Molecular Mechanisms Regulating CD8 T Cell Differentiation Following External Cues

Molecular mechanisms regulating CD8 T cell differentiation following external cues

Jolie Cullen orcid.org/0000-0001-7780-0629

November 2018

Department of Microbiology and Immunology, The Peter Doherty Institute The University of Melbourne

Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy

Table of Contents ABSTRACT ...... 5 DECLARATION ...... 7 COMMUNICATIONS ...... 8 PREFACE ...... 10 ACKNOWLEDGMENTS ...... 11 ABBREVIATIONS ...... 14 LIST OF FIGURES ...... 20 CHAPTER 1: INTRODUCTION ...... 21 1.1 ADAPTIVE IMMUNE RESPONSE ...... 21 1.1.1 CD8 T cells ...... 22 1.2 RECOGNITION OF INFECTED CELLS BY T CELLS ...... 22 1.2.1 The T cell receptor and antigen recognition ...... 22 1.2.2 Major histocompatibility complex (MHC) ...... 23 1.3 ANTIGEN PROCESSING AND PRESENTATION ...... 23 1.3.1 Endogenous processing and presentation pathway ...... 24 1.3.2 Cross presentation ...... 24 1.4 CD8 T CELL MEDIATED IMMUNITY ...... 24 1.4.1 Activation of naïve CD8 T cells ...... 24 1.4.2 Homing and migration of CD8 T cells ...... 28 1.5 EFFECTOR FUNCTIONS OF CD8 T CELLS ...... 28 1.5.1 Acquisition and expression of cytolytic molecules within effector CTL ...... 28 1.5.2 Cytotoxicity ...... 29 1.5.3 Effector Cytokines ...... 29 1.6 DIFFERENTIATION OF MEMORY CD8 T CELLS ...... 31 1.6.1 Effector/memory fate decisions ...... 31 1.7 THE MEMORY PHASE ...... 31 1.7.1 Memory cell subsets ...... 31 1.7.2 The foundation of memory T cells ...... 33 1.8 THE ROLE OF CD4 T CELL HELP DURING CD8 MEMORY DIFFERENTIATION ...... 37 1.8.1 Dependence on CD4 T cell help ...... 37 1.8.2 Delivery of CD4 T cell help ...... 38 1.8.3 Timing of CD4 T cell help ...... 38 1.8.4 CD4 T cell help and its role in exhaustion ...... 39 1.8.5 Memory cells are a heterogeneous population: heterogeneous requirement for CD4? 40 1.9 T CELL ENERGY PRODUCTION ...... 42 1.9.1 Modes of energy production ...... 42 1.9.2 Metabolism during the memory phase ...... 43 1.10 TRANSCRIPTIONAL REGULATION OF CD8 MEMORY T CELLS ...... 45 1.10.1 T-bet and eomesodermin ...... 45 1.10.2 Blimp-1 and Bcl-6 ...... 46 1.10.3 ID2 and ID3 ...... 46 1.10.4 STAT family ...... 47 1.11 THESIS AIMS ...... 49 CHAPTER 2: MATERIALS & METHODS ...... 50 2.1 MATERIALS ...... 50 2.1.1 Mice ...... 50 2.1.2 Antibodies ...... 50 2.1.3 Primers ...... 51 2.1.4 Buffers and Media ...... 52 2.1.5 Seahorse Instrument (Seahorse Bioscience) ...... 52 2.2 METHODS ...... 54 2.2.1 Infection of mice ...... 54 2.2.2 Tissue sampling, processing and counting ...... 54 2.2.3 Adoptive transfer ...... 55

2.2.4 OT-I T cell enrichment ...... 56 2.2.5 CD8 T cell purification by cell sorting ...... 56 2.2.6 Violet tracker labelling ...... 56 2.2.7 In vitro T cell activation ...... 57 2.2.8 Determination of lung viral titres ...... 57 2.2.9 Plaque Assay ...... 57 2.2.10 Purification of cells by ficoll gradient ...... 57 2.2.11 Antibody staining ...... 58 2.2.12 Intracellular staining ...... 58 2.2.13 Cytometric Bead Array ...... 58 2.2.14 Flow cytometry ...... 58 2.2.15 Measuring Bioenergetics in T Cells by Seahorse Bioscience ...... 59 2.2.16 RNA extraction and cDNA synthesis ...... 60 2.2.17 TaqMan gene expression assay ...... 61 2.2.18 Sybr green real-time PCR ...... 61 2.2.19 Mathematical modelling ...... 61 2.2.20 ATAC-seq: Assay for Transposase-Accessible Chromatin with high throughput sequencing ...... 63 2.2.21 Bioinformatic analyses ...... 65 2.2.22 Statistical Analyses ...... 66 CHAPTER 3: THE MOLECULAR BASIS OF CD4 T CELL HELP DURING THE PRIMING OF CD8 T CELL MEMORY ...... 67 3.1 INTRODUCTION ...... 67 3.2 RESULTS ...... 68 3.2.1 Characterization of the GK1.5, unhelped mouse model of Influenza ...... 68 3.2.2 Unhelped mice have similar cytokine profiles in their lungs compared to B6 mice during the primary, resting memory and secondary immune response...... 74 3.2.3 CD4 T cell help is required during the initial priming phase of CD8 T cell responses...... 76 3.2.4 CD4 T cell help is not vital during the maintenance of memory T cells...... 76 3.2.5 Helped and unhelped memory CD8 T cells have a similar transcriptional profile. 78 3.2.6 Unhelped memory CTL exhibit an altered metabolic capacity compared to helped memory 78 3.3 DISCUSSION ...... 81 CHAPTER 4: DELINEATION OF DISTINCT TRANSCRIPTIONAL PATHWAYS THAT UNDERPIN VIRUS-SPECIFIC CTL DIFFERENTIATION ...... 87 4.1 INTRODUCTION ...... 87 4.2 RESULTS ...... 88 4.2.1 Naïve, effector and memory cell quality control prior to sequencing ...... 88 4.2.2 Whole transcriptome analysis of CD8 T cells at each stage of differentiation ...... 90 4.2.3 Identification of genes involved in CD8 T cell differentiation ...... 95 4.2.4 Validation of Dmrta1 ...... 100 4.2.5 Zbtb32 and its unique transcriptional profile in memory CTL ...... 105 4.3 DISCUSSION ...... 108 CHAPTER 5: CHAPTER 5: THE MOLECULAR BASIS OF ‘SIGNAL THREE’ AND HOW IT DICTATES THE DIFFERENTIATION OF CD8 T CELLS ...... 115 5.1 INTRODUCTION ...... 115 5.2 RESULTS ...... 116 5.2.2 Phenotypic analysis of stimulated cells over time shows equivalent activation across the stimulation conditions ...... 121 5.2.3 Combined co-stimuli program earlier commitment to cell division...... 123 5.2.4 Differential metabolic profile induced by signal three molecules ...... 127 5.2.5 Signal three has a direct effect on the chromatin landscape of CD8 T cells ...... 131 5.2.6 Quality control of ATAC-seq samples ...... 134

5.1.1 Differential amounts of chrM accessibility based on alternate sources of signal 3 138 5.1.2 Combined culture has the highest number of regulated regions, and therefore highest amount of chromatin remodelling ...... 138 5.2.7 Signal three shows the same effects in vivo as shown in vitro ...... 142 5.2 DISCUSSION: ...... 145 CHAPTER 6: CONCLUDING REMARKS ...... 149 CHAPTER 7: APPENDIX ...... 152 REFERENCES ...... 157

Abstract

Upon activation, naïve CD8 T cells undergo a program of proliferation and differentiation that results in the acquisition of effector functions. Optimal T cell activation requires the integration of multiple signals including cross-linking of the T cell receptor (signal 1); co-stimulation (signal 2) and soluble factors such as cytokines (signal 3). Once a CD8 T cell has received these three signals they differentiate into an effector cell, which are able to control infection by directly killing the infected cell. Once the infection is cleared, these effector cells contract by controlled cell death and a long-lived population of memory cells remain. These potent memory cells are the defining feature of adaptive immunity as they offer protection for the life of the host due to their unique capabilities to survive in the absence of antigen and respond rapidly to secondary challenge. Therefore, effective CD8 T cell memory is the goal of cell-mediated vaccination strategies.

While it is well established that CD4 help is required for CD8 T cell memory formation, it is unclear when during CD8 differentiation this help is required. Further, the effect that CD4 help has on the transcriptional profiles of CD8 T cells and the molecular pathways they use during the generation and maintenance of memory CD8 T cells remains elusive. Using a mouse model of Influenza A virus infection, where priming occurs in the presence or absence of CD4 T cell help, we have pinpointed that help is required during the initial priming of CD8 T cells, and not during memory maintenance or recall. Genome-wide RNA-sequencing analysis of the transcriptional signatures between resting “helped” and “unhelped" memory CD8 T cells revealed surprisingly few differentially expressed genes. However, upon reactivation, “helped” memory CD8 T cells exhibited greater transcriptional up-regulation than their “unhelped” counterparts, and utilization of alternate molecular pathways. Intriguing metabolism defects combined with similarities to an ‘exhausted phenotype’ suggest that help is required to defer a cell away from terminal differentiation, towards a memory cell. Further, our analysis revealed that CD4 T cell help during initial priming is essential for establishing a memory cell pool with enhanced transcriptional potential. Thus, CD4 T cell-dependent programming likely underpins rapid responsiveness, a key characteristic of memory CD8 T cells.

Each stage of T cell differentiation; naïve, effector and memory, are characterized by distinct transcriptional and functional profiles. However, the molecular mechanisms regulating the acquisition of these profiles remain poorly defined. Further, contention remains around the pathway of differentiation towards memory cell status. This thesis has compared the transcriptional profiles of each of these cell stages, aiming to identify any

previously unappreciated genes, gene networks or TFs that may be vital during the differentiation of memory CD8 T cells. These transcriptional profiles were first compared globally, which highlighted the similarities between each of the cell stages. Based on transcriptional profiles of gene expression across each of the cell stages, two key genes were identified, Dmrta1 and Zbtb32. These were then validated and assessed to determine if they translate from mice models into human studies. The data shown in this thesis suggests each of these genes may be molecular signatures used to identify memory cells. Each gene should be further evaluated, but alone have validated the method of data mining and comparison to identify previously undescribed genes as having a role in cell differentiation.

Finally, using mathematical modelling of in vitro activated cells combined with bioinformatic analysis of ATAC-Seq, this thesis also has explored the role of signal three on chromatin remodelling during CD8 T cell activation. Our analysis has identified that each cytokine has a slightly different impact on the degree of chromatin accessibility, and a combination of signal 3 cytokines resulting in the highest level of chromatin accessibility and subsequent activation, proliferation and downstream acquisition of effector function. We suggest signal 3 itself is an under-appreciated cascade of regulation during the activation of CD8 T cells by directly controlling chromatin structure.

Taken together, this data suggests that each signal received by a CD8 T cell during activation is gearing it towards a particular fate. It can be speculated from this data that the default pathway after activation is towards terminal differentiation, that is, to become an effector cell, destined to die by controlled death after the infection is cleared. It is the cues from the environment that skew the cell away from this fate in a healthy environment, such as CD4 T cell help and signal three. Importantly, in chronic disease states such as HIV, or LCMV in mice, this balance is tilted and results in a mass of activated cells and exhaustion. Therefore, the work contained in this thesis demonstrates the importance of each factor during activation and their effect on not only the functional capacity of CD8 T cells, but also their fate.

Declaration

This is to certify that: • The thesis comprises only my original work towards the PhD except where indicated in the preface, • Due acknowledgement has been made in the text to all other materials used, • The thesis is fewer than 100 000 words in length, exclusive of tables, bibliographies and appendices.

Jolie Cullen

Communications

Part of the work presented in this thesis has resulted in the following communications: Publications • Cullen JG, Croom HA, Quinn K, Olshansky M, Prier JE, Doherty PC and Turner SJ. CD4 T cell help contributes to limiting virus-specific T cell exhaustion signatures and retention of memory potential. PNAS, 2019. • Russ, BE, Olshanksy M, Smallwood HS, Li J, Denton AE, Prier JE, Stock AT, Croom HA, Cullen JG, Nguyen ML, Rowe S, Olson MR, Finkelstein DB, Thomas PG, Speed TP, Rao S, Turner SJ. Distinct Epigenetic Signatures Delineate Transcriptional Programs During Virus-Specific CD8 T Cell Differentiation. Immunity, 2014. • Olson MR, Seah SG, Cullen JG, Greyer M, Edenborough K, Doherty PC, Bedoui S, Lew AM Turner SJ. Helping Themselves: Optimal Virus-Specififc CD4 T Cell Responses Require Help Via CD4 T Cell Licensing of Dendritic Cells. J Immunol, 2014.

This thesis was presented in the following presentations: Oral Presentations: 2017: NIF Winter School, Singapore. 2016: EMBL Australia Postgraduate Symposium, South Australian Health and Medical Research Institute, Adelaide. Biomedicine Link Conference, St. Vincent Hospital, Melbourne. Center for Advanced Molecular Imaging, Imaging CoE Summit, Torquay. RIKEN Centre for Integrative Medical Sciences (IMS), Summer Program, Yokohama, Japan. Invited speaker, Technische Universität München, Freising, Germany Invited speaker, Technische Universität München, Munich, Germany. Invited Speaker, Yale University, New Haven, USA. Invited Speaker, Columbia University, New York, USA. Invited Speaker, Indiana University of Medicine, Indianapolis, USA. Invited speaker, Benaroya Research Institute, Seattle, USA. IMMUNOLOGY 2016, AAI. Seattle, USA. Invited speaker, Fred Hutchinson Cancer Research Centre, Seattle, USA. PhD Oration, Peter Doherty Institute, Melbourne.

Lorne Infection and Immunity Conference, Lorne. 2015: Biomedicine Link Conference, St. Vincent Hospital, Melbourne. Immunology Interest Group of Victoria Annual Meeting, Creswick. Department of Microbiology and Immunology SPASIM Postgraduate Retreat, Lancemore Hill. Gene Technology Access Centre (GTAC) Youth Science Forum, WEHI, Melbourne. 2014: Immunology Interest Group of Victoria Annual Meeting, Creswick. NHMRC Influenza Program Retreat, University College, Melbourne. 2013: Immunology Interest Group of Victoria Annual Meeting, Creswick. Department of Microbiology and Immunology SPASIM Postgraduate Retreat, Lancemore Hill. NHMRC Influenza Program Retreat, University College, Melbourne.

Poster Presentations: 2017: NIF Winter School, Singapore. 2016: VIIN Young Investigator Symposium 2016, The WEHI. Center for Advanced Molecular Imaging, Imaging CoE Summit, Torquay. The International Conference of Immunology, Melbourne. RIKEN Centre for Integrative Medical Sciences (IMS), Summer Program, Yokohama, Japan. IMMUNOLOGY 2016, AAI. Seattle, USA. The Research Bazaar, Melbourne. 2015: Australasian Society of Immunology Annual Conference, Canberra. Victoria Infection and Immunity Network Young Investigator Symposium, Melbourne. 2014: Australasian Society of Immunology Annual Conference, Wollongong. 2012: Australasian Society of Immunology Annual Conference, Melbourne.

Preface

My contribution to experiments in each of the chapters was a follows: Chapter 3: 99% Chapter 4: 90%- see below Chapter 5: 99%

I acknowledge the important contributions to experiments here: Julia Prier conducted and designed the Fluidigm experiments. Jasmine Li was vital in experimental design and carrying out the ATAC-seq experiments. Kylie Quinn and Michael Dagley assisted in experimental design and helped carry out the Seahorse experiments.

I acknowledge the important contributions to the bioinformatical data analysis here: Moshe Olshanky carried out all bioinformatics analysis on RNA-seq, ATAC-seq and fluidigm data. I credit his contribution to all chapters in this thesis. Adele also contributed greatly to the ATAC-seq data analysis. Julia Marchingo gave expert advice on experimental design and data analysis for the Modelling in chapter 5.

Acknowledgments

First and foremost, thank you so much, Steve, for allowing me to do a PhD in your lab. Thank you for being so patient with me. I’ve appreciated being able to let my scientific imagination run wild and do whatever experiments I thought were necessary. I’ve learnt so many valuable lessons from my time in your lab- and I can’t thank you enough for these. But thank you! To my committee members Sammy and Andrew, thanks for the scientific advice along the way, and the extra meetings! I appreciate the time you both put into helping me. A massive thank you to the best PhD buddy a girl could ever ask for- JP. You are going to go on and do massive things in science and I am so glad I got to get to know you before you got famous. A massive thank you to Croomy for your amazing friendship and mentorship, as well as all things CD4. I hope to one day be as patient and as good at teaching as you! Thank you Moshe not only for your imperative data analysis and contribution to this thesis but also for your friendship and good humour throughout the entire process. Thanks heaps to Jas who taught me ATAC-seq, thanks for putting in the late nights. And to the rest of the Turner group; Dana, Mishy, Hatts, thanks for your friendship and support both in and out of the lab. Thank you Matthew Olsen (Olsie) for everything! One day I hope to be a scientist just like you. Thanks for your friendship, advice, mentorship, and even getting me a job! I hope you manage to keep in touch now you’re going to be super busy running your own lab! And thank you to the other Labs that made up the Doherty group, led by Katherine Kedzierska and Nicole La Gruta. Thank you for your expert advice during lab meetings and words of encouragement along the way. Thank you Nikki Bird for your help with plaque assays and CBAs, and for being an amazing friend along the way. Thanks also to Em, Sne, Jing, Kathy and Bridie. A massive thank you to my amazing mentors- Misty Jenkins, Paul Whitney and Kylie Quinn. You’ve all helped me make some pretty massive decisions and guided me through this crazy PhD process. Thanks for my ‘foster’ labs, Godfrey and Carbone. Thanks for everything you gave me, from questions at my oration to birthday cake (thanks Dale!) and all those antibodies I borrowed. And a huge thank you to the Mackay Lab, thanks for giving me a job Laura so I could afford to live while I finished my thesis, you are an amazing role model and an inspiration to me! Thanks to the whole lab for putting up with my stress and being such amazing lab mates! Thank you for the strong collaboration from the Hodgkin’s Lab- including Phil Hodgkin, Julia Marchingo, and Sue Heinzel. The signal three Chapter only came together with your input. Thanks Marie Grier for helping me with the IL-15 story and OT-II experiments (neither of which made the cut to thesis). Big thanks to Michael Dagley and Kylie Quinn for help and encouragement during the Seahorse experiments. Thank you to my ‘Human people’ for trying to teach me how to be translational- Nick Gheradin, Oanh Nyugen and Liyen Lo. Thanks also to the animal house for enabling me to do my experiments. Thanks to SPASIM who introduced me to some great friends outside the lab in the early days. And thanks to Odilia for letting me teach and pass on my knowledge to the next gen of scientists. And also for your advice along the way!

Huge thank you to Clara and Simpak. Clara, your inspiring attitude and positive outlook on life have made you a wonderful friend and colleague. And Simpak, I don’t even know where to start. Your advice and knowledge on every aspect of life is on point/ thanks for being an amazing friend and setting an example of how to get science done. Thank you Andre Mu for your endless support, music supply and amazing friendship. A massive thank you also to Scott, for the constant comedy relief, late night lab chats and for the huge amount of support you gave me. I don’t think this thesis would have ever been finished if it weren’t for all your help! To my Wagga crew, Lachie, Bug and Mel. I don’t know what I would do without you guys, I love you all. Undergrad besties Nicole, Kate, Bec, Dave and Paula- the constant Facebook ‘memories’ and ‘tags’ have pulled me through more late night experiments and writing sessions then you will ever know. Thank you. To Lisa, Brett and Evie, thank you for being such wonderful and understanding friends. You’re always there when I call, despite me being constantly caught up in the lab and horrible at replying to texts! Love you guys! To my Sea Salt crew- Josh, Kon and Serinna. You are the best bosses anyone could ever ask for. Thanks for your endless support and flexibility. Thanks for providing me with a Melbourne family, and creating a workplace that felt like home. I’ve made amazing lifelong friends from this experience- Zoe, Elise, Anita, Kon, Emily, Julia and Chris. Special thanks though to Zo and Elise. I don’t know what I have done to be blessed with such wonderful friends. I can’t put into words how much I love and owe you both. Zo- I would never have made it to my confirmation OR oration without you. You are amazing. Elise- thank you so much for always saying it how it is, and doing more than anyone could ever ask. And thanks mostly to my amazing, supportive, generous and understanding family. Rach, thanks for being an amazing big sister and for always looking after me, especially while we lived together. Thanks for always being there to talk about the important things, like Harry Potter and Bluejuice. Nai, thanks for being the voice of reason, a massive support and always being up for hanging out. Thanks for always checking up on me and ensuring I’m fed. Josie, thanks for being a supportive and understanding little bro. And thank you for bringing 3 amazing girls into our lives- Rach, we are so lucky to have you. Plus, you produce the most beautiful and happy children (Bronte and Pippa I love you!). I really do have the most amazing siblings; I love you all! Und an meinen Schatz Flo, vielen Dank für deine Unterstützung und Geduld - ohne deine Ermutigung wäre ich nicht fertig geworden. Ich liebe dich. Mum, thanks for talking to me nearly every day during this rollercoaster ride. I can’t thank you enough- I love you! Thank you for always letting me know I can do anything I want, and for letting me know that I can always change my mind if I want! Dad, thanks for always being so excited and encouraging about my science. I definitely inherited my love of science from you! Thanks for always keeping me motivated; I love you both so much! And thank you both so much for supporting my move overseas- there’s no way I could take on this next challenge without your support xx

“Happiness can be found in the darkest of times, if one only remembers to turn on the light”. – Albus Dumbledore.

Abbreviations aa amino acid Ab antibodies ac acetylation Ag antigen APC alophycocyanin APCs antigen presenting cells ATAC-seq Assay for Transposase-Accessible Chromatin with high throughput sequencing ATC ammonium tris chloride ATP andenosine triphosphate

B6 C57BL/6 mouse BAL bronchoalveolar lavage BLIMP-1 B lymphocyte-induced maturation protein-1 bp nucleotide base pairs BSA bovine serum albumin

CD cluster of differentiation cDNA complementary deoxyribonucleic acid CDR complementarity determining region ChIP chromatin immunoprecipitation

Ci precursor cohort number

CO2 carbon dioxide cRPMI complete RPMI Ct cycle threshold C-terminus carboxyl-terminus CTL cytotoxic CD8 T lymphocytes CTV celltrace violet Cy Cyanin

D diversity d day(s) DD Division density

DCs dendritic cells DMEM Dulbecco’s Modified Eagle Medium DNA deoxyribonucleic acid dNTP deoxynucleotide triphosphate DRiPs defective ribosomal proteins

ECAR extracellular acidification rate EDTA ethylenediamine tetra-acetic acid Eif2ak4 Eukaryotic Translation Initiation Factor 2 Alpha Kinase 4 EOMES Eomesodermin ER endoplasmic reticulum ESCs embryonic stem cells ETC electron transport chain ETS-1 E26 transformation-specific-1

F forward FACS fluorescence activated cell sorter FAO Fatty acid oxidation Fc region fragment crystallisable region FCS foetal calf serum FITC fluorescein isothiocyanate g gram GAPDH glyceraldehyde 3-phosphate dehydrogenase gzm(s) granzyme(s) h hours HBSS Hanks’ balanced salt solution HDAC histone deacteylase HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HMTs histone methyltransferases

IAV influenza A virus ICS intracellular cytokine staining IFN(s) interferon(s)

IFN-γ interferon-γ IL interleukin i cell’s generation i.n. intranasally i.p. intraperitoneally i.v intravenously ITAM immunoreceptor tyrosine-based activation motif

J joining JAK Janus activated kinase Jmjd3 Jumonji C domain-containing protein 3 K Lysine KLRG1 killer cell-lectin-like receptor G1 KO knock-out

LCMV lymphocytic choriomeningitis virus LFA-1 Lymphocyte function-associated antigen 1 LMP2 low molecular weight protein-2 LMP7 low molecular weight protein-7 LNs lymph nodes L32 mitochondrial ribosomal protein L32

M molar mAb monoclonal antibody mDD mean division density mDN mean division number me methylation MEF myeloid elf-1-like factor MFI mean fluorescence intensity mg milligram MHC major histocompatibility complex min minutes ml millilitre MLL Mixed-Lineage Leukemia protein MLN mediastinal lymph node

mM millimolar MPEC memory precursor effector cell

NFAT nuclear factor of activated T cells NFκB nuclear factor-κB ng nanogram NK natural killer nmole nanomole nM nanomolar NA neuraminidase NP nucleoprotein N-terminus amino-terminus

OCR Oxygen consumption rate OT-I Ly5.1xOT-I mouse

OVA ovalbumin peptide, OVA257-264, SIINFEKL, N4 OXPHOS Oxidative phosphorylation

PBS phosphate-buffered saline PCR polymerase chain reaction PE phycoerythrin PerCp peridinin-chlorophyll-protein complex p.i. post infection pfu plaque forming unit(s) pMHC peptide + major histocompatibility complex Poldip3 DNA polymerase delta interacting protein 3 pol II polymerase II PR8 influenza A/PuertoRico/8/34 pre- precursor prf perforin

R receptor RBCs red blood cells rhIL-2 recombinant human IL-2 RNA ribonucleic acid

ROS reactive oxygen species rpm rotations per minute RPMI Roswell Park Memorial Institute 1640 medium RT room temperature RUNX1 runt-related transcription factor 1 secs seconds seq sequencing siRNA short interfering RNA SLEC short-lived effector cell SP1 specificity protein 1 STAT Signal Transducer and Activator of Transcription

TCM central memory CD8 T cell

TEM effector memory CD8 T cell

TRM resident memory CD8 T cell TAE tris-sodium citrate buffer TAP transporter associated with antigen processing TCR T cell receptor TFs transcription factors Th1 CD4 T helper 1 cell Th2 CD4 T helper 2 cell Tg transgenic TNF-α tumour necrosis factor-α TSS transcriptional start site Tyr Tyrosine

U enzyme unit µg microgram µl microlitre µM micromolar

Vcpip1 valosin containing protein interacting protein 1

WT wild type x31 influenza A/HongKong-X31 virus

°C degrees Celsius -/- homozygous knockout

List of Figures

Figure 1.1 Models of T cell differentiation...... 36 Figure 1.2 Metabolic regulation of effector and memory T cell differentiation...... 45 Figure 3.1 Validation of CD4 depletion in GK1.5 mice...... 69 Figure 3.2 Primary immune response to Influenza in B6 and GK1.5 mice has slightly altered kinetics in the absence of CD4 T cells, but reaches a similar peak...... 71 Figure 3.3 Secondary immune response to Influenza in B6 and GK1.5 mice...... 73 Figure 3.4 Unhelped mice have similar cytokine profiles in their lungs compared to B6 mice during the primary, resting memory and secondary immune response...... 75 Figure 3.5 CD4 T cell help at the time of priming is required for CD8 memory T cell differentiation, and is likely to ‘program’ recall capacity into responding CTL...... 77 Figure 3.6 Helped and unhelped memory CD8 T cells have a similar transcriptional profile...... 79 Figure 3.7 Unhelped memory CTL exhibit an altered metabolic capacity compared to helped memory CTL...... 81 Figure 4.1 Flowchart of RNA-seq...... 89 Figure 4.2 Comparison of naïve, effector and memory RNA-seq...... 91 Figure 4.3 Global analysis of naïve, effector and memory RNA-seq samples...... 94 Figure 4.4 Global analysis of naïve, effector and memory RNA-seq samples...... 95 Figure 4.5 The unique expression of Dmrta1 in differentiating CD8 T cells...... 97 Figure 4.6 Dmrta1 expression from publically available datasets...... 99 Figure 4.7 Dmrta1 expression in CD8 T cells...... 101 Figure 4.8 The expression of Dmrta1 in TCM v TEM...... 103 Figure 4.9 The expression of Dmrta1 in human cells...... 105 Figure 4.10 The expression of Zbtb32...... 107 Figure 4.11 Features of Zbtb32 protein...... 108 Figure 5.1 Titration of cytokines over time and their effect on cell proliferation...... 117 Figure 5.2 Titration of cytokines over time and their effect on cell proliferation...... 120 Figure 5.3 Phenotypic analysis of stimulated cells over time...... 122 Figure 5.4 The effect of signal three on the proliferation and cell numbers during activation of naïve CD8 T cells...... 125 Figure 5.5 Signal three cytokines affect cell survival diversely...... 127 Figure 5.6 Metabolic analysis of stimulated cells after culturing with signal three cytokines and the effects of mitochondrial inhibitors on the electron transport chain. 130 Figure 5.7 Harvest and collection stimulated CD8 T cells for ATAC-seq...... 133 Figure 5.8 Results from bioanalyzer: Quality control of ATAC-seq samples...... 135 Figure 5.9 Validation of the ATAC-seq samples...... 137 Figure 5.10 Mitochondrial analysis from ATAC-seq ...... 138 Figure 5.11 Comparison of gene list of regulated regions acquired from ATAC-seq. 139 Figure 5.12 Comparing each condition to IL-2 alone using ATAC-seq...... 141 Figure 5.13 Differential expansion and phenotypes of CD8 T cells exposed to differing signal threes in vivo...... 144

Chapter 1: Introduction

The mammalian immune system has evolved to defend us against an almost infinite number of pathogens in a tightly restricted, antigen-specific manner, whilst preserving tolerance to harmless antigens and self. It can orchestrate this fine balance through the coordination of two broad branches of immunity, the adaptive and innate immune responses. Innate immunity is pre-existing and non-specific, responding immediately and equally to infection. This branch includes cells that directly eliminate infection such as macrophages, neutrophils and NK cells, and physical barriers such as the skin. In contrast, adaptive immunity is specific to the type of infection, and therefore much more complex and slower, and is the focus of this thesis.

1.1 Adaptive immune response

Adaptive immunity functions to destroy invading pathogens, and any associated by- products, to keep the host healthy. The adaptive immune response is divided into two broad classes, the antibody response, predominately comprised of B cells, and the cell- mediated immune response, dominated by T cells. Pathogen-specific recognition is mediated by membrane-bound antigen receptors expressed on B and T lymphocytes [1], termed the B cell receptor (BCR) and T cell receptor (TCR) respectively. Both BCRs and TCRs can vary greatly enabling adaptive immune responses to target a myriad of antigens unique to particular pathogens.

B cells are adaptive immune cells that upon activation differentiate into plasma cells that are capable of secreting antibodies, the soluble, or humoral component of adaptive immunity. Their activation is initiated in response to specific antigen binding to the BCR, a complex comprised of membrane immunoglobin (Ig) heavy and light chains, in association with the Igαβ heterodimer [2]. Following antigen binding, accumulation and activity of signalling molecules initiates the regulation of gene expression, reorganization of the cytoskeleton, and BCR-mediated internalization of antigen complexes. Antigen internalized through the BCR is subsequently processed within specific endosomal compartments and presented in complex with MHC II to recruit specific T cell help [3]. Antibodies secreted by mature B cells circulate in the bloodstream, binding specifically to a foreign antigen, resulting in either the inactivation of viruses or destruction by phagocytic cells.

Cellular immunity is comprised of CD4 and CD8 T cells which are able to recognize and destroy infected host cells. CD4 helper T cells regulate the immune response primarily through the release of cytokines, while CD8 T cells produce anti-viral, pro-inflammatory and cytotoxic molecules that are important for removal of virally infected cells and tumours from the host. Memory cell formation is the hallmark of adaptive immunity and the goal of vaccination. Once an antigen is recognized by T and B cells, the adaptive immune response forms memory, so the second time these cells see the same antigen they can mount a faster, bigger response resulting in combat of the infection. Memory CD8 T cell populations can be extremely long-lived, often lasting for the life of the host, and allow faster clearance and recovery from secondary infection. Although not directly focused on in this study, the distinction between self and non-self is another fundamental characteristic of the adaptive immune response.

1.1.1 CD8 T cells

CD8 cytotoxic T lymphocytes (CTLs) play a vital role in the response to intracellular pathogens and tumour cells (as reviewed in [4]). Upon recognition of foreign antigen, the T cell response advances through three distinct phases: expansion, contraction and memory establishment. When naïve CD8 T cells encounter antigen presented by antigen presenting cells (APCs), they become activated. This prompts them to undergo proliferation, during which they differentiate and acquire effector functions such as production of cytokines and the ability to kill infected cells through the release of cytotoxic molecules. Following the peak of the effector response, the CD8 T cell population contracts dramatically (90-95%), with the remainder of cells destined to be long-lived memory cells, capable of rapid proliferation and expression of effector molecules upon antigen re-encounter (as reviewed in [5]).

1.2 Recognition of infected cells by T cells

1.2.1 The T cell receptor and antigen recognition The TCR is comprised of a paired α and β chain, and somatic gene segments, which recombine during T cell development [6], encode each chain. TCR-α chains are a combination of variable (V), junctional (J), and constant (C) gene segments. TCR-β chains exhibit addition variability due to a highly variable region known as diversity (D) as well as V, J, and C segments. Complementarily-determining regions (CDRs) [7, 8] are segments of hypervariability within the Vα and Vβ domains which form the TCRαβ binding site, mediating contact with a given peptide-major histocompatibility complex (pMHC) [9]. Diversity within the TCR repertoire is generated via 4 main mechanisms:

different VJ or VDJ gene segment combinations; the imprecise joining of gene segments; the addition of non-template encoded nucleotides at V(D)J junctions; and, pairing of different TCR-α and -β chains. Of these, the imprecise joining of gene segments and the addition of non-template encoded nucleotides contribute most to the variety, consequently, TCR diversity is heavily focused toward the CDR3 loop, positioned at the junction of the V(D)J segments [10]. Generation and maintenance of an effective repertoire of T cell antigen receptors is essential for an optimal immune response [11]. A study using TCR gene amplification and sequencing showed that in the naive, highly diverse repertoire, there are approximately 106 different β chains in the blood, each pairing, with at least 25 different α chains [12]. In the memory subset, the diversity decreased to 1- 2 × 105 different β chains, each pairing with only a single α chain, contributing less than 1% of the total diversity [12]. Polyclonal T cell populations with highly conserved TCRβ CDR3 motifs are insufficient when single residue mutations were introduced in the cognate viral epitope. However, diverse clonotypic repertoires are not associated with viral escape [13]. Thus, the enormous amount of diversity in the T cell repertoire is essential for T cell immunity.

1.2.2 Major histocompatibility complex (MHC) As alluded to above, a crucial interaction during T cell activation is TCR recognition of immunogenic peptides, in the context of MHC molecules as expressed on the surface of a cell [14]. These molecules are heterodimer glycoproteins that have a transmembrane region below a peptide-binding groove, of which there are two main types. CD8 T cells express a TCR that recognizes peptides in the context of MHC-class I molecules (MHC- I), while the TCR of CD4 T cells recognizes peptides in the context of MHC-class II (MHC-II). For this reason, this review will focus on MHC-I.

