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Change of TCR V Beta CDR3 Length Distribution in CD4 T Cells of Influenza Vaccinated Subjects

Permanent Link: http://ufdc.ufl.edu/UFE0022209/00001

Material Information

Title: Change of TCR V Beta CDR3 Length Distribution in CD4 T Cells of Influenza Vaccinated Subjects
Physical Description: 1 online resource (86 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: cdr3, influenza, tcell
Medicine -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Observing TCR V beta (Vb) CDR3 length distribution tells us which specific TCR Vb families are used by a particular antigen. By observing the TCR Vb CDR3 length distribution in influenza vaccinated patients, we are able to determine which specific Vb families are used which may result in the development of a more effective influenza vaccine that targets those specific receptors. The TCR Vb CDR3 length distribution was assessed by spectratyping. The condition to amplify 21 Vb families using the minimal amount of RNA from CD4 positive T cells of influenza vaccinated subjects was optimized using RNA from cord blood and purified CD8 positive T cells of healthy donors. The change of TCR Vb CDR3 length distribution in CD4 positive T cells of flu vaccinated adults was evaluated by comparison to the Gaussian distribution of TCR Vb CDR3 length in naive CD4 positive T cells from healthy children using statistical analytical tools. The conditions needed for T cell receptor spectratyping have been optimized to perform spectratyping on 21 Vb families. Through the use of purified CD45RA and CD45RO CD8 positive T cells, the optimum condition of using the minimum amount of cDNA derived from the minimum amount of RNA was discovered. It was found that between 5 to 10 x 106 peripheral blood mononuclear cells (PBMC) are need to obtain between 3 to 5 x 106 purified (CD45RA or CD45RO) T cells from which 0.25 micrograms of RNA is derived to yield 1 micro liter (2.5 percent of the total amount) of cDNA. This amount (1 micro liter or 2.5 percent of the total amount of cDNA derived from 0.25 micrograms of RNA) was sufficient to perform one RT reaction and amplify 21 Vb families. Using the optimum condition for spectratyping obtained earlier, we sought to perform spectratyping on 21 Vb families for two influenza vaccinated subjects to study the T cell receptor Vb CDR3 repertoires within PBMC, CD4 positive, and CD4 negative cells. We only obtained results for the CD4 positive cells since the CD4 positive cells consist of a pure cell population whereas the PBMC and CD4 negative cells consist of a mixed cell population. In performing spectratyping on the CD4 positive cells, it was found that nine Vb?families were perturbed in both subjects, two Vb families did not amplify in either subject (Vb 15 and 18) and one family (Vb 9 in Subject 1 and Vb 7 in Subject 2) was found to show monoclonal expansion. Only one family, Vb 3, was normal in both subjects. In comparing our results to the cited literature, of the nine families perturbed in our study, six were found to correspond with perturbations in the CD4 positive subset in at least one study in the literature including Vb 5, 8, 9, 12, 13, and 17. These perturbations may be carryover from previous antigen recognition. The two families that did not amplify may represent a minor population in the TCR repertoire. Monoclonal expansions were observed in Vb 12, 14, and 23 in the literature, however such expansions were not observed in Vb 9 and Vb 7 as was seen in our subjects. These monoclonal expansions may be due to the influenza vaccine; however this cannot be stated with certainty without further studies being conducted with pre and post vaccination sample time points.
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.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Yin, Li.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022209:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022209/00001

Material Information

Title: Change of TCR V Beta CDR3 Length Distribution in CD4 T Cells of Influenza Vaccinated Subjects
Physical Description: 1 online resource (86 p.)
Language: english
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: cdr3, influenza, tcell
Medicine -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Observing TCR V beta (Vb) CDR3 length distribution tells us which specific TCR Vb families are used by a particular antigen. By observing the TCR Vb CDR3 length distribution in influenza vaccinated patients, we are able to determine which specific Vb families are used which may result in the development of a more effective influenza vaccine that targets those specific receptors. The TCR Vb CDR3 length distribution was assessed by spectratyping. The condition to amplify 21 Vb families using the minimal amount of RNA from CD4 positive T cells of influenza vaccinated subjects was optimized using RNA from cord blood and purified CD8 positive T cells of healthy donors. The change of TCR Vb CDR3 length distribution in CD4 positive T cells of flu vaccinated adults was evaluated by comparison to the Gaussian distribution of TCR Vb CDR3 length in naive CD4 positive T cells from healthy children using statistical analytical tools. The conditions needed for T cell receptor spectratyping have been optimized to perform spectratyping on 21 Vb families. Through the use of purified CD45RA and CD45RO CD8 positive T cells, the optimum condition of using the minimum amount of cDNA derived from the minimum amount of RNA was discovered. It was found that between 5 to 10 x 106 peripheral blood mononuclear cells (PBMC) are need to obtain between 3 to 5 x 106 purified (CD45RA or CD45RO) T cells from which 0.25 micrograms of RNA is derived to yield 1 micro liter (2.5 percent of the total amount) of cDNA. This amount (1 micro liter or 2.5 percent of the total amount of cDNA derived from 0.25 micrograms of RNA) was sufficient to perform one RT reaction and amplify 21 Vb families. Using the optimum condition for spectratyping obtained earlier, we sought to perform spectratyping on 21 Vb families for two influenza vaccinated subjects to study the T cell receptor Vb CDR3 repertoires within PBMC, CD4 positive, and CD4 negative cells. We only obtained results for the CD4 positive cells since the CD4 positive cells consist of a pure cell population whereas the PBMC and CD4 negative cells consist of a mixed cell population. In performing spectratyping on the CD4 positive cells, it was found that nine Vb?families were perturbed in both subjects, two Vb families did not amplify in either subject (Vb 15 and 18) and one family (Vb 9 in Subject 1 and Vb 7 in Subject 2) was found to show monoclonal expansion. Only one family, Vb 3, was normal in both subjects. In comparing our results to the cited literature, of the nine families perturbed in our study, six were found to correspond with perturbations in the CD4 positive subset in at least one study in the literature including Vb 5, 8, 9, 12, 13, and 17. These perturbations may be carryover from previous antigen recognition. The two families that did not amplify may represent a minor population in the TCR repertoire. Monoclonal expansions were observed in Vb 12, 14, and 23 in the literature, however such expansions were not observed in Vb 9 and Vb 7 as was seen in our subjects. These monoclonal expansions may be due to the influenza vaccine; however this cannot be stated with certainty without further studies being conducted with pre and post vaccination sample time points.
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.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Yin, Li.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022209:00001


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1 CHANGE OF TCR V BETA CDR3 LENGTH DISTRIBU TION IN CD4 T CELLS OF INFLUENZA VACCINATED SUBJECTS By MICHELE LEE LAWSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2008

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2 2008 Michele Lee Lawson

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3 To my parents, Thomas and Magdale ne, for all their love and support.

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4 ACKNOWLEDGMENTS I would like to express my sin cerest appreciation to my mentor Dr. Li Yin, for all of her guidance, support, insight, and encouragement. I would also like to express appreciation to my committee members, Dr. Maureen Goodenow and W eaver Gaines, Esq., w hose constructive and thoughtful comments helped improve the overall quali ty of my thesis proj ect. I would like to acknowledge Dr. John Sleasman and Dr. Sleasmans laboratory for providing me with samples for this project. I would also like to acknowledge the members of Dr. Goodenows laboratory for their invaluable assistance with this project These members include Dr. Guity Ghaffari, Dr. Marco Salemi, Steve McCready, Joey Brown, Steve Pomeroy, Josh Bunger, and Wilton Williams. I would also like to give speci al thanks to my parents and friends for their continued support and encouragement. I would not have been able to complete this project without them and I sincerely appreciate all of them being there for me.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 LIST OF ABBREVIATIONS.......................................................................................................... 9 ABSTRACT...................................................................................................................................10 CHAP TER 1 INTRODUCTION AND SPECIFIC AIMS........................................................................... 12 Introduction................................................................................................................... ..........12 Influenza Virus.......................................................................................................................12 Influenza Structure..........................................................................................................13 Influenza Life Cycle........................................................................................................15 Influenza Vaccine.............................................................................................................. .....16 Vaccine Types.................................................................................................................17 Vaccine Effectiveness..................................................................................................... 18 Immune Response...................................................................................................................18 Humoral Immune Response to Vaccine.......................................................................... 18 Cellular Immune Response to Vaccine........................................................................... 19 The CD4+ response...................................................................................................20 The CD8+ response...................................................................................................23 The T Cell Receptor (TCR).................................................................................................... 25 Spectratyping..........................................................................................................................28 Specific Aim 1: To Optimize the TCR Sp ectratyping Conditions Needed to Perform Spectratyping on 21 V fam ilies Using Cord Blood.......................................................... 29 Specific Aim 2: To Use the Optimal Conditions Found in Specific Aim 1 to Perform Spectratyping on 21 V Families from Influenza Vaccinated Subjects to Measure the TCR V Family Usage Caused by Vaccination................................................................. 30 Significance................................................................................................................... .........31 2 MATERIALS AND METHODS........................................................................................... 33 Cord Blood PBMC and CD8+ T Cell Subsets........................................................................33 Separation of CD45RA and CD45RO CD8 T Cells from PBMC..........................................33 Prepare Cells for Cell Sorting.......................................................................................... 34 Magnetic Labeling of CD8 Negative Cells.....................................................................34 Magnetic Separation of CD8+ Cells from CD8Cells..................................................... 34 Magnetic Labeling of CD45RO+ CD8 T Cells................................................................ 35 Magnetic Separation of CD45RA and CD45RO CD8 T Cells .......................................35

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6 Extraction of RNA..................................................................................................................36 Quantification of Concentration of RNA............................................................................... 38 Reverse Transcription.......................................................................................................... ...39 Polymerase Chain Reaction (PCR).........................................................................................39 Spectratyping..........................................................................................................................41 Determination of TCR V Perturbation ................................................................................. 43 Samples from Influenza Vaccinated Healthy Adults.............................................................. 44 3 RESULTS...............................................................................................................................49 Results Obtained for Aim 1....................................................................................................49 Estimation of Targeted Minimum Amount of RNA and cDNA Required to Am plify 21 V Families.............................................................................................................49 Determination of the Minimum Amount of RNA and cDNA Using Cord Blood .......... 50 Application of the Minimum Amount of RNA a nd cDNA of Cord Blood to Purified CD45RA and CD45RO CD8 T Cell Subsets............................................................... 51 Calculation of the Number of PBMCs Needed to Am plify 21 V Families from CD8 CD45RA and CD45RO T Cell Subsets............................................................... 52 Results Obtained for Aim 2....................................................................................................53 4 CONCLUSION..................................................................................................................... ..71 Conclusion for Aim 1: The Representation of TCR Can Be Assessed in Sm all Numbers of Cells from T Cell Subsets...............................................................................................71 Conclusion for Aim 2: Perturbations of TCR Ca n Provide a Su rrogate Marker for Immune Response...............................................................................................................71 LIST OF REFERENCES...............................................................................................................79 BIOGRAPHICAL SKETCH.........................................................................................................86

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7 LIST OF TABLES Table page 2-1. Variable beta chain prim ers for spectratyping....................................................................... 47 3-1. Separation of CD45RA and CD45RO CD8 T cell subsets ................................................... 56 3-2. Summary of optimal conditions for spectratyping................................................................ 66 3-3. Concentrations of RNA for in fluenza vaccinated subject sam ples....................................... 67

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8 LIST OF FIGURES Figure page 1-1. Spectratyping process............................................................................................................32 2-1. Statistical method for determination of TCR V perturbations. ........................................... 48 3-1. Objective................................................................................................................................57 3-2. Scale down amount of RNA per RT using cord blood cells on V 2 fam ily......................... 58 3-3. Cord blood amplification of five V f amilies using 1 L or 2.5% of cDNA that was derived from 0.25 g of cord blood RNA.........................................................................59 3-4. Comparison of unacceptable versus acceptable spectraty ping results.................................. 60 3-5. Cord blood amplification of 21 V f amilies using 1 L of cDNA derived from 0.5 g of RNA......................................................................................................................... ......61 3-6. CD8+ CD45RA cells amplification of five V families using 1 L cDNA derived from 0.25 g RNA......................................................................................................................62 3-7. CD8+ CD45RO cells amplification of five V families using 1 L cDNA derived from 0.25 g RNA......................................................................................................................63 3-8. CD8+ CD45RA T cells amplification of 18 V families using 1 L cDNA derived from 0.25 g RNA......................................................................................................................64 3-9. CD8+ CD45RO T cells amplification of 21 V families using 1 L cDNA derived from 0.25 g RNA......................................................................................................................65 3-10. Spectratyping results for subject 1.......................................................................................68 3-11. Spectratyping results for subject 2.......................................................................................69 3-12. Summary of results for both influenza vaccinated subjects................................................ 70 4-1. Summary of results as compared to previously published studies on healthy nonvaccinated adult subjects.................................................................................................... 78

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9 LIST OF ABBREVIATIONS AUC Area under the curve CD45RA Cell surface molecule for nave T cells CD45RO Cell surface molecule for memory T cells cDNA Complementary deoxyribonucleic acid CDR 3 Complementary determining region 3 CTL Cytotoxic lymphocyte DF Differentiation factor DNA Deoxyribonucleic acid FBS Fetal bovine serum HA Haemagglutinin HLA Human leukocyte antigen HSCT Hematopoietic stem cell transplantation MHC Major histocompatibility complex NA Neuraminidase PBMC Peripheral blood mononuclear cells PBS Phosphate buffered saline PCR Polymerase chain reaction RNA Ribonucleic acid RNP Ribonuclear proteins RT Reverse transcription RT-PCR Real time polymerase chain reaction TCR T cell receptor V or Vb Variable beta chain of T cell receptor

