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Regulation of the CD4+ T cell response to influenza infection Fonseca, Nicolette Marian 2019

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REGULATION OF THE CD4+ T CELL RESPONSE TO INFLUENZA INFECTION by  Nicolette Marian Fonseca  M. Sc., Goa University, 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Microbiology and Immunology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2019  © Nicolette Marian Fonseca, 2019    ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:  Regulation of the CD4+ T cell response to influenza infection  submitted by Nicolette Marian Fonseca in partial fulfillment of the requirements for the degree of PhD in Microbiology and Immunology  Examining Committee: Dr. Georgia Perona-Wright Co-supervisor Dr. Pauline Johnson Co-supervisor  Dr. Lisa Osborne Supervisory Committee Member Dr. Kelly McNagny University Examiner Dr. Tillie-Louise Hackett University Examiner    iii Abstract Surviving influenza infection requires a balance of pro- and anti- inflammatory signals, to promote viral clearance while preventing immunopathology. CD4+ T cells are central to this balance, exerting positive and negative influences on the immune response to infection. Since dysregulated CD4+ T cell responses are detrimental to survival, understanding the mechanisms controlling CD4+ T cell function during infection is essential. This thesis examines two aspects of CD4+ T cell regulation, the effect of the immunoregulatory cytokine IL-27 on CD4+ T cell function and the epigenetic control of the CD4+ T cell response to influenza.         In chapter 3, I show that IL-27 signalling can regulate the immune response to influenza by inducing IL-10 from effector CD4+ T cells in primary and recall infection. Since regulated IL-10 expression is important for a protective Th1 response, I investigated the mechanism through which IL-27 promotes CD4+ T cell derived IL-10. I found that IL-27 signalling enhances IL-10 expression from effector CD4+ T cells in a primary response but memory cells lose their responsiveness to IL-27. However, these memory cells re-express IL-10 in a recall response to influenza due to an IL-27 induced permissive epigenetic signature deposited at the Il10 locus during primary activation. In chapter 4, I investigated the effect of IL-27 on functional specialisation in Tregs during infection. Upon exposure to IL-27, airway Tregs exhibited a Th1 adapted phenotype characterised by increased T-bet, CXCR3 and IL-10 expression.           In Chapter 5, I investigated the link between histone modifications and gene-expression in naïve, effector and memory CD4+ T cells. I found that dynamic changes in histone modifications at gene promoters are associated with temporal gene expression patterns and that super-enhancer target genes encode highly expressed lineage specific determinants in naïve and effector cells. I   iv also show that memory cells contain a subset of primed effector genes and re-express key genes specifying naïve cell identity.          Collectively, my findings indicate that IL-27 regulates effector CD4+ and Treg cell function in the lung and that the control of gene expression in effector and memory CD4+ T cells can be achieved through epigenetic modifications at promoter and super-enhancer elements.       v Lay Summary Influenza is a severe respiratory infection caused by the Influenza A virus. CD4+ T cells are a key player in the immune response against influenza through their dual roles in enhancing and inhibiting the immune response. Therefore, if CD4+ T cell responses are too weak, too strong or the wrong flavour, this can be detrimental to the delicate balance of signals that control the immunity to influenza. The goal of this thesis was to understand how CD4+ T cells are precisely regulated during influenza infection. By using a combination of immunological techniques and high-throughput sequencing, I showed that infection induced cytokine signals and epigenetic changes act co-operatively to modulate the function of CD4+ T cells during initial and repeat encounter to influenza. This work provides new insight into the mechanisms that control CD4+ T cells and may inform novel therapeutic design against influenza such as universal T cell vaccines.     vi Preface A manuscript version of Chapter 3 is ready for submission Fonseca, N.M., Lorzadeh, A., Redpath, S.A., Moksa M., Welch, I., Jazdarehee, A., Zhao, B., Hirst M., Perona-Wright, G.P.W. IL-27 signaling promotes epigenetic remodelling of the Il10 locus in memory CD4+ T cells following influenza infection. I designed and performed all experiments for this chapter under Dr. Georgia Perona Wright’s supervision with the following exceptions: • Alireza Lorzadeh performed chromatin immunoprecipitation, prepared DNA libraries and analysed ChIP Seq data shown in Fig 3.10, Fig 3.11 and Fig 3.12.  • Dr. Stephen Redpath performed one of three experiments for Fig 3.1 A-D. • Michelle Moksa extracted RNA and prepared cDNA libraries for sequencing. • Dr. Ian Welch scored and imaged influenza H&E slides shown in Fig 3.14 and 3.15. • Billy Zhao, an undergraduate student performed the qPCR shown in Fig 3.5D. • Aria Jazdarehee, an undergraduate student under my mentorship, performed the qPCR shown in Fig 3.12.  A manuscript version of Chapter 4 is ready for submission.  Fonseca, N.M., Redpath, S.A., Tomisawa, M., Jazdarehee, A., Perona-Wright, G.P.W.,. Functional specialisation of Foxp3+ CD4+ T cells following influenza infection. I designed and performed all experiments for this chapter under Dr. Georgia Perona Wright’s supervision.   • Dr. Stephen Redpath helped to optimise Foxp3 and GFP recovery staining protocols.    vii • Mio Tomisawa and Aria Jazdarehee, undergraduate students under my supervision, helped prepare single cell suspensions for flow cytometric analysis.  A version of Chapter 5 will be submitted for publication later this year Fonseca, N.M., Lorzadeh, A., Bilenky, M., Moksa M., Hirst M., Perona-Wright, G.P.W. Histone modifications dynamics during the CD4+ T cell response to influenza. This study was carried out in collaboration with Dr. Martin Hirst’s laboratory at the University of British Columbia. Alireza Lorzadeh and I have contributed equally to this work. I designed and performed all in vivo and in vitro experiments required for cell isolation. Alireza Lorzadeh aligned RNA Seq and ChIP Seq bam files and performed computational analysis specifically for differential gene expression, peak calling and UCSC genome browser data visualisation. I analysed these datasets using R statistical software with Alireza Lorzadeh’s guidance.  Animal studies were conducted in accordance with protocols approved by the University of British Columbia Animal Care Committee and Canadian Council of Animal Care. The project title and corresponding certificate numbers applicable to this thesis is Cytokines in infection: A16-0179, A16-0180.     viii Table of Contents Abstract ....................................................................................................................................iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents ................................................................................................................... viii List of Tables ........................................................................................................................... xv List of Figures ........................................................................................................................ xvi List of Abbreviations .............................................................................................................. xx Acknowledgement ................................................................................................................ xxvi Dedication........................................................................................................................... xxviii Chapter 1: Introduction............................................................................................................ 1 1.1 Influenza ..................................................................................................................... 1 1.1.1 Influenza A virus and impact on global health ...................................................... 1 1.1.2 Disease and treatment of IAV infection in humans ............................................... 4 1.1.3 Current vaccines against influenza ....................................................................... 5 1.2 Immune response to IAV ............................................................................................. 7 1.2.1 Innate immune response: protection versus pathology .......................................... 7 1.2.2 B cell response to IAV ......................................................................................... 9 1.2.3 CD8+ T cell response to IAV ............................................................................. 11 1.2.4 CD4+ T cell response to IAV ............................................................................. 13 1.2.4.1 Immunopathogenic potential of CD4+ T cells during IAV ............................. 17 1.3 IL-10 as a regulator of immunity and immunopathology during IAV ......................... 17 1.3.1 IL-10 expressing regulatory CD4+ T cells in Th1 infection ................................ 20   ix 1.4 IL-27: a regulator of CD4+ T cell function in Th1 infection ....................................... 22 1.4.1 IL-27: mechanisms of CD4+ T cell regulation.................................................... 23 1.4.2 IL-27 signalling in CD4+ T cells ........................................................................ 25 1.5 CD4 T cell memory ................................................................................................... 28 1.5.1 Generation of memory CD4+ T cells ................................................................. 29 1.5.2 Models of memory T cell formation ................................................................... 31 1.5.3 Heterogeneity of memory CD4+ T cells in tissues .............................................. 32 1.5.4 A role for memory CD4+ T cells in protection against IAV ............................... 34 1.6 Epigenetic regulation of the CD4+ T cell response to infection .................................. 35 1.6.1 Cis regulatory elements ...................................................................................... 35 1.6.2 Histone modifications ........................................................................................ 36 1.6.3 Role of histone modifications at promoter regions during CD4+T cell differentiation .................................................................................................................... 38 1.6.1 Epigenetic regulation of memory CD4+ T cells .................................................. 39 1.6.2 Role of histone modifications at enhancer regions during CD4+T cell differentiation .................................................................................................................... 40 1.6.3 Super-enhancers in CD4+T cells ........................................................................ 41 1.7 Rationale and Research Aims .................................................................................... 42 Chapter 2: Materials and Methods ........................................................................................ 46 2.1 Animals ..................................................................................................................... 46 2.1.1 Influenza infection ............................................................................................. 46 2.1.2 Tissue preparation and cell isolation .................................................................. 47 2.1.3 In vitro cell culture ............................................................................................. 47   x 2.2 Flow cytometry and analysis ...................................................................................... 48 2.2.1 Surface staining ................................................................................................. 48 2.2.2 Cytokine responsiveness and pSTAT staining .................................................... 48 2.2.3 Intracellular GFP recovery and transcription factor staining ............................... 49 2.2.4 Cell sorting ........................................................................................................ 49 2.2.5 Acquisition and analysis of flow cytometry data ................................................ 50 2.3 Quantitative analysis of murine gene expression by RT-PCR ..................................... 50 2.4 Data visualisation and statistical analysis ................................................................... 51 2.5 Histological analysis of lung tissue sections ............................................................... 52 2.6 Transcriptional and epigenetic profiling of CD4+ T cells ........................................... 52 2.6.1 Cell isolation ...................................................................................................... 52 2.6.2 RNA Sequencing ............................................................................................... 52 2.6.3 Low input chromatin immunoprecipitation and sequencing ................................ 54 2.6.4 Transcription factor motif enrichment ................................................................ 56 2.6.5 Statistical analysis .............................................................................................. 56 Chapter 3: IL-27 signaling promotes epigenetic remodelling of the Il10 locus in memory CD4+ T cells following influenza infection ............................................................................. 57 3.1 Introduction ............................................................................................................... 57 3.2 Results ....................................................................................................................... 60 3.2.1 Identification and characterization of IL-10 expressing CD4+ T cells during influenza infection ............................................................................................................. 60 3.2.2 CD4+ T cell derived IL-10 expression in the lung is enhanced by IL-27 signaling during primary influenza infection ................................................................................. 65   xi 3.2.3 Primary and memory CD4+ T cells lose surface expression of the IL-27 receptor subunit gp130 .................................................................................................................. 66 3.2.4 Memory CD4+ T cells exhibit decreased responsiveness to IL-27 signaling ....... 70 3.2.5 Memory CD4+ T cells with impaired IL-27 responsiveness can express IL-10 in a recall response to influenza ............................................................................................... 73 3.2.6 CD4+ T cell IL-10 expression in a recall response to influenza requires IL-27 signaling during primary infection ..................................................................................... 76 3.2.7 Epigenetic remodelling of the Il10 locus is associated with IL-10 expression in a recall response ................................................................................................................... 78 3.2.8 IL-27 signaling is required for epigenetic remodelling of the Il10 gene locus in memory CD4+ T cells ........................................................................................................ 81 3.2.9 Establishment of an active enhancer located upstream of the Il10 gene in memory CD4+ T cells is dependent on IL-27 signaling .................................................................... 86 3.2.10 Absence of IL-27 signaling results in increased granulocytic infiltration and...... 89 3.3 Discussion ................................................................................................................. 92 Chapter 4: IL-27 promotes functional specialisation of airway Tregs during primary influenza infection ................................................................................................................. 101 4.1 Introduction ............................................................................................................. 101 4.2 Results ..................................................................................................................... 103 4.2.1 Foxp3+ CD4+ T cells expand in the lung in response to primary influenza infection.. ........................................................................................................................ 103 4.2.2 Foxp3+ CD4+ T cells upregulate CD44 and T-bet in the infected lung during primary influenza ............................................................................................................ 105   xii 4.2.3 Foxp3+ CD4+ T cells upregulate IL-10 in the infected lung during primary influenza.. ....................................................................................................................... 108 4.2.4 IL-27 signaling has no effect on the expansion of Foxp3+ CD4+ T cells during IAV……. ........................................................................................................................ 110 4.2.5 IL-27 signaling promotes an increase in proportions of Tbet+ and CXCR3+ Tregs in the airways during influenza infection ......................................................................... 112 4.2.6 IL-27 signaling enhances IL-10 expression from Foxp3+ CD4+ T cells in the airways and lungs during IAV infection .......................................................................... 114 4.3 Discussion ............................................................................................................... 116 Chapter 5: Mapping histone modification dynamics in CD4+ T cells responding to influenza infection ................................................................................................................. 121 5.1 Introduction ............................................................................................................. 121 5.2 Results ..................................................................................................................... 123 5.2.1 Generation of naïve and influenza specific CD4+ T cell gene expression datasets… ........................................................................................................................ 123 5.2.2 Transcriptional profiling of the CD4+ T cell response to influenza infection .... 126 5.2.3 Memory CD4+ T cells upregulate a unique set of genes compared with naïve or effector cells .................................................................................................................... 132 5.2.4 Identification of temporal gene expression patterns in CD4+ T cells following influenza infection ........................................................................................................... 137 5.2.5 Generation of histone modification ChIP Seq datasets from naïve, primary, memory and secondary CD4+ T cells following influenza infection ................................ 141   xiii 5.2.6 Naïve to effector or memory transition in CD4+ T cells is marked by an increase in active promoters .......................................................................................................... 142 5.2.7 Dynamic changes in repressed and bivalent promoter states are associated with naïve to effector and memory transition in CD4+ T cells ................................................. 148 5.2.8 Distinct temporal gene expression profiles are marked by changes in histone modifications at promoters .............................................................................................. 155 5.2.9 Identification of super-enhancers in CD4+ T cells responding to influenza infection.. ........................................................................................................................ 159 5.2.10 Super-enhancers delineate key cell identity genes in naïve and effector CD4+ T cells responding to influenza infection ............................................................................. 161 5.3 Discussion ............................................................................................................... 168 Chapter 6: Conclusions ......................................................................................................... 176 6.1 Interpretation and significance ................................................................................. 176 6.1.1 IL-27 as a regulator of the CD4+ T cell response to IAV .................................. 176 6.1.2 Epigenetic control of CD4+ T cell differentiation ............................................. 182 6.2 Limitations and future directions ............................................................................. 184 6.2.1 Elucidating the function of CD4+ T cell derived IL-10 in a recall response to IAV……. ........................................................................................................................ 184 6.2.2 The effect of Th1 adapted Tregs on disease outcome during influenza ............. 185 6.2.3 Epigenetic regulation of the CD4+ T cell response to IAV infection ................ 186 6.3 Concluding remarks ................................................................................................. 187 Bibliography .......................................................................................................................... 188 Appendices ............................................................................................................................ 221   xiv Appendix A Purification and sorting of in vitro cultured CD44hi CD4+ T cells and naïve CD44lo CD4+ T cells for H3K4me3 and H3K37me3 ChIP Seq .......................................... 221 Appendix B Expression of genes identified by a ‘primed’ epigenetic signature at the gene promoter ............................................................................................................................. 223   xv List of Tables Table 2.1: List of qPCR primers and target sequences ............................................................... 51 Table 5.1: Mapping statistics of naïve, primary, memory and secondary CD4+ T cell RNA Seq datasets. .................................................................................................................................. 126 Table 5.2:  Summary of ChIP Seq libraries generated for this study. ....................................... 142 Table 5.3: Select SE associated genes shared by primary, memory and secondary CD4+ T cells or shared by primary and secondary CD4+ T cells only. .......................................................... 168    xvi List of Figures Figure 1.1:  Structure of Influenza A virion ................................................................................. 3 Figure 1.2: Functional diversity of the CD4+ T cell response to primary IAV infection. ............ 16 Figure 1.3: Overview of the IL-27 signalling pathway ............................................................... 27 Figure 1.4: Overview of histone modifications at promoter, enhancer and super-enhancer elements .................................................................................................................................... 38 Figure 3.1: Influenza infection model and selection of time points for primary and recall CD4+ T cell responses. ........................................................................................................................... 62 Figure 3.2:Effector CD4+ T cells express IL-10 in the lung during primary influenza infection. 64 Figure 3.3: IL-27 signalling enhances IL-10 expression from CD4+ T cells during primary influenza infection .................................................................................................................... 66 Figure 3.4: Activated (CD44hi) CD4+ T cells downregulate gp130 expression .......................... 69 Figure 3.5: Memory CD4+ T cells lose IL-27 responsiveness. ................................................... 72 Figure 3.6: CD4+ T cells express IL-10 in the lung during secondary influenza infection. ......... 75 Figure 3.7: IL-27 signaling during primary influenza is required for IL-10 expression in a recall response. ................................................................................................................................... 77 Figure 3.8: A permissive histone modification signature at the IL-10 gene locus in memory CD4+ T cells. ...................................................................................................................................... 80 Figure 3.9: IL-27 signaling results in the epigenetic remodeling of the IL-10 locus in memory CD4+ T cells. ............................................................................................................................ 84 Figure 3.10: MLL1 and MLL2 mRNA expression is not increased in IL-27 stimulated activated or resting phase CD4+ T cells. ................................................................................................... 85   xvii Figure 3.11: An active enhancer region upstream of the Il10 gene promoter is established in response to IL-27 signaling. ...................................................................................................... 88 Figure 3.12: Effect of IL-27 signaling on lung pathology in primary and secondary influenza infection. ................................................................................................................................... 91 Figure 3.13:Increased granulocytic infiltrate in the lungs of VertX IL-27Ra-/- following secondary influenza infection. ................................................................................................... 92 Figure 4.1: Numbers and frequencies of Foxp3+ CD4+ T cells during influenza infection ...... 104 Figure 4.2: CD44 and T-bet expression in Foxp3+ and Foxp3- CD4+ T cells in the lung during influenza infection .................................................................................................................. 107 Figure 4.3: IL-10 expression in Foxp3+ CD4+ T cells in the lung and draining lymph node during influenza infection. ...................................................................................................... 109 Figure 4.4: Effect of IL-27 signaling on the expansion of Foxp3+ CD4+ T cells during influenza infection. ................................................................................................................................. 111 Figure 4.5: Effect of IL-27 signaling on T-bet and CXCR3 expression in Foxp3+ CD4+ T cells during influenza infection ....................................................................................................... 113 Figure 5.1: Overview of experimental design .......................................................................... 124 Figure 5.2: Flow cytometric sorting of naïve and influenza specific CD4+ T cells from the lung. ............................................................................................................................................... 125 Figure 5.3: Application of 1 RPKM threshold to gene expression. ........................................... 127 Figure 5.4 Multidimensional scaling of naïve and influenza specific CD4+ T cells. ................ 128 Figure 5.5: Gene expression (RPKM) of CD4+ T cell and non CD4+ T cell markers in naïve, primary, memory and secondary CD4+ T cells. ....................................................................... 129   xviii Figure 5.6: Gene expression (RPKM) of key cytokines (A), phenotypic markers (B) and transcription factors (C) in naïve, primary, memory and secondary CD4+ T cells.................... 131 Figure 5.7: Differentially expressed genes in pairwise comparisons of naïve, primary, memory and secondary CD4+ T cells.................................................................................................... 134 Figure 5.8: Memory CD4+ T cells upregulate a unique set of genes. ....................................... 136 Figure 5.9: Transcriptional profiling identifies five major temporal gene expression profiles in the CD4+ T cell response to influenza. .................................................................................... 138 Figure 5.10: Representative genes within STEM clusters identified in naïve, primary, memory and secondary CD4+ T cells.................................................................................................... 140 Figure 5.11: Global histone methylation at gene promoters and gene expression in naïve and influenza specific primary, memory and secondary CD4+ T cells. .......................................... 144 Figure 5.12: Enumeration of promoter state in CD4+ T cells following influenza infection. .... 146 Figure 5.13:  Summary plot of net increase or decrease in bivalent, active or suppressed promoters. ............................................................................................................................... 147 Figure 5.14: Histone modification dynamics in naïve, effector and memory CD4+ T cells. ..... 149 Figure 5.15: Temporal changes in H3K27me3 and H3K4me3 at gene promoter regions occur at  key Th1 specific genes. ........................................................................................................... 152 Figure 5.16: Temporal change in which promoters move from bivalent to H3K4me3 identify the Th1 transcription factor Tbx21. ............................................................................................... 154 Figure 5.17: Temporal gene expression patterns exhibit two distinct patterns of histone modifications at gene promoters. ............................................................................................. 157 Figure 5.18: Gene expression and H3K4me3 and H3K27me3 signal at promoters of representative genes in Cluster 1 (A,B)  and Cluster 4 (C,D). .................................................. 158   xix Figure 5.19: Super-enhancer profiling in naïve, primary, memory and secondary CD4+ T cells. ............................................................................................................................................... 160 Figure 5.20: Naïve SE target genes are highly expressed relative to effector and memory cells. ............................................................................................................................................... 163 Figure 5.21: SEs shared between naïve and memory are associated with target genes that promote stemness and survival in naïve cells........................................................................... 165 Figure 5.22: Shared effector and memory SEs are associated with target genes required Th1 function during infection. ........................................................................................................ 167 Figure 6.1: Model showing the effect of IL-27 signalling on (A) effector CD4+ T cells and (B) Tregs following IAV infection ................................................................................................ 181 Figure 6.2: Model showing histone modification dynamics during and after the CD4+ T cell response to IAV infection ....................................................................................................... 184     xx List of Abbreviations ARDS Acute Respiratory Distress Syndrome APC Antigen presenting cell Bcl6 B cell lymphoma 6 protein bam binary alignment map BAL Bronchoalveolar lavage CCR C-C motif chemokine receptor CXCR C-X motif chemokine receptor CREB CAMP Responsive Element Binding Protein  CDC Centre for Disease Control CXCL CXC chemokine ligand CCL CC chemokine ligand ChIP Chromatin immunoprecipitation  cDNA complementary DNA Cre Cre recombinase CTL Cytotoxic T lymphocyte DC Dendritic cell DNA Deoxyribonucleic acid Egr-2 Early growth response gene 2 ESC Embryonic stem cell EBI3 Epstein-Barr virus induced gene 3  EDTA Ethylene-diamine-tetra-acetic acid   xxi FDR False discovery rate Fc𝛾R  Fc gamma receptor FBS Fetal bovine serum FMO Fluorescence minus one FACS Fluorescence-activated cell sorting GO Gene ontology GAPDH Glyceraldehyde-3-phosphate dehydrogenase gp130 glycoprotein 130 GFP Green fluorescent protein H3K27Ac Histone 3 lysine 27 acetylation  H3K27me3 Histone 3 lysine 27 tri-methylation  H3K4me1 Histone 3 lysine 4 mono-methylation  H3K4me3 Histone 3 lysine 4 tri-methylation  HA Hemagglutinin H&E Hematoxylin and eosin HCl Hydrocholoric acid IL-27Ra IL-27 receptor alpha Ig Immunoglobulin IIV Inactivated Influenza Vaccine iTreg induced Tregulatory cell ICOS Inducible costimulator IBD Inflammatory bowel disease   xxii IAV Influenza A virus IFN Interferon IRF3 Interferon regulatory factor ISG Interferon stimulated gene IFNAR Interferon-α receptor IFNLR Interferon-λ  receptor Il6st Interleukin 6 signal transducer IL-7Ra Interleukin 7 receptor alpha JAK Janus associated kinase KLRG1 Killer cell lectin like receptor G1 kb kilo bases CD62L L-selectin L. monocytogenes Listeria monocytogenes LAIV Live attenuated Influenza Vaccine LFA-1 Lymphocyte function associated antigen 1 MHC Major Histocompatibility Complex M1 Matrix protein 1 M2 Matrix protein 2 MFI Mean fluorescence intensity MPEC Memory precursor cell Mnase Micrococcal nuclease MACS2 Model-based Analysis of ChIP-seq 2   xxiii MDS Multi dimensional scaling c-Maf Musculoaponeurotic fibrosarcoma oncogene homolog M. tuberculosis Mycobacterium tuberculosis NK Natural Killer Cell nTregs natural T regulatory cells NA Neuraminidase NET Neutrophil extracellular traps NS Non-structural protein NFkB Nuclear factor kappa B NP Nucleocapsid protein NLRP3 Nucleotide binding domain, leucine rich-containing family pyrin domain containing-3 Oct4 Octamer binding transcription factor 4 PAMP Pathogen associated molecular pattern PRR Pattern recognition receptor PBMC Peripheral blood mononuclear cell PEG Polyethylene glycol poly (I:C) Polyinosinic-polycytidylic acid  PA Polymerase acidic protein PB1 Polymerase basic protein 1 PB2 Polymerase basic protein 2 PDL1 Programmed death receptor ligand 1   xxiv PKR Protein Kinase R qPCR Quantitative polymerase chain reaction ROS Reactive oxygen species RPKM Reads Per Kilobase Million RIG-I Retinoic acid inducible gene 1 RA Rheumatoid arthritis RNA Ribonucleic acid SLEC Short lived effector cells STEM Short Time Series Expression Miner  TRAIL-DR TNF-related apoptosis-inducing ligand death receptor STAT1 Signal transducer and activator of transcription NaCl Sodium chloride spp species Sox2 SRY-box 2 SD Standard deviation SE Super-enhancer Seq Sequencing SOCS Suppressor of cytokine signalling TCF1 T cell factor 1 TCR T cell receptor TCM T central memory TEM T effector memory   xxv  Tfh T follicular helper Th1 T helper 1  Th17 T helper 17 TRM Tissue resident memory TLR Toll-like receptor T.gondii Toxoplasma gondii TSS Transcriptional start site Treg Tregulatory cell TNFα Tumour necrosis factor alpha Tr1 Type 1 regulatory cell  TYK Tyrosine kinase 2 UCSC University of California Santa Cruz VE Vaccine effectiveness ZAP Zeta-Chain-Associated Protein Kinase p.i. post infection                 xxvi Acknowledgement       My time at UBC has been a rewarding experience and the relationships fostered through this journey have contributed greatly towards the completion of this thesis. A sincere thank you to Dr. Georgia Perona-Wright, my PhD supervisor, for taking me on as your first graduate student, for your encouragement and guidance throughout my degree. You’ve always set the bar high and led with kindness. I also extend my heartfelt thanks to Dr. Pauline Johnson who took over as my supervisor after the lab re-located to Glasgow. Pauline, I’m equally thankful for your thesis writing guidance and for introducing me to Sooty, Sherlock and Swallow. I extend my deep gratitude to Dr. Mike Gold and the department for their support during my PhD and for making sure I had the resources to finish my degree. I would also like to thank my committee members Dr. Ninan Abraham and Dr. Lisa Osborne for their advice at committee meetings and for reading my thesis. A very special thank you to Darlene, for being so warm, caring and for always having your office door open to grad students. I am thankful for the UBC Four Year Fellowship that contributed to my academic and my professional development. I would like to acknowledge that the research in this thesis was conducted at UBC which is situated on the unceded ancestral territory of the Musqueam people.        In the fourth year of my PhD, my project took a new path from being purely wet lab based to include bioinformatics. This led to a collaboration with Dr. Martin Hirst’s epigenomics laboratory at UBC during which I was introduced to the world of computational biology. I’d like to extend my sincere thanks to Dr. Martin Hirst, Michelle and other Hirst lab members for being welcoming and so helpful. In particular, I’d like to thank Alireza Lorzadeh, a brilliant fellow graduate student, for his tireless efforts, enthusiasm and patience while we worked together.   xxvii Thanks also to Rashedul, Alice, Misha (Hirst) and Connor (Hallam) for your help when I was stuck in R or needed to discuss my analysis.        Having been a part of the LSI for six years, I will miss the coffee-runs, science chats and friendly banter shared with the wonderful community here. I knew I hit the science friendship jackpot when some of you, my immunologist friends, volunteered your time to help me carry out a lengthy and crucial experiment. Steve, Morgan, Sally, Amy and Abdalla, you guys are the Massive Memory Sort OGs, thank you so much! An extra special shout-out to Morgan and Steve for all the laughs, your friendship and your help when things got tough. Graham, thanks for being a great lab mate even long distance, I can’t wait to call you Dr. Graeme soon!        There are a few people outside of the lab that I must thank: Florian, for showing me that its never too cold to do anything and patiently teaching me to ski. Christel and Jacob, for your friendship and help, both of you are wonderful. Ben, Ana, Laura and Lesley who have been awesome companions on several adventures. Thanks to (soon to be Dr.) Aria for all your hard-work in the lab, trail rides and Persian food. You’re awesome, buddy-guy! Lastly, Nita and Clary; Mike and Kelly for your consideration and kindness.      I thank: my parents for their unconditional love and support. My beloved cat Ike, deserves an honourable mention for his enduring love and meowing over Skype. My two older brothers and my precious sister-in-law for never failing to ask me about graduation but also never letting me spend Christmas alone. Nathan, my dearest nephew, thank you for your hugs and long winded conversations about everything. Thanks also to my dearest Goan friends Ruella, Shanti, Pixie, Vian and Joel for your friendship over so many years and time zones.  I’ll end by thanking a tall handsome pharmacist named Mark who always manages to drag a laugh out of me even when I am at my grumpiest. You are solid gold. <3   xxviii   Dedication To my amazing parents and my dearest late Avo   1 Chapter 1: Introduction 1.1 Influenza  1.1.1 Influenza A virus and impact on global health Influenza A is a single stranded RNA virus belonging to the Orthomyxoviridae family. Human infection with Influenza A virus (IAV) causes an acute, highly contagious, respiratory disease called influenza. IAV infections occur as seasonal outbreaks and pandemics resulting in significant global health and economic burdens (1, 2). The World Health Organisation estimates the global toll of a seasonal influenza epidemic at 3-5 million cases of severe illness and 290,000 – 650,000 deaths (3, 4). In the United States, the annual cost of a seasonal influenza epidemic is estimated at $10.4 billion per year in medical expenses and $16.3 billion in lost earnings (5). Pandemic influenza is far more devastating than the seasonal kind. Historically, the most severe pandemic was the 1918 Spanish flu outbreak which infected nearly half the world’s population and caused 50 -100 million deaths (6). In the recent 2009 H1N1 pandemic outbreak, the Centre for Disease Control (CDC) estimated ~59 million cases of infection, ~ 265,000 hospitalisations and ~12,000 deaths in the United States alone (7). Understanding IAV pathogenesis and the limitations of the immune response that counteract the spread of seasonal and pandemic IAV is therefore imperative to improve global health.   The epidemiological success of IAV in seasonal and pandemic outbreaks is due to the ability of the virus to evade the immune system. Immune escape occurs through two genetic mechanisms called antigenic drift and antigenic shift. Antigenic drift is caused by the gradual accumulation of point mutations in HA and NA genes which encode the immunogenic IAV surface proteins hemagglutinin and neuraminidase (Fig 1.1). Seasonal IAV strains that undergo antigenic drift   2 can escape the neutralising antibody response elicited by previous infections and vaccinations. Antigenic shift is a more dramatic event that occurs due to reassortment of genomic segments between genotypically different IAV subtypes infecting the same host. The resulting novel IAV strain then spreads rapidly through the population due to the lack of pre-existing immunity (1, 2). For example, the 2009 H1N1 pandemic was caused by a new IAV strain with genomic segments from swine, avian and human IAV lineages which is thought to have undergone recombination in a swine host (8, 9). The occurrence of antigenic shift and drift combined with the ease with which the virus is transmitted once it enters the human population (10, 11) results in the rapid spread of IAV.    3  Figure 1.1:  Structure of Influenza A virion  The influenza virome consists of eight RNA segments that encode at least 11 proteins including Hemagglutinin (HA), Neuraminidase (NA), Matrix proteins (M1 and M2), Polymerase basic protein (PB1, PB2 and PA), Nucleocapsid protein (NP) and non-structural protein NS. With permission: Horimoto and Kawaoka (2005) Influenza: lessons from past pandemics, warnings from current incidents. Nat Reviews Microbiology. 3:591-600      4  1.1.2 Disease and treatment of IAV infection in humans A typical bout of seasonal influenza in healthy adults consists of an incubation period of 1- 4 days, during which asymptomatic viral replication occurs, followed by the abrupt onset of fever, chills, myalgia, malaise, and a dry cough. Febrile illness lasts around 3-8 days and usually recovery occurs within a few days to less than two weeks (2). Severe influenza infections manifest with pulmonary complications such as primary viral pneumonia or secondary bacterial pneumonia or a combination of both (12). Histopathological evidence indicates that influenza related pneumonia is caused by direct lung injury due to virus replication and indirect damage by the host immune response (13, 14). Inability to control IAV replication due to a weakened or suppressed immune system is thought to increase disease severity among the elderly, those with chronic medical conditions (asthma, diabetes or heart disease) or immunosuppressive disorders, pregnant women and young children (15, 16). However, during IAV pandemics such as the 1918 H1N1 Spanish influenza and 2009 H1N1 outbreaks, in addition to the very young and elderly, an increase in deaths was observed among young adults between 20-40 years of age when compared with mortality rates from seasonal outbreaks (17, 18). Although the exact cause is unknown and likely to be multi-factorial, reverse genetics experiments using a reconstructed 1918 virus (19, 20) together with immunological studies in fatal cases of H5N1 avian and 2009 H1N1 infection in humans (21, 22) indicate that dysregulated immune responses are a significant contributor to increased mortality during influenza. These studies suggest that an exaggerated immune response may be just as detrimental as a weak or ineffective immune response during influenza infection.     5 Given that high virus load and immunopathology contribute to influenza related mortality, the ideal therapeutic interventions would therefore act to limit viral replication and regulate the immune response to influenza. There are currently three classes of anti-viral drugs have been approved for treatment of influenza infection which target different stages of the influenza life cycle. They are M2 ion channel inhibitors, neuraminidase inhibitors and RNA dependent RNA polymerase inhibitors. However, the use of these anti-virals is hampered by a combination of drug resistance, side effects and limited effectiveness. In Europe and USA, only M2 ion channel and neuraminidase inhibitors have been approved for treatment but only neuraminidase inhibitors are currently in use due to widespread resistance against M2 ion channel inhibitors (23-25). Further, randomised controlled trials of neuraminidase inhibitors in otherwise healthy patients with uncomplicated influenza have shown that these drugs shorten the duration of clinical symptoms by less than a day (26, 27). Besides anti-virals, anti-inflammatory drugs such as systemic steroids were used to treat approximately one third of influenza infected patients during the 2009 H1N1 pandemic. This rescue therapy was administered to patients who presented with severe pneumonia and acute respiratory distress syndrome (ARDS). However, steroid use did not result in better outcomes and instead was associated with increased the risk of contracting superinfections (28). Together, these studies indicate that better treatments and therapeutic strategies are urgently needed to protect against influenza.   1.1.3 Current vaccines against influenza Vaccination is the most effective method to prevent and control the spread of seasonal influenza. To this effect, the World Health Organisation recommends annual vaccination for persons 6 months or older with special emphasis on pregnant women, elderly individuals, individuals with   6 chronic medical conditions, and healthcare workers. Currently licensed influenza vaccines include multivalent forms of inactivated, live attenuated and recombinant vaccines (29). Inactivated vaccines (IIV) act to elicit antibodies primarily against the HA surface protein of IAV and are available in standard dose, high dose or adjuvanted form to meet the needs of different age groups. Live attenuated vaccines (LAIV) are made by introducing HA and NA into the backbone of a replication competent but attenuated cold adapted virus. LAIVs elicit both neutralising antibody responses and cell mediated immunity and were designed with the aim of providing better protection than IIV (29). While vaccines do offer yearly protection against influenza, the problem lies in the fact that vaccine induced protection is not complete and varies from season to season. For example, vaccine effectiveness (VE) against influenza during the 2015-2016 season was 48% but only 19% in 2014-2016 (30, 31). Studies in the US that compared IIV and LAIV reveal in the 2013-2014 and 2015-2016 flu seasons, LAIV was found to be as low as 5% while VE for IIV ranged between 40-60% (31, 32) Variable influenza vaccine effectiveness is thought to occur due to a combination of factors such as antigenic drift during the amount of time it takes from selection of IAV strains to vaccine availability, acquisition of mutational changes during serial egg passage and host factors such as prior influenza exposure or vaccination history and age (29, 33). Since current vaccines that elicit a neutralising antibody response to HA and NA epitopes are rendered ineffective by antigenic shift and drift and exhibit variable effectiveness, there is great need to develop and licence therapeutics such as universal vaccines that elicit protective immunity against seasonal and pandemic IAV strains.     7 1.2 Immune response to IAV The immune response to influenza is necessary for controlling virus replication in the lung but aberrant immune responses are associated with severe respiratory disease observed in human infections with pandemic and avian influenza strains. Studying influenza pathogenesis in animal models has shown that exaggerated immune cell infiltration and over-production of pro-inflammatory cytokines and effector molecules can cause lung pathology. This section discusses the protective and pathogenic roles of the immune response during IAV and identifies the immunosuppressive cytokine IL-10 as a critical regulator of immunity during infection.   1.2.1 Innate immune response: protection versus pathology The immune response to influenza is initiated following entry of IAV into respiratory epithelial cells via attachment to α- 2,6 or α-2,3 linked sialic acid residues on the cell surface. Pattern recognition receptors (TLRs, RIG-1, NLRP3) present in endosomes or the cytosol of respiratory epithelial cells and lung resident alveolar macrophages, dendritic cells and monocytes detect viral pathogen associated molecular patterns (PAMPs) such as dsRNA and ssRNA. PRR-signaling through MyD88, TRIF or MAVS-dependent pathways and lead to the activation of the transcription factors such as NFkB, IRF7 and IRF3, which stimulate the expression of Type 1 and III interferons, pro-inflammatory cytokines and chemokines (34, 35). Type 1 and Type 3 IFNs induce an anti-viral state by signalling through their cell surface receptors IFNAR and IFNLR in an autocrine or paracrine manner to activate Interferon Stimulated Genes (ISGs). Well-knowns ISGs include Mx1, ZAP, PKR, viperin and tetherin, which function to inhibit different stages of the IAV life cycle. Type 1 IFNs, together with PRR signaling, also induce sustained expression of pro-inflammatory cytokines (TNFα, IL-1 and IL-6) and chemokines   8 (CXCL10, CCL2, CCL4, CCL5) from respiratory epithelial cells, lung dwelling alveolar macrophages and dendritic cells (36-38). Early chemokine expression recruits innate immune cells including neutrophils, monocytes and natural killer cells (NK cells) from circulation into the lung (39).  