1.3 Antigen processing and presentation

Almost all nucleated cells express MHC-I molecules. At steady state, the MHC-I processing pathway samples host-derived peptides in the cytosol from proteins generated for physiological functions. During infection, altered or non-self-peptides are processed and presented in MHC-I molecules, alerting CD8 T cells to changes in the intracellular environment. MHC-I molecules consist of a single membrane-bound heavy chain made up of three extracellular domains (α1, α2 and α3) and a non-covalently associated β2 microglobulin subunit [15]. The α1 and α2 subunits comprise the peptide-binding groove and are associated with the greatest degree of polymorphism. These subunits contain

amino acid residues that point into the groove to mediate peptide-TCR interactions [16]. The peptide-binding groove of MHC-I molecules has a closed-ended structure, restricting the length of peptides bound to 8-10 amino acid residues [17]. Each MHC-I molecule can present a very large array of peptides, resulting in a high likelihood of recognition by a CD8 T cell with a given TCR.

1.3.1 Endogenous processing and presentation pathway Cytosolic peptides that bind MHC-I molecules are postulated to be derived from defective ribosomal translocation products (DRiPs) [18]. DRiPs mark the cytosolic peptides for degradation by the proteasome through ubiquitination [19]. Following degradation, peptides are translocated into the lumen of the endoplasmic reticulum (ER) by the transporter associated with antigen processing (TAP) [20, 21]. MHC-I molecules in the ER are then loaded with peptides a complex process, which stabilizes the MHC and enables egress, which is mediated by multiple chaperone proteins including calreticulin, calnexin and tapasin and released via the Golgi apparatus into the plasma membrane, [22].

1.3.2 Cross presentation It was originally thought that cells exclusively present peptides derived exclusively from proteins synthesized within the cytosol. It has since been determined that antigens from the extracellular environment can be processed and presented on MHC-I molecules to CD8 T-cells, a process termed cross-presentation [23, 24] as reviewed in [25]. This phenomenon has been implicated in influenza virus [26], vaccinia virus [23], poliovirus [23], and lymphocytic choriomeningitis virus infection [27], although some debate does remain around Influenza and vaccinia virus. Cross-presentation is an important mechanism of antigen presentation as not all pathogens infect DCs [28]. Cross presentation is largely restricted to CD8a+ [29, 30] or CD103+ dendritic cells (DCs) [31] which have evolved the capacity to phagocytose debris from apoptotic cells [32] or capture antigen from live cells [33]. Cross-presentation allows uninfected DCs to present exogenous antigens that are not usually presented on MHC-I.

1.4 CD8 T cell mediated immunity

1.4.1 Activation of naïve CD8 T cells

DCs are the principle antigen presenting cell (APC) responsible for priming CD8 T cells and provide a link between innate and adaptive immunity [34]. Before a DC can participate in T cell priming, it must first be activated from an immature state [34], either directly by infection, or through CD40/CD40L licensing by CD4 T helper cells [35-38]. After activation, DCs process and present antigen bound to the MHC molecule, to be

recognized by naive CD8 T cells. Optimal T cell activation requires the integration of multiple signals including cross-linking of the T cell receptor (signal 1); co-stimulation (signal 2) and soluble factors such as cytokines (signal 3). Due to the importance of the integration of these 3 signals, each will now be further explored.

1.4.1.1 Signal 1: cross-linking of the T cell receptor Recognition of pMHC by a specific TCR is the first of three signals required for mature T cell activation [15]. Activated DCs expressing the peptide MHC (pMHC) migrate to the LNs draining the site of infection and present the processed antigens to the naïve CD8 T cells [30, 33, 39]. TCR mediated recognition of cognate pMHC induces CD3 clustering resulting in a CD3V-chain signal transmission via several adaptor proteins, resulting in T cell activation. The T cell signal strength, which is shown to direct differentiation of T cells [40], is the combination of the density of pMHC expression [41], the affinity of the TCR for the pMHC [42-44], the duration of antigen presentation [45, 46], signalling through accessory molecules [47-49] and other exogenous signals such as cytokines [50].

1.4.1.2 Signal two

Signal two in the activation of naïve CD8 T cells is co-stimulation which results in increased IL-2 production, up regulation of CD25, entry of the T cell into the cell cycle and enhanced T-cell survival through up regulation of the anti-apoptotic molecule Bcl- XL [51]. Numerous co-stimulatory receptors and their interacting ligands (those on DCs written in parentheses) have been described for CD8 T cells, including CD28 (CD80 and CD86), CD40 (CD40L), 4-1BB (4-1BBL), CD27 (CD70) and OX40 (OX40L) [52-56]. Transcriptional programs induced by signal 2 are orchestrated by the transcription factors (TFs) nuclear factor-κB (NF-κB), activator protein 1 (AP-1) and nuclear factor of activated T cells (NFAT) [57-59]. Activation of these TFs leads to the expression of multiple cytokines, chemokines and cell surface receptors, resulting in the promotion of T cell activation and proliferation [60]. TCR stimulation alone (without signal 2) has been shown to lead to an 'off' signal in the form of T cell anergy rather than full T cell activation [61, 62]. Under these conditions, NFAT is activated in the absence of full AP-1 activation, which leads to the expression of genes such as diacylglycerol kinase-α (DGKA) and the E3 ubiquitin-protein ligases CBLB and GRAIL, which inhibit full T cell activation [60, 63]. Despite the importance of signal 2 in activating CD8 T cells and promoting long- term cell survival [64], it has been shown that naive CD8 T cells can be fully activated by signal 1 alone [65]. Further, using CD28-deficient mice immunized with LCMV,

signal 1 alone was shown to be sufficient for pre-activated T cells to clear virus [65]. In this case, the co-stimulation is likely coming from an alternate source.

1.4.1.3 Signal 3 After a naïve CD8 T cell receives signal 1 activation and signal 2 co-stimulation, pathogen-induced inflammatory cytokines then act directly on the responding CD8 T cells as signal 3. In 2006 signal 3 was highlighted by Mescher et. al., who had identified the profound effects of lineage determining cytokines during CD4 cell-fate decisions toward their lineages [66, 67]. This third signal regulates both the expansion and differentiation of the cells, as well as playing a role in determining the fate of T cells. It is most well documented in the context of CD4 T cell activation, during which cytokines IL-12, IL-4 and IL-6 activate signal transducer and activator of transcription (STAT) 4, STAT6 and STAT3, respectively. This leads to the expression of T-bet, GATA-binding protein 3 (GATA3) and retinoic acid receptor-related orphan receptor-γt (RORγt), enabling the formation of T helper 1 (TH1), TH2 and TH17 cells. In the case of regulatory (Treg) cells, transforming growth factor-β (TGFβ) signalling through SMAD2–SMAD4 promotes the expression of forkhead box P3 (FOXP3).

The transcriptional aspect of CD8 T cell differentiation in the context of signal 3 is less well defined. The main pro-inflammatory cytokines produced intracellular infections, and therefore the most applicable to CD8 T cells are Type I IFNs (IFN-I), IFNγ, IL-2, IL-12, IL-27 and IL-33. Each of these has been shown to enhance the proliferation, differentiation and survival of CTLs [50, 67-71]. Potential mechanisms by which these cytokines promote CTL survival include increasing the expression of cytokine and co- stimulatory receptors (such as CD25, OX40 and 4-1BB), of cysteine and serine protease inhibitors (such as the granzyme B inhibitor SPI6 (encoded by Serpinb9)) and of BCL-3, which limits the transcription of nuclear factor-κB (NF-κB)-dependent genes [72]. In addition, inflammatory cytokines play an important role in regulating the rate at which responding CD8 T cells acquire memory phenotype and function [101]. For example, priming of CD8 T cells in a low inflammatory environment (i.e., DC immunization) is reported to accelerate memory CD8 T cell development [251]. This has important implications in vaccine design and implies that the inflammatory cytokine milieu present during CD8 T cell activation has long-lasting effects on the ensuing effector and memory CD8 T-cell populations.

IL-12 is one of the most studied cytokines in the context of signal 3 to CD8 T cells, and it has been shown to directly enhance expansion, promote terminal differentiation [73, 74], and regulate memory formation via induction of graded levels of T-bet expression (Joshi et al., 2007). Type I interferons (IFNs) and IL-12 have been described as critical survival signals for optimal CD8 T cell accumulation during the expansion phase. Mescher et al., have shown, using beads conjugated with pMHC (signal 1), and various membrane co-stimulatory molecules (signal 2), that for CD8 T cells, signals 1 and 2 are sufficient for proliferation and cytokine production, but that a third signal, is required for cytotoxic effector function [4]. Mescher et al. have further extended the concept of the third signal to show that the presence of signals 1 and 2 but the absence of signal 3, results in peripheral tolerance in the CD8 T-cell compartment [50]. Thus, CD8 T cells can proliferate and to produce IFN-γ in vivo in the absence of IL-12, but signal 3 is required for full maturation of effective T cell responses. The data are consistent with the work of others, showing the important role of IL-12 in driving IFN-γ effector function by T cells [72, 75].

IFN-Is were identified as antiviral proteins more than 50 years ago and since then have been shown to regulate cell proliferation, survival, migration and other specialised functions. These cytokines potentially regulate the transcription of up to 2000 genes, in an IFN subtype, dose, cell type and stimulus-dependent manner. Importantly to this study, IFN-Is as a primary product of TLR ligation of innate cells have been reported during the induction of CD4 T cell help- even replacing the need for CD4 Th cells during the activation of CD8 T cells for an efficient primary response [76], and more recently as a ‘fill-in’ for IL-12 during the priming of CD8 T cell memory [67]. IFN-Is have also been shown to curtail activation during the immune response, with ‘suppressive’ DCs being induced by IFN-I to express inhibitory molecules, such as PD-L1 and IL-10 [77]. When these IFN-I DCs are cultured with naïve LCMV-specific CD4 T cells, they suppress CD4 Th1 priming, generating a lower number of Tbet expressing and IFNγ producing cells [78].

Importantly, the three signals are time-dependent. A brief exposure (6hr) to antigen and co-stimulation is sufficient to stimulate multiple rounds of division, but clonal expansion and development of effector function are minimal even when signal 3 is present. Full activation requires collaborative signalling by antigen, co-stimulation and signal 3 for greater than 40 hours [50]. Gene expression for cell division can, therefore, be initiated

by a brief interaction with antigen and co-stimulation, but maintaining the expression of the genes needed for survival and effector function requires prolonged signalling by signal 3 cytokine in concert with antigen and co-stimulation [50].

While the studies discussed in this section have assessed the role of signal three in the proliferation of CD8 T cells, there is less research into the role of signal three cytokines in changing the epigenetic landscape of CD8 T cells, or the effect signal three has on epigenetic mechanisms which play a role in the differentiation of memory CTL. Considering the importance of different signals in determining memory potential (Section

1.7.1), signal 3 may also influence CD8 T cell differentiation through epigenetic mechanisms.

1.4.2 Homing and migration of CD8 T cells Following activation and receiving the appropriate antigenic and co-stimulatory signals, naive CD8 T cells undergo several highly coordinated changes resulting in proliferation, acquisition of effector function, differential homing, and the modulation of various cell surface markers associated with cell function and signal transduction [18]. Once a cell has become an effector T cell, it has a reduced potential for homing to lymph nodes due to a decrease in lymph-node-homing receptor expression, such as CC-chemokine receptor 7 (CCR7) and L-selectin (CD62L). TCR stimulation initially induces the rapid shedding of CD62L from the T-cell surface by proteolytic cleavage, but within 24–48 hours, CD62L can be re-expressed. To be able to migrate to inflamed tissues, effector cells upregulate expression of chemokine receptors such as CCR5 and CCR2. Cytokines such as IL-2 and IL-15 have been shown to modulate T-cell migration by altering the level of expression of CCR7 and CD62L, respectively [79, 80]. Importantly, although the expression of CD62L and CCR7 will selectively bias T-cell migration towards lymph nodes, it does not necessarily impede the migration of T cells into peripheral tissues [79, 81].

1.5 Effector functions of CD8 T cells

1.5.1 Acquisition and expression of cytolytic molecules within effector CTL After activation of naïve CD8 T cells, comes a burst of proliferation which results in a large pool of antigen specific CTLs. During this proliferation, the cells acquire the ability to effectively eliminate pathogens in a step-wise progression [82]. The well-established plethora of CD8 T cell effector functions fall into two main categories- cytotoxicity, which is the production of the cytotoxic molecules perforin and granzyme (gzm) A and

B, and pro-inflammatory, the release of antiviral cytokines interferon-gamma (IFN-γ), tumour necrosis factor-alpha (TNF-α) and IL-2 [83-85]. Naïve CD8 T cells can produce TNF-α without proliferation, but to produce IFN-γ and IL-2 they need to divide. CTLs also bind target cell surface Fas (death-inducing) molecules via their Fas ligand (FasL), activating apoptosis [86, 87].

1.5.2 Cytotoxicity 1.5.2.1 Perforin and Gzms Perforin is a pore-forming cytolytic protein found in the granules of CD8 T cells and NK cells. Upon degranulation, perforin binds to the target cell's plasma membrane, and oligomerises in a Ca2+ dependent manner to form pores on the target cell. Once perforin has successfully compromised the infected cells membrane, Gzms A & B enter and induce antigen specific cell lysis and limiting the spread of viral infection [88, 89]. When compared to that of death via FasL, perforin and granzyme are a faster mechanism of CD8 T cell induced cell death [90]. Current questions remain in the field regarding perforins ability to form holes in the target, infected cells, but not the killer cell that releases it. Adding to confusing this ongoing work is the fact that perforin is released by CD8 T cell to kill infected CD8 T cells.

1.5.3 Effector Cytokines 1.5.3.1 IFNγ

NK, NKT, CD4 T and CD8 T cells are the main producers of IFNγ. IFNγ has numerous functions in the immune response, with its main role being to limit viral replication, which it does by increasing expression of class I and II MHC [91, 92], and increasing expression of TAP and proteasomes which are involved in antigen presentation [93]. Additionally, IFNγ promotes lymphocyte recruitment to infected sites [93], and activates innate cells such as macrophages. IFNγ is vital for virus clearance, as IFNγ knockout mice have increased susceptibility to infections such as LCMV, respiratory syncytial virus (RSV) and VV [85, 94, 95]. Importantly, some infections such as influenza virus can be sufficiently cleared in the absence of IFNγ [96].

For CD8 T cells to produce IFN-γ there must be TCR engagement and division [97], except memory CD8 T cells which can produce IFN-γ in response to pro-inflammatory cytokines [98]. Cytokine protein production by CD8 T cells rapidly declines after removal of pMHC complexes [97], a mechanism which controls non-specific bystander damage [99].

1.5.3.2 TNF-α Macrophages and T cells can produce TNF-α [100, 101]. Similar to IFN-g, TNF-α also functions to limit the replication of viruses [102], but unlike IFN-g, TNF-α is involved in CD8 T cell activation. TNF-α can act as a co-stimulatory agent during CD8 T cell activation [103] and enhances CD4 and CD8 T cells proliferative responses by up regulating their expression of IL-2R [84, 104, 105]. Naive CD8 T cells are capable of producing TNF-α immediately after antigen recognition [97], without proliferation, in a transient, autocrine fashion [106-108].

TNF-α-/- mice have increased effector and memory CD8 T cell responses during LCMV infection [109], and TNFR2-/- mice have a greater magnitude of influenza A-specific CD8 T cell responses [110], suggesting a role in directing antigen-induced cell death of activated T cells. Alternatively, TNF-α may play a role in enhancing the production of other cytokines, such as IFN-g and IL-2 [109], consistent with TNF-α playing a role in enhancing the activation of CD8 T cells. While there is little evidence of CD8 T cell- derived TNF-α playing a direct anti-viral role, some studies suggest that TNF-α production by CD8 T cells at the site of infection is deleterious, increasing immunopathology during viral infection [111, 112].

1.5.3.3 IL-2 As mentioned in Section 1.4.1.3, IL-2 promotes T cell proliferation, enhancing the acquisition of effector functions [113, 114]. IL-2 is not required for the initiation of division but is required for expansion of CTL [115], and is also reported to be important for the production of IFN-g by CD8 T cells [83]. A study which found that IL-2 is important for recall of memory CD8 T cell responses also showed that autocrine IL-2 production by CD8 T cells is not necessary, however, IL-2 must be present during activation to give rise to functional memory CD8 T [116]. This suggests that CD4 T cell help is critical for the development of memory CD8 T cells [117], potentially through the production of IL-2, which will be further described later in this chapter. Thus, IL-2 production is vital for cell proliferation and important for the generation of stable long- lived memory CTL and their capacity to be recalled upon secondary infection.

1.5.3.4 Polyfunctionality Polyfunctional is a term used to describe a CD8 T cell that is capable of making IFN-γ, TNF-α and IL-2, which describes the quality of an immune response. Polyfunctionality

is desirable as many studies have shown it to correlate with better disease outcome after infection or vaccination [118-120]. CD8 T cells obtained from the site of infection have greater polyfunctional capacity, which correlates with a heightened state of activation [121-123]. Whether this pertains to preferential recruitment of highly activated polyfunctional cells to the site of infection or the tissue microenvironment, including inflammation-induced local activation, number of DC and strength of CD4 T cell help remains to be determined.

1.6 Differentiation of memory CD8 T cells

1.6.1 Effector/memory fate decisions Modulation of KLRG1 and IL-7R are reported to distinguish LCMV infected cells that are destined to die or survive as long-lived memory cells [124]. IL-7R expression is used to identify cells capable of either forming memory precursors (MPECS, IL-7Rhi) and KLRG1 identifies effector cells that appear early during infection, which die during contraction, called short-lived effector cells (SLECs, KLRG1hi IL-7Rlo). The decision between SLEC and MPEC fates is regulated by the amount and type of inflammatory cytokines present during T cell priming (i.e., IL-12 and IL-15). For example, high levels of inflammatory cytokines during differentiation induces Tbet expression which skews towards a SLEC phenotype, and vice versa [125]. More recently, the term ‘memory potential’ cell has been questioned, particularly using single cell high throughput sequencing and cluster analysis, which suggests that memory potential cells are a homogenous population of cells that give rise to both effector and memory cells [126]. Further, numerous studies have been published directly opposing the idea of MPECs, showing that these concepts don’t apply to certain infections, such as Influenza [127].

1.7 The memory phase

1.7.1 Memory cell subsets Subpopulations of memory T cells have long been known to exist [128], however, it wasn’t until 1999 that the now well-accepted model of ‘central’ memory (TCM) and

‘effector’ memory (TEM) T cells were proposed [129]. Memory cells are grouped according to their phenotypic markers, homing properties of these cells, location and functional distinctions [129]. TCM cells express high levels of CD62L, CCR5, CXCR3 and are CCR7+, whereas EMs express CD62L lo and CCR7- [130]. Based on the main roles of these phenotypic markers, it is no surprise that TCM and TEM cells have distinct

circulatory properties in vivo. CD62L enables entry into LNs by allowing circulating TCM to attach to high endothelial venules via peripheral-node addressin (PNAd), which mediates attachment and rolling [131, 132]. CCR7 binds the luminal surface of endothelial cells in the lymph nodes through the chemokines CCL19 and CCL21, which result in cell arrest and the initiation of extravasation into the tissue. Thus, CD62Lhi + lo - CCR7 TCM cells migrate efficiently to peripheral lymph nodes, and CD62L CCR7 TEM cells can be found in peripheral tissue sites such as the liver and lungs. TCM and TEM are also functionally distinct. In vitro stimulation of human CD62Lhi CCR7+ memory CD4 T cells results in the production of IL-2, but little IFN-γ, IL-4 or IL-5, and vice versa for lo - TEM [129]. Further, only the CD62L CCR7 subpopulation of CD8 T cells was found to contain intracellular perforin. Based on the trafficking potential and functionality of TEM and TCM, a model was proposed in which the tissue-homing effector memory T cells, which are capable of immediate effector functions, could rapidly control invading pathogens [129]. The lymph-node-homing central memory T cells would be available in secondary lymphoid organs ready to stimulate dendritic cells, provide B-cell help and/or generate a second wave of T-cell effectors.

An additional subtype of memory cell was described to remain in the local environment long after peripheral infections are controlled [133-136]. Tissue resident memory T (TRM) cells are transcriptionally distinct from the circulating subsets and reportedly exhibit superior protection against re-infection against certain pathogens [137, 138]. These cells do not re-enter the circulation and persist long-term in the absence of antigen without replenishment from circulating memory cells. CD8 TRM can be found in many tissues including skin, gut, lung, sensory ganglia, female genital tract (FRT), salivary glands, kidney, lymph node and brain, and are highly dependent on the tissue microenvironment for survival [139-142]. CD8 TRM are traditionally identified in skin by their location in the epithelium and the expression of the E-cadherin binding receptor CD103 and early activation marker CD69, both of which are important for tissue-retention of skin TRM

[137]. It should be noted that this only accurately identifies TRM in certain organs such as the skin and gut, but not others such as the pancreas, salivary gland and FRT [140].

Although TRM remain highly localized to sites of previous infection, repeat exposure to antigen can induce widespread lodgement of these cells throughout the skin, which mediate protection against subsequent infections [143]. In humans, CD8 TRM have been found in several organs, including the skin [144], and the genital tract of humans

following HSV-2 infection, during which they play a crucial role in containing reactivating virus [145, 146].

As memory cells are being studied more closely, further subsets of cell function and phenotype have been made. Recently, stem cell memory cells (TSCM) have been identified in mice, nonhuman primates, and humans. They have a naïve-like phenotype, as well as some memory markers such as IL-2Rb and CXCR3 [147-150]. Functionally TSCM cells are shown to be self-renewing and multi-potent, providing a potential reservoir for T cell memory throughout life [151]. These cells are yet to be fully characterized but may provide a potential vaccination target, as they have been shown to serve as precursors of central memory cells [152]. Virtual memory (VM) cells have also been focused on more recently, as a population of foreign antigen-specific memory phenotype CD8 T cells in unimmunized mice. Yet to be properly characterized, there is still debate about whether these cells arise via homeostatic mechanisms. Despite their memory cell like phenotype, these cells show poor T cell receptor induced IFN-γ synthesis, however have been shown to control Listeria monocytogenes infection better than conventional memory cells [153]. As yet, no investigations have been carried out to determine the differentiation of a TSCM or TVM compared to more established memory T cell subsets.

Despite the numerous studies done since 1999, the precise relationship between these memory T-cell subpopulations, how each population is maintained and even the signals that govern how they arise during a primary immune response are areas that remain to be fully defined. There have been theories proposed for memory cell differentiation into each of the different subtypes. In order to fully explore this, we must also consider the long- standing question in the field, when does a memory cell become a memory cell?

1.7.2 The foundation of memory T cells There are five main models proposed for memory T cell differentiation. Two linear pathways to memory differentiation have been proposed. The linear differentiation pathway (Fig. 1.1a), proposes that memory T cells are direct descendants of effector cells. That is, naive T cells first differentiate into effectors, and after Ag clearance, effectors convert into memory cells [154-156]. The second linear pathway, known as the decreasing-potential hypothesis suggests that effector T-cell functions steadily decrease as a consequence of persisting antigen (as observed in chronic infections, Fig. 1.1b) [157].

In addition, accumulative encounters with antigen lead to increased susceptibility of effector cells to apoptosis, and reduced numbers of memory T cells are formed. Similar to the linear differentiation model, the development of memory T cells occurs following antigen clearance. This model suggests that dysfunctional effector T cells such as those produced in a chronic infection, may regain function over time following the removal of antigen. This is supported more recently by studies examining LCMV specific cells produced after Clone 13 infection, which can be re-invigorated using PD-L1 therapy [158]. In order to be true, these linear pathways rely on evidence showing that memory T-cell development only occurs after Ag is removed.

A variation of the linear-differentiation pathway is described by a third model, the signal strength model (Fig. 1.1c), where a short duration of antigenic stimulation favours the development of central memory T cells, and a longer duration of stimulation favours the differentiation of effector memory T cells (as reviewed in [159]). The fate decision by this model is determined during priming and leads to early divergence of effector and memory cells. This model differs from the decreasing-potential hypothesis in that different cell fates can be specified early during the response, in a more divergent manner, according to the intensity of the signals received, rather than in a linear, stepwise manner driven by successive rounds of stimulation.

The ‘divergent pathway’ (Fig. 1.1d) shows a naive T cell that can give rise to daughter cells that develop into either effector or memory T cells, a decision that could be passive or instructive. In this model, naive T cells can differentiate into effector, or bypass an effector-cell stage, developing directly into memory T cells. This model is based on the premise that naive T cells are ‘pre-programmed’ during thymic development to adopt certain differentiation states following activation. Due to studies using the adoptive transfer of single CD8 T cells or cellular barcoding, this indeed seems unlikely, as they have shown that a single naive T cell is multipotent and can give rise to both effector and memory T cells, including both CM and EM cells [160, 161]. Importantly, these studies do not consider the potential effects of different TCR signalling strengths as they were carried out with T cell receptor (TCR)-transgenic CD8 T cells. Note that this model is not really different to the decreasing potential model as daughter cells could have distinct fates depending on receipt of differentiation signals.

The asymmetric cell fate model suggests that upon activation, naïve T cells undergo asymmetrical cell division, with a single cell able to give rise to both effector and memory daughter cells (Fig. 1.1e, [162]). During cell division, the daughter cell that receives the immunological synapse receives stronger stimulation, which is the precise contact point of interaction between T cells and APC that forms during priming, where TCR- MHC and co-stimulatory molecules cluster and signal transduction occurs [163] due to it being closer to the APC. This stronger stimulation leads to the generation of effector cells while the other daughter cell is less differentiated and will have memory potential. This process simultaneously preserves a population of less-differentiated cells that have greater memory cell potential and stem cell-like qualities.

Clear discrepancies remain regarding precise details of how memory T cells are generated, however, each model has one shared observation: that during activation of a naïve T cell, cells with a range of potentials are generated. These include terminally differentiated effector cells that can provide an immediate immune response, as well as those that are less differentiated and have the potential to become memory cells that persist long term that can rapidly respond to re-infection. This flexibility might be important for keeping the T cell response in sync with the infection, which can vary in burden, duration and tropism. Further studies are required to fully discern the pathway a CD8 T cell takes to become a memory cell. Importantly, these models may not be mutually exclusive.

Figure 1.1 Models of T cell differentiation. a) Linear-differentiation pathway- memory T-cell development does not occur until Ag is removed, and the memory T cells are direct descendants of effector cells. b) The decreasing-potential hypothesis suggests that effector T-cell functions diminish as a consequence of persisting ag, as observed in chronic infections. Similar to model 1, the development of memory T cells occurs following antigen clearance. c) Signal strength model- a short exposure to antigenic stimulation favours the development of central memory T cells, whereas a longer duration of stimulation favours the differentiation of effector memory T cells. d) Divergent pathway- a naive T cell can give rise to daughter

cells that develop into either effector or memory T cells, bypassing an effector-cell stage. e) The asymmetric cell fate model states that as a naïve cell divides, one daughter cell becomes an effector cell and one becomes a memory cell.

It is also interesting to speculate as to the developmental relationship between TCM, TEM hi + and TRM subsets. When re-stimulated in vitro, CD62L CCR7 memory CD4 T cells became CD62Llo CCR7-, which insinuates that central memory cells can give rise to effector T cells or, perhaps, effector memory cells. Studies have shown that a memory cell can be ‘programmed’ by 3 divisions. These experiments need to be further evaluated using division dyes to determine if all 3 accepted memory subtypes are determined by this third division, or if this is when the memory cells begin to branch away from terminal differentiation and it is only at this point that the broad memory versus terminal differentiation is decided.

1.8 The role of CD4 T cell help during CD8 memory differentiation

It was first recognized in 1998 that CD4 T cell help is required for the differentiation of CD8 memory T cells [123]. Helpless CTL memory cells are defective in all cardinal features of memory cells in that they persist at a lower frequency, have limited recall capacity upon secondary infection, and have a diminished effector molecule profile. Much work has been done in an attempt to determine when CD4 T cell help is required, what the mechanisms of CD4 T cell help are, and what direct effect this help has on the CD8 T cell. Whilst the definition of ‘help’ has been fully eluded, the timing and molecular effects this help has on the CD8 T cell remain elusive.

1.8.1 Dependence on CD4 T cell help Both primary and secondary phases of CD8 T-cell immunity have been shown to depend on CD4 T-cell help, although during certain infections, such as Influenza, the primary phase is help independent [164]. This variation in the requirement for help has confounded efforts to fully elucidate the timing and requirement of CD4 T cell help. One explanation for the variability in the requirement for CD4 T cell help in the effector phase relates to the strength of associated inflammatory signals, with weak signals requiring help. This explanation fits with HSV infection, which is dependent on CD4 T cell help for an effector response, versus Influenza infection, which is independent of CD4 T cell help during the primary response, but require it for functional memory cells. Many infectious agents have evolved evasion strategies to downregulate inflammation and/or

costimulatory molecules, which have been linked to memory T-cells being CD4 T cell help dependent. For example, vaccinia virus encodes for soluble receptors, which decrease IFN-I production and thereby limit inflammation [46]. Similarly, HSV has been described to modulate the level of costimulatory molecules expressed on both T cells and DCs [48]. The Influenza virus is capable of upregulating CD40L on DCs [165], which may be why the primary response is CD4 T cell help independent [164]. Current data suggest that in infections/immunizations, which lead to robust CD4 T cell-independent Type-I IFN production, CD4 T-cell help is not required for primary CD8 T-cell responses, as long as APC maturation is provided by the infecting agent or an adjuvant. In the case of infections/immunizations, which are associated with a predominant IL-12 response, the CD4 T-cell dependence of primary CD8 T-cell responses may relate to whether sufficient IL-12 production by the priming APC requires engagement with CD4 T cells. Finally, in the case of “weak” immunogens, which do not by themselves promote the maturation of DCs, CD4 T cells are required not only to induce inflammatory third signals for CD8 T-cell activation, but also to induce DC maturation.

1.8.2 Delivery of CD4 T cell help Much work has been done to describe how CD4 T cells ‘help’ CD8 T cells to become fully activated. Early studies suggested a direct interaction is required between the three cell types (DCs, CD4s and CD8s) [39], or that the CD4 T cell modifies the APC [166]. However, more recent studies have doubted the likelihood of this three-way interaction. Eickhoff et al 2015, and Hor et al 2015 have used intravital imaging during the early stages of LCMV and HSV-1 infection to visualize the DC/CD8 interaction [167, 168]. Hor et al (2015) use a cutaneous HSV-1 infection to show the HSV-1 specific CD4 T cells expand earlier than CD8 T cells, introducing a new paradigm about the order of T cell activation [168]. It is also suggested that CD4s deliver help to DCs by ‘licensing’ them through CD40-CD40L interaction [29]. This CD4 T-cell engagement of DCs promotes the upregulation of certain co-stimulatory molecules (such as CD80 and CD86) on, as well as the release of pro-inflammatory cytokines such as IL-12 and IL-15 by the DCs. This DC activation then permits the effective induction of CD8 T cell responses [169].

1.8.3 Timing of CD4 T cell help

A topic of much contention that remains in the field is the exact timing of CD4 T cell help during the differentiation of memory CD8 T cells. Reports state that help is required during the initial priming of the CD8 T cell to the antigen [166, 170], during contraction

[171] and the maintenance of this population [172]. Shen et al examined this question by generating CD8 memory T cells in wildtype mice and CD4 deficient mice and then transferring them into naïve mice [173]. The helped memory cells responded normally when transferred into CD4-deficient hosts. In contrast, the memory CD8 cells generated in CD4-deficient mice mounted defective recall responses [173]. Thus, for LCMV, vaccinia virus [174] and Listeria monocytogenes infection [15] it appears that help is not required for the maintenance of CD8 T cell memory, as reviewed in [32]. Schoenberg et al (2003) also found that regardless of whether the primary response is CD4 T cell dependent or independent, that without CD4s during the initial priming, the memory cell and secondary response are defective [117]. They suggest that during this initial priming stage, T cell help is ‘programmed’ into the CD8 T cells [117]. Further support for the importance of CD4 help during the priming stage comes from experiments showing that the fate of naïve CD8 T cells is predictable as early as after their first division [27]. These cells are directed toward either memory or effector cell differentiation much earlier than previously understood, suggesting a heightened importance on their means of activation, supporting the idea that a cells fate is imprinted on it during activation.

There is also support that the possibility that CD4 T cell help is required for long term maintenance of memory CD8 T cells [175] and reactivation on antigen re-exposure [19,

38-42]. Bevan et al (2004) report that memory CD8 T cell numbers decrease gradually in the absence of CD4 T cells, despite the presence of similar numbers of memory cell precursors at the peak of the effector phase [172]. These studies showed through the transfer of both effector and memory CD8 T cells into wild-type or CD4–deficient mice that the presence of CD4 T cells was important only after, and not during, the early CD8

T cell programming phase. Without CD4 T cell help, memory CD8 T cells became functionally impaired and decreased in prevalence. They concluded that CD4 T cells are required only during the maintenance phase of long-lived memory CD8 T cells, because in all scenarios of their experiments the environment of the secondary host completely dictates number and functionality of the memory CD8 T cell pool. There are also reports of CD4 help being required throughout the CD8 T cell response [175]. Taken together, there is a clear discrepancy between models in regards to the exact timing of the delivery of help. In order to fully answer this question, further experiments must be done to monitor the interaction of CD4 and CD8 memory T cells.

1.8.4 CD4 T cell help and its role in exhaustion CD8 T cell exhaustion is a hallmark of chronic infection, characterized by progressive, hierarchical loss of effector function, sustained upregulation of inhibitory receptors,

altered expression of transcription factors, metabolic dysregulation and failure to transition to memory cell quiescence [176, 177]. One of the most studied inhibitory receptors, a marker of T cell exhaustion is PD-1. Ligation of PD-1 and programmed death-ligand 1 (PD-L1) results in reduced signal transduction after TCR triggering. T cell exhaustion is most well defined in the mouse model of LCMV, has also been observed in humans during infections such as HIV and hepatitis C virus (HCV), as well as in cancer (as reviewed in [177]).

By transiently depleting mice of CD4 T cells before infection with a chronic strain of LCMV, exhaustion is enhanced. CD4-deficient mice exhibit profound CD8 T cell exhaustion and higher viral loads compared to mice with a normal CD4 compartment after LCMV clone 13 infection. Without CD4 T cell help other chronic infections such as gammaherpesvirus [178], and the human infections HIV and hep C [179], also result in exhaustion of the CD8 T cells. Using a mouse model of chronic LCMV, Aubert et al examined whether the restoration of CD4 T cell help could overcome, and even reverse CD8 T cell exhaustion [180]. They found that by transferring antigen specific CD4 T cells, they could enhance CD8 T cell proliferation and reduce viral burden, concluding that CD4 T cells can rescue exhausted CD8 T cells during chronic viral infection [180]. Thus, CD4 T cell help appears to play a vital role in not only circumventing CD8 T cell exhaustion, but also may aid in its rescue. Table 1.1: A comparison of exhausted and unhelped CD8 T cells Feature Functional memory cell Exhausted T cell Unhelped T cell Proliferative potential + +/- - Cytokine production + +/- +/- Memory markers + +/- + (eg CD44, CD62, CD127 or CXCR3) Inhibitory receptors (e.g. PD1, - + - LAG3, CD160 or 2B4) IL-7 and/or IL-15 driven self- + - - renewal Antigen dependency - + +?

