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Determining the relationship between Epstein-Barr virus and multiple sclerosis using a mouse model Márquez, Ana Citlali 2019

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DETERMINING THE RELATIONSHIP BETWEEN EPSTEIN-BARR VIRUS AND MULTIPLE SCLEROSIS USING A MOUSE MODEL by  Ana Citlali Márquez  B.Sc., Universidad de las Américas, Puebla, 2009 M.Sc, Universidad Nacional Autónoma de México, 2012  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)  January 2019  © Ana Citlali Márquez, 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:  Determining the relationship between Epstein-Barr virus and Multiple Sclerosis using a mouse model  submitted by         Ana Citlali Márquez  in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Microbiology and Immunology  Examining Committee: Marc Horwitz, Microbiology and Immunology Supervisor  Pauline Johnson, Microbiology and Immunology Supervisory Committee Member  Ninan Abraham, Microbiology and Immunology University Examiner Fumio Takei, Pathology and Laboratory Medicine  University Examiner  Additional Supervisory Committee Members: Jacqueline Quandt, Pathology  Supervisory Committee Member iii Abstract  Multiple Sclerosis (MS) is caused by a combination of genetic and environmental factors. It is believed that previous infection with Epstein Barr Virus (EBV) plays an important role in the development of MS. To examine this, our lab developed a murine model where latent infection with gamma herpesvirus 68 (γHV-68), the murine homolog to EBV, enhanced the symptoms of experimental autoimmune encephalomyelitis (EAE), resulting in disease that more closely resembles MS development in humans.  Latency of γHV-68 primarily occurs in memory B cells. Here, we explored which conditions were necessary for EAE enhancement. We showed that latently infected memory B cells were capable of enhancing EAE symptoms when transferred from mice infected with γHV-68 into uninfected mice. We observed a prevention of enhancement when B cells were depleted before infection. However, depletion after the establishment of latency only partially reduced EAE. This indicated the existence of a mechanism where B cells likely play an important role as antigen presenting cells (APCs) prior to EAE induction for the priming of Th1 cells, and that these signals persist in the mouse even after B cell depletion. Using RNAseq analysis we identified 22 genes that differ between B cells from infected versus uninfected mice.  Upon the establishment of latency, CD11b+CD11c+ cells show upregulation of CD40 and pSTAT1. This suggests a contribution by type I IFNs in EAE enhancement. To test this, we used IFNARko mice, however, there was no difference in EAE enhancement, suggesting that while type I IFNS are relevant for maintenance of latency and antiviral responses, they are not contributing to EAE exacerbation.  iv Finally, while infection of mice at a young age did not result in EAE enhancement, infection in adult mice left a long-lasting immune impact that directed enhanced autoimmunity even after 5 months of latent infection.  Based on these results we propose a mechanism for how gammaherpesviruses contribute to the development of autoimmunity and suggest that cells infected with EBV can be the target of new therapeutic treatments that would be highly effective and less damaging to the immune system of MS patients.   v Lay Summary Previous infection with Epstein Barr Virus (EBV) is associated with the development of multiple sclerosis (MS). However, how infection with EBV increases the chance of developing MS is unknown. Our lab developed a mouse model to research this relationship, using γHV-68 (a mouse virus) as a stand-in for EBV and experimental autoimmune encephalomyelitis (EAE) as a proxy for MS. We have shown that, in mice, previous infection with γHV-68 exacerbates EAE in a way that closely resembles MS in humans. I studied how cells infected with γHV-68, especially B cells, are connected to the worsening of EAE. We demonstrated that infected B cells change the immune system of the mouse and make it more susceptible to EAE. We also found that heightened disease depends on the age at which mice are infected with gHV-68. Through this project, we better understand how EBV contributes to the development of MS.  vi Preface All the work presented here was conducted in the Life Sciences Institute, the Modified Barrier Facility and the Centre for Disease Modelling at the University of British Columbia, Point Grey Campus. Dr. Marc S. Horwitz was the supervisor of this thesis and was involved over the course of the entire project in concept formation and manuscript composition.   A version of the literature review presented in Chapter 1 has been published (Márquez AC, Horwitz MS. The Role of Latently Infected B Cells in CNS Autoimmunity. Front Immunol. 2015 Oct 28;6:544. doi: 10.3389/fimmu.2015.00544).   A version of Chapter 2 will be submitted for publication (Ana Citlali Márquez, Iryna Shanina, Carys Croft and Marc S Horwitz. Memory B cells direct exacerbation of EAE symptoms during gHV-68 latency). I conducted all the experiments, analyzed the data and wrote the manuscript under the guidance of Dr. Marc S. Horwitz. Carys Croft designed qPCR used to quantify gHV-68. Iryna Shanina helped with mouse breeding and sample processing. RNA sequencing processing and preliminary analysis was performed by Ryan Vander werff. Signalling pathways analysis was performed by Dr. Rebecca Skalsky. aCD20 for depletion experiments was provided by Genentech. Primer and probe sequences for ORF50 qPCR were designed by Karen Simmons.  A version of Chapter 3 will be submitted for publication (Ana Citlali Márquez, Iryna Shanina and Marc S Horwitz. Latent gHV-68 EAE enhancement is independent of Type I interferon). I conducted all the experiments, analyzed the data and wrote the manuscript under the guidance of Dr. Marc S. Horwitz. Iryna Shanina helped with mouse breeding and sample processing.  vii  All animal work was approved by the University of British Columbia Animal Care Committee (protocol numbers: A17-0105, A17-0184). Human ethics approval was obtained from the University of British Columbia Clinical Ethics Review board (certificate number: 5622-12).  This work was supported by grants from the Multiple Sclerosis Society of Canada (MSSoC) and the National Multiple Sclerosis Society to Dr. Horwitz. Graduate funding was provided by the National Council from Science and Technology (CONACyT-Mexico), the MSSoC and the American Association of Immunologists (AAI).  viii Table of Contents  Abstract ......................................................................................................................................... iii	Lay Summary .................................................................................................................................v	Preface ........................................................................................................................................... vi	Table of Contents ....................................................................................................................... viii	List of Tables .............................................................................................................................. xiii	List of Figures ............................................................................................................................. xiv	List of Abbreviations ................................................................................................................ xvii	Acknowledgements .................................................................................................................... xxi	Dedication ................................................................................................................................. xxiii	Chapter 1: Introduction ................................................................................................................1	1.1	 Multiple Sclerosis ........................................................................................................... 1	1.1.1	 Epidemiology of Multiple Sclerosis ....................................................................... 1	1.1.2	 Genetic factors that affect the development of MS ................................................ 2	1.1.3	 Environmental factors that affect the development of MS ..................................... 3	1.1.3.1	 Sun exposure and vitamin D deficiency ............................................................. 3	1.1.3.2	 Cigarette smoking ............................................................................................... 4	1.1.3.3	 Viral infections .................................................................................................... 4	1.2	 Epidemiology of Epstein-Barr Virus .............................................................................. 5	1.3	 Epstein-Barr Virus Primary infection ............................................................................. 6	1.3.1	 Innate immune response to EBV primary infection ............................................... 7	1.3.2	 Adaptive immune response to EBV primary infection ........................................... 8	1.3.3	 Epstein Barr Virus Latency ..................................................................................... 8	ix 1.4	 Epstein-Barr Virus and cancer ...................................................................................... 10	1.5	 Epstein-Barr Virus and autoimmunity .......................................................................... 11	1.5.1.1	 EBV and Rheumatoid Arthritis (RA) ............................................................... 11	1.5.1.2	 EBV and Sjögren’s Syndrome (SS) .................................................................. 12	1.5.1.3	 EBV and Systemic Lupus Erythematosus (SLE) .............................................. 12	1.6	 Epidemiology of Epstein-Barr Virus and Multiple Sclerosis ....................................... 14	1.6.1	 Infectious Mononucleosis and the Hygiene Hypothesis ....................................... 15	1.6.2	 Presence of EBV in MS Patients .......................................................................... 16	1.7	 How can EBV cause MS? ............................................................................................. 17	1.7.1	 The Molecular Mimicry Hypothesis ..................................................................... 17	1.7.2	 The Bystander Damage Hypothesis ...................................................................... 18	1.7.3	 Mistaken Self hypothesis ...................................................................................... 19	1.7.4	 The EBV-infected Autoreactive B Cell hypothesis. ............................................. 19	1.8	 The Importance of B cells in Multiple Sclerosis .......................................................... 21	1.8.1	 B cells as Antigen Presenting Cells ...................................................................... 22	1.9	 Animal Models to Study Multiple Sclerosis and Epstein-Barr Virus Infection ........... 24	1.9.1	 Gammaherpesvirus 68 as a murine model for Epstein-Barr Virus ....................... 24	1.9.2	 Experimental Autoimmune Encephalomyelitis .................................................... 26	1.9.3	 The Role of B cells in EAE ................................................................................... 27	1.9.4	 A Unifying Model to Study the Effect of EBV in MS ......................................... 28	1.10	 Research Hypothesis and Rationale .............................................................................. 31	1.10.1	 Aim 1 .................................................................................................................... 32	1.10.2	 Aim 2 .................................................................................................................... 32	x 1.10.3	 Aim 3 .................................................................................................................... 33	Chapter 2: Memory B cells in gHV-68 Latency Direct Enhancement of EAE ......................35	2.1	 Introduction ................................................................................................................... 35	2.2	 Materials and Methods .................................................................................................. 36	2.3	 Results ........................................................................................................................... 40	2.3.1	 Memory B cells from gHV-68 Latently Infected Mice can Direct EAE Enhancement ......................................................................................................... 40	2.3.2	 Memory B cells from gHV-68 Mice Lead to an Increase of CD8 T cell Infiltration in the CNS ............................................................................................................. 42	2.3.3	 Memory B cells from gHV-68 Mice Alter Cytokine Production by T cells During EAE  ...................................................................................................................... 44	2.3.4	 T regulatory Population is not Affected by the Presence of gHV-68 memory B cells  ...................................................................................................................... 46	2.3.5	 B cell Depletion during EAE Slightly Improves Overall EAE Score .................. 47	2.3.6	 Depletion of B cells Before EAE Induction Moderately Affects EAE Score ...... 53	2.3.7	 gHV-68 Latency Establishes a Th1 Precondition Before EAE Induction ............ 58	2.3.8	 EAE Enhancement Depends on the Presence of B cells During gHV-68 Infection                                        ............................................................................................................................... 63	2.3.9	 Memory B cells From gHV-68 Mice have a Differential Gene Induction than Memory B cells from Naïve Mice ........................................................................ 70	2.4	 Discussion ..................................................................................................................... 76	Chapter 3: Latent gHV-68 EAE Enhancement is Independent of Type I Interferon ...........81	3.1	 Introduction ................................................................................................................... 81	xi 3.2	 Materials and Methods .................................................................................................. 82	3.3	 Results ........................................................................................................................... 85	3.3.1	 gHV68 Establishes Latency at a Similar Rate in IFNARko and WT Mice .......... 85	3.3.2	 gHV-68 Latently Infected IFNARko Mice Develop Enhanced T cell Infiltration into the CNS .......................................................................................................... 86	3.3.3	 Treg Proportion is Affected by Type 1 IFNs During gHV-68 Latent Infection ... 90	3.3.4	 B cells Infected with gHV-68 Require Type I IFNs to Direct CD8 T cell Infiltration in the CNS .......................................................................................... 91	Chapter 4: Effects of Age on gHV-68 Latency and its Influence on EAE ..............................97	4.1	 Introduction ................................................................................................................... 97	4.1.1	 Autoimmunity Development Later in Life ........................................................... 97	4.1.2	 γHV-68 infection in young mice ........................................................................... 98	4.2	 Materials and Methods ................................................................................................ 100	4.3	 Results ......................................................................................................................... 102	4.3.1	 γHV-68 Latency has a Long-Lasting Effect on EAE Symptoms ....................... 102	4.3.2	 Increased Infiltration of CD4 and CD8 T Cells in the Brain of gHV-68 Infected Mice after EAE Induction ................................................................................... 104	4.3.3	 gHV-68 in 3 Week-Old Mice Stops EAE Enhancement Later in Life ............... 107	4.3.4	 T cell Infiltration and Cytokine Production Follows a Gradient Dependent on Time of Infection with gHV-68 .......................................................................... 108	4.3.5	 Mice Infected with gHV-68 at 3 Weeks of Age Harbor Similar Amounts of Virus Compared to those Infected at 5 and 8 Weeks of Age ........................................ 112	4.4	 Discussion ................................................................................................................... 113	xii 4.4.1	 Long-Term Latency of gHV-68 and a New Model for LOMS ........................... 113	4.4.2	 Age of Infection and the Development of EAE Symptoms ................................ 114	Chapter 5: Conclusions and Future Directions ......................................................................117	5.1	 Discussion ................................................................................................................... 117	5.1.1	 Memory B cells and their Role in EAE Exacerbation ........................................ 118	5.1.2	 Type I Interferons are not Necessary for EAE Enhancement ............................. 119	5.1.3	 Age Dependent Effects on EAE Exacerbation ................................................... 121	5.2	 Future Directions ........................................................................................................ 123	5.2.1	 Development of a New EAE Scoring Method .................................................... 123	5.2.2	 How do B cells Skew the Immune System? ....................................................... 123	5.2.3	 Is Itga4 Upregulation Necessary for EAE Enhancement? .................................. 124	5.2.4	 Isolation of Memory B cells Infected with gHV-68 ........................................... 124	5.2.5	 Does EAE Worsens with Age? ........................................................................... 125	5.2.6	 The Difference Between Young and Adult Immune Systems ............................ 126	5.3	 Conclusion .................................................................................................................. 127	Bibliography ...............................................................................................................................128	Appendices ..................................................................................................................................168	 xiii List of Tables  Table 1.1 Autoimmune diseases related to Epstein Barr Virus .................................................... 13	Table 2.1 Upregulated/Downregulated genes in memory B cells from gHV-68 mice compared to uninfected mice ............................................................................................................................. 72	Table 2.2 Signaling pathways associated with the upregulated genes in memory B cells from mice latently infected with !HV-68 .............................................................................................. 73	Table 2.3 Signaling pathways associated with the downregulated genes in memory B cells from mice latently infected with !HV-68 .............................................................................................. 74 Table B.1 Differentially expressed genes in memory B cells from gHV-68 mice vs MEM …………………………………………………………………………………………..…………….... 171  xiv List of Figures  Figure 1.1 Worldwide prevalence of Multiple Sclerosis (2013) ..................................................... 2	Figure 1.2  Life cycle of Epstein-Barr Virus .................................................................................. 7	Figure 1.3  Latent infection with EBV establishes a precondition that leads to the development of MS ................................................................................................................................................. 21	Figure 2.1 Memory B cells from mice latently infected with !HV-68 trigger EAE exacerbation ....................................................................................................................................................... 42	Figure 2.2 Figure 2.2 Transfer of !HV-68 B cells leads to high infiltration of CD8 T cells in the CNS ............................................................................................................................................... 43	Figure 2.3 Transfer of !HV-68 B cells leads to upregulation of IFN! and downregulation of IL-17................................................................................................................................................... 45	Figure 2.4 Transfer of !HV-68 B cells does not affect the Treg repertoire ................................. 46	Figure 2.5 Depletion of B cells after EAE Induction .................................................................... 48	Figure 2.6 Depletion of B cells during EAE in !HV-68 mice stops EAE enhancement but not progression of disease ................................................................................................................... 49	Figure 2.7 Depletion of B cells after EAE induction in !HV-68 mice have high levels of T cells infiltrating the CNS ....................................................................................................................... 50	Figure 2.8 Depletion of B cells after EAE induction in !HV-68 mice show slight decrease in IFN! but not recovery in IL-17 ..................................................................................................... 52	Figure 2.9 B cell depletion with ⍺-CD20 ..................................................................................... 53	Figure 2.10 Depletion of B cells in mice latently infected with !HV-68 partially stops EAE enhancement ................................................................................................................................. 54	xv Figure 2.11 Depletion of B cells in mice latently infected with !HV-68 does not affect T cell infiltration into the CNS  ............................................................................................................... 55	Figure 2.12 B cell depletion leads to diminished upregulation of IFN! and upregulation of IL-17....................................................................................................................................................... 57	Figure 2.13 Quantification of !HV-68 genome after depletion with ⍺-CD20 ............................. 59	Figure 2.14 Immune cells show increased production of IFN! during !HV-68 latency ............. 60	Figure 2.15 T cells show increased levels of T-bet during !HV-68 latency ................................ 62	Figure 2.16 Depletion of B cells before !HV-68 infection .......................................................... 64	Figure 2.17 Depletion of B cells before !HV-68 infection .......................................................... 65	Figure 2.18 Mice with B cell depletion before !HV-68 infection show no sign of EAE enhancement ................................................................................................................................. 66	Figure 2.19 Depletion of B cells before infection !HV-68 prevents infiltration of CD8+ T cells into the CNS .................................................................................................................................. 67	Figure 2.20 Depletion of B cells before infection !HV-68 affects cytokine production by T cells infiltrating the CNS ....................................................................................................................... 69	Figure 2.21 Differential gene expression between !HV-68 infected mice vs uninfected ............ 71	Figure 2.22 Memory B cells from mice latently infected with !HV-68 have enhanced symptoms of EAE .......................................................................................................................................... 75	Figure 3.1 A low dose of gHV-68 infects and establishes latency in WT and IFNARko mice ..... 86	Figure 3.2 gHV-68 latently infected IFNARko mice develop enhanced EAE symptoms ............ 87	Figure 3.3 gHV-68 latently infected IFNARko mice develop enhanced T cell infiltration into the CNS compared to uninfected IFNARko and WT mice ................................................................ 89	xvi Figure 3.4 Tregs from gHV-68 latently infected IFNARko mice are downregulated before and after EAE induction ...................................................................................................................... 90	Figure 3.5 B cells from IFNARko mice latently infected with γHV-68 partially transfer EAE enhancement ................................................................................................................................. 92	Figure 4.1 Effects of gHV-68 are maintained after long term latency ........................................ 103	Figure 4.2 Long term latency increases T cell infiltration into the CNS .................................... 105	Figure 4.3 Long term latency maintains high production of IFNg ............................................. 106	Figure 4.4 Infection at an early age eliminates enhancement of EAE symptoms ...................... 108	Figure 4.5 Infection with gHV-68 at early age reduces T cell infiltration in the CNS ............... 110	Figure 4.6 Mice infected at 3 weeks of age produce similar levels of cytokines than uninfected mice ............................................................................................................................................. 111	Figure 4.7 Mice infected at 3 weeks of age harbour similar levels of gHV-68 during latency .. 112 Figure A.1 Gating strategy for T cell infiltration in the CNS ............................................................... 168 Figure A.2 Gating strategy for T regulatory cells in spleen .................................................................. 169 Figure A.3 Effectiveness of B cell depletion after EAE induction …………………………………. 170    xvii List of Abbreviations ABC – activated B cell ACC - Animal Care Committee  ANOVA - analysis of variance  APCs - antigen presenting cells  BBB - blood brain barrier  BCR - B-cell receptor BD - Becton Dickinson  BL – Burkitt’s lymphoma bp - base pairs  BSA - bovine serum albumin CD - cluster of differentiation  cDC – classical dendritic cells cDNA - complementary deoxyribonucleic acid  CFA - complete Freund’s adjuvant  CFSE - carboxyfluorescein succinimidyl ester cHL – classical Hodgkin lymphoma CIS - Clinically Isolated Syndrome CNS - central nervous system CSF – cerebrospinal fluid CTL - cytotoxic T cells  DCs - dendritic cells  DLBCL – Diffuse large B-cell lymphoma DMEM - Dubelcco’s modified eagle medium xviii DNA - deoxyribonucleic acid dNTPs - deoxynucleotide triphosphate  dsDNA - double strand deoxyribonucleic acid EAE - experimental autoimmune encephalomyelitis  EBER - Epstein-Barr virus encoded small RNAs EBI3 - Epstein-Barr induced gene 3 EBNA - Epstein-Barr virus nuclear antigen EBV - Epstein-Barr Virus  FACS - fluorescence activated cell sorting FBS - fetal bovine serum  Foxp3 - forkhead box P3  GWAS – genome-wide association studies gHV-68 - murine gammaherpesvirus 68  HHV - human herpesvirus HS- heparin sulfate IBD – Inflammatory bowel disease IFNARko - knockout mice without interferon alpha/beta receptor gene IFN-a - interferon alpha  IFN-b - interferon beta IFNg - interferon gamma  Ig - immunoglobulin IL - interleukin  i.p. - intraperitoneally xix KSV - Kaposi’s sarcoma virus LMP - Epstein-Barr virus latent membrane protein LNs - lymph nodes MBP – myelin basic protein MEM – minimum essential media MHC - major histocompatibility complex MOG - myelin oligodendrocyte glycoprotein mRNA - messenger ribonucleic acid MS - multiple sclerosis  NK - natural killer cell  NAWM – normal-appearing white matter  ORF50 - open reading frame 50 PBMCs - peripheral blood mononuclear cells PBS - phosphate buffered saline  PCR - polymerase chain reaction pDC – plasmacytoid dendritic cells PFU - plaque forming units PI or p.i. - post infection PLP – proteolipid protein PMA - phorbol 12-myristate 13-acetate PP-MS – primary progressive MS PTGER2 - prostabglandin EP2 receptor  PTX - pertussis toxin xx RBC - red blood cells  RNA - ribonucleic acid ROR-gt - retinoic-acid-receptor-related orphan receptor gamma t  RPMI - Roswell Park Memorial Institute culture medium RR-MS relapsing-remitting MS  RT - room temperature  RTA - lytic trans activator protein  RT-PCR - reverse transcription polymerase chain reaction  qPCR - quantitative real time polymerase chain reaction SSC - side scatter ssDNA - single strand deoxyribonucleic acid  T-bet - T-box expressed in T cells  TCR - T cell receptor  Th - helper T cell  TNF-a - tumour necrosis factor alpha  TLO – tertiary lymphoid organ TLR - toll like receptor  T reg - regulatory T cell  WT - wild type   xxi Acknowledgements First of all, I would like to thank Dr. Horwitz for accepting me in his lab and giving me the opportunity to learn and grow during my PhD, his support and encouragement have made my years at UBC a great experience. I would also like to thank my committee members Dr. Pauline Johnson, Dr. Jacqueline Quandt and Dr. Georgia Perona-Wright, for their help and support throughout my project.  