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10 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science CHANGE OF TCR V BETA CDR3 LENGTH DISTRIBU TION IN CD4 T CELLS OF INFLUENZA VACCINATED SUBJECTS By Michele Lee Lawson May 2008 Chair: Li Yin Major: Medical Sciences Observing TCR V beta (Vb) CDR3 length distribution tells us which specific TCR Vb families are used by a particular antigen. By observing the TCR Vb CDR3 length distribution in influenza vaccinated patients, we are able to determine which specific Vb families are used which may result in the development of a more effective influenza vaccine that targets those specific receptors. The TCR Vb CDR3 length distribution was assessed by spectratyping. The condition to amplify 21 Vb families using the minimal amount of RNA from CD4 positive T cells of influenza vaccinated subjects was optimized using RNA from cord blood and purified CD8 positive T cells of healthy donors. The change of TCR Vb CDR3 length distribution in CD4 positive T cells of flu vaccinated adults was evaluated by comparison to the Gaussian distribution of TCR Vb CDR3 length in naive CD4 positive T cells from healthy children using statistical analytical tools. The conditions needed for T cell receptor spec tratyping have been optimized to perform spectratyping on 21 Vb families. Through the use of purified CD45RA and CD45RO CD8 positive T cells, the optimum condition of using the minimum amount of cDNA derived from the

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11 minimum amount of RNA was discovered. It was found that between 5 to 10 x 106 peripheral blood mononuclear cells (PBMC) are n eed to obtain between 3 to 5 x 106 purified (CD45RA or CD45RO) T cells from which 0.25 micrograms of RNA is derived to yield 1 micro liter (2.5 percent of the total amount) of cDNA. This am ount (1 micro liter or 2.5 percent of the total amount of cDNA derived from 0.25 micrograms of RNA) was sufficient to perform one RT reaction and amplify 21 Vb families. Using the optimum condition for spectratyping obtained earlier, we sought to perform spectratyping on 21 Vb families for two influenza vaccinated subjects to study the T cell receptor Vb CDR3 repertoires within PBMC, CD4 positive, and CD4 negative cells. We only obtained results for the CD4 positive cells since the CD4 positive cells consist of a pure cell population whereas the PBMC and CD4 negative cells consist of a mixed cell population. In performing spectratyping on the CD4 positive cells, it was found that nine Vb families were perturbed in both subjects, two Vb families did not amplify in either subject (Vb 15 and 18) and one family (Vb 9 in Subject 1 and Vb 7 in Subject 2) was found to show monoclonal expansion. Only one family, Vb 3, was normal in both subjects. In comp aring our results to the cited literature, of the nine families perturbed in our study, six were found to correspond with perturbations in the CD4 positive subset in at least one study in the l iterature including Vb 5, 8, 9, 12, 13, and 17. These perturbations may be carryover from previous an tigen recognition. The two families that did not amplify may represent a minor popul ation in the TCR repertoire. Monoclonal expansions were observed in Vb 12, 14, and 23 in the literature, ho wever such expansions were not observed in Vb 9 and Vb 7 as was seen in our subjects. These monoclonal expansions may be due to the influenza vaccine; however this cannot be stated with certainty without further studies being conducted with pre and post vaccination sample time points.

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12 CHAPTER 1 INTRODUCTION AND SPECIFIC AIMS Introduction Influenza causes severe respiratory illness in a s ignificant portion of the population each year, even resulting in death for some individua ls. There are two types of vaccines that are important to prevent the pathogene sis of the disease, however effi cacy of the vaccine is difficult to assess prior to the display of symptoms from infection. The goal of this study is to evaluate a surrogate marker for effective vaccination usin g changes in TCR repert oire as the surrogate marker as assessed through spectratyping. Influenza Virus The influenza virus attacks the respiratory system through infection of the columnar epithelial cells. Symptoms of influenza infection include: coughing, sneezing, runny nose, headache, sore throat, chills, and body aches [1]. The virus is highly contagious with between 5 and 20% of the population contracting influenza each year. The influenza virus is an especially virulent virus that, according to the Centers fo r Disease Control (CDC), is thought to cause between 3 and 5 million cases of severe illne ss and approximately 36,000 deaths each year. Those most at risk for serious illness and death are the elderl y aged 65 and above, children under the age of 2 years, and those who are immunocompromised [2]. The virus is primarily transmitted from pers on to person via droplets extruded from the nose or throat of an infected person by coughing or sneezing with close contact (no more than 3 to 6 feet) required for transmission. The virus is also spread thr ough direct skin to skin contact as well as indirect contact by touching contaminat ed surfaces and then touching the nose, eyes, or mouth. Persons are considered infectious from 2 days before to 5 days after the onset of symptoms [3, 4]. The human infectious dose is between 100 and 1000 vi rus particles. A 0.1 L

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13 aerosol particle contains more than 100 virus particles and within nasal secretions, millions of virus particles are shed [5]. If the virus is able to replicate early in the co urse of infection within the lower respiratory tract then smaller droplets with higher viral load an d higher infectivity are seen [3]. The virus has three strains labeled A, B, and C although only strains A and B cause significant illness in humans [6]. Of these strain s, strain A is the most virulent giving rise, through point mutations in the viru s surface glycoproteins, to new vari ants every 1 to 2 years [4]. Some of the mutations cause changes to these viral proteins that a ffect the binding of host antibodies thus variants with these mutations are not effectiv ely inhibited by host antibodies [6]. These variants are able to elude the bodys host defenses and therefore do not permit lasting immunity against the virus whether by natural infection or by vaccination. These changes in the antigenicity of influenza A viruses are termed antigenic drift and are the basic cause for influenza epidemics [4]. In contrast to epidemics, pandemics occur approximately every 10 to 50 years [7]. There have been three major influenza pandemics within the twentieth century: 1918, 1957, and 1968. The 1918 pandemic was the most devastating with between 50 and 100 million deaths. One characteristic of a pandemic is a mortality sh ift towards younger age grou ps (those younger than age 65). Pandemics occur due to antigenic shif t whereby there is a major change in the influenza A virus caused by either the reassortme nt of the genome of two viruses or by the gradual mutation of an animal virus. This ma jor change results in new glycoproteins on the surface of the virus [4]. Influenza Structure The influenza virus is an enveloped negativ e stranded RNA vi rus that has a segmented genome composed of eight single stranded RNA segments covered by the nucleocapsid protein.

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14 Together these form the ribonucleoprotein (RNP) with each segment coding for a protein [5, 8]. The most important proteins for virulence of the virus are the surface glycoproteins haemagglutinin (HA) and neuraminidase (NA). There are sixteen (H1 to H16) subtypes of HA and nine (N1 to N9) subtypes of NA. Different combinations of the HA and NA subtypes give rise to different types of the influenza A strain [1, 5]. The viru lence of the virus depends on the compatibility of NA with HA meaning that mu tations in HA need to have compensatory mutations in NA [3]. Currently, only three type s of HA (H1, H2, H3) and two types of NA (N1, N2) have been seen in influenza virus infection in humans [1]. HA is a glycoprotein projecting outward from th e surface of the virus. The HA protein is encoded by the fourth RNA segment of the genome. HA is involved in att aching the virus to the host cell through binding sial ic acid residues, and i nduces penetration of the interior of the virus particle through membrane fusion. HA has five an tigenic sites and is the main virus antigen against which neutra lizing antibodies are produced. In antigenic drift, mutations in the antigenic sites reduce or inhibit the binding of neutralizi ng antibodies thus permitting a new subtype of the virus to enter the population and cause an epidem ic. Genome reassortment or antigenic shift arises when the HA subtype is exchanged in a virus. Exchange of HA can lead to pandemics as discussed above [5, 8]. NA is also a surface glycoprotein. NA cleaves si alic acid thus facilitating virus infection by permitting the virus to be endocytosed into the cell. The cleavage of si alic acid by NA is the major component necessary for the release of pr ogeny virus from the infected cell. NA is also involved in allowing penetrati on of the virus through the mucin layer of the respiratory epithelium. Like HA, NA also has antigenic si tes that are capable of mutation and antigenic drift. Mutations in the amino acid residues can also cause resistance to antiviral drugs [5, 8].

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15 In addition to the surface glycoproteins identi fied above, several othe r proteins are encoded by the genome. The M2 ion channel induces a low pH in the virus particle allowing the nucleocapsid to uncoat so that it can enter the cytoplasm of the hos t cell. The M2 protein then destabilizes protein binding thus allowing the nucleocapsid to be transported to the nucleus [5, 8]. The functions of the NS1 and NS2 proteins are not known for sure, however the NS1 protein is thought to inhibit the export of poly-A containing mRNA molecules from the nucleus which allows preference to be given to the vi ral RNA to be transported to the ribosome and translated. The NS2 protein is thought to facilitate the tran sport of the newly synthesized ribonuclear proteins (RNPs) from the nucleus to the cytoplasm in order to accelerate virus production [5, 8]. Influenza Life Cycle The influenza virus particle is first intr oduced to the host cell through the binding of HA to the s ialic acid on the host cell glycoproteins. After attachment, the virus particle is taken up by endocytosis into the host cell. The M2 ion channel induces a low pH which encourages the HA to fuse with the membrane of the endosome thus uncoating the virus and allowing the RNPs to enter into the cytosol of the host cell. Unco ating of the virus normally is completed within 20 to 30 minutes of virus attachment [5, 8]. The M1 protein is dissociated from the RNPs and the RNPs enter the nucleus of the ce ll through the nuclear por e. Once in the nucleus, the virus needs active host cell DNA to scavenge cap sequences from the nascent mRNA that is generated in the nucleus by transcription of the host cell DNA and a ttaches them to its ow n mRNA. This process is called cap snatching [8]. The viral mRNA is transported to the cytoplasm where it is translated by host ribosomes which then generate the viral proteins. Some of the newly synthesized viral proteins are

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16 transported back to the nucleus where they bi nd to viral RNA to form RNPs. Other newly synthesized viral proteins are processed in the endoplasmic retic ulum and the Golgi apparatus where glycosylation occurs. These modified proteins include the HA and NA glycoproteins. The HA and NA glycoproteins are tr ansported to the cell membrane and insert into the lipid bilayer. When these glycoproteins reach a hi gh enough concentration at the plasma membrane, RNPs and M1 proteins aggregate and condense to produce the viral particle. Finally, the newly synthesized viral partic le is extruded from the membrane and cleaved from the host cell by NA activity. The time from vi ral entry into the host cell to the production of a new virus averages 6 hours [5]. Influenza Vaccine The influenza vaccin e is an important compone nt to preventing the pathogenesis of the virus. The vaccine is normally administered in the fall season. The vaccine composition changes every year according to data collected by the World Health Organization throughout each year which determines what strains to put into the vaccine. The new vaccine takes approximately six months to produce. The vaccine is a trivalent vaccine consisting of two types A strains and one type B strain. Vaccine co mposition is named in the following particular nomenclature: type of influenza strain/where th e strain was isolated/the isolate number/the date the strain was isolated. Following this designa tion in parentheses is the particular HA and NA subtypes represented. For example, in 2005, when the samples used in this study were collected, the vaccine contained the fo llowing strains: A/Solomon Islands/3/2006 (H1N1 like); A/Wisconsin/67/2005 (H3N2 like); and B/Malaysia /2506/2004 like [9]. For the 2007 influenza season, the vaccine contains the following strains: A/New Caledonia/20/1999 (H1N1 like); A/California/7/2004 (H3N2 like); an d B/Shanghai/361/2002 like [2].

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17 Vaccine Types There are tw o main types of influenza vaccine : trivalent inactivated vaccine (TIV) and live, attenuated influenza viru s vaccine (LAIV). TIV contains purified HA and NA while LAIV contains a weakened form of the virus [10]. Inactivated vaccines are further divided into wh ole, split, and subunit. The whole vaccine was the first developed. In this type of inactivated vaccine, the in fluenza virus is grown in eggs and subsequently purified, concentrate d, and inactivated using formaldehyde or -propiolactone. More recently, this method has been modified using centrifuge purifica tion, density gradient purification, or filter-membrane purification [9]. Split vaccines are produced similarly to whole vaccine but the virus particles are disrupted through the use of detergents. In contrast, s ubunit vaccines are composed of only purified HA and NA proteins. The other viral proteins are removed [9 ]. The inactivated vaccines consist of killed viruses and cannot cause influenza. These vaccines are generally administered intramuscularly and cause few side effects [2]. In contrast to the inactivated vaccines, a live attenuated vaccine is available that is administered intranasally. The vaccine is compri sed of a master attenuated virus inserted with HA and NA genes. This master vaccine is cold-ada pted to grow ideally at 25 degrees Celsius. When the vaccine is administered to a pers on, the high human body temperature attenuates the virus by inhibiting its growth. The advantage of the live, attenuated vaccine administered intranasally is the development of local neut ralizing immunity and the development of cellmediated immune response which may lead to a cross-reactive and longer lasting immune response. The major disadvantages of live, attenuated vaccines are the production of mild flulike symptoms, the inability of use on immunoc ompromised subjects, and the possibility of

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18 genetic reversion and reassortment with wild-type virus causing ne w strains to be developed [2, 9]. Vaccine Effectiveness Vaccine effectiven ess is defined as the pr evention of illness in vaccinated populations. Influenza vaccine effectiveness is between 70 and 90% in children and healthy adults less than 65 years of age and between 30 and 40% in those over age 65. However, the effectiveness of the vaccine in those over age 65 can be as high as 80% effective in preventing death among this age group [2, 9]. Vaccine efficacy is defined as the preven tion of illness among vaccinated persons in controlled trials as measured by haemaglutinatio n titers used as a sero logical marker of the immunological response to the vaccine. The efficacy in healthy primed adults and children is between 80 and 100% when vaccinated once. In unprimed adults, those who have never been vaccinated or encountered antigen, two vaccinati ons are needed to reach this efficacy. The vaccination efficacy for adults over age 60 and not living in nursing facilities was found to be between 30 and 70%. Vaccine efficacy for childre n was similar to adults, about 68%, although if two doses of vaccine were given, the effi cacy of this group increased to 89% [2]. Immune Response Humoral Immune Response to Vaccine The immune response takes a few days to becom e effective against the virus but then helps to eradicate the virus as well as establish a memory response that results in long-lived resistance to that particular strain of virus. In th e humoral immune response, B lymphocytes recognize antigen and differentiate into antibody secreting ce lls that produce antibodi es to both the HA and NA glycoproteins. Peak antibodies are seen four to seven weeks after infection followed by a steady decline; however antibodies remain detectab le for years after infection even without re-

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19 exposure. The anti-HA antibody neutralizes viru s infectivity and protects against disease and infection with a homologous virus [3]. In contrast to anti-HA, the anti-NA antibody reduces the release of progeny virus from the infected host cells. Since the NA glycoprotein of the virus is used to cleave the sialic acid residues and thus release progeny virus from the host cell, the anti-NA antibody effectively blocks this cleavage. Anti-NA antibody results in decreased virus shedding and severity of flu symptoms [3, 11]. Cellular Immune Response to Vaccine In contrast to hum oral immunity, T cells cannot recognize antigens di rectly but rather recognize antigen through surface peptide fragment s that are displayed at the surface of the infected host cell. These peptide fragments ar e derived from the viru s proteins and are displayed on the cell surface by specialized peptid e-binding host-glycoproteins named the major histocompatibility complex molecules (MHC). Th ere are two types of MHC molecules: MHC I and MHC II. MHC I molecules present antigen to CD8+ T cells while MHC II molecules present to CD4+ T cells [12]. Peptides associated with MHC I molecules are endogenously produced while peptides associated with MHC II molecules are exogen ously produced [13]. In cellular immunity, dendritic cells within the lung acquire antigen and become activated [3, 14]. These dendritic cells th en travel to the lymph nodes wher e the antigen is processed and fixed on the dendritic cell surface as peptides which are presented to the T cells through the MHC molecules [3, 15]. An immune response is triggered by the dendritic cells for any T cell with a receptor that is specific for the foreign peptide-MHC complex on th e dendritic cell surface [3, 16]. The activated T cells acqui re effector cell functions and trav el to the site of infection in the lungs of the respiratory system. These effect or functions allow the T cells to help directly, release cytokines, or mediate cytotoxicity af ter recognition of antigen [3].

