive for at least 2 samples per strain.









Transmembrane spanning helices were predicted using TMHMM v2.0 (Krogh et al.,

2001). Phosphosites were predicted using NetPhos v2.0 (Blom et al., 1999). Proteins

were categorized into Clusters of Orthologous Groups (COGS) using COGNITOR

(Tatusov et al., 2000). Basic local alignment search tool (BLAST) was used locally at

www. ncbi.nlm.nih.gov.

All phosphopeptides predicted by mass spectrometry via the Mascot database

search algorithm were filtered to include only those peptides that were top-ranking hits

and had E-values less than 3.0. The resulting list of phosphopeptides spectra were

reviewed manually with Analyst QS v1.1 acquisition software (Applied Biosystems,

Foster City, CA), Scaffold software v1.6 (Proteome Software, Portland, OR) and with

spectral data generated through the Mascot search algorithm. Detected peptide fragment

masses were compared with those generated in a theoretical peptide fragmentation

spectrum generated by Protein Prospector v 4.0.8 (Baker, P.R. and Clauser, K.R.

http ://prospectoruucsf edu). Mass additions of 80 Da (HPO3) Or neutral losses of 98 Da

(H3PO4) WeTO COnfirmed mathematically through analysis of peptide fragmentation and

correlation of detected diagnostic ions present in the corresponding spectrum.

Consideration was also given to spectral quality including peak intensity and

completeness of generated ion series when screening spectral data for confirmation of

modification. Theoretical lists of modified internal ions were also generated, searched

and assigned in the actual spectra.

Radioactive asS Pulse-Chase Labeling of H. volcanii Proteins

Haloferax volcanii cultures (200 ml in 500 ml Erlenmeyer flasks) were grown in

defined high-salt media (Allers et al., 2004) at 42oC (200 rpm) and were harvested at an

OD 600 nm of 0.4-0.6 through centrifugation (10,000 x g at 40oC for 15 min). Cell









pellets were resuspended in 80 ml of defined high-salt media without methionine and

were allowed to purge existing methionine by incubation at 42oC for 1-2 h with orbital

agitation at 200 rpm. Cell cultures were then pulsed with 2 mCi of 35S-labeled

methionine and cysteine (Easytag Express, Perkin-Elmer Cat. No. NEG772002MC) for

10 min at 42oC and 200 rpm. Labeled cells were dispensed into 10 ml aliquots over 8-15

ml conical tubes containing 1 ml of stop solution (20% [w/v] sodium azide, 100 Cpg/ml

puromycin; 0-min time point only) or 1 ml of chase solution (10 mg/ml cold methionine

10 mg/ml cold cysteine, 100 Cpg/ml puromycin and 150 Cl1 general protease inhibitor

cocktail; Sigma). Tubes were mixed promptly and either instantly chilled on ice (O-min

time point only) or returned to 42oC with orbital agitation at 200 rpm. Tubes were

removed from incubation at 1, 3, 5, 10, 30 and 60 min and 12 hours, mixed with 1 ml of

stop solution and immediately chilled on ice. Stopped samples were centrifuged at

10,000 x g for 10 min, and supernatant was removed. Resulting pellets were resuspended

in approximately 1-1.5 ml of remaining media and were pelleted at 14,000 x g for 5 min

in 2 ml microcentrifuge tubes. Supernatant was removed and pellets were frozen at -

80oC for 12 to 48 h prior to lysis and immunoprecipitation.

Immunoprecipitation of Radiolabeled H. volcanii Proteins

Protein A-Sepharose beads (100 mg/ml) were equilibrated in phosphate-buffered

saline solution (PBS) at pH 7.2 containing 0.01% (w/v) sodium azide. The bead slurry

(50 Cl) was combined with 5-10 Cl~ of rabbit polyclonal antiserum, 1 ml of cold PBS and

2 Cl~ of 0.01% (v/v) Triton X-100 (to aid in mixing). The mixture was rocked at 4oC for

12 h. Charged Sepharose beads were washed 5x with cold PBS prior to the addition of

cell lysate. Labeled cell lysate was prepared by the addition of 150 Cl1 of denaturing lysis

buffer [DLB; 50 mM Tris-HCI at pH 7.4 with 1% (w/v) SDS, 5 mM EDTA, 10 mM









dithiothreitol (DTT), ImM phenylmethylsulfonyl fluoride (PMSF)] followed by heating

to 95oC for 10 min. Lysate was then brought to 1.5 ml with non-denaturing lysis buffer

[NDLB; 50 mM Tris-HCI at pH 7.4 with 1% (v/v) Triton X-100, 300 mM NaC1, 5 mM

EDTA, 0.02% (w/v) sodium azide, 10 mM iodoacetamide and 1 mM PMSF] and mixed

with 500 U of Benzonase (DNase/RNase cocktail). The mixture was incubated at room

temperature for 30 min with periodic mixing. Cell lysate was clarified by centrifugation

at 14,000 x g for 5 min (21oC) and was mixed with charged sepharose beads. The

mixtures were rocked at 4oC for 3 h prior to washing 4x with immunoprecipitation wash

buffer [50 mM Tris-HCI at pH 7.4 with 0. 1% (v/v) Triton X-100, 300 mM NaC1, 5 mM

EDTA, 0.02% (w/v) sodium azide, 0.1% (w/v) SDS and 0.1% (w/v) sodium

deoxycholate] and lx with cold PBS, pH 7.2. Supernatant was removed from beads and

20 Cl~ of reducing dye [0.5M Tris-HCI at pH 6.8 with 25% (v/v) glycerol, 2% (w/v) SDS,

0.5% (w/v) bromophenol blue and 5% (v/v) P-mercaptoethanol] was added. Beads were

boiled for 10 min and supernatant was removed and separated by 12% SDS-PAGE. Gels

were fixed in fixing/enhancing solution (50% methanol, 10% acetic acid and 1 M sodium

salicylate) and dried prior to exposure to radiographic film at -80oC for 2-4 weeks. Film

was developed using a Konica XQ-60A film developer and imaged with an Epson 3170

photo and document scanner.









Table 2-1. A list of
this work

Strain or Plasmid


strains and plasmids used and/or constructed through the course of


Phenotype, genotype and oligonucleotides used for
PCR amplification as indicated*


Source


E. coli strains:


F- recA1 endA 1 hsdR1 7(rk- mi) supE44 thi-1 gyrA
relA1

F- ompT [Ion] hsd SB (rB mB) (an E. coli B strain)
with DE3, a h prophage carrying the T7 RNA
polymerase gene

F- ara-14 leuB6 JhuA31 lacY1 tsx78 glnV44 galK2
galIT22 mcrA dcm-6 hisG4 rfbD1 rpsL 136(Str")
daml3::Tn9(CamR)dd~~dd~dd~~3 xylA5mtl-1 thi-1 mcrB1 hsdR2


Life
Technologies


DH~a


BL21(DE3)


Novagen


GM2163


NE Biolabs


H. volcanii strains:


DS70


GG102


DS2 cured of pHV2


Wendoloski
et. al., 2001

Kirkland et.
al. 2007


Mevr; recombination of pJAM906 with
chromosomal panA gene of DS70; panA knockout
mutant


Plasmids:


pCR-BluntlI-
TOPO
pET24b


Invitrogen

Novagen

New England
Biolabs

Jolley et. al.,
1997

Wendoloski
et. al., 2001


Kmr; cloning vector
Kmr; E. coli expression plasmid vector


pUC 19


Ampr; cloning vector


Ampr, Novr; E. coli-H. volcanii shuttle expression
vector

Kmr, Mevr; E. coli-H. volcanii shuttle plasmid
based on the mevinolin drug-resistent version of the
Haloarcula hispanica hmgA gene encoding 3-
hydroxy-3 -methylglutaryl coenzyme A reductase


pBAP5010


pMDS99








Table 2-1. (Continued)


Phenotype, genotype and oligonucleotides used for
PCR amplification as indicated*
Ampr; 2.2-kb SacII genomic fragment from H.
volcanii blunt-end ligated into HinclI site of
pUC19; carries panA

Kmr; blunted PCR fragment containing panA gene
inserted into Ndel-EcoRI site of pZERO vector;
primers used were 5'-
GAATTCAAAACGAAATCGAAGGA-3 and 5'-
CATATGATGACCGATACTGTGGAC-3 '

Kmr; 1.2-kb PCR fragment from pJAM631 cloned
into pCR-BluntlI-TOPO; carries panA without stop
codon; primers used were 5'-
CATATGATGACCGATACTGTGGAC-3 and 5'-
AGGC TTAGCAAAC GC GCGGGAGAC -3 '
Kmr; 1.2-kb Ndel-to-HindIII fragment of pJAM63 8
ligated into Ndel and HindIII sites of pET24b;
carries panA gene with polyhistadine tag

Apr, Novr; Xbal-to-BspEl fragment containing His-
tagged panA gene blunt-end inserted into BanHI
and KpnI sites of pBAP5010

Mevr; 4.6-kb blunt-end BbvC1 fragment of
pJAM636 ligated with 1.5-kb Mev' fragment of
pMDS99

Kmr; 1.24-kb gene fragment with panB amplified
from H. volcanii genomic DNA ligated into Ndel
and Xhol sites of pET24b; 5'-
CATATGTCACGCAGTCCATCTCTCC-3 and
5' -CTCGAGGTACTGGTAGTCCGTGAAG-3 '

Apr, Novr; 1.3-kb Ndel-to-Blpl fragment from
pJAM202 was excised and replaced with Ndel and
Blpl fragment containing panA gene with His tag

Kmr; 1.03-kb PCR fragment with gene encoding
EF-la amplified from H. volcanii genomic DNA
and ligated into Ndel and HindIII sites of pET24b
using primers 5'-
GCGAGTACATATGATGAGCGACAAACCCCA
-3' and 5'-
GCATAGAAGCTTTCGCTCGTTGACTTCG-3 '


Strain or Plasmid

pJAM631


Source

Reuter et. al.,
2004


Kirkland et.
al., 2007




Reuter et. al.,
2004



Reuter et. al.,
2004


Kirkland et.
al., 2007


Kirkland et.
al., 2007


Reuter et. al.,
2004




Kirkland et.
al., 2007


pJAM636






pJAM638




pJAM642



pJAM650



pJAM906



pJAM1006






pJAM1012


p)AM4010


This work








Table 2-1. (Continued)


Phenotype, genotype and oligonucleotides used for
PCR amplification as indicated*
Kmr; 845-bp PCR fragment with gene encoding
SufC amplified from H. volcanii genomic DNA and
ligated into Ndel and HindIII sites of pET24b using
primers 5'-
GCGCGATCATATGATGGCAACTCTCGAGAT-
3' and 5'-
ATATGCAAGCTTCGCGGCTTCGTACACTTC-


Kmr; 1.22-kb PCR fragment with gene encoding
KaiC homolog amplified from H. volcanii genomic
DNA and ligated into Ndel and HindIII sites of
pET24b using primers 5'-
CATATGGTGGTAGCTCCGCGCACGG-3 and
5'-AAGCTTGAAAATATTCGCCTGCTGG-3'
Kmr; 740-bp PCR fragment with gene encoding
elF2a amplified from H. volcanii genomic DNA
and ligated into Ndel and Xhol sites of pET24b
using primers 5'-
CATATGAAGTACAGCGGATGGCCTG-3 and
5'-AAGCTTCTCTTCGTCGCCGCTGCGT-3'
Kmr; 533-bp PCR fragment with gene encoding
PCNA amplified from H. volcanii genomic DNA
and ligated into Ndel and HindIII sites of pET24b
using primers 5'-
CATATGGTGGACCTCACGCTCGACG-3 and
5'-AAGCTTGTCGCTCTGGATGCGCGGG-3'
Kmr; 863-bp PCR fragment with gene encoding
Smc 1 amplified from H. volcanii genomic DNA
and ligated into Ndel and HindIII sites of pET24b
using primers 5'-
GCGCATGCATATGATGGTAACGAAGCAGGA
-3' and 5'-
ATATACAAGCTTGAGCAGCCCGGACTTCTG-

Kmr; 614-bp PCR fragment with gene encoding
Pfpl amplified from H. volcanii genomic DNA and
ligated into Ndel and HindIII sites of pET24b using
primers 5'-
GCTCGGACATATGATGACATCGGCGCTTTT-
3' and 5'-
AGATCCAAGCTTCAGTTCGTCCAACAGCGT-


Strain or Plasmid

pJAM4011









pJAM4012






pJAM4013






pJAM4014






pJAM4015








pJAM4016


Source
This work









This work






This work






This work






This work








This work








Table 2-1. (Continued)


Phenotype, genotype and oligonucleotides used for
PCR amplification as indicated*
Kmr; 656-bp PCR fragment with gene encoding
SBD amplified from H. volcanii genomic DNA and
ligated into Ndel and HindIII sites of pET24b using
primers 5'-
GCGCGCACATATGATGATTTCACTCGACGA-
3' and 5'-
GCATGCAAGCTTGAGTTCGTCTTCGTCCTT-


Kmr; 1.12-kb PCR fragment with gene encoding
FtsZ amplified from H. volcanii genomic DNA and
ligated into Ndel and HindIII sites of pET24b using
primers 5'-
GCCGGCACATATGATGAAGCTCGCAATGAT
-3' and 5'-
GCATGCAAGCTTGAAAAGCGACTCCAGTTC
-3'


Strain or Plasmid

pJAM4017









pJAM4018


Source

This work









This work


pJAM4019 Kmr; 1.40-kb PCR fragment with gene encoding This work
Cdc6/Oril1 amplified from H. volcanii genomic
DNA and ligated into Ndel and HindIII sites of
pET24b using primers 5'-
CGACATATGATGTTGGAGACTCTC-3 and 5'-
CTATAAGCTTCTGCTCCGAGC-3 '
*The fidelity of all PCR products was confirmed by DNA sequence analysis










Table 2-2. Summary of proteomic experiments and corresponding culture conditions
Culture Conditions*
Harvest
Proteomic Experiment Cell
Method of
Volume Density Media Notes
Extraction
(OD 600
nm)


20 CLM
inhibitor
Trizol ATCC974
added at OD
600 nm 0.15


30 CLM
inhibitor
Trizol ATCC974
added at OD
600 nm 0.20


cla~sto Lactacystin 0
Lactone Inhibition of Hv
Proteasomes (Group 1)



cla~sto Lactacystin 0
Lactone Inhibition of Hv
Proteasomes (Group 2)


IMAC
Phosphoproteomic
Analysis of H. volcanii
panA Mutant


MOAC
Phosphoproteomic
Analysis of H. volcanii
panA Mutant



MudPIT Analysis of H
volcanii panA Mutant



Development of Tnizol
Protein Extraction
Method


25 ml






25 ml


1.7-1.9






0.8-1.0


100 ml 1.0-1.2 Standard ATCC974


Defined
High-Salt
Trizol
(Allers et.
al., 2004)


Defined
High-Salt
Trizol
(Allers et.
al., 2004)

Defined
TrizolHigh-Salt
(Allers et.
al. 2004)


10 ml






10 ml





10 ml


1.0-1.2






1.0-1.2





1.0-1.2


*All cultures were grown at 42oC and 200 rpm









CHAPTER 3
OPTIMIZING ISOELECTRIC FOCUSING OF HALOPHILIC PROTEINS THROUGH A
TRIZOL-BASED SAMPLE PREPARATION METHOD

Introduction

Effective first-dimension separation of proteins by isoelectric focusing (IEF) requires

thorough desalting of the sample. Typically, IEF is tolerant of salt concentrations up to 50 mM;

however, most of this is contributed by carrier ampholytes (amphoteric electrolytes) added to

assist in the migration of proteins to their respective pl values (Cho et al., 2003). In situations

where protein samples are prepared from high salt-dwelling organisms, the minimal salt

tolerance of IEF becomes a serious complication. Methods commonly used in the past for the

preparation of halophilic proteins for separation by IEF often consisted of maintaining moderate

levels of magnesium to prevent protein aggregation but also interfered with the focusing process

by causing excessively high current or aberrant patterns in protein migration (Oren, 2002). In

the case of Haloferax volcanii, as with other halophilic archaea, methods for first-dimension

separation are further complicated by the pl values of their proteins, which are highly acidic

(Oren, 2002). The maj ority of proteins, regardless of origin, tend to collect at the mean of both

dimensions; however, the acidity of the total proteome of halophilic organisms compresses the

first dimension and dramatically restricts resolution (O'Farrell, 1975; Cho et al., 2003; Karadzic

and Maupin-Furlow, 2005; Joo and Kim, 2005). This phenomenon causes crowding of the

halophilic proteins during IEF within a narrow region of the field and prevents clear separation

and identification of these proteins within the two-dimensional (2D) map. Previous attempts to

remedy the lack of separation of acidic proteins have included using narrower pl ranges during

IEF, increasing the distance between the basic and acidic poles, and mixing carrier ampholytes to

target separation in the clustered regions (Hoving et al., 2000; Gorg et al., 2000; Leimgruber et

al., 2002). Although these previous tactics have improved the separation of halophilic proteins










by 2-DE, we report an even greater improvement by changing the sample preparation

methodology from previously reported technologies (Cho et al., 2003; Karadzic and Maupin-

Furlow, 2005; Joo and Kim, 2005). In this chapter, a phenol/guanidine isothiocyanate solution

known commercially as Trizol (Invitrogen), which is typically employed to isolate RNA from

cell and tissue samples of both prokaryotes and eukaryotes, was used to rapidly purify halophilic

protein samples from contaminating nucleic acids, carbohydrates, and lipid cellular components.

This methodology was shown to also drastically enhance all facets of sample preparation and

separation of halophilic proteins without the drawbacks of protein aggregation and excessive

sample loss experienced with other methods.

Results and Discussion

Comparative 2D maps of halophilic protein samples (Fig. 3-1) prepared using the Trizol

extraction method (see "Methods and Materials", chapter 2 for details) reveal a consistently

higher number of protein spots overall than those developed using the previous standard

methodology (Karadzic and Maupin-Furlow, 2005). In total, 2D gels generated from standard

samples yielded an average of 729 spots, whereas those produced from Trizol-prepared samples

yielded an average of 972 spots. Other qualitative advantages of the Trizol preparation method

are apparent when directly comparing 2D maps from each methodology. For example, there is a

noticeable difference in the resolution of protein spots in the crowded regions of the H. volcanii

2D map. This phenomenon of IEF crowding is common among proteins of halophilic organisms

due to the abnormally high percentage of acidic amino acids present in their proteins. The region

lying in the upper molecular weight range (>45 kDa) and in a pl range of approximately 4.2-4.5

is of particular interest in this comparison given its high complexity. Excessive lateral streaking

makes it impossible to discern any appreciable number of spots in this region of the map

developed with the standard method. However, the corresponding region in the gel developed









using the Trizol method shows a multitude of spots that can be discerned individually. It is also

worthy to point out the difference in total spot numbers that appear in the less complex regions

of the 2D map. The advantages that the Trizol preparation has in protein yield (especially of

lower abundance proteins) is clear to see in these regions, where there not only is an obvious

increase in spot numbers with the Trizol method but also are numerous instances of faint protein

spots amplified dramatically by the use of the Trizol protocol (Fig. 3-2). This increase in total

protein spots was confirmed through the use of large landmark spots, which were commonly

found in high abundance and appeared in gels from both methodologies with similar relative

intensity values, ruling out the possibility of varying amounts of total protein being applied to

each gel (Figs. 3-3 and 3-4). The effects that the Trizol methodology has on improving protein

yield and spot resolution in 2D PAGE applications, as compared with the standard method

(Table 3-1), are likely attributed to an increased efficiency in the removal of nucleic acid from a

cell preparation and dissolving lipid and carbohydrate components from the mixture, thereby

freeing a higher percentage of proteins for unimpeded migration laterally as well as vertically in

a 2D gel. Less volumetric transfer in the Trizol protocol, as compared with previous methods of

preparation, also allows a higher percentage of protein to be retained for analysis. This feature

increases the probability of detecting low-abundance proteins in complex samples through any

number of mass spectrometric techniques. Cleaner protein samples produced by Trizol

extraction may also enhance other proteomic analysis tools such as prefractionation and

ultrazoom gel analysis (Hoving et al., 2000; Herbert and Righetti, 2000; Lilley et al., 2002).

Trizol extraction methods may also be useful to proteomics beyond the scope of 2D PAGE. The

cleaner protein samples that result from this method of preparation may facilitate more thorough

separation in various liquid chromatography techniques such as immobilized metal affinity









chromatography (IMAC), making it easier to identify modified proteins in complex mixtures,

quantify levels of modification, and map peptide modification sites.












3.9 4.5 5.1



97.4 kDa

66.2

45





21.5

14.4 A I


97.4 kDa .

66.2 .






21.5 1. .

14.4 B




Figure 3-1. Two-dimensional PAGE maps of H. volcanii total cell lysate prepared using (A) the
previous standard method and (B) the Trizol extraction method.





~E~i~i~1
~c~

~-c,'L~:


f C~l". D.

Figure 3-2. Magnified regions of 2D maps of H. volcanii cell lysate prepared using (A and C)
the Trizol extraction method and (B and D) the previous standard method. Regions
of these maps have been enlarged to demonstrate the advantages of the Trizol
extraction method over the previous method. The left and right pairs (panels A and
B, panels C and D) correspond to similar regions of each respective map. Arrows
indicate landmark reference spots.
















*1 2 1 1 2

A. B. iC. iD.

Figure 3-3. Magnified regions of Gaussian images of 2D PAGE maps of H. volcanii total cell
lysate prepared using (A and C) the Trizol extraction method and (B and D) the
previous standard method. Pannels A and C demonstrate a quantitative enhancement
in the number and intensity of spots in the maps of protein samples prepared by
Trizol extraction compared with the previous method (panels B and D, respectively).
Landmark spots are inciated by arrows. Spots indicated by squares are noticeably
enhanced in the Trizol-prepared samples compared with samples prepared using the
previous method. For example, spot 1 in panel C is increased more than 2-fold
compared with its counterpart in panel D. Similarly, spot 2 in panel C is increased
more than 24-fold compared with its counterpart in panel D.











3.9 4.5 5.1


97.4 kDa
66.2 -ll




-A -








21.5 -
14.4 -B

Fiue -. assa iaeso 2 A G masoH ocnittlcl yaepeae sn
(A) 4 th rvossadadmto n B teTio xrcio ehd nfr
lanmar sptidcae ycrce nech eaecnitn n nhg bnac
insmpe peardusn etermthd











Table 3-1. Advantages of the Trizol extraction method compared to methods described
previously


Trizol Method


Standard Method


Phenol/chloroform extraction and ethanol
precipitation steps thoroughly remove RNA and
DNA, this improves the resolution of
complicated regions of 2D PAGE gels and
improves the effectiveness of peptide
enrichment by IMAC

Isopropanol precipitation followed by 300 mM
guanidine HCI and acetone washes are
thoroughly remove salt from protein samples,
making them fully compatible with IEF

Phenol (Trizol) lysis step dissolves lipid
membrane components and carbohydrate
components, enhancing the dissociation of
membrane proteins for separation by 2D PAGE

Protein pellet is soluble in TEA/glycerol buffer
supplemented with a zwitterionic detergent such
as CHAPS

Sufficient protein quantities can be obtained
from as little as 2 ml of culture

Nearly 100% of soluble protein is retained by
avoiding protein fractionation and multiple tube
transfer steps; protein loss is almost exclusively
due to incomplete precipitation

Soluble protein samples can be obtained from
culture in less than 4 h



Requires Trizol, guanidine HC1, chloroform,
ethanol, isopropanol, 2ml microcentrifuge tubes,
and a microcentrifuge


DNA and RNA are removed by Benzonase
digestion and centrifugation, which is
inefficient and can cause streaking in 2D
PAGE gels. In complex protein mixtures,
the remaining nucleic acids compete for the
IMAC matrix

Acetone precipitation and pellet washes are
less effective in desalting, given the need
for maintaining a moderate level of
magnesium throughout the preparation


Larger, insoluble cell components such as
cell wall and membrane components are
removed by differential centrifugation


Protein pellet is soluble in TEA/glycerol
buffer with CHAPS but contains fewer
proteins overall

Sufficient protein quantities usually require
a minimum of 100 ml of culture


Considerable protein loss occurs due to
fractionation, cell pellet wash steps, tube
transfers, and incomplete precipitation


Soluble protein samples can be obtained
from culture in 8 h

Requires French Press equipment, a full-
size centrifuge, 40 ml Oakridge tubes,
Benzonase, phosphatase and protease
inhibitor cocktails, and Tris and magnesium
chloride solutions


Nucleic Acids






Desalting



Non-
protemnaceous
Membrane
Components


Solubility


Starting Material
Required


Soluble Protein
Retention



Prep Time



Equipment/
Materials
Required









CHAPTER 4
EFFECT OF CLASTO-LACTACYSTINT BETA LACTONE ON THE PROTEOME OF
Haloferax volcanii

Introduction

Proteasomes are large, barrel-shaped proteases found in all three domains of life

(Maupin-Furlow et al., 2004). The 20S proteolytic core consists of four stacked heptameric rings

with 6 to 14 N-terminal nucleophile (Ntn) hydrolase-active sites sequestered within the complex

interior. Protein degradation by 20S proteasomes requires protein unfolding which can be

mediated via the hydrolysis of ATP by associated AAA ATPases such as the proteasome-

activating nucleotidases (PANs) of archaea and homologous regulatory particle ATPases of

eukaryotic 26S proteasomes (Smith et al., 2005).

It has been known for some time that eukaryal 26S proteasomes are essential for

regulating a myriad of cellular functions including: antigen processing for MHC presentation

(Kloetzel, 2004), circadian rhythmicity (Casal and Yanovsky, 2005), cell division (Devoy et al.,

2005), metabolism (Asher et al., 2006), transcription (Lipford and Deshaies, 2003), translation

(Baugh and Pilipenko, 2004; Takahashi et al., 2005; Jiang and Wek, 2005; Arora et al., 2005),

and others. Unlike eukaryotes, archaea do not have a conserved ubiquitin conjugation system for

tagging proteins for proteasome-mediated destruction. However, many fundamental aspects of

physiology and biochemistry are conserved between these two domains of life including the

highly identical proteasomes.

Recently, it was shown that cla;sto-lactacystin-p-lactone (cLPL) inhibits the 20S

proteasomes of the halophilic archaeaon Haloferax volcanii (Reuter et al., 2004). The p-lactone

component of cLPL irreversibly and specifically inhibits 20S proteasome activity via

modification of the Ntn-threonine residue of the P-type subunits (Fenteany and Schreiber, 1998).

Thus, proteins that accumulate in cLPL-treated H. volcanii cells are likely to provide insight into









the types of proteins that are regulated by archaeal proteasomes and expand our understanding of

the role these multicatalytic proteases play in archaeal cell physiology.

This chapter describes our identification of a large collection of proteins were identified

by MS/MS that accumulated as 2-DE gel isoforms in H. volcanii cells treated with the

proteasome inhibitor cLPL. These included homologs of proteins known to be essential in a

variety of functions including cell division, translation and metabolism. Evidence suggests that a

subset of these proteins may be modified post-translationally Together the results of this

chapter provide important insights into proteins that may be targeted to the proteasome-

dependent degradation pathway and/or induced after proteasome-inhibition in an archaeal cell.

Results

Little is known regarding the types of proteins targeted for proteasome-mediated

degradation or the role these multicatalytic proteases play in archaeal cells. To provide insight,

this chapter focused on establishing a set of proteins that increase in abundance and/or change in

isoform migration when H. volcanii cells are treated with the proteasome inhibitor cLPL. Until

recently, 2-DE separation of proteins isolated from halophilic cells such as H. volcanii was

complicated by the necessity to use relatively large quantities of cell material. This prevented

detailed proteomic analysis of H volcanii cells treated with cLPL, based on the restrictive

expense of the proteasome-specific inhibitor. This issue was recently resolved by developing a

new Trizol-based method of halophilic protein preparation (Kirkland et al., 2006), which

permitted proteasomal inhibition experiments on a smaller scale, thereby increasing the

efficiency with which proteomic analysis could be performed.

Growth of H. volcanii in the presence of proteasome inhibitor

The growth of H. volcanii was monitored in the presence of various concentrations of the

proteasome inhibitor cLPL (0, 20 and 30 CIM) (Fig. 4-1), to facilitate downstream proteomic









analysis. DMSO (0.5%) was included to enhance inhibitor solubility. Of the cultures examined,

those devoid of proteasome inhibitor (negative controls) grew the fastest and reached the highest

cell yield, with doubling times of 2. 1 h and a final OD 600 nm of 3.8. In comparison, cells

treated with 20 CIM and 30 CIM cLPL grew slower with doubling times of 3.1 and reached lower

cell yields with maximal OD 600 nm values of 2.9 and 2.8, respectively. Thus, addition of the

proteasome inhibitor reduced overall cell yield and growth rate. However, increasing its

concentration from 20 to 30 CIM resulted in little change in these growth characteristics.

Global differences in H. volcanii proteome in the presence of proteasome inhibitor

Haloferax volcanii cells were grown in the presence and absence of 20 and 30 CLM

proteasome inhibitor cLPL, and cellular proteins were extracted and separated by 2-DE. Six

individual gel maps (three test and three control) were generated from separately grown cultures

to ensure accuracy and statistical significance of each variably expressed protein spot between

the different groups. Each 2-DE set or 'Group' was generated from the average number of total

protein spots of cells incubated with 20 CIM cLPL (Group 1) and 30 CIM cLPL (Group 2) was

comparable at 1072 and 1000, respectively (Fig. 4-2). This represented a 1.6- to 1.8-fold (403 &

48 and 436 & 33 spot) increase over the total number of spots of the uninhibited controls which

were 669 and 584 for Group 1 and Group 2, respectively. This increase was relatively high and

was reproducible among experiments. In contrast, the differences observed between the average

spot number of the two inhibitor groups (72 & 5 1 spots) and two control groups (105 & 74 spots)

was modest and not consistent among experiments.

To compare the relative abundance of the individual spots between the different 2-DE

sets, the statistical significance for the relative intensity of each protein spot was set to a

threshold value of 4. The cultures supplemented with 20 and 30 CIM of the proteasome inhibitor









(Group 1 and 2, respectively) yielded a total of 89 spots that were at or above this threshold. Of

these spots, 60 were common to both groups, two were restricted to Group 2, and 27 were

restricted to Group 1. In contrast, the number of spots with relative intensities at least four-fold

below that of the uninhibited controls was only 14 with two spots common to both groups, two

restricted to Group 1, and 12 restricted to Group 2. Based on these results, a number of

consistent and significant differences within the proteome could be detected by 2-DE when H.

volcanii cells were treated with the proteasome specific inhibitor cLPL. Thus, in addition to a

notable reduction in the growth of proteasome-inhibited cells, changes in 2-DE migration and/or

abundance of a large group of proteins were observed.

A total of 24 spots 'unique' and/or increased 2- to 14-fold in cells cultivated in the

presence of the proteasome inhibitor (cLPL) were selected, excised and pooled from triplicate 2-

DE gels for in-gel tryptic digestion and MS/MS identification. Criteria for spot selection

included: i) reproducible and significant differences between the 2-DE gels of cLPL-treated and

non-treated cells, ii) sufficient protein quantity as determined by SYPRO Ruby fluorescent

staining, and iii) adequate separation from neighboring protein spots by 2-DE. Protein identities

for 17 of these spots were determined via HPLC-ESI MS/MS using a QSTAR and are listed in

Table 4-1 along with their corresponding probability-based Mascot ion scores, peptide coverage,

and fold increase in the presence vs. absence of proteasome inhibitor. These protein identities

are well within the significant range (p < 0.05) with Mascot ion scores from 53 to 839 (average

of 268) and peptide coverage of 6.9 to 60.3% with an average of 5.5 tryptic peptide fragment

ions detected per protein. In two cases more than one protein was identified per spot (a3 and b2)

that cannot be contributed to protein carryover from one sample to another (Table 4-1; see

Appendix A for all spot images). Spot a3 appears as a protein chain that is not well separated,









yet all of the proteins within that chain appear to increase in the presence of cLPL. The reason

for the identification of two proteins within spot b2 is less clear, since this spot appears well

separated from surrounding proteins. The remaining 7 protein spots had overall Mascot ion

scores less than 53 and were excluded from the list.

The maj ority of proteins identified migrated within 9 kDa of the molecular mass and 0.4

pl units of that calculated for the deduced polypeptide (Table 4-1). Exceptions included the

conserved archaeal coiled-coil COGl340 protein and 30S ribosomal protein S4 which migrated

more acidic (by 0.5 units); the actin-like protein (ORFO2969) which migrated more basic (by 0.5

units); and a number of 'outlier' proteins which migrated at least 10 kDa greater or less than

calculated. Whether these differences are due to post-translational modification, incomplete

denaturation (in 7 M urea and 2 M thiourea), or other factors remains to be determined. In

particular, ORF01073 is predicted to adopt a coiled-coil conformation (residues 60 to 289) which

may be somewhat resistant to unfolding. A number of the identified proteins appear to undergo

N-terminal methionine excision based on the identification of tryptic peptide ions with cleaved

N-termini (individual Mascot ion scores of 49 to 91). These include homologs of the 30S

ribosomal protein S4, 2-oxoacid decarboxylase E1P, ORF01073, and EFlA. In addition, the

detection of an STHDVDPATVEVIR- tryptic fragment with an N-acetyl group (Mascot ion

score of 62; E value 4.4 e-5) suggests the hydantoinase /oxoprolinase homolog is cleaved by

methionine aminopeptidase and acetylated on the resulting N-terminal serine. These results are

consistent with what has been observed for other haloarchaeal proteins (Humbard et al., 2006;

Falb et al., 2006), but does not account for the aberrant migration of the subset of proteins

described above. Although ions were correlated with methylated and phosphorylated tryptic









fragments of FtsZ and ORF0 1073, their identity is only tentative (individual Mascot ion scores

below 32).

The protein spots, which increased in the presence of proteasome inhibitor and were

linked to protein sequence by MS/MS, fell into three maj or categories: (i) protein quality control,

translation, and degradation; (ii) metabolism/transport; and (iii) cell division/conserved proteins

of unknown function. Those which fell into the category of protein quality control and

translation were the most extensive, with 9 proteins identified by a total of 33 tryptic peptide

fragment ions. The majority of proteins in this group were homologs of the 30S and 50S

ribosomal subunits (S3Ae, S17, S13, S4 and L7). In addition, members of the DJ-1/ThiJ/Pfpl,

SUF Fe-S cluster assembly, and elongation factor- (EF-)1A families were found. The second

category of spots identified were 4 proteins known and/or proposed to be involved in metabolism

and transport and were identified via a total of 22 peptide fragment ions with the highest Mascot

score average of 437. The group included orthologs of the divalent metal binding lipoproteins of

ABC-type transporters, 2-oxoacid decarboxylase E1P [EC 1.2.4.-], dihydroxy-acetone kinase

[EC 2.7.1.2], and aldehyde dehydrogenase [EC 1.2.1.-]. The final category included a homolog

of the cell division protein FtsZ and two conserved proteins ORFO2998 and ORF0 1703 which

respectively cluster to COG1077 and COGl340. Although these latter two COGs encompass

proteins of unknown function, ORFO2998 and ORF01703 have low (20%) identity to actin-like

(e.g. Magnetospirillunt nagneticunt amb0965) and SMC-like proteins (e.g. Haloarcula

nzarisnzortui rrnAC 1639), respectively. In further support of the potential role of ORF0 1703 in

cell division, some archaeal members of COGl340 appear to be cotranscribed with ftsZ genes

based on gene neighborhood.









Discussion


Ribosomal Proteins

Many of the protein spots which increased in the presence of cLPL were homologs of

ribosomal proteins including: S4p (S9e), S17p (RpsQ, S11e), S13p (S 18e), S3Ae and L30p

(L7e). All of these are functionally versatile and/or key to the initiation of ribosome biogenesis.

For example, S4p and S17p bind 16S rRNA and initiate 30S ribosomal subunit assembly (Stern

et al., 1986; Nowotny and Nierhaus, 1988; Weitzmann et al., 1993; Powers and Noller, 1995).

S4p associates with RNA polymerase and controls NusA-like rRNA antitermination (Torres et

al., 2001), regulates translation (Tang and Draper, 1990) including its fidelity (Topisirovic et al.,

1977), and catalyzes mRNA helicase activity (Takyar et al., 2005). S13p appears to modulate

the interactions of the small and large ribosomal subunits in multiple steps of the translation

cycle (Cukras and Green, 2005). S3Ae is associated with gene mutations that generate

transformed phenotype reversions in rats (Kho and Zarbl, 1992) and transcript levels that are

transiently regulated in log phase and after nutrient depletion in plants. L30p (L7e) family

members are also functionally diverse including the ability to inhibit translation, control gene

expression by physical interaction with receptor proteins, and induce apoptosis when

constitutively expressed in eukaryotic cells (Neumann and Krawinkel, 1997; von Mikecz et al.,

1999).

The reason for the increased levels of the ribosomal protein spots in cLPL-treated H.

volcanii cells is unclear, as is their status as targets of post-translational modification and/or 20S

proteasomal degradation. Ribosomal proteins are often covalently modified co- and/or post-

translational. In mammalian cells, ribosomal proteins are ubiquitinated and degraded by

proteasomes (Kim et al., 2006). Although this type of proteasome specificity has yet to be

established in archaea, the observed changes in the isoform abundance of ribosomal proteins do









suggest a widespread influence of cLPL on archaeal cell function. Similar alterations in

ribosomal protein levels (as determined by 2-DE gels) have been observed for other archaea after

global challenges including changes in energy source (Dopson et al., 2005), cold adaptation

(Goodchild et al., 2004), heat shock (Shukla, 2006), and osmotic stress (Shukla, 2006). Of these

ribosomal proteins, the L30p homolog commonly increases after cLPL-treatment of H. volcanii

cells as well as addition of Fe2+ as an electron donor to Ferroplasma acidarmanusdd~~~dd~~~ddd~~ Ferl cells

(Dopson et al., 2005).

Elongation Factor 1A

Elongation factor 1A (EF lA) is a maj or translational factor which catalyzes the first step

of the elongation cycle. EF lA is also implicated in the coordination of a number of other

cellular processes including cytoskeletal organization, viral RNA synthesis, and signal

transduction in cell growth, stress responses and motility (Ejiri et al., 1994). Consistent with

this, EF lA binds the cytoskeletal proteins actin and tubulin (Condeelis, 1995), QP RNA

polymerase (Blumenthal et al., 1972), valyl-tRNA synthetase (Motorin et al., 1988), calmodulin

(Kaur and Ruben, 1994), and calcium/calmodulin-dependent protein kinase (Wang and

Poovaiah, 1999). In eukaryotes, EFlA also binds proteasomes (e.g. the Rpt1 subunit) (Verma et

al., 2000; Coux, 2003) and ubiquitinated proteins after ATP depletion (Chuang and Yew, 2005)

and is essential for ubiquitin-proteasome dependent degradation of N"-acetylated proteins

(Gonen et al., 1994). In addition, EFlA has isopeptidase (Gonen et al., 1996) and chaperone

activities (Caldas et al., 2000) and binds polypeptides unable to fold after their release from the

ribosome (Hotokezaka et al., 2002). Based on these multifunctional properties of EFlA, a

number of biological factors may be responsible for the increased levels of the EF lA protein

spot in cLPL-treated H. volcanii cells. In eukaryotes, changes in EFlA abundance and post-

translational modification are correlated with growth rate, cell proliferation and differentiation,









and intracellular pH (Krieg et al., 1989; Grant et al., 1992; Ransom-Hodgkins et al., 2000; Zobel-

Thropp et al., 2000; Lopez-Valenzuela et al., 2003). In archaea, the intensities of EFlA spots are

altered on 2-DE gels by heat shock (Shukla, 2006), cold adaptation (Goodchild et al., 2004), and

energy source (Dopson et al., 2005). In addition, multiple EFlA isoforms have been detected in

archaea (Giometti et al., 2002), and post translational modification sites are predicted based on

similarity to known eukaryal sites (Whiteheart et al., 1989; Wang and Poovaiah, 1999; Lopez-

Valenzuela et al., 2003).

DJ-1/ThiJ/Pfpl Superfamily

The DJ-1/ThiJ/Pfpl superfamily (Bandyopadhyay and Cookson, 2004) protein which

accumulated as a 2-DE spot in the presence of cLPL has retained the conserved Cys, His, and

Asp residues proposed to function as a catalytic triad in the Hsp31/Pfpl clan of proteins (Malki et

al., 2005). Members of this clan are often induced by stressful conditions (e.g. heat shock

(Sastry et al., 2002) and peptide starvation (Snowden et al., 1992)) and may be responsible for

the hydrolysis of short oligopeptides generated by energy-dependent proteases such as the

proteasome (Maupin-Furlow et al., 2006) Interestingly, an E. coli member of this superfamily

(Hsp31) interacts with EFlA and ClpA (Malki et al., 2005), an AAA+ ATPase which associates

with ClpP much like the AAA proteasome-activating nucleotidase (PAN) and 20S proteasome

proteins of archaea .

Cell Division

FtsZ, actin, and SMC are important in the division of prokaryotic cells. FtsZ forms a

cytokinetic ring early in cell division (Margolin, 2005), actin-like proteins (e.g. MreB) are

partners with RNA polymerase in providing the force needed for chromosome segregation

(Kruse et al., 2006), and the coiled-coil SMC proteins are key subunits of complexes that

perform essential tasks in chromosome dynamics (Nasmyth and Haering, 2005). In bacteria, the









levels and/or activities of many of these cell division proteins are controlled by proteolysis. In E.

coli, the FtsZ inhibitor SulA is targeted for regulated proteolysis by HslVU (Kanemori et al.,

1999) and Lon proteases (Mizusawa and Gottesman, 1983). In Streptomyces coelicolor, the half-

life of FtsZ is controlled (Del Sol et al., 2006), and, in Bacillus subtilis, an SMC protein is

degraded by Lon and Clp proteases as cells enter stationary phase (Mascarenhas et al., 2005). In

addition, the AAA+ ATPase ClpX forms an energy-dependent protease with ClpP also associates

with (Flynn et al., 2003) and inhibits FtsZ assembly (Weart et al., 2005) in E. coli and B. subtilis,

respectively. In archaea, it is not clear whether the proteasome and/or proteolysis control events

in cell division. However, the cLPL-dependent accumulation of FtsZ-, actin- and SMC-like

protein spots correlates well with the reduced growth rate of H. volcanii cells under these

conditions.

2-Oxoacid Dehydrogenase

All archaea use ferredoxin oxidoreductases to oxidize 2-oxoacids to the CoA derivatives.

This has lead to question whether archaea synthesize and/or need functional 2-oxoacid

dehydrogenase (OADH) complexes of Elap, E2, and E3 (Jolley et al., 2000). El and E3 enzyme

activities have been detected (Danson et al., 1984; Heath et al., 2004); however, a functional

archaeal OADH has yet to be demonstrated. The E10-like protein spot that increased in the

presence of cLPL is encoded within a 4-gene operon with coding capacity for ElaP, E2, and E3

components of an OADH (Jolley et al., 2000) and is separate from the genomic region coding for

only ElaP components that are needed for nitrate-respirative growth (Wanner and Soppa, 2002).

This combined with the finding that an El a-type OADH protein of F. acidarmanusdd~~~dd~~~ddd~~ Ferl

increases several fold during chemoorganotrophic vs. chemomixotrophic growth (Dopson et al.,

2005) suggests archaea may modulate the ElaP 2-oxoacid decarboxylase component of OADH










complexes as nutrients shift and/or become limiting. Whether, proteasomes are involved in this

process remains to be determined.

Dihydroxyacetone Kinase

The sequence conserved family of dihydroxyacetone kinases phosphorylate

dihydroxyacetone (DHA), D-glyceraldehyde or other short-chain ketoses and aldoses (reviewed

in (Erni et al., 2006)). The source of the high energy phosphate is either ATP or a

phosphoprotein of the phosphoenolpyruvate: sugar phosphotransferase system (PTS). In E. coli,

the DHA kinase is composed of three subunits: DhaL, DhaK and DhaM, (reviewed in (Erni et

al., 2006)). DhaM is phosphorylated by the PTS system. This phosphoryl group is displaced by

a tightly bound ADP coenzyme of DhaL and transferred to the substrate, which is covalently

bound to DhaK. Besides catalysis, DhaL and DhaK serve antagonistic roles in binding the

sensing domain of an AAA+ ATPase transcriptional regulator, DhaR (Bachler et al., 2005).

Whether a DHA kinase- and PTS-dependent global regulatory system function in H. volcanii and

are controlled by proteasomes is unknown. However, the DhaL-like protein spot which

accumulated in cLPL-treated H. volcanii cells is encoded within a region of the genome with

coding capacity for DhaK and the PTS EIIA- and HPr-type domains of DhaM.

Aldehyde Dehydrogenase

The protein that accumulated as a 2-DE spot in the presence of cLPL that is related to

members of the aldehyde dehydrogenase (ALDH) family (pfam00 171; E value 7e-100) includes

conserved catalytic and NAD(P) binding residues. Thus, it is likely to function in the oxidation

of aldehydes to their corresponding carboxylic acids and, thus, detoxify a wide variety of

reactive organic compounds, toxins and pollutants. Interestingly, the levels of a highly related

ALDH isoform ofHalobacterium salinarum (gi no. 10581906; E value 6 e-147) are reduced









when cells were cultured in the presence of high vs. optimal salt conditions (6 vs. 4.3M NaC1)

(Kim et al., 2006).

Fe-S Cluster Assembly

Unlike most archaea, haloarchaeal genomes such as that of H. volcanii have the coding

capacity for both ISC/Nif- and SUF-like Fe-S cluster assembly systems. This is analogous to E.

coli in which the ISC system plays a 'housekeeping' role and SUF is required during iron

starvation (Outten et al., 2004) and induced after oxidative stress (Lee et al., 2004). The reason

for the increased abundance of the SufB and SufC protein homologs in cLPL-treated H. volcanii

cells remains to be determined; however, it is possible that inhibition of proteasome activity

increased the levels of oxidatively damaged and/or improperly folded proteins and triggered an

overall increase in the SUF system to facilitate Fe-S cluster assembly under these conditions.

Divalent Metal Transport

The ORFO2598 protein that accumulated as a 2-DE spot in the presence of cLPL is related

to 'cluster 9' lipoproteins. These proteins are proposed to regulate the high affinity uptake of

divalent metals by ABC transporters for repair of metalloenzymes, resistance to oxidative stress,

and/or maintenance of intracellular redox homeostasis (Claverys, 2001; Johnston et al., 2004;

Hantke, 2005). Interestingly, heat shock of Halobacterium NRC-1 results in the elevation of a 2-

DE protein spot related to an ABC transporter lipoprotein which functions in the import of

nutrients and/or release of toxic products (Shukla, 2006). Thus, the accumulation of unfolded

and damaged proteins after cellular stresses such as proteasomal inhibition or heat shock may be

a general signal that modulates the levels (or isoform status) of various ABC transporter-

associated lipoproteins in the halophilic archaea.









Conclusions

A number of protein spots, which represent a variety of cellular functions, differentially

accumulated in H. volcanii cells treated with the 20S proteasomal inhibitor cLPL. The general

classes of proteins identified through the proteomic analysis of chemically inhibited cells provide

insight into specific cellular functions that may be regulated by archaeal proteasomes. Many of

the identified proteins represent key components of processes vital to cell function thereby

making them prime candidates for theoretical proteasomal control points. To further understand

the role proteasomes play in these observed changes, future studies are aimed at determining the

influence cLPL has on transcript levels, protein half-life, and/or covalent modification of the

protein spots identified to increase in the presence of proteasome inhibitor.















3.5-




2-
1.5-


0.5-


0 10 20 30 40 50 60
Time (hours)



B.

O +CLBL (30uM)
S- CLBL

S100
80


.40

t;20

SAE-AMC Ac- Suc- Suc- Suc-IWV- Boc-
YVAD- AAF- LLVY- AMC FSR-
AMC AMC AMC AMC


Figure 4-1. Cellular response of H. volcanii to proteasomal inhibition. (A) Growth of H
volcanii in the presence and absence of cLPL inhibitor. O CLM (m), 20 CLM (A) and 30
CLM (A) inhibitor was added to cells at an OD600 of 0.20 (15 h growth). DMSO
(0.5% v/v) was added to all cultures at this time. (B) Select peptide-hydrolyzing
activities of cell lysate prepared from H. volcanii grown in the presence (0) or
absence (m) of inhibitor. Peptide substrates are listed on the X-axis and represent one
of three peptide-hydrolyzing activities: peptidyl-glutamyl peptide hydrolysis (AE-
AMC and Ac-YVAD-AMC), tryptic activity (Boc-FSR-AMC) or chymotryptic
activity (all others).


















3.9 4.5
~U.


~L ~--I ...--ii~pJC~ 2. 1L


rL"

i-

r-




cr



A
r d;



*_I--; ----
i~





t
rr
rsr











C


.9 ~ 4.5 ~ 5.~

h,

Jit ~



--
i ,-


~LI 1
-re
b


a



B1 I c




irlce
-- -~ --
---- ~
-
C r

~ ,
r
,~

LI -` "
-rr
r
r. i
r


I
r

D


Figure 4-2. Modified Gaussian 2-DE images ofH. volcanii proteomes isolated from cells grown

in the presence (A and C) and absence (B and D) of proteasome inhibitor cLPL.

Cultures were divided into two groups based on the phase of growth at the time of

harvest: (A and B) group 1, harvested at an OD600 of 1.0 and 1.3, respectively. (C

and D) group 2 harvested at an OD600 of 1.7 and 1.9, respectively.














I_,



QI




B)

A~CMlnl Id~3llphllLljL

1 _.


~


_L


1







B6 Control


e













B6 20pMh iiLt


~111&1~ +



r~


IlrPL'


IL


SB7 2041 4gL


B7 Control


Figure 4-3. Magnified regions of 2-DE proteome maps of H. volcanii cells grown in the absence
(right) and presence (left) of cLPL. Landmark spots common to both gels are
indicated by lowercase letters. Protein spots whose intensity increased in the
presence of cLPL are circled and those identified by MS/MS are labeled with arrows.
(A) ThiJ/Pfpl cellular protease homolog was 5-fold higher than the negative control.
(B) FtsZ cell division protein homolog with a relative intensity value 4-fold that of
the uninhibited control. (C) S3Ae ribosomal protein homolog exhibiting one of the
highest differences in relative intensity in this study at almost 14-fold over the
uninhibited control.













Table 4-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs.
ab sence of the proteasome inhibitor cla~sto-l actacy stin-p-l actone


Grp/ ORF
Spota no~b


2 a3 1073


pl 1, kDa
Increase (SD)c (cal gel)d (clld


Coverage
oo pep no.)e


27.2 4

11 3

13.9 5

25.4 5

38.6 6

22.8 3

18.3 3

23.4 3

35.5 1


18.2 5

41.8 10

38.2 12

38.9 11


Homolog Description


Mascot



220


Protein Quality Control, Degradation and Translation:
DJ-1 ThiJ family [3.4.-.-1 5.1 + 0.97 4.0 4.2


24 33


2 a3

1,2 b1

1,2 a6
1 b7

1,2 al0

1 b8


0859

0860

0359

1145

2784

2783


Fe-S assembly protein SufC

Fe-S assembly protein SufB

translation elongation factor EF-1A

30S ribosomal protein S3Ae

30S ribosomal protein S13

30S ribosomal protein S4

50S ribosomal protein L30P


5.1 +0.97 4.2 4.2 33 33


3.0 + 1.0,
6.6 +0.40
4.4 + 0.25


4.6 4.8 53 54

4.6 4.7 46 50


13.8 + 1.4 4.8 4.5 25 34


2.4+ 0.6,

U

6.5 +2.1

2.1 +0.53

Metabolism Transport:

U


5.1 4.8 19 16

5.2 4.7 20 14

4.6 4.8 17 22

4.7 4.4 13 15


4.4 4.6 40 32


1 c5 2543


2 a4 2555* 30S ribosomal protein S17


1 611 2397 lipoprotein of divalent metal ABC
transporter
2 a9 1545 dihydroxy-acetone kinase DhaL
[2.7.1.2]
1 c8 29592-oxoacid decarboxylase E113 chain
[1.2.4.-]


5.3 +0.98 4.4 4.6 25 27


2 h2 R0371


2.7 + 1.1

4.0 +2.4


4.6 4.8 36 27

4.3 4.1 53 48


aldehyde dehydrogenase [1.2.1.-]


Cell Division Conserved Proteins of Unknown Function:
Z U 4.6 4.7
5.0 + 1.4 4.6 4.2
n of COGl340 5.0 + 1.4 4.7 4.2


42 35
42 42
36 42


12.4 3
15.4 4
24.1 4

11 2


1,2 b5
2 b6
2 b6

2 a8


2204


Fts:


205 conserved protein related to actin-
like proteins of COG1077


0880 coiled-coil protei


3.0 +0.66 4.1 4.6 38 27


Outlier Data:


2 a7 2419 hydroxvmethylghttaryl coenzyme A
synthetase
2 a7 0860 Fe-S assembly protein SufB

2 al2 0359 translation elongation factor EF-1A

2 al2 2091 4-aminobutvrate aminotransferase

1,2 a6 0880 coiled-coil protein of COGl340

2 b2 AO378 hydantoinase B /oxoprolinase
2 b2 0455 thermosome subunit 2


2.9 +0.47 4.5 4.8 50 15

2.9 +0.47 4.6 4.8 53 15

3.3 +0.38 4.6 4.9 46 20

3.3 +0.38 4.6 4.9 48 20

4.4 + 0.25 4.7 4.7 36 50


60.3 14 839

8.4 2 103

31.9 8 212

34.2 13 636
15.2 6 355


4.0 +2.4
4.0 +2.4


4.3 4.1 64 48
4.2 4.1 59 48


"Group and spot numbers of protein samples analyzed by mass spectrometry, where group 1 and 2 correspond to 20
and 30 pLM cLBL, respectively.
bORFs are numbered according to the GenBank assembly (Hartman et al., in preparation) of the H. volcanii genome
as indicated.. Polypeptide sequences deduced from genome and identified by mass spectrometry are included with
this HVO number in Table A-1 for comparison of polypeptide to future annotations. Asterisk indicates HVO
number for which the polypeptide sequence was extended from the annotation.
"Increase in intensity of protein spot of cells grown in the presence /absence of cLBL + standard deviation. U,
protein spot detected only in cells grown in the presence of cLBL.
dpl and M, estimated (est.) by 2D-gel and calculated (cal.) based on deduced protein sequence with the differences
between est. and cal. included as Apl and AM,.
ePercent coverage of the deduced protein sequence, number of peptide ions detected, and overall mascot ion score of
protein detected by mass spectrometry are indicated. Protein sequences of detected peptide ions with individual
Mascot ion scores and E-values are included in supplemental data.









CHAPTER 5
PHOSPHOPROTEO1VE ANALYSIS OF PROTEASO1VE-ACTIVATING NUCLEOTIDASE A
MUTANT OF Haloferax volcanii

Introduction

Protein phosphorylation, in many instances, has been associated with ubiquitin tagging

and protein stabilization/destabilization. It has been shown to serve as a precursor to

ubiquitination for several proteins in the eukaryotic proteasomal degradation pathway; a

phenomenon further demonstrated by the association between ubiquitin ligases and protein

kinases in the COP9 signalosome in plants (Dimmeler et al., 1999; Harari-Steinberg and

Chamovitz, 2004; Perales et al., 2006). Furthermore, the phosphorylation of certain intrinsic

destabilization/degradation signals such as PEST sequences has also been linked to proteasomal

degradation (Rechsteiner and Rogers, 1996; Garcia-Alai et al., 2006). Interestingly, this chapter

reveals the observation that phosphorylated protein levels are elevated in cells where full

proteasomal activity is lacking (see Figure 5-3); perhaps an indication of an alternative

mechanism for proteasomal substrate targeting in the absence of a ubiquitinating system. The

known physical effects of phospho-modification lend feasibility to the idea that protein

phosphorylations serves as a maj or recognition factor in the archaeal proteasome pathway.

Phosphorylation is one of the most important and widespread post-translational

modifications of proteins. Its usefulness comes in the form of its strong perturbing forces which

modulate structure and function in a profoundly effective manner (Kennelly, 2003).

Phosphorylation and dephosphorylation of proteins can occur quite rapidly, making it an ideal

mechanism for controlling adaptive responses to environmental cues and changing intracellular

conditions. These appealing characteristics have made it the modification of choice for

regulating a number of vital cellular processes, many of which overlap between branches of life.

In eukaryotes, it is estimated that as much as 30% of the proteome is phosphorylated with serine









constituting 90% of the modified residues and threonine and tyrosine representing the other 10%

(Ficarro et al., 2002; Walsh et al., 2005). Many of these modified proteins represent immune cell

signaling pathways, circadian regulatory systems, cell division control components and

metabolic networks within plant and/or mammalian systems (Ptacek and Snyder, 2006; Huber,

2007; Vener, 2007). In bacteria, phosphorylation of 'two-component' signal transduction and

phosphotransferase system (PTS) elements broadens the occurrence of protein phosphorylation

to include histidine and aspartic acid residues (Walsh et al., 2005; Grangeasse et al., 2007).

Although less common, serine, threonine and tyrosine phosphorylation are also emerging as

regulatory devices of bacteria (Grangeasse et al., 2007).

To date, there are only a modest number of proteins with confirmed phosphorylation sites

among archaea (Kennelly, 2003; Eichler and Adams, 2005). Several of these mirror eukaryotic

and/or bacterial counterparts in terms of modification status and function. Based on traditional

radiolabeling experiments, such proteins as the Halobacterium salinarum CheA and CheY are

phosphorylated at sites identical to their two-component signal transduction bacterial homologs

(Rudolph et al., 1995). The archaeal translation initiation factor 2 a (alF2a) of Pyrococcus

horikoshii is phosphorylated at Ser48 by a kinase (PHO512) related to the human double-

stranded RNA-dependent protein kinase (Tahara et al., 2004). In both Crenarchaeota and

Euryarchaeota, the initiator protein Cdc6 (Orc) is autophosphorylated by a DNA-regulated

mechanism at a conserved Ser residue (Grabowski and Kelman, 2001; De Felice et al., 2003).

Likewise, the P subunit of 20S proteasomes from Haloferax volcanii is phosphorylated at a Ser

residue (Humbard et al., 2006). A zinc-dependent aminopeptidase, with a leucine zipper motif

that associates with a CCT-TRiC family chaperonin, has also been shown to be phosphorylated

in Sulfolobus solfataricus (Condo et al., 1998).









The advent of modern high-throughput phospho-enrichment and analytical tools provides

the opportunity to now identify at large-scale archaeal phosphoproteins, including those which

lack similarity to members of the bacterial or eukaryotic clades or for which modifications have

not previously been predicted. In this study, a comparative proteomic analysis of wild type H.

volcanii cells and those lacking proteasomal function through deletion of a primary proteasome-

activating nucleotidase (PanA) was performed which exemplifies such a scenario. The analysis

included a combination of high-throughput phospho-enrichment methods, sub-enrichment

strategies and tandem mass spectrometry (MS/MS).

Results and Discussion

Construction of H. volcanii Pan2A Mutant GG102

In addition to three 20S proteasomal proteins (al, a2 and P), H. volcanii encodes two

proteasome-activating nucleotidase proteins (PanA and PanB) which are 60% identical. Of

these, the al, p and PanA proteins are relatively abundant during all phases of growth (on rich

medium at moderate temperature, 37 to 42 C) (Reuter et al., 2004). In contrast, the levels of

PanB and a2 are relatively low in early phases of growth and increase several-fold during the

transition to stationary phase (Reuter et al., 2004). To further investigate the function of the

predominant Pan protein, PanA, a mutation was generated in the chromosomal copy of the

encoding panA gene (G. Gil; see "Materials and Methods", chapter 2 for details). A suicide

plasmid, pJAM906, was used to generate this mutation in which panA had a 187-bp deletion in

addition to an insertion of the H. hispanica mevinolin resistance marker. Recombinants were

selected on rich medium supplemented with mevinolin and screened by colony PCR using

primers which annealed outside the chromosomal region that had been cloned on the suicide

plasmid. Approximately 10% of the clones that were screened generated the expected panA

mutant PCR product of 2.50 kb compared to the parent strain PCR product of 1.28 kb (data not









shown). Of the PCR positive clones, one (GG102) was selected for further analysis by Western

Blot using anti-PanA antibodies. The PanA protein was readily detected by Western Blot for

parent strain DS70 (Fig. 5-1). In contrast, PanA was not detected under any growth conditions

for the panA mutant GG102, thus, confirming the mutation.