To the MS Society of Canada for their generous support and funding and for allowing me to gain a deep understanding of the effects of MS in patients and their families.  Thank you to the Horwitz lab, Jessica Allanach, Miguel Mejias, Zach Morse and Isobel Mouat. Specially Virginie Jean-Baptiste for standing all my antics and being a good movie companion. Dr. Costanza Casiraghi, for welcoming me to this lab and being a great mentor in the first months of my PhD, and to Dr. Christina Farr for being such a great lab mate during my first years here, helping and teaching me so much. I would also like to thank all the undergraduate students who I had a chance to work with through summer internships and directed studies projects, Negar Farsam-Kia, Karen Simmons, Asia Ghana, Kevin Ng, Angela Wang, Carys Croft and Matthew Dordevic. A special thanks goes Iryna Shanina, for all her help and all the things I learned from her, but specially for her friendship. During my years in Vancouver I made great friends at UBC, Monica Torres, Sandra Peña and Karen Simmons made my PhD amazing, thank you.  My biggest thanks go to my family, my sister Itzel, my mom Laura Hernández and my dad Arturo Márquez. They have been my greatest supporters, without your love and encouragement I would have never done all the things I have. I live freely and happily knowing that your unwavering love will always be there for me.  xxii Finally, I would like to thank my wife, Pascale Meehan. These years have been a team effort for us; I would not have received as many fellowships, gone to as many conferences or even obtained a position at UBC, without your endless generosity. Even when I was tired and on my worst behaviour you were patient enough to understand my frustrations and continued to help me. Thank you for all your effort, for always trying to understand what I was writing even when it was challenging, for learning new things just to help me and, for all the nights we spent practicing my presentations just because I wanted to have the right words. But above everything, thank you for understanding how important this was for me. Knowing that you are by my side makes me walk through this world certain of the steps I take. For everything you do, I will always be grateful.   xxiii Dedication  Para mis papás y Pascale 1 Chapter 1: Introduction 1.1 Multiple Sclerosis 1.1.1 Epidemiology of Multiple Sclerosis Multiple sclerosis (MS) is a chronic neurodegenerative disease that affects the central nervous system (CNS) (1) and leads to serious disability in young adults, especially women (2). MS affects more than 2.3 million people worldwide (3). Its highest prevalence is in North America and Europe, while it is least common in East Asia and sub-Saharan Africa (Figure 1.1) (4). The average age of onset is 30 years old, and it presents a wide range of symptoms that include visual disturbances, motor impairments, fatigue, pain, and cognitive deficits (5). These symptoms are caused primarily by CD4+ T cells that infiltrate the CNS initiating acute lesions characterized by the breakdown of the blood-brain barrier (BBB), which drives the inflammatory process of MS (6). In addition, CD8+ T cells that recognize myelin proteins can also be found in the perivascular regions (7). These regions also contain other immune cells such as dendritic cells (DCs), B cells, microglia, astrocytes, macrophages and Natural Killer T cells (NKT) (8). Recently, the presence of B-cell follicle-like aggregates in the meninges of secondary-progressive MS has been discovered, which has been associated with early onset of the disease and extensive neuronal damage (9, 10). Largely accepted as an autoimmune disease, the mechanism describing how MS develops is still not clear. Studies in MS patients and in Experimental Autoimmune Encephalomyelitis (EAE) (an animal model for MS), have helped determine that inflammation caused by myelin-specific T cells in the CNS leads to the activation of microglia (macrophages resident in the CNS parenchyma). When activated,  2 microglia/macrophages produce reactive-oxygen species (ROS), nitric oxide (NO) and myeloperoxidase (MPO) (10). Increase of ROS, NO and MPO has been also associated with demyelination, lesion formation, neuronal death, apoptosis of oligodendrocytes and disease activity (10-12).  Figure 1.1 Worldwide prevalence of Multiple Sclerosis (2013)   Figure 1.1 MS prevalence varies greatly by country, but a latitude gradient can be appreciated, where countries closer to the poles have a higher number of people with MS. Taken from the Multiple Sclerosis International Federation Atlas 2013.  1.1.2 Genetic factors that affect the development of MS  While the etiology of MS is still unknown, several genetic and environmental factors have been identified as possible elements that increase the risk of developing MS.  3 Among the specific genetic markers associated with MS is the presence of genes related to alleles in the human leucocyte antigen (HLA) class II region (which is part of the major histocompatibility complex (MHC)), especially genes containing HLA-DRB1*15.01 (13, 14) as well as with the genes encoding the α-chains of the IL-2 and IL-7 receptors (IL-2Rα and IL-7Rα, respectively) (2). Genome-wide association studies (GWAS) have identified several non-MHC associations with MS in Caucasian populations, such as CLEC16A (15), CD58 (16), IRF8  (17) and CD40 (18), which by themselves appear to have a modest impact in the overall risk of disease, making MHC the main susceptibility locus (14, 19). Moreover, it seems that the interaction of these genetic variants with the environment is necessary to increase the risk of MS development.   1.1.3 Environmental factors that affect the development of MS  In addition to genetic research, studies on migration (20), discordancy among identical twins (21), and geographic gradients (22) strongly suggest that environmental factors influence susceptibility. Up to forty-four environmental risk factors have been linked to MS (23). So far, the strongest evidence for increased risk of MS development has been linked to vitamin D deficiency, cigarette smoking and, more prominently, with previous viral infections.  1.1.3.1 Sun exposure and vitamin D deficiency Vitamin D is a major regulator of the immune system. Low exposure to sunlight is the main risk factor for vitamin D deficiency. Vitamin D synthesis is not triggered in environments with short days and weak sunlight, which especially effects people living in latitudes above 40 degrees North (24).  Munger et al. showed that increased intake of  4 vitamin D was associated with a decreased risk of MS (25). Moreover, conversion of vitamin D to its active metabolite, calcidiol, depends on ultraviolet radiation (26), and it has been reported that low sun exposure in early life increases the risk of developing MS later in life (27). Low calcidiol levels are observed in MS patients at the beginning of the disease, and these levels tend to decrease further as the disease progresses (28). Vitamin D supplementation has resulted in beneficial immunological effects, such as stimulation of Tregs and IL-10 as well as downregulation of Th17 lymphocytes and IL-17 production in MS patients. Similar results have been observed in EAE, where supplementation with vitamin D can prevent and alleviate EAE symptoms (28). 1.1.3.2 Cigarette smoking Cigarette smoking has also been linked to an increased risk of MS (29). Related studies have shown that elevated levels of cotinine, a nicotine metabolite recognized as a marker for recent tobacco use, is also associated with an increased risk of MS (30). However, experimental evidence and mechanistic models for this environmental factor are scarce, making it difficult to clearly determine the involvement of cigarette smoking in the onset of MS. 1.1.3.3 Viral infections A variety of viruses have been identified as possible co-factors in the onset of autoimmunity (31, 32). Viruses most prominently associated with MS are herpesviruses such as Epstein-Barr Virus (EBV), Cytomegalvirus (CMV) (33); varicella-zoster (33, 34); and human herpes virus 6 (35, 36). Establishing strong evidence for the association of MS to most of these viruses is elusive and a consistent correlation between viral infection and the autoimmune disease has been hard to establish. Complicating  5 experimental studies is the fact that these viruses only infect humans, and are widespread throughout world populations  (37, 38). Nevertheless, the existence of a link between EBV and MS has wide support from researchers and is one for which there is the strongest evidence of association (39). Studies involving MS patients, together with investigation using EAE and other animal models of MS, have yielded high quantities of data, however the extent of the contribution of EBV to the onset of autoimmunity is still widely unknown. In the following sections we explore some of the proposed mechanisms for how previous infection with EBV can contribute to MS, discuss the importance of B cells as antigen presenting cells on MS pathology, and propose an animal model that will help to further explore the relationship between EBV, B cells, and the pathology of MS. 1.2 Epidemiology of Epstein-Barr Virus EBV was originally detected in 1964 in the cells of a patient with Burkitt’s Lymphoma (BL) (40). The virus is widespread in humans, and it infects more than 90% of the world population within the first two decades of life (41). In developing countries, primary infection mostly occurs during the first years of life, having universal seroconversion by ages 3-4 years. In developed countries* however, infection often does not occur until adolescence or adulthood (42, 43).                                                    * For the purposes of this thesis, I will assume that authors citing “developed” countries are using the definition by the Organisation for Economic Co-operation and Development (OECD) which establishes that although “there is no established convention for the designation of “developed and “developing” countries or areas in the United Nations system. In common practice, Japan in Asia, Canada and the United States in northern America, Australia and New Zealand in Oceania and Europe are considered “developed” regions or areas. In international trade statistics, the Southern African Customs Union is also treated as a developed region and Israel  6 1.3 Epstein-Barr Virus Primary infection EBV is a double stranded DNA γ-herpesvirus. Its genome encodes more than 80 genes (44) and infects both epithelial cells and B cells (45). Transmission of the virus usually occurs through saliva, although it can also be transmitted sexually and, more rarely. via blood transfusion (46). During primary infection, EBV infects epithelial cells through the ephrin receptor tyrosine kinase A2 (ephA2) (47) in the oral cavity. The virus goes through lytic replication, which results in the production of new viral particles that  spread to the oral submucosa and infect B cells, thus establishing primary infection (48). Initial EBV infection in B cells occurs by the attachment of the EBV protein gp350/220 to the complement C3d receptor 2 (CR2/CD21) in the cell (45). CR2 is a B cell membrane glycoprotein that amplifies antigen receptor-mediated signal transduction (49). Infected B cells are activated and differentiate to memory B cells, which are then released to peripheral circulation where they are recognized by T lymphocytes (50). Although the immune system is able to control the EBV infection, the provirus remains latent in the host’s B-lymphocytes for the rest of their life (Figure 1.2). Primary infection with EBV is transmitted through saliva, although it has also been detected in cervical secretions (51). When infection occurs during childhood, it is asymptomatic (52). In contrast, if the infection occurs during puberty or early adulthood, it can cause Infectious                                                  as a developed country; countries emerging from the former Yugoslavia are treated as developing countries; and countries of Eastern Europe and the former USSR countries in Europe are not included under either developed or developing regions” (40). It is worth mentioning however, that most scientific papers do not provide a clear definition of what is considered a “developed/developing country”.   7 Mononucleosis (IM), which is characterized by vague malaise followed by fever, sore throat, swollen posterior cervical lymph nodes, and fatigue (53).   Figure 1.2  Life cycle of Epstein-Barr Virus  Figure 1.2 During primary infection, EBV will infect epithelial cells in the oral cavity and will later establish latency in class switched memory B cells.  1.3.1 Innate immune response to EBV primary infection  During initial infection, the innate response to EBV seems to be directed by plasmacytoid dendritic cells (pDCs) and classical dendritic cells (cDCs), that recognize unmethylated viral DNA and Epstein-Barr-virus-encoded small RNAs (EBERs) through Toll-like receptor (TLR) 9 on pDCs, and TLR3 on cDCs. This activation of DCs triggers  8 their maturation, pDCs are especially affected and produce large amounts of type I IFNs (a/b) (54), which helps to control the virus and stimulate other immune cells in order to promote an adaptive immune response (55). At the same time, B cells express TLR9, and it is possible that direct activation of infected B cells by unmethylated CpG dinucleotides during primary infection, leads to the production of proinflammatory cytokines that inhibit lytic replication (56). Additionally, natural killer (NK) cells have been implicated in restricting the spread of the virus. However, there have been contradictory findings about the function of NK cells and more research is needed (55, 57) to determine their exact function during acute infection. 1.3.2 Adaptive immune response to EBV primary infection Most of the information available about EBV primary infection comes from patients that develop IM. IM patients present symptoms four to six weeks after infection (58),. Prior to the development of symptoms, there does not seem to be an apparent change in the proportions of CD4+ or CD8+ lymphocytes (59). Once symptoms develop, there is a major expansion of activated CD8+ T cells specific for lytic and latent EBV antigens (60). In contrast, there is little expansion of CD4+ T cells (59). CD4+ T cells are initially predominantly specific to Epstein-Barr nuclear antigen (EBNA) 2, however, with the resolution of the infection, these cells decrease considerably, and a subset of T cells specific to EBNA1 appears (61). IM symptoms resolve within weeks, and a decline in the viral genome can be observed (62) as well as a decline in EBV specific T cells. 1.3.3 Epstein Barr Virus Latency  Latency is understood as a state in which an infected cell survives but viral progeny is not being produced. During latency, the main reservoir for EBV is long-lived  9 memory B cells that have gone through somatic hypermutation and immunoglobulin class-switch recombination (63). EBV’s genomic DNA exists as a highly methylated episome in the nucleus of the host cell. This episome encodes an origin of DNA synthesis, an independent maintenance element (together termed oriP), and one protein, EBNA-1, which binds oriP to mediate DNA synthesis and partitioning of the viral genomes (64).  During latent infection the host cell expresses EBV gene products including six nuclear proteins (EBNA-1/2/3A/3B/3C/LP), three membrane proteins (LMP-1/2A/2B), and EBV-encoded small RNAs (EBER-1 and EBER-2). These products can control the host’s cell cycle and prevent apoptosis. During latency none of the genes needed to induce productive replication are expressed. The pattern in which these genes are expressed depends on the type of latency that the virus establishes during EBV primary infection (53). Latency type gene expression patterns correspond to distinct primary transcripts that are initiated from different viral promoters (65). EBV can express four types of latency (66):  - Type 0: defined as the lack of viral gene expression. It is found in non-dividing B-cells (67).  -Type I: Expressed in Burkitt’s lymphoma (BL) and BL-derived cell lines, as well as in memory B-cells of healthy hosts (68, 69). Expresses EBER1/2 RNA, EBNA-1, LMP-2A/B, and BART RNA proteins (66). - Type II: Expresses EBER1/2 RNA, EBNA-1, LMP-2A/B, LMP-1 protein Type IIa) or EBNA-2 (type IIB),  and BART RNA proteins (66). Is usually found in Hodgkin lymphoma and undifferentiated nasopharyngeal carcinoma (70).   10 - Type III: Expressed during rare cases of BL and post-transplant lymphoprolipherative disorders (PTLD) (71). Expresses the following proteins: EBER1/2 RNA, EBNA-leader protein, EBER1/2 RNA, EBNA-3ABC, EBNA-1, LMP-2A/B, LMP-1 protein, and BART RNA (66). The patterns of latency are epigenetically stable for each of the types above, and they are maintained over multiple cell divisions (65).  1.4 Epstein-Barr Virus and cancer  As mentioned earlier, EBV was initially described in BL. Subsequently, its potential oncogenic ability was confirmed in vitro where it was shown that it can transform resting B cells (72). Since then, EBV has been associated with multiple lymphomas, including classical Hodgkin lymphoma (cHL), diffuse large B-cell lymphoma (DLBCL) and natural killer (NK)/T –cell lymphoma (73).  All forms of EBV- positive B-cell lymphomas carry aberrations in MYC, a family of proto-oncogenes that encode for transcription factors that regulate gene expression. In particular, c-Myc is known to be important for B cell proliferation (74). cHLs are characterized by the presence of B cells that are derived from the germinal centre (GC) but that have destructive B-cell receptor (BCR) mutations and have failed to go through apoptosis(75). DLBCLs are a heterogeneous group of malignancies that can be subdivided in two groups depending on their gene expression profiles: germinal centre B-cell (GCB) and activated B-cell (ABC). ABCs are the most common type of DLBCL and they are usually present in cases of immune suppression, such as organ transplantation and acquired immunodeficiency syndrome (AIDS) (73).   11 1.5 Epstein-Barr Virus and autoimmunity In addition to cancer, EBV infection has also been associated with the development of several autoimmune diseases (Table 1.1) such as systemic lupus erythematous (SLE), rheumatoid arthritis (RA), Sjögren’s syndrome (SS) and, more prominently, MS. However, direct evidence of how EBV might contribute to the development of these diseases is limited.  1.5.1.1 EBV and Rheumatoid Arthritis (RA) RA is a disease characterized by the development of arthritis, cardiovascular complications, metabolic syndrome, cognitive dysfunction, and depression (76). An increased number of EBV infected B cells has been described in RA patients compared to controls (77), as well as increased concentrations of IgG/IgA/IgM antibodies against EBNA-1 and the early EBV antigen diffuse (EAD) (78). Additionally, lytic EBV infection has been detected in synovial samples of RA patients by RT-PCR and in situ hybridization, which suggests a role of the virus in the synovial inflammation characteristic of RA (79). The most likely mechanism for how EBV can be implicated in RA is through molecular mimicry. The amino acid sequence QKRAA in the third hypervariable region of the DR beta-1 chain (DRB1) has been associated with susceptibility to RA, and has also  been identified in the EBV glycoprotein gp110 (80). In addition, EBNA-1 has glycine-alanine repeat sequences that are also expressed in cytoskeleton proteins like cytokeratin and type 2 collagen, as well as a citrullinated form of EBNA-1 cross-reacts with citrullinated human fibrin (81). However, more research is needed in order to better understand the role of EV in RA.  12 1.5.1.2 EBV and Sjögren’s Syndrome (SS) SS is characterized by the chronic inflammation of exocrine glands and other glands, the dysfunction of muscarinic receptors, and the development of specific autoantibodies (76). An increased EBV load in blood, lacrimal and salivary glands, as well as increased levels of antibodies against EBV have been observed in SS patients. Additionally, SS patients have an increased risk of developing lymphomas associated with EBV (82-84). Patients with SS have a higher incidence of EBV reactivation, as well as higher expression of HLA-DR antigens on salivary epithelial cells, and increased levels of EBV antigens and DNA in salivary infiltrating lymphocytes (85). It has been suggested that the activated aryl hydrocarbon receptor (AhR) (a receptor associated with inflammation when activated by exogenous ligands), may interact with latent EBV infection. Coincidentally, it has been observed that saliva from SS patients can transactivate the target genes of AhR (85). 1.5.1.3 EBV and Systemic Lupus Erythematosus (SLE) In the case of SLE, a considerable amount of data has been collected. SLE is characterized by a butterfly rash at the malar region of the face, photosensitivity, oral- and nasopharyngeal ulcers, arthritis, renal and hematologic disorders, and autoantibodies against nuclear components (86). SLE patients have an abnormally high viral load of EBV in PBMCs, this increase in viral loads has been associated with disease activity. SLE patients have also shown elevated titers of anti-EBNA-1 and EBV-VCA IgA, as well as EBV-EA/D, EBV-EA/R IgG and IgA (86). The presence of antibodies directed against lytic cycle antigens suggest a frequent reactivation of the virus in SLE patients, this lack of control also show the presence of DNA in serum (87). It has also been  13 suggested that TLR3 activation by EBERs can lead to the induction of type 1 interferon in SLE patients (88). Despite the great amount of evidence that has been collected through the years, the strongest association of an autoimmune disease caused by EBV infection is with MS.   Table A Autoimmune diseases related to Epstein Barr Virus Disease  Link to EBV  References  Systemic Lupus Erythematous  • High Viral load of EBV • Elevated titers of anti-EBV IgA and IgG • EBV DNA presence in serum (82, 87, 89-93)    Rheumatoid Arthritis  • Increased EBV infected B cells • EBV infection in the joints of RA patients  • Increased antibodies against EBV lytic and latent proteins (77, 78, 94-98)  Sjögren’s Syndrome  • Increased EBV load in blood and salivary glands, • Increase antibodies against EBV  • Increased risk of lymphomas associated with EBV (79, 83, 84, 99-101) Multiple Sclerosis  • IM increases MS risk • 100% of MS patients have anti-EBV antibodies (42, 45, 52, 92, 102-113)     14 Disease  Link to EBV  References • Increased anti-EBV antibodies in plasma and CSF Inflammatory Bowel Disease (IBD) • Increased prevalence of EBV in intestinal tissue  • EBV replication associated with severe IBD  • Increased presence in inflamed gastrointestinal mucosa (114-116) Celiac Disease • Detected increased in duodenal mucosa of patients  (117) Type I diabetes, Juvenile idiopathic arthritis, celiac disease • Gene-environment interactions. EBNA2 interaction with transcription factors and cofactors.   (118)   1.6 Epidemiology of Epstein-Barr Virus and Multiple Sclerosis A connection between MS and EBV was first suggested when it was recognized that there are similarities in the demographic distribution of MS and infectious mononucleosis (39), whereby both IM and MS occur at higher incidences in developed countries. Subsequent studies found that while 90% of the global population has circulating anti-EBV antibodies, such as anti-EBNA and anti-Viral Capsid Antigen (VCA) IgG, indicative of prior EBV infection (102), these are found in almost 100% of MS patients (52). It is also important to mention that recent infection by EBV is not a  15 trigger for MS, since evidence has been found that there is no difference in seroprevalence of anti-early Antigen (EA) IgG between MS patients and healthy controls (102). 1.6.1 Infectious Mononucleosis and the Hygiene Hypothesis  Infection with EBV during childhood is usually asymptomatic. However, when EBV primary infection occurs during early adulthood most of the patients develop IM. IM is clinically identified by sore throat, cervical lymph node enlargement, fatigue and fever. These symptoms are accompanied by atypical large activated CD8 T cells, known as Downey cells, that are mostly specific for EBV-infected cells (119). Numerous studies report a positive association between IM and MS (111). People with a history of infectious mononucleosis have a two to three times higher risk of developing MS (112). This relationship has been shown to exist across ethnicities (38). In developed countries, seroconversion occurs primarily during adolescence, which allows for the development of IM (42, 106). Contrastingly, in developing countries, where infection with EBV occurs most often within the first year of life, and universal seroconversion is seen by ages 3-4 (42), individuals show a low incidence of infectious mononucleosis and, consequently, the risk of developing MS is much lower (109, 120). This so-called “paradox” reveals that the relationship between MS and EBV is related to the stage in life when the infection with EBV occurs (52), together with the associated development, or not, of IM.  These observations have contributed to the development of the “hygiene hypothesis”, which posits that increased sanitation causes a change in the exposure to common pathogens during childhood and can predispose individuals to a higher risk of  16 developing allergies or autoimmune disorders (121). A correlation between socioeconomic status and MS incidence has been proposed (122), for example, there is evidence that infection with common parasites such as Trichuris trichura, Ascaris lumbricoides and Sternglyoides stercolaris, is inversely correlated with MS incidence, and it has been suggested that a Th2 response against the parasite prevents the production of Th1 cytokines predominant in most autoimmune disorders (123), however, conclusive links between socioeconomic status and MS have not been proven (122). 1.6.2 Presence of EBV in MS Patients In support of the epidemiological data, it has been described that MS patients show increased levels of serum or plasma IgG antibodies against the EBNA family in general, particularly against EBNA 1, EBNA2 (103), EBNA3 (EBNA3A), EBNA4 (EBNA3B), EBNA6 (EBNA3C), latent membrane protein 1 (LMP1), EBV capsid protein VP26 (110), early antigen complex (92, 103, 107), EBV viral capsid antigen (92), and the EBV lytic protein BRRF2 (124). In addition, patients with MS also have elevated levels of these antibodies in the cerebrospinal fluid (CSF), including IgG antibodies to EBNA1, viral capsid antigen, EBV early antigen, Epstein-Barr virions and BRRF2 (124). Several studies confirm the increase in risk of developing MS after EBV infection (111). Levin et al found that active-duty US Army, Navy and Marines with MS and that originally were negative for EBV, became EBV positive before development of MS, while controls that did not seroconvert did not develop MS (125). A study based on data from Swedish patients with MS, found that EBNA1 titer levels in blood of MS patients that had developed IM earlier in life, was directly correlated with time of MS onset. A low titer, suggesting an efficient immune response to the virus, showed a slow development of MS,  17 while a high titer, suggesting a deficient immune response, correlated with a more rapid MS development. Interestingly, pre-adolescent women tended to have low blood titers of EBNA1, however, if infected once adolescence started, they showed higher EBNA1 titers. Contrastingly, men tended to have high EBNA1 blood titers regardless of age (126). Furthermore, increased antibody titers have been observed in adults more than 10 years before the development of the first MS symptoms (127).  