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20 T lymphocyte responses peak around day fourteen post infection. In primary infection, viral clearance depends on CD8+ T lymphocytes whereas upon re-infection both CD4+ and CD8+ T cells respond to the site of infection to medi ate control of the influenza infection [3, 17]. Previous studies seem to indicate that virus-sp ecific CTLs respond to influenza infection and are able to clear the virus from the lungs [18-21]. However, these vi rus-specific CTLs alone are not able to efficiently clear the virus from the lungs. In fact, both CD4+ and CD8+ T cells are required for effective virus cleara nce [18], and both also act as effectors in protective immunity against influenza viral infection [22]. Immune response to influenza requires cytotoxic CD8+ cells, cytokine-secreting CD4+ cells, and antibody secreting B cells [23]. The CD4+ response CD4+ cells are important to the clearance of virus mainly through the augmentation of CD8+ cells and B cell responses [1]. CD4+ T lymphocytes assist B lymphocytes to produce antiHA and anti-NA antibodies, however the HA epitopes recognized by the CD4+ T helper cells are different from those recognized by the antibodies. CD4+ cells secrete numerous cytokines to contribute to the immune response at the site of infection [1, 24, 25]. T helper cells are divided in to Th1 and Th2 cells depending on the t ype of cytokines they produce. Nave CD4+ T cells, when in the presence of IL-12 and IFN, differentiate into Th1 cells but if in the presence of IL4, differentiate into Th2 cells. Th1 cells produce IFN, IL-2, and TNFwhile Th2 cells produce IL-4, IL-5, and IL-13 [23]. Viral infections predominantly induc e Th1 responses that activate CD8+ T cells. Correspondingly, protective immunity from influen za infection induces more of a Th1 response than a Th2 response [23]. There are conserved regions subunits of the HA glycoprotein that are homologous among different subtypes of the virus. CD4+ T cells have been found to recognize

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21 these conserved areas of the HA molecule wh ich may explain the partial protection from vaccination. One study reported that some of the conserved regions were recognized by a large proportion of subjects in the study thus implying a broadly antigenic CD4+ T cell response [26]. During influenza infection, the nave CD4+ T cell immune response is initiated in the lymph nodes and spleen. Upon stimulation these nave CD4+ T cells become a large effector cell population that contains many subsets. After these effector cells are created, only the most divided and differentiated cells trav el to the lung, thus antigen-specifi c cells travel to the site of infection only after effector cells are created. Th e highly differentiated effe ctor cells that travel to the lung have different f unctional abilities [27]. CD4+ effector T cells in the lung are directly involved in viral clearance by dir ect cytolytic effects on infected cells and/or by recruiting other cells to the site of infection by rapid expression of cytokines a nd chemokines [23]. After viral clearance, a spectrum of resting cell subsets remains which indicat es that heterogenous effector cells gives rise to corresponding memory cells [27]. CD4+ cells are needed for a long lasting effective CD8+ memory response. Memory CD4+ T cells that have an activated phenotype and are capable of immediate effector function are present in high frequencies in the lungs after clearance of a respiratory viral infection and help confer effective cellular immunity [28]. There are multiple diverse subsets of CD4+ memory T cells of which one subset of these primed CD4+ cells is positioned in the lungs for immediate response to reinfection while another subset is in the lymph nodes and can be recruited later [23]. Antigen recognition is responsible fo r the later accumulation of both CD8+ and CD4+ T cells at the site of infection [29]. There are both similarities and di fferences in the responses of CD4+ and CD8+ T cells. For both types of T cells, transient exposure to antigen is enough to induce proliferation and

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22 differentiation; however the strength and duratio n of the antigen stimulus can affect the differentiation process and regulate the function of the effector and memory cells that develop [30, 31]. Antigen exposure for nave CD8+ cells requires less time than that for nave CD4+ cells [32-35] and CD8+ T cell responses are higher in frequency when compared to CD4+ T cell responses [29, 36]. Initial activati on of both CD4+ and CD8+ T cells results in a death phase where most effector cells (about 90%) are eliminated. Nave CD8+ T cells develop into effector cells more readily than CD4+ cells after short-term primary stimulation. CD8+ effector cells are more likely to develop into long lasting memory cells as compared to CD4+ T cells since CD4+ T cells have a unique characteristic that renders them more susceptible to death when they become IFNproducing cells [36]. CD4+ and CD8+ T cells are divided into two subsets based on the expre ssion of molecular weight isoforms of the leukocyte common antig en, CD45RA and CD45RO. CD45 is cell surface protein that changes after exposure to an antig en. Repeated exposure to antigen on antigenpresenting cell allows cell to become effector cell [37]. CD45RA cells have a high molecular weight and contain nave cells while CD45RO cells are memory cells that have encountered antigen and have a low molecular weight [38]. CD45RA cells have three variable exons (A, B, C) and do not associate with the TCR or corecepto r. In contrast, CD45RO cells have variable exons removed by alternative splicing and asso ciates with both TCR and coreceptor so it transduces signals more effectively [37]. CD 45RA cells, being nave cells that have not encountered antigen, usually have a Gaussian distribution while CD45R O cells tend to show more differences in CDR3 length distributions [38]. Dominant T cell clones in peripheral blood may indicate previous exposure to antigen or previous or ongoing immune response.

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23 Oligoclonal populations have also been detected in a number of ch ronic diseases indicating that these populations may be the consequence of continuous antigen stimulation [13]. The CD8+ response CD8+ T cells can operate through direct lysis of infected cells or through the production of pro-inflammatory cytokines such as IFNand TNFand are needed for effective clearance of the virus [1]. CD8+ T cells require specific antigen for activation but memory cells can be recruited to the lungs in an antigen-nonspecific manner during influenza infection [29, 39]. Memory CD8+ T cells are recruited to the lung during influenza infection in th ree distinct phases, the first two of which are non-proliferating populations. One population is pr esent in the tissue and thus is in the lung at the outset of infec tion. A second population is antigen-dependent and recruited from the blood. The third populat ion is produced in the lymph nodes and has proliferated in response to antigen [29, 40]. The T cells that respond to influenza infecti on undergo similar cha nges in the expression of cell surface markers. Both CD4+ and CD8+ cells divide at a similar rate but CD8+ cells continue to proliferate after CD4+ cells peak and then dec line more slowly than CD4+ cells. This continued proliferation and slow decline by CD8+ T cells leads to a tenfold more increase in the number of cells. CD4+ and CD8+ cells are regulated differently to perform distinct functions [41]. Virus-specific activated CD8+ T cells can remain in the l ungs for several months postinfluenza infection. Some level of T cell activa tion or inflammation in the lungs is needed for antigen-specific CD8+ T cells to remain in the lungs. The T cells in the lungs after viral infection express surface markers that are normally found on recently activated effector cells. A prolonged effector T cell response may produce th e chronic activation of antigen-specific CD8+

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24 T cells in the lungs. Persistent antigen stimulation may be re quired for maintaining activated CD8+ T cells near the site of virus amplificatio n in the lungs. Influenza virus antigens are retained in the draining lymph nodes and are presented to CD8+ T cells for at least 2 months post-infection [42]. In memory responses with a large number of T cells before infection there is a longer delay in T cell expansion, and a limit on the number of effector T cells that can be produced which results in the same peak CTL number being r eached over a wide range of pre-infection T cell levels. This seems to indicate that repeated vaccination could lead to a stronger early response after infection; however repeat ed vaccination could also lead to decreased proliferation in responding cells thus resulting in decreased resp onsiveness with chronic antigenic stimulation [43]. CD8+ cytotoxic lymphocytes (CTL) may be eith er subtype specific, recognizing HA, or broadly cross-reactive with influenza strain A recognizing internal proteins such as the M protein [3]. Most CTL recognize one subtype of influen za strain A viruses that are currently circulating, but some CTL seem to be able to recognize bo th homosubtypic and hete rosubtypic variants. Consecutive infection with viruses containing differe nt variants of the same epitope will select for cross-reactive T cells that are reactive agains t both variants. The flexibility of the TCR of this subset of CTL recognizes naturally occurring variants of the epitope that may be escape mutants. This subset of cross-reactive CTL may cont ribute to protective immunity be increasing in number after repeated exposur e to heterologou s viruses [44]. Both CD8+ and CD4+ T cells as well as B cells contri bute to heterosubtypic immunity, which is immunity against a differe nt virus subtype than the original infection. There must be a properly diversified TCR repert oire in order to confer he terosubtypic immunity [45].

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25 The T Cell Receptor (TCR) The virus interacts with T cells through the T cell recep tor (TCR). Each T cell has approximately 30,000 antigen-receptor molecules on its surface. Each T cell receptor is comprised of a constant region and a variable region. The re arrangement of V and J gene segments in the variable region produces the V chain while the recombination of V, D, and J gene segments produces the V chain. Each of these variable region exons are transcribed and spliced to join the constant region of the T CR. The variable (V) regions of the TCR, the chain (TCR ) and the chain (TCR ), are linked together by a disulfide bond [37, 46]. There are hypervariable regions within the V and V chains that fold in close proximity to each other to form the TCR antigen binding site. These regions are called the complementary determining region (CDR) and are divide d into CDR1, CDR2, and CDR3. The V chain contains only CDR1 and CDR2 regions while V consists of CDR1, CDR2, and CDR3. The CDR1 and CDR2 regions are encoded by the V a nd J genes while the CDR3 region is encoded by the V, D, and J genes, thus making the CD R3 region the most di verse region [37]. The ability of the T cell immune system to r ecognize a vast array of pathogens rests on the diversity of the TCR V CDR3 region. This diversity occu rs mainly through two methods: (1) recombination of the variable (V), diversity (D), and joining (J) gene segments; or (2) removal or insertion of nucleotides at VD and D-J junctions by terminal deoxynucleotidyltransferase (TdT) during the recombination process. Thus the CDR3 region of the TCR V chain is variable in both length and amino acid sequence [38]. Each V gene segment can be grouped into families where each segment shares at least 80% DNA seque nce identity with each other family member [12]. The CDR3 region may vary in length by as many as six to eight amino acids within each V family [12, 47, 48]. TCR V CDR3 length diversity can serve as an indicator of diversity of

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26 antigenic specificity, and changes in the amino acid sequence and length of the CDR3 region can be used as a clonal marker to show the T cell res ponse to an antigen in the course of an immune response [38, 47]. In regards to determining whether or not TCR V spectratyping can be used as a surrogate marker of T cell activation due to vaccination, accordi ng to the literature, examining changes in the TCR V repertoire can be used as a surrogate marker of T cell activation due to vaccination [49-53]. In addition, several studies have shown that examining which V genes are involved in immune response is important since these molecules are expressed on the T cell surface and can be used as targets for immune therapy [49-51, 54, 55]. The majority of literature seems to indi cate that vaccination does indeed cause TCR activation that can be m easured, and different V families show perturbation according to what vaccine was used. Studies on TCR V repertoire activation have be en done on tetanus, measles, and hepatitis B vaccinations with pre-vaccin ation and post-vaccinati on samples taken to determine if a particular TCR V family perturbation was due to the effect of the vaccine as opposed to carry over from previous exposure to an antigen [49, 51, 52, 54]. Differences in TCR V family activation within each specific antigen type may be due to differences in time points at which the samples were taken (ranging fr om 48 hours post vaccination to 14 days post vaccination). Additionally the differences in TCR V gene usage may also be due to different HLA haplotypes between individuals [56]. Hapl otype refers to each individual person having different MHC molecules that pr esent different antigens. If a study examines subjects with a specific haplotype then more families may be shared [37]. Studies have also shown that there seems to be a difference in the TCR V activation between measles vaccination and natural measles infection which may be due to the effects of the virus strain or th e ages of the subjects [50, 53].