1.8.5 Memory cells are a heterogeneous population: heterogeneous requirement for CD4? The memory CD8 T cell population is heterogeneous, consisting of numerous subsets (as discussed in section 1.7.1). Laidlaw et al 2014 have recently highlighted the importance of CD4 T cell in the production of TRM cells after influenza virus infection of the mouse lung. By transiently depleting the CD4 T cells during memory production it was shown that CD8 T cells insufficiently up regulate CD103, CXCR3 or CD69 in the absence of

CD4 T cells. These defects attributed to the loss of the CD8 T cells ability to localize to the lung to set up residency, due to loss of CD4 T cell production of IFN-γ [171]. CD8 T lymphocyte mobilization to virus-infected tissue has been shown to require CD4 T-cell help in other models and tissues [181], including the vaginal mucosa by a mechanism involving IFN-g-mediated induction of CXCR3 ligands [181]. Studies have not yet looked into the differential need for CD4 T cell help TCM or TEM, however it has been reported that TCM are the most defective in the absence of CD4 T cell help [182]. A brief summary table of the development in determining the role of CD4 T cell help is included below (Table 1.2). Table 1.2: A summary of the major developments in determining the role of CD4 T cell help. Year Primary findings Infection model Reference CD4 T cells are required for a robust primary CD8 T cell response: 1997 • CD4 help is required for CTL induction OVA-specific CTL induced by [166] by cross-priming with cell-associated intravenous injection of ovalbumin irradiated spleen cells by • CD4 and CD8 need to recognize the same osmotic shock. APC. • CD4 T cell actively modifies the APC 1998 • Cross priming: CD40 on the APCs can OVA-specific CTL induced by [29] replace the requirement for TH cells intravenous injection of irradiated spleen cells by osmotic shock. 1998 • CD40 requirement for delivering CD4 Immunization with Ad5E1– [169] help BALB/c MECs. 1998 • The CD4, CD8 and APC don’t meet Studied responses to the male [39] simultaneously, the helper cell first antigen H–Y conditions the dendritic cell (2 step B6.bm12 mice (with mutated process) MHC II molecules.

2007 • CTLs depended on the induction of a Synthetic CD8 T-cell epitope [183] strong IFN-alpha/beta response. administered with TLR3-L and • Unhelped CTLs induced by TLR-Ls TLR9-L. differentiated into fully functional memory CTLs 2008 • IL-2 from CD4 T cells is required for Primed with peptide-pulsed [184] CD8 T cell responses to noninflammatory DCs. antigens 2008 • IL-15 is a mediator of CD4 help IL-15 codelivered with [185] • CD4 help stops TRAIL-mediated vaccines. apoptosis 2012 • CD40L and IL-2 signalling a form of Adenoviral (AdV) vectors. [186] help from CD4 T cells

CD4 T cells are critical for developing a memory CD8 T cell population 2002 • Help is delivered directly through CD40 Transgenic mice specific for the [187] • CD40 contact is essential for CD8 T cell male histocompatibility-Y (HY) memory generation. antigen were stimulated in an in vivo setting with male cells. 2003 • CD4 T cells are required for secondary Immunization with syngeneic [117] expansion in CD8+ T lymphocytes. human adenovirus type 5 E1- • T-cell help is 'programmed' into CD8+ T transformed Tap-/- mouse cells during priming.

embryo cells (Tap-/- 5E1 MECs). 2007 • Early Ag-specific CD4 T cell help Vaccination model, OT-I/II [188] required to produce functional memory SIINFEKL. CD8 T cells. • CCL3 and CCL4 chemokines is required for the efficient development of CD8 T cell memory 2008 • TRAIL deficiency does not rescue Listeria monocytogenes. [189] unhelpeed CD8 T Cell Memory. 2011 • CD4 T cells influence the biodistribution Bioluminescent imaging of [190] of CD8 T cells in the memory phase Luciferase-transgenic CD8 T • Proposed that CD4+ T cell help cells. programs the homing potential of CD8 T cells.

2012 • NKG2D signalling on CD8+ T cells Influenza and HIV, human. [191] rescues unhelped CD8+ T cell memory recall responses. 2012 • CD40L and IL-2 signalling plays a AdV vectors. [186] critical role in multiple phases of CD8 T cell development *Last example appears in both, as it shows CD4 help is required during both phases.

1.9 T cell energy production

Intracellular metabolism is central to cell activity and function, and the importance of the link between immunology and metabolism, coined immunometabolism, is currently being investigated by numerous groups (such as [192-195] and reviewed in [196]). As more groups explore the metabolism of T cells, it’s role in activation, quiescence and memory formation and even the regulation of these cell stages is being brought to the forefront. Recent studies show that the metabolism and therefore function, of T cells are influenced significantly by local environmental conditions and the availability of key metabolites. Further, it has also been reported that defined metabolic programs associate with T cell differentiation, expression of lineage-specific genes, and phenotype stabilization.

1.9.1 Modes of energy production Naive CD8 T cells primarily oxidize glucose-derived pyruvate in their mitochondria via oxidative phosphorylation (OXPHOS), but can also use fatty acid oxidation (FAO) to generate ATP [197-199]. OXPHOS is a metabolic process that encompasses two sets of reactions. Briefly, the first reaction involves the conversion of intermediate molecules (pyruvate and fatty acids) to acetyl coenzyme A (acetyl- CoA) and the degradation of acetyl-CoA to carbon dioxide in the tricarboxylic-acid cycle, yielding free electrons that are carried by NADH and FADH2. The second reaction involves the transfer of electrons

from NADH and FADH2 to the electron-transport chain, resulting in the movement of protons out of the mitochondrial matrix. The resulting electrochemical potential is used by the F1F0 ATP synthase to synthesize ATP. Once the cell is activated, metabolic remodelling of the naive T cell results in a program of anabolic growth and biomass accumulation; specifically, the engagement of aerobic glycolysis, a process in which glucose is converted into lactate even though sufficient oxygen is present to support glucose catabolism via the tricarboxylic acid (TCA) cycle and OXPHOS [200, 201]. Importantly, aerobic glycolysis is less efficient than OXPHOS at yielding an abundance of ATP per molecule of glucose, aerobic glycolysis can generate metabolic intermediates important for cell growth and proliferation, and provides a way to maintain redox balance (NAD+/NADH) in the cell [200-202].

1.9.2 Metabolism during the memory phase Memory CD8 T cells utilize mitochondrial respiration for their development, long-term persistence, and ability to robustly respond to antigen stimulation [195, 198]. Rather than using glucose to feed mitochondrial respiration through pyruvate, memory CD8 T cells perform b-oxidation of fatty acids and utilize lipolysis to generate their own fuel [203]. Recent work from numerous laboratories has highlighted that changes in metabolism are vital in driving memory T cell [195, 204].

Unlike effector CD8 T cells, memory CD8 T cells have a substantial mitochondrial spare respiratory capacity (SRC) [205]. SRC is defined as the extra capacity available in cells to produce energy in response to increased stress and is associated with cellular survival [205]. This parameter is suggested to be vital for the longevity of memory T cells, especially in times of stress or nutrient restriction, conditions that may present themselves when infection is resolved and growth factor signals are scarce. A recent study found that IL-15 regulates SRC and oxidative metabolism by promoting mitochondrial biogenesis and expression of carnitine palmitoyltransferase (CPT1a), a metabolic enzyme that controls the rate-limiting step to mitochondrial fatty acid oxidation (FAO) [162]. A follow-up study revealed that by increasing expression of CPT1a, FAO in memory T cells was enhanced and resulted in increased CD8 memory T cell numbers [162]. Formation of memory CD8 T cells in vivo has also been shown to be enhanced by activating AMP- activated protein kinase (AMPK) signalling and lipid metabolism with metformin

treatment [195], inhibiting mammalian target-of-rapamycin (mTOR) activity with rapamycin [206] or blocking glycolysis [207].

Importantly, effector cells do increase their SRC after activation, but have significantly less SRC than memory T cells. This may be explained by the high amount T cell proliferation outpacing their own mitochondrial biogenesis, resulting in less mitochondrial mass. Alternatively, mitochondria may segregate unequally as T cells asymmetrically divide resulting in terminally differentiated cells having fewer mitochondria [205]. Naïve T cells also have less mitochondrial mass than memory T cells, which correlates with their decreased SRC [162]. This could be explained by qualitative differences between the mitochondria in naïve, effector and memory T cells, such as differences in expression of ETC complexes on a per mitochondrion basis [208]. This suggests that cell-intrinsic energy-production differences underlie the disparity in functionality between naïve, effector and memory T cells, and may suggest the rapid recall of memory T cells is due to the enhanced SRC.

Little research has been done to elucidate the metabolic profiles of the heterogeneous memory population. TCM, TEM and TRM each have different microenvironments, and energy requirements (Fig. 1.2). Further work should be done in order to determine any such differences, as this may provide information of their differentiation, or ways to target certain memory subtypes through vaccination design. Research on heterologous prime- boost vaccination protocols has shown that after secondary or tertiary immunization memory T cell differentiation is accelerated and enhanced, including the acquisition of substantially greater mitochondrial mass and SRC [209]. Studies such as these do show the importance of metabolism on memory cell differentiation.

Figure 1.2 Metabolic regulation of effector and memory T cell differentiation. Resting naive and memory T cells primarily generate ATP through the catabolic processes fatty acid (fa) oxidation and mitochondrial respiration. Following activation, the T cells undergo a metabolic switch to lipid synthesis (an anabolic process) and aerobic glycolysis to meet the bioenergetic and biosynthetic demands for rapid clonal expansion and the production of effector molecules.

1.10 Transcriptional regulation of CD8 memory T cells

The underlying role of TF’s on the regulation of memory cell differentiation has been well studied. Several transcription factors that regulate effector and memory CD8 T cell development have been identified. Interestingly, several of them function in pairs that form counter-regulatory axes to simultaneously produce effector and memory cells.

1.10.1 T-bet and eomesodermin

T-bet and eomesodermin (eomes) are two T-box transcription factors with vital roles during the formation and function of effector and memory CD8 T cells. Early during activation, these TF’s cooperate in a semi-redundant fashion to form functional CD8 effector T cells by inducing the expression of IFN-g, GZMB, perforin, CXCR3 and CXCR4 [125, 161, 210-212]. It has been suggested that Eomes promotes memory development while T-bet pushes the CD8 T cell towards terminal effector differentiation [210]. IL-12 strongly induces T-bet expression and represses Eomes [213]. Thus, strong inflammatory signals promote terminal effector differentiation through T-bet [125], while

Eomes expression increases as the CD8 T cell response resolves [213], presumably because CD8 T cells with the capacity to form memory express higher levels of Eomes and survive. Further, T-bet has been shown to decrease expression of the IL-7R (CD127) [125], a marker of memory potential, while T-bet and Eomes co-operate to induce expression of the IL-15R on CD8 T cells [210], an important factor for memory survival.

1.10.2 Blimp-1 and Bcl-6 Bcl-6 and Blimp-1 are key transcriptional regulators of effector and memory differentiation of both B and T lymphocytes. Bcl-6 and Blimp-1 are antagonistic transcription factors, which can block each other’s function and can function as a self- reinforcing genetic switch for cell-fate decisions. Blimp-1 is expressed in effector and memory CD8 T cells [214, 215] and that the absence of Blimp-1 can result in excessive proliferation and increased numbers of memory CD8 T cells [215]. Blimp-1-deficient CD8 T cells also have lower expression of effector molecules important for cytotoxicity, such as granzyme B [216, 217], and they express more Bcl-6 than do wild-type CD8 T cells. Constitutive Bcl-6 expression suppresses granzyme B expression [218] and overexpressing Bcl-6 generates a larger memory cell population [219]. Conversely, Bcl6−/− CD8 T cells express more granzyme B [218], proliferate poorly and are less able to acquire a memory-like phenotype [74]. Thus, Bcl-6 and Blimp-1 for a regulatory axis for controlling effector and memory lymphocyte differentiation and function.

1.10.3 ID2 and ID3 Inhibitor of DNA binding 2 (ID2) and ID3 are also transcription factors known to have important roles in effector and memory CD8 T cell development. ID2 supports the survival of CD8 T cells during the naive to effector cell transition, whereas ID3 supports their survival during the effector to memory cell transition [150, 220]. ID2 is important

for supporting the clonal expansion and the formation of terminal KLRG1hiIL- 7Rαlow effector T cells [220]. ID3 is required for the generation of long-lived memory CD8 T cells, and its overexpression sustains the survival of effector CD8 T cells that normally die during the contraction phase of the response [150]. Despite their common ability to bind E proteins, ID2 and ID3 function in a non-redundant manner to modulate the differentiation and survival of effector CD8 T cells.

1.10.4 STAT family JAK–STAT signalling pathways are important in both effector T cell differentiation and in the survival of memory CD8 T cells. There are seven members of the STAT family in mammals (STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B and STAT6), which are mainly known for their function as transcription factors [221]. STAT1 levels decline in effector CD8 T cells as they proliferate during viral infection [222]. IFN-Is activate both STAT1 and STAT4 in effector CD8 T cells [223], suggesting that IFN-I-mediated signalling qualitatively changes from a STAT1-dominated to a STAT4-dominated response over time. Increased levels of STAT4 activity enhances T-bet expression and the terminal differentiation of KLRG1hiIL-7Rαlow T cells in L. monocytogenes infection [125]. Dimerization and tetramerization of STAT5 A and STAT5B is crucial for the normal gene expression and function both of virus-specific CD8 T cells during LCMV infection and of regulatory T cells [224].

Recent work found an important role for STAT3 and its target gene suppressor of cytokine signalling 3 (Socs3) in memory CD8 T cell development. In the absence of STAT3, LCMV-specific CD8 T cells are unable to mature into a protective population of self-renewing memory cells, instead maintaining a highly activated terminal effector cell phenotype [225]. STAT3 is required to sustain the expression of SOCS3, BCL-6, EOMES, IL-7Rα and CD62L during the effector to memory cell transition, and the decreased amount of SOCS3 in STAT3-deficient CD8 T cells caused them to become hyper responsive to IL-12 [226]. It is unclear whether the expression or function of the different STAT factors in T cells is as diametrically opposed as the other transcription factor pairs that we have described (that is, BLIMP1-BCL-6, T-bet-EOMES and ID2- ID3), but evidence suggests that such a reciprocal interaction might occur to a certain degree. Collectively, the expression of these competing sets of transcription factors regulate the transcriptional programs which control effector and memory CD8 T cell differentiation. Such a gradient, or pair wise control, provides a flexible way to manage the size and

quality of the CD8 T cell population during infection, particularly when one considers the unpredictable properties of an infection, which can vary in intensity, tropism and duration. This method of control also achieves the primary goal of a T cell response: to simultaneously generate an expendable pool of effector cells to combat the present infection and a pool of long-lived progenitors to combat future infections. As the discovery of signalling pathways, transcription factors and molecular control mechanisms involved in effector and memory CD8 T cell development continues, it will be essential to identify how these factors cooperate or impede each other’s function, the target genes that they bind to and the epigenetic profiles that they create.

1.11 Thesis aims

This PhD thesis attempts to understand the impact of external cues on CD8 T cell differentiation to effector and memory cells. The specific aims are:

• To determine the role and timing of CD4 T cell help during memory CD8 T cell differentiation, and to investigate differential gene expression between helped and unhelped memory cells.

• To compare the transcriptional profiles of each stage of CD8 T cell differentiation in an attempt to identify genes that are stage-specific and therefore vital for memory cell production.

• To investigate the direct effect role signal three has on CD8 T cell differentiation by determining the effect of different signal three stimuli on chromatin landscape.

Chapter 2: Materials & Methods

2.1 Materials

2.1.1 Mice C57BL/6 (B6), B6.GK1.5 (GK1.5) and Ly5.1xOT-I (OT-I/Ly5.1) mice were bred and housed in The Department of Microbiology and Immunology animal facility, The Peter Doherty Institute for Infection and Immunity at the University of Melbourne, under specific-pathogen free conditions. All experiments were conducted within guidelines specified by the animal ethics committee.

Table 2.1: Mouse strains

Strain Description B6 H-2b restricted, Ly5.2+ OT-I/Ly5.1 H-2Kb restricted Ly5.1+TCR transgenic (Vα2+Vβ5.2+) mice, specific for ovalbumin peptide (OVA257-264, SIINFEKL). Obtained from Prof. Francis Carbone (The Department of Microbiology and Immunology, The University of Melbourne) [227]. GK1.5 H-2b restricted, Ly5.2+. Produce high levels of serum GK2c (GK1.5 CD4 depleting Ab) from pancreas, resulting in loss of all peripheral CD4 T cells.

2.1.2 Antibodies Table 2.2: Flow cytometry antibodies

Antibody & Conjugate(s)1 Clone Company Anti-CD8 FITC, PE, BD Biosciences PharMingen (San Diego, 53-6.7 PerCPCy5.5, APC CA, USA) Anti-Va2 PB, PE B20.1 BD Biosciences PharMingen

Anti-Ly5.1+ FITC, PE A20 BD Biosciences PharMingen

Anti-CD44 FITC, PE IM7 BD Biosciences PharMingen

Anti-CD62L PE, APC MEL-14 BD Biosciences PharMingen XMG1.2 Anti- IFN-g PE, APC, BD Biosciences PharMingen FITC MP6-XT22 Anti- TNF-α PE, APC BD Biosciences PharMingen

JES6-5H4 Anti-IL-2 PE, APC BD Biosciences PharMingen

Santa Cruz Biotechnology (Santa Cruz, CA, Anti-Granzyme A FITC 3G8.5 USA) Anti-IgG2b FITC - Santa Cruz Biotechnology

Anti-Granzyme B APC GB12 Invitrogen (Camarillo, CA, USA)

Anti-IgG1 APC - Invitrogen

1FITC- Fluorescein isothiocyanate, PE- Phycoerythrin, APC- Allophycocyanin, PerCP- Peridinin-chlorophyll-protein complex, Cy- Cyanin, PB- Pacific Blue, PerCPCy5.5- Peridinin-chlorophyll-protein Complex: Cy5.5 Conjugate

Table 2.3: Antibodies for in vitro stimulation of naïve CD8 T cells Antibody1 Clone Manufacturer Anti-CD28 37.51 Biolegend Anti-IL-2 S4B6 Hybridoma stocks Wehi

1 All anti-mouse and non-conjugated

2.1.3 Primers Table 2.4: TaqMan® MGB primer/probes for real-time PCR (all FAM labelled) Primer Assay ID Species Mitochondrial Mm00777741_sH Mus musculus ribosomal protein L32 (L32) GzmA Mm00439191_m1 Mus musculus GzmB Mm00442834_m1 Mus musculus GzmK Mm00492530_m1 Mus musculus Prf Mm00812512_m1 Mus musculus GATA3 Mm00484683_m1 Mus musculus STAT6 Mm01160477_m1 Mus musculus Dmrta1 Mm00558696_m1 Mus musculus Dmrta1 Hs00403012_m1 Human IFN-g Mm01168134_m1 Mus musculus IL-2 Mm00434256_m1 Mus musculus TNF-α Mm00443258_m1 Mus musculus Zbtb32 Mm00491292_g1 Mus musculus Vcpip1 Mm00557928_m1 Mus musculus Poldip3 Mm00724315_m1 Mus musculus Eif2ak4 Mm00469222_m1 Mus musculus

All primers obtained from Applied Biosystems, Foster City, CA, USA.

Table 2.5: Primer sets for SYBR green real-time PCR analysis (Human) Primer Sequence 5’ -> 3’1 Dmrta1 F:CCAACTTTCGAGGTTTTCCA (NM_175647.3) R: CTGTCTTCTCCTCCCGATTG Zbtb32 F: AGAATTCATGCCCCAGACCCCCACAAG R: GTTCGACAATTGTAGCTGTCAGGTGGCAGCAGAGGAGG 1 F = forward primer, R = reverse primer All primers obtained from Invitrogen.

Table 2.6: Cytokines for signal three stimulation of naïve CD8 T cells Cytokine Species Manufacturer TYPE I IFNa Universal PBL assay science IL-2 Human Sigma Aldrich IL-4 Human Miltenyi Biotec IL-6 Human Miltenyi Biotec IL-7 Human Miltenyi Biotec IL-12 Human Miltenyi Biotec IL-21 Human Miltenyi Biotec

2.1.4 Buffers and Media Ammonium tris chloride (ATC) lysis buffer: Baxter water (Baxter Healthcare, NSW, Australia)/ 0.14 M NH4Cl/ 17 mM ethylenediaminetetraacetic acid (EDTA, Sigma, St.Louis, MO, USA), pH 7.3.

Complete roswell park memorial institute media 1640 (c-RPMI): RPMI (Gibco)/ 2 mM L-glutamine/ 1 mM sodium pyruvate/ 100 µM non-essential amino acids/ 5mM HEPES buffer/ 55 µM 2-mercaptoethanol/ 100 U/mL penicillin/100 µg/mL streptomycin (all from Gibco)/ 10% heat inactivated foetal calf serum (FCS, Bovogen Biologicals, Essendon, VIC, Australia). Glucose free cRPMI: RPMI 1640 Medium, no glucose (Gibco). Catalog number: 11879020

Fluorescence activated cell sorter (FACS) buffer: Phosphate Buffered Saline (PBS, Media Preparation Unit, Department of Immunology & Immunology, The University of Melbourne, Parkville, Australia)/ 1% Bovine Serum Albumin (BSA, Gibco, Carlsbad, CA, USA)/ 0.02% Sodium Azide (Sigma).

Hanks buffered salt solution (HBSS): Supplied by Media Preparation Unit, Department of Immunology & Immunology, The University of Melbourne.

Lysis Buffer: 10 mM Tris·Cl, pH 7.4, 10 mM NaCl 3 mM MgCl2 0.1% (v/v) Igepal CA-630.

Sort buffer: PBS/ 0.1%BSA

MACs buffer: PBS/ 0.5% BSA/ 2 mM EDTA.

XF medium: Media formulated for Seahorse assays, Agilent Technologies, pH 7.4

2.1.5 Seahorse Instrument (Seahorse Bioscience) Drugs: 1.4.1.1 1 mM oligomycin, Sigma 1.4.1.2 1.5 mM Fluoro-carbonyl cynade phenylhydrazon (FCCP), Sigma 1.4.1.3 100 μM rotenone, Sigma 1.4.1.4 1 mM antimycin A, Sigma

XFe96 FluxPak including:

1.1.1 Sensor cartridges 1.1.2 Cell culture microplates 1.1.3 Calibrant: Poly-D-lysine (PDL; Sigma; 50 μg/ml)

XFe96 extracellular flux analyzer (Seahorse Bioscience)

2.2 Methods

2.2.1 Infection of mice

For primary infections, mice were anaesthetized with Isoflurane (2-chloro-2- (difluoromethoxy)-1, 1, 1-trifluoro-ethane; Veterinary Companies of Australia Pty Ltd, Kings Park, NSW, Aust.) and infected intranasally (i.n.) with 104 plaque forming units (PFU) of the X31-OVA virus in 30µl phosphate buffered saline (PBS; Media Preparation Unit, University of Melbourne, Parkville, Australia). For secondary infections, mice were primed intraperitoneally (i.p.) with 600 pfu PR8-OVA virus in 30uL of PBS at least 30 days prior to i.n challenge as described above. Body weight of mice was monitored daily.

2.2.2 Tissue sampling, processing and counting

Mice were culled by CO2 asphyxiation at a slow fill rate of 20% volume/min at the experiment endpoint and tissues were collected.

2.2.2.1 Lymphocyte preparation from spleen Single cell suspensions were prepared from spleens via manual disruption with frosted glass slides, and then passed through a 70 µm sieve (BD Falcon). For cell sorting and phenotyping of memory cells, splenocytes were enriched for T cells via negative enrichment by B cell panning. A 150 cm2 petri dish (Falcom, BD Biosciences) was coated either overnight with Affinipure anti-IgG + IgM antibodies at 4 oC (Jackson o ImmunoResearch Laboratories, West Grove,PA,USA) or for 1 hr at 37 C with 5% CO2. Red blood cells (RBCs) were lysed with 4 mL of ammonium tris chloride (ATC) buffer in 1 ml HBSS for 5 mins at room temperature (RT). Following washing and centrifugation (1600 rpm, 6 mins, 4°C), single celled suspensions were resuspended in 15-25 mL of o HBSS and plated on coated petri dish for 1 hr at 37 C with 5% CO2. Enriched cells were then collected, centrifuged and resuspended in 3-5 mL of HBSS, c-RPMI, sort or sorter buffer for further analysis. 2.2.2.2 Lymphocyte preparation from lymph nodes (LNs) Cervical, mediastinal, axillary, brachial, mesenteric, inguinal and lumbar LNs were collected and pooled. Pooled LNs were disrupted through a 70 µm sieve with a 1 mL syringe plunger. Cells were pelleted (1600 rpm, 6 mins, 4oC) and resuspended in sort buffer or c-RPMI. For analysis of the lung draining, mediastinal lymph node (mLN), single cell suspensions were prepared via manual disruption with frosted glass slides, collected and resuspended in downstream appropriate buffer.

2.2.2.3 Lymphocyte preparation from Bronchoalveolar Lavage (BAL) Cells from the lung airways were obtained via bronchoalveolar lavage (BAL). A small incision was made in the trachea allowing insertion of an 18-gauge catheter, which adjoined a 1ml syringe. The lungs were washed with three washes of 1ml HBSS up to a

total of 3ml. BAL fluid was incubated in six-well tissue culture plates (Techno Plastic

Products, Trasadingen, Switzerland) for depletion of macrophages at 37°C, 5% CO2 for 1 hour. Non-adherent cells were collected, and plates were rinsed with additional media. Cells were centrifuged at 515 x g for 6 mins at 4°C and resuspended in media appropriate to downstream experiments.

2.2.2.4 Lymphocyte preparation from Lungs For phenotypic and functional analysis of cells within the lung tissue, lungs were collected, minced with scissors and digested with 2mg/mL collagenase A (Roche) at 37°C for 30 min. After digestion, samples were pushed through a 70um sieve and the collagenase inactivated with HBSS. Samples were pelleted, and erythrocyte’s lysed with ATC buffer for 5 min at room temperature after final resuspension in 1-5mL of FACS buffer or cRPMI for further analysis.

2.2.2.5 Lymphocyte preparation from Bone Marrow (BM) Bone marrow cells were harvested from the femur and tibia of mice. The kin of the hind leg is cut, and skin is pulled backward to expose the muscles. The leg is then cut above the pelvic joint and the lower portion of leg is collected after discarding the foot below the ankle. The surrounding muscles and excess tissue are removed from the bones, and both ends of the bones are cut to expose the interior marrow shaft. The contents of the marrow are then flushed using a 29G needle, into a 70uM filter over a 50mL tube. Cells are then pushed through the filter, RBC lysed as described and resuspended in appropriate media for downstream applications.

2.2.2.6 Cell Counts Viable cell counts were obtained by Trypan blue exclusion, using 0.2 % trypan blue (Gibco) using a haemocytometer (Lomb scientific, Taren Point, NSW, Aust) as:

�� = �� × �� × �� × 10

For later experiments, cells were counted using a Beckman coulter counter.

2.2.3 Adoptive transfer

LNs were harvested from naïve OT-I mice, pooled and single cell suspensions were prepared. A small sample of cells was stained with anti-CD8α, Vα2, CD45.1 and CD44.

The percentage of CD44low, Vα2+, Ly5.1+. CD8 was determined via FACS analysis. Cells were counted and resuspended in PBS at a concentration of 5x104 CD8 OT-I cells/mL. Recipient mice were intravenously (IV) injected with 1x104 cells/200μL CD8 OT-I cells via the tail vein.

2.2.4 OT-I T cell enrichment

Spleens from 20-40 B6 or GK1.5 mice that had previously received 1x104 OT-I cells IV and infected with x31-OVA virus were processed as described in Section 2.2.2, and splenocytes resuspended in 1mL sorter buffer per spleen. Cells were stained with anti- Ly5.1-PE for 30 mins on ice and were washed twice with sorter buffer. Cells were resuspended in 500 μL of sorter buffer and stained with 200 μL anti-PE conjugated magnetic microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) for 30 mins at 4°C and washed twice with sorter buffer. Cells were resuspended in 3 mL of sorter buffer and then passed over magnetized LS columns (Miltenyi Biotec). The initial flow-through was passed over the column again, followed by 3 × 3 mL washes with sorter buffer. The column was then removed from the magnet and bound Ly5.1+ OT-I cells were eluted by pushing 5 mL of sorter buffer through column.

2.2.5 CD8 T cell purification by cell sorting

Spleen and/or LN single cell suspensions from B6, OT-I/Ly5.1 or GK1.5 mice were surface stained with anti-CD8α-PE or –PerCPCy5.5 and anti-CD44-FITC to isolate naïve cells, or anti-CD8α-APC and anti-Ly5.1-FITC to isolate transferred OT-I cells for 30 mins at 4°C. All samples were passed through a 40um sieve prior to cell sorting. Naïve CD8CD44lo cells and transferred OT-1 CD8Ly5.1+ cells were sort purified to a minimum purity of 95% using a FACSAria cell sorter (BD Biosciences).

2.2.6 Violet tracker labelling

Sort purified naïve (CD44lo) CD8 B6 or GK1.5 T cells were resuspended at a concentration of 1x107 cells/mL in warm PBS and labelled with 10μM celltrace violet (CTV) for 20 mins at 37°C. Cells were washed with five times the volume of HBSS and resuspended at 1x106 cells/mL in cRPMI for in vitro cell division analysis, or IV injected in PBS for in vivo cell tracking.

2.2.7 In vitro T cell activation

CTV labeled, naïve OT-I CD8 T cells were plated in 96-welled round bottom plates at 10,000 cells/well and stimulated with the OVA-Specific peptide SIINFEKL (OVA) and cultured with or without anti-CD28, anti-IL-2 (S4B6 at 25 µg/mL) and the indicated cytokine/s alone for the indicated time points (0-110hrs). At these time points, cells were harvested and run on FACs immediately after addition of Propidium iodide (PI) (0.2 µM, Sigma (concentration 1/1000) (eBioscience) which was used for dead cell exclusion. A known number of counting beads (SPHERO Rainbow calibration beads BD Biosciences) was also added, and the ratio of beads to live cells was used to calculate the absolute cell number in each sample.

2.2.8 Determination of lung viral titres

For viral titre analysis, lungs were harvested on days 3, 5, 7 and 10 post primary infection, or days 28 (resting memory) 3, 6 and 8 days post-secondary infection. They were washed in HBSS and homogenised in 2mL HBSS, using a Polytron system PT1200 CL230V homogeniser. The homogeniser probe was rinsed with HBSS between samples, and additionally with 100% ethanol between groups. Homogenates were pelleted at 1600 g for 6mins and the supernatant stored at -80°C. Infectious virus titre (pfu/lung) was determined by plaque assay.

2.2.9 Plaque Assay

Lung homogenates or viral stocks were titred by plaque formation on MDCK monolayers. MDCK cells were pooled from 12 flasks and passed through a 23-gauge needle to disrupt clumps. Cells were then seeded at 9x105cells/well in 6 well plate and incubated overnight -2 at 37°C 5% CO2 to achieve a confluent monolayer. Lung homogenates were diluted 10 ,

10-3 and 10-4 (d3 primary and secondary infection), 10-1, 10-2 and 10-3 (d6 primary) or used neat (d10 primary, d8 secondary), while viral stocks were diluted 10-4 to 10-6.5 in half-log dilutions prior to addition to monolayers. Monolayers were washed with 3mL RPMI-anti and 150μL of sample was added. Infection proceeded for 45min at 37oC 5%CO2 before addition of TW and 0.9% type I agarose (Sigma) in L-15 media. Cells were incubated at 37C 5%CO2 for 3 days and visible plaques counted. Assays were performed in duplicate, and pfu determined per lung.

2.2.10 Purification of cells by ficoll gradient

Lymphocytes from culture were resuspended in 3 mL HBSS, gently under laid with 3 mL lymphocyte separation medium (MP Biomedicals, Solon, OH, USA) and centrifugated at 2800 rpm for 20 mins at RT with no brake. The interphase, containing viable lymphocytes, was collected and washed with HBSS. Purified cells were resuspended in cRPMI and counted prior to further downstream analysis.

2.2.11 Antibody staining

Cells (0.2-2x106) were stained with antibodies (at approximately 0.5 – 1 ug/sample) as described in Table 2.2 in 50-100 μL FACS buffer for 30 mins at 4°C. For phenotypic analyses, B6 cells were stained with anti-CD8α-PerCPCy5.5, anti-CD44-FITC, and anti- CD62L-APC, and adoptively transferred OT-I cells were stained with anti-CD8α- PerCPCy5.5 and anti-Ly5.1-PE. Cells were analyzed using flow cytometry (refer to Section 2.2.14).

2.2.12 Intracellular staining

Naïve and in vitro activated B6, GK1.5 or OT-I cells were surface stained with anti- CD8α-PerCPCy5.5 and anti-CD44-PE for B6 and GK1.5 cells, or anti-CD8α- PerCPCy5.5 and anti-LY5.1-PE for adoptively transferred OT-I cells, for 30 mins at 4°C. Cells were washed twice in FACS buffer and fixed and permeabilised using the Cytofix/Cytoperm kit (BD Biosciences), according to manufacturer’s instructions, and intracellular gzm, IL-2, IFN-g and TNF-a was detected using flow cytometry (refer to Section 2.2.14).

2.2.13 Cytometric Bead Array

BAL was collected from Influenza infected mice at the time points indicated (Section 2.2.2) except that only 1 mL was collected rather than 3. These cells were then pelleted and the 1mL was frozen at -80’C. On the day of CBA testing, supernatants were thawed and tested for the presence of f IL-2, IL-4, IL-5, IL-6, IL-10, IL-17A, TNF-α, and IFN-γ as previously described (https://www.bdbiosciences.com/). Data was detected using flow cytometry and analysed using FCAP Array software v3.0.

2.2.14 Flow cytometry

Samples in this study were acquired on FACSCantoII (BD Biosciences) and LSR Fortessa (BD Biosciences). All data analysis utilised FlowJo software (version 8.8.6, Treestar). Mean fluorescence intensity (MFI) was determined as the geometric mean of staining within the positive population. Cell sorting was conducted on an Aria (BD Biosciences).