1.7 How can EBV cause MS? Several hypotheses have been proposed to explain the relationship between EBV and MS. Among these, the most studied are the molecular mimicry hypothesis, the bystander damage hypothesis, the mistaken self-hypothesis, and the EBV infected autoreactive B cell hypothesis.    1.7.1 The Molecular Mimicry Hypothesis  This hypothesis postulates that T cells specific for EBV antigens (such as EBNA-1) are structurally related to CNS antigens like myelin basic protein (MBP). In this way, a TCR would be able to recognize more than one peptide and lead to recognition of autoantigens (128, 129). Additionally, it has been shown that anti-EBV antibodies, such as anti-EBNA-1, are cross-reactive for epitopes of neuroglial cells (130) and transaldolase, a protein expressed selectively in oligodendrocytes (131). CD4 T cells specific for EBV proteins have been found in the CSF of MS patients, moreover, a high proportion of these T cells cross-recognize MBP, suggesting that EBV specific CD4 T cells could target MBP in the CNS (129, 132). Further, the presence of latently infected B cells alone does not necessarily influence cross-reactivity. Though the presence of latently infected B cells in the brains of MS patients (133) remains controversial (134,  18 135), B cells and plasma cells are commonly found in MS lesions, appear in large numbers in chronic MS plaques, and are present in areas of active myelin breakdown (136). Lymphoid B cell follicle-like structures that feature characteristics similar to germinal centers have been observed in the cerebral meninges of MS patients with secondary progressive MS and are usually associated with cortical neuronal loss and demyelination (137). The presence of these tertiary lymphoid organs (TLOs) is characteristic of chronic inflammation and is observed in several other diseases such as tuberculosis, Crohn’s disease, and sarcoidosis (138). Although this theory explains the development of autoreactive immune cells, it is not likely to be the sole cause of the onset of the disease since CD4+ T cells need to be activated in order to cross the blood brain barrier (BBB). 1.7.2 The Bystander Damage Hypothesis  The bystander damage hypothesis establishes that the activation of CD8+ or CD4+ T cells directed against EBV antigens, particularly lytic antigens, can result in bystander damage to the CNS. CD8+ T cells outnumber CD4+ T cells in MS brain lesions during all disease and lesion stages, although a variable number of CD4+ T cells, CD20+ B cells, and plasma cells are also present (139-141). However, for this hypothesis to be possible, it would be necessary for infected B cells to be present in the CNS, which has been rather hard to prove. Serafini et al. showed that meningeal B cell follicles and acute white matter lesions express EBV nuclear transcripts (EBERs) (133). Recently, Hassani et al. used PCR to detect the presence of EBV in the meninges of human brains from MS patients post-mortem and more surprisingly they were also able to detect EBV presence in white matter, thus finding evidence of EBV in 65% of MS cases that were  19 analyzed (142).  However, further attempts to detect EBV in MS brains have been futile (134, 135, 143). Under the bystander damage hypothesis, MS would not be an autoimmune disease, although secondary autoimmune responses could occur as a result of sensitization to CNS antigens released after virus-targeted bystander damage (133). A caveat to this hypothesis would be that, overall and relative to other viruses, EBV does not directly damage the cells that it infects, leaves little bystander inflammation, and is not likely to induce disease through this type of mechanism preferentially in the CNS.  1.7.3 Mistaken Self hypothesis In the mistaken self-hypothesis, the stress protein αB-crystallin that is expressed de novo in infected lymphoid cells is recognized by T-cells that are activated by microbial antigen and the accumulation of the αB-crystallin self-antigen in oligodendrocytes provokes a CD4 T cell response with resultant demyelination (144). To date, little to no data exists to fully support this scenario.  1.7.4 The EBV-infected Autoreactive B Cell hypothesis.  Pender et al. (145) has proposed a new theory, where EBV specific CD8+ T cells do not effectively eliminate EBV-infected B cells, leading to the accumulation of autoreactive B cells infected with EBV in the CNS (146). If this theory proves true, it is possible that boosting the immune system with CD8+ T cells specific for EBV epitopes could be a successful treatment for MS patients.  In support of this theory, Pender et al. recently performed a trial where they treated a patient with secondary progressive MS using AdE-1-LMPpoly, a recombinant adenovirus vector that encodes multiple CD8+T-cell epitopes from the latent EBV proteins EBNA1, LMP1 and LMP2A (147). This treatment has been successfully used in  20 patients with EBV-associated carcinoma (147). The patient was treated with EBV specific CD8 T cells expanded with AdE-1-LMPpoly and IL-2. The results showed an improvement in symptoms including a reduction in fatigue and pain. More studies are needed in order to determine if this regimen could be effective in treating secondary progressive MS. In addition, more research is needed to investigate the treatment’s mechanism of action, which is believed to be the elimination of EBV-infected B cells in the CNS (148). It is possible that depletion of infected B cells in the CNS is not the only mechanism involved in the treatment and that a combination of factors leads to the improvement of the patients’ symptoms.  In summary, these four hypotheses explain some of the potential scenarios that contribute to the development of autoimmunity by EBV. However, each of them fails to explain key characteristics of MS pathogenesis. Since sample collection from MS patients is limited, the development of animal models to help understand and explain these hypotheses are of great importance and will help us to understand and develop new and better models that explain the link between EBV and MS. In particular, these models help elucidate the role of latently infected B cells in the EBV-MS relationship.  As an alternative to the hypotheses described above, we propose that EBV infection and latency establishes a precondition to the immune response where subsequent challenges show acceleration and/or enhanced Th1 outcomes that eventually will lead to the onset of MS (Figure 1.3).  In this scenario, the latently infected B cell is not an initiator but instead acts as a necessary co-factor in disease progression.   21 Figure 1.3  Latent infection with EBV establishes a precondition that leads to the development of MS  Figure 1.3 (A) In normal conditions, B cells will activate APCs and, depending on the stimulus they receive, can promote a fully functional immune response that will not impact the development of MS. (B) EBV latently infected B cells will activate APCs and promote a skewed Th1 response that, when combined with genetic factors, will lead to the development of MS.  1.8 The Importance of B cells in Multiple Sclerosis   B cells found in the CNS and CSF of MS patients are clonally expanded and have gone through IgG class-switch and somatic hypermutation (149-151). In MS patients, more than 90% of B cells in the CSF express the memory B cell marker CD27 and a fraction of CSF B cells express CD138 and/or CD38, suggesting stimulation of the  22 maturation of clonal activated memory B cells into antibody producing plasma blast. Alternatively, naïve B cells expressing CD27 IgD+ are significantly lower in the CSF compared to blood (152, 153). The memory B cells that can be found in the CSF have an upregulation of costimulatory molecules, which suggests an active B and T cell interaction (154). 1.8.1 B cells as Antigen Presenting Cells Until recently, it was believed that the only role B cells played in MS pathogenesis was the production of autoantibodies, however, with the realization that B cell depleting drugs such as rituximab, ocrelizumab and ofatunumab, had an important effect in diminishing relapses in Relapsing Remitting MS (RRMS), it became evident that the contribution of B cell to MS might also involve their role as antigen presenting cells (APCs). In fact, patients treated with B cell depletion therapy show a rapid response to treatment, and since these antibodies do not affect plasma cells, it is now believed that autoantibodies are not as important in the pathogenesis of MS as B cells functioning as APCs or as immunomodulators (155-158). In their role as APCs it has been suggested that B cells and DCs interact via cytokine-dependent feedback loops to shape the T cell response to viral infections. When B cells are stimulated with cytokines, TLR ligands, or antibodies, these cells release diverse cytokines including IL-10, TGFβ, IL-6 or IL-17 which have a suggested modulatory effect in DCs (159-161). One of these effects is the suppression of antigen presentation by IL-10. It has also been seen that high levels of TGFβ are produced by B cells stimulated with LPS, which regulates Th1 response in NOD mice, induces the apoptosis of T cells, and impairs the ability of APCs to present auto-antigens. In addition, IL-6 promotes the differentiation of B cells into antibody  23 secreting plasma cells in mice and humans, and IL-17 has been seen to control DC maturation in mice infected with Trypanosoma cruzi (162).  Contrastingly,  B cells have the ability to produce different subsets of pro inflammatory cytokines depending on the antigen they encounter, hence promoting polarization of CD4 T cells towards a Th1 or Th2 phenotype by producing IL-12, IFN-g and TNFa in the case of Th1 and IL-4 and IL-5 in the case of Th2 (163). MOG-stimulated CD19+ B cells from PP-MS and RR-MS patients can produce high levels of pro inflammatory cytokines, such as TNF-a, IL-12, IFNg and IL-6 compared to healthy controls, while having a reduced production of IL-10, IL-13 and TGFb (164).  In MS, memory B cells from some RR-MS patients are able to activate and elicit proliferation of CD4 T cells and increase the production of IFNg in response to MBP and MOG (165). Memory B cells are highly efficient antigen presenting cells and, in MS patients, they have a deficient capacity to produce IL-10 when stimulated ex vivo (166). Contrary to what could be expected, it has been recently observed that ex-vivo, B cells, rather than monocytes, from patients with RR-MS and Clinically Isolated Syndrome (CIS) show increased levels of CD40 and HLA-DR. This is especially visible during relapses when compared to healthy controls. Interestingly, patients with other inflammatory neurological diseases and progressive MS do not show this upregulation (167).  In another study, an upregulation in CD86 and CD80 in peripheral blood B cells and in the cerebrospinal fluid (CSF) of untreated MS patients compared to healthy controls was reported. Moreover, after antigen-specific stimulation, B cells increase their CD80 expression even more (168).   24 A population of memory B cells that expresses CD80 during their resting state has been characterized. When activated, this population efficiently secretes large amounts of class switched immunoglobulins and is very effective presenting antigen to T cells (169). It is unclear however if this particular population is upregulated in MS patients.  1.9 Animal Models to Study Multiple Sclerosis and Epstein-Barr Virus Infection While numerous lines of evidence point towards a relationship between MS and EBV, the study of this interaction is limited since EBV only infects humans and, while most patients become infected with the virus during childhood or as adolescents, the onset of MS does not occur until years later. Despite these limitations, a current murine g-herpesvirus can be used to study the relationship of these viruses with MS.  1.9.1 Gammaherpesvirus 68 as a murine model for Epstein-Barr Virus Murine g-herpesvirus 68 (gHV-68), is a g-herpesvirus that has provided a widely used model to study human g-herpesviruses, in particular EBV and Kaposi’s Sarcoma-associated Herpesvirus (KSHV) (170). gHV-68 shares most of its genome with these two viruses, and, importantly, genes that are associated with EBV cell tropism- latency and transformation- are also present in gHV-68 (171).  In the model, mice are inoculated with gHV-68 usually via intraperitoneal or intranasal methods. Despite the route of infection, the main reservoir of the virus is the spleen and it is cleared 14-16 days post infection. At that point, the virus establishes a life-long latency primarily in isotype-switched B cells CD19+ IgD-, which are considered  memory B cells (172). During early stages of latency, the virus also establishes itself in macrophages and splenic DCs, although to a much lesser extent.  In these other APCs, gHV-68 latency decreases considerably with time (173, 174).  25 In gHV-68 infection, the virus is able to modify the expression of different genes in the cells that harbour the latent virus. Many of these genes are inflammatory cytokines, such as IFNg, IL-18 receptor, SOCS3 and a wide array of known stimulated IFNαβ genes (159-161). Once the virus has established latency in B cells, it continues expressing latency genes that are able to regulate the expression of genes in B cells. In the same way, B cells express other genes to control gHV-68 reactivation. All of this brings different outcomes that differentiate latently infected B cells from uninfected B cells.  Among the viral genes expressed during gHV-68 latency, we can find M2, a protein that can suppress STAT1/2 expression and leads to the inhibition of the interferon response (175), as well as being able to induce the expression of IL-10 in primary B cells. Despite M2 being unique for gHV-68, EBV also modulates the immune response by producing its own viral IL-10 (vIL-10) (176). In addition, M1, a secreted protein with a superantigen-like activity might play an important role in maintaining latency (177). EBV also encodes for 25 pre-miRNAs that may play a role in immune response and whose target transcripts are immune recognition, apoptosis, and cell cycle pathways. gHV-68 can generate 15 mature miRNAs, however their function is less understood than in EBV (178, 179). It is known that micro RNAs are not necessary for acute replication, but that they are important in the establishment of latency in germinal center and memory B cells (180).  Infection by gHV-68 increases heparin sulphate (HS) on the surface of B cells. HS is a cofactor for cytokines, chemokines and growth factors, and its upregulation is dependent on the expression of type I IFNs that increase responsiveness to APRIL, a cytokine important for B cell survival and T cell-independent B cell responses (181). It is  26 well known that IFN α/β are important to direct gHV-68 into latency, and that they are also important in maintaining latency (182). Moreover, Latency Membrane Protein (LMP-1) is a virus protein that has been shown to control EBV’s latent life cycle. LMP-1 is upregulated in the presence of Type I IFN, in particular IFN α (183), and this unique feedback maintains the latent life cycle and as well as promotes host IFN production (184). It is important to remark that IFN α and IFN β present functional differences (185) that are in a unique balance with each other. While not completely understood, Type I IFNs have been largely used in the clinic with different purposes: while IFN α has been historically used to treat chronic hepatitis C infection (186), IFN β was the first disease-modifying therapy for MS and is currently is part of the first-line treatments used for RR-MS (187) . Addition of either IFN α or IFN β generally results in diminishment of the other. Based on the effectiveness of Betaferon in the clinic and its putative role in upsetting the balance between LMP-1 and IFN α (188, 189), a better understanding of the roles and functions of IFN α and IFN β should be explored in the context of EBV infection and MS. It would be particularly interesting to explore whether IFN α/β produced by infected B cells for the maintenance of latency are able to promote APC maturation.  1.9.2 Experimental Autoimmune Encephalomyelitis EAE has long been used as the most acceptable in vivo model of MS. In active EAE, mice are immunized with myelin peptides or proteins, such as myelin oligodendrocyte glycoprotein (MOG), myelin basic protein (MBP) or proteolipid protein (PLP), that are emulsified in complete Freund’s adjuvant (CFA, which is composed of mineral oil and desiccated Mycobacterium tuberculosis) (190). In addition, two injections  27 of pertussis toxin (PTX) might be needed, depending on the strain of mouse used. EAE leads to an ascending paralysis in 10-12 days after induction and is characterized by a CD4-mediated autoimmune reaction. SJL mice injected with PLP generally develop a relapsing-remitting disease course. In C57Bl/6 mice, EAE induction with MOG results in a chronic progressive disease (190). Alternatively, passive EAE can be induced if MOG-specific T cells are transferred to naïve mice. 1.9.3 The Role of B cells in EAE In CNS autoimmunity, the role of B cells as APCs has been mostly studied in EAE. It has been observed that B cell antigen presentation plays a critical role in the initiation of EAE (191, 192). Mice with a selective deficiency of MHC-II in B cells are resistant to EAE induced with rhMOG, a model that is known to be B cell dependent. Additionally,  mice with a BCR specific for MOG but that cannot secrete antibodies are susceptible to EAE,  thus highlighting the importance of antigen presentation by B cells in the development of pathogenesis in the CNS (193). Although MHC-II expression restricted to B cells is not enough to develop EAE (194), when B cells  are MOG-specific they are able to drive inflammation in the CNS when passive EAE is induced. Moreover, they  can cooperate with dendritic cells and enhance EAE severity (195).   This is further confirmed in patients, where it has been described that contrary to other autoimmune diseases such as rheumatoid arthritis, central tolerance of B cells is not affected in MS. Instead, only peripheral tolerance seems to be defective, which can be the result of defective Treg function (196, 197). Patients with relapsing-remitting MS (RRMS) show memory B-cells in peripheral blood that are able to respond to MBP, and  28 it has been described that closely related B cells exist on both sides of the BBB, suggesting that in patients with MS, a pool of IgG- expressing B cells is capable of bidirectional exchange through the BBB (19). An important characteristic of B cells in MS patients is cytokine production. B cells from MS patients produce higher levels of IL-6 compared to healthy controls. After depletion of B cells with anti CD20, and after B cell reconstitution, the new cells do not seem to produce the same level of IL-6 than before depletion, which might help to understand the ameliorating effect in patients. All of this is accompanied by reduced levels of IL-17 secreted by peripheral T cells (198). In EAE mice, B cell depletion seems to remove B cells that are producing IL-6, which helps to ameliorate symptoms of the disease. In RRMS it has been shown that during relapses, patients have reduced levels of Bregs (B cells, also commonly referred to as B10 cells, are known for their regulatory phenotype since they secrete IL-10 an immunoregulatory cytokine), as well as memory B cells in peripheral blood compared to healthy donors (166, 199). In EAE, Bregs are able to modulate the autoimmune response (200).  1.9.4 A Unifying Model to Study the Effect of EBV in MS There is strong evidence that gHV-68 is a successful model to help understand the relationship between EBV and MS. Peacock et al. describe that EAE induced mice infected with gHV-68 show exacerbated symptoms of EAE compared to non-infected mice (201).  Moreover, similar to what is observed in MS patients, it has been described that gHV-68 is capable of inducing the expression of αB crystallin in mice infected with the virus. These mice develop a strong immune response against heat shock protein (202).   29 Combining EAE and γHV-68 models, our research has focused on determining the relationship between EBV infection and the onset of MS. Recently, we demonstrated that mice that were latently infected with γHV-68 before the induction of EAE showed increased ascending paralysis, as well as augmented neurological symptoms and brain inflammation. This was the result of a stronger Th1 response in infected mice, characterized by higher levels of IFNg and diminished IL-17 levels. CD8 infiltration into the CNS was also noted in these latently infected mice. This is remarkable, given that EAE pathology generally lacks the presence of CD8 T cell infiltration and has a predominant Th17 response, while in MS, CD8 T cells infiltration and a combined Th1/Th17 response are important characteristics of the disease. Additionally, we observed the upregulation of the co-stimulatory molecule CD40 and MHC II in antigen presenting cells (APCs) during EAE induction in mice infected with γHV-68 (203). Recently, this upregulation of CD40 and MHC II has also been reported in B cells of RR-MS patients (167).  We also showed that the enhanced disease observed in gHV-68 infected mice depends on the latent life cycle of the virus, and that is strongly associated with pSTAT1 and CD40 upregulation on uninfected CD11b+CD11c+ cells. This CD40 upregulation leads to a decrease in the frequency of regulatory T cells (204). It is known that CD40 is important in the control of Tregs and that its upregulation is associated with an enhanced Th1 response, it also has been related to the development of autoimmunity (205, 206). The decrease in Treg frequencies has also been seen in MS patients (207, 208). Further research is needed to determine if factors such as IFN α/β are involved in the  30 enhancement of EAE symptoms, and, in particular, to understand potential differences between uninfected and latently infected B cells.  Finally, studies performed on non-human primates would be an important tool in the study of EBV and MS. In marmosets, for example, EAE is effectively inhibited when marmosets are treated with anti-CD20, however, treatment with anti-BlyS or anti-APRIL, which mainly depletes peripheral B cells, but not CD40high B cells, only delays the onset of EAE (209, 210). It has been proposed that the difference in the effectiveness of the treatments resides in the fact that cells infected with CalHV3 are among the B cells depleted by anti-CD20; CalHV3 is the marmoset equivalent of human EBV, and is a B-cell transforming lymphocryptovirus (211). Moreover, a small percentage of Japanese macaques which are naturally infected with a gamma 2- herpesvirus, named JM radhinovirus, isolated from CNS lesions, spontaneously develop an encephalomyelitis that is similar to MS in humans (212). In addition, since EBV has not just been associated with MS but with other autoimmune diseases like SLE and inflammatory bowel disease, it is possible that the mechanism of action is similar in these diseases, making gHV-68 even more important in the study of the development of autoimmunity.  It is our contention that EBV acts a co-factor that sets up a precondition in which any subsequent environmental stress runs the risk of an overly responsive, under regulated Th1 response. Specificity towards the CNS, myelin sheath and oligodendrocytes is dictated by the secondary stress event and not EBV latency. While EAE is an acceptable model that mimics many of the characteristics of MS, it does not represent how MS is induced. Given that not every person infected with EBV develops MS, genetic predispositions, as well as other environmental factors must be involved in  31 the expression of the disease. With that in mind, other environmental events and stresses that target the myelin sheath or oligodendrocytes, such as a secondary virus infection or toxins, likely act to initiate the disease in the presence of latently infected B cells. For example, agents like cuprizone, a copper-chelating agent, that is known to cause demyelination in the CNS through oligodendrocyte apoptosis (213, 214) may well be active MS inducers. By studying these models in the context of latently infected B cells, we will be able to better investigate the role of latent virus infection in the initiation and progression of MS. 1.10 Research Hypothesis and Rationale Determining the mechanism that describes how environmental factors such as EBV and infectious mononucleosis are related to the onset of MS is vital to understanding how MS pathogenesis is developed. The efficacy of treatments such as Rituximab and Betaferon that indirectly act to inhibit EBV latency in B cells by depleting B cells or upsetting the IFN balance, serves to demonstrate the important role that EBV latent infection plays in MS development. It is also important to remember that, despite being highly effective in preventing relapses (70, 187, 215-218), neither B cell depletion nor IFN I treatment are successful therapies for EAE (192, 219-221) and were instead chosen because of their efficacy in other autoimmune diseases. With the aid of new animal models that consider the role of latent infection, it is expected that these complicated causal mechanisms can be more easily studied and new and more effective treatments for MS patients will be more closely at hand. We propose that EBV infection and latency establish a precondition to the immune response where subsequent challenges show acceleration and/or enhanced Th1 outcomes. This is directed by latently infected B  32 cells through the upregulation of Type I interferons and/or other cytokines and costimulatory proteins, which generate an autoimmune process that leads to MS. In order to explore this hypothesis, we proposed the following aims:  1.10.1  Aim 1 Recently, treatments using anti-CD20 monoclonal antibodies (Rituximab) in MS patients have shown a reduction in symptoms and relapses (215). The antibody removes germinal center B cells, including memory B cells. The exact mechanism of the effect on patients has not been elucidated, but it has been proposed that indirect removal of EBV infected cells might contribute to the effectiveness of the treatment (222). In our model, we hypothesize that memory B cells from latently infected mice are determinant in the enhancement of EAE. As described in Chapter 2, we determined whether B cell latent infection with gHV68 was necessary to lead to EAE enhancement. For this we performed adoptive transfers of CD19+ IgD- cells from infected mice into naïve mice and evaluated the resulting effects in EAE.  Alternatively, to determine if B cell presence during acute and latent infection can affect the reservoir of latently infected cells and consequently the development of EAE, we treated mice with anti-CD20 antibody at different time points (that resembles Rituximab) before and after EAE induction as well as B cell depletion before gHV-68 infection. We also explored some of the differences between B cells from infected mice vs uninfected by RNAseq and propose a mechanism of how these changes can lead to EAE enhancement. 