The Role of Killer Immunoglobulin-Like Receptors In

The role of killer immunoglobulin-like receptors

in HTLV-1 infection

A thesis submitted to Imperial College London

for the degree of Doctor of Philosophy

Katie R. Twigger

Imperial College London

2014

Section of Immunology

Department of Medicine

Wright-Fleming Institute

Imperial College

Norfolk Place

London W2 1PG Declaration of originality and copyright

The copyright of this thesis rests with the author and is made available under a Creative Commons

Attribution-Non Commercial-No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or distribution, researchers must make clear to others the licence terms of this work.

Declaration of originality

All work presented in this thesis is the author’s own other than where clearly stated in the ‘Statement of Collaboration’.

Signature______

Date______

Summary

Human T-lymphotropic virus type 1 (HTLV-1) causes the debilitating neuroinflammatory disease

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) in a minority of infected individuals, while the majority are lifelong asymptomatic carriers (ACs). The effectiveness of the CD8+

T-cell response to HTLV-1 is a major determinant of the HTLV-1 proviral load (PVL) and the associated HAM/TSP risk. Host genotype, particularly of HLA class I, strongly influences CD8+ T-cell effectiveness against HTLV-1. Furthermore, possession of the KIR gene 2DL2 was recently shown to enhance protective and detrimental HLA class I-associated immunity to HTLV-1 and HCV (Seich al

Basatena et al., 2011). The proposed mechanism explaining this effect is a KIR expression-induced increase in activation thresholds, thus promoting survival. I investigated KIR expression and function on T-cells and NK cells in HTLV-1 infection, focussing on CD8+ T-cells and using new anti-KIR mAbs to analyse 2DL2 expression alongside four other KIRs.

This thesis presents evidence supporting the genetic findings and the KIR expression-induced CD8+

T-cell survival hypothesis. AC status was associated with expanded 2DL2+CD8+ T EM cell populations and 2DL2+ and 2DL3+CD8+ T EM cell frequencies were inversely correlated with PVL, consistent with a protective role for 2DL2 (and potentially 2DL3) expression on CD8+ T-cells in HTLV-

1 infection. Rather than a generalised reduction in CD8+ T-cell function, specific KIR expression was associated with a shift in effector function profile towards a cytotoxic phenotype without cytokine production. This is consistent with raised activation thresholds and may suggest fine-tuning of CD8+

T-cell effector functions by KIRs, preserving killing without causing inflammation. I further speculate that 2DL2/3 could be a context-dependent modulator of CD8+ T-cell function, optimally tuning responses and contributing to CD8+ T-cell effectiveness. Together with the longevity of 2DL2/3+CD8+

T-cells in vivo, this hypothesis requires further testing, since it may be relevant to other persistent viral infections.

Acknowledgements

Firstly, I would like to thank my supervisors Keith Gould and Charles Bangham for their supervision, guidance and advice. I would also like to thank the members of the Immunology Department at St

Mary’s for all their help, technical training, teaching and discussion on all things HTLV-1, in particularly Aileen Rowan, also Katerina Seich al Basatena, Heather Niederer, Anat Melamed, Lucy

Cook, Danny Laydon and Hiroko Yaguchi. I am also grateful to the Wellcome Trust for my funding.

I am very grateful to all of the patients at the National Centre for Human Retrovirology who donated blood for this study, and all the people who provided control samples for me. I would like to thank

Graham Taylor and his team for all their help with this.

This study would not have been possible without the assistance of my collaborators. I am very grateful to Christelle Retiere and Gaelle David for kind provision of their anti-KIR mAbs and for their advice on using these reagents. I would also like to sincerely thank James Traherne for all his help with understanding the complex world of KIR genotyping and alloypting, and also Olympe Chazara. I am also very grateful to the following for flow cytometry advice: Andrea Stacey, Seph Borrow, Kalle

Malmberg, Vivian Beziat, Thurka Poobalasingam and Suzanne Venables.

I would like to say a big thank you to all at the ICR, past and present, for getting me started with this science thing, and inspiring and encouraging me to go further, particularly Kevin Harrington, Christine

White and Virginie Mieulet, also Vickie Roulstone, Katie Copeland, Rohit Seth, the Targeted Therapy

Team, the Marais and Marshall labs. Also thanks to my Wellcome Trust Programme cohort, especially

Sorcha Cassidy and Anna Cauldwell for your encouragement.

Thank you to my amazing friends and family for all your support, particularly to my mum Roselyn

Twigger, for your complete and unwavering belief in me and especially for looking after me while I was writing, and my dad Brian Twigger, for your extremely high standard of proof-reading.

Finally and most of all, to my wonderful husband Alex Miller, thank you so much for supporting me throughout this experience, I honestly could not have done this without you.

Table of Contents

Declaration of originality and copyright ...... 2

Summary ...... 3

Acknowledgements ...... 4

Table of Contents ...... 5

List of Figures ...... 8

List of Tables ...... 10

List of abbreviations ...... 11

Statement of collaboration ...... 13

Chapter 1. Introduction ...... 14 1.1 HTLV-1 epidemiology and transmission ...... 15 1.2 HTLV-1-associated diseases ...... 17 1.2.1 HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP)...... 17 1.2.2 Adult T cell leukaemia/lymphoma (ATLL) ...... 19 1.2.3 HTLV-1 associated inflammatory/autoimmune diseases and co-infections ...... 20 1.3 Cellular and molecular biology ...... 21 1.3.1 Initial infection and integration ...... 21 1.3.2 Viral gene expression ...... 22 1.3.3 Mechanisms of viral proliferation and persistence ...... 26 1.4 Proviral load and the risk of disease ...... 28 1.5 Immune response to HTLV-1...... 29 1.5.1 CD8+ T cells ...... 29 1.5.2 CD4+ T cells ...... 34 1.5.3 NK cells and innate immunity ...... 36 1.5.4 Antibodies ...... 37 1.6 Killer immunoglobulin-like receptors (KIRs) ...... 38 1.6.1 Molecular structure ...... 38 1.6.2 KIR ligand specificities ...... 41 1.6.3 Genetic features ...... 43 1.6.4 Cellular expression of KIRs on NK cells and T cells...... 47 1.6.5 Function of KIRs on NK cells and T cells ...... 49 1.7 KIR associations with disease ...... 51 1.7.1 Viral infections ...... 51 1.7.2 Autoimmune/inflammatory conditions ...... 55 1.8 KIRs in HTLV-1 immunity ...... 60 1.8.1 Modulation of HLA class I-associated immunity by KIR2DL2 ...... 60 1.8.2 How could KIR2DL2 modulate HLA class I-associated immunity? ...... 61 1.9 Aims ...... 64

Chapter 2. Materials and methods ...... 65 2.1 Study subjects and PBMC collection ...... 66

2.2 HTLV-1 proviral load measurement ...... 66 2.3 Flow cytometry experiments ...... 67 2.3.1 Detection of global and specific KIR expression on lymphocyte populations ...... 67 2.3.2 Detection of CD8+ T cell functional responses ...... 67 2.4 Targeting and definitions of global and specific KIR expression ...... 71 2.4.1 Targeting global KIR expression ...... 71 2.4.2 Targeting specific KIR expression using novel anti-KIR mAbs ...... 71 2.4.3 Definitions of specific KIR expression ...... 78 2.5 Flow cytometry data analysis ...... 79 2.5.1 Gating ...... 79 2.5.2 Data processing ...... 89 2.6 KIR and HLA class I genotyping ...... 89 2.6.1 HLA class I genotyping ...... 89 2.6.2 KIR genotyping ...... 90 2.6.3 KIR allotyping ...... 90 2.7 Statistical analysis...... 91 2.7.1 Comparing groups and correlations ...... 91 2.7.2 Fisher’s chi-squared method of combining probabilities ...... 91

Chapter 3. Global and specific KIR expression on effector lymphocytes in HTLV-1 infection ...... 93 3.1 Introduction ...... 94 3.2 Aims ...... 96 3.3 Results: Global KIR expression ...... 97 3.3.1 Global KIR expression on effector lymphocytes in HTLV-1 infection ...... 97 3.3.2 Relationship between donor age and the frequencies of global KIR+ effector lymphocytes in HTLV-1 infection ...... 101 3.3.3 Relationships between PVL and the frequencies of global KIR+ effector lymphocytes in HTLV-1 infection ...... 102 3.4 Results: Specific KIR expression ...... 104 3.4.1 Specific KIR expression on effector lymphocytes in HTLV-1 infection ...... 104 3.4.2 Influence of cognate HLA class I ligand on the inhibitory KIR repertoire of effector lymphocytes in HTLV-1 infection ...... 113 3.4.3 Relationships between HTLV-1 PVL and the frequencies of specific KIR+ effector lymphocytes in HTLV-1 infection ...... 115 3.4.4 The effect of specific KIR gene presence/absence on HTLV-1 PVL ...... 120 3.5 Discussion ...... 122 3.5.1 Expansions of 2DL2+ CD8+ T EM cells are associated with AC status ...... 122 3.5.2 Reductions in 2DL1+ and 2DS1+ NK cells in HTLV-1 infection ...... 125 3.5.3 Expansions of global KIR+ CD4+ T cells in HTLV-1 infection correlate with age ...... 129 3.5.4 Reduced PVLs are associated with higher frequencies of 2DL2+ and 2DL3+ CD8+ T EM cells and with possession of a 2DL2 gene in HAM/TSP patients ...... 131 3.5.5 Chapter 3 summary ...... 136

Chapter 4. Specific KIR expression on HTLV-1-specific CD8+ T cells ...... 137 4.1 Introduction ...... 138 4.2 Aims ...... 140 4.3 Results ...... 141

4.3.1 Characterisation of the CD8+ T EM cell response to Tax ...... 141 4.3.2 Specific KIR expression on CD8+ T EM cells in the functional analysis cohort ...... 146 4.3.3 Presence and frequency of Tax-specific CD8+ T EM cells expressing specific KIRs ... 150 4.3.4 Influence of cognate HLA class I ligand on the inhibitory KIR repertoire of Tax-specific CD8+ T EM cells in ACs and HAM/TSP patients ...... 155 4.3.5 Relationships between HTLV-1 PVL and the frequencies of Tax-specific CD8+ T EM cells expressing specific KIRs in ACs and HAM/TSP patients ...... 157 4.4 Discussion ...... 159 4.4.1 The Tax response of CD8+ T EM cells in ACs and HAM/TSP patients ...... 159 4.4.2 Very low frequencies of Tax-specific CD8+ T EM cells express specific KIRs ...... 160 4.4.3 Chapter 4 summary ...... 165

Chapter 5. Functional consequences of specific KIR expression on CD8+ T EM cells in HTLV-1 infection ...... 166 5.1 Introduction ...... 167 5.2 Aims ...... 170 5.3 Results: Specific KIR+ and Tax-specific cell frequencies within the total CD8+ T cell population ...... 171 5.3.1 Frequencies of functionally Tax-specific and Tax-specific TCR+ cells in specific KIR- expressing CD8+ T EM cell populations...... 171 5.3.2 Summary of the proportions of CD8+ T EM cells, specific KIR+ cells, Tax-specific cells and their overlap in total CD8+ T cells in HTLV-1+ individuals ...... 174 5.4 Results: Effects of specific KIR expression on CD8+ T EM cell function ...... 181 5.4.1 The functional responses of specific KIR+ compared to KIR– CD8+ T EM cells in HTLV-1 infection ...... 181 5.4.2 Influence of cognate HLA class I ligand on the functional responses of inhibitory KIR+ CD8+ T EM cells in HTLV-1 infection ...... 188 5.4.3 The functional profile of specific KIR+ compared to specific KIR– CD8+ T EM cells in HTLV-1 infection ...... 190 5.5 Discussion ...... 198 5.5.1 Inhibitory KIR expression reduces the functional response of CD8+ T EM cells to TCR- dependent but not TCR-independent activation ...... 198 5.5.2 The functional responses of specific KIR+ CD8+ T EM cells are biased towards a cytotoxic phenotype and have reduced bifunctional capability ...... 201 5.5.3 Proportional summary: 2DL2+ Tax-specific CD8+ T EM cells make up a greater proportion of the total CD8+ T cell population in ACs ...... 204 5.5.4 Chapter 5 summary ...... 206

Chapter 6. General discussion and summary ...... 207

References ...... 220

Appendices ...... 244 Appendix 1: KIR and HLA genotypes and allotypes of all donors...... 245 Appendix 2: Summary of permissions for third party copyright works ...... 250

List of Figures

Figure 1.1: HTLV-1 proviral genome structure...... 23 Figure 1.2: KIR structures and nomenclature...... 40 Figure 1.3: Examples of KIR haplotypes...... 44 Figure 1.4: KIR gene and allele nomenclature...... 46

Figure 2.1: Detection of specific KIR expression using cross-reactive anti-KIR mAb combinations 1F12/DX27 and 8C11/EB6...... 73 Figure 2.2: The effect of potential KIR co-expression on 1F12/DX27 mAb staining patterns...... 74 Figure 2.3: The effect of potential KIR co-expression on 8C11/EB6 anti-KIR mAb staining patterns. . 75 Figure 2.4: The effect of KIR allele specificities of the anti-KIR mAb clones 8C11 and EB6 on allotype requirements for 2DL1 and 2DS1 expression analysis...... 77 Figure 2.5: Flow cytometry gating scheme for global KIR expression analysis...... 80 Figure 2.6: Flow cytometry gating scheme for specific KIR expression analysis...... 81 Figure 2.7: Flow cytometry gating scheme for the specific KIR+ CD8+ T cell function analysis...... 83 Figure 2.8: Defining gates for each specific KIR expression analysis...... 85 Figure 2.9: 8C11/EB6 staining patterns from a donor with a 2DL1*004-like allele...... 86 Figure 2.10: Staining patterns of 2DL2+ and 2DL2– control donors used to assist 2DL2 gating...... 87 Figure 2.11: Staining patterns of different 1F12 mAb batches used to assist 2DL2 gating...... 87 Figure 2.12: Setting IFNγ and CD107a gates using DMSO and media controls...... 88

Figure 3.1: Global KIR staining patterns on CD8+ T cells, CD4+ T cells and NK cells...... 98 Figure 3.2: Frequencies of global KIR expression on CD8+ T cells, CD4+ T cells and NK cells in HTLV- 1 infection...... 100 Figure 3.3: Correlations of donor age with global KIR+ CD8+ T cell, CD4+ T cell and NK cell frequencies...... 101 Figure 3.4: PVLs of the global KIR expression cohort...... 103 Figure 3.5: Correlations of PVL with global KIR+ CD8+ T cell, CD4+ T cell and NK cell frequencies in ACs and HAM/TSP patients...... 103 Figure 3.6: Specific KIR staining patterns on CD8+ T EM cells and NK cells...... 108 Figure 3.7: Frequencies of specific KIR expression on CD8+ T EM cells and NK cells in HTLV-1 infection...... 112 Figure 3.8: The effect of cognate HLA class I ligand possession on the frequencies of inhibitory KIR+ CD8+ T cell and NK cell in HTLV-1 infection...... 114 Figure 3.9: Correlations of PVL with frequencies of CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients...... 117 Figure 3.10: Correlations of high PVL with frequencies of CD8+ T EM cells expressing 2DL2...... 118 Figure 3.11: Correlations of PVL with frequencies of NK cells expressing specific KIR molecules in ACs and HAM/TSP patients...... 119

Figure 3.12: PVLs in ACs and patients with HAM/TSP with and without 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 genes...... 121

+ Figure 4.1: IFNγ/CD107a and HLA-A2/Tax11-19 pentamer staining patterns on CD8 T EM cells...... 142 Figure 4.2: Frequencies of functionally Tax-specific and Tax-specific TCR+ CD8+ T EM cells...... 145 Figure 4.3: PVLs of the mixed HLA class I cohort and the HLA-A2+ cohort...... 145 Figure 4.4: Specific KIR staining patterns on CD8+ T EM cells in the functional analysis cohort...... 148 Figure 4.5: Frequencies of specific KIR expression on CD8+ T EM cells in ACs and HAM/TSP patients in the functional analysis cohort...... 149 Figure 4.6: Frequencies of Tax-specific CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients...... 154 Figure 4.7: The effect of cognate HLA class I ligand possession on the frequencies of functionally Tax-specific CD8+ T cells expressing inhibitory KIR molecules in ACs and HAM/TSP patients...... 156 Figure 4.8: Correlations of PVL with frequencies of functionally Tax-specific CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients...... 158

Figure 5.1: Frequencies of Tax-specific cells within specific KIR+ CD8+ T EM cell populations in ACs and HAM/TSP patients...... 173 Figure 5.2: Summary bar charts of the proportions of each specific KIR+ and Tax-specific cell population within total CD8+ T cells in ACs and patients with HAM/TSP...... 179 Figure 5.3: Frequencies of total functional cells in inhibitory KIR+ compared to KIR– Tax-specific TCR+ CD8+ T EM cell populations following stimulation with Tax peptides in HLA-A2+ ACs and HAM/TSP patients...... 182 Figure 5.4: Frequencies of total functional CD8+ T EM cells following stimulation with anti-CD3/28 mAb in ACs and HAM/TSP patients...... 183 Figure 5.5: Frequencies of total functional cells in specific KIR+ compared to KIR– CD8+ T EM cell populations following stimulation with anti-CD3/28 mAb in ACs and HAM/TSP patients...... 185 Figure 5.6: Frequencies of total functional cells in specific KIR+ compared to KIR– CD8+ T EM cell populations following stimulation with PMA/CAI treatment in ACs and HAM/TSP patients...... 187 Figure 5.7: The effect of cognate HLA class I ligand possession on the frequencies of total functional cells in specific KIR+ CD8+ T EM cell populations following anti-CD3/28 mAb stimulation in ACs and HAM/TSP patients...... 189 Figure 5.8: Frequencies of IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells in specific KIR+ compared to KIR– total functional Tax-specific CD8+ T EM cells in ACs and patients with HAM/TSP...... 193 Figure 5.9: Frequencies of IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells in specific KIR+ compared to KIR– total functional CD8+ T EM cells following stimulation with anti-CD3/28 mAb in ACs and patients with HAM/TSP...... 197

List of Tables

Table 1.1: KIR ligand specificities and functions...... 42 Table 1.2: KIR/HLA disease associations...... 58

Table 2.1: Monoclonal antibody panels used in each analysis...... 69 Table 2.2: Geno/allotype requirements and definitions of specific expression for each KIR...... 78

Table 3.1: Frequencies of CD8+ T cells, NK cells and CD4+ T cells within total lymphocytes in uninfected controls, ACs and HAM/TSP patients...... 97 Table 3.2: Frequencies of CD8+ T EM cells within total CD8+ T cells in uninfected controls, ACs and HAM/TSP patients...... 105 Table 3.3: KIR mAb reactivity, geno/allotype requirements and specific expression definitions for each KIR...... 106 Table 3.4: Frequencies of CCR7– cells within specific KIR+ CD8+ T cells in uninfected controls, ACs and HAM/TSP patients...... 109

Table 4.1: Frequencies of CCR7– cells in functionally Tax-specific and Tax-specific TCR+ CD8+ T cells in ACs and HAM/TSP patients...... 143 Table 4.2: Corrected absolute numbers of specific KIR+ Tax-specific CD8+ T EM cells collected on the flow cytometer, measured by functional IFNγ/CD107a assay or by HLA-A2/Tax11-19 pentamer staining...... 151

List of abbreviations

AC Asymptomatic carrier

AICD Activation-induced cell death

AIDS Acquired immunodeficiency syndrome

ATLL Adult T-cell leukaemia/lymphoma

BMT Bone marrow transplant bZIP Basic leucine zipper

CAI Calcimycin (calcium ionophore)

CEF CMV, EBV and ‘Flu (immunodominant viral antigen pool)

CMV Cytomegalovirus

CNS Central nervous system

CSF Cerebrospinal fluid

DC Dendritic cell

DMSO Dimethyl sulfoxide

EBV Epstein Barr virus

EM Effector memory

HAM/TSP HTLV-1-associated myelopathy/Tropical spastic paraparesis

HBV Hepatitis B virus

HBZ HTLV-1 bZIP factor

HCV Hepatitis C virus

HIV Human immunodeficiency virus

HLA Human leukocyte antigen

HPV Human papilloma virus

HTLV-1 Human T-lymphotropic virus type 1

ICAM Intercellular adhesion molecule

IFN Interferon

ITAM Immunoreceptor tyrosine-based activation motif

ITIM Immunoreceptor tyrosine-based inhibitory motif

KIR Killer immunoglobulin-like receptor

LD Linkage disequilibrium

LFA lymphocyte function-associated antigen

LTR Long terminal repeats mAb Monoclonal antibody

NK Natural killer

NPC Nasopharyngeal carcinoma

ORF Open reading frame

PBMC Peripheral blood mononuclear cells

PFA Paraformaldehyde

PHA Phytohemagglutinin

PKC Protein kinase C

PMA Phorbol myristate acetate (also known as Tetradecanoylphorbol acetate, TPA)

PVL Proviral load qPCR Quantitative polymerase chain reaction

RA Rheumatoid arthritis

ROS Reactive oxygen species

SCT Stem cell transplant

SEB Staphylococcus enterotoxin B

SHP-1 Src homology 2 domain phosphotyrosine phosphatase 1

SHP-2 Src homology 2 domain phosphotyrosine phosphatase 2

SSP Sequence-specific primers

SSOP Sequence-specific oligonucleotide probes

Tax Transactivator of transcription

TCR T cell receptor

T-LGL T cell large granular lymphocytic leukaemia

Statement of collaboration

All experiments were carried out by Katie Twigger except for the following:

 HTLV-1-infected individuals and some uninfected control subjects were recruited by Dr Graham

P. Taylor and his team at the HTLV-1 clinic at St Mary’s Hospital, London, UK.

 HLA class I genotyping was carried out by Dr Franco Tavarozzi and his team at the Anthony

Nolan Histocompatibility Laboratories, Royal Free Hospital, London, UK.

 KIR genotyping and allotyping was carried out by Dr James Traherne and Dr Olympe Chazara in

Prof. John Trowsdale’s group, Cambridge Institute for Medical Research, Addenbrookes Hospital,

University of Cambridge, UK.

Chapter 1. Introduction

Human T-lymphotropic virus type 1 (HTLV-1) was the first member of the retroviridae family discovered to infect humans (Poiesz et al., 1980). HTLV-1 persists lifelong in infected hosts and infection is associated with a diverse range of diseases, including adult T cell leukaemia/lymphoma, several chronic inflammatory diseases and susceptibility to opportunistic helminth and bacterial infections (Gonçalves et al., 2010). It remains unclear how the virus causes such diverse diseases and why a minority of infected individuals develop these serious illnesses while most are lifelong asymptomatic carriers (ACs). There is currently no vaccine, and there are no effective long-term treatments and no reliable prognostic indicators for disease risk on a per-patient basis. Research over the last three decades has shown that HTLV-1 infection creates a complex dynamic host-virus interaction, in which the host immune response is a major disease determinant (Saito, 2014). Further understanding of the factors that influence this balance, and the interplay between them, is required to improve our insight into protective immunity and disease pathogenesis.

1.1 HTLV-1 epidemiology and transmission

The prevalence of HTLV-1 varies widely across different regions of the world and is characterised by clusters of endemic foci. The main endemic regions are south-western Japan, the Caribbean, South

America and sub-Saharan Africa (Proietti et al., 2005), while there are also foci in the Middle East and

Australo-Melanesia (Gessain et al., 2012). In these areas, seroprevalence is estimated to be at least

1-2% but in particular regions this can be higher, for example there have been estimates of 5% seroprevalence in Jamaica (Murphy et al., 1991) and 6-8% on the south-western island of Kyushu in

Japan (Hinuma et al., 1982; Maeda et al., 1984).

Estimates of the number of HTLV-1 infected people globally have varied from 5 to 20 million (de The et al., 1993; Gessain et al., 2012). These estimates remain imprecise due to poor epidemiological data in many endemic areas, such as sub-Saharan Africa, and because HTLV-1 prevalence has not been assessed at all in several highly populated areas such as China, India and East Africa. In the

UK, 22,500 people are estimated to be infected with HTLV-1 (Tosswill et al., 2000) and most of these

HTLV-1 carriers are of Caribbean or African ethnicity (Catovsky et al., 1982; Dougan et al., 2005;

Payne et al., 2004).

Of the four HTLVs that have been identified (HTLV-1-4; Wolfe et al., 2005) only HTLV-1 is definitively associated with disease. Over 90% of HTLV-1-infected individuals are lifelong ACs however, 0.25-4% develop a progressive debilitating neuroinflammatory disease known as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP; Kaplan et al., 1990) and up to 5% develop a malignancy of mature CD4+ T cells, called adult T cell leukaemia/lymphoma (ATLL; Shimoyama,

1991; Tajima, 1990). The incidence of both diseases varies according to ethnicity and gender. In

Japan, ATLL occurs in HTLV-1-infected individuals more frequently than HAM/TSP and there is a higher incidence of ATLL in men than women (Arisawa et al., 2000). In Jamaica, HAM/TSP is more common than ATLL, but in both populations, women have a higher risk of developing HAM/TSP than men (Hisada et al., 2004).

Cell-to-cell contact is required for transmission of HTLV-1 (Yamamoto et al., 1982). Infection can therefore occur through sexual transmission (Murphy et al., 1989; Roucoux et al., 2005), breastfeeding (Hino et al., 1985; Kinoshita et al., 1987), transfusion with blood products containing lymphocytes, (Okochi et al., 1984) and intravenous drug use (Lee et al., 1990). HTLV-1 can also be contracted following kidney and liver transplant, though this is rare (Glowacka et al., 2013; Gonzalez-

Perez et al., 2003). In endemic regions, mother-to-child transmission through breastfeeding is the most common route of infection (Carneiro-Proietti et al., 2002; Hino et al., 1997). Prior to the advent of leucodepletion and screening for HTLV-1, receipt of a blood transfusion was an important risk factor for HTLV-1 infection (Murphy et al., 1996; Schreiber et al., 1997).

Due to the known transmission routes and resulting public health intervention, screening and subsequent diagnosis is most often carried out after blood donation or during pregnancy, indeed the majority of seroprevalence studies are carried out on these groups (Gessain et al., 2012). Screening for HTLV-1 is usually carried out using serological assays, such as enzyme immunoassay or particle agglutination. A positive serological result is then confirmed, with western blot being the preferred method (Berini et al., 2008; Dow et al., 2001). Screening of blood donations for HTLV-1 antibody has been carried out in the more developed endemic nations since the mid to late 1980s but was not implemented in the UK until 2002 (Davison et al., 2009). More cost-effective diagnostics needs to be

16 made available to developing endemic countries, particularly in Africa, where screening of blood donations is still not routinely carried out in most countries (Gonçalves et al., 2010).

1.2 HTLV-1-associated diseases

1.2.1 HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP)

The association of HAM/TSP with HTLV-1 infection was identified independently in the Caribbean and

Japan in the mid-1980s. Antibodies against HTLV-1 were found in patients in Martinique with tropical spastic paraparesis (TSP; Gessain et al., 1985) and in patients with a neurological disorder prevalent in the Kagoshima region, which was named HTLV-1 associated myelopathy (HAM; Osame et al.,

1986).

Clinical features of HAM/TSP

HAM/TSP typically develops after decades of asymptomatic HTLV-1 infection and the majority of patients are over 30 years of age when diagnosed (Olindo et al., 2006); a mean age of onset of 43.8 years was shown in a comprehensive Japanese study (Nakagawa et al., 1995). The earliest symptom in ~60% of patients is the spastic paraparesis i.e. weakness or partial paralysis of the legs alongside muscle spasm, resulting in a disturbed gait (Araujo et al., 1998). In ~30% of patients, urinary dysfunction precedes the paraparesis, sometimes by many years (Nakagawa et al., 1995). Other common symptoms which may also be present initially or develop over time include lower back pain, sexual dysfunction, constipation and sensory symptoms such as overactive reflexes, reduced vibratory sensation or numbness (De Castro-Costa et al., 2006; Nagai et al., 2003; Orland et al.,

2003). The weakness and spasticity progress slowly without remission and the gait gradually deteriorates to the point of wheelchair dependence, which occurs at a median of 21 years after the initial onset of symptoms (Olindo et al., 2006). As the disease progresses, bladder dysfunction can result in recurrent urinary tract infections and eventually chronic renal failure (Oliveira et al., 2007).

The lower back pain, which often radiates to the legs, occurs in ~50% of patients and is often severe, causing more distress to patients than the paraparesis (Gotuzzo et al., 2004). The severity of

17 symptoms and the rate of disease progression can vary significantly between different HTLV-1- infected individuals making prognosis predictions for individual patients very difficult.

Current therapeutic interventions for HAM/TSP are focused on the relief of disease related symptoms.

Treatments used include laxatives, antibiotics, pain management, physiotherapy and antispasmodics

(Cooper et al., 2009). Corticosteroids are commonly prescribed for early disease and can be effective at this stage but benefit declines with repeated administration (Croda et al., 2008; Nakagawa et al.,

1996). Many small scale proof-of-concept trials have been carried out, with the aim of informing larger scale proof-of-efficacy trials with safety and outcome data (Martin et al., 2011). These have included antiretroviral drugs (Taylor et al., 2006), as well as immune modulators to reduce cytokine production or lymphocyte proliferation, such as cyclosporin A (Martin et al., 2012), IFNα (Arimura et al., 2007), anti-CD25 mAb (Lehky et al., 1998) and anti-CD122 (IL-15 receptor β) mAb (Oh et al., 2009). IFNα treatment showed some short term benefit but a high number of adverse events were also reported

(Arimura et al., 2007). Overall, efficacy in these small scale trials has been minimal and larger more controlled trials are needed.

Immunopathogenesis of HAM/TSP

Histopathological analyses of post mortem biopsies from patients with HAM/TSP have shown that the disease predominately affects the spinal cord at the lower thoracic level. Inflammatory lesions are present which are characterised by demyelination, axonal damage and mononuclear cell infiltrates

(Akizuki et al., 1987; Iwasaki, 1990). Early in the disease process, these cellular infiltrates mostly consist of CD4+ T cells (Iwasaki et al., 1992; Usuku et al., 1990). CD8+ T cells have also been found within early lesions and as the disease progresses CD8+ T cells predominate (Iwasaki et al., 1992;

Umehara et al., 1993). Examination of the cerebrospinal fluid (CSF) has shown the presence of both

HTLV-1-infected T cells and HTLV-1-specific CD8+ T cells capable of secreting IFNγ and TNFα

(Greten et al., 1998), and anti-HTLV-1 antibodies have also been detected (Nakagawa et al., 1995).

The evidence of strong anti-HTLV-1 immune activation in HAM/TSP lesions has led to the proposal of several models of disease immunopathogenesis, of which the bystander damage theory is the most widely accepted hypothesis (Ijichi et al., 1993; Jacobson, 2002). This theory proposes that the

18 activation of HTLV-1-infected CD4+ T cells could lead to their migration, along with HTLV-1-specific

CD4+ and CD8+ T cells, from the circulation, across the blood-brain-barrier and into the central nervous system (CNS). The subsequent HTLV-1 immune response within the CNS would result in the release of cytokines and chemokines from these activated HTLV-1-specific and or HTLV-1-infected T cells. The pro-inflammatory cytokines IFNγ and TNFα are proposed to cause damage to bystander cells i.e. neurons, as well as recruiting further immune cell infiltrates, augmenting the HTLV-1 immune response and exacerbating the inflammation and tissue damage.

Multiple lines of evidence suggest that the effectiveness of the CD8+ T cell response to HTLV-1 is a key determinant of whether an HTLV-1 infected individual will develop HAM/TSP, as described in

Section 1.5.1 below. A more effective CD8+ T cell response would not only contain the infection, therefore reducing antigen expression, but it has further been suggested that it may do so predominantly via lysis of infected cells, rather than the release of proinflammatory cytokines. This latter response may be more characteristic of a less effective CD8+ T cell response, and could contribute to the development of HAM/TSP (Bangham, 2003).