The 20S Proteasome and PanB Protein Levels Are Not Altered by the panzA Mutation

To determine whether the panA mutation influenced the levels of 20S proteasome and/or

PAN proteins, DS70 and GG102 were analyzed by Western blot using polyclonal antibodies

raised separately against al, a2 and PanB proteins (where al and a2 are subunits of 20S

proteasomes). No differences in the levels of any of these three proteasomal proteins were

detected between the two strains when grown to log phase in minimal medium (Fig. 5-1). Thus,

H. volcanii does not appear to have a feedback mechanism to increase the levels of PanB or 20S

proteasomes after loss of PanA under the growth conditions examined in this study.

Growth Phenotype of panzA Mutant GG102 Compared to Its Parental and Complemented
Strains

Comparison of GG102 panA to its parent DS70 revealed significant differences in growth

in rich medium (Fig. 5-2). This included an increase in doubling time from 4.1 to 5.2 h as well as

decrease in overall cell yield from an O.D. 600 nm of 2.1 to 1.8. Both doubling time and overall

cell yield were at least partially restored by complementation with plasmids pJAM650 and

pJAM1020 encoding PanA-His6 and PanB-His6 proteins, respectively. The ability of this latter

plasmid to partially complement the panA mutation for growth may be due at least in part to the

high-level expression of PanB from the pHV2-derived plasmid pJAM 1020 which includes both a

strong rRNA P2 promoter of H. cutirubrum and T7 terminator to mediate transcription of the

panB-his6 gene fusion. This is likely to alter the levels of PanB protein, normally low in early

phases of growth and increased several-fold during the transition to stationary phase. Although









the PanA and PanB proteins may have overlapping functions based on this partial

complementation for growth, these proteins differ significantly in primary sequence (by 40%)

and growth-phase dependent regulation (Reuter et al., 2004) and, thus, are likely to degrade

proteins differentially, in terms of substrate preference and/or rate.

Comparative 2-DE Analysis of the panzA Mutant to Its Parent

Expression of the panA and panB his6-gene fusions using a rRNA P2 promoter on a high

copy plasmid is likely to alter proteome composition in the complemented panA mutant

compared to 'wild type'. To avoid this complexity, parental strain DS70 was used as 'wild

type' for proteome comparison to the panA mutant GG102. Analysis of the cell lysate of DS70

and GG102 by 2-DE revealed a comparable number of total protein spots as detected by SYPRO

Ruby protein stain, at 333 (A 17) and 314 (A 14) spots, respectively. However, further analysis

of the 2-DE separated proteins by Pro-Q Diamond (through the efforts of I. Karadzic), a

phosphoprotein-specific fluorescent dye developed by Steinberg et al. (Steinberg et al., 2003)

revealed more than twice as many 'phosphoprotein' spots for the panA mutant (64 & 7)

compared to wild type (3 1 & 6) (Fig. 5-3). This difference in the number of protein spots stained

by Pro-Q Diamond in the panA mutant constitutes an average increase of 1 1.1% from total spots

with an average variability of less than 2% across experimental replicates, indicating a

reproducible difference in the phosphoproteomes of these strains. As much as 40-44% of the

Pro-Q Diamond stained phosphproteins were not detected by SYPRO Ruby and, thus, are likely

to be at relatively low levels in the cell while those detected by both methods are of substantial

abundance. These high-abundance proteins within the common proteome are likely to include

ribosomal proteins and translation elongation factor subunits, as determined through MS/MS

identification of maj or landmark spots in past analyses and remarkably high abundance of

proteins identified in both total and phosphoenriched samples in non-gel-based proteomic









experiments. Interestingly, the limited impact of the panA mutation on the total number of

protein spots detected by SYPRO Ruby contrasts with the over 1.7-fold increase after addition of

the 20 S proteasome-specific inhibitor cla~sto-lactacy stin-p-lactone (cLPL) (Kirkland et al., 2007).

This may reflect the central role of the N-terminal Thr proteolytic active site of the 20S P

subunit, which is irreversibly inactivated by cLPL, in all proteasome-mediate protein degradation

in this archaeon. In contrast, the PanA and other closely related AAA+ ATPases are more likely

to play upstream roles including differential substrate recognition, unfolding and translocation

into the 20S proteasome core for degradation.

Phosphoprotein and Phosphopeptide Enrichments

Immobilized metal affinity chromatography (IMAC) and metal oxide affinity

chromatography (MOAC) were used in four separate experiments (A to D) to enrich for

phosphoproteins and/or phosphopeptides from the cell lysate of the panA mutant (GG102) and

its parent (DS70). A similar pattern and enrichment of phosphoproteins compared to cell lysate

was confirmed by fluorescent staining with Pro-Q Diamond of 2-DE separated IMAC fractions

(data not shown). As an initial approach to protein identification, IMAC-purified samples were

separated by 12% SDS-PAGE (one-dimensionally) and bands that appeared uniquely in the

mutant or in discernibly different amounts were excised and digested in-gel with trypsin prior to

hybrid MS/MS analysis (LCQ Deca MS; Experiment A). This analysis produced a modest

number of protein identifications (10 total) with MOWSE scores at 30 or above and, thus, was

not further pursued as a viable approach for high-throughput analysis of these strains. To

overcome these limitations, biological triplicate samples of GG102 and DS70 were subj ected to

three separate approaches: 1) IMAC enrichment of proteins followed by cleavage with trypsin,

2) MOAC enrichment of methyl esterified tryptic peptides, and 3) IMAC enrichment of proteins

followed by MOAC enrichment of methyl esterified tryptic peptides (Experiments B to D,









respectively). Peptides from all three of these latter approaches were separated by C18 reversed

phase HPLC and analyzed online using ESI-QTOF hybrid mass spectrometry.

In total, these approaches resulted in the identification of 625 proteins with MOWSE

scores at 30 or above and with at least 2 peptides identified per protein. This corresponds to

15.4% of deduced proteome ofH. volcanii, based on the DS2 genome sequence minus proteins

encoded by pHV2 (the plasmid cured from DS70 and its derivatives). The identified proteins

ranged from 4.9 to 186 kDa with an average of 41.6 kDa in molecular mass. The majority of the

identified proteins (523 proteins or 83% of total) were encoded on the 2.848-Mb chromosome.

The coding sequences for the remaining 202 proteins were distributed among pHy4 (52

proteins), pHy3 (43 proteins), and pHyl (7 proteins). This represents 8 to 17% of the coding

capacity for each element (18% of the 2.848-Mb chromosome, 8.2% of pHy4, 1 1.2% of pHy3

and 7.9% of pHyl). Of the total proteins identified, 142 were based on high probability scores

and multiple peptide identifications within a single biological replicate and, thus, were not

assigned as unique or common to DS70 or GG102. The remaining proteins were categorized as

either common to GG102 and DS70 (328 proteins), unique to DS70 (57 proteins), or unique to

GG102 (98 proteins). The number of proteins identified by MS/MS in these phosphoprotein

enriched fractions that were exclusive to GG102 was nearly 2-fold higher than the number of

identified proteins exclusive to DS70. This relative difference in MS/MS identified strain

specific-proteins is consistent with the differences observed in the number of Pro-Q Diamond

stained 2-DE gel spots from the proteomes of these two strains.

The 480 proteins that were categorized as either common or exclusive to either strain

(DS70 and GG102 panA) were identified based on an average detection per protein of 4.7 tryptic

peptides in 5 biological replicates and a MOWSE score average of 76. Among the proteins









common to both strains, 8 to 9% were estimated to be more abundant in either GG102 (15

proteins) or DS70 (13 proteins) based on spectral counts. Of the total 625 proteins identified,

only a small portion (32 proteins or 5.1%) were predicted to form transmembrane spanning

helices (TMH) compared to the approximately 22% putative TMH-proteins encoded on the

genome. The TMH proteins were detected primarily in GG102 (over 80%) with nearly 50%

classified as unique to this strain. A lower proportion of TMH proteins were detected in DS70

(53%) with only 2 of these (6%) grouped as exclusive to this strain. While the significantly

higher number of identified predicted membrane proteins in the panA deletion strain compared to

the wild type is intriguing, the overall number identified is low and likely to require preparation

of membranes to enhance their detection (Klein et al., 2005).

A summary of proteins identified as either unique to or more abundant in either GG102

or DS70 are listed in Tables 5-1 and 5-2. At this stage in our understanding, it is unclear whether

these differences in proteomes between the two strains reflect a change in the phosphorylation

status of the protein or overall protein abundance. However, the identification of these

differences was reproducible. As a confirmation to the approaches, the PanA protein was

exclusively and reproducibly detected in the DS70 parent strain across all proteomic analyses.

The PanA protein was not detected in GG102, consistent with the targeted knockout of the

encoding panA gene from the chromosome of this strain (Fig. 5-1).

Categorization of 'Unique' Proteins into Clusters of Orthologous Groups

The 156 proteins identified as exclusive to either GG102 or DS70 (listed in Table 5-1)

were categorized into Clusters of Orthologous Groups (COGS) as indicated in Fig. 5-4. Overall,

the largest percentage (36%) of these proteins was equally distributed between two groups: 1)

those of unknown or general function [R/S] and 2) those involved in amino acid transport and

metabolism [E]. The remaining proteins clustered into a diversity of functional COG groups









ranging from translation to signal transduction as outlined in Fig. 5-4. Comparison of the panA

mutant to its parent (GG102 vs. DS70) revealed notable differences in the distribution of

'unique' proteins within many of these groups. In particular, the percentages of proteins

exclusive to GG102 panA were considerably higher than those unique to DS70 in energy

production and conversion [C], posttranslational modification, protein turnover, and chaperones

[O], cell envelope biogenesis and outer membrane [M], and inorganic ion transport and

metabolism [P]. In contrast, the percentages of proteins which clustered to transcription [K],

translation, ribosomal structure and biogenesis [J], and nucleotide transport and metabolism [F]

were higher in DS70 compared to GG102. The increased number of proteins involved in

translation, nucleotide biosynthesis, and transcription for DS70 may be reflection of the higher

growth rate and overall cell yield of this strain compared to GG102 panA. Although the

increased number of GG102-unique proteins clustering to energy production and conversion [C]

would seem to contradict this suggestion, the vast maj ority of these latter proteins were

oxidoreductases (10 out of 11) including a homolog of 'Old Yellow Enzyme', shown to increase

during periods of oxidative stress in Bacillus subtilis (Fitzpatrick et al., 2003). The remaining

GG102-unique protein of this cluster was a glycerophosphoryl diester phosphodiesterase (ugpQ),

an enzyme which can be an indicator of phosphate starvation (Antelmann et al., 2000). In

contrast to GG102, the two DS70-unique proteins which clustered to this general COG group [C]

were predicted to be involved in the generation of proton motive force including an ATP

synthase subunit and plastocyanin-/cytochrome-like protein.

One of the most notable differences in the protein profiles of the panA mutant and its

parent was linked to phosphorus assimilation and synthesis of polyphosphate (the PHO regulon,

many members of which cluster to group [P])(summarized in Fig. 5-5). Proteins of the PHO









regulon exclusively identified in GG102 included homologs of phosphate/phosphonate ABC-

type transporters (pstB2, pstS2, and phnC), PHO regulators (abrB ghoU2), polyphosphate kinase

(ppk), ABC-transporter glycerol-3 -phosphate binding protein (ugpB), and glycerophosphoryl

diester phosphodiesterase (ugpQ; discussed above). Many additional proteins unique to GG102

were encoded within these genomic regions (i.e. hisH, HVO_AO476, trxB, metK and cysK). In

addition, a transcriptional regulator (HVO_0730) unique to DS70 was found to be divergently

transcribed from a gene encoding inorganic pyrophosphatase (ppa). Components of the ABC-

type Pst2 transporter uniquely identified in GG102 are encoded on pHV4 and appear ancillary to

a paralogous ABC-type Pst1 transporter common to both DS70 and GG102, which is encoded on

the 2.848-Mb chromosome. The up-regulation of components of phosphate uptake and

metabolism typically corresponds to cellular stress (Tam et al., 2006; Fischer et al., 2006) with

the levels of polyphosphate impacting cell survival (Brown and Kornberg, 2004). Proteomic and

microarray analysis of archaeal species such as 3\ /I'thammi,(l L inae acetivorans has revealed the

PHO regulon to be expressed at higher levels during growth on the lower energy-yielding

substrate acetate vs. methanol (Li et al., 2007b).

In addition to alterations in the PHO regulon, the panA knockout resulted in an increased

enrichment and identification of proteins linked to protein folding, Fe-S cluster assembly and

oxidative stress response compared to parent strain DS70. These included components of a

thiosulfate sulfurtransferase (tssA, tssB) and Fe-S assembly system (ni~fU, su~f8, sufC),

thioredoxin/di sulfide reductases (trxB1 and trxB2), topoisomerase A (topA), replication A-related

ssDNA binding protein (RPA), excinuclease ABC ATPase subunit (uvrA), Old Yellow Enzyme

(ygM,~ discussed above), Hsp20 molecular chaperone (ibpA), OsmC-like regulator (osmC),

peptidyl-prolyl cis-trans-isomerase (slyD), S-adenosylmethionine synthase, and cysteine









biosynthetic enzymes (e.g. cysK, serA, OAH) (Tables 4-1 and 4-2). Interestingly, in E. coli the

osmotically-inducible OsmC is proposed to use highly reactive cysteine thiol groups to elicit

hydroperoxide reduction (Lesniak et al., 2003) and is downstream of a complex phosphorelay

system which includes regulation by the AAA+ proteases Lon and HslUV (Kuo et al., 2004;

Majdalani and Gottesman, 2005).

A number of additional differences in transcription and signal transduction proteins were

also observed between DS70 and GG102 panA For example, subunits of RNA polymerase were

altered by the panA mutation with Rpb4 more abundant in DS70 fractions and RpoA and RpoB

(a and p) higher in those of GG102 (Table 5-2). The altered levels of Rpb4 in DS70 vs. GG102

are consistent with the recent finding that the levels of this protein are directly correlated with

eukaryotic cell growth (i. e., cells expressing lower levels of Rpb4 grow slower compared to cells

expressing higher levels)(Sharma et al., 2006). In addition to the above noted differences, a

BolA-like protein and conditioned medium-induced protein 2 (Cmi2) were exclusively identified

in DS70. BolA triggers the formation of osmotically stable round cells when overexpressed in

stationary phase E. coli (Santos et al., 2002), and Cmi2, a putative metal-regulated transcriptional

repressor, may be regulated by quorum sensing in H. volcanii (Bitan-Banin and Mevarech,

GenBank AAL3 583 5). A number of putative DNA-binding proteins and homologs of methyl-

accepting chemotaxis proteins (MCP) were also found to be altered. In addition, a number of

mandelate racemase/muconate lactonizing enzymes related to the starvation sensing protein

RspA of E.coli (Huisman and Kolter, 1994) were altered by the panA mutation.

Some archaea do not encode Pan proteins (e.g. species of Thermopla~sma, Pyrobaculum

and Cenarchaeum). Thus, it is speculated that Cdc48/VCP/p97 AAA+ ATPases, which are

universal to archaea, may function with 20S proteasomes similar to eukaryotes (Jentsch and









Rumpf, 2007). Interestingly, two Cdc48-like AAA+ ATPases were identified as either exclusive

or more abundant in GG102 panA compared to DS70 (HVO_1327 and HVO_2700; Tables 1 and

2, respectively) suggesting these proteins may compensate in part for the loss of the AAA

ATPase function of PanA. A TMH homolog of prohibition (HVO_003 5) known to regulate AAA

protease function (Arnold and Langer, 2002) was also identified as exclusive to GG102.

Intriguing similarities also emerged between the proteomic profiles of this study

compared to our previous work in which cells were treated with the 20S proteasome-specific

inhibitor cla;sto-lactacystin-p-lactone (cLPL) and analyzed by 2-DE (Kirkland et al., 2007).

Specifically, three proteins that accumulated more than 5-fold in cells treated with cLPL were

also discovered in notably higher abundance in the panA mutant (vs. parent) strain of this study.

This subset of proteins included the DJ-1/ThiJ family protease, the translation elongation factor

EF-la, and the Fe-S cluster assembly ATPase SufC (Table 5-2). In addition, FtsZ cell division

protein homologs and components of the putative phosphoenolpyruvate phosphotranferase

system which appear to be coupled to dihydroxyacetone kinase (PtsI, DhaK, DhaL) were also

found at higher levels in GG102 panA and cLPL-treated cells compared to wild type.

Phosphopeptide Identification

Based on favorable likelihood scores using the Mascot search algorithm and careful

manual analysis of the peptide spectra, 9 individual phosphosites including 4 serine, 4 threonine

and 1 tyrosine modification sites were tentatively identified and mapped to a total of 8 proteins

(Table 5-3; Appendix B, Fig. B-1). Five of these phosphosites were identified exclusively by the

combined IMAC-MOAC approach (Experiment D). The remaining four sites were identified by

the combined IMAC-MOAC process as part of an overlapping data set with at least one other

phosphoanalysis method within this study (Table 5-3). Although neural networks are not yet

available for archaeal protein phosphosite predictions based on the limited dataset available for









this domain, many of the putative phosphosites identified in this study had NetPhos scores above

the threshold of 0.5, which is based on eukaryotic phosphosites.

Among the H. volcanii phosphoproteins identified, the Cdc48-related AAA+ ATPase

(HVO_2700), which was more abundant in GG102 enriched fractions than DS70 based on

spectral counting, was represented by two identical phosphorylated peptides occurring in

separate experiments for both strains (Table 5-3). Both inclusive and exclusive Y-series ions and

internal ion fragments containing a phosphorylated Thr45 residue were identified (numbering

throughout based on the deduced polypeptide sequence; Appendix B, Fig. B-1). Similarly, a

protein of unknown function (HVOAO206) was also represented by duplicate peptides

phosphorylated at Ser88 in separate experiments (Table 5-3). Although the function of this ORF

is unknown, it is transcribed in the same orientation two genes encoding members of the

clustered regularly interspaced short palindromic repeats (CRISPR) family of proteins and of

which were identified by MS/MS as common to GG102 and DS70. A DNA-directed RNA

polymerase subunit A' (RpoA') was determined to have a phospho-modiaication at Tyrl 17

(Table 5-3). The discovery of the Tyrl 17 phosphosite, which had the highest NetPhos score

(0.962) and lowest expect value (1.4 e-4) of the phosphosites identified, was supported by the

appearance of three separate internal fragment ions containing the modified tyrosine residue as

well as an inclusive and exclusive y-ion series (Appendix B, Fig. B-1). Other phosphoproteins

included a Cdc6-1/Orcl-1 ortholog (HVO_0001) and pyruvate kinase (Table 5-3). Although

both proteins were common to DS70 and GG102 based on MS/MS identification, the

phosphopeptides of these proteins were only detected in GG102 panA The reason for the

exclusive appearance of these latter phosphopeptides in the panA mutant strain remains to be

determined; however, it does implicate the proteins corresponding to these phosphopeptides as









potential proteasomal substrates that depend upon their phosphorylation status for destruction.

Phosphosites were also identified on components of membrane systems including an ATPase of

an ABC-type transporter as well as an integral membrane protein with a TrkA (NAD-binding)

domain (Table 5-3). A hypothetical protein (HVO_C0059) was also identified as a putative

phosphoprotein based on the detection of one doubly modified peptide fragment at residues

Serl36 and Thrl39 (Table 5-3).

Many of the phosphorylated proteins identified and listed in Table 5-3 are supported by

evidence of the phosphorylation and proteasome-mediated destruction of their orthologs. For

example, eukaryal RNA polymerases II and III are phosphorylated at tyrosine, serine, and/or

threonine residues (Solodovnikova et al., 2005; Jing et al., 2005), and ubiquitin E3 ligase Wwp2

targets the Rpb 1 subunit of RNA polymerase II (related to H. volcanii RpoA') for destruction by

26S proteasomes (Li et al., 2007b). Eukaryal and archaeal Cdc6 proteins undergo

autophosphorylation at serine residues as a presumed means of regulating their DNA helicase

loading activity (Grabowski and Kelman, 2001; De Felice et al., 2003). Furthermore, the

ubiquitin/proteasome-mediated destruction of Cdc6 in both mammalian and yeast cells has been

observed and is proposed to be a mechanism for uncoupling DNA replication and cell division as

part of a pre-programmed cell death response (Blanchard et al., 2002). Cdc48/p97/VCP proteins

undergo Akt-mediated phosphorylation at multiple sites as a mode of ubiquitinated protein

substrate release to facilitate degradation by proteasomes (Klein et al., 2005). In addition, these

AAA+ ATPase proteins are phosphorylated by DNA-protein kinase (DNA-PK) at serine residues

as part of a proposed mechanism to enable chaperone activity and to aid in DNA repair

(Livingstone et al., 2005).









Conclusions

Based on the results of this study, a global increase in the number of phosphoproteins

appears to occur in the presence of a panA mutant compared to wild type cells. Although several

of the proteins enriched and identified as exclusive to GG102 panA are likely involved in

phosphorous assimilation and metabolism and, thus, may bind phosphate non-covalently, this

alone cannot account for the large-scale difference in phosphoprotein staining between the panA

mutant and its parent. Therefore, we propose that the phosphorylation of protein substrates may

facilitate their recognition for proteasome-mediated destruction in organisms for which

traditional ubiquitination pathways are absent (e.g., archaea, actinomycetes). It is also possible

that the observed changes in phosphoprotein content reflect a secondary effect of the maintained

presence of a kinase or enhanced digestion of a phosphatase.

It has been demonstrated in eukaryotes that protein phosphorylation can serve as a

precursor to ubiquitin tagging and subsequent degradation by 26S proteasomes (Karin and Ben

Neriah, 2000; Lin et al., 2006). Furthermore, phosphorylation of conserved sequences rich in

proline, glutamic and aspartic acids, serine and threonine (PEST sequences) is a well-known

example of post-translational modification as a destabilizing force on substrate proteins

(Rechsteiner and Rogers, 1996; Garcia-Alai et al., 2006). The link between protein

phosphorylation and proteasomal degradation is also supported through the function of accessory

structures like the COP9 signalosome, which has been indicated as a coordinator of protein

kinases and ubiquitin ligases in plant systems (Harari-Steinberg and Chamovitz, 2004).

The predominant proteins identified as exclusive to the panA mutant GG102 (vs. DS70)

were linked to protein folding, Fe-S cluster assembly, oxidative stress response and phosphorus

assimilation and polyphosphate synthesis. A number of additional differences in transcription

and signal transduction proteins (e.g. OsmC, BolA, Cmi2) were also observed between these two









strains which, together with the growth defect, suggest the panA mutant is undergoing stress and

accumulating polyphosphate. Consistent with this, a distantly relative of PanA, the ATPase ring

forming complex of mycobacteria, is presumed to associate with 20S proteasomes and serve as a

defense against oxidative or nitrosative stress (Darwin et al., 2003). Proteasomal inhibition has

also been shown to hypersensitize differentiated neuroblastoma cells to oxidative damage (Lev et

al., 2006). Interestingly, a polyphosphate-Lon protease complex is proposed in the adaptation of

E. coli to amino acid starvation (Kuroda et al., 2001). Whether archaeal proteasomes are linked

to polyphosphate remains to be determined; however, short-chain polyphosphates identical to

those of Saccharomyces cerevisiae have been detected in H. volcanii cells grown under amino

acid starvation (Scoarughi et al., 1995).













I


I I


I I-- -


12 3 4 5 6


+ ApanA

--- --I 4 Pan B


- +


- +


--~ -


-


-


protein


2 lg


4 lg


8 lg


Figure 5-1. Western blot analysis of proteasomal subunits a1 and a2 and proteasome-associating
regulatory particles PanA and PanB. Western blot analysis was performed using
increasing amounts of total cell lysate from either DS70 (lanes 1, 3 and 5) or GG102
(lanes 2, 4 and 6).


Ir PanA

















cr 0.5


0.

23 28 33 38 43 48 53 58
Time (h)


Figure 5-2. A growth curve plotting proliferation of wild type and complemented panA
knockout mutant in H. volcanii. Wild-type cells (m), panA mutant GG102
uncomplemented (0), GG102 complemented with plasmid-based panA (0) and
GG102 complemented with plasmid-based panB (*) were compared.












** I
I *
















A.


. *
* L rr a5
**~+




I, *


I,


r*

rna


as


asI


I *


Figure 5-3. ProQ Diamond phosphoprotein stained 2D PAGE gels of total cell lysate extracted
from (A) DS70 and (B) GG102.









20
,18 0DS70 +GG102

0 14 GG102
o
a 12
2 10 O~ I DS70

th o




E R/S C G K J 0 L F M P N/U T V D/B H

COG Group

Figure 5-4. Proteins identified by mass spectrometry grouped according to COG (clusters of
orthologous groups) database. Protein members of each COG are summarized as
percent of total proteins unique to GG102 and DS70 (striped bars), unique to GG102
(black bars), and unique to DS70 (white bars). COG groups listed include: E [Amino
acid transport and metabolism], R/S [General function prediction only/Function
unknown], C [Energy production and conversion], G [Carbohydrate transport and
metabolism], K [Transcription], J [Translation, ribosomal structure and biogenesis],
O [Posttranslational modification, protein turnover, chaperones], L [DNA replication,
recombination, and repair], F [Nucleotide transport and metabolism], M [Cell
envelope biogenesis, outer membrane], P [Inorganic ion transport and metabolism],
N/U [Cell motility and secretion/Intracellular trafficking], T [Signal transduction
mechanisms], V [Defense mechanisms], D/B [Cell division and chromosome
partitioning], I [Lipid metabolism], and H [Coenzyme metabolism].
















ORF01506 ORF01507 ORF015080RF015090ORF01510 ORF01512 ORF01513 ORF01514
leuS hsp hsp pheA hisH hbp phnC phnE
ORFO2620

ORFO2618 ORFO2619 ORFO2621 ORFO2622 ORFO2623 ORFO2624
phoU1 pstB1 pstA1 pstC1 pstS1 abtB phoU2
ORFA00575

ORFA00568 ORFA00569 ORFA00570 ORFA00571 ORFA00572 ORFA00573 ORFA00574 ORFA005760RFA00577
phoU3 pstB2 pstA2 pstC2 pstS2 conserved SOD TMH protein trxB


ORFBOO348 ORFBOO349 ORFBOO350 ORFBOO3510RFBOO352 ORFBOO353
suhB ugpQ ugpB ugpA ugpE ugpC
ORF00281

ORF00279 ORF00280 ORF00282 ORF00283 ORF00284 ORF00285 ORF00286
cysK thil_tRNA modlfication ppk metK cyaA cox1

ORFO2383 ORFO2384 ORFO2385 ORFO2386 ORFO2387
ZnCad efflux His trad map prpA ppk2 ~
ORF01227 ORF01226 1000 bp
ppa transonptional
regulator





Figure 5-5. Organization of genes linked to PHO regulon which encode proteins altered by or
are related in primary sequence to those altered by the GG102 pnan mutation. Genes

highlighted in black encode proteins unique to GG102, in grey encode proteins

unique to DS70, marked with diagonal lines encode proteins common to DS70 and
GG102, and in white encode proteins not identified in this study.













Table 5-1. Phospho-enriched proteins uniquely identified in each strain ofH. volcanii



G Banoka~, Predicted Function, Descriptionb
GG102 unique:

HVO_0035 TMH protease regulator (stomatin, prohibition)
HVO_0058 oligopeptide ABC transporter ATP-binding protein
HVO_0070 NifUlthioredoxin-related protein (nifU)
HVO 0136 translation initiation factor elF-1A

HVO_0177 arsenate reductase (ArsC) and protein-tyrosine-phosphatase (VVzb) related
HVO_0393 excinuclease ABC ATPase subunit (uvrA)
HVO_0433 NADPH-dependent F420 reductase (npdG); dinucleotide binding
HVO_0446 phosphate/phosphonate ABC transporter, ATP-binding protein (phnC)
HVO_0448 imidazole glycerol phosphate synthase glutamine amidotransferase subunit (hisH)
HVO_0480 3-phosphoglycerate kinase (pgk)
HVO_0519 replication A related (single-stranded DNA-binding) protein (RPA)
HVO_0602 3-dehydroquinate dehydratase (aroD)
HVO_0620 Type II/IV secretion system and related ATPase protein
HVO_0627 dipeptide ABC transporter ATP-binding
HVO_0681 TopA DNA topoisomerase I
HVO_0766 Hsp20 molecular chaperone (ibpA)
HVO_0788 tryptophan synthase B subunit (trpB)
HVO_0829 prolyl oligopeptidase
HVO_0884 aldehyde reductase (COGO656)
HVO_0887 2-oxoacid :ferredoxin oxidoreductase, B subunit
HV_0889 FAD/NAD binding oxidoreductase
HVO_0894 acetate-CoA ligase (acsA)
HVO_1009 oxidoreductase related to aryl-alcohol dehydrogenases (aad)
HVO_1022 NADH:flavin oxidoreductase related to 'Old Yellow Enzyme' (yg/NVI)
HVO_1027 Twin arginine translocation TMH protein TatAo
HVO_1037 conserved protein
HVO_1047 NADPH:quinone oxidoreductases (qor)
HVO_1061 thioredoxin reductase (trxB1)
HVO 1113 FtsZ cell division GTPase

HVO_1121 heme biosynthesis protein (pqqE)
HVO_1170 conserved protein
HVO_1203 flagella protein E-related
HVO_1272 transcriptional regulator
HVO_1273 inosine-5'-monophosphate dehydrogenase, CBS pair domain
HVO_1289 OsmC-like regulator (osmC)
HVO_1299 HTH DNA-binding XRE-lMBF1-like protein
HVO_1309 Xaa-Pro dipeptidase
HVO 1327 Cdc48 related AAA+ ATPase
HVO_1 446 fructose-1 6-bisphosphatase (fbp)
HVO_1495 phosphotransferase system llB component (fruA)
HVO_1507 acetolactate synthase small regulatory subunit (ilvN)
HVO_1513 conserved protein
HVO_1527 glucose-1 -phosphate thymidylyltransferase (galU)
HVO_1576 UDP-glucose 4-epimerase (gmd) (COGO451)

HVO_1578 NADH dehydrogenase, FAD-binding subunit


HVO 1588 cupin (small barrel) domain protein

















G B no.a Predicted Function, Descriptionb
GG102 unique [cont.]:
HVO_1637 peptidyl-prolyl cis-trans isomerase
HVO_1649 S-adenosylmethionine synthetase
HVO_1650 polyphosphate kinase (ppk)
HVO_1654 cysteine synthase (cysK)
HVO_1788 ferredoxin-nitrite/sulfite reductase (nirA, cysl)
HVO_1830 6-phosphogluconate dehydrogenase
HVO_1894 conserved TMH protein
HVO_1932 phosphoglycerate dehydrogenase (serA)
HVO_1936 F420-dependent oxidoreductase
HVO_1973 conserved protein
HVO_2040 UDP-glucose 4-epimerase (gmd) (COGO451)
HVO_2045 hypothetical protein
HVO_2072 conserved TMH protein
HVO_2081 conserved TMH protein
HVO_2214 MCP domain signal transducer TMH protein (Htr, Tar) (COGO840)
HVO_2270 HsdM type I restriction enzyme
HVO 2271 HsdS restriction endonuclease S subunit
HVO_2361 carbamoyl-phosphate synthase large subunit (carB)
HVO_2374 PhoU-like phosphate regulatory protein
HVO_2516 2 3-bisphosphoglycerate-independent phosphoglycerate mutase
HVO_2524 phytoene/squalene synthetase (crtB)
HVO_2542 ribosomal protein L15 (rplO)
HVO_2589 asparaginase
HVO_2622 aldehyde reductase (COGO656)
HVO_2661 aspartate aminotransferase (aspC) (COG0075)
HVO_2662 dioxgenase
HVO_2671 aminotransferase class V (COG0075)
HVO 2690 MviM oxidoreductase

HVO_2716 acyl-CoA dehydrogenase (acd)
HVO_2742 methionine synthase II (metE)
HVO_2759 TET aminopeptidase
HVO_2760 non-conserved TMH protein
HVO 2767 DNA/RNA helicase

HVO_2819 phage integrase family domain protein
HVO_2883 snRNP-like protein
HVO_2948 phenylalanyl-tRNA synthetase a chain (pheS)
HVO_2997 O-acetylhomoserine sulfhydrylase (OAH, SHLase)
HVOAO267 mandelate racemase/muconate lactonizing enzyme (COG4948)
HVOAO331 mandelate racemase/muconate lactonizing enzyme (COG4948)
HVOAO339 ABC transporter extracellular solute-binding protein
HVOAO472 thioredoxin reductase (trxB2)
HVOAO476 conserved pst-operon protein
HVOAO477 PstS phosphate ABC transporter, solute binding protein
HVOAO480 PstB phosphate ABC transporter ATP-binding protein
HVOB0130 DNA binding protein (DUF296)
HVOB0173 SMC-ATPase like chromosome segregation protein
HVOBO248 oxidoreductase (COG4221)
HVO BO265 oxidoreductase

HVOBO268 alkanal monooxygenase-like
HVOBO291 glycerophosphodiester phosphodiesterase (ugpQ)
HVOBO292 ABC-type transporter, glycerol-3-phosphate-binding protein (ugpB)
HVOBO371 aldehyde dehydrogenase (putA)


Table 5-1. Continued













Table 5-1. Continued



G Banoka Predicted Function, Descriptionb
DS70 unique:
HVO_0017 conserved protein
HVO_0043 aminotransferase (gabT, argD)
HVO 0117 translation initiation factor elF-6
HV_0161 ATP phosphoribosyltransferase (hisG)
HVO_0314 archaeal/vacuolar-type H -ATPase subunit C/AC39
HVO_0353 ribosomal protein S23 (S12, RpsL)
HVO_0420 MCP domain signal transducer (Tar) (COGO840)
HVO_0549 2-keto-3-deoxygluconate kinase

HVO_0730 transcription regulator transcribed divergent from inorganic pyrophosphatase in haloarchaea

HVO_0827 conserved protein (COG4746)
HV_0850 proteasome-activating nucleotidase A (panA)

HV_0853 DNA double-strand break repair protein (mrell)

HVO_1085 bifunctional purine biosynthesis protein (purH)

HVO_1087 Usp universal stress protein (COGO589)
HVO_1173 SpoU-like RNA methylase (COG1303)
HVO_1228 PetE plastocyanin/Fbr cytochrome related protein
HVO_1245 DsbA-IDsbG-like protein-disulfide isomerase (thioredoxin domain)
HVO_1426 conserved protein
HVO_1443 ABC transporter, ATP-binding protein
HVO_1472 putative transcriptional regulator with CopG DNA-binding domain
HVO_1492 conserved protein
HVO_1502 3-isopropylmalate dehydrogenase (leuB)
HVO_1 683 4-a-glucanotransferase (malQ)
HVO_1741 conserved protein
HVO_1829 leucyl aminopeptidase T (pepB, ampS)

HVO_1901 translation initiation factor elF-2y (GTPase)

HVO_2102 PTS system enzyme II A component, putative

HVO_2126 ABC transporter, oligopeptide-binding protein
HVO_2239 Usp universal stress protein (COGO589)
HVO_2333 conserved protein
HVO_2504 3-oxoacyl- acyl-carrier protein reductase (atsC) (COG4221)

HVO_2514 transcriptional regulator (conditioned medium-induced protein 2 or Cmi2)
HVO_2614 uridine phosphorylase (udp)
HVO_2675 histidinol dehydrogenase (hisD)
HVO_2723 snRNP homolog (LSM1)
HVO_2726 glutamyl-tRNA synthetase (gitX, glnS)
HVO_2738 ribosomal protein S28e (S33)
HVO_2782 ribosomal protein S11 (rpsK)
HVO_2798 ABC transporter, branched-chain amino acid binding protein
HVO_2899 BolA, stress-induced morphogen
HVO_2916 short chain alcohol dehydrogenase (fabG)

HVO_2943 dihydroorotate oxidase (pyrD)
HVO_2981 uracil phosphoribosyltransferase (upp)
HVOA0078 helicase SNF2/RAD54 family
HVOAO279 Tnp transposase
HVOAO295 N-carbamoyl-L-amino acid amidohydrolase (amaB)
HVOAO386 hydantoin utilization protein B (hyuB)
HVOAO388 transcriptional regulator AsnC/Lrp family

HVOAO487 cobyrinic acid a c-diamide synthase (cobB)
HVOAO635 aminotransferase class V (CsdB-related)












Table 5-1. Continued


G BanoSa Predicted Function, Descriptionb
DS70 unique [cont.]:
HVqOB005 conserved protein in cobalamin operon

HVqOOO84 GlcG-like protein possibly involved in glycolate and propanediol utilization

HVqOB012 mandelate racemase / muconate lactonizing N-terminal domain protein (COG4948)

HVqOB015 Ribbon-helix-helix transcriptional regulator of CopG family

HVqOB014 OYE2 11-domain light and oxygen sensing His kinase, member of 'two component' system

HVOBO233 a-L-arabinofuranosidase (xsa, abfA)
HVOC0017 chromosome partitioning ATPase (ParA, Soj)
"ORFs are numbered according to the GenBank assembly (Hartman et al., in preparation) of the H. volcanii
genome as indicated. ORF numbers highlighted in grey are predicted to be co- or divergently-transcribed.
hLescriptions highlighted in grey indicate paralogous ORFs identified as unique to either GG102 or DS70. COG
numbers are included for these paralogues. Proteins were classified as 'unique' if they were exclusively
identified in at least two samples of either GG102 or DS70 with MOWSE score averages of 30 or higher and at
least two peptide hits.











Table 5-2. Proteins with a minimum 2-fold abundance, comparatively, as determined by spectral
counting


b DS700 GG102
ORF no. aDS70b GG102 Diff.
GenBank Predicted Function and/or Description Exp. MOWSE MOWSE Spec. Spec.
Count Count %


HVO_1073 DJ-1,Pfpl family protein B 46 126 0 8 100



HVO_1546 Dihydroxyacetone kinase subunit DhaK B 49 78 0 7 100


HVO 1496 Pstl phosphoenolpyruvate-protein B 40 112 0 7 100
phosphotransferase


HVO_2923 20S proteasome a12 subunit PsmC B 37 70 0 6 100



HVO_0025 Thiosulfate sulfurtransferase TssA B, D 41 100 0 6 100



HVO_0581 FtsZ cell division protein B, D 32 58 0 6 100


HVO 0024 Thiosulfate sulfurtransferase TssB B 35 95 0 5 100



HVO_0350 RpoA DNA-directed RNA polymerase a1 B 41 89 0 4 100



HVO_0348 RpoB DNA-directed RNA polymerase 3 B 64 92 2 7 71



HVO_0861 SufB/SufD domain protein B, D 77 176 8 24 67



HVO_0806 Pyruvate kinase B 92 147 6 15 60



HVO_2941 mc nonhistone chromosomal protein B, D 61 90 2 5 60



HVO_0859 Fe-S assembly ATPase SufC A, B 208 289 13 19 32



HVO 2700 Cdc48-like AAA+ ATPase B 171 252 14 19 26



HVO_0359 Translation elongation factor EF-la1 B, D 392 546 34 45 24









Table 5-2. Continued
ORF n. DS7b DS700 GG102
ORF o. a DS70 GG102 Diff.
GenBank Predicted Function and/or Description Exp. MOWSE MOWSE Spec. Spec.o
Count Count %
HVO_2748 RNA polymerase Rpb4 B 117 34 9 0 100


HVO_0055 conserved protein B 133 57 8 1 88



HVO_0313 ATP synthase (E/31 kDa) subunit B 92 56 5 1 80



HVO_2543 rpmD ribosomal protein L30P B 111 67 9 2 78



HVO_2561 rplB ribosomal protein L2 B 97 59 9 2 78



HVO_1000 acetyl-CoA synthetase B 100 58 8 2 75



HVO_1148 rpsl5p ribosomal protein S15 B 109 60 10 3 70


HVO 1412 dmd diphosphomevalonate B 104 61 3 1 67
decarboxylase


HVO_2384 CBS domain pair protein B 96 64 5 2 60



HVO_0214 L-lactate dehydrogenase B 166 105 16 7 56


HVO 0536 Nutrient-stress induced DNA binding B 303 148 25 11 56
protein


HVO_0044 argB acetylglutamate kinase B 117 77 8 4 50


HVO_0324 argS arginyl-tRNA synthetase B 78 46 4 2 50
'E \pei uni. Ilus A, B, C and D correspond to in-gel IMAC, total IMAC, MOAC and IMAC-MOAC respectively.
bAverage probability-based peptide matching scores from all experiments in which the protein was identified.
"Average total spectral counts for all experiments in which the protein was identified with % difference between
two strains indicated












Table 5-3. Putative phosphosites identified by MS/MS


Predicted
ORF no. Function
GenBank and/or
Description
DNA-directed
HVO_0349 RNA
polymerase


Phosphopeptidee


LSLTDEEADEpYRDK
(Y117)


Sria Epb MOWSE Total no. Pep.
Strai Exp. (p>95)C Peptd E-value


Both B, D


GG102 A to D


60 11 1.4 e-4


Pyruvate
kinase


HVO 0806


83 13 1.9 e-3 AGMpTGYPALVAR (T533)


Branched-
chain amino
acid
HVO AO579
ABC-
transporter,
ATPase
TrkA-integral
HVO_1916 membrane
protein


7 0.16MpSADETNAQSVDDPDADI
DRDGER (S2)


DS70



DS70


D 34


D 79 6 0.53


LIGRpTIR (T578)

SGDDTLYNLpSRMNSELDN
SR (S321)


HVO_0001


Cdc6, Orc GG102 D 45


8 0.97


HVO C0059 Hypothetical
protein

HVO 2700Cdc48-like
AAA+ ATPase

HVO AO206Conserved
protein


DS70


D 32 3 0.99 TSpSMQpTR (S136, T139)


171 18
Both B, C (DS70) (DS70) 2.0
252 17
(GG102) (GG102)
DS70 B, D 101 8 2.8


IDGPNDGpTAIAR (T45)


VEDVpSKLR (S88)


"Strain in which phosphopeptide was detected.
experiments A, B, C and D correspond to in-gel IMAC, total IMAC, MOAC and IMAC-MOAC respectively.
"Average probability-based peptide matching scores from all experiments in which the protein was identified.
dotal number of peptides detected by MS per protein identified to be phosphorylated.
eSequence of phosphopeptide with 'p' preceding residue proposed to be modified by phosphorylation.
Phosphorylation sites are also indicated in parenthesis and numbered according to residue position of polypeptide
deduced from the genome sequence.









CHAPTER 6
ESTABLISHMENT OF A BASELINE PROTEOME OF Haloferax volcanii

Introduction

With the genome of Haloferax volcanii now complete, global surveys of transcript and

protein levels are now possible in efforts to expand our understanding of cellular systems and

how these compare to systems of other organisms. Detailed proteomic and transcriptomic

analyses are now possible and will contribute to rapid advancement of haloarchaeal research.

Sub-specialized comparative proteomic techniques such as degradomics, metabolomics and

secretomics can now be employed to answer lingering questions about H. volcanii from a

functional point of view. In this chapter, we describe the first large-scale proteome map of H.

volcanii. Two-dimensional gel electrophoresis and multi-dimensional liquid separation and

fractionation were paired with tandem mass spectrometric technology for protein identification.

Results and Discussion

Statistical Analysis of the H. volcanii Proteome Map

Aggregate data from fiye independent proteomic investigations resulted in a pool of

protein identifications of considerable size and scope (Table 6-1). In total, 1,295 proteins of the

H. volcanii proteome were mapped, constituting nearly 32% of the total predicted coding

capacity of this organism. These proteins were identified through 14,553 statistically significant

top-ranking peptide matches (hits) with an average of 11.2 matching peptides per protein

identification. Of those proteins identified, only 81 (6.3%) were single-hit identifications,

leaving 1,214 proteins (93.7%) identified through multiple hits. An average probability-based

MOWSE score of 88.7 was assigned overall with a range of 35 to 433 for the entire dataset.

Moreover, these identifications were made over a broad range of masses and isoelectric point

(pl) values, likely resulting from the use of multiple complementary protein separation and









detection methods for the generation of a unified proteome map. Proteins were identified with a

mass range of 4.8 kDa to 232.7 kDa and an average of 39.9 kDa. These same proteins represent

a pl range of 3.10 to 13.04 with an average of 5.01. These value ranges are representative of

total proteome pl values of 2.70 to 13.04 with an average of 4.52 and estimated molecular weight

range of less than 10 Da to more than 235 kDa. While these data were achieved through a

combination of liquid chromatography MS/MS and 2D-PAGE MS/MS experiments, it was

determined that only 13.7% of proteins identified were out of the range of resolution for in-gel

experiments based on a mass range of 97.4 kDa to 14.4 kDa. A more substantial percentage

(38.4%) was determined to be beyond the 3.9-5. 1 range of pl resolution for gel-based

proteomics. Additively, 52.1% of those proteins identified across all 5 experiments were done so

with the aid of 1D and 2D liquid chromatography, covering regions of the proteome inaccessible

by our gel-based proteomic methods alone. Contributions made by an LC/MS/MS approach also

included identification of membrane-associated proteins containing trans-membrane helix

(TMH) domains. In total, 60 proteins predicted to possess TMH domains were identified by

LC/MS/MS, constituting 4.6% of the proteins in this mapping dataset and 6.3% of the 949

proteins in the total proteome predicted to possess at least one TMH domain. Of these 60 TMH

domain-containing proteins, none were identified through 2D in-gel separation.

Expectedly, the maj ority of proteins in the mapped proteome (79.2%) are coded for by

genes residing on the largest 2,848 kb chromosome while the remaining 20.8% are distributed

over the remaining three replicons in proportion to their respective sizes (Table 6.2). None were

mapped to pHV2 due to the fact that the wild-type H. volcanii strain analyzed (DS70) and the

derivative panA mutant (GG102) have been cured of this plasmid. Proteins identified were

categorized into COGs (clusters of orthologous groups) based on proposed function and are









summarized in Table 6.3. The largest category was that of proteins assigned to unknown or

general function ([R/S], 29.3%) which included hypothetical proteins and proteins that are

predicted to function in multiple capacities or possess non-descript roles. Otherwise, the largest

groups are represented by carbohydrate transport and metabolism ([G], 12.4%), energy

production and conversion ([C], 9.0%) and translation and ribosome biogenesis ([J], 7.6%).

Functional categories that were least represented included motility and secretion ([N], 0.9%),

lipid metabolism ([I], 1.2%) and signal transduction ([T], 1.5%).

Reannotation of High-Scoring Hypotheticals

Of the 379 proteins that were assigned to the unknown/general function COG, 175 were

designated as hypothetical or conserved hypothetical proteins. In order to assign putative

function to a larger portion of the annotated genome, these proteins were prioritized based on

MOWSE score and ratios of MOWSE score to assigned peptide hits. Those that exceeded the

arbitrary MOWSE score limit of 60 and MOWSE/peptide ratio of 10 were subj ected to protein

basic local alignment sequence tool (BLAST) searches. While 37 individual proteins were

considered, only 7 returned sequence similarity hits with reasonably high e-value scores greater

than le-6. These newly assigned annotations included a probable transcriptional regulator

(HVO_0 184 most closely related to Natromona~s pharaonisYP_3 2633 5; E-value of 4e- 10;), 3 -

isopropylmalate dehydratase (HVO_0697 most closely related to /rlle thammaklLinae barkeri

YP_3 05092; E-value of 3 e-53;), cobolamin (vitamin Bl12) biosynthesis CbiX protein

(HVO_1 129 most closely related to Deinococcus geothermalis YP_603746; E-value of 7e-13;),

Fmu (Sun) domain protein (HVO_1595 most closely related to Plesiocystis pacifica

ZP_0 1912358; E-value of le-6;), UDP-glucose:protein transglucosylase-like protein

(HVO_1704 most closely related to Solan2um tuberosum ABA81861; E-value of 7e-16;),

elongator protein 3/MiaB/NifB (HVO_2187 most closely related to Halorubrum lacusprofundi










ZP_0201635 7; E-value of le-158;) and probable transcriptional regulator (HVO_2718 most

closely related to Haloquadratum walsbyi YP_658583; E-value of 4e-38;). Additionally, 25 of

these 37 proteins not assigned to any putative function were similar to hypothetical proteins of

other haloarchaeal species with e-values in excess of le-80 and, thus are conserved

hypothetical. The remaining 5 proteins of the 37 considered were unique to H. volcanii yet had

probability-based MOWSE scores ranging from 36 to 111 with MOWSE/peptide ratios of 1.1 to

57.0.

While most of the newly assigned annotations clustered to proteins involved in

metabolism or transcriptional regulation, two were notable in that they were both predicted to

function in non-coding RNA modification. The Fmu protein (HVO_1595) identified through

alignment with Plesiocystis pacifica possessed a SUN domain which, in yeast, is associated with

proteins involved in a wide array of functions such as DNA replication, ageing, mitochondrial

biogenesis and cytokinesis (Hiller et al., 2007). Additionally, SUN-domain-containing proteins

have also been determined to function as RNA methyltransferases (Frye and Watt, 2006).

Interestingly, the H. volcanii Fmu SUN-domain containing protein possessed significant

similarity to the tRNA and rRNA cytosine-C5-methylase ofP. pacifica and, thus may operate to

modify non-coding RNAs in this halophile. Another protein which was functionally reassigned

(HVO_2187) showed overwhelming similarity to elongator protein 3/MiaB/NifB from

Halorubrum lacusprofimndi ( E-value of le-158). While this protein was determined to be a

probable Fe-S oxidoreductase, functionally, it possesses some overlap with the MiaB protein

from organisms such as Thermotoga, which has been shown to catalyze the posttranscriptional

methyltiolation of N-6-isopentenyladenosine in tRNAs (Hernandez et al., 2007). Together, these










newly annotated proteins putatively represent a general functional subdivision concerned with

the modification of non-coding RNA structures in H. volcanii.

Comparative COG Analysis of the H. volcanii Proteome Map

The numbers of H. volcanii proteins detected by MS/MS and categorized into each of the

17 COGs (Table 6-3) were compared to average COG data from the deduced proteomes of seven

archaeal species (Halobacterium sp. NRC-1, M\~ethanococcusjannaschiii M~ethanobacterium

thermoautotrophicus, Thermopla~sma acidophilum, Pyrococcus horikoshii, Archaeoglobus

fulgidus and Sulfolobus solfataricus) as well as two bacterial (Escherichia coli and Pseudomona~s

aeruginosa) and one eukaryotic (Saccharomyces cerevisiae) species. Overall, the numbers of

proteins in each COG mapped in H. volcanii varied from the average of the 10 reference

organisms by only 1.3%. The largest discrepancy in this comparison was a 7.3% higher number

of proteins observed in group G (carbohydrate transport and metabolism) in H. volcanii.

Similarly, when compared only to the average COG profiles of those members of the Archaea

domain listed previously, only a 1.28% discrepancy was observed with the highest (7.9%) again

belonging to carbohydrate transport and metabolism in H. volcanii. Three archaeal species

(Halobacterium sp. NRC-1, M\~ethanococcus jnannaschii and Sulfolobus solfataricus) were used in

comparison with H. volcanii to represent the similarities and differences in the archaeal COG

groupings (Fig. 6-1A). While the profile of H. volcanii was similar in numbers of proteins in

each COG to those of each of these archaeal species, H. volcanii did show an approximate 9%

increase in proteins of group G (carbohydrate transport and metabolism). A notable increase in

average percent discrepancy was also observed when comparing H. volcanii COG profile to an

average of only those members of the Eukarya and Bacteria domains listed above. This

comparison revealed a 1.92% difference overall with the largest difference belonging to the

unknown/general function (8.5% higher in H. volcanii) (Fig. 6-1B). Other significant differences









were also observed in this latter comparison in the numbers of proteins belonging to

carbohydrate transport and metabolism (group G; 5.8%), signal transduction (group T; 4.3%) and

energy production and conversion (group C; 3.4%) all higher in H. volcanii (Fig. 6-1B).

Generally, the mapped portion of the H. volcanii proteome appears to be representative of other

archaeal COG profiles as well as COG profiles of organisms from other domains. While there

were few minor discrepancies between H. volcanii and compared COG maps, these may be

attributed to differences in proteome sizes or skewed numbers of unannotated (or improperly

annotated) proteins between organisms of this comparison group.

Comparison of proteins mapped in H. volcanii (in this study) to groups of proteins

mapped by mass spectrometry in other organisms such as Mycopla~sma hyopneumoniae has

revealed surprising similarities and differences in the numbers of proteins assigned to

orthologous gene clusters. Orthologous gene clusters R/S (unknown/general function), K

(transcription), J (translation and ribosome biogenesis) and G (carbohydrate transport and

metabolism) in M~ycoplasma were similar to H. volcanii in terms of percent coverage, revealing

only 0.3%, 2.5%, 2.1% and 0.5% differences, respectively (Pinto et al., 2007). The most notable

differences in the COG profiles of these organisms were the numbers of proteins assigned to

post-translational modification, chaperones and protein turnover (0) and energy production and

conversion (C), which were lower in H. volcanii by 12.6% and 10.4%, respectively (Pinto et al.,

2007). Variability between COG profiles of these proteomes may be attributed to differences in

map sizes or bias resulting from analytical methods; however, the remarkable similarity between

key functional categories of these somewhat unrelated organisms may suggest a need for

constitutive expression of a common subset of genes for basic cellular function, irrespective of

taxonomic classification.









Identification of Paralogs in H. volcanii

Within this current proteome mapping dataset for H. volcanii, multiple proteins were

identified which possess one or two other paralogs. Less common, however, was the MS/MS

detection of 4 or more closely related protein paralogs. Nine separate proteins which possessed 4

or more paralogs were detected in this mapping dataset. These paralogs included proteins

annotated as FtsZ of which 6 were detected out of a predicted total of 8. Six of the 16 cell

division control protein 6/origin recognition complex proteins of the deduced proteome were also

detected. Other paralogs included ArcR transcriptional regulators (6 of 16), bacterio-opsin

activator-like proteins (7 of 14), non-descript oxidoreductases (6 of 8) and transposases of the

IS4 family (5 of 31i). Four of each of the following were also identified by MS/MS: cell division

control protein 48 (5 total), mutS DNA mismatch repair protein (4 total; all accounted for in this

mapping dataset) and metallo-beta-lactamase superfamily domain protein (9 total). The extent of

functional overlap between many of these paralogs is unclear. A few, such as those involved in

cell division or transcriptional regulation, may operate in a specialized way or may be co-

functional, forming multi-subunit complexes with related paralogs. Those paralogs not

identified may be conditionally expressed; therefore exploration of a greater variety of growth

conditions may assist to fully identify these "missing" proteins. Future characterization of the

identified paralogous proteins by the haloarchaeal community may facilitate the assignment of a

more specific function and reduce apparent redundancy within the currently annotated proteome.

Conclusion

This work reports the identification of approximately one-third of the total proteome of

Haloferax volcanii with coverage of both molecular weight and isoelectric point ranges related to

those of the deduced proteome. This project has also made progress towards improving the

current annotation of the H. volcanii proteome through careful analysis of high-scoring










hypothetical. The regions of the proteome which were mapped and future expansion of this

current map through proteomic analyses of changing growth conditions and various stress

challenges will serve as a useful resource for the H. volcanii research community. This

information will be particularly useful in coordination with forthcoming transcriptome data and

the release of the first published H. volcanii genome. Collectively, this work will also serve

those studying other members of the Archaea domain.









Table 6-1. Number of proteins mapped in H. volcanii, categorized by experiment in which they
were discovered
Experiment No. Identified Percent of Total ID
Strong Cation Exchange/MudPIT 1150 88.7
Immobilized Metal Affinity Chromatography (IMAC) 484 37.3
Metal Oxide Affinity Chromatography (MOAC) 49 3.8
Combined IMAC-MOAC 296 22.8
Proteasome Inhibitor (cLPL) 2DE 43 3.3










Table 6-2. Total protein identifications categorized by contia of origin


No
No. ID
Deduced
1027 2960
147 638
99 438
23 85


Deduced
34.7
23.0
25.9
25.8


Contig
Chromosome
pHV4
pHV3
pHV1
pHV2


Size (kb)
2848
636
438
85


% ID

79.2
11.3
7.6
1.8










Table 6-3. Division of identified H. volcanii proteins by functional category
COG Description No. % of Total
[R/S] Unknown/general function 379 29.3
[E] Amino acid transport and metabolism 88 6.8
[C] Energy production and conversion 116 9.0
[O] PTM, chaperones, protein turnover 45 3.5
[M] Envelope biogenesis and outter membrane 39 3.0
[P] Inorganic ion transport and metabolism 48 3.7
[K] Transcription 74 5.7
[J] Translation and ribosome biogenesis 99 7.6
[F] Nucleotide transport and metabolism 54 4.2
[L] DNA replication, recombination and repair 70 5.4
[D] Cell division and chromosome partitioning 32 2.5
[N] Motility and secretion 11 0.9
[T] Signal transduction 19 1.5
[G] Carbohydrate transport and metabolism 161 12.4
[H] Coenzyme metabolism 44 3.3
[I] Lipid metabolism 16 1.2
Total 1295 100
































































50%00%


_*1 __I 1 11_1 1 __I I_


35 0%


SHalobacterlum sp NRC-1
a Methanococcus Jannaschll
a Sulfolobus solfataricus
O Haloferax volcanil


30 0%

25 0%

S20 0%

150
10 0%o


0 0%


JAKLBDYVTMNZWUOCGEFH PQR/S
COG


35 0%


erevislae
12
uginosa









.Bl~i~n~:


m Saccharomyces c
a Escherichla coll K
a Pseudomonas aer
0 Haloferax volcanil


30 0%

25 0%

~20 0%

"15 0%
10%


JAKLBDYVTMNZWUOCGEFHIPQR/S
COG


Figure 6-1. Comparative COG profiles of H. volcanii and representative organisms from (A) the
Archaea, including Halobacterium sp. NRC-1, M\~ethanococcus jnannaschii and

Sulfolobus solfataricus. (B) Members of the Bacteria and Eukary domains were
compared. This analysis included Escherichia coli, Pseudomona~s aeruginosa and
Saccharomyces cerevisiae.


5 0%









CHAPTER 7
SUMMARY OF RESULTS AND CONTINUING INVESTIGATIONS

Summary of Results

The long-term obj ective of this work is to identify proteins of the Haloferax volcanii

proteome which are degraded by its proteasomal system. In addition, this work is focused on

examining our working model in which post-translational modification, particularly protein

phosphorylation, serves as a recognition motif or destabilizing factor of proteasomal substrate

proteins in the absence of an ubiquitin system. Both classical and modern proteomic techniques

were implemented to comparatively analyze the proteome of cells with and without full

proteasomal activity. This comparison was performed using a H. volcanii mutant strain

possessing a deletion of the panA gene encoding the primary regulatory particle (RP) of its

proteasome as well as chemical inhibition of the 20S core particle itself. Investigations into the

role of protein phosphorylation in protein recognition and/or degradation by archaeal 20S

proteasomes were based on observations of a phenotypic difference in phosphorylated protein

accumulation between wild-type and RP mutant cells. In consideration of this difference, our

extensive proteomic analyses incorporated phosphoprotein enrichment and purification strategies

to facilitate a focused exploration of the phosphoproteomes of cells with and without active

PanA. These approaches led to the establishment of a list of 10-12 proteins that represent ideal

candidates for proteasomal degradation studies, as determined by skewed abundance in cells

lacking full proteasomal function. Many of these proteins were also identified after

phosphoprotein enrichment, thus lending support to our working model suggesting protein

phosphorylation may play a role in proteasomal degradation. The status of these candidate

proteins as substrates of archaeal proteasomes has been, and continues to be determined through










pulse-chase and immunoprecipitation experimental methods using custom polyclonal antibodies

generated specifically for the H. volcanii antigens.