1.10.2 Aim 2 In Aim 1 we determined that B cells from latently infected mice play an important role in the development of EAE enhancement, we also identified some important  33 molecules that could be relevant as biomarkers in order to identify pathogenic cells. In Aim 2, we determined whether type I IFNs are part of the cytokines produced that will play a role during EAE. Alpha/Beta interferons (IFN-α/β) are expressed in large amounts during early viral infection. Production of IFN- α/β upregulates CD40 on DCs which is related to differentiation to a Th1 phenotype (223). Our model demonstrates that levels of CD40 increase during the EAE induction phase in infected mice compared to uninfected mice (204). Also, IFN- α/β has been shown to be an important factor in inhibiting the reactivation of the virus during latency (224). We hypothesized that latently infected memory B cells create a continuous state where they maintain elevated levels of Type I IFNs which are able to upregulate CD40 on DCs at early stages of EAE induction. In Chapter 3 we used IFNARko mice in order to determine the importance of type I IFNs in EAE enhancement. 1.10.3 Aim 3 Reports suggest age of infection with EBV plays an important role in the likelihood of developing MS later in life (225). Moreover, it has been established that a history of IM doubles an individual’s propensity to develop MS. IM is the symptomatic version of EBV infection; interestingly these symptoms are generally present only when the virus is acquired during adolescence or adulthood, but not during childhood (226). The fact that IM is a marker for MS propensity, but infection during childhood is not, suggests that time of infection marks an important mechanism that is not developed during childhood and that could help understand MS development.  gHV-68 behaves similarly in mice; studies have shown that when mice are infected with gHV-68 before 3 weeks of age, infection runs asymptomatic, while mice  34 infected at 8 weeks old, show similar symptoms to IM (227, 228).  In Chapter 4 our objective was to determine how age and gHV-68 would interact and affect EAE enhancement. We determined that the effect of gHV-68 latency was stable and lasted up to 20 weeks after inoculation. We also determined that young mice infected before 3 weeks of age behave like uninfected mice when EAE is induced. This highlights the importance of time of infection and underscores how the environment is an important determinant in the development of autoimmunity.     35 Chapter 2: Memory B cells in gHV-68 Latency Direct Enhancement of EAE 2.1 Introduction  While evidence strongly suggests that EBV contributes in an important way to the development of MS, the mechanism of EBV involvement in the pathogenesis of MS has been largely elusive (38, 103, 225, 226).  Previously, our lab described an experimental model that mimics MS symptoms (203). Using murine gammaherpesvirus-68 (gHV-68), an homolog of EBV (227), and Experimental Autoimmune Encephalomyelitis (EAE), we have been able to show that latent infection with gHV-68 leads to a high number of CD4+ and CD8+T cells infiltrating the CNS of mice induced with EAE. These immune cells produce an overwhelming Th1 response that leads to enhancement of EAE clinical symptoms (203). Importantly, all these events require the virus to remain latent (204). When EAE is induced during acute γHV-68 infection, the start of EAE symptoms is delayed until most of the virus is cleared and has established latency. Additionally, when mice are infected with a latency free virus, mice do not show signs of EAE enhancement, even though the virus efficiently mounts an acute infection (204). EAE enhancement is accompanied by an upregulation of CD40 and MHC class II in CD11b+CD11c+ cells (203). Although these cells can be infected during acute infection (174), they are not the main reservoir of the virus during latency (173), where splenic B cells, and particularly marginal zone CD19+ IgD- harbour most of γHV-68 during long-term latency. In humans, memory B cells are also the main reservoir of latent EBV (48). Recently, with the use of monoclonal α-CD20 (Rituximab/Ocrelizumab), the focus of MS research has switched from T cells to B cells (222). Originally, it was thought that the role of B cells in MS was limited to the generation of autoantibodies (229). However, B cell depletion  36 therapies mostly target CD20, a surface molecule present throughout the maturation cycle on B cells, from pre B cells up to memory B cells, but not in plasma cells (220)-the main producers of antibodies in the immune system. This discrepancy has caused a surge in the study of the antigen presentation ability of B cells (192, 195, 198, 220). In our model, it is not clear if the B cells infected with γHV-68 during latency are actively participating on the enhancement of EAE and how they are doing so. In this chapter, we examine the role played by memory B cells in EAE, and determine which events are necessary during acute and latent infection in order to exacerbate EAE. Finally, we explore how memory B cells from latently infected mice are different from uninfected mice and the possible repercussions of said differences. 2.2 Materials and Methods Mice and ethics statement - C57Bl/6 mice from The Jackson Laboratory and were bred and maintained in the animal facility at the University of British Columbia. All animal work was performed in accordance with the regulation of the Canadian Council for Animal Care. The protocol was approved by the Animal Care Committee (ACC) of the University of British Columbia (Protocols A17- 0105, A17-0184). Infections and EAE induction – Female and male mice between 8-10 weeks of age  were infected intraperitoneally (i.p.) with 104 pfu of  gHV-68 WUMS strain (purchased from ATCC, propagated on BHK cells) or 200µl of MEM media as a control. EAE was induced at different time points post-infection by injecting 100 µl of emulsified Complete Freund’s Adjuvant (DIFCO) containing 200 µg of MOG 35-55 (GenScript) and 400 µg of desiccated Mycobacterium tuberculosis H37ra (DIFCO) subcutaneously.  Mice also received two doses of 200ng of pertussis toxin (List Biologicals) via i.p. at the time of immunization and then again 48  37 hours later. EAE was assessed on a score from 0 to 5 as follows: 0, no clinical symptoms, 0.5 partially limp tail; 1, paralyzed tail; 2, loss of coordination; 2.5, one hind limb paralyzed; 3, both hind limbs paralyzed; 3.5, both hind limbs paralyzed accompanied by weakness in the forelimbs; 4, forelimbs paralyzed (humane endpoint); 5, moribund or dead. Viral quantification – DNA was extracted from total splenocytes and enriched memory B cells at indicated time points using either TRIzol Reagent (Invitrogen) or PureLink Genomic DNA Mini Kit (Invitrogen) following manufacturer’s instructions. qPCR analysis of DNA samples was performed using 2x Quantitect Probe Mastermix (Qiagen, USA) on the Bio-Rad CFX96 Touch™ Real Time PCR Detection system with a final volume of 20µl. Primers, probes and gBlocks® were obtained from Integrated DNA Technologies. Quantification of copies of mouse genome was done on 100ng of DNA by using primers and probe for a region of the mouse PTGER2 gene (Forward Primer: 5’ – TACCTTCAGCTGTACGCCAC – 3’; Reverse Primer: 5’ – GCCAGGAGAATGAGGTGGTC – 3’; Probe: 5’ – /56-FAM/CCTGCTGCT/ZEN/TATCGTGGCTG/3IABkFQ/ – 3’) (230) and absolutely quantified by use of a standard curve using concentrations from 5x107 copies/µl to 5x101 copies/µl. Quantification of copies of the γ-HV68 genome was done on 100ng of DNA by using primers and probe for a region of ORF50 (Forward Primer: 5’ –TGGACTTTGACAGCCCAGTA – 3’; Reverse Primer: 5’ – TCCCTTGAGGCAAATGATTC – 3’; Probe: 5’ – /56-FAM/TGACAGTGC/ZEN/CTATGGCCAAGTCTTG/3IABkFQ/ – 3’) and absolutely quantified by use of a separate standard curve using concentrations from 2x104 copies/µl to 2 copies/µl. Samples were run using a minimum of two technical replicates and all standard curves had an R2 greater than 0.95. The protocol was as follows: 95oC for 15 minutes, 95oC for 15s, 60oC for 1 min, repeated 50 times. Quantification of copy number was done using the CFX manager  38 software. The ratio of virus genome copy number to mouse genome copy number was obtained using the following equation: ##$%&'(	$*	+,-./#012345	16	789:; 	<	 =	012345	789:;=/?4@1A4B012CD;E.//?4@1A4  (simplified to #	#$%&'(	$*	+,-./#	#$%&'(	$*	FGHI,=×2).  RNA extraction and RT-qPCR- Splenocytes and memory B cells were isolated as described above. RNA was extracted using an RNeasy Mini Kit (Qiagen) following manufacturer’s instructions. 1µg RNA was reverse-transcribed into cDNA with a high capacity cDNA Transcription Kit (applied Biosciences) following manufacturer’s instructions with a final volume of 20µl. The reaction was incubated at 25°C for 10 minutes followed by 37°C for 120 minutes and 85°C for 5 minutes.  100ng of cDNA were added to a PCR mix composed of iQ SYBR Green supermix (Bio-Rad), 0.3µM of primers for mouse ITGA4 and HPRT1 with a final volume of 20µl. ITGA4 (Forward Primer: (5’-GCAGGACATTCAAGTTGCCCTTGT-3’; Reverse Primer: 5’-AGGAATTCCCACCTGCTACCAACA-3’) (231). HPRT1 (Forward Primer: (5’-GGACTAATTATGGACAGGACTG3’; 5’-GCTCTTCAGTCTGATAAAATCTAC3’) (232, 233). The reaction was incubated at 95°C for 3 minutes, followed by 40 cycles at 95°C for 15 seconds and 60 seconds at 55°C. A melt curve was performed from 55-95°C with 0.5°C increments to ensure specific amplification. Quantification of CD49d was done using the CFX manager software. Immune cells isolation and flow cytometry - Mice were euthanized 17-25 days post EAE induction. They were perfused with 30cc of PBS, and brains, spinal cords, and spleens were isolated. A single cell suspension was generated from each organ. Immune cells from the CNS were isolated using a 30% Percoll gradient. For intracellular staining, CNS mononuclear cells,  39 were stimulated for 4 hours in DMEM (Gibco) containing 10% FBS (Gibco), GolgiPlug (BD Biosciences), 10ng/ml PMA, and 500ng/ml ionomycin. Antibodies for the cell surface markers were added to the cells in PBS with 2% FBS for 30 min on ice. After washing, cells were resuspended in Fix/Perm buffer (eBiosciences) for 30-45 minutes on ice, washed twice, and incubated with Abs for intracellular antigens (cytokines and transcription factors) in Perm buffer (30 min, on ice). Fluorescently conjugated antibodies directed against CD4 (clone RM4-5), CD8 (clone 53-6.7), CD3 (clone eBio500A2), IFN-g (clone XMG1.2), Foxp3 (clone FJK-16s), and IL-17 (eBio17B7), CD19 (clone eBio1D3) , IgD (clone 11-26c), CD49d (clone R1-2), RORgt (clone AFKJS-9), T-bet (clone eBio4B10), were all purchased from eBiosciences. Samples were acquired using a FACS LSR II (BD Biosciences) and analyzed FlowJo software (Tree Star, Inc.). Adoptive transfer – Spleens from gHV-68 latently infected mice were isolated, and a single cell suspension was obtained. Memory B cell enrichment was performed using a custom kit from STEMCELL that contained a combination of monoclonal antibodies, including IgD, as negative selection antibodies. After enrichment, cells were washed in blank DMEM and were adjusted to a concentration of 1-1.5 x106 cells/200µl. Cells were injected i.p. into naïve mice and, the following day, EAE was induced.  RNAsequencing- Total splenocytes were obtained as described before. Cells were stained for CD19 and IgD. Following they were sorted at the UBC FLOW cytometry facility positively selecting CD19+IgD- cells. RNA was extracted as described before and was sent for mRNA sequencing at the Sequencing Core at the Biomedical Research Centre UBC. Sample quality control was performed using the Agilent 2100 Bioanalyzer to check for RNA integrity with RNA Integrity Numbers (RIN) over 8. Qualifying samples were then prepped following the standard protocol for the TruSeq stranded mRNA library kit (Illumina). Sequencing was  40 performed on the Illumina Nextseq500 with Paired End 75bp × 75bp reads. De-multiplexed read sequences were then aligned to the mus musculus mm10 reference sequence using TopHat2 splice junction mapper (234). Mapping and Reads Per Kilobase Per Million (RPKM) were generated using Genomic Suite (ParTek Inc). Aligned sequences were analyzed by Rebecca Skalsky at the Oregon Health and Science University as follows: Determine differentially expressed (DE) genes: Transcript read counts were analyzed by EdgeR (using TMM for normalization) to identify significantly differentially expressed transcripts (uninfected vs infected).  Gene IDs were then added back to proceed with further analysis on the gene level for FDR cutoff of 0.05. Identify potentially affected pathways: All DE genes (up or down-regulated, FDR<0.05) were uploaded to multiple pathway prediction tools (DAVID, PANTHER, REACTOME). DAVID includes KEGG, PANTHERdb, BioCarta, and REACTOME. Upregulated genes (FDR<0.05) were uploaded to DAVID and REACTOME to determine enriched pathways. Downregulated genes were queried in the same manner. Statistical analysis – Results are reported as mean + standard error of the mean (SEM). Two-way ANOVA followed by Bonferroni’s correction for multiple comparisons was employed to compare EAE scores. Unpaired Student’s t-test was used for all other analyses (GraphPad Prism). A p Value of <0.05 was considered statistically significant. ns > 0.05, *p < 0.05,  ** p  < 0.01, *** p < 0.001, **** p  < 0.0001   2.3 Results 2.3.1 Memory B cells from gHV-68 Latently Infected Mice can Direct EAE Enhancement Herpesviruses have long been suspected of contributing to the development of autoimmunity, and with the surge of B cell depleting therapies to treat MS, the possibility of a  41 determinant role by those cells that are latently infected with EBV in humans or gHV-68 in mice could make them a target for new therapies.  In order to determine whether memory B cells from latently infected mice had the ability to drive EAE enhancement, we enriched CD19+IgD- cells (the main reservoir of gHV-68 during latency (172)) from mice which had been infected with gHV-68  for 5 weeks (gHV-68 B cells) or from uninfected mice (MEM B cells) and adoptively transferred them into naïve mice.  To confirm that CD19+IgD- cells from gHV-68 mice were infected, we detected the presence of the virus by qPCR (Figure 2.1A). Then we transferred approximately 1-1.5x106 cells/mouse and induced EAE 24 hours later. The number of cells used was based on the availability of cells collected from mice. Different amounts of cells were tested (from 5x105 to 3x106 cells), but 1-1,5x106 was the one that resulted in less variability in the results.  We observed that by day 17, mice that had received gHV-68 B cells had an overall higher EAE score than mice that received MEM B cells (Figure 2.1B). Although this difference is not statistically significant, we can clearly see a trend towards a worsening of symptoms, this suggested that memory B cells form gHV-68 infected mice are capable of inducing an exacerbation of EAE score in naïve mice.         42 Figure 2.1 Memory B cells from mice latently infected with !HV-68 trigger EAE exacerbation  Figure 2.1. Mice were infected with !HV-68 i.p. 5 weeks p.i. spleens were harvested and CD19+ IgD- cells were enriched by negative selection. Cells then were transferred into naïve mice and the following day EAE was induced. A) Representative qPCR showing the presence of !HV-68 in isolated B cells (no amplification in MEM cells). B) Graph shows EAE scores up to day 17 post induction. Three independent experiments with 18-24 mice/ group.  2.3.2 Memory B cells from gHV-68 Mice Lead to an Increase of CD8 T cell Infiltration in the CNS When mice are latently infected with gHV-68, there is a high infiltration of CD8 T cells in their brains and spinal cords during EAE (203). This is particularly relevant since MS patients show an increased infiltration of CD8 T cells into the CNS (235, 236), but this infiltration is missing from regular EAE. In order to determine if gHV-68 B cells are able to drive CD8 T cell infiltration into the CNS, we stained immune cells for CD4 and CD8 T cells in brain and spinal cord during EAE after memory B cell transfer (for gating strategy see Appendix A). We were able to determine that while there is no difference in the infiltration of CD4 T cells between  43 gHV-68 B cells and MEM B cells, we do see an increase of CD8 T cells in the brain and spinal cord of the mice that received gHV-68 B cells (Figure 2.2). This confirms that memory B cells from gHV-68 mice are able to drive an increase in CD8 T cell infiltrates into the CNS.   Figure 2.2 Transfer of !HV-68 B cells leads to high infiltration of CD8 T cells in the CNS  Figure 2.2 CD19+IgD- from mice infected with gHV-68 or MEM, were transferred into naïve mice. 24 hours after the transfer, EAE was induced. gHV-68 latently infected mice and MEM mice were used as controls. At days 16-18 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain. B) Percentage of infiltrating  44 CD3+ cells expressing CD4+ or CD8+ in the spinal cord. Three independent experiments with 6-24 mice/per group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.  2.3.3 Memory B cells from gHV-68 Mice Alter Cytokine Production by T cells During EAE  Besides T cell infiltration, we have been able to observe that EAE enhancement during gHV-68 latency is directed by a strong Th1 response. In this context, T cells in the CNS produce high levels of IFNg and low levels of IL-17 compared to uninfected mice.  To test if the presence of memory B cells from gHV-68 mice could also affect the production of these cytokines, we used flow cytometry to test IFNg and IL-17 in the brain and spinal cord of mice that received gHV-68 B cells vs MEM B cells. We found that CD4 T cells from mice that received gHV-68 B cells were producing higher amounts of IFNg in both brain and spinal cord when compared both to mice that received MEM B cells and to MEM mice (Figure 2.3 A) as well as a downregulation in IL-17 (Figure 2.3B). These results suggest that memory B cells from latently infected mice are actively interacting with other immune cells and modifying the production of cytokines during latency, leading to a strong Th1 response during EAE.       45 Figure 2.3 Transfer of !HV-68 B cells leads to upregulation of IFN! and downregulation of IL-17  Figure 2.3 CD19+IgD- from mice infected with gHV-68 or MEM, were transferred into naïve mice. 24 hours after the transfer, EAE was induced. gHV-68 latently infected mice and MEM mice were used as controls. At days 16-18 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+IFNg+ and CD4+IL-17+ in the brain and B) Percentage of infiltrating CD3+ cells expressing CD4+CD4+IFNg+ and CD4+IL-17+ in the spinal cord. Three independent experiments with 6-24 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.  46 2.3.4 T regulatory Population is not Affected by the Presence of gHV-68 memory B cells  One of the most striking results of latency is the downregulation of Tregs in spleen and inguinal lymph nodes during latency. During EAE we can also see fewer Tregs in the periphery and in the CNS (204). In order to test whether the Treg population was also affected in memory B cell transfer, we measured the Treg population in the spleen after EAE induction. We found that the level of Tregs in mice that received gHV-68 B cells remain unchanged when compared to MEM B cells (Figure 2.4). This suggests that while B cells from infected mice are able to affect CNS infiltration and cytokine production by infiltrating cells, they do not affect the overall number of Tregs present in the periphery.  Figure 2.4 Transfer of !HV-68 B cells does not affect the Treg repertoire   Figure 2.4 CD19+IgD- from mice infected with gHV-68 or MEM, were transferred into naïve mice. 24 hours after the transfer, EAE was induced. gHV-68 latently infected mice and MEM mice were used as controls. At days 16-18 post EAE induction, mice were perfused; spleens were harvested and processed to isolate immune infiltrates. Percentage of CD3+ cells expressing  47 CD4+CD25+FoxP3+ in the spleen. Three independent experiments with 18-24 mice/group. Data analyzed with Student’s t-test: **p<0.01.  2.3.5 B cell Depletion during EAE Slightly Improves Overall EAE Score  Given the strong effect of gHV-68 B cells in EAE, we explored whether depleting B cells in gHV-68 mice would eliminate the effect that memory B cells have during EAE. B cell depletion therapies have had great success in the treatment of MS and other autoimmune diseases. Specifically, they have shown a great effect in controlling relapses in RR-MS. Although the mechanism for why B cell depletion helps to stop relapses has not yet been identified, it has been suggested that part of the success is due to the depletion of the main reservoir of latent EBV(103).  In order to determine if B cell depletion has a similar effect in the enhancement of EAE in gHV-68 mice, we decided to deplete B cells with a murine a-CD20 (or PBS as control±) on mice latently infected with gHV-68 (gHV-68/a-CD20 mice) or not (MEM/a-CD20 mice), and that had an EAE score of 1 > (Figure 2.5).                                                      ± Anti-CD20 clone 5D2 was provided by Genentech and is considered to be biosimilar to rituximab. In order to mimic a comparison between treated and untreated MS patients, no isotype control was used. This is why in all depletion experiments, samples were compared to MEM vs gHV-68 and PBS vs a-CD20.  48 Figure 2.5 Depletion of B cells after EAE Induction   Figure 2.5 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. When mice reached a score of >1 B cells were depleted with ⍺-CD20 i.v. At days 20-22 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) B cell depletion workflow B) Histogram shows a representative experiment of B cell population at moment of harvesting. Three independent experiments with 6-11 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001.  When B cell depletion treatment was provided when EAE symptoms first started to show, gHV-68/a-CD20 mice showed an overall improvement in their EAE score at the beginning of the therapy. However, this did not necessarily stop the progression of the disease and, by day 20,  49 mice showed an average score of 2.5-3. While this was not statistically different from MEM/a-CD20 mice, it also highlighted that B cell depletion by itself was not able to completely stop the disease (Figure 2.6). Although highly effective, a-CD20 depletion leaves some residual B cells in the spleen (Figure 2.5 B) which could be enough to maintain the course of the disease.   Figure 2.6 Depletion of B cells during EAE in !HV-68 mice stops EAE enhancement but not progression of disease   Figure 2.6 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. When mice reached a score of 1> B cells were depleted with ⍺-CD20 i.v. (dotted line represents when treatment was started). At days 20-22 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Graph shows EAE scores up to day 21 post induction. Three separate experiments with 6-11 mice/ group.    50 Figure 2.7 Depletion of B cells after EAE induction in !HV-68 mice have high levels of T cells infiltrating the CNS     51 Figure 2.7 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. When mice reached a score of 1> B cells were depleted with ⍺-CD20 i.v. At days 20-22 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain and spinal cord. B) Percentage of infiltrating CD3+ cells expressing CD4+CD25+FoxP3+ in the spleen. Three independent experiments with 6-11mice/ group. Data analyzed with Student’s t-test: **p<0.01, *p<0.05.  When we analyzed T cell infiltration into the CNS, gHV-68/a-CD20 mice had high infiltration of CD8 T cells to the brain and spinal cord at the same level as gHV-68/PBS mice (Figure 2.7A). Additionally, we saw no change in the downregulation of the Treg population between gHV-68/a-CD20 mice and gHV-68/PBS mice (Figure 2.7 B).  However, despite still having a strong T cell infiltration into the CNS, we saw a significant reduction in IFNg production in the brain and spinal cord in gHV-68/a-CD20 mice compared to gHV-68/PBS mice, although it was still elevated compared to MEM/PBS and MEM/a-CD20 mice (Figure 2.8A). In the case of IL-17, we saw that it remained downregulated in both groups of gHV-68 mice compared to the MEM/PBS group. Interestingly, we also saw a downregulation in the MEM/a-CD20 group. While this downregulation was not significant in the brain, it was more marked in the spinal cord (Figure 2.8 B). This had previously been observed in B cell depletion experiments in mice and provides evidence of a correlation between B cells and IL-17 production (236).  These results suggest that while B cell depletion during EAE reduces EAE score and alleviates some of the IFNg production, it does not affect the infiltration of T cells into the CNS  52 nor does it re-establish the balance IFNg/IL-17, and therefore does not completely eliminate the deleterious effects of gHV-68 latency.   Figure 2.8 Depletion of B cells after EAE induction in !HV-68 mice show slight decrease in IFN! but not recovery in IL-17  Figure 2.8 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. When mice reached a score of >1 B cells were depleted with ⍺-CD20 i.v. At days 20-22 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+IFN g+ and B)  53 CD4+IL-17+ in the brain and spinal cord. Three independent experiments with 6-11mice/ group. Data analyzed with Student’s t-test: ***p<0.001, **p<0.01, *p<0.05.  2.3.6 Depletion of B cells Before EAE Induction Moderately Affects EAE Score In order to determine if depleting B cells before EAE induction could eliminate disease enhancement completely, we infected mice with γ-HV68, and, 5 weeks p.i. mice were injected with anti-CD20 antibody or PBS as a control. After depletion, blood was collected and stained for CD19 to detect the presence of B cells (Figure 2.9). Once we confirmed that the depletion had been effective, EAE was induced.  Figure 2.9 B cell depletion with ⍺-CD20  Figure 2.9 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. B cells were depleted with ⍺-CD20 i.v. 2 days after depletion EAE was induced. Histogram shows a representative experiment of effectiveness of B cell depletion.   In contrast to what we observed when the depletion was performed during EAE, depletion before EAE induction did not seem to have an effect on the start of the disease. Scores seemed to remain higher in gHV-68/a-CD20 mice compared to MEM/aCD-20 mice. However,  54 after day 15 post EAE induction, mice seem to recover and have a lower overall EAE score (Figure2.10).  Despite the improvement of EAE score, depletion before EAE induction did not affect the response at the cellular level. gHV-68/a-CD20 mice still have a high level of infiltration of CD4 and CD8 T cells in the CNS (Figure 2.11A), and depletion does not help to recover the Treg population. On the contrary, we see a downregulation of Tregs not just in the gHV-68 mice, but also in the MEM/aCD-20 group (Figure 2.11 B). The latter has been an effect reported previously in the literature as a result of B cell depletion (237).  Figure 2.10 Depletion of B cells in mice latently infected with !HV-68 partially stops EAE enhancement   Figure 2.10 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. B cells were depleted with ⍺-CD20 i.v. 2 days after depletion EAE was induced. At days 21-23 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Graph shows EAE scores up to day 23 post induction. Five independent experiments  55 20-25 mice/ group. Data analyzed with two-way ANOVA test with Bonferroni’s correction for multiple comparisons: ***p<0.001, *p<0.05.  Figure 2.11 Depletion of B cells in mice latently infected with !HV-68 does not affect T cell infiltration into the CNS         56 Figure 2.11 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. B cells were depleted with ⍺-CD20 i.v. 2 days after depletion EAE was induced. At days 21-23 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain and spinal cord. B) Percentage of infiltrating CD3+ cells expressing CD4+CD25+FoxP3+ in the spleen. Five independent experiments 20-25 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.  When we tested cytokine production by T cells in the CNS, we saw that even though there was still a strong production of IFNg in the brain and spinal cord, there was a trend towards downregulation (Figure 2.8 A). This difference was only significant in the spinal cord and not in the brain. It should be noted, however, that we see a recovery of IL-17 production in the gHV-68/aCD-20 group to levels closer to the ones observed in MEM mice (Figure 2.8 B).  Overall, these results suggest that B cell depletion before EAE is less effective than depletion during EAE, since it does not affect the beginning of the disease and T cell infiltration remains unchanged.  