2.2.15 Measuring Bioenergetics in T Cells by Seahorse Bioscience

Preparation before cell harvesting The XF cell culture microplate plate was coated with 20 μl poly-D-lysine for 1-2 hr at room temperature (RT). Wells were washed, air-dried, and kept at 4°C overnight.

Cell preparation and plating

Naïve OT-I CD8 T cells were sort purified and stimulated with IL-2, IL-12, IFN-I or in combination for 48 hrs with OVA peptide and either rhIL-2 (31.6U/mL), IL-12 (10ng/mL), IFN-I (600U/mL), or both IL-12 and IFN-I. Naïve CD8 T cells were harvested on the day as a control. Cells were then washed in 5 ml XF medium, and plate cells in 40 μl XF medium at 200,000 cells/well, each sample in quadruplicate. 4 wells were used for background correction (wells A1, A12, H1, and H12); so only XF medium in these wells (no cells). Plates were centrifuged for 5 min at 400 × g, at room temperature, to allow the cells to collect into a monolayer at the bottom of the plate. 140 μl XF medium was added into each well making sure not to disrupt the cell monolayer. Cells were observed under an inverted microscope to determine that a monolayer of cells is present in all groups, and that no cells were dislodged. The plate was incubated for 30 to 60 min at 37°C in a non-CO2 incubator. Preparation for drug loading and program set-up

10x stocks of drugs in XF medium are prepared, for final concentrations: 1 μM oligomycin, 1.5 μM FCCP, 100 nM rotenone, and 1 μM antimycin A. Pipet drugs directly into the drug delivery ports on top of the sensor cartridge as follows:

Port A: 20 μl oligomycin Port B: 22 μl FCCP Port C: 24 μl rotenone + antimycin A.

Incubate cartridge at 37°C in a non-CO2 incubator while setting up the program. Standard Seahorse EFA program for running mitochondrial stress test is: a. Calibrate

b. Equilibrate c. Base line readings (Loop 3 times): d. Mix – 3 min Wait – 2 min Measure – 3 min End loop e. Inject port A (Loop 3 times): f. Mix – 3 min Wait – 2 min Measure – 3 min End loop g. Inject Port B (Loop 3 times): h. Mix – 3 min Wait – 2 min Measure – 3 min i. End loop Inject Port C (Loop 3 times): j. Mix – 3 min Wait – 2 min Measure – 3 min End loop k. End program.

For comparison of helped and unhelped memory, memory OT-I CD8 T cells were sorted from d28 mice, which had been infected with X31-OVA. These cells were plated up at 200,000 cells per well. For chapter 3/signal 3 analysis, CD8 OT-I T cells were activated with OVA peptide in the presence of IL-2 for 3 days and subsequently cultured in either IL-2, IL-12, IFN-I, no additional stimulus or a combination of all three. Each sample was done in quadruplicate, with 200,000 cells per well. Basal extracellular acidification rate (ECAR), basal OCR/ECAR ratio, and OCR under basal conditions and in response to the mitochondrial inhibitors Oligomyocin, FCCP and Rotenone + Antimycin A.

2.2.16 RNA extraction and cDNA synthesis

OT-I T cells obtained in vivo after influenza infection in B6 and GK1.5 mice at effector and memory time points, or naive OT-I T cells were lysed in 1 mL TRIzol reagent (Invitrogen), vortexed and incubated for 5 mins at RT. For RNA sequencing, chloroform (AnalaR) was added (200μL) and tubes were shaken vigorously and incubated for 2-3 mins at room temperature. Samples were then centrifuged (14,000 rpm, 20 mins, 4°C) and the aqueous phase collected. RNA was precipitated with two volumes of 100% ethanol (Chem-Supply) and 1-2μg glycogen (Invitrogen) and incubated for ≥45 mins on ice. RNA was pelleted (14,000 rpm, 60 mins, 4°C), washed with 75% ethanol (Chem- Supply), pelleted again, and dried at 60°C for approximately 10 mins or until liquid had evaporated. RNA was resuspended in 20 μl of RNase free water, or TE buffer (Invitrogen) and redissolved at 60°C for 10 mins. RNA concentration was determined using a NanoDrop 3300 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Samples were kept on dry ice and taken for sequencing immediately.

For RT-PCR for assessment of relative gene transcript levels or validation of RNA-seq samples, cDNA was synthesized from 150 – 500 ng of total RNA using the Omniscript kit (Qiagen). Each reaction contained 1 x buffer RT (Qiagen), 0.5 mM of each dNTP in a dNTP mix (Qiagen), 0.5 μg/ml oligo-dT (Promega), 2.5 U RNase inhibitor (Invitrogen) and 4 U reverse transcriptase (Qiagen). cDNA synthesis was conducted at 37°C for 90 mins, and was diluted to 5-10 ng/μl (cDNA – RNA equivalent).

2.2.17 TaqMan gene expression assay

For mRNA expression experiments, cDNA (25 – 50 ng/reaction) was used as template in a real-time PCR using TaqMan® Gene MGB primer/probes (FAM labeled) (refer to Section 2.1.5 Table 2.4). Each reaction was done in 25 μL using 1x Two-Step Universal PCR Master Mix (Applied Biosystems) and 1x primer/probes, and was performed in an iQ™5 Multicolor Real-Time PCR Detection System using iQ5 software (Bio-Rad) with the following cycle conditions: 95°C for 2 mins, followed by 40 cycles of 95°C for 15 secs and 60°C for 1 min. Each reaction was done in duplicate, and the results are reported as the mean of replicates, normalized to L32 and made relative to naïve samples using the ΔCt method [228].

2.2.18 Sybr green real-time PCR

Each reaction contained 1x Power SYBR green buffer (Applied Biosystems) with 100 nM forward and 100 nM reverse primers (refer to Section 2.1.5 Table 2.5) in a total volume of 25µL, with the PCR conducted under the following conditions: 95°C 10 min, 40 cycles of 95°C 15 sec and 60°C 1 min followed by a melt curve consisting of 81 cycles of 55°C for 10 sec with a 0.5°C temperature change per cycle. All real-time reactions were done in duplicate and conducted in an iQ™5 Multicolor Real-Time PCR Detection System and iQ5 software (Bio-Rad) was used to determine Ct for all samples. Each primer pair was tested for efficiency using two-fold serial dilutions of genomic DNA as template. The efficiency of all primer pairs was roughly equivalent (95-105%), as determined by iQ5 software.

2.2.19 Mathematical modelling

Cells were processed as described in 2.2, and flow cytometry data was analyzed using FlowJo software (Treestar). Using counting beads, the number of cells per division at each time point (up to 110hrs) was determined.

2.2.19.1 Population measurements Division destiny (DD) describes cell proliferation where cells divide for a series of generations, then return to a quiescent non-dividing state. The final generation reached is termed the cell’s division destiny. The cohort number (an estimate of the number of starting cells whose progeny are contributing to the response at a time point) was calculated by dividing the cell number per division by 2i, where i is the cell’s generation. The population mean division number (MDN) was calculated as the arithmetic mean of the cohort numbers at each time point. Assuming little death, the MDN will increase in time, plateauing at the point where the cells reach DD. Thus, the population mean division destiny (mDD) was estimated as the maximum mean division number measured over all the time points.

2.2.19.2 Precursor Cohort Based Method The precursor cohort method first removes the effect of cell division on the total cell number (24). For each time-point, the number of cells in a given division i was converted to a precursor cohort number (Ci) using the following formula:

Ci =

Undivided cells are in division i=0. In some cases, cohort number is percentage normalized (calculated from a time-point typically between 20-30 hours after stimulation). If there is no cell death or loss, the sum of cohort numbers from each generation will always equal the precursor cell number. Thus, reduction in this number indicates death and loss of either precursors or their descendants. A maximum number of 7-8 divisions can be traced using CTV. For a given time-point, the cohort numbers estimate a distribution that characterizes the number of divisions progeny cells underwent by this time-point. The mean of this distribution is called mean division number (mDD) and is estimated as:

∑ � ∙ � �� = ∑ � ∙

Where K is the number of divisions that can be resolved. Assuming little cell death, this mean division number will increase over time as cells progress through divisions and reach a plateau at mDD. The mDD using the cohort method is therefore defined as the maximum mean division number calculated in this way over all the measured timepoints.

2.2.20 ATAC-seq: Assay for Transposase-Accessible Chromatin with high throughput sequencing

Naïve, CD8 OT-I T cells were stimulated with OVA (as described in Methods section 2.2.7) and the cytokines in Table 2.7. At 72 hours, cells were sorted on division 4 and above, and counted by trypan blue exclusion.

Table 2.7: Stimulation conditions of ATAC-seq samples Sample OVA + 31.6U/mL IL-2 OVA + 31.6U/mL IL-2 + 10ng/mL IL-12 OVA + 31.6U/mL IL-2 + 100U/mL IFN-I OVA + 31.6U/mL IL-2 + 10ng/mL IL-12 + 100U/mL IFN-I 100,000 cells were centrifuged for 5 min at 500 × g, 4°C and washed with 1mL of cold, Ca2+ deficient PBS buffer. The supernatant was removed and discarded. The pellet was resuspended in 100 μl of cold lysis buffer, and each sample was divided 50uL/50uL into two tubes- one for digestion and one for undigested control, then centrifuged for 10 min at 500 × g, 4°C. Supernatant was discarded, and the nuclei pellet was immediately resuspended in the transposition reaction mix and incubated at 37°C for 30 min (Except the undigested controls which were resuspend in PBS and left on ice). Following transposition, samples were purified using a Qiagen MinElute PCR Purification Kit. Transposed DNA was eluted in 10 μl elution buffer (Buffer EB from the MinElute kit consisting of 10 mM Tris·Cl, pH 8). Samples are then amplified by PCR:

10 μl transposed DNA

10 μl nuclease-free H2O 2.5 μl 25 μM PCR Primer 1 2.5 μl 25 μM Barcoded PCR Primer

2 25 μl NEBNext High-Fidelity 2× PCR Master Mix Thermal cycle: 1 cycle: 5 min 72°C 30 sec 98°C 5 cycles: 10 sec 98°C 30 sec 63°C 1 min 72°C.

Total volume = 50uL/sample. Will then take 5 uL of this and determine number of PCR cycles using qPCR. qPCR to determine the number of amplification cycles

5 uL was taken from each each sample to perform qPCR to determine the appropriate number of PCR cycles (N). This is done to reduce GC and size bias in PCR, allowing amplification to stop prior to saturation. qPCR reaction: 5 μl of previously PCR-amplified DNA

4.41 μl nuclease-free H2O (3.5uL- in -20) 0.25 μl 25 μM Custom Nextera PCR Primer 1 0.25 μl 25 μM Custom Nextera PCR Primer 2 0.09 μl 100× SYBR Green I (we use SYTOGREEN cos more sensitive) 5 μl NEBNext High-Fidelity 2× PCR Master Mix.

Thermal cycle as follows: 1 cycle: 30 sec 98°C 20 cycles: 10 sec 98°C, 30 sec 63°C 1 min 72°C.

To calculate the number of cycles needed for amplification of samples, the linear reaction was plotted against the cycle, and the cycle number was determined by interpolating to one-third of the maximum fluorescent intensity (MFI). The remaining 45uL of each sample was run to the cycle number determined by qPCR. Cycle as follows: 1 cycle: 30 sec 98°C

N cycles: 10 sec 98°C 30 sec 63°C 1 min 72°C. Cycle for an additional N cycles, where N is determined using qPCR.

After qPCR, DNA was extracted again using the Qiagen Mini Allute PCR Purification Kit (cat number 28004). Before sequencing, the quality of purified libraries was assessed using a Bioanalyzer High-Sensitivity DNA Analysis kit (Agilent). Samples were then run on Illumina HiSeq HT chemistry 100bp paired end sequencing, 2 lanes.

2.2.21 Bioinformatic analyses

2.2.21.1 RNA-Seq: Illumina paired end reads were aligned to the mm10 genome with tophat. Only properly aligned pairs were kept. The reads have been further filtered requiring mapping quality of 5 or above. Duplicated reads were removed. Reads were assigned to genes with featureCounts function from Rsubread R package. Differential gene expression analysis was first attempted using edgeR Bioconductor R package but due to small number of replicates and large variability between them this attempt failed produce satisfactory results. Because of this RUVs function (with k=1) from RUVSeq package was used and it produced more reasonable results. Gene ontology (GO) analysis was done using DAVID and GREAT was used for assigning enhancers to genes. RNA-seq was analyzed as described in [165].

2.2.21.2 ATAC-Seq: Illumina paired ends reads were aligned to the mm10 genome with Bowtie2. Only properly aligned pairs were kept. Duplicated reads were removed and furthermore pairs with mapping quality below 10 were also removed. Peaks were called using MACS2. For each sample peaks with p-value < 1.0e-5 were deemed as strong peaks while those with p- value < 1.0e-3 were deemed as weak peaks. One sample was excluded from further analysis due to its poor quality. For the rest where there were two replicates, a strong peak was deemed as one which is a strong peak in at least one replicate and overlaps at least a weak peak in the other replicate. A weak peak was deemed as a weak peak in one replicate that overlaps a weak peak in another one. Peaks common to two conditions are strong peaks in one condition which overlap strong peaks in the other condition. Peaks unique to condition A compared to condition B are strong peaks in A which do not overlap even a weak peak in B.

2.2.22 Statistical Analyses

All statistical analyses were conducted using a two-tailed unpaired Student’s t-test. A p value of <0.05 was considered to indicate a significant difference (*).

Chapter 3: The molecular basis of CD4 T cell help during the priming of CD8 T cell memory

3.1 Introduction

Unhelped CD8 T memory cells are reportedly defective in the cardinal areas of memory cells; they exhibit poor survival, impaired secondary expansion, are not polyfunctional, and have a diminished capacity for immediate effector function upon restimulation [117,

164, 173, 229, 230]. Due to this, CD4 T helper (TH) cell involvement is key for ensuring the maturation of robust humoral and cellular immunity following immune challenge. However, the importance of CD4 T cells help in priming CTL responses is highly dependent on the nature of the immune challenge. For example, CD4 T cell-independent primary CTL responses are readily induced in the context of robust acute viral [117, 164, 173] or bacterial infections that induce a strong inflammatory response [231]. In contrast, pathogens or immunogens that induce low levels of inflammation are dependent on CD4 T cell help [29, 117]. Beyond ensuring effective CD8 T cell priming, the precise role CD4 T cell in the establishment of optimal CD8 T cell memory after immunization or infection is also controversial. Initial work demonstrated that, regardless of whether the primary CTL response was CD4 T cell-dependent or independent, CD4 T cell help during the initial priming phase was necessary for the generation of a memory T cell population capable of responding to secondary challenge [117, 173]. These data suggested that CD4 T cell help was necessary for installing a “program” into activated CTL to become memory cells. This programming likely occurs via the provision of secondary signals, such as IL- 2, delivery of co-stimulatory signals that promote optimal DC activation [29], immune regulation that serves to limit effector CTL differentiation, and/or engagement of pathways that regulate T cell survival pathways. At the molecular level, there are limited studies that suggest that CD4 T cell help is crucial for establishing and maintaining chromatin structures that ensure transcriptional stability at effector gene loci. While also reporting that CD4 T cell help was required for effective CD8 T cell memory responses, Sun and colleagues concluded that CD4 T cell help was not required at the time initial priming but for the maintenance of memory CTLs once the infection had been cleared [231]. In this case depletion of CD4 T cells after the establishment of memory resulted in a gradual decrease in cell number and loss of function. Studies utilizing either vesicular stomatitis virus (VSV) or ectromelia (mousepox) virus infection have demonstrated that memory T cell formation can be largely independent of

any CD4 T cell help [232]. Interestingly, a recent study analysing the CTL response to a minor histocompatibility antigen determined that provision of CD4 T cell help was critical for limiting eventual T cell exhaustion, rather than “programming” CTL T cell memory fate within responding effectors [233]. These studies suggest that memory CTL formation is a largely intrinsic property of responding CTLs and is highly dependent on the extent of cellular differentiation. Overall these studies highlight that, in a manner similar to primary CTL responses, CD4 dependence for establishing effective CTL memory is context-dependent. Influenza A (IAV) causes an acute, localised respiratory infection in B6 mice, and induces a robust CD8 T cell response, which has been well characterised. Infection results in vigorous viral replication in the respiratory tract, peaking around 48hrs p.i., with limited viraemia and persistence of infection [234, 235]. In the context of IAV infection, CD4 T cell help is key for the establishment of effective CTL memory [236-238]. While early studies indicated that CD4 help was required to establish optimal fitness of IAV-specific CTL memory T cell populations, there is little information about when CD4 T cell help is required and the molecular mechanisms involved in establishing the CD4-dependent IAV-specific T cell memory pool. To address this gap in knowledge, we examined the timing of CD4 T cell help that is required for the establishment of optimal IAV-specific CTL memory cells. We then performed RNA-seq analysis of helped versus unhelped memory CTL populations. Our data suggest that CD4 T cell dependent programming of IAV-specific CTL memory involves ensuring that there is appropriate engagement of molecular pathways required for ensuring the rapid responsiveness of memory CD8 T cells. These data provide insights into how CD4 dependent memory is regulated and has implications for developing approaches for promoting effective pathogen-specific CTL memory. This chapter is divided into the following aims: 1. To characterize the unhelped, GK1.5 IAV mouse model. 2. To determine if CD4 T cell help is needed for priming, or maintenance of naïve CD8 T cells to become memory cells. 3. To compare the transcriptional profiles of helped and unhelped memory CD8 T cells through bioinformatics.

3.2 Results

3.2.1 Characterization of the GK1.5, unhelped mouse model of Influenza

In order to address the long-term effect of CD4 T cell help in CD8 memory T cell differentiation we required a long lasting, stable depletion of CD4 T cells. Several studies have used transient depletion of CD4 using GK1.5 antibody injections, however these are unreliable and requires injection at least every 3 days. To overcome this issue, we utilised GK1.5 Tg mice, bred on a B6 background (GK1.5, from Andrew Lew, as published in [239]). These GK1.5 mice produce high levels of serum GK2c, resulting in total depletion of CD4 cells in the peripheral lymphoid organs (shown in blood, Fig. 3.1a). Depletion of CD4 T cells was associated with a slight increase in the percentage of CD8 T cells (shown in blood, Fig. 3.1a). Importantly, percentages of CD4-single-positive cells in thymus are not affected by circulating GK2c, which fits with previous data as thymocytes are typically not affected when mice are given depleting T-cell antibodies intravenously [239]. Naïve CD8 T cells in GK1.5 mice do express higher percentages of CD44 (shown in blood, Fig. 3.1b), and this trend continues 3, 7 and 10 days after Influenza infection. However, this is not likely to be indicative of activation, as the CD69 expression is low, and similar to that of naïve B6 mice (shown in blood, Fig. 3.1c).

Figure 3.1 Validation of CD4 depletion in GK1.5 mice.

B6 and GK1.5 mice were bled and stained for CD4, CD8 and the activation markers CD44 and CD69. a) Peripheral CD4 T cells are depleted from the blood from GK1.5 mice (0%, right), when compared to B6 (24%). This depletion was associated with a slight increase in the percentage of CD8 T cells. b) Samples were also stained for CD44, which was slightly increased in naïve GK1.5 mice compared to B6 mice. This trend was evident in mice 3, 7 and 10 days p.i. from Influenza. c) Percentages of CD69 positive cells were the same in B6 and GK1.5 mice.

In order to fully characterize the kinetics of the primary immune response in the OT-I Tg mouse model in the absence of CD4 T cell help, naïve, CD8 OT-I T cells were transferred into B6 and GK1.5 mice, which are infected with X31-OVA (as described in Methods section 2.2.1). These mice were weighed daily, and groups were harvested at d3, 5, 7 and 10 after infection for numeric analysis, as well as determination of the viral load. Similar frequencies (percentage of antigen specific CD8 T cells, left) and numbers (right) were seen in the spleen (Fig. 3.2a) during these time points. The CD4 depleted mice appeared to show a trend towards a delay in the CD8 T cells numbers particularly at d7 (Fig. 3.2a), however the number at day 10 appeared similar. Slightly higher percentages of CD8 T cells were found in the mLN at d10 (Fig. 3.2b), however when these were converted to numbers it appeared that there were much less CD8 T cells in the mLN at this timepoint. Although consistent across the 3 replicates, this may represent a slightly altered localization of the cells, as majority of the antigen specific cells would have or would be leaving the site of infection (lung) at this point and draining to this organ. There was a trend for lower frequencies and numbers of CD8 T cells recruited to the site of infection (BAL) at d7/10 (Fig. 3.2c). To the best of our knowledge, such a thorough kinetic analysis hasn’t been done in these organs in this model, so we wished to determine if these small differences in antigen-specific CD8 T cells had a functional result. To this end, we did viral titres on the supernatant collected from the lungs of B6 and GK1.5 mice, transferred with naïve OT-I CD8 T cells and infected with X31-OVA 3, 5, 7 and 10 days p.i. At d7, there is clearly more virus persisting in the lungs on CD4 depleted mice than B6, however by day 10 virus is cleared from both groups of mice (Fig. 3.2d), and these mice also failed to reach back to 100% of their starting weight by the end of the 10 days (Fig. 3.2e). Importantly, these are small changes and by the peak of the Influenza response the B6 and unhelped GK1.5 mice have similar cell numbers, are capable to clear virus, and no mice lose more than 20% of their body weight. Although the primary CD8 T cell response to Influenza is not completely independent of CD4 T cells, the shown effect on cell numbers during this phase does not affect the infection outcome.

Figure 3.2 Primary immune response to Influenza in B6 and GK1.5 mice has slightly altered kinetics in the absence of CD4 T cells, but reaches a similar peak. Naïve, CD8 OT-I T cells are transferred into B6 and GK1.5 mice, which are infected with X31-OVA, and harvested at d3, 5, 7 and 10 after infection for a full kinetic analysis. a) Percentages and numbers of antigen specific CD8 T cells recover from B6 [217] and GK1.5 (grey) mice in the spleen, b) mLN, and c) BAL. d) Viral titres from the lungs of B6 [217] and GK1.5 (grey) mice at the specified timepoints after primary infection. e) Weight loss of B6 compared to GK1.5 mice, determined as a percentage of the starting weight. Data from 1 experiment, representative of 2 repeats. N=3-5. SD shown. P=<0.05. Mann-Whitney test.

3.2.1.1 There are significantly less CD8 memory T cells formed in the absence of CD4 T cells help Consistent with previous reports, there is significantly less antigen-specific memory CD8 T cells when they are produced without CD4 T cell help [117, 173, 231]. This was evident in all organs tested, including spleen, mLN and lung (Fig. 3.3a-c). More recent reports have shown the importance of CD4 T cell help during the formation of TRM cells in the

lung [171], and our data supports these findings (Fig. 3.2d). There is a significant decrease in unhelped TEM (left), and an even further decrease in unhelped TRM (right). After secondary infection with a heterologous Influenza (PR8-OVA), unhelped memory cells do not respond to the same degree as the helped memory cells (Fig. 3.2d shown for spleen, representative of mLN, BAL and lung), as previously reported [117]. Mice which have their peripheral CD4 T cells depleted also lose more weight after secondary infection (Fig. 3.2f). Functionally, the CD4 depleted mice make slightly less IFN-γ, (Fig. 3.2g 2d shown for spleen, representative of mLN, BAL and lung), however this difference is not significant (Fig. 3.2h) and the proportion of polyfunctional cells which are produced after secondary infection are similar between helped and unhelped mice (Fig. 3.2i).

Figure 3.3 Secondary immune response to Influenza in B6 and GK1.5 mice. B6 and GK1.5 mice infected with X31-OVA, and rechallenged with PR8-OVA 28 days later, and harvested at 2, 4, 6 and 8 days after infection for a full kinetic analysis. a) Numbers of antigen specific CD8 T cells recovered from B6 [217] and GK1.5 (green) mice in the spleen, b) mLN, and c) Lung at resting memory. d) Subtypes of memory CD8 T cells (TEM left, TRM right) found in the lung at resting memory. e) Kinetics showing cell number in the spleen of B6 and GK1.5 mice after secondary infection. f) Weight loss of B6 compared to GK1.5 mice. g) Representative dot plots of the percentage of CD8 T cells that express IFN-g after 5 hr restimulation with OVA, graphed in (h). i) Proportion

of cells that are polyfunctional. B6 [217] and GK1.5 (green) mice, data from 1 experiment, representative of 3 repeats. (g), (h) and (i) are 8 days post-secondary infection. SEM shown. P=<0.05. Mann-Whitney test.

3.2.2 Unhelped mice have similar cytokine profiles in their lungs compared to B6 mice during the primary, resting memory and secondary immune response.

Many studies have suggested the mechanism of CD4 T cell help to be cytokine production, in particular IL-10 [171] and IL-2 [240]. In order to examine the environment the CD8 T cells are being recruited into along the course of the infection, and to identify key time points in which there are large difference in cytokine profiles, CBAs were performed on the supernatant of BAL on days 3, 5, 7, 10 and 28 days pst-infection with X31-OVA, and day 6 and 9 post-secondary infection. Amounts of IL-2 (Fig. 3.4a) and IL-4 (Fig. 3.4b), were determined, as well as two cytokines known to be produced by CD4 T cells as controls IL-10 (Fig. 3.4c) and IL-17 (Fig. 3.4d). Most time points show no substantial difference in the cytokine level between helped and unhelped mice, except at d5 primary infection, and d6 post-secondary infection. All cytokines were increased in the helped mice, compared to the unhelped mice. Particularly the IL10 and IL-17, which is not a surprising result, as CD4 T cells are known to produce IL-10 and IL-17 during acute infections [241]. Early in the primary response there appears to higher amount of IL-2 and IL-4, and this is similar for the beginning of the secondary immune response. However, the effect of depleting the CD4 T cells on the cytokine profiles of these mice is minimal.

Figure 3.4 Unhelped mice have similar cytokine profiles in their lungs compared to B6 mice during the primary, resting memory and secondary immune response. Mice were sacrificed at days 3, 5, 7, 10 and 28 days post infection with X31-OVA, and day 6 and 8 post-secondary infection and the supernatant of BAL was collected and a CBA performed. Amounts of IL-2 (a), IL-4 (b), IL-10 (c) and IL-17 (d) were determined. Data from 1 experiment. N=5. SD shown.

3.2.3 CD4 T cell help is required during the initial priming phase of CD8 T cell responses.

Having confirmed that the OTI transfer model recapitulates observations made for endogenous IAV-specific responses whereby optimal CTL memory formation is CD4 dependent, and this appears to be at the level of memory CTL maintenance, we could then better define if CD4 T cell help was necessary at the time of priming, or during the maintenance phase of CTL memory. To this end, we modified our adoptive transfer model to first establish OTI CTL memory in either B6 (helped) or GK1.5 (unhelped) recipients. Equal numbers 1x104 -1x105 helped or unhelped memory cells were then transferred into groups of naïve, B6 mice, where CD4 T cell help is available (Fig. 3.5a). The following day these mice were infected with influenza, and the secondary response examined. Given the same number of helped and unhelped memory cells were transferred, any differences in the number of recalled memory CTL could be directly attributed to the presence or absence of CD4 T cell help at the time of priming. Memory cells generated in the absence of any CD4 T cell help did not expand to same degree as those that received CD4 T cell help at the time priming both in terms of proportion (Fig. 3.5b-c) and absolute number (Fig. 3.5d) in spleen and BAL. Despite the difference in recall capacity of unhelped memory OIT, there was no measurable difference in cytokine production upon cognate peptide re-stimulation (Fig. 3.5e). Collectively, these data demonstrate that while the primary CTL response to IAV infection is CD4-help independent, CD4 T cell help during priming is essential for memory differentiation.

3.2.4 CD4 T cell help is not vital during the maintenance of memory T cells.

The above results indicate that CD4 T cell help at the time of initial priming is critical for memory CTL recall capacity. However, whether this early help helps sets up an autonomous memory response, or whether CD4 T cell help is also required during the secondary response was not clear. To further clarify this issue, equal numbers (104) of helped (d28) CD8 T cells, were adoptively into B6 or GK1.5 mice, and their ability to respond to rechallenge assessed (Fig 3.5a). Upon secondary challenge, there was equal expansion of WT memory CTL in both CD4 intact (B6) or CD4 deficient mice (Fig. 3.5c, d). These data suggest that the role of CD4 T cell help at the time of priming is likely to ‘program’ recall capacity into responding CTL. This data also shows that CD4 T cell help is not required during the secondary infection in order for memory CD8 T cells to

respond, highlighting the differences between the initial and secondary responses by the T cells.

Figure 3.5 CD4 T cell help at the time of priming is required for CD8 memory T cell differentiation, and is likely to ‘program’ recall capacity into responding CTL. a) Naïve, OT-I CD8 T cells were transferred into B6 and GK1.5 mice, and infected with X31-OVA and memory formed over 28 days. Equal numbers 1x104 -1x105 helped or unhelped memory cells were then transferred into groups of naïve, B6 mice. In a separate experiment, equal numbers (104) of helped (d28) CD8 T cells, were adoptively into either B6 or GK1.5Tg mice and during both experiments, the transferred cells ability to respond to rechallenge assessed. (b-c) Representative dot plots of the recovery of cells from the spleen (top) and BAL (bottom) from each experiment. (d) Graphed results from (b) and

(c). (e) Functionality of the resultant secondary memory cells after restimulation with OVA peptide. Data from 1 experiment, repeated 3 times with n=5. SD shown. ** = P=<0.01. Mann-Whitney test. 3.2.5 Helped and unhelped memory CD8 T cells have a similar transcriptional profile.

To gain further insights into the molecular mechanism behind the memory deficiency exhibited by unhelped CTL, RNA-seq was done to compare the transcriptional signature of helped and unhelped memory CTL (d28, Fig. 3.6a). This shows there was little difference in the differentially expressed genes (DEGs) observed between helped vs unhelped memory CTL. We identify 261 and 141 DEG between helped and unhelped memory CTL in the resting state, or after peptide stimulation, respectively. Interestingly, several of the genes associated with upregulation in unhelped memory CTL included T cell effector genes and receptors (Gzma, Gzmb, Ccl9, Ccr4, Ccr6 Cx3cr1) and inhibitory receptors (Tigit, Lag3, Klrg1, Klra3, Itgam) previously associated with T cell exhaustion (Fig. 3.6b). Taken together these data give an indication that unhelped CTL have some similarity to exhausted T cells.

3.2.6 Unhelped memory CTL exhibit an altered metabolic capacity compared to helped memory

Differentiation of virus-specific CTL from the naïve to effector state is associated with a change in the metabolic pathways utilised for energy production. While naïve T cells utilise oxidative phosphorylation, effector CTL switch to glycolysis taking up glucose as an energy source. This ensures that cell components can be used to for macromolecular synthesis, rather than being catabolised for energy. Transition into memory is associated with a return to OXPHOS but now with greater mitochondrial capacity for rapid energy production enabling rapid recall responses. Recent data has suggested that exhausted T cells have intrinsic defects in metabolic pathways rendering them unable to engage glycolysis. Initial gene set enrichment analysis (GSEA) indicated that unhelped memory CTL exhibited enrichment of genes associated with an effector vs memory state (Fig. 3.6c). Further analysis demonstrated that genes associated with the NADPH complex and activity, a key component of the TCA cycle, were enriched in helped but not helped memory CTL (Fig. 3.6d). The mammalian target of rapamycin (mTOR) is an important environmental sensor that regulates multiple metabolic pathways. Interestingly, genes that respond to rapamycin treatment were enriched in helped but not unhelped memory CTL (Fig. 3.6e), suggesting that mTOR sensing, and hence metabolic regulation, are dysregulated in unhelped memory CTL.

Figure 3.6 Helped and unhelped memory CD8 T cells have a similar transcriptional profile. d28 resting memory CD8 OT-I T cells were enriched and sorted from mice, and RNA- seq conducted. a) PCA analysis of helped and unhelped memory CD8 T cells, with and without stimulation. b) GO analysis of DEGs that were significantly upregulated in unhelped memory CTL. c) GSEA on set of genes associated with an effector vs memory

state. d) GSEA on genes associated with the NADPH complex and activity, a key component of the TCA cycle. e) GSEA on genes that respond to rapamycin treatment. N=2-3. FDR was set at 0.05. NES was 0.7 for Rapamycin response; NES 0.7 for NADH complex; for exhaustion signature, FDR was 0.01; NES 0.9.

To examine whether unhelped memory CTL exhibited a defect in the ability to undergo a metabolic switch and use glycolysis upon activation, we assessed uptake of the glucose analogue, 2-NBGD, after peptide stimulation. While both helped and unhelped memory CTL exhibited glucose uptake after activation, there was greater glucose uptake in helped memory CTL at both 5 and 12 hours after activation (Fig. 3.7a, b). To better assess metabolic activity and the full glycolytic profile of the unhelped memory cells compared to their helped counterparts, we utilised the Seahorse XF Glycolysis Stress Test to directly measuring extracellular acidification rate (ECAR) under different conditions (Fig. 3.7c, d), we can measure 3 main parameters of cellular glycolysis: the glycolytic switch with addition of glucose, glycolytic capacity with addition oligomycin and the glycolytic reserve with addition of 2-deoxyglucose (2-DG). In the steady state, helped and unhelped memory CTL exhibited a similar basal ECAR indicative of utilising OXPHOS and not glycolysis for energy. The addition of oligomycin, which inhibits ATP synthase in the electron transport chain and hence drives glycolysis demonstrated that unhelped memory CTL exhibited significantly lower glycolytic capacity compared to helped memory CTL (Fig. 3.7c, d). Addition of 2-DG to inhibit glycolysis resulted in a decrease in ECAR to similar levels for both helped and unhelped memory CTL (Fig. 3.7c, d). This indicates that non-glycolytic processes contributing to ECAR are also similar between the two subsets. Overall these data indicate that unhelped memory CTL have dysfunctional metabolic processes, the including the inability to fully engage the glycolytic pathway upon reactivation.

Figure 3.7 Unhelped memory CTL exhibit an altered metabolic capacity compared to helped memory CTL. (a) Helped and unhelped memory CD8 T cells were assessed for uptake of 2-NBDG directly ex vivo and after 5 and 12hr post restimulation with OVA. b) The MFI from (a) is graphed. c) the Seahorse XF Glycolysis Stress Test to directly measuring extracellular acidification rate (ECAR) of helped and unhelped memory CD8 T cells. d) Tabulated results from (c). Data from 1 experiment. N=5. SD shown. *=<0.05, ** = P=<0.01. Mann-Whitney test.