1.2.2 Adult T cell leukaemia/lymphoma (ATLL)

Adult T cell leukaemia/lymphoma (ATLL) is an aggressive malignancy of circulating HTLV-1-infected

T cells, most commonly CD4+ but occasionally tumours may be CD8+. It was first identified as a distinct clinical entity in Japan, occurring at high rates in the south-west (Uchiyama et al., 1977) and was subsequently described in other HTLV-1 endemic regions. It was the first human cancer to be associated with a retrovirus (Hinuma et al., 1981; Miyoshi et al., 1981). Similar to HAM/TSP, ATLL develops after decades of asymptomatic HTLV-1 infection, typically in the fourth decade for South

American and Caribbean individuals, and in the fifth decade for Japanese people (Proietti et al.,

2005). Individuals infected in infancy e.g. through breastfeeding are thought to have a higher risk of developing ATLL (Wilks et al., 1996).

ATLL is a heterogeneous disease and is classified into four subtypes: acute, lymphoma, chronic and smouldering (Shimoyama, 1991). A fifth purely cutaneous subtype has been recently described

(Amano et al., 2008). The clinical presentation can involve raised lymphocyte counts, enlarged lymph

19 nodes, hepatosplenomegaly, hypercalcaemia, skin lesions and infection complications, depending on the subtype (Shimoyama, 1991). Along with these abnormalities, diagnosis is made based on the detection of lymphocytes with abnormal multi-lobed nuclei, known as “flower cells”, as well as the presence of antibodies to HTLV-1 and proviral DNA (Tsukasaki et al., 2009). The prognosis for the more aggressive subtypes (acute and lymphoma) is poor, with a median survival time of 13 months from diagnosis (Tsukasaki et al., 2007). The indolent subtypes (chronic, smouldering and cutaneous) have a better prognosis but can develop into acute ATLL and there is a median survival time of 5 years from diagnosis (Takasaki et al., 2010). The long term prognosis of the disease remains extremely poor because it is refractory to chemotherapy and causes severe immunosuppression.

However, a phase II clinical trial of combination IFNα, the antiviral drug zidovudine and the chemotherapeutic drug arsenic trioxide has shown promising remission rates (Kchour et al., 2009).

1.2.3 HTLV-1 associated inflammatory/autoimmune diseases and co-infections

In addition to the confirmed associations between HTLV-1 infection and HAM/TSP and ATLL, HTLV-1 is also etiologically linked to uveitis, an inflammation of the middle vascular layers of the eye

(Mochizuki et al., 1992). HTLV-1 infection is also strongly suspected to have a role in exacerbating or increasing the susceptibility of infected individuals to other inflammatory/autoimmune diseases and other infectious pathogens. For some conditions, the presence of HTLV-1 provirus in affected tissues has been demonstrated, others are reported at higher frequencies in HTLV-1-infected individuals

(Proietti et al., 2005). Inflammatory diseases associated with HTLV-1 infection include arthritis

(McCallum et al., 1997; Nishioka et al., 1989; Yakova et al., 2005), polymyositis, a chronic inflammation of the muscles (Morgan et al., 1989), Sjögren’s syndrome, an autoimmune disease affecting the saliva and tear glands (Terada et al., 1994), and infective dermatitis, eczematous skin lesions caused by exacerbated infection with bacteria of the Staphylococcus and Streptococcus genera, which particularly affects children (LaGrenade et al., 1990). HTLV-1 infection is also associated with co-infection with other pathogens such as the helminth Strongyloides stercoralis

(Marcos et al., 2008; Robinson et al., 1994) and Mycobacterium tuberculosis (de Lourdes Bastos et al., 2009). Although HTLV-1 infection does not cause overt immunosuppression, the frequency of these conditions in infected individuals does reflect a mild immune impairment.

1.3 Cellular and molecular biology

HTLV-1 preferentially infects CD4+ T cells, although 5-10% of the proviral load (PVL) can be carried by CD8+ T cells (Hanon et al., 2000b; Nagai et al., 2001a; Richardson et al., 1990). The PVL is defined as the percentage of peripheral blood mononuclear cells (PBMC) harbouring an integrated

HTLV-1 provirus. The PVL is an important measure of viral burden because (1) HTLV-1 is almost

100% cell associated (Yamamoto et al., 1982), therefore cell-free viral particles are not detectable in vivo, and (2) the PVL is the major correlate of disease risk for both HAM/TSP and ATLL (see Section

1.4 below; Iwanaga et al., 2010; Jeffery et al., 1999; Matsuzaki et al., 2001; Nagai et al., 1998).

1.3.1 Initial infection and integration

Since HTLV-1 is highly cell-associated (Yamamoto et al., 1982), cell-to-cell contact is required for de novo HTLV-1 infection. However, in an in vitro model, cell-free HTLV-1 virions were shown to cross epithelial barriers by transcytosis and can infect dendritic cells (DCs; Jones et al., 2008; Martin-Latil et al., 2012). Thus, following transmission of HTLV-1-infected T cells via breast milk, semen or blood

(Proietti et al., 2005) free HTLV-1 particles could transcytose across epithelial barriers and infect resident DCs (Jones et al., 2008). These infected DCs can then migrate to lymphatic organs where they form cell-to-cell contacts with T cells, resulting in transmission of the virus (Matsuoka et al.,

2013). While infected DCs make up a very small fraction of the PVL they are thought to play an important role in dissemination of the virus due to their mobility and the frequency of their interactions with lymphocytes.

Although its major cellular tropism is CD4+ T cells, HTLV-1 does not use CD4 as a receptor, unlike

HIV-1. The HTLV-1 Env protein, through which the virus attaches to and enters target cells, has been shown to interact with the ubiquitously expressed heparan sulfate proteoglycan (Jones et al., 2005), neuropilin-1 (Ghez et al., 2006) and glucose transporter GLUT1 (Manel et al., 2003). It has been proposed that the sequential binding of these receptors in the order listed results in a conformational change in the Env protein which triggers fusion of viral and cell membranes but this currently remains to be demonstrated (Ghez et al., 2010).

Like other exogenous retroviruses, HTLV-1 performs two characteristic steps as part of its life cycle.

Following viral entry, the HTLV-1 RNA genome is reverse transcribed to form double stranded proviral

DNA, by the viral reverse transcriptase enzyme. The HTLV-1 provirus is then transported to the nucleus, where the viral integrase enzyme co-ordinates its integration into the host genome. Targeting of HTLV-1 integration into the host genome is not random. HTLV-1 has been shown to preferentially integrate into transcriptionally active regions of the DNA, which have an open conformation (Gillet et al., 2011; Meekings et al., 2008). There is also a strong bias for HTLV-1 integration within 100 base- pairs of particular transcription factor binding sites (Melamed et al., 2013).

1.3.2 Viral gene expression

HTLV-1 is a complex retrovirus with a genome of approximately 9 kb (Matsuoka et al., 2011). Despite its small size, multiple proteins are generated from the transcriptional products of the HTLV-1 genome by the use of multiple open reading frames (ORFs), transcription along both the plus and minus strands of the provirus, RNA splicing and proteolytic cleavage (Figure 1.1). The typical retroviral genes are present: gag and env, coding for structural proteins, as well as pol and pro, coding for enzymatic proteins, are flanked on either side by the 5’ and 3’ long terminal repeats (LTRs). Between env and the 3’ LTR lies the pX region, which is unique to the HTLVs and contains the regulatory genes tax and rex and the accessory genes p12, p13, p21 and p30. The 5’ LTR contains the promoter and enhancer elements for the initiation of transcription of these plus strand gene products, while the

3’ LTR controls transcription of the relatively recently described gene, the HTLV-1 basic leucine zipper

(bZIP) factor (HBZ), from the minus strand (Gaudray et al., 2002). The products of these pX region genes regulate viral and host gene expression, leading not only to the production of viral proteins but also to activation and proliferation of the host cell and subsequently viral persistence. They therefore have a potential role in the pathogenesis of HTLV-1 infection. The two most studied genes which are particularly important in these processes are tax and HBZ (Matsuoka et al., 2011).

Figure 1.1: HTLV-1 proviral genome structure. Schematic representation of the organisation of the HTLV-1 proviral genome and protein coding regions (illustrative and not to scale). Structural (gag, env), enzymatic (pro, pol) and plus strand pX region gene products (tax, rex, p12, p13, p30) are transcribed from the 5’ LTR. Only the HBZ gene is transcribed from the minus strand, from the 3’ LTR. Figure adapted from Matsuoka & Jeang, (2011).

Tax

The HTLV-1 tax gene encodes the transcriptional transactivator Tax which activates the expression of all the other viral genes from the integrated provirus. Tax does not bind directly to DNA itself, instead it recruits host cell transcription factors, as well as the host transcriptional machinery to the viral promoter/enhancer in the 5’ LTR. Tax has been shown to interact with many host cell factors and is able to transactivate the expression of numerous host genes as well as the viral genes (Boxus et al.,

2008). As a result, the Tax protein expression has a wide range of actions on the host cell, including promoting DNA replication and overriding cell cycle checkpoints leading to increased cellular proliferation, inhibition of DNA repair and apoptosis, and activation of signalling pathways resulting in upregulation of host gene expression of immune system components such as cytokines and their receptors. The Tax protein also has an important role in orchestrating the cell-to-cell transmission of

HTLV-1 via the virological synapse (see Section 1.3.3 below; Mazurov et al., 2010; Nejmeddine et al.,

2005).

Tax expression is usually undetectable in freshly isolated lymphocytes, however upon incubation infected CD4+ T cells start to express viral proteins and levels are elevated if CD8+ T cells are depleted prior to culture (Hanon et al., 2000a; Jacobson et al., 1990). Kinetic studies of viral gene expression using an ex vivo culture system have shown that the tax/rex mRNA is the first to be expressed, rising rapidly over the first 8 hours, followed by gag expression (Rende et al., 2011). In a rabbit model of de novo HTLV-1 infection tax/rex and gag/pol mRNA peaked at one week post- infection and then declined (Li et al., 2009b). These observations are consistent with the role of Tax in transactivating the expression of the other viral genes.

HBZ

The HBZ gene influences viral and host cell gene expression in both its mRNA and protein forms.

Through its bZIP domain, the HBZ protein can interact with a number of different host transcription factors, activating or inhibiting host gene transcription, depending on the binding partner recruited

(Matsuoka et al., 2011). Similar to Tax, HBZ expression has been shown to increase cellular proliferation, though the exact mechanisms as to how this occurs are unknown. The HBZ protein has been shown to activate mitogenic signalling pathways, however HBZ mRNA has also been shown to

24 promote host cell proliferation (Basbous et al., 2003; Satou et al., 2006). The HBZ protein also has an important role in negatively regulating Tax-mediated viral gene transactivation, by binding and sequestering the host transcription factors with which Tax interacts in order to transactivate viral genes (Basbous et al., 2003; Gaudray et al., 2002). Interestingly, HBZ expression can also directly influence the immune phenotype of infected CD4+ T cells, by reducing IFNγ production and increasing expression of FoxP3, the characteristic transcription factor of regulatory CD4+ T cells (Sugata et al.,

2012; Zhao et al., 2011).

In contrast to Tax, HBZ mRNA expression is readily detectable in ex vivo samples (Fan et al., 2010;

Saito et al., 2009; Satou et al., 2006). In the rabbit model, low HBZ mRNA levels were detected 1 week post-infection, which then increased over time and plateaued (Li et al., 2009b). HBZ protein was not detected in human ex vivo HTLV-1-infected cells, even though HBZ mRNA was detected

(Suemori et al., 2009). However, since anti-HBZ antibodies can be detected in 10% of HTLV-1 infected individuals (Enose-Akahata et al., 2013) and ~30% have HBZ-specific CD8+ T cells (Hilburn et al., 2011) this indicates that there must be some HBZ protein expressed in vivo.

Other pX region proteins

Alongside the transcription transactivating function of Tax, the Rex protein regulates viral gene expression at the post-transcriptional level. It promotes the nuclear export of unspliced and singly spliced viral mRNAs at the expense of doubly spliced transcripts, thus controlling the ratio of differentially spliced transcripts in the cytoplasm. Its expression also reduces the rate of splicing and increases viral mRNA stability (Kashanchi et al., 2005).

The HTLV-1 accessory proteins p8, p12, p13, p21 and p30 are formed by alternative splicing of pX region RNAs and have a variety of actions that influence viral and host gene expression and host cell function (Boxus et al., 2009). The p30 protein negatively regulates viral gene expression by retaining tax/rex mRNA in the nucleus (Nicot et al., 2004). It can also interact with host transcription factors to influence Tax-mediated transcription of viral and host genes involved in proliferation, T-cell activation and apoptosis (Michael et al., 2004; Taylor et al., 2009). The p13 protein is a truncated form of p30

(Koralnik et al., 1992). It localises to the mitochondria (Ciminale et al., 1999) where it increases the

25 production of reactive oxygen species (ROS), influencing cellular proliferation and sensitivity to apoptosis (Hiraragi et al., 2005; Silic-Benussi et al., 2004).

The p12 protein traffics to the endoplasmic reticulum, where it promotes cellular proliferation by activating mitogenic signal transduction pathways (Ding et al., 2002; Nicot et al., 2001) and interacting with the IL-2 receptor to enhance recruitment of downstream signalling molecules (Mulloy et al.,

1996). It also influences effector lymphocyte recognition of HTLV-1-infected cells by targeting HLA class I heavy chains for degradation via the proteasome and reducing expression of the intercellular adhesion molecules ICAM-1 and ICAM-2 (Banerjee et al., 2007; Johnson et al., 2001). The p8 protein is a proteolytic cleavage product of p12 (Van Prooyen et al., 2010a). It increases transmission of

HTLV-1 by inducing the formation of cellular projections known as conduits, through which the virus is transmitted (Van Prooyen et al., 2010b).

1.3.3 Mechanisms of viral proliferation and persistence

Mitotic and infectious replication

HTLV-1 proliferates and persists in the host using two mechanisms, (1) mitosis of an HTLV-1-infected cell, and (2) de novo infection. During mitosis of an HTLV-1-infected cell, the integrated provirus is replicated along with cellular DNA by the highly faithful host DNA polymerase and all subsequent daughter cells will contain a provirus at the same integration site. However, in order to establish infection of multiple cells within a host and to transmit between hosts, de novo infection is required.

The viral life cycle must be completed to generate progeny virions. Viral structural proteins are expressed from the integrated HTLV-1 provirus and these combine with full-length proviral transcripts, which serve as RNA genomes, making new infectious particles.

Cell-free HTLV-1 particles are not detectable in vivo and in infection assays in vitro they are not infectious to T cells (Yamamoto et al., 1982). HTLV-1 transmits extremely efficiently via cell-to-cell contact through several different mechanisms; (1) the virological synapse, (2) biofilms, and (3) cellular conduits. The virological synapse is a virally-induced organised contact formed between HTLV-1- infected cells and target cells. Tax expression upregulates ICAM-1 and lymphocyte function- associated antigen (LFA-1; which prolong cellular attachment) and triggers polarisation of the

26 cytoskeleton towards the contact. Budding viral particles are directed into synaptic clefts which completely enclose the virus, allowing them to infect the target cell without exposing them to the extracellular space (Igakura et al., 2003; Majorovits et al., 2008; Nejmeddine et al., 2005). Biofilm-like extracellular viral assemblies have also been described (Pais-Correia et al., 2010). Recently budded

HTLV-1 particles are retained on the surface of infected cells with virally-induced extracellular matrix components and can efficiently infect target cells upon contact. Finally, the HTLV-1 p8 protein can increase T cell conjugation by interacting with ICAM-1 and LFA-1 and induces the formation of intercellular conduits, through which HTLV-1 can be transmitted to target cells (Van Prooyen et al.,

2010b).

HTLV-1 persistence

In the chronic stage of infection, HTLV-1 is widely thought to persist via mitotic spread because of two major lines of evidence. There is very little variation in viral sequence within or between hosts which indicates that the majority of proviral replication is likely to be carried out using the high-fidelity host

DNA polymerase, rather than by completion of the retroviral life cycle using the error prone viral reverse transcriptase (Kubota et al., 2007; Niewiesk et al., 1994; Slattery et al., 1999). Also, long-lived expanded cell populations containing the same proviral integration site have been detected in vivo

(Cavrois et al., 1996; Furukawa et al., 1992; Gillet et al., 2011).

By activating proliferation and survival pathways in infected T cells, HTLV-1 uses host cell mobility and machinery to persist in infected hosts and maintain the PVL. However, in order to persist in this manner, HTLV-1 must maintain a balance between the induction of infected cell proliferation and activation of the immune response. This is achieved through regulation of proviral expression mainly via the actions of Tax and HBZ (Boxus et al., 2009). While expression of Tax is needed to transactivate expression of viral genes, as well as the activation and proliferation of the host cell, it is also a major target for the CD8+ T cell response, since Tax is the immunodominant HTLV-1 antigen

(see Section 1.5.1; Goon et al., 2004a; Jacobson et al., 1990; Kannagi et al., 1991; Macnamara et al.,

2010). Since expression of HBZ suppresses Tax expression (and thus expression of all the other viral genes on the positive strand) but also maintains host cell proliferation, HBZ reduces the immunogenicity of HTLV-1-infected cells whilst promoting their persistence. Indeed, the importance of

HBZ in the persistence of HTLV-1 is demonstrated by two findings: (1) mutation of the HBZ gene is less frequent than in other viral genes, suggesting HBZ mutations may be selected against (Fan et al.,

2010), and (2) rabbits infected with HBZ-defective HTLV-1 had reduced PVLs and antibody responses compared to wild-type HTLV-1-infected animals (Arnold et al., 2006). This critical role for

HBZ in immune evasion and PVL maintenance could explain the finding that CD8+ T cell responses directed against the subdominant HBZ protein, rather than the immunodominant Tax antigen, are strongly protective (Macnamara et al., 2010). Thus, since HBZ expression appears to confer a survival advantage for HTLV-1-infected cells, HBZ-specific CD8+ T cells would have an important role in preventing the proliferation of infected cells and blocking this strategy of persistence (see Section

1.5.1 below).

1.4 Proviral load and the risk of disease

The HTLV-1 PVL reaches a stable set point within each infected individual and remains relatively constant over time (Matsuzaki et al., 2001) however, it can vary over 1000 fold between infected individuals (Nagai et al., 1998). The equilibrium position of the set point PVL within an infected individual is thought to result from the balance of two opposing forces: (1) the rate of proviral expression, and (2) surveillance by the immune response, particularly CD8+ T cells (Bangham et al.,

2009). The PVL is the major correlate of disease susceptibility: a higher PVL is associated with a higher risk of HAM/TSP (Nagai et al., 1998) and ATLL (Iwanaga et al., 2010). In a cross-sectional study, the prevalence of HAM/TSP increased exponentially with log(PVL) once PVL exceeded 1%

(Jeffery et al., 1999). In a 10 year follow-up study, a higher PVL was associated with accelerated

HAM/TSP progression (Matsuzaki et al., 2001) and high ratio of the PVL found in CSF:peripheral blood also correlated with progressive HAM/TSP disease (Takenouchi et al., 2003). In a Japanese

ATLL cohort, the risk of clinical disease increased when PVL was greater than 4% (Iwanaga et al.,

2010).

Although many studies have found that HAM/TSP patients have a higher mean PVL than ACs

(Kamihira et al., 2003; Kira et al., 1991; Manns et al., 1999; Nagai et al., 1998), importantly there is a wide range and overlap in the distributions of PVL in ACs and HAM/TSP patients, indicating that other

28 factors are involved in the progression to HAM/TSP disease. Nevertheless, much work has focussed on defining correlates of the PVL, and several factors have been identified to be important in determining those who remain healthy and those who develop inflammatory disease. Two examples are: (1) the level of HBZ mRNA expression has been shown to positively correlate with PVL, demonstrating the importance of this viral gene in maintaining the PVL (Saito et al., 2009), and (2) high-throughput sequencing analyses of the number and abundance of HTLV-1-infected T cell clones

(i.e. T cells carrying a provirus at the same integration site) revealed a correlation between the PVL and the total number of distinct clones, rather than the abundance of a given clone, suggesting ACs are able to limit viral dissemination (Gillet et al., 2011).

While factors that influence HTLV-1 proviral expression undoubtedly make an important contribution to determining an individual’s set point PVL, over the last two decades the single major determinant of the PVL has been shown to be the host immune response to HTLV-1, in particular the functional effectiveness or the “quality” of HTLV-1-specific CD8+ T cells (Bangham, 2005).

1.5 Immune response to HTLV-1

1.5.1 CD8+ T cells

CD8+ T cells play an essential role in protective immune responses against viral infections. Upon T cell receptor (TCR) engagement with peptide:HLA class I complexes, CD8+ T cells can elicit multiple effector functions in order to limit viral replication, including cellular proliferation, cytotoxicity via the release of stored cytotoxic granules and the secretion of cytokines, such as IFNγ. These different effector functions are sequentially induced with increasing antigen concentration, creating a hierarchy of functionality along a gradient of CD8+ T cell activation level (Lim et al., 2000; Valitutti et al., 1995;

Valitutti et al., 1996). Thus, different functional outputs require different activation thresholds to be reached in order to be initiated (Viola et al., 1996). Investigations into correlates of protective CD8+ T cell immunity in chronic viral infection have focussed on measurable functional attributes of CD8+ T cell quality, such as polyfunctionality, the rate of clearance of infected cells (i.e. lytic efficiency) and antigen sensitivity. These measures of CD8+ T cell functional effectiveness will be affected by the

29 position of the activation threshold in different CD8+ T cell clonotypes (Seder et al., 2008). In persistent infections, prolonged exposure to high antigen levels leading to chronic CD8+ T cell activation alters the relationship between antigen load and CD8+ T cell functional effectiveness and complicates efforts to understand the properties of protective CD8+ T cell responses (Bangham, 2009;

Seder et al., 2008).

Early studies found the CD8+ T cell response against HTLV-1 to be strong and chronically activated:

HTLV-1-specific CD8+ T cells were found at high frequencies in the peripheral blood of ACs and

HAM/TSP patients (Jacobson et al., 1990; Kannagi et al., 1991; Parker et al., 1992) and are capable of immediate effector function ex vivo with no requirement for exogenous stimulation (Hanon et al.,

2000a; Jacobson et al., 1990). The majority of the response is directed against the Tax protein, which is therefore immunodominant (Goon et al., 2004a; Jacobson et al., 1990; Kannagi et al., 1991;

Macnamara et al., 2010). High frequencies of CD8+ T cells specific to the HTLV-1 Gag, Pol and Env proteins have also been detected (Goon et al., 2004a; Jacobson et al., 1990; Parker et al., 1992).

More recently, CD8+ T cell responses to the HBZ protein have been demonstrated (Hilburn et al.,

2011).

The fact that there are higher frequencies of HTLV-1-specific CD8+ T cells in patients with HAM/TSP than in ACs (Daenke et al., 1996; Goon et al., 2004a; Jeffery et al., 1999; Kubota et al., 2000a;

Kubota et al., 2000b), together with the positive correlation between PVL and HTLV-1-specific CD8+ T cell frequencies shown by some studies (Kubota et al., 2000a; Kubota et al., 2000b; Wodarz et al.,

2001) led to the suggestion that HTLV-1-specific CD8+ T cells cause the inflammatory lesions and tissue damage observed in HAM/TSP disease (Jacobson, 2002). While this does remain a possibility

(see Section 1.2.1 above on the Immunopathogenesis of HAM/TSP), there are also multiple lines of evidence that support a protective role for CD8+ T cells in HTLV-1 infection, both in lowering the PVL and reducing HAM/TSP disease risk:

1. Gene expression microarrays analysing transcripts in CD8+ T cells isolated from HTLV-1 infected

individuals showed that higher mRNA levels of cytotoxic genes, such as granzyme and perforin,

are associated with a low PVL both in ACs and patients with HAM/TSP (Vine et al., 2004).

2. Variations in the tax gene are more common in ACs than in HAM/TSP patients (Niewiesk et al.,

1994) and escape mutations have been found in known CD8+ T cell epitopes (Kubota et al., 2007;

Niewiesk et al., 1995), which is consistent with selection pressure being exerted on the tax gene

by CD8+ T cells.

3. CD8+ T cells isolated from HTLV-1 infected individuals can spontaneously kill autologous Tax-

expressing CD4+ T cells ex vivo, which is dependent on matched HLA class I, perforin release

and CD8+ T cell frequency (Asquith et al., 2005; Hanon et al., 2000a)

4. Possession of the HLA class I alleles HLA-A*02 and HLA-C*08 are associated with a lower PVL

in ACs and reduced risk of HAM/TSP in a Japanese cohort from the HTLV-1 endemic Kagoshima

region (Jeffery et al., 2000; Jeffery et al., 1999).

Lytic efficiency and functional avidity of the CD8+ T cell response to HTLV-1

Efforts to define correlates of protective CD8+ T cell responses in HTLV-1 infection have focussed on measuring quality. Two functional attributes of HTLV-1-specific CD8+ T cell quality have been assessed: (1) the rate of infected cell elimination or lytic efficiency (Asquith et al., 2005), and (2) the functional avidity, which is a measure of antigen sensitivity (Kattan et al., 2009). The fact that HTLV-1- specific CD8+ T cells can directly kill autologous Tax+ CD4+ T cells without in vitro restimulation

(Hanon et al., 2000a) was exploited to develop a method to quantify the rate of Tax+ CD4+ T cell elimination per CD8+ T cell per day (Asquith et al., 2005). This assay gives a physiologically relevant measure of the lytic efficiency of an individual’s HTLV-1-specific CD8+ T cells, in that naturally processed viral antigens are presented to autologous CD8+ T cells. The lytic efficiency of CD8+ T cells had previously been predicted to be one of the crucial measurable parameters in defining the effectiveness of the CD8+ T cell response to HTLV-1 and is a much more informative attribute of protective immunity than the frequency of HTLV-1-specific CD8+ T cells (Asquith et al., 2000;

Bangham et al., 1996; Bangham, 2003; Nowak et al., 1996). Using this assay, Asquith et al., (2005) showed that the rate of CD8+ T cell-mediated lysis was negatively correlated with the PVL in both ACs

31 and patients with HAM/TSP. Therefore, individuals whose CD8+ T cells have higher lytic efficiency had a lower PVL. While cause and effect should be attributed with caution in chronic viral infection, a reduction in PVL via killing of infected cells by CD8+ T cells is consistent with their known function.

This study also showed that the variation in the lytic efficiency of CD8+ T cells between HTLV-1- infected donors accounted for 40-50% of the variation in their PVLs, making CD8+ T cell lytic efficiency the single largest determinant of PVL and therefore disease risk (Asquith et al., 2005).

A useful method of quantifying the antigen sensitivity of CD8+ T cells is to define the functional avidity of antigen-specific CD8+ T cells. High avidity CD8+ T cells respond to low antigen concentrations and vice versa. Kattan et al., (2009) measured CD8+ T cell functional avidity by quantifying the frequency of IFNγ-producing CD8+ T cells over a gradient of Tax peptide concentrations using an ELISpot assay. They showed that higher functional avidity/antigen sensitivity was correlated with higher lytic efficiency of Tax-specific CD8+ T cells, which was in turn (as described above) associated with a lower PVL (Asquith et al., 2005). In support of these findings, Sabouri et al., (2008) showed that while

HAM/TSP patients had higher frequencies of HTLV-1-specific CD8+ T cells, these cells had lower levels of CD107a staining per cell than HTLV-1-specific CD8+ T cells from ACs.

Host genetic factors that determine disease risk and PVL

Host genotype is likely to have an important role in determining CD8+ T cell avidity, particularly HLA class I genotype (Bangham, 2009). Indeed, as described above, this is supported by the fact that specific HLA class I alleles were associated with clinical outcome and PVL in a Japanese cohort from the HTLV-1 endemic Kagoshima region (Jeffery et al., 2000; Jeffery et al., 1999; Vine et al., 2002).

Possession of the HLA class I alleles HLA-A*02 and HLA-C*08 was associated with a lower PVL in

ACs and reduced risk of HAM/TSP, whereas HLA-B*54 was associated with increased disease risk and higher PVL in patients with HAM/TSP (Jeffery et al., 2000; Jeffery et al., 1999). Given that HLA class I molecules present antigenic peptides to CD8+ T cells, these observations imply that HLA-A*02 and HLA-C*08-restricted CD8+ T cells are more effective at killing virus-infected cells while HLA-B*54- restircted CD8+ T cells are less effective. Consistent with the idea that HLA class I-restricted CD8+ T cells play an important role in determining the outcome of HTLV-1 infection, heterozygosity at all three

HLA class I loci, which provides broader recognition of viral epitopes, was also associated with a

32 lower PVL (Jeffery et al., 2000). Also, the class II allele HLA-DRB1*0101 was associated with increased risk of HAM/TSP but only in the absence of the protective effect of HLA-A*02 (Jeffery et al.,

1999).

The protective effect of HLA-A*02 is particularly strong: HLA-A*02+ individuals elicit a strong CD8+ T cell response against HTLV-1 and HLA-A*02 has particularly high affinity for the Tax11-19 epitope

(Nagai et al., 2001b). This interaction has been exploited both in structural biology, to determine the first TCR:peptide:HLA class I crystal structure (Garboczi et al., 1996), and in the use of HLA class I

+ pentamers to stain HLA-A2-restricted Tax11-19-specfic CD8 T cells, which were employed in the present study.

While Tax is the immunodominant HTLV-1 antigen, the ability of CD8+ T cells to recognise a subdominant antigen, HBZ, rather than Tax was recently shown to be associated with reduced PVL and HAM/TSP risk (Hilburn et al., 2011; Macnamara et al., 2010). This was despite the fact that compared to Tax, HBZ shows low immunogenicity. HBZ-specific CD8+ T cells were at significantly lower frequencies than Tax-specific CD8+ T cells in the same individual (Hilburn et al., 2011) and, using epitope and peptide:HLA class I binding affinity prediction software, Macnamara et al., (2010) showed that the HBZ peptides are predicted to bind HLA molecules with significantly lower affinity than Tax peptides. However, in the same study, the protective HLA-A*02 and HLA-C*08 HLA class I alleles were shown to bind HBZ peptides more strongly than the detrimental HLA-B*54 allele. Also, individuals who had HLA-A and HLA-B alleles that bound strongly to HBZ peptides were more likely to be ACs and to have lower PVLs. This association remained significant when donors with HLA-A*02,

HLA-C*08 or HLA-B*54 alleles were removed from the analysis, indicating that the ability to bind HBZ strongly is protective across multiple HLA class I alleles, not just those previously identified to confer protection from disease. Consistent with this, HBZ-specific CD8+ T cells were more frequent in ACs than in patients with HAM/TSP, confirming the importance of HBZ recognition by HLA class I molecules in HTLV-1 protective immunity (Hilburn et al., 2011). Despite their low frequency, HBZ- specific CD8+ T cells are thought to be unusually protective because of the key role HBZ has in HTLV-

1 persistence, since it reduces Tax expression and therefore the immunogenicity of HTLV-1-infected cells but also maintains their proliferation (see Section 1.3.3 on HTLV-1 persistence).

HLA class I genotype was identified as the strongest genetic factor determining PVL in a study of population immunogenetics using the Kagoshima cohort (Vine et al., 2002). These genes accounted for 5-10% of the variation in PVL between individuals. In total, host genetic factors were shown to account for up to 88% of the risk of HAM/TSP. Other non-HLA genes that were implicated included polymorphisms in TNFα, which was associated with increased risk of HAM/TSP, IL-15 which was associated with reduced PVL, and the chemokine gene SDF-1/CXCL12, which conferred reduced

HAM/TSP disease risk. In another study using the same cohort, an IL-10 polymorphism was found to be associated with decreased risk of HAM/TSP and a lower PVL in both ACs and patients with

HAM/TSP (Sabouri et al., 2004). It is important to note that the specific HLA class I alleles identified in the Japanese cohort may have less relevance in other ethnic groups, due to differing allele frequencies in different populations, however the fundamental protective influence of the CD8+ T cell response is likely to be consistent.