Despite all this, depletion before EAE induction seems to restore some of the balance dysregulated by gHV-68 latency by slightly decreasing IFNg production and increasing IL-17. Most likely due to the lack of the direct influence of B cells, which at the time of EAE induction have not been replenished, since α-CD-20 depletion lasts between 25 days to 2 months after initial depletion (238).    57 Figure 2.12 B cell depletion leads to diminished upregulation of IFN! and upregulation of IL-17  Figure 2.12 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. B cells were depleted with ⍺-CD20 i.v. 2 days after depletion EAE was induced. At days 21-23 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+IFNg + and B) CD4+IL-17+ in the brain and spinal cord. Five independent experiments 20-25 mice/ group. Data analyzed student’s t-test: ***p<0.001, **p<0.01, *p<0.05.   58 2.3.7 gHV-68 Latency Establishes a Th1 Precondition Before EAE Induction The previous sections suggest that while B cell depletion before or after EAE induction has a slight effect on EAE enhancement, these depletions are not able to completely ablate the effect of gHV-68 latency. This suggests that the therapy is not able to eliminate all the virus from the mouse and/or that the effect of latency affects different cell types in addition to the B cells infected and that this effect persists even when the infected cells are not present. To determine whether either of these two options, or a combination of both, were contributing to limit the effect of B cell depletion, we decided to quantify the amount of virus present at the different time points of depletion (Figure 2.13). qPCR for ORF50 was able to reliably and consistently detect as few as 100 copies per reaction, and was able to detect as few molecules as 1 copy per reaction What we observed was that depletion of B cells before EAE induction was highly effective, since the virus was beyond the limit of detection. However, when depletion occurred after EAE induction, the virus was still detectable at experimental endpoint. This suggests that the different effects that we observed in both treatments might be related to the presence/absence of the virus and/or an effect of latency in the immune system before EAE induction.      59 Figure 2.13 Quantification of !HV-68 genome after depletion with ⍺-CD20  Figure 2.13 B cells were depleted with ⍺-CD20 i.v. at different time points, before/ after EAE induction and before gHV-68 infection. Two independent experiments with 5-10mice/ group. Data analyzed with Student’s t-test: *p<0.05.   Next, we tested if there were any changes occurring in the immune system during latency, before EAE induction. We harvested splenocytes of gHV-68 infected mice and determined their production of IFNg and IL-17 when stimulated with PMA/Ionomycin. Surprisingly, we observed that during latency, while most of the immune cells are present in the same proportion between gHV-68 and MEM mice (except for CD19+IgD-), cells from gHV-68 mice are already primed towards a Th1 response. We see that immune cells from gHV-68 mice produce high levels if IFNg when compared to cells from MEM mice and that there is no difference in the IL-17 production. This pattern can be easily observed not just in CD4 and CD8 T cells, but also in CD19+ and CD11b+CD11c+ cells, the latter is similar to what we had observed before, were CD11b+CD11c+ cells from γHV-68 were producing high amounts of IFNγ during the antigen presentation phase of EAE (day 4 post-induction) (203) (Figure 2.14).   60 Figure 2.14 Immune cells show increased production of IFN! during !HV-68 latency     61 Figure 2.14 Mice were infected with !HV-68 i.p. 5 weeks p.i. spleens were harvested, stimulated with PMA/Ionomycin and stained for IFN ! and IL-17 in CD3+CD4+, CD3+CD8+, CD19+, CD19+IgD-, CD11b+CD11c+ cells. Two independent experiments with 6-11mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.  We also saw an increase of T-bet in CD4 and CD8 T cells, but not in B cells or CD11b+CD11c+ cells (Figure 2.15). This suggests that T cells are constantly receiving a stimulus that leads them to produce IFNg during latency. It is probable that this stimulus is coming from memory B cells infected with gHV-68.                62 Figure 2.15 T cells show increased levels of T-bet during !HV-68 latency     63  Figure 2.15 Mice were infected with !HV-68 i.p. 5 weeks p.i. spleens were harvested, for T-bet and ROR!t in CD3+CD4+, CD3+CD8+, CD19+, CD19+IgD-, CD11b+CD11c+ cells. Two independent experiments with 6-11mice/ group. Data analyzed with Student’s t-test: *p<0.05.  2.3.8 EAE Enhancement Depends on the Presence of B cells During gHV-68 Infection The previous experiments demonstrated that once gHV-68 latency was established, eliminating the Th1 skewing of immune cells was not possible by simply depleting B cells before or after EAE induction. We then explored if the effect of gHV-68 in EAE enhancement was limited to the establishment of latency in B cells or if latency in other cell types would also lead to exacerbation of EAE. gHV-68 is able to infect other cells types during acute infection, and, in the absence of B cells, the virus is able to establish latency in cell types other than B cells  64 (239). In order to determine if the effects of EAE enhancement were exclusive to the establishment of latency in B cells, we depleted B cells before gHV-68 infection (Figure 2.16).  Figure 2.16 Depletion of B cells before !HV-68 infection   Figure 2.16 B cells were depleted with ⍺-CD20 i.v. 2 days after depletion, mice where infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. At days 21 post EAE induction, mice were perfused; brains and spinal cords and spleen were harvested and processed to isolate immune infiltrates. A) Histogram shows B cell levels at experimental end point. Two independent experiments with 6-12 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, *p<0.05.  65 Since memory B cells are the main reservoir of gHV-68 after acute infection, we determined if the virus was able to establish latency in mice without B cells. We quantified gHV-68 in the spleen at 35 days p.i., before EAE was induced (Figure 2.17). Although we were able to detect virus in some samples, most of the samples were beyond the limit of detection in the spleen. This is in accordance with the literature where, in the absence of B cells, the virus is less efficient in establishing latency in the spleen but does establish latency in peritoneal cells and the lungs (239, 240).   Figure 2.17 Depletion of B cells before !HV-68 infection  Figure 2.17 B cells were depleted with ⍺-CD20 i.v. 2 days after depletion, mice where infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. Spleens were harvested at 35 days p.i., DNA was extracted, and viral genomes were quantified by qPCR. Graph shows quantification of gHV-68 in mice depleted with ⍺-CD20 vs PBS. Two independent experiments with 3-6 mice/ group. Data analyzed with Student’s t-test: ***p<0.001.    66 Figure 2.18 Mice with B cell depletion before !HV-68 infection show no sign of EAE enhancement  Figure 2.18 B cells were depleted with ⍺-CD20 i.v. 2 days after depletion, mice where infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. At days 21 post EAE induction, mice were perfused; brains and spinal cords and spleen were harvested and processed to isolate immune infiltrates. Graph shows EAE scores up to day 21 post induction. Two independent experiments with 6-12 per group.   Interestingly, we can see that in gHV-68/a-CD20 mice, the EAE score virtually follows the same pattern as MEM/a-CD20 mice (Figure 2.18). More significantly, we saw that in gHV-68/a-CD20 mice the level of infiltration of CD8 T cells in the brain and spinal cord was the same as in MEM/a-CD20 or MEM/PBS mice, completely eliminating the infiltration of CD8 T cells into the CNS in gHV-68 mice. In the periphery, however, we still saw a downregulation of Tregs in gHV-68/a-CD20, like the one seen in gHV-68/PBS mice (Figure 2.19).   67 Figure 2.19 Depletion of B cells before infection !HV-68 prevents infiltration of CD8+ T cells into the CNS    68 Figure 2.19 B cells were depleted with ⍺-CD20 i.v. 2 days after depletion, mice where infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. At days 21 post EAE induction, mice were perfused; brains and spinal cords and spleen were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain and spinal cord. B) Percentage of infiltrating CD3+ cells expressing CD4+CD25+FoxP3+ in the spleen. Two independent experiments with 6-12 per group. Data analyzed with Student’s t-test: **p<0.01, *p<0.05.  This downregulation in Tregs does not affect the overall effect of the depletion. When cytokine production was tested in CD4 T cells infiltrating brain and spinal cord, we saw that there was virtually no change in the gHV-68/a-CD20 vs the MEM groups, even though it was clearly different from the gHV-68/PBS group. In other words, we saw that CD4 T cells from gHV-68/a-CD20 mice produced considerably lower levels of IFNg and higher levels of IL-17 than CD4 T cells from gHV-68/PBS mice (Figure 2.20). Together, these results show that gHV-68 in B cells is necessary for the virus to direct the strong Th1 response that we observed during EAE.        69 Figure 2.20 Depletion of B cells before infection !HV-68 affects cytokine production by T cells infiltrating the CNS  Figure 2.20 B cells were depleted with ⍺-CD20 i.v. 2 days after depletion, mice where infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. At days 21 post EAE induction, mice were perfused; brains and spinal cords and spleen were harvested and processed to isolate immune infiltrates. A) Percentage of infiltrating CD3+ cells expressing CD4+IFN g+ and B) CD4+IL-17+ in the brain and spinal cord. Two independent experiments with 5-12mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.   70 2.3.9 Memory B cells From gHV-68 Mice have a Differential Gene Induction than Memory B cells from Naïve Mice  So far, we have identified B cells as necessary for the enhancement of EAE, but we have not identified the type of signals that these cells produce in order to activate T cells and CD11b+CD11c+ cells and generate a Th1 response. To determine the different signals that B cells produce when mice are latently infected with gHV-68, we decided to perform RNAseq. For this, we infected mice with gHV-68, 5 weeks p.i. we sorted cells that were CD19+IgD- and extracted RNA. Cells from gHV-68 and MEM mice were sent for sequencing, from which we obtained 20 million reads/sample that were aligned to the mus musculus mm10 reference sequence using TopHat2 splice junction mapper (234). We were able to obtain a list of over 242 genes that were differentially expressed (Figure 2.21,for a full list of these genes, see appendix B). After further analysis of all differentially expressed genes using the pathway prediction tool DAVID, we were able to identify the most significant pathways upregulated and downregulated in memory B cells from gHV-68 mice compared to MEM mice (Tables 2.2 and 2.3). These pathways highlight 23 genes that affect a broad range of signaling pathways relevant in the immune response of B cells, such as Jak-STAT signaling, B cell activation, T cell activation and interleukin signaling pathway. Some of these genes have been highlighted as being dysregulated in gene expression analyses done in MS patients, such as ATF2 (241) in peripheral blood samples, CREBBP (also known as CBP) (242) in tissue specimens from the normal-appearing white matter (NAWM),  EP300 in NAWM and sera (242, 243), PRKD3 (244), RGS19 (245), and c-REL, the product of REL (246) in blood samples,  STAT1 and ITGA4 were of particular interest. STAT1 is an important molecule in type I and II signaling pathways. The JAK-STAT signaling pathway has long been associated with the  71 development of MS (247) and STAT1 has been identified as being modulated by EBV and also necessary for latency (248, 249). We have previously published that CD11b+CD11c+ cells from gHV-68 have an upregulation in pSTAT1, it would be of great interest determine if memory B cells have also an upregulation in pSTAT1 (204).   Figure 2.21 Differential gene expression between !HV-68 infected mice vs uninfected  Figure 2.21 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. spleens were harvested, and cells were sorted into CD19+IgD-. RNA was extracted and sent for RNAseq analysis. Heatmap shows log reads per gene by sample. 3mice/ group.    72 Table 2.1 Upregulated/Downregulated genes in memory B cells from gHV-68 mice compared to uninfected mice  Table 2.1 Mice were infected with !HV-68 i.p. or MEM. 5 weeks p.i. spleens were harvested and sorted into CD19+ IgD-. RNA was extracted and RNAseq was performed. Table shows genes upregulated/downregulated compared to MEM mice. Analysis was done using DAVID. n= 3mice/group   73 Table 2.2 Signaling pathways associated with the upregulated genes in memory B cells from mice latently infected with !HV-68       Table 2.2 Mice were infected with !HV-68 i.p. or MEM. 5 weeks p.i. spleens were harvested and sorted into CD19+ IgD-. RNA was extracted and RNAseq was performed. Table shows genes upregulated compared to MEM mice. Analysis was done using DAVID. n= 3 mice/group       74 Table 2.3 Signaling pathways associated with the downregulated genes in memory B cells from mice latently infected with !HV-68            Table 2.3 Mice were infected with !HV-68 i.p. or MEM. 5 weeks p.i. spleens were harvested and sorted into CD19+ IgD-. RNA was extracted and RNAseq was performed. Table shows genes downregulated compared to MEM mice. Analysis was done using DAVID. n= 3 mice/group  Integrin alpha 4, also known as CD49d, is an adhesion molecule associated with cell migration to the CNS and Natalizumab, a drug used in MS for the relief of relapses, is an a-ITGA4. Most of the studies done on this molecule have focused on T lymphocytes, however, in recent years it has become evident that ITGA4 also plays an important role in B cell adhesion (250) and the generation of regulatory B cells during EAE (251). So far, we have been able to use flow cytometry to confirm the presence of a small population of CD19+IgD- B cells in gHV-68 mice that have an upregulation of CD49d (Figure 2.22A), and we have been able to confirm an increase in CD49d transcript by RT-qPCR (Figure 2.22B).       75 Figure 2.22 Memory B cells from mice latently infected with !HV-68 have enhanced symptoms of EAE                Table 2.22 Mice were infected with !HV-68 i.p. or MEM. 5 weeks p.i. spleens were harvested. A) Representative histogram showing cells stained for CD19+IgD-CD49d+. B) Cells were sorted for CD19+IgD- and RNA levels of CD49d were determined. C) CD19+IgD- cells were tested for gHV-68 infection. Two independent experiments with 7-10 mice/ group. Data analyzed with Student’s t-test: **p<0.01.   76 2.4 Discussion In recent years, the role of B cells in autoimmune diseases, and in particular MS, has gained a lot of attention by researchers. This is mainly from the unexpected efficiency of B cell depletion therapies. So far, the role of B cells in the development of autoimmunity is not well understood, but we and others (103, 252-254) have suggested that the link might be in the establishment of latency of EBV in memory B cells. Our lab has previously shown (203) that CD11b+CD11c+ cells from latently infected mice were able to direct a strong production of IFNγ and prime T cells during the antigen presentation phase of EAE. However, these cells are not infected with gHV-68 (203), which suggests that CD11b+CD11c+ cells should have been primed during latency most probably by infected B cells. In this chapter, we explored how B cells from γHV-68 mice are indispensable for the enhancement of EAE. Transfer of CD19+IgD- cells from γHV-68 mice into naïve mice showed that memory B cells are capable to direct a strong Th1 response during EAE without the need of CD11b+CD11c+ cells from γHV-68 mice (Figures 2.1-2.3).  This result suggested that B cell depletion could probably be enough to stop EAE enhancement in γHV-68 infected mice, however, depletion of B cells with an a-CD20 before and after EAE induction showed limited success in decreasing overall disease. In the literature, B cell depletion has shown contrasting results, while some groups have shown a worsening of EAE when B cells are depleted before and after EAE induction in C57Bl/6 mice (192, 219, 220), and no effect on relapsing remitting EAE on Biozzi ABH mice (221), some have shown an improvement when depletion occurs after EAE induction (198). However, all of these experiments, with the exception of Lehmann-Horn et al.(220), used a different antibody clone  77 than ours and followed different depletion courses which may account for different depletion effectiveness in blood at tissue, although this remains to be investigated. In our experiments, while we see a moderate improvement in EAE enhancement in gHV-68 a-CD20 mice when B cell depletion occurs after EAE induction, we also see that MEM a-CD20 mice show an EAE progression similar to the one observed in gHV-68/PBS mice. However,  by the time of harvesting, there is a clear trend towards worsening in gHV-68/PBS mice, while MEM a-CD-20 mice show a lower score. The moderate changes we see in EAE score do not always completely match the symptoms that we observe in mice; as we reported before, mice infected with gHV-68 display unusual neurological symptoms, such as loss of balance, ataxia and hunched posture (203), that are not considered by the traditional EAE score. Evaluating changes in these symptoms will require us to develop a new scoring system that takes into consideration the symptoms characteristic of regular EAE and the ones developed by gHV-68 infected mice. Regardless of the score however, we do not see changes in the infiltration of T cells to the CNS or in the increased production of IFN-g by CD4 T cells in gHV-68 mice where depletion occurred before or after EAE induction. This shows that, independently of the time of depletion, the EAE observed in gHV-68 mice is still primarily driven by Th1 (Figures 2.5-2.12). These results support the ideas that either the effects of latency in terms of T cell infiltration and cytokine production can be felt even after the elimination of B cells, or, given that depletion is not complete, the presence of some of the virus is enough to still drive EAE enhancement. It is still necessary to determine whether depletions at these different time points also leads to pockets of demyelination that we had previously observed in the brain of gHV-68 mice and where we saw the presence of infiltrating CD4 and CD8 T cells (203). It would be of great interest to determine whether there is also the presence of B cells and if this is affected by depletion.  78 It is worth mentioning that during depletions in both infected and uninfected mice IL-17 was one of the cytokines that changed the most depending on when depletion occurred.  When B cell depletion occurred once EAE was induced, we saw an overall downregulation of IL-17. However, when B cell depletion occurred before EAE induction, we saw an overall recovery of IL-17 production, accompanied with a slight downregulation of IFNg. This suggests that by eliminating most of the virus (Figure 2.16) before EAE induction, we helped to recover some of the balance of IFNg/IL-17.  While there still was a predominantly strong Th1 response, it was not at the same level as in non-depleted mice. We know that there is an interdependent relationship between B cells and Th17 cells. For example, IL-6, a cytokine necessary for IL-17 production, is also a cytokine needed in B cell proliferation (198, 255). Similar to what we observe in B cell depletions before EAE induction, any disturbance in the B cell repertoire in humans and mice seems to also downregulate IL-17 production (256). However, why IL-17 is not affected when depletion occurs before EAE induction and when depletion occurs before gHV-68 infection remains to be explored. It would be interesting to determine whether the residual virus found in the depleted mice affects the production of IL-17 and whether it also affects IFNγ production.  Ultimately, the fact that latency established a precondition towards a Th1 response (Figure 2.14,2.15) means that the only way to effectively eliminate the effects of latency on EAE is by depleting B cells before primary infection (Figure 2.18- 2.20), or to go through several rounds of depletion with the objective of eliminating as much virus as possible and reset any programming provided by B cells to other cells during latency. Throughout the depletions at different time points of infection or of EAE induction, we noticed that the downregulation of Tregs caused by gHV-68 latency (204, 257) could not be reverted. This suggests that downregulation of Tregs in gHV-68 mice is independent of infection in memory B cells.  79 Downregulation of Tregs has been observed in MS patients (258), and B cell therapy has also been described to downregulate the Treg population in EAE (259). It has been suggested that increasing the Treg population could be used as a therapy to restore immunological balance during MS/EAE and help downregulate the TH1/Th17 response (260). It would be interesting to determine if treating mice with Tregs from uninfected mice or boosting the Treg population in mice infected with gHV-68 would help stop EAE exacerbation.  Given the previous results, being able to identify those memory B cells that are latently infected with gHV-68 is of great interest. However, isolating this population has proven rather challenging. Despite taking advantage of several transgenic viruses such as YFP-γHV-68 (261), M3FL-γHV-68 (262), γHV-68-Cre (263) and techniques like Prime Flow (Thermo Fisher), the population of cells infected during latency is so small that we have not been able to isolate it.  RNAseq performed on B cells from γHV-68 (Figure 2.21) has provided us with a new tool, where direct detection of the virus would not be necessary. Rather, we could use a combination of differentially expressed surface markers and transcription factors that allow us to select those cells that are probably infected with the virus, and, most importantly, are potentially pathogenic and directly affecting EAE outcome. This way, it becomes paramount to validate the genes that encode for potential markers for B cells infected with γHV-68, such as CD22 and ITGA4, but also explore if the genes that intersect between MS and EBV, and that have been highlighted as upregulated between gHV-68 and MEM mice, are directly contributing to EAE enhancement (Table 2.1). Additionally, functional studies of memory B cells from gHV-68 mice should be carried out to explore their antigen presentation properties and their specificity towards CNS antigens. It is very possible that memory B cells from gHV-68 have increased antigen presentation capabilities, something that has been also observed in memory B cells from MS  80 patients (165, 166). Finally, it would also be of great interest to determine if there are any epigenetic changes of B cells from γHV-68 and of other immune cells that are in contact with these cells.  In general, the data described in this chapter provides us with a mechanism for how latency of γHV-68 is affecting the immune system and the important role that B cells play in EAE enhancement. It also highlights new avenues for treatment development in MS, where it is possible to target pathogenic cells without affecting healthy ones.    81 Chapter 3: Latent gHV-68 EAE Enhancement is Independent of Type I Interferon 3.1 Introduction  In Chapter 2 we described the importance of memory B cells in the enhancement of EAE. We also proposed a set of genes that are potentially relevant as biomarkers to identify those cells that are latently infected with gHV-68. In this chapter, we will explain a possible mechanism for how latently infected memory B cells are leading to the activation of CD11c+CD11b+ cells and possibly T cells. Previously, we identified that, in order to see EAE enhancement, it is imperative that the virus is latent. When mice are infected with a latency free gHV-68, the EAE they develop is similar to that of uninfected mice, showing how the ability to establish latency is necessary to the enhancement of EAE (204). Viral latency leads to the upregulation of phosphorylation of STAT1 and CD40 in CD11b+CD11c+ cells even before EAE induction (204).  We also see the upregulation of T-bet in CD4 T cells in the CNS, while RORgt expression is limited to Th17 CD4+ T cells and its levels are comparable to the ones of uninfected mice (203). Interestingly, pSTAT1 and T-bet upregulation has been observed in PBMCs of patients with coeliac disease and relapsing-remitting MS, and has been correlated with disease activity (264, 265). STAT1 phosphorylation plays a decisive role in the early stages of the activation of the IFNα receptor (IFNAR) (266). Although many other cytokines activate STAT1, activation through IFNAR that promotes antigen presentation and activation of T lymphocytes has been described (266). Importantly, type 1 IFN production is necessary during gHV-68 infection in order to control reactivation and maintenance of latency (182). During EBV acute infection, EBER-1, a small noncoding viral RNA, and EBV virions induce type I IFN production in several cell types. After the establishment of latency, type I IFNs upregulate the  82 latent membrane protein LMP-1, which acts as a CD40 receptor analog in B cells (183).  Finally, Interferon-beta has been widely used in the treatment of RR-MS and has shown to be effective in reducing relapses in MS patients (267). Although interferon beta-1b treatment (Betaferon) has limited success when used alone, it has a better effect when combined with other MS therapies (70). These reasons allow us to surmise that that type I IFNs are important players in the communication between latently infected B cells and CD11b+CD11c+ cells that direct EAE enhancement. This suggests that EBV infection in humans can contribute to the upregulation of type I IFNs, enabling the development of MS. In this chapter we explore the role of type I IFNs in the enhancement of EAE in mice latently infected with gHV-68.  Our results show that while type I IFNs are important for the control of gHV-68 infection and for the maintenance of latency of the virus, they do not affect EAE enhancement. This suggests that although type 1 IFNs might play an important role in the immune regulation of EAE and in the control of gHV-68, it is dispensable for the enhancement of EAE symptoms in our model. These results highlight a possible explanation for why treatment with IFN-b fails to efficiently control MS relapses and why it often needs to be combined with other treatments.   3.2 Materials and Methods  Mice and ethics statement - C57Bl/6 mice from The Jackson Laboratory and C57Bl/6 IFNARko were kindly donated by Dan Campell and were bred and maintained in the animal facility at the University of British Columbia. All animal work was performed in accordance with the regulation of the Canadian Council for Animal Care. The protocol was approved by the Animal Care Committee (ACC) of the University of British Columbia (Protocols A17- 0105, A17-018)  83 Infections and EAE induction – Female and male mice between 8-10 weeks of age were infected intranasally (i.n.) with 100 pfu of gHV-68 WUMS strain (purchased from ATCC, propagated on BHK cells) or 15 µl of PBS as a control. EAE was induced at 5- 6 weeks post infection by injecting 100µl of emulsified Complete Freund’s Adjuvant (DIFCO) with 200µg of MOG 35-55 (GenScript) and 400µg of desiccated Mycobacterium tuberculosis H37ra (DIFCO) subcutaneously.  