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27 The diversity, clonality, and speci ficity of a TCR repertoire th at is antigen-specific depend on the molecular, biophysical, and structural components of both the V and V regions [57]. Theoretically the TCR repertoi re could have a size of 1015 different TCR dimers [48]. The actual diversity of the TCR repertoire is much le ss than the theoretical st ructural diversity of 1015 due to selection of T cells in the th ymus [58, 59]. The actual number of TCR expressed in adults is closer to 107 with the complexity of the expressed repertoire varying between functionally distinct subpopul ations of T cells [60]. There are differences in cord blood versus adult repertoires that represent those T cell subsets that have survived as a result of antigen se lection [61]. Cord blood TCR expression is highly diverse while during aging the TCR repe rtoire changes by becoming less diverse and more oligoclonal [62]. This result is expected since as a person gr ows older, he or she is exposed more frequently to a diverse group of antig ens which would invoke changes in the TCR repertoire. A diverse TCR V CDR3 repertoire allows greater pr otection from infection since many diverse subtypes of virus can be recognized as well as immune escape viral variants can be controlled. In influenza virus infections, it has b een reported that a diverse TCR repertoire could be important in controlling viral CTL escap e variants. Recent research has found a novel molecular mechanism to maintain overall diversity of the TCR repertoire in order to control the escape of heterogenous influenza A virus from the recognition by virus-specific CTLs. This mechanism is shown by pairing a diverse V chain with a biased V chain. Diversity of V chains has not been extens ively explored; however V chain diversity can potentially have a profound impact on overall TCR diversity [57].

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28 All V segments can be amplified ex vivo by PCR by using V specific primers. CDR3 length distribution for each V subfamily can be measured by having an average of eight peaks spaced by three nucleotides apart. Oligoclonality comprises a discrete number of specific T cells being used. In immune responses, there can be a public response where the response is shared among individuals or a private response which has T cells clones that are different between individuals. The rationale for determining CD R3 size is that it provi des a picture of the repertoire without needing in vitro growth. The relative intensity of any given peak is proportional to the number of non-amplified cDNA molecules that share this CDR3 size. An increase in the height or area of a peak usually indicates a mo noclonal expansion. Certain TCR are not selected at the nucleoti de level but rather are selected at the amino acid level [47]. Spectratyping Spectratyping is a PCR based assay designed to measure the length vari atio n within a gene family. To measure length distribution in TCR V CDR3 region, two rounds of PCR are performed. In the first round PCR, the forwar d primer is specific to a particular V family and the reverse primer is located in the constant region of the TCR V chain. In second round PCR, the first round products are used as a template an d a pair of nested primers of which the forward primer is V family specific and is nested 3 to th e first round PCR primer while the reverse primer is constant and located 88 base pairs away from the CDR3 region. One of the second round primers is labeled with a flourochrome to allow the bands to be seen on a gel and signals to be detected by a computer. The PCR products are then run on a 6% acrylimide sequencing gel and analyzed using ABIPRISM GeneScan anal ysis software (Applied Biosystems) which calculates the relativ e intensity of each product to generate a histogram of CDR3 lengths for each V family [63] (Figure 1-1).

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29 The relative representation of the particul ar CDR3 sizes within an individual V family is reflected in the intensity of an individual peak. Expressing this data as a percentage of the total area under the curves for each V family can be used to accura tely determine the degree of CDR3 length diversity w ithin a particular V family. These results allow comparisons of TCR diversity among different V families or among different subjects [38]. In the absence of antigen stimulation, th e distribution of CDR3 lengths within a V family should be Gaussian or bell-shaped. Use of a V family after antigen stimulation is demonstrated by the loss of the Gaussian distribu tion of CDR3 lengths (pol yclonal expansion) or the predomination of particular CDR3 peaks within the family (monoclonal expansion), both of which constitute perturbation [64] Spectratyping allows examination of the antigen specific CDR3 region of the TCR by examining CDR3 length distributions for 24 V families. However, technically there are only 21 functional V families since family #10 and #19 are pseudofamilies [65]. Specific Aim 1: To Optimize the TCR Spectratyping Conditions Needed to Perform Spectratyping on 21 V families Using Cord Blood The hypothesis for specific aim 1 was that using the minimum amount of cDNA derived from the minimum amount of R NA would produce similar results as when using higher starting amounts of RNA and cDNA. We wa nt to use the minimum concentration that still allows for sufficient representation of all V families while eliminating false negatives and oligoclonality due to insufficient sample. Using the minimu m amount of RNA and cDNA needed to obtain an optimal PCR result allows the patients sample to be conserved. The objective was to find the minimum amount of cDNA derived from the mini mum amount of RNA needed to amplify 21

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30 families. The targeted optimal PCR condition was 1 L or 2.5% of the total amount of cDNA derived from 0.25 g of RNA. Specific Aim 2: To Use the Optimal Conditions Found in Specific Aim 1 to Perform Spectratyping on 21 V Families from Influenz a Vaccin ated Subjects to Measure the TCR V Family Usage Caused by Vaccination This aim was a pilot study done to determine th e feasibility of time poi nts in a larger study done in collaboration with the University of Sout h Florida. The larger study was examining the breadth of TCR immune response following i mmunization with influenza as recall antigen among healthy and bone marrow transplant patients undergoing hematopoietic stem cell transplantation (HSCT) as well as examining th e relationship between influenza-specific clonal expansions within the T cell repe rtoire and increases in influenza-specific T cell responses in healthy and post-HSCT subjects with a particul ar genotype. Samples from each subject would be drawn pre-vaccination, and 1 mont h and 3 months post vaccination. Our pilot study was done with samples that were drawn at the time of vaccination and one month post vaccination to determine if there were indeed perturbations. The rationale for this pilot study was to examine post-vaccination samp les first because if no perturbations were observed in the post-vaccination samples, then there would be no need to examine a prevaccination sample time point. The hypothesis for the pilot study is that vaccination would cause perturbations in the TCR repertoire and that changes in T cell diversity could be used as a surrogate marker to determine the efficacy of the influenza vaccine. Spectratyping was used to examine the changes in PBMC, CD4+ (enriched), and CD4(depleted) T cell subsets from two influenza vaccinated subjects for 21 V families.

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31 Significance The significance of this study was twofold. Fi rst, we wanted to determ ine the minimum amount of RNA that was needed to evaluate 21 V families. This aim was significant because samples from subjects may be in limited supply. The optimization of conditions to use the minimum amount of RNA is applicable not only to this study but also to other studies going on in the lab. The second aim is significant because as a pilot study, we can determine the appropriate time points for samples to be taken from subjects in order to observe TCR repertoire diversity. If perturbations are not observed post-vaccination, th en pre-vaccination samples do not need to be taken. As part of a larger study, we can determin e whether or not TCR diversity can be used as a surrogate marker to determine the efficacy of the influenza vaccine. By examining which specific families are perturbed, we can have a clearer idea as to what specific TCR are used during influenza viral infection. Knowing which families are perturbed can lead to development of a more effective influenza vaccine.

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32V b V D J C V V NS C C NS6-FAM* Spectratyping Applied Biosystemsmodel 377 DNA sequencer ABIPRISMGeneScan analysis software CDR3TCR chain C region V regionV b V D J C V D J C V V NS C C NS6-FAM* Spectratyping Applied Biosystemsmodel 377 DNA sequencer ABIPRISMGeneScan analysis software CDR3TCR chain C region V region Figure 1-1. Spectratyping process can examine the antig en specific CDR3 region of the TCR by examining CDR3 length distributions for twenty four V families. In first round PCR, we use a forward primer that is specific to the particular V family and a reverse primer that is in the constant re gion. In second round PCR, we use the first round products as a template and a pair of ne sted primers of whic h the forward primer is V family specific and is nested 3 to th e first round PCR primer while the reverse primer was constant and located 88 bp away from the CDR3 region. This allows the CDR3 region to be amplified. The second round primers are labeled with fluorescence (6-FAM) to allow the bands to be seen on the gel. The PCR products were then run on a 6% acrylimide seque ncing gel and analyzed with ABIPRISM GeneScan analysis software which calculate s the relative intensity of each product to generate a histogram for each V family. The area under each peak is calculated and a peak with an area that is >40% of the total area is c onsidered perturbed which can indicate oligoclonal expans ion. Each peak is one le ngth and we can observe the length distribution of the CDR3 region (pr oportion, what length is, distribution) to determine TCR recognition of Ag

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33 CHAPTER 2 MATERIALS AND METHODS Cord Blood PBMC and CD8+ T Cell Subsets Cord blood PBMC (STEMCELL technologi es) and CD45RA and CD45RO CD8+ T cell subsets isolated from the PBMC of healthy dono rs were used to find the minimum number of cells which could provide enough RNA and subsequently cDNA to amplify 21 TCR V families. Separation of CD45RA and CD4 5 RO CD8 T Cells from PBMC Peripheral blood mononuclear cells (PBMC) we re collected from leukopacks of healthy subjects (Civitan, Gainesville, FL) and frozen dow n in liquid nitrogen, were quickly thawed in a 37o water bath. Normally one cryovial contains 20 x 106 PBMC in 1 mL of freezing medium. One half microliter of fetal bovine serum (FBS) was added drop-wisely to each vial and then incubated at room temperature for 15 minutes. Th e cells were then transferred drop-wisely into two 15 mL conical tubes, each containing 6 mL RT RPMI-1640 and mixed by pipetting. Each tube was centrifuged at 800 rpm in the Beck man TJ-6 centrifuge for 5 minutes at room temperature. The cell pellet was then resuspended in an appropriate amount of phosphate buffered saline (PBS) and mixed well by pipetting. After appropriate dilution, 10 L of cells were placed on a hematocytometer and counted under the microscope. The concentration of cells was calculated by taking the mean of the total number of cells from the four quadrants and multiplying this number by the differentiation factor (DF) and multiplying this number by 104 (concentration of cells = m ean of total x (DF) x 104 = number x 106 cells / mL). The concentration of cells was then multiplied by the volume of PBS used to resuspend the cell pellet to find the total number of cells (total number of cells = concentration cells / mL x volume of cell suspension in mL).

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34 Prepare Cells for Cell Sorting The tubes with the required number of PBMC were then centrifuge d at 800 rpm in the Beckman TJ-6 centrifuge for 5 minutes at room temperature. When finished, the supernatant was pipetted off each tube as much as possible without disturbing the pellet. Magnetic Labeling of CD8 Negative Cells A CD8+ T Cell Isolation Kit II by Miltenyi Biotech (Auburn, CA) was used to separate the CD8+ T cells. The cell pellet was resuspended in wash buffer of 40 L /10 x 106 cells. The CD8cells were labeled by adding Biotin-Antibody Cocktail in the amounts of 20 L /10 x 106 cells. The content of each tube was mixed by vortexing, incubated for 10 minutes at 4-8o C, and vortexed every 3 minutes. Wash buffer was added at 30 L /10 x 106 cells. Anti-Biotin Microbeads were added to the cells at 20 L /10 x 106 cells. The cells were then mixed well by vortexing, incubated for 15 minutes at 4 to 8o C, and vortexed every 3 minutes. The cells were washed with wash buffer of 20 x labeling volum e and centrifuged at 800 rpm for 5 minutes at room temperature. After centrifugation, the supern atant was pipetted off completely and the cell pellet was resuspended in each tube with 500 L of wash buffer (up to 100 x 106 cells in 500 L wash buffer). Magnetic Separation of CD8+ Cells from CD8Cells A column labeled CD8was placed in the magnetic field of a MiniMACS Separator and the column was prepared by rinsing with 500 L of wash buffer with th e flow through discarded. One collection tube was placed under each of the three columns (representing each of the three tubes) and labeled CD8+. The cell suspension (500 L) was applied onto each column with the cells allowed to pass through and th e effluent collected as a fract ion with unlabeled cells which represents the enriched CD8+ T cell fraction. Each column was then washed with 500 L of

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35 wash buffer three times, each time on ce the reservoir was empty. The CD8+ cells were then counted (total number of cells = mean of total x DF x 104 x 2 mL) and the separation efficiency calculated (separation efficiency = cell number / total # of PBMC starting with). Magnetic Labeling of CD45RO+ CD8 T Cells The CD8+ T cells were centrifuged at 800 rpm for 5 minutes at room temperature. After centrifugation, the pellet was resu spended in wash buffer of 80 L /10 x 106 cells. We added 80 L of mouse anti-CD45RO-APC (BD PharMingen 340438) into each tube and mixed well. The tubes were incubated on ice fo r 20 minutes and vortexed ever y 5 minutes. The cells were washed carefully by adding 2 mL of buffer per 10 x 106 total cells and cent rifuged at 800 rpm for 5 minutes. The wash step was then repeated once and the supernat ant was pipetted off completely. The cell pellet was resuspended in 80 L of buffer per 10 x 106 total cells. Twenty microliters per 10 x 106 total cells of MACS Anti-APC Microbeads was added to the cells and mixed well. The cells were then incubated for 15 minutes at 4 to 8o C and vortexed every 3 minutes. The cells were washed by adding 2 mL of wash buffer per 10 x 106 cells and centrifuged at 800 rpm for 5 minutes at 4o C. The supernatant was pipetted off completely and the cell pellet was resuspended in 500 L of wash buffer (500 L of wash buffer per 100 x 106 total cells). Magnetic Separation of CD45RA and CD45RO CD8 T Cells A colum n was placed in the magnetic field of the MiniMACS Sepa rator and prepared by rinsing it with 500 L of wash buffer. The cell suspensi on was applied onto the column and the unlabeled cells were allowed to pass through and the effluents were collected into a 4 mL clear collection tubes that were labeled CD45RA. The column was then washed with 500 L of wash buffer three times, each time once the column reservoir was empty. The column was removed

