Group Title: BMC Microbiology
Title: Pathway analysis for intracellular Porphyromonas gingivalis using a strain ATCC 33277 specific database
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Title: Pathway analysis for intracellular Porphyromonas gingivalis using a strain ATCC 33277 specific database
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Language: English
Creator: Hendrickson, Erik
Xia, Qiangwei
Wang, Tiansong
Lamont, Richard
Hackett, Murray
Publisher: BMC Microbiology
Publication Date: 2009
 Notes
Abstract: BACKGROUND:Porphyromonas gingivalis is a Gram-negative intracellular pathogen associated with periodontal disease. We have previously reported on whole-cell quantitative proteomic analyses to investigate the differential expression of virulence factors as the organism transitions from an extracellular to intracellular lifestyle. The original results with the invasive strain P. gingivalis ATCC 33277 were obtained using the genome sequence available at the time, strain W83 GenBank: AE015924. We present here a re-processed dataset using the recently published genome annotation specific for strain ATCC 33277 GenBank: AP009380 and an analysis of differential abundance based on metabolic pathways rather than individual proteins.RESULTS:Qualitative detection was observed for 1266 proteins using the strain ATCC 33277 annotation for 18 hour internalized P. gingivalis within human gingival epithelial cells and controls exposed to gingival cell culture medium, an improvement of 7% over the W83 annotation. Internalized cells showed increased abundance of proteins in the energy pathway from asparagine/aspartate amino acids to ATP. The pathway producing one short chain fatty acid, propionate, showed increased abundance, while that of another, butyrate, trended towards decreased abundance. The translational machinery, including ribosomal proteins and tRNA synthetases, showed a significant increase in protein relative abundance, as did proteins responsible for transcription.CONCLUSION:Use of the ATCC 33277 specific genome annotation resulted in improved proteome coverage with respect to the number of proteins observed both qualitatively in terms of protein identifications and quantitatively in terms of the number of calculated abundance ratios. Pathway analysis showed a significant increase in overall protein synthetic and transcriptional machinery in the absence of significant growth. These results suggest that the interior of host cells provides a more energy rich environment compared to the extracellular milieu. Shifts in the production of cytotoxic fatty acids by intracellular P. gingivalis may play a role in virulence. Moreover, despite extensive genomic re-arrangements between strains W83 and 33277, there is sufficient sequence similarity at the peptide level for proteomic abundance trends to be largely accurate when using the heterologous strain annotated genome as the reference for database searching.
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Research article


Pathway analysis for intracellular Porphyromonas gingivalis using a
strain ATCC 33277 specific database
Erik L Hendricksoni, Qiangwei Xial,2,4, Tiansong Wang1,2, Richard J Lamont3
and Murray Hackett*1

Address: 'Department of Chemical Engineering, Box 355014 University of Washington, Seattle, WA 98195, USA, 2Department of Microbiology,
Box 357242 University of Washington, Seattle, WA 98195, USA, 3Department of Oral Biology, University of Florida, Gainesville, FL 32610, USA
and 4University of Wisconsin-Madison, Department of Chemistry, Madison, WI 53706, USA
Email: Erik L Hendrickson elh@u.washington.edu; Qiangwei Xia qxia@chem.wisc.edu; Tiansong Wang tswang@u.washington.edu;
Richard J Lamont rlamont@dental.ufl.edu; Murray Hackett* mhackett@u.washington.edu
* Corresponding author