Mice also received two doses of 200ng of pertussis toxin (List Biologicals) via i.p. at the time of immunization and then again 48 hours later. EAE was assessed on a score from 0 to 5 as follows: 0, no clinical symptoms, 0.5 partially limp tail; 1, paralyzed tail; 2, loss of coordination; 2.5, one hind limb paralyzed; 3, both hind limbs paralyzed; 3.5, both hind limbs paralyzed accompanied by weakness in the forelimbs; 4, forelimbs paralyzed (humane endpoint); 5, moribund or dead. Viral quantification - qPCR analysis of DNA samples was performed using 2x Quantitect Probe Mastermix (Qiagen, USA) on the Bio-Rad CFX96 Touch™ Real Time PCR Detection system. Primers, probes and gBlocks® were obtained from Integrated DNA Technologies. Quantification of copies of mouse genome was done on 10ng of DNA by using primers and probe for a region of the mouse PTGER2 gene (Forward Primer: 5’ – TACCTTCAGCTGTACGCCAC – 3’;       Reverse Primer: 5’ – GCCAGGAGAATGAGGTGGTC – 3’; Probe: 5’ – /56-FAM/CCTGCTGCT/ZEN/TATCGTGGCTG/3IABkFQ/ – 3’)(230) and absolutely quantified by use of a standard curve using concentrations from 5x107 copies/µl to 5x101 copies/µl. Quantification of copies of the γ-HV68 genome was done on 100ng of DNA by using primers and probe for a region of ORF50 (Forward Primer: 5’ –TGGACTTTGACAGCCCAGTA – 3’;   84 Reverse Primer: 5’ – TCCCTTGAGGCAAATGATTC – 3’;  Probe: 5’ – /56-FAM/TGACAGTGC/ZEN/CTATGGCCAAGTCTTG/3IABkFQ/ – 3’) and absolutely quantified by use of a separate standard curve using concentrations from 2x104 copies/µl to 2 copies/µl. Samples were run using a minimum of two technical replicates and all standard curves had an R2 greater than 0.95. The protocol was as follows: 95oC for 15 minutes, 95oC for 15s, 60oC for 1 min, repeated 50 times. Quantification of copy number was done using the CFX manager software. The ratio of virus genome copy number to mouse genome copy number was obtained using the following equation: ##$%&'(	$*	+,-./#012345	16	789:; 	<	 =	012345	789:;=/?4@1A4B012CD;E.//?4@1A4  (simplified to #	#$%&'(	$*	+,-./#	#$%&'(	$*	FGHI,=×2).  Immune cell isolation and flow cytometry - Mice were euthanized two weeks post EAE induction. They were perfused with 30cc of PBS, and brains, spinal cords, and spleens were isolated. A single cell suspension was generated from each organ. Immune cells from the CNS were isolated using a 30% Percoll gradient. For intracellular staining, CNS mononuclear cells, were stimulated for 4 hours in DMEM (Gibco) containing 10% FBS (Gibco), GolgiPlug (BD Biosciences), 10ng/ml PMA, and 500ng/ml ionomycin. Antibodies for the cell surface markers were added to the cells in PBS with 2% FBS for 30 min on ice. After washing, cells were resuspended in Fix/Perm buffer (eBiosciences) for 30-45 minutes on ice, washed twice, and incubated with Abs for intracellular antigens (cytokines and transcription factors) in Perm buffer (30 min, on ice). Fluorescently conjugated antibodies directed against CD4 (clone RM4-5), CD8 (clone 53-6.7), CD3 (clone eBio500A2), IFN-g (clone XMG1.2), Foxp3 (clone FJK-16s), and IL-17 (eBio17B7) were all purchased from eBiosciences. Samples were acquired using a FACS LSR II (BD Biosciences) and analyzed FlowJo software (Tree Star, Inc.).  85 Adoptive transfer – Spleens from IFNARko mice were isolated and a single cell suspension was obtained. Memory B cell enrichment was performed using a custom kit from STEMCELL that contained a combination of monoclonal antibodies, including IgD, as negative selection antibodies. After enrichment, cells were washed in blank DMEM and were adjusted to a concentration of 1-1.5 x106 cells/200µl. Cells were injected into WT mice and, the following day, EAE was induced.  Statistical analysis - Results are reported as mean + standard error of the mean (SEM). Two-way ANOVA followed by Bonferroni’s correction for multiple comparisons was employed to compare EAE scores. Unpaired Student’s t-test was used for all other analyses (GraphPad Prism). A p Value of <0.05 was considered statistically significant ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001    3.3 Results 3.3.1 gHV68 Establishes Latency at a Similar Rate in IFNARko and WT Mice  Both INFα and β signal through the receptor for IFNα (266). To study the effect of type I IFNs in the enhancement of EAE in mice latently infected with gHV-68, IFNARko mice, which lack the IFNa receptor, are a useful tool. It has previously been described that IFNARko mice are able to control gHV-68 infection and successfully establish a latent infection in a similar proportion as WT mice (182). However, due to the lack of type I IFN response, the virus quickly overwhelms the mouse’s immune response and, at regular doses, has a high mortality rate during acute infection, not allowing the establishment of latency (182). Prior work by Barton et al. demonstrated that a 100pfu dose given intranasal of gHV-68 reduced mortality in IFNARko mice. We found that this dose of infection resulted in a survival rate of 75% (Figure 3.1) and  86 allowed the immune system to control the acute infection while still allowing the virus to establish latent infection. Quantification of latency with this low dose shows a slightly lower number of copies in IFNARko mice compared to WT, however this difference was not significant (Figure 3.1). These results are similar to those previously described by Barton et al (182).    Figure 3.1 A low dose of gHV-68 infects and establishes latency in WT and IFNARko mice    Figure 3.1 A) IFNARko mice were infected with 100 pfu of gHV-68. Survival rate in IFNARko mice was 75%. B) 5 weeks p.i. IFNARko mice establish latency at similar rates than WT mice. Three independent experiments with 7-11 mice/group. Data analyzed with Student’s t-test was not significant.   3.3.2 gHV-68 Latently Infected IFNARko Mice Develop Enhanced T Cell Infiltration into the CNS  The presence of Type I IFNs has been associated with a protective effect in MS patients. One of the first lines of treatment for MS is IFN-beta. However, as many as 30-50% of patients A B  87 do not respond to the treatment (268). The mechanism for why this happens has not been described yet. Here, we explored whether the absence of type I IFNs had any effect on the ability of gHV-68 to drive the infiltration of immune cells into the CNS when IFNARko mice were latently infected with gHV-68. Previously, we reported that latent gHV-68 leads to a strong infiltration of CD4+ and CD8+ T cells in the CNS during EAE, and that this T cell response is heavily skewed towards a Th1 phenotype (203).  We infected IFNARko mice with gHV-68 and waited 5 weeks to allow for the establishment of latency. After this time, EAE was induced. As expected from previous reports (269), IFNARko mice demonstrated clinically enhanced EAE regardless of whether they had been infected with gHV-68 or not. However, IFNARko mice infected with gHV-68 show a higher clinical score (Figure 3.2).  Figure 3.2 gHV-68 latently infected IFNARko mice develop enhanced EAE symptoms  Figure 3.2 IFNARko mice were infected with γHV-68. Five weeks p.i. EAE was induced. Graph shows EAE scores up to day 14 post induction. Three independent experiments 4-11 mice/ group. All 3 experimental groups were performed at the same time; score graphs were separated for clarity. Data analyzed with two-way ANOVA test with Bonferroni’s correction for multiple comparisons: ***p<0.001, *p<0.05.  88 Moreover, uninfected IFNARko mice show very limited infiltration of T cells to the brain, and most of these cells are CD4+. In the case of IFNARko mice, latency with gHV-68 leads to high levels of T cells infiltrating the brain (Figure 3.3) and spinal cord (not shown), in particular CD8+ T lymphocytes. Remarkably, the percentage of T cells infiltrating the CNS of gHV-68 IFNARko mice equals the one from gHV-68 WT mice. This is accompanied by a strong production of IFNg and a downregulation of IL-17 (Figure 3.3). This T cell infiltration and cytokine profile is comparable to what we had previously observed in WT mice latently infected with gHV-68, which suggests that T cell infiltration and/or cellular enhanced infiltration into the CNS does not depend on the presence of Type I IFNs.               89 Figure 3.3 gHV-68 latently infected IFNARko mice develop enhanced T cell infiltration into the CNS compared to uninfected IFNARko and WT mice    Figure 3.3 IFNARko mice were infected with γHV-68. Five weeks p.i. EAE was induced. Mice were harvested at day 14 post EAE induction. They were perfused, brain, spinal cords, and spleens were harvested and stained. Graphs show percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain and CD4+IFNg+ and CD4+IL-17+ in the brain. Three separate experiments with 8-13 mice/ group. Data analyzed with Student’s t-test: ***p<0.001, **p<0.01, *p<0.05.   90 3.3.3 Treg Proportion is Affected by Type 1 IFNs During gHV-68 Latent Infection Both our lab and others (204, 257), have described that latent infection of gHV-68 leads to the downregulation of Tregs in the CNS and in the periphery. This downregulation is sustained after EAE induction. It has also been shown that depletion of Tregs during EAE exacerbates symptoms by facilitating an upregulation of cytokine production by T cells (270). We observed a marked downregulation in the number of Tregs in the periphery of IFNARko during gHV-68 latency (Figure 3.4); interestingly, although downregulation of Tregs is sustained after EAE induction in latently infected mice, Tregs are also downregulated in uninfected IFNARko mice (Figure 3.4). This downregulation of Tregs in uninfected IFNARko mice likely explains why they show a higher EAE score. However, this exacerbation is not accompanied by an upregulation in cytokine production (Fig3.3), contrasting with the increased IFNg production in gHV-68 IFNARko mice.   Figure 3.4 Tregs from gHV-68 latently infected IFNARko mice are downregulated before and after EAE induction   A  91 Figure 3.4 IFNARko mice were infected with γHV-68. A) 5 weeks p.i. mice were induced or not with EAE, they were harvested at day 14 post induction, spleens were harvested and stained. Graphs show percentage of infiltrating CD3+ cells expressing CD4+CD25+Foxp3+ in the spleen n= 8-10/ group 2 separate experiments. Data analyzed with Student’s t-test: ****p<0. 0001  3.3.4 B cells Infected with gHV-68 Require Type I IFNs to Direct CD8 T cell Infiltration in the CNS We tested whether the presence of type I IFNs were necessary to transfer disease into WT mice. In Chapter 2 we showed that enriched CD19+ IgD- B cells from gHV-68 mice, which are the main reservoir of gHV-68 during latency, can transfer EAE enhancement into naïve mice (Figure 3.5). When we transfer the CD19+IgD- B cells from infected IFNARko mice into WT mice, we find that, contrary to what happens with WT B cells, these cells are not able to transfer an increase in EAE score, and while there is a moderated upregulation of CD8+ T cells infiltrating the CNS, this change is not statistically significant.  These B cells however seem able to retain their ability to direct a strong Th1 response, since CD4 T cells infiltrating the CNS are still producing high levels of INFg and less IL-17 (Figure 3.5). This suggests that while type I IFNs are not directly responsible of skewing a strong Th1 response, they might have a role in the infiltration of CD8 T cells into the CNS.       92 Figure 3.5 B cells from IFNARko mice latently infected with γHV-68 partially transfer EAE enhancement     A B  93     Figure 3.5 Latently infected memory B cells from IFNARko mice partially transfer EAE enhancement. Memory B cells from IFNARko mice infected with gHV-68 or not, were transferred to uninfected WT mice. 24 hours after the transfer, EAE was induced. At days 16-18 C D  94 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Graphs show EAE scores up to day 18 post induction. B) percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ CD4+ in the brain and spinal cord. C) Percentage of infiltrating CD3+ cells expressing CD4+IFNg+ and CD4+IL-17+ in the brain and spinal cord. D) Representative experiment of viral quantification of enriched CD19+ IgD- cells from IFNARko gHV-68 infected cells. Four independent experiments with 11-20 mice/ group. Data analyzed with Student’s t-test: *p<0.05.  3.4 Discussion Type I IFNs have been identified as an important therapeutic target because of their protective role in MS and EAE development. However, therapies based on Type I Interferons only confer limited relief. This is why treatment with β-IFN is often combined with other therapies such as methotrexate, methylprednisolone, azathioprine, prednisone, statins and  occasionally Natalizumab (70, 272). IFN-α has also shown effectiveness in reducing MS relapses, although it is less effective than β-IFN (273). This suggests that while type I IFNs can modify MS, other factors are involved in causing changes in the disease. Infection with pathogenic agents that otherwise would be innocuous to the organism might override the protective effect of type I IFNs and could lead to the development of MS relapses.  Our results explore the potential role of type I IFNs in mice infected with a homolog of EBV - gHV-68. Using the model we previously developed (203), we showed that despite the importance of type I IFNs in the control and latency maintenance of the virus, and a moderate decrease in the total amount of virus present in the IFNARko mice (Figure 3.1), there is a strong Th1 response in IFNARko mice that leads to an EAE enhancement similar to the one observed in  95 gHV-68 WT mice characterized by worsening of EAE symptoms (Figures 3.2), high infiltration of T cells into the CNS and high production of IFNg (Figure 3.3), although it remains to be determined if demyelination is present in the brain and spinal cord, our results strongly suggest that type I IFNs are dispensable for the enhancement of EAE symptoms. It is very possible that even if type I IFNs are helping to lead EAE enhancement, other factors are involved that require the combination of different cytokines and chemokines produced by the B cell to activate the CD11b+CD11c+ cells. This activation directs a strong Th1 response that ultimately will develop EAE enhancement and immune cell infiltration into the CNS independent of Type I IFNs. Type I IFN upregulation during viral infection has been associated with an inhibition of Treg activation and proliferation (271). IFNARko mice have a downregulation in Tregs, which could help explain why these mice show enhanced EAE symptoms. Contrary to what was expected, Tregs remain at low levels during latent infection with gHV-68.  Finally, adoptive transfers suggest that even though B cells from infected mice do not need type I IFNs to drive a strong Th1 response, they do seem to be important in directing CD8+ T cell infiltration in the CNS (Figure 3.5). It has been suggested that type I IFNs can provide the “third signal” that is necessary for the activation and clonal expansion of CD8 T cells (274). It is possible that IFNs delivered directly by B cells are important in the activation of cells that will infiltrate the CNS, although more experiments need to be done to confirm this.  Overall, these results demonstrate that despite the importance of type I IFN in regulating the immune response during acute viral infection and the development of EAE, it does not have an effect in the enhancement of EAE symptoms caused by latent infection with gHV-68, highlighting a type I IFN independent pathway. These results can help to understand why Betaferon effectiveness’ can be so unpredictable in MS patients and suggests that EBV status  96 might be important in the predicting the effectiveness of treatment. Additional experiments need to be done to explore the role of gHV-68/ EBV latency in EAE and MS, especially, the role of memory B cells, the main reservoir of virus during latency.    97 Chapter 4: Effects of Age on gHV-68 Latency and its Influence on EAE 4.1 Introduction In the previous chapters we explored the conditions necessary to see the enhancement of EAE in adult mice. In this chapter, we explore how age of infection and age of EAE induction affect the exacerbation of EAE paralysis. 4.1.1 Autoimmunity Development Later in Life  While most patients with MS first show symptoms during their mid-20s to 30s (Adult-onset MS), there is a small proportion of patients for whom the first indications of disease appear later in life. Late-onset MS (LOMS) and Very late-onset MS (VLMS) usually develop when patients are around 50 and 60 years old, respectively (275).  In Adult-onset MS (AOMS), the disease usually presents in the form of relapsing remitting (RR-MS), and its occurrence is heavily biased towards females. LOMS and VLMS patients show a different and more aggressive disease progression with a higher incidence in males, and which is primary progressive (PP-MS) in most cases (276-278). Whenever LOMS patients develop RR-MS they show a diminished ability to recover from relapses (275, 279). This deficiency in the degree of recovery appears to have a strong correlation with age (280), however, there is no clear explanation for how age contributes to the severity of the disease in LOMS and VLMS. In EAE, 15 months old Wistar rats show a lower susceptibility to EAE as well as a less severe Th1 response (281). On 24-month-old F344 rats, histological damage can be observed, but not the presence of symptoms typical of EAE such as paralysis (282). Information on mice is limited and, on occasion, inconsistent. Some reports establish that SJL and BALB/c  mice aged12-30 months have a decreased susceptibility to EAE, but once the disease develops there  98 was no difference on the effect in severity of the symptoms (283). In contrast, it has been observed that middle-aged (8 months old) C57Bl/6 male mice show a delayed onset of clinical symptoms when EAE is induced with MOG35-55 peptide. Once symptoms develop, these mice show a similar pattern to that of humans, with an increased EAE score accompanied by a reduction in Treg activity, reduced levels of splenic CD4+ T cells, increased macrophages and MHC class II-expressing cells, as well as increased production of IFNγ and MCP-1 (284). A similar effect in disease severity has been observed in 6 month-old mice, where there was increased permeability in the BBB and upregulation in the expression of NADPH oxidase, MMP-9, ICAM-1, and VCAM-1 (285). 4.1.2 γHV-68 infection in young mice Understanding the relationship between infectious mononucleosis (IM) and an increased risk to MS also presents a challenge to researchers. As mentioned previously, infection with EBV during early childhood is usually asymptomatic; however, once a person reaches puberty, IM is more likely to occur. Given the short lifespan of mice compared to humans, and taking in consideration different stages of life, Dutta et. al propose a series of formulas to calculate the equivalence of human years’ vs mice. In their proposal, from birth, 56.77 days in mice equal one human year and during puberty (which can start as early as 24 days of age and is usually attained by day 42), 3.65 days equal 1 human year (42 mice days = 11.5 human years).  By weeks 8-12 mice reach sexual maturity, this period corresponds with adulthood in humans and 2.6 mice days equal one human year (286). When 8-day-old mice are infected intranasally with γHV-68, they show a similar acute infection as 6-week-old mice. However, pups show a more disseminated viral infection; viral DNA can be observed in spleen, liver, and heart, up to 60 days p.i. Adult mice show a localized  99 infection to the spleen, and an increase in spleen weight and expansion of Vβ4+ CD8+ T cell population, a syndrome consistent with IM. Pups lacked this syndrome entirely (228), showing a similarity in pathogenesis by γHV-68 to EBV.  Age-dependent effects from γHV-68 have been also observed in the development of Type 1 diabetes in NOD mice. When mice were infected at 8 or 9-weeks old, they showed a decreased and delayed development of Type 1 diabetes. Infection at 4, 7 and 10 weeks old, showed no difference when compared to uninfected mice (287). This suggests that there are specific time-dependent interactions of the virus with the immune system and that how these interactions play out are very much dependent on the age of the mouse when they occur. In Chapter 3 we explored whether age of infection and/or age of EAE induction affect how γHV-68 modifies EAE symptoms, we decided to test a long-term latency on mice and early age of infection with γHV-68. We observed that the effect of γHV-68+ EAE on mice infected at 8 weeks of age lasts up to 20 weeks post-infection. Moreover, we can see certain indications of a more severe disease in terms of T cell infiltration and cytokine production. We also infected mice with γHV-68 when they were 3 weeks of age, right after weaning, and 5 weeks of age, at the beginning of puberty, and we observed a striking difference to those mice infected at 8 weeks of age, adult mice that have reached sexual maturity. These results show that infection with γHV-68 provides us with a model useful to understanding crucial differences in the immune system of older people and children in terms of their predisposition to MS or more severe forms of MS.    100 4.2 Materials and Methods Mice and ethics statement - C57Bl/6 mice from The Jackson Laboratory and C57Bl/6 and were bred and maintained in the animal facility at the University of British Columbia. All animal work was performed in accordance with the regulation of the Canadian Council for Animal Care. The protocol was approved by the Animal Care Committee (ACC) of the University of British Columbia (Protocols A17- 0105, A17-0184). Infections and EAE induction – Female and male mice between 8-10 weeks of age were infected intraperitoneally (i.p.) with 104 pfu of  gHV-68 WUMS strain (purchased from ATCC, propagated on BHK cells) or 200µl of MEM media as a control. Mice between 3-8 weeks old were infected intranasally (i.n.) with 400 pfu of γHV-68 WUMS strain or 15 µl of PBS as a control. EAE was induced at different time points post infection by injecting 100µl of emulsified Complete Freund’s Adjuvant (DIFCO) with 200µg of MOG 35-55 (GenScript) and 400µg of desiccated Mycobacterium tuberculosis H37ra (DIFCO) subcutaneously.  Mice also received two doses of 200ng of pertussis toxin (List Biologicals) via i.p. at the time of immunization and then again 48 hours later. EAE was assessed on a score from 0 to 5 as follows: 0, no clinical symptoms, 0.5 partially limp tail; 1, paralyzed tail; 2, loss of coordination; 2.5, one hind limb paralyzed; 3, both hind limbs paralyzed; 3.5, both hind limbs paralyzed accompanied by weakness in the forelimbs; 4, forelimbs paralyzed (humane endpoint); 5, moribund or dead. Viral quantification – – DNA was extracted from total splenocytes and enriched memory B cells at indicated time points using either TRIzol Reagent (Invitrogen) or PureLink Genomic DNA Mini Kit (Invitrogen) following manufacturer’s instructions. qPCR analysis of DNA samples was performed using 2x Quantitect Probe Mastermix (Qiagen, USA) on the Bio-Rad CFX96 Touch™ Real Time PCR Detection system. Primers, probes and gBlocks® were  101 obtained from Integrated DNA Technologies. Quantification of copies of mouse genome was done on 10ng of DNA by using primers and probe for a region of the mouse PTGER2 gene (Forward Primer: 5’ – TACCTTCAGCTGTACGCCAC – 3’; Reverse Primer: 5’ – GCCAGGAGAATGAGGTGGTC – 3’; Probe: 5’ – /56-FAM/CCTGCTGCT/ZEN/TATCGTGGCTG/3IABkFQ/ – 3’) (230) and absolutely quantified by use of a standard curve using concentrations from 5x107 copies/µl to 5x101 copies/µl. Quantification of copies of the γ-HV68 genome was done on 100ng of DNA by using primers and probe for a region of ORF50 (Forward Primer: 5’ –TGGACTTTGACAGCCCAGTA – 3’; Reverse Primer: 5’ – TCCCTTGAGGCAAATGATTC – 3’; Probe: 5’ – /56-FAM/TGACAGTGC/ZEN/CTATGGCCAAGTCTTG/3IABkFQ/ – 3’) and absolutely quantified by use of a separate standard curve using concentrations from 2x104 copies/µl to 2 copies/µl. Samples were run using a minimum of two technical replicates and all standard curves had an R2 greater than 0.95. The protocol was as follows: 95oC for 15 minutes, 95oC for 15s, 60oC for 1 min, repeated 50 times. Quantification of copy number was done using the CFX manager software. The ratio of virus genome copy number to mouse genome copy number was obtained using the following equation: ##$%&'(	$*	+,-./#012345	16	789:; 	<	 =	012345	789:;=/?4@1A4B012CD;E.//?4@1A4  (simplified to #	#$%&'(	$*	+,-./#	#$%&'(	$*	FGHI,=×2).  Immune cell isolation and flow cytometry - Mice were euthanized 20-25 days post EAE induction. They were perfused with 30cc of PBS, and brains, spinal cords, and spleens were isolated. A single cell suspension was generated from each organ. Immune cells from the CNS were isolated using a 30% Percoll gradient. For intracellular staining, CNS mononuclear cells, were stimulated for 4 hours in DMEM (Gibco) containing 10% FBS (Gibco), GolgiPlug (BD  102 Biosciences), 10ng/ml PMA, and 500ng/ml ionomycin. Antibodies for the cell surface markers were added to the cells in PBS with 2% FBS for 30 min on ice. After washing, cells were resuspended in Fix/Perm buffer (eBiosciences) for 30-45 minutes on ice, washed twice, and incubated with Abs for intracellular antigens (cytokines and transcription factors) in Perm buffer (30 min, on ice). Fluorescently conjugated antibodies directed against CD4 (clone RM4-5), CD8 (clone 53-6.7), CD3 (clone eBio500A2), IFN-g (clone XMG1.2), Foxp3 (clone FJK-16s), and IL-17 (eBio17B7) were all purchased from eBiosciences. Samples were acquired using a FACS LSR II (BD Biosciences) and analyzed FlowJo software (Tree Star, Inc.). Statistical analysis - Results are reported as mean + standard error of the mean (SEM). Two-way ANOVA followed by Bonferroni’s correction for multiple comparisons was employed to compare EAE scores. Unpaired Student’s t-test was used for all other analyses (GraphPad Prism). A p Value of <0.05 was considered statistically significant ns > 0.05, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001.  4.3 Results  4.3.1 γHV-68 Latency has a Long-Lasting Effect on EAE Symptoms It was previously determined that C57/Bl6 mice 6 months of age or older have a diminished susceptibility to EAE with MOG35-55 (284, 285), but, if they develop symptoms, they show a more severe presentation of the disease. We set out to explore whether long-term latency of γHV-68 would maintain its ability to exacerbate EAE symptoms or if it would diminish with time. To test this, we infected 8-week-old mice with γHV-68. 13 or 20 weeks p.i. we induced EAE, when mice were 5-7 months old.  103 We found that γHV-68 infected mice show exacerbation of EAE symptoms (Figure 4.1) similar to what we observe at 5 weeks of latency. The difference in the exacerbation seems more marked in the mice that had a 13-week latency, where we also saw increased mortality in gHV-68 mice compared to MEM mice, similar to what we previously reported (203). Given this, together with the severity of the symptoms, we decided to end the experiments at day 15 post EAE induction.  (Figure 4.1A). In the case of mice with 20-week latency, the disease developed more slowly, allowing us to maintain the mice for a longer period of time. However, once gHV-68 mice developed paralysis, symptoms progressed quickly and they were considerably worse than in MEM mice (Figure 4.1B).   Figure 4.1 Effects of gHV-68 are maintained after long term latency   Figure 4.1 8 week old mice were infected with gHV-68 or MEM, 13 or 20 weeks after infection, EAE was induced. At days 15 (A) -26 (B) post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Graph shows EAE scores up to day 15 post induction after 13 weeks p.i. B) Graph shows EAE scores up to day 26 post induction after 20 weeks p.i. Two and three independent experiments respectively with 10- 104 13 mice/ group. Data analyzed with two-way ANOVA with Bonferroni’s correction for multiple comparisons: ****p<0. 0001, ***p<0.001.  4.3.2 Increased Infiltration of CD4 and CD8 T Cells in the Brain of gHV-68 Infected Mice after EAE Induction  In mice latently infected with gHV-68, we observed an upregulation in the infiltration of CD8 T cells into the CNS. However, we did not see a change in the infiltration of CD4 T cells (203). Interestingly, it seems that the longer latency is established, the higher the increase in the infiltration of T cells, including not just CD8+ but also CD4+. At 20-week latency, we see a higher number of T cells in the CNS compared to 13-week latency (Figure 4.2). Moreover, when we determined the production of IFNg and IL-17, we found a similar trend showing a high production of IFNg and lower production of IL-17 (Figure 4.3). These differences were more evident in the brain, where we saw a marked upregulation of IFNg and a considerable decrease in IL-17 production, in both gHV-68 infected mice and uninfected mice, although it was always more pronounced in gHV-68 mice.           105 Figure 4.2 Long term latency increases T cell infiltration into the CNS  Figure 4.2 Mice were infected with gHV-68 or MEM, 13 or 20 weeks after infection, EAE was induced. At days 14-26 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Percentage of infiltrating total cells expressing CD4+ or CD8+ in the brain and spinal cord. Two and three independent experiments with 7-11 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, *p<0.05.   