Characteristics of protective CD8+ T cell responses to HTLV-1

These observations of the CD8+ T cell immune response to HTLV-1 reveal the following picture of protective immune characteristics: ACs have a lower frequency of HTLV-1-specific CD8+ T cells than

HAM/TSP patients, but their cells are likely to be of higher effectiveness or quality, that is, to have higher lytic efficiency and higher functional avidity (i.e. greater antigen sensitivity). In these individuals, an equilibrium between virus and host is established where viral replication and expression is limited, resulting in a lower PVL. There is a strong genetic influence on the quality of the

CD8+ T cell response, particularly involving HLA class I alleles. However, since HLA class I genotype has only explained 5-10% of the observed variation in PVL (Vine et al., 2002), whereas the lytic efficiency of CD8+ T cells accounted for 40-50% of the observed variation in PVL (Asquith et al.,

2005), this discrepancy means that other host genetic factors must have a role in determining CD8+ T cell quality and subsequently the control of PVL and the risk of HAM/TSP (Bangham, 2009).

1.5.2 CD4+ T cells

CD4+ T cells provide help in the form of cytokines, to assist the development of B cell and CD8+ T cell responses to viral infection. Infection of CD4+ T cells by HTLV-1 complicates analyses of their function

34 and contribution to HTLV-1 immunity. Freshly isolated CD4+ T cells from HTLV-1-infected individuals spontaneously express HTLV-1 Tax protein, which reaches a peak following 12 hours of incubation

(Hanon et al., 2000a). Tax expression transactivates many host genes (Boxus et al., 2008; see

Section 1.3.2 on Viral gene expression), resulting in the activation of infected T cells to produce IFNγ and to proliferate, i.e. two of the most commonly used measures of CD4+ T cell activity. For this reason, only assays of CD4+ T cell function with short incubation times (<6hrs) can be carried out.

This incubation time limit also applies to the functional analysis of CD8+ T cell responses, since a proportion of CD8+ T cells can also become infected and spontaneously express Tax ex vivo (Hanon et al., 2000b; Nagai et al., 2001a). Thus, incubation of HTLV-1-specific CD8+ T cells in the presence of infected CD8+ T cells beyond 6 hours can induce fratricide, complicating experimental interpretation

(Hanon et al., 2000b).

The immunodominant antigen of HTLV-1-specific CD4+ T cells is Env, followed by Gag and then Tax

(Goon et al., 2004b). The phenotype of HTLV-1-specific CD4+ T cells in both ACs and patients with

HAM/TSP is predominantly Th1, as determined by their production of IFNγ (Goon, 2002). HTLV-1- specific CD4+ T cells from HAM/TSP patients show higher production of IFNγ, TNFα and IL-2 than those from ACs (Goon, 2002; Goon et al., 2003), and HAM/TSP patients have 10-25 times the frequency of HTLV-1-specific CD4+ T cells as ACs with similar PVLs (Goon, 2002; Goon et al., 2004b;

Nose et al., 2007). Interestingly, HTLV-1 preferentially infects HTLV-1-specific CD4+ T cells, which could potentially impair their immune function (Goon et al., 2004b). This phenomenon has also been shown to occur in HIV-1 infection (Betts et al., 2001; Douek et al., 2002).

The increased frequency of HTLV-1-specific CD4+ T cells in HAM/TSP patients compared to ACs, indicating their lack of control over the infection, is consistent with the idea that their activation, by contact with HTLV-1 antigens and/or by infection, may lead to the formation of the inflammatory lesions observed in HAM/TSP disease (Bangham, 2003). Indeed, infected CD4+ T cells can produce high levels of IFNγ, which may contribute to tissue damage, and a proportion of infected CD4+ T cells have an effector memory phenotype, which could enable their migration to CNS (Hanon et al., 2001).

As described above (see Section 1.2.1 on the Immunopathogeneis of HAM/TSP), CD4+ T cells are the major cell type found in early active HAM/TSP inflammatory lesions (Iwasaki et al., 1992; Usuku et

35 al., 1990) and the frequency of these infiltrating CD4+ T cells has been shown to correlate with the

PVL detected in the CSF of HAM/TSP patients (Kubota et al., 1994). Also, the PVL in CSF is greater than in the peripheral blood of individuals with the inflammatory disease, suggesting the preferential migration or expansion of HTLV-1-infected T cells in the CNS of these individuals (Nagai et al.,

2001c).

Regulatory CD4+ T cells, characterised by expression of the FoxP3+ transcription factor, have been found at high frequencies in HTLV-1 infected individuals and are associated with a high PVL and viral expression (Toulza et al., 2008). HTLV-1 infected cells were subsequently shown to secrete the chemokine CCL22, which induces and maintains FoxP3+ CD4+ T cells and could explain their elevated frequencies in HTLV-1 infected individuals (Toulza et al., 2010). Interestingly, high frequencies of FoxP3+ CD4+ T cells were strongly correlated with lower rates of CD8+ T cell-mediated lysis (Toulza et al., 2008), measured using the ex vivo assay of CD8+ T cell lytic efficiency (Asquith et al., 2005). These findings suggest that high frequencies of regulatory CD4+ T cells present in HTLV-1 infected individuals with high PVLs could repress the CD8+ T cell response against HTLV-1 and so may contribute to the mild immune impairment induced by HTLV-1 infection, which results in increased susceptibility to certain HTLV-1-associated infections, as described above (see Section 1.2 on HTLV-1-associated diseases).

1.5.3 NK cells and innate immunity

Natural killer (NK) cells are an important component of antiviral immune responses. They are able to initiate rapid cytotoxicity and cytokine secretion without the need for prior exposure to antigens. NK cell activation is regulated by the integration of signalling from a variety of different activating and inhibitory cell surface receptors interacting with their ligands, which include HLA class I molecules

(Caligiuri, 2008). Many viruses employ strategies to reduce host cell expression of HLA class I molecules, in order to evade HLA class I-restricted killing by CD8+ T cells (Seliger et al., 2006). HTLV-

1 is no exception: the HTLV-1 protein p12 has been shown to interact with the heavy chain of HLA class I, prevent its maturation and target it for degradation via the proteasome (Johnson et al., 2001).

However, NK cells can detect and kill cells with reduced surface HLA class I expression, as the

“missing self” hypothesis describes (see Section 1.6.5 below on the Function of KIRs on NK cells and

T cells; (Raulet, 2006). Interestingly, p12 has also been shown to down-regulate expression of ICAM-

1 and ICAM-2, which reduces NK cell binding to HTLV-1 infected cells and their subsequent lysis

(Banerjee et al., 2007).

There have been relatively few studies on the impact of NK cells on the HTLV-1 immune response and the different combinations of cellular markers that have been used to identify these cells complicates interpretation of this work. However, the results consistently show lower frequencies and functional activity of NK cells in HAM/TSP patients compared to ACs (Azakami et al., 2009; Fujihara et al., 1991; Saito et al., 2003; Yu et al., 1991).

Recently, a study comparing whole blood transcriptional profiles of ACs and HAM/TSP patients found that the presence of HAM/TSP disease was associated with the over-expression of a distinct subset of IFN-inducible genes, at a given PVL (Tattermusch et al., 2012b). The genes identified can be stimulated by either type I or type II IFN, which are key cytokines in the innate immune response to viral infection. This IFN-stimulated transcriptional signature was also positively correlated with the clinical disease severity of HAM/TSP (Tattermusch et al., 2012b), which confirms its clinical relevance. However, whether activation of these IFN-stimulated genes has a role in the pathogenesis of HAM/TSP or whether their upregulation results from this inflammatory condition remains to be determined (Tattermusch et al., 2012a).

1.5.4 Antibodies

High titres of anti-HTLV-1 antibodies are found in HTLV-1 infected individuals, frequently including high levels of IgM, which provides further evidence of persistent viral expression in vivo (Kira et al.,

1992; Nagasato et al., 1991). Patients with HAM/TSP typically have higher concentrations of anti-

HTLV-1 antibodies than ACs (Enose-Akahata et al., 2012; Ishihara et al., 1994; Kira et al., 1992;

Nagasato et al., 1991), particularly those directed against the Tax protein (Lal et al., 1994). Anti-Env antibodies may be protective since they have been shown to neutralise HTLV-1 in vitro (Hakoda et al.,

1995; Kiyokawa et al., 1984; Shida et al., 1987). Interestingly, although the majority of HTLV-1 infected individuals have anti-Tax antibodies (de Souza et al., 2011), only around 10% were found to have anti-HBZ antibodies (Enose-Akahata et al., 2013). The contribution of these high antibody titres

37 to protection or pathogenesis of HTLV-1 is not known. However, because HTLV-1 is almost 100% cell-associated, antibody surveillance is thought to have a limited role in controlling the infection

(Dumenil, 2011).

1.6 Killer immunoglobulin-like receptors (KIRs)

The killer immunoglobulin-like receptors (KIRs) are one of the families of activating and inhibitory transmembrane receptors in humans, through which NK cell activation is controlled by the integration of signals received through interaction with their HLA class I ligands. KIRs are mainly expressed on

NK cells but KIR expression can also be found on certain subsets of T cells. Highlighting the important role KIRs play in protective immunity, particular KIR genes have been associated with protection from or susceptibility to a range of diseases, such as autoimmune/inflammatory conditions, some forms of cancer and viral infections, including HIV-1 and HTLV-1 (Moesta et al., 2012).

1.6.1 Molecular structure

Nomenclature

Figure 1.2 outlines the basic structure and functional identity (activating or inhibitory) of the different

KIR molecules, which has been used to define their nomenclature (Middleton et al., 2010). Both the activating and inhibitory KIRs may be composed of either two or three extracellular Ig-like domains

(KIR2D or KIR3D). The extracellular Ig-like domains are numbered moving away from the cell membrane according to their arrangement in the three-domained KIRs, D0, D1 and D2. The two- domained KIRs can be divided into two groups, type 1 with D0 and D1 domains, which includes KIRs

2DL1, 2DL2 and 2DL3, as well as their activating counterparts 2DS1 and 2DS2, and type 2 with D0 and D2 domains (Vilches et al., 2000a; Vilches et al., 2000b). The inhibitory KIRs have long cytoplasmic domains (KIR2DL or KIR3DL), containing up to two immunoreceptor tyrosine-based inhibitory motifs (ITIMs) which initiate inhibitory signalling by recruiting the phosphotyrosine phosphatases SHP-1 and SHP-2 (Barrow et al., 2006). The activating KIRs have short cytoplasmic domains (KIR2DS or KIR3DS) and they associate with the signalling adaptor DAP12, through a positively charged residue in their transmembrane region. DAP12 contains immunoreceptor tyrosine-

38 based activating motifs (ITAMs) which initiate activating signalling by recruiting Syk family kinases

(Underhill et al., 2007). 2DL4 uniquely, has structural features of both activating and inhibitory receptors (Rajagopalan et al., 2001).

Sequence homology

The KIR molecules not only have a high degree of structural similarity, they also share 85-99% amino acid sequence identity. This high level of sequence similarity is both indicative of, and has facilitated, their evolution from ancestral KIR genes by duplication and recombination events (Vilches et al.,

2002). Indeed, it has been proposed that the activating KIRs evolved from their inhibitory counterparts

(Abi-Rached et al., 2005) and 2DL2 is thought to be the result of recombination of 2DL1-like and

2DL3-like ancestors (Parham, 2003). Thus, the degree of sequence similarity is very high between

2DL2 and its activating counterpart, 2DS2, and between 2DL2 and 2DL3. The 2DL2 and 2DS2 molecules share 99% amino acid sequence homology and comparisons of their extracellular domains show there are only four amino acid substitutions between 2DL2 and 2DS2 (Moesta et al., 2012), and between 2DL2 and 2DL3 (Moesta et al., 2008). Consequently, it has been extremely difficult to make antibodies that are capable of distinguishing the extracellular domains of these three KIRs in order to analyse their expression on NK and T cells. Also, the level of nucleotide sequence similarity, even between alleles of different genes, has been challenging for KIR gene and allele typing. Similar to

HLA typing, sequence-specific primers (SSPs) and sequence-specific oligonucleotide probes

(SSOPs) have been employed and DNA sequencing is usually necessary for allele typing.

Figure 1.2: KIR structures and nomenclature. Figure from the IPD-KIR database, release 2.5.0: www.ebi.ac.uk/ipd/kir/. (Robinson J, Halliwell JA, McWilliam H, Lopez R, Marsh SGE. IPD - the Immuno Polymorphism Database. Nucleic Acids Research (2013), 41: D1234-40.)

1.6.2 KIR ligand specificities

The HLA class I molecules are the ligands for the KIRs. However, for several of the KIRs, particularly the activating KIRs, the exact ligands are yet to be fully defined (Table 1.1). Despite the sequence similarity in the extracellular domains, the KIRs have very different binding specificities and affinities.

This is demonstrated particularly well by KIRs with inhibitory and activating versions. According to comparisons of their structure and sequence, the activating KIRs should recognise the same ligands as their inhibitory counterparts but there is either no binding or the interactions are of much lower affinity, as is the case for 2DS1 (Stewart et al., 2005).

3DL2 and 3DL1 are specific for HLA-A and HLA-B alleles, respectively (Table 1.1), and these interactions occur through a specific Bw4 motif (Gumperz et al., 1995; Pende et al., 1996). It is thought that roughly a quarter of the human population do not possess HLA-A and HLA-B alleles containing this motif (Norman et al., 2007). The specificity of the recognition of HLA-C alleles by

2DL1, 2DL2, 2DL3 and 2DS1, is according to the amino acid present at position 80 of the HLA-C protein. Alleles that encode an asparagine at this location belong to the C1 group (HLA-C1Asn80) and are the ligands for 2DL2 and 2DL3, while those that encode a lysine at position 80 are C2 group

(HLA-C2Lys80) members and ligate 2DL1 and 2DS1 (Colonna et al., 1993; Moretta et al., 1993;

Wagtmann et al., 1995). Since all HLA-C alleles can be classified as either C1 or C2 with roughly half in each group, almost all humans will carry a KIR that recognises HLA-C, the exceptions being very rare haplotypes where these 2-domained KIRs have been deleted. Thus, HLA-C-binding KIRs are particularly important in regulating NK cell function (Moesta et al., 2012).

The exact ligand specificity of 2DS2 remains unresolved. A weak interaction with one HLA-C1 allele

HLA-C*16:01 has been detected however, no binding was shown for six other alleles (Moesta et al.,

2010). Interestingly, very weak interactions were shown between 2DS2 and HLA-C1 expressed on

EBV infected cells but not uninfected cells, indicating that the affinity of 2DS2 for its ligand may be influenced by infection (Stewart et al., 2005). Indeed, it has been proposed that the ligands for activating KIRs may only be available under infectious or inflammatory conditions, and could be either the products of virally-encoded proteins or host proteins modified by an infecting virus (Ivarsson et al.,

2014).

The inhibitory KIR/HLA-C interactions have been shown to vary in strength along a gradient such that,

2DL1/HLA-C2 is the strongest inhibitory combination, 2DL2/HLA-C1 is intermediate, and 2DL3/HLA-

C1 elicits the weakest inhibition (Moesta et al., 2008; Winter et al., 1998). While 2DL1 is highly specific for HLA-C2, 2DL2 and to a much weaker extent 2DL3 have been shown to cross-react with

HLA-C2 (Moesta et al., 2008). The 2DL2/HLA-C2 interaction has been shown to have functional consequences ex vivo, in that primary 2DL2+ NK cells are inhibited by target cells expressing HLA-C2

(Pende et al., 2009; Schonberg et al., 2011). It will be interesting to see if this interaction has a physiological role in NK cell development and function.

Table 1.1: KIR ligand specificities and functions.

KIR genes Ligand Function

2DL1 HLA-C2 Inhibitory 2DL2 HLA-C1, (cross-reacts with HLA-C2) Inhibitory 2DL3 HLA-C1, (cross-reacts weakly with HLA-C2) Inhibitory 2DL4 HLA-G Activating & Inhibitory 2DL5A/B Not known Inhibitory 3DL1 HLA-Bw4 Inhibitory 3DL2 HLA-A3, A11 Inhibitory 2DS1 HLA-C2 (low affinity) Activating 2DS2 ? HLA-C1 (low affinity) Activating 2DS3 Not known Activating 2DS4 Not known Activating 2DS5 Not known Activating 3DS1 HLA-Bw4? Activating 3DL3 Not known Not known 2DP1, 3DP1 – (pseudogenes, not expressed)

Table adapted with information from Ivarsson et al., (2014).

1.6.3 Genetic features

KIR genes

The KIR genes are located in the leukocyte receptor complex on chromosome 19q13.4 along with other similar receptors of the immunoglobulin-like receptor superfamily (Trowsdale et al., 2001). There are 17 KIR genes (including the pseudogenes 2DP1 and 3DP1), although segregation analyses and full-length haplotype sequencing have shown that 2DL3 and 2DL2 are allelic, since they segregate at the same locus (reviewed in Moesta et al., 2012; see Figure 1.3). These analyses also showed that

3DL1 and 3DS1 are allelic as well. However, these four KIR molecules continue to be referred to as separate genes, to denote the fact that 2DL2 and 2DL3 have different ligand binding properties and are found on different KIR haplotypes (2DL3 may be on either the A or B haplotype, 2DL2 is only found on the B haplotype), and the fact that 3DL1 is an inhibitory receptor, whereas 3DS1 is an activating receptor.

KIR genotypes vary significantly between individuals. Not only are there many different KIR genes, but the number of KIR genes an individual has can vary from 6 to 16 and the copy number of those genes can also vary (Uhrberg et al., 1997). However, despite the potential variety of KIR gene combinations, there are distinct patterns in the content of KIR genotypes. These patterns are determined by the arrangements of genes within the KIR haplotypes and the linkage disequilibrium

(see below) between the different KIR genes.

KIR haplotypes

There are two broad classifications of KIR haplotypes, designated A and B, based on their KIR gene content (reviewed in Middleton et al., 2010; Figure 1.3). Both A and B haplotypes contain four framework genes that are present in all haplotypes, almost without exception. These four genes mark out the basic structure of KIR haplotypes, such that every haplotype consists of 3DP1 and 2DL4 in the middle of the gene array, flanked by 3DL3 and 3DL2, with the other KIR genes distributed between them (Martin et al., 2000; Wilson et al., 2000). The divide marked out by 3DP1 and 2DL4 separates haplotypes into the centromeric region between 3DL3 and 3DP1, and the telomeric region between

2DL4 and 3DL2. The group A haplotype generally carries up to eight genes, as shown in Figure 1.3 and is typically non-variable in gene content and organisation, although occasionally gene deletions

43 occur. The A haplotype is considered to be more inhibitory than the B haplotype, since the A haplotype contains only two activating genes, 2DS4 and 2DL4. Also, the former frequently contains a deletion rendering it non-functional, while the latter has been shown to be retained in endosomes and is therefore not expressed on the cell surface (Hsu et al., 2002; Rajagopalan et al., 2006). The gene content of the B haplotypes is much more variable, in that they generally carry a mix of inhibitory and activating genes. Some of the KIR genes are exclusive to the B haplotypes; these are almost all of the activating KIRs (2DS1-3, 2DS5 and 3DS1), as well as 2DL5A/B and 2DL2. Some examples of B haplotypes are shown in Figure 1.3.

Figure 1.3: Examples of KIR haplotypes. KIR gene content of (A) the typical group A haplotype, and (B) examples of the group B haplotypes. Figure adapted from Middleton et al., (2010).

Linkage disequilibrium (LD)

Similar to their HLA class I ligands, there is extensive and often strong linkage disequilibrium (LD) between certain KIR genes (Hollenbach et al., 2010; Shilling et al., 2002a). Strong LD occur between

KIR genes within the same haplotypic region, centromeric or telomeric. However, LD is much weaker between KIR genes from different regions. For example, 2DL2 and 2DS2, which are both found in the centromeric region, are in almost complete LD (Hollenbach et al., 2010), whereas centromeric 2DL1 and telomeric 2DS1 do not show LD.

KIR genotypes

There are three possible combinations of KIR haplotypes, AA, AB and BB, which together make up an individual’s KIR genotype. These KIR haplotype combinations, together with the arrangements of genes within the KIR haplotypes and LD between them determine the patterns of KIR genes individuals possess. Since 2DL3 and 2DL1 are found on either the A or B haplotypes, these KIRs are found at significant frequencies in all populations. The presence of the activating KIR genes 2DS1 or

2DS2 in an individual is suggestive of a B haplotype, as is possession of a 2DL2 gene, and two copies of 2DL2 would indicate a likely BB genotype. Finally, if a 2DL2 gene is present in an individual, it is extremely likely that they will also have a 2DS2 gene because they are in almost complete LD.

Allelic diversity

Similar to their HLA class I ligands, as well as being polygenic the KIRs are also highly polymorphic

(Shilling et al., 2002a). More alleles have been identified for the inhibitory KIRs compared to the activating KIRs, for example there are currently 12 known alleles of 2DL2, 24 for 2DL3 and 26 for

2DL1, compared to 8 each for 2DS1 and 2DS2. The KIR gene with the highest number of alleles identified to date is 3DL1, with 76 alleles (IPD-KIR database, release 2.5.0: www.ebi.ac.uk/ipd/kir/).

There is also a haplotypic division in the degree of polymorphism, in that the KIR genes found on the

A haplotype show extensive allelic diversity, whereas genes on the group B haplotypes generally show less allelic polymorphism (Shilling et al., 2002a; Uhrberg et al., 2002). This may compensate for the relative lack of variation in gene content and number in the A haplotype compared to the B haplotypes.

KIR alleles are named in an analogous fashion to the HLA system (Figure 1.4). Following the asterisk separator, the first three numbers indicate alleles whose encoded protein sequence differs by one or more amino acids. The next two digits distinguish alleles that differ in nucleotide sequence but not in the amino acid encoded i.e. a synonymous substitution. The final pair of digits denotes alleles that differ in nucleotide sequence within non-coding regions, e.g. introns or promoters (IPD-KIR database, release 2.5.0: www.ebi.ac.uk/ipd/kir/).

Allelic polymorphism influences many different aspects of the huge diversity that is evident in the functionality of KIRs. Different alleles have different capacities for specificity and avidity for HLA class

I ligand (Gendzekhadze et al., 2009; Moesta et al., 2008) signal transduction strength (Bari et al.,

2009) receptor integrity and expression levels at the cell surface (VandenBussche et al., 2006) as well as the frequency of cells expressing a given KIR (Gardiner et al., 2001; Yawata et al., 2006) and even the frequency of cells expressing other KIRs (Schonberg et al., 2011).

Figure 1.4: KIR gene and allele nomenclature. Figure from the IPD-KIR database, release 2.5.0: www.ebi.ac.uk/ipd/kir/ (Robinson J, Halliwell JA, McWilliam H, Lopez R, Marsh SGE. IPD - the Immuno Polymorphism Database. Nucleic Acids Research (2013), 41: D1234-40.)

1.6.4 Cellular expression of KIRs on NK cells and T cells

KIR expression on NK cells

A further level of diversity in KIR biology results from the stochastic expression of KIRs at the cell surface, generating repertoires of NK cells expressing different combinations of some (usually 3-5) but not all of the KIR genes encoded in an individual’s genome (Valiante et al., 1997). Thus, for any given KIR, individuals will vary in the frequency of NK cells expressing that receptor. For example, in one study, frequencies of 3DL1+ NK cells varied from 5-50% (Gumperz et al., 1996). NK cells can also express different amounts of a given KIR on the cell surface, which can be detected by measuring the intensity of antibody staining (Gardiner et al., 2001; Yawata et al., 2006).

As described above, allelic polymorphism has a strong influence on these features of KIR expression.

The number of copies of a KIR gene an individual has also has an influence, in that having two copies leads to higher frequencies of NK cells expressing that KIR than having one copy of the gene

(O'Connor et al., 2007; Yawata et al., 2006). Another factor affecting the control of KIR gene expression is the methylation status of KIR gene promoters (Chan et al., 2003; Santourlidis et al.,

2002). KIR genes have two promoters, one more distal to the transcription start site with weaker activity and the main proximal promoter, which has stronger activity and is also bidirectional. The probability of KIR gene expression is determined by the ratio of forward and reverse activity of the proximal promoter (Stulberg et al., 2007). However, it remains unclear how and when the methylation of KIR promoters is controlled.

The extent to which interactions with cognate HLA class I ligand influence the repertoires of KIRs expressed by NK cells is currently under debate. Some studies have concluded that the frequencies of NK cell subsets expressing particular KIRs are influenced by the possession of cognate HLA class I ligands (Schonberg et al., 2011; Shilling et al., 2002b; Yawata et al., 2006; Yawata et al., 2008) while others have found that the KIR repertoires of NK cells are expressed independently of cognate HLA class I ligands (Andersson et al., 2009; Björkström et al., 2012). However, these studies have also shown various aspects of KIR biology and NK cell function can affect the structure of KIR repertoires, of which cognate HLA class I ligand possession is only one, which could make its influence on the frequencies of specific KIR-expressing NK cell subsets difficult to detect.

KIR expression on CD8+ and CD4+ T cells

Both activating and inhibitory KIRs are expressed on subsets of CD8+ T cells (Anfossi et al., 2001;

Mingari et al., 1996) and CD4+ T cells (van Bergen et al., 2004). KIR expression on T cells is acquired after thymic development and TCR rearrangement, since T cells with the same TCR Vβ chains can vary significantly in their KIR expression patterns (Björkström et al., 2012; Mingari et al., 1996;

Uhrberg et al., 2001; Vely et al., 2001). A number of studies have shown that KIR+ T cells have an effector memory (EM) phenotype, (Anfossi et al., 2004; Anfossi et al., 2001; Mingari et al., 1996;

Speiser et al., 1999; van Bergen et al., 2004; Young et al., 2001). The most recent and comprehensive analysis showed that the major KIR-expressing CD8+ T cell subset is CD45RA+

CD57+ CCR7− CD27− CD28− CD127− (Björkström et al., 2012). The frequency of KIR+ CD8+ and CD4+

T cells has been shown to increase with age, consistent with their EM phenotype (Abedin et al., 2005;

Anfossi et al., 2001; Li et al., 2009a; van Bergen et al., 2004). A small number of studies have shown that KIR+ CD8+ T cells accumulate during persistent viral infection and an expanded KIR+ CD4+ T cell subset has been demonstrated in exacerbations of rheumatoid arthritis (see Section 1.7 below on KIR associations with disease).

The repertoires of KIRs expressed on T cells compared to NK cells have been shown to vary significantly in the same individual (Abedin et al., 2005; Björkström et al., 2012; Uhrberg et al., 2001).

Similar to NK cells, allelic polymorphism and gene copy number are likely to influence the frequency and intensity of KIR expression on the T cell surface. The regulation of KIR gene expression differs between NK cells and T cells at both the level of epigenetic modifications and the recruitment of transcription factors to the KIR gene promoters. Whereas the promoters of a KIR gene in NK cells are either fully methylated or unmethylated (Chan et al., 2003; Santourlidis et al., 2002), in T cells promoter demethylation is more patchy and increases as the cells differentiate (Li et al., 2009a).

Given the differences between NK cells and T cells in the timing of KIR expression during cell development and in the functional role KIRs have on these two cell types (see Section 1.6.5 below on the Function of KIRs on NK cells and T cells), on top of the many levels of generating stochasticity in

KIR expression, variations in KIR repertoires between NK cells and T cells are not surprising. Little is known about the influence of cognate HLA class I ligand on the KIR repertoires of KIR+ CD8+ T cells.

One study has shown that the KIRs expressed on CD8+ T cells were independent of selection by cognate HLA class I molecules (Björkström et al., 2012).

1.6.5 Function of KIRs on NK cells and T cells

Functional role of KIRs on NK cells

Along with other types of activating and inhibitory receptors that are expressed on NK cells, the broad functional role of KIRs on NK cells is to control their activation, through the integration of signals received by the receptors following binding of their HLA class I ligands (Caligiuri, 2008). The classical mechanism describing the activation of NK cells is the “missing-self” hypothesis (Raulet, 2006). When the HLA class I ligands for inhibitory receptors are down-regulated on target cells, NK cells are stimulated to kill the target cell by signals received through their activating receptors. This activation is normally kept in check by the inhibitory receptor interacting with its ligand, since the inhibitory signal dominates over the activating signal.

In order to maintain self-tolerance, the functional responsiveness of NK cells to target cells is fine- tuned in a process termed education, which is widely accepted to occur but is not fully understood mechanistically (Vivier et al., 2011). It is proposed that part of NK cell education is an HLA class I- dependent process in which NK cells are “licensed”, following the ligation of their receptors during development in the bone marrow. Licensing renders NK cells functionally competent with respect to their activating receptors (Anfossi et al., 2006; Kim et al., 2008). The exact mechanism by which licensing occurs, and the relative involvement of activating and inhibitory receptors, are yet to be determined. However, it has been shown that NK functional responsiveness increases with the number of inhibitory KIR/HLA class I ligand pairs an individual has (Yu et al., 2007) and an activating

KIR (2DS1) has also been shown to be able to influence NK cell responsiveness (Fauriat et al., 2010).

Functional effects of KIRs on T cells

Several studies have examined the effects of KIR expression on T cell function. Inhibitory KIR expression on CD8+ and CD4+ T cells has been demonstrated to inhibit cytokine production (the most frequently measured are TNFα and IFNγ) and cytotoxicity, while the expression of activating KIRs co- stimulates these functions in T cells (van Bergen et al., 2010). Interestingly, expression of 2DL2 on a

CD4+ T cell clone has been shown to uncouple effector functions, allowing target cell lysis but preventing cytokine secretion (Henel et al., 2006). This study also showed that 2DL2 expression interrupted effector cell:target cell interaction and disrupted microclusters of signalling adapter molecules. Similarly, TCR-mediated activation was inhibited in a KIR+ tumour-specific CD8+ T cell clone (Guerra et al., 2002) and in 2DL1-transfected Jurkat CD4+ T cells (Fourmentraux-Neves et al.,

2008).

Inhibitory KIR expression on T cells has also been shown to promote cell survival. In 2DL3/HLA-C1- transgenic mice, there was an accumulation of 2DL3+ CD8+ T cells due to a reduction in activation- induced cell death (AICD; Ugolini et al., 2001). Similar findings were made in human KIR+ CD8+ T cell clones, which expressed higher levels of the anti-apoptotic molecule Bcl-2 and were more resistant to

AICD than KIR− clones (Young et al., 2001). Another study using human KIR+ and KIR− CD8+ T cell clones showed that KIR engagement inhibits AICD and results in decreased caspase 8 activity (Gati et al., 2003). Transfection of KIRs into Jurkat cells also reduced apoptosis via suppression of TCR signalling-induced Fas ligand expression (Chwae et al., 2002). Interestingly, this effect was mediated in a KIR ligand and ITIM-independent manner, via direct interaction of KIR with PKC.

There are mixed reports as to whether HLA class I ligand binding is necessary for KIRs to induce the above described effects on CD8+ and CD4+ T cells. Some studies have shown that KIR engagement was required for the disruption of TCR signalling (Fourmentraux-Neves et al., 2008; Guerra et al.,

2002; Henel et al., 2006), the reduction in AICD (Gati et al., 2003; Ugolini et al., 2001) and the decrease in T cell effector function (Bonorino et al., 2007; van Bergen et al., 2009; van der Veken et al., 2009). Conversely, others have demonstrated that the inhibition of apoptotic pathways (Chwae et al., 2002) and KIR-mediated reduction in CD8+ T cell effector function can occur without cognate HLA class I ligand interaction (Alter et al., 2008; Anfossi et al., 2004; Björkström et al., 2012). The experimental systems used by these authors varied considerably and are thus likely to explain these contradictory observations.

1.7 KIR associations with disease

Several immunogenetics studies have found consistent associations between particular KIR genes and disease pathogenesis or resistance, often in conjunction with their HLA class I ligands. As Table

1.2 shows, these conditions include infectious diseases, autoimmune/inflammatory conditions and some forms of cancer, as well as pregnancy-related disorders and issues surrounding transplantation.