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36 from the separator and placed on a suitable coll ection tube labeled CD45RO. One milliliter of wash buffer was pipetted onto the column and the positive labeled cells were flushed out using the plunger supplied with the column. The column was washed once more with 1 mL of wash buffer. The cells were then counted and the se paration efficiency calculated as described earlier for both the CD45RA and CD45RO cells. The cells were centrifuged at 800 rpm for 5 minutes at room temperature. Finally, the cells were lysed by adding 0.5 mL of lysis/binding solution (RNAqueous-4PCR kit, Ambion Cat. No. 1914), vorte xed very well to break up any clumps, and left in -20oC until the RNA was extracted. Extraction of RNA An RNAqueous-4PCR kit (Am bion, Austin, Texas) was used to extract the RNA from the lysed purified cells. The manufactur ers protocol was modified to include extra elutions. In extracting the RNA, first a 64% ethanol soluti on was prepared by adding 22.4 mL of ACS grade 100% ethanol to a bottle containing 12.6 mL of RNase-free water and mixing well. A wash solution #2/3 was prepared by adding 28 mL AC S grade 100% ethanol to bottle labeled wash solution #2/3 concentrate and mixing well. We heated 200 L of elution solution in an RNasefree microfuge tube in a h eat block set to 70 to 80oC. The cells in the lysis/binding solution obtai ned from the cell separation step or cord blood PBMC were thawed and an equal volume (500 L) of 64% ethanol was added to the lysate and mixed by vortexing. A filter cart ridge was inserted into a collect ion tube and the lysate/ethanol mix was pipetted onto the cartr idge (cartridge can hold 700 L at a time can pass up to 1.8 mL of lysate/ethanol mixture without exceeding the RNA binding capacity). The collection tube containing the filter cartridge was centrifuged for 1 minute at 14,000 rpm. After centrifugation,

PAGE 37

37 the flow through was discarded a nd the collection tube was reused for the subsequent washing steps. We applied 700 L of Wash Solution #1 to the filter and the tube was centrifuged for 1 minute at 14,000 rpm until all wash solution was through the filter. The flow through was discarded and the tube reused for subsequent steps. Five hundred micro liters of Wash Solution #2/3 was added to the filter and the tube wa s centrifuged for 1 minute at 14,000 rpm. Another 500 L of Wash Solution #2/3 was adde d to the filter and the tube was centrifuged for another 1 minute at 14,000 rpm. The wash solution was di scarded and centrifugatio n was continued for another 10 to 30 additional seconds to rem ove the last traces of wash solution. The filter was then placed into a fresh collection tube and 50 L Elution Solution that was preheated to 70 to 80oC was applied to the center of the filte r and the cap of the tube was closed and the tube was centrifuged for 30 seconds at 14,000 rpm. The elution and centrifugation steps were repeated three times fo r a total of four elutions. Twenty micro liters (0.1 volumes) of 10x DNase I Buffer and 2 L of DNase I were added to the tube and mixed gently by flicking the tube. The tube was then incubated for 30 minutes at 37oC. After incubation, 20 L (0.1 volumes) of DNase Inactivat ion Reagent was added to the tube. The slurry was vortexed immediately prior to use. The tube contents were mixed gently by flicking the tube and incubated for 10 minutes at room temperature. The tube was then centrifuged at 14,000 rpm for 1 mi nute to pellet the DNase Inactivation Reagent. The supernatant was transferred to a ne w tube and the pellet discarded. Twenty micro liters (0.1 volumes ) of 5M Ammonium Acetate and 4 L (0.02 volumes) of linear acrylamide were added to the tube containing the supernatan t and the tube was vortexed to

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38 mix the contents. After mixing, 500 L (2 to 2.5 volumes) of 100% ethanol were added to the tube and mixed well. The tube was placed in -20oC overnight. The tube was centrifuged at 14,000 rpm for 15 mi nutes the next day. After centrifugation, the supernatant was removed and the tube centrifuged again for several seconds to remove the residual fluid. The RNA was then resuspended in 15 L of elution solution and was stored in 80oC for further use. Quantification of Concentration of RNA A spectroph otometer was used to calculate th e concentration of RNA in our samples. Spectrophotometers compare the light transmitted through a sample to the light transmitted through a blank which in this case is a cuvette containing 100 L of DEPC water. A spectrophotometer can be shown to be calibra ted by using a standard curve of known RNA concentrations. Calibration is important becau se even a 1 nm displacement will significantly affect the absorption reading. In spectrophotometry, a measurement of th e absorbance of standards containing known concentrations of RNA is first taken. A standard curve is plotted with absorbance on the X-axis and RNA concentration on the y-axis. The absorbance of the unknown samples is then measured. The concentration of the unknown RNA is calculated based on the standard curve. In using the spectrophotometer to calculate the optic density of the subject samples, the absorbance at 260 nm is related to the concentration of R NA in the sample. If a sample containing pure RNA has an absorbance of 1 at 260 nm then it contains approximately 40 g/ L of RNA. To find the concentrations of RNA for the samp les, the 260 nm reading was multiplied by 4 g / L. The concentration of RNA was measured us ing a Beckman DU-600 spectrophotometer at a wave length of 260 nm after blanking with DEPC water used to dilute the RNA sample. A 1:100

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39 dilution was used in which 1 mL of RNA sample was added into a cuvette containing 99 mL of DEPC water and pipetted to mix. The cuvette was then placed into the Beckman DU-600 spectrophotometer and read at 260 nm. The concen tration of the sample RNA was calculated as absorbance at 260 nm x 40 g / L because if a sample containing pure RNA has an absorbance of 1 at 260 nm then it contains approximately 40 g / L of RNA. An A260/A280 ratio of 2.0 is characteristic of pure RNA and a ratio of 1.8 to 2.0 is desirable. Any ratio below 1.7 indicates that there is probably a contaminant in th e solution, usually a protein or phenol. Reverse Transcription Reverse transcription (RT) was used to synt hesize cDNA fro m the RNA obtained earlier. One RT reaction produces 40 L of cDNA. Two mixes were prepared for each RT reaction. The first mix included 1 L of Oligo (dT)12-18 Primer (Invitrogen, Cat. No. 18418-012) and 20 L of RNA sample in DEPC water in a 250 L PCR tube. The tube was then placed on ice and the second mix was prepared and added to each tube including 8 L of 5x First Strand Buffer, 2.7 L of 0.1M DTT, 1.3 L of Rnasin Ribonuclease Inhibitor (Promega, Cat. No. N2111), 2.0 L of 10mM dNTP (Amersham Biosci ences, Cat. No. 27-2035-07), 4.0 L of Bovine Serum Albumin, Acetylated (Prome ga, Cat. No. R3961), and 1.0 L of SuperScriptTM II Reverse Transcriptase. The 5x buffer, DTT, and SuperScriptTM II Reverse Transcriptase were from Invitrogen (Cat. No. 18064-022). The tube was then centrifuged and run on the PCR machine for 60 minutes at 42oC and then at 95oC for 10 minutes, and held at 4oC. Polymerase Chain Reaction (PCR) After cDNA was synthesized by reverse transc ription, specific regions of the cDNA were am plified through the use of primer s that are specific to each V family. Inner and outer primers specific for each of the 22 V families were previously constructed in the lab to flank specific

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40 regions of the cDNA strand (Table 2-1). The master mix for the first round of PCR consisted of: dH2O, 1x buffer; MgCl2 (2.8 mM); the 3 primer (C ) for each V family (0.4 M); the 5 (V # of family) primer for each V family (0.4 M); and dNTPs (0.2 mM of each dNTP); Taq DNA Polymerase (0.05 U/ L). This master mix 1 was added to each well in the block plate along with water and the cDNA synthesized from the RT-PCR reac tion. Water was used a negative control. The first round of PCR was perf ormed on a thermocycler at 95oC for 2 minutes then 35 cycles of 95oC for 1 minute, 55oC for 1 minute, and 72oC for 1 minute with the final extension at 72oC for 10 minutes. The plate was then held at 4oC until the gel was run. The master mix for the second r ound of PCR consisted of: dH2O; 1x buffer; MgCl2 (2.8 mM); reverse primer C NS-FAM (0.1 M); forward primer V NS-family # (0.1 M); dNTPs (0.2 mM for each dNTP); and Taq DNA Polymerase (0.05 U/ L). The forward primer V NSfamily # ) is nested 3 to the first-round V -specific primer, and the reverse primer (C NSFAM) is located 88 base pairs away from the CDR3 region. The reverse primer (C NS-FAM) contains a nontemplate sequence GTTTCTT which is placed on the 5 end of the C NS primer and labeled at the 5 end with a blue fluorescent dye (6-FAM; A pplied Biosystems, Foster City, CA). The primer concentrations were redu ced in the second round of PCR to increase amplifying specificity. The master mix 2 was added to each well of a new block plate and 2 L of first round PCR product was added to each well. The plate was run for second round PCR on a thermocycler at 95oC for 2 minutes, then 20 cycles of 95oC for 1 minute, 55oC for 1 minute, and 72oC for 1 minute with a final extension cycle at 72oC for 10 minutes. The plate was then held at 4oC until the gel was run. The second round P CR product was run on an agarose gel after which bands were viewed and a picture was ta ken through the Stratagene Eagle Eye II.

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41 Spectratyping Spectratyping is a PCR based assay that is specially designed to m easure the length variation within the same gene, for example, he re we used spectratyping to measure the TCR V CDR3 length distribution. Spectra typing allows oligoclonal expa nsion within T cell populations to be detected as dominant peaks within individual V families. Spectratyping uses two rounds of PCR in order to amplify the CDR3 region of the TCR. In first round PCR, a forward primer is used that is specific to the particular V family and a reverse primer is used that is in the constant region of the TCR. In second round PCR, the first round products are used as a template and a pair of nested primers of which the forward primer is V family specific and is nested 3 to the first round PCR primer while th e reverse primer is constant and located 88 base pairs away from the CDR3 region (Table 2-1). The second round primers are labeled with a blue fluorescent dye (6-FAM, Applied Bi osystems) to allow the bands to be seen on a gel. The PCR products are then run on a 6% acrylimide se quencing gel and analyzed using ABIPRISM GeneScan analysis software (Applied Biosys tems) which not only gives the CDR3 length distribution in base pairs but also calculates the relative intensity (area under the curve, AUC) of each length product to generate a histogram for each V family [63]. In interpreting the histogram, the area under each peak is calculated and a peak with an area that is greater than 40% of the total ar ea is considered perturbed which may indicate oligoclonal expansion. Each peak is one length and by observing the proportion, measurement, and distribution of the peaks, one can determine TCR recognition of antigen [66]. The intensity of each individual peak shows the relative representation of the particular CDR3 sizes within each individual V family. The relative fluorescent intensity of individual CDR3 sizes can be quantified as the area under each peak [38, 48]. When this data is expressed as a percentage of

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42 the total area under th e curves for each V family, it can be used to accurately determine the degree of CDR3 length diversity within any particular V family. The relative contribution of each CDR3 size is determined by the relationship of each particular peak to the total area under the curve, the results of which are shown as a percentage [38]. In-frame transcripts of the CDR3 region of the TCR in adults shows six to eight peaks spaced three nucleotides apart to form a bellshaped curve. The area under each peak is proportional to the amount of transcripts of the corresponding CDR3 size in the sample and each peak corresponds to a given CDR3 length that cont ains multiple distinct sequences. An increase in the height and area of a give n peak that results in modifi cation of the bell-shaped curve represents oligoclonal or monoclonal expansions due to immune stimulation [55]. A peak is considered a predominant peak if the area under the peak is greater than 40% of the total area under the curve. A predominant peak in any given CDR3 pattern could reflect either oligoclonal expansion of T cells expressing a particular TCR or a polyclonal collection of T cells that contain multiple TCRs of the same CDR3 size. Oligoclonal or polyclonal expansion can be determined by sequencing the amplified CDR3 regions [38]. Examining intensity can signify multiple species of the same size class with each having a low concentration or it could signify the expansion of one or a few species w ithin the size class [67]. A Gaussian, or bellshaped, distribution of peaks within each fam ily is normally seen in healthy individuals consisting of multiple peaks (usually six to ei ght) that are three nucle otides apart [48, 65]. Some antigens use only a specific receptor which correlates to a specific peak while other antigens may use multiple receptors within one family. T cells for a specific antigen will activate and replicate thus a llowing monoclonal or polyclonal expansion. By examining the distribution of the T cell repertoi re, one can determine whether or not the virus has interacted

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43 with one or more families thus allowing the use of the TCR as a surrogate marker to observe the effectiveness of a vaccine. The more diversified the response, the better protection from virus infection. Determination of TCR V Perturb ation Perturbation in TCR V CDR3 repertoire was assessed by two methods. In the first method, any V family exhibiting a predominant peak is considered perturbed. A predominant peak was defined as the AUC of one peak within a given CDR3 spectratype being greater than 40% of the sum of the total AUC. In the second method, if the distribution of TCR V CDR3 lengths were significantly away from the Gaussian distribution they were considered perturbed. The method of analysis is de scribed in detail below. The AUC of each CDR3 length ( i) in a V family profile ( ) was translated into a probability distribution, P j( i ) = A j i / (i A j i), using the fraction of the area ( Ai ) under the V family profile for each CDR3 length, from mini mal to maximal length in steps of three nucleotides. The letter j represents each sample. Generall y there were ten possible amino acid lengths ( i (1 to 10)) in each V family. A control profile was established for each CDR3 length by calculating the average probability distribution, P c( i ) = (j [ P j( i )]) / nj of corresponding V profiles, where n represents the number of healthy subj ects. The resulting control profiles, P c( i ), conform to a Gaussian distribution. To define the extent of pe rturbations in CDR3 length the distance (D) between the probability distributions of sample and average pr obability distributions of healthy controls (c) was calculated as D j(i) = P j(i) P c(i). The sum of the absolute distance, D j = 100 [ i D j(i) ] / 2, was calculated in each V family. Overall, TCR length yield perturbations of the TCR profile in percentages. The average perturbation among all V families studied for each individual j is

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44 calculated as an average distance (AD), ADj = ( D j )/ m, of all V families examined. In this study, AD for a control sample is presented as ADc (j) = ( D j ) / m .. Based on approximate Z test, a perturbation within each V family was defined as AD j > [ j ADc (j)] / nj + 3 Standard Deviations (SD) [68] (Figure 2-1). TCR V perturbation was evaluated using the fi rst method (assessing predominant peaks) in Specific Aim 1 where all we wanted to determine is whether each V family was successfully amplified. Unsuccessful amplification is mainly due to insufficiency of template that leads to either no curve, loss of CDR3 lengths exhibi ting wobble at the baseline, or low fluorescent intensity (FI < 400). Unsuccessful amplification is distinguished from a successfully amplified but perturbed V family by the latter exhibiting a predomin ant peak having a straight baseline and a high fluorescent intensity (FI > 400). TCR V perturbation in Specific Aim 2 was evaluated using statistical analys is as described in the second method described above since we needed to know a detailed change in th e CDR3 length distribu tion within each V family in order to map out the V family usage after influenza vaccination. Samples from Influenza Vaccinated Healthy Adults Changes in TCR V CDR3 length reperto ire after in fluenza vaccination were evaluated within CD4+ T cells from two anonymous donors who each received a trival ent split influenza vaccine administered intramuscularly. The a nonymous donors were part of a pilot study carried on at the University of South Florida (USF) clinic and All Childrens Hospital to determine appropriate time points of sampli ng to be able to detect the re sponse of T cells to influenza vaccine. Each subject received a split inactivated trivalent influe nza vaccine intramuscularly and blood samples were taken at two separate time points: at vaccinat ion and one month post vaccination.