We have used chemical inhibition of the proteasomal core particle to analyze differential

accumulation of proteins using an in-gel format with reasonable success. Our recently developed

method of Trizol-based preparation of halophilic proteins has allowed the range of resolvable

proteins in gel to be expanded from approximately 400-500 to more than 1000 spots per Hield and

thus has made large-scale proteomic comparisons of H. volcanii more efficient. In experiments

involving the addition of the proteasome-specific inhibitor clasto-lactacystin beta lactone (cLPL),

as many as 1072 spots were resolved after addition of 20 CLM inhibitor and 1000 spots after

addition of 30 CLM inhibitor. These numbers represent a consistent 1.6-to-1.8-fold average

increase over uninhibited samples, which yielded average spot numbers of 669 and 584 for 20

CLM and 30 CLM samples, respectively. Of those appearing in difference between inhibited and

uninhibited samples, 89 were determined to be at or above a 4-fold threshold where proteasomal

activity was reduced. Ultimately, 17 of these spots were chosen for MS/MS identification based

on their clear separation from surrounding protein spots, thus resulting in cleaner excision from

the gel and more definitive MS/MS identification. Proteins identified in these experiments

included those involved in protein quality control such as a DJ-1/ThiJ/Pfpl1 family member and

proteins involved in protein synthesis such as translation elongation factor EF-la and an

assortment of ribosomal proteins. These analyses also revealed proteins involved in iron-sulfur

cluster assembly, cell division and metabolism. The diversity of these identifications suggests a

far-reaching influence of archaeal proteasomes and a functional role with some similarity to the

eukaryotic proteasome system. Interestingly, many of the proteins selected for MS/MS

identification in this study were discovered in isomer chain formations within the 2D gel maps,









indicating some form of posttranslational modification and supporting our hypothesis that there

exists a connection between post-translational modification (PTM) and proteasomal degradation

in the archaea.

In parallel to chemically inhibiting the core particle of 20S proteasomes, we also

generated a mutant in which the gene encoding the primary proteasome-activating nucleotidase

regulatory particle PanA was deleted. Close homologs of this protein from M~ethanococcus

janna~schii have unfoldase activity and catalyze the translocation of protein substrates into 20S

proteasomes in in vitro degradation assays. Interestingly, phosphoproteomic analysis of wild-

type strains and those lacking PanA revealed an approximate difference of 30 protein spots when

analyzed in gel and visualized with phospho-specific fluorescent stain ProQ Diamond. A more

in-depth analysis of the phosphorylated proteomes of each strain was conducted via

chromatographic enrichment of phosphoproteins and phosphopeptides using iron or gallium-

mediated immobilized metal affinity chromatography (IMAC) or titanium oxide-mediated metal

oxide affinity chromatography (MOAC) coupled to methyl esterifieation to eliminate

interference of proteome acidity. These combined results, along with those observed in-gel,

indicated a consistent average discrepancy in phosphoprotein numbers of more than 1 1% in

favor of the panA mutant strain. In total, 625 proteins were identified by MS/MS after

phosphoprotein enrichment with 98 of these unique to the mutant strain. In contrast, 57 were

reproducibly identified only in the wild-type strain and 328 were common to both strains. The

remaining 142 proteins, while identified with statistically significant scores and multiple peptide

hits, were assigned to only a single biological replicate and thus were discounted from the

statistical analysis. Spectral counting was also utilized to identify proteins within the

overlapping proteome that repeatedly showed at least 2-fold dominance in either strain. In total,









28 proteins were determined to be in at least 2-fold abundance in at least 3 biological replicates.

Of these, 15 were more abundant in the panA mutant while 13 were more abundant in the wild-

type strain. Proteins existing in higher levels in the panA mutant included DJ-1/ThiJ/Ppf family

protease, iron-sulfur cluster assembly components, translation elongation factor EF-la and a cell

division protein FtsZ and several metabolic enzymes; results that overlap considerably with our

chemical inhibition studies. An additional protein of interest that was more abundant in the panA

mutant than wild-type strain included the AAA+ ATPase Cdc48/valosin-containing protein/p97;

a protein that may associate with the archaeal proteasome in substitution for an absent PAN

complex. Also of interest were the accumulating and, in some cases, exclusive appearance of

transport and regulatory components of the phosphate uptake system (dominant in the panA

mutant) as well as consistently skewed levels of RNA polymerase subunits between strains.

These subunits included Rpb4 (dominant in wild-type) and RpoA and RpoB (both dominant in

the panA mutant).

Spectral data resulting from these MS/MS scans also led to the identification of 9

phosphorylation sites over 8 different proteins. Among these were the Cdc48/VCP/p97 protein

and the origin recognition complex protein 1 (Cdc6-1l/Orcl-1); the latter of which has been

identified as an ubiquitinated substrate of the 26S proteasome in higher eukaryotes (Blanchard et

al., 2002). Similarly, the DNA-dependent RNA polymerase subunit RpoA' was determined in

this study to be phosphorylated at tyrosine 117. This finding was of particular importance

considering the fact that not only is the RpoA' subunit related to eukaryotic RNA polymerase

subunits such as Rpb 1 but it has also been show to be ubiquitinated and degraded by eukaryotic

proteasomes (Li et al., 2007a). Coincidentally, Rpb 1 has also been shown to be phosphorylated

at serine, threonine and tyrosine residues (Solodovnikova et al., 2005; Jing et al., 2005).










Aggregate data resulting from each proteomic experiment was pooled and organized to

generate the first map the proteome of Haloferax volcanii. Presently, our combined proteomic

studies have yielded 1,296 protein identifications, constituting 3 1.8% of the total predicted

proteome. Of those identified, 111 (8.5%) were represented by a single peptide hit while 91.5%

were matched through multiple peptide fragments. The average number of peptides matched per

protein in this data set was 5.56 and the maximum was 46. The average probability-based

scoring for the mapped proteins was 80.7 with a range from 35 to 1,028. In demonstration of our

dynamic range of detection (likely the result of a diversified set of separation and detection

methods) we were able to identify proteins that ranged in mass from 4.8 kDa to as much as 232.7

kDa with an average of 39.9 kDa. The pl range of detection was 3.10 to 13.04. Combined with

currently established genomic data and forthcoming microarray profiles, this proteomic

information will allow the archaeal research community to delve deeper into questions of cellular

subsystems and gene expression within H. volcanii.

Recent experiments have focused on confirming the status of several identified proteins

as substrates of the H. volcanii proteasome through pulse-chase and immunoprecipitation. Of

particular interest is the proliferating cell nuclear antigen (PCNA) identified through IMAC

phosphoprotein enrichment and quadrupole/time-of-flight tandem mass spectrometry (QTOF

MS/MS). Preliminary results indicate a rapid disappearance of PCNA levels over the course of

60 min with a half life determined to be around 1 min (Fig. 7-1). This observation is supported

by the fact that PCNA orthologs in rice and tobacco plants have been shown to be degraded by

26S proteasomes in a cell cycle-dependent manner (Yamamoto et al., 2004). In consideration of

PCNA and its role in DNA replication and the identification of a phosphopeptide of the Cdc6-

1/Orcl-1 protein exclusive to the panA mutant, other components that function at the replication









fork or are associated with the pre-replication complex were selected for pulse-chase analysis.

Preliminary immunoblot analysis of several pre-replication complex elements was performed

using polyclonal antibodies generated against orthologs in other archaeal species (gifts from

Dr.Zvi Kelman, U. of Maryland Medical Center, Rockville; in collaboration with Ken Lau,

University of Florida). In these preliminary experiments, several components including a DNA

ligase, Halobacterium NRC-1 mini-chromosome maintenance (MCM) helicase paralog, a DNA

polymerase subunit and the PCNA sliding clamp loader were found to produce Western blot

signals that were dominant and, in some cases, seemingly exclusive to the panA mutant.

Continuing Investigations

Future investigations stemming from these proteomic surveys and the preliminary

immunoprecipitation analysis of PCNA will focus on these replication components and others

identified through MS/MS. Additionally, the status of several other proteins for which we have

generated polyclonal antibodies will be determined through pulse-chase labeling and

immunoprecipitation. These will include the Swachman-B odian-Diamond syndrome protein

homolog, translation elongation factor subunit EF-la and an archaeal ortholog of the bacterial

cell division septum formation protein FtsZ. Other investigations to be pursued in support of the

proteomic analyses presented here include the generation of transcriptome data from cells

lacking proteasomal function through both genetic and chemical inhibition. Overlaying

expression profies with proteome maps will expedite the process of identifying protein targets of

the proteasome by indicating the source of differences in protein levels identified through

comparative proteomic analysis.

Our ability to identify consistent differences in the proteome of H. volcanii with and

without full proteasomal function has facilitated the identification of several proteins that

represent viable candidates for degradation. These proteins represent a variety of cellular









functions ranging from cell division and DNA replication to protein synthesis and central

metabolism. This may indicate a rather wide range of influence by the archaeal 20S proteasome,

similar to that observed in eukaryotes. Moreover, the 20S proteasome is the primary energy-

dependent protease in H. volcanii, which may implicate the proteasome and its accessory

elements as key regulators of a diverse collection of cellular activities.












0 min 1 min 3 min 5 min 10 min 30 min 60 min


DS70


GG102


'Crr~


-igu -g-i


0 1 3 5 10 30 60
Time (min)


Figure 7-1. 35S pulse-chase and immunoprecipitation of H. volcanii PCNA. (A)
Immunoprecipitated protein over a 60-min time course. (B) Gradual disappearance of
PCNA protein levels over 60 min in wild-type DS70 (0) and stabilized levels in the
panA mutant GG102 (m).



































I


APPENDIX A
CLASTO LACTACYSTIN BETA LACTONE-TREATED PROTEOME SPOT DATA

A.


A4 Rib. Prot. Sl7
30CLM cLPL


A4t Rib. Prot. Sl7
Control


Figure A-1. Magnified regions of 2-DE proteome maps of H. volcanii cells grown in the absence
(right) and presence (left) of proteasome inhibitor cla~sto-lactacy stin-p-lactone in
addition to those depicted in Figure 4-3. Landmark spots common to both gels are
indicated by lowercase letters. Protein spots that increased in the presence of cLPL
are circled.


A. IPslffhil/Pfp 1
Control


Ig-lffn'~hij/Pfp l
30pM cLPL










~


~I


rlilL,


A6 Coiled Coil
(COXKl340)
30pLM cLBL


A7 HmgCoA
Syntbase
Control


&


A7 HmgCo0A
Synthase
30p~M cLBL


r
))
\u


Figure A-1. Continued


A6 Coiled Coil
(COG l340) .
Control









I


d "


I IC
A8 Related to actin-
like proteins
(COGl077)
Con ts@


A8 Related to actin-
like proteins
(COG 1 077)
30CLM L


1 ~6)


r


A9 DhaL
Control


A9 DhaL
30CrM cLjiL
*F


Figure A-1. Continued












_ ii ~


r a


~


SAl0 Rib. Prot. Sl3
30prM cLBL


L


L


Al0 Rib. Prot. Sl3
Con trol


Figure A-1. Continued


Al2 ~EF-lA
301rM cLflL


go













All EF-1A
Control










-- --7


B1 Suf B


B2 Aldehyde
Dchydrogenase
30CLM cLQL


Figure A-1. Continued


I~Lrr,


B1 SufB B--
30pM" cLPL,


*(11




B32 Aldchyde
Dchydrogenase
Control





B5 Fts~Z (1)
Control


B5 FtsZ (1)
20pM cLBL


~3r
rFFL


B6i FtsZ (2)



Figure A-1. Continued


B6 FtsZ (2)
20prM cL)L








































I


B7 Rib. Prot.
S3Ae
20ptM ctPL


B7 Rib. Prot.
S3Ae r
Control


B8 Rib. Prot. S4
Control




a


B8 Rib. Prot. S4
20pMcl cL|3L




a


Ii


Q)
''-'
'


Figure A-1. Continued





I _


d i




Bll Lipoprotein
of Divalent Metal
Transporter
Control


d**



BII Lipoprotein of
Divalent Metal
Transporter
20pM cLBL


C5 Rib. Prot.
L30P ;
Control


IC,


Figure


A-1. Continued


C5 Rib. Prot.
L30P
20pMh~ cLPL





_I ~I


Cs 2-Oxoacid
Decarboxylase El|$
Chain
Control




CD


CB 2-Oxoacid
Decarboxylase ElP
Chain
S20pLM cLPL


aIIIIAll


IQI


Figure A-1. Continued














Table A-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs.

ab sence of the proteasome inhibitor cla~sto-l actacy stin-p-l actone.


Srp toF D it 1 Increase (1
gel~d


(clgld o~pp Aascot
HO.)'


E-value




3.4e-6

9.4e-7

5.5

5.0e-5




5.9e-9

1.4

3.2e-1


Peptide Sequence


Protein Quality Control, Degradation and Translation:
5.1 +0.97 4.0 4.2 24 33 27.2 4 71

77

11

61

220


2 a3 1073


DJ- 1 ThiJ
family [3.4.-
.-]


2 a3 0859


5.1 +0.97 4.2 4.2


33 33


11 3 100

19


Fe-S
assembly
protein SufC


4.6 4.8 53 54 13.9 5 31 3.6e-2

67 1.1e-5


3.0 + 1.0,
6.6 +0.40


Fe-S
assembly
protein SufB


1,2 b1


1.5e-7

4.3e-4


51 9.1e-4


287


1,2 a6 0359


4.4 + 0.25 4.6 4.7 46 50 25.4 5 44

54


3.2e-3

3.6e-4


translation
elongation
factor EF-1A


52 7.5e-4

9 18


65 5.1e-5


224


1 b7 1145


13.8 + 1.4 4.8 4.5 25 34 38.6 6 66

50

81

67

55

40

359

2. ., 5.1 4.8 19 16 22.8 3 84

LT 35


1.1le-5

4.7e-4

5.2e-7

1.5e-5

3.1e-4

1.0e-2



2.7e-7

3.4e-2


30S
ribosomal
protein S3Ae










30S
ribosomal
protein S13


1,2 al0












Table A-1. Continued


S ota Ho Increaton se) (Cl 1 l/ed oUpe Mascot E-value
g~el~d 10.)'
35 4.1e-2

154


Peptide Sequence


1 b8 2783









1 c5 2543









2 a4 2555


U 5.2 4.7 20 14









6.5 +2.1 4.6 4.8 17 22


18.3 3 75 1.3e-6

51 5.2e-4

50 1.2e-3

176


23.4 3 39 9.5e-3

48 1.2e-3


30S
ribosomal
protein S4






50S
ribosomal
protein L30P






30S
ribosomal
protein S17


2.1 +0.53 4.7 4.4 13 15


35.5 1 53 3.7e-4

53


Metabolism Transport:


1 bl1 2397


U 4.4 4.6 40 32


18.2 5 78 6.1e-7

48 6.9e-4

19 7.5e-1

57 1.3e-4

63 4.6e-5

265


lipoprotein
of divalent
metal ABC
transporter


2 a9 1545 dihydroxy- 5.3 +0.98 4.4 4.6 25 27 41.8 10 44 9.3e-4
acetone21 3.4e-1
kinase DhaL
[2.7.1.2] 88 le-7

30 6.4e-2

18 1.2

47 1.3e-3

15 2.2

21 5.6e-1

17 1.2


27 0.2

328

18 5.1e-1


1 c8 2959 2-oxoacid 2.7 + 1.1 4.6 4.8 36 27 38.2 12













Table A-1. Continued


S oto IH. Dciptio 11 Icese (Cl 1 l/ed oUpp Alascot E-value
g~el~d 10.)'
decarboxvlas
e E113 chain 63 1.9e-5
[1.2.4.-]
26 11-

35 1.3e-2

751.6e-6

85 2.2e-7

29 8.2e-2

35 2.2e-2

19 1

18 1.6

38 2.2e-2

53 7.1e-4

494


Peptide Sequence


2 b2 BO371 aldehyde 4.0 + 2.4 4.3 4.1 53 48 38.9 11
dehydrogena
se [1.2.1.]


41 6.6e-3

74 3.9e-6


19 1.3

32 6.4e-2

57 2.3e-4

72 8.6e-6

88 2.1e-7

77 3.0e-6


49 1.4e-3


61 1.7e-4


91 1.8e-7


661


Cell Division Conserved Proteins of Unknown Function:


1 2 hS 2204


FtsZ


4 4 7 42 Ti


12.4 3 21 3.9e-1

39 7.1e-3


7 11


67


2 b6 2204


FtsZ 5.0 + 1.4 4.6 4.2 42 42


15.4 4 52 3.7e-4
48 8.8e-4













Table A-1. Continued


S oto IH. Dciptio 11 Icese (Cl 1 l/ed oUpe Mascot E-value
g~el~d 10.)'
68 1.6e-5

44 6.8e-3

212


Peptide Sequence


2 b6 0880 coiled-coil 5.0 + 1.4 4.7 4.2 36 42
protein of
COGl340


24.1 4 26 1.7e-1

30 1.2e-1


32 6.5e-2

91 9.1e-8

179


2 a8 205 conserved 3. .6 4.1 4.6 38 27
protein
related to
actin-like
proteins of
COG1077


11 2 47 1.4e-3


37 2.6e-2


Outlier Data:


2 a7 2419 hydroxy- 2.9 +0.47 4.5 4.8 50 15
methyl-
glutaryl
coenzyme A
synthetase


9.4 3 70 5.6e-6


66 1.4e-5


8.0e-6


2 a7 0860


Fe-S
assembly
protein SufB


2.9 +0.47 4.6 4.8 53 15


6.9 2 34 1.9e-2


3.0e-1


2 al2 0359 translation 3.3 +0.38 4.6 4.9 46 20
elongation
factor EF-1A


60.3 14 89 1.0e-7


2.5e-3

6.7e-4


46 2.1e-3

75 3.2e-6

69 1.2e-5


30 1.2e-1


27 2.8e-1


98 2.6e-8













Table A-1. Continued


Grpta ORF DHomolog Increase ( 1 kad 00p MactEvle
(Cl clgel)d (%peO.)ot Evau


Peptide Sequence


75 5.0e-6

72 1.1e-5


79 2.3e-6


44 7.8e-3

40 1.7e-2

839


4-
aminobutyrat
e
aminotransfe
rase


2/al2 2091


3.3 +0.38 4.6/4.9 48/20


8.4/2 47 1.7e-3


56 2.5e-4


103


1,2/a6 0880 coiled-coil 4.4 + 0.25 4.7/4.7 36/50
protein of
COGl340






















2/b2 AO378 hydantoinase 4.0 +2.4 4.3/4.1 64/48


/oxoprolinas


31.9/8 11 2.6
Pho


21 5.5e-1

KI
22 4.7e-1 K

RA
34 3.9e-2 R

C.
40 1.1e-2 R

E.
26 2.8e-1 K.

RN
39 1.1e-2 R

KV
19 1.8 K

212



34.2/13 45 1.2e-3

23 2.3e-1

51 4.5e-4

21 4.4e-1 R
MS
62 4.4e-5 M.

45 2.2e-3 R.

109 1.1e-9 K.1
RM
47 1.5e-3 R

KV
39 1.2e-2 K

RE
38 1.6e-2 R











Table A-1. Continued


SGpt OF ~ oolg1 Inrese (Cl/ (al/ed 05 pep Mascot E-value Peptide Sequence
g~el~d nO.)'
R. FNSRMPDKVITGDLNAQVA
53 6.9e-4
AVR.T + Oxid. (M)
K. YGGATVEAAI DKILE HGE
61 9.7e-5
ETSR.S
K. DSGYVLDSTDVHQEGVLF
42 7.7e-3
PGTK.V
636

2/b2 0455 thermosome 4.0 + 2.4 4.2/4.1 59/48 15.2/6 84 1.5e-7 K. FLDQEEAQLK. Q
subuit 234 2.0e-2 K. GIDDLAQHYLAK. Q

33 2.9e-2 K. FLDQEEAQLKQK. V
78 1. 1e-6 K .S S ELNKE LLADL IVR. A
R.VLAENAGLDSIDTLVDLR
61 8.1e-5
K.NAEDLLEQDIHPTAIIR.
65 2.8e-5

355


aGroup and spot numbers of protein samples analyzed by mass spectrometry, where group 1 and 2 correspond to 20
and 30 pLM cLBL, respectively.
bORFs are numbered according to the GenBank assembly (Hartman et al., in preparation) of the H. volcanii genome
as indicated. Asterisk indicates ORF number for which the polypeptide sequence was extended from the annotation.
"Increase in intensity of protein spot of cells grown in the presence/absence of cLBL + standard deviation. U,
protein spot detected only in cells grown in the presence of cLBL.
dpl and Mr estimated (est.) by 2D-gel and calculated (cal.) based on deduced protein sequence with the differences
between est. and cal. included as Apl and AM,.
ePeptide sequences of ions detected by mass spectrometry are indicated along with their percent coverage of the
deduced protein sequence, number of peptides detected, individual and overall (bold) Mascot ion scores, and E-
values.










APPENDIX B
PHOSPHOPEPTIDE TANDEM MASS SPECTRA








A.


HVO_1916
Y1 TrkA integral Membrane Protein


Y2 I


~Y5 (HPO,)















Figure B-1. MS/MS spectra of phosphorylated peptides identified in H. volcanii. Labeled
peaks indicate dominant b and y-ions and modified internal fragments are also
labeled.





I~u~.-* Ilr~l~~i: am ~ ..




-l


HVOAOS79
Branched Chain A.A. ABC Transporter ATPase

YG Y1

Sr DaPo~l'(RI 1j(






Y8

B12 (HPO )


pSAD




B3


IllIIII


Figure B-1. Continued


I




:3i; I''cl :J`i!





_~ i_ll_lU __ln
U .il-l~ II IIPi:WI ill-l


II


HVOC0059
Hypothetical Protein





pS 11


SpSMQ


Y1

B1


IlTr r1


Figure B-1. Continued


- j111. 1


1








































~ 'LYCLIIIC-IIMIY -LUI~I'YTII~'~~~'~I~I-I ---r-I-rYrYIY(I~I(I (I1U~II1 1I1I11
~C N q C ~ y -1 II n r. rr n r r
u


i ~l~~Y-- 119
U ~~~~l(lrIL Y-i~ Ill*H i*~
C


pTG

Y2

MpT
~. GMpT

-GMp


.Y4


Figure B-1. Continued


HVO_0806
Pyruvate Kinase






Y6


Y10 (H PO )



iTG
Y9 (HPO )


~i~i~r~~
















HVO_0001
Cdc6-110rc1
BB (HO)

Lp RMNSLYNLpSRMN


Yf
B4 (H20)







A6











Figure B-1. Continued































179




















































Figure B-1. Continued


18 Y5 VS Yi

s LT EE A"D p



merase
Y6' (Me.Est.)


ADEpYRD


I:E T'C:
h
L
"'
.,..,

-
;liI 1 ''


_ _____ _____IL IYI


1_


ILYUY~CUIUI lulrrrlu


L


~- T ~I L I L ~n ur Ir r. r rr r ii


Illr Cl _I ___I ~~ I!_
U sm-~l~v- Fca--~.-r


HVO 0349
DNA-Directed RNA Pol)





Y1


DEpY


R~



'''
.I~..i


V


vL'


ADEpY



































* I


0'1 '~YYI' Yl .I~ YL~Y


( I


IEd y~~uu.MMARQ YYYYI~~IMMMAI IiYLU I~ll1LU I


In ~ rl-l~ I-----' ~ ~~
U sm-~l~v- Fc:a.-1.-r.


HVO AO206
Conserved Protein


Y1 Y2


pSKL.


): 1


Y6 ('Me Est)


j~ 1/1


Figure B-1. Continued


ve r*s rs n

V pS K R


1 .9:


I*




































u~ I nl~



N
1111
cj., ....
.~..

,,
.....: ... ~ ..
,
1


"' n. n, a a ? `r a
I


LII YIILI II~~OIYII~IYI LI~YI


I *** *- *** ** -- -1 "* ****


HVO_2700
Cdc48-like AAA+ ATPase

YFn Y2 Y1


..G ND pAIARcr


Y4*2


Y7 ('Me. Est.)


.r tl


Y.


GTAIA


U


Figure B-1. Continued










APPENDIX C
COMPLETE HALOFERAX VOLCANII PROTEOME MAPPING DATA

Table C-1. A complete listing of H. volcanii proteins included in current proteome map
Acc. No.a Description MOWSEb Hitse Expd
HVqO_000 orc cell division control protein 6-like 38.00 10 D
HVO_0004 oxidoreductase FADIFMN-binding superfamily 49.00 2 A
HVO_0008 lysC aspartokinase 69.80 21 AB
HVO_0009 tnaA beta-eliminating lyase 77.64 44 AB
HVO_0011 purO IMP cyclohydrolase 43.50 7 AB
HVO_0014 RNA helicase Ski2 homolog 54.25 22 AD
HVO_0017 conserved hypothetical protein 48.25 11 AB
HVO_0024 tssB thiosulfate sulfurtransferase 62.43 18 ABD
HVO_0025 tssA thiosulfate sulfurtransferase 79.53 61 ABD
HVO_0027 transcription anti-termination factor 73.00 16 AB
HVO_0028 hypothetical protein 68.50 9 AB
HVO_0029 uvrB excinuclease ABC B subunit 56.00 24 A
HVO_0035 stomatin-prohibitin homolog transmembrane 63.67 65 AB
HVO_0039 helicase 53.00 4 A
HVO_0041 argF ornithine carbamoyltransferase 74.27 29 ABD
HVO_0043 ArgD acetylornithine aminotransferase 77.75 13 ABE
HVO_0044 argB acetylglutamate kinase 88.90 23 AB
HVO_0049 argG argininosuccinate synthase 88.83 46 AB
HVO_0054 glyS glycyl-tRNA synthetase 113.09 66 AB
HVO_0055 conserved protein 108.47 86 ABD
HVO_0058 dppF oligopeptide ABC transporter ATP-binding 129.00 22 AB
HVO_0059 oppD oligopeptide ABC transporter ATPase 8.4 2
component
HVO_0062 dipeptide ABC transporter dipeptide-binding 99.31 74 ABC
HVO_0065 polC DNA polymerase II large subunit DP2 48.50 13 D
HVO_0068 panE 2-dehydropantoate 2-reductase 47.00 7 A
HVO_0069 arylsulfatase 66.00 2 A
HVO_0070 NifUl-like domain protein 91.50 16 AD
HVO_0078 hemC porphobilinogen deaminase 67.00 15 AB
HVO_0081 Glutamate-1 -semialdehyde 2 1-aminomutase 73.45 29 AB
HVO_0083 Nitrogen regulatory protein P-ll 64.00 2 A
HVO_0085 Nitrogen regulatory protein P-ll 52.63 40 AB
HVO_0087 hemB delta-aminolevulinic acid dehydratase 51.20 13 A
HVO_0094 conserved hypothetical protein 39.00 5 D
HVO_0099 conserved hypothetical protein 39.00 7 A
putative nonsense mediated mRNA decay
HVO_0100 67.00 2 A
protein
HVO_0 103 conserved hypothetical protein 40.00 1 A
radA DNA repair and recombination protein
HVO_0104 92.19 57 ABD
RadA
HVO_0 107 SUF system FeS assembly protein NifUl family 46.50 3 A
HVO_0 109 csd aminotransferase class V superfamily 56.33 13 AB
HVO_0112 tetratricopeptide repeat protein 74.33 8 A
HVO_0115 Ribosomal L39 protein 37.00 3 A
HVO_0116 Ribosomal protein L31e 38.00 3 D









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_0117 translation initiation factor elF-6 putative 53.75 6 AB
HVO_0118 Ribosomal LX protein 51.33 7 AB
HVO_0119 pfdA prefoldin alpha subunit 42.00 2 B
HVO_0120 dpa signal recognition particle receptor-like 7.0 1
protein
HVO_0 122 serA phosphoglycerate dehydrogenase 73.00 5 A
HVO_0 123 srp Signal recognition 54 kDa protein 50.33 60 ABD
HVO_0 128 adenylate kinase 36.00 6 A
HVO_0133 cctA Thermosome subunit 1 373.48 446 ABCD
HVO_0 134 RNA-binding Pno1 homolog 62.50 10 AE
HVO_0 135 serine/threonine protein kinase 38.00 3 A
HVO_0136 eiflA translation initiation factor elF-1A 65.40 15 AD
HVO_0 138 tyrS tyrosyl-tRNA synthetase 92.17 20 A
HVO_0 140 Protein of unknown function (DUF460) family 49.00 6 D
HVO_0 141 hypothetical protein (TBD) 37.00 6 A
HVO_0 145 rfcC replication factor C small subunit 46.00 5 A
HVO_0 158 tata-box binding protein e 70.00 5 B
HVO_0 161 hisG ATP phosphoribosyltransferase 58.00 1 A
HVO_0 163 transcription regulator 69.50 4 A
HVO_0 165 chlorohydrolase family protein 77.00 15 A
HVO_0 167 ahcY adenosylhomocysteinase 59.64 51 ABD
HVO_0 175 pcn proliferating cell nuclear antigen (pcna) 71.33 9 AC
HVO_0 177 arsC arsC protein 61.75 9 BD
HVO_0 184 conserved hypothetical protein 69.00 2 A
HVO_0 190 conserved hypothetical protein TIGR00268 41.00 1 A
HVO_0 191 mutS mismatch repair protein 44.00 5 A
HVO_0 195 rpiA ribose 5-phosphate isomerase A 78.00 1 A
HVO_0 196 conserved hypothetical protein 96.44 80 ABD
HVO_0 197 hypothetical protein 37.00 4 A
pmm
HVO_0199 96.42 45 ABD
phosphoglucomutaselphosphomannomutase
HVO_0203 rfcS replication factor C small subunit 68.17 27 A
kinA signal-transducing histidine kinase
HVO_0204 38.00 3 D
homolog
HVO_0206 alaS alanyl-tRNA synthetase 92.76 99 ABD
HVO_0208 GMP synthase 38.00 2 A
HVO_0209 gcdH glutaryl-CoA dehydrogenase 87.22 44 A
HVO_0212 putative lactoyglutathione lyase 37.00 2 A
HVO_0213 Ferritin-like domain subfamily 146.93 71 ABD
HVO_0214 L-lactate dehydrogenase 95.45 68 ABCDE
HVO_0215 hdrD heterodisulfide reductase 55.00 10 A
HVO_0220 mcm cell division control protein 21 41.00 15 A
HVO_0221 conserved hypothetical protein 99.00 5 A
HVO_0233 ribose-phosphate pyrophosphokinase 64.00 2 AC
N2 N2-dimethylguanosine tRNA
HVO_0236 66.00 5 A
methyltransferase
HVO_0237 ribonuclease BN 109.00 4 A
HVO_0239 glnA glutamine synthetase type I 332.21 413 ABCD
HVO_0240 trh transcription regulator AsnC family 44.60 23 ABD









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_0242 pepB Aminopeptidase II 56.33 6 A
HVO_0243 Ita threonine aldolase 88.50 4 A
HVO_0250 hlp hemolysin protein 43.00 2 A
HVO_0253 Sua5/YciO/YrdC/YwlC family protein 58.00 7 B
HVO_0258 phage integrase/site-specific recombinase 38.00 4 A
HVO_0260 phrH PhiH1 repressor-like 58.00 1 A
HVO_0266 conserved hypothetical protein 38.00 3 A
HVO_0274 conserved hypothetical protein 42.75 29 B
HVO_0291 conserved protein 63.14 39 ABD
HVO_0292 conserved protein 77.67 40 ABE
HVO_0298 tram domain protein 66.67 8 A
HO377 signal-transducing histidine kinase
HVO_0301 43.00 4 A
homolog
HVO_0304 etfA Electron transfer flavoprotein alpha-subunit 69.50 21 AB
HVO_0305 etfB Electron transfer flavoprotein beta-subunit 74.00 1 A
HVO_0307 conserved hypothetical protein 51.00 13 A
HVO_0309 hmp membrane protein 83.00 4 A
HVO_0310 vacuolar (H+)-ATPase G subunit 91.67 36 ABD
HVO_0311 V-type ATP synthase subunit I 62.78 35 AB
HVO_0313 ATP synthase (E/31 kDa) subunit 111.83 85 ABD
HVO_0314 ATP synthase (C/AC39) subunit 52.50 28 AB
HVO_0315 ATP synthase (F/14-kDa) subunit 49.64 21 ABD
HVO_0316 ATP synthase archaeal A subunit 161.76 159 ABC
HVO_0317 ATP synthase archaeal B subunit 177.95 142 ABCD
HVO_0319 V-type ATPase D subunit 82.85 97 AB
HVO_0321 erf peptide chain release factor eRF-1 69.75 23 A
HVO_0324 argS arginyl-tRNA synthetase 69.08 53 AB
HVO_0327 ribB 3 4-dihydroxy-2-butanone 4-phosphate 9056A
synthase
ilvE branched-chain amino acid
HVO_0329 139.33 155 ABD
aminotransferase
HVO_0331 proline dehydrogenase 46.00 5 A
HVO_0339 OB-fold nucleic acid binding domain protein 46.50 10 D
HVO_0346 RNA polymerase Rpb5 C-terminal domain 41.00 1 B
HVO_0347 DNA-directed RNA polymerase beta subunit 74.13 108 ABD
rpoB DNA-directed RNA polymerase beta
HVO_0348 70.42 123 AB
subunit
rpoA1 DNA-directed RNA polymerase subunit
HVO_0349 ,107.81 153 ABD
A'
rpoA2 DNA-directed RNA polymerase subunit
HVO_0350 ,,71.44 46 AB
A"
HVO_0351 NusA family KH domain protein archaeal 43.00 3 A
HVO_0353 ribosomal protein S23 (S12) 61.50 19 AB
HVO_0354 rpsG ribosomal protein S7 89.71 88 ABD
HVO_0356 translation elongation factor aEF-2 265.16 290 ABC
tuf translation elongation factor EF-1 subunit
HVO_0359 343.59 543 ABCDE
alpha
HVO_0360 rpsJ ribosomal protein S10 79.14 33 ABD
HVO_0364 hypothetical protein 45.00 9 A
HVO_0382 gpl3 putative 46.00 6 A









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_0387 conserved hypothetical protein 88.00 5 A
HVO_0388 vacB ribonuclease R 67.00 34 AD
HVO_0390 conserved protein 80.00 2 A
HVO_0393 uvrA excinuclease ABC A subunit 97.50 15 A
HVO_0397 ygfZ folate-binding protein YgfZ 67.00 5 A
HVO_0399 conserved hypothetical protein 48.00 4 A
HVO_0400 conserved hypothetical protein 105.25 12 A
HVO_0401 universal stress protein 42.00 3 AB
HVO_0402 conserved hypothetical protein TIGR00266 64.50 6 A
HVO_0406 Predicted ribonuclease of the G/E family 63.00 2 A
HVO_0407 conserved hypothetical protein 48.00 1 A
MIF4G domain-containing protein / MA3
HVO_0410 38.00 1 D
domain-containing protein Arabidopsis thaliana .
Xaa-Pro aminopeptidase M24 family protein
HVO_0414 44.00 6 A
(TBD)
HVO_0415 uvrD repair helicase 70.20 29 A
HVO_0417 cxp metal-dependent carboxypeptidase 66.56 76 ABD
HVO_0420 MCP domain signal transducer 60.50 8 B
HVO_0421 P-loop ATPase of the PilT family 68.00 25 AB
HVO_0424 rli RNase L inhibitor homolog 80.78 51 AB
HVO_0431 HAD superfamily (subfamily IA) hydrolase 57.00 7 A
HVO_0433 npdG NADPH-dependent F420 reductase 62.60 9 AB
HVO_0435 phosphoribosyl-ATP pyrophosphohydrolase 70.00 1 A
HVO_0438 trxA thioredoxin 95.00 14 A
HVO_0441 fprA flavoprotein 53.00 1 A
phnC phosphonate ABC transporter ATP-
HVO_0446 76.50 39 AB
binding protein
HVO_0449 pheA prephenate dehydratase (EC 4.2.1.51) 46.75 10 B
HVq~O_5 hsp small heat shock protein 89.27 66 ABD
HVO_0451 hsp small heat shock protein 101.00 1 A
HVO_0452 leuS leucyl-tRNA synthetase 100.67 39 A
HVO_0454 ocd ornithine cyclodeaminase 83.08 22 AD
HVO_0455 cctB Thermosome subunit 2 433.00 431 ABCDE
HVO_0459 endoribonuclease L-PSP putative 36.00 1 A
HVO_0466 citZ Citrate synthase 83.60 58 ABD
HVO_0467 pchB potassium channel homolog 59.00 2 A
HVO_0469 sdh succinate dehydrogenase subunit 37.00 2 A
HVO_0471 bacteriophage protein homolog lin2587 42.50 15 A
pimT protein-L-isoaspartate O-
HVO_0474 52.00 6 A
methyltransferase 2
HVO_0478 glyceraldehyde-3-phosphate dehydrogenase 101 7A
type II
HVO_0480 pgk phosphoglycerate kinase 105.67 39 AD
HVO_0481 ~gap g lyceraldehyde-3-phosphate12.2 9AB
dehydrogenase type I
HVO_0484 Ribosomal L10 51.71 26 AB
HVO_0487 Pyridoxamine 5'-phosphate oxidase family 60.00 1 A
HVO_0491 Pyridoxamine 5'-phosphate oxidase family 85.00 12 AB
HVqO_057 creatinine amidohydrolase 71.00 1 A
HVqO_050 conserved hypothetical protein 65.00 23 AB









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_0511 NAD-binding domain 4 putative 41.00 2 A
HVO_0519 rpa replication A related protein 74.29 30 AD
HVO_0520 hpyA archaeal histone Al 45.00 4 A
HVO_0521 cca tRNA nucleotidyltransferase 43.00 4 A
HVO_0522 aup acetoin utilization protein 43.50 4 A
Bacterial extracellular solute-binding protein
HVO_0530 80.00 7 A
putative
HVO_0534 sugar ABC transporter ATP-binding protein 78.00 14 A
HVO_0536 Nutrient-stress induced DNA binding protein. 150.23 98 AB
HVO_0541 acnA aconitate hydratase 1 119.56 138 ABCDE
HVO_0549 kdgK 2-keto-3-deoxygluconate kinase 61.50 15 AB
HVO_0551 mutL-2 DNA mismatch repair protein mutL 61.67 35 AD
HVO_0552 mutS-2 DNA mismatch repair protein MutS 65.25 20 AD
HVqO_054 htr MCP domain signal transducer 46.75 25 D
HVO_0561 Ribosomal L15 61.33 16 A
HVO_0564 mmbp maltose ABC transporter maltose-binding 6356A
protein
HVO_0566 glucan 1 4-alpha-glucosidase 39.00 7 A
HVO_0567 alpha amylase 57.00 1 A
HVO_0572 Ipl lipoate protein ligase 48.00 9 A
HVO_0577 conserved hypothetical protein 38.00 1 A
HVO_0580 n-type ATP pyrophosphatase superfamily 72.00 7 A
HVqO_058 ftsZ cell division protein FtsZ 56.00 68 ABD
Predicted transcription regulator containing
HVO_0582 48.00 4 A
CopG/ArclMetJ DNA-binding domain
Ribbon-helix-helix protein copG family domain
HVO_0583 53.00 8 A
protein
HVqO_058 general stress protein 69 65.00 2 A
HVO_0590 Phosphori bosyltra nsferase family 53.00 3 A
L-threonine-O-3-phosphate decarboxylase
HVO_0591 45.00 1 A
putative
L-threonine-O-3-phosphate decarboxylase
HVO_0593 38.00 3 A
putative
HVO_0598 hypothetical protein 41.00 2 B
HVO_0601 hom homoserine dehydrogenase 57.00 1 B
HVO_0602 3-dehydroquinate dehydratase 67.43 32 AB
HVqO_61 3 metallo-beta-lacta mase su perfamily putative 35.50 2 AB
TadA/VirB11 type II/IV secretion system
HVO_0620 46.33 17 BD
ATPase
HVO_0627 dppF dipeptide ABC transporter ATP-binding 54.00 18 AD
HVO_0628 dppD dipeptide ABC transporter ATP-binding 63.88 44 ABD
HVO_0632 fixL pas-pac-pac sensing his kinase 37.00 9 A
HVO_0651 prefoldin beta subunit 48.00 3 A
DNA-directed RNA polymerase subunit P.-
HVO_0653 46.50 2 A
related protein
HVO_0654 RPL43A 50S ribosomal protein L37ae 43.50 8 AB
HVO_0661 dcd deoxycytidine triphosphate deaminase 72.00 4 A
HVO_0662 DNA binding protein 39.00 6 B
HVO_0665 thiazole biosynthesis enzyme 75.00 3 A
HVO_0666 pdhC dihydrolipoamide acetyltransferase 73.00 11 A










Table C-1. Continued


Expd

A
A
A
ABCD
A
ACD
AD
AD
AB
A
AB
AB
A
A
A
A
A
A
AB
A
A
ABD
ABD
B
A
A
A
AB
AB
AD
AB
A
AB
A
A
AD
ABCD
AD
A
AD
A
A
A
A
AB
ABD


MOWSEb Hitsc


Acc. No.a

HVO_0668
HVO_0669
HVO_0674
HVO_0677
HVO_0680
HVO_0681
HVO_0684
HVO_0689
HVO_0692
HVO_0694
HVO_0697
HVO_0699
HVO_0700
HVO_0701
HVO_0702
HVO_0703
HVO_0704
HVO_0711
HVO_0717
HVO_0720
HVO_0724
HVO_0725
HVO_0729
HVO_0730
HVO_0733
HVO_0735
HVO_0736
HVO_0738
HVO_0743
HVO_0753
HVO_0766
HVO_0768
HVO_0769
HVO_0773
HVO_0774
HVO_0775
HVO_0778
HVO_0783
HVO_0787
HVO_0788
HVO_0789
HVO_0790
HVO_0792
HVO_0795
HVO_0801
HVO_0806


Description
component of pyruvate dehydrogenase complex
acetoin dehydrogenase
acoA acetoin dehydrogenase (EC 1.1.1.5)
Uncharacterized protein conserved in archaea
aspS aspartyl-tRNA synthetase
PcrB protein
topA DNA topoisomerase I
glutamyl-tRNA (Gln) amidotransferase chain B
smc chromosome segregation protein SMC
mtaP methylthioadenosine phosphorylase
gptA purine phosphori bosyltransferase
conserved hypothetical protein TIGR00162
translation initiation factor 2 subunit alpha
Ribosomal protein S27
Ribosomal protein L44
membrane protein putative
conserved hypothetical protein
dgs dolichol-P-glucose transferase
conserved hypothetical protein
ftsZ cell division protein FtsZ
conserved hypothetical protein
arsA arsenical pump-driving ATPase
oxidoreductase
ppa inorganic pyrophosphatase
transcription regulator
tfb Transcription initiation factor llB 7
hypothetical protein
Domain of unknown function DUF302
superfamily
conserved hypothetical protein
sulfatase arylsulfatase A-like
conserved hypothetical protein
putative Hsp20
radical HhH
hypothetical protein (TBD)
pnm N-methyltransferase-like
glycosyl transferase
Amidohydrolase family superfamily
cctA Thermosome subunit 3
ATP-dependent protease Lon protease
transmembrane
trpC indole-3-glycerol phosphate synthase
trpB tryptophan synthase beta subunit
trpA tryptophan synthase alpha subunit
conserved hypothetical protein
predicted 3-dehydroquinate synthase
tfb transcription initiation factor
stomatin-prohibitin homolog transmembrane
pyk pyruvate kinase


48.00
65.00
52.00
107.91
50.00
56.00
80.38
62.60
67.20
63.00
69.67
85.29
56.00
42.00
43.00
104.50
64.00
83.50
105.67
65.00
57.50
69.31
113.05
64.00
81.00
50.00
71.00
80.36
80.67
47.67
72.67
70.00
63.08
80.00
38.50
76.50
355.33
79.33
91.00
63.25
114.80
65.00
75.00
86.00
96.42
100.69









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_0807 hypothetical protein 48.00 2 A
HVO_0808 hypothetical protein 55.00 1 A
HVO_0809 metG methionyl-tRNA synthetase 97.50 58 AB
HVO_0812 ppsA phosphoenolpyruvate synthase 113.80 96 ABD
HVO_0817 flavin-containing amine-oxidoreductase 72.00 22 A
HVO_0818 thrC threonine synthase 49.50 3 A
HVO_0819 sirR transcription repressor 73.80 18 A
HVO_0822 3-dehydroquinate synthase 42.00 2 A
HVO_0826 TET aminopeptidase homolog 58.00 7 A
HVO_0827 conserved protein 66.25 8 AB
HVO_0829 prolyl oligopeptidase family protein 74.00 16 AB
HVO_0831 LAO/AO transport system ATPase 63.00 14 A
HVO_0832 predicted hydrolase or acyltransferase 76.67 4 AB
HVO_0835 acaB 3-ketoacyl-CoA thiolase 70.50 5 A
HVO_0836 aminopeptidase 76.10 56 AB
HVO_0837 ATP-NAD kinase 46.00 2 A
HVO_0841 cytochrome B(C-terminal)/b6 63.00 2 A
HVO_0850 panA proteasome-activating nucleotidase A 109.67 68 AB
mrel l DNA double-strand break repair protein
HVO_0853 60.00 12 AB
mrell.
rad50 DNA double-strand break repair rad50
HVO_0854 70.29 97 A
ATPase.
HVO_0858 polB DNA polymerase B 67.00 53 AD
HVO_0859 sufC FeS assembly ATPase SufC 171.00 138 ABCDE
HVO_0860 sufB FeS assembly protein SufB 75.05 100 ABCDE
HVO_0861 sufB/sufD domain protein 162.43 143 ABD
HVO_0862 conserved hypothetical protein 72.00 1 A
HVO_0867 HD domain protein 42.00 3 D
3-beta hydroxysteroid
HVO_0868 72.67 8 A
dehydrogenaselisomerase family superfamily
HVO_0869 glutamate synthase nadph large chain 81.00 118 ABD
HVO_0870 proS prolyl-tRNA synthetase 87.80 38 AD
HVO_0874 epf mRNA 3"-end processing factor homolog 120.08 106 AB
HVO_0876 mgsA methylglyoxal synthase 85.38 16 AB
HVO_0880 coiled-coil protein of COG 1340 134.96 219 ABDE
HVO_0884 aldehyde reductase 99.71 30 AB
porB 2-oxoglutarate ferredoxin oxidoreductase
HVO_0887 72.78 24 AB
beta subunit
porA 2-oxoglutarate ferredoxin oxidoreductase
HVO_0888 102.21 161 ABD
alpha subunit
HVO_0889 FADINAD binding oxidoreductase 87.67 13 AB
HVO_0891 nosF ABC transporter ATP-binding protein 41.00 2 A
mcmA methylmalonyl-CoA mutase subunit
HVO_0893 57.67 7 A
alpha
HVO_0894 acsA acetate--CoA ligase 93.63 42 AD
HVO_0896 alkK medium-chain acyl-CoA ligase 70.00 9 A
HVO_0911 GTP-binding protein 59.00 13 A
HVO_0914 lactoylglutathione lyase 74.33 34 ABD
HVO_0921 trh transcription regulator Asn family 36.00 6 A
HVO_0931 universal stress protein 50.00 5 AC









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_0935 fdhA format dehydrogenase alpha subunit 36.00 12 AC
HVO_0936 conserved hypothetical protein 42.00 5 D
HVO_0940 cpx heavy-metal transporting CPx-type ATPase 87.00 16 A
HVO_0944 subunit II-homologue of cytochrome oxidase 63.50 3 A
HVqO_095 conserved hypothetical protein 42.00 6 C
HVO_0960 oxidoreductase 110.50 78 ABD
HVO_0962 conserved hypothetical protein 45.00 3 D
HVO_0964 trxC thioredoxin 66.00 7 A
HVO_0965 putative thymidine phosphorylase 44.00 3 D
eif2ba translation initiation factor alF-2Bll
HVO_0966 59.00 6 A
translation initiation factor
HVO_0968 ndhG nadh-quinone oxidoreductase chain c/d 93.83 64 AB
HVO_0970 rbcL ribulose bisphosphate carboxylase type Ill 73.60 19 A
Protein of unknown function (DUF1628)
HVO_0972 38.00 1 B
superfamily
HVO_0973 aspB aspartate aminotransferase 94.62 53 AB
HVO_0974 ribH 6 7-dimethyl-8-ribityllumazine synthase 51.00 1 A
purK phosphoribosylaminoimidazole
HVO_0976 42.00 6 D
carboxylase ATPase subunit
HVO_0979 ndhG nadh dehydrogenaseloxidoreductase 93.50 30 A
HVO_0980 ndhG nadh-quinone oxidoreductase chain c/d 174.13 114 AB
nolD nadh dehydrogenaseloxidoreductase-like
HVO_0982 49.50 6 A
protein
HVO_0989 conserved hypothetical protein 55.67 4 A
HVO_0991 imd inosine-5"-monophosphate dehydrogenase 54.33 13 AD
HVO_0997 Xaa-Pro aminopeptidase M24 family protein 69.75 18 A
HVO_0999 cobyrinic acid ac-diamide synthase 131.50 93 AB
HVO_1000 acetyl-CoA synthetase 124.00 67 ABD
HVO_1004 conserved hypothetical protein 46.67 3 B
HVO_1006 nosF ABC transporter ATP-binding protein 76.50 3 A
HVO_1008 dehydrogenase probable putative 68.50 6 A
HVO_1009 aad oxidoreductase 84.64 79 ABD
HVO_1018 conserved protein 78.50 75 ABD
HVO_1020 PBS lyase HEAT-like repeat domain protein 60.79 65 ABD
HVO_1022 nadh-dependent flavin oxidoreductase 115.38 42 AB
HVO_1023 conserved hypothetical protein 58.00 5 A
HVO_1024 Domain of unknown function putative 71.00 1 A
HVO_1025 acaB 3-ketoacyl-CoA thiolase 64.50 14 A
HVO_1027 TatAo twin arginine translocation 83.67 16 ABD
HVO_1031 thioredoxin reductase 41.50 3 A
flaJlTadC archaeal flagellin biosynthesis/type II
HVO_1033 43.50 16 A
secretion system protein
HVO_1034 TadA/VirB11 type II/IV secretion system4304C
ATPase
HVO_1036 dsbh domain containing protein 87.00 1 D
HVO_1037 conserved hypothetical protein 63.00 15 AB
HVO_1038 conserved hypothetical protein 44.00 1 A
HVO_1040 DnaJ domain protein 41.00 15 AD
metallo-beta-lactamase superfamily domain
HVO_1041 67.33 3 A
protein









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_1042 RNA polymerase Rpb3/Rpbl1 dimerisation 6955A
domain
Protein of unknown function (DUF1684)
HVO_1043 35.00 2 A
superfamily
HVO_1047 qor nadph quinone oxidoreductase 62.00 14 ABDE
HVO_1053 gatC glutamyl-tRNA(Gln) amidotransferase C 81.67 6 A
HVO 1054 gatA Glutamyl-tRNA(Gln) amidotransferase 8.7 1
subunit A
HVO_1058 trkA potassium uptake protein TrkA 52.00 3 A
HVO_1060 conserved hypothetical protein 39.00 5 B
HVO_1061 txrB thioredoxin reductase 76.83 11 AB
HVO_1072 Adenine phosphoribosyltransferase 87.27 30 AB
HVO_1073 DJ-1/Pfpl/ThiJ superfamily protein 104.36 50 ABDE
graD glucose-1-phosphate
HVO_1076 90.29 50 ABD
thymidylyltransferase
HVO_1077 conserved protein 70.00 4 A
HVO_1078 nodP phospho-adenylyl-sulfate Reductase 44.00 2 A
HVO_1079 nodP phospho-adenylyl-sulfate Reductase 56.00 4 AB
HVO_1080 conserved hypothetical protein 56.50 29 AB
HVO_1 082 o rotate ph os ph oribosyltra nsfe rase 126.69 64 ABD
HVO_1083 glucose dehydrogenase 59.00 6 A
HVO_1084 purB adenylosuccinate lyase 91.00 5 A
purH bifunctional purine biosynthesis protein
HVO_1085 49.00 8 AB
PurH
HVO_1087 universal stress protein 62.60 14 ABC
HVO_1088 folP FolC-P protein 84.00 2 A
HVO_1091 psmA proteasome subunit alphal 83.65 116 ABC
HVO_1093 protein-L-isoaspartate O-methyltransferase 60.00 2 A
HVO_1098 lysA diaminopimelate decarboxylase 37.00 1 A
HVO_1099 dapD 23 4 5-tetrahydropyridine-2-carboxylate 64.57 41 AB
N-succinyltransferase
HVO_1100 dapB dihydrodipicolinate reductase 60.00 22 ABD
HVO_1101 dapA dihydrodipicolinate synthase 35.00 1 B
HVO_1113 ftsZ cell division protein 52.29 57 ABD
HVO_1117 boa bacterio-opsin activator-like protein 62.00 2 A
HVO_1119 hcpl halocyanin 1 42.00 2 A
HVO_1120 conserved hypothetical protein 70.50 18 AB
HVO_1121 pqqE heme biosynthesis protein 102.67 22 BD
HVO_1123 trxB thioredoxin-disulfide reductase 60.67 4 A
HVO_1125 cysS cysteinyl-tRNA synthetase 96.20 22 AB
HVO_1129 conserved hypothetical protein 69.00 4 A
Protein of unknown function (DUF343)
HVO_1131 62.50 6 A
superfamily
HVO_1132 purA adenylosuccinate synthetase 84.56 80 ABD
HVO_1133 conserved protein 73.33 43 ABDE
HVO_1134 conserved hypothetical protein 82.29 61 A
HVO_1139 fdfT farnesyl-diphosphate farnesyltransferase 95.00 4 A
HVO 1140 acd Acyl-CoA dehydrogenase short-chain 00
specific
HVO_1141 DNA binding putative transcriptional regulator 89.00 2 A









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_1145 RPS1A 30S ribosomal protein S3Ae. 127.15 82 ABE
HVO_1148 rpsl5p ribosomal protein S15 89.00 61 ABD
HVO_1162 TatAt twin arginine translocation 68.42 31 ABD
HVO_1163 bcp peroxiredoxin 68.00 1 A
HVO_1164 HD domain protein 80.56 44 ABD
HVO_1170 conserved hypothetical protein 63.17 12 AD
HVO_1172 galE UDP-glucose 4-epimerase 74.67 12 A
HVO_1173 SpoU-like RNA methylase 40.00 5 AD
HVO_1174 TFllE alpha subunit 59.78 24 AB
HVO_1175 conserved protein 69.17 19 AE
HVO_1181 prp 2-domain regulatory protein 99.00 5 A
HVO_1187 conserved hypothetical protein 57.67 8 B
HVO_1189 Betaine aldehyde dehydrogenase 68.67 64 ABD
HVO_1198 syrB universal stress protein 106.40 38 BCD
HVO_1199 acd Acyl-CoA dehydrogenase. 60.50 8 A
HVO_1202 conserved hypothetical protein 40.00 6 A
HVO_1203 flagella-related protein E 49.00 10 B
HVO_1206 CheA histidine kinase 37.00 2 B
HVO_1209 conserved hypothetical protein 57.00 1 A
HVO_1212 circadian regulator 87.75 29 ABD
HVO_1223 cheA CheA 37.00 6 D
Chemotaxis response regulator protein-
HVO_1224 36.00 2 C
glutamate methylesterase.
HVO_1228 fbr cytochrome-like protein Fbr 77.60 20 AB
HVO_1235 creatinine amidohydrolase 58.20 18 ABD
HVO_1 236 acetate-CoA ligase 93.00 7 A
HVO_1238 oxidoreductase aldolketo reductase family 96.40 44 ABD
HVO_1240 Tat (twin-arginine translocation) pathway signal 7355A
sequence domain protein
HVO_1241 prrC regulatory protein PrrC 81.50 6 A
HVO_1245 DSBA-like thioredoxin domain putative 47.33 3 AB
HVO_1249 conserved protein 64.50 3 A
HVO_1250 thiol-disulfide isomeraselthioredoxin 55.33 8 AB
moxR methanol dehydrogenase regulatory
HVO_1257 48.00 7 A
protein
HVO_1258 conserved hypothetical protein 42.00 6 A
HVO_1259 conserved hypothetical protein 40.00 5 D
HVO_1260 von Willebrand factor type A domain protein 37.00 6 A
HVO_1261 conserved hypothetical protein 37.00 6 A
HVO_1264 Bacterial membrane flanked domain family 85.00 1 A
HVO_1265 Irp leucine responsive regulatory protein 76.63 21 AB
HVO_1268 conserved hypothetical protein 39.00 2 A
HVO_1271 hrg HoxA-like transcriptional regulator 65.89 33 AB
HVO_1272 conserved hypothetical protein 46.00 4 D
guaB inosine-5'-monophosphate
HVO_1273 81.86 32 AB
dehydrogenase
HVO_1283 putative zinc-binding dehydrogenase 52.00 3 A
HVO_1284 gch GTP cyclohydrolase Ill 1. 75.67 42 AB
HVO_1 287 Pfpl-ThiJ-DJ-1 superfamily 59.89 11 AB









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_1289 OsmC-like protein superfamily 67.00 25 AD
HVO_1290 metallo-beta-lactamase family protein 46.00 3 A
HVO 1291 hpcE 2-hydroxyhepta-24-diene-1 7-dioate 5305A
isomerase
HVO_1295 hisC histidinol-phosphate aminotransferase 82.25 8 A
HVO_1299 HTH DNA-binding protein 49.67 12 D
HVO_1300 tpiA triosephosphate isomerase 45.00 3 B
HVO_1301 conserved hypothetical protein 36.00 4 D
beta 2-oxoglutarate ferredoxin oxidoreductase
HVO_1304 101.39 76 ABDE
beta subunit
2-oxoglutarate ferredoxin oxidoreductase
HVO_1305 188.75 96 ABC
subunit alpha
HVO_1306 aroC chorismate synthase 49.67 7 AB
aroA 3-phosphoshikimate 1-
HVO_1308 47.67 13 B
carboxyvinyltransfe rase
HVO_1309 pepQ Xaa-Pro dipeptidase 59.56 31 ABD
HVO_1311 dehydrogenase putative (TBD) 97.33 8 A
HVO_1314 surE Acid phosphatase surE 63.00 2 A
HVO_1322 pheA chorismate mutase 48.50 11 AB
HVO_1323 shikimate kinase 46.00 2 A
HVO_1324 conserved hypothetical protein 49.50 2 A
HVO_1327 cdc48 cell division control protein 48 63.20 54 AB
transcriptional regulator ArsR family domain
HVO_1331 40.00 1 A
protein
HVO_1337 conserved protein 67.33 8 A
HVO_1340 LPS glycosyltransferase 61.00 1 A
HVO_1342 trx thioredoxin 61 .00 2 AB
HVO_1344 conserved hypothetical protein TIGR00291 75.54 69 ABD
HVO_1346 hflX GTP-binding protein HflX 35.00 7 A
HVO 1348 Short chain dehydrogenase fused to sugar 116
kinase
HVO_1351 rad3a helicase 39.00 18 AD
HVO_1354 mutS DNA mismatch repair protein 42.50 14 AD
HVO_1356 bacterio-opsin activator-like protein 47.50 10 AD
HVO_1357 bacterio-opsin activator-like protein putative 49.00 9 A
HVO_1359 conserved hypothetical protein 38.00 1 A
HVO_1360 Sugar-specific transcriptional regulator TrmB 5109A
superfamily
HVO_1370 proA gamma-glutamyl phosphate reductase 91.86 33 AB
HVO_1371 proB glutamate 5-kinase 60.89 24 ABD
HVO_1372 proC pyrroline-5-carboxylate reductase 51.00 1 A
HVO_1373 acd acyl-CoA dehydrogenase 94.00 2 A
HVO_1374 alkK medium-chain-fatty-acid-CoA ligase 75.00 20 A
mcmA methylmalonyl-CoA mutase subunit
HVO_1380 88.50 20 AD
alpha
HVO_1381 mdmC caffeoyl-CoA O-methyltransferase 51.00 4 A
HVO_1382 alpha-NAC homolog 115.50 4 A
HVO_1385 rpoM DNA-directed RNA-polymerase subunit M 58.00 4 A
HVO_1387 fad 3-hydroxybutyryl-CoA dehydratase 39.00 2 A
HVO_1388 conserved hypothetical protein 57.00 5 A