In the majority of studies, NK cells have been implicated as the effector cells mediating the protection or susceptibility correlated with particular KIR genes. However, there is growing evidence that T cells may also have a role to play in mediating these effects.

1.7.1 Viral infections

For viral infections, many of the protective associations with KIR genes identified by immunogenetics are those which theoretically result in increased NK cell activation and/or lower inhibition. There is a small amount of functional data which are consistent with the proposed protective functionality of NK cells expressing the implicated KIRs. Also, a recent immunogenetic finding has shown that KIRs can influence HLA class I-restricted antiviral immunity and a small number of studies have analysed the expression and function of KIRs on T cells in persistent viral infection.

Immunogenetic associations with viral infections

As Table 1.2 indicates, the outcome of a number of viral infections has been associated with particular KIR genes and (in most cases) their cognate HLA class I ligands. The first identified was in

HIV infection, in which both 3DS1 and 3DL1, alongside HLA-Bw480Ile alleles, are associated with slower progression to AIDS (Martin et al., 2002a; Martin et al., 2007). In HCV infection, individuals who are homozygous for both 2DL3 and its ligands (HLA-C1 group alleles) were more likely to spontaneously clear HCV infection than individuals who are heterozygous at either of these loci

(Khakoo et al., 2004; Romero et al., 2008). The same homozygous compound genotype was also found to be protective in another hepatotropic virus, namely HBV, while 2DL1/HLA-C2 was associated with susceptibility to HBV infection (Gao et al., 2010). The activating KIRs 2DS1, 2DS5, and 3DS1 are associated with protection from respiratory involvement in HPV infection (Bonagura et al., 2010) and higher numbers of activating KIR genes in bone marrow transplant (BMT) donors results in lower occurrence of CMV reactivation in BMT recipients (Cook et al., 2006).

Very few studies have examined the impact of KIR genes on HTLV-1 infection. A study on a small cohort from Peru analysed the association of KIR genes and HLA-C alleles with HAM/TSP susceptibility and PVL but found no significant relationships (Talledo et al., 2010). However, another recent study considered the influence of KIR genotype on HLA class I-associated immunity in HTLV-1 infection and in HCV infection, and made some important observations (Seich al Basatena et al.,

2011). The analysis demonstrated that possession of a 2DL2 gene strengthened known HLA class I associations with multiple metrics of the outcome of these viral infections. Several lines of evidence suggest that the effector cells mediating the protective effects of 2DL2 could be CD8+ T cells. This is described in more detail below (see Section 1.8.1 below on the Modulation of HLA class I-associated immunity by KIR2DL2).

NK cell-mediated functional mechanisms

While the idea that activating receptors confer protection through increasing NK cell activation is relatively straightforward, there are two main mechanisms proposed to explain how increased NK cell activity could be conferred by inhibitory receptors. In the case of 3DL1/HLA-Bw480Ile in HIV infection, the suggested mechanism is linked to the NK cell licensing process (see Section 1.6.5 above on the

Function of KIRs on NK cells), whereby inhibitory receptor/HLA class I ligand interactions tune up the level of response an NK cell can elicit when its activating receptors are ligated. Therefore, 3DL1 is thought to have a strong licensing effect during NK cell development (Carrington et al., 2008). In the case of 2DL3/HLA-C1 in HCV infection, this interaction affords relatively weak inhibition compared to other inhibitory KIR2DL/HLA-C ligand pairs (Moesta et al., 2008; Winter et al., 1998). Therefore, increased NK cell activation is thought to result from the ability of activating signals to dominate more readily over this weaker inhibitory signal (Jamil et al., 2011). The requirement for homozygosity of

2DL3/HLA-C1 could be explained by the copy number effect on KIR expression i.e. individuals with two copies of 2DL3 will have more NK cells expressing 2DL3 (Alter et al., 2007; O'Connor et al., 2007;

Yawata et al., 2006).

For HIV and HCV infection, some functional evidence confirms a role for NK cells expressing the implicated KIRs in mediating the protective effects found by immunogenetics. 3DS1+ NK cells were significantly more effective at killing HIV-infected T cells expressing HLA-Bw480Ile than 3DS1– NK cells

(Alter et al., 2007) and HIV+ individuals with a 3DS1 gene were also shown to have greater NK cell function and a lower HIV viral load (Long et al., 2008). Also, NK cells from 3DL1+/HLA-B*57+ individuals have been shown to have high functionality (Boulet et al., 2010; Eller et al., 2011).

Consistent with 2DL3/HLA-C1 conferring an activating phenotype in HCV infection, NK cells from individuals with an AA KIR genotype that were homozygous for HLA-C1 (ligating 2DL3) activated more readily than NK cells from individuals with an AA KIR genotype that were homozygous for HLA-

C2 (ligating 2DL1; (Ahlenstiel et al., 2008). Also, individuals who cleared HCV infection had higher frequencies of 2DL3+ NK cells than those who progressed to chronic infection (Alter et al., 2011).

KIR expression on T cells in viral infections

Since KIR-expressing T cells are mainly CD8+ and have an effector memory phenotype (Anfossi et al., 2001; Young et al., 2001) and chronic viral infection results in the expansion of distinct memory T cell subsets (Appay et al., 2002; Champagne et al., 2001; Kuijpers et al., 2003; van Leeuwen et al.,

2004), it was anticipated that KIR expression patterns on these cells may be altered during infection with persistent viruses. Indeed, it has been proposed that KIR expression on T cells may be upregulated by sustained antigenic stimulation during chronic viral infection (Anfossi et al., 2001).

Thus, a small number of studies have analysed the expression and function of KIRs on T cells in persistent viral infections.

The presence of KIR+ virus-specific CD8+ T cells in infected individuals has been reported for all the viral infections in which KIR-expressing CD8+ T cells have been analysed to date. These are HIV-1

(Alter et al., 2008; Anfossi et al., 2004), EBV (Poon et al., 2005), CMV (Anfossi et al., 2004; van der

Veken et al., 2009), and HCV (Bonorino et al., 2007). However, KIR+ virus-specific CD8+ T cells were not found in all individuals tested and they have not been observed in all studies. For example, van der Veken et al., (2009) found KIR+ CMV-specific CD8+ T cells in 6 out of 28 healthy individuals and in all stem cell transplant (SCT) patients, but van Stjin et al., (2008) found no KIR expression on CMV- specific CD8+ T cells. Although most studies describe KIR+ virus-specific CD8+ T cell frequencies as very low, Alter et al., (2008) reported that HIV-specific CD8+ T cells were enriched for KIR expression, and frequencies of KIR+ CMV-specific CD8+ T cells in SCT patients (van der Veken et al., 2009) and

KIR+ EBV-specific CD8+ T cells in the persistent phase of infection (Poon et al., 2005) were increased compared to the frequencies of KIR expression in total CD8+ T cells.

There are also mixed reports regarding the frequencies of KIR expression in total CD8+ T cells in individuals infected with persistent viruses. Two studies observed increases in KIR+ CD8+ T cell frequencies in HIV-infected individuals (Alter et al., 2008; Sirianni et al., 2001) while two others found no difference compared to uninfected controls (Anfossi et al., 2004; Galiani et al., 1999). In CMV infection, increased frequencies of KIR+ CD8+ T cells have been found, however these cells were not

CMV-specific (van Stijn et al., 2008). Clearly, there is a lack of consensus about the effects chronic viral infection has on the frequencies of KIR+ total CD8+ and virus-specific CD8+ T cells observed in infected individuals, with the data varying significantly between different viruses, patient groups and study designs.

The expression of KIRs on CD4+ T cells has been analysed in CMV infection by a handful of studies.

KIR+ CMV-specific CD4+ T cells were detected at low frequencies and there was an enrichment of

KIR expression on CMV-specific CD4+ T cells (Saez-Borderias et al., 2006; van Bergen et al., 2009; van Bergen et al., 2004). Similarly, van Bergen et al., (2009) made the latter observation in healthy individuals and in patients with rheumatoid arthritis (RA). The influence of persistent viral infection on the frequency of KIR expression in total CD4+ T cells has not been addressed to date.

A small number of studies have compared the functional capacity of KIR+ and KIR– virus-specific T cells, some using PBMCs (Alter et al., 2008; Bonorino et al., 2007), but most using clones (Alter et al.,

2008; Anfossi et al., 2004; van Bergen et al., 2009; van Bergen et al., 2004; van der Veken et al.,

2009), which were necessary due to the very low frequencies of KIR+ virus-specific T cells often found. These studies either used T cell clones expressing inhibitory KIRs or identified KIR-expressing cells using pools of mAbs, which almost exclusively recognised inhibitory KIR. While the findings of the KIR expression analyses are varied, the functional studies consistently show reduced effector output in inhibitory KIR+ compared to inhibitory KIR– CD8+ and CD4+ T cells. There have been several suggestions as to the influence KIR+ T cells could have on immune responses during persistent viral infection. These range from protective roles such as the preservation of antigen-specific T cells or the

54 prevention of autologous damage caused by persistent stimulation of inflammatory responses

(Anfossi et al., 2004; Henel et al., 2006; Ugolini et al., 2001), to more detrimental roles such as contributing to the functional impairment of T cells which can be observed in chronic viral infections

(Alter et al., 2008; Poon et al., 2005). Due to the difficulties in analysing KIR+ virus-specific CD8+ and

CD4+ T cells because of their low frequency and the lack of KIR genes in typical laboratory animals, such as rodents, the precise function of these cells in vivo remains to be determined.

To date, only one small study has examined KIR expression in HTLV-1 infection. In ATLL patients, high frequencies of CD4+ T cells were found to express 3DL2 (Obama et al., 2007), which has previously been reported to be a useful marker of the malignant T cells found in Sezary syndrome, a type of cutaneous lymphoma (see Table 1.2; Poszepczynska-Guigne et al., 2004).

1.7.2 Autoimmune/inflammatory conditions

Activating KIR genotypes have also been associated with autoimmune and inflammatory disease.

However, unlike their protective effects in viral infections, in chronic inflammatory conditions and in some cancers with inflammatory aetiology (see Table 1.2) activating KIRs are associated with disease susceptibility (Parham, 2005). Interestingly, where this has been tested, cellular and functional data more strongly implicates T cells rather than NK cells in mediating the inflammatory disease susceptibility correlated with particular KIR genes, though the inflammatory response of NK cells with an activating phenotype may also have a role to play.

Immunogenetic associations with autoimmune/inflammatory conditions

The first identified link between activating KIR genes and susceptibility to autoimmune diseases involving chronic inflammation was for rheumatoid arthritis (RA), in which possession of 2DS2 alongside HLA-Cw*03 alleles (HLA-C1 group ligands) is associated with the development of vasculitis, a complication of RA involving vascular inflammation (see Table 1.2; Yen et al., 2001).

Another study confirmed these observations and showed that other extra-articular complications of

RA were also associated with 2DS2 (Majorczyk et al., 2007). In patients with the inflammatory skin disorder psoriasis, activating B group haplotypes (Suzuki et al., 2004), as well as possession of a

2DS1 gene alone (Luszczek et al., 2004) or alongside HLA-Cw*06 alleles (HLA-C2 group ligands;

Holm et al., 2005), have all been associated with disease susceptibility. In individuals with the related condition psoriatic arthritis, a model of the KIR/HLA genetic data has proposed that disease severity correlates with increasingly activating KIR2D/HLA-C compound genotypes, such that individuals with either 2DS1 or 2DS2 alongside two copies of cognate HLA-C ligand were most likely to develop disease (Nelson et al., 2004). Interestingly, both scleroderma (Momot et al., 2004) and Sjogren’s syndrome (Lowe et al., 2009) are associated with a 2DS2+/2DL2– genotype, which is an unusual genotype given the strong LD between these two genes (Hollenbach et al., 2010). Possession of activating KIR genes has also been associated with type I diabetes (van der Slik et al., 2003).

KIR expression on T cells in autoimmune/inflammatory conditions

The initial association of RA with 2DS2 was in fact observed in a cellular study, which showed an expansion of CD4+ CD28– T cells expressing 2DS2 but not 2DL2 in the peripheral blood of RA patients (Namekawa et al., 2000). These CD4+ CD28– T cells were previously shown to be greatly expanded in patients with severe complications of RA, such as vasculitis, and are thought to have a causative role in RA complications (Martens et al., 1997). Similarly, 2DS2+ CD4+ CD28– T cells have also been observed in the inflammatory infiltrates of atherosclerotic plaques found in acute coronary syndrome (Nakajima et al., 2003). Thus, consistent with the observed functional effects of activating

KIR expression on CD4+ T cells, 2DS2 expression on CD4+ CD28– T cells is thought to co-stimulate their expansion and IFNγ production, leading to exacerbation of existing inflammation in these conditions (reviewed in van Bergen et al., 2010).

In the same way it is inferred that HLA class I-associated immune effects are mediated via CD8+ T cells, NK cells are proposed to be the effector cells behind KIR/HLA class I associations with disease outcomes. In fact, as the above studies demonstrate, either cell type could potentially have a role in mediating either immunogenetic association. It is possible that multiple mechanisms are involved which could function simultaneously or in synergy. In line with this, the strongest genetic protection against HIV progression was afforded by 3DL1 in combination with HLA-B*57 (Martin et al., 2007), which is not only a strong binding ligand for 3DL1 (Cella et al., 1994) but is also associated with slower progression to AIDS on its own, without the KIR, and is enriched in HIV controllers (Kaslow et

56 al., 1996; Migueles et al., 2000). In general, functional studies of NK cells and T cells relating to the immunogenetic findings are lacking, not least because of the paucity of monoclonal antibodies available to differentiate between different KIRs, in order to monitor their functional properties. Further functional analyses are needed to determine the mechanisms underlying these immunogenetic associations, in particular, the functional consequences of KIR expression on T cells in terms of its potential impact on disease requires further investigation.

Table 1.2: KIR/HLA disease associations. Disease KIR-HLA ligand pair Effect Reference

Infections HIV 3DS1/Bw4-80I Slower progression (Martin et al., 2002b) 3DL1*004/Bw4 Slower progression (Martin et al., 2007) 3DL1*h/Bw4 80I 3DS1 Reduced risk of infection (Boulet et al., 2008) HCV 2DL3/HLA-C1 (homozygous for both) Resolution of infection (Khakoo et al., 2004) 2DL2 enhances HLA-B*57-associated immunity Enhanced HLA class I-mediated protection (Seich al Basatena et al., 2011) HTLV-1 2DL2 enhances HLA-C*08-associated immunity Enhanced HLA class I-mediated protection (Seich al Basatena et al., 2DL2 enhances HLA-B*54-associated immunity Enhanced HLA class I-mediated susceptibility 2011) CMV >1 activating KIR in BMT donor Protection from CMV reactivation in recipient (Cook et al., 2006) HBV 2DL3:HLA-C1 (homozygous for both) Protection (Gao et al., 2010) 2DL1:HLA-C2 Susceptibility HPV 2DS1, 2DS5, and 3DS1 Protection from respiratory involvement (Bonagura et al., 2010) M. tuberculosis 2DL1; 2DL3 Susceptibility (Mendez et al., 2006) P. falciparum 3DL2*002 High IFNγ response to infected RBCs (Artavanis-Tsakonas et al., 2003) Inflammation & autoimmunity Psoriatic arthritis 2DS1/2DS2 plus HLA-C ligand homozygosity Susceptibility (Nelson et al., 2004) Psoriasis 2DS1/HLA-Cw6 Susceptibility (Holm et al., 2005; Luszczek 2DS1; KIR haplotype B et al., 2004; Suzuki et al., 2004) Rhuematoid vasculitis 2DS2/HLA-Cw03; 2DS2+ CD4+CD28– T cells Susceptibility (Majorczyk et al., 2007; Yen et al., 2001) Scleroderma 2DS2+/2DL2– Susceptibility (Momot et al., 2004)

Acute coronary syndrome 2DS2+ CD4+CD28– T cells Susceptibility (Nakajima et al., 2003) Diabetes 2DS2/HLA-C1 Susceptibility (van der Slik et al., 2003) Sjogren’s syndrome 2DS2+/2DL2– plus HLA-C1 Susceptibility (Lowe et al., 2009) Cancer Leukaemia 2DL1; 2DL2; 2DL3 Susceptibility (Verheyden et al., 2004) Hodgkin’s lymphoma ≥5 activating KIR Protection (Besson et al., 2007) NPC (EBV associated) 3DS1 and absence of HLA-C2 and/or HLA-Bw4 Susceptibility (Butsch Kovacic et al., 2005) Cervical cancer 2DS1; 3DS1 Susceptibility (Carrington et al., 2005) T-LGL Expression of inhibitory KIR without ligands More severe disease (Nowakowski et al., 2005) Sezary syndrome Expression of 3DL2 Useful diagnostic marker (Poszepczynska-Guigne et al., 2004) ATLL (HTLV-1 causative) Expression of 3DL2 Potential diagnostic marker? (Obama et al., 2007) Pregnancy Pre-eclampsia AA KIR genotype in mother; HLA-C2 in foetus Susceptibility (Hiby et al., 2004) Recurrent miscarriages 2DS1– mothers and HLA-C2 in both parents Susceptibility (Hiby et al., 2008)

BMT, bone marrow transplant; NPC, Nasopharyngeal carcinoma; T-LGL, T cell large granular lymphocytic leukaemia; ATLL, Adult T cell leukaemia/lymphoma Table adapted with information from Kulkarni, S Martin, MP and Carrington, M. The Yin and Yang of HLA and KIR in human disease. Seminars in Immunology 20 (2008) 343–352.

1.8 KIRs in HTLV-1 immunity

As described above in Section 1.5.1 on the CD8+ T cell response in HTLV-1, host genes, particularly the HLA class I genes have a strong influence on the effectiveness or quality of the CD8+ T cell response. However, since the lytic efficiency of HTLV-1-specific CD8+ T cells explained 40-50% of the observed variation in PVL (Asquith et al., 2005) but HLA class I genotype only explained 5-10% of this variation (Vine et al., 2002), there are likely to be further genetic factors that have a role in determining CD8+ T cell quality and subsequently the control of PVL and the risk of HAM/TSP

(Bangham, 2009). Given the association of particular KIR genes with the outcome of other viral infections (as described above in Section 1.7.1 on KIR associations with viral infection) and the fact that KIRs have the potential to influence T cell responses (van Bergen et al., 2010), the influence of

KIR genotype on HLA class I-mediated immunity was studied using immunogenetics (Seich al

Basatena et al., 2011).

1.8.1 Modulation of HLA class I-associated immunity by KIR2DL2

Seich al Basatena et al., (2011) investigated whether the effects of HLA class I alleles known to be associated with the outcome of infection with HTLV-1 or HCV were altered by KIR genotype. The

HTLV-1 analysis was carried out using the same cohort from the Kagoshima region of Japan in which the original HLA class I associations were identified (Jeffery et al., 2000; Jeffery et al., 1999). The immunogenetic analysis showed that in individuals with a 2DL2 gene, the protective effect of HLA-

C*08 on the risk of HAM/TSP was enhanced and the association of HLA-C*08 allele with a lower PVL was also stronger in both 2DL2+ ACs and HAM/TSP patients. Similarly, the detrimental effect of HLA-

B*54 on HAM/TSP risk and PVL was also strengthened in 2DL2+ individuals. Interestingly, possession of a 2DL2 gene had no effect on the protection from HAM/TSP afforded by HLA-A*02.

Following the finding made previously, that individuals who had HLA-A and HLA-B alleles that bound strongly to HBZ peptides were more likely to be ACs and to have a lower PVL (Macnamara et al.,

2010), this association was analysed in individuals with and without a 2DL2 gene. This protective effect was also enhanced in 2DL2+ individuals (Seich al Basatena et al., 2011).

In HCV infection, HLA-B*57 is associated with spontaneous viral clearance (Kim et al., 2011) and in the cohort analysed by Seich al Basatena et al., (2011) it was found to reduce HCV viral load in individuals with chronic infection. Again, these two associations were independently strengthened in individuals who had a 2DL2 gene alongside HLA-B*57 and there was a gene dose effect, with the strongest association found in 2DL2 homozygotes, although this result was speculative for viral load due to low donor numbers.

The consistency of the effect of 2DL2 on HLA class I-associated immunity is striking, as are the effect sizes. The presence of a 2DL2 gene modulated HLA class I-mediated immune responses across multiple infections and different metrics of infection outcome in cohorts of varying ethnicity

(Japanese for HTLV-1, Caucasian and African-American for HCV). Individuals with a 2DL2 gene alongside HLA-C*08 had a 6-fold increase in their odds of remaining asymptomatic compared to those with HLA-C*08 alone, while the risk of developing HAM/TSP was increased by 12-fold in 2DL2+

HLA-B*54+ individuals compared to HLA-B*54+ individuals without 2DL2. In HCV infection, HLA-B*57+

2DL2 homozygotes were 5 times more likely to clear the virus than those with HLA-B*57 alone and if they did develop chronic HCV infection their viral loads were lowered by 6.5 logs.

1.8.2 How could KIR2DL2 modulate HLA class I-associated immunity?

There are several mechanisms that could potentially explain how 2DL2 modulates HLA class I- mediated antiviral immune responses. The protective KIR/HLA class I associations reported for HCV and HIV have been attributed to the direct action of NK cells (Khakoo et al., 2004; Martin et al.,

2002a; Martin et al., 2007). However, as described above (see Section 1.7 on KIR associations with disease), it is possible that CD8+ T cells or NK cells, or both, simultaneously or in synergy, could have a role in mediating the protective effects of KIR/HLA class I combinations. Certainly for the modulation of HLA class I-associated immunity by 2DL2, the involvement of NK cells (either directly or indirectly) cannot be ruled out while potential mechanisms are untested, and other more complex interactions between NK cells, CD8+ T cells, DCs and/or CD4+ T cells could potentially be involved (reviewed in

Cook et al., 2013). However, several strong lines of evidence point to CD8+ T cells as the effector cells that are modulated by the presence of a 2DL2 gene:

1. 2DL2 does not bind any of the HLA class I alleles whose effects on antiviral immunity were

enhanced, with possible exception of HLA-C*08, which is an HLA-C1 ligand. However, the most

frequent HLA-C*08 allele present in the study binds 2DL2 very weakly.

2. Possession of a 2DL2 gene alone, in the absence of a protective or detrimental HLA class I allele

(and either with or without the presence of a gene encoding an HLA-C1 ligand), had no effect on

disease status or viral burden in HTLV-1 or HCV infection.

3. The fact that 2DL2 can enhance associations with HLA class I molecules that are both protective

and detrimental is difficult to reconcile with NK cells as the direct effector cells.

4. Although NK cells show broad selectivity for certain peptide motifs (reviewed in Cassidy et al.,

2014), the protective effect of binding peptides from HBZ by multiple HLA-A and HLA-B alleles,

which was enhanced by 2DL2, is more consistent with the antigen specificity of CD8+ T cells.

5. Given that HLA class I molecules present antigenic peptides to CD8+ T cells, HLA class I alleles

that are associated with disease outcomes have been assumed to affect immune responses via

their influence on the function of CD8+ T cells.

It was therefore hypothesised that the effects of 2DL2 on HLA class I-associated antiviral immunity are mediated via the expression of 2DL2 on CD8+ T cells restricted by protective or detrimental HLA class I molecules (Seich al Basatena et al., 2011). The proposed mechanism is based on previous reports which suggest that KIR expression on CD8+ memory T cells could promote their survival, which may help to maintain CD8+ T cells that have been chronically exposed to viral antigens during persistent infection (Anfossi et al., 2001; Ugolini et al., 2001). As reviewed by Vivier et al., (2004) it is thought that the initiation of inhibitory KIR signalling, resulting in the recruitment of phosphatases to

ITIM domains, could disrupt activating signalling via tyrosine kinase cascades initiated from the TCR.

While effector response levels may be reduced, rather than preventing CD8+ T cell activation entirely

KIR expression may raise their activation thresholds, thus protecting them from AICD, preventing their exhaustion despite chronic stimulation with antigen during persistent viral infection, and prolonging

62 their survival (Chwae et al., 2002; Gati et al., 2003; Ugolini et al., 2001; Young et al., 2001). Thus, according to this mechanism, the survival of 2DL2+ CD8+ T cells restricted by HLA class I molecules associated with either positive or negative effects on antiviral immunity would be increased, explaining the contrasting ability of 2DL2 to enhance both protection from and susceptibility to disease (Seich al

Basatena et al., 2011).

The effect of 2DL2 on the survival of CD8+ T cells is beyond the scope of the present study. However,

KIR expression and function has not been studied in HTLV-1 infection, either on T cells or NK cells. I therefore undertook a detailed analysis of the expression patterns and functional effects of 2DL2 and four further KIR molecules in HTLV-1-infected individuals, focussing mainly on CD8+ T cells. I used newly characterised anti-KIR mAbs which allow the identification of 2DL2 separately from its allele

2DL3 and its activating counterpart 2DS2 for the first time.

1.9 Aims

The central aim of this work was to investigate the patterns and functional consequences of the expression of specific KIR molecules on lymphocytes in HTLV-1 infection.

More specifically, I aimed:

1. To profile the frequency of global KIR expression and the expression of 2DL2 and other 2-

domained KIRs on circulating effector lymphocytes in HTLV-1-infected and uninfected individuals

and relate this to HTLV-1 PVL and donor parameters known to influence KIR biology.

2. To profile the frequency of 2DL2 expression and the expression of other 2-domained KIRs on

HTLV-1-specific CD8+ T cells in HTLV-1-infected individuals and relate this to HTLV-1 PVL and

donor parameters known to influence KIR biology.

3. To analyse the functional effects of 2DL2 expression and the expression of other 2-domained

KIRs on CD8+ T cells in HTLV-1 infected individuals.

Chapter 2. Materials and Methods

2.1 Study subjects and PBMC collection

The 103 blood donors recruited for this study signed informed consent for research, including genetic analysis, in accordance with ethical approval from the National Research Ethics Service, Oxfordshire

REC C no. 09/H0606/106 (16.11.2009) and the St Mary’s NHS Trust Local Research Committee EC no. 02.31 (27.06.2002). All HTLV-1-infected individuals and some uninfected donors were recruited and consented via attendance at the HTLV-1 clinic at the National Centre for Human Retrovirology,

St. Mary’s Hospital. The remaining uninfected donors were recruited through the Immunology

Department, St Mary’s Hospital Medical School. The cohort consisted of 30 uninfected donors, 39

HTLV-1-infected healthy asymptomatic carriers (ACs) and 34 HTLV-1-infected patients with

HAM/TSP. None of the HAM/TSP patients were receiving immunosuppressive therapies at the time the samples used for this study were donated. Each study subject donated ~30mls of blood per visit, collected in EDTA-coated vacutainers. PBMC were isolated by density gradient centrifugation on

Histopaque and cryopreserved until use.

2.2 HTLV-1 proviral load measurement

DNA was extracted from 2x106 freshly thawed and washed PBMC using a DNeasy kit, according to the manufacturer’s instructions (Qiagen). Two elutions of 100ul each were performed in Qiagen’s EB buffer. The starting dilution for the qPCR reactions contained 250ng of sample DNA, diluted in 50ul

DNase-free water and was serially diluted 3-fold to make two further test dilutions. 8ul of each DNA dilution was added to two separate reaction mixes, one to detect HTLV-1 Tax and one to detect β- actin. Each reaction mix contained 7ul of Fast SYBR Green Master Mix (Applied BioSystems) and

0.5ul (0.3uM final concentration) of each appropriate forward and reverse primer (Kwok et al., 1988), giving a total reaction volume of 16ul. To generate a standard curve of known proviral copy number, the same reactions were set up using six 3-fold dilutions of 250ng DNA diluted in 50ul DNase-free water from the rat lymphoid cell line TARL-2, which carries one copy of the HTLV-1 provirus per cell

(Nagai et al., 1998). Thermal cycling and quantification of amplified products by measuring SYBR

Green incorporation was carried out on an ABI7900HT Fast Real-Time PCR machine (Applied

BioSystems). Copy numbers of amplified products for both HTLV-1 Tax and β-actin were estimated

66 by interpolation from the standard curves and proviral loads were expressed as the percentage of

PBMC carrying one copy of the provirus.

2.3 Flow cytometry experiments

2.3.1 Detection of global and specific KIR expression on lymphocyte populations

A detailed explanation of how global and specific KIR expression was targeted and defined, using panels of commercially available and newly characterised cross-reactive anti-KIR mAbs (David et al.,

2009), alongside the KIR genotyping and allotyping data provided by Dr J. Traherne and Dr O.

Chazara (see Statement of collaboration), can be found below in Section 2.4.

For both the global and specific KIR expression analyses, 0.5–2x106 freshly thawed PBMC per tube were stained with Live/Dead Fixable Dead Cell Stain (Invitrogen) for 30mins at room temperature

(RT) and blocked with 5% human AB serum/PBS (Sigma) for 20mins RT. For specific KIR expression analysis, cells were initially stained with unconjugated 1F12 mAb for 30mins RT, followed by staining with DyLight-488-conjugated polyclonal secondary Ab for 10mins RT (see Table 2.1). Samples were then stained with all other monoclonal antibodies (mAbs) recognising KIRs and lymphocyte cell surface markers for 30mins RT, as detailed in Table 2.1. For global KIR analysis, all mAb staining was carried out in one step. Cells for both analyses were fixed in 2% paraformaldehyde/PBS

(Electron Microscopy Sciences) and samples were stored at 4ºC overnight until acquisition. Global

KIR expression samples were acquired on a CyAn ADP flow cytometer using Summit software

(Beckman Coulter), and specific KIR expression samples were acquired on an LSRFortessa using

FACSDiva software (BD Bioscience). Data were analysed using FlowJo (Tree Star Inc) or Kaluza software (Beckman Coulter).

2.3.2 Detection of CD8+ T cell functional responses

Stimulation and culture

2x106 freshly thawed PBMC per tube were resuspended in RPMI medium (Sigma), supplemented with

10%FCS, 50units/ml penicillin, 50ug/ml streptomycin, 2mM L-Glutamine (all Gibco), 20ug/ml DNase

(Sigma) and 2uM monensin (eBioscience). Cells were stimulated with 2uM of 20mer Tax peptides, which overlapped by 14 amino acids and spanning the whole Tax protein (Mimotopes Pty Ltd). Tax peptides were divided into two pools, A (peptides 1-28) and B (peptides 29-57), and stimulations were set up in separate tubes to minimise dimethyl sulfoxide (DMSO) carrier toxicity. Positive controls were stimulated with 10ul anti-CD3 and anti-CD28 mAbs conjugated to microbeads, according to the manufacturer’s protocol (Miltenyi Biotec), or 1ng/ml PMA and 50ng/ml calcimycin (CAI; Sigma).

DMSO vehicle controls and media only negative controls were also included. Tubes for all the stimulated conditions and the DMSO control were set up in duplicate, to enable staining with the two panels of anti-KIR mAbs (see Table 2.1). CD107a mAb was added to tubes as necessary and culture volumes were equalised to 1ml. All tubes were mixed well by vortexing and incubated for 5h at 37 ºC,

5% CO2.

Staining cell surface and intracellular markers

Following stimulation and culture, cells were stained with Live/Dead Fixable Dead Cell Stain

(Invitrogen) for 30mins RT, followed as necessary by HLA-A2/Tax11-19 pentamer (ProImmune) staining

(HLA-A2+ donors only). Samples were blocked with 5% human AB serum/PBS (Sigma) for 20mins RT and stained for 30mins RT with monoclonal antibodies, as detailed in Table 2.1. Cells were fixed and permeabilised using the Foxp3/transcription factor fix/perm buffer set, according to the manufacturer’s instructions (eBioscience). Intracellular staining for IFNγ was carried out in perm buffer for 20mins RT and samples were stored at 4ºC overnight until acquisition on an LSRFortessa using FACSDiva software (BD Bioscience). As many cells as possible were acquired from each sample, to maximise detection of low frequency specific KIR+ Tax-specific CD8+ T EM cells. Data were analysed using

Kaluza software (Beckman Coulter).