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45 This pilot study was done in collaboration with a larger study carried out at the University of South Florida that sought to measure the breadth of the T cell receptor immune response within nave and activated memory/effector CD8+ T cells following immunization with influenza as recall antigen for 15 healthy subjects as well as 15 post bone marrow transplant subjects. IRB approval was obtained for the larger study at the University of S outh Florida. The larger study design was designed to use MHC I: influenza tetramers, flow cytometry, and spectratyping to determine the magnitude and br eadth of the memory immune response to influenza recall antigen. Tetramers are complexes of four MH C molecules which are bou nd to four antigenic peptides to form a complex that is recognized by the TCR. These tetramers are conjugated to a flourochrome and to fluorescently conjugated monoclonal antibodies thus facilitating the detection of the antigen specific T cell respons e through flow cytometry. The MHC I: peptide complexes are mutated to bind primarily to T ce lls of a specific HLA allele. Subjects in the larger study were HLA typed a nd only subjects with the HLAA0201 genotype were included in the study. The larger study sought to first use spectra typing to determine the memory effector response to influenza recall antigen at prevaccination, and 1 mont h and 3 months postvaccination for 15 healthy adult subjects and 15 one-year post-transplant bone marrow patients with immune reconstitution. E ach subject was to have the HLA-A0201 genotype in order to detect the influenza recall antigen with the use of specific tetramers sensitive to this genotype. The two anonymous subjects in this paper were excluded from the original larger study at the University of South Florida due to not having the specific HLA genotype. However, cell lysates from these two anonymous subject samples were sent to the Goodenow la b to perform a pilot study to determine if perturbations were seen within either PBMC or CD4+ T cells in the 1

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46 month post-vaccination sample when compared to the sample taken at vaccination. Unfortunately, the larger projec t was stopped before the 3 month post vaccination sample could be drawn. Cell separation was performed at the University of South Florida. PBMC were isolated from each subjects sample and then CD4+ and CD4were separated and isolated using a T Cell Isolation Kit II (Miltenyi Biotec h, Auburn, CA). Cell lysates were then sent to the Goodenow lab for RNA extraction and spectratyping. RNA was extracted from PBMC, CD4+, and CD4cells of each patient using the Ambion RNAqueous-4PCR kit according to the protocol stated earlier. Reverse transcription and PCR (described earlier in this section) were performed on each sample for all 21 V families. Spectratyping (described earl ier in this section) was finally used to measure the T cell receptor diversity.

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47 Table 2-1. Variable beta ch ain p rimers for spectratyping

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48 V k Pi i total A) x 100%Ctrl n = 8 Control profile Average Pi of ctrl (APic) V k Length i Length iSample profileV k Length i Di= Pi -APic Di Di (Absolute distance for length i) Absolute distance (D) = sum of absolute distance for all lengths Average D of ctrl 1 Average D of ctrl 8D24D22 D21D20 D18 D17 D16D15D14D13D12D11D9D8D7D6D5D4D3D2D1Ctrl 1 Ctrl 8 Average D (AD) 2422 V families 2120181716 15141312 11 987654321 Average D of ctrl 1 Average D of ctrl 8D24D22 D21D20 D18 D17 D16D15D14D13D12D11D9D8D7D6D5D4D3D2D1Ctrl 1 Ctrl 8 Average D (AD) 2422 V families 2120181716 15141312 11 987654321 Perturbation = D > AD + 3SD Figure 2-1. Statistical method for determ ination of TCR V perturbations. First find area under each peak for each family for each control to get probability distribution (Pi) = (area under peak/total area) x 100. Second, averag e each peak for each family for all controls to get average probability distribution (APi) ex=Vb1 for all 8 controls. Third, get absolute distance, Di, for each peak for each sample Di=Pi-APi. Fourth, add Di of all families for each contro l and divide by # of families (exVb1+Vb2+./21 = Average distance of each control=AD). Fifth, add average distance of each control together and divi de by # controls = average D of AD or AAD. Sixth, perturbation of family if D of each family of each sample >AAD+3sd.

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49 CHAPTER 3 RESULTS Results Obtained for Aim 1 The objective of Specific Aim 1 was to de fine the minimum amount of cDNA derived from the minimum amount of RNA extrac ted from purified CD45RA and CD45RO CD8+ T cells needed to be able to amplify 21 V families. The strategies to achieve the goal were: (1) to use PBMC from cord blood to scale down the amount of RNA and the volume of cDNA used to amplify 21 V families; and (2) to test the mini mum amount of RNA and volume of cDNA determined in cord blood PBMC on purified CD45RA and CD45RO CD8+ T cells from healthy donors to ensure the conditions also work for purified T cells. We chose cord blood PBMC because the T cells in cord blood PBMC always demonstrate a Gaussian distribution of CDR3 length for each V family due to lack of antigen s timulation. We hypothesized that the minimum amount of RNA and cDNA determin ed in cord blood, a mixed population of mononuclear cells, will also appl y to purified T cell subsets. The purpose of using cord blood and T cell subsets from healthy donor s was to minimize the chance of V perturbation during the optimization process. Estimation of Targeted Minimum Amount of RNA a nd cDNA Required to Amplify 21 V Families Based on previous experience, normally 2 g of RNA is used in each RT reaction to obtain 40 L of cDNA. Two microgram s of RNA is extracted from approximately 3 x 106 cells. A subjects PBMC sample usually contains 5 x 106 to 10 x 106 PBMC per vial. Around 0.3 x 106 and 0.5 x 106 CD45RA and CD45RO CD8+ T cells can be purified from 5 x 106 to 10 x 106 PBMC respectively from which 0.2 g and 0.3 g of RNA can be extracted. In our study, we wanted to determine if we could amplify 21 V families from 40 L of cDNA that was reverse

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50 transcribed from 0.25 g of RNA which was extracted from 0.2 0.3 x 106 purified CD45RA or CD45RO CD8+ T cells (Figure 3-1). Determination of the Minimum Amount of RNA a nd cDNA Using Cord Blood PBMC from cord blood was used to scal e down the amount of RNA and cDNA used to amplify 21 V families. First, cord blood PBMC were used to derive differing amounts of RNA, ranging downward from 2 g to 0.25 g. These differing amounts of RNA were then used to make cDNA. Differing volumes of cDNA, ranging downward from 10 L to 0.5 L (40 L total volume), were used to amplify the V 2 family. The V 2 family was amplified showing an acceptable CDR3 length distribution when lower limit conditions of 1 L of cDNA reversely transcribed from 0.25 g of RNA were used (Figure 3-2). We found the upper limit to be 6 L of cDNA derived from 2 g of RNA, given that amounts ove r this limit did not exhibit amplification of the V 2 family. Next, 1 L of cDNA derived from 0.25 g RNA of cord blood PBMC was tested on four other V families two easy families (V 14 and V 21) and two hard families (V 5 and V 15). Families are classified as easy or hard according to the ease of which they have been successfully amplified in previous studies. The easy families have been amplified in almost all of the previous studies while the hard families have been amplified in fewer studies. The easy families may be more representative, i.e. they may have more cells expressing these families, or their primers may be more efficient, whereas the hard families may be less representative or have less efficient primers. The results showed that all four families amplif ied well with this amount of cDNA (Figure 3-3). Since the minimum amount of RNA and cDNA was able to successfully amplify five representative V families, all 21 V families were amplified using 1 L of cDNA derived from

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51 0.25 g RNA. However, using this condition, so me families did not amplify well including V 4, 7, 8, 13, 16, 17, 18, and 22. These families were amplified after increasing the volume of cDNA to 3 L thus indicating that the failure of amplification of these families was due to having insufficient template. Figure 3-4 demonstrates the histographs of V 4, 7, 13, and 17 with insufficient (1 L) and sufficient (3 L) template respectively. When the template is insufficient, there are distortions at the baseline, the fluorescent intensity is very low (FI < 400), or there are no peaks shown. In order to am plify all the families, the amount of RNA was increased to 0.5 g and the amount of cDNA remained at 1 L. This amount is equivalent to using 0.25 g of RNA and 2 L of cDNA. With this adjustme nt, all families were amplified successfully (Figure 3-5). Application of the Minimum Amount of RNA and cDNA of Cord Blood to Purified CD45RA a nd CD45RO CD8 T Cell Subsets The 1 L cDNA derived from 0.25 g RNA was used to amplify V families of T cell subsets since these were pure T cells. Since co rd blood is a mixed population of cells and only contains 17% T cells, if 1 L cDNA derived from 0.5 g RNA was able to amplify each V family for cord blood PBMC, we hypothesized that 1 L cDNA derived from 0.25 g RNA should be able to amplify each V family from purified T cells. As in the cord blood experiments, five V families, three easy families (V 2, V 14, and V 21) and two hard families (V 5, V 15) were first amplified. These five families were amplified for CD8+ CD45RA cells and CD8+ CD45RO cells separately. The five V families within CD8+ CD45RA T cells showed a Gaussian distribution of CDR3 lengths, which is expected since these are nave cells (Figure 3-6). However, one of the five V families (V 14) in the CD8+ CD45RO cells exhibited perturbation with a prominent CDR3 size of 201bp whose AUC is greater than 40%, indicating

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52 monoclonal expansion. Sufficient template was used since the fluorescent intensity was greater than 400 for all five V families (Figure 3-7). In healthy adults, perturbed V families can often be seen in effector memory CD8+ CD45RO+ T cells. The CD45RO+ T cells express a number of activation markers and respond to recall antigen readily thus thes e cells represent memory cells. A strongly positive expression of CD45RO+ CD8+ T cells is generally consistent with T cell activation [54, 69]. Patterns of perturbations in CD45RO+ CD8+ T cells have been observed to be persistent over many months in different healthy adults [54]. Given the encouraging results from amplifying five V families, all 21 V families were amplified using 1 L cDNA derived from 0.25 g RNA for CD45RA and CD45RO CD8+ T cells separately. Eighteen families amplified well from CD8+ CD45RA T cells and exhibited normal distributions (Figure 3-8). All 21 V families were amplified from CD8+ CD45RO T cells with four V families exhibiting predominant peaks, including V 11, 14, 17, and 24 (Figure 3-9). Again, these predominant peaks we re not due to having an insufficient template since the fluorescen t intensity was greater than 400. Calculation of the Number of PBMCs Needed to Amplify 21 V Families from CD 8 CD45RA and CD45RO T Cell Subsets Based on the above results, 40 L of cDNA derived from 0.25 g RNA from purified CD45RA and CD45RO CD8+ T cells was able to amplify 21 V families using only 1 L of cDNA per V family. We obtained 0.25 g of RNA from 0.3 to 0.5 x 106 purified CD45RA and CD45RO CD8+ T cells which were purified from 5 to 10 x 106 PBMC. Therefore 5 to 10 x 106 PBMC are needed to successfully amplify 21 V families of CD45RA and CD45RO CD8+ T cell subsets (Table 3-2).

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53 A greater quantity of RNA should be extracted from CD45 RA and CD45RO CD4+ T cells as compared to CD45RA and CD45RO CD8+ T cells due to the PBMC of healthy adults having a higher percentage of CD4+ T cells than CD8+ T cells. The ratio of CD4+ T cells to CD8+ T cells is 1.5 to 1, meaning that for every one CD8+ T cell in the PBMC of healthy adults, there are one to two CD4+ T cells. In other words, for every 10 CD8+ T cells, there are 15 to 20 CD4+ T cells. This ratio seems to be constant acros s different studies. An average of 800 cells / L has been observed for CD4+ T cells in healthy adults wh ile an average of 550 cells / L has been observed for CD8+ T cells in healthy adults [70, 71]. Given our results for specific aim 1, we estimate that 5 to 10 x 106 PBMC should contain 0.3 to 0.5 x 106 CD45RA or CD45RO CD8+ T cells which should provide enough RNA and cDNA be able to successfully amplify 21 V families from CD45RA and CD45RO CD4+ T cell subsets. Results Obtained for Aim 2 The results obtained for the second aim of th is study are described in this part of the chapter. This aim uses the optimal conditions found in specific aim 1 to perform spectratyping on 21 V families in two anonymous influenza vaccinated subjects. Spectratyping was used to examine the changes in PBMC, CD4+, and CD4T cells in two influen za vaccinated subjects for 21 V families. The hypothesis for the pilot study is that vaccination would cause perturbations in the TCR V repertoire and that changes in TCR V CDR3 length distribution could be used as a surrogate marker to determine the efficacy of the influenza vaccine as measured by spectratyping. In examining the RNA concentration for the sa mples examined in this study, we observed that all of the RNA concentrations for each of th e cell types were below the concentrations that were expected based on the starting cell number. If there is too much or too little of a sample,

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54 the spectrophotometer cannot read the absorbance accurately. Since the concentrations of RNA for the samples were low, perhaps our samples were too dilute and thus were outside of the linear range of the reading or that the starting cell number was in correct. The linear range of absorbance values should be between 0.1 and 1.0. Given that the linear range of the samples here were below 0.1, it would seem as if our sample s were too dilute and t hus gave an inaccurate reading of absorbance (Table 3-3). Cell lysates containing PBMC, CD4+, and CD4cells from two anonymous influenza vaccinated healthy adult subjects were sent to us by a collaborator to examine the change of TCR V repertoire in each of the above cell types from vaccination to 1 month post vaccination. After RNA extraction, it was found that only CD4+ T cells from both subjects had sufficient number to obtain the 0.25 g RNA needed to amplify 21 V families. Unfortunately, the PBMC and CD4cells did not contain the minimum amount of RNA needed for TCR V amplification, therefore TCR V spectratyping was performed only on the two CD4+ T cell samples from both subjects. Eighteen V families were amplified from the CD4+ T cells from Subject 1. After statistical analysis, thirteen V families exhibited a perturbation, including V 1, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20, 21, and 24. Of those V families exhibiting a perturbation, only the V 9 family displayed a monoclonal expansion. V 15, 16, and 18 could not be amplified (Figure 310). Fifteen V families were amplified from Subject 2, fourteen of which showed perturbation including V 1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, and 22. Of these fourteen families exhibiting a perturbation, only V 7 displayed a monoclonal expansion. V 14, 15, 18, 20, 21, and 24 could not be amplified (Figure 3-11). The sample from Subject 2 had

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55 significantly less RNA than the other subject which may explain why more families could not be amplified. In comparing the two subjects, nine V families were perturbed in both subjects including V 1, 5, 7, 8, 9, 11, 12, 13, and 17. These perturbations are not due to insufficient template since the fluorescent intensity is higher than 400 and ther e is no evidence of distortions at the baseline. Only V 3 was normal in both subjects. Two V families, V 15 and 18, failed to amplify in either subject (Figure 3-12).