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_1389 SAM-dependent methyltransferase 47.50 3 A
HVO_1393 conserved hypothetical protein 61.00 1 A
HVO_1395 indole-3-acetyl-L-aspartic acid hydrolase 81.00 8 A
HVO_1396 P-hydroxybenzoate hydroxylase 61.11 53 AD
HVO_1400 rbsA ribose ABC transporter ATP-binding 52.00 8 A
HVO_1401 ABC transporter 79.63 17 AE
HVO_1402 pmu phosphomannomutase 107.88 24 A
HVO_1412 dmd diphosphomevalonate decarboxylase 74.33 26 AB
HVO_1413 nadh dehydrogenase 54.00 6 AB
kinA signal-transducing histidine kinase
HVO_1414 43.00 3 C
homolog
HVO_1 424 hypothetical protein 60.00 7 A
HVO_1 425 hypothetical protein 41.00 1 A
HVO_1 434 hypothetical protein 41.00 6 D
HVO_1439 cysM cystathionine beta-synthase 55.00 5 A
HVO_1440 conserved hypothetical protein 65.00 4 A
HVO_1443 ABC transporter ATP-binding protein 40.00 6 CD
HVO_1444 hbd 3-hydroxybutyryl-CoA dehydrogenase 51.75 16 AB
HVO_1446 fbp fructose-1 6-bisphosphatase 47.00 18 BD
HVO_1451 Glutamate dehydrogenase 136.33 8 A
HVO_1452 pro-3S citrate-lyase 62.50 8 A
HVO_1453 gdhA Glutamate dehydrogenase 113.29 32 A
HVO_1454 pyrB aspartate carbamoyltransferase 91.25 71 ABD
HVO_1459 AF trehalose utilization protein 105.00 4 A
HVO_1463 txrB thioredoxin reductase 43.50 6 AD
HVO_1464 ferrichrome-binding protein 42.50 11 A
HVO_1465 menB naphthoate synthase 41.00 2 A
HVO_1471 sulfite oxidase homolog 75.00 1 A
HVO_1472 surface glycoprotein precursor-related protein 73.50 5 AB
HVO_1478 tfb transcription initiation factor 74.25 22 A
HVO_1481 universal stress protein 77.33 28 ABCD
HVO_1484 htr Heme-based aerotactic transducer hemAT. 35.00 6 A
rspA mandelate racemase/muconate lactonizing
HVO_1488 120.09 62 A
enzyme family
HVO_1491 conserved hypothetical protein 63.00 23 A
HVO_1492 conserved hypothetical protein 55.60 16 AB
HVO_1493 conserved hypothetical protein 102.38 22 AB
HVO_1494 fba fructose-1 6-bisphosphate aldolase class II 76.00 34 AB
HVO_1495 fruA phosphotransferase system IlB component 50.83 13 AB
HVO_1496 ~ptsI phosphoenolpyruvate-protein79169A
phosphotransfe rase
HVO_1497 FruB phosphocarrier protein Hpr 50.00 3 A
HVO_1502 LeuB_ 3-isopropylmalate dehydrogenase 49.33 9 AB
HVO_1503 leuD 3-isopropylmalate dehydratase small 7502B
subunit
HVO_1504 leuC 3-isopropylmalate dehydratase large 633 12A
subunit
HVO_1505 conserved hypothetical protein 55.00 3 A
HVO_1506 ilvC ketol-acid reductoisomerase 72.67 56 ABD
HVO_1507 ilvN acetolactate synthase small subunit 47.00 13 AB









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
ilvB acetolactate synthase large subunit
HVO_1508 54.20 13 AB
biosynthetic type
HVO_1510 leuA (R)-citramalate synthase. 37.00 5 D
HVO_1513 conserved hypothetical protein 65.33 7 AB
HVO_1522 LPS? 128.00 5 A
HVO_1527 glucose-1-phosphate thymidylyltransferase 4.08A
putative
HVO_1529 exoM succinoglycan biosynthesis protein 61.00 2 A
HVO_1530 tot transmembrane oligosaccharyl transferase 53.67 6 A
HVO_1531 ugd UDP-glucose dehydrogenase 85.00 3 A
HVO_1536 SPP-like hydrolase Archaeal 60.33 3 A
gpdA Anaerobic glycerol-3-phosphate
HVO_1538 dehydrogenase subunit A(G-3-P 67.67 25 AB
dehydrogenase).
HVO_1539 g lycerol-3-phosphate dehydrogenase chain B 49.40 10 AB
HVO_1540 gpdC glycerol-3-phosphate dehydrogenase 5.07A
chain C
HVO_1541 glpK glycerol kinase 98.60 26 AB
HVO_1543 ptsH Phosphocarrier protein HPr 101.50 3 A
HVO 1544 dihydroxyacetone kinase phosphotransfer 9.3 1
subunit
HVO_1545 dihydroxyacetone kinase L subunit 106.80 85 ABDE
HVO_1546 dihydroxyacetone kinase DakL subunit 93.73 43 ABD
HVO_1547 ileS isoleucyl-tRNA synthetase 98.33 13 A
HVO_1553 ttuD putative hydroxypyruvate reductase 48.00 4 A
HVO_1558 cytochrome P450 40.00 6 A
HVO_1560 Uncharacterized protein conserved in archaea 65.17 24 ABD
HVO_1562 psmB proteasome subunit beta 84.57 24 AB
HVO_1565 lig DNA ligase ATP dependent 71.00 19 A
HVO_1568 HydD putative 77.00 5 A
HVO_1570 top6A Type II DNA topoisomerase VI subunit A. 62.00 2 A
HVO_1571 top6B DNA topoisomerase VI B subunit 78.11 47 AB
HVO_1572 gyrB DNA gyrase B subunit 91.00 36 AB
HVO_1573 gyrA DNA gyrase A subunit 76.20 37 AB
HVO_1574 MutT/nudix family protein 72.00 3 A
HVO_1575 rocF arginase 101.50 7 A
HVO_1576 gmd UDP-glucose 4-epimerase 70.25 22 AB
HVO_1577 imd inosine-5"-monophosphate dehydrogenase 104.89 99 ABD
HVO_1578 nadh dehydrogenase 85.92 31 AB
HVO_1579 udp Uridine phosphorylase 69.00 2 A
HVO_1584 acetyltransferase family 61.50 4 A
HVO_1585 acs Acetyl-coenzyme A synthetase 52.00 7 A
HVO_1588 Cupin domain protein 42.00 7 B
HVO_1589 dnaJ chaperone protein DnaJ 39.00 4 A
HVO_1590 dnaK chaperone protein DnaK 147.68 157 ABD
HVO_1591 conserved hypothetical protein 42.00 2 D
HVO_1592 grpE co-chaperone GrpE 81.67 22 A
HVO_1593 ppd 3-isopropylmalate dehydratase 105.00 2 A
HVO_1594 cna tRNA and rRNA cytosine-C5-methylases 51.50 6 A
HVO_1595 conserved hypothetical protein 80.75 7 AD









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_1599 conserved hypothetical protein 66.00 1 A
HVO_1604 Aspartate aminotransferase 53.33 13 AB
HVO_1608 gbp GTP-binding protein homolog 63.00 2 A
HVO_1613 gst glycosyltransferase 59.67 6 A
HVO_1619 conserved hypothetical protein 69.00 4 A
HVO_1 625 hypothetical protein 83.00 10 A
HVO_1626 metallo-beta-lactamase 51.00 3 A
HVO_1 629 hypothetical protein 42.00 10 B
HVO_1631 dph Diphthamide synthase subunit 68.50 13 A
HVO_1637 slyD peptidyl-prolyl cis-trans isomerase 101.80 32 AD
HVO_1649 S-adenosylmethionine synthetase 81.62 64 ABD
HVO_1650 ppk polyphosphate kinase 66.67 63 BD
HVO_1654 cysK Cysteine synthase 70.60 25 ABD
HVO_1655 conserved hypothetical protein 46.33 3 A
HVO_1656 thioredoxin domain containing protein 55.50 7 AD
HVO_1657 purD phosphoribosylamine--g lycine ligase 36.00 1 A
HVO_1673 conserved hypothetical protein 117.00 4 A
HVO_1676 tfb Transcription initiation factor llB 2 91.00 12 A
HVO_1677 conserved hypothetical protein 45.00 2 A
HVO_1678 elF-2 Translation initiation factor 76.33 8 A
HVO_1683 malQ 4-alpha-glucanotransferase 57.60 19 AB
HVO_1684 thrS threonyl-tRNA synthetase 99.93 107 AB
HVO_1690 conserved protein 85.67 10 A
HVO_1692 4Fe-S protein 51.33 12 B
HVO_1 693 hypothetical protein (TBD) 67.00 1 A
HVO_1697 glcD glycolate oxidase subunit 84.50 53 AB
HVO_1699 aminotransferase classes I and II superfamily 80.33 8 AB
HVO_1704 conserved hypothetical protein 66.50 3 A
HVO_1705 ibp iron-binding protein 88.50 7 A
HVO_1710 alpha amylase 70.00 11 AB
HVO_1711 glucoamylase 84.33 59 AC
HVO_1713 HTR-like protein 39.00 17 AD
HVO_1716 exsB protein 64.00 5 A
HVO_1717 radical SAM protein putative (TBD) 37.00 4 A
HVO_1718 6-pyruvoyl tetrahydropterin synthase 69.00 3 A
HVO_1723 rhl DNA repair helicase 37.00 4 A
HVO_1725 cdc Cell division control protein 6 homolog 6 48.00 19 AD
HVO_1727 tata-box binding protein e 106.44 32 BCD
HVO_1733 conserved hypothetical protein 62.00 4 A
HVO_1741 conserved hypothetical protein 106.67 8 A
HVO_1744 ATP-dependant helicase 37.00 8 D
HVO_1 745 hypothetical protein 39.00 4 A
HVO_1753 copA copper-translocating P-type ATPase 47.00 1 A
HVO_1758 txrB thioredoxin reductase 58.75 10 AD
HVO_1760 fepC Ferric enterobactin transport ATP-binding 85.33 8 A
protein fepC.
HVO_1762 cbpA Ferredoxin 2. 36.00 5 A
HVO_1769 appF Oligopeptide transport ATP-binding 3705A
protein appF.









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO 1775 oxygen-insesitive NAD(P)H700 6D
nitroreductaseldihydropteridine reductase
HVO_1778 katG catalaselperoxidase HPI 107.06 86 ABD
HVO_1785 conserved hypothetical protein 89.50 2 A
HVO_1788 nirA ferredoxin-nitrite reductase 69.71 40 AB
HVO_1792 trh transcription regulator 39.00 5 A
HVO_1799 FAD dependent oxidoreductase putative 70.33 18 AB
HVO_1803 Uncharacterized conserved protein 49.00 2 A
nadC nicotinate phosphoribosyltransferase
HVO_1804 44.50 6 AB
(TBD)
HVO_1811 ark HTR-like protein 39.00 7 A
HVO_1816 pyrC Dihydroorotase 45.00 6 A
HVO_1819 conserved hypothetical protein 46.33 6 A
HVO_1824 RecJ-like exonuclease 73.89 27 AD
HVO_1825 conserved hypothetical protein 47.00 2 A
HVO_1827 Ribosomal protein S6e 99.67 46 ABD
HVO_1829 pepB aminopeptidase homolog 55.13 24 AB
HVO_1830 gndA 6-phosphogluconate dehydrogenase 68.00 18 AB
HVO_1831 2Fe-2S iron-sulfur cluster binding domain 84.00 3 A
HVO_1838 conserved hypothetical protein 42.00 6 A
HVO_1847 pepF oligoendopeptidase F 68.00 16 AD
HVO_1853 universal stress protein 1 36.00 2 A
HVO_1854 hisS histidyl-tRNA synthetase 70.17 22 AB
HVO_1857 conserved protein 54.00 5 A
HVO_1858 Ribosomal protein S19e 159.31 105 ABE
HVO_1867 lysS lysyl-tRNA synthetase 48.75 8 AB
HVO_1 869 hypothetical protein 57.43 16 AB
HVO_1871 Chlorite dismutase family 73.50 41 AB
HVO_1874 gsp general stress protein 69 82.50 61 AB
HVO_1 875 acetyltransferase-like 36.00 7 AB
HVO_1878 nadE NAD+ synthetase 60.00 1 B
HVO_1888 extracellular tungstate binding protein 112.50 4 A
HVO_1892 conserved hypothetical protein 37.00 1 A
HVO_1 893 Ham1 family 78.00 3 A
HVO_1894 conserved hypothetical protein 58.33 4 AD
HVO_1896 Ribosomal protein S24e 52.31 45 ABD
HVO 1901 elF2g translation initiation factor elF-2 subunit 61021A
gamma
HVO_1907 cdc48 cell division control protein 48 homolog 67.17 53 AB
HVO_1914 3-ketoacyl-CoA thiolase 86.71 16 A
HVO_1916 TrkA-N domain family 78.33 19 D
HVO_1917 Ifl long-chain fatty-acid-CoA ligase 55.00 9 A
metallo-beta-lactamase superfamily domain
HVO_1918 54.50 3 A
protein
HVO_1921 serS seryl-tRNA synthetase 109.64 104 ABD
HVO_1925 gbp GTP-binding protein 73.67 14 A
HVO_1 928 5-fo rmyltetra hyd rofo late cyclo-l ig ase 42.00 2 A
HVO_1931 GTP-binding protein 57.00 2 A
HVO_1932 SerA phosphoglycerate dehydrogenase 56.80 21 AB









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_1936 F420-dependent oxidoreductase putative 46.00 5 B
mer 5 10O-methylen etetrahyd rometha nopte rin
HVO_1937 reductase(Coenzyme F420-dependent N(5) 43.25 9 AB
N(10)-methylenetet
HVO_1939 mutL DNA mismatch repair protein mutL. 49.17 26 BD
HVO_1940 mutS DNA mismatch repair protein MutS 90.00 17 A
HVO_1946 translation initation factor SUll putative 65.42 22 ABD
HVO_1949 conserved hypothetical protein 60.00 2 A
11-domain light and oxygen sensing his kinase
HVO_1953 39.50 9 AD
putative
HVO_1955 aconitate hydratase putative 156.77 183 ABD
HVO_1957 panB proteasome-activating nucleotidase B 83.20 27 AD
HVO_1959 signal-transducing histidine kinase-like 36.00 7 A
HVO_1963 infB translation initiation factor alF-2 92.86 40 A
HVO_1967 pgi glucose-6-phosphate isomerase 108.00 5 A
HVO_1973 conserved hypothetical protein 70.20 19 AB
HVO_1976 secD SecD 50.00 2 A
HVO_1979 conserved hypothetical protein TIGR01213 64.25 10 A
HVO_1982 expressed protein 44.00 4 BD
HVO_1999 htr Halobacterial transducer protein V. 54.00 10 B
HVO_2001 tgtA archaeal tRNA-guanine transglycosylase 76.00 2 A
HVO_2008 tgtA archaeosine tRNA-ribosyltransferase 145.00 5 A
HVO_2010 conserved hypothetical protein 70.00 2 A
HVO_2013 ftsZ cell division protein 92.70 37 A
HVO_2015 conserved hypothetical protein 111.18 67 ABDE
HVO_2016 putative permease 45.00 13 A
HVO_2029 trh transcription regulator AsnC family 69.00 16 AD
HVO_2030 HTR-like protein 47.50 24 AD
HVO_2031 ABC transporter 82.50 2 A
HVO_2032 rbsA ribose ABC transporter ATP-binding 46.00 4 A
HVO_2040 gmd UDP-glucose 4-epimerase 102.83 20 AB
HVO_2046 N-acetylgalactosamine-4-sulfatase. putative 42.75 12 A
HVO_2049 rfbF rhamnosyl transferase homolog 50.00 2 A
HVO_2056 spsK spore coat polysaccharide synthesis spsK 63.50 34 AB
HVO_2057 glucose-1-phosphate thymidylyltransferase 84.80 72 ABD
HVO_2059 rfbB dTDP-glucose 4 6-dehydratase 64.91 30 AB
HVO_2068 ftsZ cell division protein 44.00 4 A
HVO_2070 sialidase 90.00 4 A
HVO_2071 conserved hypothetical protein 51.00 17 A
HVO_2072 csg Cell surface glycoprotein precursor 61.00 5 AB
HVO_2073 sugar-specific transcriptional regulator 36.00 3 A
HVO_2080 conserved hypothetical protein 58.50 2 A
HVO_2081 conserved hypothetical protein 54.00 32 AD
HVO_2088 xloA xylosid aselarabinosidase 40.00 5 A
HVO_2091 gabT 4-aminobutyrate aminotransferase 95.50 15 AE
HVO_2092 arcR transcription regulator 47.00 1 A
HVO_2094 ferrichrome-binding protein 35.67 9 B
HVO_2095 putative nadp-dependent oxidoreductase yncb. 55.00 7 A
HVO_2102 PTS system galactitol-specific enzyme II A 77.33 9 B










Table C-1. Continued


Expd

AD
A
A
A
A
AB
A
D
ABE
AB
AB
A
A
AB
AD
B
D
A
AB

B
AB
A
A
C
ABE
A
A
AD
AD
AB
AB
ABD
A
C
A
B
ABE
A
A
ABD


MOWSEb Hitsc


Acc. No.a

HVO_2108
HVO_2110
HVO_2113
HVO_2118
HVO_2120
HVO_2126
HVO_2131
HVO_2144
HVO_2153
HVO_2156
HVO_2158
HVO_2160
HVO_2163
HVO_2168
HVO_2169
HVO_2175
HVO 2183

HVO_2187
HVO_2188

HVO_2189
HVO_2191
HVO_2193
HVO_2194
HVO_2195
HVO_2204
HVO_2205
HVO_2209
HVO_2214
HVO_2220
HVO_2222
HVO_2225
HVO_2226
HVO_2227
HVO_2232
HVO_2234
HVO_2239
HVO_2242
HVO_2253
HVO_2255
HVO_2256


Description
component putative
arcR transcription regulator
arcR transcription regulator
hypothetical protein
sugar ABC transporter ATP-binding protein
BNR/Asp-box repeat domain protein
oligopeptide ABC transporter oligopeptide-
binding protein putative
fbp fructose-1 6-bisphosphatase
pqqE coenzyme PQQ synthesis protein
pan membrane protein Pan1
universal stress protein
mdh nadp-dependent malic enzyme
Mucl9 precursor putative
ABC transporter ATP-binding protein homolog
moxR methanol dehydrogenase regulatory
protein
conserved hypothetical protein
hypothetical protein (TBD)
aor Tungsten-containing aldehyde ferredoxin
oxidoreductase.
conserved hypothetical protein TIGR01210
purQ phosphoribosylformylg lycinamidine
synthase I
purS phosphoribosylformylglycinamidine
synthase purS protein
purU Formyltetrahyd rofolate deformylase
purC phosphoribosylaminoimidazole-
succinocarboxamide synthase
transcriptional regulator Sir2 family
ark adaptive-response sensory-kinase
ftsZ cell division protein
nolA nadh dehydrogenaseloxidored uctase-like
protein
pdhA pyruvate dehydrogenase alpha subunit
htr MCP domain signal transducer
ugpB MCP domain signal transducer
ppiA peptidyl-prolyl cis-trans isomerase slrl251
succinylglutamate desuccinylase /
aspartoacylase family protein
trpD anthranilate phosphoribosyltransferase
nirD heme d1 biosynthesis protein NirL
ATPase RecA superfamily
msrB methionine-R-sulfoxide reductase
universal stress protein
eif probable translation initiation factor 2 beta
subunit
hypothetical protein
hypothetical protein
Protein of unknown function DUF262 family


68.00
45.00
47.00
66.00
35.00
101.40
53.00
40.00
51.63
88.70
58.67
70.00
61.00
66.29
43.00
71.50
42.00
63.00
49.00

49.25
136.88
59.00
71.00
39.00
85.47
65.40
57.50
38.00
58.50
75.70
63.75
119.89
63.50
37.00
57.00
47.00
94.07
64.00
39.50
51.33









Table C-1. Continued
Acc. No.a Description MOWSEb Hits" Expd
HVO_2263 hypothetical protein 55.00 28 A
HVO_2265 hypothetical protein 54.80 15 ABD
HVO_2266 hypothetical protein 52.80 16 AB
type I restriction-modification system restriction
HVO_2269 52.67 18 AC
subunit
HVO_2270 Type I restriction enzyme EcoKI M protein 42.20 19 AB
HVO_2271 HsdS Restriction endonuclease S subunits 94.00 11 A
HVO_2273 integ rase/reco mbinase xerd putative 39.00 5 A
HVO_2275 N-6 DNA Methylase family 49.00 6 A
HVO_2276 conserved hypothetical protein 76.00 15 A
HVO_2278 conserved hypothetical protein 52.00 17 A
HVO_2289 putative ATP-binding protein 56.00 11 A
HVO_2290 integrase putative 44.00 7 A
HVO_2300 eif5A translation initiation factor elF-5A 53.20 17 ABD
HVO_2302 ABC1 family domain protein 39.00 19 A
HVO_2304 moeA Molybdopterin biosynthesis protein 8.0 1
moeA.
HVO_2305 moeA molybdopterin biosynthesis protein moeA 59.00 9 AC
HVO_2309 pterin-4-alpha-carbinolamine dehydratase 44.33 17 AB
HVO_2311 hemA glutamyl-tRNA reductase 64.00 5 A
HVO_2313 nirH heme biosynthesis protein 122.00 8 A
serine protease inhibitor family protein
HVO_2316 55.00 5 A
(SERPIN)
HVO_2321 DnaG dnaG 63.00 2 A
HVO_2322 AP endonuclease family 2 superfamily 105.00 3 A
HVO_2325 conserved hypothetical protein 45.33 6 AB
HVO_2327 conserved hypothetical protein 53.67 3 A
HVO_2328 isochorismatase 65.00 2 A
HVO_2333 conserved hypothetical protein 51.00 8 AB
HVO_2334 conserved hypothetical protein 44.50 10 B
HVO_2336 pyridoxine biosynthesis protein 87.00 68 AB
HVO_2340 conserved protein 50.00 1 A
HVO_2341 transcription regulator 75.33 9 A
HVO_2344 conserved hypothetical protein 48.00 1 A
HVO_2345 noxA nadh oxidase 82.75 53 AB
HVO_2347 conserved hypothetical protein 83.00 3 A
HVO_2348 conserved hypothetical protein TIGR00294 66.55 27 AB
HVO_2349 transcription regulator 56.00 2 B
HVO_2356 conserved hypothetical protein 71.45 24 AD
HVO_2361 carB carbamoyl-phosphate synthase large 131 7A
subunit
HVO_2368 conserved hypothetical protein 96.00 4 A
HVO_2370 putative hydrolase of the HAD superfamily 61.00 3 A
HVO_2371 conserved hypothetical protein 69.00 2 A
HVO_2373 ribosomal protein S8.e 64.75 19 A
HVO_2374 prp phosphate regulatory protein-like 138.38 64 ABD
HVO_2375 phoX phosphate ABC transporter phosphate- 51356B
binding protein
HVO_2378 pstB phosphate ABC transporter ATP-binding 4106A
protein









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_2379 phoU phosphate transport system regulatory 7.0 6 B
protein PhoU
HVO_2380 cdc48 cell division control protein 48 137.45 124 AB
radB DNA repair and recombination protein
HVO_2383 61.50 4 A
RadB
HVO_2384 CBS domain pair putative 77.38 73 ABCDE
HVO_2387 conserved hypothetical protein 49.50 8 A
HVO_2396 glutaredoxin-like 58.00 2 A
lipoprotein component of divalent metal ABC
HVO_2397 113.43 123 ABDE
transporter putative
HVO_2398 zurA ABC transporter ATP-binding protein 60.75 22 ABC
HVO_2400 conserved hypothetical protein 49.00 2 A
HVO_2401 Glycine cleavage system P-protein 56.00 6 A
HVO_2402 Glycine cleavage system P-protein 56.33 10 AB
HVO 2413 tuf translation elongation factor EF-1 subunit 37 AD
alpha
HVO_2417 conserved protein 59.57 40 ABD
HVO_2419 mvaB hydroxymethylglutaryl-CoA synthase 89.40 49 ABDE
HVO_2423 acetyltransferase GNAT family family 56.67 7 B
ribosomal-protein-alanine acetyltransferase
HVO_2425 90.33 34 ABD
putative
glnH glutamine ABC transporter periplasmic
HVO_2432 80.50 5 A
glutamine-binding protein homolog
HVO_2434 ntp neutral proteinase 59.00 9 D
HVO_2435 Xaa-Pro aminopeptidase M24 family protein 82.00 13 A
HVO_2436 tme nadp-dependent malic enzyme 54.00 49 ABD
HVO_2444 dppF dipeptide ABC transporter ATP-binding 81.00 4 A
HVO_2448 aad aryl-alcohol dehydrogenase 44.00 3 A
ribonucleoside-diphosphate reductase
HVO_2452 152.86 118 ABD
adenosylcobalamin-dependent
HVO_2454 trpE anthranilate synthase component I 56.00 15 AD
HVO_2464 sucD succinyl-CoA synthase alpha subunit 131.69 61 AB
HVO_2465 sucC succinyl-CoA synthase beta subunit 85.82 77 ABD
HVO_2467 zinc-binding dehydrogenase 68.00 3 A
HVO_2471 propionyl-CoA carboxylase complex B chain 59.67 11 A
HVO_2475 30S ribosomal protein S17e.-related protein 60.67 7 A
HVO_2481 phosphohydrolase 61.50 4 A
HVO_2485 biotin--acetyl-CoA-carboxylase ligase 57.00 6 A
HVO_2486 bccA biotin carboxylase 69.67 51 ABCE
HVO_2487 asd aspartate-semialdehyde dehydrogenase 82.80 21 AB
HVO_2494 cytidylate kinase putative 64.00 14 AB
HVO_2495 htlB HTR-like protein 60.17 20 AD
HVO_2496 adk Adenylate kinase 75.23 55 ABD
HVO_2498 conserved hypothetical protein 52.33 4 A
HVO_2504 atsC 3-oxoacyl- acyl-carrier protein reductase 90.00 2 A
HVO_2506 Isopentenyl-diphosphate delta-isomerase 65.17 8 A
carA carbamoyl-phosphate synthase small
HVO_2508 80.75 7 AB
subunit
HVO_2510 gnat family acetyltransferase 73.00 1 A
HVO_2511 gatD glutamyl-tRNA(Gln) amidotransferase 85.43 23 A










Table C-1. Continued


Expd

D
ABD
AB
A
AB
A
A
A
A
A
ABD
ABDE
AB
A
ABC
ABD
A
AB
A
AB
AB
AB
AB
AE
AB
ABD
AB
AB
AB
AB
AB
AB
A
A
ABDE
ABC
A
A
A
A
A
AB
A
AD
ABD


MOWSEb Hitsc


Acc. No.a

HVO_2514
HVO_2516
HVO_2519
HVO_2528
HVO_2529
HVO_2530
HVO_2533
HVO_2536
HVO_2537
HVO_2538
HVO_2542
HVO_2543
HVO_2544
HVO_2545
HVO_2546
HVO_2547
HVO_2548
HVO_2549
HVO_2550
HVO_2551
HVO_2552
HVO_2553
HVO_2554
HVO_2555
HVO_2557
HVO_2558
HVO_2559
HVO_2560
HVO_2561
HVO_2562
HVO_2563
HVO_2564
HVO_2575
HVO_2577
HVO_2588
HVO_2589
HVO_2590
HVO_2598
HVO_2600
HVO_2601
HVO_2606
HVO_2614
HVO_2616
HVO_2620
HVO_2621


Description
subunit D
conditioned medium-induced protein 2
gpml 2 3-bisphosphoglycerate-independent
phosphoglycerate mutase
conserved hypothetical protein
crtl phytoene dehydrogenase
short chain dehydrogenase
phosphori bosylamine-glycine ligase
conserved hypothetical protein
conserved hypothetical protein TIGR00300
predicted protein putative
hydrolase alphalbeta fold family putative
rpll5p Chain L ribosomal protein
rpmD ribosomal protein L30P
rpsE ribosomal protein S5
rpll8p 50S ribosomal protein L18P
Ribosomal protein L19e
rpl32e ribosomal protein
rplep 50S ribosomal protein L6P
rpsH ribosomal protein S8
rpsN ribosomal protein S14plS29e
rpl5p ribosomal protein L5.
rps4E ribosomal protein S4.eR
rplX ribosomal protein L24
Chain J Trigger Factor Ribosome Binding
Domain
RpsQ 30S ribosomal protein S17
rpmC ribosomal protein L29
30S ribosomal protein S3P
rplV ribosomal protein L22
rpsS ribosomal protein S19
rplB ribosomal protein L2
rplW ribosomal protein L23
rplD ribosomal protein L4/L1 family
rplC ribosomal protein L3
gbp GTP-binding protein-like
pyrF orotidine 5'-phosphate decarboxylase
icd isocitrate dehydrogenase NADP-dependent
asparaginase
oxidoreductase short-chain
dehydrogenase/reductase family
ppk polyphosphate kinase
map methionine aminopeptidase type II
hit histidine triad protein
quinohemoprotein alcohol dehydrogenase
udp Uridine phosphorylase
pchA potassium channel-like
rieske 2Fe-2S domain protein
ppc phosphoenolpyruvate carboxylase


44.00
69.00
81.88
59.50
61.17
46.00
73.50
58.00
88.00
52.67
89.00
74.23
76.64
86.00
72.21
97.40
189.00
80.11
77.40
67.75
91.80
96.00
69.80
88.40
71.82
112.69
107.50
90.44
76.13
60.50
90.71
104.64
41.00
38.00
176.35
88.86
90.00
46.00
84.33
66.00
46.25
55.40
57.50
84.69
77.39









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_2622 ara aldehyde reductase 80.67 15 AB
HVO_2624 pyrG CTP synthase 71.64 78 AB
HVO_2625 guaA GMP synthase C-terminal domain 100.89 55 AB
HVO_2630 conserved hypothetical protein 48.00 8 AD
HVO_2636 transcription regulator 175.67 19 A
HVO_2638 tcbD chloromuconate cycloisomerase 40.00 1 A
HVO_2639 Uncharacterized conserved protein 69.50 2 A
HVO_2640 conserved hypothetical protein 40.00 5 A
HVO_2643 adh quinone oxidoreductase 48.00 9 ABD
HVO_2644 conserved hypothetical protein 59.00 8 A
HVO_2646 ilvD dihydroxy-acid dehydratase 69.18 55 ABDE
HVO_2648 SerA phosphoglycerate dehydrogenase 77.00 11 A
HVO_2650 4-hydroxybenzoate 3-monooxygenase 62.06 74 ABDE
HVO_2654 cytochrome B6 54.00 1 A
HVO_2655 qcrA rieske iron-sulphur protein 50.00 2 A
HVO_2661 aspC aspartate aminotransferase 75.67 21 B
HVO_2662 dioxgenase 61.14 20 AB
HVO_2663 oxidoreductase 56.00 10 A
HVO_2665 hpcH HpcH/Hpal aldolase family protein 77.50 4 A
HVO_2670 glcD glycolate oxidase subunit GlcD 52.00 6 A
HVO_2671 spt agt aminotransferase class V 59.75 12 AB
HVO_2675 hisD histidinol dehydrogenase 54.00 22 AB
HVO_2677 acetyltransferase (gnat) family 51.50 2 AB
HVO_2688 sugar-specific transcriptional regulator TrmB 66.67 5 A
HVO_2690 MviM oxidoreductase 78.82 31 AB
MalK sugar ABC transporter ATP-binding
HVO_2692 71.67 5 A
protein
HVO_2695 maltose binding protein 126.09 68 A
HVO_2697 DNA primase eukaryotic-type small subunit 53.00 4 A
putative
HVO_2699 bcp Bacterioferritin comigratory protein. 66.00 16 AD
HVO_2700 cdc48 cell division control protein 48 138.65 184 AB
panB 3-methyl-2-oxobutanoate
HVO_2703 49.00 18 B
hyd roxymethyltransferase
eif2bd translation initiation factor elF-2B subunit
HVO_2706 71.57 20 AB
delta
HVO_2707 oxidoreductase 86.40 16 A
HVO_2708 AP-endonuclease/AP-lyase 92.00 3 A
RtcB RNA terminal phosphate cyclase operon
HVO_2712 79.33 16 A
orfB homolog UPF0027 family
HVO_2714 conserved hypothetical protein 36.00 1 A
HVO_2715 gph Phosphoglycolate phosphatase 58.50 5 A
HVO_2716 acd Acyl-CoA dehydrogenase. 80.67 35 AB
HVO_2718 conserved hypothetical protein 83.00 4 A
HVO_2721 purF amidophosphoribosyltransferase 36.00 1 A
HVO_2723 snp snRNP homolog 62.67 4 B
HVO_2724 predicted hydrolase 73.77 73 AB
HVO_2725 idsA geranylgeranyl diphosphate synthase 102.30 44 AB
HVO_2726 gltX glutamyl-tRNA synthetase 56.80 19 AB
HVO_2734 acetyl transferase gnat family 54.00 1 A









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_2736 hsp small heat shock protein 48.00 8 A
HVO_2737 RPL8A 50S ribosomal protein L7Ae 89.11 22 AB
HVO_2738 Ribosomal protein S28e 64.50 11 AB
HVO_2739 Ribosomal protein L24e 46.75 8 A
HVO_2740 ndk Nucleoside diphosphate kinase 89.39 65 ABCD
metE 5- methyltetra hyd ro pte royltriglIuta mate-
HVO_2742 67.25 24 AB
homocysteine methyltransferase
HVO_2743 methionine synthase vitamin-B12 independent 73.11 24 ABD
HVO_2747 Predicted RNA-binding protein 45.00 1 A
HVO_2748 RNA polymerase Rpb4 72.57 19 AB
HVO_2749 Ribosomal protein L21e 68.50 6 A
HVO_2750 metB cystathionine alpha synthase 53.33 7 A
HVO_2752 translation elongation factor aEF-1 beta 39.50 10 AD
HVO_2753 Domain of unknown function (DUF1610) family 76.33 8 A
HVO_2756 RplJ Acidic ribosomal protein PO homolog 63.78 33 AB
HVO_2757 L1P family of ribosomal proteins 110.95 101 ABD
HVO_2758 ribosomal protein L11 65.43 38 ABD
HVO_2759 TET aminopeptidase homolog 51.20 13 AD
HVO_2760 hypothetical protein 52.67 4 D
HVO_2761 mvk mevalonate kinase 74.25 8 A
HVO_2766 Ihr ATP-dependent DNA helicase 41.00 4 A
HVO_2767 hel DNA helicase 52.25 31 AD
HVO_2770 conserved hypothetical protein 91.00 3 A
HVO_2773 rpsB ribosomal protein S2 82.31 39 ABE
HVO_2774 eno phosphopyruvate hydratase 91.18 91 ABC
DNA-directed RNA polymerase subunit N.-
HVO_2776 60.33 5 ABD
related protein
HVO_2777 rpsI ribosomal protein S9 86.73 49 ABC
HVO_2778 rplM ribosomal protein L13 50.00 12 ABC
HVO_2779 rpll8e Chain O 66.40 16 A
HVO_2781 RNA polymerase Rpb3/Rpbl1 dimerisation 7.1 20A
domain
HVO_2782 rpsK ribosomal protein S11 49.25 15 AB
HVO_2783 rpsD ribosomal protein S4 117.00 84 ABDE
HVO_2784 rpsM ribosomal protein S13plS18e 102.41 84 ABDE
HVO_2790 mrp Mrp protein homolog 96.25 50 ABD
HVO_2796 conserved hypothetical protein 54.67 27 BD
HVO_2797 udk uridine kinase 49.67 8 B
HVO_2798 Tat (twin-arginine translocation) pathway signal 5704B
sequence domain protein
sdhA succinate dehydrogenase flavoprotein
HVO_2808 126.47 124 ABDE
subunit A
sdhB succinate dehydrogenase chain B
HVO_2809 75.80 19 AB
homolog
HVO_2812 succinate dehydrogenase subunit 50.00 3 A
HVO_2813 conserved hypothetical protein 95.67 11 AB
HVO_2814 alkA DNA-3-methyladenine glycosylase 37.00 4 A
HVO_2815 hbd 3-hydroxyacyl-CoA dehydrogenase 89.00 21 AD
HVO_2827 UvrDIREP helicase domain protein 54.00 19 A
HVO_2836 bacterio-opsin activator-like protein 49.00 8 D









Table C-1. Continued
Acc. No.a Description MOWSEb Hitse Expd
HVO_2843Deoxyri bod ipyri mid i ne ph otolyase5305A
(photreactivation)
HVO_2846 spoVR stage V sporulation protein R-like 42.33 23 AD
HVO_2848 prkA kinase anchor protein 40.50 9 A
HVO_2849 prkA PrkA protein. 63.00 23 A
HVO_2850 conserved hypothetical protein 63.00 1 A
HVO_2851 conserved protein 39.00 1 A
HVO_2857 suhB extragenic suppressor homolog 81.50 8 A
HVO_2861 conserved hypothetical protein 77.00 1 B
HVO_2862 glyA serine hyd roxymethyltransferase 88.43 41 ABD
HVO_2865 FolD bifunctional protein. 60.75 11 AB
HVO_2869 DNA binding domain similar to hmvngl864 64.17 20 AB
HVO_2871 gabT 4-aminobutyrate aminotransferase 39.00 2 A
HVO_2879 ocd ornithine cyclodeaminase 87.25 12 A
HVO_2883 conserved protein 54.00 12 AD
HVO_2884 membrane protein putative 47.00 1 A
HVO_2888 hat histone acetyltransferase 57.67 23 A
HVO_2889 putative nuclease 104.24 114 ABD
HVO_2892 nadph-dependent fmn reductase 55.00 12 A
HVO_2894 bacterio-opsin activator-like protein 42.00 3 A
HVO_2899 conserved domain protein 58.75 6 AB
HVO_2900 Fumarate hydratase class II 161.27 108 AB
HVO_2902 gatE glutamyl-tRNA(Gln) amidotransferase 89.14 80 ABD
subunit E
HVO_2906 RNA methyltransferase TrmH family group 1 56.00 2 A
HVO_2907 conserved hypothetical protein 55.67 3 A
HVO_2908 dchpS dihydropteroate synthase 78.40 16 AB
HVO_2915 conserved hypothetical protein 71.00 1 A
2 5-dichloro-2 5-cyclohexadiene-1 4-diol
HVO_2916 86.00 13 AB
dehydrogenase(2 5-ddol dehydrogenase).
HVO_2918 thyA thymidylate synthase 66.67 9 AB
HVO_2923 psmC Proteasome subunit alpha2 47.90 48 AB
HVO_2929 SsnA cytosine deaminase 50.00 1 A
HVO_2930 Phosphinothricin N-acetyltransferase 39.00 1 A
HVO_2932 GTP-binding protein 55.00 4 A
HVO_2941 mc nonhistone chromosomal protein 74.77 29 ABD
HVO_2943 pyrD dihydroorotate oxidase 42.00 2 A
HVO_2945 valS valyl-tRNA synthetase 59.67 49 AB
HVO_2946 cgs cystathionine gamma-synthase 100.00 9 A
HVO_2947 pheT phenylalanyl-tRNA synthetase beta 73837A
subunit
HVO_2948 phenylalanyl-tRNA synthetase alpha chain 78.57 16 AD
HVO_2951 trpS tryptophanyl-tRNA synthetase 66.00 13 A
pdhA Pyruvate dehydrogenase El component
HVO_2958 54.00 7 A
alpha subunit
HVO_2959 2-oxoacid decarboxylase El beta chain 122.88 34 AE
2-oxo acid dehydrogenases acyltransferase
HVO_2960 99.82 82 AB
(catalytic domain) protein
HVO_2961 IpdA dihydrolipoamide dehydrogenase 98.00 26 A
HVO_2965 serB phosphoserine phosphatase SerB 144.00 6 D










Table C-1. Continued


MOWSEb
55.00
63.71
69.64
46.00
65.20
72.33
74.67
88.29
80.33
35.50
65.00
38.00
83.64
65.00

49.00
50.00
43.00
41.00
66.67
37.00
61.40
50.00
76.00
56.00
40.00
37.00
41.00
44.00
41.00
37.00
38.67
42.00
65.00

68.14

60.33
55.60
78.60
45.33
96.00
45.00
55.00
76.00
41.00
38.60


Expd
A
AB
AB
A
AB
AE
AB
ABD
B
B
A
A
AB
B

A
A
A
A
ABD
D
ABD
A
AD
A
A
D
A
A
A
A
AD
A
A


H itsc
1
29
59
8
11
5
14
71
8
10
1
7
43
10


Acc. No.a
HVO_2966
HVO_2968
HVO_2969
HVO_2979
HVO_2981
HVO_2985
HVO_2989
HVO_2995
HVO_2997
HVO_3000
HVO_3003
HVO_3006
HVO_3007
HVO_3008

HVO_3010
HVO_3013
HVO_3014
HVOA0002
HVOA0003
HVOA0010
HVOA0012
HVOA0014
HVOA0021
HVOA0047
HVOA0048
HVOA0049
HVOA0056
HVOA0064
HVOA0065
HVOA0070
HVOA0078
HVOA0079
HVOA0084

HVOA0087

HVOA0088
HVOA0090
HVOA0092
HVOA0095
HVOA0097
HVOA0114
HVOA0118
HVOA0119
HVOA0127
HVOA0137


Description
hypothetical protein
serA D-3-phosphoglycerate dehydrogenase
thrC threonine synthase
cobalamin synthesis protein/P47K
upp uracil phosphoribosyltransferase
conserved hypothetical protein
pmu phosphomannomutase
2Fe2S ferredoxin.
thiol O-acetylhomoserine (thiol)-lyase
ligA DNA ligase NAD-dependent
conserved hypothetical protein
uvrC excinuclease ABC C subunit
mdhA L-lactate dehydrogenase
Uncharacterised protein family (UPF0148)
family
eif4a ATP-dependent RNA helicase homolog
elF-4A
conserved protein
gbp GTP-binding proteinlike
unknown putative
parA3 chromosome partitioning protein ParA
family ATPase
hypothetical protein
hypothetical protein
tnp transposase
unknown
Uspl universal stress protein 1
unknown
lin2334 Magnesium and cobalt efflux protein
corC
unknown
Orc5 orc / cell division control protein 6
polB2 DNA polymerase B2
hypothetical protein
helicase SNF2/RAD54 family putative
conserved hypothetical protein
bll7610 putative
xlnF 2-hydroxyhepta-2 4-diene-1 7-dioate
isomerase/5-carboxymethyl-2-oxo-hex-3-ene1
7-dioate decar
oxidoreductase FAD-binding putative
phdl gentisate 1 2-dioxygenase
acdD acyl-coA dehydrogenase
AGR_C_2623 amidohydrolase
HBD2 3-hydroxybutyryl-CoA dehydrogenase
hypothetical protein
transcription regulator
Putative cyclase superfamily
trpS tryptophanyl-tRNA synthetase
tfbE transcription initiation factor llB 5


A
A
A
A
A
AB
ABD
A
A
B










Table C-1. Continued


MOWSEb
38.00
45.00
95.13
56.67
62.00
45.00
49.00
54.50
36.00
36.00
64.00
70.63
123.10
91.94
70.50
49.00
39.00
57.00
50.00
40.00
53.00
39.00
44.00
45.00
93.67
70.43
63.00
86.11
66.50
78.00
56.00
66.67
75.33
44.00

52.50
54.40
42.00
37.00
50.00
57.88

83.00


Expd
A
D
A
A
B
D
A
A
A
B
A
AB
ABD
ABD
A
A
D
A
A
A
A
D
D
A
A
AB
A
BD
A
A
A
A
A
D


H itsc
7
5
37
15
2
5
3
3
4
4
6
32
181
91
4
16
8
2
6
6
2
4
4
3
9
56
5
51
6
6
4
11
7
3


Acc. No.a
HVOA0144
HVOA0149
HVOA0156
HVOA0158
HVOA0161
HVOA0168
HVOA0170
HVOA0177
HVOA0180
HVOA0189
HVOA0197
HVOAO205
HVOAO206
HVOAO207
HVOAO208
HVOAO209
HVOAO222
HVOAO228
HVOAO234
HVOAO237
HVOAO245
HVOAO247
HVOAO266
HVOAO267
HVOAO268
HVOAO269
HVOAO271
HVOAO274
HVOAO281
HVOAO283
HVOAO286
HVOAO288
HVOAO289
HVOAO294
HVO AO295

HVOAO304
HVOAO306
HVOAO324
HVOAO328
HVOAO331

HVOAO339


Description
transposase (IS4 family)
cga-2 glucan 1 4-alpha-glucosidase
acsA acetate--CoA ligase
ACS5 acetyl-CoA synthase
transcriptional regulator TetR family domain
protein putative
boa-8 Bat-like protein
hypothetical protein
ABC transporter ATP-binding protein
hypothetical bps2 homolog (TBD)
unknown putative
polysaccharide biosynthesis protein putative
cas6 CRISPR-associated protein Cas6
unknown putative
csh2 CRISPR-associated protein Csh2 family
cas5h CRISPR-associated protein Cas5 Hmari
su btype
TM1799 CRISPR-associated helicase Cas3
conserved hypothetical protein
surE stationary phase survival protein
unknown
adenine specific DNA methyltransferase
unknown
hypothetical protein H1420
transcriptional regulator putative
dgoA3 mandelate racemase/muconate
lactonizing enzyme family protein
Fuca L-fuculose phosphate aldolase
gpdA glycerol-3-phosphate dehydrogenase
gpdC glycerol-3-phosphate dehydrogenase
chain C
suhB extragenic suppressor homolog
sugC sugC
sugar ABC transporter periplasmic sugar-
binding protein putative
unknown
fabG 3-oxoacyl-(acyl-carrier-protein) reductase
endoribonuclease L-PSP putative
potA polyamine ABC transporter ATP-binding
protein
amaB N-carbamoyl-L-amino acid
amidohydrolase
groL NEQ141
goaG 4-aminobutyrate aminotransferase
unknown
Pfk1l 2-keto-3-deoxygluconate kinase
dgoA3 mandelate racemase/muconate
lactonizing enzyme family protein
extracellular solute-binding protein family 5
putative










Table C-1. Continued


MOWSEb
41.00
39.50
42.00
37.00
50.00
45.00
85.00
97.00
223.75
137.40
50.50

41.00
68.00
66.25
62.33
38.00
46.50
39.00
38.00
37.00
41.00
60.00
38.00
72.50
56.00
56.00
63.00
106.53
133.36
62.75
331.50
168.33
43.00
63.50
82.00
44.80
88.50
48.00
47.00
66.00
57.00
62.00
37.00


Expd
D
AC
D
D
A
C
A
A
AE
A
A

D
A
A
AB
D
A
A
D
A
A
B
A
A
A
A
A
ABD
ABCD
AB
BD
BD
A
A
A
AB
A
A
B
A
A
A
A


H itsc
2
7
7
2
5
3
3
16
98
59
13

8
12
12
14
4
13
4
4
5
3
2
5
5
2
3
2
56
51
10
77
32
6
5
2
16
3
3
5
3
10
5
2


Acc. No.a
HVOAO343
HVOAO356
HVOAO358
HVOAO371
HVOAO372
HVOAO373
HVOAO376
HVOAO377
HVOAO378
HVOAO379
HVOAO380

HVOAO384
HVOAO385
HVOAO386
HVOAO388
HVOAO394
HVOAO406
HVOAO424
HVO AO429

HVOAO431
HVOAO432
HVOAO437
HVOAO439
HVOAO458
HVOAO459
HVOAO465
HVOAO469
HVOAO472
HVOAO475
HVOAO476
HVOAO477
HVOAO480
HVOAO481
HVOAO485
HVOAO486
HVOAO487
HVOAO488
HVOAO490
HVOAO496
HVOA0500
HVOA0501
HVOA0503
HVOA0505


Description
hypothetical protein
hypothetical protein
hypothetical protein H1420
hypothetical protein
metallo-beta-lactamase superfamily domain
protein
hypothetical protein
Xaa-Pro aminopeptidase M24 family protein
hydantoin racemase putative
hydantoin utilization protein B putative
agaF N-methylhydantoinase A
oligopeptide ABC transporter periplasmic
oligopeptide-binding protein putative
appF peptide ABC transporter ATP-binding
protein
huyA-1 N-methylhydantoinase A
hydantoin utilization protein B putative
transcriptional regulator AsnC family family
hypothetical protein
hypothetical protein
boa-8 bacterio-opsin activator-like protein
ureX N-acyl-D-amino acid deacylase family
protein
transposase (IS4 family)
hypothetical protein
H1513 ORF H1513
H1016 ORF H1016
sojE Spo0A activation inhibitor
hypothetical protein
transcription regulator putative
ACC1 accessory protein
trxB2 thioredoxin reductase
SOD1 superoxide dismutase
hypothetical protein
pstS phosphate ABC transporter binding
pstB phosphate ABC transporter ATP-binding
protein
Prpl transcription regulator
CACO221 Aspartate aminotransferase
HypE hydrogenase expression/formation protein
putative
cobB cobyrinic acid a c-diamide synthase
cobO cob(1)alamin adenosyltransferase
PAO882 CAIB/BAlF family protein
Uspl universal stress protein 1
ppiB2 peptidylprolyl isomerase
pyridine nucleotide-disulphide oxidoreductase
class II putative
amidohydrolase
fad-4 enoyl-CoA hydratase










Table C-1. Continued


MOWSEb
51.40
47.67
112.33
63.00
47.33
43.00
71.25
40.00
76.40
62.67
70.00
39.50
76.00
44.00
41.00
54.33

61.00
96.00
45.50
66.00
52.00
42.00
75.67
62.00
48.63
53.00
64.33
40.00
60.00
113.27
37.00
52.33
45.00
51.75
63.00
60.00

41.50
52.50
39.00
57.00
61.00


Expd
AD
A
A
A
A
A
A
A
A
AB
A
AD
A
A
B
A

ABD
A
AD
A
A
A
A
A
AB
A
AD
A
A
BCD
A
AD
A
AB
D
A


H itsc
26
5
20
1
8
2
17
4
13
8
1
10
3
9
1
5


Acc. No.a

HVOA0507
HVOA0508
HVOA0509
HVOA0511
HVOA0512
HVOA0520
HVOA0521
HVOA0522
HVOA0523
HVOA0524
HVOA0525
HVOA0529
HVOA0534
HVOA0535
HVOA0542
HVOA0545

HVOA0547
HVOA0549
HVOA0550
HVOA0551
HVOA0552
HVOA0561
HVOA0562
HVOA0569
HVOA0571
HVOA0575
HVOA0583
HVOA0586
HVOA0587
HVOAO611
HVOAO619
HVOAO624
HVOAO627
HVOAO635
HVOAO636
HVOAO637

HVOB0001
HVOB0004
HVOB0009
HVOB0026
HVOBOO31


Description
paaA phenylacetic acid degradation protein
PaaA
pacF PacF protein
paal phenylacetate-CoA oxygenase Paal
subunit
paaD phenylacetic acid degradation protein
aldY5 aldehyde dehydrogenase
uncharacterized domain 1 putative
paaK1 phenylacetyl-coenzyme A ligase
acaB2 3-ketoacyl-CoA thiolase
Domain of unknown function domain protein
fadB2 3-hydroxyacyl-CoA dehydrogenase family
protein
ech4 enoyl-CoA hydratase
ilvB3 acetolactate synthase large subunit
unknown
putative CAAX amino terminal protease family
transmembrane
hypothetical protein
tetR putative TetR-family transcriptional
reg ulator
Family of unknown function (DUF1028)
superfamily
nosL NosL protein
11-domain light and oxygen sensing his kinase
alkK3 AMP-binding domain protein
fadD14 fadD14
hutG formimidoylglutamase
hutU urocanate hydratase
baiF-1 CAIB/BAlF family protein
hydrolase isochorismatase family
3-ketosteroid dehydrogenase putative
arcR-6 transcription regulator
rffH1 glucose-1-phosphate
thymidylyltransferase
htlD HTR-like protein
lipoprotein component of divalent metal ABC
transporter putative
unknown putative
zntA1 zinc-transporting ATPase
unknown
aminotransferase class V
vba~lebsC protein putative
alF1A2 Translation initiation factor 1A-2 (alF-
1 A-2).
Orc4 Cell division control protein 6 homolog 6
(CDC6 homolog 6).
glyA serine hyd roxymethyltransferase
Rieske 2Fe-2S family protein
fabG 3-oxoacyl- acyl-carrier-protein reductase
kduD 2-deoxy-D-gluconate 3-dehydrogenase










Table C-1. Continued


MOWSEb

61.50

49.50

38.00
55.00
58.00
69.00
124.25
50.00
85.25
42.00
66.13
77.57
107.20
69.75

61.00
81.00
45.00
50.33
107.40

99.00
90.44
85.75

75.00

93.67
58.63
90.00
53.50

61.00
80.33
72.50
54.00
53.00
65.00
63.00

92.00

48.25

67.57
94.00


Expd
A


H itsc

6


Acc. No.a

HVOBOO37

HVOBOO38

HVOBOO40
HVOBOO45
HVOBOO47
HVOBOO49
HVOB0050
HVOB0051
HVOB0053
HVOB0055
HVOB0057
HVOB0058
HVOB0059
HVOBOO60

HVOBOO61
HVOBOO62
HVOBOO64
HVOBOO69
HVOBOO70

HVOBOO74
HVOBOO76
HVOBOO77

HVOBOO78
HVO B0082
-V B08
HVOB0084

HVOBOO87

HVOBOO89
HVOBOO93
HVOBOO95
HVOBOO96
HVOB0100
HVOB0102
HVOB0109

HVOB0110

HVOB0111

HVOB0112
HVOB0113


Description
ugpC3 sugar ABC transporter ATP-binding
protein (UGPC)
ugpC3 sugar ABC transporter ATP-binding
protein (UGPC)
arcR-6 transcription regulator
bdb L-2 4-diaminobutyrate decarboxylase
unknown
cbiC precorrin isomerase
cobN cobalamin biosynthesis protein
hmcA protporphyrin IX magnesium chelatase
hypothetical protein (TBD)
unknown
cobJ precorrin-3B C1 7-methyltransferase
cobH precorrin-3B C1 7-methyltransferase
CbiG
cbiF cobalamin biosynthesis precorrin-3
methylase
cbiL cobalamin biosynthesis
cbiT precorrin-8W decarboxylase
unknown
gabD succinate-semialdehyde dehydrogenase
aminotransferase
Protein of unknown function (DUF917)
superfamily
hydantoinase
Protein of unknown function (DUF917)
superfamily
appF putative ABC transporter ATP-binding
protein
mll9136 oligopeptide ABC transporter
periplasmic oligopeptide-binding protein
orfY
possible polygalacturonase putative
Mandelate racemase / muconate lactonizing
enzyme C-terminal domain protein
dppF dipeptide ABC transporter ATP-binding
Bacterial extracellular solute-binding proteins
family 5 family
hypothetical protein (TBD)
unknown
aldY3 aldehyde dehydrogenase
Glycosyl Hydrolase Family 88 superfamily
ugpC1 sugar ABC transporter ATP-binding
protein
sorbitol dehydrogenase (L-iditol 2-
dehydrogenase) putative
dgoA4 mandelate racemase/muconate
lactonizing enzyme family
Mandelate racemase / muconate lactonizing
enzyme N-terminal domain protein
Luciferase-like monooxygenase superfamily


A
ABD
A
AB

A
A
A
B
A
A
A










Table C-1. Continued


MOWSEb
49.00
47.00
126.78
170.25
53.33
50.50
43.00
48.00
41.00
60.11
91.67
63.86
37.00
41.00
42.00
46.00
38.33
85.00
66.67

35.00
45.50
75.82
40.00
82.67
42.00
65.80

36.00
72.00
68.00
35.00
36.00
44.00
35.00
59.33
54.50
73.00
71.00
38.00
58.00
96.25
97.00


Expd
AD
D
A
ABD
B
A
A
B
D
AB
AB
AB
A
A
C
D
AD
A
A


H itsc
8
3
55
219
5
7
3
14
4
56
13
60
3
3
4
6
20
6
12


Acc. No.a
HVOB0114
HVOB0115
HVOB0117
HVOB0118
HVOB0130
HVOB0134
HVOB0137
HVOB0138
HVOB0140
HVOB0143
HVOB0149
HVOB0154
HVOB0157
HVOB0162
HVOB0167
HVOB0169
HVOB0173
HVOB0182
HVOB0184

HVOB0187
HVOB0196
HVOB0198
HVOBO203
HVOBO213
HVOBO215
HVOBO217

HVOBO220
HVOBO223
HVOBO227
HVOBO233
HVOBO238
HVOBO244
HVOBO248
HVOBO253
HVOBO265
HVOBO266
HVOBO268
HVOBO272
HVOBO287
HVOBO291
HVOBO292


Description
arcR-6 transcription regulator
FAHD2A fumarylacetoacetate hydrolase domain
containing 2A
unknown
unknown
Domain of unknown function (DUF296)
superfamily
trp2 ABC transport protein
putative DNA binding protein
afsK serine/threonine protein kinase
afsK serine/threonine protein kinase
htlD HTR-like protein
orf3 ORF3
OYE2 11-domain light and oxygen sensing his
kinase
transposase (IS4 family)
qcrB menaquinol-cytochrome-c reductase
narJ chaperonin-like protein
Prp protein phosphatase
unknown
deoxyhypusine synthase
oppA oligopeptide ABC transporter solute-
binding protein
PROBABLE SOLUTE-BINDING PERIPLASMIC
(PBP) ABC TRANSPORTER PROTEIN putative
kinA8 signal-transducing histidine kinase-like
lipoprotein putative
glcD1 glycolate oxidase subunit GlcD
SCE29.12c Myo-inositol-1-phosphate synthase
probable secreted glycosyl hydrolase
livK-2 branched-chain amino acid ABC
transporter amino acid-binding protein
livG-9 branched-chain amino acid ABC
transporter ATP-binding protein
transcriptional regulator
ugpC1 sugar ABC transporter ATP-binding
protein
xsa alpha-L-arabinofu ran osidase
endoribonuclease L-PSP putative
aor2 aldehyde ferredoxin oxidoreductase
oxidoreductase
suhB extragenic suppressor homolog
glcD2 putative oxidoreductase
gdhB glutamate dehydrogenase imported -
Halobacterium sp. NRC-1
alkanal monooxygenase-like
boa bacterio-opsin activator-like protein
Ftsj cell division protein
glycerophosphodiester phosphodiesterase
putative
ugpB glycerol-3-phosphate-binding protein










Table C-1. Continued


Expd


ABC
B
A
D
D
A
A
A
A
D
A
A
ABE
A
AB
A
A
AD
AE
A
AB
A
A
D
A
A
A
A
A
D


MOWSEb Hitsc


Acc. No.a


HVOBO295
HVOBO297
HVOBO305
HVOBO316
HVOBO317
HVOBO321
HVOBO324
HVOBO334
HVOBO343
HVOBO361
HVOBO364
HVOBO365
HVOBO371
HVOBO375
HVOBO376
HVOC0001
HVOC0006
HVOC0007
HVOC0011
HVOC0013
HVOC0017
HVOC0028
HVOC0036
HVOC0038
HVOC0041
HVOC0042
HVOC0043
HVOC0046
HVOC0053
HVOC0054

HVOC0057

HVOC0065

HVOC0067
HVOC0069
HVOC0074
HVO C0075

HVOC0077
HVOC0081
HVOD0003


Description
precursor
ugpC sn-glycerol-3-phosphate transport system
ATP-binding
hypothetical protein
hypothetical protein
RbsA ribose ABC transporter ATP-binding
ade adenine deaminase
NADH-dependent dyhydrogenase putative
unknown
dehydrogenase homolog
IplD hydrolytic enzyme IplD
transcription regulator
hmoA molybdopterin oxidoreductase
moz molybdopterin oxidoreductase
aldehyde dehydrogenase
unknown
oxidoreductase
orc / cell division control protein 6
unknown
unknown
hypothetical protein
transposase (IS4 family)
parA1 chromosome partitioning protein ParA
hypothetical protein
hypothetical protein
hypothetical protein
hypothetical protein
putative helicase family protein
hypothetical protein
hypothetical protein (TBD)
transposase (IS4 family)
Tat (twin-arginine translocation) pathway signal
sequence domain protein
Orc4 Cell division control protein 6 homolog 6
(CDC6 homolog 6).
Transposase for insertion sequence-like
element IS431mec.
sarcosine oxidase putative
gfo1 glucose-fructose oxidoreductase
dppF dipeptide ABC transporter ATP-binding
oligopeptide/dipeptide ABC transporter
periplasmic substrate-binding protein putative
soxA sarcosine oxidase
chromosome segregation protein putative
unknown protein


48.78
36.00
48.00
37.00
45.00
70.00
64.00
37.00
38.00
37.00
45.00
37.00
120.08
77.00
102.29
59.00
37.00
88.40
45.50
40.00
49.20
48.00
67.67
53.00
40.00
94.00
84.50
83.00
37.00
42.00

54.67

46.00

67.00
97.13
106.33
72.80
67.00
86.00
43.50


aORFs are numbered according to the GenBank assembly (Hartman et al., in preparation).
bProbability-based MOWSE scores are calculated averages of all individual peptide scores for a given protein, irrespective of experiment.
cPeptide hits are additive and represent all top-ranking peptides matched to a protein sequence, irrespective of experiment.
dExperiment(s) from which each protein identification resulted. A, B, C, D and E denote SCX MudPIT, IMAC, MOAC, combined IMAC-
MOAC and CLBL inhibitor experiments, respectively.