Neutrophils arrive as early as 6 hours post infection and together with alveolar macrophages are critical for the control of viral replication through phagocytosis of apoptotic virus infected cells (40, 41). However, neutrophils also contribute to acute lung injury through the production of  reactive oxygen species, myeloperoxidase and neutrophil extracellular traps (NETs) (42-44). NK cells directly control virus replication through lysis of infected cells by binding IAV-HA with cytotoxicity receptors NKp44 and NKp46 receptors (45, 46) but NK cell derived IL-6 and IL-12 expression is associated with increased IAV morbidity and mortality (47). Monocytes are recruited to the lung in a CCR2 dependent manner and differentiate to form monocyte derived dendritic cells and exudate macrophages. While monocyte derived DCs produce Type 1 IFNs and stimulate robust CD8+ T cell proliferation in the lung these DCs together with exudate macrophages also cause lung damage by producing large amounts of TNF𝛼 and nitric oxide synthase (48-50). When CCR2 deficient mice are infected with IAV, a reduction in pulmonary immunopathology due to decreased accumulation of monocyte, macrophage and dendritic cells was observed but viral titres were increased (51). Interestingly, reducing but not eliminating trafficking of TNF-	𝛼 and nitric oxide synthase producing dendritic cells through pharmacological suppression was shown to ameliorate lung pathology (50). Together these studies show that innate immune cells have key roles in limiting viral replication but can cause   9 collateral damage in the lung due to imbalanced inflammatory responses and highlight the need for precise immunoregulation.   While the activation of the innate system establishes an anti-viral state and limits virus replication, virus clearance occurs through activation of the adaptive immune system. Lung resident dendritic cells called respiratory DCs act as a bridge between the innate and adaptive immune system by priming naïve T cells and viral clearance is hampered in their absence (52). Respiratory DCs acquire IAV antigen primarily through phagocytosis though direct infection has been observed in in vitro studies (53-56). The main antigen presenting respiratory DCs are CD103+CD11blo DCs and CD103-CD11bhi DCs which, upon viral encounter, upregulate the chemokine receptor CCR7 and migrate to the draining lymph node along a chemokine gradient (57, 58). In the lymph node, these respiratory DCs present IAV antigen to CD4 and CD8 T cells via MHC Class I and MHC Class II dependent pathways (52, 58-60). Respiratory DCs are also able to transfer antigen to lymph node resident DCs that may serve to prime B cell responses (61).    1.2.2 B cell response to IAV The importance of B cells in combating IAV infection can be observed in B cell deficient mice that are highly susceptible to pathogenic IAV infection and are unable to control viral load compared with wild type control that fully recover (62, 63). Antibody production is the primary mechanism through which B cells protect against IAV as demonstrated by complete clearance of infection upon transfer of HA specific neutralizing antibodies into SCID mice lacking both T and B cells (64). The HA-specific antibody prevents viral entry due to its ability to bind the trimeric   10 globular head of HA and is used a measurable correlate of protection against IAV in hemagglutinin inhibition assays. B cells also produce non-neutralizing antibodies against NA and internal viral proteins such as NP and M2 which cannot clear the infection but instead reduce viral titres and mortality through Fc𝛾R	mediated	cell	lysis	by	natural	killer	cells	or	macrophage	phagocytosis	(65-68). The presence of neutralizing antibodies prevents re-infection with the same strain of IAV (64) while non-neutralizing antibodies to conserved IAV proteins can provide protection against heterosubtypic IAV strains that possess different HA and NA subtypes (65, 66).  The priming of naïve B cells occurs in secondary lymphoid organs upon encountering soluble IAV antigen or through DC presentation of intact antigens (69-71). Once activated, B cells differentiate into effector cells, and antibody production can occur through an extra-follicular T cell independent pathway or T cell dependent pathway in the germinal center (72). The T cell independent pathway rapidly produces low affinity IgM producing B cells that are an important part of the early, local antibody response but are short lived.  The T cell dependent pathway requires antigen-specific T follicular helper (Tfh) cells to interact, through CD40-CD40L pairing, with cognate antigen bearing B cells in the presence of cytokines. These interactions induce immunoglobulin class switching to high affinity IgG and IgA antibodies (73). Of note, IgA secreting B cells can also be generated within mucosa associated lymphoid tissue  or induced bronchus associated lymphoid tissue (74, 75). Antibody secreting B cells peak around 7 days post infection and return to pre-infection levels by Day 28 (76) while serum anti IAV antibody titres peak around 2 weeks post infection and are maintained for life (77-79).     11 Following viral clearance by Day 10 (80, 81) majority of IAV specific B cells undergo apoptosis leaving behind class switched long lived plasma cells and memory B cells. IgG producing long lived plasma cells home to survival niches in the bone marrow (82, 83) while IgA secreting plasma cells home to the respiratory mucosa (84, 85). Memory B cells are distributed systemically through the lungs, secondary lymphoid organs and bone marrow (86). Long lived plasma cells constitutively produce immunoglobulins whereas memory B cells are reactivated upon re-infection and can undergo an additional round of affinity maturation within the germinal cente. The immunological memory of B cells harnessed by currently licensed influenza vaccines consists of these long lived  antibody producing plasma cells and memory B cells (87).    1.2.3 CD8+ T cell response to IAV CD8+ T cells are essential for optimal viral clearance which occurs through direct lysis of virus infected cells (80, 88). While mice lacking CD8+ T cells are able to clear low dose IAV after a short delay (80,	89),	protection mediated by CD8+ T cells appears to be especially important during virulent influenza infection because CD8+ T cell deficient mice exhibit both delayed viral clearance and accelerated mortality compared to wild type controls (89). Naïve CD8+ T cells in the lymph node are activated by IAV antigen bearing CD103+ and CD11b+ DCs migrating from the infected lung in the early phase of infection (52, 58). Following antigenic stimulation, naïve CD8+ T cells undergo a developmental program in which they proliferate and differentiate into effector cells with cytotoxic activity and pro-inflammatory cytokine expression within the lymph node (90, 91). Activation also results in the upregulation of chemokine receptors and adhesion molecules on the surface of effector CD8+ T cells in the lymph node thereby enabling trafficking   12 into the lung. The accumulation of effector CD8+ T cells with a CD62lowCD25high, CD11ahigh, CD49dhigh phenotype in the lung can be observed at Day 6-7 post infection and peaks between Day 8-10 (91). Optimal CD8+ T cell responses in the lung are dependent on interactions with non-migratory dendritic cell populations which enhance CD8+ T cell proliferation, survival and effector function (59, 92-95).     CD8+ T cells exert their effector functions in the lung through the lysis of virus infected respiratory epithelial cells and by producing inflammatory cytokines and chemokines. Cytotoxic T lymphocytes (CTLs) recognize and destroy virus infected cells by two main mechanisms: the release of cytotoxic granules granzyme and perforin or through the engagement of death receptors (Fas or TRAIL-DR) on virus infected cells (88). The cytokine profile of CTLs responding to IAV include proinflammatory molecules such as IFNγ, TNFα, low levels of IL-2 and the anti-inflammatory cytokine IL-10 (96-98). In addition to CTLs, a population of CD8+ T cells that produce IL-17 called Tc17 cells have been identified that lack cytotoxicity and offer partial protection but their effector functions are not well-defined (99). Finally, effector CD8+ T cells can express chemokines such as CCL3, CCL4, CXCL9 and CXCL10 which promote the recruitment of neutrophils, NK cells, macrophages and B cells into the lung (100) thus establishing a positive feedback loop that sustains lung inflammation. While CD8+ T cells utilize multiple effector mechanisms to promote viral clearance, these cells can also contribute to lung injury when dysregulated. Studies have shown that CTLs can cause lung immunopathology by overexpression of IFNg and TNFa in the lung which leads to increased apoptosis of bystander alveolar epithelial cells and increased recruitment of immune cells (101-104). Together, these   13 studies show that CD8+ T cells function as a double-edged sword contributing to both protection and pathology during influenza infection.   1.2.4 CD4+ T cell response to IAV The primary CD4+ T cell response to IAV is classically described as the ‘Thelper’ response due to the distinct roles these cells play in providing help to CD8+ T cells and B cells via co-stimulation and inflammatory cytokine production which promote optimal IAV clearance (105). Recent studies have identified roles for CD4+ T cells that extend beyond their classical helper functions such as in the direct killing of virus infected cells (106), promoting the formation of functional memory CD8+ T cells (107, 108) or by suppressing effector T cell proliferation and cytokine expression (97, 109, 110). These multi-functional roles that CD4+ T cells play during the immune response IAV are due to their ability to differentiate into multiple Thelper lineages (Fig 1.2) with specialized functions designed to promote viral clearance and offer protection during IAV infection.    Naïve CD4+ T cells are primed the in the draining lymph node upon recognition of cognate IAV antigen on CD103+ or CD11bhi respiratory dendritic cells (60). Following activation, CD4+ T cells expand in the lymph node and can be detected in the lung around Day 6 and peak around Day 10 post infection (97, 111). These effector CD4+ T cells in the lung possess a predominantly Th1 phenotype characterized by expression of the canonical Th1 transcription factor T-bet (112) and the pro-inflammatory Th1 cytokines IFNg and TNFa and IL-2 (111). IFNg is known to stimulate the expression of chemokines, directly activate innate immune cells such as macrophages, NK cells and dendritic cells and induce production of IgG2a isotype antibodies   14 (113, 114). TNFa is a complex cytokine known to induce apoptosis of infected cells but can also regulate the CD8 T cell response and innate cell function (115). Interestingly Th1 cells also express the anti-inflammatory cytokine IL-10 and subsets of IL-10+ IFNg+ CD4+ T cells can be observed in the lung at the peak of the T cell response. It is worth nothing that IFNg and IL-10 expression from CD4+ T cells is highest in the lung compared to the draining lymph node which suggests that environmental signals received at the infected tissue site (97, 106) may further modulate the function of these cells. In addition to cytokine secretion, CD4+ T cells in the lung can express both Granzyme B and perforin and therefore possess the ability to directly lyse MHC-Class II expressing epithelial cells (106, 116). These CD4+ T cells with cytolytic ability are called CD4 CTLs and are generated through a Th1 differentiation pathway and rely on IL-2 and Type 1 IFNs to attain full cytotoxic potential (117-119). Th17 cells can  also be identified in the infected lung at the peak of primary IAV infection (99, 120, 121) although these IL-17 expresing cells are present at ~200-fold lower frequencies than IFNg+ Th1 cells (99). The balance between Th1 and Th17 cells in the lung depends on IL-10 expression as IL-10 deficient mice express increased amounts of Th17 associated cytokines such as IL-17, IL-22 and IL-6 (121). Lastly, IAV infection induces the expansion of Foxp3+ CD4+ Tregulatory (Tregs) cells within the lung which can suppress the proliferation of antigen specific CD4+ and CD8+ T cell responses. Within the lymph node, activated CD4+ T cells exposed to IL-6 and co-stimulation via ICOS differentiate into follicular helper T (TFH) cells. TFH cell are distinguished by the expression of the lineage defining transcription factor Bcl6 and upregulate CXCR5 which allow them to migrate into the germinal centre (GC) follicle and form stable interactions with B cells. Within the GC, TFH cells promote the differentiation, survival and high affinity maturation of B cells through the production of IL-4, IL-21, CXCL13 and CD40-CD40L interactions (122, 123).   15 Taken together, these studies indicate that CD4+ T cells offer protection during IAV by differentiating into multiple Thelper subsets that can boost the function of other immune cells, suppress inflammation and even directly contribute to viral clearance.   The ability of CD4+ T cells to carry out their protective roles in the lung is dependent on their migration from the site of priming in lymph node to the site of infection. Naïve CD4+T cells express high levels of CD62L and CCR7 which is required for the entry of T cells into the lymph node through high endothelial venules (124, 125). Following activation by a cognate antigen bearing DC, CD4+ T cells downregulate CD62L and CCR7 and upregulate the chemokine receptors CCR5, CXCR3 and CCR4 (126) and adhesion molecules CD44, LFA-1 and CD49d  (97, 106, 111, 127). CXCR3 in particular is important for virus specific CD4+ T cells to home to the lung during IAV infection as CD4+ T cells lacking CXCR3 expression exhibit impaired migration to the lung compared with wild type or CXCR5 deficient CD4+ T cells (127). The Th1 transcription factor T-bet is essential for CXCR3 expression in Th1 cells (112, 128) which then migrate to the lung in response to a gradient of CXCR3 ligands CXCL9, CXCL10 or CXCL11 secreted by lung resident cells including alveolar epithelial cells and stromal cells (129, 130). Since all three CXCR3 ligands can be induced by IFNg, Groom and Luster (131) proposed that CXCR3+ Th1 cells may be part of a chemokine mediated amplification loop wherein IFNg expression from these cells stimulates expression of CXCR3 ligands from lung resident cells which results in the recruitment of additional CXCR3+ IFNg+ cells such as NK cells and CD8+ T cells to the infected lung. While a strong Th1 response may be beneficial, the potential of such an amplification loop to cause immunopathology can be observed during pathogenic H5N1 infection where CXCR3 and CXCL10 expression contribute to acute lung injury (132).   16 Interestingly, Tregs  present in the lung during primary IAV display a T-bet+CXCR3+ phenotype (109, 110) and therefore may be able to home to and suppress the cellular sources of CXCR3 ligands but this aspect of Treg function has not been investigated during influenza infection.     Figure 1.2: Functional diversity of the CD4+ T cell response to primary IAV infection.     17 1.2.4.1 Immunopathogenic potential of CD4+ T cells during IAV The protective functions of CD4+ T cells during influenza are evident in their ability to provide help to antigen presenting cells, B cells and CD8+ T cells via co-stimulation or by the secretion of inflammatory cytokines. However, dysregulated CD4+ T cell responses may cause lung immunopathology through excessive stimulation of their target immune cells. The pathogenic role of CD4+ T cells in IAV is supported by studies showing that elevated levels of Th1 and Th17 cytokines are present in patients with severe pandemic H1N1 infection compared to patients with mild infections (22, 133) and that IL-17 signalling can cause lung injury via recruitment of damage inducing neutrophils (134). CD4+ T cells can also cause lung damage through their cytolytic activity which is mediated through perforin and FasL dependent pathways (135, 136). Therefore, CD4+ T cells are another immune cell subset that must be precisely regulated during influenza infection to achieve a balance between viral clearance and immunopathology. Given the role of CD4+ T cells as orchestrators of innate and adaptive immune response, understanding the immune mechanisms that control their pathogenic potential may in turn enable regulation of APCs, CD8+ T cells and B cells, the targets of their helper activity, in a feed forward loop.  1.3 IL-10 as a regulator of immunity and immunopathology during IAV  Given the pathogenic potential of the immune response as described in Section 1.2.4, regulatory mechanisms that can turn off the immune response following infection are as important as the inflammatory signals turned on to protect against IAV. The immune system has multiple control mechanisms, including regulatory cells, immunosuppressive cytokines, and inhibitory molecules. During influenza, critical roles for controlling immunopathology in the lung have been described   18 for inhibitory molecules such as CD200 (137) and NKG2A (138) and the immunosuppressive cytokines TGF-𝛽 (139) and IL-10 (97). Interestingly, these immunoregulatory molecules are either expressed by or act on effector T cells to limit their accumulation and the production of inflammatory cytokines such as IFNg and TNFa (97, 137-139) thus serving to highlight the importance of regulated T cell function during IAV infection. IL-10 is particular has a well-established role in protecting the host from immunopathology caused by exaggerated T cell responses in parasitic and bacterial infections (140-142). In acute respiratory viral infections with influenza and RSV, IL-10 has also been described as a critical regulator of inflammation and immunopathology. When IL-10 signalling is blocked, mice infected with either IAV or RSV succumb early with fatal lung pathology caused by  T cell and innate cell associated pro-inflammatory cytokine production compared to wild type controls (97, 143). The inflammatory cytokine profile and histopathological changes in these mice are reminiscent of the cytokine storm observed in pathogenic IAV infections. While these reports support the protective role of IL-10 in the lung, two conflicting reports showed that mice genetically deficient in IL-10 showed better recovery from IAV and this was associated with higher IAV specific antibody production (144) or developed a Th17 response (121) capable of mediating protection against IAV. Intriguingly, IL-10-/- RSV infected mice show similar impairment of the protective immune response as αIL-10R treated mice (145). One possible explanation for these conflicting data in influenza infection is that the absence of IL-10 when the immune response is initiated may be beneficial for the development of a robust immune response. In contrast, during natural infection, while inflammation is at its peak, IL-10 expression is necessary to limit the pathogenicity of the immune response to influenza. Interestingly, effector CD4+ and CD8+ T cells themselves are the main source of IL-10 in the infected lung during influenza and RSV infection (97, 143). These   19 effector cells have a highly activated phenotype and simultaneously express high levels of T-bet, IFNγ and TNFα indicating that IL-10 could act in an self-regulatory manner to limit the pathogenicity of the T cell response to acute viral infection. This protective role of IL-10 is further supported by the finding that in IAV and RSV, IL-10 acts to limit tissue pathology but does not affect viral clearance which remains comparable between wild type and αIL-10R treated groups (97, 143). Together, these studies indicate that T cell derived IL-10 is an important regulator of immunity in the lung when the risk of T cell mediated immunopathology is highest and that IL-10 may act in an auto-regulatory manner on effector CD4+ and CD8+ T cells (143, 146) .    The potent immunosuppression associated with Il-10 expression during infection is due to the pleiotropic effects of this cytokine on innate and adaptive immune cells. IL-10 is known to inhibit TNFa and ROS from macrophages (147-150), inhibit the differentiation of monocytes to TNF iNOS+ DCs (151) and suppress the expression of costimulatory and MHC-II molecules on conventional DCs (152, 153) (154). The inhibitory effects of IL-10 on macrophages and dendritic cells in turn suppresses T cell proliferation and inhibits IFN𝛾 production from T cells and NK cells (152, 154, 155). While beneficial in limiting immunopathology, the widespread immunosuppression mediated by IL-10 carries the risk of inadvertently suppressing a protective pro-inflammatory immune response and leading to pathogen persistence as seen in chronic infections caused by Plasmodium spp (156, 157), Leishmania spp (158, 159), T. cruzi (160), Mycobacterium spp (161) and LCMV (162). While not observed in self-resolving influenza infection (163), persistence of IAV measured by high viral loads is a feature of severe and fatal infections caused by pathogenic avian H5N1 and pandemic H1N1 2009 IAV strains (21, 22,   20 164). Therefore, IL-10 is an effective and critical regulator of immunopathology during influenza infection but in order for IL-10 to be beneficial its expression must be well-timed during the immune response.   1.3.1 IL-10 expressing regulatory CD4+ T cells in Th1 infection  The role of IL-10 expressing CD4+ T cells during influenza is of particular interest given the need for precise immunoregulation of both innate and adaptive immune cells during pathogenic IAV infections. As mentioned in Section 1.2.4, CD4+ T cells provide help to innate and adaptive immune cells and are present at the site of infection therefore CD4+ T cells could utilize IL-10 to modulate the inflammatory activity of multiple immune cell types in the lung. In addition, CD4+ T cells themselves require precise regulation due to their immunopathogenic potential (Section 1.2.4.1). Therefore, CD4+ T cell derived IL-10 may not only target multiple immune cell types but act in an auto-regulatory manner during IAV infection.   In Th1 infections, IL-10 expressing CD4+ T cells can be divided into three main regulatory subsets; IL-10+ Th1 cells, Tr1 cells and Foxp3+ Tregulatory cells. In acute Th1 infections caused by intracellular pathogens such as  Toxplasma gondii (165) and Plasmodium chabaudi (166), effector Th1 cells that express IL-10 are essential regulators of immunopathology and act by suppressing pro-inflammatory cytokines and chemokines. The expression of IL-10 does not hamper the effector functions of Th1 cells as IL-10+ IFNg+ CD4+ T cells were able to simultaneously stimulate parasite killing via IFNg and suppress IL-12 (165). IL-10 expression from Th1 cells in these parasitic infections appears to be transient and induced upon activation (165) which is reminiscent of activated Th1 cells expressing IL-10 in the infected lung at the   21 peak of the primary response (167). Therefore, it is possible the timed expression of Th1 derived IL-10 could play an important role in limiting deleterious inflammation in the infected lung.    Another subset of IL-10 expressing Foxp3- CD4+ T cells called Type 1 regulatory cells (Tr1) cells have been identified in mice infected with Plasmodium yoelli (168)  and Bordetella pertussis (169). These Tr1 cells express high levels of IL-10 but very little IFNg (168, 169) and are identified by their Lag3+CD49b+ phenotype (170). In P. yoelli, high level IL-10 expression from CD25- CD127- Foxp3- Tr1 cells is essential to limit fatal immunopathology but at the cost of inhibiting parasite killing (168). In B pertussis infection, antigen specific Tr1 cells inhibit IL-2 production and proliferation of Th1 cells (169) but the effect on pathology is not known. There is currently some debate on the phenotype of Tr1 cells during infection as these cells share surface markers with effector CD4+ T cells or Foxp3+ Tregs. In addition, Tr1 cells do not express a lineage defining transcription factor. While IL-10 expressing Tr1 cells can be generated in vitro and in vivo, their relationship to IL-10 expressing Th1 cells during infection is not fully understood (171).   Another key source of IL-10 is the CD4+ Foxp3+ T cell population known as Tregs, Foxp3+ Tregs can be divided into two categories according to their development. Thymic or “natural” Tregs recognise self-antigens in the thymus and develop in response to high affinity self-peptide:MHCII interactions and IL-2 stimulation (172-174). The second category of Tregs are induced in the periphery by the conversion of conventional CD4+ Foxp3- CD4+ T cell to CD4+ Foxp3+ CD4+ T cells, called induced Tregs (iTregs). High affinity TCR signaling together with suboptimal co-stimulation in the presence of TGFβ (175, 176) (177) (178) or chronic low dose   22 exposure to non-self-antigens (179)  is thought to favour the generation of iTregs. Foxp3+ Tregs responding to influenza infection are at least in part IAV antigen specific as demonstrated by their expansion in response to BMDC presenting influenza antigen (110).  During primary influenza infection, activated Foxp3+ CD4+ T cells with an CD44hi CD69hi CTLA4+ ICOS+ phenotype expand in the lungs of infected mice. The peak of Treg accumulation in the lung occurs on day 7 and precedes that of effector T cells (97, 110). Functionally, Tregs can suppress effector CD4+ and CD8 T cell accumulation and cytokine production in the lung in an IL-10 dependent manner (109, 110). The expression of IL-10 from Tregs appears to be lung specific as very little IL-10 can be detected in the draining lymph node (109). Of note, the depletion of Tregs during influenza infection results in a skewing of epitope specific CD8+ T cells towards the immune dominant epitope which indicates that Tregs limit high affinity cytotoxic CD8 T cells that can contribute to immunopathology in the lung (180, 181). Currently, the cellular signals that promote the recruitment and regulate the immunosuppressive functions of Tregs in the lung in response to influenza  are not clear.   1.4 IL-27: a regulator of CD4+ T cell function in Th1 infection IL-27 is nuanced cytokine that can exert both pro- and anti-inflammatory effects on CD4+ T cell responses. The pro-inflammatory effects of IL-27 include the STAT1 dependent upregulation of T-bet and IL-12Rβ2 expression in CD4+ T cells cultured in vitro (182-185). Increased IL-12Rβ2 expression is thought to sensitise newly activated CD4+ T cells to IL-12 and promote early Th1 differentiation (182, 186). IL-27 has also been shown to induce STAT3 dependent proliferation   23 and survival of CD4+ T cells following activation in vitro (182, 186, 187).  However, in vivo studies in multiple infection models revealed an important role for IL-27 in regulating inflammation and immunopathology in models of viral and parasitic infection. In Th1 infections with T. gondii, Plasmodium spp and T. cruzi, the absence of IL-27 signalling results in severe immunopathology due to exaggerated T cell responses which are characterised by excessive pro-inflammatory cytokine production, proliferation and activation (188-191). Similarly, in acute viral infections with influenza and RSV, the absence of IL-27 causes more immunopathology which is associated with increased T cell expansion, IFNγ and TNFα expression (192, 193). Interestingly, in infections with L. major, T. gondii and influenza, IL-27 acts specifically to limit immunopathology and has no effect on pathogen clearance (191, 192, 194). In malaria infection with Pl. berghei, the absence of IL-27 signalling results in better pathogen control but IL-27Ra-/- mice succumb early due to Th1 mediated pathology (188). The presence of IL-27 also acts to limit the development of inappropriate Th2 and Th17 responses during Th1 infection which impair pathogen clearance (190, 194, 195) and increase tissue pathology (194, 196, 197) respectively.  Taken together, these studies indicate IL-27 is an essential regulator of Th1 inflammation and tissue immunopathology during infection.    1.4.1 IL-27: mechanisms of CD4+ T cell regulation Given the role of IL-27 in limiting the pathogenicity of the Th1 response, the regulatory pathways employed by this cytokine are of interest to understand how immunity is regulated during infection.  IL-27 can induce expression of the immunosuppressive cytokine  IL-10 expression in a STAT1 and STAT3 dependent manner from Th1, Th2, Th17 and Tr1 in vitro   24 (198, 199) and during infection (166, 192, 194, 200). The importance of the IL-27/IL-10 axis during Th1 responses is evident during malaria infection where IL-10 expressed by IFN𝛾+ CD4+ T cells in response to IL-27 signalling limits immunopathology and increases survival (166). In influenza, IL-27 signalling promotes IL-10 expression primarily from IFN𝛾+ CD4+ T cells which acts to suppress IL-17 expression but has no effect on IFN𝛾	(192). Instead, IL-27 signalling appears to directly limit IFN𝛾 expression from CD4+ T cells in a STAT3 dependent manner (192, 201) but the downstream mechanism is not known. It is worth noting that, in this study, the effect of IL-10 on IFN𝛾 and IL-17 expression was evaluated in an IL-10-/- mouse which is known to have altered responses to inflammatory stimuli and could therefore complicate the results obtained.  Intriguingly, IL-27 can induce the pro-inflammatory Th1 master transcription factor T-bet and the anti-inflammatory cytokine IL-10 in Foxp3+ Tregs within the gut mucosa during parasitic Th1 infection with T gondii (202). The expression of T-bet in Tregs results in enhanced fitness, survival and immunosuppressive ability of these cells compared with T-bet- Foxp3+ Tregs (203). T-bet also directly promotes expression of the chemokine receptor CXCR3 on Tregs which enables trafficking of these cells to the effector site (203). IL-27 signalling induced T-bet+ Tregs suppress Th1 inflammation and promote survival in during T. gondii infection through the expression of IL-10 (202). There is some evidence that IL-27 may be important for Treg function in the lung because treatment of IL-6 depleted RSV infected mice with recombinant IL-27 has been shown to increase expression T-bet and suppressive molecules such as KLRG1, CTLA4 and GITR in lung dwelling Tregs (193). In influenza, Tregs that express T-bet and CXCR3 have been identified in the lung where they limit effector T cell proliferation (109). However, the role   25 of IL-27 in promoting a Th1 effector like phenotype in Foxp3+ CD4+ Tregs in the lung is not known.  As mentioned in the previous section (Section 1.4), IL-27 also acts to limit deleterious Th2 and Th17 responses in Th1 infections. IL-27 can suppress Th17 responses by multiple mechanisms that rely on STAT1 and T-bet expression in CD4+ T cells. These include IFN𝛾 mediated suppression of IL-17 expression, direct repression of the master Th17 regulator ROR𝛾t and induction of the of programmed death receptor ligand 1 (PDL1) on CD4 T cells which suppresses Th17 cells in trans (204). IL-27 signalling can directly inhibit expression of the Th2 master regulator GATA3 expression in CD4+ T cells cultured under neutral or Th1 polarising conditions (184). Since GATA3 inhibits STAT4 and IL-12Rβ expression (205, 206), the upstream inhibitory action of IL-27 on GATA3 expression may enforce and stabilize the Th1 lineage by preventing Th2 differentiation during infection. Together these studies indicate that IL-27 can dampen inflammation during Th1 infection by employing multiple immunosuppressive pathways however current knowledge on the role of IL-27 in regulating CD4+ T cell function during influenza infection is lacking and requires further investigation.   1.4.2 IL-27 signalling in CD4+ T cells IL-27 is a heterodimeric cytokine comprised of two subunits, Epstein-Barr virus induced gene 3 (EBI3) and IL-27p28. The cellular sources of EBI3 and IL-27p28 under inflammatory conditions are myeloid cells such as activated dendritic cells, macrophages and monocytes. The inflammatory signals that promote IL-27 expression from myeloid cells include microbial stimuli such as LPS, poly (I:C) and CpG, Type 1 IFNs and IFN𝛾	(207). During influenza infection, IL-  26 27 can be detected early in the infected lung around Day 2 and peaks at Day 6 post infection before returning to baseline by Day 12 (208, 209).   Naive CD4+ T cells express the IL-27 receptor, which is composed of two subunits, IL-27Rα and gp130, a shared subunit of the IL-6 receptor family (210). The binding of IL-27 to its receptor activate JAK1, JAK2 and TYK2 (211) which then leads to the downstream phosphorylation of  STAT1 and STAT3 proteins (185, 212). Activation of STAT1 is required for T-bet expression which in turn upregulates IL-12Rβ on the surface of CD4+ T cells exposed to IL-27 (213). The activation of STAT1 and STAT3 by IL-27 are both essential for IL-10 expression (198) but STAT3 can also activate other transcription factors such as c-Maf and Egr-2 which then promote IL-10 expression (214, 215). Besides STAT proteins, IL-27 signalling activates a MAPK pathway which has been shown to promote Th1 differentiation in vitro (186).   Since IL-27 is an important regulator of inflammation during infection through mechanisms such as the production of IL-10, sustained IL-27 signalling carries the risk of excessive immunosuppression. Therefore, it is important to understand the cellular mechanisms that turn off IL-27 once the pathogen is cleared. CD4+ T cells that are activated in vitro downregulate surface expression of the signaling subunit gp130 (216). In addition, the IL-27 signalling pathway may possess an intrinsic ability to self-regulate by inducing expression of the negative regulator SOCS3 (186) which can bind to the phosphorylated intracellular tail of gp130 and inhibit IL-27 signal transduction (217). In CD8+ T cells, there is evidence that the loss of surface gp130 results in decreased STAT1 and STAT3 phosphorylation and less IL-10 secretion during   27 influenza infection (218). However, in CD4+ T cells, the biological significance of impaired IL-27 signal transduction has not been determined.    Figure 1.3: Overview of the IL-27 signalling pathway     28   1.5 CD4 T cell memory Immunological memory, as described by Janeway (219), is the ability of the adaptive immune system to remember a previous encounter with a pathogen and mount a rapid and more effective immune response. This enhanced recall ability is attributed to a pool of long lived antigen-specific memory T and B cells derived from clonally expanded precursors during the primary response (220, 221). The long-lasting protection offered by memory cells has been harnessed in the form of vaccines against infectious diseases such as small pox, measles and polio. Most vaccines currently in use protect by eliciting an antibody response and therefore provide a safe and effective disease prevention strategy against pathogens that are antigenically stable. In contrast, pathogens such as IAV that undergo antigenic shift and drift are able to evade the neutralizing antibody response towards surface HA and NA proteins elicited by influenza vaccines resulting in seasonal and pandemic outbreaks (29). As a result, recent efforts have been directed towards developing novel therapeutics such as universal influenza vaccines that generate antibody and cell mediated immunity towards conserved IAV epitopes such as HA stem specific antibodies and memory T cells that recognise internal NP and M1 viral proteins (222, 223). Since optimal anti-viral immunity against IAV requires the synergistic action of different arms of the immune system, universal vaccines that harness both antibody and T cell mediated memory responses would offer the most effective protection. In this respect, eliciting a robust memory CD4+ T cell population that can boost memory CD8+ T cell and B cell function in a recall response could enhance the protective capacity of universal vaccines against influenza. However, several questions regarding the induction, location and longevity of memory T cells   29 following exposure to IAV antigen still remain unanswered. This section discusses the function, generation and heterogeneity of the memory CD4+ T cell response and highlights the need for further studies to understand the development of memory CD4+ T cells and their behaviour in a recall response to IAV.   1.5.1 Generation of memory CD4+ T cells Following pathogen clearance, the expanded T cell compartment undergoes contraction during which 90-95% of the cells die by apoptosis leaving behind a small pool of memory cells. Environmental signals such as sustained peptide-MHC II interactions, co-stimulation and the cytokine signals IL-2 and IL-7 (224-226) promote the formation of memory CD4+ T cells. Memory T cells are also subject to regulation at the transcriptional and epigenetic level but much of our understanding of these aspects of memory formation arise from studies on CD8+ T cells. Advances on how memory formation occurs in CD8+ T cells has been made possible by the identification of memory precursor cells (MPECs) and short lived effector cells (SLECs), based on differential expression of KLRG1 and IL-7Ra (227-229). For example, studies on MPEC versus SLEC formation in CD8+ T cells have shown that transcription factors such as Eomes, Bcl6, Id3 and TCF-1 (230-233) are required for memory CD8+ T cell development while T-bet, Id2 and Blimp1 promote terminal differentiation (233-235). However, this model of divergent SLEC (KLRG1hi IL-7Ralo) and MPEC (KLRG1hi IL-7Ralo) differentiation has been called into question by a fate mapping study showing that KLRG1hi effector cells can enter the memory pool following loss of KLRG1 expression during contraction. The formation of these ‘ex-KLRG1’ memory cells were dependent on the transcription factor Bach2 and the ex KLRG1 cells gave rise to all memory cell lineages (236). The sequential transition from naïve to effector   30 and memory stages is regulated in part at the epigenetic level through changes in DNA methylation and chromatin accessibility. In humans, long term memory CD8 T cells retain demethylated DNA at a set of effector genes which suggest that the effector functions of these cells are poised for rapid recall (237). Interestingly, these memory CD8+ T cells also express several naïve associated genes such as CD127, CCR7 and BCL2 that are essential for long term survival and whose expression is downregulated in effector cells (237). DNA methylation also plays a key role in the MPEC model of memory formation wherein naïve associated genes Sell, Tcf7 and Ccr7 are marked with Dnmt3a-dependent DNA methylation at the effector stage but MPECs can erase these methylation program and re-express naïve genes as they transition to memory cells in a process termed de-differentation (238).  In the case of CD4+ T cells, our understanding of what regulates their effector to memory transition is less clear. It is tempting to assume that memory CD4+ T cells are regulated by the same molecules and mechanism as memory CD8+ T cells because CD4+ T cells go through the same stages of expansion, contraction and memory formation. However, studies that compare the responses of CD4+ T cells and CD8+ T cells suggest that this may not be the case for the following reasons. Effector CD4+ T cells expansion is limited compared to that of effector CD8+ T cells (239). The contraction phase of the CD4+ T cell response is prolonged and the memory CD4+ T cell pool that forms slowly decline with time compared with the sharp contraction phase of memory CD8+ T cells and their long term persistence (240). Importantly, within the effector CD4+ T cells, it has not been possible to identify classical MPECs and SLECs (241) which has imposed limitations on understanding the factors controlling effector to memory transition in CD4+ T cells. Adding another layer of complexity to the memory CD4 T cell puzzle is the   31 presence of multiple Thelper subsets during the expansion stage which raises questions about the entry of these functionally and phenotypically diverse subset into the memory stage. Recent studies using a combination of T-bet, Ly6C and CXCR5 markers in LCMV infection have shown that Th1 and Tfh memory precursor cells can be identified during effector differentiation which then form memory Th1 and Tfh cells (241, 242). In addition, Tregs activated during viral infection can survive contraction and enter the memory pool (243, 244). Together, these studies highlight the need for a better understanding of memory CD4+ T cell formation and indicate that the memory CD4+ T cell pool is likely to contain memory cells that reflect the diversity of the primary Thelper response.   1.5.2 Models of memory T cell formation T cell contraction is regulated by extrinsic environmental signals such as antigen and growth factor availability and by the cell intrinsic balance between pro- and apoptotic molecules.  Identifying CD4+ T cells that are destined to die during contraction versus those that persist into memory has proven to be a challenge. The differentiation pathway of CD4+ T cells from effector to memory state continues to be a matter of debate across the T cell biology field with two main models proposed - the asymmetric or divergent division model and the classical linear differentiation model. In the first model, the fate of an activated CD4+ T cell is made very early during asymmetric cell division to effector or memory daughter states. In contrast, the classical linear model proposes that memory CD4+ T cells arise from a sub-population of effector CD4+ T cells that undergo a cell fate decision to survive (245). Support for the divergent model in IAV infection comes from a study showing that naïve CD4+ T cells activated in vitro for a day is sufficient to give rise to a memory population (CD25low CD44high IL-7Rhigh) capable of rapid   32 effector function (IFNg and IL-2) upon re-stimulation (246). Moreover, in another study, IFNg expressing CD4+ T cells failed to develop into a resting memory population suggesting that memory cells do not develop from effectors (247). However, strong evidence for the linear differentiation model exists from studies showing that virus specific IFNg+ effector Th1 progenitors isolated at Day 8 p.i. can develop into memory cells (CD25low CD44high IL-7Rhigh) that produce IFNg (248, 249) and IL-2 (248) upon re-stimulation. Given the evidence for both models, it is possible that memory CD4+ T cells are generated by more than one pathway following IAV and further studies are needed to resolve this outstanding question.   1.5.3 Heterogeneity of memory CD4+ T cells in tissues  Memory CD4+ T cells formed after the termination of the primary immune response are a heterogeneous pool distributed among different anatomical locations. Based on the localization and phenotype of memory CD4+ and CD8+ T cells within lymphoid and peripheral tissues, three populations of memory T cells have been identified: T central memory, (TCM), T effector memory (TEM) and Tissue resident memory (TRM) cells.  TCM and TEM were the earliest memory T cell subsets to be described based on differential expression of the lymph node homing receptors CD62L and CCR7 (250), which allow T cells to home to the T cell zone of secondary lymphoid organs. TCM which express high levels of CCR7 and CD62L circulate through secondary lymphoid tissues via the blood and expresses high amounts of IL-2 upon stimulation. In contrast, TEM are a CCR7lo CD62Llo subset that display high levels of various tissue homing receptors and therefore circulate between peripheral tissues. When stimulated TEM expresses high levels of inflammatory cytokines such as IFNγ but are less proliferative than TCM (250, 251). While TCM and TEM are characterized by their distinct patterns of   33 circulation, TRM are described as a memory T cell population that do not circulate but are retained within peripheral tissues such as the lung and skin and can be identified by increased expression of CD69, CD11a and CD103. Consistent with their localization in peripheral tissue, TRM also possess a CCR7lo CD62Llo phenotype similar to TEM (252).   Following bacterial and viral infections, antigen specific memory CD4+ T cells that fit the above described model of TCM, TEM and TRM have been identified. CD4 TCM is localized within secondary lymphoid organs (253), possess a CCR7hi phenotype and express low levels of lineage defining transcription factors such as T-bet or Bcl6  (254, 255). Interestingly, TCM cells identified in a Listeria model of infection were shown to possess a CXCR5+ phenotype and their generation was dependent on Bcl6 expression during the primary response. However, while CXCR5+ TCM share a Bcl6-dependent developmental path with Tfh cells, these TCM cells represent a distinct population within the T cell area of the lymphoid tissue and are separate from germinal center dwelling Tfh cells (256). CD4 TEM possess a CD62Llo phenotype and are formed from IFN𝛾 or IL-4 expressing effector CD4+ T cells generated during the primary response (248, 249, 255). The CD4+ TCM and CD4+ TEM subsets appear to possess distinct differentiation potential because when stimulated, TCM can differentiate into effector cells, TCM precursors and TEM while CD4 TEM mainly differentiate into effector cells (256, 257).   Recently, a population of non-circulating tissue resident memory CD4 T cells (CD4 TRM) have been identified within previously infected tissues such as the skin, lung, female reproductive tract and lung in several infection models including influenza (258, 259). The CD4 TRM population present in the lung possess a CD44hi CD62Llo phenotype and upregulate canonical   34 TRM markers CD69 and CD11a but not CD103. Upon rechallenge with IAV, lung CD4 TRM cells express more IFN𝛾 than circulating memory CD4+ T cells  (260) and promote increased viral clearance and survival compared to spleen derived memory CD4+ T cells (261). CD4 TRM cells are found within a distinct anatomical niche around the airways and vasculature compared to circulating memory CD4+ T cells that are located within the lung parenchyma  (260). The close proximity of CD4 TRM to the site of IAV attachment in the lung (260) and their ability to promote viral clearance and reduce morbidity (261) suggests that the primary role for CD4 TRM may be in controlling early IAV replication before circulating memory CD4+ T cells are recruited. Interestingly, tissue resident CD4 memory cells have been identified in the human lung where majority of the cells possess a CD45RO+ CD69hi VLA1hi phenotype and a subset of these cells proliferate in response to influenza antigen presented via APCs (262). However, it is not clear if these proliferating human lung resident CD4+ memory cells represent a truly non-circulating population as this study was conducted on the total memory CD4+ T cell population extracted from the lung without sorting for cells with a TRM phenotype (262). Taken together, the studies discussed in this section indicate that memory CD4+ T cells in different anatomical locations have distinct functional roles which should be taken into consideration when designing therapeutics suchs as universal vaccines that elicit T cell responses.   1.5.4 A role for memory CD4+ T cells in protection against IAV Memory CD4+ T cells participating in recall responses reprise their roles as helpers of CD8 T cells and B cells (263) but also provide enhanced protection compared to primary effectors by inducing early innate immune responses and accelerating B cell antibody production (264) (265) (266). Moreover, in contrast to primary effectors (63, 267) memory CD4+ T cells can directly   35 control viral load in the absence of CD8+ T cells and B cells (268). Studies on memory CD4+ T cells in a recall response to influenza have relied mainly on using a combination of TCR transgenic mice and adoptive transfer animal models of influenza (269). Therefore, while indicative of the protective role of memory CD4+ T cells, whether these studies truly reflect the behavior of memory CD4+ T cell during natural infection is debatable due to the in vitro priming of CD4+ T cells or the artificial environment created by transfer of high numbers of adoptively transferred precursor cells before assaying CD4+ T cell function ex vivo. However these experimental approaches have been necessary because IAV antigen specific memory CD4+ T cells function in vivo are present in low numbers and are difficult to detect ex vivo using MHC Class II tetramers (244, 269). Therefore, although memory CD4+ T cells may possess the potential to enhance recall responses to influenza there is an unmet need to understand how influenza virus specific memory CD4+ T cells are formed following natural influenza infection and function to protect against IAV.  In humans, pre-existing CD4+ T cells are a correlate of protection against disease severity (116) and universal vaccine induced memory CD4+ T cells can enhance early recall antibody production and CD8+ T cell responses compared to unvaccinated but infected control mice (270).    1.6 Epigenetic regulation of the CD4+ T cell response to infection 1.6.1 Cis regulatory elements Biological processes such as development and metabolism are dependent on the precise spatio-temporal control of gene expression. Approximately 24,000 protein coding genes have been identified in mouse and human genomes but only a subset of these genes are expressed in a specific cell or tissue at a given time (271) (272, 273). The precise spatio-temporal control of   36 gene expression relies on cis- acting regulatory elements which act as binding sites for transcription factors. The main classes of cis-regulatory elements are promoters, enhancers, silencers and insulators. Of these, promoters and enhancers are relevant to this section and discussed further. Promoters consist of a core region that contain the transcriptional start site (TSS) and proximal elements which function as binding sites for basic transcriptional machinery and formation of the transcriptional pre-initiation complex (274). Enhancers are distal regulatory regions that consist of closely grouped clusters of transcription factor binding sites. A functional hallmark of enhancers is their ability to augment gene expression independently of their distance and orientation from the target gene. That is, enhancer elements can be found upstream or downstream of genes and also within introns. Despite being located at a distance from target genes, enhancers interact with promoters by looping out intervening DNA sequences thereby bringing bound transcriptional activators into close proximity with the TSS (275).  