Published: I September 2009 Received: 20 March 2009
8MC Microbiology 2009, 9:185 doi: 10.1 186/1471-2180-9-185 Accepted: I September 2009
This article is available from: http://www.biomedcentral.com/1471-2180/9/185
2009 Hendrickson et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Porphyromonas gingivalis is a Gram-negative intracellular pathogen associated with
periodontal disease. We have previously reported on whole-cell quantitative proteomic analyses to
investigate the differential expression of virulence factors as the organism transitions from an extracellular
to intracellular lifestyle. The original results with the invasive strain P. gingivalis ATCC 33277 were obtained
using the genome sequence available at the time, strain W83 [GenBank: AEO 15924]. We present here a
re-processed dataset using the recently published genome annotation specific for strain ATCC 33277
[GenBank: AP009380] and an analysis of differential abundance based on metabolic pathways rather than
individual proteins.
Results: Qualitative detection was observed for 1266 proteins using the strain ATCC 33277 annotation
for 18 hour internalized P. gingivalis within human gingival epithelial cells and controls exposed to gingival
cell culture medium, an improvement of 7% over the W83 annotation. Internalized cells showed increased
abundance of proteins in the energy pathway from asparagine/aspartate amino acids to ATP. The pathway
producing one short chain fatty acid, propionate, showed increased abundance, while that of another,
butyrate, trended towards decreased abundance. The translational machinery, including ribosomal
proteins and tRNA synthetases, showed a significant increase in protein relative abundance, as did proteins
responsible for transcription.
Conclusion: Use of the ATCC 33277 specific genome annotation resulted in improved proteome
coverage with respect to the number of proteins observed both qualitatively in terms of protein
identifications and quantitatively in terms of the number of calculated abundance ratios. Pathway analysis
showed a significant increase in overall protein synthetic and transcriptional machinery in the absence of
significant growth. These results suggest that the interior of host cells provides a more energy rich
environment compared to the extracellular milieu. Shifts in the production of cytotoxic fatty acids by
intracellular P. gingivalis may play a role in virulence. Moreover, despite extensive genomic re-arrangements
between strains W83 and 33277, there is sufficient sequence similarity at the peptide level for proteomic
abundance trends to be largely accurate when using the heterologous strain annotated genome as the
reference for database searching.



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Background
The Gram-negative anaerobe Porphyromonas gingivalis is an
important periodontal pathogen. Amongst the most com-
mon infections of humans, periodontal diseases are a
group of inflammatory conditions that lead to the
destruction of the supporting tissues of the teeth [ 1 ] and
may be associated with serious systemic conditions,
including coronary artery disease and preterm delivery of
low birth weight infants [2]. P. gingivalis is a highly inva-
sive intracellular oral pathogen [3] that enters gingival
epithelial cells through manipulation of host cell signal
transduction and remains resident in the perinuclear area
for extended periods without causing host cell death [4].
The intracellular location appears to be an integral part of
the organism's lifestyle and may contribute to persistence
in the oral cavity. Epithelial cells can survive for prolonged
periods post infection [5] and epithelial cells recovered
from the oral cavity show high levels of intracellular P.
gingivalis [6,7]. Intracellular P. gingivalis is also capable of
spreading between host cells [8].

We have previously reported a whole-cell quantitative
proteomic analysis of the change in P. gingivalis between
extracellular and intracellular lifestyles [9]. P. gingivalis
strain ATCC 33277 internalized within human gingival
epithelial cells (GECs) was compared to strain ATCC
33277 exposed to gingival cell culture medium. The anal-
ysis focused on well-known or suspected virulence factors
such as adhesins and proteases and employed the genome
annotation of P. gingivalis strain W83. In order to be effec-
tive, quantitative proteomic analysis requires that mass
spectometry results be matched to an annotated genome
sequence to specifically identify the detected proteins. At
the time, the only available whole genome annotation for
P. gingivalis was that of strain W83 [10]. Recently, the
whole genome sequence of P. gingivalis strain ATCC
33277 was published [11].

We re-analyzed the proteomics data using the P. gingivalis
strain ATCC 33277 genome annotation. Use of the strain
specific genome annotation increased the number of
detected proteins as well as the sampling depth for
detected proteins. As the quantitative accuracy of whole
genome shotgun proteomics is dependent on sampling
depth [12] the new analysis was expected to provide a
more accurate representation of the changes in protein rel-
ative abundance between intracellular and extracellular
lifestyles.