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BIOGRAPHICAL SKETCH

Phillip Aaron Kirkland was born in May of 1981 at Keesler AFB in Biloxi, MS, to parents

Ron and Kelly Kirkland. He lived in Huntsville, AL, from the age of 18 months until graduation

from S.R. Butler High School in May of 1999. He began post-secondary studies at Auburn

University, Auburn, AL, in August 1999, at the age of 18. Phillip Kirkland graduated from

Auburn University in May 2003 with a Bachelor of Science in microbiology. In August 2003,

he was admitted to the graduate program in the Department of Microbiology and Cell Science at

the University of Florida where he made progress toward a Doctor of Philosophy degree,

completed in Fall 2007. In September of 2005, he became engaged to his college sweetheart,

Shelley Cooper of Reno, NV and was married on June 14, 2008 in Evergreen, CO.





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1 IDENTIFICATION OF NATIVE 20S PR OTEASOMAL SUBSTRATES IN THE HALOARCHAEON Haloferax volcanii THROUGH DEGRADOMIC AND PHOSPHOPROTEOMIC ANALYSIS By PHILLIP A. KIRKLAND A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Phillip Kirkland

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3 To my mother, Kelly Kirkland; my grandmother Judy Jansen; and to the loving memory of Mr. and Mrs. L.V. Stevens.

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4 ACKNOWLEDGMENTS I would like to express my d eepest gratitude to Dr. Julie Maupin for her patience and guidance. Many thanks go to the Maupin lab st aff (Matt Humbard, Wei Li, and Guanguin Zhou) and to former graduates Dr. Chris Reuter and Go sia Gil for all of their help and support. My sincerest thanks go to my supervisory committee (Dr. James Preston, Dr. Madeline Rasche, Dr. Richard Lamont, Dr. Peter Kima and Dr. Eric Triplett ) for their insight. I owe a debt of gratitude to John Moore, Phi Do, Dr. Franz St. John, Youngnyun Kim, John Rice and the entire Microbiology and Cell Science Department. I am also extremely grateful to all of those who have offered help and support with instrume ntation, bioinformatics and general proteomic endeavors including Dr. Stan Stevens, Scott Mc Clung, Dr. Marjorie Chow and Dr. Alfred Chung and the entire ICBR staff as well as Dr. Jennifer Bu sby and Valerie Cavett at Scripps Florida. Finally, I am eternally grateful for the support and encouragement of my family and friends: my mom, Kelly Kirkland, my grandmother Judy Janse n, my fianc Shelley Cooper, Jeremy and Matt Jones, William Knapp, and the Smith, Bright, Blai r, Stevens and Jansen families and my great grandparents, the late Lavert V. and Una B. Stevens.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES................................................................................................................ .........9 LIST OF ABBREVIATIONS........................................................................................................10 ABSTRACT....................................................................................................................... ............15 CHAPTER 1 LITERATURE REVIEW.........................................................................................................17 Introduction................................................................................................................... ..........17 Proteasome Structure and Function........................................................................................17 Core Particle Structure and Arrangement........................................................................18 Associated ATPase Re gulatory Components..................................................................20 The 19S regulatory particle......................................................................................20 Proteasome-activating nucleotidases........................................................................21 Bacterial ARC ATPase.............................................................................................22 Other proteasome-associated complexes.................................................................22 Protein Degradation............................................................................................................ ....23 Protein Degradation Signals............................................................................................24 Ubiquitination and other PTMs..............................................................................25 The N-end rule.........................................................................................................28 PEST sequences.......................................................................................................30 Oxidative damage and h ydrophobic patch exposure................................................32 Known Substrates and Cellular In fluences of the Proteasome........................................34 Cell cycle..................................................................................................................34 Transcription and translation....................................................................................36 Cellular metabolism and stress response..................................................................38 Proteomics and Biological Mass Spectrometry......................................................................41 The Current State of Bottom-Up Proteomics..................................................................42 Protein/peptide separation........................................................................................43 Post-translational modification analysis...................................................................46 Shotgun proteomics and quantitativ e, high-throughput strategies...........................49 Mass spectrometry a nd bioinformatics....................................................................53 Special Considerations for Extreme Proteomes..............................................................60 Proteomes with extreme pI value bias......................................................................60 Hydrophobic proteomes and membrane proteomics................................................62 Project Rationale and Design..................................................................................................64

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6 2 METHODS AND MATERIALS..............................................................................................67 Chemicals and Reagents.........................................................................................................67 Strains, Plasmids and Culture Conditions..............................................................................67 Construction of panA Knockout Mutant GG102....................................................................69 Protein Preparation and Quantification..................................................................................70 Standard Protein Extraction.............................................................................................71 Trizol-Mediated Protein Extraction.................................................................................71 Protein Modification........................................................................................................... ....73 Protein Reduction, Alkylati on and Tryptic Digestion.....................................................73 Peptide Methyl Esterification..........................................................................................73 Phosphoprotein Enrichment and Purification.........................................................................74 Immobilized Metal Affinity Chromatography................................................................74 Titanium Dioxide Phosphopeptide Enrichment..............................................................74 Nickel Purification of Pol yhistadine-Tagged Proteins....................................................74 2D PAGE Analysis and Imaging............................................................................................75 Liquid Chromatography and Mass Spectrometry...................................................................76 Reversed Phase HPLC Coupled w ith Nano-ESI-QTOF (QSTAR) MS/MS...................76 Three-Dimensional LCQ Deca Ion Trap MS..................................................................77 MS Data and Protein Identity Analyses..................................................................................77 Radioactive 35S Pulse-Chase Labeling of H. volcanii Proteins..............................................78 Immunoprecipitation of Radiolabeled H. volcanii Proteins...................................................79 3 OPTIMIZING ISOELECTRIC FOCUSING OF HALOPHILIC PROTEINS THROUGH A TRIZOL-BASED SAMPLE PREPARATION METHOD................................................86 Introduction................................................................................................................... ..........86 Results and Discussion......................................................................................................... ..87 4 EFFECT OF CLASTO -LACTACYSTIN BETA LACTONE ON THE PROTEOME OF Haloferax volcanii ..................................................................................................................95 Introduction................................................................................................................... ..........95 Results........................................................................................................................ .............96 Discussion..................................................................................................................... ........101 Ribosomal Proteins........................................................................................................101 Elongation Factor 1A....................................................................................................102 DJ-1/ThiJ/PfpI Superfamily..........................................................................................103 Cell Division..................................................................................................................103 2-Oxoacid Dehydrogenase............................................................................................104 Dihydroxyacetone Kinase.............................................................................................105 Aldehyde Dehydrogenase..............................................................................................105 Fe-S Cluster Assembly..................................................................................................106 Divalent Metal Transport..............................................................................................106 Conclusions.................................................................................................................... .......107

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7 5 PHOSPHOPROTEOME ANALYSIS OF PROTEASOME-ACTIVATING NUCLEOTIDASE A MUTANT OF Haloferax volcanii .....................................................112 Introduction................................................................................................................... ........112 Results and Discussion.........................................................................................................114 Construction of H. volcanii PanA Mutant GG102........................................................114 The 20S Proteasome and PanB Protein Levels Are Not Altered by the panA Mutation.....................................................................................................................115 Growth Phenotype of panA Mutant GG102 Compared to Its Parental and Complemented Strains...............................................................................................115 Comparative 2-DE Analysis of the panA Mutant to Its Parent.....................................116 Phosphoprotein and Phosphopeptide Enrichments.......................................................117 Categorization of Unique Proteins into Clusters of Orthologous Groups..................119 Phosphopeptide Identification.......................................................................................123 Conclusions.................................................................................................................... .......126 6 ESTABLISHMENT OF A BA SELINE PROTEOME OF Haloferax volcanii ......................140 Introduction................................................................................................................... ........140 Results and Discussion.........................................................................................................140 Statistical Analysis of the H. volcanii Proteome Map...................................................140 Reannotation of High-Sc oring Hypotheticals...............................................................142 Comparative COG Analysis of the H. volcanii Proteome Map....................................144 Identification of Paralogs in H. volcanii ........................................................................146 Conclusion..................................................................................................................... .......146 7 SUMMARY OF RESULTS AND C ONTINUING INVESTIGATIONS..............................152 Summary of Results............................................................................................................. .152 Continuing Investigations.....................................................................................................157 APPENDIX A CLASTO LACTACYSTIN BETA LACTONE-T REATED PROTEOME SPOT DATA....160 B PHOSPHOPEPTIDE TANDEM MASS SPECTRA.............................................................175 C COMPLETE Haloferax volcanii PROTEOME MAPPING DATA......................................183 LIST OF REFERENCES.............................................................................................................213 BIOGRAPHICAL SKETCH.......................................................................................................236

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8 LIST OF TABLES Table page 3-1. Advantages of the Trizol extraction method.........................................................................94 4-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs. absence of the proteasome inhibitor clasto -lactacystin-lactone......................................111 5-1. Phospho-enriched proteins uniqu ely identified in each strain of H. volcanii ......................133 5-2. Proteins with a minimum 2-fold abundan ce, comparatively, as determined by spectral counting....................................................................................................................... ........137 5-3. Putative phosphosites identified by MS/MS.......................................................................139 6-1. Number of proteins mapped in H. volcanii categorized by experiment.............................148 6-2. Total protein identifications categorized by contig of origin..............................................149 6-3. Division of identified H. volcanii proteins by functional category.....................................150 A-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs. absence of the proteasome inhibitor clasto -lactacystin-lactone......................................169 C-1. A complete listing of H. volcanii proteins included in current proteome map...................183

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9 LIST OF FIGURES Figure page 1-1. Proteasomal core particle arrangment...................................................................................65 1-2. Assorted composition and association co mbinations for the 20S proteasomal core particle and the PAN regulatory complexes of Haloferax volcanii ......................................66 3-1. Two-dimensional PAGE maps of H. volcanii total cell lysate..............................................90 3-2. Magnified regions of 2D maps of H. volcanii cell lysate......................................................91 3-3. Magnified regions of Gaussian images of 2D PAGE maps of H. volcanii total lysate.........92 3-4. Gaussian images of 2D PAGE maps of H. volcanii total cell lysate.....................................93 4-1. Cellular response of H. volcanii to proteasomal inhibition.................................................108 4-2. Modified Gaussian 2-DE images of H. volcanii proteomes isolated from cells grown in the presence and absence of proteasome inhibitor cL L....................................................109 4-3. Magnified regions of 2-DE proteome maps of H. volcanii cells grown in the absence and presence of cL L..........................................................................................................110 5-1. Western blot analysis of proteasomal subunits 1 and 2 and proteasome-associating regulatory particles PanA and PanB...................................................................................128 5-2. A growth curve plotting proliferati on of wild type and complemented panA knockout mutant in H. volcanii ...........................................................................................................129 5-3. ProQ Diamond phosphoprotein stained 2D PAGE gels of total cell lysate extracted from H. volcanii ..................................................................................................................130 5-4. Proteins identified by mass spectrome try grouped according to COG (clusters of orthologous groups) database.............................................................................................131 5-5. Organization of genes linked to PHO regul on which encode proteins altered by or are related in primary sequence to those altered by the GG102 panA mutation. ...................132 6-1. Comparative COG profiles of H. volcanii and representatives from other domains..........151 7-1. 35S pulse-chase and immunoprecipitation of H. volcanii PCNA. ......................................159 A-1. Magnified regions of 2-DE proteome maps of H. volcanii cells grown in the absence and presence of proteasome inhibitor clasto -lactacystin-lactone....................................160 B-1. MS/MS spectra of phosphoryl ated peptides identified in H. volcanii ...............................175

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10 LIST OF ABBREVIATIONS 16-BAC 16-benzyl dimethyl-n-hexadecylammonium chloride 2DE Two-dimensional electrophoresis AAA+ Atpase Associated with Va rious Cellular Activities AAEE Acryloylaminoethoxyethanol ABC Atp binding cassette ACN Acetonitrile ALDH Aldehyde dehydrogenase Amp Ampicillin AP Activator protein AQUA Absolute quantification ARC AAA+ atpase Forming Ring-Shaped Complexes ATCC American type culture collection ATP Adenosine triphosphate BN-PAGE Blue native polyacrylam ide gel electrophoresis Cdc Cell division control Cdk Cyclin-dependent kinase CE Capillary electrophoresis CHAPS 3-Cholamidopropyl dimethylam monio-1-propanesulfonate CID Collision-induced dissociation CILAT Cleavable isobaric labeled affinity tag c L L Clasto-Lactacystin-beta-lactone CN-PAGE Colorless native polyacryl amide gel electrophoresis COG Clusters of Orthologous Groups

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11CP Core particle CSN Cop9 signalosome CTAB Cetyl trimethyl ammonium bromide DHA Dihydroxy acetone DMA Dimethylacrylamide DMSO Dimethyl sulfoxide DTT Dithiothreitol ER Endoplasmic reticulum ERAD Endoplasmic reticulum associated degradation Fc Antibody constant domain FT-ICR Fourier transform ion cyclotron resonance Fv Antibody variable domain GAPDH Glyceraldehyde-3-phosphate dehydrogenase HMG CoA Hydroxymethylglutaryl coenzyme a HPLC High performance liquid chromatography Hsp Heat shock protein ICAT Isotope-coded affinity tag IDA Information-dependent acquisition IEF Isoelectric focusing IMAC Immobilized metal affinity chromatography IPG Immobilized ph Gradient ISC Iron-sulfur cluster Kan Kanamycin

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12LB Luria-bertani media LC Liquid chromatography m/z Mass-to-charge ratio MALDI Matrix-assisted laser desorption ionization MCM Mini-chromosome maintenance Mev Mevinolin MOAC Metal oxide affinity chromatography mODC Mouse Ornithine Decarboxylase MS Mass spectrometry MS/MS Tandem mass spectrometry MudPIT Multidimensional protein identificaiton technology MW Molecular weight NEPHGE Non-Equilibrium ph Gradient Electrophoresis NHS N-hydroxysuccinimidyl ester Nov Novabiocin OADH 2-oxoacid dehydrogenase OD Optical density ORC Origin recognition complex ORF Open reading frame PAGE Polyacrylamide gel electrophoresis PAN Proteasome-activating nucleotidase PCNA Proliferating cell nuclear antigen PCR Polymerase chain reaction

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13PDDAC Polydiallyldimethylammonium chloride PEST Proline, Glutamic acid, Serine and Threonine pI Isoelectric point PTM Post-translational modification PTS Phosphotransferase system Q Quadrupole QIT Quadrupole ion trap QTOF Quadrupole-Time of Flight RC Replication complex ROS Reactive oxygen species RP Regulatory particle RP Reversed phase RPA Replication protein a Rpn Regulatory particle non-atpase Rpt Regulatory particle trip le-A atpase type I SCX Strong cation exchange SDS Sodium dodecal sulfate (sodium laurel sulfate) SILAC Stable Isotope Labeling by Amino Acids in Cell Culture TEA Triethanolamine TFA Trifluoro acetic acid TNF Tumor necrosis factor TOF Time of Flight ULS Universal linkage system

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14UPR Unfolded protein response UV Ultraviolet VCP Valosin-containing protein YPC Yeast peptone caseamino acid media ZE-FFE Zone electrophoresis-fre e flow electrophoresis

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15 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IDENTIFICATION OF NATIVE 20S PR OTEASOMAL SUBSTRATES IN THE HALOARCHAEON Haloferax volcanii THROUGH DEGRADOMIC AND PHOSPHOPROTEOMIC ANALYSIS By Phillip A. Kirkland December 2007 Chair: Julie A. Maupin-Furlow Major: Microbiology and Cell Science The fact that protein levels in the archaeal cell frequently do not correlate with transcript levels suggests the importance of understanding the details of posttranscriptional regulation. In addition, there is a widespread distribution of proteasome machinery among the archaea and a noticeable absence of other energy-dependent proteases. This s uggests that archaeal proteasomes are key factors in controlling the quality and quantity of proteins within the cell and are a driving force behind protein turnover. At present, the details of how proteins are recognized for degradation by the proteasome in prokaryotes remain poorly understood. The lack of an ubiquitination system in archaea co mplicates the identificati on of native substrates; however, recent developments in degradomics (c omparative proteomics of protease mutants) reveal a promising future in the investigati on of phosphorylated protei ns as potential native substrates. With at least 30 unique phosphoproteins (detected from a total proteome through the use of a phospho-specific dye) that accumulate within the proteo me of a proteasome-activating nucleotidase (PanA) -deficient mutant, eviden ce is mounting that supports our hypothesis that protein phosphorylation plays a ke y role in mediating substrate recognition and degradation by the PanA/proteasome complex.

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16 Our efforts have focused on high-throughput degr adomic and phosphoproteomic analysis of the H. volcanii proteome in order to identify multiple candidates for native degradation targets of the 20S proteasome and the assembly of a list of th e ten most supported putative substrates based on overlapping proteomic data and literary support beyond the archaeal domain. To date, we have identified a wide consortium of proteins w ith likely involvement in the 20S proteasomal degradation pathway. These candidates represent a myriad of cellular functions ranging from cell division, translational and tr anscriptional control to supplem entary proteolysis, circadian rhythm and central metabolism.

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17 CHAPTER 1 LITERATURE REVIEW Introduction The structure and function of the proteasomal machinery across eukaryotic, bacterial and archaeal domains of life will be presented in this chapter. In addition, the modes of protein substrate recognition and degradation by these cellular components and other commonly encountered cellular proteases will be summarized. This review of current literature in the disciplines of microbiology, molecular biology, pr oteomics and bioinformatics will also outline various post-translational modifications made to proteins and how these influence protein stability and degradation. The most current technical approaches for analyzing total cell proteomes and phosphoproteomes in a high-th roughput capacity will also be reviewed. Proteasome Structure and Function Proteasomes are energy-dependent barrel-sh aped proteases that can be found across eukaryotic and archaeal domains and, in a mo re limited capacity, in the eubacterial domain (existing primarily in the actinomycetes) (Volker and Lupas, 2002; Wolf and Hilt, 2004; De Mot, 2007). They are key quality contro l factors in the cell, assuming the responsibility of degrading damaged or misfolded proteins (Kostova and Wo lf, 2003). Additionally, the proteasome exerts a regulatory role in a myriad of essential cellula r functions as evidenced by the mediation of the turnover of proteins functioning in translati on, oxidative damage repair, cell division, DNA replication, metabolism and antigen presentation (Guo et al., 1995; Gillette et al., 2001; Liu et al., 2002; Xu-Welliver and Pegg, 2002; Blanchard et al., 2002; Yamamoto et al., 2004; Baugh and Pilipenko, 2004; Rock et al., 2004; Takahash i et al., 2005; Harada et al., 2005; Chuang and Yew, 2005; Hershko, 2005; Gallego et al., 2006; Pera les et al., 2006). In higher eukaryotic systems, the proteasome has also been shown to play a role in signal tr ansduction, apoptosis and

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18 the development of several neurodegenerative diseases such as ALS, Alzheimers and Parkinsons as well as the genera tion of and therapy for numerous forms of cancer (Kabashi and Durham, 2006; Hung et al., 2006; van Leeuwe n et al., 2006; Olanow and McNaught, 2006; Zavrski et al., 2007; Meyer et al ., 2007; Stapnes et al., 2007). Core Particle Structure and Arrangement The proteasome is often defined by a characteri stic barrel-shaped 20S core particle (CP) and an associated ATPase regulatory component (M aupin-Furlow et al., 2006) The core particle is a series of 4 stacked heptameric rings each co nsisting entirely of either alpha-type or beta-type subunits (Fig. 1-1). In the yeast 26S proteasome, these rings are heteroheptamers, composed of seven different protein subunits wh ile those characterized in proka ryotes have been found to be homoheptameric, consisting of seven identical subunits (Zuhl et al., 1997b; Kaczowka and Maupin-Furlow, 2003; Groll and Huber, 2005). Pr oteasomes of the archaeal domain are often composed of a single alpha-type and one or two beta-type subunits (Zuhl et al., 1997a). There are some exceptions within the haloarchaeal clade, one of which being Haloferax volcanii which possesses two distinct alpha-type subunits and a single beta-type subunit (De Mot et al., 1999; Kaczowka and Maupin-Furlow, 2003) (Fig. 1-2). Proteasomes of the higher eukaryotes are notably more complex due, not only to their mixed subunit arrangements but also to their tendency to produce additional, sub-specializ ed proteasomal complexes such as the immunoproteasome in mammals (Groettrup et al., 2001; Ahtoniemi et al., 2007). Coincidentally, some archaea also produce subspecialized proteomes given a demonstrated shift in proteasome subunit and regulatory particle isoforms, depe ndent on growth phase, as is the case with H. volcanii (Kaczowka and Maupin-Furlow, 2003). The stacked rings of the proteasome core part icle are generally arra nged with the betatype rings paired together formi ng the central proteolytic core. Th is beta ring core is flanked on

PAGE 19

19 both ends by alpha-type rings that form gates. The cylinder measures approximately 15 nm in height and 12 nm in width, according to cr ystallographic analysis of both yeast and Thermoplasma acidophilum CPs (Lowe et al., 1995; Groll and Huber, 2005). Once assembled into the 20S complex, a cross-sectional view reve als the formation of th ree distinct chambers within the proteasome. Two ante-chambers are formed at the interface between the alpha and beta rings. These ante-chambers are entered th rough restrictively narrow gated axial pores (~1.3 nm) which ensure exclusion of folded protei ns and create the necessity for a substrate recognition system which provides unfoldase and ATP-dependent translocation activities making protein entrance into the barrel possible (Groll et al., 2000; Benaroudj et al., 2001; Kohler et al., 2001a). The N-termini of the alpha subunits ar e thought to guard the entrance to the 20S proteasome and require an associat ed regulatory particle (RP) fo r ATP-dependent opening of the pore (Kohler et al., 2001b). Past studies have shown that deletion of the N-terminal region of the alpha subunits notably reduces th e selectivity of the proteaso me and creates an uncontrolled opening for folded substrate passage (Kohl er et al., 2001a; Groll and Huber, 2005). Furthermore, N-terminal acetylation of the alpha subunits has been suggested to enhance the alpha rings ability to restrict unregulated substrate entrance into the proteasome aperture (Kimura et al., 2000; Humbard et al., 2006). The central chamber formed at the interface of the beta rings represents the catalytic center of the proteasome. It houses the N-termin al threonine residues of each beta subunit, which, after exposure due to an autocatalytic processing step, me diate the nucleophi lic attack of substrate peptide backbones and provides the pr oteasome with its tryptic, chymotryptic and postacidic cleavage capabilities (Lowe et al., 1995; Brannigan et al., 1995). This combination of proteolytic activities results in the release of degradation produc ts of 3 to 24 amino acids in

PAGE 20

20 length which can, in turn, be recycled to ami no acids or smaller products by other cellular peptidases (Akopian et al., 1997; Kisselev et al., 2006). Associated ATPase Regulatory Components Proteasomes are energy-dependent proteases that require ATP-hydrolyzing accessory components for the capture, unfolding and transloca tion of protein substrates into their catalytic core. The specific function and composition of e ach associated ATPase is variable among the domains and physiological subsystems; however, so me degree of relatedness is evident when considering the general arrangeme nt of these complexes and thei r roles across all three domains (Fenton and Horwich, 1997; Bukau and Horwich, 1998; Glickman et al., 1999). Categorically, these accessory complexes belong to the AAA+ superfamily of proteins ( A TPases a ssociated with diverse cellular a ctivities) which can also serve th e cell by mediating any number of nonproteolytic tasks ranging from control of the cel l cycle to vesicle-mediated transport (Ogura and Wilkinson, 2001; Lupas and Martin, 2002). The 19S regulatory particle The ATP-dependent regulatory ATPase known as the 19S cap (or PA700), found in eukaryotic 26S proteasomal complexes, is one of the more complex examples of an AAA+ regulatory particle. It consists of 17 subunits which compose two substructures: the lid and the base (Finley et al., 1998; Glickman et al., 1998). Of these 17 structural components, 6 of them are classified as r egulatory p article t riple-A type I (Rpt) prot eins and are of the AAA+ superfamily (Glickman et al., 1998). These Rpt subunits collectivel y compose the base structure of the cap and are responsible for the unfolding of globular prot eins for degradation by the proteasome (Glickman et al., 1998). The base structure also possesses three r egulatory p article n on-ATPase (Rpn) subunits Rpn1, Rpn2 and Rpn10; the latter of which is thought to form a hinge or common anchor point for both s ubstructures (Glickman et al., 1998). The lid

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21 structure is required for ubiquiti n-tagged substrate binding and degradation (Glickman et al., 1998). This lid is composed of the remainder of the 17 subunits of the 19S cap and consists of a heterogeneous combination of Rpn proteins (Rpn3, Rpn5-9, Rpn11 and Rpn12) (Glickman et al., 1998). This full 19S cap struct ure in association with the 20S core particle yields a 30S structure in which the catalytic cylinder is cappe d at both ends or, alternatively, a 26S structure in which only a single CP terminus is cap-a ssociated (Hendil and Hartmann-Petersen, 2004). Proteasome-activating nucleotidases Some members of the archaeal domain also posse ss ATPase regulatory particles that are very similar in structure and func tion to the base structure of th e 19S cap in eukaryotes (Zwickl et al., 1999; Wilson et al., 2000). These RPs are known as PAN ( P roteasomeA ctivating N ucleotidases) and are composed of subunits that are homologous to the eukaryotic Rpt proteins. Generally, archaea encoding PAN proteins produce at least one isoform, however, in some cases, such as Haloferax volcanii multiple subtypes have been identif ied (Reuter et al., 2004). It is evident that PAN proteins are able to both asso ciate with the proteasome and unfold/translocate proteins into the cataly tic chamber without the aid of other components, therefore it is believed that PAN is capable of recognizing and binding select substrates (Goldberg, 1990). In actuality, it is the binding of the substrate to PAN that stimulates ATP hydrolysis, unfoldase activity and subsequent movement of the polypeptide into the proteolytic center of the proteasome (Benaroudj et al., 2003). Characterization of both Methanococcus jannaschii and Haloferax volcanii PAN complexes reveal seemingly conserved features among proteins of this type in the archaeal domain. Both PAN isoforms possess well-conser ved Walker A and Walker B motifs, each of which are predicted to function in magnesium binding and ATP hydrolysis (Wilson et al., 2000; Reuter et al., 2004). In the M. jannaschii model, PAN also appears to assemble into

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22 dodecameric complexes which, in turn, associate with the 20S proteasome (Zwickl et al., 1999; Wilson et al., 2000). While there are examples of proteins with ubiquitin-like folds or ubiquitin-associated domains in prokaryotes, there has yet to be a tr ue ubiquitin protein found to be encoded in any prokaryotic genome; therefore the signal infl uencing substrate binding by PAN is unclear (Bienkowska et al., 2003; Spreter et al., 2005). There are, however, several theoretical possibilities that will be disc ussed in future sections. Bacterial ARC ATPase The Bacteria domain is no exception with regards to having a proteasome as well as an ATPase regulatory component. An assembly known as ARC ( A AA ATPase forming R ingshaped C omplexes) is found in multiple gram-positive bacteria such as Rhodococcus and Streptomyces species (Nagy et al., 1998; Wolf et al ., 1998; De Mot, 2007; De Mot et al., 2007). It was shown that ARC forms homohexameric rings that associate with th e 20S proteasome with general similarity in structure and function to Lon proteases as well as ClpA and ClpX ATPases (Wolf et al., 1998). Interestingly, it was also noted that ARC is a divergent relative of the AAA ATPase family which deviates from the bacter ial branch of proteasom e/proteasome-associated proteins much earlier than most other, more familiar AAA representatives of the eubacterial domain, such as the FtsH homologs (Wolf et al ., 1998). This indicate s a closer relationship, evolutionarily, between ARC a nd archaeal AAA+ ATPases such as PAN compared to other bacterial counterparts. Other proteasome-associated complexes There is an assortment of other ATPase a nd non-ATPase complexes that associate with the proteasome to modulate function in some capacity. One such complex is the COP9 signalosome (CSN) found in higher eukaryotes. CSN is an eight-membered complex (Csn1-8)

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23 which possesses domains that indi cate its relationship to other eukaryotic regulatory components such as translation initiation f actor 3 (eIF3) and the lid of the 19S proteasome cap (Wei and Deng, 2003; Harari-Steinberg and Chamovitz, 2004) From a functional standpoint, CSN plays vital roles in both plant and in sect development and may act as a switchboard that is responsible for the coordination of protein kinases and kinase substrates to ubiquitin ligase proteins, linking protein phosphor ylation to the poly-ubiquitin ation-proteasomal degradation pathway (Harari-Steinber g and Chamovitz, 2004). Another notable group of proteasome-related proteins are the cell division control 48 (Cdc48) homologs, which can be found across a ll three domains. In eukaryotes, these AAA ATPases function in multiple ways, including e ndoplasmic r eticuluma ssociated protein d egradation (ERAD) and nuclear envelope forma tion (Rabinovich et al., 2002; Bar-Nun, 2005). Eukaryotic Cdc48/VCP/p97 has al so been shown to exhibit segregase activity by removing ubiquitinated substrates from larger complexes possessing unmarked subunits and unfolding them for proteasomal degradation (Golbik et al., 1999; Jentsch and Rumpf, 2007). In the archaea, Cdc48 homologs may operate in the proteasomal substrate recognition and unfolding aspect where PAN complexes are absent (MaupinFurlow et al., 2004). Cd c48 homologs such as valosine-containing protein (VCP), valosine-containing protein-like ATPase from Thermoplasma. acidophilum (VAT) and p97 all form stacked he xameric ring structures that are somewhat similar to other ring -shaped nucleotidases of the AAA superfamily (Frohlich et al., 1995; Rockel et al., 1999). Protein Degradation In the past two decades, considerable insight has been gained into how energy-dependent proteolysis works throughout all domains. Much of the mach inery has been identified and significant progress has been made towards resolv ing details of assembly and mechanistics.

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24 More attention is now turning towards gaining a clearer understanding of the what and why aspects of proteolysis. Questions still remain about the structural motifs or modifications that make proteins recognizable to these degradati on systems, which protei ns are under control of these systems and what effect does the degrad ation of these protei ns have on global cell physiology. The following sections provide some in sight into what is known on these topics to date. Protein Degradation Signals The Eukarya Bacteria and Archaea domains have adapted an array of mechanisms designed to ear-mark protein substrates for de gradation by a diverse collection of proteases within the cell. Often these protein substrates are designated by some form of post-translational modification; such is the case with eukaryot ic polyubiquitin-tagging systems. Other times, intrinsic signals are used to promote recogniti on of proteins destined to be destroyed or processed. These signals include internal seque nces such as PEST and conserved degrons as with the N-end rule in bacteria Yet another strategy for promo ting substrate degradation is the use of molecular adapters to tether substrate proteins to their appr opriate protease complex (Chien et al., 2007). This has been demonstrated in Escherichia coli where RssB and SspB serve as adapters mediating interacti on and degradation betw een ClpX and substrate proteins such as sigma factors (Levchenko et al., 2005; Chien et al., 2007). Development of these regulatory systems may be necessary for controlled regulat ion of certain cellular subsystems through the careful selection of proteins to be overturned. Such regulatory di scipline is demonstrated in the controlled degradation of specific targets involved in cell division of higher plants and animals or the degradative processing of proteins for presen tation by major histocompatability complexes in the immune system (Rock et al., 2004; Hershko, 2005). In many cases, the signaling system for degradation is less regulated and complex. Many proteins are targeted for destruction simply

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25 through the act of becoming misfolded or da maged. Oxidation and improper exposure of internal, hydrophobic residues may provide the cue for proteolysis (Grune et al., 2003). This section will discuss known mechan isms for introducing substrate into a proteolytic system as well as specific proteins and functional categorie s that are targeted by the proteasome and the influence these events have on the cell. Ubiquitination and other PTMs Post-translational modification is used by the cell to toggle between functional states and to create sub-populations of prot eins. One of the most commonl y studied sub-proteomes in the eukaryotic cell is that which is designated for proteasomal degradation by ubiquitination. Ubiquitin is a 76-residue polypeptide that is presen t in virtually all eukaryotic cells (Ciechanover and Schwartz, 1994). The ubiquitination of prot eins is primarily managed by a system of activating, carrier and ligase comp onents collectively referred to as the ubiquitin cascade. The process of ubiquitination starts with the activ ation of the C-terminal glycine of the ubiquitin polypeptide by the first member of the cascade system, E1 (Ciechanover and Schwartz, 1994). The E1 or ubiquitin-activating enzyme converts the glycine residue into a high-energy thiol-ester intermediate in an ATP-dependent manner (Ciech anover and Schwartz, 1994). The next step in the cascade involves the ubiquitin ca rrier protein E2 which removes the activated unit from E1 and delivers it to an ubiquitin ligase complex known as E3 (Ciechanover and Schwartz, 1994). Subsequently, the E3 complex carries out the last step in the ubiquitin li gation process; addition of ubiquitin to the amino side chains of lysine residues within the target protein or continued addition of ubiquitin subunits to a growing polyubiquitin chain (Ciechanover and Schwartz, 1994; Gilon et al., 1998). In some cases, ubiquitin is transferred to the substrate directly by E2, however, this variation of the ligation proce ss usually involves monoubiquitination and does not

PAGE 26

26 usually serve a role in proteolysis but rath er in functional modifi cation (Ciechanover and Schwartz, 1994). There are numerous examples in bacteria, yeast and higher eukaryotes of structural signals that impart instability of proteins and direct them into a degradation pathway (Gilon et al., 1998). An interesting example of sequence and/or structural si gnals that influence ubiquitination and degradation is the destruction box (D-box) found in mitotic cyclins. This 9amino acid sequence found in the N-terminal domain has been shown to dest abilize proteins and lead directly to ubiqui tin ligation and degradation by the proteasome in mammalian cells (King et al., 1996). Similarly, in Saccharomyces cerevisiae the C-terminal domain SINNDAKSS of the alpha-factor pheromone receptor Ste2p ha s been determined to be necessary for ubiquitination and internalization into the e ndomembrane system where vacuolar degradation follows (Hicke et al., 1998). Destabilizing motifs are not always constitutively available for ubiquitination as demonstrated by the Unfolded Protein Response (UPR)-associated transcription factor Hac1p, which undergoes alte rnative splicing at the mRNA le vel to preferentially expose either a stabilizing C-terminus or a C-termin al domain which leads to degradation (Cox and Walter, 1996). While intrinsic destabilizing signals in bot h eukaryotes and bacteria appear to be commonplace, they are not the only aiding forces at work in the cells effort to recognize and overturn protein substrates. There are numerous examples of both pr otein phosphorylation and acetylation serving as modulators of ubiquitination and degradation in eu karyotes. G proteincoupled receptors such as the Ste2p mentioned previously have been demonstrated to be hyperphosphorylated at a C-terminal domain; an ev ent that appears to be necessary for ubiquitin ligation and subsequent degrada tion (Hicke et al., 1998). A mu ch more common example of the

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27 cooperative phosphorylation-ubiquiti nation relationship is found in the circadian regulatory systems of plants and higher eukaryotes. In Arabidopsis, the phosphorylated form of the CK2 regulatory subunit CKB4 in its light-dependent biological clock syst em is preferentially ubiquitinated and degraded in a proteasomedependent manner (Perales et al., 2006). Interestingly, phosphorylation has also been shown to have the opposite effect on protein stability when considering its role in stabiliz ing the small G protein RhoA from ubiquitination and proteasomal degradation through modification of serine 188 in a vascular muscle cell model (Rolli-Derkinderen et al., 2005). The in terplay between protein phosphorylation and ubiquitination/degradation is even more evident when considering the COP9 signalosome (CSN) and its coordinated association with a multitude of kinases such as CK2 and PKD, which are known to phosphorylate the p53 tumor suppressor and enhance interaction with the Mdm2 ubiquitin ligase (Harari-Steinberg and Chamovitz, 2004). In contra st, the CSN also coordinates kinases for the modification of c-Jun; a subun it of the AP-1 transcri ption complex (HarariSteinberg and Chamovitz, 2004). This event serves to stabilize c-Jun against ubiquitination and degradation by the proteasome (Harar i-Steinberg and Chamovitz, 2004). In addition to protein phosphorylation, acetylat ion also affects the stability of certain proteins in the presence of an ubiquitin-proteasome degradation system. In 1989 it was discovered that N-terminal acetylati on of proteins in reticulocyte ly sate interacted directly with ubiquitin activating enzymes and served as a beacon for ubiquitination and subsequent proteasomal degradation (Mayer et al., 1989). Several years later, it was also discovered that Nterminally acetylated proteins required a separate factor for interaction with the ubiquitin/proteasome machinery (G onen et al., 1994). This factor was determined to be the alpha subunit of the translation elongation factor EF-1 and was ultimately determined to be an

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28 isopeptidase that presumably functions to trim the N-terminally blocked proteins into more manageable or recognizable fragments for de gradation by the 26S pr oteasome (Gonen et al., 1996). N-terminal acetylated substrates of the pr oteasome may also be recognized by an entirely separate group of ubiquitin ligases which rec ognize internal sequences downstream of the modified N-terminus (Bartel et al., 1990). The N-end rule It has been acknowledged for a number of y ears that there exists a real relationship between the N-terminal amino acid residue of a give n protein and its stability in the presence of proteolytic machinery (Varshav sky, 1996). This phenomenon is known as the N-end rule and has been demonstrated in yeast, mammals, plants and even bacteria (Varshavsky, 1996). The Nend method of bacterial substr ate recognition is well conserve d among eukaryotes, despite the major differences in molecular components that comprise each system (Mogk et al., 2007). As a whole, the N-end protein dest abilization/degradation pathway is surprisingly extensive and meticulously regulated. The eukaryotic N-end rule pathway naturally represents the more complicated version compared to bacteria as it serves as an in termediary substrate recognition system for the exclusively eukaryotic ubiquitin/ proteasome degradation system. It consists of a hierarchical Nterminal degradation signal (N-degron) and a sp ecialized subset of ubiquitin ligases (N-recognin) in conjunction with 26S proteas omal machinery (Varshavsky, 1996; Mogk et al., 2007). Destabilizing residues for mammalia n and yeast cells have been cat egorized as either type one (Arg, Lys and His) or type two (Phe, Leu, Trp, Ile and Tyr), which corresponds to one of two ubiquitin ligase (N-recognin) binding sites (Varshavsky, 1996) Degradation signals in eukaryotic systems are typically accompandied by internal lysine resi dues to accommodate

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29 ubiquitin chain formation (Mogk et al., 2007). Additionally, plant systems expand their list of destabilizing residues to include basic and aromatic amino acids as well (Mogk et al., 2007). Co-translational, methionine aminopeptidasemediated removal of the N-terminal and naturally stable methionine residues (or formyl -methionine in bacteria) from newly synthesized proteins occurs only in the pr esence of a stable penultimate residue (Gly, Cys, Ala and Ser) (Mogk et al., 2007). There are, however, other methods employed by the cell to regulate the stability of certain proteins based on terminal residue exposure. One alternative method includes the endoproteolytic cleavage of a protein to expo se an otherwise internal degron (Mogk et al., 2007). A variation of this mechanism has been obs erved in the processing of viral polyproteins to generate destabilized produc ts such as the HIV-1 integr ase or the Sindbis virus RNA polymerase (de Groot et al., 1991; Mulder and Muesing, 2000). In eukaryotes, the canon of N-degron signals is a three-tiered hier archy. The first tier, consisting of primary destabilizing residues, requires no modification other than exposure through proteolytic cleavage or methionine removal and binding to N-recognin or E3 (Varshavsky, 1996). The second tier, termed th e secondary destabilizing residues include Asp and Glu and, in the case of mammalian cells, Cys (Varshavsky, 1996). These residues become destabilizing only after modification by an arginyl-tRNA-prot ein transferase (R-transferase) (Varshavsky, 1996). This category gives way to a third tier known as te rtiary destabilizing residues which include Asn and Gln (Varshavsky, 1996). Like the secondar y destabilizers, these residues also require exposure to a modifyi ng enzyme in order to become destabilizing themselves. This process is mediated by th e enzyme N-terminal amidohydrolase (Varshavsky, 1996).

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30 Interestingly, the N-end rule is rather we ll conserved between bact eria and eukaryotes despite the absence of an ubiquitin system or, in most cases, an actual proteasomal degradation system in bacteria. Regulated proteolysis in bacteria is often regulat ed by the Hsp100 category of proteolytic components such as ClpA and S (Mogk et al., 2007). These components either interact directly with selected substrates or serve as a platf orm for adapter molecules which intermediate the binding of destabilized protein substrates (Mogk et al., 2007). Some differences are present, however, when considering the details of the N-terminal degradation signal itself. In bacteria, the destabilizing amino acids differ from those of eukar yotes and only include Phe, Leu, Trp and Tyr (Varshavsky, 1996; Mogk et al., 2007). In addition, the tiered system of substrate destabilization is also not as extensive when compared to its e ukaryotic counterpart and varies in terms of amino acids included in each group. In bacteria, secondary destabilizers include Arg and Lys and are converted to a destabilizing fo rm by way of the leucine/phenylalanyl t-RNAprotein transferase enzyme (Varshavsky, 1996). In contrast, tertiary dest abilizing residues have not been identified at this point in the prokaryot ic division, creating a less extensive system for proteolytic substrate selection and destabilization. PEST sequences Since the mid-1980s, protein modifications and N-terminal composition have been recognized as proven mechanisms for proteoly tic substrate recogniti on; however, as the collection of energy-dependent proteolytic substr ates grew, it became evid ent that alternative mechanisms for making stable proteins vulnerable to degradation must exist. Collective primary sequence analysis of important cellular compone nts such as transcript ion factors, signal transduction proteins and kinases have revealed a pattern of re occurring sequences of definite order and composition that have come to be kno wn as PEST sequences. Examination of key factors such as p53, Jun and Fos tr anscription proteins has revealed consistent regions that are

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31 rich in proline (P), glutamate (E), serine (S ) and threonine (T) (Rechst einer and Rogers, 1996). These PEST sequences are uninterrupted by positiv ely charged amino acids and typically appear in the form of 12 or more hydr ophilic residues (Rechsteiner and Rogers, 1996). They invariably contain at least one proline and at least one glutamic or aspartic acid as well as at least one serine or threonine (Rechsteiner and Rogers, 1996). These hydrophilic stretches are also typically flanked by histadine, lysine or arginine residues (Rechsteiner and Rogers, 1996). While PEST sequence composition is well-cons erved, there is no apparent conservation of secondary structure among the sequences (Rechs teiner and Rogers, 1996). In fact, educated predictions have been made that, given thei r dominantly hydrophilic nature, these sequences would likely form soluble protrusions or tails as opposed to compact st ructural organization (Rechsteiner and Rogers, 1996). Because the PEST sequence is ever-present within the protein, it was concluded that covalent modification of this sequence wa s the determining factor in destabilization of a protein substr ate (Garcia-Alai et al., 2006). PEST motifs were discovered to be finely tunable by phosphorylati on of the internal serine and threonine residues and mutational analysis and NMR spectroscopy of substrate protei ns revealed that entry into a proteolytic pathway correlated to thermodynamic instabil ity provided by phospho-pert urbation rather than the phosphate moiety itself (Gar cia-Alai et al., 2006). The PEST sequence motif has been demonstrat ed to operate both in the presence and absence of a ubiquitin-tagging system (Kornitzer et al., 1994; Yaglom et al., 1995; Rechsteiner and Rogers, 1996). In yeast, bot h the transcriptional activator Gcn4 and the Cln3 cyclin protein are degraded by the ubiquitin-proteasome degradation pathway as a result of the presence and phosphorylation of internal PEST sequences (Korni tzer et al., 1994; Yaglom et al., 1995). E2 proteins from papillomavirus particles also co ntain a PEST sequence within a flexible loop

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32 region of its structure, which directly influen ces its half-life in the presence of the ubiquitinproteasome pathway (Garcia-Alai et al., 2006). The influence of PEST sequences on protein stability was established through both mutational analysis and tran sposition of PEST tags to otherwise stable proteins. A favorite model of PEST-tagged substrate degradation is the mouse ornithine decarboxylase (m ODC). Removal of the mODC C-terminal PEST sequence increased its half-life dramatically and transfer of th e mODC PEST sequence to the more stable Trypanosoma ODC imparted extreme instability in the presence of 26S proteasomes (Ghoda et al., 1989; Ghoda et al., 1990). Mammalian ornithine decarboxylase, however, differs from other eukaryotic PEST substrates in that it does require proteasomal degr adation machinery and antizyme but its degradation is not mediated by ubiquitin (Murakami et al., 1992; Murakami et al., 1999). The transcription factor Jun, in similarity, also does not require ubiquitin for its PEST-dependent turnover in the cell (Rechsteiner and Rogers, 1996). Oxidative damage and hydrophobic patch exposure The intrinsic properties that make proteins su sceptible to proteolysis have been a subject of interest for more than 25 years and over time it has become apparent that not all degradationpromoting modifications made to proteins are deliberate. Over time, an aerobic cell generates oxidative free radicals or becomes exposed to chemical oxidants which can damage and contort proteins and necessitate their de gradation (Grune et al., 2003). It is observed that proteins experiencing oxidative stress undergo a biphasic mode of degradation wh ereby initial exposure of hydrophobic residues prompt an increasing su sceptibility to prot easomal degradation consistent with an increase in oxidant concentr ation (Grune et al., 2003). After an apex in degradation is reached, exposed hydrophobic patches begin to facilitate pr otein aggregation and conceal the recognition motifs from pr oteases (Grune et al., 2003). Th is leads to a return to basal levels of degradation (Grune et al., 2003). Th e specific motifs which ma ke proteins vulnerable

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33 to proteasomal degradation are unclear but re present a long-standing que stion in the study of protease substrate degradation. There is consider able evidence that exposu re and/or oxidation of individual amino acids can be a major determining factor for degradation of a particular protein. Popular examples include the degradation of glut amine synthetase after oxidation of methionine residues and the relationship between tyrosine ox idation and degradation of RNase A (Levine et al., 1996; Lasch et al., 2001). Evidence also strong ly supports changes in secondary and tertiary structure as cues for recognition by degradative machinery. Lasch and colleagues were able to establish, through work with RNase A, that oxida tive modification of certain domains caused as much as 50% unfolding which was observed to si gnificantly increase proteolytic susceptibility (Lasch et al., 2001). As supporting evidence fo r the relationship between protein unfolding and degradation, a correla tion between the proteolytic suscep tibility and overall hydrophobicity of multiple proteins has been shown through primary sequence analysis, hydrophobic interaction chromatography and fluorescent detection of hyd rophobic surface regions (P acifici et al., 1993; Giulivi et al., 1994; Levine et al., 1996; Levine et al., 2000). This proposed mode of substrate recognition is further supporte d by the observation th at the proteasome gives preference to binding hydrophobic and aromatic amino acids with in substrate protein se quences (Hough et al., 1987). In addition to intrinsic sequence characteristic s which lead proteins into a proteolytic system, other means by which hydrophobicity ca n modulate protein stability have been discovered. The most notable exam ple of a mechanism of this type is the ssrA tagging system in E. coli Robert Sauer and colleagues observed that tr anscripts which lacked terminal translation stop sequences were modified with a terminal sequence coding for an 11-residue tag that is predominantly hydrophobic in nature (AANDE NYALAA) (Keiler et al., 1996). They

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34 hypothesized that this system s ubstituted for a termination sequence and made provisions to rid the cell of the delete rious effects of a mis-transcribe d gene by marking its product for degradation in the pre-translational stage. This system is likened to another terminal tagging system; the Tsp protease-dependent degrada tion of substrates po ssessing the non-polar Cterminal recognition sequence WVAAA (Keiler et al., 1995). Known Substrates and Cellular In fluences of the Proteasome There is a broad spectrum of proteins, repr esenting a multitude of cellular subsystems that are degraded by proteasomes. Proteasomes and accessory ATPase components control the how and when of cell divison, help to m odulate the appropriate cell response to changing metabolic conditions and serve as a major influence in the ability of the cell to express certain crucial genes. This section will discuss, in greater detail, the role of the 26S proteasomal degradation system in manipulating three essen tial and well-characteriz ed functions of the eukaryotic cell: division, transc ription/translation and metabolism. Cell cycle One aspect of eukaryotic cell division, DNA re plication, has been studied in great detail over the years. It has become evident that the process of cell division an d the assembly of the necessary pre-replication complex (pre-RC) is very well conserved in the eukaryotic domain, from budding yeast to human cell lin es (Chuang and Yew, 2005). It has also been observed that the 26S proteasome system is a vital part of DNA replication as well as other phases of cell division. The assembly of the pre-replication complex involve s the assembly of 6 origin recognition complex proteins (ORC), cell division control protein 6 (Cdc6) and other components such as mini-chromosome maintenance (MCM) proteins (Blanchard et al., 2002). A key component of the pre-RC is Cdc6, which has been found to be a target of the proteasomal degradation system in both yeast and mammalian cel ls (Blanchard et al., 2002; Luo et al., 2003).

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35 This pre-replication element is primarily res ponsible for coupling DNA re plication to mitosis by helping to assemble pre-initiation complexes in late mitosis and working to maintain these complexes until replication begins in S phase (Bla nchard et al., 2002). The depletion of Cdc6 or the addition of geminin, an origin licensing inhibitor, causes uncoupling of mitosis and DNA replication and has been demonstrated to cause unwarranted mitotic behavior in Xenopus egg extract nuclei (Hekmat-Nejad et al., 2000; Michae l et al., 2000; Blanchard et al., 2002). The degradation of Cdc6 in both Saccharomyces cerevisiae and Arabidopsis thaliana has been shown to be mediated by both caspases and proteasomes in response to apoptos is-inducing factors such as TNFand cycloheximide (Blanchard et al., 2002). Another essential component of the euka ryotic DNA replication system is the proliferating cell nuclear antigen (PCNA). PCNA is a sliding cl amp protein which functions to keep DNA polymerase in contact with the DNA helix and to enhance processivity of DNA synthesis (Yamamoto et al., 2004). It has also been found to play pivo tal roles in DNA repair, apoptosis and cytosine methylation (Chuang et al., 1997). Recent evidence reveals that a homolog of PCNA (OsPCNA) found in rice plants ( Oryza sativa ) interacts with the Rpt6 ATPase subunit of the proteasomal 19S cap (Yamamoto et al., 2004). Furthermore, it was determined that OsPCNA was degraded by the ubiquitin/prot easome system over an eight-hour time course in vitro and in vivo and that the addition of proteasome-speci fic inhibitor MG132 consistently stabilized OsPCNA levels in the cell (Yamamoto et al., 2004). More recent an alysis of PCNA in Xenopus egg extracts has revealed that PCNA is vital to the temporal regulation of DNA replication through binding to th e cyclin-dependent kinase (CDK) inhibitor Xic1 and promoting its degradation by the ubiquitin/prot easome system (Chuang and Yew, 2005).

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36 Perhaps the most studied and arguably th e most important proteasomal substrate discoveries to date are the cyclin proteins, considering that their discovery as substrates directly associates proteasomal function to cancer developm ent. Cyclin proteins from the A, B, D and E categories as well as their regulatory cyclin-depe ndent kinases such as Cdc2 have been shown to be targeted for destruction by the ubiquitin-proteasome pathway in yeast and mammals as a means of controlling progression through the G1 phase (Cyclin D, Cdk4, Cdk6), S phase transition (Cyclin E, Cdk2), S phase (CyclinA, C dk2) and G2 or Mitotic phases (Cyclin A and B and Cdk1) (Casanovas et al., 2004; Fung et al., 2005; Lahne et al., 2006 ; Tanaka et al., 2006; Aggarwal et al., 2007). Proteasomal inhibitory drugs such as the newly developed Velcade (bortezomib) are designed with the purpose of limiting the proteasome itself as a means of stabilizing cyclins and cyclin-dep endent kinases in an effort to inhibit rampant cell division and may prove to be an invaluable tool in the treat ment of many types of can cer (Ishii et al., 2007). Transcription and translation DNA transcription and protein s ynthesis are also major target s for proteasomal regulation in eukaryotes. Regulation of these functions by the ubiquitin/proteasome pathway has been demonstrated through the degradation of tran scription factors, tr anslation elongation and specifically initiation factors, ribosomes and RNA polymerase II (Baugh and Pilipenko, 2004; Takahashi et al., 2005; Ribar et al., 2006; Carl e et al., 2007; Lam et al., 2007). Additionally, interference with proteas ome degradation has also been demo nstrated to induce the accumulation of 90S preribosomes, alter the dynamic properties of a number of processing factors, slow the release of mature rRNA from the nucleolus, and lead to the depletion of 18S and 28S rRNAs (Stavreva et al., 2006). A popular characterization of the relationship between the ubiquitin-proteasome system and transcriptional control is the degradation of activator protein 1 (AP-1)-family pre-oncogene

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37 transcription factors c-Jun and c-Fo s. It has been demonstrated in recent years that these and other members of the AP-1 family are ubiquitinated and degraded by proteasomes in mammalian cells (Ito et al., 2005; Matsumot o et al., 2005; Carle et al., 2007) These components, along with a host of other AP-1 family protei ns have been demonstrated to e xhibit control over such things as cell differentiation, apoptosis and tumorgenesis often in response to inflammatory conditions (Matsumoto et al., 2005). The proteasome regulates overa ll protein levels not only th rough degradation of damaged or unwanted proteins but also through selective degradation of protein synthesis components. An interesting example of this regulatory duty is the demonstrated proteolysis of translation initiation factors eIF4F and eIF3 as a means of affecting pre-initiation complex formation on various cellular and viral mRNAs which results in an overall decrease in translation (Baugh and Pilipenko, 2004). Other examples of transl ational control carried out by the ubiquitinproteasome system include degradation of ribosomal proteins themselves within the nucleus of the cell (Lam et al., 2007). This process is thought to balance the ribosomal protein overexpression in the nucleoplasm which prevents ri bosomal protein availability from becoming the limiting factor in initiation comp lex formation (Lam et al., 2007). Proteasomes are not simply destructive in this respect, howev er, considering the fact that they have also been indicated as key factors in multiple steps in the ribosome biogenesis process and ultimately, the assembly of the 90S pre-ribosome (Stavreva et al., 2006). Finally, a proteasomal effect on transcription/tran slation not to be overlooked is the ubiquitin-dependent degradation of RNA pol ymerase II, most notably in the yeast Saccharomyces cerevisiae (Ribar et al., 2006) In cells exposed to UV light or DNA-damaging chemical agents, polyubiquitination of Rpb1, the largest subunit of RNA pol II, leads to the

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38 subsequent proteasomal degradation of the RNA pol II complex and serves as an obvious control point for preventing transcription of damaged genes (Ribar et al ., 2006). In this capacity, the ubiquitin-proteasome system is integral to adap tive response to acute environmental stresses and can confer flexibility to the cell in a harsh or changing environment. Cellular metabolism and stress response While the regulatory capabilities of the 26S proteasomal system are often thought of as being most commonly exploited at the control points of the cell, such as cell division or transcription, ubiquitin conjuga tion and proteasomal degradation also control metabolism and conditional responses. Those who study the 26S pr oteasome in a capacity other than cell cycle control or transcription factor degradation have discovered a whol e new level of involvement of the proteasome in processes like light-dependent circadian rhythmicity, lipid and cholesterol metabolism, carbon metabolism and metabolic switching in response to changing cellular conditions like oxidative stress. Arguably the most studied function of 26S proteasomes, as it relates to metabolic regulation, is the turnover of light-dependent cir cadian regulatory elements for the establishment of an oscillatory pattern. This topic has been explored in a wide array of model systems including photosynthetic algae, yeast, plants, mice and humans. In Arabidopsis thaliana it was determined that, in contrast to previous notions about the circadian system in this organism as a transcriptional feedback loop, the oscillatory pattern of its biorhyt hmic system was the result of post-translational modification and nuclear loca lization of the regulator y CKB4 protein through phosphorylation and subsequent ubiquitination and degradation by proteasomes (Perales et al., 2006). This concept is very much in line with current models of how mammalian circadian systems appear to function. While mammalian ci rcadian rhythms do appear to function in a negative transcriptional feedback loop, major commonalities do exist between plant and algal

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39 systems and higher eukaryotes. One such similar ity is exemplified thr ough analysis of mouse CRY2 proteins and PER2 proteins in Xenopus egg extract and the fact that they both require phosphorylation for effective localiz ation in the nucleus (Harada et al., 2005; Gallego et al., 2006). In all cases, the phosphoryl ation/localization process also appears to destabilize the proteins in the presence of th e ubiquitin/proteasome system and thus serves to establish and maintain a tightly controlled 24 -hour internal clock (Harada et al., 2005; Gallego et al., 2006; Perales et al., 2006). Specific pathways for lipid and carbon metabol ism in eukaryotes have also been shown to be regulated by 26S proteasomes. The rate-det ermining enzyme for cholesterol biosynthesis, hydroxyl-methyl-glutaryl coenzyme A (HMG CoA) reductase, is degraded by the ER-based ubiquitin/proteasome pathway in mammalian cel ls (McGee et al., 1996; Song and Debose-Boyd, 2006). Further work in this area has identi fied the membrane-bound ubiquitin ligase, gp71 and its necessary cofactor, Ufd1, res ponsible for the endoplasmic reticulum-associated degradation (ERAD) of this essential chol esterol synthesis enzyme; a target of great interest in the development of new therapies for high choleste rol conditions and heart disease (Cao et al., 2007). Eukaryotic 26S proteasomes have been indi cated in limited regulation of various carbon metabolism pathways in the cell. One such case is the known degr adation of glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (G APDH) by proteasomes (Sukhanov et al., 2006). The proteasomal destruction of this enzyme is al so reported to be significantly enhanced by the presence of reactive oxygen species (ROS), ex emplifying the role of the ubiquitin/proteasome system in protein turnover as a means of re gulating central carbohydrat e metabolism as well as helping to coordinate a response to deteriora ting cellular conditions (Sukhanov et al., 2006).