1.6.2 Histone modifications Chromosomal DNA is packaged into nucleosomes which consist of DNA wrapped around a core of histone octamers. Core histone possess N-terminal ‘tails’ that are subject to modifications including methylation, acetylation, phosphorylation and ubiquitination (276). Histone modifications can activate or repress gene expression in two ways. First, histone modifications such as acetylation and phosphorylation can disrupt electrostatic interactions between DNA and core histones thereby directly altering compact chromatin structure and facilitating DNA accessibility. Second, histone modification can regulate the binding of chromatin modifying enzymes that contain histone methyl lysine and histone acetyl lysine binding domains (277, 278).  Histone modifications themselves are written or erased by histone modifying enzymes which   37 include histone acetyltransferases, histone deacetylases, histone methyl transferases and histone demethylases (279) (280). Together with other epigenetic mechanisms such as DNA methylation, histone modifications act as gate-keepers that allow or inhibit the access of cellular transcriptional machinery to DNA thereby modulating gene expression.   Histone tail modifications are associated with regions of ‘open’ or ‘closed’ chromatin called euchromatin and heterochromatin respectively. Genome wide chromatin profiling studies have identified combinatorial patterns of histone modifications associated with distinct chromatin states at promoter and enhancer regions of the genome (281). Active genes possess high amounts of histone H3 lysine 4 tri-methylation (H3K4me3) and H3K27Ac at the transcriptional start site (282, 283) while H3K27me3 is present at promoters of genes that are silenced (284). Enhancers are marked by H3K3me1 combined with low amount of H3K4me3 (285, 286). H3K27Ac also separates active enhancers (H4K4me1 and H3K27Ac) from poised enhancers (H3K4me1) (287). Recently, large enhancer domains that are associated with exceptionally high H3K27Ac levels compared to typical enhancers have been identified (288). These H3K27Ac marked regulatory regions are called super enhancers (SEs) and occur in close proximity to key cell identity genes such as master transcription factors such as Oct4, Sox2 and Nanog in embryonic stem cells.  H3K27Ac marked SEs have been identified 86 different human tissues and cell types including ESCs, monocytes, T cells and B cells (288).      38   Figure 1.4: Overview of histone modifications at promoter, enhancer and super-enhancer elements   1.6.3 Role of histone modifications at promoter regions during CD4+T cell differentiation The use of chromatin immunoprecipitation combined with next generation sequencing technologies (ChIP Seq) has provided insight the role of histone modifications in controlling the transcriptional program of CD4+ T cell differentiation. One major area of CD4+ T cell biology where global epigenetic profiling has provided new insight is that of CD4+ T cell plasticity. Differentiated CD4+ T cells were initially thought to form stable lineages regulated by the expression of a master transcription factor, but it has gradually become apparent that there is significant plasticity between different effector CD4+ T cell subsets. For example, Th17 cells are EnhancerEnhancerPromoterTFTFTFTFTFSuperenhancerClustered enhancersHigh H3K27AcRelated to cell identity H3K27me3 H3K27Ac H3K4me1 H3K4me3Histone modificationsPromoterActive: High H3K4me3 Repressed: High H3K27me3Bivalent: H3K4me3 and H3K27me3 EnhancerIdentified by H3K4me1Low/no H3K4me3Active: High H3K27AcPoised: Only H3K4me1  HATRNAPHATTFRNAP RNA polymerase II Transcription factor Histone acetyl transferase  39 known to express the signature Th1 cytokine IFN𝛾 under inflammatory conditions or when exposed to IL-12 (289, 290). In addition, regulatory Foxp3+ CD4+ T cells can express master transcription factors such as T-bet and IRF4 belonging to inflammatory effector CD4+ T cell subsets when present in a Th1 or Th2 environment (178, 203). A study conducted by Wei et al (291) on in vitro polarized naïve, Th1, Th2, Th17, iTreg and nTreg cells showed that the gene loci of master transcription factors such as T-bet and GATA3 were marked with permissive H3K4me3 in Th1 and Th2 cells but in opposing Thelper lineages these master TFs displayed a bivalent (H3K4me3 and H3K27me3) histone modification signature known to be associated with genes poised for expression (292). When nTregs, which displayed a bivalent signature at the Tbx21 gene, were activated in vitro under Th1 polarising conditions, T-bet expression was induced in these cells within 72 hrs (291). These observations suggest that in terminally differentiated Thelper subsets, the poised epigenetic state of master transcription factors contribute to CD4+ T cell plasticity in a changed cytokine environment.    1.6.1 Epigenetic regulation of memory CD4+ T cells Memory CD4+ T cells can provide enhanced protection during recall responses compared to primary CD4+ T cells, but the regulatory networks that underlie memory CD4+ T cell function are not clear. Evidence that the recall ability of CD4+ T cells may be encoded in their epigenome comes from a study by Barski et al (293) who analysed the genome wide distribution of four permissive chromatin marks (H3K27Ac, H3K4me1, H3K4me3 and histone variant H2A.Z) at promoter regions in naïve, TCM and TEM CD4+ T cells. This study showed that effector cytokine gene loci such as IFNG, IL4, IL13 and IL17A were marked with permissive histone modifications at promoter and enhancer regions, despite low level expression of these genes in   40 memory CD4+ T cells. In contrast, in naïve CD4+ T cells, these permissive marks were either present at very low levels or absent, thereby indicating a poised state at effector genes in memory CD4+ T cells.  The gain of H3K4me3 within a subset of promoters of genes related to the immune response, chemotaxis and cell adhesion (293) memory CD4+ T cells was associated with an increase in gene expression over naïve CD4+ T cells. These observations suggest that gene poising via H3K4me3 at promoters may underlie the rapid recall ability of memory CD4+ T cells upon reactivation.    1.6.2 Role of histone modifications at enhancer regions during CD4+T cell differentiation In CD4+ T cells, H3K4me1 marked enhancer regions can be observed as early as 72 hrs after activation irrespective of the polarising (Th0, Th1 or Th2 ) conditions. A subset of these enhancers are Th1 or Th2 lineage specific and correlate with lineage specific gene expression profiles. Interestingly, Th1 specific enhancers were enriched for transcription factor motifs related to Th1 differentiation including STAT1, STAT4 and JUN while Th2 specific enhancers were enriched for TFs including STAT6, GATA3 and GFI1 (294). These observations suggest that early lineage commitment in differentiating CD4+ T cells may be regulated by enhancer regions that promote cell fate specification by binding Thelper lineage specific transcription factors.   Evidence that enhancers may regulate CD4+ T cell differentiation in vivo was provided by a study conducted by He et al (295) on active and poised enhancers states in naïve, effector and memory CD8 T cells.  Following acute LCMV infection, He et al showed that the transition from   41 naïve to effector or memory states in CD8+ T cells was accompanied by the gain of stage specific enhancer repertoires. In addition, the transition of enhancers from poised to active states or vice versa in naïve, effector and memory CD8 T cells correlated with the ability of these distal regulatory elements to up- or downregulate the expression of their target genes. Network analysis carried out by integrating promoter-enhancer interactions and gene expression enabled the identification of transcription factors, such as Tcf1 in naïve CD8+ T and T-bet and Eomes in effector CD8+ T cells (295) which provides insight into how TF binding at enhancer regions can control the  gene program in naïve and effector CD8+ T cells. In another study, subset specific active enhancers were identified SLEC and MPEC cells and in combination with DNA accessibility, transcription factor regulatory networks were predicted for each of these cell populations (296). From this analysis, two previously unknown TFs YY1 and Nr3c1 were identified and functionally validated as being important for effector and memory differentiation (296). Since our understanding of the transcription factors that regulated CD4 memory formation is limited, profiling the poised and active enhancer repertoire of effector and memory cells may help to identify candidate transcription factors that regulate effector versus memory CD4+ T cell fate following infection.   1.6.3 Super-enhancers in CD4+T cells Super-enhancers (SEs) in CD4+ T cells have been cataloged by the high level binding of p300, a histone acetyltransferase, in Th1, Th2 and Th17 cells (297). The lineage specific SE regions identified were associated with higher transcriptional activity compared to typical enhancers within a cell type. When compared across different cell types, SE associated genes were upregulated in a lineage specific manner. When transcription factor binding profiles were   42 integrated with the SE catalog, transcription factors that controlled CD4+ T cell lineage were enriched within SE regions. For example, STAT4 and T-bet were associated with in Th1 specific SEs.  Gene ontology analysis of SE regions from Th1, Th2 and Th17 cells yielded enriched GO terms for cytokines, chemokines and their receptors. Interestingly, SE regions in effector CD4+ T cells are enriched for single nucleotide polymorphisms associated with IBD, RA and  Type 1 diabetes when compared with typical enhancers. Together, these observations suggest that super enhancer regulate genes that are critical for CD4+ T cell biology and these regulatory regions may play an important role in controlling the pathogenicity of CD4+ T cell responses (297).   1.7 Rationale and Research Aims Influenza is a highly infectious disease and pandemic IAV outbreaks are a serious threat to global health. While a strong immune response is necessary to clear IAV, an excessive or dysregulated immune response can cause fatal lung damage. Therefore, it is important to understand the mechanisms that regulate the immune response to IAV so that protection can be maximized while limiting immunopathology. CD4+ T cells have pro- and anti-inflammatory roles during IAV infection which make them an attractive target for future therapeutics such as universal vaccines that aim to elicit a trained and balanced immune response. However, many aspects of CD4+ T cell regulation and their impact on the outcome of influenza infection are not well understood. Therefore, the overall goal of this thesis was to elucidate the molecular and epigenetic mechanisms that regulate CD4+ T cell function during primary and recall responses to IAV infection.     43 Aim 1: Investigate the role of IL-27 in regulating IL-10 expression from CD4+ T cells during primary and recall responses to IAV  CD4+ T cells are a source of the critical immunoregulatory cytokine IL-10 which acts to limit immunopathology during infections with intracellular pathogens including influenza. IL-27 is an infection induced environmental cue that triggers the release of IL-10 from CD4+ T cells during primary IAV infection. However, the role of IL-27 in regulating IL-10 expression from memory CD4+ T cells is not known. I hypothesized that changes in IL-27 receptor expression would regulate the ability of memory CD4+ T cell to secrete IL-10 in a recall response. I used an animal model of influenza where VertX (IL-10 GFP) or VertX IL-27Ra-/- reporter mice were infected with primary influenza and then rechallenged with a heterosubtypic strain in order to measure IL-10 GFP expression from naïve or memory CD4+ T cells in the lung. I tracked the expression of gp130, the signaling subunit of the IL-27 receptor, to determine whether changes in IL-27 receptor expression accompanied the secretion of CD4+ T cell derived IL-10. I also investigated the role of epigenetic modifications in regulating IL-10 expression by performing ChIP Seq on naïve, effector and memory CD4+ T cells.    Aim 2: Investigating the effect of IL-27 on functional specialisation of Foxp3+ Tregulatory cells in influenza Foxp3+ Tregulatory cells are better at suppressing Th1 responses when they express the Th1 lineage defining transcription factor T-bet and chemokine receptor CXCR3. Within mucosal tissues such as the gut, IL-27 has been show to promote this functionally specialized T-bet+   44 CXCR3+ phenotype in Tregs but the effect of IL-27 on Treg phenotype during IAV infection not known. Given the role of IL-27 in promoting Treg function within the gut, I hypothesized that IL-27 would be a regulator of Treg function within lung which is also a mucosal site. To test this hypothesis, I isolated CD4+ T cells from the airways and lungs of IAV infected VertX and VertX IL-27Ra-/- and used intracellular and intranuclear staining to study T-bet, CXCR3 and IL-10 expression in Foxp3+ Tregs and Foxp3- CD4+ T cells.   Aim 3: The role of histone modification changes in CD4+ T cell responding to  IAV infection Following IAV infection, CD4+ T cells undergo a program of differentiation to form primary effector cells which promote viral clearance and then contract to form a long lived memory pool. Upon re-exposure to IAV, memory CD4+ T cells differentiate into secondary effectors that can provide enhanced protection compared to primary effectors. The protective functions of these primary, memory and secondary CD4+ T cells are known but the molecular mechanisms that regulate gene expression at each stage of the CD4+ T cell response to infection is not well understood. Studies have shown that gene expression in effector and memory CD8+ T cells can be controlled at the epigenetic level by dynamic changes in histone modifications at gene promoters and enhancers. In addition, super-enhancers have been linked to lineage specification in in vitro polarised Thelper cells. Therefore, I hypothesised that that tracking histone modification dynamics at gene promoters and super-enhancer regions in CD4+ T cells  would identify genes encoding key regulatory molecules that establish naïve, effector and memory states in CD4+ T cells following influenza infection. To test this hypothesis, naïve and antigen specific primary, memory and secondary CD4+ T cells from IAV infected mice were isolated and their genome wide distribution of H3K4me3, H3K27me3, H3K4me1, H3K27Ac marks and   45 gene expression were profiled using high throughput sequencing. RNA Seq and ChIP Seq datasets were then integrated to identify genes that are subject to epigenetic regulation during the  CD4+ T cell response to influenza.      46 Chapter 2: Materials and Methods 2.1 Animals Vert-X (C57BL/6 IL-10/ eGFP) (298) and IL-27Ra-/- mice (299) background were rederived, bred and housed at the University of British Columbia at the start of this project. VertX+/+ IL-27Ra-/- mice were generated by crossing VertX and IL-27Ra-/- mice and breeding  heterozygous offspring to obtain homozygous pups.  The colony was then maintained as a homozygous line. Genotyping of this line was carried out by the Biomedical Research Centre genotyping facility.  C57BL/6 mice were purchased from Jackson Laboratories. All animals were kept under identical specific pathogen-free conditions and were used at 8-12 weeks of age. Animals used in experimental groups were not direct littermates. All experimental procedures were conducted in accordance with protocols approved by the University Animal Care Committee and Canadian Council of Animal Care/ institutional guidelines.  2.1.1 Influenza infection Mice were weighed, anesthetized and infected intranasally with 50 pfu influenza A/ HK-x31 (x31, H3N2) or 5 pfu A/PR/8 (PR8, H1N1) and weight loss monitored between Day 6 to Day 10. For in vivo experiments (see Fig 3.1A), mice were sacrificed at Day 10 for primary infections and Day 30-35 for the memory response. For virus re-challenge experiments, memory stage mice that were infected with x31 were challenged with heterosubtypic PR8 IAV and sacrificed at Day 6 post infection.       47 2.1.2 Tissue preparation and cell isolation Bronchoalveolar lavage fluid (BAL) was obtained by flushing the airways twice with 1 ml of FACS buffer, using a cannula (Nipro Safelet IV catheter) inserted into the trachea. Lungs were perfused using 5 ml of PBS (137 mM NaCl, 10mM PO4, 2.7mM KCl, pH 7.4)  injected into the heart, then extracted and cut into small pieces followed by digestion with 100 units/ml of Collagenase IV (Worthington Biochemical)  and 1 µg/ml of DNAse1 (Sigma Aldrich)  in RPMI 1640 (Thermofisher Scientific) for 45’ at 37 degrees Celsius on a rotary shaker. After digestion, the lungs were mashed through a 70µM cell strainer (BD Falcon). Percoll separation was not necessary. Spleens and lymph nodes were harvested and mashed directly through a 70µM filter followed by erythrocyte lysis for 5 minutes  in ACK lysis buffer (0.15M NH4Cl, 1mM KHCO3, 0.1 mM Na2EDTA, pH 7.4).     2.1.3 In vitro cell culture CD4+ T cells were purified from the spleens of VertX or VertX IL-27Ra-/- mice with the EasySepTM CD4+ T cell negative isolation kit (STEMCELL). CD4+ T cells of 90-95% purity were activated in a 96 well plate with 1µg/ml plate bound aCD3 (145 2C11, UBC Antibody Lab) and 1µg/ml soluble aCD28 (37.51, UBC Antibody Lab) antibodies in complete RPMI media (10% (v/v) heat inactivated FBS (Gibco), 10units/ml Penicillin and Streptomycin (Life Technologies),  55 µM b mercaptoethanol (manufacturer), 2 mM GlutaMax (Life Technologies)) supplemented with 1X non- essential amino acids and 1mM sodium pyruvate for 4 days in the presence or absence of 20 ng/ml of IL-27 (R&D) followed by four days of rest in complete RPMI media containing 10 ng/ml of IL-2.      48  2.2 Flow cytometry and analysis 2.2.1 Surface staining Single cell suspensions were incubated with blocking FcgRIII/ FcgRII- specific monoclonal antibody (2.4G2, UBC Antibody Lab) for 15 mins on ice. The following monoclonal antibodies to mouse antigens were used as direct fluorochrome conjugated or biotinylated antibodies for flow cytometry: CD4 (RM 4-5), CD44 (IM7), CXCR3 (FAB1685P), CD25 (PC61), CD69 (L78), CD62L(MEL-14) and biotinylated gp130 (125623), all from eBioscience, R&D Systems or BD Biosciences. Streptavidin conjugates (eBioscience) were used to label cells stained with biotinylated antibodies. Dead cells were stained using a Live Dead Fixable AQUA dead cell stain kit (Invitrogen) or propidium iodide (ThermoFisher Scientific). Influenza specific CD4+ T cells were stained with APC conjugated MHC Class II tetramers (NIH Tetramer Facility) specific for the Influenza A virus nucleocapsid protein (FluNP 311-325 I-A(b)) for 1h at 37°C, followed by a wash step with FACS buffer.   2.2.2 Cytokine responsiveness and pSTAT staining Sort purified CD44lo and CD44hi CD4+ T cells were stimulated for 15 mins at 37°C with 20ng/ml of recombinant IL-27 (R&D) in PBS pre-warmed to 37°C, immediately fixed and permeabilised using PhosFlow reagents (BD Biosciences). Cells were then stained with pSTAT1 (pY701) and pSTAT3 (pY705) antibodies and surface antibodies CD4 (RM-45) and CD44 (IM7) for 1h at room temperature in the dark.  Stained cells were washed with FACS buffer and acquired immediately on an LSR II flow cytometer.      49 2.2.3 Intracellular GFP recovery and transcription factor staining Surface stained cells were fixed and permeabilized with 100ul of 0.01% Triton X (Sigma Aldrich) in BD Cytofix/Cytoperm (BD Biosciences) for 20 minutes at 4°C. To recover GFP, cells were then stained with primary rabbit polyclonal αGFP antibody (ab6556, Abcam) diluted in BD Wash buffer for 30 minutes at 4°C. Secondary antibody labelling with Zenon A647 conjugated Rabbit Ig (Thermo Fisher Scientific)  was carried out for 40 minutes at 4°C. For transcription factor staining, surface and aGFP stained cells were fixed and permeabilized with Fixation/Permeabilisation buffer from the Foxp3/Transcription factor staining set (eBioscence) for 30 minutes at 4°C. Cells were then stained with Foxp3 (FJK-16s) and T-bet (eBio4B10) antibodies for 30 minutes at 4°C, before washing and immediate data acquisition.   2.2.4 Cell sorting Sorting from in vivo experiments:  When sorting cells from in vivo experiments, CD4+ T cells were first enriched from the lungs or lymph nodes using an EasySepTM CD4+ T cell negative isolation kit (STEMCELL). Cells were then surface stained with CD4 (RM-45) and CD44 (IM7) followed by MHC Class II:NP tetramer staining (as described in  Section 2.5.1). Labelled cells were then sort-purified using BD Influx or BD Aria cell sorters in the UBC Flow Facility into naïve (CD4+ CD44lo, >95% purity), activated (CD4+ CD44hi, >90% purity) or activated influenza specific (CD4+ CD44hi FluNP+ populations, 70-80% purity).  Sorting from in vitro experiments: Activated and resting phase splenic CD4+ T cells cultured in the absence or presence of IL-27, as described in Section 2.1.3, were surface stained with CD4 (RM-45) and CD44 (IM7), Live Dead Fixable AQUA and sorted into CD4+ CD44hi populations   50 of >90% purity. Naïve CD4+ CD44lo cells (>95% purity) were sorted from uncultured splenic cells to be used as unstimulated controls.   2.2.5 Acquisition and analysis of flow cytometry data Flow cytometry data was acquired using an LSRII or FACS Canto flow cytometer using FACS Diva software (BD Biosciences), or a MACSQuant (Miltenyi Biotec) flow cytometer with built-in software. Flow cytometry data was analysed using a Flow Jo analysis software (Treestar).   2.3 Quantitative analysis of murine gene expression by RT-PCR For RNA extraction from tissues, the left lung lobe extracted and stored in 250ul of RNAlater (Qiagen) and frozen at -20°C. Tissue was homogenized using a TissueLyser (Qiagen) in buffer RLT with 10ul of  𝛽-mercaptoethanol from the RNeasy kit (Qiagen) and total RNA was isolated from homogenate according to manufacturer’s instructions. RNA was extracted from cell pellets of purified CD4+ T cells or in vitro cultured cells as per manufacturer’s instructions. Synthesis of cDNA was performed using iScript cDNA Synthesis Kit. Quantitative real time PCR was performed using SsoFast EvaGreen Supermix (Bio-Rad) and the Bio-Rad CDX86 real time system. Thermocycle conditions included an initial denaturing step at 95°C (3 mins), followed by 40 cycles at 95°C (15 sec) and 60 °C (1 min). After 40 cycles, a melting curve was generated by slowly increasing (0.5°C/s) the temperature from 60 °C to 95 °C.  Primers listed in Table 2.1. Gene expression of target samples calculated by DDCT method with normalisation to house-keeping gene Gapdh.  Bar graphs display relative normalised expression of target sample over reference sample (set as 1).     51 Primer name Sequence GAPDH F: GTG TTC CTA CCC CCA ATG TGT R: ATT GTC ATA CCA GGA AAT GAG CTT IL-10 F: CTG AAG ACC CTC AGG ATG CG R: TGG CCT TGT AGA CAC CTT GGT C IFNg F: GGA TGC ATT CAT GAG TAT TGC C R: CCT TTT CCG CTT CCT GAG G MLL1 F: CTC CTC CTC TTT GCT GTA TTG R: CAG ATG TGA TGG CGA ATG T MLL2 F: CTC TAT CCT GTG GGC TAT GA R: GTG AAG ACC AGA TCC TCT AAA C Il6st F: ATA GTC GTG CCT GTG TGC TTA GC R: GGT GAC CAC TGG GCA ATA TGA Table 2.1: List of qPCR primers and target sequences   2.4 Data visualisation and statistical analysis Data from flow cytometry and qPCR experiments was graphed using GraphPad Prism (MacKiev Software). P-values were calculated using two tailed unpaired Students t tests with Welch’s correction. Error bars represent standard deviation. P values of <0.05, <0.01 and <0.001 were used as cut-offs for statistical significance and are represented in the figures by one, two or three asterisks respectively.   52 2.5 Histological analysis of lung tissue sections Lungs were isolated and cross section of each lobe fixed in 10% neutral buffered formalin, embedded in paraffin and staining with hematoxylin and eosin by Ingrid Barta at the BRC Histology Facility. All bright field microscopy images were captured with an Olympus DP73 camera mounted on an Olympus BX51 microscope, using 4X, 10X and 20X apochromatic objective lenses. All images were obtained using Olympus CellSens Dimension software. Lung sections from naïve and influenza infected mice were assessed by pathologist Dr. Ian Welch to identify and describe the pathology and cellular infiltrate.   2.6 Transcriptional and epigenetic profiling of CD4+ T cells 2.6.1 Cell isolation  ~ 200,000 naïve CD44lo or influenza specific Flu NP+ CD44hi CD4+ T cells from the lungs and lymph nodes of age and sex matched C57Bl/6 mice at primary, memory and secondary time points p.i. were sort purified as described in Section 2.2.4 using the sorting strategy in Fig 5.1. During the sort, cells were collected in FACS buffer containing 1X protease inhibitor cocktail (Calbiochem). Equal volumes of the sorted cell suspension were divided into two Eppendorf tubes and centrifuged at 300 rcf (Eppendorf) to obtain two cell pellets each containing approximately 100,000 cells for RNA Seq and ChIP Seq respectively.   2.6.2 RNA Sequencing The cell pellet (~100,000 cells) obtained was frozen in liquid nitrogen and submitted to our collaborators at Dr. Martin Hirst’s laboratory at UBC for RNA extraction and sequencing. Total RNA was extracted using a combination of mirVana miRNA Isolation kit (Thermofisher,   53 AM1560) and All prep DNA/RNA Mini Kit (Qiagen, 80204) and then assessed for quality and quantified using Agilent Bioanalyzer (Life Technologies). Total RNA was rRNA depleted using NEBNext rRNA Depletion Kit (New England BioLabs, E6310L).  1st strand cDNA was generated using Maxima H minus First Strand cDNA Synthesis Kit (Thermo Scientific, K1652) with the addition of 1ug of Actinomycin D (Sigma, A9415). The product was purified using in-house prepared 20% PEG, 1M NaCL Sera-Mag bead solution at 1.8X ratio and eluted in 35µL of Qiagen EB buffer.  Second Strand cDNA was synthesized in a 50µL volume using SuperScript Choice System for cDNA Synthesis (Life Technologies, 18090-019) with 12.5mM GeneAmp dNTP Blend with dUTP.  Double stranded cDNA was then purified with 20% PEG, 1M NaCL Sera-Mag bead solution at 1.8X ratio, eluted in 40µL of Qiagen EB buffer, and fragmented using Covaris E220 (55 seconds, 20% duty factor, 200 cycles per burst).  Sheared cDNA was End Repaired/Phosphorylated, single A-tailed, and Adapter Ligated using custom reagent formulations (New England BioLabs, E6000B-10) and in-house prepared Illumina forked small adapter.  20% PEG, 1M NaCl Sera-Mag bead solution was used to purify the template in-between each of the enzymatic steps.  To complete the process of generating strand directionality, adapter-ligated template was digested with 5U of AmpErase Uracil N-Glycosylase (Life Technologies, N8080096).  Libraries were then PCR amplified and indexed using Phusion Hot Start II High Fidelity Polymerase (Thermo Scientific, F 549-L).  An equal molar pool of each library was sequenced on HiSeq2000 (Illumina) PE75. Standard operating procedures for RNA-Seq library construction and sequencing are available at (http://www.epigenomes.ca/protocols-and-standards).   54  Paired end reads were aligned to the mouse genome ( UCSC GRCm38/mm10) by the JAGuar v 17.6 pipeline.  The alignment result files in bam format were converted to bedgraph format using Samtools to display on UCSC genome browser. An in-house quality control and analysis pipeline was used to generate a report and calculate a normalisation constant for computing RPKM values (reads per kilobase per million mapped reads). Pairwise comparisons between the four sample types were performed to identify differentially expressed genes using a custom Define matlab tool (cut off: FDR<0.015) created by Misha Bilenky.   2.6.3 Low input chromatin immunoprecipitation and sequencing 100,000 naïve and influenza specific CD4+ T cells were sorted from the lungs and lymph nodes of naïve, primary, memory and secondary stage mice infected with influenza as described in Section 2.2.4. Cells were lysed in mild non-ionic detergents (0.1% Triton X-100 and Deoxycholate) and protease inhibitor cocktail (Calbiochem) followed by flash freezing and submission to our collaborators at Dr. Martin Hirst’s laboratory (UBC) for low input ChIP Seq as described in Lorzadeh et al  (300). 10, 000 cells were used for a single immunoprecipitation assay. In brief, cells were digested by Micrococcal nuclease (MNase) at room temperature for 5 minutes and 0.25mM EDTA was used to stop the reaction.  Antibodies against H3K4me1 (Diagenode), H3K4me3 (Cell Signaling Technology), H3K27Ac and H3K27me3 (Diagenode) were incubated with anti-IgA magnetic beads for 2 hours. Pre-cleared chromatin was incubated with antibody bead complex overnight in immunoprecipitation buffer (20mM Tris-HCl, pH 8.0, 2 mM EDTA, 150mM NaCl, 1% Triton X-100, 0.1% SDS) and High Salt (20mM, Tris-HCl pH 8.0, 2 mM EDTA, 500mM NaCl, 1% Triton X-100, 01.% SDS) was buffers. IPs were eluted in   55 elution buffer(1% SDS, 100mM Sodium Bicarbonate) for 1.5 hour at 65 °C. Histones were digested by Protease for 30 mins at 50°C and DNA fragments were purified using Sera Mag magnetic beads in 30% polyethylene glycol. Illumina sequencing libraries were generated by end repair, 3’ A-addition and Illumina sequencing adaptor ligation (New England Biolabs). Indexed libraries were PCR amplified (10 cycles) and sequenced on an Illumina HiSeq 2500 sequencing platform following the manufacturer’s protocols (Illumina, Hayward, CA). Control libraries for each  peak calling library was the input DNA from the respective cell type.  Sequence reads were aligned to GRCm38/mm10 using Burrows-Wheeler Aligner (BWA) 0.5.7 (301) and converted to bam format by Sambamba (version 0.5.5) (302). Sequence reads with BWA mapping quality scores <5 are discarded and reads that aligned to the same genomic coordinate were counted only once in the profile generation. Input normalized tracks were generated by MACS2 (303) and converted to bigwig format for display on the UCSC genome browser using UCSC utility tool bedGraphToBigWig.  Promoter analysis: Promoters were defined as genomic region ± 2kb from the transcription start site (TSS).  H3K27me3, H3K4me3, and H2K27Ac peaks were identified by MACS2 (303) in paired end mode at q value of 0.1, 0.01 and 0.01 respectively and input DNA as a control. Promoters were defined as the following: (1) H3K4me3 marked if at least 50% of the ± 2kb region was enriched for H3K4me3 signal; (2) H3K27me3 if at least 50% of the ± 2kb region was enriched for H3K27me3; (3) bivalent if H3K4me3 and H3K27me3 overlapped with the following exceptions. Bivalent promoters overlapping with H3K27Ac or with less than 20% of the promoter covered H3K27me3 were considered H3K4me3 marked.     56 Super-enhancer (SE) analysis: To delineate super-enhancer domains, we used the approach proposed by Young et al (304). First, MACS2 identified H3K27Ac marked regions within 12.5kb were merged using Bedtools. Then all regions were ranked within each cell type by increasing H3K27Ac signal. To geometrically define when the H3K27Ac signal increases rapidly, the x-axis point for which a line with a slope of 1 was tangent to the curve was determined. Genomic regions above this point were identified as super-enhancers.  2.6.4 Transcription factor motif enrichment  The genomic sequence at the center of the MACS2 identified H3K27Ac enriched region was matched with the GRCm38/mm10 genomic assembly using Bedtools fastaFromBed. Significantly matched motifs from 2016 JASPAR Vertebrates CORE Catalogue (305) were identified using Fimo at default setting as previously described (306). Motifs were also identified by Transcription factor Affinity Prediction (TRAP) Web Tools using single sequence application at default setting as previously described (307).  2.6.5 Statistical analysis All plots for bioinformatics data visualisation were generated in RStudio version 3.2.3. R packages used include ggplot2, pheatmap and UpSetR. Boxplots represent the median (centre line), the first and third quartiles (top and bottom line of box) and confidence intervals (95%; black lines). Violin plots display rotated kernel density plots. P-values were generated in R using two-tailed Students t-tests (t-test).      57 Chapter 3: IL-27 signaling promotes epigenetic remodelling of the Il10 locus in memory CD4+ T cells following influenza infection   3.1 Introduction Influenza evokes a strong pro-inflammatory immune response in the lung that is necessary for viral clearance and survival (308-310).  In infections caused by pathogenic strains of IAV, this inflammatory response can act as a double-edged sword causing severe tissue pathology (311-314). A controlled immune response to IAV is therefore critical to ensure that the virus is eliminated while limiting lung immunopathology. The immunosuppressive cytokine IL-10 limits tissue immunopathology and is expressed within infected tissues following infection (97, 315, 316). However, the expression of IL-10 must be precisely controlled because excessive or mistimed IL-10 can dampen host immunity (162, 317) and reduce survival (97, 318).   During acute respiratory infection, effector T cells are the main source of IL-10 in the lung (97, 143, 319). The importance of T cell derived IL-10 during IAV infection is evident by the striking increase in mortality of influenza infected mice when IL-10 signalling is blocked (97). This impairment of IL-10 signalling results in excessive pro-inflammatory cytokine production and increased immune cell infiltration which causes lethal lung pathology (97). Th1 cells present in the lung are an important source of IL-10 but these cells also produce IFNg with similar kinetics and both cytokines peak around Day 10 post infection (97). This evidence suggests that transient IL-10 expression by effector CD4+ T cells may provide protection at the peak of the T cell response during influenza infection by dampening pathogenic T cell mediated inflammation. The   58 protective role of IL-10 at the site of infection is supported by studies in other Th1 infections models with intracellular pathogens such Plasmodium chabaudi (316) and Toxoplasma gondii (165). In influenza, the signals that promote host protective IL-10 from effector CD4+ T cells and the mechanism through which CD4+ T cell derived IL-10 controls pathology is less clear.   IL-27 is an infection-induced cytokine that restrains pathogenic T cell responses during acute Th1 infection. The absence of IL-27 or the IL-27 receptor leads to increased T cell proliferation (191), T cell mediated inflammation (191, 192, 320) and immunopathology (192, 316, 321). IL-27 exerts control over T cells and limits immunopathology in part by inducing IL-10 from activated Th1 cells (194, 199, 200, 316). During primary influenza, exposure to IL-27 in the infected lung induces IL-10 from Th1 cells and this IL-10 can suppress inflammation by inhibiting the expansion of IL-17 expressing CD4+ T cells (192) with immunopathogenic potential (134, 322). Importantly, both IL-27 and IL-10 act specifically to limit immunopathology during influenza infection without hampering viral clearance (97, 192).These in vivo studies indicate that IL-27 induced IL-10 production from effector CD4+ T cells present at the site of infection is protective. Since timed IL-10 expression is essential for surviving infection (97, 317), I investigated the role of the IL-27 signalling pathway in modulating IL-10 expression from effector CD4+ T cells in vivo.   Evidence that IL-27 signalling can regulate T cell derived IL-10 arises from a study conducted by our laboratory which showed that the downregulation of the IL-27 receptor subunit gp130, on CD8+ T cells impaired CD8+ T cell intrinsic IL-10 expression (218). Activated CD4+ T cells also downregulate gp130 following TCR ligation (216) which suggests that surface expression of   59 the IL-27 receptor may modulate the release of IL-10 from effector CD4+ T cell by altering the cell’s ability to receive IL-27 signals. In CD8+ T cells, the impact of decreased gp130 expression on IL-10 production was observed in a recall response to influenza (218). Whether memory CD4+ T cells are similarly impaired due to changes in IL-27 responsiveness is presently unclear.   To determine if IL-27 regulates IL-10 from CD4+ T cells during primary and secondary influenza, I used a combination of flow cytometry and high throughput ChIP sequencing. This approach allowed the identification of key signalling events and the epigenetic changes associated with IL-10 expression in CD4+ T cells. My results show that in a primary response to influenza, IL-27 signaling was required to enhance IL-10 expression from primary effector CD4+ T cells. A permissive histone modification signature was observed at the IL-10 locus in influenza-specific memory CD4+ T cells compared to naïve precursors. We found that exposure of CD4+ T cells to IL-27 during primary activation was required for establishment of these epigenetic changes. Intriguingly, although gp130low memory CD4+ T cells were less responsive to IL-27 signaling, we observed that memory CD4+ T cells still expressed IL-10 in a recall response to influenza. We propose that IL-27 signaling in a primary response to influenza promotes an increase in permissive histone modifications at the Il10 gene locus which allows primed memory CD4+ T cells to express IL-10 in a recall response.       60 3.2 Results 3.2.1 Identification and characterization of IL-10 expressing CD4+ T cells during influenza infection To investigate the regulation of IL-10 from CD4+ cells during influenza infection, first I determined the kinetics of IL-10 expression from activated CD4+ T cells in the lung.  FluNP 311-325 I-A(b)] MHC Class II tetramers were used to measure the expansion of influenza specific CD4+ T cells and IL-10 GFP reporter mice (VertX) were used to track IL-10 expression. VertX mice were infected with PR8 IAV to elicit a primary response or with a primary dose of x31 IAV infection followed by rechallenge with heterosubtypic PR8 IAV (secondary) as shown in Fig 3.1 A. At the time points shown in Fig 3.1B-C, the frequencies of tetramer positive cells and activated IL-10 expressing CD4+ T cells were measured by flow cytometry. In primary influenza infected mice, we observed an increase in the frequencies of tetramer+ cells in the lung and lymph nodes which peaked at Day 10 p.i. (Fig 3.1B, left). In a recall response, a similar but more rapid increase in the frequencies of tetramer+ cells observed with peak expansion on Day 6 in the lymph node (Fig 3.1C, left) . IL-10 eGFP+ CD4+ T cells showed peak expansion on Day 10 during primary infection (Fig 3.1B, right), while the frequencies of activated IL-10+ CD44hi CD4+ T cells were highest between Day 4-6 (Fig 3.1C, right). Based on these results, the Day 10 time point was selected for assessing the peak of CD4 IL-10 expression in the primary response and Day 6 for the peak of CD4 IL-10 in a secondary response. These time points are consistent with published reports on the kinetics of  CD4 IL-10 expression in the primary response  (97) and CD4+ T cell activation in the secondary response (266).    61 Both effector CD4 T cells and Foxp3+ Tregs express IL-10 in response to respiratory infection but activated Th1 cells have been reported to be the primary source of CD4+ T cell derived IL-10 at the site of infection in the lung (97). To determine if IL-10 expression is highest in the lung in our infection model, we infected VertX (IL-10 GFP reporter) mice with influenza (PR8 IAV) and measured IL-10 eGFP expression and the phenotype of IL-10+ CD4+ T cells in the lung and spleen. We observed an increase in the number and frequency of IL-10 expressing activated CD44hi CD4+ T cells in the lung on Day 10 p.i (Fig 3.2 A,B) but no change in the percentage of IL-10+ CD44hi CD4+ T cells between naïve and infected mice in the spleen (Fig 3.2 B). When IL-10 expression from antigen specific CD4+ T cells present in the lung and spleen were compared, (Fig 3.2C) higher frequencies of IL-10+ CD44hi CD4+ T cells were detected in lung than the spleen (Fig 3.2 D). Together, these results indicate that activated influenza specific CD4+ T cells can express IL-10 locally within the infected lung.    62  Figure 3.1: Influenza infection model and selection of time points for primary and recall CD4+ T cell responses.  Primary IAVSecondary IAVCBA PR8/ H1N1 Memory1º infection2º infectionRechallenge with PR8/H1N1              (Day 10 p.i.)(Day 30-35 p.i.) (Day 6 p.i.)1º  effector CD44hi    CD4+ T cellsMemory CD44hi   CD4+ T cells 2º effector CD44hi    CD4+ T cellsIn vivo model1º infection(Day 10 p.i.) x31/ H3N20 5 10 150510152025Days post  infection% IL-10 GFP+ of CD44hi LungLN0 5 10 1501234Days post  infection% FluNP+ of CD44hi0 2 4 6 8 1005101520Days post infection% FluNP+ of CD44hi0 2 4 6 8 1005101520Days post infection% IL-10+ GFP of CD44hi LungLNPrimary influenzaSecondary  influenza  63 (A) Primary infection: VertX or VertX IL-27Ra-/- mice were infected with 5 pfu PR8/H1N1 Influenza A virus. On Day 10, CD4+ T cells are isolated from the BAL, lung, LN and spleen. Memory: Mice infected with 5 pfu PR8/H1NI were allowed to recover from influenza infection until Day 30-35 post infection. Secondary infection: Mice infected with 50 pfu x31/H3N2 were allowed to recover until Day 30-35 and then re-challenged with 5 pfu of heterosubtypic PR8/H1N1. BAL, lung, LN and spleen were harvested on Day 6 p.i. (B) VertX mice were infected with PR8/H1N1 and frequencies of Flu NP+ CD4+ T cells (left) and IL-10+ CD4+ T cells (right) in the lung and LN were measured by flow cytometry on Days 8, 10 and 12 post infection. (C) Memory stage VertX (x31primed) were re-infected with PR8/IAV and frequencies of IL-10+ CD4+ T cells and Flu NP tetramer+ CD4+ T cells in the lung and LN were measured on Days 4, 6 and 8 post infection. Graphs show frequencies of Flu NP tetramer+ CD4+ T IL-10+ CD4+ T cells or cells at the stated time points. Uninfected VertX mice were used as naïve controls. Graphs show all data from a single experiment.   To determine if IL-10 expressing CD4+ T cells in the lung possess a Th1 effector phenotype, I used intracellular cytokine staining to assay expression of the Th1 transcription factor T-bet (112) and Treg transcription factor Foxp3 (323) expression in CD4+ IL-10+ T cells. Primary IAV infection induced an increase in the frequency of T-bet+ CD4+ T cells (Fig 3.2E) and T-bet expression was higher in activated IL-10+CD44hi cells compared to naïve CD44lo IL-10 CD4+ T cells (Fig 3.2F). Approximately 10% of IL-10+ CD4+ T in the lung expressed Foxp3 while ~90% of the IL-10 expressing CD4+ T cells were Foxp3- (Fig 3.2 G). Together, these data indicate that IL-10 expressing CD4+ T cells exhibit a Tbet+ Th1 phenotype and the majority of CD4+ T cell derived IL-10 is expressed by Foxp3- CD4+ T cells.        64  Figure 3.2:Effector CD4+ T cells express IL-10 in the lung during primary influenza infection. VertX IL-10 reporter mice were infected with influenza PR8 virus and 10 days later, cells in the lung and spleen were analysed by flow cytometry. (A) Graph shows the number of IL-10 GFP+ FluNP+ as %CD4+02468**LungNaive1o PR80246810IL-10 GFP+ as %CD44hi ****nsNaive1o PR8Lung SpleenBCFluNPNaive1o PR8IL-10 GFP+ as %FluNP+05101520 *Lung SpleenIL-10 GFPFluNPLungSpleenDE GCD44Naive 1o PR8LungSpleenA0.36 8.750.51 2.280.0364.684.28 1.5587.0 7.130.59 0.01398.6 0.79CD4IL-10 GFP02 1044 1046 1048 1041 105# IL-10 GFP+ CD4+T cellsNaive 1o PR8****T-bet% of maxLungNaive1o PR801020304050Foxp3+ as %IL-10 GFP+ 05001000150020002500T-bet MFI***LungIL-10 GFP- CD44loIL-10 GFP+ CD44hi *4.9994.44 0.360.21 3.7192.66 2.800.82IL-10 GFPFoxp3Naive 1o PR8IL-10 GFP- CD44loIL-10 GFP+ CD44hiIL-10 GFP- CD44lo FMOIL-10 GFP+ CD44hi FMOTbetIL-10 GFP0.256.800.1892.86.8035.07.5950.7Naive 1o PR8F  65 CD4+ T cells per lung in naïve and infected VertX mice. Gated on total live CD4+ T cells. (B) Representative FACS plots (left) and bar graph (right) displaying the percentage of IL-10 expressing CD4+ T cells in the lungs and spleen of naive and infected VertX mice. C) Frequency of influenza specific CD4+ T cells in the naïve and infected lung identified with MHC Class II tetramers.  (D) Frequency of IL-10 producing tetramer+ CD4+ T cells in the lung and spleen of infected VertX mice. (E) T-bet MFI in activated (CD44hi) IL-10 GFP+ and naïve (CD44lo) IL-10 GFP- CD4+ T cells in the infected lung of VertX mice. (F) IL-10 GFP and Foxp3 staining in the lungs of naïve and infected VertX mice (above). Graph (below) shows the frequencies of Foxp3+ IL-10 GFP+ CD4+ T cells in naïve and infected VertX lungs. Data are representative of three independent experiments with 3-4 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.  3.2.2 CD4+ T cell derived IL-10 expression in the lung is enhanced by IL-27 signaling during primary influenza infection IL-27 induces IL-10 expression from effector CD4+ cells present at the site of infection in multiple Th1 infections including influenza (192, 194, 316, 324), and Th1 cell derived IL-10 has the potential to limit tissue immunopathology (316). To determine the effect of IL-27 signaling on IL-10+ CD4+ T cells in my influenza model, VertX and VertX IL-27Ra-/- mice were infected with IAV and IL-10 expression from CD4+ T cells in the BAL, lung and draining lymph node and spleen was measured on Day 10 post infection. The BAL and lungs of infected VertX IL-27Ra-/- mice contained 2 fold lower frequencies of IL-10+ CD4+ T cells than infected VertX (wildtype) mice (Fig 3.3). In contrast to the lung, the absence of IL-27 signaling in the lymph node and spleen had no effect on the frequencies of IL-10+CD4+ T cells in these tissues (Fig 3.3). These results indicate that IL-27 signaling enhances IL-10 expression from CD4+ T cells at the effector site (BAL and lung) during primary influenza infection.    66  Figure 3.3: IL-27 signalling enhances IL-10 expression from CD4+ T cells during primary influenza infection  VertX and VertX IL-27Ra-/- were infected with influenza PR8 and 10 days later, IL-10 GFP+ CD4+ T cells in the BAL, lung, mLN and spleen were analysed by flow cytometry. Gated on total live CD4+ T cells. (A) Representative FACS plots (left) displaying the percentage of IL-10 GFP expressing CD44hi CD4+ T cells in the BAL, lungs, draining lymph node (mediastinal) and spleen of naïve and infected VertX mice. Graphs (right) depict IL-10 GFP expression from CD44hi CD4+ T cells from naïve and infected VertX mice. Data are representative of three experiments with 3-4 mice per group.  Error bars indicated the mean ± SD and *=p <0.05, ** = <0.01, *** = p <0.001.  3.2.3 Primary and memory CD4+ T cells lose surface expression of the IL-27 receptor subunit gp130 IL-27 promotes IL-10 expression from Th1 cells by signaling through its receptor, IL-27R (Fig 1.3). The IL-27 receptor consists of a ligand binding subunit, IL-27Ra, and a signal transducing subunit, gp130 (325). Activated CD4+ T cells downregulate surface gp130 expression in vitro BALmLNLungVertX VertX IL-27Ra -/-CD44IL-10 (GFP) Spleen19.07 6.3214.34 4.641.44 0.940.94 0.84**A0510152025IL-10 GFP+ as %CD44hi BALVertX   VertX IL-27Ra-/- 0123LNVertX  VertXIL-27Ra-/- **05101520LungVertX  VertXIL-27Ra-/- 0.00.20.40.60.81.0SpleenVertX  VertXIL-27Ra-/- Infected NaiveIL-10 GFP+ as %CD44hiIL-10 GFP+ as %CD44hiIL-10 GFP+ as %CD44hi  67 and in vivo upon TCR engagement (216) but it is not clear if this occurs during infection, nor whether CD4+ T cells recover gp130 after initial downregulation. To determine how gp130 is modulated in CD4+ T cells, we measured gp130 expression in naïve, primary, and memory CD4+ T cells from the lungs and spleen of VertX mice by flow cytometry. We found that CD44hi primary CD4+ T cells in the lung and spleen downregulate surface gp130 expression compared to CD44lo naïve CD4+ T cells (Fig 3.4 A). At the memory stage, CD44hi CD4+ T cells in the lungs and spleens also showed reduced gp130 expression compared with naïve CD44lo CD4+ T cells (Fig 3.4B). We also measured IL-10 expression from memory CD4+ T cells and observed that this cytokine returned to baseline levels by Day 35 p.i. (Fig 3.4C). These results indicate that gp130 downregulation occurs in activated CD4+ T cells during influenza infection, and that memory CD4+ T cells retain this gp130low phenotype.   Since we observed a decrease in surface gp130 expression on memory CD4+ T cells isolated from VertX mice with a wild-type phenotype, it was possible that either TCR stimulation or gp130 signal transduction in CD4+ T cells triggered gp130 downregulation. To investigate the signals responsible for the loss of surface gp130, I used an in vitro system where CD4+ T cells activated by TCR ligation were cultured with or without IL-27 followed by 4 days of rest in culture media containing IL-2. gp130 MFI was then measured in rested CD4+ T cell groups and compared with naïve controls. I observed that rested CD4+ T cells lost surface gp130 expression (Fig 3.4 D, right) following TCR ligation, and that IL-27 exposure had no effect on gp130 expression in this context. Next, to determine whether gp130 downregulation occurred only at the surface or was a result of decreased transcription, gp130 mRNA (Il6st) from naïve and activated CD4+ T cells was quantified by qPCR.  A decrease in Il6st mRNA expression was observed in activated cells compared to naïve   68 controls and this decrease was evident in both IL-27 treated and untreated CD4+ T cell groups (Fig 3.4 E). These results indicate the loss of surface gp130 on activated CD4+ T cells is dependent on TCR activation not IL-27 signaling. Further, the loss of surface gp130 is associated with the transcriptional downregulation of Il6st mRNA.    69  Figure 3.4: Activated (CD44hi) CD4+ T cells downregulate gp130 expression  VertX mice were infected with influenza PR8 virus. At primary (D10 p.i)  and memory (~D35 p.i). time points, lungs and spleens were harvested and cells stained with monoclonal gp130, EDCIl6st Naive+IL-27-IL-270.00.51.01.5Relative expression% of max% of max% of max% of maxgp130LungSpleenNaive Memory-500050010001500gp130 MFI**-500050010001500gp130 MFI**Naive Memory Naive MemoryBgp130 LungSpleenNaive Primarygp130FMONaive PrimaryNaive Primarygp130FMO0246810IL-10 GFP+ as %CD44hinsnsAFMOgp130-500050010001500gp130 MFISpleen-500050010001500gp130 MFILung* * * * *gp130Naive Resting +IL-27Resting+ no cytokineNaive RestingIL-27 – +– + – –aCD3/aCD28 – +– + + +0200400600800gp130 MFI% of maxgp130FMOFMOgp130NaiveMemoryLung SpleenFMOgp130SpleenLung  70 CD4 and CD44 antibodies. FMOs were used as negative controls. Data shown are gated on live CD44lo CD4+ T cells for naive groups and live CD44hi CD4+ T cells for primary or memory groups.  