Given the prolonged periods of intracellular residence
[4,5] it is likely that, in addition to changes in virulence
factors, metabolic changes in response to the intracellular
environment may play an important role in the intracellu-
lar lifestyle of P. gingivalis, including shifts in energy path-
ways and metabolic end products [13].


Results and discussion
Re-analysis using the P. gingivalis strain ATCC 33277
genome annotation
The proteomics data previously analyzed using the strain
W83 genome annotation [GenBank: AE015924] [9] was
recalculated employing the strain specific P. gingivalis
strain ATCC 33277 annotation [GenBank: AP009380].
Accurately identifying a proteolytic fragment using mass
spectrometry-based shotgun proteomics as coming from a
particular protein requires matching the MS data to a pro-
tein sequence. Differences in amino acid sequence
between the proteins expressed by strain ATCC 33277 and
the protein sequences derived from the strain W83
genome annotation rendered many tryptic peptides from
the whole cell digests employed unidentifiable in the orig-
inal analysis [9]. Given that the quantitative power of the
whole cell proteome analysis is dependent on the number
of identified peptides [12,14], the new analysis was
expected to give a more complete picture of the differen-
tial proteome, an expectation that proved accurate. In
addition, some proteins in the strain ATCC 33277
genome are completely absent in the strain W83 genome
and were thus qualitatively undetectable in the original
analysis.

Overall, 1266 proteins were detected with 396 over-
expressed and 248 under-expressed proteins observed
from internalized P. gingivalis cells compared to controls
(Table 1). Statistics based on multiple hypothesis testing
and abundance ratios for all detected proteins can be
found in Additional file 1: Table S 1, as well as pseudo M/
A plots [15] of the entire dataset. The consensus assign-
ment given in Additional file 1: Table S 1 of increased or
decreased abundance was based on two inputs, the q-val-
ues for comparisons between internalized P. gingivalis and
gingival growth medium controls as determined by spec-
tral counting and summed signal intensity from detected
peptides that map to a specific ORF [9,14,15]. If one or the
other of the spectral counting or protein intensity indi-
cated a significant change (q < 0.01) and the other meas-
ure showed at least the same direction of change with a
log2 ratio of 0.1 or better, then the consensus was consid-
ered changed in that direction, coded red for over-expres-
sion or green for under-expression. A simple "beads on a
string" genomic map of the consensus calls is shown in
Fig. 1.

Whole cell proteomics measurements of this type are
noisy and the trade off between quantitative FDR (false
discovery rate) and FNR (false negative rate) is made
based on the informed judgment of the analyst, and often
tends to be ad hoc and arbitrary in practice [9,14]. The q-
value cut-off of 0.01 used here for statistical significance
based on formal hypothesis testing was in good agree-
ment with experimentally derived error distributions, as


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Figure I
Map of relative abundance trends based on the ATCC 33277 gene order and annotation. This plot shows the
entire set of consensus calls given in Additional file I: Table SI arranged by ascending PGN number [I I], which follows the
physical order of genes in the genome sequence. Color coding: red indicates increased relative protein abundance for internal-
ized P. gingivalis, green decreased relative abundance, grey indicates qualitative non-detects and black indicates an unused ORF
number.










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Table 1: A comparison of the proteomics results employing either the W83 [10] or ATCC 33277 [I I] genome annotations.


ATCC 33277
Increased


W83
Increased
Decreased
Unchanged
Not detected
Total


Decreased

248
10
140
75
23


Unchanged

622
124
79
345
74


Not detected


Total


The numbers of proteins showing increased, decreased or unchanged abundance in the internalized state for each analysis are given. Entries indicate
the number of proteins from each category in one analysis that are assigned to the categories in the other analysis, including proteins that are not
detected in a specific analysis.


illustrated by the two pseudo M/A plots given in Addi-
tional file 1. The present findings serve to show the value
of examining trends in groups of proteins, both as an end
in itself with respect to biological questions and as feed-
back in the determination of proper cut-off values for the
quantitative significance testing of individual proteins. As
proteomics technology improves and it becomes econom-
ically feasible to run a greater number of independent cul-
tures (biological replicates) than what was possible here,
the overall noise issue in any one set of measurements will
be less of a concern, and it will be easier to distinguish
biological noise from deficiencies with respect to analyti-
cal repeatability, and thus identify biological trends that
are truly significant rather than stochastically driven.
Nonetheless, as in our previous work [9] the trends iden-
tified here are consistent with what we know about the
behavior of the organism under intracellular conditions
[3,9,16].