106  In the spinal cord, the pattern is similar for IFNg production. The longer the latency, the greater the increased production of IFNg. IL-17 however, did not follow the same pattern as IFNg and seemed to diminish naturally when mice were older. Overall, these results might suggest that the quick progression of EAE symptoms can be related to the high amount of T cells present in the CNS and a strong production of IFNg by these T cells. It also seems that a diminished production of IL-17 was not only evident in gHV-68 mice, but also in uninfected mice, although it was always more pronounced when the virus was present.   Figure 4.3 Long term latency maintains high production of IFNg   107 Figure 4.3 Mice were infected with gHV-68 or MEM, 13 or 20 weeks after infection, EAE was induced. At days 14-26 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Percentage of infiltrating total cells expressing CD4+IFNg+ and CD4+IL-17+ in the brain and spinal cord. Two and 3 independent experiments with 7-11 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01.  4.3.3 gHV-68 in 3 Week-Old Mice Stops EAE Enhancement Later in Life  Infectious mononucleosis is a syndrome characterized by the atypical production of activated CD8 T cells mostly specific to EBV (119). In adult mice (8 weeks old), a similar syndrome can be observed where mice show splenomegaly and expansion of Vβ4+ CD8+ T cells. These cells are particularly interesting because they persist during latency and their response is characterized by being independent of MHC Class Ia and b2-microglobuling expression in vivo. It has been largely suggested that a superantigen is responsible for their activation, but so far this ligand has not been identified (288). Similar to what occurs in humans, younger mice do not show any signs of this syndrome, highlighting important differences between the juvenile and the adult immune systems.  To test whether these differences would have an effect on EAE induced later in life we infected mice at different time points; at 3 weeks (after weaning) and at 5 weeks of age. Once mice were infected, we waited until they reached adulthood (13 weeks old) and we induced EAE. Results showed that while 5-week-old mice still demonstrated exacerbated EAE symptoms (Figure 4.4 B), mice infected at 3 weeks of age were not different from uninfected mice (Figure  108 4.4 A). This suggests that the effect on EAE symptoms by gHV-68 is dependent on the age at which mice were infected.   Figure 4.4 Infection at an early age eliminates enhancement of EAE symptoms  Figure 4.4 Mice were infected with gHV-68 or MEM at 3 or 5 weeks of age. When mice reached 13 weeks old, EAE was induced. At day 20 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. A) Graph shows EAE scores on mice infected at 3 weeks of age B) Graph shows EAE scores on mice infected at 3 weeks of age. Three independent experiments with 8-20 mice/ group. All 3 experimental groups were performed at the same time; score graphs were separated for clarity. Data analyzed with two-way ANOVA test with Bonferroni’s correction for multiple comparisons: ****p<0. 0001, **p<0.01, *p<0.05.  4.3.4 T cell Infiltration and Cytokine Production Follows a Gradient Dependent on Time of Infection with gHV-68 In order to test if these changes in EAE scores were accompanied by changes in T cell infiltration and cytokine production in the CNS, we first measured infiltration of CD4 and CD8 T  109 cells into the CNS. We saw a small increase in CD8 T cells in the brain of mice infected at 3 and 5 weeks old, but this increase was only significant in 3-week-old mice. In the spinal cord we saw increased infiltration of CD8 T cells at 5 weeks of age, where levels reached almost the same as mice infected at 8 weeks old. However, a high amount of infiltration of CD8 T cells was only consistently reached when mice are infected at 8 weeks of age. We did not observe any consistent differences in CD4 T cell infiltration on any of the groups compared to uninfected mice (Figure 4.5).  When we examined cytokine production, we observed that IFNg was elevated in the brain starting at 5 weeks of age, but not in the spinal cord. This lack of IFNg production in the spinal cord is not surprising since we had previously seen that even though gHV-68 latency leads to an increase of CD4 T cells producing IFNg, this difference is very subtle, and often is not significant (203). It is possible that mice need to be infected during adulthood in order to see full inflammation in brain and in spinal cord. The opposite occurred with IL-17, where a significant downregulation of IL-17 in the brain was only observed in the mice infected at 8 weeks old, but it was clearly observed in the spinal cord of the mice infected at 5 weeks of age. In the case of the mice infected at 3 weeks of age, we were not able to observe any differences in IFNg or IL-17 compared to uninfected mice (Figure 4.6). These results highlight important differences in how the immune system responds to gHV-68 infection, and subsequent EAE induction, depending on the age of infection.      110 Figure 4.5 Infection with gHV-68 at early age reduces T cell infiltration in the CNS  Figure 4.5 Mice were infected with gHV-68 or MEM at 3, 5 or 8 weeks of age. When mice reached 13 weeks old, EAE was induced. At day 20 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Percentage of infiltrating CD3+ cells expressing CD4+ or CD8+ in the brain and spinal cord. Three independent experiments with 8-20 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, **p<0.01, *p<0.05.   111 Figure 4.6 Mice infected at 3 weeks of age produce similar levels of cytokines than uninfected mice  Figure 4.6 Mice were infected with gHV-68 or MEM at 3 or 5 weeks of age. When mice reached 13 weeks old, EAE was induced. At day 20 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Percentage of infiltrating CD3+ cells expressing CD4+IFNg+ and CD4+IL-17+ in the brain and spinal cord. Three independent experiments with 8-20 mice/ group. Data analyzed with Student’s t-test: ****p<0. 0001, ***p<0.001, **p<0.01, *p<0.05.   112 4.3.5 Mice Infected with gHV-68 at 3 Weeks of Age Harbor Similar Amounts of Virus Compared to those Infected at 5 and 8 Weeks of Age  To determine whether the differences observed in the presentation of EAE symptoms, T cells infiltration, and cytokine production in younger mice was due to changes in the amount of virus present in the mice during latency, we quantified gHV-68 at the moment of EAE induction. We observed that despite a different immune response to EAE, younger mice showed similar levels of gHV-68 compared to adult mice. This suggests that the changes observed in younger mice are due to differences in the immune system of young mice vs adult ones, and that these differences make them less likely to have an exacerbation of EAE (Figure 4.7).  Figure 4.7 Mice infected at 3 weeks of age harbour similar levels of gHV-68 during latency   Figure 4.7 Mice were infected with gHV-68 or MEM at 3 or 8 weeks of age. When mice reached 13 weeks old spleens were harvested and gHV-68 quantities were obtained by qPCR n= 3 mice/group.    113 4.4 Discussion 4.4.1 Long-Term Latency of gHV-68 and a New Model for LOMS In this chapter, we began to explore how latent infection with gHV-68 can affect the immune response during EAE in middle-aged and young mice. In middle-aged mice, we found a persistent effect of gHV-68 latency compared to young-adult mice. Moreover, we found that in middle-aged mice, the longer the latency period, the higher the infiltration of T cells in the CNS (Figures 4.1-4.3). This observation could be associated with a worsening of disease or a fast progression of paralysis in mice.  Previous research describes a delay in the beginning of EAE symptoms in C57Bl/6 mice, but once symptoms occurred, the progression of disease was faster (284, 285). Our study did not see a delay or worsening of disease in uninfected mice. We did notice a significant exacerbation of symptoms in mice that had 13 weeks of latency (5-month-old mice) and 20 weeks of latency (7-month-old mice). The presentation of the disease was different between these two groups. In 5-month-old mice we saw an enhancement similar to what we observed in mice with 5 weeks of latency (203), and in 7-month-old mice we saw a slower start of paralysis that eventually turned more severe for those infected with gHV-68 (Figure 4.1B). We also observed an increase in the infiltration of CD4 an CD8 T cells in the brain and the spinal cord in older mice, as well as a slight increase in the IFNg production in the brain. At 20 weeks of latency we also observed an overall decrease of IL-17 production in older mice regardless of state of infection, although it was consistently less in gHV-68 mice. Given these results, it is important to determine whether changes in demyelination of brain and spinal cord, as well as the presence of T cells in the areas of demyelination, can account for the changes we see in the timing of the onset of EAE symptoms. Interestingly, old age in mice (between 18-31 months old) has been associated with a  114 decrease in the T cell repertoire, while B cells remain constant (289), it would be important to determine if, despite lower numbers, these T cells maintain their pathogenic properties and ability to migrate to EAE lesions or, if their role is less prominent in EAE in old mice while other cell types gain relevance.  Given that gHV-68 preferentially affects memory B cells, it is possible that the events occurred during primary infection and latency are enough to alter the aging of the immune system, thus causing a more severe autoimmune disease when EAE is induced regardless of how much time has passed since primary infection. More research is needed to fully understand how the virus affects the development of EAE. It is necessary to evaluate whether the effects of the virus are still felt after 20 weeks of latency, and testing ranges of 10 to 18 months would be ideal. It is also necessary to understand the effectiveness of B cells and T cells in older mice and their differences with uninfected mice. Research in LOMS and VLMS is very limited, we propose our model as a tool that could be useful in the study of these presentations of MS. 4.4.2 Age of Infection and the Development of EAE Symptoms  Just as the immune system of older mice is different from that of adult mice, the immune system of neonates or young mice is also different. In order to better understand how these differences affect the response to EAE induction later in life, we infected mice at different time points before reaching adulthood. We infected 3- and 5- week-old mice with gHV-68. When EAE was induced, it resulted in a different presentation than in mice who were infected as adults (8 weeks of age).  One of the most striking differences was that 3-week-old mice do not appear to have an exacerbation of EAE symptoms, while 5-week-old mice do (Figure 4.4). When we explored T cell infiltration into the CNS, we observed a marked trend where the older the mouse, the greater  115 the infiltration of CD8 T cells into the brain and spinal cord (Figure 4.5). A similar trend was observed in the production of IFNg: in the brain, we saw a strong production of IFNg in mice that were 5 weeks old when they were infected, but in the spinal cord the production of IFNg was only significant when the mice were infected as adults. An opposite trend was seen with the production of IL-17: in the brain, a significant downregulation in IL-17 production was only noticed in the group were mice were infected as adults. In the spinal cord, we saw a consistent downregulation of IL-17 in the mice that were 5 weeks old when they were infected (Figure 4.6). Given that we do not observe exacerbation of symptoms in mice infected at 3 weeks of age, but we still see some infiltration of CD8 T cells to the brain, it would be of great interest to determine where these cells are localized in the brain, and whether they are capable to start a process of demyelination, even in small amounts.  Studies of gHV-68 in young mice have mostly been done in neonates (8 days old), but at 3 weeks of age, mice are not considered adults as they have just recently been weaned and have not yet reached adulthood (286). This developmental stage is closer to the age at which human children get infected with EBV, were most of the primary infections in the developing world occur between 14-18 months of age (290).  While acute infection is widely different in neonates, they eventually establish similar levels of latency as adults (228). In mice infected at 3 weeks of age, we also found that, while lower in one mouse, in the other mice, latency is not different than in those infected at 8 weeks of age (Figure 4.7). It is possible that the differences we observed in young mice vs adults are due to a different immune response towards the virus during acute phase. It has been shown that during the first 24 hours of life, newborn mice show a skewed immune response towards Th2 pathway (291), but otherwise have the same ability as adult mice to produce cytokines such as  116 IL-2 and IFNg. Before 21 days of age, mortality rate due to infection is highly increased, but the immune system of the pups is mostly mature (292).  More research is needed to understand changes in the immune system of old versus young mice. A longer latency period is needed to determine if there is a time limit on the effect of gHV-68 latency. In young mice, it is necessary to do assays that help describe how the immune system of these mice differ from those of adult mice, as well as detailing the immune response to the virus during acute infection and determining any changes in the cellular composition during latency. It would be especially important to determine if immune cells from young mice maintain a skewed response towards a Th1 phenotype before EAE induction.   117 Chapter 5: Conclusions and Future Directions 5.1 Discussion  Since the disease was first discovered, defining the aetiology of MS has been a challenge (293). As research has shown, a combination of genetic and environmental factors is involved in order to develop MS symptoms. However, as happens with most autoimmune diseases, pinpointing the combination of events that triggers the disease is still a mystery. The role of infectious agents in MS development has been long suspected, but the lack of suitable experimental models and an unclear mechanism for how viruses can affect the development of autoimmunity has made it very hard to study the interaction between viruses and multiple sclerosis. The model developed in the Horwitz lab provides an invaluable tool to understand the mechanism of action of EBV and autoimmunity, and it allows us to test potential therapies and the best way to deliver them.   The goal of this project has been to begin understanding how viral latency influences the immune response in an autoimmune environment, and to describe some of the basic differences between the immune system during latency versus when no infection is present. For this, our strategy was to directly target those cells that are considered the main reservoir of gHV-68; memory B cells. By doing B cell depletions at different time points, disturbing the establishment of latency with IFNARko mice and changing age of infection, and sequencing memory B cells from infected mice, we hoped to achieve a more complete image of how herpesviruses can lead to the development of MS, and even how their presence can affect the effectivity of drugs used for MS treatment. However, it is important to point out that, as has been described previously (294, 295), no differences in EAE susceptibility was observed between male and female mice in any of the experiments performed in this thesis.  118 5.1.1 Memory B cells and their Role in EAE Exacerbation  Our lab has demonstrated that CD11b+CD11c+ from mice latently infected with gHV-68 can direct a strong Th1 response after EAE induction (203). However, these cells are not infected during latency. The main reservoir of gHV-68 during latency is memory B cells. This suggests that memory B cells are contributing to polarize CD11b+CD11c+ cells.   In Chapter 2 our objective was to determine whether memory B cells were also able to direct a strong Th1 response and whether they were necessary for EAE enhancement. We also described some of the characteristics that differentiate B cells from latently infected mice vs uninfected mice.  By transferring enriched memory B cells from gHV-68 mice to uninfected mice, we were able to determine that B cells actively contributed to the enhancement of EAE by skewing the immune response. Moreover, B cell depletions confirm that this skewing also happened before EAE induction, and that, once the virus establishes latency, the effects on EAE can only be partially reversed. It is only when establishment of latency in B cells was altered that we could observe a complete reversal in EAE enhancement (Figures 2.5-2.12).  In humans, it has been observed that depletion of B cells with Rituximab is able to control and eliminate EBV replication (296). These observations were made in transplant patients suffering graft versus host disease (GHVD) or as a way to prevent GHVD (297), in cases where the virus was actively replicating. Currently, it is assumed that since B cell depletion therapies target memory B cells, the latent EBV reservoir should also be downregulated (222). This has not been measured directly, however, making it necessary to determine how B cell depletion therapies affect the number of cells latently infected with EBV in MS patients.  119  We were also able to understand some of the differences between cells from gHV-68 mice vs uninfected mice. RNAseq provided us with a set of molecules that are differentially expressed between infected and uninfected mice. Interestingly, one of these molecules was Itga4, also known as CD49d.  Along with b1 integrin, a4 integrin forms the very late activation antigen (VLA)-4. This integrin has been associated with mediating adhesion and migration of T cells into the CNS through interaction with its ligand, vascular cell adhesion molecule (VCAM)-1 on the surface of endothelial cells (298). VLA-4 also interacts with ligands such as fibronectin and osteopontin, thus modulating the survival, priming, and activation of leukocytes present in the CNS (299). Originally, it was thought that this was the mechanism of Natalizumab, a monoclonal antibody against Itga4 (299). However, VLA-4 is expressed in high quantities in B cells, and patients with clinically isolated syndrome (CIS) have an upregulation of Itga4 in all B ell subtypes compared to patients with other inflammatory neurological diseases (300). It has been observed that CD19Cre Itga4fl/fl mice have decreased symptoms of EAE when it is induced with rhMOG (301). Contrastingly, when EAE is induced with MOG35-55 peptide CD19Cre Itga4fl/fl mice show a worsening of the disease (251). These results suggest different mechanisms for how Itga4 can modulate the pathogenic effect of B cells during EAE and probably during MS. With latent infection of gHV-68, it would be important to determine if the upregulation of Itga4 is contributing to the pathogenic phenotype of infected cells and if specific blockage of Itga4 in memory B cells can help reduce the enhancement of EAE symptoms.  5.1.2 Type I Interferons are not Necessary for EAE Enhancement During our RNAseq analysis, STAT1 was upregulated, and we had previously demonstrated that pSTAT1 was upregulated in CD11b+CD11c+ cells from gHV-68 mice during  120 EAE (204). STAT1 is a transcription factor that is part of the JAK-STAT signalling pathway and is activated upon recognition of IFNa or IFNb by IFNAR. During a viral response, activation of IFNAR leads STAT1 and 2 to be tyrosine-phosphorylated. They then dimerize and translocate to the nucleus, where they assemble with IFN-regulatory factor 9 (IRF9) and form IFN-stimulated gene factor 3 (ISGF3). This binds to its cognate sequences the IFN-stimulated response elements (ISREs) and activate the IFN-stimulated genes (ISGs) (266). During gHV-68 latency, type I IFNs are necessary for the control of the viral infection and maintenance of latency (182).  In Chapter 3, we were able to corroborate the importance of type I interferons in terms of viral control. Similar to what has been observed previously (182), IFNARko mice were highly susceptible to gHV-68 infection. The infection was lethal in 100% of the cases when mice were infected via i.p. with 104 pfu. Alternatively, we were able to obtain a 75% survival rate when infection was made via i.n. and with a low dose of 100pfu (Figure 3.1). Despite the considerable reduction in the number of pfu administered, surviving mice were able to establish latency at similar rates as WT mice (Figure 3.1). When IFNARko mice were latently infected with gHV-68 they showed similar EAE exacerbation compared to WT mice infected with gHV-68 (Figures 3.2- 3.5). This means that these mice still presented a higher EAE score, high infiltration of T cells, and high production of IFNg. Interestingly, we observed that during EAE, IFNARko mice show a downregulation of Tregs regardless of if they were infected or not. A more detailed analysis describing why we see such a profound downregulation in Tregs during EAE in uninfected mice is necessary. Overall, these results suggest that while Type I IFNs play an important role in the Treg phenotype, they do not affect the ability of gHV-68 to enhance EAE symptoms and cell infiltration.   121 It is possible that the effectiveness of IFN-b treatment in MS patients is related to the role IFNb plays in the increase of IL-10 and IL-4 production by T cells as well as limiting migration of leukocytes through the BBB, rather than a downregulation of the effects of EBV latency in the immune response.  5.1.3 Age Dependent Effects on EAE Exacerbation Chapters 2 and 3 described how B cells from infected mice are able to drive a systemic Th1 response during gH-68 latency that leads to an enhanced response when EAE is induced. In Chapter 4 we explored how long that effect lasts and if age of infection affects the response of the immune system during latency.  We found that latency has a long-lasting effect on mice, and that up to 20 weeks p.i. mice show an exacerbation of EAE when compared to uninfected mice. Moreover, the aggressiveness and the higher CD4 T cell infiltration and IFNg production suggests that, in old mice, gHV-68 latency leads to a disease similar to the primary progressive MS observed in patients who develop MS later in life (276) (Figure 4.1-4.3). If this proves to be true, long-term latent infection with gHV-68 could serve as an invaluable model to study LOMS and VLOMS. As such, a better characterization of gHV-68 latency in old mice needs to be done, and whether these effects are maintained in old mice needs to be explored. It will be also pertinent to determine if we can observe clinical differences depending on the sex of the mice at later stages of life. In humans, while women and men experience the same types of aging-related changes in the immune system, men experience them earlier or more dramatically than women. For example, prior to menopause, women are more prone to autoimmunity, while post-menopausal women have a lower incidence (302), moreover, LOMS and VLMS have a higher incidence in  122 men where it tends to be PP-MS (276, 278). It would be important to determine if we can observe some of these age related differences in our model.  We also studied the effect of gHV-68 in EAE when primary infection occurs as soon as 3 weeks of age. While these young mice show a similar level of gHV-68 latent infection (Figure 4.7), they do not show signs of EAE enhancement (Figure 4.4-4.6). These results are similar to what has been described in humans, where a history of IM increases the risk of developing MS later in life. At the same time, infection with EBV during the first years of life does not increase the risk of developing MS (278).  Although preliminary, our data strongly suggest that gHV-68 infection in young mice crucially affects the immune response to EAE induction. Neonate mice are known to have a preferential immune response towards Th2, as they are able to produce high levels of IL-4 but not IFN-g (291, 303, 304). gHV-68 infection in neonates is also characterized by longer persistence of infection in the lungs and spleen compared to adults, although eventually, the virus establishes long-term latency in the spleen (228). This latency seems to be different between neonates and adults. Injection of latently infected mice with CpG (TLR9 ligand) shows differences in viral gene expression between neonates and adults (305), thus confirming an age dependent effect of gHV-68 infection.  In future research, it will be necessary to take a closer look into the differences between acute and latent infection of neonate/young mice versus adults. It is possible that the combination of the acute response and latency is responsible for the absence of EAE enhancement in mice infected at 3 weeks of age. Identifying these differences will provide a valuable tool to better understand why young adults develop IM and children do not. It will also allow a deeper understanding why a history of IM increases the risk of developing MS.   123 5.2 Future Directions Overall, the data presented in this thesis opens several new avenues of study that will be useful to mechanistically describe the processes that lead to the development and exacerbation of EAE. In the following sections, I describe some of the unanswered questions that should be addressed. 5.2.1 Development of a New EAE Scoring Method One of the biggest limitations of this project has been the accurate depiction of the symptomatology observed in gHV-68 mice induced with EAE. As mentioned before, mice latently infected with gHV-68 show a worsening of disease, that not only includes the characteristic development of ascending paralysis, but other atypical neurological symptoms such as loss of balance, ataxia and hunching  (203). Taking into consideration these atypical symptoms in the EAE score has been rather challenging. Although the current EAE scoring method is widely used and accepted (306), it does not fully fit our model, making it necessary for us to develop a new scoring method that takes typical and atypical EAE symptoms into account. The development of this method will likely require extensive training in neurological and behavioral tests (307, 308) that comprehensibly and reliably determine any changes between infected and uninfected mice.  5.2.2 How do B Cells Skew the Immune System?  In Chapter 2 we described how B cells are able to establish a precondition wherein latency skews the immune system towards a Th1 response. However, how latently infected cells are specifically directing this skewing remains to be answered. In Chapter 3 we proposed that type I IFNs was one of the molecules that was strongly contributing to this shift of the immune system. However, it became evident that that was not the case. It is very possible that a  124 combination of molecules and signals are the ones that ultimately lead to the strong Th1 response. One of the ways in which B cells are possibly doing this is through their antigen presentation ability. Antigen presentation assays as well as migration and motility assays are necessary in order to determine if B cells from gHV-68 are able to directly activate the CD4 and CD8 T cells that infiltrate the CNS. If that is the case, it would be of great interest to know what type of antigens these cells are presenting, and which cytokines and chemokines are produced upon contact with T cells. Additionally, a timeline of when this antigen presentation occurs (during latency or after EAE induction) will be crucial to better understand the mechanism of EAE enhancement. 