Table 2.1: Monoclonal antibody panels used in each analysis. Global KIR Specific KIR KIR+ CD8+ T expression expression cell function Colour Marker Clone Supplier 2DL2/ 2DL1/ 2DL2/ 2DL1/ NK T cell L3/S2 S1 L3/S2 S1 Anti-mouse Poly- Stratech DyLight-488 secondary clonal Scientific Unconjugated KIR2DL3/S2 1F12 C. Retierea

FITC KIR2DL3/S2 1F12 C. Retierea

KIR2DL1/ a FITC 8C11 C. Retiere L2/L3/S2 AbD FITC KIR2D NKVFS1 Serotec CCR7 PerCP-Cy5.5 G043H7 BioLegend (CD197) PacBlue CD107a H4A3 BioLegend

PacBlue CD16 3G8 BioLegend

Live/dead Violet — Invitrogen Fixable Stain Brilliant CD14 61D3 BioLegend Violet 570 Brilliant CD19 HIB19 BioLegend Violet 570 Qdot605 CD3 UCHT1 Invitrogen

HLA-A2/Tax APC 11- — ProImmune 19 pentamer Beckman APC KIR3DL1/S1 Z27 Coulter AbD AF700 CD8 LT8 Serotec APCeF780 CD3 UCHT1 eBioscience

KIR2DL2/ PE DX27 BioLegend L3/S2 Beckman PE KIR2DL1/S1 EB6 Coulter SFCI- Beckman ECD CD4 12TD411 Coulter PE-TexasRed CD16 3G8 Invitrogen

PE-Cy7 CD8 RPA-T8 eBioscience

PE-Cy7 CD56 HCD56 BioLegend

PE-Cy7 IFNγ 4S.B3 BioLegend

Live/dead Blue — Invitrogen Fixable Stain

69 aAnti-KIR mAb clones 1F12 and 8C11 were kind gifts from Dr. C. Retiere, Etablissement du Sang Francais, Nantes, France (David et al., 2009).

2.4 Targeting and definitions of global and specific KIR expression

2.4.1 Targeting global KIR expression

Global KIR expression was targeted using two commercially available anti-KIR mAb clones mAbs.

Clone NKVFS1 recognises all 2-domained KIRs (KIR2D) and clone Z27 stains 3DL1 and 3DS1. This anti-KIR mAb combination covered all KIRs except 3DL2 and 3DL3, the latter of which is not expressed at the cell surface (Trundley et al., 2006). Unfortunately, it was not possible to obtain mAbs to target 3DL2.

2.4.2 Targeting specific KIR expression using novel anti-KIR mAbs

The majority of anti-KIR mAbs are cross-reactive, i.e. they recognise epitopes found in multiple KIRs and do not distinguish activating and inhibitory KIR counterparts. This is due to the high level of amino acid sequence homology between the KIRs resulting in very few polymorphisms in the extracellular domains which distinguish the different receptors. This is particularly true for the 2-domained KIRs and until recently, it was not possible to identify cells expressing 2DL2, in the absence of 2DS2 since there are only four amino acid substitutions in their extracellular domains that distinguish these two receptors (Moesta et al., 2012). In this study, expression of 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 was targeted using combinations of commercially available and newly generated cross-reactive anti-KIR mAbs, the latter kindly provided by Dr Christelle Retiere (David et al., 2009). These mAb combinations were, clone 8C11 (Retiere) with EB6 (Beckman Coulter), and clone 1F12 (Retiere) with

DX27 (BioLegend). The specificities of the mAbs are shown in Table 2.1 and Figure 2.1. As detailed in Figure 2.1, for the detection of some KIRs, the overlap in the specificities of these anti-KIR mAbs allows identification of a particular KIR via its recognition by one of the anti-KIR mAbs in the pair and the exclusion of the KIRs recognised by both anti-KIR mAbs in the pair. For other KIRs, the gene and allele specificity of the anti-KIR mAbs, and the potential for KIRs to be co-expressed in different combinations, mean that donors with particular KIR genotypes and allele types have to be selected for each KIR analysis (Figures 2.2-4). Together, these factors result in definitions of specific KIR expression which overlap but are focussed on one of the five KIRs in the analysis (see Table 2.2 for summary). Overall, these definitions represent an improvement in the resolution of KIR expression analysis at the protein level, particularly for detection of 2DL2 expression.

Detection of specific KIR expression using 8C11/EB6 and 1F12/DX27

The anti-KIR mAb combination 8C11/EB6 was used to detect 2DL1 and 2DS1 expression, and anti-

KIR mAb combination 1F12/DX27 was used to detect 2DL2, 2DS2 and 2DL3 expression. As illustrated in Figure 2.1, the overlap in the specificities of these anti-KIR mAb pairs can target a specific KIR by excluding the KIRs recognised by both anti-KIR mAb clones. For example, 2DL2 is targeted by DX27 (2DL2/2DS2/2DL3-specific) because 1F12 (2DS2/2DL3-specific) excludes 2DS2+ and 2DL3+ cells from the 2DL2+ population, therefore 2DL2+ cells are 1F12–DX27+. Detection of 2DS1 expression using 8C11/EB6 is similar (Figure 2.1). 2DS1 is recognised by EB6 (2DL1/2DS1-specific) and 8C11 (2DL1/2DL2/2DS2/2DL3-specific) excludes 2DL1+ cells from the 2DS1+ population, so

2DS1+ cells are 8C11–EB6+. For 2DS2 and 2DL3 analysis however, the overlapping specificity of the

1F12/DX27 anti-KIR mAb combination means that both of these KIRs stain 1F12+DX27+. Therefore,

2DS2 expression can only be analysed in donors without a 2DL3 gene, and vice versa. Although detection of 2DL1 expression using 8C11/EB6 appears straightforward, in that 2DL1+ cells are

8C11+EB6+ as depicted in Figure 2.1, this analysis is affected by the potential for NK cells and T cells to express multiple combinations of KIRs, as detailed below.

Figure 2.1: Detection of specific KIR expression using cross-reactive anti-KIR mAb combinations 1F12/DX27 and 8C11/EB6. Theoretical flow cytometry staining patterns of cells expressing 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 obtained using the anti-KIR mAb combinations 1F12/DX27 and 8C11/EB6. The positions where cells expressing the different KIR molecules appear in the flow plots are shown, along with the anti-KIR mAb reactivity of each KIR.

Co-expression of different KIRs on the same cell

The stochastic expression of KIRs at the cell surface results in the potential for multiple combinations of different KIRs to be expressed on NK cells and T cells. For both 1F12/DX27 and 8C11/EB6 anti-

KIR mAb staining, this affects the precise definition of KIR expression that can be attributed to the different populations of cells identified with these anti-KIR mAbs. For example, Figure 2.2, showing the theoretical effect of co-expression on staining patterns for the 1F12/DX27 anti-KIR mAb combination, reveals that cells detected in the 1F12+DX27+ quadrant, defined as 2DS2+ or 2DL3+

(depending on the donor’s KIR genotype) could also be expressing 2DL2. A more extensive theoretical effect of KIR co-expression on staining patterns can be seen for the 8C11/EB6 mAb combination, shown in Figure 2.3, largely because 8C11 recognises four different KIR molecules. For this KIR mAb pair, the potential for co-expression of different KIRs on the same cell also affects the

KIR genotypes that can be analysed with the 8C11/EB6 anti-KIR mAb combination. This is because in donors who have both a 2DL1 and a 2DS1 gene, the 8C11+EB6+ quadrant may contain cells that do not express 2DL1. In these donors, 8C11+EB6+ cells could co-express 2DS1 alongside 2DL2, 2DS2 or 2DL3. Therefore, 2DL1 expression can only be analysed in donors without a 2DS1 gene.

Figure 2.2: The effect of potential KIR co-expression on 1F12/DX27 mAb staining patterns. Theoretical flow cytometry staining patterns of cells expressing single, double and triple combinations of KIRs recognised by the anti-KIR mAb combination 1F12/DX27. The positions of the cell populations expressing the different combinations of KIR molecules demonstrates the potential for KIR co-expression to affect the precise definitions of 2DL2, 2DS2 and 2DL3 expression that apply to the populations identified in the flow plot gates (see Table 2.2 for expression definitions).

Figure 2.3: The effect of potential KIR co-expression on 8C11/EB6 anti-KIR mAb staining patterns. Theoretical flow cytometry staining patterns of cells expressing single, double, triple and quadruple combinations of the KIRs recognised by the anti-KIR mAb combination 8C11/EB6. The positions of the cell populations expressing the different combinations of KIR molecules demonstrates the potential for KIR co-expression to affect the precise definitions of 2DL1 and 2DS1 expression that apply to the populations identified in the flow plot gates and the requirement for donors lacking a 2DS1 gene for the analysis of 2DL1 expression (see Table 2.2 for expression definitions and geno/allotype requirements).

KIR allele recognition by 8C11 and EB6

Both mAb clones 8C11 and EB6 have KIR allele specificities which affect the KIR allotypes that can be analysed with this anti-KIR mAb pair. Clone EB6 has been shown to recognise 2DL3*005, along with 2DL1 and 2DS1. This is because the mature 2DL3*005 protein has an arginine at position 50

(R50), within the D1 extracellular domain, which is required for EB6 binding (Falco et al., 2010). The

R50 residue is also present in 2DL1 and 2DS1 but is not found in other 2DL3 alleles. As Figure 2.4A shows, the additional recognition of 2DL3*005 by EB6 prevents accurate 2DL1 detection in donors with this 2DL3 allele, because 2DL1 and 2DL3*005 would both be 8C11+EB6+. Therefore, donors with a 2DL3*005 allele were excluded from the 2DL1 expression analysis. Similarly, David et al., (2009) show that their clone 8C11 does not recognise the 2DL1 alleles *004, *007, *010, *011, *013, *024

(termed 2DL1*004-like alleles). These 2DL1 alleles have a threonine at position 154 (T154) in the mature protein sequence, which is within exon 5, corresponding to the D2 extracellular domain. 8C11 binding requires a proline at position 154 (P154), found in non-2DL1*004-like alleles, and in 2DL2,

2DS2 and 2DL3. As Figure 2.4B demonstrates, the lack of recognition of T154 by mAb 8C11 interferes with accurate 2DS1 detection in donors with a 2DL1*004-like allele, because both 2DS1 and 2DL1*004-like alleles would stain 8C11–EB6+. Therefore, donors with a 2DL1*004-like allele were not analysed for 2DS1 expression.

Exclude 2DL3*005+ donors from 2DL1 analysis Exclude 2DL1*004-like+ donors from 2DS1 analysis

Figure 2.4: The effect of KIR allele specificities of the anti-KIR mAb clones 8C11 and EB6 on allotype requirements for 2DL1 and 2DS1 expression analysis. Theoretical flow cytometry staining patterns of cells expressing (A) 2DL3*005 alleles, which are recognised by EB6 mAb in addition to 2DL1 and 2DS1, and (B) 2DL1*004-like alleles, which are not recognised by 8C11 mAb. Donors with 2DL3*005 alleles cannot be analysed for 2DL1 expression and donors with 2DL1*004-like alleles cannot be analysed for 2DS1 expression. These allotype requirements also affect the definitions of 2DL1 and 2DS1 expression that apply to the populations identified in the flow plot gates (see Table 2.2 for expression definitions).

2.4.3 Definitions of specific KIR expression

A summary of the KIR genotypes and allotypes required for expression analysis of each KIR, and the resulting definitions of specific KIR expression, is presented in Table 2.2. While there is overlap in the

KIRs expressed by the cell populations identified using 8C11/EB6 and 1F12/DX27 staining, these definitions represent a more specific analysis of individual KIR expression than has been possible previously. Importantly, it is possible to analyse 2DL2 expression, the main KIR of interest in this study, in the absence of 2DS2 and 2DL3 in all donors with a 2DL2 gene, so there is no restriction on donor numbers for 2DL2 analysis. Expression of 2DL1 and 2DS1 can also be analysed independently of each other using the 8C11/EB6 combination. The effects that the overlapping gene and allele specificity of the anti-KIR mAbs and the potential for KIR co-expression have on accurate interpretation of staining patterns, together with the requirement for donors to have appropriate genotypes to analyse particular KIRs, highlights the necessity of KIR gene and allele typing for accurate KIR expression analysis at the protein level.

Table 2.2: Geno/allotype requirements and definitions of specific expression for each KIR. KIR KIR geno/allotype requirements Definitions of specific KIR expressiona

2DL1 2DL1+ 2DS1– 2DL3*005– 2DL1+ 2DS1– 2DL2+/– 2DS2+/– 2DL3+/– (*005–) 2DS1 2DS1+ 2DL1*004– 2DS1+ 2DL1– 2DL2– 2DS2– 2DL3– 2DL2 2DL2+ 2DL2+ 2DS2– 2DL3– 2DL1+/– 2DS1+/– 2DS2 2DS2+ 2DL3– 2DS2+ 2DL3– 2DL2+/– 2DL1+/– 2DS1+/– 2DL3 2DL3+ 2DS2– 2DL3+ 2DS2– 2DL2+/– 2DL1+/– 2DS1+/– a Cells could also be +/– for all other KIRs not covered by 1F12/DX27 and 8C11/EB6 anti-KIR mAb specificities

2.5 Flow cytometry data analysis

2.5.1 Gating

General gating schemes

General gating schemes are shown for the global KIR expression analysis in Figure 2.5, generated in

FlowJo (Tree Star Inc), the specific KIR expression analysis in Figure 2.6 and the KIR/CD8+ T cell function analysis in Figure 2.7, which were both analysed using Kaluza software (Beckman Coulter).

Fluorescence minus one (FMO) controls were initially tested for all colours, to check for colour interactions and to verify gate placement. Where staining for common lymphocyte markers showed clear separation, FMOs were not continuously used. For CD8 AF700 and CCR7 PerCP-Cy5.5, used in both the specific KIR expression and the functional analyses, significant compensation was required and limited separation of the two markers was achieved. Therefore, FMO controls were included for every donor to guide the positioning of a diagonal polygon gate which avoided the spill- over of PerCP-Cy5.5 into AF700 (see Figures 2.6 and 2.7). Another colour interaction was between

CD14/19 Brilliant Violet 570 and DX27 PE or EB6 PE, which was used in the functional analysis.

While good separation between these markers was achieved, so an FMO was not necessary, the significant compensation required between the two fluorochromes resulted in a risk of losing the tail end of DX27+ staining if the CD14/19– gate was not set low enough. Therefore, these two parameters were plotted against each other in the gating scheme to monitor this and biexponential scaling was used for CD14/19 Brilliant Violet 570 to improve visualisation of the DX27+ population (see Figure

2.7).

Figure 2.5: Flow cytometry gating scheme for global KIR expression analysis. Single lymphocytes were identified in PBMC using their forward and side scatter properties. NK cells were defined as live CD3–CD16+CD56dim. CD4+ T cells were defined as live CD3+CD4+. CD8+ T cells were defined as live CD3+CD8+. Global KIR+ cells were defined as KIR2D+3DL1/S1+ using the anti- KIR mAb combination NKVFS1/Z27. Images and percentage data were generated in FlowJo (Tree Star Inc).

Figure 2.6: Flow cytometry gating scheme for specific KIR expression analysis. Single lymphocytes were identified in PBMC using their forward and side scatter properties. NK cells were defined as live CD3–CD16+CD56dim. CD8+ T effector memory (EM) cells were defined as live CD3+CCR7–CD8+. 2DL2+, 2DS2+ and 2DL3+ cells were identified using the 1F12/DX27 anti-KIR mAb combination and 2DL1+ and 2DS1+ cells were identified using the 8C11/EB6 anti-KIR mAb combination. Images and percentage data were generated in Kaluza software (Beckman Coulter).

Figure 2.7: Flow cytometry gating scheme for the specific KIR+ CD8+ T cell function analysis. Single lymphocytes were identified in PBMC using their forward and side scatter properties. Monocytes and B cells were excluded by gating on CD14– and CD19– cells, respectively. The interaction between BV570 and PE was monitored to ensure no loss of DX27+ cells. Total CD8+ T cells were defined as live CD3+CD8+. CD8+ T EM cells were defined as live CD3+CD8+CCR7–. 2DL2+, 2DS2+ and 2DL3+ cells were identified using the 1F12/DX27 anti-KIR mAb combination, and 2DL1+ and 2DS1+ cells were identified using the 8C11/EB6 anti-KIR mAb combination. Functional cells were + + + defined for each of these populations as IFNγ and/or CD107a and/or HLA-A2/Tax11-19 pentamer . Boolean gating equations were constructed (not shown) to define single+, double+ and triple+ cell populations for the three functional markers, using the individual functional gates shown. Data was extracted as cell numbers to facilitate multiple percentage calculations and subtraction of DMSO background in very low frequency populations. Images and cell numbers were generated in Kaluza software (Beckman Coulter).

Gating specific KIR+ populations

Setting gates for the different KIR+ cell populations identified with the two anti-KIR mAb combinations was complicated by the variation in the staining patterns observed between different donors, particularly on CD8+ T cells. Since the 1F12 and 8C11 anti-KIR mAbs are newly characterised, little precedent exists to guide gating decisions. Therefore, a combination of controls and comparisons were used to standardise the gating procedure. To set a threshold for positive staining for all anti-KIR mAbs, KIR FMO controls stained with all markers except the anti-KIR mAbs, were set up for each donor (Figure 2.8). Careful observations were made of general staining patterns and background staining for each KIR analysed. All KIR gates were set individually for each donor, using box, polygon and spider gates to accommodate individual donor staining patterns and the different requirements of each KIR analysis on the different cell types (Figure 2.8). Box gates were used for 2DL1 and 2DL3 gating, because only one population of cells is identified by the anti-KIR mAb pairs in the donor genotypes used to analyse these two KIRs. Spider gates were used to gate 2DS1+ cells, to allow flexibility in defining the separation between 8C11–EB6+ and 8C11+EB6+ cells. 2DL2 and 2DS2 were gated using polygons, as they gave the most options to accommodate the different staining patterns observed for 2DL2+ cells (see below). Some donors analysed for 2DL1 expression had 8C11–EB6+ cell populations, which did not correspond to 2DS1+ cells, because these donors were required to lack a 2DS1 gene in order to be analysed for 2DL1 expression (see above Section 2.4.2). These cells were likely to be expressing 2DL1*004-like alleles, whose presence was not tested for in 2DS1– donors. Since these 8C11–EB6+ cell populations represented genuine 2DL1 staining, 2DL1 box gates were set to include them (Figure 2.9). This staining pattern was clearer in NK cells than CD8+ T cells, as were staining patterns for all the KIRs, because KIR+ population resolution and cell number was higher for NK cells than for CD8+ T cells (Figure 2.8). Thus, KIR+ NK cells provided a comparison for gating KIR+ CD8+ T cell staining in many donors.

Figure 2.8: Defining gates for each specific KIR expression analysis. Different types of gates and gating controls were required for expression analysis of each KIR. Box gates were used for 2DL1 and 2DL3, spider gates were used for 2DS1 and polygons were used to gate 2DL2 and 2DS2. Gates for KIR staining on NK cells were set first, then used to assist the gating of KIR staining on CD8+ T cells in the frequency analysis and the functional analysis. KIR FMOs were used to set negative boundaries in each analysis. Staining patterns shown for each KIR are from the same donor in the two different analyses for comparison.

Figure 2.9: 8C11/EB6 staining patterns from a donor with a 2DL1*004-like allele. Box gates for 2DL1 gating were set wide to include 8C11–EB6+ staining in 2DS1– donors, since this results from possession of a 2DL1*004-like allele and represents genuine 2DL1 staining.

2DL2 staining had more gating issues than the other KIRs, so as this was the main KIR of interest in the study, additional gating controls were used. The main difficulty was caused by the limited separation of positive and negative populations stained with the 1F12 mAb clone, particularly for the

FITC-conjugated 1F12 format. Also, a low MFI 1F12–DX27+ background was evident in the staining patterns of individuals without a 2DL2 gene. This made distinguishing genuine 2DL2 staining in donors with a 2DL2 gene difficult because 2DL2+ cells are also 1F12–DX27+. 2DL2 polygon gates were therefore set diagonally to exclude this DX27 PE background, using comparisons of its location in donors with and without a 2DL2 gene, which were stained alongside test donors in each experiment (Figure 2.10). Gate boundaries for positive staining with mAb clone 1F12 were set stringently, to reduce the inclusion of cells co-expressing 2DL2 alongside 2DS2 or 2DL3 in the 2DL2 gate. The staining patterns using the two different 1F12 formats and the different staining/experimental procedures varied considerably, sometimes even within the same donor.

Therefore, unique 2DL2 gates were set for each donor, for each of the two analyses and for each cell type analysed for 2DL2 expression, starting with the most separated staining and using those gates to guide gating for less obvious 1F12+/– population boundaries, as follows: Beginning with the specific

KIR expression analysis, using the unconjugated 1F12 format with DyLight-488 secondary Ab, FMOs were used to set 2DL2 gate positions for NK cells, and these gates, alongside the FMOs, were then used to inform 2DL2 gating for CD8+ T cells (Figure 2.8). The most difficult 2DL2 gating to set was for

86 staining with the 1F12 FITC-conjugated format, used in the functional analysis. Different 2DL2 gate comparisons were useful and/or available for different donors. For some individuals, the unconjugated

1F12/DyLight-488 staining was better separated, and provided a good comparison. For other donors, previous staining from set up experiments using an earlier batch of 1F12 FITC had better resolution than the 1F12 FITC batch used in the final panel and was used to guide gating (Figure 2.11).

Figure 2.10: Staining patterns of 2DL2+ and 2DL2– control donors used to assist 2DL2 gating. Control donors with and without a 2DL2 gene were stained alongside test donors to assist with 2DL2+ polygon gate placement to exclude the low MFI DX27 PE background staining.

Figure 2.11: Staining patterns of different 1F12 mAb batches used to assist 2DL2 gating. Comparisons of staining patterns using an earlier batch of 1F12 FITC-conjugated mAb with better resolution were used to assist with 2DL2+ polygon gating of staining with the 1F12 FITC-conjugated mAb batch used for the functional analysis which showed more limited resolution, as well as comparison of 1F12 staining from the frequency analysis which showed better separation.

Gating functional responses

The positive staining thresholds for IFNγ and CD107a was set firstly using the DMSO vehicle control, and when particularly high background signal was observed in DMSO controls, the unstimulated media control was also used for comparison (Figure 2.12). The gates were set high, especially for

CD107a, to accommodate the background staining observed with the two functional markers, and because of the large numbers of cells collected. The signal-to-noise ratio for both IFNγ and CD107a was improved by gating out dead cells which can bind these mAbs non-specifically. Accuracy of

CD107a detection was further enhanced by gating out CD19+ B cells and CD14+ monocytes, both of which can bind CD107a mAb (see Figure 2.7). The remaining background can be attributed to spontaneous cytokine production and/or degranulation by CD8+ T cells during culture, or potentially

+ stimulation of Tax-specific CD8 T cells by HLA-A2/Tax11-19 pentamer binding. FMO controls were used to set gate boundaries for positive staining with HLA-A2/Tax11-19 pentamer (see Figure 2.7). To analyse HLA-A2/Tax11-19 pentamer staining in combination with IFNγ and CD107a staining, Boolean equations were used to define cell populations expressing single, double and triple positive combinations of these three markers, using the individual gates that had already been defined.

Figure 2.12: Setting IFNγ and CD107a gates using DMSO and media controls. DMSO vehicle controls were used to set positive thresholds for IFNγ and CD107a staining and these gates were compared to media only controls when DMSO background became high.

2.5.2 Data processing

Flow cytometry data from the global and specific KIR frequency analyses were extracted as percentages of parent cell populations from FlowJo (Tree Star Inc) or Kaluza software (Beckman

Coulter), respectively. For the functional analysis, data were extracted from Kaluza software

(Beckman Coulter) as cell numbers so that cell frequencies could be calculated in Excel. It was not possible to generate percentage data for this analysis within Kaluza because of (1) the number of calculations that were made for the different analyses performed using this data, (2) the complexity of the gating strategy that would have been involved if this had been attempted, and (3) the number of events collected was at the limit of the processing power of the software. Following a viability assessment, cell numbers were normalised, within each donor’s set of samples, to the number of total

CD8+ T cells. This population had the most consistent staining and all further cell subsets analysed were within this population. Since as many cells as possible were acquired for each sample, to enable analysis of very low frequency Tax-specific CD8+ T EM cells expressing specific KIRs, the normalisation was necessary to equalise the differences in the number of cells acquired on the flow cytometer. The normalisation also allowed corrections for backgrounds staining to be made using cell numbers rather than percentages which proved more reliable when analysing the very low frequency

Tax-specific CD8+ T EM cells expressing specific KIRs. Once DMSO background was subtracted for

IFNγ and CD107a analysis, responses to Tax peptide pools A and B were combined, since they represented responses to different epitopes spanning the whole Tax protein. Where comparisons were made between the results of the functional IFNγ/CD107a assay and HLA-A2/Tax11-19 pentamer staining in HLA-A2+ donors, only IFNγ/CD107a responses to Tax peptide pool A were considered, since this pool contained the Tax11-19 epitope and reduced the inclusion of responses from other HLA alleles to other parts of the Tax protein in this analysis.

2.6 KIR and HLA class I genotyping

2.6.1 HLA class I genotyping

HLA class I genotyping was carried out under the direction of Dr Franco Tavarozzi at the Anthony

Nolan Histocompatibility Laboratories, Royal Free Hospital, using xMAP Technology (Luminex).

Genomic DNA was genotyped for HLA-A, B and C loci using locus-specific PCR amplification, followed by detection of allelic polymorphisms using sequence specific oligonucleotide probes

(SSOPs) immobilised on fluorescent microbeads. Binding of SSOPs to PCR products is determined by reporter dye fluorescence and cross referenced with the fluorescent signal from the microbead to identify the SSOP and hence the polymorphism detected. This medium resolution typing method returns strings of potential HLA class I alleles, listed in frequency order. The purposes of HLA class I genotyping for this study were to identify donors carrying HLA-A*02 alleles and to classify the HLA-C alleles as group 1 or 2, according to the amino acid present at position 80. Therefore, the first allele in the 6 strings determined for each donor was taken to be the allele present.

2.6.2 KIR genotyping

KIR genotyping was carried out by Dr James Traherne. Genomic DNA was genotyped for KIR gene content and copy number, using a recently developed high-throughput technology known as quantitative KIR automated typing (qKAT), as described in detail in (Jiang et al., 2012). Briefly, a set of 10 multiplex qPCR assays were carried out in quadruplicate for each donor. Each assay detected two different KIR genes and one reference gene, using sequence specific primers and probes labelled with different fluorophores. Monitoring of fluorescence signal for each probe in each reaction and subsequent Ct determination allowed relative quantification of copy numbers for the 17 KIR genes and their major variants.

2.6.3 KIR allotyping

2DL3*005 allotyping

Dr James Traherne carried out PCR amplification using sequence specific primers (PCR-SSP), followed by DNA sequencing, on genomic DNA to identify donors carrying the 2DL3*005 allele, as detailed in Beziat et al., (2013). The typing was performed for all donors that were analysed for 2DL1 expression, to ensure that the presence of a 2DL3*005 allele did not interfere with 8C11/EB6 mAb staining interpretation (see above, Section 2.4.2).

2DL1*004-like allotyping

Dr Olympe Chazara carried out PCR-SSP, followed by DNA sequencing, on genomic DNA to identify donors carrying the 2DL1 alleles *004, *007, *010, *011, *013, *024 (2DL1*004-like alleles). Two sets of intronic SSPs were used to amplify the whole of exons 4 and 5, corresponding to the two extracellular domains of 2DL1. DNA sequencing was carried out in both directions on both PCR products, to ensure that polymorphisms close to the sequencing primers were detected. Both extracellular domains were sequenced, to provide sufficient nucleotide information to differentiate these highly homologous alleles. The typing was performed for all donors that were analysed for

2DS1 expression, to ensure that the presence of a 2DL1*004-like allele did not interfere with

8C11/EB6 mAb staining interpretation (see above, Section 2.4.2).

2.7 Statistical analysis

2.7.1 Comparing groups and correlations

To determine whether data were normally distributed, D’Agostino and Pearson normality tests were performed. If the data for any study group within an analysis was not normally distributed, nonparametric statistical methods were used. Differences between groups of donors were assessed using Kruskal-Wallis and/or Mann-Whitney tests. Lines of best-fit were plotted using linear regression and correlations were tested using Spearman’s rank. Pairwise comparisons of cell frequencies within the same donor were assessed using Wilcoxon matched pairs test. All tests were 2-tailed and results where p<0.05 were considered statistically significant. All graphs and statistical analyses were produced using Prism 5 (GraphPad).

2.7.2 Fisher’s chi-squared method of combining probabilities

To calculate the combined statistical significance of the Spearman’s rank correlations for PVL and

2DL2+ CD8+ T EM cell frequency over the two tests performed for the two different donor groups (see

Chapter 3, Section 3.4.3), Fisher’s chi-squared method of combining probabilities was used:

where pi is the significance level obtained from the ith experiment, and k is the number of experiments; –2Σln(pi) is distributed as a chi-squared variate with 2k degrees of freedom (Sokal et al.,

2004).

Chapter 3. Global and specific KIR expression on effector lymphocytes in HTLV-1 infection

3.1 Introduction

Several strong lines of evidence suggest that the modulation of HLA class I-mediated antiviral immunity by 2DL2 observed in the immunogenetic study by Seich al Basatena et al., (2011) could be mediated by 2DL2 expression on CD8+ T cells (see Chapter 1 Introduction, Section 1.8.2). KIRs are known to be expressed on CD8+ and CD4+ T cells with a memory phenotype in healthy humans

(reviewed in van Bergen et al., 2010; Vivier et al., 2004). Interestingly, increased frequencies of KIR+

CD8+ T cells have been reported in HIV-infected individuals (Alter et al., 2008; Sirianni et al., 2001) and in CMV-infected individuals (van der Veken et al., 2009; van Stijn et al., 2008), and an expanded

KIR+ CD4+ T cell subset has been observed in inflammatory autoimmune diseases (Nakajima et al.,

2003; Namekawa et al., 2000).

KIR expression has not been evaluated in HTLV-1 infection, therefore I investigated the patterns of

KIR expression on T cells and NK cells in ACs, HAM/TSP patients and uninfected controls, initially at a global KIR level and then targeting the expression of specific KIRs. The analysis of specific KIR expression employed the newly generated and characterised anti-KIR mAb clone 1F12 (kindly provided by C. Retiere; David et al., 2009) in combination with the commercial clone DX27, which allows cell surface expression of 2DL2 to be assessed, independently of its activating counterpart

2DS2 and its allele 2DL3 for the first time. The use of 1F12/DX27 and clone 8C11, another novel mAb clone from the Retiere lab, in combination with commercial clone EB6, enabled in depth expression profiling of two further inhibitory KIRs, 2DL1 and 2DL3, and two activating KIRs, 2DS1 and 2DS2.

These four further KIRs were analysed to compare/contrast with 2DL2 expression and identify any specific features of 2DL2 expression that could relate to its influence on HLA class I-mediated immunity. I chose to focus the analysis on two-domained KIRs and to compare KIR molecules that are closely related to 2DL2, while separating activating and inhibitory counterparts as much as possible. The anti-KIR mAb combinations do not allow exclusive detection of one specific KIR gene product. Instead, they provide definitions of specific KIR expression (see Table 3.3 below and Chapter

2 Methods, Sections 2.4.2-3 for a detailed explanation). However, this analysis of specific KIR expression is more detailed than has been previously possible.

Several donor parameters have been shown to influence the total frequency and repertoire of specific

KIRs expressed by NK cells and T cells. The frequency of global KIR+ CD8+ and CD4+ T cells has been reported to increase with donor age (Anfossi et al., 2001; van Bergen et al., 2004), and since

HAM/TSP tends to be diagnosed later in life (Nakagawa et al., 1995; Olindo et al., 2006), I tested the relationships between donor age and the frequencies of global KIR expression on CD8+ T cells, NK cells and CD4+ T cells in HTLV-1 infection.