Comparison between W83 and ATCC 33277 annotations
for proteomics
As expected, the new analysis identified more proteins,
1266 proteins compared to 1185 in the previous analysis
(Table 1). The number of proteins with statistically signif-
icant changes between internalized and medium incu-
bated cells also increased, from 380 proteins with
increased abundance to 396 proteins and from 235 pro-
teins with decreased abundance to 248 proteins. This was
a consequence of the higher number of proteolytic frag-
ments detected across the proteome. However, there was
a fairly large shift as to which proteins made the cut-off for
statistically significant change: 168 proteins called
unchanged in the W83 analysis now show statistically sig-
nificant changes in the ATCC 33277-based analysis, while
203 proteins previously called significantly different no
longer make the cut-off (Table 1), at q <: 0.01. This is not
surprising as values reasonably close to the cut-off point
for significance would be expected to be very sensitive to
changes in protein detection and sampling depth, with a
small shift in the peptides involved in the calculations
moving the protein over or under the significance cut-off


Table 2: The 15 proteins with opposite abundance trends.


PGN0148
PGN0152
PGN0294
PGN0302
PGN0503
PGN0678
PGN0914
PGN1032
PGN1403
PGN1529
PGN1590
PGN 1830
PGN1849
PGNI1904
PGN2070


conserved domain protein
immunoreactive 61 kDa antigen PG91
ragB lipoprotein RagB
rubrerythrin
mmdC methylmalonyl-CoA decarboxylase gamma subunit
thiL thiamine monophosphate kinase
peptidase M24 family
hypothetical protein PG_0914
acetylornithine aminotransferase putative
oxidoreductase putative
rplM ribosomal protein LI 3
TonB-dependent receptor putative
rplO ribosomal protein LIS
hemagglutinin protein HagB
hypothetical protein PG_2204


Out of 1, I 13 detected (see Table I) using both annotations, these 15 proteins showed inconsistent trends for significant (q < 0.01) abundance
change depending on whether the W83 [10] or ATCC 33277 [I I] genome annotations were used for database searching. The ORF numbers and
descriptors given are those for ATCC 33277.


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point. A small number of proteins, 15, switched trend
direction, moving from statistically significant increased
or reduced abundance in internalized cells in the W83
analysis to the opposite trend in the ATCC 33277 analysis.
The 15 proteins are listed in Table 2. In every case these 15
proteins showed inconsistency between two control cul-
tures. In these cases the direction of change differed
between the two controls with one control giving statisti-
cally significant change in one direction and the other giv-
ing change in the other direction but without making the
statistical cut-off. Again, we saw shifts in borderline cases,
in these 15 instances enough to shift the direction of
abundance change. We also found that some proteins
detected using the W83 genome annotation were no
longer detected using the ATCC 33277 annotation. In
most cases this was due to the presence of a second similar
protein in the ATCC 33277 annotation, but not in the
W83 annotation. Peptides that could not be unambigu-
ously assigned to a single protein were not retained for the
finished dataset given in Additional file 1: Table Si. The
presence of the same peptide sequence in another protein
eliminated the data from consideration both here and in
the original W83-based analysis. Despite the shifts in
assigned q-values and abundance ratio magnitudes as a
consequence of the change in annotations, the abundance
trends observed for P. gingivalis virulence factors did not
differ greatly from those reported previously [9], except as
noted in Table 2.