5.2.3 Is Itga4 Upregulation Necessary for EAE Enhancement?  Initial results validating RNAseq data have shown a small population of B cells that have an upregulation of Itga4. This is of great interest given the importance of Natalizumab in the treatment of MS. So far, we do not know if upregulation of Itga4 has any effect on the enhancement of EAE. We currently have acquired mice that are CD19cre and Itga4fl/fl mice. We are in the process of breeding these mice to obtain CD19creItga4fl/fl mice. These mice will allow us to investigate if this molecule has any effect on gHV-68 latency and, ultimately, on EAE enhancement.  If successful, these experiments will provide an alternative mechanism of action of Natalizumab.  5.2.4 Isolation of Memory B cells Infected with gHV-68  During this project we faced the great disadvantage of not being able to differentiate cells that are infected with gHV-68 versus uninfected cells within the same mouse. As mentioned in Chapter 2, many different strategies were attempted in order to address this problem (eg. YFP-gHV-68, M3FL-gHV-68). However, given the very small number of cells infected and the  125 limited viral gene expression during latency, any attempts to differentiate the cells has been unsuccessful. The list of upregulated genes described in Table 2.1 suggests strong candidates that could be used as biomarkers of cells that are infected with gHV-68. A validation of the genes mentioned in Table 2.1, and a detailed characterization of memory B cells, is necessary to determine if it is possible to differentiate between latently infected cells versus uninfected ones, and, ultimately, between pathogenic and non-pathogenic cells.  Additionally, validation of the genes from RNAseq will provide further insight on the processes occurring during latency in memory B cells. This will be incredibly valuable to continue examining the possible mechanism by which latency leads to EAE enhancement. 5.2.5 Does EAE Worsens with Age? Literature suggests that LOMS is different from MS developed in young adults (286). Experiments described in Chapter 4 show that the effect of latency is long lasting, and that up to 20 weeks post infection, the effects of gHV-68 can be observed in the infected mice. The experiments performed in this thesis suggest that we are not only able to see exacerbation of EAE in middle age mice, but that we also can measure a worsening of the disease. This effect is likely occurring not only because of the presence of the virus, but also as a result of changes in the immune systems of older mice. So far, there has been a lack of studies done in older mice. Our experiments did not go beyond 7-month-old mice given our concern that the longer we waited, the less we would be able to see any changes in EAE. Now, since we have established that the phenotype observed in gHV-68 mice is robust and that it seems to last for the rest of the mice’s life experiments should be designed to understand how changes in the immune system of old mice affect gHV-68 latency and how it impacts EAE development. Ideally, these experiments would be conducted on mice that are at least one year old. This way infection with gHV-68 will  126 provide us with a great tool to explore EAE in old and very old mice and will allow us to gain a greater understanding of how infection with EBV affects the onset of MS later in life.   5.2.6 The Difference Between Young and Adult Immune Systems As mentioned previously, important differences between the immune systems of young versus adult mice and their differing responses to viral infections have been described (228, 291, 303, 304). Determining how these differences in the immune system and how acute infection is resolved in young mice can shed light onto understanding why children who do not develop IM do not have an increased risk of development of MS later in life. This is particularly relevant since primary infection in children is extremely hard to observe and has not yet been characterized. The results provided in Chapter 4 propose an ideal model to explore the difference in the immune response in neonates/young mice versus adults, and how these differences are contributing to EAE enhancement. A better characterization of the acute response, latency, and the response to EAE induction, is necessary since experiments performed in this project are limited to mice that were 3 weeks-old while most of the experiments performed in young mice are done in neonates, which average around 8 days old.  Given that mice infected as pups develop an acute infection where the virus spreads systemically, and that the latent virus can be detected in the lungs (228), it is possible that the localization of the virus during latency affects how it will enhance EAE. It would be of great interest to transfer memory B cells from young mice to naïve mice, including cells that come from the spleen, the lungs, and the lymph nodes to determine if they are able to induce enhanced EAE. Conducting RNAseq in mice infected at 3 weeks of age or younger would be of great interest, as well as determining if immune cells of young mice are producing IFNg during  127 latency. Ultimately, results from these experiments would provide researchers with a better picture of how the age of infection is involved with the development of MS later in life.  5.3 Conclusion The main purpose of this project was to understand how latent infection with gHV-68 contributes to the exacerbation of EAE, and how different conditions, including B cell depletion/transfer, length of latency, and age of infection, ultimately affected the presence of symptoms and overall immune response.  With this project we were able to identify B cells as key participants in the development of a strong Th1 immune response during EAE. We also identified that B cells were modifying the immune response even before of the induction of autoimmunity, and that latency had long-lasting effects even after depletion of B cells or a long period of latency. We also started to identify some of the factors that differentiate B cells from infected versus uninfected mice, such as the upregulation of Itga4 and CD22, and discarded type I IFNs as regulators of that immune response. Finally, we identified age and depletion of B cells before gHV-68 as key determinants in stopping the strong autoimmune response in EAE characteristic of mice latently infected with gHV-68.  This project provides us with important insights on how EBV contributes to the development of MS in humans and with novel models that allow further research into understanding this relationship.  128 Bibliography 1. Sospedra M, Martin R. Immunology of multiple sclerosis. Annual review of immunology. 2005;23:683-747. 2. Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nature r eviews Immunology. 2015;15(9):545-58. 3. MSIF. Atlas of MS 2013: Mapping Multiple Sclerosis around the World. London: Multiple Sclerosis International Federation; 2013. 4. 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Representative plots of gating strategies used throughout this thesis.       169 Figure A.2 Gating strategy for T regulatory cells in spleen   Figure A.2 CD19+IgD- from mice infected with gHV-68 or MEM, were transferred into naïve mice. 24 hours after the transfer, EAE was induced. gHV-68 latently infected mice and MEM mice were used as controls. At days 16-18 post EAE induction, mice were perfused; spleens were harvested and processed to isolate immune infiltrates. Representative plots of gating strategies used throughout this thesis.     170 Figure A.3 Effectiveness of B cell depletion after EAE induction    Figure A.3 Mice were infected with gHV-68 or MEM i.p. 5 weeks p.i. EAE was induced. When mice reached a score of >1 B cells were depleted with ⍺-CD20 i.v. At days 20-22 post EAE induction, mice were perfused; brains and spinal cords were harvested and processed to isolate immune infiltrates. Plots shows a representative experiment of B cell population at moment of harvesting.    171 Appendix B Chapter 2 Differentially expressed genes B.1 Differentially expressed genes in memory B cells from gHV-68 mice vs MEM Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Ncoa7	 NM_001111267	 0.130194	 0.0278254	 0.0159898	 24.767	 27.8059	 73.21	 0.058003067	 41.92763333	Celf1	 NM_198683	 0.571835	 0.293464	 0.147858	 8.76447	 45.8525	 258.451	 0.337719	 104.35599	Luc7l	 NM_025881	 0.00226257	 7.18E-05	 0.000623212	 3.2462	 50.4237	 6.75376	 0.000985853	 20.14122	Tpm1	 NM_024427	 0.205047	 0.421633	 0.0604875	 121.306	 1.34587	 2.07644	 0.229055833	 41.57610333	Mllt3	 NM_029931	 0.230485	 0.0246286	 0.0437529	 4.68362	 2.19759	 54.3849	 0.099622167	 20.42203667	Fbxw7	 NM_080428	 0.302571	 0.121395	 3.83914	 29.8615	 44.1244	 144.344	 1.421035333	 72.77663333	Homer1	 NM_152134	 1.04706	 0.00643634	 0.279039	 0.125527	 20.5879	 57.4866	 0.444178447	 26.06667567	Dok1	 NM_001291799	 0.104794	 0.0184845	 2.51107	 5.72513	 26.2151	 63.4123	 0.878116167	 31.78417667	Uba3	 NM_001301858	 0.205728	 0.372427	 1.01369	 8.66116	 9.83475	 44.4049	 0.530615	 20.96693667	Sec23b	 NM_001281816	 0.854074	 0.171374	 3.43838	 12.1328	 29.9865	 88.3598	 1.487942667	 43.49303333	Tex264	 NR_104459	 0.00316728	 1.27073	 0.000740745	 10.9184	 14.3068	 20.7521	 0.424879342	 15.32576667	Mlst8	 NM_001252465	 2.05052	 0.00205691	 1.55759	 23.6078	 42.2204	 23.0701	 1.20338897	 29.63276667	Ptbp1	 NM_001283013	 1.0022	 3.55298	 11.8832	 39.5348	 43.6957	 314.941	 5.47946	 132.7238333	Adam17	 NR_102380	 0.949269	 0.253928	 4.76927	 19.9179	 25.1282	 87.3963	 1.990822333	 44.14746667	Tmem173	 NM_001289591	 1.64304	 0.464821	 5.01514	 5.58375	 91.5675	 19.4971	 2.374333667	 38.88278333	Chchd7	 NM_001190323	 2.30107	 0.0691574	 0.000500961	 11.9945	 16.9204	 20.7985	 0.790242787	 16.57113333	Fam89b	 NM_023166	 0.606186	 0.908157	 1.50164	 14.1641	 35.748	 6.34572	 1.005327667	 18.75260667	Sh3kbp1	 NM_001135728	 3.45322	 0.146613	 22.9225	 84.1678	 7.79978	 353.215	 8.840777667	 148.3941933	Gabpb1	 NM_001271467	 0.932837	 1.40145	 3.7463	 36.2529	 30.3875	 30.8838	 2.026862333	 32.50806667	Crk	 NM_001277221	 0.708335	 1.09918	 10.3845	 15.5092	 11.7831	 157.599	 4.064005	 61.63043333	Mtf2	 NM_001253880	 9.8534	 0.0257863	 2.72189	 54.7179	 9.97131	 134.383	 4.200358767	 66.35740333	Xiap	 NR_125870	 6.77147	 2.36386	 23.8791	 102.095	 61.2722	 284.17	 11.00481	 149.1790667	Kat7	 NM_177619	 4.58678	 0.299876	 4.04334	 28.3895	 22.5819	 70.955	 2.976665333	 40.64213333	 172 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Msra	 NM_001253716	 0.260129	 0.000787316	 5.1672	 26.4235	 12.446	 28.8805	 1.809372105	 22.58333333	Chfr	 NM_001289578	 3.32579	 1.86636	 6.89539	 7.69305	 60.3005	 80.3747	 4.02918	 49.45608333	Prkd3	 NM_001171005	 3.04999	 3.28232	 23.1298	 44.7547	 65.5151	 226.512	 9.820703333	 112.2606	Pum1	 NM_001159606	 17.2409	 2.37465	 0.602491	 40.6771	 44.0663	 180.248	 6.739347	 88.33046667	Eps15	 NM_001159964	 25.4513	 4.71516	 7.38433	 50.7714	 82.7312	 343.987	 12.51693	 159.1632	Cdc14a	 NM_001173553	 2.32748	 3.0644	 1.61573	 24.6782	 23.2098	 41.1974	 2.33587	 29.69513333	Bfar	 NM_001177552	 4.7293	 2.56229	 6.05622	 22.4714	 23.5058	 104.942	 4.44927	 50.3064	Dffa	 NR_104263	 2.96934	 0.878116	 15.7883	 37.696	 31.5431	 106.432	 6.545252	 58.55703333	Lonp2	 NM_001168591	 11.5526	 3.42591	 13.2139	 54.122	 25.4633	 200.107	 9.39747	 93.23076667	Dhx32	 NM_001286032	 2.70915	 0.894414	 8.92041	 27.6645	 35.2429	 45.871	 4.174658	 36.25946667	Lrmp	 NM_001281980	 13.5752	 12.1713	 7.75836	 65.8071	 90.0167	 188.188	 11.16828667	 114.6706	Rreb1	 NR_033615	 4.86225	 0.101195	 15.2131	 48.1004	 15.945	 104.975	 6.725515	 56.34013333	Lrrc40	 NM_001289525	 4.45852	 1.61488	 4.82697	 38.5223	 22.309	 24.5356	 3.633456667	 28.45563333	Arap1	 NM_198096	 35.1555	 4.51147	 45.6644	 112.683	 192.632	 306.065	 28.44379	 203.7933333	Blvrb	 NM_001290525	 11.678	 0.228132	 8.64297	 29.7247	 24.2487	 105.578	 6.849700667	 53.1838	Swi5	 NM_001290552	 0.908617	 2.40703	 16.9838	 49.7907	 24.4426	 71.0833	 6.766482333	 48.43886667	Tulp4	 NM_001103181	 3.98172	 19.9085	 31.7755	 110.406	 106.525	 186.993	 18.55524	 134.6413333	Pik3r1	 NM_001024955	 13.2424	 5.0838	 79.1004	 91.6727	 124.696	 364.391	 32.47553333	 193.5865667	Mbnl1	 NM_001253710	 63.9461	 29.9234	 80.6975	 200.278	 305.857	 623.987	 58.189	 376.7073333	Zfp277	 NM_178845	 15.8377	 1.87604	 6.40987	 31.9415	 48.6772	 68.6681	 8.041203333	 49.76226667	Itpripl1	 NM_001163528	 2.23618	 5.79158	 26.8114	 60.4872	 53.1555	 59.9038	 11.61305333	 57.84883333	Phf23	 NM_001291127	 39.1484	 10.6716	 29.1393	 95.5606	 129.203	 189.307	 26.31976667	 138.0235333	Luc7l2	 NM_001170848	 15.668	 24.7496	 22.0816	 158.192	 101.189	 79.9436	 20.83306667	 113.1082	Cbfb	 NM_001161456	 9.38952	 16.4499	 37.2753	 60.4506	 66.7397	 203.077	 21.03824	 110.0891	Maml2	 NM_173776	 20.9481	 20.7623	 35.4015	 114.18	 118.233	 141.144	 25.70396667	 124.519		        		 		Ikzf1	 NM_001301866	 144.06	 59.7525	 167.178	 228.164	 261.033	 1254.52	 123.6635	 581.239	 173 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Aup1	 NM_001301649	 23.0039	 17.3726	 17.3787	 59.1048	 82.7805	 114.716	 19.25173333	 85.53376667	2310035C23Rik	 NM_029349	 14.2737	 36.8386	 26.3318	 98.1019	 75.1674	 207.181	 25.8147	 126.8167667	Camk2d	 NM_001293665	 18.2261	 10.113	 36.8678	 45.8599	 63.6689	 150.273	 21.73563333	 86.6006	Mif4gd	 NM_001243587	 13.1719	 19.193	 37.182	 99.8585	 43.9794	 145.656	 23.1823	 96.49796667	Gatad2a	 NM_145596	 27.9445	 12.358	 25.9837	 69.2754	 110.137	 71.9196	 22.0954	 83.77733333	Mical1	 NM_001164433	 18.1225	 16.9594	 27.2697	 52.3691	 58.4586	 136.397	 20.78386667	 82.40823333	Dnaja1	 NM_001164672	 186.245	 50.9941	 121.051	 280.402	 242.18	 816.521	 119.4300333	 446.3676667	Dnaja1	 NM_001164671	 52.7222	 14.4349	 34.2654	 79.3778	 68.5547	 231.191	 33.8075	 126.3745	Mbnl1	 NM_001253709	 206.39	 76.3845	 183.506	 401.867	 298.101	 921.965	 155.4268333	 540.6443333	Tspan15	 NM_197996	 30	 13	 60	 96	 126	 92	 34.33333333	 104.6666667	Nedd9	 NM_017464	 166.795	 52.6694	 503.316	 475.907	 608.65	 1045.9	 240.9268	 710.1523333	Prkacb	 NM_001164199	 49.0404	 24.5554	 65.2212	 97.8037	 163.572	 185.358	 46.27233333	 148.9112333	Tcea1	 NM_001159751	 144.092	 43.9272	 155.419	 228.765	 221.409	 641.478	 114.4794	 363.884	Sirt1	 NM_001159589	 89.4784	 25.192	 85.8105	 148.16	 123.813	 314.299	 66.82696667	 195.424	Trim30d	 NM_001167828	 57.1194	 17.1421	 100.651	 117.597	 137.165	 212.205	 58.30416667	 155.6556667	Lrif1	 NM_001039478	 55.6773	 31.0562	 61.9281	 112.525	 115.687	 201.58	 49.55386667	 143.264	Cnbp	 NM_001109745	 84.7889	 48.7703	 164.015	 224.31	 174.388	 394.269	 99.1914	 264.3223333	Eif5	 NM_178041	 248.277	 126.87	 396.476	 420.537	 398.729	 1267	 257.2076667	 695.422	Slc44a2	 NM_152808	 122.063	 37.0297	 232.515	 262.219	 229.774	 469.523	 130.5359	 320.5053333	Cd22	 NM_009845	 251.709	 107.321	 660.36	 941.84	 512.386	 933.911	 339.7966667	 796.0456667	Nup210	 NM_018815	 1010	 640	 1112	 514	 554	 594	 920.6666667	 554	Cep350	 NM_001039184	 595	 404	 1047	 385	 386	 402	 682	 391	Atp8a1	 NM_009727	 648.631	 390.186	 656.501	 356.89	 310.178	 344.972	 565.106	 337.3466667	Itpr1	 NM_010585	 676	 506	 1020	 407	 456	 413	 734	 425.3333333	Med13	 NM_001080931	 1880	 1453	 3313	 1249	 1300	 1231	 2215.333333	 1260	Hook3	 NM_207659	 356	 223	 557	 232	 196	 215	 378.6666667	 214.3333333	Prrc2c	 NM_001081290	 1684	 1016	 2380	 1096	 866	 916	 1693.333333	 959.3333333	 174 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Gabpb2	 NM_029885	 493.264	 319.699	 925.479	 358.706	 278.076	 309.324	 579.4806667	 315.3686667	Atp2a3	 NM_001163336	 744.477	 578.257	 851.514	 378.621	 448.498	 437.794	 724.7493333	 421.6376667	Bach2	 NM_001109661	 1023.93	 752.965	 1148.93	 493	 510	 725	 975.275	 576	Ep300	 NM_177821	 791	 566	 1179	 478	 443	 495	 845.3333333	 472	Vps13a	 NM_173028	 688	 470	 1056	 420	 381	 414	 738	 405	Usp34	 NM_001190401	 1393.23	 752.898	 1900.61	 775.128	 743.136	 642.352	 1348.912667	 720.2053333	Itga4	 NM_010576	 1695.76	 1055.89	 2901.75	 1157.9	 986.653	 825.651	 1884.466667	 990.068	Kat6a	 NM_001081149	 839	 720	 1421	 580	 579	 472	 993.3333333	 543.6666667	Cnot1	 NM_178078	 803.17	 450.086	 1211.74	 437.9	 439.136	 431.053	 821.6653333	 436.0296667	Cep192	 NM_027556	 431	 214	 525	 241	 177	 214	 390	 210.6666667	Akap13	 NM_029332	 1801	 1208	 2566	 1076	 1067	 841	 1858.333333	 994.6666667	Herc1	 NM_145617	 905.793	 564.459	 1543.26	 574.496	 550.073	 443.23	 1004.504	 522.5996667	Pik3cd	 NM_001029837	 316.715	 286.318	 402.016	 217.624	 165.653	 187.271	 335.0163333	 190.1826667	Bptf	 NM_176850	 1185	 658	 1239	 514	 552	 607	 1027.333333	 557.6666667	Wnk1	 NM_001185021	 364.435	 268.611	 562.731	 210.354	 141.158	 311.385	 398.5923333	 220.9656667	Mon2	 NM_153395	 293.815	 157.436	 332.753	 150.464	 126.745	 139.282	 261.3346667	 138.8303333	Rapgef6	 NM_175258	 585.962	 361.249	 845.423	 344.206	 304.568	 271.297	 597.5446667	 306.6903333	Malat1	 NR_002847	 6676	 3850	 9630	 4637	 2872	 2690	 6718.666667	 3399.666667	Smg1	 NM_001031814	 2988	 1734	 4656	 1576	 1644	 1488	 3126	 1569.333333	4932438A13Rik	 NM_172679	 952	 567	 1365	 495	 511	 455	 961.3333333	 487	Ssh2	 NM_001291190	 178.675	 192.059	 298.528	 104.211	 113.34	 147.462	 223.0873333	 121.671	Atm	 NM_007499	 798	 492	 1109	 397	 404	 428	 799.6666667	 409.6666667	Ino80d	 NM_001114609	 301.242	 248.57	 593.204	 204.054	 200.469	 166.725	 381.0053333	 190.416	Nup214	 NM_172268	 281	 227	 486	 190	 161	 151	 331.3333333	 167.3333333	Gm608	 NM_001029889	 583	 343	 923	 293	 310	 303	 616.3333333	 302	Polr2a	 NM_001291068	 1351	 716	 1734	 655	 697	 497	 1267	 616.3333333		        		 		 175 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Dmxl1	 NM_001081371	 1023	 733	 1772	 585	 561	 605	 1176	 583.6666667	Ubr3	 NM_001303033	 231.053	 126.247	 406.32	 97.0288	 123.281	 148.017	 254.54	 122.7756	Nipbl	 NM_027707	 860.859	 567.786	 1476.36	 475.168	 474.343	 457.327	 968.335	 468.946	Tnrc6b	 NM_144812	 417	 245.974	 608.854	 239.168	 175.432	 210.487	 423.9426667	 208.3623333	Dync1h1	 NM_030238	 1234	 882	 1800	 625	 752	 538	 1305.333333	 638.3333333	Atf7ip	 NM_019426	 303	 257	 570	 226	 185	 137	 376.6666667	 182.6666667	Acin1	 NM_023190	 484.531	 315.013	 645.014	 285.791	 250.57	 162.009	 481.5193333	 232.79	Htt	 NM_010414	 500	 289	 778	 247	 273	 224	 522.3333333	 248	Mecp2	 NM_010788	 238.867	 95.8156	 438.017	 113.337	 87.1836	 158.934	 257.5665333	 119.8182	Mycbp2	 NM_207215	 2016	 1372	 3322	 1099	 1053	 1047	 2236.666667	 1066.333333	Rel	 NM_009044	 379	 251	 607	 263	 181	 132	 412.3333333	 192	Vps13b	 NM_177151	 648	 390	 960	 305	 333	 302	 666	 313.3333333	Ahnak	 NM_009643	 1485.67	 806	 1976.9	 776.655	 753	 425	 1422.856667	 651.5516667	Huwe1	 NM_021523	 2333	 1223	 3378	 1172	 1106	 902	 2311.333333	 1060	Tnpo1	 NM_178716	 359.642	 220.647	 399.086	 209.636	 122.562	 144.934	 326.4583333	 159.044	Atp11c	 NM_001037863	 175.24	 77.902	 169.52	 69.739	 63.7639	 65.9873	 140.8873333	 66.49673333	N4bp2	 NM_001024917	 431	 292	 574	 258	 204	 145	 432.3333333	 202.3333333	Ep400	 NM_029337	 506.207	 171.728	 517.345	 175.237	 183.71	 184.341	 398.4266667	 181.096	Trrap	 NM_001081362	 551	 420	 695	 270	 294	 229	 555.3333333	 264.3333333	Akap9	 NM_194462	 524	 377	 704	 267	 268	 215	 535	 250	Birc6	 NM_007566	 1665	 965	 2429	 715	 842	 734	 1686.333333	 763.6666667	Cacna1e	 NM_009782	 712	 516	 1328	 531	 364	 239	 852	 378	Mdn1	 NM_001081392	 918	 532	 1353	 441	 476	 333	 934.3333333	 416.6666667	Smarca4	 NM_001174079	 307.525	 138.982	 168.174	 111.4	 81.2465	 109.204	 204.8936667	 100.6168333	Ikzf3	 NM_011771	 2571	 2040	 3734	 1494	 1258	 1163	 2781.666667	 1305	Tnrc6b	 NM_177124	 412	 346.028	 783.148	 228.832	 282.568	 181.513	 513.7253333	 230.971	Gm15800	 NM_181421	 291	 254	 418	 165	 157	 125	 321	 149	 176 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Glg1	 NM_009149	 310	 208	 452	 183	 158	 83	 323.3333333	 141.3333333	Prkd3	 NM_001171004	 396.6	 128.897	 323.525	 165.207	 111.447	 90.2092	 283.0073333	 122.2877333	Sorl1	 NM_011436	 832	 634	 1121	 422	 415	 300	 862.3333333	 379	Fry	 NM_172887	 148	 138	 232	 71	 86	 75	 172.6666667	 77.33333333	Cd22	 NR_102723	 664.719	 585.565	 847.962	 315.695	 320.551	 320.772	 699.4153333	 319.006	Vps13d	 NM_001276465	 344.625	 240.016	 562.002	 155.822	 185.879	 145.813	 382.2143333	 162.5046667	Pou2f1	 NM_198934	 146.406	 77.2218	 189.988	 48.7473	 71.2657	 53.8782	 137.8719333	 57.96373333	Chd6	 NM_173368	 475.137	 231.495	 545.76	 182.463	 228.327	 94.4989	 417.464	 168.4296333	Aak1	 NM_001040106	 134.808	 139.162	 287.73	 72.4324	 98.4112	 60.8851	 187.2333333	 77.2429	Gm4070	 NM_001243040.1	 698.528	 1361.42	 2022.46	 669.857	 456.748	 701.535	 1360.802667	 609.38	Gvin1	 NM_001039160.1	 712.036	 1338.97	 1987.26	 658.747	 449.287	 689.955	 1346.088667	 599.3296667	Abca3	 NM_013855	 207.175	 96.4034	 235.265	 81.0888	 77.2493	 53.9125	 179.6144667	 70.7502	Ubr4	 NM_001160319	 1231	 915	 1764	 518	 618	 422	 1303.333333	 519.3333333	Ralgapa1	 NM_001112714	 200.253	 181.303	 329.736	 113.171	 83.8569	 94.7588	 237.0973333	 97.26223333	Spen	 NM_019763	 234	 242	 352	 136	 133	 69	 276	 112.6666667	Vps13c	 NM_177184	 233	 161	 417	 130	 123	 51	 270.3333333	 101.3333333	Ankrd11	 NM_001081379	 475.157	 391.042	 480.113	 223.071	 231.58	 93.5814	 448.7706667	 182.7441333	Ccdc88c	 NM_026681	 291	 266	 317	 152	 132	 73	 291.3333333	 119	Pitpnm2	 NM_011256	 127.484	 136.112	 160.595	 59.4147	 52.9794	 67.8621	 141.397	 60.0854	Ciita	 NM_007575	 618.1	 419.547	 798.164	 280.585	 264.581	 152.771	 611.937	 232.6456667	Mga	 NM_013720	 775.131	 496.496	 1399.74	 404.554	 333.896	 223.727	 890.4556667	 320.7256667	Pik3cd	 NM_008840	 163.627	 84.5629	 225.67	 24.0229	 80.4398	 70.7454	 157.9533	 58.4027	Elk4	 NM_007923	 348	 254	 538	 216	 133	 69	 380	 139.3333333	Xrn1	 NM_011916	 237	 126	 339	 237	 126	 339	 234	 234	Pou2f1	 NM_198933	 224.534	 101.269	 292.223	 103.877	 47.9329	 70.6573	 206.0086667	 74.15573333	Lyst	 NM_010748	 720	 407	 1161	 313	 320	 151	 762.6666667	 261.3333333	Uhmk1	 NM_010633	 129	 88	 192	 73	 38	 36	 136.3333333	 49	 177 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Helz	 NM_198298	 216	 144	 304	 93	 81	 61	 221.3333333	 78.33333333	Lpp	 NM_178665	 169.338	 70.7788	 160.563	 70.5709	 30.9947	 40.8603	 133.5599333	 47.4753	Lnpep	 NM_172827	 882	 515	 1337	 422	 302	 197	 911.3333333	 307	Cdk19	 NM_001168304	 246.166	 211.147	 448.742	 135.342	 87.5517	 96.6967	 302.0183333	 106.5301333	Tcf12	 NM_001253862	 235.52	 124.779	 334.454	 136.214	 62.3817	 29.7913	 231.5843333	 76.129	Stat1	 NM_009283	 683.139	 1813.02	 1358.15	 485.316	 488.702	 626.808	 1284.769667	 533.6086667	Cic	 NM_001110131	 86.5573	 128.121	 207.902	 50.8657	 32.0987	 75.8592	 140.8601	 52.9412	Gatad2b	 NM_139304	 132	 100	 260	 72	 64	 24	 164	 53.33333333	Nbeal1	 NM_173444	 151	 92	 205	 66	 50	 34	 149.3333333	 50	Lpp	 NM_001145952	 188.792	 66.5976	 160.376	 51.8922	 17.3701	 78.4632	 138.5885333	 49.24183333	Zbtb37	 NM_173424	 74	 52	 131	 29	 29	 24	 85.66666667	 27.33333333	Tmem245	 NM_175518	 198	 104	 350	 89	 59	 52	 217.3333333	 66.66666667	Prrc2b	 NM_001159634	 323.815	 328.086	 327.345	 157.445	 129.89	 44.1717	 326.4153333	 110.5022333	Foxn3	 NM_183186	 141	 107	 210	 65	 55	 22	 152.6666667	 47.33333333	Crebbp	 NM_001025432	 318	 263	 526	 155	 131	 51	 369	 112.3333333	Cux1	 NM_001291234	 74.3953	 149.937	 150.68	 38.9627	 39.1589	 54.2974	 125.0041	 44.13966667	Hgs	 NM_008244	 63.6891	 58.1899	 96.3098	 17.6058	 20.2275	 32.8501	 72.7296	 23.56113333	Trim56	 NM_201373	 160	 142	 216	 80	 55	 22	 172.6666667	 52.33333333	Gbp7	 NM_001083312	 135.549	 271.509	 307.792	 90.0464	 72.4157	 74.5253	 238.2833333	 78.9958	Gse1	 NM_198671	 107.401	 124.429	 150.781	 64.5916	 14.9678	 44.2364	 127.537	 41.26526667	Pdpr	 NM_198308	 73	 43	 125	 30	 23	 13	 80.33333333	 22	Clcn3	 NM_007711	 110.211	 69.7271	 63.356	 34.1547	 11.6595	 32.049	 81.09803333	 25.9544	Maml2	 NM_001013813	 118.052	 79.2377	 265.599	 39.8205	 51.7668	 31.8561	 154.2962333	 41.1478	Zmiz2	 NM_028601	 257.844	 342.201	 278.048	 97.8471	 101.772	 81.6643	 292.6976667	 93.76113333	Sptbn1	 NM_009260	 310.859	 234.422	 479.265	 114.222	 120.038	 41.4953	 341.5153333	 91.91843333	9930111J21Rik1	 NM_001114679	 1203.82	 834.144	 2452.06	 589.647	 463.258	 77.8574	 1496.674667	 376.9208	Cbl	 NM_007619	 535	 278	 789	 215	 145	 48	 534	 136	 178 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Atf2	 NM_001025093	 101.493	 64.0132	 127.095	 20.8572	 21.4494	 40.2017	 97.53373333	 27.50276667	Arap1	 NM_001040111	 92.9394	 257.468	 146.515	 59.0547	 52.1506	 39.5572	 165.6408	 50.25416667	Tmem229b	 NM_178745	 105.656	 148.736	 192.426	 62.0554	 10.2108	 53.6046	 148.9393333	 41.95693333	Cdk12	 NM_026952	 133.075	 81.1633	 229.444	 48.7917	 42.7456	 5.98236	 147.8941	 32.50655333	Wnk1	 NM_198703	 269.779	 118.168	 324.784	 67.3299	 63.8772	 25.6268	 237.577	 52.27796667	Hnrnpr	 NM_001277122	 72.5145	 87.2875	 73.5654	 10.117	 20.7421	 28.9094	 77.78913333	 19.92283333	Psd3	 NM_177698	 72.4675	 68.8064	 120.264	 11.3272	 36.8986	 6.468	 87.1793	 18.23126667	Hivep3	 NM_010657	 100	 109	 122	 28	 26	 15	 110.3333333	 23	Vasn	 NM_139307	 77	 44	 93	 21	 12	 4	 71.33333333	 12.33333333	Fnbp1	 NM_019406	 64.729	 18.4293	 78.0414	 17.2093	 3.64087	 5.23893	 53.73323333	 8.696366667	Plec	 NM_201393	 19.4128	 44.2319	 82.7862	 5.53875	 10.9822	 8.87019	 48.8103	 8.463713333	Cnot1	 NM_153164	 154.568	 129.453	 200.162	 36.4829	 16.7644	 34.1716	 161.3943333	 29.13963333	Tulp4	 NM_054040	 238.018	 135.091	 304.225	 59.594	 36.4753	 14.0072	 225.778	 36.69216667	Gm10825	 NR_028580	 48.8306	 20	 110	 8	 8.82276	 10	 59.6102	 8.94092	Zfp318	 NM_021346	 83.754	 55.9679	 103.282	 25.0423	 4.96801	 7.71291	 81.0013	 12.57440667	Macf1	 NM_001199136	 4223.1	 2448.85	 6517.46	 986.134	 734.55	 127.503	 4396.47	 616.0623333	Cic	 NM_001302811	 97.0773	 160.843	 118.458	 26.201	 21.6734	 7.68956	 125.4594333	 18.52132	Tgtp1	 NM_011579	 22.1049	 160.183	 94.222	 15.9643	 15.9781	 11.5585	 92.16996667	 14.5003	Pgap2	 NM_145583	 55.4252	 12.597	 27.4335	 0.258817	 5.61004	 4.50976	 31.81856667	 3.459539	Otud5	 NM_138604	 31.4032	 131.263	 34.7515	 17.3441	 6.91609	 4.11006	 65.8059	 9.45675	Plec	 NM_201394	 16.1095	 103.525	 87.7635	 8.64268	 9.11355	 7.36093	 69.13266667	 8.372386667	Slc35c1	 NM_211358	 57.9856	 28.6225	 32.2593	 6.47581	 0.0376813	 6.48051	 39.62246667	 4.331333767	Gm4759	 NR_003967	 188.369	 228.555	 506.818	 52	 29	 4	 307.914	 28.33333333	Zbtb20	 NM_019778	 31.9453	 18.1899	 28.2337	 4.16563	 2.77706	 0.238225	 26.12296667	 2.393638333	Eps8	 NM_007945	 48.6946	 18.1225	 53.8754	 4.38976	 4.38172	 0.000231356	 40.23083333	 2.923903785	Gas7	 NM_001109657	 41.8456	 37.8214	 7.02253	 3.01465	 3.15486	 1.82281	 28.89651	 2.664106667	Rtel1	 NM_001166665	 53.2996	 45.5273	 71.4075	 6.72176	 2.07007	 5.14414	 56.7448	 4.645323333	 179 Gene	 Transcript	 973(Reads)	 974	(Reads)	 972	(Reads)	 937	(Reads)	 922	(Reads)	 923	(Reads)	 Mean	gHV-68	 Mean	MEM	Rgs19	 NM_001291206	 29.9999	 30.2584	 12.9653	 3.6995	 1.84789	 0.523214	 24.40786667	 2.023534667	Serpina3f	 NM_001168294	 31.0129	 324.286	 123.839	 28.8922	 3.69713	 7.62674	 159.7126333	 13.40535667	Aqr	 NM_001290788	 31.1633	 21.7682	 48.4571	 0.0766501	 0.410292	 5.57001	 33.7962	 2.018984033	Slc14a1	 NM_001171010	 23.8871	 10.7343	 11.5117	 0.668335	 0.121411	 1.80234	 15.3777	 0.864028667	A730008H23Rik	 NM_172505	 73.2308	 17.5992	 29.5631	 4.46712	 1.34378	 3.65E-08	 40.13103333	 1.936966679	Itsn2	 NM_001198969	 24.0808	 2.14681	 61.6273	 3.73E-07	 1.31E-05	 3.06727	 29.28497	 1.022427831	Aptx	 NM_001025445	 21.8214	 33.5485	 1.93226	 0.0762239	 0.179802	 2.21461	 19.10072	 0.8235453	Iigp1	 NM_021792	 14.9841	 387.753	 86.436	 2.85204	 1.90136	 19.9643	 163.0577	 8.239233333	Nup98	 NM_001287167	 9.11988	 5.31715	 7.66824	 0.0116944	 0.00563256	 0.293933	 7.368423333	 0.10375332	Wars	 NM_011710	 30.722	 9.75822	 16.4713	 0.449607	 0.0135073	 0.00834939	 18.98384	 0.157154563	Zscan22	 NM_001290438	 4.36916	 10.5212	 14.0043	 0.00747547	 0.00145718	 0.0211763	 9.631553333	 0.010036317	Mical3	 NM_153396	 14.3886	 2.05433	 15.9756	 0.016376	 6.52E-12	 8.48E-34	 10.80617667	 0.005458667				 	 	 	 	 	 	 	 	 	

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