3.3 Discussion

Memory CD8 T cells offer long term protection of against virus and tumour challenge due to their unique capacity for long term survival in the absence of persisting antigen, and their ability to respond rapidly upon secondary challenge. There is continued interest in developing novel vaccine strategies to boost T cell immunity against pathogens, such as influenza A virus, where T cell immunity has the capacity to protect from disease when antibody immunity is ineffective. While it is well established that CD4 help is required for optimal CD8 T cell memory formation, when and how CD4 T cell help contributes to CD8 memory T cell formation is still controversial. Using a mouse model of Influenza A virus infection, where IAV-specific CD8 T cell priming occurred in the

presence or absence of CD4 T cell help, we demonstrate that CD4 T cell help was required during the initial priming phase of the response, and not required for the maintenance or subsequent recall responses. Genome wide RNA-sequencing analysis of the transcriptional signatures between “helped” and “unhelped" memory CD8 T cells identified that “helped” memory CD8 T cells exhibited greater transcriptional changes than their “unhelped” counterparts upon reactivation, and that “unhelped” memory CD8 T cells exhibited a more “exhausted” signature for optimal T cell expansion. Thus, CD4 T cell dependent programming likely helps program molecular pathways required for engagement of metabolic processes which control the rapid responsiveness of memory CD8 T cells. It has long been appreciated that CD4 T cell help is required for maturation of effective CTL cell memory responses. CD4 T cells help CTL memory formation via secretion of growth factors required for CTL differentiation and maturation, particularly IL-2, and licensing of dendritic cells via CD154 ligation of CD40 expressed on activing dendritic cells. These two mechanisms can act during the initial priming of CTL responses however; other data suggest that CD4 T cell help is also required for the maintenance. Paradoxically, acute IAV-specific CTL responses are CD4 independent, whereas the establishment of memory IAV-specific CTL populations are CD4 dependent. Hence, this study set out to pin point the time at which CD4 T cell help was required for effective IAV-specific memory formation. We established that priming of adoptively transferred OTI CTL within CD4-deficient hosts resulted in diminished recall responses. This data support previous studies that showed a CD4 dependence for establishment of endogenous IAV-specific CTL memory populations [242]. These data are also consistent with the findings in other infectious systems such as LCMV and Listeria monocytogenes. What wasn’t clear from the earlier IAV studies was when CD4 help was required. It had been shown for LCMV and Listeria monocytogenes infection models that CD4 T cell help was not required at the time of priming but was required for the maintenance of memory CTL [172]. In this instance, LCMV-specific effector CTL were adoptively transferred into either CD4 competent or CD4 deficient (MHC class II KO) recipients. There was a greater level of contraction of LCMV specific memory CTL numbers after adoptive transfer into CD4 deficient recipients with the loss of memory potential correlating with decreased levels of IL7R expression and decreased functionality. A recent study by our Lab also identified diminished memory OT-I numbers in CD4 deficient mice that correlated with lower levels of central memory precursors (CD62LhiKLRG1lo) (Data acquired by Dr. Hayley

Croom). This observed decrease in the number of unhelped OT-I memory precursors

(TCM) with effective recall capacity is one explanation for the diminished recall response in CD4 deficient mice infected with IAV. The concept that CD4 +T cell help is only required for maintenance of memory populations is in contrast to other studies that provide evidence that CD4 T cell help at the time of initial priming is critical for programming T cell memory capacity [173] [230]. In this case, it is the provision of extrinsic IL-2 that is able to install memory potential into responding CD8 T cells. In this study, adoptive transfer unhelped memory CTL into a CD4 competent animals was not able to restore memory recall capacity when compared to an equal number of “helped” memory CTL. Moreover, “helped” memory CTL were able to respond equivalently in both CD4 competent and deficient environments when infected soon after transfer. Hence, it would appear that in the context of IAV infection, CD4 T cell help is required at both the initial time of priming and also during the maintenance phase. As yet unpublished data has shown that there is a defect in IAV-specific MPEC formation in the absence of CD4 T cell help (Data acquired by Dr. Hayley Croom). This suggests that one outcome of providing CD4 T cell help at the time of initial priming is to ensure that helped memory CTL are able to respond to homeostatic signals such as IL-7 and IL- 15. This is supported by previous reports showing that unhelped memory CTL exhibit lower levels of CD122 (IL-2Rb), a key co-receptor for IL-15 signalling [243]. Just how CD4 T cells impart the “programming signal” to promote memory establishments is still not completely clear. Much of the evidence suggests that the impact of CD4 T cell help is indirect. For example, CD40 mediated licensing of DCs is often critical to support mature CD8 T cell activation and autocrine IL-2 production [230], as well as upregulation of the high affinity IL-2 receptor, CD25, by activated CD8 T cells [244]. The resulting IL-2 signal promotes CD8 T cell expansion via a Nab2 dependent inhibition of TRAIL expression [245]. The inhibition of TRAIL expression then limits antigen induced cell death upon subsequent memory CTL reactivation. Another indirect mechanism is via the impact of CD4 T regulatory (Treg) cells on the maturation of CD8 T cell responses and memory formation. It is generally considered that Tregs serve to dampen effector T cell differentiation and responses via modulating dendritic cell interactions [246]. However, it has also been reported that Treg modulation of DC function is required for maturation of high avidity CTL responses and establishment of effective CTL memory [247]. In this case, depletion of Tregs prior to immunisation resulted in the over production of inflammatory chemokines that resulted in prolonged antigen activation and a subsequent

over-proliferation of low avidity CTL responses. A consequence of this outgrowth of poor-quality CTL responses was the inability to generate long-lived memory CTL populations capable of responding to Listeria monocytogenes challenge. Thus, it is possible that the absence of Tregs, rather than the absence of specific signals, in the GK1.5 mice could result in memory dysfunction of unhelped OTIs. It will therefore be of interest to examine whether specific Treg depletion also results in memory dysfunction in the context of an acute influenza infection. It is interesting to note that depletion of CD4 T cells during chronic LCMV infection can accelerate development of T cell exhaustion. Even a transient depletion of CD4 T cells during the initial CD8 T-cell priming results in the establishment of severely exhausted CD8 T cells and high viral loads in chronically infected mice [248]. In contrast, transfer of antigen-specific CD4 T cells during chronic LCMV infection enhanced CD8 T-cell number and function, resulting in decreased viral burden. A recent study concluded that deficient CD8 T cell memory in a non-replicating antigen model was a consequence of the prolonged high antigen load that resulted from a poorly-expanded primary response, with high antigen load driving effector exhaustion rather than memory quiescence [249]. While the primary IAV-specific CTL responses is largely intact in the GK1.5 mice, the lack of CD4 T cells does result in prolonged effector CTL expansion and delayed viral clearance. Hence, unhelped effector and memory IAV-specific CTL may be exposed to factors that promote eventual T cell exhaustion. In a model of adenoviral vector vaccination, assessment of unhelped effector CTL demonstrated that “exhaustion signatures” were apparent early after priming suggesting that T cell dysfunction is programmed early after activation. Similarities were observed between genes identified as being differentially expressed in this study and those identified in this study of helpless CD8 T cell responses [250]. Provine et al. concluded that the genes induced under helpless conditions are similar to those transcriptional signatures observed in models of T cell exhaustion and T cell anergy. This included reduced transcription of Sell (CD62L) and increased transcription of the costimulatory genes Cd40lg and Tnfsf11 by effector CTL in the absence of CD4 T cell help. Further, we observed that there were higher levels of mRNA transcripts in unhelped versus helped OTI memory CTL for the inhibitory receptors Lag3 and Tigit, which are associated with a more complete T cell exhaustion state, and a decreased proportion of MPEC (IL-7Rlo KLRG1hi) cells. Together these data support the notion that the memory dysfunction in unhelped IAV-specific OTIs is more reminiscent of T cell exhaustion versus a specific defect in memory programming per se.

Upon T cell activation, there is a switch from mitochondrial oxidative phosphorylation, to aerobic glycolysis that involves the uptake of glucose to fuel the energy needs of the dividing cell. Upon memory formation, there is a switch back to OXPHOS that is largely dependent on fatty acid oxidation. It was therefore of interest that in our limited RNA- seq data that metabolic genes encoding for components of the NADH complex and pathway were deficient in unhelped memory OTIs. The observation that unhelped memory CTL are deficient for components of the electron transport cycle suggested a defect in metabolism pathways. We were able to demonstrate that unhelped memory OTI demonstrated lower levels of glucose uptake and decreased mitochondrial respiratory capacity. This is reminiscent of a recent study demonstrating that T cell exhaustion is associated with active suppression of metabolic pathways including decreased glucose uptake and mitochondrial respiratory capacity. Given that CD4 T cells are not dependent on the IAV-specific effector response; it suggests that CD4 help is not necessarily required for engagement of the glycolytic pathway. However, it is tempting to speculate that CD4 T cell help is required for ensuring that memory CTL retain functional metabolic pathways that then enable long term persistence upon resolution of infection. These data reconcile earlier observations that CD4 T cell help appeared to be required both at the time of priming, whilst there also being a defect in maintenance of memory CTL in the absence of CD4 T cell help. It remains to be determined if it is CD4 signals acting directly on activated CTL that installs this program, or whether it is an intrinsic program that is compromised when activated CTL undergo a process of differentiation that results in a more exhausted phenotype. Certainly, activation of CTL using allogeneic tumours demonstrated that memory potential was retained at if unhelped CTL were isolated at earlier phases of the CTL. It is interesting to note that unhelped memory OTI CTL induced after IAV-infection were unable to recover function when transferred into a CD4 competent host. This suggests that memory potential is lost relatively early after activation. While we favour a model whereby CD4 help is important for helping retain this intrinsic memory potential via limiting extensive T cell differentiation, further analysis is required to delineate the precise time after activation when such potential is lost. Moreover, this thesis has demonstrated that unhelped memory CTL have dysfunctional metabolic capacity, it is unclear whether such dysfunction can be reversed reinstalling memory potential and effective recall capacity. While there is some indication this might be possible, further work is required to assess whether such interventions will be more broadly applicable.

Chapter 4: Delineation of distinct transcriptional pathways that underpin virus-specific CTL differentiation

4.1 Introduction

Upon infection, naïve CD8 T cells have the capacity to differentiate into many subtypes of cells along the effector and memory T cell continuum. These cells vary in phenotype, functionality, location, migratory patterns, metabolism and long-term fate (as reviewed in [225]). Underlying the formation and regulation of this heterogeneous population of naïve, effector and memory CD8 T cells are numerous signalling pathways and transcriptional programs, with numerous transcription factors (TFs) being identified and characterized as being vital for this process (as reviewed in [225]). Despite immunological memory being intensively studied in the last few decades, the mechanisms underlying the generation and maintenance of memory lymphocytes during and after an immune response remain only partially understood. Many questions remain; do all memory cells ‘branch off’ at once? When, during activation, is a memory cell fate determined? Is there a global marker of memory cell potential? What marks the commitment to a memory cell? And how does a memory cell acquire its gene expression pattern?

The transcriptional profile of a cell dictates the distinctive features of a cell (as reviewed in [251]). The data presented in the previous chapter highlighted the importance of transcriptional analysis when comparing two populations of memory CD8 T cells. Accordingly, phenotypic and functional characteristics of memory lymphocytes is underpinned by distinct patterns of gene expression, and their epigenetic profiles (as reviewed in [251]). Through the comparison of the transcriptional profiles of naive, effector and memory CTL, both with and without antigen stimulation, this chapter attempts to identify key genes and regulatory networks that are vital for the programming and differentiation of virus-specific CD8 memory T cells. Using bulk RNA-seq, combined with publically available datasets this chapter aims to identify any previously novel genes, gene networks or TFs, which may be vital during the differentiation of memory CD8 T cells during differentiation. These will then be validated, and determined if they translate from mice models into human studies, providing further insight into the differentiation of memory CD8 T cells following infection. Such information will have clear implications for vaccine design.

4.2 Results

4.2.1 Naïve, effector and memory cell quality control prior to sequencing

In order to investigate the differentiation of CD8 T cells via comparison of transcriptomes from naïve, effector and memory CD8 T cells, the OT-I transgenic TCR system was utilized (as outlined in Materials and methods). Naïve, effector (d10 p.i.) and memory (d28 p.i.) cell populations were generated by either directly harvesting naive OT-I cells (Ly5.1+), or by harvesting the naïve cells and adoptively transferring them into naïve B6 congenic host mice (Ly5.2+), and infecting recipient mice with a genetically engineered

IAV containing the ovalbumin T cell epitope, OVA257-264, in the neuraminidase stalk (X31-OVA) [61]. CD8Ly5.1+ effector and memory cells were sort purified at the peak of the immune response (d10 p.i., effector) and at memory (d28 p.i.). RNA-seq was then performed as described (Methods section 2.2.16, Fig. 4.1) on naïve, effector (d10), and memory (d28), OT-I CD8 T cells. Prior to sequencing, samples were collected and sorted to above 95% purity. These samples were then stimulated for 5 hrs in the presence of IL- 2, with or without 1uM OVA. At this point cells were split, and either frozen in TRIzol for qPCR validation (transcriptional analysis), frozen in TRIzol for RNA sequencing or examined for effector function via FACS (protein) to determine the outcome of the stimulation. Once samples were validated for the stimulation via FACs, RNA was extracted as described (Methods section 2.2.16). Samples were then sent to AGRF sequencing facility for Illumina whole transcriptome library preparation with rRNA depletion, followed by HiSeq HT Chemistry paired end (50/100/125) 100bp RNA sequencing.

Figure 4.1 Flowchart of RNA-seq. Naive, CD8 CD44lo, Va2+, CD45.1+ OT-I T cells were harvested from LNs of 6-8 wk old OT-I mice. For effector, and memory time points, 104 cells were IV transferred in mice, which were infected with X31 OVA the following day. Effector cells were

harvested at d10 p.i., by harvesting the spl and mLNs. Memory cells were harvested at d28 p.i., by harvesting the spls. Organs from each time point were processed as described in Section 2.2.2, and resuspended in 1mL sorter buffer per spleen. Effector and memory cells were stained with anti-Ly5.1-PE and anti-CD8 on PB, APC or FITC for 30 mins on ice and washed twice with sorter buffer. Cells were resuspended in 500 μL of sorter buffer and stained with 200 μL anti-PE conjugated magnetic microbeads/mouse (Miltenyi Biotec) for 20 mins at 4°C and washed twice with sorter buffer. Cells were resuspended in 3 mL of sorter buffer and then passed over magnetized LS columns (Miltenyi Biotec). The initial flow-through was passed over the column again, followed by 3 × 3 mL washes with sorter buffer. The column was then removed from the magnet and bound Ly5.1+ OT- I cells were eluted by pushing 5 mL of sorter buffer through column. Naïve cells were not enriched in this way. OT-1 CD8Ly5.1+ cells were then sort purified to a minimum purity of 95% using a FACSAria cell sorter (BD Biosciences). After sorting, each sample was divided in 2, half of which were stimulated for 5 hrs with OVA peptide, and the other half unstimulated. Samples were then split in 4 for phenotype analysis (upreg of CD69), functionality (TNF-α and IFN-g production), effector function (qPCR) and RNA-seq. Once sample quality and stimulation efficiency was determined, RNA-seq samples were then run on Illumina HiSeq HT chemistry 100bp paired end sequencing, 2 lanes. Samples were then normalized, and read counts assigned for each gene. In order to compare each sample, differentiation gene expression was determined, and GO term analysis conducted. Depending on the results, downstream applications such as Interferome, network analysis, pathway analysis or GSEA were performed. Spl- spleen, LN- lymph nodes, d- day, GO- gene ontology.

4.2.2 Whole transcriptome analysis of CD8 T cells at each stage of differentiation

First, the quality of data was determined by PCA plot (Fig. 4.2). The replicate samples grouped together as shown, giving further confidence in the reproducibility of the data. Importantly, the stimulated samples sit to the right of the plot, separate from the unstimulated samples that sit to the left. This suggests that the biggest difference between the samples is the stimulation condition. Of note, there is only one memory time point for this analysis, due to limiting numbers of cells. Interestingly, the naive samples are the furthest away, with effector and memory grouped closer together, with memory in between the effector and naïve. Thus, each cell stage is transcriptionally distinct, with memory and effector cells appearing closely related. The most substantial differences between samples are seen after stimulation, and not due to cell differentiation stage. Thus, each of the replicates are validated and each cell stage is transcriptionally distinct, with memory and effector cells appearing closely related. The most substantial differences between samples are seen after stimulation, and not due to cell differentiation stage.

Figure 4.2 Comparison of naïve, effector and memory RNA-seq. a) MDS Plot of the naive (3), effector (2) and memory [252] RNA-seq samples. N=1-3. Each sample is 1-25 pooled mice, stimulated and unstimulated samples paired.

4.2.2.1 Global comparison of naïve, effector and memory CD8 T cells

Next, the transcriptional signature of naïve, effector and memory CTL were compared to determine key differences in the transcriptional landscape. Initially, the average expression of each gene in the ‘core gene list’ was compared between each cell stage in a heat map (Fig. 4.3a). The gene expression was hierarchically clustered, resulting in two main branches; the stimulated (to the right) and unstimulated (left) groups. This result confirmed what was displayed in the PCA plot (Fig. 4.2). In each of these groups, were two subgroups, the naive sat alone, whilst the memory and effector grouped together, again confirming the results from the PCA plot. The clustering on a gene basis separated the gene list into 3 broad branches; the top branch consists of genes which are mostly downregulated after stim, the second branch appears to be predominately effector molecules, such as Tnf-α and IL-2 (mostly up-regulated after stimulation), and the bottom branch is a mixture of up and down regulated. This list of core CD8 T cells genes is a relatively small number of 96 genes, and is used to validate our dataset compared to the

literature. Each of these genes are expressed as previously reported based on the heat map.

4.2.2.2 TCR dependent, non cell-stage specific genes

In order to further investigate any global similarities or differences between the cell stages from the RNA-seq data, genes which are upregulated by more than two fold after stimulation were compared between each cell stage (Fig. 4.3b). The Venn diagram shows those genes that were shared amongst all stages (614), and those which are cell-stage dependent (Venn diagrams produced by Venny). To further look into those genes which were shared by each cell stage, GO searches were executed on the 614 genes shared by all cell stages (PANTHER) (Fig. 4.3c). These genes represent global changes to a CD8 T cell upon TCR stimulation and are not cell-stage dependent. 52% of these genes are directly associated with metabolic processes such as Slc1a4, the gene encoding for ASCT1 which is known to play a role in the metabolism in mature neurons [253].

Figure 4.3 Global analysis of naïve, effector and memory RNA-seq samples. Normalized read counts from each RNA-seq sample were compared. a) A heat map of the core CD8 T cell genes and their expression across the three differentiation stages, with and without OVA peptide stimulation. b) Genes which were upregulated by more than two fold after OVA-peptide stimulation were compared. The Venn diagram shows those genes which were shared amongst all stages, and those which are cell-stage dependent (Venn diagrams produced by Venny). c) GO searches were executed on those genes shared by all cell stages (PANTHER). N=2-3, SD shown. Each sample is 1-25 pooled mice, stimulated and unstimulated samples paired.

Next, genes that were down regulated by more than 2-fold change after stimulation at each differentiation stage (N, E and M) were compared (Fig. 4.4a). This resulted in several groups of genes, importantly a group of shared genes between all three cell stage (553 genes) which represent global changes to a CD8 T cell upon TCR stimulation, which are not cell stage dependent. Gene ontology (GO) search analysis was performed using PANTHER (Fig. 4.4b). 47% of these genes were involved in metabolic processes such as Atp5g1, and Pfas, whilst the majority of others were unclassified. These down regulated genes do as we would predict based on their function, for example Atp5gl is part of a mitochondrial ATPase, and is down regulated upon activation as part of the switch from mitochondrial metabolism to glycolytic metabolism.

Figure 4.4 Global analysis of naïve, effector and memory RNA-seq samples. Normalized read counts from each RNA-seq sample were compared. a) Genes which were down regulated by more than two fold after OVA-peptide stimulation were compared. The Venn diagram shows those genes which were shared amongst all stages, and those which are cell-stage dependent (Venn diagrams produced by Venny). b) GO searches were executed on those 553 genes shared by all cell stages (PANTHER). N=2- 3, SD shown. Each sample is 1-25 pooled mice, stimulated and unstimulated samples paired.

4.2.3 Identification of genes involved in CD8 T cell differentiation

Following the global analysis of the RNA-seq data, the aim was to identify key genes that were specific to each differentiation stage, with a focus on memory cells. To do this, the transcriptional profiles of genes were examined for any interesting patterns along differentiation. First, those genes with high expression exclusively in resting memory cells, suggesting importance for memory maintenance and not quiescence (not in naïve cells) or effector function (not in stimulated or effector CD8 T cell populations) were identified. One such gene was Dmrta1 (doublesex and mab-3 related transcription factor like family A1, Fig. 4.5a and b). Little has been reported about Dmrta1 in T cells, or other immune cells. However, it was shown to control differentiation in other systems including regulating gene expression during brain formation [254] and during sexual development [255]. Additionally, there is a suggested functional link with STAT5B, which is of interest due to STAT5s role in immunology and T cells. STAT5 is activated by the IL-2 signalling pathway, through IL-2Rb stimulation and STAT5b in humans is shown to regulate Gzmb and Prf (as reviewed in [256]).

A study from Wirth and colleagues (2010), examined the transcriptional regulation of CD8 T cells after primary, secondary, tertiary and quaternary infection ([257], Fig. 4.5c).

This data shows that Dmrta1 transcription is maintained in resting memory cells, and re- expressed after 2, 3 and 4 infections. Publically available ChIP-Seq data on naïve, effector and memory CD8 T cells after Influenza infection reveals a potential regulation method of Dmrta1 expression ([182], Fig. 4.5d). The promoter of Dmrta1 has low enrichment of the active mark (K4) and repressive mark (K27) in naïve and effector T cells. Memory CD8 T cells have low enrichment of K27, but high enrichment of K4, suggesting it’s the acquisition of this mark that dictates the transcription of Dmrta1 (Fig. 4.5d). Before further analysis, the RNA-seq data was confirmed by qPCR on naïve, effector and memory CD8 OTI T cells, with and without stimulation (Fig. 4.5e). This data confirmed that memory CTL exhibit high levels of Dmrta1, which is down regulated upon stimulation. Notably, the small amount of transcript detected at d10, effector unstimulated was also detected by qPCR.

Figure 4.5 The unique expression of Dmrta1 in differentiating CD8 T cells. a) Expression of Dmrta1 in naïve, effector and memory CD8 T cells was determined based on read counts from bulk RNA-seq data. b) Reads from RNA-seq data sets of the Dmrta1 gene body. c) Transcriptional data as published in [257]. d) ChIP-seq data from an active and repressive mark at the promoter of Dmrta1 in naïve, effector and memory CD8 T cells. e) qPCR validation of the expression of Dmrta1 in naïve, effector and memory CD8 T cells. Data representative if 10+ pooled mice, sorted and split in half for direct ex vivo analysis or stimulation.

In order to look broadly at the expression of Dmrta1, Immgen was referred to, which contains publically available microarray data (http://www.immgen.org/) [258] (Fig. 4.6). Examining bulk tissue, there is high transcription of Dmrta1 in liver, kidney and adipose tissues (Fig. 4.6a). From bulk cells, there is highest expression in NKT cells sorted from the spleens of naïve mice, and there is a conserved mark in all cells examined on Immgen (Fig. 4.6b). In memory CD8 T cells induced after vaccinia virus infection, Dmrta1’s expression is high at d45 and further increased at d100 (Fig. 4.6c, data from [257]), validating our observations that Dmrta1 is expressed by memory CD8 T cells. Taken together, the data from Fig. 3 and 4, and the lack of knowledge surrounding this gene during CD8 T cell differentiation, lead us to hypothesis that Dmrta1 is required for the persistence of CD8 T cell memory.

Figure 4.6 Dmrta1 expression from publically available datasets. a) Whole tissue transcriptome data of Dmrta1 expression. b) OT-I cells specific for VSV at an effector time point. c) Expression of Dmrta1 in CD8 T cells after Vaccinia Infection (Data from [257]). d) Functional data of Dmrta1 acquired from Immungen.

4.2.4 Validation of Dmrta1

To further validate Dmrta1 expression as a marker of memory cells, OT-I CD8 bulk memory cells were sorted from influenza infected mice at d60 (RNA-seq data from Julia Prier, Fig. 4.7a), d90 (Fig. 4.7b). In the resting samples, these cells maintained a high level of expression of Dmrta1. Thus, Dmrta1 transcriptional profile matches that of what would be expected for a memory-specific gene.

4.2.4.1 Dmrta1 is downregulated 1 hr after stimulation

The RNA-seq data looks at gene expression in a snapshot, 5 hrs after in vitro stimulation with peptide. In both effector and memory cells Dmrta1 expression is drastically down regulated during this time. In order to investigate the kinetics behind this down regulation we examined transcription over a time course (1, 5, 12, 24 and 48 hours post stimulation). Strikingly, Dmrta1 is down regulated by 2 fold by 1 hr, and was undetectable by 5 hrs as previously shown (Fig. 4.7c, later time points not shown). In order to assess when Dmrta1 is re-transcribed after its down regulation, stimulated memory cells were also rested in IL-2 and media alone after 5 hr stimulation for 12 and 24 hrs post stim (Fig. 4.7c). Dmrta1 transcript was not detected during this rest period.

Previous chapters in this thesis have shown that in the absence of CD4 T cell help, CD8 T cell memory is defective and this evident on a transcript level. Due to the unique transcriptional pattern of Dmrta1 its expression in defective memory cells was assessed by qPCR, with the hypothesis that it will not be as highly expressed as wild type memory. To this end, helped and unhelped memory OT-I CD8 T cells were sorted, and RNA was extracted (Fig. 4.7d). The expression of Dmrta1 appeared similar to that of helped memory cells, indicating CD4 T help has no effect on its regulation in resting memory CTL.

RNA-seq data and Dmrta1 expression has been investigated thus far in the OT-I mouse model of X31-OVA IAV infection. In order to determine if this Dmrta1 is also expressed in the same pattern in endogenous IAV specific cells, memory CD8 T cells were enriched and sorted on tetramer for resting NP and PA specific cells. They were bulk sorted, stimulated for 5 hrs and frozen in TRIzol for RNA extraction and PCR quantification.

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The same pattern of transcription was seen as in OT-I model, Dmrta1 is transcribed in unstimulated memory cells, but not after stimulation (Fig. 4.7e). Interestingly there appeared to be less transcript in NP compared to PA- specific cells. Similar to the Tg model, there was no Dmrta1 transcript detected after stimulation. This data implies Dmrta1 expression is not a Tg-only phenomenon, and suggests it is feasible that all memory cells transcribe Dmrta1.

Figure 4.7 Dmrta1 expression in CD8 T cells. a) Expression of Dmrta1 in late memory (d60) was determined based on read counts from bulk RNA-seq data. b) The time course was then extended by qPCR to look at d90 and d150 (data not shown). c) To determine the speed that Dmrta1 is downregulated after stimulation, memory CD8 OT-I T cells were sorted, and stimulated with their specific peptide as described in Methods section 2.2.7. Samples were taken for qPCR 1 and 5 hrs post stimulation. To assess the upregulation of Dmrta1 after removal of stimuli, the remaining cells were taken off stimulation after 5 hrs, and collected at 12 and 24 hrs after resting. d) The affect of CD4 T cell help during memory CD8 T cell differentiation was determined based on its expression in GK1.5 mice. Memory CD8 T cells were sorted from the GK1.5 mice and again, stimulated for 5hours. The level of expression of Dmrta1 was determined by qPCR. e) Expression of Dmrta1 was determined in endogenous cells

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in response to Influenza infection. Memory NP and PA+ CD8 T cells were sorted from memory mice using tetramers, stimulated and qPCR determined the expression of Dmrta1. Data representative if 10+ pooled mice, sorted and split in half for analysis ex vivo or following stimulation.

The RNA-seq data is on a heterogeneous population of memory T cells, which provides a global picture of what the memory cells transcribe as a population. In order to further tease apart the role of Dmrta1, it’s expression was determined between the TEM and TCM memory subsets. We looked at d30 p.i. as this time point there is more even numbers of

TCM v TEM, and this time point is when we looked with RNA-seq. Naïve OT-I cells were transferred into mice that were infected with IAV as described (Methods section 2.2.1). Cells were enriched and sorted for resting memory cells, and further divided into central memory (CD62L+CD44+) and effector memory (CD62L-CD44-), stimulated and frozen in TRIzol for RNA extraction and PCR quantification. Equal expression was detected in

TEM and TCM unstimulated memory cells (Fig. 4.8a). Interestingly, after 5 hrs restimulation effector memory cells completely stop transcribing Dmrta1, however low levels of transcript remain present in central memory population. Thus, it is the TCM cells that, in the bulk RNA-seq data, are retaining the ability to transcribe Dmrta1 after restimulation.

4.2.4.2 Single-cell analysis of Dmrta1 expression in TCM versus TEM

Current technology is revealing the importance of transcriptional analysis on a single cell level to identify the heterogeneity in a population of cells. In order to fully elucidate the pattern of transcription of Dmrta1, and to further investigate the above findings, transcription was examined on a single-cell level using fluidigm (data acquired by Julia

Prier). Not all memory CD8 T cells transcribe Dmrta1, out of 54 single cells, 6 TEM and

5 TCM did (Fig. 4.8b). But those cells that do transcribe Dmrta1 transcribe a high amount (Fig. 4.8c), in a bi-modal fashion. The cells in these experiments were not stimulated; therefore, it remains unclear if it is the TCM cells that retain the ability to transcribe

Dmrta1. Regardless, these data show a small portion of TEM and TCM cells are transcribing Dmrta1, and those that are transcribe large amounts. Thus, Dmrta1 is uniquely expressed in a small subset of memory CD8 T cells.

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Figure 4.8 The expression of Dmrta1 in TCM v TEM. a) Bulk qPCR of Dmrta1 done on d31 TEM v TCM cells, with and without stimulation. b) Fluidigm data on 54 TEM and 54 TCM cells, with black representing those which Dmrta1 transcript was detected. c) Level of transcript detected in each subtype of memory cell.

4.2.4.3 Dmrta1 is expressed in human memory CD8 T cells Having confirmed and characterized expression of Dmrta1 in mouse CD8 T cells, Dmrta1 expression in human memory cells was next investigated. Due to its known expression in NKT cells (Immugen), and the recent attribution of ‘memory-like’ innate cells (as reviewed in [259]), we also sort to determine the transcription of Dmrta1 in human MAIT, NKT and gamma delta cells. Human PBMCs were collected and processed as previously described and sorted as shown (Fig. 4.9a). Briefly, B cells and dead cells were excluded and lymphocytes were gated on, followed by doublet exclusion. γδ T cells were isolated by γδ TCR+ Va7.2-. MAIT cells were gated on CD161+, Va7.2+. Bulk memory cells were sorted on CD8 CD3+ CD45RA- (Fig. 4.9b). Naïve T cells were gated on CD8 CD3+ CD27+ CD45RA+. After sorting, the naïve, EM and M were stimulated for 5 hrs with PMA/ionmycin. Samples were then frozen in TRIzol, and RNA extracted as described (Methods section 2.2.16). qPCR was conducted on these sample and normalized to the HK gene GAPDH. Low expression was detected in naïve CD8 T cells (Fig. 4.9c). High expression was detected in both EM and M, regardless of if they received stimulation or not. Low expression was detected in MAIT cells, with high expression in γδ and NKT cells. Thus, Dmrta1 has a different transcriptional profile in human cells compared to mouse, but key signature is the same and it is present in both EM and M cells. Included is the up regulation of the effector molecules IFN-γ and TNF-α, to validate the stimulation (Fig. 4.9d and e).

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Figure 4.9 The expression of Dmrta1 in human cells. a) Sort gating strategy of donated PBMCs; B cells and dead cells were excluded and lymphocytes were gated on, followed by doublet exclusion. γδ T cells were isolated by γδ TCR+ Va7.2-. MAIT cells were gated on CD161+, Va7.2+. Bulk memory cells were sorted on CD8 CD3+ CD45RA negative. Naïve T cells were gated on CD8 CD3+ CD27+ CD45RA+. b) Subdivision of cell subtypes into naïve, effector and central memory cells, based on expression of CD27 and CD45RA. c) Dmrta1 expression normalized to GAPDH in naïve, effector memory (EM), memory (M) cells with and without stimulation of PMA ionmycin, MAIT, gamma delta and NKT cells. d-e) qPCR results confirming stimulation efficiency based on upregulation of the effector molecules Ifn-γ and Tnf-α, normalized to unstim (unstimulated).

4.2.5 Zbtb32 and its unique transcriptional profile in memory CTL

A key function of memory CD8 T cells is their ability to respond rapidly to secondary infection. This study aimed to identify a gene or network of genes that instruct this unique ability. To do this we reasoned there would be transcriptional up regulation after stimulation in memory cells, but may also be transcribed in resting memory and effector CD8 T cells. One gene that fit this transcriptional profile was zinc finger and BTB domain containing 32 (Zbtb32, also known as ROG; Repressor of GATA3) [260] (Fig. 4.10a). The read counts from the RNA-seq data shows low transcription in naïve CD8 T cells, and low transcription in effector and memory CD8 T cells without stimulation. After the 5 hr stimulation in both effector and memory cells the expression is dramatically up

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regulated (Fig. 4.10b). This is similar to what had been found in effector CD4 T cells, in which Zbtb32 is rapidly induced upon stimulation with CD3. In CD4 T cells, Zbtb32 has been suggested to play a critical role in regulating differentiation and activation. Importantly to this thesis, Zbtb32 has been shown to repress GATA3 in CD8s by inhibiting IL-4 production [260]. It has also been identified as a major TF in CD8 T cell differentiation [261], but had not been associated with memory cells. Zbtb32 is a DNA- binding protein that binds to the to a 5’- TGTACAGTGT-3’ core sequence, and likely functions as a transcriptional transactivator and transcriptional repressor [261]. Other transcription factors in this classification have been shown to have essential roles in the regulation of lineage commitment, development and effector function for example, LRF in B and T cell commitment, ThPOK in CD8 and CD4 T cell lineage commitment, PLZF in NKT and gamma delta lineage commitment and Bcl6 in B and ThF cells (as reviewed in [262]). ZBTB32 has also been reported to inhibit GATA3 and PRMD1. Based on these initial observations we hypothesized that Zbtb32 may play an important role in the activation and differentiation of memory CD8 T cells, enabling them to respond rapidly to secondary infection.

4.2.5.1 Assessment of Zbtb32 as a marker of memory T cells

Epigenetically, naïve CD8 T cells have enrichment of the negative mark K27, and very little amount of the active mark K4 (Fig. 4.10c). Effector cells retained the same level of K27 expression, but are highly enriched for K4. Memory CD8 T cells have a unique pattern of these epigenetic marks as identified in the recent study from our lab, which identified poised molecular profiles [182]. Genes with poised profiles at memory time points have been identified as able to start transcription fast after activation, and are likely playing vital roles in memory cell speed to respond after secondary infection. To validate the gene expression of Zbtb32, OT-I CD8 T cells where harvested at naïve, effector and memory time points post Influenza infection, and RNA extracted as previously described (Methods section 2.2.16). qPCR was performed and confirmed the transcriptional profile (Fig. 4.10b). Zbtb32 expression was also confirmed in late memory (d60) using RNA- seq counts (Fig. 4.10d).