Metabolic pathways differentially regulated in
internalized P. gingivalis
The consensus assignments (see Additional file 1: Table
Sl) of differentially expressed proteins were used to pop-
ulate metabolic pathways. The results were analyzed man-
ually using the ATCC 33277 genome annotation [11]. In
addition, an ontology analysis was done using DAVID
(the Database for Annotation, Visualization and Integra-
tion Discovery) to identify over- or under-expressed
ontology categories [17]. Putative changed categories were
then checked manually. DAVID has proven to be useful
for prokaryotes when compared with other ontology pro-
grams [18].

Energy metabolism
P. gingivalis is an asaccharolytic bacterium and cannot sur-
vive on glucose or carbohydrates alone. While some genes
for carbohydrate metabolism are found in the genome, P.
gingivalis derives its energy from the metabolism of amino
acids [11,13]. Takahashi and colleagues measured amino
acid usage in culture and found that glutamate/glutamine
and aspartate/asparagine were preferentially metabolized
[13]. When grown on dipeptides of these substrates, P.
gingivalis produced different amounts of metabolic
byproducts. Importantly, aspartylaspartate produced sig-
nificantly higher amounts of acetate, which is associated


with ATP formation (Fig. 2 and Additional file 1: Table
Sl). Internalized P. gingivalis cells showed an increase in
the energy pathway from aspartate/asparagine to acetate
and energy (Fig. 2). The corollary of this trend is that the
intracellular environment is energy rich for P. gingivalis.
Interestingly, the protein that converts glutamate, the
other favored amino acid, to 2-oxoglutarate (PGN1367,
glutamate dehydrogenase) showed a decrease in abun-
dance (Fig. 2). This may represent a preference for energy
production in internalized cells or be part of a more gen-
eral shift in the metabolic byproducts. We also observed a
decrease in protein abundance of maltodextrin phospho-
rolase (PGN0733). Maltodextrin phospholase plays a role
in digesting starches and, despite being an asaccharolytic
organism, P. gingivalis may make some use of the starches
available in the oral cavity, but restricts this activity after
internalization.

Cytotoxic byproducts
P. gingivalis metabolism produces several short chain fatty
acid byproducts that are cytotoxic (Fig. 2) and has been
found to shift production between these compounds
depending on growth conditions [13]. We have found a
general increase in the pathway from 2-oxoglutarate to the
cytotoxin propionate while the proteins in the pathways
for production of the cytotoxin butyrate showed
unchanged or reduced expression (Fig. 2). This is consist-
ent with hints that byproduct production shifts away from
butyrate and towards propionate during P. gingivalis infec-
tions [19]. The results are the opposite of what would be
expected from substrate studies. As mentioned previously,
the proteomics shows an increase in the aspartate/aspar-
agine pathway and a reduction in glutamate/glutamine.
Culture growth studies found that P. gingivalis grown on
aspartylaspartate had significantly more butyrate produc-
tion than propionate compared to cultures grown on
glutamylglutamate [13]. However, a recent flux balance
model of P. gingivalis metabolism predicts that there is
abundant flexibility in the production of butyrate, propi-
onate and succinate with the metabolic routes to each
being equivalent with respect to redox balancing and
energy production [20]. Thus a shift towards propionate
could be easily explained if it presented an advantage to
internalized cells. In that regard, it has been shown that
butyrate is a more potent apoptosis inducing agent than
propionate [21]. Hence, the diminished production of
butyrate by internalized P. gingivalis may contribute to the
resistance of P. gingivalis-infected GECs to apoptotic cell
death [22]. There is also the question of the reduced abun-
dance of glutamate dehydrogenase (PGN1367), the pro-
tein that converts glutamate to 2-oxoglutarate (Fig. 2). If
this is the primary substrate for propionate production it
could limit that production even with increased abun-
dance in the rest of the pathway. However, 2-oxoglutarate
is a common metabolic intermediate and glutamate/


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,sparagine L-Glutamate 2-Oxoglutarate
rPGN0170A PGN1367 PGN17561APGN1755,APGN1753A,