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40 Additional modes of proteasomal regulation of primary metabolism have been suggested in which metabolic sensors such as the Arabidopsis hexokinase-1 glucose sensor associate with regulatory components of the 26S proteasomal sy stem (namely the Rpt5b subunit of the 19S cap) and work in concert with other regulatory elements to control the transcri ption of specific carbon metabolism genes (Cho et al., 2006). In addition to its various roles in assorted metabolic pathways, transcription, protein synthesis and cell cycle progression, one vitally important role of the ubiquitin/proteasome function is its response to environmental challe nges such as oxidative stress, osmotic or temperature shock and UV exposure. Multiple comparative proteomic studies have been conducted on cells grown under stressing conditions and have consistently revealed the increased appearance of structural and regul atory proteins of the proteaso me, including instances where the proteasome itself is inhibited through chemical modification (Zang and Komatsu, 2007; Zhang et al., 2007a). Focused investigation into the precise mechanisms for stress adaptation has revealed transcriptional upregulation of 26S proteasom al machinery during heat shock, enhanced proteasomal activities duri ng prolonged cold shock (4oC) and proteasomal degradation of oxidatively damaged proteins within the cytoso l, nucleus and endoplasmic reticulum (Davies, 2001; Perepechaeva et al., 2006; Szustakowski et al., 2007). Exposure to UV radiation or DNAdamaging chemical agents has also been an i nducing factor in proteas ome-mediated damage control. As observed in the degradation of DNA polymerase delta subunit p12, the proteasome is proactive in slowing rep lication where DNA damage has occurred (Zhang et al., 2007b). Interestingly, eukaryotic proteas omes have also been identified as key figures in cell aging. Experiments in Caenorhabditis elegans have shown that post-tran scriptional changes in the levels of forkhead transcription factor DAF16, mediated by the proteasome, affect cellular

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41 lifespan by a distinguishable margin (Li et al ., 2007c). Complementary experiments also indicate that over expression of the DAF-16 prot ein or disruption of the RLE-1 ubiquitin ligase (E3) that is responsible for marking DAF-16 for destruction, has a prolonging affect on the lifespan of cells (Li et al., 2007c). Work done by other groups indicates th at FoxO transcription factors and mammalian orthologs of the C. elegans DAF-16 are also controlled by the ubiquitin/proteasome pathway. Furthermore, FoxO controls such vital cellular processes as apoptosis, cell cycle control, stress response, DNA damage repair and cell differentiation (Huang and Tindall, 2007). Proteomics and Biological Mass Spectrometry As the age of genomics continues, we are accruing overwhelming amounts of information about the coding capac ities of organisms across the fu ll spectrum of life on earth. With complementing transcriptome technology ava ilable, we can now start to understand which genes are constitutive and which are regulated in response to varying conditions encountered by the cell, however, this is only a portion of the information required for comprehensive understanding of the cell. The fact that transcription of a gene does not equal expression is often overlooked but the fact remains that a large portion of the work car ried out within a cell or by a cell is facilitated by its toolbox of proteins. When considering this, the need for highthroughput characterization of prot ein expression profiles become s apparent. Basic proteomic techniques have opened the door to this type of information and continue to expand and evolve to meet the needs of the biology community a nd to answer the questions that arise from transcriptional analysis. With the advent of advanced mass spectrometric devices and protein resolution techniques, we are ab le to delve deeper into our curiosity about changing protein levels, adapting metabolic pathways and, perh aps most importantly, the added complexity afforded by endless post-translational modification possibilities. This section outlines the current

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42 state of proteomics and explores the docum ented application of many of these modern techniques across a ll domains of life. The Current State of Bottom-Up Proteomics Bottom-up proteomic strategies are most comm only associated with typical large-scale, high-throughput analyses. They involve enzymatic digestion of protein prior to mobilization and analysis by mass spectrometry. These methods are becoming increasingly important as dramatic advances in genomics and systems biology are made. A bottom-up approach allows investigators to compare expression profiles, an alyze post-translational modification sites and identify disease biomarkers with increasing sensiti vity. Bottom-up proteomics is not without its drawbacks, however. One major limitation is the fact that protease-medi ated digestion coupled with MS/MS analysis rarely, if ev er, produces full coverage of a pr otein. Also, peptides that are generated through enzymatic digesti on may be biased in one directi on or another, with respect to ionization and MS flight (e.g. due to post-translational modifica tion, size or native charge). These features represent inherent limitations to this approach. While the alternative top-down approach (focused MS analysis of whole proteins) satisfies some of these bottom-up shortcomings, its primary drawback is the continua l struggle to mobilize fully intact proteins for MS fragmentation. Experts in th e fields of proteomics and bi oinformatics feel as though both categories of proteomic analysis are gradua lly moving towards a hybrid strategy in which specific protein domains or struct ural features are cleaved and an alyzed in an abbreviated topdown analysis which may rely upon classical bot tom-up pre-MS methodologies combined with conventional top-down MS/MS anal ysis with optimized desorpti on and ionization steps. The following sections explore standard techniques a nd recent advancements in the field of bottomup proteomics and comparative expression profile analysis.

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43 Protein/peptide separation The success of any complex proteomic analysis relies, in large part, on the ability to effectively separate proteins and peptides base d on specific characteristics such as isoelectric point or covalent modification. In mass spectrometry, peptid e scans are improved through subfractionation of complex mixtures as a means of eliminating signal masking and bias in ionization and detection of dominant peptides From its genesis in 1975 (O'Farrell, 1975) through the modern era of proteo mics, two-dimensional polyacryl amide gel electrophoresis or 2D PAGE has been a consistent and reliable method for effective separation of proteins in complex mixtures and has served as the first link in a chain of proteomic evolution that continues today (Arrigoni et al., 2006a). Two-dimensiona l PAGE traditionally separates proteins based on isoelectric point (pI) and molecula r weight (MW) and has the abil ity to resolve as many as 5,000 proteins simultaneously with pI resolution as fine as 0.001 units (Gorg et al., 2004). Additionally, with the advent of advanced pre-fractionation met hods and a collection of reliable ultra-high sensitivity fluorescent protein stains such as Krypton, Deep Purple, Flamingo and Sypro Ruby, minimal protein detection limits have been reported at less than 1 ng of protein (Gorg et al., 2004; Harris et al., 2007). In fact with the development of ultrazoom focusing methods for the analysis of proteins with ex treme pI shifts, a minimal detection limit of 300 protein copies per cell has been demonstrated in B-lymphoma cell lines (Hoving et al., 2000). As in-gel enzymatic digestion procedures have become routine in proteomics, this method of protein separation has become amenable to dow nstream mass spectrometric analysis and has reaffirmed its role as the workhorse of modern proteomics. With the obvious limitations of in-gel proteomi c separation such as protein insolubility, sub-detectable abundance and failu re to resolve within a given set of mass and pI parameters, attention in recent years has di verted to the development of practical, high-throughput liquid

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44 separation methods that would eliminate these problems and provide a more reliable and unrestricted view of full proteomes. One genera l approach to proteomi c pre-fractionation that has become standard protocol for many proteomic investigators is multidimensional protein identification technology or MudPIT. This appro ach to pre-MS separation requires little sample manipulation and therefore limits opportunity for sample loss or contamination. The process is generally performed with fully digested protei n samples which are loaded onto a fused silica microcolumn (50-100 m inner diameter) packed with multiple matrix materials in tandem that are non-redundant with regards to the peptide se lection properties (Delah unty and Yates, III, 2005). In theory, MudPIT can be carried out over an infinite linkage of complementary enrichment matrices but current applications typically utilize a biphasic or triphasic chromatography scheme with strong cation excha nge (SCX) as a higher-capacity first phase, followed by either C4 or C18 reversed phase zo nes and, in some instances, materials used for specialized selection such as iron or gallium (for phosphopeptide enrichment) may be added to the sequence in place of or in addition to the basic SCX/revers ed phase pairing (Delahunty and Yates, III, 2005; Florens and Washburn, 2006). A low-voltage current is applied to the micocolumn to facilitate na no-electrospray elutio n into an MS/MS system through a pulled proximal-end tip with a diameter of 2-5 m (Delahunty and Yates, III, 2005). Primary limitations of MudPIT-based MS/MS experiments are that it can be a lengthy, time-consuming process with runs that last from 6 to 24 hour s followed by as much as 8 hours of database searching (Delahunty and Yates, III, 2005). A second drawback is the sheer amount of data generated by single complex-sample MudPIT e xperiments. As many as 70,000 mass spectra can be generated in a single run, leading to overwhe lming amounts of data an alysis and organization

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45 and placing pressure on bioinformaticists for improved data management technology (Delahunty and Yates, III, 2005). Other, less common methods for enriching a nd simplifying complex peptide mixtures are making their way to the forefront of proteomi c technology. Among these methods is an altered approach to capillary electrophores is (CE). In the past, capillary electrophoresis has been less than desirable for large-scale proteomic applicat ions due to its relatively limited abilities in effective separation of peptides and proteins; however, recent adaptations involving addition of high-concentration poly(dial lyldimethylammonium chlori de) (PDDAC) have allowed simultaneous resolution of both ca tionic and anionic peptide species over a pI range of 4.7 to 11.1 and a mass range of 6.5 kDa to 198 kDa by se rving as an ion-pairing agent for the separation of anionic proteins and a coating agent for separation of cationic proteins {Lin, 2007 481 /id}. This innovation to capilla ry electrophoresis creates a much broader dynamic range in separation of peptides and may represent a powerful new tool for proteomic analysis. Finally, a considerable amount of thought and innovation has been invested in the development of enrichment methods for post-tran slational modification su ch as phosphorylation. Currently, there are two majo r categories of non-immunoaffin ity phosphoprotein enrichment: metal-mediated chromatography and isoelectric separation. In the former category, iron and gallium-based immobilized metal affinity chro matography (IMAC) have traditionally been the most commonly used in the enrichment of low-abundance phosphoproteins from complex samples; however, these materials are being used less frequently due to their deficiencies in phosphoprotein/peptide selectivity an d the discovery of higher-affinity metal oxides (Imanishi et al., 2007). Materials such as t itanium dioxide and zirconium di oxide are now the standards for phospho-enrichment due to their superior ability to bind phosphate moieties specifically and to

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46 reduce non-specific binding of acidic peptides (Thingholm et al., 2006). The effectiveness of metal oxide separation has also been enhanced through the use of 2,5dihydroxybenzoic acid in coordination with low pH contributed by trifluor oacetic acid (TFA) in mobile phase, which has been demonstrated to restrict binding of non-phosphorylated components to titanium or zirconium dioxide nanoparticles with greater e fficiency than traditional TFA/acetonitrile (ACN) gradients alone (Thingholm et al., 2006; Zhou et al., 2007). The improved phospho-enrichment characteristics of metal oxide chromatography have been demonstrated recentl y in the analysis of mouse liver lysate which was digested and enri ched with zirconium dioxide nanoparticles prior to LC/MS/MS (Zhou et al., 2007). This experiment yielded the detection of femtomole levels of low-abundance phosphopeptides and facilitate d the identification of over 240 phosphosites (Zhou et al., 2007). Isoelectric separation of proteins based on pI values has served as an alternate approach to phosphoenrichment, as phosphorylated proteins possess a discernable acid shift compared to their non-phosphorylated forms. Recent advancemen ts in this particular separation method make use of protein methyl esterifi cation, creating a greater differen ce in average isoelectric point values of phosphorylated and non-phosphorylated prot eins and making it feasible to effectively separate modified proteins in the acidic and neutral ranges of an IEF field while nonphosphorylated methyl esterifications are excluded from the field due to the fact that they exceed the upper limits of the pI range (Xu et al., 2007). While this me thod is not optimal for proteomes with extreme acidic bias in average pI value, it has been proven as a viable method for phosphoenrichment in more neutral proteomes. Post-translational modification analysis Bottom-up proteomic identification of post-tr anslationally modified peptides from a complex protein digest pool is fraught with obs tacles, the most signifi cant of which are their

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47 naturally low abundance, their tran sient nature or their invisibil ity with respect to purification based on distinguishing features or identification through mass sp ectrometry. One of the best characterized and most frequently studied modificat ions is protein phosphor ylation. As a result, it also has the most extensive co llection of enrichment and identif ication methods of any PTM. Most phosphoanalyses begin with one of a few enri chment strategies disc ussed in the previous section; however, the effective identification of a phospho-modificat ion site often requires more sophisticated analysis. One appr oach to phospho-enrichment and modification site identification involved the beta elimination of the phosphate mo ieties on pSer and pThr residues and replacing them with ethanedithiol to facilitate the addition of any one of a variety of thiol-reactive affinity tags such as commercially available maleamide-PEO2-biotin (Oda et al., 2001; Kalume et al., 2003). This labeling method allows for specific affinity-based enrichment of phosphoproteins and is reasonably efficient; however, it is not wi thout its disadvantages. The beta elimination and Michael addition chemistry does not dist inguish between phosphorylations and other Omodificaitons such as O-glycosylation (Oda et al ., 2001). The thiol-reactive affinity tags also have been found to bind to cysteins, negating thei r selective nature (Oda et al., 2001). Another major drawback to this technique is that it re quires large amounts of material, thereby masking any analysis of lower abundan ce phosphopeptides (Kalume et al., 2003). Other methods of phosphoanalysis based on beta elimination/Michael addition chemistry involve pSer and pThr derivitizaiton with ethylpyridyl to promote better mobilization and thus, more efficient detection of modified peptides using MA LDI MS, however, similar complicat ions persist (Arrigoni et al., 2006b). An alternative approach to phosphopeptide id entification which also relies on beta elimination and Michael addition chemistry ha s found a way to side-s tep the usual problems

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48 associated with an affinity tagging type of an alysis through targeted conversion of modification sites at pSer and pThr to dehydroalanine or -methyldehydroalanine, resp ectively (Knight et al., 2003; Stevens, Jr. et al., 2005). The beta-elimin ated residues act as Michael acceptors for cystamine, creating aminoethylcysteine or -methylaminoethylcystamine for modified serine or threonine residues, respectively, and become isot eric with lysine and thus recognizable for cleavage by trypsin, Lys-C or any other lysine-specific peptidase (Knight et al., 2003). Simple digestion and analysis by LC-MS/MS or MALD I MS yields a unique y1 ion representing the modified site (Knight et al ., 2003). This method has been tested for effectiveness in phosphopeptide enrichment from complex mixtures and has been shown to be successful for such applications (Knight et al., 2003; Stevens, Jr et al., 2005). Disadvant ages of this approach, however, have been identified as an obvious inability to label large ra nges of proteins or peptides efficiently due to their physiochemical variab ility (Waugh, 2005) as we ll as variability (or transient nature) of particular modifica tions used as the basis for labeling. Protein glycosylation is also a major post-translational modification within the cell. The realization that the types and quantity of carbohydrate modification made to proteins can be directly related to certain disease states has given rise to the fiel d of glycoproteomics (Sparbier et al., 2005). The challenge of selective enrichment and analysis of glyc osylated proteins or peptides is being met with some interesting solutions. One approach to the selective enrichment of O-GlcNAc-modified proteins from complex mixtures involv es the use of a genetically modified galactosyltransferase (Y289L GalT mutant) which possesse s the ability to selectively label proteins containing an O-GlcNAc moiety with a ketone-biotin tag to facilitate avidin enrichment and subsequent ESI-MS (Khidekel et al., 2004). This method has been employed to effectively analyze the glycoproteomic profile of the mammalian brain (Khi dekel et al., 2004).

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49 Other glycoprotein analysis methods are mo re straight-forward and include affinity chromatography-based separation utilizing func tionalized magnetic beads. These beads are covalently coated with glyco-affinity chemical s such as Concanavalin A or diboronic acid, depending on the type of glycosylation being enriched (high mannose, hybrid or complex) (Sparbier et al., 2005). Samples are anal yzed through MALDI-TOF and enrichment, identification and mapping of glyco-modified pep tides of various types has been demonstrated using this latter approach, as was the specificity of this technique in the presence of competing free mannose concentrations, proving it to be an effective tool in glycoproteomic analysis (Sparbier et al., 2005). Other functionally important pos t-translational modifications such as methylation and acetylation are proving to be much more difficult to enrich for and analyze due to the fact that they are not amenable to conventional affinity tagging or labeling t echniques pioneered by phosphoproteomics and glycoproteomics (Leitner and Lindner, 2006). Studies focused on these types of post-translational modi fication, especially the character ization of histone modification, have relied almost exclusively on analytical ma ss spectrometry for confirmation of modification and mapping of modification site s (Karanam et al., 2006; Wisnie wski et al., 2007). These mass spectrometry-based approaches, including ion trapping and tandem MS, will be discussed in future sections. Shotgun proteomics and quantitativ e, high-throughput strategies The power and versatility of proteomi c analysis extends well beyond protein identification and analysis of post-translational modification. Technical advances of the past decade have permitted us to explore such aspects as relative and absolute protein quantification, protein-protein interaction and subcellular localization en rout e to mapping changing expression profiles of cells and identifying better disease biomarkers. Th is section will explore several

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50 modern developments that have extended our ab ility to analyze large, complex mixtures of proteins in a comparative and/or quantitative capacity. Array-style experiments have revolutionized the fi elds of genomic and transcriptional analysis as well as biochemical characterization. Similar arraybased procedures are now being applied to large-scale proteomic analysis through the use of an tibody arrays. Currently, antibody microarrays are being designed to probe progressively larger divisions of the proteome through the use of human recombinant single-chain vari able domain (Fv) antibody fragments (scFv) designed to be adapted to solid support attachment for microarray applications and selected from a substantial phage display libra ry (Ingvarsson et al., 2007). This recombinant approach allows for flexible design and expanded range of sp ecificity. Methods for quantifying protein microarray results are also being expanded a nd improved upon. Current methods for antibody microarray analysis include dire ct labeling of protein samples using muliplex fluorescent dyes; however, this method has given way to much mo re sensitive indirect labeling approaches (Kusnezow et al., 2007). Pres ently, N-hydroxysuccinimidyl ester (NHS) and Universal Linkage System (ULS)-based labels using fluorescein or biotin are being used for immunoaffinity or extravidin detection, respectively, with severa l dozen-fold improvement over direct labeling techniques (Kusnezow et al., 2007). While antibody microarray technology is pr omising with regards to its universal application to proteomic analysis and its ability to be customized to specific sub-proteomic analyses, its current cost and as-of-yet unresolve d technical restrictions force investigators to turn attention to other methods for comparative quantification of complex protein samples. Among the alternatives is the Is otope-Coded Affinity Tagging (ICAT) method. This technique has become standard for comparative analysis since its inception in 1999 (Gygi et al., 1999).

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51 This method employs thiol-reactive labels pos sessing isotopic components, an affinity purification element such as bi otin and in many cases, a linking region capable of acidor photocleavability (Gygi et al., 1999; Bo ttari et al., 2004; Chiang et al., 2007). Separate conditions are analyzed, pair-wise, through diffe rential labeling with light and heavy isotope-coded tags. MS and MS/MS analysis of sample mixtures can si multaneously determine relative quantities and identities of specific peptides within complex mixtures (Gygi et al., 1999). While ICAT has immense value in its high throughput and high se nsitivity features, its negatives are that it adversely complicates resulting spectra, particularly in instances where multiple cysteines are labeled (Li and Zeng, 2007). Also, peptides l acking cysteines are not available for ICAT labeling, representing a severe limitati on with this method (Li and Zeng, 2007). Another method of large-scale quantitative comparison in frequent use today involves sample labeling with isobaric tags. This approach has been made popular through Applied Biosystems introduction of the iT RAQ system. This system is composed of four (or available up to 8) different primary amine-reactive isobaric tags that are used to label as many different samples. Labeled peptide samples are then pooled and separated through LC-MS/MS where fragmentation (CID) produ ces signature mass peaks at 114.1, 115.1, 116.1 and 117.1 m/z which are distinctive for each tag (Aggarwal et al., 200 6). This approach shows a vast improvement over conventional gel-based analys is or even isotope-coded affinity tagging (ICAT) methods due to the fact that it allows for multiplex analysis and enables quantification of zero protein levels; a feature not afforded by conventional ICAT e xperiments due to its requirement for observed mass shifts (Aggarwal et al., 2006). Additionally, because the tags are isobaric in nature, resulting spectra are simplified by revealing single addi tive peaks resulting from elimination of the tag (Aggarwal et al., 2006). Isobaric tagging does, however, po ssess some drawbacks in that

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52 iTRAQ experiments are relatively costly and ther e exists some variability in peptide labeling between comparable samples. Clevable Isobar ic Labeled Affinity Tags (CILAT) are also making their way into large-scale quantitative pr oteomics, offering the be nefits of both ICAT and iTRAQ labels with none of the apparent drawbacks (Li and Zeng, 2007). CILAT combines the multiplexing capabilities of an isobaric tagging procedure with the utility and convenience of an affinity purification option and cl eavable linking region (Li and Zeng, 2007). Mass spectrometrybased approaches to comp arative quantification are, perhaps, the most commonly relied upon, offering a large number of labeling and separation tools with everincreasing sensitivity and reproducibility. One me thod, however, has proven to be of equal value and effectiveness of any discussed here, but wi thout the complication of tagging inefficiencies and convoluted spectra. This method, known as AQUA or the Absolute Quantification method, utilizes synthesized internal standard peptides as the basis for quantification in actual molar quantities as opposed to relative quantity ratios (Kirkpatrick et al., 2005). Precise absolute quantification of a specific pe ptide within a complex mixture is achieved through Selected Reaction Monitoring (SRM) of a peptide of a defined mass and comparing to a known quantity of stable isotope-labeled intern al peptide standard (Kirkpatrick et al., 2005). This allows for actual quantities of a given protei n to be determined and is also used as a reliable method for quantifying certain post-translational modifi cation occurrences within complex samples (Kirkpatrick et al., 2005). Finally, a method very similar to AQUA in bot h simplicity and power has been used for quite some time for straight-forward comparative analysis of multiple complex protein mixtures. This technique is known as Stable Isotope Labe ling by Amino Acids in Cell Culture (SILAC) (Ong et al., 2002; Mann, 2006). SILAC is a simp lified version of other differential isotope

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53 labeling methods which uses metabolic labeling of total protein in cell culture or during tryptic digestion via heavy and light isotopic amino acids (often 15N/14N or 18O/16O coded) (Ong et al., 2002; Mann, 2006). Sample analysis and the style of data generated is similar to that of ICAT with similar complications in that it too produ ces somewhat convoluted spectra (Ong et al., 2002; Mann, 2006). However, the advantages of SILAC are plenty, considerin g the simplicity of labeling and how much less time-consuming this pr ocess is compared to other methods utilizing an external tag (Ong et al., 2002; Mann, 2006). SILAC is also advantageous in that it allows for total proteome comparison in cont rast to a much more restricted type of analysis exhibited by AQUA. Finally, SILAC also inco rporates differential label comp letely without restriction, as opposed to ICAT or other affinity labeling methods. Mass spectrometry and bioinformatics Refinement of mass spectrometric tools over the past 30 years has ushered its migration from the physics and chemistry labs of academia to the forefront of biology and applied health sciences. In fact, innovative development of new mass analysis and ionization instruments, data analysis methods and assembly of hybrid MS configurations have transformed mass spectrometry and bioinformatics into independent divisions of science. This subsection will discuss, in general terms, the advantages and disadvantages of the most recent innovations in biological mass spectrometry as well as the mo st heavily-relied upon me thods, instrumentation and software and their inherent feat ures that made them so popular. Two basic types of mass spectrometer curren tly exist: ion-trapping instruments and beam-type instruments. Each type of MS t echnology has evolved independently but has crossed paths with the other, particularly where hybrid in strumentation is concerned. These categories of MS differ fundamentally in how they operate with respect to tandem function. Beam-type instruments typically work to se parate and analyze fragment ions spatially (a tandem-in-space

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54 approach) as opposed to ion-trapping devices which work by scanning and re-scanning ion pools on a time scale (a tandem-in-time approach). For this primary reason, ion traps hold several advantages over beam instruments when it comes to tandem mass analysis. One obvious advantage is that separation and recurrent mass an alysis in space is limited by space, cost and ion intensity. Considering the case of peptide an alysis through tandem mass spectrometry, after ionization and fragmentation, the number of successive mass analyses to be performed depends on how many analyzers one can physically conf igure. To compete with a theoretical MSn instrument (such as those in the ion trap ca tegory) one would require unrealistic (or likely impossible) amounts of space and resources. Additionally, such analysis would require impractical levels of starting ma terial and unattainable efficiency for continued tandem analysis. In the case of an ion trap MS, fragment ions are scanned, trapped a nd re-scanned through time, using the same mass analyzer. Th is configuration allows for a th eoretically infinite sequence of dissociation and analysis limited only by the size and quantity of peptide to be analyzed. A common practical limitation to any type of ion trap in us e today, however, is that 100% efficiency in product ion trapping is rarely, if ever, achieved. This presents the issue of additive loss whereby the combined effects of ineffici ency between each MS scan ultimately reaches a point where product ion levels dimini sh below detectability. This e ffect also exists in beam-type instruments as well and has been demonstrated to be much more severe as this type of MS/MS instrument possesses a level of efficiency at leas t one order of magnitude lower than its ion trap counterparts. There are two major types of ion trapping instruments in use today. These are the quadrupole ion trap mass spectrometer (QIT-MS) and the Fourier transf orm MS, of which the Ion Cyclotron Resonance MS (FT-ICR) is the mo st recognized. The quadrupole configuration is

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55 of the simplest design and is also the most wide ly used among biological MS laboratories. Its widespread use is a result of several factor s, including considerably lower cost and space commitment compared to its FT-MS alternatives. Its relative simplicity also translates to less maintenance and ease of operation. Besides thes e factors, QIT-MS designs are considered a workhorse for biological MS applications due to its speed and adaptability to most any commonly-used ionization methods as we ll as its considerable resolution (106 spectra per m/z) and mass range of about 70,000 Da/charge. Comparatively, Fourier transform instrument s such as the FT-ICR require much more commitment in terms of space and monetary reso urces. These instruments often occupy entire laboratories and can exceed the one million dollar mark for instrumentation and hardware alone. This instrument is also costly in terms of maintenance and required expertise for operation. These negatives are offset, however, by the fact that the Fourier Transform Ion Cyclotron Resonance MS instrument is widely considered to be the superior device for biological and small molecule analysis. Its mass resolution and ma ss accuracy capabilities are unsurpassed by any instrument in current use. Its mass resolution routinely reaches the upper limits of any QIT-MS device (106 spectra per m/z) and its mass range exceeds 105 Da/charge, making it appealing for top-down and MS-based structural experiments. Its mass accuracy can also reach sub-ppm range, compared to QIT-MS devices which are bound by a 1-2 ppm limit. While ion trap instruments have a number of advantages, beam instruments such as the triple quadrupole (Q3) configuratio n are in heavy use in biological MS facilities, particularly those interested in post-translational modification. Instruments of this design are used for an assortment of PTM experiments such as precurs or ion scanning, product ion scanning and neutral loss scanning. This configuration lends itself to tandem processes in three sectors starting with

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56 an initial scan of peptides in the first quadrupole (Q1) and a final, diagnostic scan in the third quadrupole (Q3) with Q2 typically serving as a co llision cell for fragmentation of input peptides (collision-induced dissociat ion or CID). In precursor ion scan ning experiments, peptides in Q1 are scanned over a pre-determined m/z range, all of which are subj ected to fragmentation in Q2. Mass analysis by the third quadrupole selects fo r a specific CID product, often possessing some form of post-translational modification. Neut ral loss scanning proce sses are identical to precursor ion scans with the exception that Q3 sc ans are broad range scans that match the initial Q1 scan and facilitate the isolation of pep tide fragments that have lost a signature mass indicative of a particular PTM. Product ion scans, in contrast, star t with the selection of a given m/z value prior to CID and subsequent broad-rang e scanning of resulting fragment ions. This approach allows for efficient PTM characterization of a single m/z. Hybrid MS/MS devices merging desirable f eatures of both ion trapping and beam-type devices are also commonplace, not only for mass fingerprinting and PTM analysis but also for metabolomic, toxicological and dr ug discovery applications. Tw o hybrid configurations are of particular importance to these fields. These are the quadrupole/time-of -flight (QTOF) MS/MS and the triple quadrupole/linear ion trap (QTrap) MS/MS hybrid instruments. The QTOF arrangement is one of the original hybrid MS/MS devices. It c ontains an initial mass filter (Q1) and a collision cell (Q2) with a time-of-flight (TOF) mass anal yzer in the distal position. This combination of quadrupole ion trapping technology and TOF mass analysis is advantageous for a variety of reasons. One positive aspect of this instru ment is that it is modular with respect to ion sources and is designed to inte rface with either a pulsed ion source such as matrix-assisted laser desorption ionization (MALDI) or a continuous i on source such as electrospray ionization (ESI) and because of its TOF back-end arrangement, it is insensitive to changes in ionization source.

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57 Perhaps the greatest advantage of a QTOF hybrid instrument is that the TOF mass analyzer can measure a full daughter ion spectrum and thus has unparalleled sensitivity. The TOF component is also theoretically limitless with respect to mass range; however, in practice, a mass limit is determined by the ion source and quadrupole mass filters in the proximal position. While the time-of-flight mass analyzer is the major asset to a hybrid instrument of th is type, it is also the major drawback. Because it is strictly a mass an alysis tool and has no scanning capabilities, the TOF component of the QTOF hybrid MS/MS limits the instruments capabilities to a scan-anddetect function and prevents ty pical PTM analyses, such as pr ecursor ion scanning, from being performed. Another hybrid device that is, arguably, the most versatile combination MS/MS instrument is known commercially as the QTrap. It is a quadrupole/li near ion trap hybrid machine arranged in a triple quadrupole format. This instrument combines the PTM analysis capabilities of a triple quadrupole with the enhanced ion trapping effi ciency of a linear ion trap to provide structural/qualitative da ta and quantification simultaneously. The key component of the QTrap, the linear ion trap, is th e primary reason for its sensitivit y and versatility. The enhanced design of a linear ion trap as opposed to other qu adrupole or three-dimensi onal ion traps is that fragment ion ejection from the tr ap during scanning occurs bilate rally through exit slits that either lead to separate mass analyzers or cha nnel all fragment ions to a third quadrupole for analysis. This configuration tran slates to a theoretical 100% rate of efficiency with respect to fragment ions detected from the trap as opposed to conventional ion trap s such as a quadrupole arrangement in which fragment ion expulsion is an elastic event, leadi ng to significant loss of material through the proximal (ion source) end of the trap.

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58 Finally, an aspect of mass spectrometry not to be overlooked is data acquisition and analysis, which has become the rate-limiting st ep in the high-throughput proteomic workflow. Advances in mass scanning and dete ction have pushed MS/MS to the limits of what we are able to manage given the software technology currentl y coupled to this process. Additionally, the staggering amounts of spectral data that can be generated often become too much to manage efficiently. On-going developmen t of new software and MS/MS data search algorithms as well as frequent improvements on existing algorithms is helping to keep data analysis and management up to speed with increasingly ra pid data production by modern MS systems. From the computational aspect of mass spect rometry, database searching algorithms have received the most attention over the past several years as pep tide mass fingerprinting (PMF) has become routine. Such popular algorithms as ChemApplex, PeptIdent or the more recently developed Aldente all operate on sl ightly different principles, account ing for such factors as peak intensity, mass accuracy and matching hit frequency; however, the most widely used and most recognizable PMF search algorithms are MOWSE (Mascot and MS-FIT) and XCorr (SEQUEST). Both MOWSE and SEQUEST are first-generation algorithms widely used for large-scale database searching and MS/MS peptid e matching but each operates in a distinctively different way. The MOWSE algorithm is a probability-based search tool which assigns a score to each matching MS peptide based primarily on database size and governed by use r-defined parameters which set acceptable margins for mass difference values and PTM occurrence. MOWSE scoring also uses signal intensities a nd consecutive fragment heuristics as additional guidelines for peptide matching.

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59 The most widely used alternative to the MOWSE algorithm is SEQUEST. This program differs from Mascot or MS-FIT programs in that it uses a cross-correlation (XCorr) approach to assigning probable peptide hits to database memb ers. This is primarily accomplished through comparative analysis of m/z and intensity valu es of actual peptide sp ectra with the top 500 candidate peptides predicted from the database. A final identification and ultimate confidence score is then assigned based on cross-correlati on of the top two hits of a given protein. SEQUEST also has some similarity to the MOWSE algorithm in that it gives consideration to signal intensity and does not make assignm ents exclusively based on m/z values. Both SEQUEST and MOWSE programs have gained enormous popularity in the proteomic field but unfortunately, th eir popularity is not entirely at tributed to accuracy. Perhaps the main selling point for each of these algorithms is that they produce a user-friendly matching report with an easy-to-interpret confidence score. They also have the capacity for customization where fixed and variable modifications, mass tolera nces and charge states can all be defined by the user. These and many other MS/MS search algorithms tend to fall short where common PMF pitfalls exist. These primarily include instances where detected peptide masses do not match appropriately with database members for a variety of reasons. These reasons include (but are not limited to) errors within the database sequence polymorphism, non-specific cleavage of peptides and, of particular importance, failure to match due to PTM. These represent some of the greatest obstacles to overcome in developi ng an accurate and dependable tandem MS search algorithm, along with the inclusion of an uncomplicat ed user interface. Efforts to alleviate these complications have included more advanced algor ithms such as Aldente, which matches peptides to database members based on two-stage scori ng at both the protein and peptide level or assumptive algorithms such as FindPept which can account for mass deviat ions resulting from

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60 non-specific cleavage, common contaminants and a wide range of PTMs annotated in the SwissProt feature tables. Special Considerations for Extreme Proteomes Inherent properties of certain proteomes often prevent conventi onal analysis with standard tools and methods. In instances wher e proteomes are seemingl y resistant to routine manipulation, alternative approach es have been adapted. This section will briefly discuss two common examples of extreme proteomes and meas ures that have been taken to enhance the efficiency with which they can be explored. Th ese examples include proteomes with extreme pI value bias (highly acidic or basic proteomes) and membrane-associated protein components. Proteomes with extreme pI value bias In silico analysis of several select prokaryotic proteomes has revealed a bimodal pattern in their migration with respect to isoelectric po int value (Lamberti et al., 2007). In fact, the majority of proteomes subjected to this analysis clustered in pI ranges of 4-7 and 9-11 (Lamberti et al., 2007). Similar pI bias has been demonstr ated in the archaea and in certain cellular subproteomes of eukaryotes (Corton et al., 2004; Joo and Kim, 2005; Kirkland et al., 2006; Lamberti et al., 2007). Furthermore, highly modified se ctions of a proteome such as those possessing numerous phosphorylations or gl ycosylations can also exhibi t poor resolution during chargebased separations due to impartation of str ong net negative charges. Methods have been developed in these special cases that allow for enhanced reso lution and thus more complete analysis of these proteins, particularly wher e gel-based proteomic strategies are employed (Molloy et al., 2002; Ki rkland et al., 2006). The issue of protein acidity and its compli cation of the proteome has had a notable impact on proteomic investigations of prokaryotes. Average pI values have been determined to be remarkably low among ha loarchaeal species like Haloferax volcanii (4.5) and Halobacterium

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61 NRC-1 (4.9) and even organisms of importance in food and industrial microbiology such as Lactococcus lactis with a large portion of its proteome resolving near 5.0 (Kennedy et al., 2001; Guillot et al., 2003; Joo and Kim, 2005). Common methods for improving resolution of acidic proteomes have included the use of ultrazoom ranges for IEF separation. Commercially available immobilized pH gradients (IPG) w ith ranges as narrow as 3.5-4.5 or 3.9-5.1 (Amersham Biosciences and BioRad) are in common use while custom IPG ranges can be purchased or synthesized that encompass even na rrower stretches of the pI scale. The use of narrow-range carrier ampholytes (a mphoteric electrolytes) has been demonstrated to have some effect on improving resolution during focusing as well. Perhaps the simplest measure for separation of clustered proteomes, however, is th e use of larger IPG/gel formats, with 11 cm being a standard improvement over the mini-gel (7 cm) format and 18 and 24 cm formats being commercially available as well. All of these improvements have been implemented together with vast improvements in re solving power of acidic proteo me clusters using gel-based proteomic methods. Extremely basic (alkaline) proteomes have also posed similar pr oblems with proteomic surveys and like extreme proteome acidity, alkalin ity places serious restric tions on our ability to thoroughly investigate the prot eomes of many common, well-studi ed organisms. In fact, proteins with alkaline pI values (> 7.5) have been shown to constitute significantly large portions of a wide variety of organisms such as E. coli (38%), Methanococcus jannaschii (49%) and Helicobacter pylori (62%). Additionally, in eukaryotes, the average pI distribution tends to be trimodal with clustering at 5, 7 and 9, leaving hundr eds of proteins out of reach. Earlier attempts to effectively resolve proteins within the alkalin e range involved the use of Non-Equilibrium pH Gradient Electrophoresis (NEPHGE ) but resulted in a significant resoluti on deficit when

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62 compared to steady-state IPG separation. Attemp ts were also made to adapt conventional IPG focusing methods for basic proteins; however, th is required a highly basic buffer system for establishing the pH gradient and overcoming th e buffering power of water. This approach eventually let to reverse endo-osmotic flow of water towards the anode during focusing and ultimately interfered with steady-state focusing c onditions. In addition, extreme pH buffers were found to have a degenerative effect on acrylam ide IPG support matricies, causing hydrolysis and preventing normal IEF. Current efforts toward s resolving the basic proteome have involved combined developments in commercial and cust om alkaline-range ultrazoom IPGs (typically in focused regions between 8 and 11) and the use of immobilized pH support material alternatives such as dimethylacrylamide (DMA) a nd acryloylaminoethoxyethanol (AAEE). Hydrophobic proteomes and membrane proteomics As much as one-third of the genes in a given organism encode for membrane-bound proteins (Hopkins and Groom, 2002; Wu et al., 2003). Additionally, as much as two-thirds of all medications being prescribed today target membrane-associated components (Hopkins and Groom, 2002). When considering these statistics, it is evident that there is a definite need for indepth proteomic analysis of this considerable subset of protei ns. Unfortunately, these proteins come with an inherent set of difficulties that include their hydrophobic nature and thus a tendency to aggregate and to resi st solubilizaiton. These properti es make membrane proteomics one of the most challengi ng endeavors in biology. In recent years, simple approaches have been taken to adapt in-gel proteomics to membrane protein analysis. Most notably, modi fied two-dimensional PAGE techniques have been devised which rely upon elec trophoretic separation of proteins in the first dimension as opposed to IEF and which use strong cationic detergents such as 16-benzyldimethyl-nhexadecylammonium chloride (16-BAC) and cet yl trimethyl ammonium bromide (CTAB) or

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63 strong anionic detergents like SDS (Braun et al ., 2007). Because two-dimensional SDS-PAGE (SDS/SDS-PAGE) results in a diagonal migration pattern due to separation by protein mass in both dimensions, proteins can be resolved indivi dually by deviation from the diagonal which is achieved through varying the buffer composition in each dimension (Braun et al., 2007). An additional adaptation to the 2DE format has been the use of native gel electrophoresis in the first dimension and denaturing SDS-PAGE in the s econd. Native gel electrophoresis has typically been used for determination of membrane comple xes or for in-gel activity assays but further modification of this process has given rise to colorless native gel electrophoresis (CN-PAGE) and blue native gel electrophores is (BN-PAGE) (Braun et al., 2007; Heinemeyer et al., 2007). These processes differ only by the pI range in which they resolve hydroph obic proteins natively. Those proteins with pI values less than 7 have been found to resolve reas onably well in colorless gels, however, it was discovered that the addition of the negatively charged protein binding dye Coomassie Brilliant Blue G-250 to samples higher on the pI scale could provi de a charge shift to the proteins and allow for bett er resolution (Braun et al., 2007 ; Heinemeyer et al., 2007). Finally, gel-free methods have al so been explored for enhanced fractionation and analysis of membrane proteins. In particular, zone electroph oresis using a free-flow apparatus (ZE-FFE) has been of interest in this field primarily for its greater sample loading capacity and simplicity in comparison to in-gel hydrophobic pr otein analysis (Zischka et al., 2006; Braun et al., 2007). Zone electrophoresis operates by separating membranes, organelle s, proteins and even entire cells based on their charge-to-mass ratio where sa mples are placed into an electrophoretic buffer field of uniform conductivity and pH oriented perp endicular to the buffer stream. The speed and versatility of this method in membrane proteomi cs has made it an appealing method for large and insoluble components of any proteome.

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64 Project Rationale and Design The goals of this study are focused on identi fying native substrates of the proteasomal degradation system in the haloarchaeal model organism Haloferax volcanii and determining potential signals for degradation/destabilization of substrate proteins in an ubiquitin-free system. Established genetic tools fo r detailed analysis of Haloferax volcanii have allowed proteasomedeficient mutants to be created through deletion or disruption of the prim ary regulatory particles of the proteasome. Development of these mutant s, in addition to chemical inhibition of the proteasome core particle, has facil itated a degradomic approach to be used to identify proteins in the absence of proteasomal activity levels which are altered and/or isof orm conformations. The potential for these proteins as proteasomal substrates was also st udied. Extensive highthroughput comparative proteomic an alysis has led to the establishment of a putative substrate list of 10 proteins for which polyclonal antibodi es have been produced and subsequent pulsechase and immunoprecipitation methods have been appl ied to further investigate their status as substrates of the proteasome. These analyses ha ve also revealed significant differences in the levels of accumulating phosphorylated proteins between wild-type cells and those lacking full proteasome-activating nucleotidase activity; a po tential indicator of a substrate recognition mechanism in the prokaryotic system. Identificati on of substrate proteins in this study represent the first confirmed proteasomal substrate identifications in an y archaeon and among the first in prokaryotes. Additionally, these proteins may serve as a starting point for the detailed characterization of a degradation signaling system comparable to that of the ubiquitin cascade in eukaryotes.

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65 Figure 1-1. Proteasomal core part icle arranged in typical alpha-be ta-beta-alpha ring order. Beta ring structures possess N-terminal threonine residues which act as catalytic sites for protease activity. ring ring 20S Core Particle

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66 Figure 1-2. Assorted composition and association combinations for the 20S proteasomal core particle and the PAN regulatory complexes of Haloferax volcanii The ability to produce two separate alpha subunit types as well as two distinctly different PAN protein subunits suggests the assembly of multiple sub-specialized proteasomal complexes. The pattern of association between the various PAN complexes and the assortment of core particle arrangements is undetermined, however. All proteasome variations presented here have been c onfirmed with the exception of that which possesses homogenous alpha ri ngs of different types. PanA PanB PanA/B 1/ 2/ 1/ 2 (hetero)/ 1/ 2 (homo)/

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67 CHAPTER 2 METHODS AND MATERIALS Chemicals and Reagents Immobilized metal ion affinity chromat ography (IMAC) and metal oxide affinity chromatography (MOAC) materials were supplie d by Qiagen (Valencia, CA) and PerkinElmer (Waltham, MA), respectively. Triz ol reagent and Pro-Q Diamond phosphoprotein stain were purchased from Invitrogen (Car lsbad, CA). Mevinolin was from Merck (Whitehouse Station, NJ). Two-dimensional gel electrophoresis (2-DE) materials were supplied by BioRad (Hercules, CA). Reversed phase C18 spin columns and trypsin were respectively purchased from The Nest Group, Inc. (Southborough, MA) and Promega (Madison, WI). Pre-packed HPLC C4 and C 18 reversed phase columns were supplied by LC Packings, San Francisco, CA). Rest riction enzymes and other DNA modifying enzymes were from New England BioLabs (Ipswitch, MA). Hi-Lo DNA and Precision Plus protein molecular mass standards we re from Minnesota Molecular, Inc. (Minneapolis, MN) and BioRad, respectively. All other chemicals and reagents were supplied by Sigma-Aldrich (St. Louis, MO) and Fisher Scientific (Pittsburgh, PA). Strains, Plasmids and Culture Conditions Strains and plasmids used and/or construc ted through the course of this work are listed in Table 2.1. All Haloferax volcanii cultures (with the excep tion of those used for genetic studies, described in the next s ection) were grown in ATCC 974 medium or defined high-salt medium (Allers et al., 2004) at 42C with orbital shaking at 200 rpm (see Table 2-2 for culture condi tions of specific experiments) Growth was monitored at an optical density using a wavelength of 600 nanometers (OD 600 nm) using a 50-2000 l, 220-1600 nm Uvette (Eppendorf) and Sm artSpec 3000 spectrophotometer (BioRad).

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68 To determine growth rates of proteasome inhib itor-treated cells, triplicate cultures of 6 ml were inoculated at 0.33% (v /v) with log-phase cells ( OD 600 nm of 0.426) grown in liquid culture from freshly isolated colonies. Proteasome inhibitor clasto -lactacystin beta-lactone ( c L L; 0, 20, and 30 M) with 0.5% (v/v ) dimethyl sulfoxide (DMSO) was added at an OD 600 nm of 0.20 (15 h). Growth was monitored at 12 separate intervals over the course of 55 h. For preparation of proteins for two-dimensional PAGE analysis of inhibitor-treated samples, cells were grown similar to ab ove (see Table 2-2 for specific conditions). Log phase cells (OD 600 nm of 0.55, 1 ml in 13 x 100 mm tubes) from freshly isolated colonies were used as a 0.33% (v/v) inoculum for triplicate cultures (25 ml in 125 ml Erlenmeyer flasks). Group 1 cultures were grown to an OD 600 nm of 0.15, supplemented with 0.5% (v/v) DMSO 20 M c L L, and harvested after 18 to 24 h of growth (final OD 600 nm of 1.7 to 1.9). Gr oup 2 cultures were grown to an OD 600 nm of 0.2, supplemented with 0.5% (v/v) DMSO 30 M cL L, and harvested at an OD 600 nm of 0.8-1.0. Escherichia coli DH5 and GM2163 strains (NE BioLab s) were respectively used for routine cloning and purification of plasmid DNA for transformation of H. volcanii (Cline et al., 1989). E. coli strains were grown at 37oC (200 rpm) in Luria Broth (LB) supplemented with 100 mg ampicillin, 50 mg kanamycin and/or 30 mg of chloramphenicol per liter as needed. For the analysis of proteins through I mmobilized Metal Affinity Chromatography (IMAC) or Metal Oxide Affinity Chroma tography (MOAC), cells were grown as indicated in Table 2-2. Ce lls were harvested at a final OD 600 nm of 1.0-1.2 for both analyses and were collected by centrifugation at 10,000 to 14,000 x g at 4oC. Cell pellets

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69 that were not immediately subjected to prot ein extraction were flash frozen in liquid nitrogen and stored at -80oC for up to 6 months. Construction of panA Knockout Mutant GG102 For genetic analysis, H. volcanii strains were grown at 42oC in YPC medium as described by Allers et. al (2004). Medium was supplemented with 4 5 mg mevinolin per ml and/or 0.1 mg novobioc in per liter as needed. Haloferax volcanii DS70 (Wendoloski et al., 2001) served as the pa rent strain for the generation of the panA mutant strain GG102. The chromosomal copy of the GG102 panA has a 187-bp deletion and an insertion of a modified Haloarcula hispanica hmgA gene ( hmgA* ) encoding 3hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase which renders H. volcanii cells resistant to mevinolin (Wendoloski et al., 2001). The following approach was used to generate GG102 (through the efforts of G. Gil). Vent DNA polymerase was used for polymerase chain reaction (PCR) amplification of the panA gene from H. volcanii genomic DNA using primers (Primer 1: 5'CATATG ATGACCGATACTGTGGAC-3' and Primer 2: 5'GAATTC AAAACGAAATCGAAG GAC-3') (N deI and EcoRI sites in bold). Haloferax volcanii genomic DNA was prepared for P CR from colonies of cells freshly grown on YPC plates. In brie f, cells were transferred into 30 l deionized H2O using a toothpick, boiled (10 min), chille d on ice (10 min), and centrifuged (10 min, 14,000 x g ). The supernatant (10 l) was used as the template for PCR. The 1.28-kb PCR fragment was cloned into pCR-BluntII-TOPO (Invitroge n) to generate plasmid pJAM636. The fidelity of this insert wa s confirmed by Sanger dideoxy DNA sequencing at the University of Florid a Interdisciplinary Center for Biotechnology Research. The 1.5-kb NotI fragment of pl asmid pMDS99 which carries hmgA* (Wendoloski et al., 2001) was inserted into the B bvCI to NruI sites of panA carried on pJAM636 by blunt-

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70 end ligation to generate plasmid pJAM906. This suicide plasmid (pJAM906), which carries hmgA* and panA in the same orientation as determined by restriction mapping using KpnI and AgeI, was transformed into H. volcanii DS70, and recombinants were selected on YPC solid medium supplemented with mevinolin (YPC + Mev). Isolated colonies were re-streaked for isolation on fresh YPC + Mev and screened by PCR using primers which annealed outside the panA region of suicide plasmid pJAM906 (Primer 3: 5-TACGATAAGGACTCGGCGTCGCAGC-3 and Primer 4: 5-TACGTC GCGTTCGCGGCGTAGTCAC-3). Clones, which generated the appropriate PCR product, were further screened by West ern Blot using anti-PanA, -PanB, 1, 2 and antibodies as previously described (Reuter et al., 2004). For complementation studies, H. volcanii E. coli shuttle plasmids carrying panA and panB genes were transformed into mutant strain GG102. These plasmids, pJAM650 and pJAM1012, encode C-terminal poly-histidine tagged PanA a nd PanB (PanA-His6 and PanB -His6), respectively, under control of the Halobacterium cutirubrum rRNA P2 promoter and were constructed by C.J. Reuter and J.A. Maupin-Furlow as fo llows. The 1.2-kb NdeI-HindIII fragment of pJAM642 and the 1.24-kb NdeI-XhoI fragment of pJAM1006 (Reuter et al., 2004) were separately blunt-end ligated into the Nde I-BlpI site of pJAM202 (Kaczowka and MaupinFurlow, 2003) to replace psmB his6 with panA-his6 (pJAM650) and panB-his6 (pJAM1012), respectively. Protein Preparation and Quantification Protein was extracted using either a standard method (Karadzic and MaupinFurlow, 2005) or Trizol-mediated method optim ized from the standard method (Kirkland et al., 2006) of protein extr action as described below. Samples were dried through vacuum centrifugation and stored at -80oC. Protein concentration was determined by

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71 Bradford Protein Assay using bovine serum al bumin as a standard according to supplier (BioRad). Standard Protein Extraction The standard protein extraction met hod described by Karadzic and MaupinFurlow (2005) was used at the onset of this study and was performed as follows. Haloferax volcanii cells were grown as indicated in Table 2-2 and harvested by centrifugation at 10,000 x g for 15 min at 4C. Cell pellets were washed two times with 300 ml of 1.5 M TrisHCl (pH 7.1), and the fi nal pellet was resuspended in 2 ml of 50 mM TrisHCl (pH 7.1) containing 0.8% (w/v) sodium dodecyl sulfate (SDS) and supplemented with phosphatase and protease inhi bitor cocktails (Sigma ) according to the suppliers instructions. Resusp ended cells were passed through a French pressure cell at 10,000 psi. Extract was clarif ed by centrifugation at 15,600 x g for 20 min at 4C. Cell lysate was boiled for 5 min, chilled on ice for 5 min, and incubated with 300 U of Benzonase for 30 min at room temperat ure. Final cell extr act was obtained by centrifugation at 15,600 x g for 30 min. Cell extract was pr ecipitated by 50% (v/v) cold acetone, followed by centrifugation at 14,000 x g for 10 min. The pellets were resuspended in 250 mM glycerol supplement ed with 10 mM triethanolamine (TEA). Sample was diluted (80-fold) and stored in isoelectric focu sing (IEF) rehydration buffer (250 mM glycerol, 10 mM trieth anolamine (TEA), and 4% (w/v) 3cholamidopropyl dimethylammonio-1-pr opanesulfonate (CHAPS) detergent). Trizol-Mediated Protein Extraction Haloferax volcanii cells were grown as indicated in Table 2-2 and were collected by centrifugation at 12,000 x g for 10 min at 4 C. The resulting pellet was resuspended in 1 ml of Trizol per 100 mg cells (wet we ight). The sample was incubated at 65C

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72 for 15 min to fully homogenize the cells. The mixture was incubated at room temperature for 5 min to allow debris to set tle. After the addition of 0.2 ml chloroform (Sigma) per 1 ml of starting Trizol volume the sample was mixed by vortexing for 10 s and incubated at room temperature for 5 min to allow phase separation. Once separated, the sample was centrifuged at 12,000 x g for 15 min at 4C. The upper aqueous layer containing extracted microarray-quality R NA was removed, and the lower phenol layer was combined with 0.3 ml of 100% ethanol per 1ml of initial Trizol volume to precipitate DNA. This Trizol-containing solution was mixed by inversio n and incubated at room temperature for 2 min to allow nonproteinaceous material to settle. The sample was centrifuged at 2000 x g for 5 min at 4C to pellet remaining nucleic acid. The phenol/ethanol supernatant was removed from the DNA pellet and placed into a new tube, where the protein was pr ecipitated by the addition of 1.5 ml of 100 % isopropanol per 1 ml of Trizol initially used. The sample was incubate d at room temperature for 15 min to allow for complete protein precip itation before collecting the protein by centrifugation at 12,000 x g for 10 min at 4C. The resulting protein pellet was mechanically disrupted, with the aid of a sterile pipette tip or ethanol-cleansed spatula, in 2 ml of 300 mM guanidine HCl (Sigma) in 95% ethanol to remove residual phenol. The mixture was incubated for 20 min at room temperature prior to centrifugation at 12,000 x g for 5 min at 4C. This step was repeated four to six times to thoroughly remove phenol from the sample. The resulting protein pellet was washed twice with 2 ml of cold 100% acetone to remove any residual salt (12,000 x g for 5 min at 4C) and air-dried to rid the sample of acetone. The dry pellet was resuspended in an IEF-compatible buffer (described above). Appropr iate quantities of this resuspended sample (50 g protein)

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73 were added to IEF rehydration buffer (7 M ur ea, 2 M thiourea, 4% [w/v] CHAPS, 2 mM tributylphosphine, 0.2% [v/v] ampholyte 3 and 3 mixture [2:1], and 0.001% [w/v] bromophenol blue) and prepared for rehydrat ion and subsequent focusing. Remaining samples, including unused dry protein pellet as well as resuspended sample, were frozen in liquid nitrogen and stored at -70C for up to 1 year. Protein Modification Protein Reduction, Alkylation and Tryptic Digestion Dried protein samples (300 g) from Trizol preparations were resuspended in 100 l of 50 mM NH4HCO3 (pH 7.5). Samples were re duced by the addition of 5 l of 200 mM dithiothreitol (DTT solution) (1 h, room temperature or 21oC). Samples were alkylated by the addition of 4 l of 1M iodoacetamide (1 h, 21oC). Alkylation was stopped by the addition of 20 l of DTT solution (1 h, 21oC). Samples were digested with a 1:20 microgram ratio of tr ypsin to protein for 18-24 h at 37oC. Digested peptides were purified using 300 l C18 spin columns and dried under vacuum centrifugation. Ingel proteins were reduced, al kylated, and digested with trypsin using an automated platform for protein digestion (ProGest, Genomics Solutions, Ann Arbor, MI). Peptide Methyl Esterification 2 N methanolic acid was generated by adding 60 l of 99% (v/v) acetyl chloride dropwise to 300 l of 100% anhydrous methanol in a glass tube. The mixture was sealed and incubated (10-15 min, 21oC). Dried tryptic peptid es were mixed with 30 l of the methanolic acid reagent and inc ubated in a sealed jar contai ning desiccate material for 90 min at 21oC. Methyl esterified samples were dried under vacuum centrifugation and reconstituted in mobile phase B [0.1% (v/v) acetic acid, 0.01 % (v/v) trifluoroacetic acid and 95% (v/v) acetonitrile] for MS/MS analysis.

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74 Phosphoprotein Enrichment and Purification Immobilized Metal Affinity Chromatography Phosphoprotein enrichment by immobilized metal affinity chromatography (IMAC) was performed using a Phosphopurific ation System according to suppliers instructions (Qiagen) with the following modi fications: protein (2.5 mg) prepared via the standard method of extraction (Karadzic a nd Maupin-Furlow, 2005) was resuspended in 25 ml of supplied lysis buffer to a final con centration of 0.1 mg protein per ml. Six 500 l fractions per sample, as opposed to the sugge sted four, were collected for analysis. Titanium Dioxide Phosphopeptide Enrichment Metal oxide affinity chromatography (MOAC) of H. volcanii DS70 and GG102 tryptic peptides (with and without methyl es terification) was performed using the PhosTrap Phosphopeptide Enrichment Kit (Perkin Elmer, cat. no. PRT301001KT) with the following modifications: samples were agitated in the presence of 40 l of elution buffer as opposed to the recommended 20 l. Samples were incubated with the TiO2 resin for 10 min instead of 5 min and were incubated with elution buffer for 15 min instead of 10 min. Resulting samples were drie d under vacuum centrifugation at 42oC for 30 min and reconstituted in mobile phase B for MS/MS analysis. Nickel Purification of Po lyhistadine-Tagged Proteins Escherichia coli BL21(DE3) (Table 2-1) cells in culture at 37oC were induced to synthesize polyhistidine-tagged proteins th rough the addition of 1 mM isopropyl-beta-Dthiogalactopyranoside (IPTG). Cells were incubated at 30oC (200 rpm) for 12 h postinduction. Cultures (12-15 h) were collected through cen trifugation (10,000 x g ) and resuspended in 30 ml of lysis buffer (20 mM Tris-HCl, pH 7.2 and 150 mM NaCl) prior to mechanical lysis using a French pre ssure cell (SLM-Aminco Cat. No. FA-030)

PAGE 75

75 operated at 20,000 psi. Lysate was cl arified through centr ifugation at 10,000 x g and tagged proteins were purified as follows. A 5 ml HisTrap HP Ni Sepharose column (Amersham-GE Biosciences, Cat. No. 17-5248-01 ) was equilibrated with 10 ml of buffer A (20 mM Tris-HCl, pH 7.2 with 150 mM Na Cl and 5 mM imidazole). The 30 ml sample was applied to the column at a flow rate of approximately 2 ml per min. The column was washed with 20 ml of buffer A a nd 20 ml of buffer B (20 mM Tris-HCl, pH 7.2 with 150 mM NaCl and 60 mM imidazole). The polyhistadine-tagged sample was eluted into 10-1 ml fractions with 10 ml of buffer C (20 mM Tris-HCl, pH 7.2 with 150 mM NaCl and 500 mM imidazole). Fracti ons were analyzed by 12% SDS-PAGE and Coomassie staining. 2D PAGE Analysis and Imaging Isoelectric focusing was performed using 11-cm immobilized pH gradient (IPG) strips (Bio-Rad) with a p I range of 3.9.1. The strips were loaded with 150 g of protein in rehydration buffer (as described above) at 20 C for 18 h. Rehydrated IPG strips were placed in an 11-cm focusing tr ay (Bio-Rad) and covered with 2 ml of mineral oil. The proteins were focused at a maximum of 8,000 V for 35,000 volt-hours (V-h) at 20C. Once complete, the strips were removed from the mineral oil, rinsed with proteomics grade water, and equilibrated for 10 min in 2 ml of equilibration buffer A (375 mM TrisHCl, pH 8.8 with 6 M urea, 2% [w/v] SDS, 20% [v/v ] glycerol, and 2% [w/v] dithiothreitol [DTT]) and again for 10 min in 2 ml equilibration buffer B (375 mM TrisHCl, pH 8.8 with 6 M urea, 2% [w/v] SDS, 20% [v/v] glycerol, and 2.5% [w/v] iodoacetamide). The equilibrated IPG strips were placed in the upper well of an 11-cm Criterion precast gel (Bio-Rad) and set in pl ace with a 0.5% (w/v) agarose overlay (BioRad). The second dimension was run at 200 V for 55 min at 16C. The completed gel

PAGE 76

76 was removed from the cassette and stai ned overnight in 150 ml of SYPRO Ruby fluorescent protein stain (B io-Rad) or ProQ Diamond phosphoprotien stain (Molecular Probes) and destained accordi ng to the suppliers instructi ons. The gels were imaged with a Molecular Imager FX Scanner (Bio-Rad) with a 532nm excitation laser and a 555-nm LP emissions filter. Acquired images were analyzed with PDQuest (version 7.0.1) software (Bio-Rad). Liquid Chromatography and Mass Spectrometry Reversed Phase HPLC Coupled with Nano-ESI-QTOF (QSTAR) MS/MS Capillary RP HPLC separation of tryptic peptides with and without methyl esterification (desalted with a PepMap C 18 cartridge) was performed using a PepMap C18 column (15 cm x 75 m i.d.) and Ultimate Capillary HPLC System (LC Packings, San Francisco, CA). A linear gradient of 5% to 40% (v/v ) acetonitrile for 25 min at 200 nlmin-1 was used for separation. MS/MS analysis was performed online using a hybrid quadrupole time-of-flight instrument (QST AR XL hybrid LC/MS/ MS) equipped with a nanoelectrospray source (Applied Biosystems Foster City, CA) and operated with the Analyst QS v1.1 data acquisition software. Information dependent acquisition (IDA) was employed in which each cycle consisted of a full scan from m/z 400-1500 (1 sec) followed by MS/MS (3 sec) of the two ions that exhibited the highest signal intensity. In the full scan acquisition m ode, ions were focused through the first quadrupole by focusing and declustering potentials of 275 V and 55 V, respectively, and guided to the TOF region via two quadrupole filt ers operated in rf-only mode Ions were orthogonally extracted, accelerated through th e flight tube (plate, grid, and offset voltages were 340, 380, and -15 V, respectively), and refocused to a 4-anode microchanne l plate detector via an ion mirror held at 990 V. The same pa rameters were utilized with MS/MS mode of

PAGE 77

77 operation; however, the second quadrupole wa s employed to filter a specific ion of interest while the third quadrupole operated as a collision cell. Nitr ogen was used as the collision gas and collision energy values were optimized automatically using the rolling collision energy function based on m/z and th e charge state of the peptide ion. Three-Dimensional LCQ Deca Ion Trap MS A portion of the IMAC-enriched samples separated by 1D SDS PAGE and were analyzed with a Thermo LCQ Deca quadrupole io n trap MS (LCQ Deca MS) in line with a 5 cm x 75 m inner diameter Pepmaptm C18 5 m/ 300 capillary column (LC Packings). The RP HPLC C18 column ope rating upstream of the MS system was run with a 60-min gradient from 5% to 50% m obile phase B with a flow rate of 12 lmin-1. MS parent ion scans were followed by f our data-dependent MS/MS scans. MS Data and Protein Identity Analyses Spectra from all experiments were c onverted to DTA file s and merged to facilitate database searchi ng using the Mascot search al gorithm v2.1 (Matrix Science, Boston, MA) against the deduced H. volcanii proteome (05/26/06 assembly; http://archaea.ucsc.edu/). Search parameters included trypsin as the cleavage enzyme. Carbamidomethylation was defined as the only fixed modification in the search while methionine oxidation, pyro-glu from glutam ine or glutamic acid, acetylation, and phosphorylation of serine, threonine and ty rosine residues were set as variable modifications. Mass tolerances for all LCQ analyses were 2 Da for MS and 1 Da for MS/MS. Mass tolerances for all QSTAR anal yses were 0.3 Da for both MS and MS/MS. Protein identifications for which a probability-based MOWSE score average of 30 or above was not assigned were excluded. Proteins were onl y considered unique to GG102 panA or DS70 if protein identities were exclus ive for at least 2 samples per strain.