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Figure 4.10 The expression of Zbtb32. a) Normalized read counts of Zbtb32 expression from RNA-seq data of naïve CD8 OT-I T cells, d10, and memory (d28) with or without stimulation. b) qPCR validation of Zbtb32 expression, normalized to the house keeping gene L32. c) ChIP-seq data of H3K27me3 (active mark) and H3K4me3 (inactive mark) at the Zbtb32 promoter in naïve, effector and memory cells, with and without stimulation. d) RNA-seq data from (a) compared to a late memory time point, d60.

Once the transcription was validated and confirmed in models other than our own, how ZBTB32 may be interacting with CD8 T cells was investigated, to confirm if it was in a similar fashion to that described in CD4 T cells. In order to determine any protein protein interactions, ‘String’ was used ([263], Fig. 4.11a). This analysis revealed 10 known proteins that interact with Zbtb32, including the previously reported GATA3. No GO terms were returned for these 10 genes (data non shown). The RNA-seq data was further probed for the transcript of genes reported to be regulated, and to interact with Zbtb32, in an attempt to show any co-regulation of the genes. Gata3 shows an almost opposite transcription pattern to Zbtb32, which may indicate a level of regulation as they are known to bind to each other (Fig. 4.11b, c).

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Figure 4.11 Features of Zbtb32 protein. a) Protein protein interactions of Zbtb32 from ‘STRING’. Pink and blue lines indicate known interactions, all others represent predicated interactions. B) RNA-seq data showing the number of reads for Gata3 (b) compared to Zbtb32 (c).

4.3 Discussion

Naïve, effector and memory CD8 T cells are epigenetically, functionally and phenotypically distinct, yet share many common features. Understanding the similarities and differences between each of the cell stages during CD8 memory T cell differentiation may help to identify targets for vaccines, or new vaccine strategies. The transcriptional profile of a cell principally determines each of these features. Based on this information, this study utilized RNA-seq to compare CD8 T cells at each stage of differentiation, naïve, effector and memory, in order to tease apart any unappreciated differences and

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similarities. Additionally, this chapter has highlighted genes with unique and intriguing transcriptional profiles that appear to play roles in CD8 memory T cell differentiation.

A recent review of the molecular basis of the memory T cell response, published in Nature Reviews Immunology in 2015, has reviewed the global transcriptional profiles of memory T cells [251]. This study analyses gene expression characteristics in memory T cells by comparing them to precursor naive T cells. Despite their substantial functional differences, naive and memory T cells share a great degree of similarity (~95%) in their overall gene expression profiles [251]. Similar to this thesis, the 2015 study focused on genes that are highly expressed in memory T cells in order to identify genes essential for memory cell differentiation [251]. Unlike this thesis, the study only concentrated on previously well-characterized genes. This thesis took a global approach to the transcriptional analysis of memory T cells, and offered a linear comparison of cells along differentiation, examining not only at naïve and memory CD8 T cells, but also the effector cell population, with the same antigen specificity and experience. Additionally, the transcriptional profiles of each cell population were examined after 5 hr peptide stimulation. This allowed us to look at how the functional capacity of naïve, effector cells and memory CTL was altered during the differentiation process. Despite the differences between the 2015 study and this thesis, similar conclusions were drawn, such as the high amount of similarities between naïve and memory CD8 T cells.

This thesis demonstrated that effector T cells are transcriptionally more similar to memory T cells than memory to naïve T cells. One possible explanation for this similarity is that the memory time point we used (d28) was too early. However, these cells fit all the requirements to be considered memory. That is, they survive long term and the population in wildtype mice don’t reduce in numbers from this point (Chapter 3, [136]). Further, they have the capacity to respond upon secondary infection demonstrating rapid transcriptional up regulation of IFN-γ and IL-2 upon activation without the need for further division (data not shown). While CTL also rapidly up regulate these effector molecules, phenotypically our memory cells are very distinct from effector cells (Chapter 3 and 4). More recent data from our lab has demonstrated that memory CTL taken from a later time point (day 60 and beyond) also demonstrate the same properties giving us confidence that d28 is a bonafide memory time point.

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By examining the gene transcription of CTL at different stages of differentiation, after stimulation with their specific peptide, this study picked up intriguing differences that were missed by previous studies that only compare naïve to memory T cells [204, 251, 264]. Hierarchal clustering and PCA plots have identified separate stimulated and unstimulated groups. Unsurprisingly, this suggests that regardless of differentiation state, antigen stimulation induces significant changes in the transcriptional signature of virus- specific CTL. Importantly, the landscape of transcriptional potential was distinct between naïve, and effector/memory CTL indicating there is significant reprogramming of CTL upon differentiation. A possible reason for this cell-type specific reprogramming is that the epigenetic changes that ‘install’ transcriptional activation of key lineage specific genes occurs early during CD8 T cell activation [182, 265]

When we looked globally, at all of the RNA-seq samples, we saw a large overlap between cell-stages in the number of genes differentially transcribed after activation (Fig. 4.2/3). There were 614 genes up regulated after stimulation more than 2 fold in naïve, effector and memory CD8 T cells. These genes are TCR dependent, non-cell stage specific. GO searches on these cells revealed most (over 50%) are considered ‘metabolic processes’. The second highest hit is also related to metabolism, as are many of the hits from the GO term analysis. This is expected, as any T cell that has been stimulated will, within 5 hours, begin to alter its metabolic profile. Such processes are common to cells that need to divide rapidly, providing the components required for cell proliferation. This overlap in cell regulation between cell stages indicates the need for similar biological processes regardless of differentiation state.

By comparing RNA-seq data from naïve, effector and memory T cells after virus infection, we identified Dmrta1 as a potential marker of memory cells based on its high transcription exclusively in unstimulated memory T cells. Dmrta1 transcript was not found in naïve cells, possibly because its transcription is regulated in a similar way to the effector molecules IFN-γ and IL-2, that is that division is required for its acquisition. Low levels of transcription in effector cells without stimulation may mark those cells within the effector cell population that are destined to become memory cells. It is tempting to speculate that after stimulation with peptide for 5 hrs this transcript is no longer detected, so it is either down regulated or, those cells expressing Dmrta1 are not recruited into the effector response, therefore the transcript is so low compared to the rest of the upregulated cells that it is below the limit of detection. When KLRG1- and CD127hi effector cells were

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probed for Dmrta1 expression, no transcript was identified. These cells have been previously identified as memory precursors [125], however KLRG1 is not a strong predictor of memory in Influenza [127, 266]. Thus, despite clearly showing Dmrta1 is transcribed at the peak of the effector response, this thesis has not identified when exactly the Dmrta1 is actively transcribed, or its mode of activation. To this end, Immgen was utilized to determine if any functional data was publically available. No CD8 T cell specific hits were returned, however 3 out of the 10 hits were related to transcription or regulation of transcription, such as ‘Transcription, DNA-templated’, and ‘Sequence- specific DNA binding transcription factor activity’. Despite the return of these interesting, transcriptional control related hits, there is no current publically available data looking at Drmta1 in Influenza infection, or memory cells soon after re-stimulation.

Dmrta1 appears to be a unique marker of memory cells, which is found not only in Influenza-specific memory T cells, but also in other viral specific memory CTL populations. Previous studies have shown its expression in LCMV [267], and vaccinia virus [257]. This study has shown Dmrta1 expression is not only in transgenic models of infection, but is transcribed in Influenza specific CD8 T cells. Of note, Dmrta1 expression is significantly lost in chronic infection [176]. This paper also confirmed Dmrta1 as highly expressed in acute infection at d30 and identified it as one of the most differentially regulated between chronic and acute infection [176]. This data compliments the data acquired in this thesis, and suggests that Dmrta1 expression is important during chronic infection to avoid exhaustion.

The expression pattern of Dmrta in TCM and TEM cells may allude to the pattern of CD8

T cell differentiation. Both resting TCM and TEM transcribe Dmrta1, however it is only

TCM cells which retains transcription after restimulation. As discussed in the literature review, a theory explaining the formation of T cell memory is where there is gradual loss of recall potential as activated CTL differentiation towards a terminal effector state. In this model, memory potential is established early and is more prevalent in less differentiated CTL. Studies have proposed TCM’s to retain the most potential, and are found at an earlier stage of differentiation spectrum having not received the full/constant signals driving terminal differentiation, followed closely by TRM [141] and lastly, due to their similarities, the TEM [268]. Dmrta1 is transcribed in both resting TEM and TCM, but is only retained after restimulation of TCM and not TEM. It is tempting to speculate that Dmrta1 reflects retention of potential within a CD8 memory T cell. There is also evidence

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in the literature that TCM cells are more potent during recall responses than TEM, which do not proliferate to the same degree [269]. Again, this supports the hypothesis that Dmrta1 is a marker of memory cells. Further, if Dmrta1 is a marker of memory cells, it may 1) provide further evidence for this suggested model of T cell differentiation, and 2) imply that TCM are more long lived, and retain memory-potential after reactivation, in contrast to TEM which are more terminal differentiated so once reactivated don’t go back to a ‘memory cell’ state (Fig. 1.1).

Fluidigm data found that approximately 10% of memory OT1 T cells are actively transcribing Dmrta1 (Fig. 4.8). Despite this being less than what the bulk RNAseq suggested, it doesn’t completely disprove the hypothesis that Dmrta1 is a marker of memory cells. The transcriptional data only reveals a snapshot of time, so these cells may express the marker transiently. Further work needs to be done in order to explore if this sub-population of memory cells is different to those which do not express Dmrta1.

The approach used to identify Dmrta1 in this thesis can be used to explore other data sources for relationships/patterns of transcriptional profiles to generate new hypotheses. For example, utilizing a similar approach, we looked for other gene transcripts that, similar to Dmrta1, were largely unique to resting memory CTL. Examination of RNA- seq data for gene with a similar transcriptional profile and marks positive for K4me3 in memory but not naïve or effector cells identified Cdh1, a gene reported to play important roles in embryonic development and cancer metastasis [270]), as fitting this profile. Epigenetically, cdh1 in memory CD8 T cells loses K27, the negative mark, and gains the positive mark K4. This enrichment in memory is similar to that seen in Dmrta1. Interestingly, Cdh1 interacts with HDAC1 and HDAC2 [271] suggesting it may play a role in modulating the epigenetic landscape during the differentiation into a memory CTL. This gene was not further explored in this thesis, but provides further evidence for this method of generate new hypotheses in the age of ‘big-data’.

This thesis also identified Zbtb32 as a potential gene required for CD8 T cell differentiation. A recent paper found following infection with LCMV-Armstrong, Zbtb32-/- mice exhibited normal viral clearance, but generated increased numbers of virus-specific CD8 T cells, relative to wild-type mice, resulting in an increased memory cell population [272]. This paper also revealed Zbtb32’s potential role as an epigenetic regulator, which binds to regulatory regions of genes (such as Eomes and CD27) and

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recruits HDAC1 and HDAC2 to promote repressive histone modifications. This study came after Zbtb32 had been shown to control the proliferative burst of virus specific natural killer cells responding to infection with NK cells from Zbtb32-/- mice demonstrating a defect in proliferation in response to MCMV, VSV or VacV. Our data suggests Zbtb32 is most highly transcribed after stimulation in effector and memory CD8 T cells, supporting these studies. Combining previous reports with the data acquired in this thesis encourages us to consider that Zbtb32 may be acting as a molecular handbrake driving memory formation by limiting differentiation. Based on the above studies, it may be preventing the establishment of a permissive histone landscape, which is known to be acquired with T cell differentiation. Further, Zbtb32 has recently been shown to have a similar effect in memory B cells [273], showing it is not only specifically a T cell epigenetic regulator, but is universal.

This thesis, and previous studies have identified a possible protein-protein interaction between ZBTB32 and GATA3 (String analysis). This is of particular interest as GATA3 has been shown to control T cell maintenance, proliferation, and activation, resulting in it being known to be essential for development of the mature T-cell lineage [274, 275]. Thus, the interaction of these two proteins is of clear interest given the intriguing transcriptional profile and the known association of ZBTB32 as an epigenetic regulator. An interesting and as yet unanswered question remains, are ZBTB32 and GATA3 co- expressed, and bound together? This question could be answered by doing sequential ChIP at the promoters of each of the genes, or by pulling down GATA3, and probing for ZBTB32 via western plot and vice versa. Such data could further describe the molecular regulation of CD8 T cell differentiation. The intricate details of how many of the core transcriptional controllers regulate and bind to one another is still elusive.

Overall, this chapter has highlighted the high similarity between effector and memory CD8 T cells, and between naïve and memory CD8 T cells. It has shown that the biggest transcriptional ‘shifts’ in CD8 T cells occur not during differentiation, but after restimulation. Dmrta1 was identified as a candidate as a memory-exclusive gene, and may represent a gene to be targeted during the differentiation of memory CD8 T Cells. Zbtb32 was identified as a potential key gene during CD8 T cell differentiation, with a poised molecular profile and known interactions with other key genes such as GATA3. This analysis has proposed 2 genes that have the potential to modulate the CD8 T cells response through novel vaccine design. The identification of the novel memory marker

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Dmrta1, and its expression in unstimulated effector CTLs proposes that memory CD8 T cells are indeed formed early after priming, and that memory cells don’t go through the effector response, but perhaps are formed through a unique differentiation pathway separate to that of effector cells. Therefore, this chapter has implications not only in vaccine design but also in further our knowledge of the way memory CD8 T cells form following infection.

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Chapter 5: Chapter 5: The molecular basis of ‘signal three’ and how it dictates the differentiation of CD8 T cells

5.1 Introduction

Upon activation, naïve CD8 T cells initiate a program of proliferation and differentiation that results in the acquisition of effector functions, enabling them to control infection [139, 276-279]. Optimal T cell activation requires the integration of multiple signals including cross-linking of the T cell receptor (signal 1); co-stimulation (signal 2) and soluble factors produced during inflammation such as cytokines (signal 3) [50]. Chapter 3 and 4 have described the essential role of CD4 T cell help during activation of a naive CD8 T cell to become a memory cell. Provision of signal three has also been shown to be a fundamental step for optimal CD8 T cell activation [75], and has been suggested to be one way which CD4 T cells offer the ‘help’. Inflammatory cytokines, derived from DCs and from CD8 T cells themselves (autocrine), can also act directly on the CD8 T cells to regulate their expansion and differentiation.

Previous studies have demonstrated that Type I IFNs (IFN-I) and IL-12 are critical signal 3 cytokines for optimal CD8 T cell differentiation during activation [50, 75], memory differentiation [280] and more recently TRM [281]. Lack of IL-12 signalling to CD8 T cells accelerates the development of central memory phenotype in both primary and secondary antigen-specific memory CD8 T cells, suggesting its requirement for effector CD8 T cell differentiation. This supports the concept that the level of inflammation can dictate memory T cell fate [73, 282, 283].

A stochastic model which is able to measure regulation of cell proliferation and death in lymphocytes has previously been used to investigate the role of signal 3 in initiating T cell responses [284]. Key studies using the Cyton Model have shown the importance of IL-12 during the initial phase of CD8 T cell activation [284-286]. Remaining questions include, are signal three cytokines additive in promoting virus-specific CD8 T cell differentiation? Can we measure and predict the effect of multiple signal three signals on an activated T cell? Do all signal three cytokines drive T cells towards to same fate?

During the program of proliferation and differentiation that follows T cell activation, there are dynamic changes in CD8 T cell transcriptional signatures which correlate with

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distinct changes in the epigenetic landscape [82, 182, 265]. One of the key changes seen during differentiation, is the degree of openness of the chromatin landscape [287]. Open chromatin regions are areas of chromatin that exhibit high DNA accessibility [288], and are associated with genes that are being actively transcribed. Despite the importance of signal 3 in ensuring optimal T cell activation, the molecular mechanisms of how signal 3 imprints its action on the genome are unclear. Thus, the second half of this chapter aims to determine if and how signal 3 orchestrates chromatin remodelling and the genomic location of transcription factor binding of CD8 T cells.

5.2 Results

5.2.1 Titration of singular cytokines over time and their effect on cell proliferation In order to determine if signal three was having a direct impact on CD8 T cell activation, acquisition of effector function and division, naïve OT-I CD8 T cells were stimulated using an in vitro approach based on work done previously by the labs of Mescher and Hodgkin [50, 284]. First, the impact of signal 3 (hIL-2, IL-12 and IFN-I) on cellular division was assessed. To this end, naïve OT-I CD8 T cells were sort purified from the spleens and LNs of naïve OT-I mice (Fig. 5.1a) and plated in triplicate at 1x104 cells/well with increasing concentrations of IL-2, IL-12 and IFN-I (Fig. 5.1b). The proliferation of cells was assessed using CTV at 94 hrs (Fig. 5.1c). IL-2 promotes more cell proliferation at every time point when compared to IL-12 and IFN-I. Any IL-2 concentration above 0.316U/mL promotes cell proliferation beyond resolution by CTV dye. IL-12 also promotes cell proliferation, although not to the same degree as IL-2. Cells cultured with IFN-I show the least amount of cell proliferation at 94 hrs, compared to IL-2 and IL-12. The higher concentrations increase the number of proliferations slightly, although not greatly when compared to the cell alone control.

Next, the effect of signal three on cell death was determined. 94 hrs was the chosen time point, as all cultures had some degree of death at this time (Fig. 5.1d). Degree of death was determined by comparing the MFI of the uptake of the cell death dye propidium iodide (PI), with the higher the MFI the higher the cell death. As the concentration of IL- 2 goes up (grey) the amount of cell death decreases. As the concentrations of IFN-I goes up, cell death increases, and there is considerable cell death at even low concentrations of IFN-I (blue). IL-12 had intermediate cell death and did not change significantly when the concentration was increased (red). Thus, IL-2 is promoting cell survival, IL-12 appears to have little effect on cell survival, and IFN-I is causing cell death.

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Figure 5.1 Titration of cytokines over time and their effect on cell proliferation. Naïve OT-I CD8 T cells were sort purified from the spleens and LNs of naïve OT-I mice, CTV labeled and plated at 1x104 cells/well with increasing concentrations of hIL-2, IL- 12 and IFN-I. a) The sort profile and purity check of naïve, CD44lo, CD8 +, OT-I T cells, and the experiment layout. b) Concentrations of cytokines used. c) CTV plots at 94 hrs post plating up with IL-2, IL-12 and IFN-I (from left to right). Light pink is the ‘blank’ with no cytokine added. The bottom histogram on each plot is the highest concentration (red) with the least amount of cytokine added shown in dark green. d) The cell death at 94 hrs in each of the stimulation conditions (MFI of PI staining). All cultures contained S4B6 at 25 μg/mL, included to block endogenous IL-2 action. Representative of 3 independent experiments, Mean ± SEM from triplicate culture wells.

5.2.1.1 IL-2, IL-12 and IFN-I show different effects on the division of naïve CD8 T cells To finalise the ideal concentrations of each cytokine, each titration series was assessed over time (Fig. 5.2). To do this, naive OT-I CD8 T cells were sort purified from the spleens and LNs of naïve OT-I mice and plated at 1x104 cells/well with increasing concentrations of hIL-2, IL-12 and IFN-I. CTV FACs plots at 22 (red), 39 (blue), 44 (yellow) 62 (green), 68 (dark green), 86 (yellow) and 110 (purple) hrs post stimulation

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are compared (Fig. 5.2a-c). Again, all cultures contained S4B6 at 25 μg/mL. All concentrations of IL-2 resulted in full dilution of the CTV by 110hrs (purple), and all dilutions resulted in either 1 or 2 cell division by 39hrs (blue). Cells stimulated in the presence of IL-12 also divide beyond dilution of CTV by the final time point (purple). A noteworthy result from the titration of IFN-I is its clear effect on cell death at all time points. The cell number is low by 62hrs (light green), making the data beyond this time point unreliable. There is little difference between each dilution of IFN-I.

Additionally, each cytokine alone was compared to a culture containing no signal 3 as a control for how the cells behave in the absence of any signal 3 (Fig. 5.2d). Fitting with previously published data, the cells cultured with no signal 3 cytokines took longer to divide, and the majority of them were dead by 62h. Clearly, during course in an infection no cell is exposed to only one cytokine, with a fixed amount of stimulation and co- stimulation. Hence the impact of a combination of signal 3s on cellular proliferation was assessed, (herein referred to as ‘combination’) (Fig. 5.2e). The CTV profiles of these cells show a similar profile to that of the IL-2, with the majority of cells having divided beyond CTV dilution at 110 hrs. They also look similar to the IL-2 alone culture group as the CTV profiles are only spread out over 2-3 cell divisions per time point. The IL-2 and combined culture condition show a much more uniform proliferation than the IL-12, which has cells in 3-5 divisions at any time point. This suggests the combination culture is promoting cell proliferation, and perhaps ensuring all the cells become activated immediately as opposed to cells entering cell division at different times.

A comparison of cell division from the highest concentrations was compared at each time point (Fig. 5.2f). This analysis allows a direct comparison of each of the cytokines. Without any cytokine (red) cells die in culture. Il-2 pushes survival and division. IL-12 also promotes division although it is less efficient than IL-2. IFN-I results in the same degree of cell death as no signal three. This may be due to the IFN-I promoting cell death, or the cells dying from neglect due to IFN-I not promoting survival. The combination culture pushes cell division to the same degree as IL-2 alone. Therefore, each cytokine alone is having a unique result on the cell proliferation, however the combination culture is markedly similar to that of IL-2 alone suggesting it is the main driver of cellular proliferation and survival.

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Figure 5.2 Titration of cytokines over time and their effect on cell proliferation. Naïve OT-I CD8 T cells were sort purified for the spleens and LNs of naïve OT-I mice, CTV labeled and plated at 1x104 cells/well with increasing concentrations of IL-2, IL-12 and IFN-I. CTV FACs plots at 22 (red), 39 (blue), 44 (yellow) 62 (green), 68 (dark green), 86 (yellow) and 110 (purple) hrs post stimulation. Increasing concentrations of the cytokines IL-2 (a), IL-12 (b), IFN-I (c), no signal 3 (d) and a combination of all 3 (e). f) A comparison of cell division from the highest concentrations compared at each time point. All cultures contained S4B6 at 25 μg/mL. Representative of 3 independent experiments, triplicate culture wells.

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5.2.2 Phenotypic analysis of stimulated cells over time shows equivalent activation across the stimulation conditions To further characterize the activation properties of each signal three, a phenotypic analysis of stimulated cells was conducted over time (Fig. 5.3). Naïve, OT-I CD8 T cells were sorted and plated as previously described, at 1x104 cells per well in a round bottom 96 well plate and stimulated with 1µM OVA. Cells were cultured with either 31.6U/mL IL-2 (yellow), 10ng/mL IL-12 (light green), 600U/mL of IFN-I (dark green), a combination of all three [229] or with no signal three (blue). Cells were assessed for expression of CD44, CD69 and CD25 at the start of the experiment (grey) and after 25, 38, 51, 62, 77, 90 and 125 hrs. CD44 shows intermediate expression at first, and is upregulated in all culture conditions. CD69 is dramatically upregulated in IL-12 and IL- 2 cultures by 24 hrs. CD69 is also upregulated in the combined and IFN-I cultures at this time point, although not to the same degree (Fig. 5.3). No upregulation is seen in the unstimulated control, and only slight upregulations is seen on the no signal three cells. By 62 hrs the transient expression of CD69 has been downregulated. CD25 upregulation is at its peak after 51 hrs, but it too is downregulated after 62 hrs. The IL-2 culture appears to upregulate CD25 first; however little difference is seen between the culture conditions in which signal three is provided. Without signal three or OVA (unstimulated) most of the cells die by 77hrs. Thus Signal 3 does influence phenotypic changes in CD44, CD69 and CD25 after T cell activation, and each condition results in similar activation of the cells.

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Figure 5.3 Phenotypic analysis of stimulated cells over time. Naïve, OT-I CD8 T cells were sorted and plated as previously described, at 1x104 cells per well in a round bottom 96 well plate and stimulated with 1µM OVA. Cells were also cultured with either 31.6U/mL IL-2 (yellow), 10ng/mL IL-12 (light green), 600U/mL of IFN-I (dark green), a combination of all three [229] or with no signal three (blue). Cells were assessed for expression of CD44, CD69 and CD25 at the start of the experiment (grey) and after 25, 38, 51, 62, 77, 90 and 125 hrs, by FACs. All cultures contained S4B6 at 25 μg/mL. Representative of 2 independent experiments, from triplicate culture wells.

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5.2.3 Combined co-stimuli program earlier commitment to cell division. Having determined stimulation conditions and concentrations, the intricate effect of what each cytokine was having on the activation of T cells was examined. To do this, naive OT-I CD8 T cells were plated as described in Methods section 2.2, and again the cell numbers per harvest time were acquired. Cell number was graphed against the division number (Fig. 5.4a). These graphs further show the similarities between the combination culture and IL-2 alone culture. Both of these graphs show promotion of proliferation in a uniform fashion by the combination culture and IL-2 alone. The IL-12 culture has more diverse numbers of cells in each cell proliferation, implying slower division when compared to when IL-2 has been added to the culture (data not shown). The high amount cell death is evident in both the cells alone and IFN-I alone cultures. Taken together, this data shows that when IL-2 is added, either alone or in culture, cells are promoted into later divisions.

Next, to determine the impact of signal 3 on cell division and death rate, the precursor cohort method was employed [285]. In short, a table of precursor numbers was generated, based on the acquired cell number and division data. This is done by dividing the total number of cells in the ith division by 2i, where i is the division number. 0.5 is added to division numbers as CTV analysis does not distinguish between cells that have just divided (ie division 1.1), or those that are just about to divide (ie division 1.9). For quantitative analysis, a continuous scale of division progression is required. The Cohort number, as described in methods section 2.2.19.2, was graphed against the division number at 22, 39, 44, 62, 68, 86 and 110 hrs (Fig. 5.4b). These graphs are similar for the cells alone, combination, IL-2 and IFN-I cultures as the above graphs. The only graph that appears different is the IL-12 alone culture. This may be because there appears to be more cell death towards the end of the time course. The cells have still divided as evidenced from the graph in (a), however there is more cell death (PI+) resulting in lower cohort numbers after 62 hrs.

Next a Normal distribution is fit to the precursor cohort plot for each time point, excluding division 0, to determine the mean division number (MDN) of CTV profiles at each harvest time. The MDN is then plotted against time (Fig. 5.4c). All data points are shown on the graph, but the most reliable and less variable 4 data points were selected for a line of best fit, as MDN that are too high or too low are often considered less accurate. Therefore, the

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range was set between 25 and 100 hours. Extrapolation to the intercept with division 1 (dotted line) gives the mean time taken for lymphocytes to enter their first division, whereas the gradient of the slope is a measure for the division time (number of division per hr) and the reciprocal of the gradient of the slope give the division time (hrs) (resulted tabulated in Fig. 5.4d).

Based on this analysis IFN-I has the shortest time to first division, before 22 hrs (the first time point analyzed). No signal 3, IL-2 alone and the combination cultures all have the time of first division of 22 hrs. IL-12 is delaying the time to first division, out to 31 hrs. This fits the data in panel a, suggesting that the reason the divisions were more diverse was in fact due to the longer time to first division. IL-2 alone has the highest number of divisions per hr, and the shortest division time (9.94hrs). The combination culture was next highest in both these parameters, with a division time of 10.32. IL-12 was next, followed by no signal 3 and IFN-I alone (21.18, 53.96 and 82.32 respectively). Whilst IFN-I increases the speed to the cells first division, the following divisions are the slowest of all the culture conditions. On the other hand, IL-12 seems to slow down the first division, but speed up the following division times. Based on this analysis, IL-2 alone and the combination signal 3 appear to induce similar cellular behavior implying that IL- 2 within the combination culture may be contributing the most to this culture condition.

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Figure 5.4 The effect of signal three on the proliferation and cell numbers during activation of naïve CD8 T cells. Naive OT-I CD8 T cells were plated up as described. a) Cell number was graphed against the division number. b) Cohort number was graphed against the division number at 22, 39, 44, 62, 68, 86 and 110 hrs. Did 0.5 correction, non-linear fit, Gaussian with a range above 1 division. Non-fitted data is included on the graph as dot points, with the connecting line removed. c) The MDNs were determined from the cohort number against division graphs. This number was graphed against the harvest time. A line of best fit was

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produced, excluding outliers. Graphs were interpolated at division = 1 to determine time to first division (dotted line). d) Results from this graph were tabulated. Gradient of slope = # of divisions per hr. Interpolated 1/slope to get division time. NB ~ means data is uncertain/ambigious. Data was excluded from analysis. All cultures contained S4B6 at 25 μg/mL. Representative of 2 independent experiments, Mean ± SEM from triplicate culture wells.

5.2.1.3 Signal three cytokines affect cell survival diversely Having established the effect of signal three cytokines on the division and proliferation of activated CD8 T cells, next the cell death was examined. To do this, purified and CTV labeled OT-I T cells (104) were stimulated with 1uM of OVA either alone (no signal three, pink) or in the presence of 31.6U/mL IL-2 (orange), IL-12 (10ng/mL yellow), IFN-I (600U/mL, green) or all three in combination (blue) and harvested at various time points (Fig. 5.5a). Viability was measured by PI uptake, and total live cell numbers were determined and converted to cohort numbers as previously described. To normalize, 100% survival was set as the cohort number at 39hrs, and each cohort number was then graphed as percentage of this. A non-linear regression was used to fit a straight line to the data for each culture condition using GraphPad Prism. The graph was interpolated at 50% survival to estimate the time it took for 50% of the cells to die. As previously reported, and in support of the earlier data in this chapter, IL-2 supports cell survival [289]. This culture condition does not get below 50% cell death for the duration of the experiment (110 hrs). The combination culture also pushes cell survival, although not the same degree at IL-2 alone. IL-12 alone, IFN-I alone and no signal three each result in faster cell death, with 50% cell survival at 66.27 hrs, 52.16 hrs and 49.46 hrs respectively. Results are tabulated in Fig 5.5b. This data does not exclude the possibility that the IFN-I and IL-12 are more detrimental to cell survival, and are reducing the amount of survival for the combination group. The Y-intercept of each culture condition represents the predicted time that all cells would be dead.

In order to fully examine the cell death, it is important to document the number of cells which are recruited, and make it into the first division (Fig. 5.5c). This was determined by the number of cells at the time point immediately prior to the first division (22hrs). Again, the IL-2 alone and combination culture are indistinguishable, with the highest number of cells entering the first division (22,673 and 23,138 respectively). In this case, IL-12 culture has no difference to the no signal three control, but IFN-I is reducing the number of cells that enter division (8,179, 9,198, 5,505 respectively). Thus, signal three cytokines affect cell survival diversely. IL-2 promotes cell survival, as does the

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combination culture. IL-12 benefits cell survival, whereas IFN-I is disadvantageous to cell survival over the no signal three control. In order to determine the value of the contributions from each signal three to the combination culture, further modelling is required.

Figure 5.5 Signal three cytokines affect cell survival diversely. Purified and CTV labeled OT-I T cells (104) were stimulated with 1uM of OVA either alone (no signal three, pink) or in the presence of 31.6U/mL IL-2 (orange), IL-12 (10ng/mL yellow), IFN-I (600U/mL, green) or all three in combination (blue) and harvested at various time points. a) Viability was measured by propidium iodide uptake, total live cell numbers were determined and converted to cohort numbers as previously described. To normalize, 100% survival was set as the cohort number at 39hrs, and each cohort number was then graphed as percentage of this. A non-linear regression was used to fit a straight line to the data for each culture condition using GraphPad Prism. The graph was interpolated at 50% survival to estimate the time it took for 50% of the cells to die. b) The results from the graph in (a) are tabulated, the Y-intercept represents the time which all cells are dead. c) The estimated number of cells entering division for each stimuli condition is tabulated. This was determined by the number of cells at the time point immediately prior to the first division (22hrs). All cultures contained S4B6 at 25 μg/mL. Representative of 2 independent experiments, mean ± SEM from triplicate culture wells.

5.2.4 Differential metabolic profile induced by signal three molecules Recent work, including that described in this thesis (Chapter 3), has unveiled the importance of metabolic changes and switches in the early programming on CD8 T cells. Particularly relevant for this thesis is the switch from OXPHOS to glycolysis upon

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activation (as reviewed in [290]). Therefore, we utilized the Seahorse platform to determine what impact signal 3 has on the metabolism of activated CD8 T cells and more specifically. Briefly, Naïve OT-I CD8 T cells were sort purified and stimulated with OVA and IL-2, IL-12, IFN-I or in combination for 48 hrs. Naïve CD8 T cells were harvested on the day as a control. Cells were then harvested, rinsed and resuspended in XF Base Medium (pH 7.4) supplemented with sodium pyruvate (1 mM), L-glutamine (2 mM), and glucose (10 mM) 1 hr before performing the XF assay. Even numbers of cells were plated up in quadruple in a 96 well plate. During the assay, 3 readings were taken to determine the basal oxygen consumption rate (OCR), before injection of oligomycin (1 µM, to block ATP synthesis), FCCP (1 µM, to uncouple ATP synthesis from the electron transport chain, ETC), rotenone (0.5 µM) and antimycin A (to block complex I and III of the ETC, respectively) where indicated (Fig. 5.6a-c).

First the basal OCR, which is an indicator of OXPHOS, was examined for each of the conditions (Fig. 5.6d). The naïve OT-I T cells has a basal OCR of zero. The highest OCR was for IL-12, followed by IFN-I, and IL-2 then the combination culture, however all culture conditions with TCR stimulation show a similar basal OCR. After determining the basal OCR, oligomycin was added, which inhibits Complex V of the ETC. By inhibiting complex V, ATP synthesis is blocked, allowing determination of how much energy is dependent on ATP (Fig. 5.6e). ATP dependency is determined by subtracting the average OCR after addition of oligomycin, from the basal OCR. Naïve CD8 T cells have the lowest ATP production, followed by the combination culture, with IFN-I alone inducing the biggest change in ATP production. This data supports that seen earlier in this chapter, which showed that the combination culture was the most potent at activating and therefore different to the naïve cells.

Oligomycin injection also allows determination of the ‘proton leak’, which is determined by subtracting the average OCR after the addition of antimycin A and rotenone, from the average OCR after the addition of oligomycin (Fig. 5.6f). Rotenone and antimycin A block Complex I and III, respectively, thereby completely inhibiting the ETC, and as such any remaining oxygen consumption after administration of these drugs is non- mitochondrial. Naïve CD8 T cells have the lowest proton leak, and each of the stimulated cultures have an increased proton leak when compared to the naive. This data fits well with current literature, suggesting activated cells switch from mitochondrial energy production to the higher energy-producing alternative, glycolysis. There are only

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differences between the amount of proton leak between each of the TCR stimulated samples.