Aspartate
|PGN0377A4 PGN0498 4 PGN0457 f
PGN0497 PGN1341Y V PGN0456
Fumarate p Succinate P- Succinyl-CoA ---- I ma yl-CA
"malonyl-CoA
4PGN0893A_ IPGN0723 Unknowni

Malate Succinyl-semialdehyde S Methyl-
rmalonyl-CoA
PGN1880 4PGN0724E PGN0500APGNO504 ny
) t0 PGN0503 0,
xaloacetate 4-Hydroxybutyrate Propionyl-CoA


IPGN0351A Acetyl-CoA PGN0725P Unknown Acetyl-CoA
S11P PGN0727Y 1Acetyl-CoA
Pyruvate 4-hydroxybutyryl-CoA - Vinylacetyl-CoA Propionate
PGN1530A4 Unknown
r Unknown PGN1176 PGN11750 V
Acetyl-CoA p Aceto (S)-3-Hydroxy- I Crotonoyl-CoA
acetyl-CoA butanoyl-CoA i


SPGN1172Y
Butyryl-CoA

aPGN1171
Butanoate


IPGN1179,A
Acetyl Phosphate
ADP
A PGN1178
ATP 4
Acetate


Figure 2
Metabolic Map of Energy and Cytotoxin Production. Proteins catalyzing each step are shown by their P. gingivalis PGN
designation. Red up arrows indicate increased levels upon internalization, green down arrows decreased levels, and yellow
squares no statistical change. Acetyl-CoA appears as a substrate and product at multiple points and is shown in purple. Metab-
olites and metabolic precursors discussed in the text are shown in bold.


glutamine may not be the only source of 2-oxoglutarate
for propionate production. Even if it is the primary source,
given the flexibility in byproduct production, a significant
shift away from butyrate production from glutamate/
glutamine to propionate production could still occur in
the presence of an overall reduction in glutamate/
glutamine usage. Interestingly, some similar shifts are
seen between planktonic cells and biofilms of P. gingivalis
strain W50. A mass spectrometry analysis of planktonic
cells versus biofilm cells identified 81 proteins and found
several energy metabolism proteins with significant differ-
ences between planktonic and biofilm lifestyles [23]. In
biofilms fumarate reductase (PGN0497, 0498) had
reduced abundance while oxaloacetate decarboxylase
(PGN0351) had increased abundance similar to what we
see in internalized cells (Fig. 2). Obviously, biofilms and
the interior of GECs are different environments, and the


energy metabolism protein glyceraldehyde-3-phosphate
dehydrogenase (PGN0173) was increased in biofilms
[23] relative to planktonic cells, while it is decreased in
internalized cells relative to external controls. A compari-
son between the two conditions would really require the
identification of more metabolic proteins from biofilm
cells, but given the relevance of biofilm formation to P.
gingivalis pathogenicity in vivo [24-26], the relation
between biofilm conditions and internalized cells is an
interesting one that we intend to pursue further at the
whole proteome level.

Translation machinery
Proteomics revealed a significant increase in proteins
responsible for translation, including many of the ribos-
omal proteins (Table 3, 4 and 5, Additional file 1: Table
Sl). Increased abundance of ribosomal proteins is seen


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Table 3: A list of detected proteins, by P. gingivalis PGN number [I I], assigned to ribosomal proteins as determined using DAVID.


Unchanged (19)


Decreased Levels (I)


PGN 0394


Proteins are indicated as increased, decreased or unchanged in abundance for internalized P. gingivalis versus external control cells. The totals for
each category are given in parentheses.


under conditions of increased growth rate in all domains
of life [27-29]. However, we have found that internalized
P. gingivalis maintain viability and replicate slowly within
gingival epithelial cells [3]. Thus, an overall increase in
protein expression due to increased energy production
may be responsible for the increased abundance of trans-
lational machinery, more so than growth under these con-
ditions.