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78 Transmembrane spanning helices were predicted using TMHMM v2.0 (Krogh et al., 2001). Phosphosites were predicted using Ne tPhos v2.0 (Blom et al., 1999). Proteins were categorized into Clusters of Orthologous Groups (COGS) using COGNITOR (Tatusov et al., 2000). Basic lo cal alignment search tool (BLAST) was used locally at www. ncbi.nlm.nih.gov. All phosphopeptides predicted by mass spectrometry via the Mascot database search algorithm were filtered to include only those peptides that were top-ranking hits and had E-values less than 3.0. The resu lting list of phosphopeptides spectra were reviewed manually with Analyst QS v1.1 ac quisition software (Applied Biosystems, Foster City, CA), Scaffold software v1.6 (Proteome Software, Portland, OR) and with spectral data generated through the Mascot search algorithm. Detected peptide fragment masses were compared with those generate d in a theoretical peptide fragmentation spectrum generated by Protein Prospector v 4.0.8 (Baker, P.R. and Clauser, K.R. http://prospector.ucsf.edu). Mass additions of 80 Da (HPO3) or neutral losses of 98 Da (H3PO4) were confirmed mathematically through analysis of peptide fragmentation and correlation of detected diagnostic ions present in the corr esponding spectrum. Consideration was also given to spectra l quality including peak intensity and completeness of generated ion series when sc reening spectral data for confirmation of modification. Theoretical lists of modified internal ions were al so generated, searched and assigned in the actual spectra. Radioactive 35S Pulse-Chase Labeling of H. volcanii Proteins Haloferax volcanii cultures (200 ml in 500 ml Erle nmeyer flasks) were grown in defined high-salt media (Allers et al., 2004) at 42oC (200 rpm) and were harvested at an OD 600 nm of 0.4-0.6 through centrifugation (10,000 x g at 40oC for 15 min). Cell

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79 pellets were resuspended in 80 ml of defi ned high-salt media without methionine and were allowed to purge existing methionine by incubation at 42oC for 1-2 h with orbital agitation at 200 rpm. Ce ll cultures were then pu lsed with 2 mCi of 35S-labeled methionine and cysteine (Easytag Expre ss, Perkin-Elmer Cat. No. NEG772002MC) for 10 min at 42oC and 200 rpm. Labeled cells were di spensed into 10 ml aliquots over 8-15 ml conical tubes containing 1 ml of st op solution (20% [w/v] sodium azide, 100 g/ml puromycin; 0-min time point only) or 1 ml of chase solution (10 mg/ml cold methionine 10 mg/ml cold cysteine, 100 g/ml puromycin and 150 l general protease inhibitor cocktail; Sigma). Tubes were mixed promptly and either instantly chilled on ice (0-min time point only) or returned to 42oC with orbital agitation at 200 rpm. Tubes were removed from incubation at 1, 3, 5, 10, 30 and 60 min and 12 hours, mixed with 1 ml of stop solution and immediately chilled on i ce. Stopped samples were centrifuged at 10,000 x g for 10 min, and supernatant was removed. Resulting pellets were resuspended in approximately 1-1.5 ml of remaini ng media and were pelleted at 14,000 x g for 5 min in 2 ml microcentrifuge tubes. Supernatan t was removed and pellets were frozen at 80oC for 12 to 48 h prior to lysis and immunoprecipitation. Immunoprecipitation of Radiolabeled H. volcanii Proteins Protein A-Sepharose beads (100 mg/ml) were equilibrated in phosphate-buffered saline solution (PBS) at pH 7.2 containing 0.01% (w/v) sodi um azide. The bead slurry (50 l) was combined with 5-10 l of rabbit polyclonal antiseru m, 1 ml of cold PBS and 2 l of 0.01% (v/v) Triton X-100 (to aid in mi xing). The mixture was rocked at 4oC for 12 h. Charged Sepharose beads were washed 5x with cold PBS prior to the addition of cell lysate. Labeled cell lysate was prepared by the addition of 150 l of denaturing lysis buffer [DLB; 50 mM Tris-HCl at pH 7.4 w ith 1% (w/v) SDS, 5 mM EDTA, 10 mM

PAGE 80

80 dithiothreitol (DTT), 1mM phenylmethylsulfonyl fluorid e (PMSF)] followed by heating to 95oC for 10 min. Lysate was then brought to 1.5 ml with non-denaturing lysis buffer [NDLB; 50 mM Tris-HCl at pH 7.4 with 1% (v/v) Triton X-100, 300 mM NaCl, 5 mM EDTA, 0.02% (w/v) sodium azide, 10 mM i odoacetamide and 1 mM PMSF] and mixed with 500 U of Benzonase (DNase/RNase cockta il). The mixture was incubated at room temperature for 30 min with periodic mixing. Cell lysate was clarified by centrifugation at 14,000 x g for 5 min (21oC) and was mixed with charged sepharose beads. The mixtures were rocked at 4oC for 3 h prior to washing 4x with immunoprecipitation wash buffer [50 mM Tris-HCl at pH 7.4 with 0.1% (v/v) Triton X-100, 300 mM NaCl, 5 mM EDTA, 0.02% (w/v) sodium azide, 0.1% (w/v) SDS and 0.1% (w/v) sodium deoxycholate] and 1x with cold PBS, pH 7.2. Supernatant was removed from beads and 20 l of reducing dye [0.5M Tris-HCl at pH 6.8 with 25% (v/v) glycer ol, 2% (w/v) SDS, 0.5% (w/v) bromophenol blue and 5% (v/v) -mercaptoethanol] was added. Beads were boiled for 10 min and supernatant was rem oved and separated by 12% SDS-PAGE. Gels were fixed in fixing/enhancing solution (50% methanol, 10% acetic acid and 1 M sodium salicylate) and dried prior to expos ure to radiographic film at -80oC for 2-4 weeks. Film was developed using a Konica XQ-60A film developer and imaged with an Epson 3170 photo and document scanner.

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81 Table 2-1. A list of strains and plasmids used and/or constructed th rough the course of this work Strain or Plasmid Phenotype, genotype and o ligonucleotides used for PCR amplification as indicated* Source E. coli strains: DH5 FrecA1 endA1 hsdR17 (rk mk +) supE44 thi-1 gyrA relA1 Life Technologies BL21(DE3) FompT [ Ion ] hsd SB (rB mB) (an E. coli B strain) with DE3, a prophage carrying the T7 RNA polymerase gene Novagen GM2163 Fara-14 leuB6 fhuA31 lacY1 tsx78 glnV44 galK2 galT22 mcrA dcm-6 hisG4 rfbD1 rpsL 136(StrR) dam13::Tn9(CamR) xylA5mtl-1 thi-1 mcrB1 hsdR2 NE Biolabs H. volcanii strains: DS70 DS2 cured of pHV2 Wendoloski et. al. 2001 GG102 Mevr; recombination of pJAM906 with chromosomal panA gene of DS70; panA knockout mutant Kirkland et. al 2007 Plasmids: pCR-BluntIITOPO Kmr; cloning vector Invitrogen pET24b Kmr; E. coli expression plasmid vector Novagen pUC19 Ampr; cloning vector New England Biolabs pBAP5010 Ampr, Novr; E. coli-H. volcanii shuttle expression vector Jolley et. al ., 1997 pMDS99 Kmr, Mevr; E. coli-H. volcanii shuttle plasmid based on the mevinolin drug-resistent version of the Haloarcula hispanica hmgA gene encoding 3hydroxy-3-methylglutaryl coenzyme A reductase Wendoloski et. al. 2001

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Table 2-1. (Continued) 82 Strain or Plasmid Phenotype, genotype and o ligonucleotides used for PCR amplification as indicated* Source pJAM631 Ampr; 2.2-kb SacII genomic fragment from H. volcanii blunt-end ligated into HincII site of pUC19; carries panA Reuter et. al ., 2004 pJAM636 Kmr; blunted PCR fragment containing panA gene inserted into NdeI-EcoRI site of pZERO vector; primers used were 5GAATTCAAAACGAAATCGA AGGA-3 and 5CATATGATGACCGATACTGTGGAC-3 Kirkland et. al ., 2007 pJAM638 Kmr; 1.2-kb PCR fragment from pJAM631 cloned into pCR-BluntII-TOPO; carries panA without stop codon; primers used were 5CATATGATGACCGATACTGTGGAC-3 and 5AGGCTTAGCAAACGCGCGGGAGAC-3 Reuter et. al ., 2004 pJAM642 Kmr; 1.2-kb NdeI-to-HindIII fragment of pJAM638 ligated into NdeI and HindIII sites of pET24b; carries panA gene with polyhistadine tag Reuter et. al. 2004 pJAM650 Apr, Novr; XbaI-to-BspEI fragment containing Histagged panA gene blunt-end inserted into BanHI and KpnI sites of pBAP5010 Kirkland et. al. 2007 pJAM906 Mevr; 4.6-kb blunt-end B bvC1 fragment of pJAM636 ligated with 1.5-kb Mevr fragment of pMDS99 Kirkland et. al. 2007 pJAM1006 Kmr; 1.24-kb gene fragment with panB amplified from H. volcanii genomic DNA ligated into NdeI and XhoI sites of pET24b; 5CATATGTCACGCAGTCCATCTCTCC-3 and 5-CTCGAGGTACTGGTAGTCCGTGAAG-3 Reuter et. al ., 2004 pJAM1012 Apr, Novr; 1.3-kb NdeI-to-BlpI fragment from pJAM202 was excised and replaced with NdeI and BlpI fragment containing panA gene with His tag Kirkland et. al. 2007 pJAM4010 Kmr; 1.03-kb PCR fragment with gene encoding EF-1 amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCGAGTACATATGATGAGCGACAAACCCCA -3 and 5GCATAGAAGCTTTCGCTCGTTGACTTCG-3 This work

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Table 2-1. (Continued) 83 Strain or Plasmid Phenotype, genotype and o ligonucleotides used for PCR amplification as indicated* Source pJAM4011 Kmr; 845-bp PCR fragment with gene encoding SufC amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCGCGATCATATGATGGCAACTCTCGAGAT3 and 5ATATGCAAGCTTCGCGGCTTCGTACACTTC3 This work pJAM4012 Kmr; 1.22-kb PCR fragment with gene encoding KaiC homolog amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5CATATGGTGGTAGCTCCG CGCACGG-3 and 5-AAGCTTGAAAATATTCGCCTGCTGG-3 This work pJAM4013 Kmr; 740-bp PCR fragment with gene encoding eIF2 amplified from H. volcanii genomic DNA and ligated into NdeI and XhoI sites of pET24b using primers 5CATATGAAGTACAGCGGATGGCCTG-3 and 5-AAGCTTCTCTTCGTCGCCGCTGCGT-3 This work pJAM4014 Kmr; 533-bp PCR fragment with gene encoding PCNA amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5CATATGGTGGACCTCACGCTCGACG-3 and 5-AAGCTTGTCGCTCTGGATGCGCGGG-3 This work pJAM4015 Kmr; 863-bp PCR fragment with gene encoding Smc1 amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCGCATGCATATGATGGTAACGAAGCAGGA -3 and 5ATATACAAGCTTGAGCAGCCCGGACTTCTG3 This work pJAM4016 Kmr; 614-bp PCR fragment with gene encoding Pfp1 amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCTCGGACATATGATGACATCGGCGCTTTT3 and 5AGATCCAAGCTTCAGTTCGTCCAACAGCGT3 This work

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Table 2-1. (Continued) 84 Strain or Plasmid Phenotype, genotype and o ligonucleotides used for PCR amplification as indicated* Source pJAM4017 Kmr; 656-bp PCR fragment with gene encoding SBD amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCGCGCACATATGATGATTTCACTCGACGA3 and 5GCATGCAAGCTTGAGTTCGTCTTCGTCCTT3 This work pJAM4018 Kmr; 1.12-kb PCR fragment with gene encoding FtsZ amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5GCCGGCACATATGATGAAGCTCGCAATGAT -3 and 5GCATGCAAGCTTGAAAAGCGACTCCAGTTC -3 This work pJAM4019 Kmr; 1.40-kb PCR fragment with gene encoding Cdc6/Ori1 amplified from H. volcanii genomic DNA and ligated into NdeI and HindIII sites of pET24b using primers 5CGACATATGATGTTGGAGACTCTC-3 and 5CTATAAGCTTCTGCTCCGAGC-3 This work *The fidelity of all PCR products wa s confirmed by DNA sequence analysis

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85 Table 2-2. Summary of proteomic experi ments and corresponding culture conditions Culture Conditions* Proteomic Experiment Volume Harvest Cell Density (OD 600 nm) Method of Extraction Media Notes clasto Lactacystin Lactone Inhibition of Hv Proteasomes (Group 1) 25 ml 1.7-1.9 Trizol ATCC974 20 M inhibitor added at OD 600 nm 0.15 clasto Lactacystin Lactone Inhibition of Hv Proteasomes (Group 2) 25 ml 0.8-1.0 Trizol ATCC974 30 M inhibitor added at OD 600 nm 0.20 IMAC Phosphoproteomic Analysis of H. volcanii panA Mutant 100 ml 1.0-1.2 Standard ATCC974 MOAC Phosphoproteomic Analysis of H. volcanii panA Mutant 10 ml 1.0-1.2 Trizol Defined High-Salt (Allers et. al. 2004) MudPIT Analysis of H. volcanii panA Mutant 10 ml 1.0-1.2 Trizol Defined High-Salt (Allers et. al. 2004) Development of Trizol Protein Extraction Method 10 ml 1.0-1.2 Trizol Defined High-Salt (Allers et. al. 2004) *All cultures were grown at 42oC and 200 rpm

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86 CHAPTER 3 OPTIMIZING ISOELECTRIC FOCUSING OF HALOPHILIC PROTEINS THROUGH A TRIZOL-BASED SAMPLE PREPARATION METHOD Introduction Effective first-dimension sepa ration of proteins by isoel ectric focusing (IEF) requires thorough desalting of the sample. Typically, IEF is tolerant of salt concentrations up to 50 mM; however, most of this is cont ributed by carrier amphol ytes (amphoteric electrolytes) added to assist in the migration of pr oteins to their respective pI values (Cho et al., 2003). In situations where protein samples are prepared from high salt-dwelling organisms, the minimal salt tolerance of IEF becomes a serious complication. Methods commonly used in the past for the preparation of halophilic proteins for separation by IEF often cons isted of maintaining moderate levels of magnesium to prevent protein aggregati on but also interfered w ith the focusing process by causing excessively high current or aberrant pa tterns in protein migra tion (Oren, 2002). In the case of Haloferax volcanii as with other halophilic archaea, methods for first-dimension separation are further complicated by the pI values of their proteins which are highly acidic (Oren, 2002). The majority of proteins, regardless of origin, tend to collect at the mean of both dimensions; however, the acidity of the total proteome of hal ophilic organisms compresses the first dimension and dramatically restricts resolution (O'Farrell, 1975; Cho et al., 2003; Karadzic and Maupin-Furlow, 2005; Joo and Kim, 2005) This phenomenon causes crowding of the halophilic proteins during IEF within a narrow re gion of the field and pr events clear separation and identification of these proteins within the tw o-dimensional (2D) map. Previous attempts to remedy the lack of separation of acidic pr oteins have include d using narrower pI ranges during IEF, increasing the distance between the basic an d acidic poles, and mixing carrier ampholytes to target separation in the cluste red regions (Hoving et al., 2000; Gorg et al., 2000; Leimgruber et al., 2002). Although these pr evious tactics have improved the separation of halophilic proteins

PAGE 87

87 by 2-DE, we report an even greater improve ment by changing the sample preparation methodology from previously reported technolo gies (Cho et al., 2003; Karadzic and MaupinFurlow, 2005; Joo and Kim, 2005). In this chapte r, a phenol/guanidine is othiocyanate solution known commercially as Trizol (In vitrogen), which is typically employed to isolate RNA from cell and tissue samples of both pr okaryotes and eukaryotes, was us ed to rapidly purify halophilic protein samples from contaminating nucleic aci ds, carbohydrates, and lipid cellular components. This methodology was shown to also drastically enhance all facets of sample preparation and separation of halophilic proteins without the drawbacks of prot ein aggregation and excessive sample loss experienced with other methods. Results and Discussion Comparative 2D maps of ha lophilic protein samples ( Fig. 3-1 ) prepared using the Trizol extraction method (see Methods and Materials, chapter 2 for details) re veal a consistently higher number of protein spots overall than those developed using the previous standard methodology (Karadzic and Maupin-Furl ow, 2005). In total, 2D ge ls generated from standard samples yielded an average of 729 spots, whereas those produced from Trizol-prepared samples yielded an average of 972 spots. Other qualita tive advantages of the Tr izol preparation method are apparent when directly comparing 2D maps from each methodology. For example, there is a noticeable difference in the resolution of pr otein spots in the crowded regions of the H. volcanii 2D map. This phenomenon of IEF crowding is co mmon among proteins of halophilic organisms due to the abnormally high percenta ge of acidic amino acids present in their proteins. The region lying in the upper molecular weig ht range (>45 kDa) and in a pI range of approximately 4.2.5 is of particular interest in th is comparison given its high complexi ty. Excessive lateral streaking makes it impossible to discern any appreciable number of spots in th is region of the map developed with the standard method. However, the corresponding region in the gel developed

PAGE 88

88 using the Trizol method shows a multitude of spots th at can be discerned indi vidually. It is also worthy to point out the difference in total spot numbers that appear in the less complex regions of the 2D map. The advantages that the Trizol preparation has in protei n yield (especially of lower abundance proteins) is clear to see in th ese regions, where there not only is an obvious increase in spot numbers with the Trizol method but also are numerous in stances of faint protein spots amplified dramatically by th e use of the Trizol protocol ( Fig. 3-2 ). This increase in total protein spots was confirmed through the use of large landmark spots, which were commonly found in high abundance and appeared in gels fr om both methodologies with similar relative intensity values, ruling out the possibility of va rying amounts of total pr otein being applied to each gel ( Figs. 3-3 and 3-4 ). The effects that the Trizol methodology has on improving protein yield and spot resolution in 2D PAGE applications, as comp ared with the standard method ( Table 3-1 ), are likely attributed to an increased effi ciency in the removal of nucleic acid from a cell preparation and dissolving lipid and carbohydrate components from the mixture, thereby freeing a higher percentage of proteins for unimped ed migration laterally as well as vertically in a 2D gel. Less volumetric transfer in the Trizol protocol, as compared wi th previous methods of preparation, also allows a higher pe rcentage of protein to be retain ed for analysis. This feature increases the probability of detecting low-abundance proteins in complex samples through any number of mass spectrometric techniques. Cleaner protein samples produced by Trizol extraction may also enhance other proteomic an alysis tools such as prefractionation and ultrazoom gel analysis (Hoving et al., 2000; He rbert and Righetti, 2000; Lilley et al., 2002). Trizol extraction methods may also be useful to proteomics beyond the scope of 2D PAGE. The cleaner protein samples that resu lt from this method of preparat ion may facilitate more thorough separation in various liquid ch romatography techniques such as immobilized metal affinity

PAGE 89

89 chromatography (IMAC), making it easier to iden tify modified proteins in complex mixtures, quantify levels of modification, and ma p peptide modification sites.

PAGE 90

90 Figure 3-1. Two-dimensional PAGE maps of H. volcanii total cell lysate prepared using (A) the previous standard method and (B) the Trizol extraction method. 3.9 4.5 5.1 B 97.4 kDa 66.2 45 31 21.5 14.4 A 97.4 kDa 66.2 45 31 21.5 14.4

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91 Figure 3-2. Magnified re gions of 2D maps of H. volcanii cell lysate prepared using (A and C) the Trizol extraction method and (B and D) the previous standard method. Regions of these maps have been enlarged to de monstrate the advantages of the Trizol extraction method over the previous method. The left and right pairs (panels A and B, panels C and D) correspond to similar regions of each respective map. Arrows indicate landmark reference spots. A.B. C.D.

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92 Figure 3-3. Magnified regi ons of Gaussian images of 2D PAGE maps of H. volcanii total cell lysate prepared using (A and C) the Tr izol extraction method and (B and D) the previous standard method. Pannels A and C demonstrate a quantitative enhancement in the number and intensity of spots in the maps of protein samples prepared by Trizol extraction compared with the previ ous method (panels B and D, respectively). Landmark spots are inciated by arrows. Spots indicated by squares are noticeably enhanced in the Trizol-prepared samples co mpared with samples prepared using the previous method. For example, spot 1 in panel C is increased more than 2-fold compared with its counterpart in panel D. Similarly, spot 2 in panel C is increased more than 24-fold compared with its counterpart in panel D. B. A. C. 1 2 D. 1 2

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93 Figure 3-4. Gaussian images of 2D PAGE maps of H. volcanii total cell lysate prepared using (A) the previous standard method and (B ) the Trizol extraction method. Uniform landmark spots, indicated by circles in each gel, are consistent and in high abundance in samples prepared using either method. 97.4 kDa 66.2 45 31 21.5 14.4 B 3.9 4.5 5.1 A 97.4 kDa 66.2 45 31 21.5 14.4

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94 Table 3-1. Advantages of the Trizol extr action method compared to methods described previously Trizol Method Standard Method Nucleic Acids Phenol/chloroform extraction and ethanol precipitation steps thoroughly remove RNA and DNA, this improves the resolution of complicated regions of 2D PAGE gels and improves the effectiveness of peptide enrichment by IMAC DNA and RNA are removed by Benzonase digestion and centrifugation, which is inefficient and can cause streaking in 2D PAGE gels. In complex protein mixtures, the remaining nucleic acids compete for the IMAC matrix Desalting Isopropanol precipitation followed by 300 mM guanidine HCl and acetone washes are thoroughly remove salt from protein samples, making them fully compatible with IEF Acetone precipitation and pellet washes are less effective in desalting, given the need for maintaining a moderate level of magnesium throughout the preparation Nonproteinaceous Membrane Components Phenol (Trizol) lysis step dissolves lipid membrane components and carbohydrate components, enhancing the dissociation of membrane proteins for separation by 2D PAGE Larger, insoluble cell components such as cell wall and membrane components are removed by differential centrifugation Solubility Protein pellet is soluble in TEA/glycerol buffer supplemented with a zwitterionic detergent such as CHAPS Protein pellet is soluble in TEA/glycerol buffer with CHAPS but contains fewer proteins overall Starting Material Required Sufficient protein quantities can be obtained from as little as 2 ml of culture Sufficient protein quantities usually require a minimum of 100 ml of culture Soluble Protein Retention Nearly 100% of soluble protein is retained by avoiding protein fractionation and multiple tube transfer steps; protein lo ss is almost exclusively due to incomplete precipitation Considerable protein loss occurs due to fractionation, cell pellet wash steps, tube transfers, and incomplete precipitation Prep Time Soluble protein samples can be obtained from culture in less than 4 h Soluble protein samples can be obtained from culture in 8 h Equipment/ Materials Required Requires Trizol, guanidine HCl, chloroform, ethanol, isopropanol, 2ml microcentrifuge tubes, and a microcentrifuge Requires French Press equipment, a fullsize centrifuge, 40 ml Oakridge tubes, Benzonase, phosphatase and protease inhibitor cocktails, and Tris and magnesium chloride solutions

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95 CHAPTER 4 EFFECT OF CLASTO -LACTACYSTIN BETA LACTONE ON THE PROTEOME OF Haloferax volcanii Introduction Proteasomes are large, barrel-shaped prot eases found in all th ree domains of life (Maupin-Furlow et al., 2004). The 20S proteolytic core consists of four stacked heptameric rings with 6 to 14 N-terminal nucleophi le (Ntn) hydrolase-active sites se questered within the complex interior. Protein degradati on by 20S proteasomes requires pr otein unfolding which can be mediated via the hydrolysis of ATP by asso ciated AAA ATPases such as the proteasomeactivating nucleotidases (PANs) of archaea and homologous regulatory particle ATPases of eukaryotic 26S proteasomes (Smith et al., 2005). It has been known for some time that e ukaryal 26S proteasomes are essential for regulating a myriad of cellular functions includ ing: antigen processing for MHC presentation (Kloetzel, 2004), circadian rhythmicity (Cas al and Yanovsky, 2005), cell division (Devoy et al., 2005), metabolism (Asher et al., 2006), transcript ion (Lipford and Deshaies, 2003), translation (Baugh and Pilipenko, 2004; Takahashi et al., 20 05; Jiang and Wek, 2005; Arora et al., 2005), and others. Unlike eukaryotes, archaea do not ha ve a conserved ubiquitin conjugation system for tagging proteins for proteasome-mediated destru ction. However, many fundamental aspects of physiology and biochemistry are conserved betw een these two domains of life including the highly identical proteasomes. Recently, it was shown that clasto -lactacystin-lactone ( c L L) inhibits the 20S proteasomes of the halophilic archaeaon Haloferax volcanii (Reuter et al., 2004). The -lactone component of c L L irreversibly and specifically i nhibits 20S proteasome activity via modification of the Ntn-th reonine residue of the -type subunits (Fenteany and Schreiber, 1998). Thus, proteins that accumulate in c L L-treated H. volcanii cells are likely to provide insight into

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96 the types of proteins that are regulated by ar chaeal proteasomes and expand our understanding of the role these multicatalytic protease s play in archaeal cell physiology. This chapter describes our identification of a large collection of proteins were identified by MS/MS that accumulated as 2-DE gel isoforms in H. volcanii cells treated with the proteasome inhibitor c L L. These included homologs of proteins known to be essential in a variety of functions including cell division, translation and metabolis m. Evidence suggests that a subset of these proteins may be modified post-transl ationally. Together the results of this chapter provide important insights into protei ns that may be targeted to the proteasomedependent degradation pathway and/or induced after proteasome-inhibition in an archaeal cell. Results Little is known regarding th e types of proteins target ed for proteasome-mediated degradation or the role these multicatalytic protease s play in archaeal cells. To provide insight, this chapter focused on establishing a set of proteins that increase in abundance and/or change in isoform migration when H. volcanii cells are treated with the proteasome inhibitor c L L. Until recently, 2-DE separation of proteins isolated from halophilic cells such as H. volcanii was complicated by the necessity to use relatively la rge quantities of cell material. This prevented detailed proteomic analysis of H. volcanii cells treated with c L L, based on the restrictive expense of the proteasome-specific inhibitor. This issue was recently resolved by developing a new Trizol-based method of halophilic protei n preparation (Kirkland et al., 2006), which permitted proteasomal inhibition experiments on a smaller scale, thereby increasing the efficiency with which proteomic an alysis could be performed. Growth of H. volcanii in the presence of proteasome inhibitor The growth of H. volcanii was monitored in the presence of various concentrations of the proteasome inhibitor c L L (0, 20 and 30 M) (Fig. 4-1), to facilitate downstream proteomic

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97 analysis. DMSO (0.5%) was included to enhance inhibitor solubility. Of the cultures examined, those devoid of proteasome inhibitor (negative c ontrols) grew the fastest and reached the highest cell yield, with doubling times of 2.1 h and a final OD 600 nm of 3.8. In comparison, cells treated with 20 M and 30 M c L L grew slower with doubling times of 3.1 and reached lower cell yields with maximal OD 600 nm values of 2.9 and 2.8, respectively. Thus, addition of the proteasome inhibitor reduced overall cell yield and growth rate. However, increasing its concentration from 20 to 30 M resulted in little change in these growth characteristics. Global differences in H. volcanii proteome in the presence of proteasome inhibitor Haloferax volcanii cells were grown in the pr esence and absence of 20 and 30 M proteasome inhibitor c L L, and cellular proteins were extr acted and separated by 2-DE. Six individual gel maps (three test and three control) were generated from separately grown cultures to ensure accuracy and statisti cal significance of each variably expressed protein spot between the different groups. Each 2-DE set or Group was generated from the average number of total protein spots of cells incubated with 20 M c L L (Group 1) and 30 M c L L (Group 2) was comparable at 1072 and 1000, respectively (Fig. 4-2) This represented a 1.6to 1.8-fold (403 48 and 436 33 spot) increase over the total number of spots of the uninhibited controls which were 669 and 584 for Group 1 and Group 2, respectivel y. This increase was relatively high and was reproducible among experiments. In contra st, the differences observed between the average spot number of the two inhibitor groups (72 51 spots) and tw o control groups (105 74 spots) was modest and not consiste nt among experiments. To compare the relative abundance of the i ndividual spots between the different 2-DE sets, the statistical significance for the relative intensity of each protei n spot was set to a threshold value of 4. The cultures supplemented with 20 and 30 M of the proteasome inhibitor

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98 (Group 1 and 2, respectively) yielded a total of 89 spots that were at or above this threshold. Of these spots, 60 were common to both groups, tw o were restricted to Group 2, and 27 were restricted to Group 1. In contrast, the number of s pots with relative intens ities at least four-fold below that of the uninhibited controls was only 14 with two spots common to both groups, two restricted to Group 1, and 12 restricted to Group 2. Based on these results, a number of consistent and significant differences within the proteome could be detected by 2-DE when H. volcanii cells were treated with the proteasome specific inhibitor c L L. Thus, in addition to a notable reduction in the growth of proteasome-inhibited cells, cha nges in 2-DE migration and/or abundance of a large group of proteins were observed. A total of 24 spots unique and/or increased 2to 14-fold in cells cultivated in the presence of the proteasome inhibitor ( c L L) were selected, excised and pooled from triplicate 2DE gels for in-gel tryptic digestion and MS/M S identification. Criteria for spot selection included: i) reproducible a nd significant differences between the 2-DE gels of c L L-treated and non-treated cells, ii) sufficient protein quant ity as determined by SYPRO Ruby fluorescent staining, and iii) adequate separa tion from neighboring protein spot s by 2-DE. Protein identities for 17 of these spots were determined via HP LC-ESI MS/MS using a QSTAR and are listed in Table 4-1 along with their corresponding probability-based Mascot ion scores, peptide coverage, and fold increase in the presence vs. absence of proteasome inhibitor. These protein identities are well within the significant range (p < 0.05) with Mascot ion scores from 53 to 839 (average of 268) and peptide coverage of 6.9 to 60.3% with an average of 5.5 tryptic peptide fragment ions detected per protein. In two cases more th an one protein was identifi ed per spot (a3 and b2) that cannot be contributed to protein carryover from one sample to another (Table 4-1; see Appendix A for all spot images). Spot a3 appear s as a protein chain that is not well separated,

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99 yet all of the proteins within that chain appear to increase in the presence of c L L. The reason for the identification of two proteins within spot b2 is less clear, sinc e this spot appears well separated from surrounding proteins. The remain ing 7 protein spots had overall Mascot ion scores less than 53 and were excluded from the list. The majority of proteins identified migrat ed within 9 kDa of the molecular mass and 0.4 p I units of that calculated for the deduced pol ypeptide (Table 4-1). Exceptions included the conserved archaeal coiled-coil COG1340 protein and 30S ribosoma l protein S4 which migrated more acidic (by 0.5 units); the actin-like protein (ORF02969) whic h migrated more basic (by 0.5 units); and a number of outlier proteins whic h migrated at least 10 kD a greater or less than calculated. Whether these differences are due to post-translational m odification, incomplete denaturation (in 7 M urea and 2 M thiourea), or other factors remains to be determined. In particular, ORF01073 is predicted to adopt a co iled-coil conformation (residues 60 to 289) which may be somewhat resistant to unf olding. A number of the identif ied proteins appear to undergo N-terminal methionine excision based on the identif ication of tryptic peptide ions with cleaved N-termini (individual Mascot ion scores of 49 to 91). These include homologs of the 30S ribosomal protein S4, 2-oxoacid decarboxylase E1 ORF01073, and EF1A. In addition, the detection of an STHDVDPATVEVIRtryptic fra gment with an N-acetyl group (Mascot ion score of 62; E value 4.4 e-5) suggests the hyda ntoinase /oxoprolinase homolog is cleaved by methionine aminopeptidase and acetylated on the re sulting N-terminal serine. These results are consistent with what has been observed for ot her haloarchaeal proteins (Humbard et al., 2006; Falb et al., 2006), but does not account for the ab errant migration of the subset of proteins described above. Although ions were correlate d with methylated and phosphorylated tryptic

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100 fragments of FtsZ and ORF01073, their identity is only tentative (individual Mascot ion scores below 32). The protein spots, which increased in the presence of proteasome inhibitor and were linked to protein sequence by MS/MS, fell into thr ee major categories: (i) protein quality control, translation, and degradation; (ii) metabolism/tran sport; and (iii) cell divi sion/conserved proteins of unknown function. Those which fell into th e category of protein quality control and translation were the most extensive, with 9 pr oteins identified by a tota l of 33 tryptic peptide fragment ions. The majority of proteins in this group were homologs of the 30S and 50S ribosomal subunits (S3Ae, S17, S13, S4 and L7). In addition, members of the DJ-1/ThiJ/PfpI, SUF Fe-S cluster assembly, and elongation f actor(EF-)1A families were found. The second category of spots identified were 4 proteins known and/or proposed to be involved in metabolism and transport and were identified via a total of 22 peptide fragment ions with the highest Mascot score average of 437. The group in cluded orthologs of the divalent metal binding lipoproteins of ABC-type transporters, 2-oxoacid decarboxylase E1 [EC 1.2.4.-], dihydroxy-acetone kinase [EC 2.7.1.2], and aldehyde dehydrogenase [EC 1.2.1.-]. The final category included a homolog of the cell division protein FtsZ and two c onserved proteins ORF02998 and ORF01703 which respectively cluster to COG1077 and COG1340. Although these latter two COGs encompass proteins of unknown function, ORF02998 and ORF01703 have low (20%) identity to actin-like ( e.g. Magnetospirillum magneticum amb0965) and SMC-like proteins ( e.g. Haloarcula marismortui rrnAC1639), respectively. In further s upport of the potential role of ORF01703 in cell division, some archaeal members of CO G1340 appear to be cotranscribed with ftsZ genes based on gene neighborhood.

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101 Discussion Ribosomal Proteins Many of the protein spots which increased in the presence of c L L were homologs of ribosomal proteins including: S4p (S9e), S 17p (RpsQ, S11e), S13p (S18e), S3Ae and L30p (L7e). All of these are functiona lly versatile and/or key to the in itiation of ribosome biogenesis. For example, S4p and S17p bind 16S rRNA and initiate 30S ribosomal subunit assembly (Stern et al., 1986; Nowotny and Nierhaus, 1988; Weitz mann et al., 1993; Powers and Noller, 1995). S4p associates with RNA polymerase and contro ls NusA-like rRNA antitermination (Torres et al., 2001), regulates translation (Tan g and Draper, 1990) including its fidelity (Topisirovic et al., 1977), and catalyzes mRNA helicase activity (Takya r et al., 2005). S13p appears to modulate the interactions of the small and large ribosomal subunits in multiple steps of the translation cycle (Cukras and Green, 2005). S3Ae is associ ated with gene mutations that generate transformed phenotype reversions in rats (Kho and Zarbl, 1992) and transcript levels that are transiently regulated in log phase and after nut rient depletion in plan ts. L30p (L7e) family members are also functionally diverse including th e ability to inhibit translation, control gene expression by physical interaction with r eceptor proteins, and induce apoptosis when constitutively expressed in eukaryotic cells (Neumann and Krawinkel, 1997; von Mikecz et al., 1999). The reason for the increased levels of the ribosomal protein spots in c L L-treated H. volcanii cells is unclear, as is their status as target s of post-translational modification and/or 20S proteasomal degradation. Ribosomal proteins are often covalently modified coand/or posttranslational. In mammalian cells, ribosomal proteins are ubiquitinated and degraded by proteasomes (Kim et al., 2006). Although this type of proteasome specificity has yet to be established in archaea, the obs erved changes in the isoform ab undance of ribosomal proteins do

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102 suggest a widespread influence of c L L on archaeal cell function. Similar alterations in ribosomal protein levels (as determined by 2-DE gels) have been observed for other archaea after global challenges including changes in energy source (Dopson et al., 2005), cold adaptation (Goodchild et al., 2004), heat shock (Shukla, 2006), and osmotic stress (Shukla, 2006). Of these ribosomal proteins, the L30p ho molog commonly increases after c L L-treatment of H. volcanii cells as well as addition of Fe2+ as an electron donor to Ferroplasma acidarmanus Fer1 cells (Dopson et al., 2005). Elongation Factor 1A Elongation factor 1A (EF1A) is a major transl ational factor which cat alyzes the first step of the elongation cycle. EF1A is also implicated in the coordination of a number of other cellular processes incl uding cytoskeletal organization, viral RNA synthesis, and signal transduction in cell growth, stress responses and motility (Ejiri et al., 1994). Consistent with this, EF1A binds the cytoskeletal protei ns actin and tubulin (Condeelis, 1995), Q RNA polymerase (Blumenthal et al., 1972), valyl-tRNA synthetase (Motorin et al., 1988), calmodulin (Kaur and Ruben, 1994), and calcium/calmodulin -dependent protein kinase (Wang and Poovaiah, 1999). In eukaryotes, EF1A also binds proteasomes ( e.g. the Rpt1 subunit) (Verma et al., 2000; Coux, 2003) and ubiquitina ted proteins after ATP depletion (Chuang and Yew, 2005) and is essential for ubiquitin-proteasome dependent degradation of N-acetylated proteins (Gonen et al., 1994). In addition, EF1A has isop eptidase (Gonen et al., 1996) and chaperone activities (Caldas et al., 2000) and binds polypeptides unable to fold after their re lease from the ribosome (Hotokezaka et al., 2002). Based on th ese multifunctional properties of EF1A, a number of biological factors may be responsible for the increased levels of the EF1A protein spot in c L L-treated H. volcanii cells. In eukaryotes, changes in EF1A abundance and posttranslational modification are co rrelated with growth rate, cell proliferation and differentiation,

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103 and intracellular pH (Krieg et al., 1989; Grant et al., 1992; Rans om-Hodgkins et al., 2000; ZobelThropp et al., 2000; Lopez-Valenzuela et al., 2003). In archaea, th e intensities of EF1A spots are altered on 2-DE gels by heat shock (Shukla, 2006), cold adaptation (Goodchild et al., 2004), and energy source (Dopson et al., 2005). In addition, mu ltiple EF1A isoforms have been detected in archaea (Giometti et al., 2002), and post translat ional modification sites are predicted based on similarity to known eukaryal sites (Whitehe art et al., 1989; Wang and Poovaiah, 1999; LopezValenzuela et al., 2003). DJ-1/ThiJ/PfpI Superfamily The DJ-1/ThiJ/PfpI superfamily (Bandyopadhyay and Cookson, 2004) protein which accumulated as a 2-DE spot in the presence of c L L has retained the conserved Cys, His, and Asp residues proposed to function as a catalytic tria d in the Hsp31/PfpI clan of proteins (Malki et al., 2005). Members of this clan are often induced by stressful conditions ( e.g. heat shock (Sastry et al., 2002) and peptid e starvation (Snowden et al., 1992 )) and may be responsible for the hydrolysis of short oligopept ides generated by energy-depe ndent proteases such as the proteasome (Maupin-Furlow et al ., 2006) Interestingly, an E. coli member of this superfamily (Hsp31) interacts with EF1A and ClpA (Malki et al., 2005), an AAA+ ATPase which associates with ClpP much like the AAA proteasome-activ ating nucleotidase (PAN) and 20S proteasome proteins of archaea Cell Division FtsZ, actin, and SMC are important in the divi sion of prokaryotic cells. FtsZ forms a cytokinetic ring early in cell division (Margolin, 2005), actin -like proteins ( e.g MreB) are partners with RNA polymerase in providing th e force needed for chromosome segregation (Kruse et al., 2006), and the coiled-coil SMC proteins are key subunits of complexes that perform essential tasks in chromosome dynamics (Nasmyth and Haering, 2005). In bacteria, the

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104 levels and/or activities of many of these cell division proteins are controlled by proteolysis. In E. coli the FtsZ inhibitor SulA is targeted for re gulated proteolysis by HslVU (Kanemori et al., 1999) and Lon proteases (Mizusawa and Gottesman, 1983). In Streptomyces coelicolor the halflife of FtsZ is controlled (D el Sol et al., 2006), and, in Bacillus subtilis an SMC protein is degraded by Lon and Clp proteases as cells enter sta tionary phase (Mascarenhas et al., 2005). In addition, the AAA+ ATPase ClpX forms an energy-de pendent protease with Cl pP also associates with (Flynn et al., 2003) a nd inhibits FtsZ assembly (Weart et al., 2005) in E. coli and B. subtilis respectively. In archaea, it is not clear whether the proteasome and/or proteolysis control events in cell division. However, the c L L-dependent accumulation of FtsZ-, actinand SMC-like protein spots correlates well with the reduced growth rate of H. volcanii cells under these conditions. 2-Oxoacid Dehydrogenase All archaea use ferredoxin oxidoreductases to oxi dize 2-oxoacids to the CoA derivatives. This has lead to question whether archaea synthesize and/or need functional 2-oxoacid dehydrogenase (OADH) complexes of E1 E2, and E3 (Jolley et al., 2000). E1 and E3 enzyme activities have been detected (Danson et al., 1984; Heath et al., 2004); however, a functional archaeal OADH has yet to be demonstrated. The E1 -like protein spot that increased in the presence of c L L is encoded within a 4-gene operon with coding capacity for E1 E2, and E3 components of an OADH (Jolley et al., 2000) and is separate from the genomic region coding for only E1 components that are needed for nitrate-re spirative growth (Wanner and Soppa, 2002). This combined with the finding that an E1 -type OADH protein of F. acidarmanus Fer1 increases several fold during chemoorganotrophic vs. chemomixotrophic growth (Dopson et al., 2005) suggests archaea may modulate the E1 2-oxoacid decarboxylase component of OADH

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105 complexes as nutrients shift and/or become lim iting. Whether, proteasomes are involved in this process remains to be determined. Dihydroxyacetone Kinase The sequence conserved family of dihydroxyacetone ki nases phosphorylate dihydroxyacetone (DHA), D-glyceral dehyde or other short-chain ke toses and aldoses (reviewed in (Erni et al., 2006)). The source of th e high energy phosphate is either ATP or a phosphoprotein of the phosphoenolpy ruvate: sugar phosphotransferase system (PTS). In E. coli the DHA kinase is composed of three subunits: DhaL, DhaK and DhaM, (reviewed in (Erni et al., 2006)). DhaM is phosphorylated by the PTS system. This phosphoryl group is displaced by a tightly bound ADP coenzyme of DhaL and transferred to the substrate, which is covalently bound to DhaK. Besides catalysis, DhaL and Dh aK serve antagonistic roles in binding the sensing domain of an AAA+ ATPase transcripti onal regulator, DhaR (Bachler et al., 2005). Whether a DHA kinaseand PTS-dependent gl obal regulatory system function in H. volcanii and are controlled by proteasomes is unknown. Ho wever, the DhaL-like protein spot which accumulated in c L L-treated H. volcanii cells is encoded within a region of the genome with coding capacity for DhaK and the PTS EIIAand HPr-type domains of DhaM. Aldehyde Dehydrogenase The protein that accumulated as a 2-DE spot in the presence of c L L that is related to members of the aldehyde dehydrogenase (ALDH) family (pfam00171; E value 7e-100) includes conserved catalytic and NAD(P) bi nding residues. Thus, it is likel y to function in the oxidation of aldehydes to their corresponding carboxylic ac ids and, thus, detoxify a wide variety of reactive organic compounds, toxins and pollutants. Interestingly, the levels of a highly related ALDH isoform of Halobacterium salinarum (gi no. 10581906; E value 6 e-147) are reduced

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106 when cells were cultured in the presence of hi gh vs. optimal salt conditions (6 vs. 4.3M NaCl) (Kim et al., 2006). Fe-S Cluster Assembly Unlike most archaea, haloarchaeal genomes such as that of H. volcanii have the coding capacity for both ISC/Nifand SUF-like Fe-S cluste r assembly systems. This is analogous to E. coli in which the ISC system plays a housekeep ing role and SUF is required during iron starvation (Outten et al., 2004) and induced after oxidative stre ss (Lee et al., 2004). The reason for the increased abundance of the SufB and SufC protein homologs in c L L-treated H. volcanii cells remains to be determined; however, it is possible that inhibiti on of proteasome activity increased the levels of oxidatively damaged and/ or improperly folded proteins and triggered an overall increase in the SUF system to facilitate Fe-S cluster assembly under these conditions. Divalent Metal Transport The ORF02598 protein that accumulated as a 2-DE spot in the presence of c L L is related to cluster 9 lipoproteins. These proteins are pr oposed to regulate the high affinity uptake of divalent metals by ABC transporte rs for repair of metalloenzymes, resistance to oxidative stress, and/or maintenance of intrace llular redox homeostasis (Clavery s, 2001; Johnston et al., 2004; Hantke, 2005). Interestingly, heat shock of Halobacterium NRC-1 results in the elevation of a 2DE protein spot related to an ABC transporter lipoprotein whic h functions in the import of nutrients and/or release of t oxic products (Shukla, 2006). Thus, the accumulation of unfolded and damaged proteins after cellular stresses such as proteasomal inhibition or heat shock may be a general signal that modulates the levels (o r isoform status) of various ABC transporterassociated lipoproteins in the halophilic archaea.

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107 Conclusions A number of protein spots, which represent a variety of cellular functions, differentially accumulated in H. volcanii cells treated with the 20S proteasomal inhibitor c L L. The general classes of proteins identified thr ough the proteomic analysis of ch emically inhibited cells provide insight into specific cellular functions that ma y be regulated by archaeal proteasomes. Many of the identified proteins represen t key components of processes vital to cell function thereby making them prime candidates for theoretical prot easomal control points. To further understand the role proteasomes play in these observed changes, future studies are aimed at determining the influence c L L has on transcript levels, protein half-lif e, and/or covalent modification of the protein spots identified to increase in the presence of pr oteasome inhibitor.

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108 A. 0 0.5 1 1.5 2 2.5 3 3.5 4 0102030405060Time (hours)Optical Density (600 nm) B. 0 20 40 60 80 100 AE-AMCAcYVADAMC SucAAFAMC SucLLVYAMC Suc-IIWAMC BocFSRAMCSpecific Activity (nMol/min/mg) + CLBL (30uM) CLBL Figure 4-1. Cellular response of H. volcanii to proteasomal inhibi tion. (A) Growth of H. volcanii in the presence and absence of cL L inhibitor. 0 M ( ), 20 M ( ) and 30 M ( ) inhibitor was added to cells at an OD600 of 0.20 (15 h growth). DMSO (0.5% v/v) was added to all cultures at this time. (B) Select peptide-hydrolyzing activities of cell lysate prepared from H. volcanii grown in the presence ( ) or absence ( ) of inhibitor. Peptide substrates ar e listed on the X-axis and represent one of three peptide-hydrolyzing activities: pepti dyl-glutamyl peptide hydrolysis (AEAMC and Ac-YVAD-AMC), tryptic activity (Boc-FSR-AMC) or chymotryptic activity (all others).

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109 Figure 4-2. Modified Gaussian 2-DE images of H. volcanii proteomes isolated from cells grown in the presence (A and C) and absence (B and D) of proteasome inhibitor cL L. Cultures were divided into two groups base d on the phase of growth at the time of harvest: (A and B) group 1, harvested at an OD600 of 1.0 and 1.3, respectively. (C and D) group 2 harvested at an OD600 of 1.7 and 1.9, respectively.

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110 A. B. C. Figure 4-3. Magnified regions of 2-DE proteome maps of H. volcanii cells grown in the absence (right) and presence (left) of cL L. Landmark spots common to both gels are indicated by lowercase letters. Protein spots whose intensity increased in the presence of cL L are circled and those identified by MS/MS are labeled with arrows. (A) ThiJ/Pfp1 cellular protease homolog was 5-fo ld higher than the negative control. (B) FtsZ cell division protein homolog with a relative intensitiy value 4-fold that of the uninhibited control. (C) S3Ae ribos omal protein homolog exhibiting one of the highest differences in relative intensity in this study at almost 14-fold over the uninhibited control.

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111 Table 4-1. Proteins unique and/or increased in H. volcanii cells cultivated in the presence vs. absence of the proteasome inhibitor clasto -lactacystin-lactone Grp/ Spota ORF no.b Homolog Description Increase (SD)c p I (cal/gel)d Mr kDa (cal/gel)d Coverage (%/pep no.)e Mascot Protein Quality Control, De gradation and Translation: 2/a3 1073 DJ-1/ThiJ family [3.4.-.-] 5.1 0.97 4.0/4.2 24/33 27.2/4 220 2/a3 0859 Fe-S assembly protein Su fC 5.1 0.97 4.2/4.2 33/33 11/3 145 1,2/b1 0860 Fe-S assembly protein SufB 3.0 1.0, 6.6 0.40 4.6/4.8 53/54 13.9/5 287 1,2/a6 0359 translation elongation factor EF-1A 4.4 0.25 4.6/4.7 46/50 25.4/5 224 1/b7 1145 30S ribosomal protein S3Ae 13.8 1.4 4.8/4.5 25/34 38.6/6 359 1,2/a10 2784 30S ribosomal protein S13 2.4 0.6, U 5.1/4.8 19/16 22.8/3 154 1/b8 2783 30S ribosomal protein S4 U 5.2/4.7 20/14 18.3/3 176 1/c5 2543 50S ribosomal protein L30P 6.5 2.1 4.6/4.8 17/22 23.4/3 104 2/a4 2555* 30S ribosomal protein S 17 2.1 0.53 4.7/4.4 13/15 35.5/1 53 Metabolism/Transport: 1/b11 2397 lipoprotein of divalent metal ABC transporter U 4.4/4.6 40/32 18.2/5 265 2/a9 1545 dihydroxy-acetone kinase DhaL [2.7.1.2] 5.3 0.98 4.4/4.6 25/27 41.8/10 328 1/c8 2959 2-oxoacid decarboxylase E1 chain [1.2.4.-] 2.7 1.1 4.6/4.8 36/27 38.2/12 494 2/b2 B0371 aldehyde dehydr ogenase [1.2.1.-] 4.0 2.4 4.3/4.1 53/48 38.9/11 661 Cell Division/Conserved Prot eins of Unknown Function: U 4.6/4.7 42/35 12.4/3 67 1,2/b5 2/b6 2204 FtsZ 5.0 1.4 4.6/4.2 42/42 15.4/4 212 2/b6 0880 coiled-coil protein of COG 1340 5.0 1.4 4.7/4.2 36/42 24.1/4 179 2/a8 2015 conserved protein related to actinlike proteins of COG1077 3.0 0.66 4.1/4.6 38/27 11/2 84 Outlier Data: 2/a7 2419 hydroxymethylglutaryl coenzyme A synthetase 2.9 0.47 4.5/4.8 50/15 9.4/3 206 2/a7 0860 Fe-S assembly protein Su fB 2.9 0.47 4.6/4.8 53/15 6.9/2 60 2/a12 0359 translation elongation factor EF-1A 3.3 0.38 4.6/4.9 46/20 60.3/14 839 2/a12 2091 4-aminobutyrate am inotransferase 3.3 0.38 4.6/4.9 48/20 8.4/2 103 1,2/a6 0880 coiled-coil protein of C OG1340 4.4 0.25 4.7/4.7 36/50 31.9/8 212 2/b2 A0378 hydantoinase B /oxoprolinas e 4.0 2.4 4.3/4.1 64/48 34.2/13 636 2/b2 0455 thermosome subunit 2 4.0 2.4 4.2/4.1 59/48 15.2/6 355 aGroup and spot numbers of protein samples analyzed by mass spectrometry, where group 1 and 2 correspond to 20 and 30 M cL L, respectively. bORFs are numbered according to th e GenBank assembly (Hartman et al ., in preparation) of the H. volcanii genome as indicated.. Polypeptide sequences deduced from genome and identified by mass spectrometry are included with this HVO number in Table A-1 for comparison of polypeptide to future annotations. Asterisk indicates HVO number for which the polypeptide sequence was extended from the annotation. cIncrease in intensity of protein spot of cells grown in the presence absence of cL L standard deviation. U, protein spot detected only in cells grown in the presence of cL L. dp I and Mr estimated (est.) by 2D-gel and calculated (cal.) based on deduced protein sequence with the differences between est. and cal. included as p I and Mr. ePercent coverage of the deduced protein sequence, number of peptide ions detected, and overall mascot ion score of protein detected by mass spectrometry are indicated. Protein sequences of detected peptide ions with individual Mascot ion scores and E-values are included in supplemental data.

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112 CHAPTER 5 PHOSPHOPROTEOME ANALYSIS OF PROT EASOME-ACTIVATING NUCLEOTIDASE A MUTANT OF Haloferax volcanii Introduction Protein phosphorylation, in many instances, has been associated with ubiquitin tagging and protein stabilization/destab ilization. It has been shown to serve as a precursor to ubiquitination for several protei ns in the eukaryotic protea somal degradation pathway; a phenomenon further demonstrated by the associ ation between ubiquitin ligases and protein kinases in the COP9 signalosome in plants (Dimmeler et al., 1999; Harari-Steinberg and Chamovitz, 2004; Perales et al., 2006). Furtherm ore, the phosphorylation of certain intrinsic destabilization/degradation signals such as PEST sequences has al so been linked to proteasomal degradation (Rechsteiner and Roge rs, 1996; Garcia-Alai et al., 2006) Interestingly, this chapter reveals the observation that phosphorylated prot ein levels are elevated in cells where full proteasomal activity is lacki ng (see Figure 5-3); perhaps an indication of an alternative mechanism for proteasomal substrate targeting in the absence of a ubiquitinating system. The known physical effects of phosphomodification lend feasibility to the idea that protein phosphorylations serves as a major recognition factor in the archaeal proteasome pathway. Phosphorylation is one of the most impor tant and widespread post-translational modifications of proteins. Its usefulness comes in the form of its strong perturbing forces which modulate structure and function in a pr ofoundly effective manner (Kennelly, 2003). Phosphorylation and dephosphorylat ion of proteins can occur qu ite rapidly, making it an ideal mechanism for controlling adaptiv e responses to environmental cu es and changing intracellular conditions. These appealing characteristics have made it the modification of choice for regulating a number of v ital cellular processes, many of which overlap between branches of life. In eukaryotes, it is estimated that as much as 30% of the proteome is phosphorylated with serine

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113 constituting 90% of the modified residues and threonine and tyro sine representing the other 10% (Ficarro et al., 2002; Walsh et al ., 2005). Many of these modified proteins represent immune cell signaling pathways, circadian regulatory syst ems, cell division control components and metabolic networks within plant and/or mamm alian systems (Ptacek and Snyder, 2006; Huber, 2007; Vener, 2007). In bacteria, phosphorylatio n of two-component signal transduction and phosphotransferase system (PTS) elements broade ns the occurrence of protein phosphorylation to include histidine and aspartic acid residues (Walsh et al., 2 005; Grangeasse et al., 2007). Although less common, serine, threonine and tyro sine phosphorylation are also emerging as regulatory devices of bacteria (Grangeasse et al., 2007). To date, there are only a m odest number of proteins with confirmed phosphorylation sites among archaea (Kennelly, 2003; Eichler and Adams, 2005). Several of these mirror eukaryotic and/or bacterial counterparts in terms of modification status a nd function. Based on traditional radiolabeling experiments, such proteins as the Halobacterium salinarum CheA and CheY are phosphorylated at sites identical to their two-component signal transduction bacterial homologs (Rudolph et al., 1995). The archaeal translation initiation factor 2 (aIF2 ) of Pyrococcus horikoshii is phosphorylated at Ser48 by a kinase (PH0512) related to the human doublestranded RNA-dependent protein kinase (Tahara et al., 2004). In both Crenarchaeota and Euryarchaeota the initiator protein Cdc6 (Orc) is autophosphorylated by a DNA-regulated mechanism at a conserved Ser residue (Grabow ski and Kelman, 2001; De Felice et al., 2003). Likewise, the subunit of 20S proteasomes from Haloferax volcanii is phosphorylated at a Ser residue (Humbard et al., 2006). A zinc-dependent aminopeptidase, with a leucin e zipper motif that associates with a CCT-TRiC family chap eronin, has also been shown to be phosphorylated in Sulfolobus solfataricus (Condo et al., 1998).