Level of glycolysis is determined through measurements of the extracellular acidification rate (ECAR) of the media surrounding the cells, which is predominately from the excretion of lactic acid after its conversion from pyruvate (Fig. 5.6). This reading is shown over time, and is significantly lower in naïve cells, until the addition of FCCP to the media, after which it spikes. FCCP uncouples respiration from oxidative phosphorylation, allowing oxidation of any oxidizable substrate present in the assay medium to occur. Each cell condition that receives TCR stimulation has a similar ECAR during the course of the experiment, with IL-2 alone being slightly higher, and IL-12 alone ending up the lowest. Interestingly, the already stimulated samples did not have the same peak of glycolysis as the naïve cells, suggesting they were already at their maximum level.

The OCR response to FCCP provides a semi-quantitative assessment of cells' ability to oxidize glucose, and is referred to here as ‘spare respiratory capacity’ (SRC, Fig. 5.6h). SRC is the extra mitochondrial capacity available in a cell to produce energy under conditions of increased work or stress and thought to be important for long-term cellular survival and function [162, 195, 198, 291, 292]. FCCP stimulates a spike in the OCR in all culture conditions, but is highest in the combination culture indicating that the biochemical pathways for glucose oxidation are more active in the combination culture compared to all other conditions. Consistent with this, the combination culture possessed a higher OCR/ECAR ratio than any other culture condition (Fig. 5.6f). This indicates that cells stimulated with IL-2, IL-12 and IFN-I in combination preferentially use OXPHOS rather than glycolysis.

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Figure 5.6 Metabolic analysis of stimulated cells after culturing with signal three cytokines and the effects of mitochondrial inhibitors on the electron transport chain. Cells were stimulated with OVA either alone or in the presence of IL-2, IL-12, IFN-I, a combination of all three and analyzed for their mitochondrial capacity using the Seahorse

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platform. Briefly, Naïve OT-I CD8 T cells were sort purified and stimulated with IL-2, IL-12, IFN-I or in combination for 48 hrs. Cells were then harvested and rinsed and resuspended in XF Base Medium (pH 7.4) supplemented with sodium pyruvate (1 mM), L-glutamine (2 mM), and glucose (10 mM) at 45 - 60 minutes before performing the XF assay. a) During the assay, 3 readings were taken to determine the basal OCR, before injection of oligomycin (1 µM), FCCP (1 µM), antimycin A (0.5 µM), and rotenone (0.5 µM) where indicated. 3 readings were taken after the addition of each drug. b) Oligomycin is an ATP synthase inhibitor, FCCP is a protonophore that uncouples ATP synthesis from the electron transport chain (ETC), rotenone is a Complex I inhibitor, and antimycin A is a Complex III inhibitor. Rotenone and Antimycin A together render a complete shutdown of the ETC. c) Summary data of the OCR over time, showing the times that oligo, FCCP and myx/RotA were injected into the cells. d) Basal OCR. e) ATP coupled. f) Proton leak. g) Spare respiratory capacity. h) Ratio of OCR/ECAR. i) ECAR over time. OCR- Oxygen consumption rate, ECAR- Extracellular Acidification Rate, FCCP- carbonyl cyanide 4-trifluoromethoxy phenylhydrazone, oligo- oligomycin, myx, rot- rotenone. Experiment done once, with quadruple wells. d-i show each data point of the 3 readings, and 4 replicate wells. Mean and SEM shown.

5.2.5 Signal three has a direct effect on the chromatin landscape of CD8 T cells Chromatin remodelling by DNA demethylation or histone acetylation results in the establishment of an open or closed area, which promotes gene activation or repression. This method of gene regulation is known to play a role in differentiation of many cell types, including differentiation of CD4 T cells to become Th1 or Th2 effector cells [293] and CD8 T cell differentiation [294]. There is emerging evidence that these mechanisms may contribute to the more rapid responsiveness of memory CD8 T cells [295-297]. Based on this knowledge and the unexpected results showing that addition of signal 3 cytokines did not appear to play a role in cell division dynamics, we wanted to examine whether they are having a direct impact on the epigenetic landscape of differentiating CD8 T cells. To interrogate the role of signal three in genome accessibility during CD8 T cell differentiation, ATAC-seq was utilized to assess genome-wide accessibility to the Tn5 transpose, which determine the level of openness of the chromatin of these samples. Briefly, naïve (CD44lo) CD8 T cells were sort purified to above 98% from the spleen and lymph nodes of naïve OT-I mice (Fig. 5.7a) and stimulated for 72 hrs with OVA and rhIL-2 (31.6U/mL), either or alone or in conjunction with IL-12 (10ng/mL), IFN-I (600U/mL), or both IL-12 and IFN-I (referred to as combination). At 72 hrs cells were sorted these cells on those which had divided over 4 times (Fig. 5.7b). Cells were also harvested and collected at 24 hrs, and counted (Fig. 5.7c). Some differences in the culture conditions explain the difference in cell proliferation seen in this result, compared to that in previous experiments in this chapter. Here, the mouse IL-2 blocking antibody S4BG

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was not added to the cultures, but hIL-2 (31.6U/mL) was added to all cultures. This allows the comparison of each culture back to the IL-2 alone, baseline culture. In previous experiments where S4BG was added, there was more cell death present in the IFN-I alone and IL-12 only cultures. From (c) we can see that IL-2, IL-12, IFN-I and combination cultures result in considerable expansion (3.5, 3.6, 4.7 and 2.4 times, respectively). Based on the CTV plots we can also determine the rate of proliferation, by comparing the number of cells at 72hrs which have made 3 division or less, compared to the number of cells which have divided more than 4 times. Previous experiments determined IL-2 alone to result in the highest cell survival, but in these culture conditions IL-12 results in the highest proliferation, followed by the combination culture, IL-2 alone and then IFN-I alone (2.3, 2, 1.8 and 1.5 times as many cells in 4+ divisions rather than 3 or less, respectively). Key activation markers CD69, CD25, CD44 were assessed by flow cytometry after sorting from division 4 (Fig. 5.7d). All three markers were upregulated compared to the unstimulated (red), and gave similar results to those seen earlier in this chapter, validating their activation. In order to look at the epigenetic profiles of cells cultured with each of these cytokines, alone and in combination, those cells which had divided more than 4 times were sorted at 72hr from IL-2 alone, IL-2 and IL-12, IL-2 and IFN-I, and IL-2, IL-12 and IFN-I cultures.

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a Sort Profile Purity Check 68.2% 98.7% CD44

CD8 b 24h 72h

Div 4+ Div 0 Div 0-3

Number of CellsNumber Violet Tracker

c Sample Number of cells at Number of cells at 72h (x103) 24h (x103) Div 0-3 Div 4+

Unstim 35.5 63 188

OVA alone 38.7 790 78

+IL-2 119 147 270

+IL-12+IL-2 143.6 157 366

+IFN+IL-2 169.7 315 479

+IL-12+IFN+IL-2 200.3 157 318

24h Comb IFN+IL-2 IL-12+IL-2 IL-2 No Sig 3 Unstim Number of CellsNumber 72h

CD69 CD25 CD44

Figure 5.7 Harvest and collection stimulated CD8 T cells for ATAC-seq. Briefly, naïve (CD44lo) CD8 T cells were sort purified from the spleen and lymph nodes of naïve OT-I mice and stimulated for 72 hrs with OVA and rhIL-2 (31.6U/mL), either alone or in conjunction with IL-12 (10ng/mL), IFN-I (600U/mL), or both IL-12 and IFN- I. (a) Sort profile of naïve, OT-I CD8 T cells. b) Sort profiles from cells at 24 hrs (left) which had not yet divided, and at 72 hrs (right) which had divided over 4 times. c) Cell yield from the sorts. d) The key activation markers CD69, CD25 and CD44 were assessed by flow cytometry after sorting from div 0 (24 hrs) or above division 4 (72 hrs).

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5.2.6 Quality control of ATAC-seq samples The samples were subjected to QC before and during the processing of samples for ATAC-seq. Briefly, 50,000 cells from each sample was digested with Tn5 transposes to ‘chop’ the open chromatin. 50, 000 cells from each sample were treated the same, except were not digested with Tn5 (undigested controls). This accessible chromatin was then fragmented, and high throughput sequencing was performed. During this extraction process qPCR is performed to determine how many cycles are required for successful amplification (Fig. 5.8a). This is also carried out on the undigested controls, to ensure no nonspecific amplification occurs (these lines are the same color as their matching digested samples, but don’t reach above the determined threshold indicated by dashed line). Next, their concentration and quality was determined using the bioanalyzer (Fig. 5.8). In order to to determine how successful the fragmentation step was, the fragment sizes for amplified ATAC-seq libraries were determined by gel electrophoresis (Fig. 5.8b). This gel also allowed for a visual comparison of the replicates, which appear to be fragmented efficiently and similarly. Bioanalyzer determined these fragmented sizes for amplified ATAC-seq libraries for each sample, showing IL-2 as an example (Fig. 5.8c), with tabulated results of each culture condition (Fig. 5.8d). All samples had an average size between 386 and 452bp, and a concentration above 937pg/uL suggesting efficient fragmentation and yield. Having validated the samples, ATAC-seq was performed.

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Figure 5.8 Results from bioanalyzer: Quality control of ATAC-seq samples. a) Representative amplification plots demonstrating the correct number of additional cyles to perform for four ATAC-seq libraries at 24 and 48 hours after stimulation. b) Fragment sizes for amplified ATAC-seq libraries, determined by gel electrophoresis. c) Fragment sizes for amplified ATAC-seq libraries, determined by Bioanalyzer. d) Tabulated results from representative sample from Bioanalyzer.

5.2.1.6 Validation of ATAC-seq data A PCA analysis was utilized to validate the replicates of ATAC-seq data, and to determine the amount of similarity between each sample (Fig. 5.9a). These analyses indicated there were inconsistencies with one of the combination culture duplicates and due to this; we excluded it from the PCA plot and all further analysis. The PCA shows clearly the naïve

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sample sitting furthest away from all other samples, indicating it is the most different. Although it appears the duplicates are spread out, they are in fact all very close together, as the x-axis has a small range (-5 to -0.25). Thus, all the stimulated ATAC-seq samples actually cluster close together, including the duplicates, giving confidence in the dataset.

Next, a list of well-studied genes with anticipated changes under the conditions considered were compared by the peaks called in their promoters, resulting in a list of gene counts in a hierarchical clustered heat map (Fig. 5.9b). The samples were compared to a naïve CD8 sample (data acquired by Jasmine Li). The clustering resulted in the samples falling into two classes, separating the naïve from the stimulated. The stimulated samples were further divided into 2 groups of 2: IL-12 and IL-2, and the IFN-I alone with the combination culture. Thus, based on this analysis the IL-12 and IL-2 cultures are similar, and the IFN-I and combination culture are similar.

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Naive 0.8 0.6 .2 0.4 p Com 0.2 0.0

IL12 IFN IL12 IL2 IFN Combination

−0.2 IL2

−0.45 −0.40 −0.35 −0.30 Comp.1

Figure 5.9 Validation of the ATAC-seq samples. a) PCA analysis plot. b) A hierarchial cluster heat map of the ATAC-seq samples.

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5.1.1 Differential amounts of chrM accessibility based on alternate sources of signal 3 Despite the largest difference being seen between the unstimulated and stimulated samples during the Seahorse experiments, minor differences in the metabolic profiles of cells have been previously shown to be of great importance. Because of this, we utilized the ATAC-seq data to probe for differential metabolic regulation between the stimulation conditions. To do this, the ratio of mitochondrial DNA to genomic DNA was determined (Fig. 5.10). Naïve OT-I CD8 T cells had a similar percentage of mitochondrial DNA to that of cells with IL-2 in the culture (around 20%). When IL-12 or IFN-I were added to the culture, the amount of mitochondrial DNA decreased, suggesting these cells were switching from primarily using mitochondrial energy, to the use of glycolysis. When all 3 cytokines were added to the cells at once, this swing to glycolysis was enhanced. This data confirmed early suggestions that by giving all cytokines at once it resulted in higher activation. Combining this data with the seahorse data, which showed the combination culture to have an elevated SRC and OCR/ECAR ratio, it is clear that the addition of alternate signal three has a differential effect on the metabolism of the CD8 T cells. These differences are seen molecularly and on a chromatin level, and through the study of the cells energy processes. Thus, metabolic programs of CD8 T cells are highly plastic and adapt to facilitate the changing function of these cells in the inflammatory process. Further, the 3 cytokines added together activate a differential pathway of metabolism, which may explain the unique pattern of differentiation and cell proliferation.

Figure 5.10 Mitochondrial analysis from ATAC-seq a) Ratio of reads aligned to chrM.

5.1.2 Combined culture has the highest number of regulated regions, and therefore highest amount of chromatin remodelling

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In order to explore the effect signal three is having on the regulatory molecules during activation, genome region associations were explored in the ATAC-seq data using GREAT. Using this tool, peaks were called and lists of regulated genes were generated for each condition, compared to IL-2 alone (Appendix, Table 2.7). The first striking difference from this analysis was evident from the number of genes from each list, which indicate the amount of chromatin remodelling compared to the IL-2 alone control. The combination culture had 1458 genes that were identified as being regulated when compared to that of the IL-2 alone culture. The IFN-I alone culture had 589 and the IL- 12 culture had 280. Based on this data there is clearly more chromatin remodelling and therefore potential activation occurring when all 3 cytokines are present at once.

In order to examine these gene lists more thoroughly, they were compared for overlap and similarities were identified (Fig. 5.11). Surprisingly, as all cells received the same stimulation including TCR signal strength and amount of IL-2, only 32 regulated genes were shared between all 3 conditions. This highlights the differences between the effects of each cytokine on the cell at a molecular level. Despite relatively small differences being highlighted during the early activation through read outs such as cell division and phenotype markers, we can see that there are clear distinctions on a molecular level, depending on which cytokines were given during this activation phase. Hence, not only are the signal threes having a direct effect on the chromatin, but the addition of multiple signal three cytokines results in the highest amount of effect.

Figure 5.11 Comparison of gene list of regulated regions acquired from ATAC-seq. Venn diagram showing the overlap and differences between the different stimulation conditions. 5.1.2.1 Comparison of IL-2 alone against other samples of ATAC-seq data Based on the above data, which revealed a heightened level of cell activation as described the higher degree of chromatin regulation in the combination culture, we wished to assess

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the amount of gene regulation in each culture condition. To do this, the ATAC-seq data was next examined individually by comparing each stimulation condition to the IL-2 alone condition, as it is the base condition. From these comparisons, non-overlapping peaks were determined for each sample. The number of associated genes per region was determined for each condition (Fig. 5.12a-c, left). This shows how many genes each genomic region is assigned as putatively regulating, based on the association rule used. The most striking results from these graphs are how many more genomic regions are ‘called’ in the combination culture, and that there is a higher amount of associated genes per region in the combination culture. Thus, the combination of signal threes is resulting in more changes to the chromatin during this early time point, suggesting they may be additive.

Next, the non-overlapping peaks were sorted by orientation and distance to transcription start site (TSS) which shows the distance between input regions and their putatively regulated genes (Fig. 5.12a-c, right). The distances are divided into four groups: one from 0 to 5 kb, another from 5 kb to 50 kb, a third from 50 kb to 500 kb, and all associations over 500 kb. Again, the differences between each cytokine condition are clear. Whilst the distributions of the distance to TSS in IL-12 and IFN-I appear binomial, whereas the genes regulated in the combinational culture are more dispersed around the TSS, and higher in number. This indicates that the peaks induced by the combination culture are further away from the annotated genes that those called in the IL-2, IL-12 and IFN-I alone. Thus, there is more open chromatin further away from the TSS of annotated genes in the combination culture and therefore more chromatin modification and regulation occurring. These indicate a more activated state is induced in the combination culture, when compared to IL-12 alone or IFN-I alone. Also note the discrepancy between the numbers of genes (Fig. 5.11) and regions (Fig. 5.12) called during this analysis. There are more regions called than genes, indicating that genes can have multiple regions.

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Figure 5.12 Comparing each condition to IL-2 alone using ATAC-seq. BED files were obtained by determining non-overlapping peaks from the ATAC-seq data for each condition when compared to cells cultured with IL-2 alone. To do this, each stimulation condition was compared to the IL-2 alone condition, as it is the base condition. From these comparisons, non-overlapping peaks were determined for each sample. These were first examined individually. The number of associated genes per region were determined for IL-2 alone compared to IL-12 a), IFN-I b) and the combination of both c) (left). The orientation and distance to TSS of each non- overlapping peak were determined for each condition (right). Analysis done using GREAT.

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5.2.7 Signal three shows the same effects in vivo as shown in vitro In order to determine if the results obtained from the in vitro system could be applied in vivo, naïve (CD44lo) OT-I CD8 T cells were sort purified from the spleen and lymph nodes of naïve OT-I mice and stimulated for 2 days with with anti-CD28 Ab co- stimulation and OVA peptide. Cells were cultured with recombinant human interleukin- 2 (rhIL-2), rIL-12, IFN-I, or a combination of all three to give 5 experimental groups: unstimulated, OVA, OVA+IL-2, OVA+IL-12 OVA+IFN-I and OVA+COMB. All cultures contained CD28 as co-stimulation. At 48 hrs, cells were removed from stimulation, CTV labeled and 5x105 cells were intravenously transferred to naïve, B6 mice (Fig. 5.13a). 2.5 days later, spleen and mLN were collected from the mice. The combination culture had the highest number of cells recovered in both organs (Fig. 5.13b). Followed by IL-12, and IL-2 and TCR stimulation alone. The combination culture did not have the highest proportion of antigen specific CD8 T cells, implying the delivery of these cells caused inflammation (Fig. 5.13c). No other macroscopic signs of inflammation were noted. The down regulation of CD62L was assessed to determine the level of activation. Unexpectedly, the unstimulated appeared to have the lowest MFI, however this is most likely due to a very low number of cells being recovered, and this data should not be considered reliable. CD44 was highest on the group stimulated in the presence of IL-2.

Functionality and cytotoxicity were also examined. Boolean gating was used to determine the polyfunctionality of cells retrieved from the spleens of the mice (Fig. 5.13d). They were intracellularly stained for IL-2, TNF-α and IFN-γ immediately ex vivo and after 5 hrs of re-stimulation with OVA. Ex vivo all culture conditions had cells which produce IFN-γ, and were predominately triple negative (non-producers). Interestingly, the most polyfunctional culture condition was OVA alone, in the absence of any signal three cytokine. After re-stimulation, all culture conditions were similar. Each had approximately 25% IFN-γ single producers. Next, the fold-increase of IFN-γ, TNF-α and IL-2 was determined after the re-stimulation. The combination culture showed the highest increase in IFN-γ and TNF-α production, followed by the IL-12 and IL-2 alone groups. There was more variability in the production of IL-2, with IL-2 alone, IL-12 alone and the combination culture showed marked increases in the production. A similar pattern was found when the Gzm B was examined (Fig. 5.13e). The combination culture showed the highest production, followed by IL-2, OVA alone, IL-12 alone and IFN-I alone. The

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unstimulated showed no Gzm B production. These results show similar results to those obtained using the in vitro method, further supporting our results and validating the method.

a Naïve OT-I CD8+ T cells sort purified (CD44lo CD8+) Stimulated with OVA+CD28 and: Spleens and mLN from • IL-2 mice collected at day 2.5 • IL-12 • Type I IFN 5 • All cytokines (Comb) Collected at 48 hrs 10 cells VT labeled and transferred into naive mice

b Spleen mLN Unstimulated OVA 0.0159 0.0079 0.0317 OVA+IL-2 20 20 0.0317 ) 0.0079 0.0159 OVA+IL-12 5 0.0317 OVA+IFN 15 15 OVA+COMB specific - 10 10

5 5

0 0 CD8+ T cells (x 10

Number of Ag 0.0317 OVA 100 OVA 90 0.0317 OVA+IL-2 OVA+IFN OVA+IL-12 OVA+IL-2 OVA+IFN Unstimulated OVA+COMB OVA+IL-12 Unstimulated80 OVA+COMB 80 60 70 40 specific alive - 60 20 CD8+ T cells ND 0

% Ag 50

OVA OVA

OVA+IFN OVA+IL-2 OVA+IFN OVA+IL-2OVA+IL-12 OVA+IL-12 OVA+COMB c Unstimulated CD62L OVA+COMB Unstimulated

10 0.0159 0.0317 0.0159 0.0079 8 0.0317 )

5 0.0079 0.0079 6 Count 4

MFI (xMFI 10 2

OVA CD62L

CD44 OVA+IFN OVA+IL-2OVA+IL-12 OVA+COMB Unstimulated15 0.0159 0.0079 ) 5 0.0317 0.0079 10 Count MFI (xMFI 10 5

0 OVA CD44 OVA+IFN OVA+IL-2OVA+IL-12 Unstimulated OVA+COMB

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Figure 5.13 Differential expansion and phenotypes of CD8 T cells exposed to differing signal threes in vivo. a) Timeline of in vivo experiment. Naïve, CD8 OT-I T cells were harvested and purified as described. Cells were stimulated with OVA and aCD28, in the presence of either IL- 2, IL-12, IFN-I or all cytokines together for 48 hrs. 105 cells Violet Tracker labeled and transferred into naive mice, and spleens were collected after 2.5 days in vivo. b) The number of OT-I CD8 T cells recovered from the spleen and mLN of mice. c) MFI of CD62L and CD44 from OT-I CD8 T cells from spleens. d) functionality of OT-I CD8 + t cells from spleen directly ex vivo, or after 5 hr re-stim with OVA (left) and the fold- increase of cytokines in stimulated cells compared to unstimulated (right). e) Normalized percentage of GzmB expression. N= 5 mice per group, filled lines are isotype controls. Experiment done once, Mann-Whitney, SEM shown. All stimulation conditions also contain aCD28.

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5.2 Discussion:

This chapter investigated the roles of different signal three cytokines alone, and in combination on the effect of activation, proliferation and acquisition of effector function, and how this correlates with chromatin accessibility. Considering the continued interest in novel vaccination strategies that induce and sustain CD8 T cell responses, understanding the mechanisms that induce optimal CD8 T cell responses is vital. Thus, it is of great interest to determine which internal/intrinsic cues allow the cells to ‘decide’ which environmental cues to accept and which to ignore. Using mathematical modelling of in vitro activated cells, combined with bioinformatic analysis of ATAC-Seq, we have shown that signal three has a direct effect on the chromatin landscape. Each cytokine has a different impact on the degree of chromatin accessibility and thus differentially regulates genes during this vital activation phase of CD8 T cell differentiation, with a combination of signal 3 cytokines results in the highest level of accessibility and therefore activation, proliferation and downstream acquisition of effector function. Signal 3 itself is an under-appreciated cascade of regulation during the activation of CD8 T cells by directly controlling chromatin structure which is required for establishment of transcriptional signatures and this is mediated via specific transcription factor binding to key gene loci.

Unexplored in this thesis, yet an important feature of signal 3 during CD8 T cell differentiation is timing. A brief exposure (6hr) to antigen and co-stimulation (B7-1) is sufficient to stimulate multiple rounds of division, but clonal expansion and development of effector function are minimal [50]. Full activation requires combined signalling by antigen, co-stimulation and IL-12 for greater than 40 hours. The 2003 study concludes that gene expression for cell division was initiated by a brief interaction with antigen and co-stimulation, but maintenance of the expression of the genes needed for survival and effector function requires prolonged signalling by signal 3 cytokine in concert with antigen and co-stimulation [50]. This is in contrast to our study, which shows signal three having a direct effect on the chromatin landscape, thus controlling gene expression and activation. However, if this study was repeated as a time course, starting from 6hrs, it may show more parallels to or explain the results found in the 2003 study. For example, the TCR stimulation and costimulation result in enough chromatin remodelling to result in the genes involved in cell division to be activated, but the signal three is required, along with the time and division, to result in full chromatin remodelling.

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Due to the Tg OT-I T cell system being used in this thesis, the role of affinity on the chromatin landscape was not explored. Previous studies have shown that both the frequency of cells responding to a stimulus, and the average time taken for cells to reach their first division are affected by peptide concentration and affinity [284]. This study implied that affinity is the sole regulator of cell death in subsequent division. The chicken OVA-derived peptide N4 and variants D7, E1, G4, R4, S4, and V4 could be used to repeat these experiments, which would reveal the role of affinity. The strength of TCR stimulation has been suggested to influence if a naïve T cell will differentiate into a memory or effector cell, based on the data presented in this chapter signal three is also playing a vital role in this process. In order to fully elucidate the early factors affecting memory T cell differentiation, one should assess factors (affinity and signal three) together.

A previous study with a similar set up to ours has investigated the metabolic consequence of stimulated cells with IL-2 or IL-15 [162]. In this case the OT-I cells were activated with OVA peptide for 3 days, and subsequently cultured in either IL-2 or IL-15 to generate IL-2 TE and IL-15 TM cells, respectively. In response to FCCP, those cells cultured with IL-15 have significantly higher OCR than those to generate Te cells. This is fitting with previously published data on memory cells, showing that memory cells have a lower basal extracellular rate (ECAR) and a substantially higher mitochondrial spare respiratory capacity [162]. Despite the changes to protocol, we see a similar result in the IL-2 alone cultures. Based on the data that a higher OCR results in memory formation, is could be speculated that the combination culture is able to promote better memory cell differentiation, especially given their increased spare respiratory capacity. This metabolic data is of particular interest given the knowledge that the programming of memory cells starts during initiation of activation (Chapter 3).

Agarwal et. al., (2009) have shown that cytokines program the development of function and memory within 3 days of initial stimulation. Much of the gene regulation program was initiated in response to TCR stimulation and co-stimulation within 24 hours, but this is not enough to ‘set’ the cells on a path to differentiation, unless a cytokine signal is available. In this paper they show that the use of histone deacetlyase inhibitor mimics the effects of IL-12 and IFN-I signalling, indicating that the cytokines are also relieving repression and allow continued gene expression by promoting increased histone

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acetylation [72]. From this data it was concluded that IL-12 and IFN-I enforce in common a complex gene regulation program that involves, at least in part, chromatin remodelling to allow sustained expression of a large number of genes critical for CD8 T cell function and memory. Our data confirms this papers claims. In order to look further into this, with the possibility that maybe the cytokines are working in numerous ways to remodel chromatin, as opposed to just promoting histone acetylation, we looked more globally at the entire genome.

Memory CD8 T cells have been previously shown to exhibit increased histone acetylation levels for genes that are expressed more rapidly than in naive cells following activation, including eomes, perforin, and gzm B [204, 295-297]. The results described here and in other papers [72], together with the evidence that a signal from IL-12 or IFN-I is required to program for memory development [75], suggest that much of the chromatin remodelling may be initiated by these cytokines. An important difference between the Mescher study and ours is the fact that we looked at these cytokines in combination, and alone. The previously unaddressed heightened effect of cytokines during the differentiation of CD8 T cells can now be suggested to be another vital part of memory differentiation. It is not the delivery of just one cytokine that allows chromatin remodelling and the downstream increased histone acetylation levels, but the coming together of numerous cytokines to have this result. Thus, this data proposes signal three cytokines to be a much more complicated level of regulation that the original ‘3 signal model’. Each cytokine is an individual additional signal, and the strength (amount per cytokine) and number (of types of cytokine) is what orchestrates the fine-tuning of CD8 T cell differentiation and ultimately, fate.

Cell localization in vivo and the timing of the motile cells should also be considered when discussing T cell activation and Signal 3. Inflammation, and therefore cytokines, are likely heightened in inflamed tissue during infections. Thus, it is likely that the DCs which activate the CD8 T cells later in the lymph node would be in direct interaction with these cytokines. This chapter has not considered the effect of cytokines on APCs. Further work should explore how the 3 signals in this chapter also affect the DCs, including their epigenetic landscape. This would provide more clues into their motility, and the licensing of CD8 T cells. When the T cells are in direct contact with the inflammation and cytokines, is likely to be after they have been activated, and are entering the infected

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tissue later. To assess this, signal 3 molecules could have been applied to the cells at time intervals after their activation, to better recapitulate what happens in vivo.

Further work should be done in order to determine how a naïve T cell not only receives these ‘messages’ and the result, but what is involved in the decision-making process between the stimulus and outcome. In a complex infection model each individual cell will be exposed to a milieu of different signals and it is important to determine how these decisions are made.

In summary, this chapter gives insight into the intricate balance of different activating signals and their direct impact on the chromatin architecture and downstream CD8 T cell activation. Signal three is important for the remodelling of chromatin, allowing TFs to dock and result in the acquisition of effector function. Moreover, the amount and number of signal three cytokine directly affects the amount of remodelling and accessibility of the chromatin. Therefore, it is of little surprise that different infections cause the release of particular inflammatory cytokines during an immune response, as each cell must be directed to carry out an infection-specific function. Finally, this data suggests that signal three is having a direct effect on the chromatin of activating CD8 T cells, imprinting on them how to react metabolically to their new environments.

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Chapter 6: Concluding remarks

In an effort to examine the timing and external cues that contribute to the establishment of immunological T cell memory to virus infection, this thesis has provided important insights into the activation, regulation and differentiation of CD8 T cells. It examined the impact of a variety of external cues that dictate CD8 T cell fate following activation. The results of this study have important implications for numerous therapies and medical interventions intended to manipulate the CD8 T cell response, particularly the design of vaccines to induce cross-protective anti-viral CD8 T cell responses.

In response to the specific aims as set out in the beginning of this thesis:

• To determine the role and timing of CD4 T cell help during memory CD8 T cell differentiation, and to investigate differential gene expression between helped and unhelped memory cells.

Chapter 3 pin pointed the timing of CD4 T cell help during memory CD8 memory T cell formation to be at the point of priming, during early activation. By doing so, this study contributed to the field by reconciling early disparities in that CD4 T cell help appeared to be required both at the time of priming, whilst there also being a defect in maintenance of memory CTL in the absence of CD4 T cell help. A key finding from this chapter was that unhelped memory OTI CTL induced after IAV-infection were unable to recover function when transferred into a CD4 competent host, suggesting that memory potential is lost relatively early after activation in the absence of CD4 T cell help. Moreover, this thesis interrogated differential gene expression profiles between helped and unhelped memory cells to demonstrate that unhelped memory CTL have dysfunctional metabolic capacity and a more exhausted phenotype, a previously unreported finding despite the immense amount of studies done in this area.

• To compare the transcriptional profiles of each stage of CD8 T cell differentiation in an attempt to identify genes that are stage-specific and therefore vital for memory cell production.

Using RNA-seq, chapter 4 has emphasized the high similarity between effector and memory CD8 T cells, and between naïve and memory CD8 T cells. It has shown that the

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biggest transcriptional ‘shifts’ in CD8 T cells occur not during differentiation, but after re-stimulation with cognate antigen. Dmrta1 was identified as a candidate for a memory- exclusive gene, and may represent a gene which can be targeted to selectively drive differentiation of memory CD8 T cells. The capacity to selectively induce T cell memory cells, and not effector T cells, may provide novel methods for stimulating protective immunity and innovative vaccine strategies. Zbtb32 was identified as a potential key gene during CD8 T cell differentiation, with a poised molecular profile and known interactions with other key genes such as GATA3. Collectively, we nominate Dmrta1 and Zbtb32 as genes important in the transcriptional network responsible for the balance between terminal differentiation and formation of memory CD8 T cells. Thus, this analysis has proposed 2 genes that have the potential to modulate the CD8 T cells response through novel vaccine design.

• To investigate the direct effect role signal three has on CD8 T cell differentiation by determining the effect of different signal three stimuli on chromatin landscape.

Chapter 4 has given novel insight into the intricate balance of cytokines and their direct impact on CD8 T cell activation, proliferation and chromatin architecture. Signal three acts directly on the chromatin by remodelling its accessibility, presumably allowing TFs to dock and result in cell proliferation and acquisition of effector function. Cell division data suggested that IL-2 is more important for activation that IL-12 or IFN-I, yet that each of these cytokines in combination results in a heightened response and induces the most change in the cells metabolic profile. This chapter provided evidence of signal three dictating the cell's metabolism, which is imprinted at a chromatin level instructing cells how to react metabolically to their new environments.

Taken as a whole, the data generated in this thesis highlights the importance of early events during activation of a naïve T cell, to be programmed to become a successful memory cell. There is a very tightly controlled cascade of events to drive the process of memory cell differentiation, in order to retain potential, or to terminally differentiate. While we favour a model whereby CD4 T cell help and signal 3 is important for helping retain memory potential via limiting extensive T cell differentiation, further analysis is required to delineate the precise time after activation when such potential is lost. A point to highlight is that the changes induced on the cells at the point of activation, as dictated by the signal three cytokines and CD4 T cell help directly affect the metabolism and cell

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division not just at the point of activation, but influence the long-term status of the cell. This work is contributing to solving the currently unknown differentiation steps of a CD8 T cell, destined to become either a terminally differentiated effector cell, or a memory cell subtype.