Representative FACS plots (left) show mean fluorescence intensity (MFI) of gp130 and FMO in (A) naive and primary or (B) naive and memory CD4+ T cells. Graphs (right) display gp130 MFI (closed circles) or FMO controls (open circles) on (A) naïve and primary or (B) naïve and memory CD4+ T cells. (C) Percentage of IL-10+CD4+ T cells from naïve and CD44hi memory CD4+ T cells. (D) Representative histograms show gp130 and FMO MFI on naïve and in vitro generated resting phase CD4+ T cells. Graph shows gp130 MFI measured from naïve and in vitro generated resting phase CD4+ T cells. (E) Gene expression of Il6st (gp130) assayed by qPCR in naïve and resting phase CD4+ T cells stimulated with or without IL-27. Data in each panel are representative of three independent in vivo or in vitro experiments. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.  3.2.4 Memory CD4+ T cells exhibit decreased responsiveness to IL-27 signaling IL-27 induction of IL-10 in activated CD4+ T cells is dependent on the transcription factors STAT1 and STAT3 (324). Given the observation that memory cells have lower gp130 expression than naïve cells (Fig 3.4), I hypothesized that gp130low memory CD4+ T cells present in the lung following influenza infection would be impaired in their ability to activate and phosphorylate STAT1 and STAT3 proteins following stimulation with IL-27. To test if gp130 downregulation resulted in a reduction in IL-27 responsiveness, we measured the phosphorylation of STAT1 and STAT3 (326) in naïve and memory CD4+ T cells stimulated with or without IL-27. We observed a reduction in the mean fluorescence intensity (MFI) of pSTAT1 and pSTAT3 in memory CD4+ T cells compared to naïve cells (Fig 3.5 A-B) when both groups were stimulated with IL-27. We then calculated the fold difference between unstimulated and IL-27 stimulated samples as shown in Fig 3.5A-B, right. Memory CD4+ T cells also showed impaired responsiveness to IL-6 stimulation (Fig 3.5 C-D), consistent with gp130 also being a signalling subunit in the IL-6 receptor. In addition, it appeared that the decrease in phosphorylation of STAT1 in cytokine-stimulated memory cells, compared to naïve, was more   71 pronounced than that of STAT3; this was true in cells exposed to either IL-27 or IL-6. Together, these results demonstrate that downregulation of surface gp130 expression on CD4+ T cells results in decreased responsiveness of memory CD4+ T cells to IL-27 and IL-6 signaling.               72  Figure 3.5: Memory CD4+ T cells lose IL-27 responsiveness. VertX mice were infected with influenza PR8 and lungs from naïve or memory stage mice harvested. Naïve (CD44lo) and memory (CD44hi) CD4+ T cells were stimulated with or without IL-27 or IL-6 for 15 minutes and phosphorylation of STAT1 and STAT3 proteins analysed by DNaive Memory pSTAT3pSTAT3BNaive Memory CpSTAT1UnstimulatedIL-6FMOUnstimulatedIL-27FMOpSTAT1Naive Memory Naive Memory % of max% of max% of max% of max012345pSTAT1 fold change ***Naive MemoryNaive Memory012345pSTAT3 fold change **pSTAT3 fold changeNaive Memory01234***ANaive MemorypSTAT1 fold chnage012345 ***  73 flow cytometry. (A-D, left) are representative histograms displaying pSTAT1 and pSTAT3 MFI of IL-27 or IL-6 stimulated naïve and memory CD4+ T cells. Each FACS plots displays pSTAT1 or pSTAT3 histogram(black line) overlaid on FMO (dotted line) and unstimulated (grey) controls. (A-D, right) Graphs show fold difference of pSTAT1 and pSTAT3 on naive (CD44lo) and memory (CD44hi) CD4+ T cells stimulated with IL-27 or IL-6. Fold difference was calculated by dividing the MFI of stimulated sample by the MFI of unstimulated sample. Data shown are gated on CD44lo CD4+ T cells for naive groups and CD44hi CD4+ T cells for memory groups. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001. Data is representative of three independent experiments.  3.2.5 Memory CD4+ T cells with impaired IL-27 responsiveness can express IL-10 in a recall response to influenza A previous study from our lab showed that gp130low memory CD8+ T cells express less IL-10 in a recall due to decreased IL-27 responsiveness (218). Given my observations of downregulated gp130 (Fig 3.4) and decreased IL-27 responsiveness (Fig 3.5) in memory CD4+ T cells, I expected these cells to express less IL-10 in a recall response to IAV. To test whether gp130low memory CD4+ T cells could express IL-10, memory stage (~Day 35 p.i.) mice previously infected with x31 IAV, were challenged with the heterosubtypic PR8 virus and IL-10 GFP expression from memory CD4+ T cells was measured at Day 6 p.i. Surprisingly, I observed that gp130low memory CD4+ T cells isolated from the lungs of infected mice were able to express IL-10 (Fig 3.6 A). Approximately 15% of activated CD44hi CD4+ T cells were IL-10+ and 15% of flu tetramer+ CD4+ T cells were IL-10+ (Fig 3.6 B-C). Similar to the primary response (Fig 3.2 B, D), higher frequencies of IL-10+ CD4+ T cells were found in the lung compared to the spleen in infected mice (Fig 3.6, A-B). I also measured gp130 expression to determine whether a change in surface gp130 expression on secondary CD4+ T cells could underlie their ability to re-express IL-10 during secondary IAV infection. I found that secondary effector CD4+ T cells displayed the same gp130low phenotype as their memory counterparts (Figure 3.6C) indicating   74 that secondary CD4+ T cells do not upregulate surface gp130 expression following IAV rechallenge. Together these results suggest that decreased IL-27 responsiveness in memory CD4+ T cells does not inhibit IL-10 expression in a recall response to influenza.       75  Figure 3.6: CD4+ T cells express IL-10 in the lung during secondary influenza infection. VertX IL-10 GFP mice in the memory stage of x31 IAV infection were re-challenged with influenza PR8 and 6 days later, CD4+ T cells in the lung and spleen were analysed by flow cytometry. (A) Representative FACS plots (left) and graphs (right) displaying the percentage of IL-10 (GFP) expressing cells from the CD44hi CD4+ T cell population in the lungs and spleen of naive and re-infected VertX mice. (B) FACS plots (left) and graph (right) displaying IL-10 GFP gp130FMOgp130 LungSpleenNaive Secondary% of max% of maxC FMOgp130Lung-500050010001500gp130 MFI***Naive SecondarySpleen-500050010001500gp130 MFI***Naive SecondaryIL-10 GFPFluNPLungSpleen0.21 0.03799 0.740.28 0.00598.3 1.436.41 1.4874.2 17.91.13 0.05995.7 3.12Naive 2o PR8IL-10 GFPLung Spleen0510152025IL-10+ as % FluNP+**BCD44Naive 2o PR8LungSpleen0.94 12.311.38 3.050510152025IL-10 GFP+ as %CD44hi**Naive2o PR8Lung SpleenA  76 expression from influenza specific (FluNP+) CD4+ T cells and in the lung and spleen of naïve and re-challenged VertX mice. Gated on total live CD4+ T cells. Graph (right) shows the frequency of IL-10 GFP expressing cells within the influenza specific NP+ population. (C) FACS plots (left) and graph (right) show gp130 MFI of naïve (CD44lo) and secondary (CD44hi) CD4+ T cells in the lung and spleen. FMO used as a negative control. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001. Data are representative of three independent experiments with 3-4 mice per group.    3.2.6 CD4+ T cell IL-10 expression in a recall response to influenza requires IL-27 signaling during primary infection Since memory CD4+ T cells express IL-10 despite impaired IL-27 responsiveness, it is possible that these cells do not require IL-27 signaling to stimulate IL-10 expression. To test this hypothesis, I rechallenged resting phase VertX and VertX IL-27Ra-/- mice with influenza and measured IL-10 GFP expression in the BAL and lung on Day 6 post infection. When 2º VertX and VertX IL-27Ra-/- infected groups were compared, I observed reduced frequencies of IL-10+ CD4+ T cells in in the lung and BAL of VertX IL-27Ra-/- mice compared to VertX mice (Fig 3.7A,B). When 1º and 2º IAV infected groups were compared, the frequencies of IL-10+GFP+ CD4+ T cells in 1º and 2º IAV infected VertX mice were unchanged while 2º CD4+ cells in VertX IL-27Ra-/- mice showed a very small but significant reduction in IL-10 expression from secondary CD4+ T cells. This result suggested that primary exposure to IL-27 may be required for recall IL-10 expression from gp130lowmemory CD4+ T cells because 2º CD4+ T cells from VertX mice expressed significantly more IL-10 than 2º CD4+ T cells from VertX IL-27Ra-/- mice. Overall, my observations suggested that primary IAV infection can intrinsically change the dependence of memory CD4+ T cell derived IL-10 on IL-27 signalling in a recall response.     77   Figure 3.7: IL-27 signaling during primary influenza is required for IL-10 expression in a recall response. VertX and VertX IL-27Ra-/- mice in the resting phase were rechallenged with heterosubtypic influenza PR8 on Day 30. (A, B) FACS plots (top) showing CD44 versus IL-10 GFP expression within the CD4+ T cell population in the BAL and lungs of naïve and infected VertX mice. Graphs (bottom) show the frequency of IL-10+ CD44hi CD4+ T cells in the BAL and lungs of naïve and infected mice. Error bars indicate the mean ± SD of individual mice and data are representative of two independent experiments with 3-5 mice per group.       IL-10 GFPCD441° IAV 2° IAV 22.61 20.257.55 5.75VertX  VertX IL-27Ra-/-IL-10 GFPCD441° IAV 2° IAV Naive 0.42 16.38 15.710.31 6.66 1.96VertX  VertX IL-27Ra-/-VertXVertX IL-27Ra-/-2° IAV 1° IAV 010203040BAL IL-10 GFP+ as %CD4+**ns*05101520IL-10 GFP+ as %CD4+**Lungns**1° IAV 2° IAV Naive A B BAL Lung   78 3.2.7 Epigenetic remodelling of the Il10 locus is associated with IL-10 expression in a recall response One mechanism that could explain the ability of memory CD4+ T cells from VertX (wildtype) mice to express IL-10 despite decreased IL-27 responsiveness is epigenetic modification of the Il10 gene through activating histone marks that maintain an ‘open’ configuration. The permissive histone modifications H4K4me3 and H3K27Ac are present at the cytokine genes IFNG and IL4 in memory CD4+ T cells which are consequently highly expressed upon re-stimulation (293). An increase in the permissive marks H3K4me3 and H3K36me3 is also associated with IL-10 expression from NK cells (327) and H3K4me3, H3K27Ac and H3K4me1 in monocytes (328) and Th2 cells (329). Given this evidence, I hypothesized that memory CD4+ T cells exposed to IL-27 in the primary response would acquire a permissive histone modification signature thereby increasing chromatin accessibility at the Il10 gene locus. To test this hypothesis, we sorted naïve and flu-specific memory CD4+ T cells and assessed their histone modification profile using low input native ChIP Seq (330). We examined the distribution of permissive histone marks H3K27Ac, H3K4me1 and H3K4me3 and the repressive histone mark H3K27me3 at the Il10 gene locus. As shown in Fig 3.8A, H2K27Ac, H3K4me1 and H3K4me3 enrichment was observed across the Il10 gene locus in memory CD4+ T cells compared to naïve CD4+ T cells. In contrast, the repressive H3K27me3 mark was lower in memory CD4+ T cells compared to naïve CD4+ T cells (Fig 3.8A). Since enrichment of H3K4me3 and H3K27me3 at promoters is predictive of gene activation and repression respectively (282, 331), we compared H3K4me3 and H3K27me3 normalised signal across the Il10 promoter region (±  2Kb from TSS) in naïve and memory CD4+ T cells. Memory CD4+ T cells were marked with higher H3K4me3 and lower H3K27me3 signal than in naïve CD4+ T cells (Fig 3.8B). These observations suggest that the   79 Il10 promoter and gene body in memory CD4+ T cells has a permissive histone modification signature compared to naïve CD4+ T cells.  Next, to determine whether the permissive histone signature correlated with Il10 transcription in memory CD4+ T cells, we measured the Il10 mRNA levels in the same cohort of naïve and antigen specific CD4+ T cells used for low input ChIP Seq. As shown in (Fig 3.8C), Il10 mRNA expression increased when memory CD4+ T cells were reactivated to form secondary effectors. However, when compared with Il10 expression from primary CD4+ T cells, secondary CD4+ T cells expressed less Il10 RNA. My observations at this point suggested that the Il10 locus in memory CD4+ T cells was more accessible than naïve cells but Il10 transcription, though increased following reactivation, was lower than in primary CD4+ T cells. Since the RNA Seq experiment was performed using single replicates, I measured the Il10 RNA expression by sorting antigen specific CD4+ T cells from the lungs of primary and secondary stage IAV infected mice and asked if there was a statistically significant difference in gene expression. Consistent with the pattern of IL-10 expression from my flow cytometric data (Fig 3.7), no significant difference in Il10 mRNA expression was observed between primary and secondary antigen specific CD4+ T cells (Fig 3.8D). From these observations, I concluded that primary T cell activation following IAV infections results in the deposition of a permissive histone signature at the Il10 gene locus in memory CD4+ T cells which is associated with an increase in Il10 gene expression in a recall response.   To determine if the increase in permissive histone modification observed at the Il10 promoter also occurred at cytokine loci, we compared H3K27me3 and H3K4me3 tag densities across Th1 and Th17 cytokines Il2, Ifng, Tnf, Il17a, Il21, Tgfb1 and Il10 gene promoters. The Il10 gene locus displayed the greatest loss of H3K27me3 compared to other cytokines while the Ifng   80 promoter displayed the highest gain of H3K4me3 (Fig 3.8 E). The Il2 and Il21 promoters were also associated with a gain of H3K4me3 and a corresponding decrease in H3K27me3 while Il17 shows an increase in only H3K27me3 (Fig 3.8 E). These data indicate influenza infection results in histone modification changes at the promoters of Ifng and other CD4 T cell derived cytokines in memory CD4+ T cells.    Figure 3.8: A permissive histone modification signature at the IL-10 gene locus in memory CD4+ T cells. Naive and influenza specific memory (Flu NP+) CD4+ T cells were sorted from the lungs and lymph nodes of infected mice. The distribution of permissive (H3K27Ac, H3K4me1, H3K4me3) and repressive (H3K27me3) histone marks was assessed by low input ChIP Seq. A. UCSC genome browser view of the Il10 gene in naive and Flu NP+ memory CD4+ T cells. B. Heat map profiling of input normalised H3K4me3 and H3K27me3 signal ± 2Kbp upstream and   81 downstream from the transcriptional start site (TSS) of the Il10 gene in naïve and Flu NP+ memory CD4+ T cells. (C) Normalised Il10 mRNA expression measured by RNA Seq in the same cohort of naïve, primary, memory and secondary CD4+ T cells shown in (B). (D) Relative normalised Il10 mRNA expression measured by qPCR from naïve and influenza specific primary and secondary CD4+ T cells. Data from a single experiment with 3-4 mice per group. (E) Z-score of H3K4me3 and H3K27me3 tag density at pro- and anti- inflammatory cytokine gene promoters in naïve and memory CD4+ T cells. Data shown in (A-C, E) are from an experiment that was performed once by pooling 75 memory stage mice to obtain 100K rare tetramer positive CD4+ cells.   3.2.8 IL-27 signaling is required for epigenetic remodelling of the Il10 gene locus in memory CD4+ T cells Given the presence of a permissive histone signature at the Il10 locus in memory CD4+ T cells, we sought to determine the signals that promote this epigenetic state. Based on our in vivo studies on wildtype and IL-27Ra-/- mice where wildtype memory CD4+ T cells expressed more IL-10 than IL-27Ra-/- cells (Fig 3.7), I hypothesised that exposure to IL-27 signaling would increase H3K4me3 and decrease H3K27me3 at the Il10 promoter. To test this, naïve CD4+ T cells were isolated from VertX and VertX IL-27Ra-/- mice and cultured in vitro in the presence of IL-27 for a period of four days followed by sorting of CD44hi CD4+ T cells from wells containing cells from VertX or VertX IL-27Ra-/- mice. Sorted cells were then rested for a four day period in the presence of IL-2 alone to generate rested ‘memory like’ CD4+ T cells. Naïve CD44lo CD4+ T cells were sorted as controls. Then, the enrichment of H3K4me3 and H3K27me3 was assessed at the Il10 promoter in naïve and rested CD4+ T cells using ChIP Seq. As shown in the heatmaps displaying normalised H3K4me3 and H3K27me3 signal across the Il10 promoter in Fig 3.9A, rested CD4+ T cells from VertX mice respond to IL-27 signaling during primary activation showed a striking gain in H3K4me3 across the Il10 promoter region   82 which was consistent with our in vivo observations (Fig 3.8). In contrast, memory CD4+ T cells from VertX IL-27Ra-/- mice do not display an increase in H3K4me3 signal at the Il10 promoter region. We also observed that IL-27 exposed VertX rested CD4+ T have reduced H3K27me3 at the Il10 promoter region compared to naïve VertX CD4+ T cells (Fig 3.9A, right panel), while VertX IL-27Ra-/- rested CD4+ T cells do not show an identical decrease in H3K27me3. This data indicated that IL-27 signalling was required for the deposition of a permissive histone modification signature at the Il10 promoter in resting phase CD4+ T cells.   To confirm whether the effect of IL-27 was specific to the Il10 promoter, we compared H3K4me3 enrichment at the Ifng gene in VertX and VertX IL-27Ra-/- CD4+ T cells cultured in the presence or absence of IL-27. When Ifng and Il10 gene promoters were ranked based on increasing H3K4me3 signal, we found that the Il10 promoter was ranked higher than Ifng in rested CD4+ T cells from VertX mice were exposed to IL-27 during primary activation (Fig 3.9B). Since rested CD4+ T cells from VertX IL-27Ra-/- mice did not display a similar increase in H3K4me3 rank (Fig 3.9B), I concluded that IL-27 signalling specifically induced increased H3K4me3 deposition at the Il10 promoter in CD4+ T cells following primary activation.   Our results so far suggested that IL-27 signalling was required for the increase in deposition of H3K4me3 at the Il10 promoter region. Since the methyltransferases MLL1 and MLL2 have been reported to mediate H3K4 trimethylation (332-334), we tested whether exposure to IL-27 resulted in increased Kmt2a (MLL1) and Kmt2d (MLL2) mRNA expression. We measured Kmt2a and Kmt2d mRNA expression from IL-27 stimulated CD4+ T cells from VertX or VertX IL-27Ra-/- mice using quantitative PCR. We did not observe an increase in MLL1 or MLL2   83 expression in activated or resting phase groups in the presence or absence of IL-27 (Fig 3.10 A,B). In fact, naïve CD4+ T cells from VertX and VertX IL-27Ra-/- mice had higher Kmt2a and Kmt2a mRNA expression (Fig 3.10A, B). These results suggest that a global measurement of MLL1 and MLL2 transcripts is not suitable for assessing Il10 gene specific H3K4me3 deposition or an MLL1/2 independent cellular mechanism of H3K4me3 deposition exists at the Il10 locus.      84  Figure 3.9: IL-27 signaling results in the epigenetic remodeling of the IL-10 locus in memory CD4+ T cells. (A) Outline of in vitro CD4+ T cell culture system. Naïve CD4+ T cells were activated in the presence or absence of IL-27 for 4 days followed by culture in IL-2 for two days to generate rested CD4+ T cells. Naïve and rested CD4+ T cells from VertX and VertX IL-27Ra-/- were sort CellVertXGene2025Cell05H3K27me3 normalised signal H3K4me3 normalised signalACell IL27 2Kb1KbTSS1Kb2KbCellIL27Cell00.10.20.30.40.50.60.72Kb1KbTSS1Kb2KbCellIL27GeneCell00.050.10.150.20.250.3Cell IL27CNaiveRestedVertX VertX IL-27Ra-/-Naive CD4+ T cellsDay 0αCD3, αCD28± IL-27Activated CD4+ T cellsDay 4Rested CD4+ T cells+ IL-2BDay 8cell sortingrestIL-27ra+/+IL-27ra-/-NaiveMemory  85 purified and the distribution of histone marks (H3K27Ac, H3K4me1, H3K4me3 and H3K27me3) was assessed by ChIP Seq. (B) Heat map profiling of H3K4me3 and H3K27me3 signal ± 2Kbp from the TSS of the Il10 and Ifng gene. (C) Normalized coverage of H3K4me3 signal across all ranked gene promoters.      Figure 3.10: MLL1 and MLL2 mRNA expression is not increased in IL-27 stimulated activated or resting phase CD4+ T cells.  Activated or resting phase CD4+ T cells from VertX and VertX IL-27Ra-/- were generated in vitro in the absence or presence of IL-27 signaling followed by RNA extraction. (A, B) Kmt2a VertXVertX IL-27Ra-/-AB0.00.51.01.52.0MLL1 Relative normalised expression0.00.51.01.5MLL2 Relative normalised expression0.00.51.01.5MLL1Relative normalised expression0.00.51.01.5MLL2 Relative normalised expression Naive Activated RestingIL-27 – –+ + –aCD3/aCD28 – ++ + +IL-27 – –+ + –aCD3/aCD28 – ++ + + Naive Activated RestingIL-27 – –+ + –aCD3/aCD28 – ++ + +IL-27 – –+ + –aCD3/aCD28 – ++ + +  86 (MLL1) and Kmt2d (MLL2) mRNA expression was measured relative to naïve controls by quantitative PCR. Data are representative of a single experiment with 3 replicate wells per group. Error bars indicate standard deviation.     3.2.9 Establishment of an active enhancer located upstream of the Il10 gene in memory CD4+ T cells is dependent on IL-27 signaling In addition to epigenetic modifications at promoters, gene expression can also be influenced by distal regulatory elements such as enhancers and super-enhancers. Indeed, studies have shown that IL-10 expression in macrophages is associated with enhancer-like DNase I hypersensitive sites upstream of the Il10 promoter, that contain conserved transcription factor binding sites and are marked by hyperacetylated histones (335). Active enhancers can be identified by the high level enrichment of H3K27Ac at H3K4me1 marked enhancer regions (287).  When the distribution of the active enhancer elements in the vicinity of the Il10 promoter was examined, we observed an H3K27Ac peak approximately 25kb upstream of the Il10 TSS in primary, memory and secondary CD4+ T cells (Fig 3.11A). Interestingly, this active enhancer peak was established only in in vitro cultured CD4+ T cells following exposure to IL-27. This suggests that a transcription factor or chromatin remodeling enzyme associated with the IL-27 signaling pathway may bind at the active enhancer site. We then assessed putative transcription factor binding sites within this active enhancer using the transcription factor motif prediction tools, FIMO (306) and TRAP (307). We selected transcription factors that had a two-fold higher expression in memory compared to naïve CD4+ T cells in order to identify highly expressed TFs at the memory stage. With these criteria, both FIMO and TRAP predicted the binding of CREB1 with the highest significance as shown in Fig 3.11B, C. These results indicate that Il10 gene   87 expression may be regulated through an active distal enhancer region in CD4+ T cells that binds CREB1 and is activated in response to IL-27.       88  Figure 3.11: An active enhancer region upstream of the Il10 gene promoter is established in response to IL-27 signaling.  (A) UCSC genome browser screenshot displays the distribution of H3K27Ac signal across a ~100kb region containing the Il10 gene locus in the labelled CD4+ T cell subsets. Box shows the location of the active enhancer peak (~ 25 kb) upstream of the Il10 TSS. Key transcription factor candidates binding to the active enhancer predicted by motif enrichment analysis tools (B) FIMO H3K27AcIl10 Mapkapk2NaivePrimaryMemorySecondaryWT naiveWT restingIL-27Ra-/- naiveIL-27Ra-/- restingA B FIMO C NR3C1AP1FEVNKX3-1CREB10.0 0.5 1.0 1.5 2.0-log10 qValue1.51.61.71.81.92.0TRAPAtf3Creb1Elf4Elk3Irf5Irf8Jdp2Nr3c1Runx2SpibZbtb7b4.04.55.05.56.0-2.5 0.0 2.5 5.0 7.5log2(Memory expression/Naive expression)-log10 p valueaablackredlog2(Memory expression/Naive expression)-log10 P value-log10 P value-log 10 p valueTRAPFIMOB CAlog2 (Memory RPKM/Naïve RPKM)-log 10 p value  89 and (C) TRAP. X-axis displays ratio of Memory RPKM/Naïve RPKM. Y-axis displays p-value of motif occurrence.    3.2.10 Absence of IL-27 signaling results in increased granulocytic infiltration and  lung pathology  Since IL-27 signaling is important to limit lung immunopathology during primary influenza infection (192), we wanted to assess the impact of IL-27 on immunopathology in a recall response. We assessed histopathological changes in H&E stained lung sections from VertX and VertX IL-27Ra-/- mice at primary (Day 10 p.i.) and secondary time points (Day 6 p.i.). Uninfected mice were used as controls. Lung sections from healthy uninfected VertX and VertX IL-27Ra-/- mice have a fine lace like appearance, clear airways, thin walled alveoli and bronchioles lined with intact epithelium (Fig 3.12, A). In contrast, all of the infected lung sections displayed a range of pathology from normal to severe.  Within infected lung tissue, the distribution ranges from multifocal to regionally extensive as seen at 4X magnification (Fig 3.12: 4 B-C, E, F 4X). At Day 10 p.i. lungs of both VertX and VertX IL-27Ra-/- mice showed extensive alveolar damage, peri-bronchial and perivascular inflammation (Fig 3.12B, Fig 4E 10X, features identified by black arrows). The lumen of airways of VertX mice contained mucus, necrotic inflammatory and bronchial epithelial cells as seen at 20X magnification (Fig 3.12B). Lungs from infected VertX IL-27ra-/- mice showed similar but more extensive histopathological changes compared with infected VertX mice with increased cellular infiltrate making the IL-2Ra-/- lungs appear more consolidated (Fig 3.12: E, 20X). In mice rechallenged with influenza, we observed increased alveolar damage compared to groups of mice infected with primary influenza in both VertX and VertX IL-27Ra-/- groups. A striking observation was the presence of increased   90 perivascular cuffing in lungs from VertX and VertX IL-27Ra-/- mice in recall responses, compared to primary (Fig 3.12C and F, 20X).   We also examined the type of cellular infiltrate present in the lung during secondary influenza using 40X magnification. VertX mice displayed a predominantly mixed lymphocytic infiltrate with the occasional presence of a neutrophil. In contrast, lung sections from a recall response in VertX IL-27Ra-/- mice showed a striking increase of granulocytes, likely neutrophils (black arrows), scattered in perivascular regions of inflammation (Fig 3.13 A, 40X). Together, these pathological observations indicate that the absence of IL-27 signaling in a recall response results in increased lung pathology and is associated with the infiltration of granulocytic cells into the lung.          91  Figure 3.12: Effect of IL-27 signaling on lung pathology in primary and secondary influenza infection.  4X 20X10XVertX  1ºPR8NaiveABC 2ºPR8 2ºPR8NaiveVertX IL-27Ra-/- 1ºPR8DEF Scale bar: 200µm  Scale bar: 100µm  Scale bar: 50µmScale: 50μmScale: 100μmScale: 200μmWTKO20X10X4X  92 Lung sections from naïve and infected VertX (WT) and VertX IL-27Ra-/- (KO)mice were stained with hematoxylin and eosin (H&E). H&E stained sections from (A-C) VertX mice or (D-E) VertX IL-27Ra-/-  mice in naïve, primary or secondary groups. Black arrows indicate key pathological observations.  Data are representative of two experiments with 5-6 mice per group and scored by an independent pathologist.      Figure 3.13:Increased granulocytic infiltrate in the lungs of VertX IL-27Ra-/- following secondary influenza infection.  40X magnification of H&E stained lung sections from secondary stage influenza in VertX (WT) and VertX IL-27Ra-/- (KO) mice. Black arrows show granulocytic cells. Data are representative of two experiments with 5-6 mice per group and scored by an independent pathologist.     3.3 Discussion Effector CD4+ T cells are an important source of IL-10 during Th1 infection but how IL-10 expression is regulated during infection is not well understood.  In this study, I investigated the role of IL-27 in regulating IL-10 expression from CD4+ T cells during primary and recall influenza infection. I found that primary and memory CD4+ T cells downregulated surface  Scale bar: 20µm40X 40XVertX VertX IL-27Ra-/-WT KO40X 40XScale: 20μm  93 expression of gp130, the beta subunit of the IL-27 receptor, which led to impaired IL-27 responsiveness in memory CD4+ T cells. Surprisingly, despite a decrease in IL-27 responsiveness, memory CD4+ T cells could still express IL-10 in a recall response. Mechanistically, we demonstrated that exposure to IL-27 signaling in the primary response led to establishment of a permissive epigenetic signature at the Il10 locus in memory CD4+ T cells. While IL-27 is known to promote IL-10 expression from activated T helper cells, these data highlight a novel role for IL-27 in promoting epigenetic remodelling of the Il10 locus in primary and memory CD4+ T cells.  IL-27 is known to promote IL-10 from effector CD4+ T cells during Th1 infection (192, 194, 316, 324). Consistent with these reports, IL-27 signalling during primary IAV infection was required to enhance IL-10 production from effector CD4+ T cells present in the BAL and lungs. The majority of these IL-10 producing CD4+ T cells were Foxp3- cells that expressed T-bet, which indicates a Th1 phenotype. The functional importance of IL-27 induced IL-10 from Th1 cells in primary infection is in limiting tissue damage and promote survival by suppressing pathogenic Th1 and Th17 responses (192, 316). I observed that IL-10 levels from activated CD4+ T cells were highest in the airways and lung and peaked around Day 8 post IAV infection. The transient high-level expression of activated Th1 CD4+ IL-10 has also been demonstrated in an IAV model by Braciale et al (97) and during T.gondii infection and has been proposed as a self-regulatory mechanism in activated effector CD4+ T cells (146). Therefore, my findings suggest that IL-27 acts as an environmental signal (208) to promote expression of host protective IL-10 from activated effector CD4+ T cells present in the BAL and lung during primary influenza.   94 Surface level expression of gp130, the beta subunit of the IL-27R, is required for IL-27 signal transduction via STAT1 and STAT3 phosphorylation and the expression of IL-10 from activated CD4+ T cells (324, 325). A study by Betz et al demonstrated that activated CD4+ T cells downregulate gp130 after TCR activation in vitro (216) and antigenic exposure in vivo. Whether infection induces changes in gp130 expression on CD4+ T cells was not known. I observed that activated CD4+ T cells downregulated gp130 (IL-27Rbeta) following primary IAV infection and that memory CD4+ T cells retained this gp130low phenotype. In addition, the reduction in gp130 expression on the cell surface was associated with a decrease in Il6st mRNA when compared to naïve controls. My data indicates that primary IAV infection results in the downregulation of gp130 on primary and memory CD4+ T cells and that the gp130low phenotype exhibited by these cells may be due to inhibition of Il6st (gp130) gene expression. Studies on hepatocytes (336) and monocytes (337) have shown that surface gp130 can also be internalised and degraded in p38 MAPK dependent manner following exposure to IL-1β (336). Since TCR signalling activates the p38 MAPK pathway (338) it is possible that receptor internalisation contributes to gp130 downregulation which future studies using live cell imaging on naive and activated CD4+ T cells could elucidate.   A publication from our group (218) first established a link between the quantity of IL-10 expressed by T cells and gp130 expression on the surface of T cells. Memory CD8+ T cells that downregulate gp130 (IL-27Rb) are less responsive to IL-27 signaling and secrete less IL-10 in a recall response (218). Consistent with this report, I observed that gp130low memory CD4+ T cells were impaired in their ability to respond to IL-27 compared to gp130hi naïve CD4+ T cells. Interestingly, despite a reduction in IL-27 responsiveness, there was no decrease in CD4+ IL-10   95 expression in a recall response. These results suggested that IL-10 expression in a recall response may be independent of IL-27 signalling. However, IL-27Ra-/- memory CD4+ T cells were impaired in their ability to express IL-10 in a recall response compared to memory CD4+ T cells from wild type mice. Together, this evidence indicated that exposure to IL-27 signalling in a primary response is required to prime memory CD4+ T cell that later express IL-10 in a recall response.    The requirement for IL-27 signalling during a primary response for IL-10 expression from reactivated CD4+ T cells in my model is supported by studies showing that Th1 memory like lymphocytes stimulated in vitro cannot re-express IL-10 unless the cells are exposed to an alternate signal such as IL-12 during reactivation (329) and that CD4+ T cells in the BAL have >10 fold higher Il10 mRNA transcripts 48h after secondary influenza infection Chapman et al (339).  However, other reports (121) showed that in vitro generated Th1 cells transferred into naïve hosts secrete less IL-10 during secondary influenza infection (121, 266). The requirement for IL-27 during primary activation of CD4+ T cells in vivo, as shown in our model, could explain the discrepancy between our observations and these two conflicting reports. Another report by Dong et al (340) show that IL-10 producing human memory CD4+ T cells that undergo expansion in vitro re-express less IL-10 and co-produce IFNγ. Here, in addition to the requirement for IL-27 signaling during primary activation, it is possible that the different re-stimulation procedures used in this study versus ours could explain the contrasting observations on IL-10 from reactivated memory CD4+ T cells.     96 We investigated epigenetic changes as a mechanism of IL-10 regulation because cytokine production in T cells is associated with changes in histone modifications such as H3K4me3 and H3K27me3 at cytokine gene loci (341, 342). In the case of IL-10, activating histone marks have been observed at the Il10 gene in Th2 cells (329), monocytes (328), and NK cells (327) expressing this cytokine. Evidence that cytokine expression by memory cells may be controlled by histone modifications arises from a report that showed permissive H3K4me3 deposition at the promoters of genes encoding cytokines such as IFNγ and IL-4 in memory CD4+ T cells, and these cytokines are rapidly expressed upon re-stimulation (293). These studies suggested that the Il10 gene may be epigenetically remodelled by permissive histone modifications in memory CD4+ T cells following primary activation.  ChIP-Seq analysis of histone modifications revealed that memory CD4 T cells exhibited a permissive histone signature at the Il10 gene locus marked by increased H3K4me3, H3K27Ac and reduced H3K27me3 compared to naïve CD4+ T cells. These results confirmed that the Il10 locus displayed an ‘open’ configuration in memory CD4+ T cells imposed by permissive epigenetic changes after primary IAV infection. Evidence for IL-27 as an epigenetic modifier is supported by two reports showing that activated CD4+ T cells exposed to IL-27 have a reduction in DNA methylation at the Il10 locus compared to cells activated in the absence of IL-27 (343, 344). In addition, IL-27 promotes Foxp3 expression in iTregs by increasing the binding of STAT1 and H4Ac at the FOXP3 promoter compared to iTregs not exposed to IL-27 (345). However, the role of IL-27 in controlling the changes in histone modifications at the Il10 gene in CD4+ T cells was not known. Therefore, we tested whether IL-27 had a role in inducing epigenetic changes at the Il10 gene using an in vitro system. When rested CD4+ T cells from wild type and IL-27Ra-/- mice were cultured in the presence of IL-27, only wild type CD4+ T cells exhibited an increase in H3K4me3 at the Il10   97 promoter thereby confirming that IL-27 signalling is capable of inducing permissive histone modification changes at the Il10 gene promoter.   A caveat in our experiments to determine the role of IL-27 as an epigenetic modifier is the use of an in vitro system to generate resting effector CD4+ T cells. Ideally, influenza specific memory CD4+ T cells sorted from IL-27Ra-/- mice would constitue an ideal sample to investigate the role of IL-27 in inducing epigenetic changes at the Il10 locus in response to infection. However, we were able to identify very low numbers of influenza specific CD4+ T cells using MHC Class II tetramers in the lungs and lymph nodes of memory stage mice  i.e. 1000 cells per mouse which posed a significant technical limitation to carrying out ChIP-Seq. Therefore, an in vitro system developed by McKinstry et al  (266, 346) was used to generate sufficient numbers of resting phase CD4+ T cells that have similar patterns of cytokine expression as memory CD4+ cells generated in vivo (121).   Along with gene promoters, distal regulatory regions called enhancers contain binding sites for transcription factors that activate or increase gene expression in a cell type specific manner (347, 348). For example, Th1 and Th2 specific gene expression is regulated by the binding of STAT1 and STAT4 to enhancers in Th1 cells and by STAT6 to enhancers in Th2 cells (349). The Locus Control Region is an enhancer element in Th2 cells that licenses gene expression of the Th2 cytokine locus by chromosomal looping that is dependent in part on GATA3 binding (350, 351). Enhancer elements identified by Dnase1 hypersensitivity analysis have been identified near the Il10 gene in Th1 and Th2 cells cultured in vitro (352). Recently, an active enhancer that bound c-Maf, an IL-27 induced transcription factor required for IL-10 production from CD4+ T cells, was   98 identified in human memory Th17 cells (353, 354). However, in an infectious setting, it is not clear if IL-10 expression can be regulated distally by enhancer regions. The histone marks H3K4me1 and H3K27Ac were selected for enhancer identification with ChIP Seq based on previous studies that identified poised and active enhancers using a combination of these histone modifications in multiple cell types including CD8+ T cells (287, 296, 355). We found a previously unknown putative active enhancer present ~25kb from the Il10 promoter in CD4+ T cells following primary exposure to influenza in vivo and determined that IL-27 was required for the establishment of this region. TF motif prediction revealed that CREB was a putative TF that could bind this active enhancer. CREB is known to induce IL-10 expression in macrophages by binding to the IL-10 promoter in response to TLR signalling (356). In CD4+ T cells, CREB is reported to be important for T cell function because expression of a dominant negative CREB protein inhibits Th1 and Th2 differentiation and the expression of the antiapoptotic molecule Bcl2 (357). However, a role for CREB in modulating IL-10 expression in CD4+ T cells has not been identified. Interestingly, CREB is activated via phosphorylation and the binding of pCREB to DNA leads to recruitment of the co-activator proteins, CREB binding protein and p300 (CBP/p300). The CBP/p300 complex can act as a scaffold for the binding of additional transcription factors (358, 359). In addition, CBP/p300 possess histone acetyl transferase activity (360) and therefore could be responsible for the formation of this active enhancer at the Il10 locus in CD4+ T cells. CREB is phosphorylated, following TCR stimulation or exposure to IL-2, by a mitogen and stress activated kinase (MSK) downstream of the p38 and ERK1/2 pathways (361, 362). Interestingly, IL-27 signaling has also been shown to activate the p38 MAPK and ERK1/2 pathways (186)  but its role in activating CREB has not been investigated. One possibility is that IL-27 signalling in a primary response leads to phosphorylation and binding of   99 CREB to the Il10 enhancer region and results in the recruitments of CBP/p300. This CBP/p300 could then serve a dual role. First, CBP/p300 could establish an active enhancer via HAT activity which is maintained in memory CD4+ T cells. Second, CREB phosphorylation by TCR signalling in a recall response would then result in recruitment of additional transcription factors via CBP/p300 that may promote Il10 transcription by interacting with the transcriptionally licensed Il10 promoter in memory CD4+ T cells. Investigating the effects of IL-27 and TCR stimulation on the phosphorylation of CREB in naïve and memory CD4+ T cells is the next step to determine the role of CREB in promoting IL-10 from CD4+ T cell in a recall response to IAV.   Since memory CD4+ T cell express IL-10 in a recall response to influenza, we then determined whether if the IL-27/IL-10 axis could control tissue pathology during secondary influenza infection. Following a secondary challenge, both wild type and IL-27Ra-/- groups had more severe pathology compared to naïve or primary stage mice. Lung sections from all infected mice were extensively infiltrated by immune cells. On closer examination, we observed that the absence of IL-27 signaling resulted in increased accumulation of granulocytic cells compared to the predominantly lymphocytic infiltrate present in wild type infected mice. Primary influenza infection in IL-27Ra-/- mice has been shown to increase inflammation and tissue damage (192) which we also observed here and is consistent with the role of IL-27 in limiting Th1 inflammation (188, 189, 316, 363). At the cellular level, the absence of IL-27 signaling has been shown to result in enhanced IL-17 expression from CD4+ T cells (192, 364), and the presence of IL-17 recruits neutrophils into infected tissues (192, 364, 365). Therefore, the increased granulocytes observed in the lung in the absence of IL-27 during primary or secondary influenza challenge could be attributed to neutrophil recruitment as a result of reduced IL-10 expression,   100 and increased IL-17 expression. Confirming this hypothesis would require profiling of the inflammatory cells and cytokine and chemokine signatures present in the lungs of wild type and IL-27Ra-/- mice in primary and recall influenza infection.   In conclusion, this study shows that IL-27 signaling primes CD4+ T cells during a primary infection for subsequent IL-10 production in a secondary response. Mechanistically, I showed that IL-27 signaling during primary influenza infection is required for the deposition of a permissive histone signature at the Il10 gene locus, thus allowing memory CD4+ T cells to express IL-10 despite decreased responsiveness to IL-27. We also identified IL-27 as a suppressor of granulocyte associated tissue damage in the lung during primary and secondary influenza. Our results suggest that CD4+ T cells exposed to infection induced cytokine signals during a primary response can retain an epigenetic imprint that predicts their behavior in a recall response.    101 Chapter 4: IL-27 promotes functional specialisation of airway Tregs during primary influenza infection 4.1 Introduction Tregs are a subset of regulatory CD4+ T cells that express the lineage specifying transcription factor Foxp3 (366, 367). The importance of Foxp3+ Tregs in regulating the immune system and maintaining self-tolerance is evident in the lethal auto-immune disease observed in mice genetically deficient in Foxp3 (323, 366, 368). Tregs are present within tissues and suppress inflammation through multiple effector mechanisms that include the secretion of immunosuppressive cytokines such as IL-10 and TGFβ, expression of inhibitory receptors and metabolic disruption (369, 370). Recent studies have shown that Tregs can adapt to suppressing Th1, Th2 and Th17 inflammatory responses by differentiating into specialised subsets (371). A hallmark of these functionally specialised Tregs is the expression of transcription factors that are characteristic of their target effector cells (371). In Type 1 inflammatory responses, Tregs that express the Th1 specifying transcription factor T-bet are able to effectively suppress Th1 inflammation and pathology compared to T-bet deficient Tregs (372, 373). The superior immunosuppression mediated by T-bet+ Tregs is in part due to the upregulation of the Th1 chemokine receptor CXCR3 which is required for the migration of Tregs to the site of inflammation (372). In addition, T-bet also promotes the survival and proliferation of Th1 adapted Tregs (372).   The cellular signals that promote functional specialisation in Tregs are important because they dictate how the environment can shape Treg differentiation, their suppressive capacity and   102 influence disease outcome. In the case of Th1 adapted Tregs, the inflammation induced cytokines IFNγ and IL-27 act as environmental cues to promote T-bet and CXCR3 expression in Tregs in a STAT1 dependent manner (372). Interestingly, there is a division of labour between these cytokine signals as IL-27 plays a dominant role in T-bet induction at the site of infection such as the laminal propria of the gut while IFNγ promotes T-bet expression in Tregs present in peripheral lymphoid organs such as the spleen (202). IL-27 is essential for surviving infection with the intracellular parasite T. gondii as IL-27-/- mice succumb early and with severe immunopathology compared to wild type mice. The transfer of IL-27 primed Tregs can rescue IL-27-/- mice but these Tregs must express IL-10, suggesting that IL-10 expression is another hallmark of Th1 adapted Tregs (202). While IL-27 regulates functional specialisation in Tregs present in the gut mucosa, the role of this cytokine on Treg differentiation at mucosal sites such as the lung is not clear. This is especially relevant in the case of acute respiratory infection with influenza that triggers a strong Th1 response capable of causing lung immunopathology when dysregulated (374).  Foxp3+ Tregs proliferate in response to primary IAV infection and carry out important regulatory functions such as controlling innate and effector T cell responses and dampening IAV associated pathology in the lung (109, 110, 375). IAV specific Tregs present in the lung have an activated CD44hi CD69+ phenotype and express the immunosuppressive molecules CTLA4, GITR, ICOS and IL-10 (109, 110). Tregs in IAV infected mice also display evidence of functional specialisation by expressing T-bet and CXCR3 (109). Since IL-27 regulates lung immunopathology and induces IL-10 from effector T cells during IAV infection (97, 192), it is possible that suppressive program of IL-27 may also include the functional specialisation of   103 Tregs. However, this aspect of IL-27 dependent Treg differentiation has not been investigated in the lung during influenza infection.   In this study, I examined the effect of IL-27 on the expression of T-bet, CXCR3 and IL-10 in Tregs in order to determine if IL-27 acts an environmental cue for Treg differentiation during IAV infection.   4.2 Results 4.2.1 Foxp3+ CD4+ T cells expand in the lung in response to primary influenza infection To determine if Foxp3+ CD4+ T cells expand in response to influenza, we infected VertX mice with PR8 IAV and measured the increase in Foxp3+ CD4+ T cell numbers and frequency in the lungs, draining lymph nodes and spleen on Day 10 post infection. We observed an increase in the numbers of Foxp3+ CD4+ T cells in the lungs of infected mice but not in the lymph node or spleen (Fig 4.1 A). Next, the proportions of Foxp3+ CD4+ T cells in naïve and influenza infected mice were compared. We did not observe a significant difference in Foxp3+ CD4+ T cell frequencies in the lungs of naïve and infected mice (Fig 4.1 B). Together these results indicate that within the infected lung, the numbers of Foxp3+ CD4+ T cells increase in response to influenza whereas unchanged Foxp3+ CD4+ T cell frequencies suggest an expansion of both Foxp3+ and Foxp3- CD4+ T cells.    104  Figure 4.1: Numbers and frequencies of Foxp3+ CD4+ T cells during influenza infection (A) Bar graphs show the numbers of Foxp3+ CD4+ T cells calculated from total live cells in the lung, lymph node and spleen of naïve and PR8 influenza infected VertX mice on Day 10 p.i. (B-D) Representative FACS plots (left) show the percentage of Foxp3+ CD4+ T cells of total live Naive Infected0.05.0 1031.0 1041.5 1042.0 1042.5 104#Foxp3+ CD4+ T cellsLung*05000001 1062 1062 1063 106#Foxp3+ CD4+ T cellsSpleenNaive Infected3.77 3.214.93 8.6911.37 10.470.070.040.08FSCFoxp3FSCFoxp3FSCFoxp3Naive InfectedNaiveNaiveInfectedInfectedFMO lungFMO LNFMO SpleenNaive Infected0.05.0 1041.0 1051.5 1052.0 105#Foxp3+ CD4+ T cellsLNnsNaive Infected012345 %Foxp3+CD4+ T cellsLungnsNaive Infected051015LNns %Foxp3+CD4+ T cellsNaive Infected051015Spleen %Foxp3+CD4+ T cellsABCD  105 CD4+ T cells present in the lung, lymph node and spleens of naïve and influenza infected VertX mice. A Foxp3 FMO was used to set the gate for Foxp3+ cells. Bar graphs (right) depict the percent Foxp3+ CD4+ T cells in the lung, lymph node and spleen. Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.   4.2.2 Foxp3+ CD4+ T cells upregulate CD44 and T-bet in the infected lung during primary influenza  Since Treg numbers increase in response to influenza, we wanted to determine if Foxp3+ CD4+ T cells acquire an activated phenotype during infection that is similar to effector CD4+ T cells. We measured the expression of the T cell activation marker CD44 on Tregs in the lungs of naïve and infected VertX mice on day 10 post infection. We observed that influenza infection results in the upregulation of CD44 on lung dwelling Tregs (Fig 4.2 B) with an ~20% increase in the proportion of CD44hi Foxp3+ cells compared to naïve mice (Fig 4.2 A). In comparison, we observed a ~30% increase in the frequencies of CD44hi Foxp3- CD4+ T cells compared to naïve controls (Fig 4.2A). These results suggest that Tregs accumulating in the lungs of influenza infected mice have an activated phenotype.  T-bet expressing Foxp3+ CD4+ T cells are reported to be better at suppressing Th1 inflammatory responses compared to T-bet negative Tregs (203). Since influenza elicits a strong Th1 response, we next determined if Foxp3+ CD4+ T cells express T-bet in the lung which is the site of effector T cell mediated inflammation. We measured the frequencies of T-bet+ Foxp3+ CD4+ T cells in naïve and influenza infected mice on Day 10 post infection. In the lung, a significant increase in the frequency of T-bet+ Foxp3+ CD4+ T cells was observed compared to   106 naïve controls. Foxp3- (Fig 4.2 C). A concomitant increase in frequencies of T-bet+ Foxp3- CD4+ T cells was also observed lungs of influenza infected mice (Fig 4.2C). An increase in T-bet MFI was observed  in Foxp3+ CD4+ T cells, and Foxp3- CD4+ T cells in infected mice compared to naïve controls (Fig 4.2D). Together these data show that there is an increase T-bet expressing Treg cells in the lung and that Tregs increase T-bet expression on a per cell basis.       