The influence of cognate HLA class I ligand possession on the formation of KIR repertoires on NK cells is contentious. However, there is strong evidence that the frequency of NK cells bearing KIRs for which an individual has an HLA class I ligand is increased during viral infection (Alter et al., 2009;

Béziat et al., 2012; Béziat et al., 2013; Björkström et al., 2011; Eller et al., 2011; Petitdemange et al.,

2011; Wong et al., 2010). Little is known about this effect for KIR+ CD8+ T cells. Since KIR expression has been shown to promote CD8+ T cell survival (Vivier et al., 2004) the presence of cognate HLA class I ligands able to stimulate inhibitory KIR signalling could amplify this process, resulting in the accumulation of CD8+ T cells expressing KIRs that can bind their ligands. I therefore investigated the influence of cognate HLA class I ligand possession on the frequencies of CD8+ T EM cells and NK cells expressing the inhibitory KIRs 2DL1, 2DL2 and 2DL3 in HTLV-1 infection.

Since HTLV-1 PVL is the major correlate of disease susceptibility in HTLV-1 infection (Nagai et al.,

1998), correlates of the PVL indicate the potential for such factors to influence its set point and hence, the associated risk of developing HAM/TSP. However, these relationships require cautious interpretation because it is difficult to separate cause and effect in the context of persistent infection and particularly in the context of immune correlates because many other unknown inter-related factors may also influence a given relationship with PVL. In HIV infection, global KIR+ CD8+ T cell frequency has been shown to correlate positively with viral load (Alter et al., 2008) and Seich al

Basatena et al., (2011) showed that 2DL2 can influence PVL, via its enhancement of HLA class I associations with PVL. Thus, I analysed the relationship between HTLV-1 PVL and the frequency of global and specific KIR+ T cells and NK cells.

3.2 Aims

The specific aims of this chapter were:

 To determine the frequencies of CD8+ T cells, NK cells and CD4+ T cells that express global KIRs

and compare them in uninfected donors, ACs and HAM/TSP patients

 To examine the relationships between donor age and the frequencies of global KIR+ CD8+ T cells,

NK cells and CD4+ T cells

 To examine the relationships between PVL and the frequencies of global KIR+ CD8+ T cells, NK

cells and CD4+ T cells

 To determine the frequencies of CD8+ T EM cells and NK cells that express specific KIRs and

compare them in uninfected donors, ACs and HAM/TSP patients

 To investigate the influence of cognate HLA class I ligand possession on the inhibitory KIR

repertoire of CD8+ T EM cells and NK cells

 To examine the relationship between PVL and the frequencies of specific KIR+ CD8+ T cells and

NK cells

 To assess the effect of KIR gene possession on PVL

3.3 Results: Global KIR expression

3.3.1 Global KIR expression on effector lymphocytes in HTLV-1 infection

Frequencies of CD8+ T cells, NK cells and CD4+ T cells in total lymphocytes

The frequencies of effector lymphocytes (CD8+ T cells, NK cells and CD4+ T cells) in total lymphocytes (singlets defined by forward and side scatter patterns) were compared in uninfected controls, ACs and HAM/TSP patients, to establish any differences in the frequencies of the parent cell populations between the donor groups, before the global KIR expression analysis was carried out. As

Table 3.1 shows, frequencies of NK cells in total lymphocytes were reduced in patients with HAM/TSP compared to ACs (p=0.012) and uninfected controls (p<0.0001). Frequencies of CD8+ and CD4+ T cells in total lymphocytes were comparable in all three donor groups (Table 3.1).

Table 3.1: Frequencies of CD8+ T cells, NK cells and CD4+ T cells within total lymphocytes in uninfected controls, ACs and HAM/TSP patients. % Effector lymphocytes UN AC HAM in total lymphocytes Median 22.96 20.94 22.74

CD8+ T cells IQR 17.91 - 29.27 14.46- 25.94 17.02 - 27.22 No. donors 30 39 34

Median 9.17*** *7.57 *5.12***

NK cells IQR 6.68 - 14.99 4.95 - 15.72 2.81 - 7.21 No. donors 30 33 28

Median 40.34 38.77 43.12

CD4+ T cells IQR 30.64 - 46.44 33.21 - 43.77 38.92 - 52.83 No. donors 18 21 21

There were significantly lower frequencies of CD3– CD56dim CD16+ NK cells in total lymphocytes in patients with HAM/TSP than in ACs *(p=0.012) and in uninfected controls ***(p<0.0001). Frequencies of CD3+ CD8+ and CD3+ CD4+ T cells in total lymphocytes were similar between all three donor groups. Median and IQR shown are percentages of each type of effector lymphocyte per total lymphocytes. The number (No.) of donors that could be analysed for each cell type are shown. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP; IQR, interquartile range.

Frequencies of CD8+ T cells, NK cells and CD4+ T cells expressing global KIRs in uninfected controls, ACs and HAM/TSP patients

To analyse the frequencies of global KIR expression on CD8+ T cells, CD4+ T cells and NK cells in

HTLV-1 infection, PBMC from uninfected donors, ACs and HAM/TSP patients were stained with mAbs detecting all 2-domained KIRs (KIR2D) and 3DL1/S1. This anti-KIR mAb combination covered all KIRs except 3DL2 and 3DL3, since it was not possible to obtain mAbs to target these KIRs.

Examples of typical global KIR staining patterns observed for CD8+ T cells, CD4+ T cells and NK cells are shown in Figure 3.1, together with staining from donors with more unusual global KIR expression profiles on CD8+ T cells.

Figure 3.1: Global KIR staining patterns on CD8+ T cells, CD4+ T cells and NK cells. Examples of (A) typical global KIR (KIR2D and 3DL1/S1) staining patterns on CD3+ CD8+ T cells, CD3+ CD4+ T cells and CD3– CD56dim CD16+ NK cells, and (B) atypical global KIR (KIR2D and 3DL1/S1) staining patterns on CD3+ CD8+ T cells.

To identify changes in the frequencies of global KIR expression on CD8+ T cells, CD4+ T cells and NK cells that are associated with HTLV-1 infection or HAM/TSP disease, KIR+ cell frequencies were compared in uninfected donors, ACs and patients with HAM/TSP. CD8+ and CD4+ T cells from all donors except one expressed global KIRs at average frequencies of 5% and 0.5% respectively across all three donor groups. Patients with HAM/TSP had significantly higher frequencies of global KIR+

CD4+ T cells than uninfected controls (p=0.0432 by Mann-Whitney test, ns by Kruskal-Wallis test;

Figure 3.2), and the median frequency of global KIR+ CD4+ T cells in ACs was also higher than for uninfected controls, although this was not statistically significant. A subset of HTLV-1 carriers had relatively high percentages of global KIR+ CD8+ T cells, with 27.9% being the highest. This pattern was not observed in uninfected controls, suggesting an accumulation of global KIR+ CD8+ T cells in a minority of HTLV-1-infected individuals (Figure 3.2). However, comparisons of the median frequencies show that patients with HAM/TSP had slightly lower frequencies of global KIR+ CD8+ T cells than ACs or uninfected controls but this was not statistically significant. Overall, there were no statistically significant differences in the median global KIR+ CD8+ T cell frequencies between any of the donor groups.

Average frequencies of global KIR+ NK cells were around 60% and the ranges of frequencies were broad for all three donor groups. Although there was a step-wise decline in the median frequencies of global KIR+ NK cells, with ACs having fewer than uninfected donors and HAM/TSP patients having fewer still than ACs, the differences between the donor groups were not statistically significant (Figure

3.2).

Figure 3.2: Frequencies of global KIR expression on CD8+ T cells, CD4+ T cells and NK cells in HTLV-1 infection. Freshly thawed PBMC were stained with characteristic markers of effector lymphocytes and anti-KIR mAb clones NKVFS1 (KIR2D) and Z27 (3DL1/S1) to analyse global KIR expression on CD3+ CD8+ T cells, CD3+ CD4+ T cells, and CD3– CD56dim CD16+ NK cells by flow cytometry. Data were not normally distributed in all study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using nonparametric Kruskal-Wallis and 2-tailed Mann-Whitney tests. The p value indicated is from a Mann- Whitney test. Statistical significance was not reached by Kruskal-Wallis test. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.3.2 Relationship between donor age and the frequencies of global KIR+ effector lymphocytes in HTLV-1 infection

To test for relationships between donor age and frequencies of global KIR+ CD8+ T cells, NK cells and

CD4+ T cells, correlations analyses were performed for all three cell types in uninfected controls, ACs and patients with HAM/TSP. There was a positive correlation between the frequency of KIR+ CD4+ T cells and donor age in ACs (p=0.0272; Figure 3.3), but not in uninfected controls or patients with

HAM/TSP. There were also no statistically significant relationships between donor age and KIR+ CD8+

T cell or NK cell frequencies in any of the three donor groups.

Overall, there was no effect of donor age on the frequency of KIR+ effector lymphocytes in these donors that needed to be accounted for when making comparisons of KIR+ effector lymphocyte frequencies between the different groups. Only KIR+ CD4+ T cell frequency showed a relationship with donor age, and CD4+ T cells were not investigated beyond the global KIR frequency analysis. Also, the different donor groups were each well matched for age range in any case.

Figure 3.3: Correlations of donor age with global KIR+ CD8+ T cell, CD4+ T cell and NK cell frequencies. Donor age is plotted against the frequencies of global KIR+ (KIR2D+ and 3DL1/S1+) CD3+ CD8+ T cells, CD3+ CD4+ T cells, and CD3– CD56dim CD16+ NK cells, as measured by flow cytometry, in each donor group. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.3.3 Relationships between PVL and the frequencies of global KIR+ effector lymphocytes in HTLV-1 infection

The frequencies of global KIR expression on CD8+ T cells, CD4+ T cells and NK cells were tested for correlations with PVL in ACs and HAM/TSP patients. The distributions of PVLs measured for ACs and patients with HAM/TSP in this cohort were typical for these two groups of HTLV-1 infected individuals

(Nagai et al., 1998). ACs had a lower median PVL than HAM/TSP patients and the difference between them was highly statistically significant (p=0.0001; Figure 3.4). However, ACs had a wider range of PVLs than patients with HAM/TSP, such that at values >1%, the PVLs of ACs overlapped with those of the HAM/TSP patient population (Figure 3.4). Due to the highly significant difference in

PVLs between ACs and patients with HAM/TSP, correlation tests were performed separately on each donor group. The correlation analyses of PVL against global KIR+ CD8+ T cell, CD4+ T cell and NK cell frequencies revealed no statistically significant relationships in ACs or patients with HAM/TSP for any of the three KIR+ cell types (Figure 3.5).

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Figure 3.4: PVLs of the global KIR expression cohort. Genomic DNA was extracted from PBMC and analysed for the number of copies of the HTLV-1 provirus present by q-PCR. PVL is expressed as the percentage of PBMC carrying one copy of the provirus, which was calculated by estimating HTLV-1 tax and β-actin copy numbers from standard curves. PVLs that were undetectable in this assay were arbitrarily set to a value of 0.0001. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

Figure 3.5: Correlations of PVL with global KIR+ CD8+ T cell, CD4+ T cell and NK cell frequencies in ACs and HAM/TSP patients. HTLV-1 PVL, as measured by q-PCR, is plotted against the frequencies of global KIR+ (KIR2D+ and 3DL1/S1+) CD3+ CD8+ T cells, CD3+ CD4+ T cells, and CD3– CD56dim CD16+ NK cells in each patient group, as measured by flow cytometry. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.4 Results: Specific KIR expression

3.4.1 Specific KIR expression on effector lymphocytes in HTLV-1 infection

Frequencies of CD8+ T EM cells in total CD8+ T cells

Specific KIR expression was analysed on CD8+ T cells with an effector memory (EM) phenotype for two related reasons, (1) it has been widely reported that KIR+ CD8+ T cells have EM phenotype (van

Bergen et al., 2010; Vivier et al., 2004) and (2) the analysis reported in Chapter 5 comparing the function of specific KIR+ and KIR– CD8+ T cells required the KIR– CD8+ T cells to be the same differentiation stage as the KIR+ CD8+ T cells, to ensure that they had similar potential functional capacities. Thus, the frequency of specific KIR expression was assessed on CD8+ T EM cells, to maintain phenotypic consistency between the KIR expression analysis and the functional analysis.

The differentiation marker CCR7, expressed on naïve and central memory CD8+ T cells, was included in the mAb panel to exclude these phenotypes and focus the analysis on the CCR7– EM population.

This marker was selected based on the analysis by (Björkström et al., 2012), which showed that almost all KIR+ CD8+ T cells were CCR7–, whereas they have been shown to be variable in their expression of CD45RA, and CD45RO (Young et al., 2001).

The frequencies of CCR7– cells (i.e. CD8+ T EM cells) in total CD8+ T cells were compared in uninfected controls, ACs and HAM/TSP patients. This was to establish any differences in the frequencies of this parent cell population between the donor groups, before the specific KIR expression analysis was carried out. HAM/TSP patients were found to have significantly higher frequencies of CD8+ T EM cells in total CD8+ T cells than either ACs (p=0.0001) or uninfected controls

(p<0.0001,Table 3.2). Although frequencies of CD8+ T EM cells in total CD8+ T cells were higher in

ACs than uninfected donors there was no statistically significant difference.

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Table 3.2: Frequencies of CD8+ T EM cells within total CD8+ T cells in uninfected controls, ACs and HAM/TSP patients. % CCR7– cells in: UN AC HAM

Median ***39.38 44.17*** ***69.40***

CD8+ T cells IQR 32.65 - 60.54 33.01 - 62.20 55.81 - 81.43 No. donors 29 39 34

There were significantly higher frequencies of CD3+ CD8+ CCR7– T cells in CD3+ CD8+ T cells in patients with HAM/TSP than in ACs ***(p=0.0001) and in uninfected controls ***(p<0.0001). Median and IQRs shown are percentages of CD3+ CD8+ T cells that are CCR7–. The number (No.) of donors in each group are shown. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP; IQR, interquartile range.

Detecting specific KIR expression on CD8+ T EM cells and NK cells

To analyse specific KIR expression on CD8+ T EM cells and NK cells in HTLV-1 infection, PBMC from uninfected controls, ACs and HAM/TSP patients were stained with anti-KIR mAb clone combinations

8C11/EB6 to detect expression of 2DL1 and 2DS1, and 1F12/DX27 to detect expression of 2DL2,

2DS2 and 2DL3. Targeting of specific KIR expression was achieved by using the overlapping specificities of these cross-reactive anti-KIR mAb pairs to exclude KIRs recognised by both mAb clones and by analysing expression in donors lacking certain KIR genes or alleles (see Chapter 2

Methods, Section 2.4.2 for details). The analysis gave rise to definitions of specific KIR expression that overlap but that focus on one of the five KIRs analysed, summarised in Table 3.3. These expression definitions result from the KIR gene and allele cross-reactivity of the anti-KIR mAb clones, the potential for different KIR molecules to be co-expressed on the same cell and the KIR genotypes and allotypes required to exclude one KIR from the analysis of another. Also, as it was not possible to exclude all of the remaining KIRs from the analysis (i.e. KIRs other than the five covered by the cross- reactive anti-KIR mAb pairs), any of these other KIRs could technically be co-expressed. It is also important to note that any one donor can be analysed for expression of up to three of the five KIRs measured in the analysis depending on their KIR genotype, because individuals possess multiple KIR genes and the KIR geno/allotype requirements for 8C11/EB6 and 1F12/DX27 staining are not mutually exclusive.

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Table 3.3: KIR mAb reactivity, geno/allotype requirements and specific expression definitions for each KIR.

KIR mAb KIR geno/allotype a KIR Definitions of specific KIR expression reactivity requirements 2DL1 8C11+EB6+ 2DL1+ 2DS1– 2DL3*005– 2DL1+ 2DS1– 2DL2+/– 2DS2+/– 2DL3+/– (*005–) 2DS1 8C11–EB6+ 2DS1+ 2DL1*004– 2DS1+ 2DL1– 2DL2– 2DS2– 2DL3– 2DL2 1F12–DX27+ 2DL2+ 2DL2+ 2DS2– 2DL3– 2DL1+/– 2DS1+/– 2DS2 1F12+DX27+ 2DS2+ 2DL3– 2DS2+ 2DL3– 2DL2+/– 2DL1+/– 2DS1+/– 2DL3 1F12+DX27+ 2DL3+ 2DS2– 2DL3+ 2DS2– 2DL2+/– 2DL1+/– 2DS1+/– a Cells could potentially also express other KIRs not covered by the specificities of 1F12/DX27 and 8C11/EB6 anti-KIR mAbs

Examples of 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 staining patterns that were observed on CD8+ T EM cells and NK cells are shown in Figure 3.6. The different shapes and intensities of the staining profiles emphasise the considerable donor-to-donor variability in the compositions of KIR+ cell populations and their size. Consequentially, the gates drawn for the different KIR analyses were donor, cell type and experiment specific. Control donors for KIR staining and comparisons between the different KIR expression analyses were used to standardise the gating procedures, as described in Chapter 2

Methods, Section 2.5.1.

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Figure 3.6: See following page for legend.

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Figure 3.6: Specific KIR staining patterns on CD8+ T EM cells and NK cells. Examples of 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DS1 (2DL1– 2DL2– 2DS2– 2DL3–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DS2 (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) and 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–) staining patterns on CD3+ CD8+ CCR7– T cells (CD8+ T EM cells) and CD3– CD56dim CD16+ NK cells, showing the variation in staining between individuals. U codes, uninfected controls; H codes, asymptomatic carriers; T codes, HAM/TSP patients.

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Effector memory phenotype of specific KIR+ CD8+ T cells

To confirm in the present study that the dominant phenotype of specific KIR+ CD8+ T cells was effector memory (EM), as previously reported (Anfossi et al., 2004; Anfossi et al., 2001; Mingari et al.,

1996; Speiser et al., 1999; van Bergen et al., 2004; Young et al., 2001), the frequencies of CCR7– cells in total KIR+ CD8+ T cell populations expressing the five KIR definitions were determined. Table

3.4 confirms that the majority of KIR+ CD8+ T cells were indeed CCR7– for all five definitions of KIR expression and there were no statistically significant differences in the median frequency of CCR7– cells (i.e. KIR+ CD8+ T EM cells) in total KIR+ CD8+ T cells between any of the donor groups for any of the five KIRs analysed.

Table 3.4: Frequencies of CCR7– cells within specific KIR+ CD8+ T cells in uninfected controls, ACs and HAM/TSP patients.

%CCR7– cells in: UN AC HAM

Median 83.73 86.89 87.42

2DL1+ CD8+ T cells IQR 61.97 - 92.10 79.62 - 91.55 80.52 - 96.82 No. donors 16 17 19

Median 86.72 76.03 90.60

2DS1+ CD8+ T cells IQR 61.23 - 88.83 72.64 - 93.46 N/A No. donors 9 5 2

Median 84.40 87.79 92.26

2DL2+ CD8+ T cells IQR 64.71 - 95.72 75.59 - 95.72 80.32 - 96.50 No. donors 11 17 21

Median 79.07 75.99 71.97

2DS2+ CD8+ T cells IQR 72.36 - 86.48 62.88 - 81.18 34.09 - 87.19 No. donors 4 6 5

Median 70.92 74.99 83.67

2DL3+ CD8+ T cells IQR 63.38 - 77.91 63.76 - 82.40 68.98 - 86.57 No. donors 18 14 7

Specific KIR+ CD3+ CD8+ T cells are mostly CCR7– and therefore have an effector memory phenotype (CD8+ T EM). There were no statistically significant differences in the median frequencies of KIR+ CD8+ T EM cells in total KIR+ CD8+ T cells between any of the donor groups for any of the five KIRs analysed. Median and IQRs shown are percentages of specific KIR+ CD3+ CD8+ T cells that are CCR7–. The number (No.) of donors that could be analysed for each specific KIR definition are shown. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP; IQR, interquartile range.

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Frequencies of CD8+ T EM cells and NK cells expressing specific KIRs in uninfected controls,

ACs and HAM/TSP patients

The frequencies of 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 expression, within their specific definitions

(Table 3.3), were analysed on CD8+ T EM cells and NK cells in uninfected donors, ACs and HAM/TSP patients who met the KIR genotype and allotype requirements for each KIR analysis, and their specific KIR+ cell frequencies were compared to identify any changes that were associated with

HTLV-1 infection and HAM/TSP. Each specific KIR definition was detected on CD8+ T EM cells and

NK cells, as shown in Figure 3.7, although frequencies of specific KIR+ CD8+ T EM cells were low in general. Median frequencies of CD8+ T EM cells expressing 2DL1 or 2DL2 were ~1%, those expressing 2DL3 or 2DS2 ~3%, and 2DS1 ~0.5% across all three donor groups. Strikingly, ACs had significantly higher frequencies of 2DL2+ CD8+ T EM cells than patients with HAM/TSP (p=0.0345 by

Mann-Whitney test, ns by Kruskal-Wallis test), though not compared to uninfected subjects, despite the similarity in the median frequencies of 2DL2+ CD8+ T EM cells in HAM/TSP patients and uninfected donors (Figure 3.7A). 2DS2+ CD8+ T EM cells, which could potentially include 2DL2 co- expression (see Table 3.3), were also at higher frequencies in ACs compared to HAM/TSP patients but, although close, this difference was not statistically significant (p=0.0519; Figure 3.7A). A subset of individuals had relatively high frequencies of 2DL1+ and 2DL3+ CD8+ T EM cells and showed similar frequency distributions to those of global KIR+ CD8+ T cells in HTLV-1 carriers, although unlike in the global KIR+ CD8+ T cell analysis, uninfected donors also had higher frequencies of 2DL1+ and

2DL3+ CD8+ T EM cells (see above, Section 3.3.1). Also, wider ranges of frequencies were observed for 2DL3+ and 2DS2+ CD8+ T EM cells, compared to 2DL1+, 2DL2+ and 2DS1+ CD8+ T EM cells.

The median frequencies of specific KIR+ NK cells varied between the different KIRs even more so than for KIR+ CD8+ T EM cells, with a median of ~25% NK cells expressing 2DL1, 2DL3 or 2DS2, and

~5% 2DL2 or 2DS1. Also similar to CD8+ T EM cells, wider ranges of 2DL1+, 2DL3+, and 2DS2+ NK cell frequencies were observed than for 2DL2+ and 2DS1+ NK cells, as Figure 3.7B shows.

Interestingly, patients with HAM/TSP had significantly lower frequencies of 2DL1+ NK cells than ACs

(p=0.0245 by Mann-Whitney test, ns by Kruskal-Wallis test; Figure 3.7B) and there was also a significant decrease in 2DS1+ NK cell frequencies in ACs compared to uninfected controls (p=0.0120 by Mann-Whitney test, p=0.0341 by Kruskal-Wallis test; Figure 3.7B). While there was a similar trend

110 towards a decrease in 2DS1+ NK cell frequencies in HAM/TSP patients compared to uninfected controls, statistical analysis could be performed for 2DS1 expression in HAM/TSP patients, as n=2 for this donor group. No further statistically significant differences were observed for frequencies of specific KIR+ NK cells between any donor groups.

Figure 3.7: See following page for figure legend.

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Figure 3.7: Frequencies of specific KIR expression on CD8+ T EM cells and NK cells in HTLV-1 infection. Freshly thawed PBMC were stained with characteristic markers of effector lymphocytes and anti-KIR mAb clone combinations 1F12/DX27 to analyse expression of 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DS2 (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) and 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), and 8C11/EB6 to analyse expression of 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–) and 2DS1 (2DL1– 2DL2– 2DS2– 2DL3–) on (A) CD3+ CD8+ CCR7– T cells (CD8+ T EM cells), and (B) CD3– CD56dimCD16+ NK cells, by flow cytometry. Data were not normally distributed in all study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using nonparametric Kruskal-Wallis and 2-tailed Mann-Whitney tests. The p values indicated are from Mann-Whitney tests. For 2DS1+ NK cell frequencies in UN compared to AC, statistical significance was also reached using Kruskal-Wallis tests (p=0.0341). The other analyses reached statistical significance by Mann-Whitney test only. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.4.2 Influence of cognate HLA class I ligand on the inhibitory KIR repertoire of effector lymphocytes in HTLV-1 infection

The influence of HLA class I ligand possession on the frequencies of 2DL1+, 2DL2+ or 2DL3+ CD8+ T

EM cells and NK cells was investigated in uninfected controls, ACs and patients with HAM/TSP. Only the inhibitory KIRs were considered in this analysis, because the exact ligand specificity of 2DS1 and

2DS2 is uncertain, the interactions that are known are weak and donor numbers were much lower than for their inhibitory counterparts. Study subjects were grouped according to their possession of at least one copy of a cognate HLA class I ligand for each inhibitory KIR. 2DL1 interacts exclusively with group HLA-C2 allotypes, which have a lysine at position 80 (HLA-C2lys80), while 2DL2 and 2DL3 bind group HLA-C1 allotypes which have an asparagine at position 80 (HLA-C1asn80). Although 2DL2 and to a much weaker extent 2DL3 cross-react with HLA-C2 allotypes these are much weaker interactions than with HLA-C1 allotypes (Moesta et al., 2008), therefore the higher affinity interactions with HLA-

C1 allotypes were analysed. Comparisons of inhibitory KIR+ CD8+ T cell and NK cell frequencies between individuals with and without cognate HLA class I molecules, revealed no statistically significant differences, in any of the three donor groups (Figure 3.8). Therefore, the inhibitory KIR repertoire of CD8+ T EM cells and NK cells is independent of HLA class I ligand possession in this cohort of HTLV-1 carriers and uninfected controls.

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KIR HLA class I ligand 2DL1 Group HLA-C2lys80

2DL2 Group HLA-C1asn80 2DL3 Group HLA-C1asn80

Figure 3.8: The effect of cognate HLA class I ligand possession on the frequencies of inhibitory KIR+ CD8+ T cell and NK cell in HTLV-1 infection. Frequencies of 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–) and 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (CD8+ T EM cells) and CD3– CD56dimCD16+ NK cells, as measured by flow cytometry, are shown in study subjects without any cognate HLA class I ligands and in those with and at least one copy for each inhibitory KIR analysed. Data were not normally distributed in all study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between groups was determined using 2- tailed Mann-Whitney tests. UN, uninfected control; AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.4.3 Relationships between HTLV-1 PVL and the frequencies of specific KIR+ effector lymphocytes in HTLV-1 infection

The frequencies of 2DL1+, 2DS1+, 2DL2+, 2DS2+ and 2DL3+ CD8+ T EM cells and NK cells were tested for correlations with PVL in ACs and HAM/TSP patients. The distributions of PVLs in the cohort of HTLV-1 carriers used for specific KIR expression analysis were essentially the same as those measured for the global KIR expression cohort (see Figure 3.4), since almost all of the study subjects analysed in the latter cohort were also tested in the former and there were only a few additional donors (see Appendix).

Due to the highly significant difference in PVLs between ACs and patients with HAM/TSP, correlation tests were performed separately on each donor group. Strikingly, a significant inverse correlation was observed between PVL and the frequency of 2DL2+ CD8+ T EM cells in patients with HAM/TSP

(p=0.0132), but there was no relationship in ACs (p=0.5985; Figure 3.9). Interestingly, when ACs with a PVL of >1% (i.e. matching the range of PVLs observed in the HAM/TSP patients) were tested for a correlation between PVL and the frequency of 2DL2+ CD8+ T EM cells there was also an inverse trend, which was very similar to the relationship that was observed in HAM/TSP patients, but was not statistically significant (p=0.1095; Figure 3.10). Fisher’s chi-squared method was then used to combine the two p values obtained separately for patients with HAM/TSP and high load ACs (Sokal et al., 2004; see also Chapter 2 Methods, Section 2.7.2). This resulted in an improved significance level over that observed for HAM/TSP patients alone (p=0.0108, Figure 3.10) and revealed that there was

+ + an inverse relationship between the frequency of 2DL2 CD8 T EM cells and PVL in HTLV-1 carriers when the load is >1%.

There was also a negative trend in HAM/TSP patients between PVL and the frequency of 2DS2+

CD8+ T EM cells, which have the potential to co-express 2DL2. This association was not far from statistical significance (p=0.0833; Figure 3.9) and considering n=5 for this donor group, the p value was relatively low. In contrast, in ACs there was no relationship between PVL and the frequency of

2DS2+ CD8+ T EM cells. A highly significant inverse correlation was observed between PVL and

2DL3+ CD8+ T EM cell frequency in ACs (p=0.0032) but there was no correlation in patients with

HAM/TSP (p=0.1095; Figure 3.9). When correlations between PVL and the frequency of 2DL3+ CD8+

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T EM cells were tested in ACs with a PVL of >1%, as they were for frequency of 2DL2+ CD8+ T EM cells, no relationship was observed (p=0.7825) and the combination of p values for high load ACs with

HAM/TSP patient was also not significant (p=0.2962; both Figure 3.10). There were no statistically significant relationships between PVL and the frequencies of 2DL1+ CD8+ T EM cells in ACs or

HAM/TSP patients, however the negative trend in patients with HAM/TSP was close to statistical significance (p=0.0650; Figure 3.9). Correlation analyses for 2DS1+ CD8+ T EM cell frequency and

PVL were hampered by the low number of donors that could be assessed for expression of this activating KIR (n=5 for ACs, n=2 for HAM/TSP patients), resulting in no statistically significant associations for either ACs or patients with HAM/TSP (Figure 3.9).

Correlation analyses of PVL and the frequencies of NK cells expressing 2DL1, 2DS1, 2DL2, 2DS2 or

2DL3 revealed no significant relationships in either ACs or patients with HAM/TSP for any of the five

KIRs (Figure 3.11), in marked contrast to the associations observed for specific KIR expressing CD8+

T EM cells.

It is striking that the significant correlations with PVL were either with the frequency of CD8+ T EM cells expressing the inhibitory KIRs 2DL2 or 2DL3, but there were no correlations with the frequencies of specific KIR+ NK cells. Also, all the significant relationships observed were inverse, therefore higher frequencies of these inhibitory KIR+ CD8+ T EM cells were associated with lower

PVLs. The frequency of 2DL2+ CD8+ T EM cells appears to have a particularly strong relationship with

PVL in HTLV-1 carriers with a PVL greater than 1%. Interestingly however, the strongest correlation observed was for the inverse relationship between the frequency of 2DL3+ CD8+ T EM cells and the full range of PVLs in ACs.

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Figure 3.9: Correlations of PVL with frequencies of CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients. HTLV-1 PVL, as measured by q-PCR, is plotted against the frequencies of 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (CD8+ T EM cells), as measured by flow cytometry, in each patient group. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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Figure 3.10: Correlations of high PVL with frequencies of CD8+ T EM cells expressing 2DL2 or 2DL3. HTLV-1 PVL, as measured by q-PCR, is plotted against the frequencies of 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1 +/–) or 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (CD8+ T EM cells), as measured by flow cytometry, in each patient group for study subjects with a PVL greater than 1%. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. The combined statistical significance of the p values from Spearman’s ranks for ACs and patients with HAM/TSP for each KIR were calculated using Fisher’s chi-squared method (see Methods, Section 2.7.2). AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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Figure 3.11: Correlations of PVL with frequencies of NK cells expressing specific KIR molecules in ACs and HAM/TSP patients. HTLV-1 PVL, as measured by q-PCR, is plotted against the frequencies of 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) CD3– CD56dim CD16+ NK cells in each patient group, as measured by flow cytometry. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.4.4 The effect of specific KIR gene presence/absence on HTLV-1 PVL

The immunogenetic analysis of the Kagoshima cohort by Seich al Basatena et al., (2011) showed that possession of a 2DL2 gene in the absence of the known protective HLA-C*08 or detrimental HLA-

B*54 alleles, either with or without an HLA-C1 ligand, had no effect on PVL or the outcome of HTLV-1 infection in. However, in the present cohort containing individuals with mixed HLA class I alleles, a significant association between 2DL2+ CD8+ T EM cell frequency and PVL was detected. Therefore, to test whether this relationship was reflected at the genetic level in the St. Mary’s cohort, ACs and patients with HAM/TSP were grouped according to the presence/absence of each KIR gene within their KIR genotype and their PVLs were compared.