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Chapter 7: Appendix

Table 7.8: Regulated genes for each condition, compared to IL-2 alone. Combination Culture IL-2 + Type I IFN IL-2 + IL-12 1110034G24Rik Clvs1 Gm5 8 6 1 Myl6b Rrn3 Xaf1 1500035N22Rik Isg15 Vgll3 1700017B05Rik 1110059G10Rik Cmah Gm5 8 6 2 Myo10 Rrs1 Xdh 1700021F07Rik Islr2 Vmn 1 r2 1700048O20Rik 1500015O10Rik Cmklr1 Gm6 4 6 0 Myo1d Rsad2 Xpnpep1 1700028K03Rik Itsn1 Vp reb2 2700062C07Rik 1700001F09Rik Cnbd2 Gm6 4 8 3 Myo9a Rsbn1 Xpr1 2300009A05Rik Jak1 Vsig1 0 l 4732465J04Rik 1700018B08Rik Cnih Gm7 0 7 5 Myo9b Rsg1 Xrcc5 2410066E13Rik Jarid2 Whrn 4930558K02Rik 1700021P04Rik Cnnm4 Gm7 1 2 0 Myom1 Rtf1 Xrn2 3110018I06Rik Jph2 Wnt7b 4930572O03Rik 1700024P04Rik Cnot6l Gm7 6 5 Myom2 Rundc3b Ypel3 3830408C21Rik Kank1 Xcr1 4931408C20Rik 1700056E22Rik Cntln Gm8 1 3 8 Myoz3 Rusc2 Ywhaz 4921511H03Rik Katnal1 Xxylt1 4932442E05Rik 2010015L04Rik Cobl Gm8 3 2 5 N4bp1 Ruvbl1 Zbed6 4930566N20Rik Kcnh2 Xylt1 A530032D15Rik 2010109I03Rik Col12a1 Gm9 7 5 8 N4bp2 Rxra Zbtb24 4930572O03Rik Kcnk12 Yes1 AW822073 2010111I01Rik Col15a1 Gm9 8 4 3 N4bp2l1 Ryk Zbtb42 5031425E22Rik Kcns1 Ypel2 Abhd17b 2310009B15Rik Col4a6 Gm9 8 8 4 Naa16 S1 0 0 a1 0 Zc3h11a 5330438I03Rik Kctd3 Zan Abra 2310061I04Rik Commd3 Gm9 8 8 7 Naa20 S1 0 0 a6 Zc3h18 9330101J02Rik Kdm6b Zbp1 Actbl2 2610002J02Rik Commd9 Gm9 9 2 2 Nanos1 S1 0 0 a7 a Zcchc13 9930021J03Rik Kdr Zbtb17 Adam6a 2610018G03Rik Cops2 Gm9 9 3 3 Napb Sall3 Zcchc2 A130010J15Rik Kif21b Zbtb26 Ago1 2610301B20Rik Cops4 Gm9 9 5 6 Nat10 Sap 1 3 0 Zdhhc18 A530032D15Rik Klf9 Zbtb33 Alcam 2810004N23Rik Cops5 Gm9 9 6 7 Nbea Sc4 m o l Zer1 A630001G21Rik Klhl2 Zbtb44 Alms1 2810428I15Rik Coq2 Gm9 9 9 2 Nbeal2 Scaf 1 Zfand4 A830010M20Rik Klhl20 Zcchc11 Anapc15-ps 2900026A02Rik Cotl1 Gn a1 2 Ncald Scan d1 Zfhx3 A930033H14Rik Klhl3 Zdhhc14 Anxa6 3110001I22Rik Cox7b Gn a1 3 Ncapd3 Scn 1 0 a Zfp141 AA467197 Kmo Zfp236 Apba1 3110018I06Rik Cpb2 Gn aq Ncoa2 Scn 1 1 a Zfp146 AI597468 Ksr1 Zfp251 Arl13b 3110062M04Rik Cpe Gn as Ncoa4 Sdh b Zfp148 AW554918 Lacc1 Zfp280d Arl4d 3110070M22Rik Cpm Gn b2 l1 Ncoa6 Sec2 3 b Zfp185 AW822073 Lasp1 Zfp36l2 Arpp21 3830408C21Rik Cpn2 Gn g1 2 Ndc80 Sec6 3 Zfp2 Aamdc Ldoc1l Zfp397 Atl1 4922502B01Rik Cpne2 Gn g2 Ndfip1 Sem a3 f Zfp202 Abcb1a Lemd3 Zfp534 Atp6v1h 4930412O13Rik Cpq Gn p at Ndnl2 Sem a4 d Zfp217 Abhd8 Lgals9 Zfp600 Atxn7 4930447A16Rik Cpt2 Go t 2 Ndufaf3 Sep w1 Zfp235 Abi1 Lgalsl Zfp709 Aven 4930485B16Rik Cramp1l Gp aa1 Ndufb5 Ser p 2 Zfp236 Actbl2 Lmo1 Znrf2 Bace2 4930505A04Rik Crtc2 Gp at ch 1 Ndufv1 Sf i1 Zfp366 Adam29 Lpin1 Zranb2 Bbx 4930542C12Rik Csk Gp c5 Necap2 Sf n Zfp36l1 Adsl Lpp Zswim6 Bcar3 4930558K02Rik Cspp1 Gp cp d1 Nedd9 Sf r p 2 Zfp385a Aes Lrmp Zxdb Bnc1 4930566N20Rik Csrp2bp Gp n 2 Nek4 Sf t 2 d2 Zfp459 Aff3 Lrp3 C130026I21Rik 4930572O03Rik Ctdsp2 Gp n 3 Nek7 Sf x n 5 Zfp516 Aftph Lrrc38 Cacng3 4930597O21Rik Ctla4 Gp r1 3 5 Nelfcd Sh 3 glb1 Zfp52 Agtpbp1 M5C1000I18Rik Card11 4931408C20Rik Ctnna3 Gp r1 5 1 Nelfe Sh f m 1 Zfp523 Ahi1 Man2a1 Cblb 4932442E05Rik Ctxn1 Gp r1 7 6 Nfe2l2 Siah 1 a Zfp526 Ak4 Map3k15 Ccdc114 4933412E24Rik Cubn Gp r8 4 Nfe2l3 Sik 3 Zfp53 Aldh1l2 Map3k2 Ccdc32 4933426M11Rik Cuedc1 Grb1 0 Nfkb1 Sip a1 l2 Zfp534 Amfr Map4k3 Cd47 5033430I15Rik Cul4b Grb2 Nfkbia Sir t 1 Zfp553 Amigo2 Mapk4 Cdh5 5830415F09Rik Cxadr Grb7 Nfkbid Sir t 2 Zfp600 Ampd3 Mapkapk3 Cdkl1 5830473C10Rik Cxcr3 Grsf1 Ngly1 Sk iv 2 l Zfp608 Ank Mapre2 Cenpj 5930403N24Rik Cyb5 Gst p 1 Nhlrc3 Slc1 6 a1 4 Zfp617 Ankle1 Mark1 Cenpw 6030458C11Rik Cyb5d2 Gt dc1 Nhp2 Slc1 6 a6 Zfp62 Ankrd44 Mb21d1 Chaf1b 8430410A17Rik Cyp2b19 Gt f2 a1 l Nid2 Slc2 5 a1 5 Zfp637 Ankrd6 Mbd3 Chek1 9230109A22Rik Cyp2g1 Gx y lt 2 Nipal4 Slc2 5 a3 1 Zfp709 Anxa6 Mbp Chmp6 9530068E07Rik Cyp2r1 Gy p c Nkx6-1 Slc2 9 a4 Zfp740 Ap1m2 Mcfd2 Chst14 9930021J03Rik Cyp3a41a Gzmf Nlrc5 Slc3 0 a7 Zfp760 Ap1s2 Mcidas Cldn14 A130051J06Rik Cyp3a41b Gzmn Nme4 Slc4 5 a1 Zfp784 Ap2a2 Mest Cnot7 A330041J22Rik Cyp4f16 H2-Oa Nmi Slc4 a7 Zfp874a Apex2 Mettl4 Coq2 A430093F15Rik Cyp4f17 H2-Q7 Nmnat2 Slc7 a1 1 Zfpm1 Aph1b Mfng Cpn2 A530032D15Rik Cypt2 H2-T24 Nobox Slc7 a5 Zfyve19 Aplp2 Mfsd4 Cuedc1 A530088E08Rik Cysltr2 H6pd Nol11 Slc9 a9 Zfyve26 Arf6 Mitf Cyp2c29 A630001G21Rik Cystm1 Hand1 Npc1 Slk Zhx3 Arhgap12 Mll5 Cyp2c66 A630007B06Rik Cytip Hap1 Npy2r Slx 1 b Zmiz1 Arhgef11 Mmrn1 Cyp8b1 A630091E08Rik D16Ertd472e Haus6 Nr2e3 Sm ad7 Znrf4 Arid2 Mon2 D14Abb1e AA386476 D17Wsu92e Havcr2 Nr3c1 Sm ch d1 Zpbp Arid5b Mppe1 D730045B01Rik

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AB041806 D4Wsu53e Hcst Nr3c2 Sm cr 7 Zranb2 Arl11 Mpped2 Dctd AI837181 D5Ertd579e Hddc3 Nr4a2 Sm g7 Zscan22 Arl4d Mroh3 Depdc1b AW822073 D930015E06Rik Heatr5a Nrbf2 Sm im 1 3 Zswim6 Arrb1 Mrps18a Depdc7 AY761185 Dab2 Hecw2 Nrbp2 Sm im 1 4 Zxdb Arvcf Mtor Dnajc7 Abat Dalrd3 Helz Nrcam Sm n dc1 Zzef1 Asb2 Mtx3 Dpp4 Abca1 Dazl Herpud1 Nrip1 Sm o k 2 a Asl Muc6 Dscam Abcb4 Dcp2 Herpud2 Nrm Sm o k 2 b Atg4a Mvb12b Dsg2 Abcb6 Dctn3 Hes1 Nsun5 Sm s Atp11b Myom2 Ebag9 Abce1 Ddr1 Hic1 Nt5c3b Sn ap 2 5 Atp5o Nanos1 Eef2k Abhd10 Ddx18 Hif1a Nubpl Sn n Atp6v1g3 Nck2 Egln2 Abhd14a Ddx19a Hist1h2af Nucks1 Sn w1 Atrnl1 Ncoa2 Eid2 Abhd14b Ddx19b Hist1h2ag Nudt14 Sn x 2 5 Atxn7 Ndc80 Elmo1 Acadl Ddx28 Hist1h2bb Numa1 Sn x 2 7 B230120H23Rik Nek7 Elovl6 Acbd6 Ddx3x Hist1h2bj Nup188 Sn x 8 B430319F04Rik Nek8 Elovl7 Acot7 Ddx43 Hist1h3c Oas1c Sn x 9 B4galnt3 Nemf Emp3 Acoxl Ddx60 Hist1h3e Oas1f So cs1 B630005N14Rik Nfkbia Enpep Actr3b Decr1 Hist1h3g Ogfrl1 So cs2 Bbs12 Ngf Ephb2 Acvr1c Dedd2 Hist1h4h Olfr1393 So ga2 Bcat1 Nhlrc1 Eri1 Adam19 Defb33 Hist1h4i Olfr1402 So s1 Bcl2 Ninj2 Etv1 Adam6b Degs1 Hist2h2bb Olfr1423 So x 4 Bcor Nlk Ezh2 Adamts14 Degs2 Hist2h3b Olfr161 So x 5 Begain Nmi F8a Adamts20 Dennd1b Hivep1 Olfr266 Sp 1 0 0 Bin1 Nostrin Fam210a Adck3 Dexi Hk2 Olfr750 Sp 1 1 0 Birc6 Nrf1 Fam69a Adcy7 Dffa Hmgcs1 Olig3 Sp 1 4 0 Bloc1s4 Nsmf Fam69c Adgb Dgkk Hn1l Onecut1 Sp 4 Brf2 Nubpl Fasl Adhfe1 Dhrs11 Hnf4g Oprm1 Sp 8 Btbd11 Nup133 Fcgr3 Adora3 Dhrs13 Hook1 Osbp Sp at a1 3 Btla Nup43 Fcho1 Adprhl2 Dhx36 Hps6 Otud4 Sp def C130026I21Rik Nupl2 Fkbp9 Adrb1 Diap1 Hrh1 Oxnad1 Sp eer 4 c C2cd4a Ocrl Fnbp1l Aff1 Dicer1 Hs3st3b1 Padi2 Sp eer 4 d Cacul1 Olfr161 Fos Ago2 Dio2 Hs6st1 Paip1 Sp eer 4 e Cadm1 Osbpl5 Galc Agps Dio3 Hsd17b12 Paip2 Sp g7 Casr Osbpl8 Galn t 9 Agrn Diras2 Hsd17b4 Parn Sp in 1 Cbl Otud3 Gas2 l3 Agtrap Dmrta1 Hsf2 Parp14 Sp in 4 Cbr4 Pag1 Gbp 2 b Ahr Dnahc11 Hus1 Parp9 Sp in k 1 3 Ccdc112 Pard3b Gbp 3 Aig1 Dnahc3 Hus1b Pask Sp in k 7 Ccdc114 Pax8 Ggact Aipl1 Dnahc6 Iars2 Pawr Sp ir e2 Ccdc162 Pcmt1 Glcci1 Ak4 Dnahc7a Ica1l Pbx3 Sp o n 1 Ccdc17 Pdcd1 Gm1 0 1 7 6 Akap13 Dnajc12 Id2 Pcdh17 Sp p 2 Ccdc88a Pde3b Gm1 0 2 7 3 Alkbh3 Dnajc8 Ier2 Pcdhga11 Sp r y 4 Ccnd1 Pde4d Gm1 0 2 7 5 Als2cr12 Dnase2a Ier3 Pclo Sp sb1 Ccnd2 Pde7b Gm1 0 3 5 4 Alyref2 Dnttip2 Ifi35 Pcmtd1 Sp t 1 Ccr1 Pde8a Gm1 0 4 8 7 Amigo2 Dock10 Ifih1 Pcnxl3 Sp t bn 1 Ccser1 Pdpn Gm1 0 6 3 9 Amot Dolk Ifna13 Pcp4 Sp t ssa Cd164 Pdss1 Gm1 0 7 7 2 Ankdd1a Dos Ifng Pcsk1 Sr bd1 Cd200 Pdyn Gm1 3 1 3 9 Ankfy1 Dph3 Ift46 Pde10a Sr ebf 1 Cd59b Pdzd7 Gm1 3 1 4 5 Ankib1 Drap1 Ift57 Pde12 Sr p 1 9 Cd83 Pebp1 Gm1 3 1 5 0 Ankrd11 Dscam Ighg2c Pde3b Sr p 7 2 Cd86 Peli2 Gm1 3 1 5 1 Ankrd34c Dtx2 Igsf5 Pde4a Sr p k 2 Cdc20b Pfkfb1 Gm1 3 1 5 2 Ankrd42 Dus2l Ikzf1 Pde8a Sr sf 5 Cdk17 Phf19 Gm1 3 2 3 5 Ankrd50 Dusp10 Ikzf3 Pdlim5 Sr x n 1 Cdk18 Phf8 Gm1 3 2 4 2 Anxa3 Dusp4 Il16 Pdlim7 Ssr 1 Cdkl4 Phip Gm1 3 2 4 7 Anxa6 Dusp5 Il1rl1 Pdp1 St 3 gal2 Ceacam15 Phlpp1 Gm1 3 2 5 1 Ap1b1 Dvl1 Il1rl2 Pdpk1 St 7 Cebpb Pisd Gm1 5 3 1 9 Ap4m1 Dym Il22 Pdpn St ar d5 Celf4 Plac8 Gm1 6 0 3 9 Ap5z1 Dynlt1a Il2rb Pdzd2 St ar d6 Cenpw Plcl2 Gm2 0 9 3 9 Apba2 Dynlt1b Il5 Peli1 St at 5 a Cers6 Pld3 Gm2 1 0 9 2 Apoa1 Dyrk1a Il6ra Pepd St at 5 b Cetn4 Plekhh2 Gm2 1 4 1 1 Aqp11 Dzank1 Iltifb Pex14 St bd1 Chml Plk1s1 Gm2 7 9 0 Arf5 E130311K13Rik Inadl Pfas St k 1 7 b Chpf2 Plk2 Gm7 0 9 4 Arhgap10 E330009J07Rik Ing1 Pfkfb3 St k 3 8 l Chrna9 Pls1 Gm8 5 9 7

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Arhgap15 ENSMUSG00000097084 Ing2 Pfn2 St o n 1 Chst15 Pmepa1 Gm9 9 1 2 Arhgap26 ENSMUSG00000097437 Ins1 Pgap1 St t 3 b Chst2 Pml Gm9 9 8 9 Arhgap5 ENSMUSG00000098133 Ints1 Pgbd5 St x 1 1 Cnksr3 Ppa2 Gn a1 2 Arhgef17 Ech1 Ints5 Pgk1 St x 2 Cobll1 Ppara Go rasp 2 Arhgef19 Edem1 Intu Phb St x bp 5 Cog3 Ppm1h Gp at ch 1 Arhgef33 Edem3 Ipcef1 Phf11c St y x Col19a1 Ppp2ca Gp r1 8 3 Arid2 Edn1 Ipmk Phf11d Sulf 2 Col25a1 Ppp2r2a Gt f2 a1 l Arl4c Eed Irf1 Phf19 Sup t 3 Col4a6 Ppp4r1 Heatr5a Arl8b Eef1a1 Irf4 Phldb2 Susd4 Comt Pramel1 Hes1 Armc12 Efcab11 Irg1 Phlpp1 Sv ip Copg2 Prdm10 Hiatl1 Arntl2 Egln2 Itga2 Phpt1 Sy n e2 Coq2 Prdm2 Hint1 Arpc3 Egln3 Itga6 Phxr4 Szt 2 Cpxm2 Prdm5 Hmcn1 Arpp19 Ehbp1 Itgb2l Pias4 T Crcp Prkar2a Hnf4g Arsg Ehd3 Itgb7 Picalm Tab1 Crygf Prkch Hoxd13 Art2b Eif2s2 Itm2b Piezo2 Tacc1 Cstad Proser2 Ifng Asf1a Elavl1 Itpr2 Pigv Taco1 Cubn Prox1 Ift57 Asic4 Ell2 Itsn1 Pigyl Tagap Cwc27 Prpf38a Ighe Asmt Elovl2 Itsn2 Pik3c3 Tanc1 Cysltr2 Prps2 Il12a Asph Elp3 Ivns1abp Pik3cb Tax1bp1 D730045B01Rik Prr3 Il7r Atad3a Emd Jak1 Pik3r6 Tbl1x Dazl Psd4 Itgb1 Atf6b Emilin2 Jak3 Pkn2 Tbl1xr1 Dcbld2 Psma1 Ivns1abp Atg14 Eml3 Jam3 Pla2g10 Tbrg4 Ddx31 Psma6 Jdp2 Atg4a Eml4 Jazf1 Plac8 Tbx19 Dgcr6 Pth2r Kcnh2 Atg4d Emp1 Jdp2 Plagl2 Tbx2 Dlk1 Pvrl1 Kcnh5 Atg7 Ep300 Jhdm1d Plau Tbx6 Dmrtb1 Rab14 Kcnj16 Atl3 Epb4.9 Jup Plb1 Tcerg1 Dnahc2 Rab3gap2 Kcnj2 Atp13a2 Epc2 Kansl1l Plcb1 Tcf25 Dpp10 Rab40b Kcnj8 Atp5d Epha6 Katnal1 Plcb4 Tcf7l1 Dtnb Rabl5 Kcnn4 Atp5g3 Ephb3 Kazn Plcg1 Tcp10c Duox1 Rai14 Klf12 Atp5o Erbb2ip Kcna3 Plcl2 Tdpoz4 Dusp10 Ralgapa2 Klf13 Atp6v0e Ercc3 Kcnj10 Pld2 Tex2 Dync2li1 Ralgps2 Klf5 Atp6v1g3 Ercc6l Kcnj6 Plekho2 Tgfb3 Dynlt3 Rap1gds1 Klf6 Atp6v1h Erdr1 Kcnk9 Plk1s1 Tgfbr1 Dyrk2 Rapgef4 Klhl20 Atp8b2 Erg Kctd3 Plp2 Tgfbr2 Ebpl Rasl2-9 Lat Atpif1 Ergic1 Kdelc1 Pls1 Tha1 Egln1 Rassf3 Ldlrad4 Atxn1 Ern1 Kdelr2 Pmp22 Timm23 Egln2 Rbl2 Lgalsl Atxn10 Esr2 Kdm2b Pnma5 Timp2 Egln3 Rbm43 Lpxn Avpr1a Etnk1 Keap1 Pnoc Timp4 Eif3f Rc3h2 Lrp3 B230219D22Rik Ets2 Khdc3 Pnpla7 Tjp2 Eif4e3 Rffl Maf B3galt4 Evl Khdrbs1 Pnpt1 Tk2 Elf2 Rgs1 Mctp1 B3gat2 Exog Kif15 Poc1a Tldc1 Eml1 Rgs18 Med31 B3gnt2 Exosc4 Kit Pofut1 Tle4 Emp3 Rheb Mettl4 B3gnt8 Extl2 Klc2 Pole4 Tlr13 Epg5 Rhobtb1 Mpnd B3gnt9 Fa2h Klf1 Polr1a Tlr2 Ephb4 Rhoh Mrps23 B4galt5 Fam117b Klf13 Polr1b Tm9sf4 Erbb2ip Rhox13 Ms4a4d BC003965 Fam129a Klhl10 Pou4f1 Tmem120a Ercc3 Ric3 Msra BC023829 Fam13b Klhl25 Pou4f3 Tmem120b Erdr1 Rilpl1 Mtus2 BC024139 Fam160b2 Klhl31 Pparg Tmem131 Erf Rnaseh2b Mtx2 Bahcc1 Fam166b Klrb1b Ppie Tmem161b F2rl2 Rnf125 Myo1b Basp1 Fam175a Kmo Ppif Tmem17 F8a Rnf138 Narf Batf Fam49a Kpna2 Ppip5k1 Tmem174 Fabp5 Rnf169 Ncoa3 Bbc3 Fam65b Krit1 Ppm1h Tmem178 Fam102a Rnf186 Nedd1 Bbx Fam69c Ktn1 Ppp1r12a Tmem194 Fam105b Rnf216 Nkiras2 Bcar3 Fam76a Lacc1 Ppp1r7 Tmem196 Fam107b Robo3 Noc4l Bcas1 Fam98b Lactb2 Ppp2ca Tmem235 Fam120c Rp9 Nol11 Bcas2 Fancc Lamc2 Ppp2cb Tmem25 Fam174a Rpap3 Nr1h4 Bcas3 Fbf1 Lamp2 Ppp2r2a Tmem42 Fam181a Rps15a Nrp1 Bcat1 Fbxl3 Larp1 Ppp2r5c Tmem51 Fam26f Rps6kc1 Nt5c3 Bckdha Fbxo42 Lcp1 Ppp4r2 Tmem65 Fam43a Rpusd3 Nubpl Bcl2 Fbxo45 Ldb1 Ppt1 Tmem66 Fam76b Rragd Nudt14 Bcl2l11 Fcf1 Leprotl1 Pptc7 Tmem68 Fam86 Rsad2 Nupl2

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Bcl6 Fcho1 Letm2 Prdm2 Tmpo Fam92b Rsf1 Olfm3 Becn1 Fcho2 Lgals3bp Prex1 Tmsb10 Farsb Rsu1 Olfr136 Bfar Fech Lhx5 Prickle1 Tmsb4x Fcho1 Rtkn2 Omt2a Bfsp2 Fer1l6 Lhx6 Prkag1 Tmub2 Fem1c Sam d1 1 Oxr1 Bivm Fgf16 Lim2 Prkch Tnc Fhl4 Sam d9 l P4ha1 Bloc1s5 Fgf7 Lin37 Prkcq Tnfaip3 Fis1 Scaf 1 1 Palm2 Bmi1 Fgfr1 Lin54 Prkcz Tnfrsf1b Fndc3a Sec6 3 Pard3 Bmp2k Fggy Lipt1 Prl5a1 Tnfrsf21 Foxp1 Ser p 2 Parp4 Bms1 Fgr Litaf Prmt2 Tnfrsf22 Gabrr1 Sesn 3 Pbk Bola2 Fibcd1 Lman2 Prokr2 Tnfrsf26 Gabrr2 Set d2 Pcca Bpnt1 Fignl1 Lmo1 Proser1 Tnfrsf8 Galc Sf i1 Pde4d Bptf Fkbp5 Lnp Prr5l Tnfsf8 Galn t 1 8 Sf p q Pdia4 Brd2 Flii Lpin2 Prune2 Tnik Galn t 3 Sf r p 2 Peli1 Brd7 Flna Lpp Psat1 Tnpo1 Galr1 Sf r s1 8 Phxr2 Brf2 Fndc1 Lrp3 Psen2 Tnrc18 Gat m Sf swap Pitpnc1 Btbd11 Fndc3a Lrrc1 Psma6 Top1 Gfra1 Sf x n 3 Plac8 Btf3 Fosl2 Lrrc28 Psmd4 Tor1a Glcci1 Sgt a Plk1 C030017K20Rik Foxd1 Lrrc32 Psme1 Tox3 Glis1 Sh f Plk2 C130026I21Rik Foxk2 Lrrc3b Ptcd3 Tpk1 Glt scr2 Sh q1 Ppcdc C1qtnf6 Foxn2 Lrrc63 Pten Tra2a Gm1 0 2 2 1 Siglec1 5 Prkar2b C2cd5 Foxn3 Lrrc8b Ptger4 Traf1 Gm1 0 2 2 2 Sir p a Prss52 C330018D20Rik Foxp1 Lrrc8c Ptp4a2 Traf5 Gm1 0 2 7 5 Slain 1 Rab11fip2 C630004L07Rik Fpgs Lsm11 Ptpn1 Tram1 Gm1 0 3 5 4 Slc1 0 a3 Rc3h2 C87499 Fras1 Lsm2 Ptpn22 Trappc6b Gm1 0 4 8 7 Slc1 3 a5 Rex2 Cabin1 Frmd7 Ltbp2 Ptprn2 Trdmt1 Gm1 0 6 4 7 Slc1 6 a1 4 Rpl5 Cacul1 Fscn3 Ly6e Ptprs Trhr2 Gm1 0 7 9 9 Slc2 5 a2 5 Rplp1 Cage1 G9 3 0 0 4 5 G2 2 Rik Lynx1 Pum1 Trib1 Gm1 3 1 3 9 Slc2 9 a3 Rpsa-ps10 Camkk2 Gadd4 5 b Lyrm1 Pura Trim2 Gm1 3 1 4 5 Slc2 a1 Rptor Cap1 Gadd4 5 g Macc1 Pus1 Trim50 Gm1 3 1 5 1 Slc2 a2 Rpusd3 Capza2 Gadl1 Maf Pus7 Trit1 Gm1 3 1 5 2 Slc3 8 a1 Rrm1 Card11 Galn t 1 Magt1 Pwp1 Trp53i11 Gm1 3 1 5 4 Slc3 8 a1 1 Rtn4rl2 Cars2 Galn t 2 Mamdc4 Qk Trpc1 Gm1 3 1 5 7 Slc3 8 a2 Ryr3 Cask Galn t 3 Maml2 Qk Trpm1 Gm1 3 2 5 1 Slc6 a1 S1 0 0 a1 1 Casp16 Galr1 Man1a2 Qrich2 Tslp Gm1 3 2 7 7 Slc9 a7 S1 p r 1 Casp6 Gan Manba Qser1 Tspan5 Gm1 3 2 7 8 Sm ch d1 Sbk 1 Cbfb Gap v d1 Map10 Qsox2 Tsx Gm1 4 4 0 9 Sm p dl3 a Sch ip 1 Ccdc12 Gas2 Map1lc3b Rab27a Ttc21b Gm1 4 4 1 2 So cs5 Sec2 4 d Ccdc122 Gat a3 Map2k2 Rab27b Ttc39c Gm1 4 4 2 0 So r cs1 Sgt a Ccdc174 Gbp 2 Map2k5 Rab44 Ttc8 Gm1 5 3 1 9 So x 2 Sh f m 1 Ccdc30 Gcc1 Map3k11 Rad51b Ttll5 Gm1 6 0 3 9 Sp 1 0 0 Slc1 6 a1 4 Ccdc54 Gclc Map3k4 Rae1 Ttll7 Gm2 0 9 3 9 Sp 1 1 0 Slc4 3 a1 Ccdc6 Gdf6 Map4k3 Rai1 Tubgcp3 Gm2 1 0 9 2 Sp ag1 6 Slc4 a1 0 Ccdc66 Gdi2 Mapk13 Rai14 Tulp1 Gm2 1 9 5 7 Sp eer 4 c Slc7 a1 Ccdc79 Gemin 5 Mapk14 Ralgds Txlnb Gm2 1 9 6 7 Sp eer 4 d Slf n 1 Ccdc82 Ggt a1 Mapk9 Rap1gds1 Txnl1 Gm2 7 9 0 Sp eer 4 e Slf n 2 Ccdc9 Gh it m Mapkap1 Rapgef5 Tyrp1 Gm4 9 0 6 Sp en Sm g9 Ccl25 Glis3 Mapkapk2 Rapgefl1 U2af1l4 Gm4 9 5 2 Sp in 1 Sm o k 2 a Ccl28 Glrp 1 Marcks Rasal3 U2af2 Gm4 9 8 0 Sp in 4 Sm p dl3 a Ccnb3 Gm1 0 0 6 8 Mbd5 Rasgef1c Ubald2 Gm4 9 8 1 Sp o ck 3 So d1 Ccr2 Gm1 0 1 3 4 Mbnl1 Rasgrf1 Ubc Gm6 6 5 8 Sp t ssa Sp 1 0 0 Ccr5 Gm1 0 1 7 6 Mboat1 Rassf2 Ube2e1 Gm6 9 4 Sr ek 1 ip 1 Sp 1 1 0 Ccrn4l Gm1 0 2 7 5 Mbtd1 Rassf6 Ube2g1 Gm7 1 2 0 St ar d1 3 Sp 1 4 0 Cd109 Gm1 0 3 5 4 Mcc Rassf8 Ubl3 Gm9 7 5 8 St k 1 0 Sp 4 Cd151 Gm1 0 3 7 5 Mcee Rbm19 Ubxn1 Gm9 9 2 5 St k 4 Sp 8 Cd28 Gm1 0 4 8 7 Mcrs1 Rbm27 Uchl3 Gn a1 1 Tab3 Sp eer 4 c Cd300lg Gm1 0 7 1 5 Mdm1 Rbm46 Ugcg Gn ao 1 Tacstd2 Sp eer 4 d Cd47 Gm1 0 7 1 7 Me1 Rdh10 Ugdh Gn l1 Tada1 Sp ef 2 Cd6 Gm1 0 7 1 8 Med12 Rela Uggt1 Gp at ch 1 Tbk1 Sp in 4 Cda Gm1 0 7 1 9 Med4 Rere Unc13a Gp m6 a Tchh Sp r ed1 Cdc40 Gm1 0 9 7 4 Med8 Ret Unc45a Gp r1 5 8 Tchhl1 St ac Cdca7l Gm1 1 1 4 6 Mef2a Rex2 Unc93a Gp r8 5 Tdrd3 St ap 2

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Cdh17 Gm1 1 1 6 8 Mef2c Rfx3 Unkl Gp rasp 2 Tead1 St ar d1 3 Cdh26 Gm1 1 2 3 Megf8 Rfx4 Uprt Grlf1 Tet2 St x 1 9 Cdh4 Gm1 1 6 9 6 Mettl21a Rfx5 Urgcp Grp el2 Thnsl1 Sulf 2 Cdh7 Gm1 1 7 3 3 Mettl21d Rgl2 Usp12 Grp r Tlcd1 Sult 5 a1 Cdipt Gm1 2 1 6 9 Mex3b Rgmb Usp18 Gse1 Tlr2 Sy bu Cdk17 Gm1 2 2 1 6 Mfsd6l Rgs1 Usp25 Gsk 3 a Tmed1 Tbc1d30 Cdk5r1 Gm1 3 0 5 1 Mgat1 Rgs12 Usp9x Gt f3 c4 Tmem117 Tbpl1 Cdk9 Gm1 3 1 3 9 Mgll Rgs13 Utp18 Gt sf1 l Tmem189 Tchhl1 Cdkl3 Gm1 3 1 4 5 Mgmt Rgs18 Utrn H3f3a Tmem260 Tcp10c Cdkn1a Gm1 3 1 5 1 Midn Rhbdd2 Uxs1 Haao Tnfrsf23 Tenm3 Cdkn2b Gm1 3 1 5 2 Mier2 Ric8b Vcl Hdac11 Tnfrsf8 Tiam1 Cdv3 Gm1 3 1 5 7 Mis18a Rictor Vdac1 Hexim1 Tnik Timm8a2 Cebpb Gm1 3 2 3 5 Mki67 Rin2 Vgll4 Hif1a Tnp1 Tle3 Cenpw Gm1 3 2 4 2 Mks1 Rinl Vmn 1 r2 Hipk4 Tnrc6b Tmco5 Cep128 Gm1 3 2 4 7 Mnd1 Riok3 Vmn 1 r2 3 8 Hlx Tns1 Tmem121 Cep170b Gm1 3 2 4 8 Mon2 Rit2 Vmn 1 r3 Hmbox1 Tom1l1 Tmem131 Cep97 Gm1 3 2 5 1 Morn5 Rmi2 Vmn 1 r7 6 Hnrnpa0 Tpbg Tmem2 Cgref1 Gm1 4 1 3 7 Mpp2 Rnase12 Vmn 1 r7 7 Hnrnpu Tpcn2 Tmpo Chaf1b Gm1 4 4 0 9 Mpped2 Rnaset2a Vmn 2 r1 1 7 Hook1 Tprg Tnfsf18 Chd1l Gm1 4 4 1 2 Mroh2a Rnf111 Vp s3 3 a Hsh2d Tram1 Trpm1 Chd7 Gm1 5 3 1 9 Mrpl1 Rnf122 Vp s3 3 b Hspb7 Trib2 Ttr Chd9 Gm1 6 5 0 5 Mrpl21 Rnf130 Vp s4 1 Htr1b Trmt11 Unc93a Chek1 Gm1 7 5 7 6 Mrpl22 Rnf144a Vp s5 2 Icos Trpm3 Usp1 Chic1 Gm1 8 1 8 Mrpl38 Rnf34 Vp s5 4 Idnk Tsnax Vmn 2 r-p s1 0 4 Chpf Gm2 0 0 9 1 Mrpl41 Rnls Vt cn 1 Ids Tspan2 Vp s3 7 a Chst2 Gm2 1 0 9 2 Mrpl42 Rom1 Vwa3 b Ier5l Tub Vwa3 a Chsy1 Gm2 1 8 1 1 Mrpl47 Rpap3 Vwc2 Ifi30 Tubb4b-ps1 Vwa3 b Chuk Gm2 6 6 Mrpl55 Rpe65 Wasf1 Ifnar1 Tyms Wbp5 Cisd1 Gm2 7 9 0 Mrps23 Rpl13a-ps1 Wbp7 Ifngr2 Tyrp1 Wbscr27 Cited2 Gm2 9 3 3 Mrps31 Rpl21 Wbscr27 Iglon5 U2surp Wif1 Cklf Gm2 9 6 4 Msantd1 Rpl24 Wdr33 Ikzf2 Ube2cbp Wnt10a Ckmt1 Gm3 1 5 0 Mt1 Rpl28-ps4 Wdr5 Il21 Ube2h Wnt6 Clca1 Gm4 8 8 9 Mterfd2 Rpl36-ps3 Wdr53 Il23r Ubiad1 Wwox Cldn14 Gm5 1 1 3 Mto1 Rpl7 Wdr59 Impg1 Ubl3 Zbtb26 Clec16a Gm5 1 4 8 Mtx3 Rpp14 Wdr72 Inadl Ubl4 Zdhhc14 Clec2i Gm5 2 1 8 Mucl1 Rps17 Whsc1l1 Ints9 Unc5b Zfp709 Clic5 Gm5 4 2 6 Mup21 Rps18 Wls Iqgap2 Upf2 Zfp91 Clint1 Gm5 4 5 8 Mxra8 Rps7 Wscd1 Irak1bp1 Ush2a Zkscan7 Clip2 Gm5 6 0 0 Myc Rps8 Wscd2 Irf6 Usp47 Zmynd8 Clpb Gm5 6 1 Myl6 Rras Wwox Irg1 Vegfa Zxdb

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Cullen, Jolie Gai

Title: Molecular mechanisms regulating CD8+ T cell differentiation following external cues

Date: 2018

Persistent Link: http://hdl.handle.net/11343/230836

File Description: Final thesis file

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