107  Figure 4.2: CD44 and T-bet expression in Foxp3+ and Foxp3- CD4+ T cells in the lung during influenza infection CD44Percent of maxFoxp3+ CD4+ T cells Foxp3- CD4+ T cellsNaiveInfectedANaive Infected020406080%CD44hi cells Naive Infected0204060%CD44hi T cells* **Foxp3+ CD4+ T cells Foxp3- CD4+ T cells1.8324.332.1471.701.8247.893.2947.0CD44Foxp3Naive Infected3.687.351.0687.921.3161.763.2333.70T-betFoxp3Naive InfectedNaive Infected020406080100 %T-bet+ FoxP3+ CD4+ T cellsNaive Infected020406080** **%T-bet+ Foxp3- CD4+ T cellsFoxp3+ CD4+ T cells Foxp3- CD4+ T cellsCBTbetPercent of maxDFoxp3+ CD4+ T cells Foxp3- CD4+ T cellsNaiveInfected  108 (A) FACS plots (left) show CD44 versus Foxp3 staining in naïve and infected lungs of VertX mice on Day 10 p.i. Bar graphs (right) show the frequency of CD44hi cells in Foxp3+ and Foxp3- CD4+ T cells in naïve and infected VertX mice. (B) Histograms show CD44 expression levels in Foxp3+ and Foxp3- CD4+ T cells from naïve (black line) and infected (red line) VertX mice. (C) T-bet versus Foxp3 staining in naïve and infected lungs of VertX mice on Day 10 p.i. (D) Histograms show T-bet expression levels in Foxp3+ and Foxp3- CD4+ T cells from naïve (black line) and infected (red line) VertX mice. Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.   4.2.3 Foxp3+ CD4+ T cells upregulate IL-10 in the infected lung during primary influenza  Foxp3+ Tregs are known to express the inhibitory cytokine IL-10 to suppress effector T cell responses during T. gondii, L. monocytogenes and S. typhimurium infection (202, 376). In response to influenza infection, we observed a striking increase in the frequencies of IL-10 expressing Foxp3+ CD4+ T cells in the lungs of infected mice on Day 10 post infection (Fig 4.3A). In the draining lymph node, an increase in the frequencies of Foxp3+ CD4+ T cells was also observed but to a lesser extent than in the lung (Fig 4.3B). To determine if IL-10+ Foxp3+ cells possess a Th1 adapted T-bet+ phenotype, T-bet expression was compared between IL-10+ and IL-10- Foxp3+ cells in naïve and infected mice. In the lungs of influenza infected mice, a trend was observed towards higher T-bet expression by IL-10+ Foxp3+ CD4+ T cells in the lung compared to IL-10- Foxp3+ CD4+ cells, although this difference was not statistically significant (Fig 4.3B). In the draining lymph node, a similar increase in T-bet MFI expression was seen which was statistically significant when compared to the Foxp3+ IL-10- population. These data indicate that following influenza infection, IL-10 expressing Tregs in the dLN and to a lesser extent in the lung exhibit a functionally specialised phenotype.    109  Figure 4.3: IL-10 expression in Foxp3+ CD4+ T cells in the lung and draining lymph node during influenza infection.  Representative FACS plots (left) and bar graph (right) show the percentage of IL-10 GFP+ Foxp3+ CD4+ T cells in the (A) lungs and (B) mediastinal lymph nodes of naïve and infected VertX mice. Gated on live Foxp3+ CD4+ T cells. Numbers to the left of the gate in FACS plot indicate the geometric mean channel fluorescence (MFI). T-bet expression (MFI) in IL-10+ Foxp3+ and IL-10- Foxp3+ CD4+ T cells in the (C) lungs and (D) mediastinal lymph nodes of 1.17 32.76139723474001020304050IL-10 GFP as % Foxp3+          CD4+ T cells*ADNaive InfectedFSCIL-10 GFPNaive InfectedFSCIL-10 GFP1.55 7.801196620743Naive Infected051015IL-10 GFP as % Foxp3+ CD4+ T cells**BCNaive InfectedLungLN02000400060008000T-bet MFILung p=0.123 IL-10+  IL-10-01000200030004000T-bet MFI* IL-10+  IL-10-LNLungLN  110 infected VertX mice. Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.  4.2.4 IL-27 signaling has no effect on the expansion of Foxp3+ CD4+ T cells during IAV IL-27 has been reported to promote Treg development and survival (187, 202) suggesting that IL-27 signalling may have an effect on the increase in Treg numbers observed within the infected lung (Fig 4.1). To test this,  VertX and VertX IL-27Ra-/- mice were infected with PR8, and Treg numbers and frequencies measured on Day 10 post infection. In the BAL and lung, an significant difference in the numbers of Foxp3+ CD4+ T cells was not observed in both groups of infected VertX and VertX IL-27Ra-/- mice compared with naïve controls (Fig 4.4 A). The frequencies of Foxp3+ CD4+ T cells in the lungs of infected mice were significantly lower than naïve controls (Fig 4.4 B) which may be due to the accumulation of Foxp3- effector CD4+ T cells in response to infection. IL-27 had no effect on frequencies of Tregs in the BAL or lungs of infected VertX and VertX IL-27Ra-/- mice (Fig 4.4 B). These results suggest that IL-27 signaling does not affect the accumulation of Foxp3+ CD4+ T cells in the airways or lung during influenza infection.     111  Figure 4.4: Effect of IL-27 signaling on the expansion of Foxp3+ CD4+ T cells during influenza infection.  0.05.0 1041.0 1051.5 1052.0 105#Foxp3+ CD4+ T cells***ns01 1032 1033 1034 103#Foxp3+ CD4+ T cellsns051015%FoxP3+ of CD4+ T cellsns *****ns051015%FoxP3+ of CD4+ T cellsA BALBALLungLungVertX  VertX IL-27Ra-/-VertX  VertX IL-27Ra-/-VertX  VertX IL-27Ra-/-VertX  VertX IL-27Ra-/-NaiveInfectedNaiveInfected0.05.0 1041.0 1051.5 1052.0 1052.5 105#Foxp3+ CD4+ T cellsLN 05.0x1051.0 1062.0 106#Foxp3+ CD4+ T cellsSpleen *0.057VertX  VertX IL-27Ra-/- VertX  VertX IL-27Ra-/-0102030 LN FoxP3+ as %CD4+ T cellsVertX  VertX IL-27Ra-/-05101520FoxP3+ as %CD4+ T cellsSpleen *VertX  VertX IL-27Ra-/-nsB  112 (A) Numbers of Foxp3+ CD4+ T cells calculated from total live cells in the lung, draining lymph node and spleen of naïve and PR8 IAV infected VertX and VertX IL-27Ra-/- mice on Day 10 p.i. BAL shows number of Foxp3+CD4+ T cells from only infected groups.  (B) Frequencies of Foxp3+ CD4+ T cells present in the BAL, lung, draining lymph node and spleen of naïve and PR8 IAV infected VertX and VertX IL-27Ra-/- mice on Day 10 p.i. BAL shows frequencies of Foxp3+CD4+ T cells from only infected groups. Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.    4.2.5 IL-27 signaling promotes an increase in proportions of Tbet+ and CXCR3+ Tregs in the airways during influenza infection IL-27 signaling can promote functional specialisation of Tregs in the gut mucosa infection by inducing T-bet and CXCR3 expression in these cells during parasitic T. gondii infection (202). To determine if IL-27 signaling influenced the Th1 adapted phenotype of Tregs during respiratory infection with influenza, we measured T-bet and CXCR3 expression in the BAL and lungs of VertX and VertX IL-27Ra-/- mice. In the BAL, IL-27 signaling resulted in a small but significant increase in the frequencies of Tbet expressing cells while a 30% increase in CXCR3 expressing cells was seen in infected VertX compared to VertX IL-27Ra-/- mice (Fig 4.5 A,C). No change was observed in T-bet or CXCR3 MFI between the infected groups (Fig 4.5 B,D). Interestingly, the lack of IL-27 signaling in the lungs of infected VertX IL-27Ra-/- mice cells led to a 35% reduction in the frequencies of CXCR3+ cells but no appreciable change in T-bet+ cells compared to infected VertX mice (Fig 4.5 F). Taken together, these data suggest that IL-27 signaling leads to an increase in the accumulation of Tregs with a T-bet+ CXCR3+ Th1 adapted phenotype in the airways (BAL) but has a limited effect on Treg phenotype in the lungs of influenza infected mice.   113  Figure 4.5: Effect of IL-27 signaling on T-bet and CXCR3 expression in Foxp3+ CD4+ T cells during influenza infection FACS plots and bar graph show the percentage of (A, E) T-bet+ Foxp3+ CD4+ T cells and (C, G) CXCR3+ Foxp3+ CD4+ T cells in the BAL and lungs of VertX and VertX IL-27Ra-/- mice on Day 10 p.i. Gated on live Foxp3+ CD4+ T cells.  Numbers to the left of the gate in FACS plot BAL 020406080100Tbet as %FoxP3+ CD4+ T cellsLung 020406080CXCR3+ as % Foxp3+      CD4+ T cells42.04 74.24 32.61CD4T-betCD4CXCR313.09 39.9013.63 36.0614.07 57.7419.92 28.72CD4T-betCD4CXCR3A CE G0500100015002000T-bet MFI05001000150020002500CXCR3 MFI0204060Tbet as %FoxP3+ CD4+ T cells020406080100CXCR3+ as % Foxp3+ CD4+ T cells05001000150020002500T-bet MFI0200040006000CXCR3 MFI185733.31980 3890419611971667139115531097162914091710Naive InfectedVertX IL-27Ra-/- Naive InfectedVertX IL-27Ra-/- VertX VertX VertX IL-27Ra-/- VertX * **ns**VertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- Naive InfectedVertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- VertX VertX IL-27Ra-/- B DF H** **  114 indicate the geometric mean channel fluorescence (MFI). T-bet MFI and CXCR3 MFI from Foxp3+ CD4+ T cells in the BAL (B, D)  and lungs (F, H) on Day 10 p.i. BAL shows infected VertX and VertX IL-27Ra-/- groups. Naïve VertX and VertX IL-27Ra-/- mice used as controls . Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.   4.2.6 IL-27 signaling enhances IL-10 expression from Foxp3+ CD4+ T cells in the airways and lungs during IAV infection  To determine if IL-27 signaling promotes IL-10 expression in Foxp3+ cells during influenza infection, we measured the frequencies of IL-10 expressing Tregs in the lungs of VertX and VertX IL-27Ra-/- mice. In the BAL fluid and lung, the absence of IL-27 signaling resulted in a significant decrease in the frequencies of IL-10+ Foxp3+ CD4+ T cells compared to wild-type VertX mice (Fig 4.6 A, B). We also observed that Treg IL-10 was higher in the BAL compared to the lung in both VertX and VertX IL-27Ra-/- mice (Fig 4.6 B). These results indicate that, during primary influenza, IL-27 signaling promotes Treg IL-10, and that the BAL fluid contains more Tregs that express IL-10 compared with the lung.    115  Figure 4.6: Effect of IL-27 signaling on IL-10 expression in Foxp3+ CD4+ T cells during influenza infection.  FACS plots (left) and bar graphs (right) show the percentage of IL-10 GFP+ Foxp3+ CD4+ T cells in the (A) BAL and (B) lungs of VertX and VertX IL-27Ra-/- mice. Naïve VertX and VertX IL-27Ra-/- mice were used as controls. Numbers to the left of the gate in the FACS plots indicate the geometric mean channel fluorescence (MFI). Data are representative of two independent experiments with 4-5 mice per group. Error bars indicate the mean ± SD, *=p <0.05, ** = <0.01, *** = p <0.001.   B58.4 50.14CD4IL-10 GFP24222235A VertX IL-27Ra-/- VertX 1.5424.49CD4IL-10 GFP53433.77169115564.171903VertX IL-27Ra-/- VertX Fig 4.5VertX VertX IL-27Ra-/- 01020304050IL-10 GFP as % Foxp3+ CD4+ T cellsLung *020406080IL-10 GFP as %Foxp3+CD4+ T cellsBAL *VertX VertX IL-27Ra-/- BAL Lung Naive Infected    116 4.3 Discussion IL-27 signalling is a critical regulator of T cell responses and tissue immunopathology during Th1 infection (191, 192, 316, 377) and its regulatory program is imposed on multiple cell types (363). Recently, a new role for IL-27 was identified in promoting the differentiation of a specialised subset of T-bet+ Tregs that were required to limit Th1 inflammation and pathology during parasitic Th1 infection (202). During primary IAV infection, IL-27 is expressed in the lung (208) but its effects on Treg differentiation are not known. In this chapter, I present evidence that IL-27 signalling promotes a functionally specialised phenotype in airway dwelling Tregs by inducing expression of T-bet, CXCR3 and IL-10.  Primary IAV infection elicits both Treg and effector T cell responses. Treg numbers in the lung gradually increase and are highest on Day 7-8 before declining to baseline by Day 14 (110). Effector T cells expansion peaks on Day 10 just after that of maximal Tregs expansion (110). I selected Day 10 as the time point to investigate the functional phenotype of Tregs in the lung because the risk of T cell mediated immunopathology is likely highest at the peak of the effector T cell response (97, 378). At this Day 10 time point, an increase in Treg numbers was observed in the lung but not in the lymph node and spleen which is consistent with previous reports (109, 110). The increase in Treg numbes in the lung was not accompanied by an increase in Treg frequencies which suggests that the Foxp3 negative effector CD4 T cell compartment is also undergoing expansion. These data are consistent with the sharp decline in Treg frequencies and concomitant increase in effector CD4+ and CD8+ T cell frequencies between Day 7-11 shown by Bedoya et al (109).     117 Tregs that express essential immunosuppressive and tissue protective molecules  such as IL-10 and amphiregulin within tissues during infection exhibit an activated CD44hi CD69+ CD62Llo phenotype (379-381). I found that primary influenza infection resulted in a small but significant increase in the frequencies of activated CD44hi CD4+ T cells compared to naïve controls. These observations are consistent with previous studies on influenza and respiratory syncytial virus infection, which showed that Tregs in the lung upregulate CD44, CD69 and CD11a, downregulate CD62L, and express classical surface markers of immunosuppressive Tregs including ICOS, GITR and CTLA4 (109, 382).   The expression of T-bet in Tregs is essential for the suppression of pathogenic Th1 inflammation (372, 373). Consistent with these reports, I observed that Tregs upregulate T-bet in the lungs in IAV infected mice which suggests that a T-bet dependent suppressive program may be employed by Tregs to suppress effector T cell expansion in the lung during primary infection.  Since Treg derived IL-10 is critical for regulating immunity at mucosal surfaces during homeostasis and infection (202, 383) and Il10 mRNA levels are higher in T-bet+ Tregs compared to T-bet- Tregs (373), IL-10 could be a hallmark of functionally specialised Tregs within the lung. When T-bet expression in IL-10+ and IL-10- Treg was compared, I observed a trend towards higher T-bet expression in IL-10+ Tregs in the lung and lymph node which suggests that IL-10 could be an effector molecule used by functionally adapted Tregs to suppress Th1 responses in the lung and lymph node during IAV.   Evidence for IL-27 in promoting a functionally specialised phenotype in Tregs was first reported in a T. gondii infection model where Tregs from IL-27-/- mice expressed less T-bet and CXCR3   118 compared to wild type controls (202). I tested the effect of IL-27 on airway and lung dwelling Tregs during influenza infection using wild type and IL-27Ra-/- mice. IL-27 signalling resulted in an increase in the proportions of T-bet+ and CXCR3+ cells in the BAL which indicates that IL-27 can act as a differentiation signal for Tregs present in the airway lining. Intriguingly, IL-27 had no effect on T-bet expressing cells in the lung, but caused a significant increase in CXCR3 expressing cells wild-type controls compared to IL-27Ra-/- mice.  Since T-bet is known to promote CXCR3 expression (372), this finding could be explained in the following ways. First, IAV infected lung epithelial cells can express high levels of monocyte and dendritic cell recruiting chemokines (384, 385) and these myeloid cells are a source of IL-27 (386, 387). IL-27 is known to induce expression of the CXCR3 ligands CXCL9, CXCL19 and CXCL11 (388) so intact IL-27 signalling in wild type mice would promote the recruitment of more CXCR3 expressing cells into the BAL and lung compared to IL-27Ra-/- mice.  Secondly, previous studies have reported roles for IFNγ and IL-27 in promoting an increase in T-bet+ Tregs in lymphoid organs versus mucosal surfaces such as the lamina propria in the gut (202); therefore, it is possible that IL-27 has a dominant role only at the respiratory mucosa while in the lung tissue, IFNγ secreted by infiltrating immune cells could be driving T-bet expression in Tregs. In future experiments, the amount of IFNγ and IL-27 expressed in the lung and BAL could be quantified by ELISA or qPCR to test the differential, site-specific expression of these cytokines during primary influenza infection. It would also be interesting to determine if CXCR3+ Tregs are able to traffic and interact with effector T cells present at the epithelial surface better that CXCR3- Tregs by confocal microscopy.     119  IL-27 is known to promote IL-10 expression from effector CD4+ T cells during influenza (192) and from Tregs in T. gondii infection (202) but the cellular signals that promote Treg IL-10 expression during IAV infection have not been clearly defined. I observed that IL-27 signaling has a mild but significant effect on the expression of IL-10 in Foxp3+ CD4+ T cells present in the airways and lung tissue during IAV. This finding indicates that IL-27 can modulate IL-10 induction from Foxp3+ T regulatory cells during respiratory infection. Previous studies have shown that the transcriptional program in T-bet+ Tregs (373) included increased Il10 mRNA expression suggesting that IL-27 mediated functional specialisation program in Tregs involves IL-10 expression in addition to T-bet for the suppression of Th1 specific inflammation in the lung.  It should be noted that in the absence of IL-27, more than 40% of the Foxp3+ cells in the lung could still express IL-10 indicating that in addition to IL-27, other signals such as ICOS (389), CD44 crosslinking (390) and TCR signalling (391) could increase IL-10 expression.    IL-27 signalling had no effect on the numbers or frequencies of Tregs present in the BAL, lung or lymphoid organs during influenza infection. This finding was in contrast to previous studies which have shown that IL-27 promotes Treg development (202) and survival (187). However, other conflicting reports have shown that IL-27 can inhibit the generation of Tregs in vitro (324) and in a colitis model (392). In yet another study, the Treg specific deletion of IL-27Ra caused an increase in Tregs infiltrating the spinal cord during EAE but in this same model IL-27 signalling also promoted the stable expression of Foxp3 in Tregs  (393). While the inhibitory effect of IL-27 on Treg numbers may be explained in part by the ability of IL-27 to suppress IL-2 and Il2ra expression in Tregs (394), both of which are essential for Treg development and survival (368, 395), there is still a lack of understanding about the role of IL-27 in Treg   120 generation. The use of an inducible Treg specific IL-27Ra knockout mice (Foxp3Cre-ERT2 IL-27Rafl/fl) would enable a more robust study on the effect of IL-27 on Treg development before and during infection.     In conclusion, our findings highlight the effect of the inflammatory environment on Treg differentiation and identify a new role for IL-27 in promoting a functionally specialised phenotype in airway Tregs during primary IAV infection.          121 Chapter 5: Mapping histone modification dynamics in CD4+ T cells responding to influenza infection  5.1 Introduction Exposure to influenza results in the rapid proliferation and expansion of effector CD4+ T cells with pro- and anti-inflammatory functions which act to promote viral clearance and to limit lung damage  (97, 105, 111, 396-398). Once the virus is cleared, responding CD4+ T cells then undergo a contraction phase which results in the generation of a memory CD4+ T cell pool (224, 246, 397, 399). Upon repeat exposure to influenza infection, these long term memory CD4+ T cells  (254, 256, 258) differentiate into secondary effector cells that offer superior protection compared to primary effectors (261, 266, 268). While naïve, effector and memory CD4+ T cells have been extensively profiled in terms of their function and phenotype, the molecular mechanisms that control their differentiation during and after IAV infection are not well understood.   The gene expression program that underlies CD4+ T cell differentiation from naïve to effector stage is controlled by transcription factors (TFs) such as STAT proteins and lineage-defining master transcription factors which are activated in response to environmental stimuli (400-402). Another layer of regulation exists at the epigenetic level where DNA modifications such as methylation and histone tail modifications demarcate chromatin regions as ‘open’ or ‘closed’ to transcriptional machinery (403). Global profiling of histone modifications using ChIP Seq has shown that cis-regulatory regions such as gene promoters and enhancers are marked by signature   122 histone modifications that can distinguish active, repressed, bivalent or poised chromatin states (285, 287, 292, 355). H3K27Ac can also be used to identify super-enhancers which are associated with key genes that control cell identity (304). In CD8+ T cells responding to influenza, dynamic changes in H3K4me3 and H3K27me3 at the promoters of lineage-defining genes such as Ifng, Tbx21 and Gzmb highlight the underlying role of epigenetic regulation in the T cell response to infection (342). Further, global mapping of enhancers using H3K4me1 and H2K27Ac in CD8+ T cells has shown that a cell type specific enhancer repertoire is associated with changes in gene expression in naïve, effector and memory cells (296). The construction of transcription factor networks based on these cell type specific enhancers have identified new regulators of CD8+ T cell differentiation (296, 404). In contrast, our current knowledge of histone modification dynamics in CD4+ T cells is limited to studies on in vitro differentiated T helper subsets where lineage specific histone modifcation changes at promoter, enhancer and super enhancer regions have been identified (294, 297, 341). The histone modification dynamics that accompany the CD4+ T cell response to infection in vivo have not been investigated. We hypothesised that histone modification dynamics at promoter and super-enhancer loci would identify genes encoding key regulatory molecules that establish naïve, effector and memory states in CD4+ T cells following influenza infection.   To test our hypothesis, we first used RNA Sequencing to profile gene expression changes in naïve and IAV antigen specific primary, memory and secondary CD4+ T cells. We identified clusters of genes with temporal expression patterns that confirmed the stage specific differentiation of naïve, effector and memory cells. Next, we carried out histone mark chromatin immunoprecipitation and deep sequencing on H3K4me3, H3K27me3, H3K4me1 and H3K27Ac   123 to identify the dynamic changes in histone modifications that accompany the CD4+ T cell response to influenza. In order to understand the relationship between gene expression and histone modification dynamics we then combined RNA-Seq and ChIP-Seq datasets. We found that temporal gene expression patterns were associated with changes in histone modifications at gene promoters. We also found that target genes of super-enhancers encode key lineage specific determinants in naïve, effector and memory CD4+ T cells. This epigenomic study serves as a starting point for the in-depth delineation of molecular mechanisms that underlie the CD4+ T cell response to infection.   5.2 Results 5.2.1 Generation of naïve and influenza specific CD4+ T cell gene expression datasets To measure gene expression changes in CD4+ T cells at each stage of influenza infection, we sorted and extracted RNA from naïve and influenza NP specific CD4+ T cells at primary, memory and secondary time points. The experimental outline and sorting strategy used is illustrated in Fig 5.1 and Fig 5.2. High throughput RNA sequencing was performed on these samples as detailed in Materials and Methods Section 2.6.2. Briefly, naive (CD44lo) and influenza specific FluNP+CD44hi CD4+ T cells were sorted from pooled samples of lungs and lymph-nodes from infected age and sex matched C57BL/6 mice at primary (D10), memory (D30) and secondary (D6, post re-challenge) timepoints. Our sort purity ranged from ~70-100% across samples (Fig 5.2). RNA was then extracted from single replicate samples followed by construction of ribodepleted cDNA libraries. RNA sequencing was carried out on an Illumina platform followed by alignment and quality control (Section 2.6.2). The mapping statistics for the four RNA Seq datasets generated are displayed in Table 5.1 and meet expected mapping   124 quality standards (405). To compare gene expression across cell types, raw read counts were normalised using the RPKM method (reads per kilobase million) which takes into account sequencing depth and gene length. Normalised gene expression is reported as RPKM in this study.    Figure 5.1: Overview of experimental design Primary infection: C57/Bl6 mice were infected with 5 pfu PR8/H1N1 Influenza A virus. On Day 10, CD4+ T cells are isolated from the BAL, lung, LN and spleen. Memory: Mice infected with 5 pfu PR8/H1NI were allowed to recover from influenza infection until Day 30-35 post infection. Secondary infection: Mice infected with 50 pfu x31/H3N2 were allowed to recover until Day 30-35 and then re-challenged with 5 pfu of heterosubtypic PR8/H1N1. Naïve and influenza specific CD4+ T cells were sorted from the lungs and lymph nodes of C57/Bl6 mice at the indicated times points. Gene expression profiling was performed by RNA-Seq. ChIP-Seq was used to assess the genome wide distribution of H3K4me3, H3K27me3, H3K4me1 and H3K27Ac. This experiment was performed once with NP-specific cells pooled from a sample size of 30 mice for each primary and secondary time-point. For the memory time point, NP-specific cells were pooled from 75 mice and the experiment conducted once.   Influenza A virus Memory1º infection 2º infectionRechallenge with heterosubtypic              Influenza A strain(Day 10) (Day 30-35) (Day 6)C57Bl/6 mouse             Naive            (CD44lo        CD4+ T cells)1º  effectors(Flu NP+ CD44hi CD4+ T cells)   Memory (Flu NP+ CD44hi CD4+ T cells)2º effectors(Flu NP+ CD44hi CD4+ T cells)Low input native ChIP Seq RNA SeqH3K4me3H3K27me3H3K4me1H3K27Ac}10,000 cells/histone mark   125  Figure 5.2: Flow cytometric sorting of naïve and influenza specific CD4+ T cells from the lung.  Initial gates (A-C) selected for live singlet CD4+ lymphocytes followed by gating on CD44 (D) to identify CD44hi and CD44lo populations. (E) MHC Class II tetramers displaying restricted IAV peptide nucleoprotein (FluNP) identified influenza specific (FluNP+) CD4+ T cells at primary, memory and secondary stages of infection. CD44lo CD4+ T cells from secondary time point were sorted as the naïve population. (F) Percentage of purified naïve and influenza specific CD4+ T cells obtained after sorting.      FSC-ASSCFSC -WSSCCD44FluNP6.69CD4Live DeadCD44CD4CD4PICD44CD4CD44FluNPFSC- ASSCFSC-WSSCCD4PICD44CD4CD44FluNPFSC-WSSCFSC- ASSC4.53PrimaryMemorySecondary24.826.147081.6369.44100CD44FluNPCD44FluNPCD44FluNPCD44FluNPA B C D E FNaiveGHLymphocytesSingletsLive CD4 CD44lo/hiFluNP+LymphocytesLymphocytesSingletsSingletsLive CD4Live CD4FluNP+FluNP+Naive CD44lo/hi CD44lo/hi  126  Naive Primary Memory Secondary Total reads  236979542  131266046  167425794  273332200  Uniquely mapped  204835683  127227059  131470131  176091342  % uniquely mapped  86.44  96.92  78.52  64.42  Table 5.1: Mapping statistics of naïve, primary, memory and secondary CD4+ T cell RNA Seq datasets.    5.2.2 Transcriptional profiling of the CD4+ T cell response to influenza infection First, to obtain a general overview of the distribution of the gene numbers at different expression levels, histograms showing the gene expression distribution of all 21767 protein coding genes were plotted (Fig 5.3A). We observed that naïve, primary, memory and secondary CD4+ T cells had similar RPKM distributions however the memory sample uniquely had an additional sharp peak around ~0.1 RPKM. We therefore applied an RPKM threshold across all samples which selected for genes with an expression of 1 RPKM in at least one cell type as shown in Fig 5.3B. This 1 RPKM threshold was used for all further RNA Seq analysis.      127  Figure 5.3: Application of 1 RPKM threshold to gene expression.  Histogram distribution of the number of genes (Y-axis) at the expression level indicated by RPKM on the X-axis for (A) 21767 protein coding genes and (B) 11197 genes filtered by a > 1 RPKM threshold.    To determine if variation in global gene expression could distinguish naïve, primary, memory and secondary CD4+ T cells, we performed multi-dimensional scaling (MDS) which clusters objects in a two-dimensional space. As shown in Fig 5.4,  MDS analysis demonstrated that primary and secondary CD4+ T cells cluster together while naïve and memory CD4+ T cells form distinct groups. These results suggested that primary and secondary CD4+ T cells possess similarities in their transcriptional profiles when compared with naïve or memory cells which represent distinct CD4+ T cell genotypes.  A B  128                           Figure 5.4 Multidimensional scaling of naïve and influenza specific CD4+ T cells.  Dimension 1 and Dimension 2 separate the four RNA Seq libraries based on the expression values of 11197 genes.    CD4+ T cells and other immune cells express an array of genes that encode cell surface antigens, transcription factors and effector molecules that can be used to distinguish different immune cell types. With this in mind, we used a combination of signature phenotypic markers to determine if the sequenced naïve, primary, memory and secondary CD4+ T cells expressed genes commonly associated with their phenotype and function. First, we first verified gene expression of lineage specific markers in CD4+ and non-CD4+ T cell types including CD8+ T cells, B cells, myeloid and granulocytic cells as displayed in Fig. 5.5. We observed very low expression of genes Cd14, Cd19, Cd8a, Itga2, Fcgr3, Ly6g and Siglec5 which encode the non CD4+ T cell markers CD19, CD8, CD49b, FCRγ3, Ly6G and SiglecF while Cd3e (CD3), Cd4 (CD4) and Itgae (CD103) known to be expressed in CD4+ T cells had high expression values. These observations indicate   129 low contamination from other immune cell types in the RNA Seq datasets and are indicative of an enriched CD4+ T cell population in naïve, primary, memory and secondary cell samples.    Figure 5.5: Gene expression (RPKM) of CD4+ T cell and non CD4+ T cell markers in naïve, primary, memory and secondary CD4+ T cells.  Note differences in scale on the y-axes.    We also profiled the gene expression of known T cell cytokines and effector molecules (Fig 5.6A), phenotypic markers (Fig 5.6B) and transcription factors (Fig 5.6C) to verify naïve, effector and memory cell identities. We found that genes encoding canonical inflammatory CD4+ T cell cytokines and effector molecules such as Granzyme B (Gzmb), IFNγ (Ifng), IL-10 (Il10), TGFβ (Tgfb1) and TNF (Tnfa) were highest in primary and secondary CD4+ T cells compared to naïve cells. In contrast, relatively low expression of Th17 specific (Il17), Th2 (Il4) and Tfh specific (Il21) cytokine genes was observed. Naïve CD4+ T cells exhibited high level expression of genes encoding CCR7 (Ccr7) and CD62L (Sell) which were lower in effector and memory CD4+ T cells although memory cells exhibited higher expression compared to effector cells. Interestingly, Cd127 which encodes the IL-7Ra subunit was highest in naïve CD4+ T cells, downregulated in primary and increased in memory and also secondary CD4+ T cells.  Prf1 Cd14 Cd19 Cd3e Cd4 Cd8a Fcgr3 Itga2 Itgae Ly6g Siglec5naiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondary0.00.10.20.30.40.50.000.050.100.150102030012340.00.51.01.52.00.00.51.01.50100200300400020040060002460369RPKM  130 (Perforin) displayed highest expression in naïve CD4+ T cells compared to effector or memory cell types. Genes encoding activation markers such as CD44 (Cd44), CD69 (Cd69), CD25 (Il2ra) and KLRG1 (Klrg1) were highly expressed in effector and memory cells compared to naïve. Gene expression of the Th1 lineage specific transcription factor CXCR3 (Cxcr3) was lowest in naïve CD4+ T cells, highest in effector cells and decreased in memory CD4+ T cells. Gene expression of transcription factors required for naïve T cell maintenance and survival such as Lef1 (Lef1), Tcf1 (Tcf7) and IRF1 (Irf1) were elevated in naïve CD4+ T cells while genes encoding transcription factors necessary for effector T cell differentiation such as Tbx21, Runx3, Id2 and Elf4 were elevated in effector CD4+ T cells. Taken together, these results indicated that these naïve, primary, memory and secondary CD4+ T cell datasets expressed the appropriate activation and lineage specific CD4+ T cell markers and were therefore suitable for downstream analysis.      131  Figure 5.6: Gene expression (RPKM) of key cytokines (A), phenotypic markers (B) and transcription factors (C) in naïve, primary, memory and secondary CD4+ T cells.      Ccr7 Cd127 Cd44 Cd62l Cd69 Cxcr3 Il2ra Klrg1naiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondary0510152002550751000306090010020001002000501001500501001502002500100200300400500RPKMGzmb Ifng Il10 Il17 Il2 Il21 Il4 Prf1 Tgfb1 Tnfanaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondary02550750501001500.02.55.07.510.00.000.250.500.751.000.02.55.07.510.00123024680306090050100150200250050100150RPKMBcl2 Elf4 Eomes Foxp3 Gata3 Id2 Id3 Irf1 Irf4 Lef1 Rorc Runx3 Tbx21 Tcf7naiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondarynaiveprimarymemorysecondary0100200300050100150200020400.00.51.01.52.00501001500255075100010020030002460100200300010203040010203001230204060800204060RPKMABC  132 5.2.3 Memory CD4+ T cells upregulate a unique set of genes compared with naïve or effector cells In order to quantify significant gene expression changes across naïve, primary, memory and secondary CD4+ T cells, differential gene expression analysis was carried out using DEfine using pairwise comparisons between the four cell types. The numbers of differentially expressed genes in each pairwise combination is displayed in Fig 5.7. We observed a large number of upregulated genes in primary (795 genes), memory (1010) and secondary (685 genes) cells compared to naïve cells which indicates their transcriptionally active state.  Interestingly, memory CD4+ T cells upregulated a higher number of genes than either primary or secondary cells compared to naïve cells. To better understand the gene expression profile of memory cells, we identified genes that were upregulated in primary, memory and secondary CD4+ T cells (422 genes) or uniquely upregulated in memory cells (387) (Fig 5.8A) and carried out a gene ontology analysis. Genes commonly upregulated in primary, memory and secondary CD4+ T cells encoded well known molecules associated with effector T cells (Ifng, Tbx21, Ccr8, Ccl4, Ctla4, Lag3, Lgals1, Pdcd1) and were highly enriched in GO terms such as inflammatory response, regulation of cell adhesion and cell chemotaxis (Fig 5.8B). In contrast, genes uniquely upregulated in memory were significantly enriched in GO terms related to regulatory function such as leukocyte differentiation, the regulation of cytokine production and the regulation of the immune response  (Fig 5.8C). Notable examples of genes uniquely upregulated in memory cells compared to primary and secondary cells include Ccr9, Dgat2, Eomes and Klf4 which are known to participate in migration of Th1 cells (406) and lipid metabolism (407) and formation of memory CD8+ T cells (230). Together these results suggest that genes upregulated in memory   133 CD4+ T cells consist of a subset shared with effector cells as well as a subset of genes that uniquely defines the memory stage.                          134  Figure 5.7: Differentially expressed genes in pairwise comparisons of naïve, primary, memory and secondary CD4+ T cells. −4−202−4 −2 0 2log10(RPKM)Naivelog10(RPKM)PrimaryUP:795NC:10208DN:19402460 2 4 6log10(RPKM) Secondarylog10(RPKM) MemoryUP:335NC:10829DN:33−4−202−4 −2 0 2log10(RPKM) Naivelog10(RPKM) SecondaryUP:685NC:10368DN:14502460 2 4 6log10(RPKM) Primarylog10(RPKM) MemoryUP:255NC:10763DN:179−4−202−4 −2 0 2log10(RPKM) Primarylog10(RPKM) SecondaryUP:62NC:10802DN:33302460 2 4 6log10(RPKM)Naivelog10(RPKM)MemoryUP:1010NC:10026DN:119A BC DE F  135 (A-F) Red dots ( ) denote genes upregulated (UP) while blue dots ( ) denote genes downregulated (DN) in the cell type displayed on the Y-axis compared with the cell type on the X-axis. Grey dots indicate no change (NC) in expression.      136  Figure 5.8: Memory CD4+ T cells upregulate a unique set of genes.  ABCEffector and memoryMemory  137 (A) UpsetR plot showing the intersection of genes upregulated in memory cells (red box, 387 genes) alone or in effector and memory CD4+ T cells (green box, 422 genes). Panel (B) and (C) shows gene expression (RPKM), statistically significant Gene Ontology results from Metascape and expression (in RPKM) of genes of interest for each comparison described in (A).    5.2.4 Identification of temporal gene expression patterns in CD4+ T cells following influenza infection  The CD4+ T cell response to influenza is divided into distinct stages of differentiation that include activation, expansion, contraction and recall (397). When all differentially expressed genes were combined in a heatmap as shown in Fig 5.9A, we observed stage specific gene expression patterns that reflected the in vivo dynamics of the CD4+ T cell response. In order to identify temporal gene expression trends in naïve, primary, memory and secondary CD4+ T cells, we used the Short Time Series Expression Miner (STEM) algorithm (408)which clustered all 1885 differentially expressed genes into 5 expression clusters. Fig 5.9B displays the gene expression profile of each STEM-identified cluster and Fig 5.9 C shows that differences in gene expression associated with each profile are significant.     138  Figure 5.9: Transcriptional profiling identifies five major temporal gene expression profiles in the CD4+ T cell response to influenza.  ACBZ scoreAll differentially expressed genes(557) (437) (196)(126) (35)  139 (A) Z-score scaled heatmap of 1885 differentially expressed genes in at least one comparison. (B) Expression profiles of the five clusters identified by STEM algorithm. Line plots show the average expression of genes in each cluster across naïve (N), primary (P), memory (M) and secondary (S) CD4+ T cells. Numbers of genes in each cluster shown in brackets next to cluster name. (C) Box and violin plots of gene expression in each STEM cluster with p-values calculated by Student’s t-test indicating statistical significance across cell types.  Cluster 1 contained genes upregulated in primary and secondary CD4+ T cells compared with naïve cells and memory cells (Fig 5.9B,C). Genes encoding well known Th1 associated molecules such as T-bet, IFNγ, and CXCR3 were found in this cluster (Fig 5.10). Cluster 2 contained genes that were upregulated in effector and memory cells compared to naïve but with average expression higher in memory (Fig 5.9C).  Klf4, Il15 and Eomes which are known to regulate memory formation (230, 409, 410) were found in this cluster (Fig 5.10). Cluster 3 was representative of genes that were low in naïve cells but had higher expression in primary cells compared with memory or secondary cells (Fig 5.9C). Genes within this cluster included Arg2, Cd14 and Clec4e (Fig 5.10). Cluster 4 contained genes that were upregulated in naïve cells compared to effector or memory cells (Fig 5.9C). Genes with well-known roles in T cell development such as Tcf7, Sell, Lef1 and Ccr7 were found in this cluster (Fig 5.10). Cluster 5 contained genes with an off-on-off pattern in naïve, primary, memory and secondary CD4+ T cells (Fig 5.9C). Genes encoding the Th1 effector cell specific transcription factor Id2 (411) and inflammasome related protein Pycard (412) were present in this cluster (Fig 5.10).  Overall, our transcriptional profiling of CD4+ T cells identified genes encoding molecules with established functions in T cells and identified new dynamically regulated target genes that may play important roles in the CD4+ T cell response to influenza infection.    140  Figure 5.10: Representative genes within STEM clusters identified in naïve, primary, memory and secondary CD4+ T cells.  Each heatmap shows the Z-score of gene expression (RPKM) of the indicated genes within a STEM identified cluster across the four cell types in this study.      Cluster 4 Cluster 5Cluster 1 Cluster 2 Cluster 3naiveprimarymemorysecondary naiveprimarymemorysecondarynaiveprimarymemorysecondary naiveprimarymemorysecondary naiveprimarymemorysecondaryArg2Cd14CebpbClec4eCsf1rDgat1HckIcoslLrp1Nlrp3EomesFoxp3Il15Irf8Klf4LtfLynSmad3Socs5Hist1h1dHist3h2baId2Isg20Plscr1PycardStmn1Sytl1Txn1Bcl2l1Cxcr3IfngIl12rb1Il21Itga4Lgals3MafPdcd1Tbx21Amigo2Ccr7Cd55Lef1S1pr1Satb1SellSox4St6gal1Tcf7Representative genes Z−score−1.0−0.50.00.51.0  141 5.2.5 Generation of histone modification ChIP Seq datasets from naïve, primary, memory and secondary CD4+ T cells following influenza infection Having identified temporal gene expression changes during the CD4+ T cell response to influenza, we wanted to examine the link between histone modifications at cis-regulatory regions such as promoters and enhancers and gene expression in naïve, primary, memory and secondary CD4+ T cells. We generated genome wide maps of H3K4me3, H3K27me3, H3K4me1 and H3K27Ac in naïve, primary, memory and secondary CD4+ T cells by using ChIP Seq,  following an identical experimental and sorting strategy to that of our RNA Seq experiments (Fig 5.1 and Fig 5.2). A summary of the ChIP Seq libraries and the mapping statistics is displayed in Table 5.2. Cell type Epigenetic mark Total number of reads Mapped reads % mapped reads Naïve H3K4me3 46785754 44159012 94.39 Primary H3K4me3 44463010 39843481 89.61 Memory H3K4me3 55085409 54275666 98.53 Secondary H3K4me3 32611040 30420571 93.28 Naïve H3K27me3 97745100 85798537 87.78 Primary H3K27me3 112937128 100354093 88.86 Memory H3K27me3 105489750 104575090 99.13 Secondary H3K27me3 84565242 81465691 96.33 Naïve H3K4me1 86402766 84056951 97.29   142 Primary H3K4me1 119178956 105888513 88.85 Memory H3K4me1 97984096 97070445 99.07 Secondary H3K4me1 124581384 121443016 97.48 Naïve H3K27Ac 17892010 14573358 81.45 Primary H3K27Ac 44721224 37442887 83.73 Memory H3K27Ac 44234944 42866633 96.9 Secondary H3K27Ac 12591590 10351784 82.21 Table 5.2:  Summary of ChIP Seq libraries generated for this study.  5.2.6 Naïve to effector or memory transition in CD4+ T cells is marked by an increase in active promoters  In our first analysis of cis-regulatory regions, we profiled histone modification changes occurring at gene promoters in naïve, primary memory and secondary CD4+ T cells. Promoters (±2 kb from TSS) were divided into active (H3K4me3), suppressed (H3K27me3) or bivalent (H3K4me3 and H3K27me3) based on the significant enrichment of H3K4me3, H3K27me3 and H3K27Ac signal (see Section 2.6.3 of Materials and Methods) (Fig 5.11A). When the expression of genes whose promoters were marked by histone modifications in each cell type were calculated (Fig 5.11B), we observed a significant increase in expression associated with active gene promoters while repressed and bivalent promoters were associated with a decrease in gene expression in all cell types. We observed a net gain of active gene promoters in primary (876), memory (1306) and secondary (874) cells compared to naïve (Fig 5.12A) while a net loss in primary (486), memory (1211) and secondary (313) suppressed promoters occurred (Fig 5.12C).  A less striking   143 but similar trend to repressed promoters was observed at bivalent promoters with primary (85), memory (252) and secondary cells (5) losing bivalency (Fig 5.12E). The overall gain or loss of active, suppressed or bivalent promoters as CD4+ T cells transition from naive to primary, memory or secondary stages is shown in Fig 5.13.  We used the UpsetR package in R to calculate the numbers of active, bivalent or suppressed promoters that were common or unique to naïve, primary, memory and secondary CD4+ T cells (Fig 5.12 B, D, E). Within the set of active promoters, ~ 90% of the promoters (1026) (Fig 5.12B) were shared by primary, memory and secondary cells while the number of uniquely suppressed promoters (933) (Fig 5.12D) or bivalent promoters (388) (Fig 5.12F) was highest in naïve cells. When the net gain or loss of active, suppressed or bivalent promoters in primary, memory and secondary cells was plotted (Fig 5.13), we observed that memory cells displayed the highest increase in active promoters and this was associated with a decrease in suppressed and bivalent promoters. Together, these results suggested that naïve CD4+ T cells are associated with a bivalent or repressive promoter signature but as naïve cells transition to effector or memory state, a common subset of gene promoters becomes active in primary, memory and secondary cells. In addition, in memory cells, gene promoters are in a more permissive epigenetic state than primary or secondary CD4+ T cells.      144  Figure 5.11: Global histone methylation at gene promoters and gene expression in naïve and influenza specific primary, memory and secondary CD4+ T cells.  Gene promoters were divided into active, bivalent and suppressed states based on enrichment and overlap of H3K4me3 and H3K27me3 signal. (A) The proportion of each active, suppressed or bivalent promoter was calculated within each cell types. Promoters not identified by BivalentSuppressedActiveUnmarkedAB< 2.2e−165.1e−06< 2.2e−16< 2.2e−160.64< 2.2e−16< 2.2e−164.9e−08< 2.2e−16< 2.2e−161e−07< 2.2e−16naive primary memory secondaryActiveBivalentSuppressedActiveBivalentSuppressedActiveBivalentSuppressedActiveBivalentSuppressed−2024−2024−2024−2024log10(RPKM)  145 significant enrichment of either H4K4me3 or H3K27me3 mark were labelled as Unmarked. (B) The expression of genes associated with each promoter state within each cell type was calculated and significance of gene expression changes between active, bivalent or suppressed promoters determined by Student’s t test. The number above the bar reports the p-value for the indicated comparison.    146  Figure 5.12: Enumeration of promoter state in CD4+ T cells following influenza infection.  ActiveSuppressed81321026400 178 124 116 28 25 13 7 6 5 5 20250050007500Intersection SizeSecondaryMemoryPrimaryNaive   025005000750010000Set SizeABivalent805388336213 209 184 17667 30 26 11 11 10 10 60250500750Intersection SizeSecondaryMemoryPrimaryNaive   050010001500Set SizeActiveBivalentSuppressedGainLossIntersection sizeIntersection sizeGainLossGainLossCEBDF1802933449 420315 240 211 185 168 161 145 143 80 65 540500100015002000Intersection SizeSecondaryMemoryPrimaryNaive   01000200030004000Set SizeIntersection size  147 Barplots show the gain or loss of active (A), suppressed (C) and bivalent (E) promoters in primary, memory and secondary CD4+ T cells compared to naive. UpsetR plots display the number of promoters (Y-axis) present in the indicated intersections (X-axis) of active (B), suppressed (D) and bivalent (F) promoters in naïve, primary, memory and secondary CD4+ T cells.                                                           Figure 5.13:  Summary plot of net increase or decrease in bivalent, active or suppressed promoters. Y-axis shows the number of promoters that gain or lose the indicated promoter state  in primary, memory or secondary CD4+ T cells compared with naïve cells.        BivalentActive    SuppressedNumber of promoters –Number of naïve promoters  148 5.2.7 Dynamic changes in repressed and bivalent promoter states are associated with naïve to effector and memory transition in CD4+ T cells Since we observed an increase in active promoters in primary, memory and secondary cells compared to naïve, we determined the proportion of promoters that switched their state in naïve cells from bivalent, repressed or unmarked to active. We tracked the percentage change of active, repressed or bivalent promoters in naïve cells across primary, memory and secondary CD4+ T cells (Fig 5.14). 8746 active promoters in naïve cells were largely unchanged in primary, memory and secondary cells but a small ~2% increase in the percentage of bivalent promoters were observed in primary and secondary CD4+ T cells. Approximately 13% of bivalent promoters in naïve cells switched to an active state in primary and secondary CD4+ T cells while 20% switched to suppressed in primary and secondary cells. Memory cells displayed a slightly greater increase in active promoters than primary or secondary cells with a 21% gain relative to naive. Approximately 30% of genes with suppressed promoters state in naïve cells switched to an unmarked state while ~1% percent gained bivalent or active marks in primary, memory and secondary cells. Together, these results demonstrate that at the global level, active gene promoters in naïve cells are largely maintained across primary, memory and secondary cells while repressed and bivalent states are subject to more dynamic changes in effector and memory CD4+ T cells.     149  Figure 5.14: Histone modification dynamics in naïve, effector and memory CD4+ T cells.  NaïveActiveNaïveBivalentNaïveSuppressedABC  150 Stacked bar-plots show the percentage change of 8746 active (A), 4000 suppressed (B) or 1672 bivalently (C) marked promoters in naïve cells across primary, memory and secondary CD4+ T cells.     In the next stage of analysis, we asked whether histone modification changes occurred at promoters of genes important for CD4+ T cell function. Since, effector and memory cells upregulate expression of inflammatory response genes following influenza infection, we hypothesised that the set of promoters that switched from bivalent or suppressed in naïve to active in primary, memory and secondary cells would contain genes with immune related function. To test this hypothesis, we identified genes that displayed a suppressed-active-active-active transition (Fig 5.15) or bivalent-active-active-active (Fig 5.16) in naïve, primary, memory and secondary CD4+ T cells respectively. We observed a significant increase in gene expression associated with promoters that went from suppressed to active (Fig 5.15A) and these genes were enriched in GO terms related to CD4+ T cell function such as regulation of tumour necrosis factor superfamily cytokine production and leukocyte apoptotic process (Fig 5.15B). Examples of genes within these GO terms, include the Th1 specific cytokine IFNγ (Ifng), Th1 associated inhibitory molecule Tim3 (Havcr2)(413)  and cytotoxicity associated molecule NKG7 (Nkg7) (414) (Fig 5.15 C-E). Gene promoters that switched from bivalent to active (Fig 5.16) were also associated with a significant increase in gene expression (Fig 5.16A) in effector and memory cells compared to naïve. We did not observe a significant enrichment for immune related GO terms (Fig 5.16B) which suggested that genes with immune function are not highly represented within the group of gene promoters that switch from bivalent to active state. However, the   151 promoter region of Tbx21, the gene encoding T-bet, is known to be marked with both H3K4me3 and H3K27me3 in naïve cells which then lose H3K27me3 upon Th1 polarisation (291). Although present but not significantly enriched within the GO term: response to interferon alpha, we found that the Tbx21 gene promoter was indeed marked as bivalent in naïve cells and active in primary, memory and secondary CD4+ T cells (Fig 5.16C). We also identified genes encoding the chemokine receptor Ccr2 and inhibitory receptor Lag3 (Fig 5.16 D, E) that followed a similar switch from bivalent to active as Tbx21. These observations indicate that distinct histone modification patterns can identify genes encoding key Th1 associated molecules that are marked by an increase in permissive histone modifications at their promoter regions in effector and memory CD4+ T cells.                 