These comparisons revealed that HAM/TSP patients with a 2DL2 gene had a significantly lower PVL than those without a 2DL2 gene (p=0.027) but there was no effect of 2DL2 gene possession on PVL in ACs (Figure 3.12). Also, HAM/TSP patients without a 2DS1 gene had a lower PVL than those with a 2DS1 gene, which was just significant (p=0.0469) but again, there was no effect of 2DS1 gene possession on PVL in ACs (Figure 3.12). There were no effects of 2DL1, 2DL3 or 2DS2 gene possession on PVL in either ACs or patients with HAM/TSP (Figure 3.12). The comparison analyses for 2DL1, and to some extent 2DL3 were hampered by the low number of both ACs and patients with

HAM/TSP lacking these genes. This was also an issue for the immunogenetic analysis of the

Kagoshima cohort, and prevented the inclusion of 2DL1 and 2DL3 in this study (Seich al Basatena et al., 2011). Interestingly, the ranges of PVLs in ACs with and without all five KIR genes overlapped completely, resulting in the striking absence of any KIR gene possession effects in these disease-free

HTLV-1 carriers. While the two-fold reduction in PVL in HAM/TSP patients with a 2DL2 gene is a mild effect, this result is consistent with the statistically significant observation that higher frequencies of

2DL2+ CD8+ T EM cells are associated with lower PVLs in individuals with inflammatory disease.

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Figure 3.12: PVLs in ACs and patients with HAM/TSP with and without 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 genes. HTLV-1 PVLs are shown in study subjects with and without 2DL1, 2DS1, 2DL2, 2DS2 and 2DL3 genes, as determined by multiplex q-PCR using SSPs and SSOPs. Data were not normally distributed in all study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between donors with and without each KIR was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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3.5 Discussion

3.5.1 Expansions of 2DL2+ CD8+ T EM cells are associated with AC status

Global KIR+ CD8+ T cells were detected in all donors analysed and, similar to previous studies, average frequencies across the whole cohort were ~5% (Anfossi et al., 2001; Björkström et al., 2012;

Mingari et al., 1996). Although there was an expansion of global KIR+ CD8+ T cells in a small subset of HTLV-1 carriers, HTLV-1 infection was not associated with an enrichment of global KIR-expressing

CD8+ T cells. There are conflicting reports in the literature on whether persistent viral infection causes expansions of KIR+ CD8+ T cell populations, which have been proposed to result from the sustained antigenic stimulation during chronic infection (Anfossi et al., 2001). Consistent with my findings are studies showing that CD8+ T cells expressing KIRs were not enriched in treated or untreated HIV infection (Anfossi et al., 2004; Galiani et al., 1999), in chronic HCV infection (Bonorino et al., 2007) or in symptomatic primary or persistent EBV infection (Poon et al., 2005). In contrast, other studies have shown that KIR+ CD8+ T cells accumulate in HIV-infected individuals over time and with increasing viral load (Alter et al., 2008; Sirianni et al., 2001) and in individuals experiencing CMV reactivation following SCT (van der Veken et al., 2009) or kidney transplant (van Stijn et al., 2008). Therefore, the dynamics of KIR+ CD8+ T cell populations in chronic viral infections appear to vary substantially between different viruses, patient groups and study designs.

Frequencies of global KIR+ CD8+ T cells did not increase with donor age in HTLV-1+ or uninfected individuals. This finding was unexpected, since this relationship has been reported previously in healthy individuals (Abedin et al., 2005; Anfossi et al., 2001; Li et al., 2009a) and is consistent with the fact that KIRs are expressed on memory CD8+ T cells (van Bergen et al., 2010; Vivier et al., 2004), which increase in frequency with age (Nikolich-Zugich, 2008). Since HTLV-1 infection was not associated with an increase in the frequency of global KIR+ CD8+ T cells, HTLV-1 infection itself is unlikely to explain the lack of relationship with donor age in HTLV-1+ individuals in the present study. It is more likely that the lack of correlation in both HTLV-1+ and uninfected individuals in this study compared to previous work stems from differences in study design. Only one other analysis included a correlation test (Anfossi et al., 2001), while the others compared two age categories for their frequencies of KIR+ CD8+ T cells (Abedin et al., 2005; Li et al., 2009a). Also, Anfossi et al., (2001) detected KIR expression using 3 anti-KIR mAbs recognising 7 KIRs, whereas I analysed the

122 expression of all but two KIRs. The study subjects of these authors also had a wider range of ages than those in the present study and it was those individuals at the extremes of age that had the largest differences in their frequencies of KIR+ CD8+ T cells.

CD8+ T EM cells expressing the five KIRs within the specific KIR definitions described in the present study were detected, and average frequencies were approximately 1% for 2DL1, 1.5% for 2DL2, 2.5% for 2DL3, 0.5% for 2DS1 and 3% for 2DS2. Very little is known about KIR repertoires of CD8+ T cells at this level of detail. The study describing the production and characterisation of the 1F12 and 8C11 anti-KIR mAb clones demonstrated staining patterns for the following specific KIR definitions on

CD3+CD56– and CD3+CD56+ T lymphocytes: 2DL2+2DL3/2DS2– (1F12–GL183+),

2DL1+2DL2/2DL3/2DS2– (8C11+GL183–) and 2DS1+2DL1/2DL2/2DL3/2DS2– (8C11–EB6+; David et al., 2009). My staining patterns were very similar to those shown by these authors, despite some differences in the anti-KIR mAbs used. However, David et al. did not assess the frequencies of these cells in a cohort of donors. More recently, Bjorkstrom et al., (2012) performed a high-resolution analysis of specific KIR expression using commercially available single KIR-specific mAb clones for

2DL1 and 2DL3. Analysing individuals who were homozygous for the group A KIR haplotype, they showed that on average ~3% of CD45RA+CD57+CD8+ T cells expressed 2DL1 and ~5% expressed

2DL3. Furthermore, these authors showed that in study subjects with a group B haplotype ~0.4% of

CD57+CD8+ T cells expressed 2DS1, using a 2DL1/S1-specific mAb in combination with the 2DL1- specific clone. Despite the different mAbs used, both to detect specific KIRs and to define CD8+ T EM cells, these frequencies of 2DL1+, 2DL3+ and 2DS1+ CD8+ T EM cells are very similar to those detected in the present study. Bjorkstrom et al., (2012) also showed that the KIR repertoire of CD8+ T

EM cells is skewed toward single KIR+ cells and one particular KIR tends to dominate, much more so than on NK cells. Analyses of KIR mRNA expression in sorted PBMC and in clones have also shown that the KIR repertoire is more restricted on T cells than NK cells (Abedin et al., 2005; Uhrberg et al.,

2001). These observations suggest that the specific KIR staining in my study could represent CD8+ T

EM cells expressing only the particular KIR targeted by my combinations of anti-KIR mAbs, rather than cells co-expressing multiple KIRs. However, in individuals with a group B KIR haplotype, which were included in the present study, it is perhaps less likely that KIR+ CD8 T cells predominantly express one KIR, because more KIR genes are available to be expressed.

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HAM/TSP patients had higher proportions of CD8+ T cells with an EM phenotype than ACs, as demonstrated previously (Tattermusch, 2012), however, AC status was associated with an expansion of 2DL2+ CD8+ T EM cells, compared to HAM/TSP disease. This result was significant by Mann-

Whitney test but not by Kruskal-Wallis test; had more donors been available, the statistical results may have been stronger. Nevertheless, this finding suggests a protective role for 2DL2 expression on

CD8+ T EM cells in HTLV-1 infection and is therefore an extremely interesting observation, because it is consistent with the finding by Seich al Basatena et al., (2011) that possession of a 2DL2 gene can confer protection from inflammatory disease in HTLV-1 infection, by enhancing HLA class I- associated immunity. It also supports the hypothesis proposed by these authors that the effects of

2DL2 on HLA class I-associated antiviral immunity are mediated via the expression of 2DL2 on CD8+

T cells. The fact that no differences were detected between ACs and HAM/TSP patients when global

KIR expression was analysed highlights the need to analyse the expression of particular KIRs on memory CD8+ T cells, otherwise subtle changes in KIR expression may be missed. This level of detail is also necessary to investigate the mechanisms behind the different genetic associations found for particular KIR genes with the outcome of disease. This has only become possible recently, with the availability of more specific anti-KIR mAbs (David et al., 2009). Moreover, the development of reagents to allow detection of individual KIR expression will be invaluable for studies in this field.

The inhibitory KIR repertoire expressed by CD8+ T EM cells was independent of cognate HLA class I ligand possession, in HTLV-1+ and uninfected individuals. The same conclusion was reached for the expression of 2DL1, 2DL3 and 3DL1 on terminally differentiated CD8+ T cells in healthy donors

(Björkström et al., 2012). This finding suggests that the ligation of KIRs expressed on CD8+ T EM cells is not required for the maintenance of these KIR+ CD8+ T EM cells, if indeed the promotion of survival by KIR expression is a major contributing factor to the propagation of KIR+ CD8+ T cell populations.

However, there are several aspects of KIR biology which could result in any effect of particular

KIR:HLA class I ligand pairs being very subtle and therefore difficult to detect using this approach.

Firstly, since the KIR expression definitions in the present study can include co-expressed KIRs, these definition may not be specific enough to detect the influence of a particular KIR:HLA class I ligand pair, because other combinations could also affect the CD8+ T cell population being analysed.

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Secondly, 2DL2 and 2DL3 have been shown to cross-react with HLA-C2 as well as HLA-C1 (Moesta et al., 2008). Though these are weaker interactions than with their main HLA-C1 ligands, especially for 2DL3, the 2DL2/HLA-C2 interaction does have functional consequences ex vivo (Pende et al.,

2009; Schonberg et al., 2011). Thus, individuals with two HLA-C2 group alleles do not necessarily constitute donors without an HLA class I ligand, for 2DL2 in particular. Finally, the present analysis was limited by the low number of individuals without a cognate HLA class I ligand. Further work on how KIR+ CD8+ T cell populations are induced and maintained is also needed, in order to fully understand any contribution that cognate HLA class I ligand may make to this process.

3.5.2 Reductions in 2DL1+ and 2DS1+ NK cells in HTLV-1 infection

HTLV-1 infection was not associated with any alterations in the frequencies of global KIR+ NK cells and there was no relationship with donor age. However, the analysis of specific KIR expression revealed a reduction in 2DL1+ NK cells in individuals with the inflammatory disease compared to ACs.

This result was significant by Mann-Whitney test but not by Kruskal-Wallis test; had more donors been available, the statistical results may have been stronger. Also, lower frequencies of 2DS1+ NK cells were observed in HTLV-1 infection, at least in ACs compared to uninfected controls, since

HAM/TSP patients could not be statistically analysed for 2DS1 expression because of low donor numbers. This finding was significant by both Mann-Whitney and Kruskal-Wallis tests. These observations are similar to those made in two studies of chronically HIV-1-infected, antiretroviral therapy-naïve individuals, which showed reductions in 2DL1+ NK cells alongside expansions of 3DL1+

NK cell subsets in a Ugandan cohort (Eller et al., 2011) and decreased 2DL1/S1+ NK cell frequencies in a South-African cohort (Wong et al., 2010). Skewed KIR repertoires have also been found in NK cell populations from individuals infected with CMV (Béziat et al., 2013), chronic HCV/HBV (Béziat et al., 2012), and in acute infections with Hantavirus (Björkström et al., 2011) and Chikungunya virus

(Petitdemange et al., 2011). Together, these findings suggest that the NK cell compartment has the ability to adapt to acute and chronic viral infection, through the expansion and contraction of different

KIR-expressing populations.

The reductions in 2DL1+ and 2DS1+ NK cells observed in HTLV-1 infection might be compensated by expansions of NK cell subsets expressing other KIRs which have not been measured in the present

125 study, such as 3DL1, as observed by Eller et al., (2011). Indeed, since there was no change in the frequency of global KIR+ NK cells, this seems a likely scenario. Alternatively, the loss of 2DL1+ NK cells in individuals with HAM/TSP could simply reflect the lower proportions of total NK cells that were observed in these patients compared to ACs, which has been shown previously (Azakami et al., 2009;

Fujihara et al., 1991; Tattermusch, 2012; Yu et al., 1991). These studies also reported that HAM/TSP disease was associated with low NK cell activity so, the reductions in 2DL1+ and 2DS1+ NK cells observed in the present study could potentially be related to reduced NK cell function in individuals with inflammatory disease. Whether the changes in the KIR repertoires of NK cells reported in the various viral infections are in response to, or are caused by the infecting viruses remains to be determined. These findings further emphasise the need to analyse expression of particular KIR molecules on NK cells and T cells, since differences in their expression patterns may not be captured at the global KIR level. Together, these observations suggest NK cells play an important role in controlling HTLV-1 infection, which deserves further research.

The average frequencies of NK cells expressing the five KIRs within the expression definitions described in the present study were approximately 20% for 2DL1, 5% for 2DL2, 30% for 2DL3, 5% for

2DS1 and 25% for 2DS2. These data reveal the substantial variability between the different KIRs in the proportions of NK cells expressing them. David and colleagues showed previously using their

1F12 and 8C11 anti-KIR mAb clones that on average ~9% of NK cells were 2DL2+2DL3/2DS2–

(1F12–GL183+), ~10% were 2DL1+2DL2/2DL3/2DS2– (8C11+GL183–), and 6.25% were

2DS1+2DL1/2DL2/2DL3/2DS2– (8C11–EB6+; David et al., 2009). The differences in the percentages of

2DL2+ and 2DL1+ NK cells compared to those I observed are most likely due to my use of DX27 and

EB6 respectively, rather than GL183. For 2DL2 staining, this substitution only changes the clone used, while for 2DL1 staining it also changes the definition of KIR expression. Recently, a number of studies have profiled 2DL1, 2DL3 and 3DL1 expression on NK cells using single KIR-specific mAb clones which are commercially available. Average frequencies of 2DL1+ NK cells were 10-30% and between 15-30% of NK cells were 2DL3+ (Béziat et al., 2012; Béziat et al., 2013; Björkström et al.,

2012; Eller et al., 2009; Eller et al., 2011; Petitdemange et al., 2011; Wong et al., 2010). Thus, considering the different anti-KIR mAb clones used, my data are in line with all of these reports and

126 thus represent accurate staining of specific KIR-expressing NK cell populations using the new 1F12 and 8C11 clones.

Similar to the average frequencies, the ranges of NK cell frequencies expressing each KIR definition also varied considerably between the five different KIRs. 2DL1+, 2DL3+ and 2DS2+ NK cells had wider ranges in frequency, while 2DL2+ and 2DS1+ NK cells had much narrower ranges. These ranges also demonstrate the variability between individuals in the proportions of NK cells expressing the same

KIR. David et al., (2009) showed similar ranges of NK cells expressing 2DL2 and 2DS1 to those found in the present study, which was expected given that exactly the same anti-KIR mAb specificities were used in both analyses. However, I detected a broader range of 2DL1+ NK cell frequencies than David et al., (2009) and the ranges of frequencies reported for staining with the single 2DL1 and 2DL3 specific mAbs were even wider than those I observed (Béziat et al., 2013; Eller et al., 2009; Eller et al., 2011; Petitdemange et al., 2011; Wong et al., 2010). The different ranges shown by different studies for the same KIR, and potentially the different ranges observed between the different KIRs, are likely to be affected by the differing stringencies of the KIR expression definitions i.e. the degree of KIR co-expression that is included in each KIR+ population, resulting from the specificities of the anti-KIR mAbs that have been used. Thus, David et al., (2009) analysed 2DL1+2DL2/2DL3/2DS2– NK cells, whereas my definition of 2DL1 expression only excluded 2DS1, and staining with the single

2DL1 and 2DL3 specific mAbs can include co-expression of all other KIRs. Also, in the present study, the 2DL2 definition was more stringent than the definitions for 2DL1 or 2DL3, which may be reflected in the fact that fewer NK cells expressed 2DL2 than 2DL1 or 2DL3. Differences in KIR expression definition stringency will also have affected the CD8+ T cell analysis but the variation observed was more subtle because frequencies of specific KIR+ CD8+ T cells are lower.

The stochastic nature of KIR expression is demonstrated by, (1) the variation in the shapes of the distributions of NK cell frequencies expressing the five different KIRs, (2) the diversity between individuals in the proportions of NK cells expressing the same KIR, and (3) the different KIR staining patterns and intensities observed in different individuals. Together, these observations are testimony to the many different aspects of KIR biology that can influence the widely varying KIR repertoires expressed by NK cells from different individuals.

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The reduction in 2DL1-expressing NK cells in HAM/TSP patients, and the inhibitory KIR repertoire expressed by NK cells in general, was independent of cognate HLA class I ligand possession in both

HTLV-1+ and uninfected individuals. It is widely accepted, though not fully understood mechanistically, that NK cells interact with self HLA class I molecules in order to fine-tune their functional responsiveness during licensing (Vivier et al., 2011). However, the influence of this interaction with

HLA class I ligand on the formation of the KIR repertoire of NK cells is contentious, with several studies reporting conflicting conclusions. Consistent with my findings, two studies have shown that the presence of cognate HLA class I ligand did not bias NK cells towards expression of specific KIRs

(Andersson et al., 2009; Björkström et al., 2012). In contrast, others have shown increased frequencies of NK cells expressing particular KIRs in individuals carrying cognate HLA class I ligand, and this effect was particularly evident in individuals that were homozygous for group A KIR haplotype

(Schonberg et al., 2011; Shilling et al., 2002b; Yawata et al., 2006; Yawata et al., 2008). However, these studies also demonstrated that the strength of the influence that cognate HLA class I molecules have may be modulated by other factors, because the frequencies of NK cell subsets expressing particular KIRs were (1) reduced as the number of cognate KIR/HLA class I pairs an individual had increased (Yawata et al., 2006), (2) reduced in the presence of a group B KIR haplotype (Schonberg et al., 2011), (3) adjusted according to the strength of effector response produced by the KIR/HLA class I pair(s) expressed (Yawata et al., 2008), and (4) modulated by the presence of other NK cell receptors, such as NKG2A (Yawata et al., 2008) and NKG2C (Béziat et al., 2012). The stochastic control of KIR expression is also likely to influence these points, as is the sequential acquisition of

KIRs on NK cells during development, for which there is emerging evidence (Fischer et al., 2007;

Stern et al., 2010). Together, these findings show that, the structure of KIR repertoires is influenced by complex interplay between many different aspects of KIR biology, one of which is cognate HLA class I ligand.

In contrast to my finding that the reduced frequencies of 2DL1+ NK cells in HAM/TSP patients was not associated with cognate HLA class I ligand possession, the skewing of NK cell KIR repertoires by viral infection was shown to be either dependent on, or strengthened by, the presence of cognate HLA class I ligand (Alter et al., 2009; Béziat et al., 2012; Béziat et al., 2013; Björkström et al., 2011; Eller et

128 al., 2011; Petitdemange et al., 2011; Wong et al., 2010). This observation provides evidence that the

KIR repertoires of NK cell continue to be influenced by their ability to interact with cognate HLA class I following their development in the bone marrow. Indeed, there is growing consensus that further maturation of the NK cell compartment may occur in the periphery (Sun, 2010). Consequently, it has even been suggested that an underlying viral infection might explain previous observations that cognate HLA class I ligand influences KIR repertoires (Béziat et al., 2013). Thus, given the complexity of the KIR system and the dynamic nature of the phenotype of NK cells, it seems impossible to predict what the frequency of NK cells expressing specific KIRs may be, in individuals with mixed KIR haplotypes and persistent viral infection, based simply on the genetic presence/absence of cognate

HLA class I molecules.

This analytical approach is also subject to the same limitations discussed above for the CD8+ T cell analysis, namely (1) there is a lack of individuals without a cognate HLA class I ligand, (2) individuals with HLA-C2/C2 genotypes can ligate 2DL2 and 2DL3 to a degree (Moesta et al., 2008), thus these individuals are not necessarily without an HLA class I ligand, particularly for 2DL2, and (3) the co- expression of multiple KIRs included in the KIR expression definitions may make it difficult to detect the influence of a particular KIR/HLA class I ligand pair, especially for NK cells since they are known to express on average 3-5 different KIRs (Valiante et al., 1997). Despite these caveats, it was anticipated that the increased detection specificity possible with the 1F12 and 8C11 mAb clones might reduce the effect of at least this latter issue. Indeed, David et al., (2009) showed an accumulation of

2DL1+2DL2/2DL3/2DS2– NK cells in healthy HLA-C2 homozygotes, in contrast to my findings.

However, these authors also found no effect of cognate HLA class I molecules on 2DL2+2DL3/2DS2–

NK cell frequencies, which is consistent with the present study.

3.5.3 Expansions of global KIR+ CD4+ T cells in HTLV-1 infection correlate with age

CD4+ T cells expressing global KIRs were found in all donors, with the exception of one AC. In the majority of individuals, frequencies were low and in uninfected subjects they were consistent with previous work which showed an average of 0.2% KIR+ CD4+ T cells in healthy donors (van Bergen et al., 2004). Interestingly, the global KIR+ CD4+ T cell population was expanded in HAM/TSP patients compared to uninfected controls. This result was significant by Mann-Whitney test but not by Kruskal-

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Wallis test; had more donors been available, the statistical results may have been stronger. The widely accepted bystander damage theory of HAM/TSP pathogenesis (Ijichi et al., 1993; Jacobson,

2002) implicates activated HTLV-1-infected and HTLV-1-specific CD4+ T cells in causing the characteristic inflammatory spinal cord lesions of HAM/TSP. Evidence supporting this hypothesis comes from the observations that CD4+ T cells are the major cell type found in early active lesions

(Iwasaki et al., 1992; Usuku et al., 1990) and that the high frequencies of HTLV-1-specific CD4+ T cells found in HAM/TSP patients fail to control infection (Goon, 2002; Goon et al., 2004b; Nose et al.,

2007), contributing to the higher PVL of HAM/TSP patients (Nagai et al., 1998). Therefore, this finding could indicate a potential role for KIR expression on CD4+ T cells in the pathogenesis of this inflammatory disease.

In an analogous manner, the large accumulations of CD4+ CD28– T cells are found in patients with severe complications of rheumatoid arthritis (RA), and which are thought to have a causative role in extra-articular exacerbations of this inflammatory condition (Martens et al., 1997), have been shown to express 2DS2 (Namekawa et al., 2000; Yen et al., 2001). More recently, these expanded CD4+

CD28– T cell populations were shown to be present only in CMV+ RA patients and the majority of the

CD4+ CD28– T cells in these individuals responded to CMV antigens (Fasth et al., 2007; Thewissen et al., 2007; van Bergen et al., 2009). Furthermore, van Bergen et al., (2009) demonstrated that amongst CD4+ CD28– T cell clones from two RA patients generated using polyclonal stimulation, a larger fraction of KIR+ CD4+ CD28– T cell clones responded to CMV antigens than their KIR– counterparts. These authors extended a previous hypothesis stating that the CD4+ CD28– T cells detected in RA patients with severe extra-articular complications are autoreactive cells whose function may be modulated by 2DS2 expression in individuals with this KIR gene (Goronzy et al., 2005), to propose that the expansion of this subset could be driven by persistent CMV infection. They further suggest that if CMV-specific CD4+ CD28– T cells cross-react with self-antigens, CMV infection coupled with activating KIR expression could stimulate their proliferation and cytokine production, exacerbating the tissue damage observed in these RA patients (van Bergen et al., 2010). It is possible that in patients with HAM/TSP, activating KIR expression on HTLV-1-infected and/or HTLV-

1-specific CD4+ T cells could similarly augment their proliferation and release of pro-inflammatory cytokines, thus contributing to HAM/TSP pathology. This raises a number of interesting questions

130 regarding the expanded global KIR+ CD4+ T cells observed in the peripheral blood of HAM/TSP patients, such as (1) are these cells HTLV-1-specific? (2) are they infected with HTLV-1? (3) do they represent expansions of cells expressing activating KIRs, and 2DS2 specifically? (4) are expansions of KIR+ CD4+ T cells found in the CSF as well as in peripheral blood?

Similar to the distribution of global KIR+ CD8+ T cell frequencies in HTLV-1+ individuals, there was an accumulation of CD4+ T cells expressing global KIRs in a small subset of individuals, this time within all three donor categories, and in ACs this accumulation was age-dependent. This relationship has been described previously in healthy donors and has been linked to the memory phenotype of KIR- expressing CD4+ T cells and the expansion of the memory compartment with increasing age (Anfossi et al., 2001; van Bergen et al., 2004). Since global KIR+ CD4+ T cells were more frequent in HAM/TSP patients than in uninfected controls, the positive correlation between the frequency of these cells and donor age in ACs could reflect an increasing risk of HAM/TSP with age in these HTLV-1 carriers. This suggestion is consistent with the fact that HAM/TSP tends to develop after decades of asymptomatic

HTLV-1 infection (Nakagawa et al., 1995; Olindo et al., 2006) which, in turn, could also be related to the declining ability of an aging immune system to cope with chronic viral infection. Indeed, as memory T cells specific for persistent viruses also accumulate with age (Nikolich-Zugich, 2008), this mechanism could also contribute to the expansion of the KIR+ CD4+ T cell population in ACs with increasing age, if these cells are HTLV-1 specific. Again, these observations highlight the need for further study of the KIR+ CD4+ T cell subset in HTLV-1 infection, although this will be technically challenging due to the low frequencies of these cell types.

3.5.4 Reduced PVLs are associated with higher frequencies of 2DL2+ and 2DL3+

CD8+ T EM cells and with possession of a 2DL2 gene in HAM/TSP patients

The HTLV-1 PVL is the major correlate of disease susceptibility in HTLV-1 infection (Nagai et al.,

1998). As such, the prevalence of HAM/TSP has been shown to increase exponentially once PVL exceeds 1% (Jeffery et al., 1999). Therefore, correlates of the PVL indicate the potential for these factors to influence its set point and consequently, the associated risk of developing HAM/TSP. There were no relationships between PVL and the frequencies of global KIR+ CD8+ T cells, NK cells or CD4+

T cells, in either ACs or individuals with HAM/TSP. In the case of global KIR+ CD8+ T cells, this result

131 contrasts with a previous study in HIV-1 infection, which found that these cells accumulated with increasing viral load (Alter et al., 2008). However, when the frequencies of specific KIR-expressing

CD8+ T EM cells were analysed for PVL correlations, several interesting inverse relationships were observed. The most striking was the inverse correlation between the frequency of 2DL2+ CD8+ T EM cells and PVL in individuals with inflammatory disease, along with a trend towards the same relationship in ACs with a PVL greater than 1%, the threshold above which the incidence of HAM/TSP was shown to increase exponentially (Jeffery et al., 1999). When this trend in ACs was statistically combined with the analysis of HAM/TSP patients it enhanced the strength of the correlation that had been found in patients with HAM/TSP, indicating that reduced PVLs are strongly associated with higher frequencies of 2DL2+ CD8+ T EM cells in individuals with PVLs in the higher range. This finding is consistent with the observation of Seich al Basatena et al., (2011) that possession of a 2DL2 gene can reduce HTLV-1 PVL by enhancing HLA class I-associated immunity and importantly, it further supports a protective role for 2DL2 expression on CD8+ T EM cells in HTLV-1 infection. Alongside the observation that there were no relationships between the frequencies of specific KIR+ NK cells and

PVL, it also provides further evidence supporting the proposal that 2DL2 expression on CD8+ T EM cells, rather than NK cells, mediates the effects of the 2DL2 gene on HLA class I-associated antiviral immunity (Seich al Basatena et al., 2011). In a similar manner, the protective 3DL1/HLA-Bw480Ile association reported in HIV-1 infection has been attributed to the direct action of NK cells (Martin et al., 2002a; Martin et al., 2007) and is also supported by a correlation between HIV-1 viral load and the frequency of 3DL1+ NK cells in HLA-Bw4+ individuals (Eller et al., 2011), as well as functional data showing that 3DL1+ NK cells have high functional activity (Boulet et al., 2010; Eller et al., 2011).

Another finding of particular interest was that the strongest relationship observed with PVL was the negative correlation with the frequency of 2DL3+ CD8+ T EM cells in ACs. However, there was no relationship in HAM/TSP patients and this did not change when the analysis of high load ACs (PVL

>1%) was statistically combined with that of HAM/TSP patients, in contrast to the improved significance observed when this analysis was performed for 2DL2. Thus, while higher frequencies of

2DL2+ CD8+ T EM cells are strongly associated with reduced PVLs in HTLV-1 carriers with PVL>1%, higher 2DL3+ CD8+ T EM cell frequencies are associated with reduced PVLs in ACs across the full range of PVLs. This finding suggests a protective role for 2DL3 expression on CD8+ T EM cells in

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HTLV-1 infection, which is unexpected and therefore very interesting, since it implies that other inhibitory KIRs, in addition to 2DL2, may have a protective effect in HTLV-1 infection via expression on CD8+ T EM cells. Indeed, while the work of Seich al Basatena et al., (2011) implicates 2DL2 gene possession specifically in enhancing HLA class I-associated immunity, preliminary results from the immunogenetic analysis of the Kagoshima cohort in this study indicated that other inhibitory KIR genes could be having a similar effect to 2DL2 (N-K. Seich al Basatena, personal communication).

Furthermore, the numbers of donors lacking particular KIR genes, especially 2DL1 and 2DL3, were not high enough to give adequate statistical power to comparisons of the effects of these KIRs in this

HTLV-1 carrier group, although they could be compared in the HCV infected cohort. Thus, it remains a possibility that the possession of other inhibitory KIR genes could influence HLA class I-associated immunity in HTLV-1 infection. The two strongly statistically significant inverse correlations observed in the present study, between the frequencies of 2DL2+ or 2DL3+ CD8+ T EM cells and PVL, suggest that there could be a particularly protective effect of expressing gene products from this locus on

CD8+ T EM cells, since 2DL2 and 2DL3 are alleles. Further inverse trends were also observed between PVL and the frequency of 2DL1+ and 2DS2+ CD8+ T EM cells in HAM/TSP patients. For the

2DS2, it is possible that this speculative result may in fact be due to co-expression of 2DL2 and its association with PVL in individuals with inflammatory disease, because the expression definition for

2DS2 includes potential co-expression of 2DL2 and these KIRs are in high LD. Although the 2DL3 expression definition also includes potential co-expression of 2DL2, none of the subjects analysed for

2DL3 expression had a 2DL2 gene. Together with the significant negative correlation result for 2DL3, the inverse trends between PVL and the frequency of 2DL1+ and 2DS2+ CD8+ T EM cells further support the notion that other inhibitory KIRs, besides 2DL2, could also have a protective effect in

HTLV-1 infection via expression on CD8+ T EM cells. Furthermore, these data, revealing the influence of particular KIRs rather than total KIRs on PVL, once again emphasise the need to analyse the expression of individual KIRs, in order to detect the differing strengths of their effects.

HAM/TSP patients with a 2DL2 gene had a lower PVL than those without a 2DL2 gene, and those that had a 2DS1 gene had a higher PVL than those that did not. These are interesting findings, given that in the Kagoshima cohort there was no effect of 2DL2 possession (or any other KIR) on PVL or the outcome of HTLV-1 infection in the absence of the known protective HLA-C*08 or detrimental

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HLA-B*54 alleles, either with or without cognate HLA class I ligand (Seich al Basatena et al., 2011).