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56 Table 3-1. Separation of CD45RA a nd CD45RO CD8 T cell subsets Starting PBMC # Cell type Cell # after separation (x 106) Separation efficiency (%) RNA ( g) RNA conc. ( g/ l) CD8+ 0.7 14% -----------------CD8+ CD45RA+ 0.24 34% 20.1 1.34 5x106 CD8+ CD45RO+ 0.3 42% 16.4 1.09 CD8+ 1.12 11% -----------------CD8+ CD45RA+ 0.48 43% 16.7 1.13 10x106 CD8+ CD45RO+ 0.5 45% 17 1.11 CD8+ 2.38 12% -----------------CD8+ CD45RA+ 0.56 24% 16.7 1.22 20x106 CD8+ CD45RO+ 0.58 25% 18.2 1.12

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57 1.1 x 1060.7 x 106CD8 0.5 x 1060.3 x 106CD45RO 0.5 x 1060.3 x 106CD45RA10 x 1065 x 106PBMC 1.1 x 1060.7 x 106CD8 0.5 x 1060.3 x 106CD45RO 0.5 x 1060.3 x 106CD45RA10 x 1065 x 106PBMC 0.2 0.3 0.3 0.5 2.00 3.0RNA ( g) Cells (x 106) 0.2 0.3 0.3 0.5 2.00 3.0RNA ( g) Cells (x 106) 1 x RT reaction 40 LcDNA 21 V families Scale down Figure 3-1. Objective is to find the minimu m a mount of cDNA that is derived from the minimum amount of RNA that can be used to amplify 21 V families. The targeted optimal condition is 1 L cDNA (2.5% of total amount) derived from 0.25 g of RNA.

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58 RNA per RT reaction ( g) Volume of cDNA to amplify V 2 family ( L) 2.00 g 1.00 g 0.50 g 0.25 g Size range 133-160 bp (10 peaks)0.5 1 2 3 4 5 6 8 10 Figure 3-2. Scale down amount of RNA per RT using cord blood cells on V 2 f amily

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59 V 2 (133 -160 bp) V 5 (143 170 bp) 162 V 14 (194 221 bp) V 15 (166 193 bp) 189 V 21 (193 220 bp)Easy families Hard families Figure 3-3. Cord blood amplification of five V f amilies using 1 L or 2.5% of cDNA that was derived from 0.25 g of cord blood RNA. The hard families length distribution is shorter possibly due to cord blood having s horter range, needing more template, or a smaller proportion of cells expr ess this particular family.

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60 V 4 (143170 bp) V 4 (143170 bp) V 7 (198 -225 bp) V 7 (198 -225 bp) V 13 (179 -206 bp) V 17 (153 bp) Acceptable (0.5 g RNA) Unacceptable (0.25 g RNA) Figure 3-4. Comparison of unacceptable versus acceptable s pectratyping results. Unacceptable results have distortions at the baseline, low fluorescent in tensity, or lack of length distribution including no peak s. Acceptable results have an even length distribution, no wobble at the baseline, and good fluorescent intensity.

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61 Figure 3-5. Cord blood amplification of 21 V fam ilies using 1 L of cDNA derived from 0.5 g of RNA. This amount is equivalent to 3 L cDNA derived from 0.25 g RNA. The amount of RNA had to be increased due to the lack of amp lification of some families when 0.25 g RNA was used.

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62 V 5 (143 170 bp)V 15 (166 193 bp)Easy families V 2 (133 -160 bp) V 14 (194 221 bp)V 21 (193 220 bp) Hard families Figure 3-6. CD8+ CD45RA cells amplification of five V families using 1 L cDNA derived from 0.25 g RNA.

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63 V 5 (143 170 bp)V 15 (166 193 bp) V 14 (194 221 bp)V 21 (193 220 bp)Hard familiesV 2 (133 -160 bp) Easy families Figure 3-7. CD8+ CD45RO cells amplification of five V families using 1 L cDNA derived from 0.25 g RNA. Perturbations are observed which is expected since these are effector and memory cells.

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64 Figure 3-8. CD8+ CD45RA T cells amplification of 18 V families using 1 L cDNA derived from 0.25 g RNA. Normal length di stribution is observed wh ich is expected since these are nave cells.

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65 Figure 3-9. CD8+ CD45RO T cells amplification of 21 V families using 1 L cDNA derived from 0.25 g RNA. Perturbations are observed which is expected since these are effector and memory cells.

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66 Table 3-2. Summary of optimal conditions for spectratyping Cord Blood CD8 CD45RACD8 CD45ROPBMC Starting Cell Number 0.8 x 106 0.3-0.5 x 106 0.3-0.5 x 106 5-10 x 106 RNA/RT 0.5 g 0.25 g 0.25 g N/A cDNA per V family 1 L 1 L 1 L N/A

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67 Table 3-3. Concentrations of RNA fo r influenza vaccin ated subject samples RNA Subject # Cell Type Cell Number (x 106) Concentration ( g/ L) Total amount ( g) PBMC 0.5 0.015 0.30 1 CD4+ 0.5 0.059 1.18 CD42.1 0.089 1.78 PBMC 1.0 0.108 2.16 2 CD4+ 0.5 0.039 0.78 CD41.5 0.050 1.00

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68 PNPPPPPPPPPPNPNNNPPerturbation by statistics Histograph 2422 2120 17 1413 12 11 987654321 CD4 PNPPPPPPPPPPNPNNNPPerturbation by statistics Histograph 2422 2120 17 1413 12 11 987654321 CD4 168 168 Figure 3-10. Spectratyping resu lts for subject 1. Eighteen V f amilies were studied with thirteen families showing perturbations as shown by the letter P. One family (V 9) displayed monoclonal expansion. Thr ee families could not be amplified (V 15, 16, and 18) which may be due to primer inefficiency or minor populations.

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69 PPPPPPPPPPPPNPPPerturbation by statistics Histograph 2217161312 11987654321 CD4 PPPPPPPPPPPPNPPPerturbation by statistics Histograph 2217161312 11987654321 CD4 214 Figure 3-11. Spectratyping resu lts for subject 2. Fifteen V f amilies were studied with fourteen showing perturbations denoted by the letter P. One family (V 7) displayed monoclonal expansion. Five fam ilies could not be amplified (V 14, 15, 18, 20, 21, and 24) due to primer effi ciency or minor populations.

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70 P N PP --P ----P P P P P P P N P N NN P P1 --P -------PP ----P P P P P PP PP N P P P2 24 22212018171615 1413 12 11 987654321 V P N PP --P ----P P P P P P P N P N NN P P1 --P -------PP ----P P P P P PP PP N P P P2 24 22212018171615 1413 12 11 987654321 V Figure 3-12. Summary of results for both influe n za vaccinated subjects. Nine families were perturbed in both patients (s haded in yellow). Only V 3 was normal in both patients (shaded in green). Two families (V 15 and 18) did not amplify for either patient (shaded in blue).

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71 CHAPTER 4 CONCLUSION Conclusion for Aim 1: The Representation of TCR Can B e Assessed in Small Numbers of Cells from T Cell Subsets Through experiments performed in specific aim 1, it was found that 1 L (or 2.5% of the total amount of a 40 L reaction) cDNA derived from 0.25 g of RNA extracted from approximately 0.3 to 0.5 x 106 CD45RA or CD45RO CD8+ T cells separated from 5 to 10 x 106 PBMC was sufficient to amplify each TCR V family. If using cord blood, then the amount of RNA used for one RT reaction should be scaled up to 0.5 g (equivalent to approximately 0.8 x 106 cord blood PBMC) because the cord blood PBMC is a mixed population of cells and contains only 17% T cells. These conditions help to conserve the subjects sample and can be applied to CD45RA and CD45RO CD4+ T cells. Conclusion for Aim 2: Perturbations of TCR Can Provide a Surrogate Marker for Immune Response. In com paring spectratyping results for both infl uenza vaccinated subjects, it was found that nine V families were perturbed in bo th. In addition, one family, V 9 in subject 1 and V 7 in subject 2, showed monoclonal e xpansion. If no perturbations were observed in the postvaccination sample, then no pre-vacc ination sample would be needed. Since perturbations and at least one monoclonal expansion were observed in each subject, it can be concluded that in order to determine whether or not the expansion is due to influenza vaccination, a pre-vaccination sample is needed. The pilot study conducted here implies that the design of the larger study to include a pre-vaccination time point sample in the analysis was correct. In regards to determining whether or not TCR V spectratyping can be used as a surrogate marker of T cell activation due to vaccination, according to the literature, ex amining changes in the TCR V repertoire could be used as a surroga te marker of T cell activation due to

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72 vaccination [49-53]. In fact, several studies have used TCR V usage to check response to a vaccine and to evaluate the ability of a response to a vaccine. Wh ile there are conflicting reports regarding the measurement of TCR activation in response to vaccination, the majority of literature seems to indicate that vaccination doe s indeed cause TCR activation that can be measured. In addition, several studies have shown that examining which V genes are involved in immune response is important since these molecules are expressed on the T cell surface and can be used as targets for imm une therapy [49-51, 54, 55]. In a heterogeneous cell population, T cells that recognize the same epitope may have a similar TCR structure that uses the same V segments [72]. In examining the V TCR repertoire in encounteri ng other vaccines, different V families are perturbed according to what vaccine is use d. For instance, studies on the tetanus vaccine define perturbations in V 2, 4, 6, 13, 14 families of CD8+ T cells 14 days pos t-vaccination due to response to the tetanus toxoid as opposed to a generalized response to vaccination [51]. However, in examining three studies regarding he patitis B vaccinations, there is no concurrence between the studies as to which V families are activated [49, 52, 54]. This lack of concurrence may be due to the difference in time points at which the samples were taken (ranging from 48 hours post vaccination to 14 days post vaccination). The differences in TCR V gene usage may also be due to different HLA haplotypes between individuals [ 56]. Haplotype refers to each individual person having different MHC molecules that present different antigens. If a study examines subjects with a specific haplotype th en more families may be shared [37]. In addition, in studies done on m easles, it was found that the TCR V families that are perturbed differ between children who are vaccinated with a live attenuate d virus as opposed to contracting a natural measles in fection [50, 53]. The differenc e between the vaccine and the

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73 natural infection may be due to th e effects of the virus strain or the effect of the age of the children [50]. In applying these results to our study, given the wi de variety of strains of the influenza virus, samples taken from naturally infected subjects should be compared with samples taken from vaccinated subjects to examine any differences in TCR V activation. In contrast to the amount of research done on TCR V activation in the above vaccinations, little resear ch has been done to date examining the TCR activation by the influenza vaccine. One study found that after stimulati on with an immunodominant synthetic peptide (matrix protein of influenza A, M55-66), V 17 perturbed but it is not kno wn if this same family would be perturbed with exposure to natural infection or vaccination [73]. Another study examining influenza found that vaccinati on does not substantially perturb CD4+ T cells since no new perturbations were observed after influenza vaccina tion [74]. This study examined subjects who were over age 70 and vaccinate d with the influenza vaccine. It compared pre-vaccination samples to 1 month post vaccination samples using a PCR-heteroduplex method. The same perturbations present pre-vaccination were found post-vaccination. Perturbations were found in different families for CD45RA and CD45RO subsets of CD4+ T cells, but the perturbation was never present in both subsets. The V 2 family was found to be perturbed in CD45RA CD4+ T cells and families V 1, V 5, or V 13 were found to be perturbed in CD45RO CD4+ T cells [74]. This lack of perturbations due to the vaccine may be the result of the age of the subjects. It has previously been reported that the TCR V CD4+ T cell subset exhibits pe rturbations in subjects over the age of 65 while it is normally bell-shaped in healthy adults under age 65. Since the same perturbations were found in both pre and po st-vaccination samples, it may be that these subjects were exposed to the influenza antigen previously and the expansions could represent

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74 dominant usage of those particular families. A lternatively, the subjects may have been exposed to some other antigen previously and the perturbed V families persisted and were carried over. In examining the literature, there seem to be differences between the expansions of CD4+ and CD8+ T cells in healthy persons. Th ere is abundant literature obser ving that there is little to no expansion in the CD4+ T cell subset in healthy persons [56, 61, 62, 75]. Age, however, seems to increase the incidence of perturbation within the CD4+ T cells of healthy subjects. It was found that 70% of healthy ag ed subjects (aged 65 to 85 year s with a mean age of 76) had perturbed V families in CD4+ cells. The perturbed V families varied from individual to individual and were stable over an 18 to 24 month period. Since these perturbations were persistent they did not repr esent a transient response to antigen. Among aged donors, perturbations were seen in CD4+ T cells for families V 1, 3, 8, 9, 11, 14, 16, 17, 20, 21, and 23 while in adult donors (aged 22 to 34 years with a mean age of 26) dominant peaks were seen only in V 14 and 23. The T cell expansions in CD4+ T cells of aged persons may occur from repeated infections from viruses such as infl uenza or EBV. The expansions may represent dominant usage of V families in response to antigen [75]. Differences in TCR V gene usage from person to person may be due to different antigen exposure or di fferent HLA haplotypes between individuals [56]. In healthy subjects, CD8+ T cells display more perturbations than CD4+ T cells. Skewing refers to the observation that some V genes react more favorably to CD4+ T cells than to CD8+ T cells. Skewing is thus represen tative of a particular V region gene product interacting more favorably with MHC class II molecules for the CD4+ subset or MHC class I molecules in the CD8+ subset during positive selection in thymic maturation [76]. The majority of the CD4+ T cells gave Gaussian bell-shaped curves while there was a higher frequency of perturbations per