Transcription machinery
Most of the proteins responsible for transcription also
showed increased abundance (Table 6, Additional file 1:
Table Si). This is consistent with the overall increase in
translational machinery as well as the larger number of
proteins showing increased versus decreased abundance
within gingival epithelial cells.

Conclusion
P. gingivalis is an opportunistic, intracellular pathogen
that survives for extended periods of time within gingival
epithelial cells without causing excessive harm to the host
and thus provides a window into host cell adaptive
responses by pathogens [3-5]. Re-analysis of whole cell
proteomics data using the recently published strain spe-


cific genome annotation for ATCC 33277 allowed several
novel conclusions. As expected, the strain specific annota-
tion yielded better overall proteome coverage and sam-
pling depth at the level of the number of proteins
identified. However, most of the overall trends identified
for major P. gingivalis virulence factors and other proteins
using the W83 genome annotation remain unchanged,
showing the viability of employing similar annotations
when a strain specific sequence is unavailable. This obser-
vation is especially important for oral and gut microbes,
where a rapidly increasing body of genomic and RNA-Seq
data suggests that genomic re-arrangements in the absence
of major changes in amino acid sequence for the
expressed proteins may be a widespread occurrence.
Although some differences in protein primary structure
exist among P. gingivalis strains [30], the primary differ-
ences observed by Naito et al. are extensive genome re-
arrangements [11]. The proteomic methods used here are
highly sensitive to sequence similarity, but not at all to the
order in which genes occur on the chromosome. How-
ever, the ways in which proteome data are interpreted in
terms of operon and regulon structure are greatly influ-
enced by the physical arrangement of the genome.


Table 4: A list of detected proteins, by P. gingivalis PGN number [I I], assigned to translation initiation, elongation and termination as
determined using DAVID.


Unchanged (3)


Decreased Levels (0)


PGN 1014


Proteins are listed by ORF number in the same manner as in Table 3.


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Increased (32)


Increased (8)

PGN 0355
PGN 1405
PGN 1587
PGN 1870


PGN 0963
PGN 1578
PGN 1846
PGN 2022


PGN 0313
PGN 1244


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Table 5: A list of detected proteins, by P. gingivalis PGN number [I I], assigned to tRNA synthetases and transferases as determined
using DAVID.


Decreased Levels (3)


PGN 0266
PGN 1157


PGN 0278


Proteins are listed by ORF number in the same manner as in Table 3.

When the data were organized in terms of metabolic path-
ways the whole cell proteomics analysis revealed what
appears to be a nutritionally rich intracellular environ-
ment for P. gingivalis. The energy metabolism pathway
from the preferred amino acids aspartate/asparagine
showed a significant increase. Transcription and transla-
tion proteins also showed significant increases, consistent
with energy not being limiting. The production of cyto-
toxic metabolic byproducts also appears to shift in inter-
nalized cells, reducing production of butyrate and
increasing production of propionate. This may be simply
a byproduct of metabolic shifts, or it may play a role in P.
gingivalis adaptive response to internalization.

Methods
Proteomic methods
The bacterial and gingival cell culturing, sample prepara-
tion, proteome extraction, proteolytic digestion, HPLC
pre-fractionation, 2-D capillary HPLC [31,32], LTQ linear
ion trap mass spectral data acquisition parameters,
Sequest database searching [33], DTASelect [34]in silicon
assembly of the P. gingivalis proteome, protein relative
abundance calculations, statistical methods and analytical
validation for FDR and FNR [14] were all as published in
the previous paper [9], with the following exceptions. The
processing of the raw mass spectral data differs in this
report due to the genome sequence annotation specific to
strain ATCC 33277 [11], [GenBank: AP0093801 which
served as the basis for a new ORF database prepared by
LANL (Los Alamos National Laboratory, Gary Xie, private
communication). The custom database prepared by LANL

Table 6: A list of detected proteins, by P. gingivalis PGN number
[I I], assigned to transcription as determined using DAVID.