152  Figure 5.15: Temporal changes in H3K27me3 and H3K4me3 at gene promoter regions occur at  key Th1 specific genes. Havcr2ABC DE1.8e−067.3e−062.6e−06−2024naiveprimarymemorysecondarylog10(RPKM)IfngNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryH3K4me3H3K27me3NaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNkg7H3K4me3H3K27me30 2 4 60.0 0.5 1.0 1.5apoptotic signaling pathwaymyeloid leukocyte activationregulation of establishment of protein localization to mitochondrionresponse to interferon−alphacellular response to heattelencephalon developmentorganic hydroxy compound metabolic processregulation of membrane protein ectodomain proteolysisleukocyte apoptotic processregulation of tumor necrosis factor superfamily cytokine production−log FDR q−value)H3K27me3H3K4 3Ifng Havcr2Nkg7H3K27me3H3K4me3  153 (A) Expression levels of genes whose promoters transition from suppressed in naïve cells to active in effector and memory cells. (B) Top 5 gene ontologies enriched in this data set. C-E Genome browser tracks of the ChIP Seq signals for H3K4me3 (red) and H3K27me3 (brown) within a 2kb of the TSS defined as the promoter region in naïve, primary, memory and secondary CD4+ T cells.     154  Figure 5.16: Temporal change in which promoters move from bivalent to H3K4me3 identify the Th1 transcription factor Tbx21.  < 2.2e−16< 2.2e−16< 2.2e−16−2024naiveprimarymemorysecondarylog10(RPKM)ABC DETbx21NaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryCcr2Lag3NaivePrimaryMemorySecondaryNaivePrimaryMemorySecondary0 2 4 60.0 0.5 1.0 1.5apoptotic signaling pathwaymyeloid leukocyte activationregulation of establishment of protein localization to mitochondrionresponse to interferon−alphacellular response to heattelencephalon developmentorganic hydroxy compound metabolic processregulation of membrane protein ectodomain proteolysisleukocyte apoptotic processregulation of tumor necrosis factor superfamily cytokine production−log FDR q−value)H3K27me3H3K4me3H3K27me3H3K4me3Tbx21 Ccr2Lag3H3K27me3H3K4me3NaivePrimaryMemorySecondaryNaivePrimaryMemorySecondary  155 (A) Expression levels of genes associated with bivalent promoters in naïve cells and H3K4me3 marked promoters in effector and memory cells. (B) Top 5 gene ontologies enriched in association with bivalent promoters in naïve and  H3K4me3 promoters in effector and memory cells. C-E Genome browser tracks of the ChIP Seq signals for H4K4me3 (red) and H3K27me3 (brown) within the 2kb promoter region in naïve, primary, memory and secondary CD4+ T cells.     5.2.8 Distinct temporal gene expression profiles are marked by changes in histone modifications at promoters Having identified clusters that displayed temporal changes in gene expression (Fig 5.9), we then determined if dynamic changes in histone modifications at promoters were associated with these clusters. Therefore, we merged the five temporal expression profiles identified in Section 5.2.4 with H3K4me3 (active), H3K27me3 (suppressed) and bivalent (H3K4me3 and H3K27me3) in naïve, primary, memory and secondary CD4+ T cells as shown in Figure 5.17B. In Cluster 1, we observed a gain in active gene promoters that matched the increase in gene expression observed in primary, memory and secondary cells compared to naive. Interestingly, although the genes identified in this cluster displayed an average decrease in gene expression in memory cells compared to effectors (Fig 5.17A), the promoter regions of memory cells remained active and did not show increased in bivalent or suppressed promoters. Genes with this ‘primed’ epigenetic state included Bhlhe40, Ccl3, Gzmb, Havcr2, Hk2 and Il10 (Appendix B, Fig 7.2). Fig 5.18 A-B shows H3K4me3 and H3K27me3 enrichment at the promoters of  the primed genes Ccl3 and Gzmb.    Cluster 2 and 3 showed a similar albeit less striking gain in active promoters as Cluster 1 in primary, memory and secondary CD4+ T cells. In Cluster 4 where gene expression decreased in   156 primary, memory and effector cells compared to naïve, we observed a corresponding decrease in active promoters in primary and secondary CD4+ T cells. Unlike Cluster 1, the small increase in Cluster 4 gene expression in memory cells when compared to primary and secondary cells was associated with the gain of active promoters. In Cluster 5, we did not observe changes in promoter state that match the off-on-off-on pattern of gene expression. Previously, we showed that Clusters 1, 2 and 3 are enriched in GO terms and genes that promoter effector and memory function in CD4+ T cells while Cluster 4 contains genes that promote the development and maintenance of naïve CD4+ T cells (Fig 5.9 and Fig 5.10). Therefore, our results suggest that in CD4+T cells responding to influenza, the expression of genes required for effector function is associated with the gain of H3K4me3 at promoters and a corresponding loss of bivalency or H3K27me3 in effector and a subset of memory cells retain active promoters despite a decrease in gene expression. On the other hand, genes that promote naïve CD4+ T development and maintenance exhibit a loss of H3K4me3 in effector cells but regain a naïve-like promoter profile in memory. Examples of genes that follow the histone modification dynamic of Cluster 4 includes well known genes associated with naïve T cell development and function including Tcf7, Sell, Lef1 and Bach2 (415-419) (420) (Fig 5.18 C-D).     157  Figure 5.17: Temporal gene expression patterns exhibit two distinct patterns of histone modifications at gene promoters.  (A) Profile of temporal gene expression clusters identified in Section 5.8 displayed a reference. (B) Heatmap of temporal gene expression clusters associated with H3K4me3, H3K27me3 and bivalent promoters in naïve, effector and memory cells. Heatmap colors represent binarized values for histone modification at promoter regions where red = present and white = absent.   Cluster4 Cluster5Cluster1 Cluster2 Cluster3N P M S N P M SN P M S N P M S N P M S1.001.251.501.750.60.81.01.21.41.61.82.00.751.001.251.501.750.91.11.31.51.71.9log10(RPKM)ABActiveBivalentSuppressedActiveBivalentSuppressedActiveBivalentSuppressedActiveBivalentSuppressedNaive Primary Memory Secondary(557) (437) (196)(126) (35)  158   Figure 5.18: Gene expression and H3K4me3 and H3K27me3 signal at promoters of representative genes in Cluster 1 (A,B)  and Cluster 4 (C,D).   A BC DSellSt6gal1Ccl3 GzmBH3K27me3H3K4me3H3K27me3H3K4me3H3K27me3H3K4me3H3K27me3H3K4me3NaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondaryNaivePrimaryMemorySecondary  159 5.2.9 Identification of super-enhancers in CD4+ T cells responding to influenza infection Super-enhancers (SE) are a subset of enhancers that span a broader genomic range than active enhancers and can be identified by significant enrichment of H3K27Ac peaks within a 12.5kb region (304). SE-profiling in CD8+ T cells and in vitro polarised CD4+ T cells have revealed that SE regions mark key cell identity genes such as Tcf7 and Ccr7  in naive CD8+ T cells (404), Tbx21 in Th1 cells, and Gata3 in Th2 cells (297). However, the super-enhancer repertoire utilised by CD4+ T cells following infection has not yet been profiled. We used the SE identification method  proposed by Whyte et al (304) (See Materials and Methods, Section 2.6.3) which yielded a total of 1510 SEs in the four cell types i.e. 300, 401, 419 and 390 in naïve, primary, memory and secondary CD4+ T cells respectively (Fig 5.19 A). To evaluate the relationships between SE regions and transcriptional activity, we assigned SEs to the nearest gene (304) within a distance of 20kb. We found that SE-associated genes in naïve, primary and memory cells displayed a significant increase in gene expression compared to active enhancer associated genes within the same cell type (Fig 5.19B). This observation suggests that the establishment of super-enhancers in naïve, and IAV specific effector and memory CD4+ T cells is associated with higher gene expression compared to active enhancers.    160  Figure 5.19: Super-enhancer profiling in naïve, primary, memory and secondary CD4+ T cells.  25312829 17 9 8 2 10100200Intersection SizeSecondaryMemoryPrimaryNaive   0100200300400Set SizeABCD−50050100150PrimaryMemorySecondaryGainLossPrimaryMemorySecondaryE< 2.2e−16 < 2.2e−16< 2.2e−16< 2.2e−16024naive_aenaive_sepri_aepri_semem_aemem_sesec_aesec_selog10(RPKM)NaivePrimaryMemorySecondaryAE SE AE SE AE SE AE SENaive Primary Memory Secondary  161 (A) Total number of super-enhancers in the indicated cell types. (B) Expression levels of target genes (RPKM) associated with active enhancers (AE) or super enhancers (SE) in the indicated cell types (x-axis). (C) Gain and loss of super enhancers in primary, memory and secondary CD4+ T cells compared to naïve cells. (D) UpsetR plot showing the overlap of SE regions in the indicated cell types. (E) Top 5 gene ontologies enriched in association with all SE target genes identified in CD4+ T cells.   5.2.10 Super-enhancers delineate key cell identity genes in naïve and effector CD4+ T cells responding to influenza infection Since SEs mark key lineage specific genes in Th1, Th2 and Th17 cells in vitro (297), we sought to determine if dynamic changes in SEs in influenza specific CD4+ T cells could identify genes that specify CD4+ T cell identity at naïve, effector and memory stages.  We measured the gain and loss of SEs as naïve cells transition to effector and memory stages (Fig 5.19 C) and calculated the overlap in SE regions across the four cell types (Fig 5.19D).  As naïve CD4+ T cells transitioned to effector or memory stages, we observed an increase in the number of SE regions (Fig 5.19 C). While a core set of 253 SEs were preserved between naïve, effector and memory cells, an increase of ~100 new SE regions in effector and memory cells occurred which were absent in naïve cells (Fig 5.19D). Interestingly, all SE regions identified in memory cells that were not present in primary or secondary cells were shared with naive cells. For subsequent analysis, we divided the SE regions into 2 groups based on their overlap in Figure 5.19D. SEnaive (46 SEs) were defined as SEs present in naïve CD4+ T cells but not in primary or secondary CD4+ T cells. SEactivation (145 SEs) were defined as those SE regions shared by primary, memory or secondary CD4+ T cells but not in naïve cells. When the expression levels of target genes of SEnaive were assessed, we observed significantly higher levels in naïve cells than primary, memory or secondary CD4+ T cells (Fig 5.20A). Within this subset were several genes encoding molecules known to regulate naïve T cell homeostasis and survival including Tcf7, Ccr7, S1pr1   162 and Bach2. We also observed that 27 SEnaive genes (Fig 5.20B) had higher expression (FC>1) in memory cells compared to primary or secondary cells. Fig 5.20 C shows the SE region within the Sell gene locus which encodes CD62L and is highly expressed in naïve cells. Within the SEnaive set, there were 29 genes that shared their SE  regions with memory cells (SEnaive/mem ) (Fig 5.21A) of which Tcf7, Ccr7 and S1pr1 exhibited a significant increase in gene expression in memory cells compared to primary cells (Fig 5.21B). Interestingly, although identified as SEs and associated with increased expression in memory cells, the H3K27Ac signal within these regions was lower than in naïve CD4+ T cells (Figure 5.21 C,D).  These results suggested that memory cells upregulate a set of genes that were highly expressed in naïve cells and that the expression of some of these genes may be regulated by super-enhancer elements in memory cells.   In the activation specific SE set, we observed significantly higher gene expression in primary, memory and secondary CD4+ T cells compared to naïve cells (Fig 5.22A). SEs belonging to the SEactivation group were associated with genes such as Tbx21, Il10, Cxcr3, Havcr2, Il2ra and Pdcd1 (Fig 5.22B) which encode effector and regulatory molecules associated with Th1 inflammation.  Activation specific SEs could further be divided into 9 SEs shared by primary and secondary CD4+ T cells or 128 SEs shared by primary, memory and secondary CD4+ T cells (Fig 5.19D and Table 5.3).  Taken together, these observations suggest that effector and memory CD4+ T cells share a large set of super-enhancers, not present in naïve cells, that are associated with inflammatory response genes with effector and regulatory functions.      163     Figure 5.20: Naïve SE target genes are highly expressed relative to effector and memory cells. 2.9e−111.2e−110.861.2e−070.11 0.08101234naiveprimarymemorysecondarylog10(RPKM)SEnaiveACBSellH3K27Ac  164 (A) Expression of SEnaive target genes in naïve cells versus primary, memory or secondary CD4+ T cells. Bars with numbers denote statistical significance of indicated pairwise comparisons calculated by Student’s t test. (B) Heatmap showing SEnaive genes with expression FC>1 in memory cells compared to primary or secondary cells (C) UCSC genome browser screenshot showing ChIP Seq signals for H3K27Ac within SE region of Sell gene encoding CD62L. Light blue rectangle indicate the SE marked regions.       165  Figure 5.21: SEs shared between naïve and memory are associated with target genes that promote stemness and survival in naïve cells. 1.8e−061.3e−060.649.3e−050.590.361234naiveprimarymemorysecondarylog10(RPKM)A BDSEnaive/memTcf7Ccr7CH3K27AcH3K27Ac***  166   (A) Expression of SEnaive/memory target genes in naïve ,primary, memory and secondary CD4+ T cells. Bars with numbers denote statistical significance of indicated pairwise comparisons calculated by Student’s t test. (B) Heatmap showing expression of 29 genes that share SE regions between memory and naïve cells. Asterisk denotes differentially expressed genes between primary and memory CD4+ T cells. (C and D) UCSC genome browser screenshot showing ChIP Seq signals for H3K27Ac within SEs in the four CD4+ T cell types. Light blue rectangles indicate the SE marked regions.     167  Figure 5.22: Shared effector and memory SEs are associated with target genes required Th1 function during infection.  < 2.2e−16< 2.2e−160.241.5e−151e−050.0013012345naiveprimarymemorysecondarylog10(RPKM)SEactivationA BCDCxcr3Tbx21naiveprimarymemorysecondaryIl2rbHavcr2TigitPdcd1Lag3Il10Cd44Ctla4Cxcr3Tbx21−1−0.500.51Ctla4Cxcr3Tbx21Il10Cd44Pdcd1Lag3Il2rbHavcr2Tigit  168 (A) Expression of SEactivation specific target genes in naïve ,primary, memory and  secondary CD4+ T cells. Bars with numbers denote statistical significance of indicated pairwise comparisons calculated by Student’s t test. (B) Heatmap showing expression of key Th1 associated  SE target genes in effector and memory cells. (C and D) UCSC genome browser screenshot showing ChIP Seq signals for H3K27Ac within SEs in the four CD4+ T cell types.  Light blue rectangles indicate the SE marked regions.    Table 5.3: Select SE associated genes shared by primary, memory and secondary CD4+ T cells or shared by primary and secondary CD4+ T cells only.      5.3 Discussion The goal of this study was to determine whether changes in histone modifications are linked to the stage specific differentiation of CD4+ T cells responding to influenza. To answer this question, we carried out transcriptional and histone modification profiling in naïve and influenza specific effector and memory CD4+ T cells using RNA Seq and ChIP Seq. The genome wide maps of histone modifications generated as part of this study are the first of their kind in antigen specific CD4+ T cells since prior studies on epigenetic regulation of gene expression in T cells during infection have been limited to antigen specific CD8+ T cells (296, 342, 421, 422). This SE overlap Number Select genes Primary, memory and secondary 128 Lck, Tbx21, Cd44, CD274, Il10, Ctla4, Il2ra, Itga4,  Nfatc2, Bhlhe40, Zbtb1, Cxcr3, Il2rb, Tigit, Ccr5 Primary and secondary  9 Bcl2l1, Havcr2, Ehd1, Pdcd1, Plac8, Lag3, Ccr8,  Osgin1, Gm21987      169 analysis provides new insight into how histone modification dynamics at promoter and super-enhancer regions can regulate gene expression as naïve CD4+ T cells differentiate into primary, memory and secondary cells in response to IAV infection.    First, transcriptional profiling by RNA Seq was carried out on naïve, primary, memory and secondary CD4+ T cells to identify temporal changes in gene expression. Multi-dimensional scaling revealed that primary and secondary CD4+ T cells had similar global patterns of gene expression compared to naïve or memory CD4+ T cells. The clustering of primary and secondary CD4+ T cells is consistent with their effector roles in the primary and secondary immune response to IAV infection (266, 397). Interestingly, memory cells have been reported to exhibit over 95% similarity in gene expression with naïve cells by a series of studies conducted using microarrays (423). In contrast, we found that memory cells comprised a distinct group in our RNA-seq analysis. The differences between previously published studies and ours could be explained by the superior ability of RNA Seq to detect low abundance transcripts compared to microarrays (424). In addition, a study conducted by Russ et al where gene expression in naïve, primary and memory OT-I CD8+ T cells was evaluated using RNA Seq also showed a similar MDS based clustering as our study wherein memory cells cluster as a separate group from naïve or primary cells (342). We then used differential gene expression analysis to identify genes that were significantly enriched in memory cells versus naïve, primary or secondary. With this approach, we identified 387 genes that were enriched for GO terms pertaining to the regulation of the immune response and identified candidate genes such as Ccr9, Dgat2, Eomes and Klf4 that may play important roles in memory CD4+ T cell differentiation. These results suggest that memory CD4+ T cells upregulate a unique set of genes compared to naïve or effector CD4+ T   170 cells following infection. Sorting memory CD4+ T cells from IAV infected mice at later time points than Day 30 used in this study may aid in better characterisation of the gene signature that uniquely define memory cells.   The CD4+T cell response to infection is divided into four distinct stages that occur in a temporal manner i.e. naïve, primary, memory and secondary. At each stage, CD4+ T cells can be identified by functional and phenotypic characteristics such as activation markers, adhesion molecules and cytokine production. We asked whether gene expression changes were also regulated in a temporal manner that was reflective of the stage specific differentiation of CD4+ T cells. The use of the bioinformatics tool STEM (408) which is designed to identify statistically significant temporal gene expression patterns in short time series experiments enabled us to cluster differentially expressed genes into 5 expression clusters. Cluster 1 contained genes related to known effector CD4+ T cells functions (Ifng, Cxcr3, Tbx21) but also genes encoding molecules such as Galectin 3, PD1 and BCL2L1 and c-Maf whose function may be worth investigating during the primary and secondary CD4+ T cell response to influenza. Cluster 6 contained genes with expression highest in naïve cells and included genes known to be important for naïve T cell development (Tcf7, Lef1) (425, 426) and homing to secondary lymphoid organs (Sell, Ccr7) (427). Interestingly, we also observed an increased expression of genes within this cluster in memory cells which suggests that naïve and memory cells share a subset of genes. In support of this notion, memory cells showed increased expression of Sell which encodes CD62L and Ccr7 which encodes the chemokine receptor CCR7 and are used as phenotypic markers for central memory T cells (250). We observed that Lef1 expression was also upregulated in memory cells which is consistent with previous studies showing that LEF1 is a transcription factor in the   171 WNT/β catenin pathway which is downregulated in effector cells but required for memory T cell formation (418, 428, 429). Two candidate genes St6gal1 and Sox4 were also strikingly upregulated in naïve and memory CD4+ T cells and merit further investigation during IAV infection based on reports that demonstrate the ability of ST6GAL1 to promote stemness (430) and Sox4 to inhibit effector CD4+ T cell differentiation (431). In summary, the identification of temporal gene expression clusters provides a resource to identify genes for future validation experiments which are associated with an ‘effector state’ versus a ‘resting state’ in CD4+ T cells during or after IAV infection.   In order to understand the relationship between histone modifications and gene expression, we generated genome wide maps of H3K4me3 and H3K27me3 in naïve, primary, memory and secondary CD4+ T cells. These genome wide maps allowed us to classify gene promoters as active, suppressed or bivalent based on statistically significant enrichment of H3K4me3 and H3K27me3. Our initial exploratory analysis showed a correlation between active promoters and gene expression while bivalent or suppressed gene promoters were associated with decreased gene expression which is consistent with previous studies in T cells (282, 331). When the net change in active, suppressed or bivalent promoters was assessed, we observed that memory cells had the highest increase in active promoters which was consistent with the transcriptionally active state of memory cells identified by differential gene expression analysis in this study. It is possible that memory CD4+ T cells at this time-point consist of a heterogenous population of cells in a stage of effector-memory transition and therefore exhibit transcriptional programs associated with both effector and memory CD4+ T cells. Future studies where single cell RNA Seq is performed on CD4+ T cells isolated from the lungs of IAV infected mice at a Day 30 time   172 point would help to determine the transcriptional signatures of these heterogenous memory cell populations.   Since Th1 lineage specific genes such as Tbx21 and Ifng are known to acquire permissive H3K4me3 and lose repressive H3K27me3 modifications as naïve CD4+ T cells differentiate into Th1 cells in vitro (341), we sought to identify if these gene promoters became more accessible in CD4+ T cells following IAV infection. We found that Tbx21 and Ifng promoters in primary, memory and secondary CD4+ T cells had increased H3K4me3 and decreased H3K27me3 which indicates that effector and memory CD4+ T cells responding to IAV infection have a permissive epigenetic signature at the promoters of lineage defining Th1 genes. We also identified the genes Havcr2 and Lag3 encoding the inhibitory molecules Tim-3 and Lag-3 which underwent a similar change from bivalent or suppressed to active. Interestingly, Tim-3 and Lag-3 are known to be expressed by IL-10+ Tr1 cells with immunosuppressive functions (170, 432) which indicates that genes encoding anti-inflammatory molecules may be epigenetically regulated in CD4+ T cells during IAV infection.   In the next stage of our analysis, we aimed to determine if temporal gene expression patterns were accompanied by dynamic changes in histone modifications at gene promoters. To do this, we integrated temporal gene expression clusters with genome wide maps of active, suppressed and bivalent gene promoters. When the gene expression between naïve and primary or secondary CD4+ T cells was compared, we observed a correlation between active/H3K4me3 marked promoters and the increased gene expression in Clusters 1-4. In contrast, within Cluster 1, memory cells appeared to retain H3K4me3 at promoters despite a downregulation in gene   173 expression compared to primary and secondary CD4+ T cells. The presence of a permissive histone signature at the promoter loci, but decreased expression, of these genes suggests that this subset of genes may exist in a ‘primed’ epigenetic state in memory CD4+ T cells. Our observations are supported by studies in memory CD8+ T cells (421) and human memory CD4+ T cells isolated from PBMCs (293) wherein a set of primed genes have also been identified based on increased chromatin accessibility (421) or increased levels of H3K4me3 (293) and decreased gene expression. Notable examples of primed genes identified in these studies and ours are Gzma and Havcr2. In Cluster 4, we observed an increase in permissive histone modifications at gene promoters such as Ccr7, Sell, Lef1, St6gal1 and Cd55 in memory cells compared to primary and secondary cells. Since these genes were highly expressed in naïve cells but low in primary and secondary CD4+ T cells, our results suggest that memory cells share a subset of genes in common with naïve CD4+ T cells and that the re-expression of these genes is associated with epigenetic changes. The notion that epigenetic changes regulate re-expression of genes associated with naïve cell identity are supported by a previous study which showed that memory CD8+ T cells exhibit increased expression of Sell and Ccr7 and decreased DNA methylation at gene promoters compared to effector CD8+ T cells (238).   Super-enhancers are distal regulatory regions which control the expression of highly active genes that specify cell identify or function (304, 433, 434). A previous study showed that super-enhancers were associated with highly expressed lineage defining cytokines and transcription factors in in vitro polarised Th1, Th2 and Th17 cells (297) but the super-enhancer landscape in antigen-specific CD4+ T cells generated in vivo has not been profiled. We identified super-enhancers in naïve, primary, memory and secondary cells based on the high level enrichment of   174 H3K27Ac (304). When active enhancers and SE enhancers were compared in each cell type, we found that SE target genes were expressed at higher levels than the target genes of active enhancers (297). This feature of SE regions is consistent with previous studies in CD8+ T cells and in vitro polarised Th1, Th2 and Th17 cells (297, 404). Naïve cells contained a subset of naïve specific SE regions (SEnaive) compared to primary or secondary cells, and SE associated target genes in naïve cells included Ccr7, Sell and Tcf7 which have established roles in naïve T cell development and homing (425-427). Among SE regions shared by primary, memory and secondary CD4+ T cells (SEactivation) we identified genes that encoded molecules associated with Th1 effector differentiation and function such as CD44, IL-10, T-bet and CXCR3 (111, 402, 435). Interestingly, the SE repertoire in memory cells was shared between naïve cells or effector cells and memory specific SEs were not detected, which suggests that cell identity in Day 30 memory CD4+ T cells may be specified by SE target genes of both naïve and effector CD4+ T cells. The lineage specific factors STAT4, STAT6 and STAT3 exhibit preferential enrichment at SE regions in Th1, Th2 and Th17 cells respectively (297). Applying transcription factor motif analysis to SEnaive or SEactivation regions will, in the future, identify candidate transcriptional regulators of naïve and effector cell identity in CD4+ T cells.     An enigmatic observation in our study is the identification of H3K4me3-marked promoters and H3K27Ac-marked SE regions in memory cells that are shared with naïve cells, but display lower H3K4me3 and H3K27Ac enrichment when compared to naïve cells. Examples of genes that exhibit this pattern are Tcf7, Sell and Ccr7 which are known to be selectively expressed by naïve T cells and TCM. Since the memory T cells in our study are likely a heterogenous population of TCM, TEM and TRM, given that the influenza specific CD4+ cells in this experiment were pooled   175 from the lung and lymph node on Day 30 post infection, it is possible that TCM cells within our memory population may only represent a small percentage of all memory phenotypes present, thus explaining the lower enrichment of H3K4me3 and H3K27Ac in the memory samples.  In summary, the transcriptional and epigenomic profiling carried out in this study elucidated that dynamic changes in histone modifications occur at promoters and super-enhancer regions of genes that specify differentiation state and function in naïve and influenza specific effector and memory CD4+ T cells. The datasets generated through this study provide an epigenomic resource for future work that aims to identify the transcriptional regulators controlling CD4+ T cell differentiation during infection.        176 Chapter 6: Conclusions 6.1 Interpretation and significance  A balanced immune response is critical to surviving infection. CD4+ T cells are an essential component of the immune response to IAV infection but can also contribute to immunopathology when dysregulated. Few studies have explored the regulatory mechanisms that limit the immunopathogenic potential of CD4+ T cells during infection. In this thesis, I provide new insights into the cellular signals and epigenetic changes that regulate the CD4+ T cell response to primary and secondary IAV infection.   6.1.1 IL-27 as a regulator of the CD4+ T cell response to IAV  IL-27 is essential to limit inflammation and pathology during acute Th1 infection, however previous studies have focused on this regulatory aspect of IL-27 during a primary response. I used a heterologous challenge model of IAV infection to study the role of IL-27 signalling in regulating expression of the immunosuppressive cytokine IL-10 from CD4+ T cells during primary and recall infection. I demonstrated that IL-27 signalling is required for the establishment of a permissive epigenetic signature at the Ill0 locus in memory CD4+ T cells which allows these cells to re-express IL-10 in a recall response despite a decrease in IL-27 responsiveness. (Fig 6.1A). My findings highlight a new aspect of IL-27 signalling as an environmental trigger that promotes epigenetic changes within memory CD4+ T cells. The presence of a ‘primed’ epigenetic state defined by activating histone modifications and increased chromatin accessibility at promoters of effector genes in memory T cells has been proposed as an explanation for the rapid recall response exhibited by T cells following repeat exposure to a pathogen (293, 421) . This primed state has been identified at pro-inflammatory genes such as   177 Ifng, Il4 and Gzma in memory CD8+ T cells and PBMC derived human memory CD4+ T cells (293, 421). To the best of our knowledge, my report is the first to identify a primed epigenetic signature at the Il10 gene promoter and enhancer elements in influenza specific memory CD4+ T cells. The IL-27/IL-10 axis is important because it can influence the outcome of infection. As such, the expression of IL-10 from re-activated memory CD4+ T cells could aid in regulating the pathogenic potential of the CD8+ T cell response. This immunoregulatory function performed by memory CD4+ T cells may be important because memory CD8+ T cells are known to express less IL-10 in a recall response (218). Therefore, in the context of viral clearance, memory CD8+ T cells secreting less IL-10 would be more efficient at killing IAV infected cells while memory CD4+ T cells that retain the ability to express IL-10 could act to restrict the pathogenicity of highly inflammatory CD8+ T cells and dampen lung immunopathology. IL-10 from CD4+ T cells is also known to promote memory CD8 T cell formation (436) suggesting that CD4+ T cell IL-10 may be important for the generation of memory CD8+ T cells following secondary infection (437). I also showed that the absence of IL-27 signalling in a memory response results in an increase infiltration of granulocytes cells into the lung. The IL-27/IL-10 axis regulates the infiltration of granulocytes such as neutrophils by inhibiting IL-17 expression which acts a neutrophil attractant (192, 364, 365). IL-27 also suppresses inappropriate Th2 responses that are associated with increased eosinophil recruitment and lung pathology during murine Sendai virus infection but the role of CD4+ T cell derived IL-10 in this setting is unclear (438). Future studies should  therefore aim to assess the functional requirement of IL-10 from CD4+ T cells in promoting a balanced Th1 response and limiting lung immunopathology during recall influenza infection (see Section 6.2.2).     178 The possibility that IL-27 can shape the memory CD4+ T cell response to influenza by promoting IL-10 expression is supported by a study in a malaria model which showed that IL-27 signalling regulates the accumulation of effector CD4+ T cells and IFNγ production in the early stages of a secondary response to re-infection (439). The absence of IL-27 signalling during the secondary response in this model resulted in increased expression of Th1 and Th17 cytokines which was associated with enhanced parasite clearance but at the cost of higher morbidity. Although the mechanism of IL-27 mediated suppression was not investigated, the disease phenotype observed is consistent with the function of IL-27 in limiting inflammation and tissue immunopathology by promoting effector CD4+ T cell derived IL-10 production (189, 316, 440).  In the setting of vaccination where pathogen control is not a concern but instead where vaccine induced inflammatory T cell responses could carry the risk of causing immunopathology, IL-27 may be an attractive candidate to temper the T cell response by priming memory CD4+ T cells to express IL-10. This could be accomplished by including adjuvants such as TLR agonists that elicit IL-27 production from antigen presenting cells or with self adjuvanted viral vectors. However, considering IL-27 is a pleiotropic cytokine that can promote or limit T cell responses in a context dependent manner (190-192, 441, 442), an assessment of the action of IL-27 on vaccine induced cellular responses to IAV is essential. An emerging body of literature supports this context dependent role of IL-27 during vaccination. For example, a study by Pennock et al showed that administration of a subunit vaccine which included a TLR agonist and αCD40 to mice elicited high affinity memory CD8+ and CD4+ T cells that were dependent on IL-27 signalling and could protect against infectious challenge with recombinant Listeria monocytogenes (443). On the other hand, immunization with the viral vector Adenovirus serotype 5 expressing OVA elicited a population of IL-10 expressing CD4+ T cells that suppress   179 inflammatory CD8+ T cell responses in an IL-27 dependent manner and IL-10 blockade improved the pro-inflammatory functions of CD8+ T cells which provided protection against infectious challenge (444). Together these studies indicate that evaluating the pathogenic potential of the T cell response and the effect of IL-27 signalling on the positive or negative regulation of the CD4+ and CD8+ T cell response should be a consideration in the development of different classes of universal vaccines (445) against influenza.   I investigated another aspect of IL-27 mediated immune regulation by examining the impact of this cytokine on Treg adaptation to the Th1 response elicited by primary IAV infection.IL-27 was previously shown to promote a functionally specialised T-bet+ CXCR3+ phenotype in Tregs which was essential to limit pathogenic Th1 inflammation in the gut mucosa during infection with T. gondii (202). However, the effect of IL-27 on Treg differentiation at other mucosal surfaces such as the lung was not clear. I demonstrated a novel role for IL-27 in promoting Treg differentiation in the airways by inducing T-bet and CXCR3 expression in Tregs present in the airways of IAV infected mice (Fig 6.1B). I also showed that IL-27 enhances the ability of Tregs to secrete IL-10 in the airways and lung during respiratory infection (Fig 6.1B). This finding is new because IL-27 is known to promote IL-10 from effector CD4+ T cells during IAV (192) but its effect on Tregs in the lung was unknown. While I have shown that Tregs exhibit a functionally specialised phenotype in response to influenza, future studies (see Section 6.2.2) should aim to assess the functional impact of IL-27 primed Tregs within the respiratory tract during infection. The expression of CXCR3 by Tregs suggests that these cells can home to cellular source of CXCR3 ligands which are alveolar epithelial cells (129) and suppress CXCR3+ effector T cells with immunopathogenic potential (131, 132). IAV antigen specific   180 Tregs accumulate in the lung in highest numbers by Day 7 just prior to the peak of T cell response (110) which also coincides with the peak of IL-27 expression (208) and I have shown that IL-27 exposed Tregs are functionally specialised by Day 10 post infection. Together, these findings suggest that Tregs may accumulate in the lung just before effector T cells undergo maximal expansion in order to undergo functional specialiation in response to IL-27 signalling. The upregulation of CXCR3 on these Th1 adapted Tregs would allow them to be spatially positioned within the airways when the potential for effector CD4+ and CD8+ T cell mediated damage is highest.   Functionally specialised Tregs are known to be more effective at suppressing Th1 responses and the role of CXCR3 and IL-10  in Th1 adapted Tregs has been highlighted in this study and others (202, 372, 446). Transcriptional profiling of T-bet+ Tregs and IL-27 primed Tregs has revealed that these  cells exhibit a distinct gene expression profile compared to T-bet- Tregs or Tregs cultured in neutral conditions (202, 373). T-bet drives a transcriptional program in Tregs that includes genes such as Ccr5, Gzmb and Ebi3 in addition to Cxcr3. Interestingly, despite upregulating T-bet, Tregs express very little IFNγ (447) unless present in a very highly inflammatory setting (167). These studies suggest that T-bet+ Tregs in self resolving influenza infections may be beneficial but could carry the risk of contributing to inflammation in the lung in case of a highly pathogenic IAV infection. At the moment, we do not fully understand how T-bet and Foxp3 coordinate to promote the suppressive actions of Tregs while limiting their conversion to a Th1 effector phenotype. To dissect the regulation of transcriptional program in  Th1 adapted Tregs, future studies could compare the gene modules regulated by T-bet and Foxp3   181 in wild type and  IL-27Ra-/- mice during low pathogenic and highly pathogenic IAV infection using transcription factor ChIP Seq.   In summary, examining the effect of IL-27 on effector CD4+ T cell derived IL-10 in a recall setting and the functional specialisation of Tregs reveals new roles for these regulatory CD4+ T cell subsets in modulating the immune response to influenza. Further exploration of the functional importance of these mechanisms may pave the way for new avenues of therapeutic treatment to regulate damage inducing lung inflammation in response to vaccination or acute respiratory virus infection.     Figure 6.1: Model showing the effect of IL-27 signalling on (A) effector CD4+ T cells and (B) Tregs following IAV infection    182 6.1.2 Epigenetic control of CD4+ T cell differentiation The CD4+ T cell response to influenza has been extensively profiled in terms of phenotype and function. However, our understanding of the transcriptional and epigenetic mechanisms that regulate CD4+ T cell response to IAV infection is limited. I studied dynamic changes in histone modifications at promoters and super-enhancers regions in naïve, primary, memory and secondary CD4+ T cells in order to understand how epigenetic changes regulate gene expression in CD4+ T cells. Previously, global histone modification changes in CD4+ T cells were profiled in Thelper cells polarised in vitro (297, 341) or human memory CD4+ T cells isolated from PBMCs (293, 448) so my study provided new insight into how epigenetic changes can regulate gene expression in CD4+ T cell responding to infection in vivo. We generated genome wide maps of H3K4me3, H3K27me3, H3K4me1 and H3K27Ac that were used to identify epigenetic changes at gene promoters and to map the super-enhancer repertoires in IAV specific CD4+ T cells. The creation of these genome wide histone modification maps from IAV antigen specific CD4+ T cells represent a significant achievement because epigenomic studies on pathogen specific CD4+ T cells are lacking. Our approach of pooling IAV specific CD4+ T cells from large groups of mice allowed us to study the endogenous CD4+ T cell response without the artificial environment created by TCR Tg systems (269). One of my main findings was the presence of a permissive histone modification signature at promoters of effector genes such as Ifng, Tbx21 and Cxcr3 in influenza specific effector CD4+ T cells compared to naïve CD4+ T cells. We also identified changes at the gene promoters of anti-inflammatory genes encoding the inhibitory molecules Lag3, Tim3 and IL-10. The presence of striking changes permissive epigenetic marks associated with increased gene expression of both pro-and anti-inflammatory molecules support the idea that effector CD4+ T cells have dual role in promoting and   183 dampening inflammation (97, 146). Interestingly, IL-27 is known to promote CD4 T cell expression of IL-10, Lag3 and Tim3 during infection (192, 449) and we have shown that IL-27 can promote epigenetic remodelling at the Il10 gene. Therefore, it is possible that IL-27 signalling promotes an anti-inflammatory program during infection by inducing epigenetic remodelling at gene promoters of effector CD4+ T cells. My second set of findings provided insight into memory CD4+ T cell formation following influenza infection. I was able to link the on-off-on gene expression pattern of Tcf7, Sell and Ccr7 in naïve, effector and memory CD4+ T cells also reported in previous transcriptional profiling studies (450, 451) to epigenetic changes at gene promoters and the establishment of super-enhancers. I also observed that memory CD4+ T cells shared a subset of SE regions with naïve cells suggesting that memory T cell fate may be specified by genes specifying naïve T cell identity. This notion is supported by a very recent report (450) that identified Thpok, a transcription factor required for early CD4 lineage commitment (452, 453), as a critical regulator of CD4 memory formation following  LCMV infection. I also identified a subset of genes that were highly expressed in primary CD4+ T cells but retained a permissive histone signature at their promoter loci in memory CD4+ T cells which suggested that epigenetic priming (293, 421) may be a mechanism for the rapid and enhanced memory CD4+ T cell responses reported during IAV infection (266, 268). Lastly, I found that primary, memory and secondary effector CD4+ T cells contained a unique set of SE regions not present in naïve CD4+ T cells which indicates that SE regions established at the effector stage can be maintained into memory. Overall, I propose a model (Fig 6.2) wherein the distinct temporal gene expression profiles of naïve and effector CD4+ T cells are regulated by naïve and effector cell type specific epigenetic changes at gene promoters and through the establishment of super-enhancers. In contrast, memory CD4+ T cells exhbit an epigenetically active state that is   184 associated with primed effector gene loci but also with a permissive epigenetic signature at promoters and super-enhancers of genes that specify naïve cell identity.    Figure 6.2: Model showing histone modification dynamics during and after the CD4+ T cell response to IAV infection   6.2 Limitations and future directions 6.2.1 Elucidating the function of CD4+ T cell derived IL-10 in a recall response to IAV I showed that an IL-27 induced a permissive epigenetic signature in memory CD4+ T cells that was required for the expression of IL-10 in a recall response. Since memory CD4+ T cells are less responsive to IL-27, this raises the question of which signal drives IL-10 expression in a recall response. We have identified CREB as a candidate TF that promotes IL-10 in a recall response and additional experiments can be carried out to assess the ability of CREB to transcribe Il10 mRNA from memory CD4+ T cells. An in vitro T cell culture system could be used in which IL-27 primed resting effector cells are re-stimulated in the presence or absence of a CREB inhibitor (454) and the effect on Il10 transcription or protein expression evaluated by   185 qPCR or flow cytometry. We do not expect IL-6 to play a role in eliciting IL-10 from memory CD4+ T cells because these gp130 low cells are less responsive to both IL-27 and IL-6.  The impact of IL-27 signalling on pathology was assessed in a recall response to influenza and more pathology was observed in IL-27Ra-/- mice which was associated with a granulocytic infiltrate. Since IL-27 can suppress inflammation through IL-10 dependent and independent pathways (189, 192, 455, 456) future experiments should aim to isolate the effect of memory CD4+ T cell derived IL-10 in a recall setting. This could be tested directly using tamoxifen inducible, CD4 T cell specific IL-10 knockout mice (Il10fl/fl CD4-ERT2-Cre+) (457). The temporal flexibility afforded by these transgenic mice would allow the deletion of CD4+ T cell specific IL-10 only during the recall response following which the effects on viral titre, inflammation and immunopathology can be assessed.   6.2.2 The effect of Th1 adapted Tregs on disease outcome during influenza While we have shown that IL-27 signaling promotes the functional specialisation of Tregs during primary influenza, we do not know whether IL-27 primed T-bet+ Tregs are more effective at suppressing inflammation and pathology in the lung compared to Tregs activated under neutral conditions or in the presence of IFNγ. To address this, future experiments could be conducted in which Tregs isolated from Foxp3GFP mice are activated in vitro in the presence of IL-27, IFNγ or blocking antibodies to both cytokines followed by transfer into IL-27Ra-/- mice. The effects on inflammatory cytokine production, survival and pathology can then be assessed.     186 6.2.3 Epigenetic regulation of the CD4+ T cell response to IAV infection One significant challenge encountered while generating the RNA Seq and ChIP Seq datasets in this study were the very low numbers of antigen specific CD4+ T cells that could be isolated with MHC Class II tetramers from the lungs of IAV infected mice. Due to these technical limitations, we pooled antigen specific CD4+ T cells from the lungs and lymph nodes of mice and conducted this analysis on single replicates of primary, memory and secondary CD4+ T cells. It is therefore necessary to validate candidate targets identified in effector or memory CD4+ T cells is an essential next step. CRISPR-Cas9 genome editing could be used to ablate constituent enhancers within SEs of primary CD4+ T cells (458, 459) to determine if these regions have a functional impact on gene expression. The accessibility of gene promoters could be validated by the CRISPR-dCas9 activation system (460) which would increase expression of genes whose promoters have permissive histone modification. Validating candidate genes with roles in CD4 memory formation will prove to be a challenge in endogenous memory CD4+ T cells but an HA TCR Tg system (268) could be used to generate sufficiently large numbers of memory CD4+ T cells in vivo for validation assays.   In the future we plan to investigate dynamic changes in enhancer landscape during the CD4+ T cell response to influenza by mapping poised and active with the H3K4me3 and H3K27Ac datasets generated in this study. Since enhancers recruit transcription factors, we will use TF motif prediction tools to identify enriched TF binding sites within the cell type specific active enhancers (296) of naïve, effector and memory CD4+ T cells. Ultimately, we hope to construct a transcription factor regulatory network that will reveal novel TFs regulating the generation of effector and memory CD4+ T cells.    187 6.3 Concluding remarks This thesis is the collation of research carried out to determine how the CD4+ T cell response is regulated to provide a better understanding of how protective and pathogenic immunity is balanced following IAV infection. The endogenous CD4+ T cell response during a primary, memory stage and recall IAV infection was investigated using a combination of immunological tools such as gene reporter mice, MHC Class II tetramers and flow cytometry in combination with high throughput ChIP and RNA sequencing technology. This approach allowed the CD4+ T cell response to be studied in the setting of a natural IAV infection and to elucidate the mechanisms that regulate CD4+ T cell function at the cellular and epigenetic level. My work showed that regulation in CD4+ T cells is multi-layered and identified regulatory mechanisms at the cell surface level through IL-27 receptor signalling and at the epigenetic level through changes is histone modifications occurring at gene promoters and through super-enhancers. This thesis provides new understanding of the regulation of CD4+ T cell responses to IAV which may be exploited for the design of novel therapeutics such as universal vaccines that generate a protective and balanced immunity to seasonal and pandemic IAV.           188 Bibliography 1. Fields BN, Knipe DM, Howley PM. 2013. Fields virology. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins 2. Paules C, Subbarao K. 2017. Influenza. Lancet 390: 697-708 3. WHO. 2018. https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal).  4. Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, Cohen C, Gran JM, Schanzer D, Cowling BJ, Wu P, Kyncl J, Ang LW, Park M, Redlberger-Fritz M, Yu H, Espenhain L, Krishnan A, Emukule G, van Asten L, Pereira da Silva S, Aungkulanon S, Buchholz U, Widdowson MA, Bresee JS, Global Seasonal Influenza-associated Mortality Collaborator N. 2018. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391: 1285-300 5. Molinari NA, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, Weintraub E, Bridges CB. 2007. The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine 25: 5086-96 6. Saunders-Hastings PR, Krewski D. 2016. Reviewing the History of Pandemic Influenza: Understanding Patterns of Emergence and Transmission. Pathogens 5 7. MMWR. 2010. https://www.cdc.gov/H1N1flu/estimates/April_February_13.htm.  8. Novel Swine-Origin Influenza AVIT, Dawood FS, Jain S, Finelli L, Shaw MW, Lindstrom S, Garten RJ, Gubareva LV, Xu X, Bridges CB, Uyeki TM. 2009. Emergence of a novel swine-origin influenza A (H1N1) virus in humans. N Engl J Med 360: 2605-15 9. Smith GJ, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, Ma SK, Cheung CL, Raghwani J, Bhatt S, Peiris JS, Guan Y, Rambaut A. 2009. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 459: 1122-5 10. Killingley B, Nguyen-Van-Tam J. 2013. Routes of influenza transmission. Influenza Other Respir Viruses 7 Suppl 2: 42-51 11. Cowling BJ, Ip DK, Fang VJ, Suntarattiwong P, Olsen SJ, Levy J, Uyeki TM, Leung GM, Malik Peiris JS, Chotpitayasunondh T, Nishiura H, Mark Simmerman J. 2013. Aerosol transmission is an important mode of influenza A virus spread. Nat Commun 4: 1935 12. Rothberg MB, Haessler SD, Brown RB. 2008. Complications of viral influenza. Am J Med 121: 258-64 13. Taubenberger JK, Morens DM. 2008. The pathology of influenza virus infections. Annu Rev Pathol 3: 499-522 14. Kuiken T, van den Brand J, van Riel D, Pantin-Jackwood M, Swayne DE. 2010. Comparative pathology of select agent influenza a virus infections. Vet Pathol 47: 893-914 15. Cox NJ, Subbarao K. 1999. Influenza. Lancet 354: 1277-82 16. Mertz D, Kim TH, Johnstone J, Lam PP, Science M, Kuster SP, Fadel SA, Tran D, Fernandez E, Bhatnagar N, Loeb M. 2013. Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis. BMJ 347: f5061 17. Taubenberger JK, Morens DM. 2006. Influenza revisited. Emerg Infect Dis 12: 1-2 18. Chowell G, Bertozzi SM, Colchero MA, Lopez-Gatell H, Alpuche-Aranda C, Hernandez M, Miller MA. 2009. Severe respiratory disease concurrent with the circulation of H1N1 influenza. N Engl J Med 361: 674-9   189 19. Kash JC, Tumpey TM, Proll SC, Carter V, Perwitasari O. 2006. Genomic analysis of increased host immune and cell death responses induced by 1918 influenza virus. Nature  20. Kobasa D, Jones SM, Shinya K, Kash JC, Copps J. 2007. Aberrant innate immune response in lethal infection of macaques with the 1918 influenza virus. Nature  21. de Jong MD, Simmons CP, Thanh TT, Hien VM, Smith GJ, Chau TN, Hoang DM, Chau NV, Khanh TH, Dong VC, Qui PT, Cam BV, Ha do Q, Guan Y, Peiris JS, Chinh NT, Hien TT, Farrar J. 2006. Fatal outcome of human influenza A (H5N1) is associated with high viral load and hypercytokinemia. Nat Med 12: 1203-7 22. To KK, Hung IF, Li IW, Lee KL, Koo CK, Yan WW, Liu R, Ho KY, Chu KH, Watt CL, Luk WK, Lai KY, Chow FL, Mok T, Buckley T, Chan JF, Wong SS, Zheng B, Chen H, Lau CC, Tse H, Cheng VC, Chan KH, Yuen KY. 2010. Delayed clearance of viral load and marked cytokine activation in severe cases of pandemic H1N1 2009 influenza virus infection. Clin Infect Dis 50: 850-9 23. CDC. 2018. https://www.cdc.gov/flu/.  24. Lackenby A, Besselaar TG, Daniels RS, Fry A, Gregory V, Gubareva LV, Huang W, Hurt AC, Leang SK, Lee RTC, Lo J, Lollis L, Maurer-Stroh S, Odagiri T, Pereyaslov D, Takashita E, Wang D, Zhang W, Meijer A. 2018. Global update on the susceptibility of human influenza viruses to neuraminidase inhibitors and status of novel antivirals, 2016-2017. Antiviral Res 157: 38-46 25. Beigel JH, Farrar J, Han AM, Hayden FG, Hyer R, de Jong MD, Lochindarat S, Nguyen TK, Nguyen TH, Tran TH, Nicoll A, Touch S, Yuen KY, Writing Committee of the World Health Organization Consultation on Human Influenza AH. 2005. Avian influenza A (H5N1) infection in humans. N Engl J Med 353: 1374-85 26. Dobson J, Whitley RJ, Pocock S, Monto AS. 2015. Oseltamivir treatment for influenza in adults: a meta-analysis of randomised controlled trials. Lancet 385: 1729-37 27. Jones M, Jefferson T, Doshi P, Del Mar C, Heneghan C, Onakpoya I. 2015. Commentary on Cochrane review of neuraminidase inhibitors for preventing and treating influenza in healthy adults and children. Clin Microbiol Infect 21: 217-21 28. Martin-Loeches I, Lisboa T, Rhodes A, Moreno RP, Silva E, Sprung C, Chiche JD, Barahona D, Villabon M, Balasini C, Pearse RM, Matos R, Rello J, Contributors EHNR. 2011. Use of early corticosteroid therapy on ICU admission in patients affected by severe pandemic (H1N1)v influenza A infection. Intensive Care Med 37: 272-83 29. Houser K, Subbarao K. 2015. Influenza vaccines: challenges and solutions. Cell Host Microbe 17: 295-300 30. Zimmerman RK, Nowalk MP, Chung J, Jackson ML, Jackson LA, Petrie JG, Monto AS, McLean HQ, Belongia EA, Gaglani M, Murthy K, Fry AM, Flannery B, Investigators USFV, Investigators USFV. 2016. 2014-2015 Influenza Vaccine Effectiveness in the United States by Vaccine Type. Clin Infect Dis 63: 1564-73 31. Jackson ML, Chung JR, Jackson LA, Phillips CH, Benoit J, Monto AS, Martin ET, Belongia EA, McLean HQ, Gaglani M, Murthy K, Zimmerman R, Nowalk MP, Fry AM, Flannery B. 2017. Influenza Vaccine Effectiveness in the United States during the 2015-2016 Season. N Engl J Med 377: 534-43 32. Gaglani M, Pruszynski J, Murthy K, Clipper L, Robertson A, Reis M, Chung JR, Piedra PA, Avadhanula V, Nowalk MP, Zimmerman RK, Jackson ML, Jackson LA, Petrie JG, Ohmit SE, Monto AS, McLean HQ, Belongia EA, Fry AM, Flannery B. 2016. Influenza   190 Vaccine Effectiveness Against 2009 Pandemic Influenza A(H1N1) Virus Differed by Vaccine Type During 2013-2014 in the United States. J Infect Dis 213: 1546-56 33. Wu NC, Zost SJ, Thompson AJ, Oyen D, Nycholat CM, McBride R, Paulson JC, Hensley SE, Wilson IA. 2017. A structural explanation for the low effectiveness of the seasonal influenza H3N2 vaccine. PLoS Pathog 13: e1006682 34. Chen X, Liu S, Goraya MU, Maarouf M, Huang S, Chen J-L. 2018. Host Immune Response to Influenza A Virus Infection. Frontiers in Immunology 9: 320- 35. Wang J, Oberley-Deegan R, Wang S, Nikrad M, Funk CJ, Hartshorn KL, Mason RJ. 2009. Differentiated human alveolar type II cells secrete antiviral IL-29 (IFN-lambda 1) in response to influenza A infection. Journal of immunology (Baltimore, Md. : 1950) 182: 1296-304 36. Pirhonen J, Sareneva T, Kurimoto M, Julkunen I, Matikainen S. 1999. Virus infection activates IL-1 beta and IL-18 production in human macrophages by a caspase-1-dependent pathway. J. Immunol. 162: 7322-9 37. Chan MCW, Cheung CY, Chui WH, Tsao SW, Nicholls JM, Chan YO, Chan RWY, Long HT, Poon LLM, Guan Y, Peiris JSM. 2005. Proinflammatory cytokine responses induced by influenza A (H5N1) viruses in primary human alveolar and bronchial epithelial cells. Respiratory Research 6: 135- 38. Kumagai Y, Takeuchi O, Kato H, Kumar H, Matsui K, Morii E, Aozasa K, Kawai T, Akira S. 2007. Alveolar macrophages are the primary interferon-alpha producer in pulmonary infection with RNA viruses. Immunity 27: 240-52 39. Pulendran B, Palucka K, Banchereau J. 2001. Sensing pathogens and tuning immune responses. Science 293: 253-6 40. Tumpey TM, Basler CF, Aguilar PV, Zeng H, Solórzano A, Swayne DE, Cox NJ, Katz JM, Taubenberger JK, Palese P, García-Sastre A. 2005. Characterization of the reconstructed 1918 Spanish influenza pandemic virus. Science 310: 77-80 41. Tate MD, Deng YM, Jones JE, Anderson GP, Brooks AG, Reading PC. 2009. Neutrophils Ameliorate Lung Injury and the Development of Severe Disease during Influenza Infection. The Journal of Immunology 183: 7441-50 42. Narasaraju T, Yang E, Samy RP, Ng HH, Poh WP, Liew A-A, Phoon MC, van Rooijen N, Chow VT. 2011. Excessive Neutrophils and Neutrophil Extracellular Traps Contribute to Acute Lung Injury of Influenza Pneumonitis. The American Journal of Pathology 179: 199-210 43. Perrone LA, Belser JA, Wadford DA, Katz JM, Tumpey TM. 2013. Inducible Nitric Oxide Contributes to Viral Pathogenesis Following Highly Pathogenic Influenza Virus Infection in Mice. The Journal of Infectious Diseases 207: 1576-84 44. Zhu L, Liu L, Zhang Y, Pu L, Liu J, Li X, Chen Z, Hao Y, Wang B, Han J, Li G, Liang S, Xiong H, Zheng H, Li A, Xu J, Zeng H. 2018. High Level of Neutrophil Extracellular Traps Correlates With Poor Prognosis of Severe Influenza A Infection. The Journal of Infectious Diseases 217: 428-37 45. Arnon TI, Lev M, Katz G, Chernobrov Y, Porgador A, Mandelboim O. 2001. Recognition of viral hemagglutinins by NKp44 but not by NKp30. European Journal of Immunology 31: 2680-9 46. Mendelson M, Tekoah Y, Zilka A, Gershoni-Yahalom O, Gazit R, Achdout H, Bovin NV, Meningher T, Mandelboim M, Mandelboim O, David A, Porgador A. 2010. NKp46   191 O-glycan sequences that are involved in the interaction with hemagglutinin type 1 of influenza virus. Journal of virology 84: 3789-97 47. Abdul-Careem MF, Mian MF, Yue G, Gillgrass A, Chenoweth MJ, Barra NG, Chew MV, Chan T, Al-Garawi AA, Jordana M, Ashkar AA. 2012. Critical Role of Natural Killer Cells in Lung Immunopathology During Influenza Infection in Mice. The Journal of Infectious Diseases 206: 167-77 48. Lin KL, Suzuki Y, Nakano H, Ramsburg E, Gunn MD. 2008. CCR2+ monocyte-derived dendritic cells and exudate macrophages produce influenza-induced pulmonary immune pathology and mortality. Journal of immunology (Baltimore, Md. : 1950) 180: 2562-72 49. Cao RG, Suarez NM, Obermoser G, Lopez SMC, Flano E, Mertz SE, Albrecht RA, García-Sastre A, Mejias A, Xu H, Qin H, Blankenship D, Palucka K, Pascual V, Ramilo O. 2014. Differences in Antibody Responses Between Trivalent Inactivated Influenza Vaccine and Live Attenuated Influenza Vaccine Correlate With the Kinetics and Magnitude of Interferon Signaling in Children. The Journal of Infectious Diseases 210: 224-33 50. Aldridge JR, Moseley CE, Boltz DA, Negovetich NJ, Reynolds C, Franks J, Brown SA, Doherty PC, Webster RG, Thomas PG. 2009. TNF/iNOS-producing dendritic cells are the necessary evil of lethal influenza virus infection. Proceedings of the National Academy of Sciences of the United States of America 106: 5306-11 51. Dawson TC, Beck MA, Kuziel WA, Henderson F, Maeda N. 2000. Contrasting effects of CCR5 and CCR2 deficiency in the pulmonary inflammatory response to influenza A virus. Am J Pathol 156: 1951-9 52. GeurtsvanKessel CH, Willart MAM, van Rijt LS, Muskens F, Kool M, Baas C, Thielemans K, Bennett C, Clausen BE, Hoogsteden HC, Osterhaus ADME, Rimmelzwaan GF, Lambrecht BN. 2008. Clearance of influenza virus from the lung depends on migratory langerin+CD11b- but not plasmacytoid dendritic cells. The Journal of experimental medicine 205: 1621-34 53. Desch AN, Randolph GJ, Murphy K, Gautier EL, Kedl RM, Lahoud MH, Caminschi I, Shortman K, Henson PM, Jakubzick CV. 2011. CD103+ pulmonary dendritic cells preferentially acquire and present apoptotic cell-associated antigen. The Journal of experimental medicine 208: 1789-97 54. Hargadon KM, Zhou H, Albrecht RA, Dodd HA, Garcia-Sastre A, Braciale TJ. 2011. Major histocompatibility complex class II expression and hemagglutinin subtype influence the infectivity of type A influenza virus for respiratory dendritic cells. J Virol 85: 11955-63 55. Hao X, Kim TS, Braciale TJ. 2008. Differential response of respiratory dendritic cell subsets to influenza virus infection. J Virol 82: 4908-19 56. Manicassamy B, Manicassamy S, Belicha-Villanueva A, Pisanelli G, Pulendran B, García-Sastre A. 2010. Analysis of in vivo dynamics of influenza virus infection in mice using a GFP reporter virus. Proceedings of the National Academy of Sciences of the United States of America 107: 11531-6 57. Heer AK, Harris NL, Kopf M, Marsland BJ. 2008. CD4+ and CD8+ T cells exhibit differential requirements for CCR7-mediated antigen transport during influenza infection. Journal of immunology (Baltimore, Md. : 1950) 181: 6984-94   192 58. Kim TS, Braciale TJ. 2009. Respiratory Dendritic Cell Subsets Differ in Their Capacity to Support the Induction of Virus-Specific Cytotoxic CD8+ T Cell Responses. PLoS ONE 4: e4204-e 59. McGill J, Rooijen NV, Legge KL. 2008. Protective influenza-specific CD8 T cell responses require interactions with dendritic cells in the lungs. Journal of Experimental Medicine 205: 1635-46 60. Nakano H, Lin KL, Yanagita M, Charbonneau C, Cook DN, Kakiuchi T, Gunn MD. 2009. Blood-derived inflammatory dendritic cells in lymph nodes stimulate acute T helper type 1 immune responses. Nature Immunology 10: 394-402 61. Belz GT, Smith CM, Kleinert L, Reading P, Brooks A, Shortman K, Carbone FR, Heath WR. 2004. Distinct migrating and nonmigrating dendritic cell populations are involved in MHC class I-restricted antigen presentation after lung infection with virus. Proceedings of the National Academy of Sciences of the United States of America 101: 8670-5 62. Lee BO, Rangel-Moreno J, Moyron-Quiroz JE, Hartson L, Makris M, Sprague F, Lund FE, Randall TD. 2005. CD4 T cell-independent antibody response promotes resolution of primary influenza infection and helps to prevent reinfection. Journal of immunology (Baltimore, Md. : 1950) 175: 5827-38 63. Graham MB, Braciale TJ. 1997. Resistance to and recovery from lethal influenza virus infection in B lymphocyte-deficient mice. The Journal of experimental medicine 186: 2063-8 64. Gerhard W, Mozdzanowska K, Furchner M, Washko G, Maiese K. 1997. Role of the B-cell response in recovery of mice from primary influenza virus infection. Immunological reviews 159: 95-103 65. Carragher DM, Kaminski DA, Moquin A, Hartson L, Randall TD. 2008. A novel role for non-neutralizing antibodies against nucleoprotein in facilitating resistance to influenza virus. Journal of immunology (Baltimore, Md. : 1950) 181: 4168-76 66. Rangel-Moreno J, Carragher DM, Misra RS, Kusser K, Hartson L, Moquin A, Lund FE, Randall TD. 2008. B cells promote resistance to heterosubtypic strains of influenza via multiple mechanisms. Journal of immunology (Baltimore, Md. : 1950) 180: 454-63 67. Sambhara S, Kurichh A, Miranda R, Tumpey T, Rowe T, Renshaw M, Arpino R, Tamane A, Kandil A, James O, Underdown B, Klein M, Katz J, Burt D. 2001. Heterosubtypic Immunity against Human Influenza A Viruses, Including Recently Emerged Avian H5 and H9 Viruses, Induced by FLU–ISCOM Vaccine in Mice Requires both Cytotoxic T-Lymphocyte and Macrophage Function. Cellular Immunology 211: 143-53 68. Vanderven HA, Ana-Sosa-Batiz F, Jegaskanda S, Rockman S, Laurie K, Barr I, Chen W, Wines B, Hogarth PM, Lambe T, Gilbert SC, Parsons MS, Kent SJ. 2016. What Lies Beneath: Antibody Dependent Natural Killer Cell Activation by Antibodies to Internal Influenza Virus Proteins. EBioMedicine 8: 277-90 69. Bergtold A, Desai DD, Gavhane A, Clynes R. 2005. Cell Surface Recycling of Internalized Antigen Permits Dendritic Cell Priming of B Cells. Immunity 23: 503-14 70. Pape KA, Catron DM, Itano AA, Jenkins MK. 2007. The Humoral Immune Response Is Initiated in Lymph Nodes by B Cells that Acquire Soluble Antigen Directly in the Follicles. Immunity 26: 491-502   193 71. Batista FD, Harwood NE. 2009. The who, how and where of antigen presentation to B cells. Nature Reviews Immunology 9: 15-27 72. Mozdzanowska K, Furchner M, Zharikova D, Feng J, Gerhard W. 2005. Roles of CD4+ T-Cell-Independent and -Dependent Antibody Responses in the Control of Influenza Virus Infection: Evidence for Noncognate CD4+ T-Cell Activities That Enhance the Therapeutic Activity of Antiviral Antibodies. Journal of Virology 79: 5943-51 73. Takahashi Y, Onodera T, Adachi Y, Ato M. 2017. Adaptive B Cell Responses to Influenza Virus Infection in the Lung. Viral Immunology 30: 431-7 74. Moyron-Quiroz JE, Rangel-Moreno J, Kusser K, Hartson L, Sprague F, Goodrich S, Woodland DL, Lund FE, Randall TD. 2004. Role of inducible bronchus associated lymphoid tissue (iBALT) in respiratory immunity. Nature Medicine 10: 927-34 75. Boyden AW, Legge KL, Waldschmidt TJ. 2012. Pulmonary infection with influenza A virus induces site-specific germinal center and T follicular helper cell responses. PloS one 7: e40733-e 76. Huang K-YA, Li CK-F, Clutterbuck E, Chui C, Wilkinson T, Gilbert A, Oxford J, Lambkin-Williams R, Lin T-Y, McMichael AJ, Xu X-N. 2014. Virus-Specific Antibody Secreting Cell, Memory B-cell, and Sero-Antibody Responses in the Human Influenza Challenge Model. The Journal of Infectious Diseases 209: 1354-61 77. Waffarn EE, Baumgarth N. 2011. Protective B cell responses to flu--no fluke! Journal of immunology (Baltimore, Md. : 1950) 186: 3823-9 78. Slifka MK, Antia R, Whitmire JK, Ahmed R. 1998. Humoral immunity due to long-lived plasma cells. Immunity 8: 363-72 79. Yu X, Tsibane T, McGraw PA, House FS, Keefer CJ, Hicar MD, Tumpey TM, Pappas C, Perrone LA, Martinez O, Stevens J, Wilson IA, Aguilar PV, Altschuler EL, Basler CF, Crowe JE, Jr. 2008. Neutralizing antibodies derived from the B cells of 1918 influenza pandemic survivors. Nature 455: 532-6 80. Eichelberger M, Allan W, Zijlstra M, Jaenisch R, Doherty PC. 1991. Clearance of influenza virus respiratory infection in mice lacking class I major histocompatibility complex-restricted CD8+ T cells. The Journal of experimental medicine 174: 875-80 81. Hou S, Doherty PC, Zijlstra M, Jaenisch R, Katz JM. 1992. Delayed clearance of Sendai virus in mice lacking class I MHC-restricted CD8+ T cells. Journal of immunology (Baltimore, Md. : 1950) 149: 1319-25 82. Slifka MK, Matloubian M, Ahmed R. 1995. Bone marrow is a major site of long-term antibody production after acute viral infection. Journal of virology 69: 1895-902 83. Chu VT, Berek C. 2013. The establishment of the plasma cell survival niche in the bone marrow. Immunological Reviews 251: 177-88 84. Liang B, Hyland L, Hou S. 2001. Nasal-associated lymphoid tissue is a site of long-term virus-specific antibody production following respiratory virus infection of mice. Journal of virology 75: 5416-20 85. GeurtsvanKessel CH, Willart MAM, Bergen IM, van Rijt LS, Muskens F, Elewaut D, Osterhaus ADME, Hendriks R, Rimmelzwaan GF, Lambrecht BN. 2009. Dendritic cells are crucial for maintenance of tertiary lymphoid structures in the lung of influenza virus-infected mice. The Journal of experimental medicine 206: 2339-49   194 86. Joo HM, He Y, Sangster MY. 2008. Broad dispersion and lung localization of virus-specific memory B cells induced by influenza pneumonia. Proceedings of the National Academy of Sciences of the United States of America 105: 3485-90 87. Chiu C, Ellebedy AH, Wrammert J, Ahmed R. 2014. B Cell Responses to Influenza Infection and Vaccination. pp. 381-98: Springer, Cham 88. Duan S, Thomas PG. 2016. Balancing Immune Protection and Immune Pathology by CD8+ T-Cell Responses to Influenza Infection. Frontiers in Immunology 7: 25- 89. Bender BS, Croghan T, Zhang L, Small PA. 1992. Transgenic mice lacking class I major histocompatibility complex-restricted T cells have delayed viral clearance and increased mortality after influenza virus challenge. The Journal of experimental medicine 175: 1143-5 90. van Stipdonk MJB, Lemmens EE, Schoenberger SP. 2001. Naïve CTLs require a single brief period of antigenic stimulation for clonal expansion and differentiation. Nature Immunology 2: 423-9 91. Lawrence CW, Braciale TJ. 2004. Activation, differentiation, and migration of naive virus-specific CD8+ T cells during pulmonary influenza virus infection. Journal of immunology (Baltimore, Md. : 1950) 173: 1209-18 92. McGill J, Heusel JW, Legge KL. 2009. Innate immune control and regulation of influenza virus infections. J. Leukoc. Biol. 86: 803-12 93. McGill J, Van Rooijen N, Legge KL. 2010. IL-15 trans-presentation by pulmonary dendritic cells promotes effector CD8 T cell survival during influenza virus infection. The Journal of experimental medicine 207: 521-34 94. Dolfi DV, Duttagupta PA, Boesteanu AC, Mueller YM, Oliai CH, Borowski AB, Katsikis PD. 2011. Dendritic Cells and CD28 Costimulation Are Required To Sustain Virus-Specific CD8+ T Cell Responses during the Effector Phase In Vivo. The Journal of Immunology 186: 4599-608 95. Hufford MM, Kim TS, Sun J, Braciale TJ. 2011. Antiviral CD8+ T cell effector activities in situ are regulated by target cell type. Journal of Experimental Medicine 208: 167-80 96. Peperzak V, Xiao Y, Veraar EAM, Borst J. 2010. CD27 sustains survival of CTLs in virus-infected nonlymphoid tissue in mice by inducing autocrine IL-2 production. The Journal of clinical investigation 120: 168-78 97. Sun J, Madan R, Karp CL, Braciale TJ. 2009. Effector T cells control lung inflammation during acute influenza virus infection by producing IL-10. Nature Medicine 15: 277-84 98. La Gruta NL, Turner SJ, Doherty PC. 2004. Hierarchies in cytokine expression profiles for acute and resolving influenza virus-specific CD8+ T cell responses: correlation of cytokine profile and TCR avidity. Journal of immunology (Baltimore, Md. : 1950) 172: 5553-60 99. Hamada H, Garcia-Hernandez MdlL, Reome JB, Misra SK, Strutt TM, McKinstry KK, Cooper AM, Swain SL, Dutton RW. 2009. Tc17, a Unique Subset of CD8 T Cells That Can Protect against Lethal Influenza Challenge. The Journal of Immunology 182: 3469-81 100. Hamada H, Bassity E, Flies A, Strutt TM, Garcia-Hernandez MdL, McKinstry KK, Zou T, Swain SL, Dutton RW. 2013. Multiple redundant effector mechanisms of CD8+ T cells protect against influenza infection. Journal of immunology (Baltimore, Md. : 1950) 190: 296-306   195 101. Zhao MQ, Stoler MH, Liu AN, Wei B, Soguero C, Hahn YS, Enelow RI. 2000. Alveolar epithelial cell chemokine expression triggered by antigen-specific cytolytic CD8(+) T cell recognition. The Journal of clinical investigation 106: R49-58 102. Ramana CV, DeBerge MP, Kumar A, Alia CS, Durbin JE, Enelow RI. 2015. Inflammatory impact of IFN-γ in CD8 <sup>+</sup> T cell-mediated lung injury is mediated by both Stat1-dependent and -independent pathways. American Journal of Physiology-Lung Cellular and Molecular Physiology 308: L650-L7 103. Liu AN, Mohammed AZ, Rice WR, Fiedeldey DT, Liebermann JS, Whitsett JA, Braciale TJ, Enelow RI. 1999. Perforin-Independent CD8 <sup>+</sup> T-Cell–Mediated Cytotoxicity of Alveolar Epithelial Cells Is Preferentially Mediated by Tumor Necrosis Factor- α. American Journal of Respiratory Cell and Molecular Biology 20: 849-58 104. Xu L, Yoon H, Zhao MQ, Liu J, Ramana CV, Enelow RI. 2004. Cutting edge: pulmonary immunopathology mediated by antigen-specific expression of TNF-alpha by antiviral CD8+ T cells. Journal of immunology (Baltimore, Md. : 1950) 173: 721-5 105. Brown DM, Roman E, Swain SL. 2004. CD4 T cell responses to influenza infection. Semin Immunol 16: 171-7 106. Brown DM, Lee S, Garcia-Hernandez MdlL, Swain SL. 2012. Multifunctional CD4 Cells Expressing Gamma Interferon and Perforin Mediate Protection against Lethal Influenza Virus Infection. Journal of Virology 86: 6792-803 107. Laidlaw BJ, Zhang N, Marshall HD, Staron MM, Guan T, Hu Y, Cauley LS, Craft J, Kaech SM. 2014. CD4+ T cell help guides formation of CD103+ lung-resident memory CD8+ T cells during influenza viral infection. Immunity 41: 633-45 108. Sun JC, Bevan MJ. 2003. Defective CD8 T cell memory following acute infection without CD4 T cell help. Science 300: 339-42 109. Bedoya F, Cheng G-S, Leibow A, Zakhary N, Weissler K, Garcia V, Aitken M, Kropf E, Garlick DS, Wherry EJ, Erikson J, Caton AJ. 2013. Viral antigen induces differentiation of Foxp3+ natural regulatory T cells in influenza virus-infected mice. Journal of immunology (Baltimore, Md. : 1950) 190: 6115-25 110. Betts RJ, Prabhu N, Ho AWS, Lew FC, Hutchinson PE, Rotzschke O, Macary PA, Kemeny DM. 2012. Influenza A virus infection results in a robust, antigen-responsive, and widely disseminated Foxp3+ regulatory T cell response. Journal of Virology 86: 2817-25 111. Roman E, Miller E, Harmsen A, Wiley J, Von Andrian UH, Huston G, Swain SL. 2002. CD4 effector T cell subsets in the response to influenza: heterogeneity, migration, and function. J Exp Med 196: 957-68 112. Szabo SJ, Kim ST, Costa GL, Zhang X, Fathman CG, Glimcher LH. 2000. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100: 655-69 113. Billiau A, Matthys P. 2009. Interferon-γ: A historical perspective. Cytokine & Growth Factor Reviews 20: 97-113 114. Snapper CM, Paul WE. 1987. Interferon-gamma and B cell stimulatory factor-1 reciprocally regulate Ig isotype production. Science (New York, N.Y.) 236: 944-7 115. Brenner D, Blaser H, Mak TW. 2015. Regulation of tumour necrosis factor signalling: live or let die. Nature Reviews Immunology 15: 362-74 116. Wilkinson TM, Li CKF, Chui CSC, Huang AKY, Perkins M, Liebner JC, Lambkin-Williams R, Gilbert A, Oxford J, Nicholas B, Staples KJ, Dong T, Douek DC,   196 McMichael AJ, Xu X-N. 2012. Preexisting influenza-specific CD4+ T cells correlate with disease protection against influenza challenge in humans. Nature Medicine 18: 274-80 117. Brown DM, Kamperschroer C, Dilzer AM, Roberts DM, Swain SL. 2009. IL-2 and antigen dose differentially regulate perforin- and FasL-mediated cytolytic activity in antigen specific CD4+ T cells. Cellular Immunology 257: 69-79 118. Hua L, Yao S, Pham D, Jiang L, Wright J, Sawant D, Dent AL, Braciale TJ, Kaplan MH, Sun J. 2013. Cytokine-dependent induction of CD4+ T cells with cytotoxic potential during influenza virus infection. Journal of virology 87: 11884-93 119. Workman AM, Jacobs AK, Vogel AJ, Condon S, Brown DM. 2014. Inflammation Enhances IL-2 Driven Differentiation of Cytolytic CD4 T Cells. PLoS ONE 9: e89010-e 120. Kudva A, Scheller EV, Robinson KM, Crowe CR, Choi SM, Slight SR, Khader SA, Dubin PJ, Enelow RI, Kolls JK, Alcorn JF. 2011. Influenza A inhibits Th17-mediated host defense against bacterial pneumonia in mice. The Journal of Immunology 186: 1666-74 121. McKinstry KK, Strutt TM, Buck A, Curtis JD, Dibble JP, Huston G, Tighe M, Hamada H, Sell S, Dutton RW, Swain SL. 2009. IL-10 deficiency unleashes an influenza-specific Th17 response and enhances survival against high-dose challenge. Journal of immunology (Baltimore, Md. : 1950) 182: 7353-63 122. Chiu C, Openshaw PJ. 2015. Antiviral B cell and T cell immunity in the lungs. Nat Immunol 16: 18-26 123. Crotty S. 2014. T Follicular Helper Cell Differentiation, Function, and Roles in Disease. Immunity 41: 529-42 124. Berg EL, Robinson MK, Warnock RA, Butcher EC. 1991. The human peripheral lymph node vascular addressin is a ligand for LECAM-1, the peripheral lymph node homing receptor. The Journal of cell biology 114: 343-9 125. Gunn MD, Tangemann K, Tam C, Cyster JG, Rosen SD, Williams LT. 1998. A chemokine expressed in lymphoid high endothelial venules promotes the adhesion and chemotaxis of naive T lymphocytes. Proceedings of the National Academy of Sciences of the United States of America 95: 258-63 126. Mikhak Z, Strassner JP, Luster AD. 2013. Lung dendritic cells imprint T cell lung homing and promote lung immunity through the chemokine receptor CCR4. Journal of Experimental Medicine 210: 1855-69 127. Kohlmeier JE, Cookenham T, Miller SC, Roberts AD, Christensen JP, Thomsen AR, Woodland DL. 2009. CXCR3 Directs Antigen-Specific Effector CD4+ T Cell Migration to the Lung During Parainfluenza Virus Infection. The Journal of Immunology 183: 4378-84 128. Lord GM, Rao RM, Choe H, Sullivan BM, Lichtman AH, Luscinskas FW, Glimcher LH. 2005. T-bet is required for optimal proinflammatory CD4+ T-cell trafficking. Blood 106: 3432-9 129. Wareing MD, Lyon AB, Lu B, Gerard C, Sarawar SR. 2004. Chemokine expression during the development and resolution of a pulmonary leukocyte response to influenza A virus infection in mice. Journal of Leukocyte Biology 76: 886-95   197 130. Fadel SA, Bromley SK, Medoff BD, Luster AD. 2008. CXCR3-deficiency protects influenza-infected CCR5-deficient mice from mortality. European Journal of Immunology 38: 3376-87 131. Groom JR, Luster AD. 2011. CXCR3 ligands: redundant, collaborative and antagonistic functions. Immunology and cell biology 89: 207-15 132. Ichikawa A, Kuba K, Morita M, Chida S, Tezuka H, Hara H, Sasaki T, Ohteki T, Ranieri VM, dos Santos CC, Kawaoka Y, Akira S, Luster AD, Lu B, Penninger JM, Uhlig S, Slutsky AS, Imai Y. 2013. CXCL10-CXCR3 Enhances the Development of Neutrophil-mediated Fulminant Lung Injury of Viral and Nonviral Origin. American Journal of Respiratory and Critical Care Medicine 187: 65-77 133. Bermejo-Martin JF, Ortiz de Lejarazu R, Pumarola T, Rello J, Almansa R, Ramírez P, Martin-Loeches I, Varillas D, Gallegos MC, Serón C, Micheloud D, Gomez JM, Tenorio-Abreu A, Ramos MJ, Molina ML, Huidobro S, Sanchez E, Gordón M, Fernández V, del Castillo A, Marcos MÁ, Villanueva B, López CJ, Rodríguez-Domínguez M, Galan J-C, Cantón R, Lietor A, Rojo S, Eiros JM, Hinojosa C, Gonzalez I, Torner N, Banner D, Leon A, Cuesta P, Rowe T, Kelvin DJ. 2009. Th1 and Th17 hypercytokinemia as early host response signature in severe pandemic influenza. Critical Care 13: R201-R 134. Crowe CR, Chen K, Pociask DA, Alcorn JF, Krivich C, Enelow RI, Ross TM, Witztum JL, Kolls JK. 2009. Critical Role of IL-17RA in Immunopathology of Influenza Infection. The Journal of Immunology 183: 5301-10 135. Brown DM, Dilzer AM, Meents DL, Swain SL. 2006. CD4 T cell-mediated protection from lethal influenza: perforin and antibody-mediated mechanisms give a one-two punch. J Immunol 177: 2888-98 136. McCormick S, Shaler CR, Small C-L, Horvath C, Damjanovic D, Brown EG, Aoki N, Takai T, Xing Z. 2011. Control of pathogenic CD4 T cells and lethal immunopathology by signaling immunoadaptor DAP12 during influenza infection. Journal of immunology (Baltimore, Md. : 1950) 187: 4280-92 137. Snelgrove RJ, Goulding J, Didierlaurent AM, Lyonga D, Vekaria S, Edwards L, Gwyer E, Sedgwick JD, Barclay AN, Hussell T. 2008. A critical function for CD200 in lung immune homeostasis and the severity of influenza infection. Nature Immunology 9: 1074-83 138. Zhou J, Matsuoka M, Cantor H, Homer R, Enelow RI. 2008. Cutting edge: engagement of NKG2A on CD8+ effector T cells limits immunopathology in influenza pneumonia. Journal of immunology (Baltimore, Md. : 1950) 180: 25-9 139. Carlson CM, Turpin EA, Moser LA, O'Brien KB, Cline TD, Jones JC, Tumpey TM, Katz JM, Kelley LA, Gauldie J, Schultz-Cherry S. 2010. Transforming growth factor-beta: activation by neuraminidase and role in highly pathogenic H5N1 influenza pathogenesis. PLoS Pathog 6: e1001136 140. Gazzinelli RT, Wysocka M, Hieny S, Scharton-Kersten T, Cheever A, Kühn R, Müller W, Trinchieri G, Sher A. 1996. In the absence of endogenous IL-10, mice acutely infected with Toxoplasma gondii succumb to a lethal immune response dependent on CD4+ T cells and accompanied by overproduction of IL-12, IFN-gamma and TNF-alpha. J. Immunol. 157: 798-805   198 141. Hunter CA, Ellis-Neyes LA, Slifer T, Kanaly S, Grunig G, Fort M, Rennick D, Araujo FG. 1997. IL-10 is required to prevent immune hyperactivity during infection with Trypanosoma cruzi. J Immunol 158: 3311-6 142. Redpath SA, Fonseca NM, Perona-Wright G. 2014. Protection and pathology during parasite infection: IL-10 strikes the balance. Parasite Immunol. 36: 233-52 143. Sun J, Cardani A, Sharma AK, Laubach VE, Jack RS, Müller W, Braciale TJ. 2011. Autocrine regulation of pulmonary inflammation by effector T-cell derived IL-10 during infection with respiratory syncytial virus. PLoS Pathog. 7: e1002173 144. Sun K, Torres L, Metzger DW. 2010. A detrimental effect of interleukin-10 on protective pulmonary humoral immunity during primary influenza A virus infection. Journal of virology 84: 5007-14 145. Loebbermann J, Schnoeller C, Thornton H, Durant L, Sweeney NP, Schuijs M, O&apos;Garra A, Johansson C, Openshaw PJ. 2012. IL-10 regulates viral lung immunopathology during acute respiratory syncytial virus infection in mice. PLoS ONE 7: e32371 146. Jankovic D, Kugler DG, Sher A. 2010. IL-10 production by CD4+ effector T cells: a mechanism for self-regulation. Mucosal Immunol 3: 239-46 147. Murray PJ. 2005. The primary mechanism of the IL-10-regulated antiinflammatory response is to selectively inhibit transcription. In Proceedings of the National Academy of … 148. Bogdan C, Vodovotz Y, Nathan C. 1991. Macrophage deactivation by interleukin 10. The Journal of experimental medicine 174: 1549-55 149. Gazzinelli RT, Oswald IP, James SL, Sher A. 1992. IL-10 inhibits parasite killing and nitrogen oxide production by IFN-gamma-activated macrophages. Journal of immunology (Baltimore, Md. : 1950) 148: 1792-6 150. Oswald IP, Wynn TA, Sher A, James SL. 1992. Interleukin 10 inhibits macrophage microbicidal activity by blocking the endogenous production of tumor necrosis factor alpha required as a costimulatory factor for interferon gamma-induced activation. Proceedings of the National Academy of Sciences of the United States of America 89: 8676-80 151. Guilliams M, Movahedi K, Bosschaerts T, VandenDriessche T, Chuah MK, Hérin M, Acosta-Sanchez A, Ma L, Moser M, Van Ginderachter JA, Brys L, De Baetselier P, Beschin A. 2009. IL-10 dampens TNF/inducible nitric oxide synthase-producing dendritic cell-mediated pathogenicity during parasitic infection. Journal of immunology (Baltimore, Md. : 1950) 182: 1107-18 152. Perona-Wright G, Mohrs K, Szaba FM, Kummer LW, Madan R, Karp CL, Johnson LL, Smiley ST, Mohrs M. 2009. Systemic but Not Local Infections Elicit Immunosuppressive IL-10 Production by Natural Killer Cells. Cell Host & Microbe 6: 503-12 153. Poncini CV, Alba Soto CD, Batalla E, Solana ME, González Cappa SM. 2008. Trypanosoma cruzi induces regulatory dendritic cells in vitro. Infection and immunity 76: 2633-41 154. Owens BM, Beattie L, Moore JW, Brown N, Mann JL, Dalton JE, Maroof A, Kaye PM. 2012. IL-10-producing Th1 cells and disease progression are regulated by distinct CD11c(+) cell populations during visceral leishmaniasis. PLoS Pathog 8: e1002827   199 155. Murray Henry W, Moreira Andre L, Lu Cristina M, DeVecchio Jennifer L, Matsuhashi M, Ma X, Heinzel Frederick P. 2003. Determinants of Response to Interleukin-10 Receptor Blockade Immunotherapy in Experimental Visceral Leishmaniasis. The Journal of Infectious Diseases 188: 458-64 156. Omer FM, de Souza JB, Riley EM. 2003. Differential induction of TGF-beta regulates proinflammatory cytokine production and determines the outcome of lethal and nonlethal Plasmodium yoelii infections. J Immunol 171: 5430-6 157. Wu Y, Wang Q-h, Zheng L, Feng H, Liu J, Ma S-h, Cao Y-m. 2007. Plasmodium yoelii: Distinct CD4+CD25+ regulatory T cell responses during the early stages of infection in susceptible and resistant mice. Experimental Parasitology 115: 301-4 158. Anderson CF, Mendez S, Sacks DL. 2005. Nonhealing infection despite Th1 polarization produced by a strain of Leishmania major in C57BL/6 mice. Journal of immunology (Baltimore, Md. : 1950) 174: 2934-41 159. Belkaid Y, Hoffmann KF, Mendez S, Kamhawi S, Udey MC, Wynn TA, Sacks DL. 2001. The role of interleukin (IL)-10 in the persistence of Leishmania major in the skin after healing and the therapeutic potential of anti-IL-10 receptor antibody for sterile cure. The Journal of experimental medicine 194: 1497-506 160. Reed SG, Brownell CE, Russo DM, Silva JS, Grabstein KH, Morrissey PJ. 1994. IL-10 mediates susceptibility to Trypanosoma cruzi infection. Journal of immunology (Baltimore, Md. : 1950) 153: 3135-40 161. Roque S, Nobrega C, Appelberg R, Correia-Neves M. 2007. IL-10 underlies distinct susceptibility of BALB/c and C57BL/6 mice to Mycobacterium avium infection and influences efficacy of antibiotic therapy. J Immunol 178: 8028-35 162. Brooks DGD, Trifilo MJM, Edelmann KHK, Teyton LL, McGavern DBD, Oldstone MBAM. 2006. Interleukin-10 determines viral clearance or persistence in vivo. Nat Med 12: 1301-9 163. Cowling BJ, Chan KH, Fang VJ, Lau LLH, So HC, Fung ROP, Ma ESK, Kwong ASK, Chan C-W, Tsui WWS, Ngai H-Y, Chu DWS, Lee PWY, Chiu M-C, Leung GM, Peiris JSM. 2010. Comparative Epidemiology of Pandemic and Seasonal Influenza A in Households. New England Journal of Medicine 362: 2175-84 164. Li C-C, Wang L, Eng H-L, You H-L, Chang L-S, Tang K-S, Lin Y-J, Kuo H-C, Lee I-K, Liu J-W, Huang E-Y, Yang KD. 2010. Correlation of pandemic (H1N1) 2009 viral load with disease severity and prolonged viral shedding in children. Emerging infectious diseases 16: 1265-72 165. Jankovic D, Kullberg MC, Feng CG, Goldszmid RS, Collazo CM, Wilson M, Wynn TA, Kamanaka M, Flavell RA, Sher A. 2007. Conventional T-bet(+)Foxp3(-) Th1 cells are the major source of host-protective regulatory IL-10 during intracellular protozoan infection. Journal of Experimental Medicine 204: 273-83 166. do Rosário APAF, Lamb TT, Spence PP, Stephens RR, Lang AA, Roers AA, Muller WW, O&apos;Garra AA, Langhorne JJ. 2012. IL-27 promotes IL-10 production by effector Th1 CD4+ T cells: a critical mechanism for protection from severe immunopathology during malaria infection. J. Immunol. 188: 1178-90 167. Oldenhove G, Bouladoux N, Wohlfert EA, Hall JA, Chou D, Dos santos L, O&apos;Brien S, Blank R, Lamb E, Natarajan S, Kastenmayer R, Hunter C, Grigg ME,   200 Belkaid Y. 2009. Decrease of Foxp3+ Treg cell number and acquisition of effector cell phenotype during lethal infection. Immunity 31: 772-86 168. Couper KN, Blount DG, Wilson MS, Hafalla JC, Belkaid Y, Kamanaka M, Flavell RA, de Souza JB, Riley EM. 2008. IL-10 from CD4+CD25−Foxp3−CD127− Adaptive Regulatory T Cells Modulates Parasite Clearance and Pathology during Malaria Infection. PLoS Pathogens 4: e1000004-e 169. McGuirk P, McCann C, Mills KHG. 2002. Pathogen-specific T Regulatory 1 Cells Induced in the Respiratory Tract by a Bacterial Molecule that Stimulates Interleukin 10 Production by Dendritic Cells. Journal of Experimental Medicine 195: 221-31 170. Gagliani N, Magnani CF, Huber S, Gianolini ME, Pala M, Licona-Limon P, Guo B, Herbert DR, Bulfone A, Trentini F, Di Serio C, Bacchetta R, Andreani M, Brockmann L, Gregori S, Flavell RA, Roncarolo MG. 2013. Coexpression of CD49b and LAG-3 identifies human and mouse T regulatory type 1 cells. Nat Med 19: 739-46 171. Roncarolo MG, Gregori S, Bacchetta R, Battaglia M. 2014. Tr1 cells and the counter-regulation of immunity: natural mechanisms and therapeutic applications. Curr Top Microbiol Immunol 380: 39-68 172. Jordan MS, Boesteanu A, Reed AJ, Petrone AL, Holenbeck AE, Lerman MA, Naji A, Caton AJ. 2001. Thymic selection of CD4+CD25+ regulatory T cells induced by an agonist self-peptide. Nature Immunology 2: 301-6 173. Lio C-WJ, Hsieh C-S. 2008. A Two-Step Process for Thymic Regulatory T Cell Development. Immunity 28: 100-11 174. Xing Y, Hogquist KA. 2012. T-Cell Tolerance: Central and Peripheral. Cold Spring Harbor Perspectives in Biology 4: a006957-a 175. Kretschmer K, Apostolou I, Hawiger D, Khazaie K, Nussenzweig MC, von Boehmer H. 2005. Inducing and expanding regulatory T cell populations by foreign antigen. Nature Immunology 6: 1219-27 176. Chen W, Jin W, Hardegen N, Lei K-J, Li L, Marinos N, McGrady G, Wahl SM. 2003. Conversion of peripheral CD4+CD25- naive T cells to CD4+CD25+ regulatory T cells by TGF-beta induction of transcription factor Foxp3. The Journal of experimental medicine 198: 1875-86 177. Selvaraj RK, Geiger TL. 2008. Mitigation of Experimental Allergic Encephalomyelitis by TGF-β Induced Foxp3+ Regulatory T Lymphocytes through the Induction of Anergy and Infectious Tolerance. The Journal of Immunology 180: 2830-8 178. Zheng Y, Chaudhry A, Kas A, deRoos P, Kim JM, Chu T-T, Corcoran L, Treuting P, Klein U, Rudensky AY. 2009. Regulatory T-cell suppressor program co-opts transcription factor IRF4 to control TH2 responses. Nature 458: 351-6 179. Apostolou I, von Boehmer H. 2004. In Vivo Instruction of Suppressor Commitment in Naive T Cells. The Journal of Experimental Medicine 199: 1401-8 180. Haeryfar SM, DiPaolo RJ, Tscharke DC, Bennink JR, Yewdell JW. 2005. Regulatory T cells suppress CD8+ T cell responses induced by direct priming and cross-priming and moderate immunodominance disparities. J Immunol 174: 3344-51 181. Ruckwardt TJ, Bonaparte KL, Nason MC, Graham BS. 2009. Regulatory T cells promote early influx of CD8+ T cells in the lungs of respiratory syncytial virus-infected mice and diminish immunodominance disparities. J Virol 83: 3019-28   201 182. Pflanz S, Timans JC, Cheung J, Rosales R, Kanzler H, Gilbert J, Hibbert L, Churakova T, Travis M, Vaisberg E, Blumenschein WM, Mattson JD, Wagner JL, To W, Zurawski S, McClanahan TK, Gorman DM, Bazan JF, de Waal Malefyt R, Rennick D, Kastelein RA. 2002. IL-27, a heterodimeric cytokine composed of EBI3 and p28 protein, induces proliferation of naive CD4+ T cells. Immunity 16: 779-90 183. Hibbert L, Pflanz S, de Waal Malefyt R, Kastelein RA. 2003. IL-27 and IFN- <i>α</i> Signal via Stat1 and Stat3 and Induce T-Bet and IL-12R <i>β</i> 2 in Naive T Cells. Journal of Interferon & Cytokine Research 23: 513-22 184. Lucas S, Ghilardi N, Li J, de Sauvage FJ. 2003. IL-27 regulates IL-12 responsiveness of naive CD4+ T cells through Stat1-dependent and -independent mechanisms. Proc Natl Acad Sci U S A 100: 15047-52 185. Takeda A, Hamano S, Yamanaka A, Hanada T, Ishibashi T, Mak TW, Yoshimura A, Yoshida H. 2003. Cutting edge: role of IL-27/WSX-1 signaling for induction of T-bet through activation of STAT1 during initial Th1 commitment. Journal of immunology (Baltimore, Md. : 1950) 170: 4886-90 186. Owaki T, Asakawa M, Fukai F, Mizuguchi J, Yoshimoto T. 2006. IL-27 induces Th1 differentiation via p38 MAPK/T-bet- and intercellular adhesion molecule-1/LFA-1/ERK1/2-dependent pathways. Journal of immunology (Baltimore, Md. : 1950) 177: 7579-87 187. Kim G, Shinnakasu R, Saris CJ, Cheroutre H, Kronenberg M. 2013. A novel role for IL-27 in mediating the survival of activated mouse CD4 T lymphocytes. J Immunol 190: 1510-8 188. Rosas LE, Satoskar AA, Roth KM, Keiser TL, Barbi J, Hunter C, de Sauvage FJ, Satoskar AR. 2006. Interleukin-27R (WSX-1/T-cell cytokine receptor) gene-deficient mice display enhanced resistance to leishmania donovani infection but develop severe liver immunopathology. Am J Pathol 168: 158-69 189. Findlay EG, Greig R, Stumhofer JS, Hafalla JCR, de Souza JB, Saris CJ, Hunter CA, Riley EM, Couper KN. 2010. Essential role for IL-27 receptor signaling in prevention of Th1-mediated immunopathology during malaria infection. J. Immunol. 185: 2482-92 190. Hamano S, Himeno K, Miyazaki Y, Ishii K, Yamanaka A, Takeda A, Zhang M, Hisaeda H, Mak TW, Yoshimura A, Yoshida H. 2003. WSX-1 is required for resistance to Trypanosoma cruzi infection by regulation of proinflammatory cytokine production. Immunity 19: 657-67 191. Villarino A, Hibbert L, Lieberman L, Wilson E, Mak T, Yoshida H, Kastelein RA, Saris C, Hunter CA. 2003. The IL-27R (WSX-1) is required to suppress T cell hyperactivity during infection. Immunity 19: 645-55 192. Liu FDM, Kenngott EE, Schröter MF, Kühl A, Jennrich S, Watzlawick R, Hoffmann U, Wolff T, Norley S, Scheffold A, Stumhofer JS, Saris CJM, Schwab JM, Hunter CA, Debes GF, Hamann A. 2014. Timed Action of IL-27 Protects from Immunopathology while Preserving Defense in Influenza. PLoS Pathogens 10: e1004110-e 193. Pyle CJ, Uwadiae FI, Swieboda DP, Harker JA. 2017. Early IL-6 signalling promotes IL-27 dependent maturation of regulatory T cells in the lungs and resolution of viral immunopathology. PLoS Pathog 13: e1006640   202 194. Anderson CF, Stumhofer JS, Hunter CA, Sacks D. 2009. IL-27 Regulates IL-10 and IL-17 from CD4+ Cells in Nonhealing Leishmania major Infection. The Journal of Immunology 183: 4619-27 195. Artis D, Johnson LM, Joyce K, Saris C, Villarino A, Hunter CA, Scott P. 2004. Cutting Edge: Early IL-4 Production Governs the Requirement for IL-27-WSX-1 Signaling in the Development of Protective Th1 Cytokine Responses following Leishmania major Infection. The Journal of Immunology 172: 4672-5 196. Stumhofer JS, Laurence A, Wilson EH, Huang E, Tato CM, Johnson LM, Villarino AV, Huang Q, Yoshimura A, Sehy D, Saris CJM, O'Shea JJ, Hennighausen L, Ernst M, Hunter CA. 2006. Interleukin 27 negatively regulates the development of interleukin 17–producing T helper cells during chronic inflammation of the central nervous system. Nature Immunology 7: 937-45 197. de Almeida Nagata DE, Demoor T, Ptaschinski C, Ting H-A, Jang S, Reed M, Mukherjee S, Lukacs NW. 2014. IL-27R-mediated regulation of IL-17 controls the development of respiratory syncytial virus-associated pathogenesis. The American journal of pathology 184: 1807-18 198. Stumhofer JS, Silver JS, Laurence A, Porrett PM, Harris TH, Turka LA, Ernst M, Saris CJM, O'Shea JJ, Hunter CA. 2007. Interleukins 27 and 6 induce STAT3-mediated T cell production of interleukin 10. Nature Immunology 8: 1363-71 199. Awasthi A, Carrier Y, Peron JPS, Bettelli E, Kamanaka M, Flavell RA, Kuchroo VK, Oukka M, Weiner HL. 2007. A dominant function for interleukin 27 in generating interleukin 10-producing anti-inflammatory T cells. Nat. Immunol. 8: 1380-9 200. Batten M, Kljavin NM, Li J, Walter MJ, de Sauvage FJ, Ghilardi N. 2008. Cutting Edge: IL-27 Is a Potent Inducer of IL-10 but Not FoxP3 in Murine T Cells. J. Immunol. 180: 2752-6 201. Yoshimura A, Yoshida H, Miyazaki Y, Kinjyo I, Ishibashi T, Yoshimura T, Takeda A, Hamano S. 2018. STAT3-Dependent Mechanism T Cells Partially Through + Activated CD4 Production Including IL-23-Induced IL-17 on Suppression of Proinflammatory Cytokine T Cells versus + Differentiation on Naive CD4 Two-Sided Roles of IL-27: Induction of Th1. J Immunol References 177: 5377-85 202. Hall AO, Beiting DP, Tato C, John B, Oldenhove G, Lombana CG, Pritchard GH, Silver JS, Bouladoux N, Stumhofer JS, Harris TH, Grainger J, Wojno ED, Wagage S, Roos DS, Scott P, Turka LA, Cherry S, Reiner SL, Cua D, Belkaid Y, Elloso MM, Hunter CA. 2012. The cytokines interleukin 27 and interferon-gamma promote distinct Treg cell populations required to limit infection-induced pathology. Immunity 37: 511-23 203. Koch MA, Tucker-Heard Gas, Perdue NR, Killebrew JR, Urdahl KB, Campbell DJ. 2009. The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation. Nature immunology 10: 595-602 204. Hirahara K, Ghoreschi K, Yang X-P, Takahashi H, Laurence A, Vahedi G, Sciumè G, Hall AOaH, Dupont CD, Francisco LM, Chen Q, Tanaka M, Kanno Y, Sun H-W, Sharpe AH, Hunter CA, O&apos;Shea JJ. 2012. Interleukin-27 priming of T cells controls IL-17 production in trans via induction of the ligand PD-L1. Immunity 36: 1017-30 205. Ouyang W, Ranganath SH, Weindel K, Bhattacharya D, Murphy TL, Sha WC, Murphy KM. 1998. Inhibition of Th1 development mediated by GATA-3 through an IL-4-independent mechanism. Immunity 9: 745-55   203 206. Usui T, Nishikomori R, Kitani A, Strober W. 2003. GATA-3 suppresses Th1 development by downregulation of Stat4 and not through effects on IL-12Rbeta2 chain or T-bet. Immunity 18: 415-28 207. Hall AOH, Silver JS, Hunter CA. 2012. The immunobiology of IL-27. Adv. Immunol. 115: 1-44 208. Robinson KM, Lee B, Scheller EV, Mandalapu S, Enelow RI, Kolls JK, Alcorn JF. 2015. The role of IL-27 in susceptibility to post-influenza Staphylococcus aureus pneumonia. Respiratory Research 16: 10- 209. Sun J, Dodd H, Moser EK, Sharma R, Braciale TJ. 2011. CD4+ T cell help and innate-derived IL-27 induce Blimp-1-dependent IL-10 production by antiviral CTLs. Nature Immunology 12: 327-34 210. Silver JS, Hunter CA. 2010. gp130 at the nexus of inflammation, autoimmunity, and cancer. J. Leukoc. Biol. 88: 1145-56 211. Kamiya S, Owaki T, Morishima N, Fukai F, Mizuguchi J, Yoshimoto T. 2004. An indispensable role for STAT1 in IL-27-induced T-bet expression but not proliferation of naive CD4+ T cells. Journal of immunology (Baltimore, Md. : 1950) 173: 3871-7 212. Charlot-Rabiega P, Bardel E, Dietrich C, Kastelein R, Devergne O. 2011. Signaling events involved in interleukin 27 (IL-27)-induced proliferation of human naive CD4+ T cells and B cells. The Journal of biological chemistry 286: 27350-62 213. Hibbert L, Pflanz S, de Waal Malefyt R, Kastelein RA. 2003. IL-27 and IFN-alpha signal via Stat1 and Stat3 and induce T-Bet and IL-12Rbeta2 in naive T cells. J. Interferon Cytokine Res. 23: 513-22 214. Apetoh L, Quintana FJ, Pot C, Joller N, Xiao S, Kumar D, Burns EJ, Sherr DH, Weiner HL, Kuchroo VK. 2010. The aryl hydrocarbon receptor interacts with c-Maf to promote the differentiation of type 1 regulatory T cells induced by IL-27. Nature Immunology 11: 854-61 215. Iwasaki Y, Fujio K, Okamura T, Yanai A, Sumitomo S, Shoda H, Tamura T, Yoshida H, Charnay P, Yamamoto K. 2013. Egr-2 transcription factor is required for Blimp-1-mediated IL-10 production in IL-27-stimulated CD4 <sup>+</sup> T cells. European Journal of Immunology 43: 1063-73 216. Betz UA, Müller W. 1998. Regulated expression of gp130 and IL-6 receptor alpha chain in T cell maturation and activation. Int. Immunol. 10: 1175-84 217. Nicholson SE, De Souza D, Fabri LJ, Corbin J, Willson TA, Zhang JG, Silva A, Asimakis M, Farley A, Nash AD, Metcalf D, Hilton DJ, Nicola NA, Baca M. 2000. Suppressor of cytokine signaling-3 preferentially binds to the SHP-2-binding site on the shared cytokine receptor subunit gp130. Proceedings of the National Academy of Sciences 97: 6493-8 218. Perona-Wright G, Kohlmeier JE, Bassity E, Freitas TC, Mohrs K, Cookenham T, Situ H, Pearce EJ, Woodland DL, Mohrs M. 2012. Persistent loss of IL-27 responsiveness in CD8+ memory T cells abrogates IL-10 expression in a recall response. Proc. Natl. Acad. Sci. U.S.A. 109: 18535-40 219. Janeway C. 2001. Immunobiology 5 : the immune system in health and disease: Garland Pub. 732- pp. 220. Ahmed R, Gray D. 1996. Immunological memory and protective immunity: understanding their relation. Science (New York, N.Y.) 272: 54-60   204 221. Seder RA, Ahmed R. 2003. Similarities and differences in CD4+ and CD8+ effector and memory T cell generation. Nature Immunology 4: 835-42 222. Sautto GA, Kirchenbaum GA, Ross TM. 2018. Towards a universal influenza vaccine: different approaches for one goal. Virology Journal 15: 17- 223. Sridhar S. 2016. Heterosubtypic T-Cell Immunity to Influenza in Humans: Challenges for Universal T-Cell Influenza Vaccines. Frontiers in Immunology 7: 195- 224. Jelley-Gibbs DM, Brown DM, Dibble JP, Haynes L, Eaton SM, Swain SL. 2005. Unexpected prolonged presentation of influenza antigens promotes CD4 T cell memory generation. The Journal of Experimental Medicine 202: 697-706 225. Dooms H, Wolslegel K, Lin P, Abbas AK. 2007. Interleukin-2 enhances CD4+ T cell memory by promoting the generation of IL-7R alpha-expressing cells. The Journal of experimental medicine 204: 547-57 226. Li J, Huston G, Swain SL. 2003. IL-7 promotes the transition of CD4 effectors to persistent memory cells. The Journal of experimental medicine 198: 1807-15 227. Kaech SM, Tan JT, Wherry EJ, Konieczny BT, Surh CD, Ahmed R. 2003. Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells. Nature Immunology 4: 1191-8 228. Joshi NS, Cui W, Chandele A, Lee HK, Urso DR, Hagman J, Gapin L, Kaech SM. 2007. Inflammation Directs Memory Precursor and Short-Lived Effector CD8+ T Cell Fates via the Graded Expression of T-bet Transcription Factor. Immunity 27: 281-95 229. Chang JT, Wherry EJ, Goldrath AW. 2014. Molecular regulation of effector and memory T cell differentiation. Nature Immunology 15: 1104-15 230. Banerjee A, Gordon SM, Intlekofer AM, Paley MA, Mooney EC, Lindsten T, Wherry EJ, Reiner SL. 2010. Cutting edge: The transcription factor eomesodermin enables CD8+ T cells to compete for the memory cell niche. J Immunol 185: 4988-92 231. Zhou X, Yu S, Zhao DM, Harty JT, Badovinac VP, Xue HH. 2010. Differentiation and persistence of memory CD8(+) T cells depend on T cell factor 1. Immunity 33: 229-40 232. Cui W, Liu Y, Weinstein JS, Craft J, Kaech SM. 2011. An interleukin-21-interleukin-10-STAT3 pathway is critical for functional maturation of memory CD8+ T cells. Immunity 35: 792-805 233. Yang CY, Best JA, Knell J, Yang E, Sheridan AD, Jesionek AK, Li HS, Rivera RR, Lind KC, D'Cruz LM, Watowich SS, Murre C, Goldrath AW. 2011. The transcriptional regulators Id2 and Id3 control the formation of distinct memory CD8+ T cell subsets. Nat Immunol 12: 1221-9 234. Intlekofer AM, Takemoto N, Kao C, Banerjee A, Schambach F, Northrop JK, Shen H, Wherry EJ, Reiner SL. 2007. Requirement for T-bet in the aberrant differentiation of unhelped memory CD8+ T cells. J Exp Med 204: 2015-21 235. Rutishauser RL, Martins GA, Kalachikov S, Chandele A, Parish IA, Meffre E, Jacob J, Calame K, Kaech SM. 2009. Transcriptional repressor Blimp-1 promotes CD8(+) T cell terminal differentiation and represses the acquisition of central memory T cell properties. Immunity 31: 296-308 236. Herndler-Brandstetter D, Ishigame H, Shinnakasu R, Plajer V, Stecher C, Zhao J, Lietzenmayer M, Kroehling L, Takumi A, Kometani K, Inoue T, Kluger Y, Kaech SM, Kurosaki T, Okada T, Flavell RA. 2018. KLRG1(+) Effector CD8(+) T Cells Lose   205 KLRG1, Differentiate into All Memory T Cell Lineages, and Convey Enhanced Protective Immunity. Immunity 48: 716-29 e8 237. Akondy RS, Fitch M, Edupuganti S, Yang S, Kissick HT, Li KW, Youngblood BA, Abdelsamed HA, McGuire DJ, Cohen KW, Alexe G, Nagar S, McCausland MM, Gupta S, Tata P, Haining WN, McElrath MJ, Zhang D, Hu B, Greenleaf WJ, Goronzy JJ, Mulligan MJ, Hellerstein M, Ahmed R. 2017. Origin and differentiation of human memory CD8 T cells after vaccination. Nature 552: 362-7 238. Youngblood B, Hale JS, Kissick HT, Ahn E, Xu X, Wieland A, Araki K, West EE, Ghoneim HE, Fan Y, Dogra P, Davis CW, Konieczny BT, Antia R, Cheng X, Ahmed R. 2017. Effector CD8 T cells dedifferentiate into long-lived memory cells. Nature 552: 404-9 239. Foulds KE, Zenewicz LA, Shedlock DJ, Jiang J, Troy AE, Shen H. 2002. Cutting edge: CD4 and CD8 T cells are intrinsically different in their proliferative responses. J Immunol 168: 1528-32 240. Homann D, Teyton L, Oldstone MB. 2001. Differential regulation of antiviral T-cell immunity results in stable CD8+ but declining CD4+ T-cell memory. Nat Med 7: 913-9 241. Marshall Heather D, Chandele A, Jung Yong W, Meng H, Poholek Amanda C, Parish Ian A, Rutishauser R, Cui W, Kleinstein Steven H, Craft J, Kaech Susan M. 2011. Differential Expression of Ly6C and T-bet Distinguish Effector and Memory Th1 CD4+ Cell Properties during Viral Infection. Immunity 35: 633-46 242. Hale JS, Youngblood B, Latner Donald R, Mohammed Ata Ur R, Ye L, Akondy Rama S, Wu T, Iyer Smita S, Ahmed R. 2013. Distinct Memory CD4+ T Cells with Commitment to T Follicular Helper- and T Helper 1-Cell Lineages Are Generated after Acute Viral Infection. Immunity 38: 805-17 243. Sanchez AM, Zhu J, Huang X, Yang Y. 2012. The Development and Function of Memory Regulatory T Cells after Acute Viral Infections. The Journal of Immunology 189: 2805-14 244. Brincks EL, Roberts AD, Cookenham T, Sell S, Kohlmeier JE, Blackman MA, Woodland DL. 2013. Antigen-specific memory regulatory CD4+Foxp3+ T cells control memory responses to influenza virus infection. The Journal of Immunology 190: 3438-46 245. Jameson SC, Masopust D. 2018. Understanding Subset Diversity in T Cell Memory. Immunity 48: 214-26 246. Moulton VR, Bushar ND, Leeser DB, Patke DS, Farber DL. 2006. Divergent generation of heterogeneous memory CD4 T cells. J Immunol 177: 869-76 247. Wu C-Y, Kirman JR, Rotte MJ, Davey DF, Perfetto SP, Rhee EG, Freidag BL, Hill BJ, Douek DC, Seder RA. 2002. Distinct lineages of T(H)1 cells have differential capacities for memory cell generation in vivo. Nature immunology 3: 852-8 248. Harrington LE, Janowski KM, Oliver JR, Zajac AJ, Weaver CT. 2008. Memory CD4 T cells emerge from effector T-cell progenitors. Nature 452: 356-60 249. Löhning M, Hegazy AN, Pinschewer DD, Busse D, Lang KS, Höfer T, Radbruch A, Zinkernagel RM, Hengartner H. 2008. Long-lived virus-reactive memory T cells generated from purified cytokine-secreting T helper type 1 and type 2 effectors. The Journal of Experimental Medicine 205: 53-61   206 250. Sallusto F, Lenig D, Förster R, Lipp M, Lanzavecchia A. 1999. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401: 708-12 251. Gasper DJ, Tejera MM, Suresh M. 2014. CD4 T-cell memory generation and maintenance. Critical reviews in immunology 34: 121-46 252. Schenkel JM, Masopust D. 2014. Tissue-resident memory T cells. Immunity 41: 886-97 253. Reinhardt RL, Khoruts A, Merica R, Zell T, Jenkins MK. 2001. Visualizing the generation of memory CD4 T cells in the whole body. Nature 410: 101-5 254. Pepper M, Linehan JL, Pagan AJ, Zell T, Dileepan T, Cleary PP, Jenkins MK. 2010. Different routes of bacterial infection induce long-lived TH1 memory cells and short-lived TH17 cells. Nat Immunol 11: 83-9 255. Pepper M, Pagán Antonio J, Igyártó Botond Z, Taylor Justin J, Jenkins Marc K. 2011. Opposing Signals from the Bcl6 Transcription Factor and the Interleukin-2 Receptor Generate T Helper 1 Central and Effector Memory Cells. Immunity 35: 583-95 256. Pepper M, Jenkins MK. 2011. Origins of CD4(+) effector and central memory T cells. Nature immunology 12: 467-71 257. Stephens R, Langhorne J. 2010. Effector Memory Th1 CD4 T Cells Are Maintained in a Mouse Model of Chronic Malaria. PLoS Pathogens 6: e1001208-e 258. Turner DL, Farber DL. 2014. Mucosal resident memory CD4 T cells in protection and immunopathology. Front Immunol 5: 331 259. Wilk MM, Mills KHG. 2018. CD4 TRM Cells Following Infection and Immunization: Implications for More Effective Vaccine Design. Frontiers in immunology 9: 1860- 260. Turner DL, Bickham KL, Thome JJ, Kim CY, D'Ovidio F, Wherry EJ, Farber DL. 2014. Lung niches for the generation and maintenance of tissue-resident memory T cells. Mucosal Immunology 7: 501-10 261. Teijaro JR, Turner D, Pham Q, Wherry EJ, Lefrancois L, Farber DL. 2011. Cutting Edge: Tissue-Retentive Lung Memory CD4 T Cells Mediate Optimal Protection to Respiratory Virus Infection. The Journal of Immunology 187: 5510-4 262. Purwar R, Campbell J, Murphy G, Richards WG, Clark RA, Kupper TS. 2011. Resident Memory T Cells (TRM) Are Abundant in Human Lung: Diversity, Function, and Antigen Specificity. PLoS ONE 6: e16245-e 263. McKinstry KK, Strutt TM, Kuang Y, Brown DM, Sell S, Dutton RW, Swain SL. 2012. Memory CD4+ T cells protect against influenza through multiple synergizing mechanisms. The Journal of clinical investigation 122: 2847-56 264. Strutt TM, McKinstry KK, Dibble JP, Winchell C, Kuang Y, Curtis JD, Huston G, Dutton RW, Swain SL. 2010. Memory CD4+ T cells induce innate responses independently of pathogen. Nature Medicine 16: 558-64 265. MacLeod MKL, David A, McKee AS, Crawford F, Kappler JW, Marrack P. 2011. Memory CD4 T cells that express CXCR5 provide accelerated help to B cells. Journal of immunology (Baltimore, Md. : 1950) 186: 2889-96 266. Strutt TM, McKinstry KK, Kuang Y, Bradley LM, Swain SL. 2012. Memory CD4+ T-cell-mediated protection depends on secondary effectors that are distinct from and superior to primary effectors. Proc Natl Acad Sci U S A 109: E2551-60   207 267. Mozdzanowska K, Maiese K, Gerhard W. 2000. Th cell-deficient mice control influenza virus infection more effectively than Th- and B cell-deficient mice: evidence for a Th-independent contribution by B cells to virus clearance. J Immunol 164: 2635-43 268. Teijaro JR, Verhoeven D, Page CA, Turner D, Farber DL. 2010. Memory CD4 T Cells Direct Protective Responses to Influenza Virus in the Lungs through Helper-Independent Mechanisms. Journal of Virology 84: 9217-26 269. MacLeod MKL, Clambey ET, Kappler JW, Marrack P. 2009. CD4 memory T cells: What are they and what can they do? Seminars in Immunology 21: 53-61 270. Valkenburg SA, Li OTW, Li A, Bull M, Waldmann TA, Perera LP, Peiris M, Poon LLM. 2018. Protection by universal influenza vaccine is mediated by memory CD4 T cells. Vaccine 36: 4198-206 271. Mouse Genome Sequencing C, Waterston RH, Lindblad-Toh K, Birney E, Rogers J, et al. 2002. Initial sequencing and comparative analysis of the mouse genome. Nature 420: 520-62 272. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, et al. 2001. The Sequence of the Human Genome. Science 291: 1304-51 273. Shen Y, Yue F, McCleary DF, Ye Z, Edsall L, Kuan S, Wagner U, Dixon J, Lee L, Lobanenkov VV, Ren B. 2012. A map of the cis-regulatory sequences in the mouse genome. Nature 488: 116-20 274. Maston GA, Evans SK, Green MR. 2006. Transcriptional Regulatory Elements in the Human Genome. Annual Review of Genomics and Human Genetics 7: 29-59 275. Shlyueva D, Stampfel G, Stark A. 2014. Transcriptional enhancers: from properties to genome-wide predictions. Nature Reviews Genetics 15: 272-86 276. Kouzarides T. 2007. Chromatin Modifications and Their Function. Cell 128: 693-705 277. Mujtaba S, Zeng L, Zhou MM. 2007. Structure and acetyl-lysine recognition of the bromodomain. Oncogene 26: 5521-7 278. Bannister AJ, Kouzarides T. 2011. Regulation of chromatin by histone modifications. Cell Research 21: 381-95 279. Parthun MR. 2007. Hat1: the emerging cellular roles of a type B histone acetyltransferase. Oncogene 26: 5319-28 280. Marmorstein R, Trievel RC. 2009. Histone modifying enzymes: Structures, mechanisms, and specificities. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1789: 58-68 281. Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, Zhang X, Wang L, Issner R, Coyne M, Ku M, Durham T, Kellis M, Bernstein BE. 2011. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473: 43-9 282. Barski A, Cuddapah S, Cui K, Roh T-Y, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K. 2007. High-Resolution Profiling of Histone Methylations in the Human Genome. Cell 129: 823-37 283. Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A. 2008. Combinatorial patterns of histone acetylations and methylations in the human genome. Nature  284. Bracken AP, Dietrich N, Pasini D, Hansen KH, Helin K. 2006. Genome-wide mapping of Polycomb target genes unravels their roles in cell fate transitions. Genes & Development 20: 1123-36   208 285. Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA, Wang W, Weng Z, Green RD, Crawford GE, Ren B. 2007. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nature Genetics 39: 311-8 286. Ghisletti S, Barozzi I, Mietton F, Polletti S, De Santa F, Venturini E, Gregory L, Lonie L, Chew A, Wei C-L, Ragoussis J, Natoli G. 2010. Identification and Characterization of Enhancers Controlling the Inflammatory Gene Expression Program in Macrophages. Immunity 32: 317-28 287. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton GM, Sharp PA, Boyer LA, Young RA, Jaenisch R. 2010. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proceedings of the National Academy of Sciences of the United States of America 107: 21931-6 288. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-André V, Sigova AA, Hoke HA, Young RA. 2013. Super-enhancers in the control of cell identity and disease. Cell 155: 934-47 289. Zhou L, Chong MMW, Littman DR. 2009. Plasticity of CD4+ T Cell Lineage Differentiation. Immunity 30: 646-55 290. Lee YK, Turner H, Maynard CL, Oliver JR, Chen D, Elson CO, Weaver CT. 2009. Late Developmental Plasticity in the T Helper 17 Lineage. Immunity 30: 92-107 291. Wei G, Wei L, Zhu J, Zang C, Hu-Li J, Yao Z, Cui K, Kanno Y, Roh T-Y, Watford WT, Schones DE, Peng W, Sun H-w, Paul WE, O'Shea JJ, Zhao K. 2009. Global Mapping of H3K4me3 and H3K27me3 Reveals Specificity and Plasticity in Lineage Fate Determination of Differentiating CD4+ T Cells. Immunity 30: 155-67 292. Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES. 2006. A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells. Cell 125: 315-26 293. Barski A, Cuddapah S, Kartashov AV, Liu C, Imamichi H, Yang W, Peng W, Lane HC, Zhao K. 2017. Rapid Recall Ability of Memory T cells is Encoded in their Epigenome. Sci Rep 7: 39785 294. Hawkins RD, Larjo A, Tripathi SK, Wagner U, Luu Y, Lonnberg T, Raghav SK, Lee LK, Lund R, Ren B, Lahdesmaki H, Lahesmaa R. 2013. Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization. Immunity 38: 1271-84 295. He B, Xing S, Chen C, Gao P, Teng L, Shan Q, Gullicksrud JA, Martin MD, Yu S, Harty JT, Badovinac VP, Tan K, Xue H-H. 2016. CD8 + T Cells Utilize Highly Dynamic Enhancer Repertoires and Regulatory Circuitry in Response to Infections. Immunity 45: 1341-54 296. Yu B, Zhang K, Milner JJ, Toma C, Chen R, Scott-Browne JP, Pereira RM, Crotty S, Chang JT, Pipkin ME, Wang W, Goldrath AW. 2017. Epigenetic landscapes reveal transcription factors that regulate CD8+ T cell differentiation. Nature immunology 18: 573-82 297. Vahedi G, Kanno Y, Furumoto Y, Jiang K, Parker SC, Erdos MR, Davis SR, Roychoudhuri R, Restifo NP, Gadina M, Tang Z, Ruan Y, Collins FS, Sartorelli V,   209 O'Shea JJ. 2015. Super-enhancers delineate disease-associated regulatory nodes in T cells. Nature 520: 558-62 298. Madan R, Demircik F, Surianarayanan S, Allen JL, Divanovic S, Trompette A, Yogev N, Gu Y, Khodoun M, Hildeman D, Boespflug N, Fogolin MB, Gröbe L, Greweling M, Finkelman FD, Cardin R, Mohrs M, Müller W, Waisman A, Roers A, Karp CL. 2009. Nonredundant roles for B cell-derived IL-10 in immune counter-regulation. The Journal of Immunology 183: 2312-20 299. Chen Q, Ghilardi N, Wang H, Baker T, Xie MH, Gurney A, Grewal IS, de Sauvage FJ. 2000. Development of Th1-type immune responses requires the type I cytokine receptor TCCR. Nature 407: 916-20 300. Lorzadeh A, Lopez Gutierrez R, Jackson L, Moksa M, Hirst M. 2017. Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis. J Vis Exp  301. Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-95 302. Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. 2015. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31: 2032-4 303. Feng J, Liu T, Qin B, Zhang Y, Liu XS. 2012. Identifying ChIP-seq enrichment using MACS. Nat Protoc 7: 1728-40 304. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, Rahl PB, Lee TI, Young RA. 2013. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153: 307-19 305. Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R, Bessy A, Cheneby J, Kulkarni SR, Tan G, Baranasic D, Arenillas DJ, Sandelin A, Vandepoele K, Lenhard B, Ballester B, Wasserman WW, Parcy F, Mathelier A. 2018. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res 46: D260-D6 306. Grant CE, Bailey TL, Noble WS. 2011. FIMO: scanning for occurrences of a given motif. Bioinformatics 27: 1017-8 307. Thomas-Chollier M, Hufton A, Heinig M, O'Keeffe S, Masri NE, Roider HG, Manke T, Vingron M. 2011. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 6: 1860-9 308. Wu H, Haist V, Baumgärtner W, Schughart K. 2010. Sustained viral load and late death in Rag2-/- mice after influenza A virus infection. Virology journal 7: 172- 309. Palladino G, Mozdzanowska K, Washko G, Gerhard W. 1995. Virus-neutralizing antibodies of immunoglobulin G (IgG) but not of IgM or IgA isotypes can cure influenza virus pneumonia in SCID mice. Journal of virology 69: 2075-81 310. Tate MD, Ioannidis LJ, Croker B, Brown LE, Brooks AG, Reading PC. 2011. The role of neutrophils during mild and severe influenza virus infections of mice. PloS one 6: e17618-e 311. Brandes M, Klauschen F, Kuchen S, Germain RN. 2013. A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection. Cell 154: 197-212 312. Perrone LA, Plowden JK, Garcia-Sastre A, Katz JM, Tumpey TM. 2008. H5N1 and 1918 pandemic influenza virus infection results in early and excessive infiltration of macrophages and neutrophils in the lungs of mice. PLoS Pathog 4: e1000115   210 313. Kash JC, Tumpey TM, Proll SC, Carter V, Perwitasari O, Thomas MJ, Basler CF, Palese P, Taubenberger JK, Garcia-Sastre A, Swayne DE, Katze MG. 2006. Genomic analysis of increased host immune and cell death responses induced by 1918 influenza virus. Nature 443: 578-81 314. Damjanovic D, Small C-L, Jeyanathan M, Jeyananthan M, McCormick S, Xing Z. 2012. Immunopathology in influenza virus infection: uncoupling the friend from foe. Clin. Immunol. 144: 57-69 315. Couper KN, Blount DG, Riley EM. 2008. IL-10: the master regulator of immunity to infection. J. Immunol. 180: 5771-7 316. Freitas do Rosario AP, do Rosário A, Lamb T, Spence P, Stephens R, Lang A, Roers A, Müller W, O&apos;Garra A, Langhorne J. 2012. IL-27 promotes IL-10 production by effector Th1 CD4+ T cells: a critical mechanism for protection from severe immunopathology during malaria infection. The Journal of …  317. Anderson CF, Oukka M, Kuchroo VJ, Sacks D. 2007. CD4(+)CD25(-)Foxp3(-) Th1 cells are the source of IL-10-mediated immune suppression in chronic cutaneous leishmaniasis. The Journal of experimental medicine 204: 285-97 318. Wu Y, Wang QH, Zheng L, Feng H, Liu J, Ma SH, Cao YM. 2007. Plasmodium yoelii: distinct CD4(+)CD25(+) regulatory T cell responses during the early stages of infection in susceptible and resistant mice. Exp Parasitol 115: 301-4 319. Weiss KA, Christiaansen AF, Fulton RB, Meyerholz DK, Varga SM. 2011. Multiple CD4+ T cell subsets produce immunomodulatory IL-10 during respiratory syncytial virus infection. The Journal of Immunology 187: 3145-54 320. Batten M, Li J, Yi S, Kljavin NM, Danilenko DM, Lucas S, Lee J, de Sauvage FJ, Ghilardi N. 2006. Interleukin 27 limits autoimmune encephalomyelitis by suppressing the development of interleukin 17–producing T cells. Nat. Immunol. 7: 929-36 321. Holscher C, Holscher A, Ruckerl D, Yoshimoto T, Yoshida H, Mak T, Saris C, Ehlers S. 2005. The IL-27 Receptor Chain WSX-1 Differentially Regulates Antibacterial Immunity and Survival during Experimental Tuberculosis. The Journal of Immunology 174: 3534-44 322. Li C, Yang P, Sun Y, Li T, Wang C, Wang Z, Zou Z, Yan Y, Wang W, Chen Z, Xing L, Tang C, Ju X, Guo F, Deng J, Zhao Y, Yang P, Tang J, Wang H, Zhao Z, Yin Z, Cao B, Wang X, Jiang C. 2012. IL-17 response mediates acute lung injury induced by the 2009 pandemic influenza A (H1N1) virus. Cell Res 22: 528-38 323. Fontenot JD, Gavin MA, Rudensky AY. 2003. Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nat Immunol 4: 330-6 324. Stumhofer JS, Silver JS, Laurence A, Porrett PM, Harris TH, Turka LA, Ernst M, Saris CJM, O&apos;Shea JJ, Hunter CA. 2007. Interleukins 27 and 6 induce STAT3-mediated T cell production of interleukin 10. Nat. Immunol. 8: 1363-71 325. Pflanz S, Hibbert L, Mattson J, Rosales R, Vaisberg E, Bazan JF, Phillips JH, McClanahan TK, de Waal Malefyt R, Kastelein RA. 2004. WSX-1 and glycoprotein 130 constitute a signal-transducing receptor for IL-27. J. Immunol. 172: 2225-31 326. Krutzik PO, Nolan GP. 2003. Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55: 61-70   211 327. Tarrio ML, Lee S-H, Fragoso MF, Sun H-W, Kanno Y, O&apos;Shea JJ, Biron CA. 2014. Proliferation conditions promote intrinsic changes in NK cells for an IL-10 response. The Journal of Immunology 193: 354-63 328. Tamassia N, Zimmermann M, Castellucci M, Ostuni R, Bruderek K, Schilling B, Brandau S, Bazzoni F, Natoli G, Cassatella MA. 2013. Cutting edge: An inactive chromatin configuration at the IL-10 locus in human neutrophils. The Journal of Immunology 190: 1921-5 329. Chang H-DH, Helbig CC, Tykocinski LL, Kreher SS, Koeck JJ, Niesner UU, Radbruch AA. 2007. Expression of IL-10 in Th memory lymphocytes is conditional on IL-12 or IL-4, unless the IL-10 gene is imprinted by GATA-3. Eur. J. Immunol. 37: 807-17 330. Lorzadeh A, Bilenky M, Hammond C, Knapp D, Li L, Miller PH, Carles A, Heravi-Moussavi A, Gakkhar S, Moksa M, Eaves CJ, Hirst M. 2016. Nucleosome Density ChIP-Seq Identifies Distinct Chromatin Modification Signatures Associated with MNase Accessibility. Cell Rep 17: 2112-24 331. Roh T-Y, Cuddapah S, Cui K, Zhao K. 2006. The genomic landscape of histone modifications in human T cells. Proc. Natl. Acad. Sci. U.S.A. 103: 15782-7 332. Wang P, Lin C, Smith ER, Guo H, Sanderson BW, Wu M, Gogol M, Alexander T, Seidel C, Wiedemann LM, Ge K, Krumlauf R, Shilatifard A. 2009. Global analysis of H3K4 methylation defines MLL family member targets and points to a role for MLL1-mediated H3K4 methylation in the regulation of transcriptional initiation by RNA polymerase II. Mol Cell Biol 29: 6074-85 333. Demers C, Chaturvedi CP, Ranish JA, Juban G, Lai P, Morle F, Aebersold R, Dilworth FJ, Groudine M, Brand M. 2007. Activator-mediated recruitment of the MLL2 methyltransferase complex to the beta-globin locus. Mol Cell 27: 573-84 334. Hughes CM, Rozenblatt-Rosen O, Milne TA, Copeland TD, Levine SS, Lee JC, Hayes DN, Shanmugam KS, Bhattacharjee A, Biondi CA, Kay GF, Hayward NK, Hess JL, Meyerson M. 2004. Menin associates with a trithorax family histone methyltransferase complex and with the hoxc8 locus. Mol Cell 13: 587-97 335. Saraiva M, Christensen JR, Tsytsykova AV, Goldfeld AE, Ley SC, Kioussis D, O&apos;Garra A. 2005. Identification of a macrophage-specific chromatin signature in the IL-10 locus. J. Immunol. 175: 1041-6 336. Radtke S, Wüller S, Yang X-P, Lippok BE, Mütze B, Mais C, de Leur HS-V, Bode JG, Gaestel M, Heinrich PC, Behrmann I, Schaper F, Hermanns HM. 2010. Cross-regulation of cytokine signalling: pro-inflammatory cytokines restrict IL-6 signalling through receptor internalisation and degradation. J Cell Sci 123: 947-59 337. Honke N, Ohl K, Wiener A, Bierwagen J, Peitz J, Di Fiore S, Fischer R, Wagner N, Wüller S, Tenbrock K. 2014. The p38-Mediated Rapid Down-Regulation of Cell Surface gp130 Expression Impairs Interleukin-6 Signaling in the Synovial Fluid of Juvenile Idiopathic Arthritis Patients. Arthritis & Rheumatology 66: 470-8 338. Ashwell JD. 2006. The many paths to p38 mitogen-activated protein kinase activation in the immune system. Nature Reviews Immunology 6: 532-40 339. Chapman TJ, Lambert K, Topham DJ. 2011. Rapid reactivation of extralymphoid CD4 T cells during secondary infection. PLoS ONE 6: e20493 340. Dong J, Ivascu C, Chang H-D, Wu P, Angeli R, Maggi L, Eckhardt F, Tykocinski L, Haefliger C, Möwes B, Sieper J, Radbruch A, Annunziato F, Thiel A. 2007. IL-10 is   212 excluded from the functional cytokine memory of human CD4+ memory T lymphocytes. J. Immunol. 179: 2389-96 341. Wei G, Wei L, Zhu J, Zang C, Hu-Li J, Yao Z, Cui K, Kanno Y, Roh TY, Watford WT, Schones DE, Peng W, Sun HW, Paul WE, O'Shea JJ, Zhao K. 2009. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30: 155-67 342. 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, Kelso A, Thomas PG, Speed TP, Rao S, Turner SJ. 2014. Distinct epigenetic signatures delineate transcriptional programs during virus-specific CD8(+) T cell differentiation. Immunity 41: 853-65 343. Hedrich CM, Ramakrishnan A, Dabitao D, Wang F, Ranatunga D, Bream JH. 2010. Dynamic DNA methylation patterns across the mouse and human IL10 genes during CD4+ T cell activation; influence of IL-27. Mol. Immunol. 48: 73-81 344. Hwang W, Lee CG, Lee C, Verma R, Rudra D, Park ZY, Im SH. 2018. Locus-Specific Reversible DNA Methylation Regulates Transient IL-10 Expression in Th1 Cells. J Immunol 200: 1865-75 345. Ouaked N, Mantel P-Y, Bassin C, Burgler S, Siegmund K, Akdis CA, Schmidt-Weber CB. 2009. Regulation of the foxp3 gene by the Th1 cytokines: the role of IL-27-induced STAT1. Journal of immunology (Baltimore, Md. : 1950) 182: 1041-9 346. Strutt TM, McKinstry KK, Swain SL. 2009. Functionally diverse subsets in CD4 T cell responses against influenza. Journal of Clinical Immunology 29: 145-50 347. Nguyen MLT, Jones SA, Prier JE, Russ BE. 2015. Transcriptional Enhancers in the Regulation of T Cell Differentiation. Frontiers in immunology 6: 462- 348. Heintzman ND, Hon GC, Hawkins RD, Kheradpour P, Stark A, Harp LF, Ye Z, Lee LK, Stuart RK, Ching CW, Ching KA, Antosiewicz-Bourget JE, Liu H, Zhang X, Green RD, Lobanenkov VV, Stewart R, Thomson JA, Crawford GE, Kellis M, Ren B. 2009. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459: 108-12 349. Vahedi G, Takahashi H, Nakayamada S, Sun H-W, Sartorelli V, Kanno Y, O&apos;Shea JJ. 2012. STATs shape the active enhancer landscape of T cell populations. Cell 151: 981-93 350. Lee GR, Spilianakis CG, Flavell RA. 2005. Hypersensitive site 7 of the TH2 locus control region is essential for expressing TH2 cytokine genes and for long-range intrachromosomal interactions. Nat Immunol 6: 42-8 351. Spilianakis CG, Flavell RA. 2004. Long-range intrachromosomal interactions in the T helper type 2 cytokine locus. Nat Immunol 5: 1017-27 352. Jones EA, Flavell RA. 2005. Distal enhancer elements transcribe intergenic RNA in the IL-10 family gene cluster. J Immunol 175: 7437-46 353. Aschenbrenner D, Foglierini M, Jarrossay D, Hu D, Weiner HL, Kuchroo VK, Lanzavecchia A, Notarbartolo S, Sallusto F. 2018. An immunoregulatory and tissue-residency program modulated by c-MAF in human TH17 cells. Nat Immunol 19: 1126-36 354. Pot C, Jin H, Awasthi A, Liu SM, Lai C-Y, Madan R, Sharpe AH, Karp CL, Miaw S-C, Ho I-C, Kuchroo VK. 2009. Cutting edge: IL-27 induces the transcription factor c-Maf, cytokine IL-21, and the costimulatory receptor ICOS that coordinately act together to   213 promote differentiation of IL-10-producing Tr1 cells. The Journal of Immunology 183: 797-801 355. Heintzman ND, Hon GC, Hawkins RD, Kheradpour P. 2009. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature  356. Sanin DE, Prendergast CT, Mountford AP. 2015. IL-10 Production in Macrophages Is Regulated by a TLR-Driven CREB-Mediated Mechanism That Is Linked to Genes Involved in Cell Metabolism. J Immunol 195: 1218-32 357. Zhang F, Rincon M, Flavell RA, Aune TM. 2000. Defective Th function induced by a dominant-negative cAMP response element binding protein mutation is reversed by Bcl-2. J Immunol 165: 1762-70 358. Goodman RH, Smolik S. 2000. CBP/p300 in cell growth, transformation, and development. Genes Dev 14: 1553-77 359. Merika M, Williams AJ, Chen G, Collins T, Thanos D. 1998. Recruitment of CBP/p300 by the IFN beta enhanceosome is required for synergistic activation of transcription. Mol Cell 1: 277-87 360. Chan HM, La Thangue NB. 2001. p300/CBP proteins: HATs for transcriptional bridges and scaffolds. J Cell Sci 114: 2363-73 361. Kaiser M, Wiggin GR, Lightfoot K, Arthur JS, Macdonald A. 2007. MSK regulate TCR-induced CREB phosphorylation but not immediate early gene transcription. Eur J Immunol 37: 2583-95 362. Hughes-Fulford M, Sugano E, Schopper T, Li CF, Boonyaratanakornkit JB, Cogoli A. 2005. Early immune response and regulation of IL-2 receptor subunits. Cell Signal 17: 1111-24 363. Hunter CA, Kastelein R. 2012. Interleukin-27: balancing protective and pathological immunity. Immunity 37: 960-9 364. Quirino GFS, Nascimento MSL, Davoli-Ferreira M, Sacramento LA, Lima MHF, Almeida RP, Carregaro V, Silva JS. 2016. Interleukin-27 (IL-27) Mediates Susceptibility to Visceral Leishmaniasis by Suppressing the IL-17-Neutrophil Response. Infect Immun 84: 2289-98 365. Gonzalez-Lombana C, Gimblet C, Bacellar O, Oliveira WW, Passos S, Carvalho LP, Goldschmidt M, Carvalho EM, Scott P. 2013. IL-17 mediates immunopathology in the absence of IL-10 following Leishmania major infection. PLoS Pathog. 9: e1003243 366. Brunkow ME, Jeffery EW, Hjerrild KA, Paeper B, Clark LB, Yasayko SA, Wilkinson JE, Galas D, Ziegler SF, Ramsdell F. 2001. Disruption of a new forkhead/winged-helix protein, scurfin, results in the fatal lymphoproliferative disorder of the scurfy mouse. Nat Genet 27: 68-73 367. Bennett CL, Christie J, Ramsdell F, Brunkow ME, Ferguson PJ, Whitesell L, Kelly TE, Saulsbury FT, Chance PF, Ochs HD. 2001. The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nat Genet 27: 20-1 368. Fontenot JD, Rasmussen JP, Williams LM, Dooley JL, Farr AG, Rudensky AY. 2005. Regulatory T cell lineage specification by the forkhead transcription factor foxp3. Immunity 22: 329-41 369. Zhao H, Liao X, Kang Y. 2017. Tregs: Where We Are and What Comes Next? Front Immunol 8: 1578   214 370. Vignali DAA, Collison LW, Workman CJ. 2008. How regulatory T cells work. Nature reviews. Immunology 8: 523-32 371. Campbell DJ, Koch MA. 2011. Phenotypical and functional specialization of FOXP3+ regulatory T cells. Nat Rev Immunol 11: 119-30 372. Koch MA, Tucker-Heard Gs, Perdue NR, Killebrew JR, Urdahl KB, Campbell DJ. 2009. The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation. Nature immunology 10: 595-602 373. Levine AG, Mendoza A, Hemmers S, Moltedo B, Niec RE, Schizas M, Hoyos BE, Putintseva EV, Chaudhry A, Dikiy S, Fujisawa S, Chudakov DM, Treuting PM, Rudensky AY. 2017. Stability and function of regulatory T cells expressing the transcription factor T-bet. Nature 546: 421-5 374. Damjanovic D, Small C-L, Jeyananthan M, McCormick S, Xing Z. 2012. Immunopathology in influenza virus infection: Uncoupling the friend from foe. Clinical Immunology 144: 57-69 375. Antunes I, Kassiotis G. 2010. Suppression of innate immune pathology by regulatory T cells during Influenza A virus infection of immunodeficient mice. J Virol 84: 12564-75 376. Rowe JH, Ertelt JM, Way SS. 2012. Foxp3(+) regulatory T cells, immune stimulation and host defence against infection. Immunology 136: 1-10 377. Villegas-Mendez A, de Souza JB, Lavelle S-W, Gwyer Findlay E, Shaw TN, van Rooijen N, Saris CJ, Hunter CA, Riley EM, Couper KN. 2013. IL-27 Receptor Signalling Restricts the Formation of Pathogenic, Terminally Differentiated Th1 Cells during Malaria Infection by Repressing IL-12 Dependent Signals. PLoS Pathog. 9: e1003293 378. La Gruta NL, Kedzierska K, Stambas J, Doherty PC. 2007. A question of self-preservation: immunopathology in influenza virus infection. Immunol. Cell Biol. 85: 85-92 379. Huehn J, Siegmund K, Lehmann JC, Siewert C, Haubold U, Feuerer M, Debes GF, Lauber J, Frey O, Przybylski GK, Niesner U, de la Rosa M, Schmidt CA, Brauer R, Buer J, Scheffold A, Hamann A. 2004. Developmental stage, phenotype, and migration distinguish naive- and effector/memory-like CD4+ regulatory T cells. J Exp Med 199: 303-13 380. Arpaia N, Green JA, Moltedo B, Arvey A, Hemmers S, Yuan S, Treuting PM, Rudensky AY. 2015. A Distinct Function of Regulatory T Cells in Tissue Protection. Cell 162: 1078-89 381. Yu L, Yang F, Zhang F, Guo D, Li L, Wang X, Liang T, Wang J, Cai Z, Jin H. 2018. CD69 enhances immunosuppressive function of regulatory T-cells and attenuates colitis by prompting IL-10 production. Cell Death & Disease 9: 905- 382. Fulton RB, Meyerholz DK, Varga SM. 2010. Foxp3+ CD4 regulatory T cells limit pulmonary immunopathology by modulating the CD8 T cell response during respiratory syncytial virus infection. Journal of immunology (Baltimore, Md. : 1950) 185: 2382-92 383. Rubtsov YP, Rasmussen JP, Chi EY, Fontenot J, Castelli L, Ye X, Treuting P, Siewe L, Roers A, Henderson Jr. WR, Müller W, Rudensky AY. 2008. Regulatory T Cell-Derived Interleukin-10 Limits Inflammation at Environmental Interfaces. Immunity 28: 546-58 384. Lam WY, Yeung ACM, Chu IMT, Chan PKS. 2010. Profiles of cytokine and chemokine gene expression in human pulmonary epithelial cells induced by human and avian influenza viruses. Virology journal 7: 344-   215 385. Stegemann-Koniszewski S, Jeron A, Gereke M, Geffers R, Kröger A, Gunzer M, Bruder D. 2016. Alveolar Type II Epithelial Cells Contribute to the Anti-Influenza A Virus Response in the Lung by Integrating Pathogen- and Microenvironment-Derived Signals. mBio 7: e00276-16 386. Pirhonen J, Sirén J, Julkunen I, Matikainen S. 2007. IFN-α regulates Toll-like receptor-mediated IL-27 gene expression in human macrophages. Journal of Leukocyte Biology 82: 1185-92 387. Liu J, Guan X, Ma X. 2007. Regulation of IL-27 p28 gene expression in macrophages through MyD88- and interferon-gamma-mediated pathways. The Journal of experimental medicine 204: 141-52 388. Basset L, Chevalier S, Danger Y, Arshad MI, Piquet-Pellorce C, Gascan H, Samson M. 2015. Interleukin-27 and IFNgamma regulate the expression of CXCL9, CXCL10, and CXCL11 in hepatitis. J Mol Med (Berl) 93: 1355-67 389. Redpath SA, van der Werf N, Cervera AM, MacDonald AS, Gray D, Maizels RM, Taylor MD. 2013. ICOS controls Foxp3(+) regulatory T-cell expansion, maintenance and IL-10 production during helminth infection. Eur J Immunol 43: 705-15 390. Bollyky PL, Falk BA, Long SA, Preisinger A, Braun KR, Wu RP, Evanko SP, Buckner JH, Wight TN, Nepom GT. 2009. CD44 costimulation promotes FoxP3+ regulatory T cell persistence and function via production of IL-2, IL-10, and TGF-beta. Journal of immunology (Baltimore, Md. : 1950) 183: 2232-41 391. Sprouse ML, Scavuzzo MA, Blum S, Shevchenko I, Lee T, Makedonas G, Borowiak M, Bettini ML, Bettini M. High self-reactivity drives T-bet and potentiates Treg function in tissue-specific autoimmunity.  392. Cox JH, Kljavin NM, Ramamoorthi N, Diehl L, Batten M, Ghilardi N. 2011. IL-27 promotes T cell-dependent colitis through multiple mechanisms. J Exp Med 208: 115-23 393. Do J, Kim D, Kim S, Valentin-Torres A, Dvorina N, Jang E, Nagarajavel V, DeSilva TM, Li X, Ting AH, Vignali DAA, Stohlman SA, Baldwin WM, 3rd, Min B. 2017. Treg-specific IL-27Ralpha deletion uncovers a key role for IL-27 in Treg function to control autoimmunity. Proc Natl Acad Sci U S A 114: 10190-5 394. Wojno ED, Hosken N, Stumhofer JS, O'Hara AC, Mauldin E, Fang Q, Turka LA, Levin SD, Hunter CA. 2011. A role for IL-27 in limiting T regulatory cell populations. J Immunol 187: 266-73 395. Setoguchi R, Hori S, Takahashi T, Sakaguchi S. 2005. Homeostatic maintenance of natural Foxp3(+) CD25(+) CD4(+) regulatory T cells by interleukin (IL)-2 and induction of autoimmune disease by IL-2 neutralization. J Exp Med 201: 723-35 396. Topham DJ, Tripp RA, Sarawar SR, Sangster MY, Doherty PC. 1996. Immune CD4+ T cells promote the clearance of influenza virus from major histocompatibility complex class II -/- respiratory epithelium. J Virol 70: 1288-91 397. Zens KD, Farber DL. 2015. Memory CD4 T cells in influenza. Curr Top Microbiol Immunol 386: 399-421 398. Braciale TJ, Sun J, Kim TS. 2012. Regulating the adaptive immune response to respiratory virus infection. Nat. Rev. Immunol. 12: 295-305 399. Garcia S, DiSanto J, Stockinger B. 1999. Following the development of a CD4 T cell response in vivo: from activation to memory formation. Immunity 11: 163-71   216 400. Christie D, Zhu J. 2014. Transcriptional regulatory networks for CD4 T cell differentiation. Curr Top Microbiol Immunol 381: 125-72 401. Ciofani M, Madar A, Galan C, Sellars M, Mace K, Pauli F, Agarwal A, Huang W, Parkhurst CN, Muratet M, Newberry KM, Meadows S, Greenfield A, Yang Y, Jain P, Kirigin FK, Birchmeier C, Wagner EF, Murphy KM, Myers RM, Bonneau R, Littman DR. 2012. A validated regulatory network for Th17 cell specification. Cell 151: 289-303 402. Zhu J, Yamane H, Paul WE. 2010. Differentiation of effector CD4 T cell populations (*). Annu. Rev. Immunol. 28: 445-89 403. Kouzarides T. 2007. SnapShot: Histone-modifying enzymes. Cell 131: 822-0 404. He B, Xing S, Chen C, Gao P, Teng L, Shan Q, Gullicksrud JA, Martin MD, Yu S, Harty JT, Badovinac VP, Tan K, Xue HH. 2016. CD8(+) T Cells Utilize Highly Dynamic Enhancer Repertoires and Regulatory Circuitry in Response to Infections. Immunity 45: 1341-54 405. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. 2016. A survey of best practices for RNA-seq data analysis. Genome Biol 17: 13 406. Papadakis KA, Landers C, Prehn J, Kouroumalis EA, Moreno ST, Gutierrez-Ramos JC, Hodge MR, Targan SR. 2003. CC chemokine receptor 9 expression defines a subset of peripheral blood lymphocytes with mucosal T cell phenotype and Th1 or T-regulatory 1 cytokine profile. J Immunol 171: 159-65 407. O'Sullivan D, van der Windt GJ, Huang SC, Curtis JD, Chang CH, Buck MD, Qiu J, Smith AM, Lam WY, DiPlato LM, Hsu FF, Birnbaum MJ, Pearce EJ, Pearce EL. 2014. Memory CD8(+) T cells use cell-intrinsic lipolysis to support the metabolic programming necessary for development. Immunity 41: 75-88 408. Ernst J, Bar-Joseph Z. 2006. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7: 191 409. Mamonkin M, Shen Y, Lee PH, Puppi M, Park CS, Lacorazza HD. 2013. Differential roles of KLF4 in the development and differentiation of CD8+ T cells. Immunol Lett 156: 94-101 410. Mamonkin M, Puppi M, Lacorazza HD. 2014. Transcription factor ELF4 promotes development and function of memory CD8(+) T cells in Listeria monocytogenes infection. Eur J Immunol 44: 715-27 411. Lin YY, Jones-Mason ME, Inoue M, Lasorella A, Iavarone A, Li QJ, Shinohara ML, Zhuang Y. 2012. Transcriptional regulator Id2 is required for the CD4 T cell immune response in the development of experimental autoimmune encephalomyelitis. J Immunol 189: 1400-5 412. Latz E, Xiao TS, Stutz A. 2013. Activation and regulation of the inflammasomes. Nat Rev Immunol 13: 397-411 413. Anderson AC, Joller N, Kuchroo VK. 2016. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity 44: 989-1004 414. Jenner RG, Townsend MJ, Jackson I, Sun K, Bouwman RD, Young RA, Glimcher LH, Lord GM. 2009. The transcription factors T-bet and GATA-3 control alternative pathways of T-cell differentiation through a shared set of target genes. Proc Natl Acad Sci U S A 106: 17876-81   217 415. Yu S, Zhou X, Steinke FC, Liu C, Chen SC, Zagorodna O, Jing X, Yokota Y, Meyerholz DK, Mullighan CG, Knudson CM, Zhao DM, Xue HH. 2012. The TCF-1 and LEF-1 transcription factors have cooperative and opposing roles in T cell development and malignancy. Immunity 37: 813-26 416. Kakugawa K, Kojo S, Tanaka H, Seo W, Endo TA, Kitagawa Y, Muroi S, Tenno M, Yasmin N, Kohwi Y, Sakaguchi S, Kowhi-Shigematsu T, Taniuchi I. 2017. Essential Roles of SATB1 in Specifying T Lymphocyte Subsets. Cell Rep 19: 1176-88 417. Jeannet G, Boudousquie C, Gardiol N, Kang J, Huelsken J, Held W. 2010. Essential role of the Wnt pathway effector Tcf-1 for the establishment of functional CD8 T cell memory. Proc Natl Acad Sci U S A 107: 9777-82 418. Willinger T, Freeman T, Herbert M, Hasegawa H, McMichael AJ, Callan MF. 2006. Human naive CD8 T cells down-regulate expression of the WNT pathway transcription factors lymphoid enhancer binding factor 1 and transcription factor 7 (T cell factor-1) following antigen encounter in vitro and in vivo. J Immunol 176: 1439-46 419. Richer MJ, Lang ML, Butler NS. 2016. T Cell Fates Zipped Up: How the Bach2 Basic Leucine Zipper Transcriptional Repressor Directs T Cell Differentiation and Function. J Immunol 197: 1009-15 420. Tsukumo S, Unno M, Muto A, Takeuchi A, Kometani K, Kurosaki T, Igarashi K, Saito T. 2013. Bach2 maintains T cells in a naive state by suppressing effector memory-related genes. Proc Natl Acad Sci U S A 110: 10735-40 421. Scharer CD, Bally AP, Gandham B, Boss JM. 2017. Cutting Edge: Chromatin Accessibility Programs CD8 T Cell Memory. J Immunol 198: 2238-43 422. Scott-Browne JP, Lopez-Moyado IF, Trifari S, Wong V, Chavez L, Rao A, Pereira RM. 2016. Dynamic Changes in Chromatin Accessibility Occur in CD8(+) T Cells Responding to Viral Infection. Immunity 45: 1327-40 423. Weng N-p, Araki Y, Subedi K. 2012. The molecular basis of the memory T cell response: differential gene expression and its epigenetic regulation. Nat. Rev. Immunol. 12: 306-15 424. Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X. 2014. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9: e78644 425. Verbeek S, Izon D, Hofhuis F, Robanus-Maandag E, te Riele H, van de Wetering M, Oosterwegel M, Wilson A, MacDonald HR, Clevers H. 1995. An HMG-box-containing T-cell factor required for thymocyte differentiation. Nature 374: 70-4 426. Schilham MW, Wilson A, Moerer P, Benaissa-Trouw BJ, Cumano A, Clevers HC. 1998. Critical involvement of Tcf-1 in expansion of thymocytes. J Immunol 161: 3984-91 427. Weninger W, Crowley MA, Manjunath N, von Andrian UH. 2001. Migratory properties of naive, effector, and memory CD8(+) T cells. J Exp Med 194: 953-66 428. Ding Y, Shen S, Lino AC, Curotto de Lafaille MA, Lafaille JJ. 2008. Beta-catenin stabilization extends regulatory T cell survival and induces anergy in nonregulatory T cells. Nat Med 14: 162-9 429. Gattinoni L, Zhong XS, Palmer DC, Ji Y, Hinrichs CS, Yu Z, Wrzesinski C, Boni A, Cassard L, Garvin LM, Paulos CM, Muranski P, Restifo NP. 2009. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat Med 15: 808-13 430. Wang YC, Stein JW, Lynch CL, Tran HT, Lee CY, Coleman R, Hatch A, Antontsev VG, Chy HS, O'Brien CM, Murthy SK, Laslett AL, Peterson SE, Loring JF. 2015.   218 Glycosyltransferase ST6GAL1 contributes to the regulation of pluripotency in human pluripotent stem cells. Sci Rep 5: 13317 431. Kuwahara M, Yamashita M, Shinoda K, Tofukuji S, Onodera A, Shinnakasu R, Motohashi S, Hosokawa H, Tumes D, Iwamura C, Lefebvre V, Nakayama T. 2012. The transcription factor Sox4 is a downstream target of signaling by the cytokine TGF-beta and suppresses T(H)2 differentiation. Nat Immunol 13: 778-86 432. White AM, Wraith DC. 2016. Tr1-Like T Cells - An Enigmatic Regulatory T Cell Lineage. Front Immunol 7: 355 433. Loven J, Hoke HA, Lin CY, Lau A, Orlando DA, Vakoc CR, Bradner JE, Lee TI, Young RA. 2013. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153: 320-34 434. Peeters JG, Vervoort SJ, Tan SC, Mijnheer G, de Roock S, Vastert SJ, Nieuwenhuis EE, van Wijk F, Prakken BJ, Creyghton MP, Coffer PJ, Mokry M, van Loosdregt J. 2015. Inhibition of Super-Enhancer Activity in Autoinflammatory Site-Derived T Cells Reduces Disease-Associated Gene Expression. Cell Rep 12: 1986-96 435. Groom JR, Luster AD. 2011. CXCR3 in T cell function. Exp Cell Res 317: 620-31 436. Laidlaw BJ, Cui W, Amezquita RA, Gray SM, Guan T, Lu Y, Kobayashi Y, Flavell RA, Kleinstein SH, Craft J, Kaech SM. 2015. Production of IL-10 by CD4(+) regulatory T cells during the resolution of infection promotes the maturation of memory CD8(+) T cells. Nat Immunol 16: 871-9 437. Jabbari A, Harty JT. 2006. Secondary memory CD8+ T cells are more protective but slower to acquire a central-memory phenotype. J Exp Med 203: 919-32 438. Muallem G, Wagage S, Sun Y, DeLong JH, Valenzuela A, Christian DA, Harms Pritchard G, Fang Q, Buza EL, Jain D, Elloso MM, Lopez CB, Hunter CA. 2017. IL-27 Limits Type 2 Immunopathology Following Parainfluenza Virus Infection. PLoS Pathog 13: e1006173 439. Gwyer Findlay E, Villegas-Mendez A, O&apos;Regan N, de Souza JB, Grady L-M, Saris CJ, Riley EM, Couper KN. 2014. IL-27 Receptor Signaling Regulates Memory CD4+ T Cell Populations and Suppresses Rapid Inflammatory Responses during Secondary Malaria Infection. Infect. Immun. 82: 10-20 440. Li C, Corraliza I, Langhorne J. 1999. A defect in interleukin-10 leads to enhanced malarial disease in Plasmodium chabaudi chabaudi infection in mice. Infect Immun 67: 4435-42 441. Schneider R, Yaneva T, Beauseigle D, El-Khoury L, Arbour N. 2011. IL-27 increases the proliferation and effector functions of human naive CD8+ T lymphocytes and promotes their development into Tc1 cells. Eur J Immunol 41: 47-59 442. Salcedo R, Hixon JA, Stauffer JK, Jalah R, Brooks AD, Khan T, Dai RM, Scheetz L, Lincoln E, Back TC, Powell D, Hurwitz AA, Sayers TJ, Kastelein R, Pavlakis GN, Felber BK, Trinchieri G, Wigginton JM. 2009. Immunologic and therapeutic synergy of IL-27 and IL-2: enhancement of T cell sensitization, tumor-specific CTL reactivity and complete regression of disseminated neuroblastoma metastases in the liver and bone marrow. J Immunol 182: 4328-38 443. Pennock ND, Gapin L, Kedl RM. 2014. IL-27 is required for shaping the magnitude, affinity distribution, and memory of T cells responding to subunit immunization. Proc Natl Acad Sci U S A 111: 16472-7   219 444. Larocca RA, Provine NM, Aid M, Iampietro MJ, Borducchi EN, Badamchi-Zadeh A, Abbink P, Ng'ang'a D, Bricault CA, Blass E, Penaloza-MacMaster P, Stephenson KE, Barouch DH. 2016. Adenovirus serotype 5 vaccine vectors trigger IL-27-dependent inhibitory CD4(+) T cell responses that impair CD8(+) T cell function. Sci Immunol 1 445. Soema PC, van Riet E, Kersten G, Amorij JP. 2015. Development of cross-protective influenza a vaccines based on cellular responses. Front Immunol 6: 237 446. Tan TG, Mathis D, Benoist C. 2016. Singular role for T-BET+CXCR3+ regulatory T cells in protection from autoimmune diabetes. Proc Natl Acad Sci U S A 113: 14103-8 447. Koch MA, Thomas KR, Perdue NR, Smigiel KS, Srivastava S, Campbell DJ. 2012. T-bet(+) Treg cells undergo abortive Th1 cell differentiation due to impaired expression of IL-12 receptor beta2. Immunity 37: 501-10 448. Durek P, Nordstrom K, Gasparoni G, Salhab A, Kressler C, de Almeida M, Bassler K, Ulas T, Schmidt F, Xiong J, Glazar P, Klironomos F, Sinha A, Kinkley S, Yang X, Arrigoni L, Amirabad AD, Ardakani FB, Feuerbach L, Gorka O, Ebert P, Muller F, Li N, Frischbutter S, Schlickeiser S, Cendon C, Frohler S, Felder B, Gasparoni N, Imbusch CD, Hutter B, Zipprich G, Tauchmann Y, Reinke S, Wassilew G, Hoffmann U, Richter AS, Sieverling L, Consortium D, Chang HD, Syrbe U, Kalus U, Eils J, Brors B, Manke T, Ruland J, Lengauer T, Rajewsky N, Chen W, Dong J, Sawitzki B, Chung HR, Rosenstiel P, Schulz MH, Schultze JL, Radbruch A, Walter J, Hamann A, Polansky JK. 2016. Epigenomic Profiling of Human CD4(+) T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development. Immunity 45: 1148-61 449. DeLong JH, O’Hara Hall A, Rausch M, Moodley D, Perry J, Park J, Phan AT, Beiting DP, Kedl RM, Hill JA, Hunter CA. 2019. IL-27 and TCR Stimulation Promote T Cell Expression of Multiple Inhibitory Receptors. ImmunoHorizons 3: 13 LP-25 450. Ciucci T, Vacchio MS, Gao Y, Tomassoni Ardori F, Candia J, Mehta M, Zhao Y, Tran B, Pepper M, Tessarollo L, McGavern DB, Bosselut R. 2019. The Emergence and Functional Fitness of Memory CD4+ T Cells Require the Transcription Factor Thpok. Immunity 50: 91-105.e4 451. Kaech SM, Hemby S, Kersh E, Ahmed R. 2002. Molecular and functional profiling of memory CD8 T cell differentiation. Cell 111: 837-51 452. Wang L, Bosselut R. 2009. CD4-CD8 lineage differentiation: Thpok-ing into the nucleus. Journal of immunology (Baltimore, Md. : 1950) 183: 2903-10 453. He X, He X, Dave VP, Zhang Y, Hua X, Nicolas E, Xu W, Roe BA, Kappes DJ. 2005. The zinc finger transcription factor Th-POK regulates CD4 versus CD8 T-cell lineage commitment. Nature 433: 826-33 454. Li BX, Gardner R, Xue C, Qian DZ, Xie F, Thomas G, Kazmierczak SC, Habecker BA, Xiao X. 2016. Systemic Inhibition of CREB is Well-tolerated in vivo. Sci Rep 6: 34513 455. Villarino AV, Stumhofer JS, Saris CJM, Kastelein RA, de Sauvage FJ, Hunter CA. 2005. IL-27 Limits IL-2 Production during Th1 Differentiation. J. Immunol. 176: 237-47 456. Do J-S, Visperas A, Sanogo YO, Bechtel JJ, Dvorina N, Kim S, Jang E, Stohlman SA, Shen B, Fairchild RL, Baldwin Iii WM, Vignali DAA, Min B. 2016. An IL-27/Lag3 axis enhances Foxp3+ regulatory T cell-suppressive function and therapeutic efficacy. Mucosal Immunol 9: 137-45 457. Aghajani K, Keerthivasan S, Yu Y, Gounari F. 2012. Generation of CD4CreER(T(2)) transgenic mice to study development of peripheral CD4-T-cells. Genesis 50: 908-13   220 458. Hultquist JF, Hiatt J, Schumann K, McGregor MJ, Roth TL, Haas P, Doudna JA, Marson A, Krogan NJ. 2019. CRISPR-Cas9 genome engineering of primary CD4(+) T cells for the interrogation of HIV-host factor interactions. Nat Protoc 14: 1-27 459. Sen DR, Kaminski J, Barnitz RA, Kurachi M, Gerdemann U, Yates KB, Tsao HW, Godec J, LaFleur MW, Brown FD, Tonnerre P, Chung RT, Tully DC, Allen TM, Frahm N, Lauer GM, Wherry EJ, Yosef N, Haining WN. 2016. The epigenetic landscape of T cell exhaustion. Science 354: 1165-9 460. Simeonov DR, Gowen BG, Boontanrart M, Roth TL, Gagnon JD, Mumbach MR, Satpathy AT, Lee Y, Bray NL, Chan AY, Lituiev DS, Nguyen ML, Gate RE, Subramaniam M, Li Z, Woo JM, Mitros T, Ray GJ, Curie GL, Naddaf N, Chu JS, Ma H, Boyer E, Van Gool F, Huang H, Liu R, Tobin VR, Schumann K, Daly MJ, Farh KK, Ansel KM, Ye CJ, Greenleaf WJ, Anderson MS, Bluestone JA, Chang HY, Corn JE, Marson A. 2017. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549: 111-5          221 Appendices Appendix A  Purification and sorting of in vitro cultured CD44hi CD4+ T cells and naïve CD44lo CD4+ T cells for H3K4me3 and H3K37me3 ChIP Seq    Gating strategy for the purification of in vitro activated CD44hi CD4+ T cells and naïve CD44lo CD4+ T cells for histone modification ChIP Seq  FSC -ASSCFSC -ASSCFSC -ASSCFSC -ASSCFSC -WFSC (perp)FSC -WFSC (perp)FSC -WFSC (perp)FSC -WFSC (perp)CD4PICD4PICD4PICD4PICD44CD4CD44CD4CD4CD44CD4CD44ABCDVertX activated + IL-27VertX IL-27Ra -/- activated + IL-27VertX naiveVertX IL-27Ra -/- naiveActivated lymphocytesActivated lymphocytesSingletsSinglets CD4CD4CD44hiCD44hiCD44loCD44loSingletsSingletsCD4CD4LymphocytesLymphocytes  222  (A-B) CD4+ T cells were purified from the spleens of  VertX or VertX IL-27Ra-/- mice and activated by aCD3/CD28 stimulation in the presence of IL-27 for 4 days followed by cell sorting  of live activated CD44hi CD4+ T cells and further rested in IL-2 for 4 days. (C-D) Bulk purified CD4+ T cells from the spleens of VertX or VertX IL-27Ra-/- mice were sorted to isolate live naïve CD44lo CD4+ T cells.                       223 Appendix B  Expression of genes identified by a ‘primed’ epigenetic signature at the gene promoter  Memory CD4+ T cells contain a subset of primed effector genes Bar plots showing expression (RPKM) of representative genes within the H3K4me3 marked ‘primed’ gene set (93 genes) identified within Cluster 1. A fold change cut off of 2 (FC>2) was applied to those genes with higher expression in primary or secondary cells compared to memory cells.  Il10 Lgals1Gzmb Havcr2 Hk2Bhlhe40 Ccl3 Ccl5N P M S N P M SN P M S N P M S N P M SN P M S N P M S N P M S0100200300010203040051015200102030010020030040002004006000501001500306090RPKM

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