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75 individual in the CD8+ subset [65]. One study examini ng healthy subjects found that CD4+ T cells were skewed in five V families, including V 2, 5, 13, 17, 22 [65] while another study found that in CD4+ cells V 3, 9, 12, and 18 were skewed [76]. Still another study found that V 2, 5, 6, 9, and 22 were skewed towards CD4+ T cells [77]. No single V family was shown to be used in all three studies, however V 2, 5, and 9 are seen in two of the studies. This skewed usage may be the result of a sel ection process in the thymus that gives preferentia l selection of some V genes to CD4+ over CD8+ [65] and seems to indicate that the V genes are not randomly used within the CD4+ and CD8+ T cell subsets [76]. In examining the present pilot study, nine V families were perturbe d in both subjects. These families included V 1, 5, 7, 8, 9, 11, 12, 13, and 17, with V families V 7 and V 9 exhibiting monoclonal expansion. The perturbations seen in our subjects are not the result of insufficient template since the fl uorescent intensity is higher than 400 and there is no evidence of distortions at the baseline. Given that CD4+ T cells were studied and our review of the literature, we would expect not to observe a ny perturbations in the healthy subj ects that were not due to an encounter with an antigen. However, in compar ing the spectratyping resu lts for both subjects to the literature regarding perturbations in the CD4+ T cell subset of healt hy individuals within the under age 65 age group, it was found that V 5, 8, 9, 12, 13, and 17 were perturbed both in our study and in at least one study in the literature (Figure 4-1). In acco rdance with the literature, the perturbations of the V families in our two vaccinated healthy subjects may be carryover from previous antigen recognition or may be due to a reaction from the vacc ine as opposed to carry over from previous antigen recogn ition. Further studies would need to be done to determine if these perturbations can be observe d within a larger hea lthy population of subjects or are due to vaccine response.

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76 Also, in comparing the present study to the majority of other studies and given that our subjects were in the 30 to 40 year age group, it would be expected th at there would be no perturbations in the CD4+ subset since perturbations normally increase in frequency with age. However, we observed a m onoclonal expansion of V 9 in subject 1 and V 7 in subject 2. Monoclonal expansions have been observed V 14 and V 23 in one study [75] and V 12 in another study [77]. Given that monoclonal expansions in the V families of the CD4+ T cells are not normally present in the 30 to 40 year old age group, we believe that it is possible that this monoclonal expansion in each subject may be th e result of the influenza vaccination, however without a pre-vaccination sample we are unable to absolutely conclude that the expansion is due to vaccination. In conclusion, according to the cited literatur e, very few, if any, expansions are normally observed within the CD4+ T cell subset of healthy adults below the age of 65. Some studies have observed perturbations in the CD4+ subset of which perturbations in six V families coincide with the results of our p ilot study. These include V 5, 8, 9, 12, 13, and 17 [65, 76, 77]. Further studies should be done to pinpoint whether these perturbations are th e result of previous exposure to antigen or if they correspond to exposure to the influenza vaccine. In our results, V 15 and 18 did not amplify in either subject. This may be the result of these two families being underrepresented in the TCR repertoire due to being a minor population. There is little information in the literature regarding what V families are minor populations in the CD4+ T cell subset. Further studies should be done which examine the CD8+ T cell subset for expansions or clonalities due to the influenza vaccine since more expansions are normally observed in the CD8+ subset than in the CD4+ subset. In addition, a pre-vaccination sample should be taken

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77 along with a sample at vaccination, 2 weeks post-vaccination, 1 month post-vaccination, 3 months post-vaccination, and 6 months post-va ccination. The pre-vaccination sample gives a baseline for the individual subj ect to which the other samples can be compared. The 2 week and 1 month post-vaccination samples will help to provide an idea as to the timing of the peak response to the influenza vaccine. The 3 month post-vaccination sample will examine if the TCR repertoire has returned to the baseline. The 6 month post-vaccination sample will help to examine the length of the immune response. Determining what TCR V families are utilized during a response to influenza antigen has great implications on the development of an improved vaccine. By knowing which specific V families are perturbed, a vaccine could be developed that targets those specific V families thus ameliorating the risk of influenza infection by a different strain of the virus in those who were v accinated. If each different strain of the virus activates the same V families, production of a vaccine that targets those specific activated V families may eliminate the need for changing the infl uenza strains in the vaccinations every year.

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78 P P P P P P P P P PP Lit --P ------PP ----P P P P P PP PP N P P P2 P N PP --P ----P P P P P P P N P N NN P P1 2422 2120181716 15 14131211 987654321 V P P P P P P P P P PP Lit --P ------PP ----P P P P P PP PP N P P P2 P N PP --P ----P P P P P P P N P N NN P P1 2422 2120181716 15 14131211 987654321 V Figure 4-1. Summary of results as com pared to previous ly published studies on healthy nonvaccinated adult subjects. Six V families were perturbed in both of our subjects as well as in at least one out of three st udies in the literature (shaded in red).

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79 LIST OF REFERENCES 1. La Gruta, N. L., K. Kedzierska, J. Stambas, and P. C. Doherty. 2007. A question of selfpreservation: immunopathology in influenza virus infection. Immunology and cell biology 85:85-92. 2. CDC. 2007. Prevention and Control of Influenza. In Morbidity and Mortality Weekly Report Department of Health and Human Services, Atlanta. 1-60. 3. Behrens G, M. S. 2006. Pathogenesis and Immunology. In Influenza Report H. C. Kamps BS, Preiser W, ed. Flying Publisher, Paris. 92-105. 4. Kamps BS, R.-T. G. 2006. Influenza 2006. In Influenza Report H. C. Kamps BS, Preiser W, ed. Flying Publisher, Paris. 17-38. 5. Gurtler, L. 2006. Virology of Human Influenza In Influenza Report H. C. Kamps BS, Preiser W, ed. Flying Publisher, Paris. 87-91. 6. Carrat, F., and A. Flahault. 2007. Influenza v accine: the challenge of antigenic drift. Vaccine 25:6852-6862. 7. Potter, C. W. 2001. A history of influenza. Journal of applied microbiology 91:572-579. 8. Lamb RA, K. R. 2001. Ort homyxoviridae: The Viruses and Their Replication. In Fields Virology Fourth Edition H. P. Knipe DM, ed. Lippencott, Williams, and Wilkins, Philadelphia. 725-769. 9. Korsman, S. 2006. Vaccines. In Influenza Report H. C. Kamps BS, Preiser W, ed. Flying Publisher, Paris. 127-144. 10. Nichol, K. L., and J. J. Treanor. 2006. V accines for seasonal and pandemic influenza. J Infect Dis 194 Suppl 2:S111-118. 11. Johansson, B. E., D. J. Bucher, and E. D. Kilbourne. 1989. Purified influenza virus hemagglutinin and neuraminidase are equivale nt in stimulation of antibody response but induce contrasting types of immunity to infection. Journal of virology 63:1239-1246. 12. Janeway CA, T. P., Walport M, Shlomchik M. 2001. Immunobiology 5th ed. Garland Publishing, New York. 13. Halapi, E., M. Jeddi-Tehrani, A. Blucher, R. Andersson, P. Rossi, H. Wigzell, and J. Grunewald. 1999. Diverse T-cell receptor CDR3 length patterns in human CD4+ and CD8+ T lymphocytes from newborns and adults. Scandinavian journal of immunology 49:149-154.

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84 59. Nishio, J., M. Suzuki, T. Nanki, N. Miya saka, and H. Kohsaka. 2004. Development of TCRB CDR3 length repertoire of human T lymphocytes. International immunology 16:423-431. 60. Arstila, T. P., A. Casrouge, V. Baron, J. Even, J. Kanellopoulos, and P. Kourilsky. 1999. A direct estimate of the human al phabeta T cell receptor diversity. Science (New York, N.Y 286:958-961. 61. Hall, M. A., J. L. Reid, and J. S. Lanchbury. 1998. The distribution of human TCR junctional region lengths shifts with age in both CD4 and CD8 T cells. International immunology 10:1407-1419. 62. Wedderburn, L. R., A. Patel, H. Vars ani, and P. Woo. 2001. The developing human immune system: T-cell receptor repertoire of children and young adults shows a wide discrepancy in the frequency of pers istent oligoclonal T-cell expansions. Immunology 102:301-309. 63. Kou, Z. C., J. S. Puhr, S. S. Wu, M. M. Goodenow, and J. W. Sleasman. 2003. Combination antiretroviral therapy results in a rapid increase in T cel l receptor variable region beta repertoire diversity w ithin CD45RA CD8 T cells in human immunodeficiency virus-infected children. J Infect Dis 187:385-397. 64. Kim, J. A., P. Rao, H. Graor, K. Rothchild, C. O'Keefe, and J. P. Maciejewski. 2004. CDR3 spectratyping identifies clonal expans ion within T-cell subpopulations that demonstrate therapeutic antitumor activity. Surgery 136:295-302. 65. Bonfigli, S., M. G. Doro, C. Fozza, D. Derudas, F. Dore, and M. Longinotti. 2003. T-cell receptor repertoire in healthy Sardinian subjects. Human immunology 64:689-695. 66. Worrell, S., J. Deayton, P. Hayes, V. C. Emery, F. Gotch, B. Gazzard, and E. L. LarssonSciard. 2001. Molecular correla tes in AIDS patients followi ng antiretroviral therapy: diversified T-cell receptor repe rtoires and in vivo control of cytomegalovirus replication. HIV medicine 2:11-19. 67. Gorski, J., M. Yassai, X. Zhu, B. Kissela, B. Kissella, C. Keever, and N. Flomenberg. 1994. Circulating T cell repertoire complex ity in normal individuals and bone marrow recipients analyzed by CDR3 size spectra typing. Correlation with immune status. J Immunol 152:5109-5119. 68. Kou ZC, P. J., Wu SS, Goodenow MM, and Sleasman JW. 2003. Combination antiretroviral therapy results in a rapid incr ease in T cell receptor variable region beta repertoire diversity within CD45RA CD8 T cells in human immunodeficiency virusinfected children. J Infect Dis 187:385-397. 69. Beverley, P. C., A. Daser, C. A. Michie and D. L. Wallace. 1992. Functional subsets of T cells defined by isoforms of CD45. Biochemical Society transactions 20:184-187.

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85 70. Jiang, W., L. Kang, H. Z. Lu, X. Pan, Q. Lin, Q. Pan, Y. Xue, X. Weng, and Y. W. Tang. 2004. Normal values for CD4 and CD8 lymphoc yte subsets in healthy Chinese adults from Shanghai. Clinical and diagnostic laboratory immunology 11:811-813. 71. Nag, V. L., P. Agarwal, V. Venkatesh, P. Rastogi, R. Tandon, and S. K. Agrawal. 2002. A pilot study on observations on CD4 & CD 8 counts in healthy HIV seronegative individuals. The Indian journal of medical research 116:45-49. 72. Fink, P. J., L. A. Matis, D. L. McElligott, M. Bookman, and S. M. Hedrick. 1986. Correlations between T-cell specificity and the structure of the antigen receptor. Nature 321:219-226. 73. Lehner, P. J., E. C. Wang, P. A. Moss, S. W illiams, K. Platt, S. M. Friedman, J. I. Bell, and L. K. Borysiewicz. 1995. Human HLA-A0 201-restricted cytotoxic T lymphocyte recognition of influenza A is dominated by T cells bearing the V beta 17 gene segment. The Journal of experimental medicine 181:79-91. 74. Wack, A., D. Montagna, P. Dellabona, and G. Casorati. 1996. An improved PCRheteroduplex method permits high-sensitivity de tection of clonal expansions in complex T cell populations. Journal of immunological methods 196:181-192. 75. Schwab, R., P. Szabo, J. S. Manavalan, M. E. Weksler, D. N. Posnett, C. Pannetier, P. Kourilsky, and J. Even. 1997. Expanded CD4+ and CD8+ T cell clones in elderly humans. J Immunol 158:4493-4499. 76. Hawes, G. E., L. Struyk, and P. J. va n den Elsen. 1993. Differential usage of T cell receptor V gene segments in CD4+ and CD8+ subsets of T lymphocytes in monozygotic twins. J Immunol 150:2033-2045. 77. van den Beemd, R., P. P. Boor, E. G. van Lo chem, W. C. Hop, A. W. Langerak, I. L. Wolvers-Tettero, H. Hooijkaas, and J. J. van Dongen. 2000. Flow cyto metric analysis of the Vbeta repertoire in healthy controls. Cytometry 40:336-345.

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86 BIOGRAPHICAL SKETCH Michele Lawson was born in 1972 in W heeling, West Virginia. She graduated from Bridgeport High School in Bridge port, Ohio, in 1991 and headed off to Otterbein College in Westerville, Ohio. She graduated from Otterbein with a Bachelor of Science in life science and psychology in 1995 and moved to Akron, Ohio to attend law school. She graduated from the University of Akron School of Law in 1998 with a Juris Doctor and was admitted to practice in Ohio. In 2001, she passed the patent bar exam and was licensed as a patent attorney by the United States Patent and Trademark Office. After practicing in a va riety of areas of the law for 7 years, she entered the joint degree Master of Science/Master of Business Administration (MS/MBA) Program at the Univ ersity of Florida in 2005.