Increased (7)


Unchanged (3)


Decreased (0)


PGN 0423 PGN 0638 PGN 0792 PGN 1190
PGN 1570 PGN 1571 PGN 1202
PGN 1576 PGN 1578
PGN 1630


was combined with reversed sequences from P. gingivalis
ATCC 33277, human and bovine proteins as with our
W83 database [GenBank: AE015924] described previ-
ously. The total size of the combined fasta file was 116
Mbytes. The estimated random qualitative FDR for pep-
tide identifications based on the decoy strategy [35,36]
was 3%.

Assignment of ORF numbers
Additional file 1: Table S1 is arranged in ascending order
by PGN numbers assigned for the experimental strain
used here by Naito et al. [11]. They have been cross refer-
enced to the W83 PG numbers originally assigned both by
TIGR-CMR and LANL, where it was possible to do so. Cer-
tain ATCC 33277 genes do not have a counterpart in the
older annotations based on the W83 genome, and will
thus be blank in the summary table for PG numbers.

DAVID
An overall list of detected proteins as well as lists of pro-
teins that showed increased or decreased levels between
internalized and gingival growth medium cultured cells
were prepared using Entrez gene identifiers, as DAVID [ 17]
does not recognize PGN numbers. Ontology analyses
were then conducted using the DAVID functional annota-
tion clustering feature with the default databases. Both
increased and decreased protein level lists were analyzed
using the overall list of detected proteins as the back-
ground. Potentially interesting clusters identified by
DAVID were then examined manually.

Abbreviations
ATCC: American Type Culture Collection; DAVID: Data-
base for Annotation, Visualization and Integrated Discov-
ery; FDR: false discovery rate; FNR: false negative rate;
GEC: gingival epithelial cell; LANL: Los Alamos National
Laboratory; MS: Mass spectrometry; ORF: open reading
frame; TIGR-CMR: The Institute for Genomic Research
Comprehensive Microbial Resource, now part of the J.
Craig Venter Institute.


Proteins are listed by ORF number in the same manner as in Table 3.


Page 8 of 10
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Increased (16)


Unchanged (8)


PGN 0209
PGN 0365
PGN 0543
PGN 0819
PGN 0987
PGN 1229
PGN 1805
PGN 2045


PGN 0137
PGN 0281
PGN 0569
PGN_ 1711


PGN 0278
PGN 0366
PGN 0981
PGN 1883


BMC Microbiology 2009, 9:185








http://www.biomedcentral.com/1471-2180/9/185


Authors' contributions
QX calculated the protein abundance ratios and abun-
dance change statistics. TW performed the mass spectrom-
etry measurements. ELH performed the pathway and
ontology analyses. MH and RJL conceived the experi-
ments. ELH and MH wrote the manuscript. All authors
read and approved the final manuscript.


Additional material


Additional file 1
This file contains explanatory notes, two diagnostic pseudo M/A plots
and Table Sl, a summary of all the relative abundance ratios for
internalized/control P. gingivalis mentioned in this report. Prior to
permanent archiving at LANL with the raw mass spectral data, summa-
ries of the ATCC 33277-based protein identifications in the form of
DTASelect filter.txt files will be available on a University of Washington
server tIn .1. ,- i ,,, ,, .,, .- .in i tl t- rather than on the BM C
Microbiology web site due to their large size. Request a password from
the corresponding author. These files include details such as SEQUEST
scores, peptide sequence, percentage of peptide coverage by observed ions
in the CID spectrum, spectral counts, and other information described in
the headers accompanying the filter files. More detail regarding the type
of information contained in the filter files can be found in Tabb et al.
[34].
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2180-9-185-Sl.pdf]


Acknowledgements
The authors wish to thank the Institute for Systems Biology for advice con-
cerning the pathway analysis and LANL-ORALGEN for the machine reada-
ble fasta database. This work was supported by the NIH NIDCR under
grants DEO 14372 and DEI 1111. Additional funding was provided by the
UW Office of Research, College of Engineering and the Department of
Chemical Engineering. We thank Fred Taub for the FileMaker database.

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