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114 The advent of modern high-throughput phospho -enrichment and analytical tools provides the opportunity to now identify at large-scal e archaeal phosphoproteins, including those which lack similarity to members of the bacterial or eu karyotic clades or for which modifications have not previously been predicted. In this study, a comparative prot eomic analysis of wild type H. volcanii cells and those lacking proteasomal functi on through deletion of a primary proteasomeactivating nucleotidase (PanA) was performed whic h exemplifies such a scenario. The analysis included a combination of high-throughput phospho-enrichment methods, sub-enrichment strategies and tandem ma ss spectrometry (MS/MS). Results and Discussion Construction of H. volcanii PanA Mutant GG102 In addition to three 20S proteasomal proteins ( 1, 2 and ), H. volcanii encodes two proteasome-activating nucleotidase proteins (PanA and PanB) wh ich are 60% identical. Of these, the 1, and PanA proteins are re latively abundant during all phases of growth (on rich medium at moderate temperature, 37 to 42C) (Reuter et al., 2004). In contrast, the levels of PanB and 2 are relatively low in early phases of grow th and increase several-fold during the transition to stationary phase (R euter et al., 2004). To further investigate the function of the predominant Pan protein, PanA, a mutation wa s generated in the chromosomal copy of the encoding panA gene (G. Gil; see Materia ls and Methods, chapter 2 for details). A suicide plasmid, pJAM906, was used to gene rate this mutation in which panA had a 187-bp deletion in addition to an insertion of the H. hispanica mevinolin resistance marker. Recombinants were selected on rich medium supplemented with mevinolin and screened by colony PCR using primers which annealed outside the chromosoma l region that had been cloned on the suicide plasmid. Approximately 10% of the clones th at were screened generated the expected panA mutant PCR product of 2.50 kb compared to the pa rent strain PCR product of 1.28 kb (data not

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115 shown). Of the PCR positive clones, one (GG102) was selected for further analysis by Western Blot using anti-PanA antibodies. The PanA prot ein was readily detected by Western Blot for parent strain DS70 (Fig. 5-1). In contrast, PanA was not detected unde r any growth conditions for the panA mutant GG102, thus, confirming the mutation. The 20S Proteasome and PanB Prot ein Levels Are Not Altered by the panA Mutation To determine whether the panA mutation influenced the levels of 20S proteasome and/or PAN proteins, DS70 and GG102 were analyzed by Western blot using polyclonal antibodies raised separately against 1, 2 and PanB proteins (where 1 and 2 are subunits of 20S proteasomes). No differences in the levels of any of these three proteasomal proteins were detected between the two strains when grown to log phase in minimal medium (Fig. 5-1). Thus, H. volcanii does not appear to have a feedback mechanis m to increase the levels of PanB or 20S proteasomes after loss of PanA under the growth conditions examined in this study. Growth Phenotype of panA Mutant GG102 Compared to Its Parental and Complemented Strains Comparison of GG102 panA to its parent DS70 revealed significant differences in growth in rich medium (Fig. 5-2). This included an in crease in doubling time from 4.1 to 5.2 h as well as decrease in overall cell yield from an O.D. 600 nm of 2.1 to 1.8. Both doubling time and overall cell yield were at least part ially restored by complementation with plasmids pJAM650 and pJAM1020 encoding PanA-His6 and PanB-His6 proteins respectively. The ab ility of this latter plasmid to partially complement the panA mutation for growth may be due at least in part to the high-level expression of PanB from the pHV2-d erived plasmid pJAM1020 which includes both a strong rRNA P2 promoter of H. cutirubrum and T7 terminator to mediate transcription of the panB-his6 gene fusion. This is likely to alter the levels of PanB protein, normally low in early phases of growth and increased several-fold during the transiti on to stationary phase. Although

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116 the PanA and PanB proteins may have ove rlapping functions ba sed on this partial complementation for growth, these proteins di ffer significantly in primary sequence (by 40%) and growth-phase dependent regulation (Reuter et al., 2004) and, thus, are likely to degrade proteins differentially, in terms of substrate preference and/or rate. Comparative 2-DE Analysis of the panA Mutant to Its Parent Expression of the panA and panB his6 -gene fusions using a rRNA P2 promoter on a high copy plasmid is likely to alter proteo me composition in the complemented panA mutant compared to wild type. To avoid this comp lexity, parental strain DS70 was used as wild type for proteome comparison to the panA mutant GG102. Analysis of the cell lysate of DS70 and GG102 by 2-DE revealed a comparable number of total protein spots as detected by SYPRO Ruby protein stain, at 333 ( 17) and 314 ( 14) spots, respectively. However, further analysis of the 2-DE separated proteins by Pro-Q Diam ond (through the efforts of I. Karadzic), a phosphoprotein-specific fluorescen t dye developed by Steinberg et al. (Steinberg et al., 2003) revealed more than twice as many phosphoprotein spots for the panA mutant (64 7) compared to wild type (31 6) (Fig. 5-3). This difference in the number of protein spots stained by Pro-Q Diamond in the panA mutant constitutes an average in crease of 11.1% from total spots with an average variability of less than 2% across experimental re plicates, indicating a reproducible difference in the phosphoproteomes of these strains. As much as 40-44% of the Pro-Q Diamond stained phos phproteins were not detected by SYPRO Ruby and, thus, are likely to be at relatively low levels in the cell while those detected by both methods are of substantial abundance. These high-abundance proteins within the common proteome are likely to include ribosomal proteins and transl ation elongation factor subunits as determined through MS/MS identification of major landmark spots in past analyses and remarkably high abundance of proteins identified in both total and phosphoe nriched samples in non-gel-based proteomic

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117 experiments. Interestingly, the limited impact of the panA mutation on the total number of protein spots detected by SYPRO Ruby contrasts with the over 1.7-fo ld increase after addition of the 20S proteasome-specific inhibitor clasto -lactacystin-lactone ( c L L) (Kirkland et al., 2007). This may reflect the central role of the N-terminal Thr proteolytic active site of the 20S subunit, which is irreversibly inactivated by c L L, in all proteasome-mediate protein degradation in this archaeon. In contrast, the PanA and ot her closely related AAA+ ATPases are more likely to play upstream roles including differential substrate recognition, unfol ding and translocation into the 20S proteasome core for degradation. Phosphoprotein and Phosphopeptide Enrichments Immobilized metal affinity chromatogr aphy (IMAC) and metal oxide affinity chromatography (MOAC) were used in four sepa rate experiments (A to D) to enrich for phosphoproteins and/or phosphopeptides from the cell lysate of the panA mutant (GG102) and its parent (DS70). A similar pa ttern and enrichment of phosphoprot eins compared to cell lysate was confirmed by fluorescent staining with ProQ Diamond of 2-DE separated IMAC fractions (data not shown). As an initia l approach to protein identifica tion, IMAC-purified samples were separated by 12% SDS-PAGE (one-dimensionally) and bands that appeared uniquely in the mutant or in discernibly different amounts were excised and digested in-gel with tr ypsin prior to hybrid MS/MS analysis (LCQ Deca MS; Experime nt A). This analysis produced a modest number of protein identifications (10 total) with MOWSE scores at 30 or above and, thus, was not further pursued as a viable approach for high-throughput analysis of these strains. To overcome these limitations, biological triplicat e samples of GG102 and DS70 were subjected to three separate approaches: 1) IMAC enrichment of proteins followed by cleavage with trypsin, 2) MOAC enrichment of methyl es terified tryptic peptides, and 3) IMAC enrichment of proteins followed by MOAC enrichment of methyl esteri fied tryptic peptides (Experiments B to D,

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118 respectively). Peptides from all three of these latter approaches were separated by C18 reversed phase HPLC and analyzed online using ESI-QTOF hybrid mass spectrometry. In total, these approaches resulted in the identificati on of 625 proteins with MOWSE scores at 30 or above and with at least 2 peptides identified pe r protein. This corresponds to 15.4% of deduced proteome of H. volcanii based on the DS2 genome sequence minus proteins encoded by pHV2 (the plasmid cured from DS70 a nd its derivatives). The identified proteins ranged from 4.9 to 186 kDa with an average of 41.6 kDa in molecular mass. The majority of the identified proteins (523 proteins or 83% of total) were enc oded on the 2.848-Mb chromosome. The coding sequences for the remaining 202 proteins were distributed among pHv4 (52 proteins), pHv3 (43 proteins), a nd pHv1 (7 proteins). This repr esents 8 to 17% of the coding capacity for each element (18% of the 2.848Mb chromosome, 8.2% of pHv4, 11.2% of pHv3 and 7.9% of pHv1). Of the total proteins iden tified, 142 were based on high probability scores and multiple peptide identificati ons within a single biological replicate and, thus, were not assigned as unique or common to DS70 or GG102. The remaining pr oteins were categorized as either common to GG102 and DS70 (328 proteins), unique to DS70 (57 prot eins), or unique to GG102 (98 proteins). The number of proteins identified by MS/MS in these phosphoprotein enriched fractions that were exclusive to GG102 was nearly 2-fold higher than the number of identified proteins exclusive to DS70. This relative difference in MS/MS identified strain specific-proteins is consistent with the differences observed in the number of Pro-Q Diamond stained 2-DE gel spots from the proteomes of these two strains. The 480 proteins that were cate gorized as either common or exclusive to either strain (DS70 and GG102 panA ) were identified based on an average detection per protein of 4.7 tryptic peptides in 5 biological rep licates and a MOWSE score aver age of 76. Among the proteins

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119 common to both strains, 8 to 9% were estima ted to be more abundant in either GG102 (15 proteins) or DS70 (13 proteins) based on spectral counts. Of th e total 625 proteins identified, only a small portion (32 proteins or 5.1%) were predicted to form transmembrane spanning helices (TMH) compared to the approximately 22% putative TMH-proteins encoded on the genome. The TMH proteins were detected pr imarily in GG102 (over 80%) with nearly 50% classified as unique to this st rain. A lower proportion of TMH pr oteins were detected in DS70 (53%) with only 2 of these (6%) grouped as excl usive to this strain. While the significantly higher number of identified predic ted membrane proteins in the panA deletion strain compared to the wild type is intriguing, the overall number identified is low and likely to require preparation of membranes to enhance their de tection (Klein et al., 2005). A summary of proteins identif ied as either unique to or more abundant in either GG102 or DS70 are listed in Tables 5-1 and 5-2. At this stage in our understanding, it is unclear whether these differences in proteomes between the two strains reflect a change in the phosphorylation status of the protein or over all protein abundance. However, the identifica tion of these differences was reproducible. As a confirmation to the appr oaches, the PanA protein was exclusively and reproducibly detect ed in the DS70 parent strain across all proteomic analyses. The PanA protein was not dete cted in GG102, consistent with the targeted knockout of the encoding panA gene from the chromosome of this strain (Fig. 5-1). Categorization of Unique Proteins into Clusters of Orthologous Groups The 156 proteins identified as exclusive to either GG102 or DS70 ( listed in Table 5-1) were categorized into Clusters of Orthologous Gr oups (COGS) as indicated in Fig. 5-4. Overall, the largest percentage (3 6%) of these proteins was equally distributed between two groups: 1) those of unknown or general function [R/S] and 2) those involved in amino acid transport and metabolism [E]. The remaining proteins cluste red into a diversity of functional COG groups

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120 ranging from translation to signal transduction as outlined in Fig. 5-4. Comparison of the panA mutant to its parent (GG102 vs DS70) revealed notable differe nces in the distribution of unique proteins within many of these groups. In particular, the per centages of proteins exclusive to GG102 panA were considerably higher than those unique to DS70 in energy production and conversion [C], posttranslational m odification, protein turnover, and chaperones [O], cell envelope biogenesis and outer memb rane [M], and inorganic ion transport and metabolism [P]. In contrast, th e percentages of proteins which clustered to transcription [K], translation, ribosomal structure and biogenesis [J], and nucleotid e transport and metabolism [F] were higher in DS70 compared to GG102. The in creased number of proteins involved in translation, nucleotide biosynthesi s, and transcription for DS70 ma y be reflection of the higher growth rate and overall cell yield of this strain compared to GG102 panA Although the increased number of GG102-unique proteins clustering to ener gy production and conversion [C] would seem to contradict this suggestion, the vast majority of these latter proteins were oxidoreductases (10 out of 11) including a homolog of Old Yellow Enzyme, shown to increase during periods of oxidative stress in Bacillus subtilis (Fitzpatrick et al., 2003). The remaining GG102-unique protein of this cluster was a glycerophosphoryl diester phosphodiesterase ( ugpQ ), an enzyme which can be an indicator of phos phate starvation (Antelmann et al., 2000). In contrast to GG102, the two DS70-uni que proteins which clustered to this general COG group [C] were predicted to be involved in the gene ration of proton motive force including an ATP synthase subunit and plastocy anin-/cytochrome-like protein. One of the most notable differences in the protein profiles of the panA mutant and its parent was linked to phosphorus assimilation an d synthesis of polyphosphate (the PHO regulon, many members of which cluster to group [P])(su mmarized in Fig. 5-5). Proteins of the PHO

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121 regulon exclusively id entified in GG102 included homol ogs of phosphate/phosphonate ABCtype transporters ( pstB2 pstS2 and phnC ), PHO regulators ( abrB_phoU2 ), polyphosphate kinase ( ppk ), ABC-transporter glycerol-3 -phosphate binding protein ( ugpB ), and glycerophosphoryl diester phosphodiesterase ( ugpQ ; discussed above). Many additional proteins unique to GG102 were encoded within these genomic regions ( i e. hisH HVO_A0476, trxB metK and cysK ). In addition, a transcriptional regu lator (HVO_0730) unique to DS70 was found to be divergently transcribed from a gene enc oding inorganic pyrophosphatase ( ppa ). Components of the ABCtype Pst2 transporter uniquely identified in GG 102 are encoded on pHV4 a nd appear ancillary to a paralogous ABC-type Pst1 transporter comm on to both DS70 and GG102, which is encoded on the 2.848-Mb chromosome. The up-regulati on of components of phosphate uptake and metabolism typically corresponds to cellular stress (Tam et al., 2006; Fischer et al., 2006) with the levels of polyphosphate impacting cell surviv al (Brown and Kornberg, 2004). Proteomic and microarray analysis of archaeal species such as Methanosarcina acetivorans has revealed the PHO regulon to be expressed at higher leve ls during growth on the lower energy-yielding substrate acetate vs. meth anol (Li et al., 2007b). In addition to alterations in the PHO regulon, the panA knockout resulted in an increased enrichment and identification of proteins linked to protein folding, Fe-S cluster assembly and oxidative stress response compared to parent strain DS70. These included components of a thiosulfate sulfurtransferase ( tssA tssB ) and Fe-S assembly system ( nifU sufB sufC ), thioredoxin/disulfide reductases ( trxB1 and trxB2 ), topoisomerase A ( topA ), replication A-related ssDNA binding protein (RPA), excinuclease ABC ATPase subunit ( uvrA ), Old Yellow Enzyme ( yqjM discussed above), Hsp20 molecular chaperone ( ibpA ), OsmC-like regulator ( osmC ), peptidyl-prolyl cis-trans-isomerase ( slyD ), S-adenosylmethionine synthase, and cysteine

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122 biosynthetic enzymes ( e.g. cysK serA, OAH) (Tables 4-1 and 4-2). Interestingly, in E. coli the osmotically-inducible OsmC is proposed to use highly reactive cysteine thiol groups to elicit hydroperoxide reduction (Lesniak et al., 2003) and is downstr eam of a complex phosphorelay system which includes regulation by the AAA+ proteases Lon and HslUV (Kuo et al., 2004; Majdalani and Gottesman, 2005). A number of additional differences in transc ription and signal trans duction proteins were also observed between DS70 and GG102 panA For example, subunits of RNA polymerase were altered by the panA mutation with Rpb4 more abundant in DS70 fractions and RpoA and RpoB ( and ) higher in those of GG102 (Tab le 5-2). The altered leve ls of Rpb4 in DS70 vs. GG102 are consistent with the recent findi ng that the levels of this prot ein are directly correlated with eukaryotic cell growth ( i.e. cells expressing lower levels of R pb4 grow slower compared to cells expressing higher levels)(Sharma et al., 2006). In addition to the above noted differences, a BolA-like protein and conditioned medium-induced pr otein 2 (Cmi2) were ex clusively identified in DS70. BolA triggers the formation of osmo tically stable round cells when overexpressed in stationary phase E. coli (Santos et al., 2002), and Cmi2, a puta tive metal-regulated transcriptional repressor, may be regula ted by quorum sensing in H. volcanii (Bitan-Banin and Mevarech, GenBank AAL35835). A number of putative DNAbinding proteins and ho mologs of methylaccepting chemotaxis proteins (MCP) were also f ound to be altered. In addition, a number of mandelate racemase/muconate lactonizing enzyme s related to the starvation sensing protein RspA of E.coli (Huisman and Kolter, 1994) were altered by the panA mutation. Some archaea do not en code Pan proteins ( e.g. species of Thermoplasma, Pyrobaculum and Cenarchaeum ). Thus, it is speculated that Cdc48/VCP/p97 AAA+ ATPases, which are universal to archaea, may function with 20S pr oteasomes similar to e ukaryotes (Jentsch and

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123 Rumpf, 2007). Interestingly, two Cdc48-like AAA+ ATPases were identifie d as either exclusive or more abundant in GG102 panA compared to DS70 (HVO_1327 and HVO_2700; Tables 1 and 2, respectively) suggesting these proteins may compensate in part for the loss of the AAA ATPase function of PanA. A TMH homolog of prohibitin (HVO_0035) known to regulate AAA protease function (Arnold and Langer, 2002) was also identified as exclusive to GG102. Intriguing similarities also emerged betw een the proteomic profiles of this study compared to our previous work in which cells were treated with the 20S proteasome-specific inhibitor clasto -lactacystin-lactone (cL L) and analyzed by 2-DE (Kirkland et al., 2007). Specifically, three proteins that accumulated mo re than 5-fold in cells treated with cL L were also discovered in notabl y higher abundance in the panA mutant (vs. parent) strain of this study. This subset of proteins include d the DJ-1/ThiJ family protease, the translation elongation factor EF-1 and the Fe-S cluster assembly ATPase SufC (Table 5-2). In addition, FtsZ cell division protein homologs and components of the putative phosphoenolpyruvate phosphotranferase system which appear to be coupled to dihydroxy acetone kinase (PtsI, DhaK, DhaL) were also found at higher levels in GG102 panA and cL L-treated cells compared to wild type. Phosphopeptide Identification Based on favorable likelihood scores using the Mascot search algorithm and careful manual analysis of the peptide spectra, 9 indivi dual phosphosites including 4 serine, 4 threonine and 1 tyrosine modification sites were tentatively identified and mapped to a total of 8 proteins (Table 5-3; Appendix B, Fig. B-1) Five of these phosphosites we re identified ex clusively by the combined IMAC-MOAC approach (Experiment D). The remaining four sites were identified by the combined IMAC-MOAC process as part of an overlapping data set wi th at least one other phosphoanalysis method within this study (Table 53). Although neural networks are not yet available for archaeal protein phosphosite predic tions based on the limited dataset available for

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124 this domain, many of the putative phosphosites identified in this study had NetPhos scores above the threshold of 0.5, which is based on eukaryotic phosphosites. Among the H. volcanii phosphoproteins identified, the Cdc48-related AAA+ ATPase (HVO_2700), which was more abundant in GG 102 enriched fractions than DS70 based on spectral counting, was represented by two iden tical phosphorylated peptides occurring in separate experiments for both strains (Table 5-3). Both inclusive and exclusive Y-series ions and internal ion fragments contai ning a phosphorylated Thr45 residue were identified (numbering throughout based on the deduced polypeptide sequ ence; Appendix B, Fig. B-1). Similarly, a protein of unknown function (HVO_A0206) was al so represented by duplicate peptides phosphorylated at Ser88 in separa te experiments (Table 5-3). Although the function of this ORF is unknown, it is transcribed in the same orientation two ge nes encoding members of the clustered regularly interspaced short palindromic repeats (CRISPR) family of proteins and of which were identified by MS/MS as comm on to GG102 and DS70. A DNA-directed RNA polymerase subunit A (RpoA) was determined to have a phospho-modification at Tyr117 (Table 5-3). The discovery of the Tyr117 phosphosite, which had the highest NetPhos score (0.962) and lowest expect valu e (1.4 e-4) of the phosphosites identified, was supported by the appearance of three separa te internal fragment io ns containing the modified tyrosine residue as well as an inclusive and exclus ive y-ion series (Appendix B, Fi g. B-1). Other phosphoproteins included a Cdc6-1/Orc1-1 ortholog (HVO_0001) a nd pyruvate kinase (Table 5-3). Although both proteins were common to DS70 and GG102 based on MS/MS identification, the phosphopeptides of these proteins were only detected in GG102 panA The reason for the exclusive appearance of these latter phosphopeptides in the panA mutant strain remains to be determined; however, it does implicate the prot eins corresponding to these phosphopeptides as

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125 potential proteasomal substrates that depend upon their phosphoryla tion status for destruction. Phosphosites were also identified on components of membrane systems including an ATPase of an ABC-type transporter as we ll as an integral membrane pr otein with a TrkA (NAD-binding) domain (Table 5-3). A hypothetical protein (HVO_C0059) was also identified as a putative phosphoprotein based on the detect ion of one doubly modified pe ptide fragment at residues Ser136 and Thr139 (Table 5-3). Many of the phosphorylated proteins identifi ed and listed in Table 5-3 are supported by evidence of the phosphorylation and proteasome-mediated destruction of their orthologs. For example, eukaryal RNA polymerases II and III are phosphorylated at tyro sine, serine, and/or threonine residues (Solodovnikova et al., 2005; Jing et al., 2005), and ubiquitin E3 ligase Wwp2 targets the Rpb1 subunit of RNA polymerase II (related to H. volcanii RpoA) for destruction by 26S proteasomes (Li et al., 2007b). Eukary al and archaeal Cdc6 proteins undergo autophosphorylation at serine residues as a pr esumed means of regulat ing their DNA helicase loading activity (Grabowski and Kelman, 2001; De Felice et al., 2003). Furthermore, the ubiquitin/proteasome-mediated dest ruction of Cdc6 in both mammalian and yeast cells has been observed and is proposed to be a mechanism fo r uncoupling DNA replicati on and cell division as part of a pre-programmed cell death response (B lanchard et al., 2002). Cdc48/p97/VCP proteins undergo Akt-mediated phosphorylation at multiple sites as a mode of ubiquitinated protein substrate release to facilitate degradation by prot easomes (Klein et al., 2005). In addition, these AAA+ ATPase proteins are phosphorylated by DNA-pr otein kinase (DNA-PK) at serine residues as part of a proposed mechanism to enable chaperone activit y and to aid in DNA repair (Livingstone et al., 2005).

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126 Conclusions Based on the results of this study, a global in crease in the number of phosphoproteins appears to occur in the presence of a panA mutant compared to wild type cells. Although several of the proteins enriched and id entified as exclusive to GG102 panA are likely involved in phosphorous assimilation and metabolism and, thus may bind phosphate non-covalently, this alone cannot account for the large-scale diffe rence in phosphoprotein staining between the panA mutant and its parent. Therefore, we propose th at the phosphorylation of protein substrates may facilitate their recognition fo r proteasome-mediated destruc tion in organisms for which traditional ubiquitination pathways are absent ( e.g ., archaea, actinomycetes). It is also possible that the observed changes in phosphoprotein content reflect a secondary effect of the maintained presence of a kinase or enhan ced digestion of a phosphatase. It has been demonstrated in eukaryotes that protein phosphorylation can serve as a precursor to ubiquitin tagging a nd subsequent degradation by 26 S proteasomes (Karin and Ben Neriah, 2000; Lin et al ., 2006). Furthermore, phosphorylation of conserved sequences rich in proline, glutamic and aspartic acids, serine and threonine (PEST sequences) is a well-known example of post-translational modification as a destabilizing force on substrate proteins (Rechsteiner and Rogers, 1996; Garcia-Alai et al., 2006). The link between protein phosphorylation and proteasomal degradation is also supported through the function of accessory structures like the COP9 signalosome, which ha s been indicated as a coordinator of protein kinases and ubiquitin ligases in plant systems (Harari-Steinb erg and Chamovitz, 2004). The predominant proteins identified as exclusive to the panA mutant GG102 (vs. DS70) were linked to protein folding, Fe-S cluster as sembly, oxidative stress response and phosphorus assimilation and polyphosphate synt hesis. A number of additional differences in transcription and signal transduction proteins ( e.g. OsmC, BolA, Cmi2) were also observed between these two

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127 strains which, together with th e growth defect, suggest the panA mutant is undergoing stress and accumulating polyphosphate. Consistent with this, a distantly relative of PanA, the ATPase ring forming complex of mycobacteria, is presumed to associate with 20S proteasomes and serve as a defense against oxidative or nitrosative stress (D arwin et al., 2003). Prot easomal inhibition has also been shown to hypersensitize differentiated neuroblastoma cells to oxidative damage (Lev et al., 2006). Interestingly, a polyphosphate-Lon prot ease complex is proposed in the adaptation of E. coli to amino acid starvation (Kur oda et al., 2001). Whether ar chaeal proteasomes are linked to polyphosphate remains to be determined; how ever, short-chain polyphosphates identical to those of Saccharomyces cerevisiae have been detected in H. volcanii cells grown under amino acid starvation (Scoarughi et al., 1995).

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128 Figure 5-1. Western blot anal ysis of proteasomal subunits 1 and 2 and proteasome-associating regulatory particles PanA and PanB. West ern blot analysis was performed using increasing amounts of total cell lysate from either DS70 (lanes 1, 3 and 5) or GG102 (lanes 2, 4 and 6).

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129 0 0.5 1 1.5 2 2328333843485358 Time (h)Optical Density (600nm) Figure 5-2. A growth curve plotting proliferation of wild type and complemented panA knockout mutant in H. volcanii Wild-type cells ( ), panA mutant GG102 uncomplemented ( ), GG102 complemented with plasmid-based panA ( ) and GG102 complemented with plasmid-based panB ( ) were compared.

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130 Figure 5-3. ProQ Diamond phosphoprot ein stained 2D PAGE gels of total cell lysate extracted from (A) DS70 and (B) GG102.

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131 0 2 4 6 8 10 12 14 16 18 20ER/SCGKJOLFMPN/UTVD/BIHCOG GroupMembers of COG (%) DS70 +GG102 GG102 DS70 Figure 5-4. Proteins identified by mass spectr ometry grouped according to COG (clusters of orthologous groups) database. Protein me mbers of each COG are summarized as percent of total proteins unique to GG102 and DS70 (striped bars), unique to GG102 (black bars), and unique to DS70 (white ba rs). COG groups listed include: E [Amino acid transport and metabolism], R/S [Gen eral function prediction only/Function unknown], C [Energy production and convers ion], G [Carbohydrat e transport and metabolism], K [Transcription], J [Transla tion, ribosomal structure and biogenesis], O [Posttranslational modification, protein tu rnover, chaperones], L [DNA replication, recombination, and repair], F [Nucleo tide transport and metabolism], M [Cell envelope biogenesis, outer membrane], P [Inorganic ion transport and metabolism], N/U [Cell motility and secre tion/Intracellular trafficki ng], T [Signal transduction mechanisms], V [Defense mechanisms], D/B [Cell division and chromosome partitioning], I [Lipid metabolism], and H [Coenzyme metabolism].

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132 ORFA00568 phoU3 ORFA00569 pstB2 ORFA00570 pstA2 ORFA00571 pstC2 ORFA00572 pstS2 ORFA00573 conserved ORFA00574 SOD ORFA00575 ORFA00576 TMH protein ORFA00577 trxB ORF02618 phoU1 ORF02620 ORF02619 pstB1 ORF02621 pstA1 ORF02622 pstC1 ORF02623 pstS1 ORF02624 abrB_phoU2 ORF00279 cysK ORF00280 ORF00281 ORF00282 thiI _tRNAmodification ORF00283 ppk ORF00284 metK ORF00285 cyaA ORF00286 cox1 ORF02383 ZnCad efflux ORF02384 His triad ORF02385 map ORF02386 prpA ORF02387 ppk2 ORF01506 leuS ORF01507 hsp ORF01508 hsp ORF01509 pheA ORF01510 hisH ORF01512 hbp ORF01513 phnC ORF01514 phnE ORF01227 ppa ORF01226 transcriptional regulator ORFB00348 suhB ORFB00349 ugpQ ORFB00350 ugpB ORFB00351 ugpA ORFB00352 ugpE ORFB00353 ugpC 1000 bp Figure 5-5. Organization of genes linked to P HO regulon which encode pr oteins altered by or are related in primary sequence to those altered by the GG102 panA mutation. Genes highlighted in black encode proteins uni que to GG102, in grey encode proteins unique to DS70, marked with diagonal li nes encode proteins common to DS70 and GG102, and in white encode proteins not identified in this study.

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133 Table 5-1. Phospho-enriched proteins uni quely identified in each strain of H. volcanii ORF no. GenBanka Predicted Function, Descriptionb GG102 unique: HVO_0035 TMH protease regulator (stomatin, prohibitin) HVO_0058 oligopeptide ABC transporter ATP-binding protein HVO_0070 NifU/thioredoxin-related protein ( nifU ) HVO_0136 translation initiation factor eIF-1A HVO_0177 arsenate reductase (ArsC) and protein-tyrosine-phosphatase (Wzb) related HVO_0393 excinuclease ABC ATPase subunit ( uvrA ) HVO_0433 NADPH-dependent F420 reductase ( npdG ); dinucleotide binding HVO_0446 phosphate/phosphonate ABC transporter, ATP-binding protein ( phnC ) HVO_0448 imidazole glycerol phosphate synthase glutamine amidotransferase subunit ( hisH ) HVO_0480 3-phosphoglycerate kinase ( pgk ) HVO_0519 replication A related (single-stranded DNA-binding) protein (RPA) HVO_0602 3-dehydroquinate dehydratase ( aroD ) HVO_0620 Type II/IV secretion system and related ATPase protein HVO_0627 dipeptide ABC transporter ATP-binding HVO_0681 TopA DNA topoisomerase I HVO_0766 Hsp20 molecular chaperone ( ibpA ) HVO_0788 tryptophan synthase subunit ( trpB ) HVO_0829 prolyl oligopeptidase HVO_0884 aldehyde reductase (COG0656) HVO_0887 2-oxoacid:ferredoxin oxidoreductase, subunit HVO_0889 FAD/NAD binding oxidoreductase HVO_0894 acetate-CoA ligase ( acsA ) HVO_1009 oxidoreductase related to aryl-alcohol dehydrogenases ( aad ) HVO_1022 NADH:flavin oxidoreductase related to Old Yellow Enzyme ( yqjM ) HVO_1027 Twin arginine translocation TMH protein TatAo HVO_1037 conserved protein HVO_1047 NADPH:quinone oxidoreductases ( qor ) HVO_1061 thioredoxin reductase ( trxB1 ) HVO_1113 FtsZ cell division GTPase HVO_1121 heme biosynthesis protein ( pqqE ) HVO_1170 conserved protein HVO_1203 flagella protein E-related HVO_1272 transcriptional regulator HVO_1273 inosine-5'-monophosphate dehydrogenase, CBS pair domain HVO_1289 OsmC-like regulator ( osmC ) HVO_1299 HTH DNA-binding XRE-/MBF1-like protein HVO_1309 Xaa-Pro dipeptidase HVO_1327 Cdc48 related AAA+ ATPase HVO_1446 fructose-1 6-bisphosphatase ( fbp ) HVO_1495 phosphotransferase system IIB component ( fruA ) HVO_1507 acetolactate synthase small regulatory subunit ( ilvN ) HVO_1513 conserved protein HVO_1527 glucose-1-phosphate thymidylyltransferase ( galU ) HVO_1576 UDP-glucose 4-epimerase ( gmd ) (COG0451) HVO_1578 NADH dehydrogenase, FAD-binding subunit HVO_1588 cupin (small barrel) domain protein

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134 Table 5-1. Continued ORF no. GenBanka Predicted Function, Descriptionb GG102 unique [cont.]: HVO_1637 peptidyl-prolyl cis-trans isomerase HVO_1649 S-adenosylmethionine synthetase HVO_1650 polyphosphate kinase ( ppk ) HVO_1654 cysteine synthase ( cysK ) HVO_1788 ferredoxin-nitrite/sulfite reductase ( nirA, cysI ) HVO_1830 6-phosphogluconate dehydrogenase HVO_1894 conserved TMH protein HVO_1932 phosphoglycerate dehydrogenase ( serA ) HVO_1936 F420-dependent oxidoreductase HVO_1973 conserved protein HVO_2040 UDP-glucose 4-epimerase ( gmd ) (COG0451) HVO_2045 hypothetical protein HVO_2072 conserved TMH protein HVO_2081 conserved TMH protein HVO_2214 MCP domain signal transducer TMH protein (Htr, Tar) (COG0840) HVO_2270 HsdM type I restriction enzyme HVO_2271 HsdS restriction endonuclease S subunit HVO_2361 carbamoyl-phosphate synthase large subunit ( carB ) HVO_2374 PhoU-like phosphate regulatory protein HVO_2516 2 3-bisphosphoglycerate-independent phosphoglycerate mutase HVO_2524 phytoene/squalene synthetase ( crtB ) HVO_2542 ribosomal protein L15 ( rplO ) HVO_2589 asparaginase HVO_2622 aldehyde reductase (COG0656) HVO_2661 aspartate aminotransferase ( aspC ) (COG0075) HVO_2662 dioxgenase HVO_2671 aminotransferase class V (COG0075) HVO_2690 MviM oxidoreductase HVO_2716 acyl-CoA dehydrogenase ( acd ) HVO_2742 methionine synthase II ( metE ) HVO_2759 TET aminopeptidase HVO_2760 non-conserved TMH protein HVO_2767 DNA/RNA helicase HVO_2819 phage integrase family domain protein HVO_2883 snRNP-like protein HVO_2948 phenylalanyl-tRNA synthetase chain ( pheS ) HVO_2997 O-acetylhomoserine sulfhydrylase (OAH, SHLase) HVO_A0267 mandelate racemase/muconate lactonizing enzyme (COG4948) HVO_A0331 mandelate racemase/muconate lactonizing enzyme (COG4948) HVO_A0339 ABC transporter extracellular solute-binding protein HVO_A0472 thioredoxin reductase ( trxB2 ) HVO_A0476 conserved pst -operon protein HVO_A0477 PstS phosphate ABC transporter, solute binding protein HVO_A0480 PstB phosphate ABC tr ansporter ATP-binding protein HVO_B0130 DNA binding protein (DUF296) HVO_B0173 SMC-ATPase like chromosome segregation protein HVO_B0248 oxidoreductase (COG4221) HVO_B0265 oxidoreductase HVO_B0268 alkanal monooxygenase-like HVO_B0291 glycerophosphodiester phosphodiesterase ( ugpQ ) HVO_B0292 ABC-type transporter, glyc erol-3-phosphate-binding protein ( ugpB ) HVO_B0371 aldehyde dehydrogenase ( putA )

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135 Table 5-1. Continued ORF no. GenBanka Predicted Function, Descriptionb DS70 unique: HVO_0017 conserved protein HVO_0043 aminotransferase ( gabT argD ) HVO_0117 translation initiation factor eIF-6 HVO_0161 ATP phosphoribosyltransferase ( hisG ) HVO_0314 archaeal/vacuolar-type H+-ATPase subunit C/AC39 HVO_0353 ribosomal protein S23 (S12, RpsL) HVO_0420 MCP domain signal transducer (Tar) (COG0840) HVO_0549 2-keto-3-deoxygluconate kinase HVO_0730 transcription regulator transcribed divergent from inorganic pyrophosphatase in haloarchaea HVO_0827 conserved protein (COG4746) HVO_0850 proteasome-activating nucleotidase A ( panA ) HVO_0853 DNA double-strand break repair protein ( mre11 ) HVO_1085 bifunctional purine biosynthesis protein ( purH ) HVO_1087 Usp universal stress protein (COG0589) HVO_1173 SpoU-like RNA methylase (COG1303) HVO_1228 PetE plastocyanin/Fbr cytochrome related protein HVO_1245 DsbA-/DsbG-like protein-disulf ide isomerase (thioredoxin domain) HVO_1426 conserved protein HVO_1443 ABC transporter, ATP-binding protein HVO_1472 putative transcriptional regulator with CopG DNA-binding domain HVO_1492 conserved protein HVO_1502 3-isopropylmalate dehydrogenase ( leuB ) HVO_1683 4-glucanotransferase ( malQ ) HVO_1741 conserved protein HVO_1829 leucyl aminopeptidase T ( pepB, ampS ) HVO_1901 translation initiation factor eIF-2 (GTPase) HVO_2102 PTS system enzyme II A component, putative HVO_2126 ABC transporter, oligopeptide-binding protein HVO_2239 Usp universal stress protein (COG0589) HVO_2333 conserved protein HVO_2504 3-oxoacylacyl-carrier protein reductase ( atsC ) (COG4221) HVO_2514 transcriptional regulator (conditioned medium-induced protein 2 or Cmi2) HVO_2614 uridine phosphorylase ( udp ) HVO_2675 histidinol dehydrogenase ( hisD ) HVO_2723 snRNP homolog (LSM1) HVO_2726 glutamyl-tRNA synthetase ( gltX, glnS ) HVO_2738 ribosomal protein S28e (S33) HVO_2782 ribosomal protein S11 ( rpsK ) HVO_2798 ABC transporter, branched-chain amino acid binding protein HVO_2899 BolA, stress-induced morphogen HVO_2916 short chain alcohol dehydrogenase ( fabG ) HVO_2943 dihydroorotate oxidase ( pyrD ) HVO_2981 uracil phosphoribosyltransferase ( upp) HVO_A0078 helicase SNF2/RAD54 family HVO_A0279 Tnp transposase HVO_A0295 N-carbamoyl-L-amino acid amidohydrolase ( amaB ) HVO_A0386 hydantoin utilization protein B ( hyuB ) HVO_A0388 transcriptional regulator AsnC/Lrp family HVO_A0487 cobyrinic acid a c-diamide synthase ( cobB ) HVO_A0635 aminotransferase class V (CsdB-related)

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136 Table 5-1. Continued ORF no. GenBanka Predicted Function, Descriptionb DS70 unique [cont.]: HVO_B0053 conserved protein in cobalamin operon HVO_B0084 GlcG-like protein possibly involved in glycolate and propanediol utilization HVO_B0112 mandelate racemase / muconate lactonizing N-terminal domain protein (COG4948) HVO_B0151 Ribbon-helix-helix transcriptional regulator of CopG family HVO_B0154 OYE2 11-domain light and oxygen sensing Hi s kinase, member of two component system HVO_B0233 -L-arabinofuranosidase ( xsa, abfA ) HVO_C0017 chromosome partitioning ATPase (ParA, Soj) aORFs are numbered according to the GenBank assembly (Hartman et al ., in preparation) of the H. volcanii genome as indicated. ORF numbers highlighted in grey are predicted to be coor divergently-transcribed. bDescriptions highlighted in grey indicate paralogous ORFs identified as unique to e ither GG102 or DS70. COG numbers are included for these paralogues. Proteins were classified as unique if they were exclusively identified in at least two samples of either GG102 or DS 70 with MOWSE score averages of 30 or higher and at least two peptide hits.

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137 Table 5-2. Proteins with a minimum 2-fold abunda nce, comparatively, as determined by spectral counting ORF no. GenBank Predicted Function and/or Description Exp.a DS70b MOWSE GG102 MOWSE DS70c Spec. Count GG102 Spec. Count Diff. (%) HVO_1073 DJ-1,PfpI family protein B 46 126 0 8 100 HVO_1546 Dihydroxyacetone kinase subunit DhaK B 49 78 0 7 100 HVO_1496 PstI phosphoenolpyruvate-protein phosphotransferase B 40 112 0 7 100 HVO_2923 20S proteasome 2 subunit PsmC B 37 70 0 6 100 HVO_0025 Thiosulfate sulfurtransferase TssA B, D 41 100 0 6 100 HVO_0581 FtsZ cell division protein B, D 32 58 0 6 100 HVO_0024 Thiosulfate sulfurtransferase TssB B 35 95 0 5 100 HVO_0350 RpoA DNA-directed RNA polymerase B 41 89 0 4 100 HVO_0348 RpoB DNA-directed RNA polymerase B 64 92 2 7 71 HVO_0861 SufB/SufD domain protein B, D 77 176 8 24 67 HVO_0806 Pyruvate kinase B 92 147 6 15 60 HVO_2941 mc nonhistone chromosomal protein B, D 61 90 2 5 60 HVO_0859 Fe-S assembly ATPase SufC A, B 208 289 13 19 32 HVO_2700 Cdc48-like AAA+ ATPase B 171 252 14 19 26 HVO_0359 Translation elongation factor EF-1 B, D 392 546 34 45 24

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Table 5-2. Continued 138 ORF no. GenBank Predicted Function and/or Description Exp.a DS70b MOWSE GG102 MOWSE DS70c Spec. Count GG102 Spec. Count Diff. (%) HVO_2748 RNA polymerase Rpb4 B 117 34 9 0 100 HVO_0055 conserved protein B 133 57 8 1 88 HVO_0313 ATP synthase (E/31 kDa) subunit B 92 56 5 1 80 HVO_2543 rpmD ribosomal protein L30P B 111 67 9 2 78 HVO_2561 rplB ribosomal protein L2 B 97 59 9 2 78 HVO_1000 acetyl-CoA synthetase B 100 58 8 2 75 HVO_1148 rps15p ribosomal protein S15 B 109 60 10 3 70 HVO_1412 dmd diphosphomevalonate decarboxylase B 104 61 3 1 67 HVO_2384 CBS domain pair protein B 96 64 5 2 60 HVO_0214 L-lactate dehydrogenase B 166 105 16 7 56 HVO_0536 Nutrient-stress induced DNA binding protein B 303 148 25 11 56 HVO_0044 argB acetylglutamate kinase B 117 77 8 4 50 HVO_0324 argS arginyl-tRNA synthetase B 78 46 4 2 50 aExperiments A, B, C and D correspond to in-gel IMAC, total IMAC, MOAC and IMAC-MOAC respectively. bAverage probability-based peptide matching scores from all experiments in which the protein was identified. cAverage total spectral counts for all experiments in wh ich the protein was identifie d with % difference between two strains indicated

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139 Table 5-3. Putative phos phosites identified by MS/MS ORF no. GenBank Predicted Function and/or Description Straina Exp.b MOWSE (p>95)c Total no. Peptd Pep. E-value Phosphopeptidee HVO_0349 DNA-directed RNA polymerase Both B, D 60 11 1.4 e-4 LSLTDEEADEpYRDK (Y117) HVO_0806 Pyruvate kinase GG102 A to D 83 13 1.9 e-3 AGMpTGYPALVAR (T533) HVO_A0579 Branchedchain amino acid ABCtransporter, ATPase DS70 D 34 7 0.16 MpSADETNAQSVDDPDADI DRDGER (S2) HVO_1916 TrkA-integral membrane protein DS70 D 79 6 0.53 LIGRpTIR (T578) HVO_0001 Cdc6, Orc GG102 D 45 8 0.97 SGDDTLYNLpSRMNSELDN SR (S321) HVO_C0059 Hypothetical protein DS70 D 32 3 0.99 TSpSMQpTR (S136, T139) HVO_2700 Cdc48-like AAA+ ATPase Both B, C 171 (DS70) 252 (GG102) 18 (DS70) 17 (GG102) 2.0 IDGPNDGpTAIAR (T45) HVO_A0206 Conserved protein DS70 B, D 101 8 2.8 VEDVpSKLR (S88) aStrain in which phosphopeptide was detected. bExperiments A, B, C and D correspond to in-gel IMAC, total IMAC, MOAC and IMAC-MOAC respectively. cAverage probability-based peptide matching scores from all experiments in which the protein was identified. dTotal number of peptides detected by MS per protein identified to be phosphorylated. eSequence of phosphopeptide with p preceding residue proposed to be modified by phosphorylation. Phosphorylation sites are also indicated in parenthesis and numbered according to residue position of polypeptide deduced from the genome sequence.

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140 CHAPTER 6 ESTABLISHMENT OF A BASELINE PROTEOME OF Haloferax volcanii Introduction With the genome of Haloferax volcanii now complete, global surveys of transcript and protein levels are now possible in efforts to expand our understanding of cellular systems and how these compare to systems of other organi sms. Detailed proteomic and transcriptomic analyses are now possible and will contribute to rapid advancement of haloarchaeal research. Sub-specialized comparative proteomic techni ques such as degradomics, metabolomics and secretomics can now be employed to answer lingering questions about H. volcanii from a functional point of view. In this chapter, we describe the first largescale proteome map of H. volcanii Two-dimensional gel electrophoresis a nd multi-dimensional liquid separation and fractionation were paired with tandem mass spect rometric technology for protein identification. Results and Discussion Statistical Analysis of the H. volcanii Proteome Map Aggregate data from five independent proteo mic investigations resulted in a pool of protein identifications of considerable size and sc ope (Table 6-1). In total, 1,295 proteins of the H. volcanii proteome were mapped, constituting n early 32% of the total predicted coding capacity of this organism. These proteins we re identified through 14,553 st atistically significant top-ranking peptide matches (hits) with an average of 11.2 matching peptides per protein identification. Of those proteins identified, only 81 (6.3%) were singl e-hit identifications, leaving 1,214 proteins (93.7%) identified through multiple hits. An average probability-based MOWSE score of 88.7 was assigned overall with a ra nge of 35 to 433 for the entire dataset. Moreover, these identifications were made over a broad range of masses and isoelectric point (pI) values, likely resulting from the use of multiple complementary protein separation and

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141 detection methods for the generation of a unified proteome map. Proteins were identified with a mass range of 4.8 kDa to 232.7 kDa and an average of 39.9 kDa. These same proteins represent a pI range of 3.10 to 13.04 with an average of 5.0 1. These value ranges are representative of total proteome pI values of 2.70 to 13.04 with an average of 4.52 and estimated molecular weight range of less than 10 Da to more than 235 kDa. While these data we re achieved through a combination of liquid chromatography MS/M S and 2D-PAGE MS/MS experiments, it was determined that only 13.7% of proteins identified were out of the range of resolution for in-gel experiments based on a mass range of 97.4 kDa to 14.4 kDa. A more substantial percentage (38.4%) was determined to be beyond the 3.95.1 range of pI reso lution for gel-based proteomics. Additively, 52.1% of t hose proteins identified across all 5 experiments were done so with the aid of 1D and 2D liqui d chromatography, covering regions of the proteome inaccessible by our gel-based proteomic method s alone. Contributions made by an LC/MS/MS approach also included identification of membrane-associated proteins containing trans-membrane helix (TMH) domains. In total, 60 proteins predic ted to possess TMH domains were identified by LC/MS/MS, constituting 4.6% of the proteins in this mapping dataset and 6.3% of the 949 proteins in the total proteome predicted to pos sess at least one TMH domain. Of these 60 TMH domain-containing proteins, none were iden tified through 2D in-gel separation. Expectedly, the majority of proteins in the mapped proteome (79.2%) are coded for by genes residing on the largest 2,848 kb chromosome while the remaining 20.8% are distributed over the remaining three replicons in proportion to their respective sizes (Table 6.2). None were mapped to pHV2 due to the fact that the wild-type H. volcanii strain analyzed (DS70) and the derivative panA mutant (GG102) have been cured of th is plasmid. Proteins identified were categorized into COGs (clusters of orthol ogous groups) based on proposed function and are

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142 summarized in Table 6.3. The largest category wa s that of proteins assigned to unknown or general function ([R/S], 29.3%) which included h ypothetical proteins an d proteins that are predicted to function in multiple capacities or po ssess non-descript roles. Otherwise, the largest groups are represented by carbohydrate transp ort and metabolism ([G], 12.4%), energy production and conversion ([C], 9.0%) and translat ion and ribosome biogenesis ([J], 7.6%). Functional categories that were least represented included motil ity and secretion ([N], 0.9%), lipid metabolism ([I], 1.2%) and si gnal transduction ([T], 1.5%). Reannotation of High-Scoring Hypotheticals Of the 379 proteins that were assigned to the unknown/general function COG, 175 were designated as hypothetical or conserved hypothetic al proteins. In order to assign putative function to a larger portion of the annotated ge nome, these proteins we re prioritized based on MOWSE score and ratios of MOWSE score to assi gned peptide hits. Those that exceeded the arbitrary MOWSE score limit of 60 and MOWSE/pep tide ratio of 10 were subjected to protein basic local alignment sequence to ol (BLAST) searches. While 37 individual proteins were considered, only 7 returned sequenc e similarity hits with reasonabl y high e-value scores greater than 1e-6. These newly assigned annotations included a probable tran scriptional regulator (HVO_0184 most closely related to Natromonas pharaonis YP_326335; E-value of 4e-10;), 3isopropylmalate dehydratase (HVO _0697 most closely related to Methanosarcina barkeri YP_305092; E-value of 3e-53;), cobolamin (v itamin B12) biosynthesis CbiX protein (HVO_1129 most closely related to Deinococcus geothermalis YP_603746; E-value of 7e-13;), Fmu (Sun) domain protein (HVO _1595 most closely related to Plesiocystis pacifica ZP_01912358; E-value of 1e-6;), UDP-glucose:p rotein transglucosylase-like protein (HVO_1704 most closely related to Solanum tuberosum ABA81861; E-value of 7e-16;), elongator protein 3/MiaB/NifB (H VO_2187 most closely related to Halorubrum lacusprofundi

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143 ZP_02016357; E-value of 1e-158;) and probable transcriptional regulator (HVO_2718 most closely related to Haloquadratum walsbyi YP_658583; E-value of 4e-38;). Additionally, 25 of these 37 proteins not assigned to any putative f unction were similar to hypothetical proteins of other haloarchaeal species with e-values in excess of 1e-80 and, thus are conserved hypotheticals. The remaining 5 proteins of the 37 considered were unique to H. volcanii yet had probability-based MOWSE scores ranging from 36 to 111 with MOWSE/peptide ratios of 1.1 to 57.0. While most of the newly assigned annotati ons clustered to proteins involved in metabolism or transcriptional regul ation, two were notable in that they were both predicted to function in non-coding RNA modification. Th e Fmu protein (HVO_1595) identified through alignment with Plesiocystis pacifica possessed a SUN domain which, in yeast, is associated with proteins involved in a wide array of functi ons such as DNA replication, ageing, mitochondrial biogenesis and cytokinesis (Hille r et al., 2007). Additionally, S UN-domain-containing proteins have also been determined to function as RNA methyltransferases (Frye and Watt, 2006). Interestingly, the H. volcanii Fmu SUN-domain containing protein possessed significant similarity to the tRNA and rRNA cytosine-C5-methylase of P. pacifica and, thus may operate to modify non-coding RNAs in this halophile. A nother protein which was functionally reassigned (HVO_2187) showed overwhelming similarity to elongator protein 3/MiaB/NifB from Halorubrum lacusprofundi ( E-value of 1e-158). While this protein was determined to be a probable Fe-S oxidoreductase, functionally, it po ssesses some overlap with the MiaB protein from organisms such as Thermotoga which has been shown to cat alyze the postt ranscriptional methyltiolation of N-6-isopentenyladenosine in tRNAs (Hernandez et al., 2007). Together, these

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144 newly annotated proteins putativ ely represent a general functi onal subdivision concerned with the modification of non-c oding RNA structures in H volcanii Comparative COG Analysis of the H. volcanii Proteome Map The numbers of H. volcanii proteins detected by MS/MS and categorized into each of the 17 COGs (Table 6-3) were compared to average COG data from the deduced proteomes of seven archaeal species ( Halobacterium sp. NRC-1, Methanococcus jannaschii Methanobacterium thermoautotrophicus Thermoplasma acidophilum Pyrococcus horikoshii Archaeoglobus fulgidus and Sulfolobus solfataricus ) as well as two bacterial ( Escherichia coli and Pseudomonas aeruginosa ) and one eukaryotic ( Saccharomyces cerevisiae ) species. Overall, the numbers of proteins in each COG mapped in H. volcanii varied from the average of the 10 reference organisms by only 1.3%. The largest discrepancy in this comparison was a 7.3% higher number of proteins observed in group G (car bohydrate transport and metabolism) in H. volcanii Similarly, when compared only to the aver age COG profiles of those members of the Archaea domain listed previously, only a 1.28% discrepancy was observed with the highest (7.9%) again belonging to carbohydrate tran sport and metabolism in H. volcanii Three archaeal species ( Halobacterium sp. NRC-1, Methanococcus jannaschii and Sulfolobus solfataricus ) were used in comparison with H. volcanii to represent the similarities a nd differences in the archaeal COG groupings (Fig. 6-1A). While the profile of H. volcanii was similar in numbers of proteins in each COG to those of each of these archaeal species, H. volcanii did show an approximate 9% increase in proteins of group G (carbohydrate transport and metabo lism). A notable increase in average percent discrepancy was also observed when comparing H. volcanii COG profile to an average of only those members of the Eukarya and Bacteria domains listed above. This comparison revealed a 1.92% difference overall with the largest diffe rence belonging to the unknown/general function (8.5% higher in H. volcanii ) (Fig. 6-1B). Other significant differences

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145 were also observed in this latter compar ison in the numbers of proteins belonging to carbohydrate transport and metabolism (group G; 5.8 %), signal transduction (group T; 4.3%) and energy production and conversion (group C; 3.4%) all higher in H. volcanii (Fig. 6-1B). Generally, the mapped portion of the H. volcanii proteome appears to be representative of other archaeal COG profiles as well as COG profiles of organisms from other domains. While there were few minor discrepancies between H. volcanii and compared COG maps, these may be attributed to differences in proteome sizes or skewed numbers of unannotated (or improperly annotated) proteins between orga nisms of this comparison group. Comparison of proteins mapped in H. volcanii (in this study) to groups of proteins mapped by mass spectrometry in other organisms such as Mycoplasma hyopneumoniae has revealed surprising similarities and differences in the numbers of proteins assigned to orthologous gene clusters. Or thologous gene clusters R/S (unknown/general function), K (transcription), J (translati on and ribosome biogenesis) a nd G (carbohydrate transport and metabolism) in Mycoplasma were similar to H. volcanii in terms of percent coverage, revealing only 0.3%, 2.5%, 2.1% and 0.5% differences, respectiv ely (Pinto et al., 2007). The most notable differences in the COG profiles of these organism s were the numbers of proteins assigned to post-translational modification, chaperones and protein turnover (O) and energy production and conversion (C), which were lower in H. volcanii by 12.6% and 10.4%, respectively (Pinto et al., 2007). Variability between COG profiles of these pr oteomes may be attributed to differences in map sizes or bias resulting from analytical me thods; however, the remarkable similarity between key functional categories of these somewhat unrelated organisms may suggest a need for constitutive expression of a co mmon subset of genes for basic cellular function, irrespective of taxonomic classification.

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146 Identification of Paralogs in H. volcanii Within this current proteome mapping dataset for H. volcanii multiple proteins were identified which possess one or two other pa ralogs. Less common, ho wever, was the MS/MS detection of 4 or more closely related protein pa ralogs. Nine separate proteins which possessed 4 or more paralogs were detected in this mapping dataset. Th ese paralogs included proteins annotated as FtsZ of which 6 were detected out of a predicted total of 8. Six of the 16 cell division control protein 6/origin recognition complex proteins of th e deduced proteome were also detected. Other paralogs included ArcR transcri ptional regulators (6 of 16), bacterio-opsin activator-like proteins (7 of 14), non-descript oxidoreductases (6 of 8) and transposases of the IS4 family (5 of 31). Four of each of the follo wing were also identifie d by MS/MS: cell division control protein 48 (5 total), mutS DNA mismatch re pair protein (4 total; a ll accounted for in this mapping dataset) and metallo-beta-lactamase superf amily domain protein (9 total). The extent of functional overlap between many of these paralogs is unclear. A few, such as those involved in cell division or transcriptiona l regulation, may operate in a sp ecialized way or may be cofunctional, forming multi-subunit complexes with related paralogs. Those paralogs not identified may be conditionally e xpressed; therefore exploration of a greater variety of growth conditions may assist to fully identify these mi ssing proteins. Future characterization of the identified paralogous proteins by the haloarchaea l community may facilitat e the assignment of a more specific function and reduce apparent redundancy within the currently annotated proteome. Conclusion This work reports the identification of appr oximately one-third of the total proteome of Haloferax volcanii with coverage of both molecular weight and isoelectric point ranges related to those of the deduced proteome. This projec t has also made progress towards improving the current annotation of the H. volcanii proteome through careful analysis of high-scoring

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147 hypotheticals. The regions of the proteome which were mapped and future expansion of this current map through proteomic analyses of ch anging growth conditions and various stress challenges will serve as a useful resource for the H. volcanii research community. This information will be particularly useful in coordination with forthcoming transcriptome data and the release of the first published H. volcanii genome. Collectively, this work will also serve those studying other members of the Archaea domain.

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148 Table 6-1. Number of proteins mapped in H. volcanii categorized by experi ment in which they were discovered Experiment No. Identifie d Percent of Total ID Strong Cation Exchange/MudPIT 1150 88.7 Immobilized Metal Affinity Chromatography (IMAC) 484 37.3 Metal Oxide Affinity Chromatography (MOAC) 49 3.8 Combined IMAC-MOAC 296 22.8 Proteasome Inhibitor ( c L L) 2DE 43 3.3

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149 Table 6-2. Total protein identificati ons categorized by contig of origin Contig Size (kb) No. ID No. Deduced % ID % Deduced Chromosome 2848 1027 2960 79.2 34.7 pHV4 636 147 638 11.3 23.0 pHV3 438 99 438 7.6 25.9 pHV1 85 23 85 1.8 25.8 pHV2 6 -6 --

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150 Table 6-3. Division of identified H. volcanii proteins by functional category COG Description No. % of Total [R/S] Unknown/general function 379 29.3 [E] Amino acid transport and metabolism 88 6.8 [C] Energy production and conversion 116 9.0 [O] PTM, chaperones, protein turnover 45 3.5 [M] Envelope biogenesis and outter membrane 39 3.0 [P] Inorganic ion transport and metabolism 48 3.7 [K] Transcription 74 5.7 [J] Translation and ribosome biogenesis 99 7.6 [F] Nucleotide transport and metabolism 54 4.2 [L] DNA replication, recombination and repair 70 5.4 [D] Cell division and chromosome partitioning 32 2.5 [N] Motility and secretion 11 0.9 [T] Signal transduction 19 1.5 [G] Carbohydrate transport and metabolism 161 12.4 [H] Coenzyme metabolism 44 3.3 [I] Lipid metabolism 16 1.2 Total 1295 100

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151 A. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% JAKLBDYVTMNZWUOCGEFHIPQR/S COGPercent of Total Halobacterium sp. NRC-1 Methanococcus jannaschii Sulfolobus solfataricus Haloferax volcanii B. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% JAKLBDYVTMNZWUOCGEFHIPQR/S COG Percent of Total Saccharomyces cerevisiae Escherichia coli K12 Pseudomonas aeruginosa Haloferax volcanii Figure 6-1. Comparativ e COG profiles of H. volcanii and representative organisms from (A) the Archaea including Halobacterium sp. NRC-1, Methanococcus jannaschii and Sulfolobus solfataricus (B) Members of the Bacteria and Eukarya domains were compared. This analysis included Escherichia coli Pseudomonas aeruginosa and Saccharomyces cerevisiae

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152 CHAPTER 7 SUMMARY OF RESULTS AND CONTINUING INVESTIGATIONS Summary of Results The long-term objective of this work is to identify proteins of the Haloferax volcanii proteome which are degraded by its proteasomal system. In addition, this work is focused on examining our working model in which post-tran slational modification, particularly protein phosphorylation, serves as a recognition motif or de stabilizing factor of proteasomal substrate proteins in the absence of an ubiquitin system. Both classical and modern proteomic techniques were implemented to comparatively analyze th e proteome of cells with and without full proteasomal activity. This comparison was performed using a H. volcanii mutant strain possessing a deletion of the panA gene encoding the primary regul atory particle (RP) of its proteasome as well as chemical inhi bition of the 20S core particle itself. Investigations into the role of protein phosphorylation in protein recognition and/or degradation by archaeal 20S proteasomes were based on observations of a phenotypic difference in phosphorylated protein accumulation between wild-type and RP mutant cells In consideration of this difference, our extensive proteomic analyses in corporated phosphoprotein enrichment and purification strategies to facilitate a focused explor ation of the phosphoproteomes of cells with and without active PanA. These approaches led to the establishment of a list of 10-12 proteins that represent ideal candidates for proteasomal degradation studies, as determined by skewed abundance in cells lacking full proteasomal func tion. Many of these proteins were also identified after phosphoprotein enrichment, thus lending support to our working model suggesting protein phosphorylation may play a role in proteasomal degradation. The status of these candidate proteins as substrates of archaeal proteasomes has been, and continues to be determined through

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153 pulse-chase and immunoprecipita tion experimental methods using custom polyclonal antibodies generated specifically for the H. volcanii antigens. We have used chemical inhibition of the prot easomal core particle to analyze differential accumulation of proteins using an in-gel format w ith reasonable success. Our recently developed method of Trizol-based preparati on of halophilic proteins has a llowed the range of resolvable proteins in gel to be expanded from approximate ly 400-500 to more than 1000 spots per field and thus has made large-scale proteomic comparisons of H. volcanii more efficient. In experiments involving the addition of the prot easome-specific inhibitor clasto -lactacystin beta lactone (cL L), as many as 1072 spots were re solved after addition of 20 M inhibitor and 1000 spots after addition of 30 M inhibitor. These numbers represen t a consistent 1.6-to-1.8-fold average increase over uninhibited samples, which yiel ded average spot numbers of