Although the comparisons of PVL in the presence/absence of different KIR genes in the present study are very low powered and not especially robust, they indicate that the KIRs may have different effects or mechanisms of action in different ethnic groups, since the individuals in the St Mary’s cohort are mostly of Afro-Caribbean ethnicity. However, it is reassuring that the same KIR, 2DL2, appears to show protective effects in Afro-Caribbean individuals, as well as in Japanese individuals. In ACs, the lack of influence of 2DL2 gene possession on PVL is consistent with the absence of a relationship between PVL and the frequency of 2DL2+ CD8+ T EM cells in these healthy HTLV-1 carriers. This may be explained by the fact that ACs have higher frequencies of 2DL2+ CD8+ T EM cells than HAM/TSP patients, which may further reduce their already lower PVL, thus resulting in no relationship between

2DL2+ CD8+ T EM cell frequency and PVL, particularly in individuals with a PVL of less than 1%, below which the risk of HAM/TSP is lower. Unfortunately, it is not possible to relate the association of

2DS1 gene possession with increased PVL in HAM/TSP patients to the frequency of 2DS1+ CD8+ T

EM cells in these individuals because 2DS1 expression could only be analysed in two patients with

HAM/TSP disease, due to the high occurrence of 2DL1*004-like alleles in these individuals, which prevented the analysis of the frequency of 2DS1 expression.

While several of the KIRs showed inverse trends and correlations between the frequencies at which they were expressed on CD8+ T EM cells and PVL, only 2DL2 and 2DS1 had associations with PVL that were observed at the level of KIR gene presence/absence. Taken together, these different associations with PVL provide further information about the relationship between 2DL2 and the outcome of HTLV-1 infection. Large cohorts are required to sufficiently power studies of the added protection or susceptibility KIR genes can confer in viral infections, because the associations can be weakened by multiple underlying contributory factors to the severity and outcome of infection, and by the stochastic nature of KIR expression at the cell surface (Khakoo et al., 2006). This suggests that in order for protective or detrimental effects on infection outcome to be detected at the genetic level, the influence a KIR has on this must be relatively strong. Therefore, since a lower PVL was associated with possession of a 2DL2 gene and with higher frequencies of 2DL2+ CD8+ T EM cells, whereas the influence of the other KIRs on PVL were only observed at the level of expression, this could indicate that the influence of 2DL2 on the outcome of HTLV-1 infection is particularly strong compared to the

134 other KIRs. This idea is consistent with the finding that possession of a 2DL2 gene, but not other KIR genes, enhanced HLA class I-associated immunity (Seich al Basatena et al., 2011). Furthermore, both of the associations of 2DL2 gene possession with reduced PVL provide evidence for the direction of causality in the correlation between 2DL2+ CD8+ T EM cell frequency and PVL. Thus,

2DL2+ CD8+ T EM cells may have a direct role in reducing the PVL in HTLV-1 infection.

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3.5.5 Chapter 3 summary

In this Chapter, I analysed the frequency of global and specific KIR expression on circulating T cells and NK cells in HTLV-1 carriers and uninfected controls, and related this to donor age, HLA class I ligand possession and HTLV-1 PVL. The expression of global and specific KIRs was detected on

CD8+ T cells and CD4+ T cells, as well as NK cells, in HTLV infection. The global KIR+ CD4+ T cell population was expanded in HAM/TSP patients, suggesting a role for KIR expression on CD4+ T cells in HAM/TSP pathogenesis. Thus, the observed increase in global KIR+ CD4+ T cell frequency with donor age in ACs could reflect an increasing risk of HAM/TSP with age. Changes in KIR expression on CD8+ T EM cells and NK cells in HTLV-1 infection, and the influence of KIR+ CD8+ T EM cells on

PVL, were observed when specific rather than global KIR expression was analysed. Strikingly, several findings strongly support a protective role for 2DL2 expression on CD8+ T EM cells in HTLV-1 infection, these were (1) AC status was associated with an expansion of 2DL2+ CD8+ T EM cells, (2) higher frequencies of 2DL2+ CD8+ T EM cells were strongly associated with reduced PVLs in HTLV-1 carriers with PVLs greater than 1%, and (3) HAM/TSP patients with a 2DL2 gene had lower PVLs than those without a 2DL2 gene. These observations are all consistent with the finding that possession of a 2DL2 gene can confer protection from inflammatory disease and can lower PVL in

HTLV-1 infection by enhancing HLA class I-associated immunity (Seich al Basatena et al., 2011) and they support the proposal by these authors, that the effects of 2DL2 on HLA class I-associated antiviral immunity are mediated via the expression of 2DL2 on CD8+ T cells, as opposed to NK cells.

Unexpectedly, higher frequencies of 2DL3+ CD8+ T EM cells were also strongly associated with reduced PVLs in ACs, supporting a protective effect of expressing gene products from the 2DL2/3 locus on CD8+ T EM cells, in different patient groups. Finally, reductions in the frequency of 2DL1+ and 2DS1+ NK cells were observed in HTLV-1+ individuals, which are consistent with previous observations of reduced frequency and function of NK cells in HTLV-1 infection however, the inhibitory KIR repertoires expressed by NK cells, and by CD8+ T EM cells, were independent of cognate HLA class I ligand possession.

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Chapter 4. Specific KIR expression on HTLV-1-specific CD8+ T cells

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4.1 Introduction

The findings from the previous Chapter provide further support for the proposal that 2DL2 expression on CD8+ T cells mediates the effects of 2DL2 gene possession on HLA class I-associated antiviral immunity (Seich al Basatena et al., 2011). Furthermore, there was evidence to suggest that the expression of other inhibitory KIRs on CD8+ T cells may also have a protective effect in HTLV-1 infection. In order to influence HLA class I-restricted anti-HTLV-1 immunity via the proposed KIR expression-induced CD8+ T cell survival mechanism (see Introduction, Section 1.8.2), it follows that expression of 2DL2 (and potentially other KIRs) would need to protect HTLV-1-specific CD8+ T cells from AICD. KIR+ virus-specific CD8+ T cells have previously been reported in HIV-1 (Alter et al., 2008;

Anfossi et al., 2004), EBV (Poon et al., 2005), CMV (Anfossi et al., 2004; van der Veken et al., 2009) and HCV (Bonorino et al., 2007). Therefore, I investigated the patterns of KIR expression on HTLV-1- specific CD8+ T cells in ACs and HAM/TSP patients. I was also interested to determine whether the same observations in Chapter 3, regarding the influence of cognate HLA class I ligand possession on the frequencies of specific KIR+ CD8+ T EM cells and their relationships with HTLV-1 PVL, were also made for the frequencies of HTLV-1-specific CD8+ T cells expressing specific KIRs.

The antigen selected for the analysis of HTLV-1-specific CD8+ T cells was the immunodominant Tax protein because of the high abundance of Tax-specific CD8+ T cells which can be detected in

peripheral blood (Goon et al., 2004a; Jacobson et al., 1990) and the availability of the HLA-A2/Tax11-19 pentamer. It was hoped these two factors would assist with minimising the effect of two potential issues with detecting HTLV-1-specific CD8+ T cells expressing KIRs. Firstly, the frequencies of these cells were predicted to be low, given that frequencies of specific KIR+ CD8+ T EM cells shown in

Chapter 3 were ~0.5-3% (Figure 3.7A, Section 3.4.1) and frequencies of Tax-specific CD8+ T cells are typically ~0.1-2.5% (Asquith et al., 2007a). By analysing responses to the immunodominant antigen and by collecting as many PBMC as possible on the flow cytometer, the detection of specific KIR+

HTLV-1-specific CD8+ T cells should be maximised. Secondly, since the expression of KIRs on CD8+

T cells has been reported to reduce their functional capacity (van Bergen et al., 2010), this could potentially reduce detection of specific KIR+ Tax-specific CD8+ T cells when using their functionality to detect Tax specificity. To address this, high concentrations of Tax peptide (2uM) were used to

+ maximise cell stimulation and the HLA-A2/Tax11-19 pentamer was used in HLA-A2 donors to identify

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Tax11-19-specific TCRs, as an alternative to detecting Tax specificity by functional output. Since the frequencies of Tax-specific CD8+ T cells measured can differ between ACs and HAM/TSP patients, and between different detection methods, I characterised this response in my cohort.

Due to the novelty of the anti-KIR mAb clones 1F12 and 8C11, I was also interested to compare the

KIR expression data obtained in the functional analysis with the specific KIR expression analysis in

Chapter 3, to verify its reliability. Also, there were differences in the staining protocols and different formats of the 1F12 mAb were used, which could have affected the staining patterns and KIR+ cell frequencies. Given that there is almost complete overlap in the donors used for the two analyses (see

Appendix), if the data are consistent, they would be a biological repeat of the specific KIR expression analysis and would confirm the results for the comparisons of specific KIR+ CD8+ T EM cell frequencies in ACs and patients with HAM/TSP shown in Chapter 3.

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4.2 Aims

The specific aims of this chapter were:

 To characterise the CD8+ T EM cell response to the immunodominant HTLV-1 protein Tax in ACs

and patients with HAM/TSP using a functional IFNγ/CD107a assay and HLA-A2/Tax11-19 pentamer

staining

 To verify the reproducibility of the KIR staining with different 1F12 formats and using different

experimental protocols and the frequencies of specific KIR+ CD8+ T EM cells observed in Chapter

3 in the cohort of donors used for the functional analysis

 To determine the presence and frequencies of functionally Tax-specific and Tax-specific TCR+

CD8+ T EM cells expressing specific KIRs and compare them in ACs and HAM/TSP patients

 To investigate the influence of cognate HLA class I ligand possession on the inhibitory KIR

repertoire of functionally Tax-specific CD8+ T EM cells

 To examine the relationship between PVL and the frequencies of functionally Tax-specific CD8+ T

EM cells that express specific KIRs

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4.3 Results

4.3.1 Characterisation of the CD8+ T EM cell response to Tax

Detecting and measuring Tax-specific CD8+ T EM cells

CD8+ T cell specificity for the HTLV-1 protein Tax was measured in two ways, (1) by functional

IFNγ/CD107a assay following Tax peptide stimulation in a cohort of donors with mixed HLA class I alleles, including those with an HLA-A2 allele, and (2) by HLA-A2/Tax11-19 pentamer staining in HLA-

A2+ donors only. Briefly, the experimental procedure carried out was as follows (see Methods 2.3.1-2 for full details): PBMC from ACs and patients with HAM/TSP were stimulated for 5 hours with a panel of overlapping Tax peptides, divided into two pools to minimise DMSO toxicity. Cells were stained

with CD107a and IFNγ mAbs, HLA-A2/Tax11-19 pentamer, and the combinations of cross-reactive anti-

KIR mAbs to detect the specific KIRs: 1F12/DX27 for 2DL2, 2DS2 and 2DL3 detection and 8C11/EB6 for 2DL1 and 2DS1 detection.

Examples of HLA-A2/Tax11-19 pentamer staining and IFNγ/CD107a staining, together with DMSO background, are shown in Figure 4.1. Considerable variation was detected in IFNγ and CD107a responses to Tax peptide stimulation, in terms of the overall magnitude of the responses, and in the balance of IFNγ to CD107a production, which was fairly even in some subjects but was skewed towards one functional output or a combination of the two in others (Figure 4.1A). Most donors in the cohort with mixed HLA class I alleles responded to both Tax peptide pools to some extent, though there was usually a stronger response towards one of the pools. In HLA-A2+ donors, responses to

Tax peptide pool A which contained the Tax11-19 epitope dominated. When functional responses were assessed in the HLA-A2+ cohort, only responses to pool A were analysed, to maintain as much consistency between the two measure of Tax-specific CD8+ T EM cells in HLA-A2+ donors as possible. A few subjects had very low Tax responses, once DMSO background was subtracted,

particularly for CD107a which is highly background-sensitive. The sizes of the HLA-A2/Tax11-19 pentamer+ CD8+ T EM cell populations detected also varied between HLA-A2+ donors (Figure 4.1B).

Reductions in the intensity of HLA-A2/Tax11-19 pentamer staining were observed in samples which were also stimulated with Tax peptide, due to the downregulation of TCR expression upon antigen

stimulation. However, there were no marked decreases in the frequencies of HLA-A2/Tax11-19 pentamer+ CD8+ T EM cells detected (Figure 4.1B).

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+ Figure 4.1: IFNγ/CD107a and HLA-A2/Tax11-19 pentamer staining patterns on CD8 T EM cells. Examples of CD3+ CD8+ CCR7– T cells (CD8+ T EM cells) staining patterns with (A) IFNγ and CD107a mAbs, following stimulation with Tax peptide pools A and B, or in DMSO control samples, showing the variation between individuals, or with (B) HLA-A2/Tax11-19 pentamer, following stimulation with Tax peptide pool A or in DMSO control samples, showing the variation in responses between individuals and the effect of Tax peptide stimulation on HLA-A2/Tax11-19 pentamer staining. Tax pool A contains the Tax11-19 epitope. H codes, asymptomatic carriers; T codes, HAM/TSP patients; DMSO, Dimethyl sulfoxide; FCS, forward scatter.

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Effector memory phenotype of Tax-specific CD8+ T cells

The majority of Tax-specific CD8+ T cells in chronic phase HTLV-1 infection are an expanded, continually stimulated population of antigen-experienced effector lymphocytes. Therefore, it was anticipated that the majority of these cells would have an effector memory (EM) phenotype, and therefore lack CCR7 expression, similar to the majority of CD8+ T cells that express KIRs (see Table

+ 3.4, Section 3.4.1). Indeed, it has been reported that almost all Tax11-19-specific CD8 T cells that were detected in patients with HAM/TSP were CCR7– (Johnson-Nauroth et al., 2006). As Table 4.1 shows, this was also the case in the present study, for both methods of defining Tax-specificity. Also, patients with HAM/TSP had higher frequencies of CCR7– cells (i.e. Tax-specific CD8+ T EM cells) in total Tax- specific CD8+ T cells than ACs, which was statistically significant for functionally Tax-specific CD8+ T cells (p=0.0005) and Tax-specific TCR+ CD8+ T EM cells (p=0.0470). This difference is consistent with the observation that HAM/TSP patients have higher frequencies of CD8+ T EM cells per total CD8+ T cell compared to ACs (see Table 3.2, Section 3.4.1). Therefore, to maintain consistency with the dominant EM phenotype of KIR+ CD8+ T cells, Tax-specific CD8+ T EM cells are described throughout.

Table 4.1: Frequencies of CCR7– cells in functionally Tax-specific and Tax-specific TCR+ CD8+ T cells in ACs and HAM/TSP patients. %CCR7– cells in total: AC HAM

Median 68.80*** 89.07***

Functionally Tax-specific CD8+ T cells IQR 46.75 - 87.59 79.14 - 94.49 No. donors 38 33

Median 62.40* 74.17*

Tax-specific TCR+ CD8+ T cells IQR 45.36 - 67.88 65.52 - 83.54 No. donors 12 7

Tax-specific CD8+ T cells are mostly CCR7– and therefore have an effector memory phenotype (CD8+ T EM). There were significantly higher frequencies of Tax-specific CD8+ T EM cells in total Tax- + specific CD8 T cells in patients with HAM/TSP than in ACs when measured by HLA-A2/Tax11-19 pentamer staining *(p = 0.0470) or by functional IFNγ/CD107a assay ***(p = 0.0005). Median and IQRs shown are percentages of Tax-specific CD8+ T cells that are CCR7–. The number (No.) of donors that could be analysed by each measure of Tax specificity are shown. AC, asymptomatic carrier; HAM, patient with HAM/TSP; IQR, interquartile range.

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Frequencies of Tax-specific CD8+ T EM cells and distributions of PVLs in ACs and HAM/TSP patients

Patients with HAM/TSP typically have higher frequencies of Tax-specific CD8+ T cells than ACs, when measured by various methods. However, it is also not unusual to observe a large overlap in the frequencies of these cells between ACs and HAM/TSP patients (reviewed in Bangham, 2003). In line with this, Figure 4.2A shows that in the cohort of donors with mixed HLA class I alleles, HAM/TSP patients had significantly higher frequencies of functionally Tax-specific CD8+ T EM cells than ACs

(p=0.0009). However, the median frequencies of Tax-specific CD8+ T EM cells were similar in HLA-

+ A2 ACs and patients with HAM/TSP, whether they were measured by HLA-A2/Tax11-19 pentamer staining (p=0.4725) or by functional IFNγ/CD107a assay (p=0.6421; Figure 4.2B and C). These observations indicate that the two different cohorts give different results for the frequencies of Tax- specific CD8+ T EM cells, particularly in HAM/TSP patients, which may be due to differences in the assays or in the Tax responses of HLA-A2+ donors compared to donors with mixed HLA class I alleles.

The distributions of PVLs measured for ACs and patients with HAM/TSP in the two cohorts were typical for these two groups of HTLV-1+ individuals (Nagai et al., 1998). Despite the difference in donor numbers between the two cohorts, the median PVL values for ACs and HAM/TSP patients were almost identical in HLA-A2+ donors and in those with mixed HLA class I alleles (Figure 4.3).

There were differences in the significance levels for the comparisons of ACs and HAM/TSP patients in donors with mixed HLA class I alleles and in HLA-A2+ individuals, although both were highly statistically significant (p<0.0001 and p=0.0005, respectively; Figure 4.3).

144

Figure 4.2: Frequencies of functionally Tax-specific and Tax-specific TCR+ CD8+ T EM cells. Freshly thawed PBMC were stimulated with pools of overlapping Tax peptides for 5 hours then + stained with CD107a and IFNγ mAbs, plus HLA-A2/Tax11-19 pentamer as necessary (HLA-A2 donors only) and analysed by flow cytometry. Frequencies shown are CD3+ CD8+ CCR7– T cells (CD8+ T EM cells) stained with (A) IFNγ and CD107a, in all donors with mix HLA class I alleles and including HLA- + + + A2 donors, (B) HLA-A2/Tax11-19 pentamer, in HLA-A2 donors, and (C) IFNγ and CD107a, in HLA-A2 donors only. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between patient groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

Figure 4.3: PVLs of the mixed HLA class I cohort and the HLA-A2+ cohort. Genomic DNA was extracted from PBMC and analysed for the number of copies of the HTLV-1 provirus present by q-PCR in all donors with mix HLA class I alleles and including HLA-A2+ donors and in HLA-A2+ donors only. PVL is expressed as the percentage of PBMC carrying one copy of the provirus, which was calculated by estimating HTLV-1 tax and β-actin copy numbers from standard curves. PVLs that were undetectable in this assay were arbitrarily set to a value of 0.0001. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

145

4.3.2 Specific KIR expression on CD8+ T EM cells in the functional analysis cohort

The specific KIR staining patterns from the functional analysis were compared with those observed in the specific KIR expression analysis in Chapter 3, to assess the effects on the KIR staining of the different formats of 1F12 mAb that were used in the two analyses, as well as the effects of the additional stimulation, incubation and staining steps that were required for the functional experiments.

Examples of staining obtained for each of the five KIRs in the functional analysis are shown in Figure

4.4, some of which are from the same donors as shown in the examples of staining for the specific

KIR expression analysis in Chapter 3 (Figure 3.6, Section 3.4.1). There were negligible effects of the different protocols used, as exemplified by 2DL1 and 2DS1 staining, for which the same anti-KIR mAbs (8C11-FITC and EB6-PE) were used in both analyses. However, there was a reduction in the brightness of the 1F12+ populations when directly FITC-conjugated 1F12 was used in the functional experiments, compared to the unconjugated 1F12 with DyLight488 secondary used in the expression analysis. This reduction particularly affects 2DL2 and 2DS2 separation and is also evident in 2DL3 staining. Consequently, the better separated staining observed using the unconjugated 1F12 format with DyLight488 secondary were used to guide the gating of staining with the directly FITC- conjugated 1F12, as described in detail in Methods, Section 2.5.1.

To assess the effect of the reduced separation of 1F12 staining on the reproducibility of the frequencies of specific KIR+ CD8+ T EM cells obtained in the cohort of donors used for the functional analysis (Figure 4.5), these data were compared with the frequencies specific KIR+ CD8+ T EM cells observed in the specific KIR expression analysis (see Figure 3.7A, Section 3.4.1). The shapes of the distributions of KIR+ CD8+ T EM cell frequencies for each KIR were very similar between the two experiments. In line with the specific KIR expression analysis, ACs had significantly higher frequencies of 2DL2+ CD8+ T EM cells compared to patients with HAM/TSP in the functional analysis cohort and the significance level was improved by the additional donors used (p=0.0299; Figure 4.5).

However, these extra study subjects did not improve the significance level for the comparison of

2DS2+ CD8+ T EM cell frequencies in ACs and HAM/TSP patients (p=0.2977; Figure 4.5), which was close to significance in the expression analysis. There was considerable variability between donors in the frequencies of CD8+ T EM cells expressing this KIR and despite the few additional individuals the number of donors that could be analysed for 2DS2 expression remained low. Interestingly, the same

146 donors showed particularly high frequencies of CD8+ T EM cells expressing e.g. 2DL1 or 2DL3 in both experiments, indicating these KIR+ cell frequencies are stable over time, since the samples analysed in the different experiments were from different time points. Therefore, despite some differences in the staining patterns produced by the two 1F12 formats, the resulting frequencies of specific KIR+ CD8+ T

EM cells were very similar in the two experiments. Furthermore, as biological repeats, they demonstrate the reproducibility of the specific KIR frequency data.

147

Figure 4.4: Specific KIR staining patterns on CD8+ T EM cells in the functional analysis cohort. Examples of 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DS1 (2DL1– 2DL2– 2DS2– 2DL3–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DS2 (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) and 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–) staining patterns on CD3+ CD8+ CCR7– T cells (CD8+ T EM cells), showing the variation in staining between individuals. H codes, asymptomatic carriers; T codes, HAM/TSP patients.

148

Figure 4.5: Frequencies of specific KIR expression on CD8+ T EM cells in ACs and HAM/TSP patients in the functional analysis cohort. Following the 5 hour incubation of freshly thawed PBMC for the functional analysis, cells were stained with the anti-KIR mAb clone combinations to analyse expression of 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1 (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2 (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) on CD3+ CD8+ CCR7– T cells (CD8+ T EM cells) by flow cytometry. Frequencies shown are from the DMSO vehicle control samples. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between patient groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

149

4.3.3 Presence and frequency of Tax-specific CD8+ T EM cells expressing specific

KIRs

Presence of specific KIR+ Tax-specific CD8+ T EM cells

To determine the presence of specific KIR+ Tax-specific CD8+ T EM cells, the number of these cells

measured by functional IFNγ/CD107a assay or by HLA-A2/Tax11-19 pentamer staining was calculated for each definition of KIR expression. Firstly, the cell numbers generated by flow cytometry were normalised within each individual donor. The sample with the lowest number of total CD8+ T cells was used to calculate normalisation factors for each of the other samples within each donor’s set of sample, to bring the number of total CD8+ T cells in line in each sample. The normalisation factor was then applied to all of the other cell populations within each sample. DMSO background was then subtracted and responses to Tax peptide pools A and B were combined, as described in Methods,

Section 2.5.2. Thus, these numbers are absolute cell numbers collected by flow cytometry, which have been corrected to account for the fact that, within a set of samples from a given donor, different numbers of cells were collected in each sample.

Table 4.2 shows the corrected numbers of specific KIR+ Tax-specific CD8+ T EM cells collected by flow cytometry for each donor, using the two methods of measuring Tax-specificity. 2DL1+, 2DS1+,

2DL2+, 2DS2+ and 2DL3+ functionally Tax-specific CD8+ T EM cells were detected in most donors, but they were mainly present in very low numbers. Similarly, very low numbers of 2DL1+, 2DL2+ and

2DL3+ Tax-specific TCR+ CD8+ T EM cells were also detected in just over half of the HLA-A2+ donors.

Activating KIR+ Tax-specific TCR+ CD8+ T EM cells could not be analysed, due to low numbers of

HLA-A2+ donors with suitable KIR genotypes. A few individuals had relatively high numbers of these cells e.g. 201 2DL2+ functionally Tax-specific CD8+ T EM cells in one AC, 178 2DL2+ Tax-specific

TCR+ CD8+ T EM cells in a HAM/TSP patient and 105 2DL3+ functionally Tax-specific CD8+ T EM cells in another HAM/TSP patient. These higher corrected absolute cell numbers could result from more cells being collected for these donors due to the availability of PBMC, hence the importance of further analysis of these numbers as proportions of Tax-specific or specific KIR+ CD8+ T EM cells.

Overall these data indicate that there is little overlap between populations of specific KIR+ CD8+ T EM cells and Tax-specific CD8+ T EM cells. It should also be noted that these very low cell numbers are at the absolute threshold of flow cytometric detection, which will affect their accuracy.

150

Table 4.2: Corrected absolute numbers of specific KIR+ Tax-specific CD8+ T EM cells collected on the flow cytometer, measured by functional IFNγ/CD107a assay or by HLA-A2/Tax11-19 pentamer staining.

Numbers of functionally Tax-specific CD8+ T EM cells expressing: 2DL1 2DL2 2DL3 2DS1 2DS2

AC HAM AC HAM AC HAM AC HAM AC HAM

0 0 0 0 0 3 0 0 0 1

0 0 0 0 0 6 2 15 0 2

0 1 0 0 0 11 2 5 9

0 1 0 0 0 12 2 5 11

1 1 0 1 1 13 2 8 34

2 1 1 1 2 13 4 17

3 2 2 1 2 13 5 18

3 2 2 2 3 24 56

4 2 2 2 3 53 75

4 2 2 3 5 90

4 2 3 3 8 105

6 3 3 3 14

7 3 4 3 18

7 5 6 3 21

7 5 7 4 79

7 5 7 5

12 8 8 6

23 19 16 9

28 17 13

62 17 27

86 33 29

201 36 n = 18 21 22 22 15 11 7 2 9 5

Corrected absolute numbers of specific KIR+ Tax- Numbers of Tax-specific TCR+ specific CD8+ T EM cells collected for each donor. CD8+ T EM cells expressing: + Functionally Tax-specific CD8 T EM cell numbers 2DL1 2DL2 2DL3 have been corrected for DMSO background and AC HAM AC HAM AC HAM responses to peptide pools A and B have been combined. 0 0 0 0 0 0 NB. Donors that have higher numbers of specific KIR+ 0 0 1 0 2 6 Tax-specific CD8+ T EM cells may either have a lot of 0 0 4 0 3 these cells or it was possible to collect more cells from them in total as more PBMC were available or 0 1 8 1 6

remained after the staining procedure. 2 2 10 4 9

2 178 15 n=number of donors analysed for each KIR definition. 6 21

n = 7 5 5 6 7 2

151

Comparison of Tax-specific CD8+ T EM cell frequencies expressing specific KIRs in ACs and

HAM/TSP patients

In Chapter 3, AC status was found to be associated with an expansion of 2DL2+ CD8+ T EM cells

(Figure 3.7A, Section 3.4.1). Therefore, the frequencies of functionally Tax-specific and Tax-specific

TCR+ CD8+ T EM cells expressing each specific KIR were determined and compared in ACs and patients with HAM/TSP, to identify any changes that were associated with HAM/TSP. As Figure 4.6A shows, frequencies of functionally Tax-specific CD8+ T EM cells expressing the five specific KIRs were mostly either very low or absent. Despite this, ACs did show higher median frequencies of

2DL1+, 2DL2+ and 2DS2+ functionally Tax-specific CD8+ T EM cells than HAM/TSP patients and for

2DL1 and 2DL2, this difference was almost statistically significant (p=0.0518 and p=0.0532, respectively). This was not the case though for the median frequencies of 2DL3+ and 2DS1+ functionally Tax-specific CD8+ T EM cells, which were very similar between the two donor groups.

Also, particularly in ACs, the range of frequency values for functionally Tax-specific CD8+ T EM cells expressing specific KIRs was very wide. Indeed, frequencies of 92% and 100% functionally Tax- specific CD8+ T EM cells expressing 2DL3 were observed in two donors and 250% for functionally

Tax-specific CD8+ T EM cells expressing 2DS2 in another donor (Figure 4.6A). These three individuals had very low numbers of CD8+ T EM cells that responded to Tax peptide stimulation (the denominator population), which therefore resulted in very low frequencies of specific KIR+ functionally

Tax-specific CD8+ T EM cells as well (the numerator population). Thus, when Tax-specific CD8+ T EM cell numbers were at the absolute threshold of flow cytometric detection, less accurate percentages could be calculated. However, these values were included for completion of the data set.

The frequencies of Tax-specific TCR+ CD8+ T EM cells expressing specific inhibitory KIRs in HLA-A2+ donors were also very low or absent and again, the ranges of frequency values were wide, particularly for 2DL3+ Tax-specific TCR+ CD8+ T EM cells in ACs. Most donors from either group had no 2DL1 expression on Tax-specific TCR+ CD8+ T EM cells and similarly, the majority of HAM/TSP patients had no 2DL2+ Tax-specific TCR+ CD8+ T EM cells (Figure 4.6B). While there were slight trends towards higher median frequencies of Tax-specific TCR+ CD8+ T EM cells expressing 2DL2 or 2DL3 in ACs compared to HAM/TSP patients, these were not close to statistical significance.

152

Figure 4.6: See following page for figure legend.

153

Figure 4.6: Frequencies of Tax-specific CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients. Freshly thawed PBMC were stimulated with pools of overlapping Tax peptides for 5 hours then + stained with CD107a and IFNγ mAbs, plus HLA-A2/Tax11-19 pentamer as necessary (HLA-A2 donors only) and the anti-KIR mAb clone combinations. Frequencies shown are (A) total IFNγ/CD107a+ CD3+ CD8+ CCR7– T cells (functionally Tax-specific CD8+ T EM cells) expressing 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1 – – – – – +/– +/– +/– (2DL1 2DL2 2DS2 2DL3 ) and 2DS2 (2DL3 2DL2 2DL1 2DS1 ), and (B) HLA-A2/Tax11-19 pentamer+ CD3+ CD8+ CCR7– T cells (Tax-specific TCR+ CD8+ T EM cells) expressing 2DL1, 2DL2 and 2DL3 with the same specific KIR expression definitions, all measured by flow cytometry. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between patient groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

154

4.3.4 Influence of cognate HLA class I ligand on the inhibitory KIR repertoire of Tax- specific CD8+ T EM cells in ACs and HAM/TSP patients

The influence of cognate HLA class I molecules on the frequencies of functionally Tax-specific CD8+ T

EM cells expressing 2DL1, 2DL2 or 2DL3 was analysed in ACs and patients with HAM/TSP. 2DS1 and 2DS2 were not included in the analysis, because the exact ligand specificity of these activating

KIRs is uncertain, the interactions that are known are weak and donor numbers were much lower than for their inhibitory counterparts. Also, it was not possible to perform this analysis using the HLA-

A2/Tax11-19 pentamer data because the donor groups became too small once they were stratified for the presence of cognate ligand. Study subjects were grouped according to their possession of the highest affinity HLA class I ligands for each KIR: group HLA-C2 for 2DL1 and group HLA-C1 for 2DL2 and 2DL3. Comparisons of the frequencies of functionally Tax-specific CD8+ T EM cell expressing the three inhibitory KIRs between donors with and without cognate HLA class I molecules revealed no statistically significant differences in ACs or HAM/TSP patients (Figure 4.7). Therefore, the inhibitory

KIR repertoire of the Tax-specific CD8+ T EM cell population is independent of cognate HLA class I possession ligand in this cohort, as was the inhibitory KIR repertoire of CD8+ T EM cells in Chapter 3.

155

KIR HLA class I ligand

2DL1 Group HLA-C2lys80

asn80 2DL2 Group HLA-C1 2DL3 Group HLA-C1asn80

Figure 4.7: The effect of cognate HLA class I ligand possession on the frequencies of functionally Tax-specific CD8+ T cells expressing inhibitory KIR molecules in ACs and HAM/TSP patients. Frequencies of total IFNγ/CD107a+ CD3+ CD8+ CCR7– T cells (functionally Tax-specific CD8+ T EM cells) expressing 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–) and 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), stimulated with Tax peptides and analysed by flow cytometry, are shown in HTLV-1+ study subjects with and without at least one copy of the cognate HLA class I ligand for each inhibitory KIR analysed. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

156

4.3.5 Relationships between HTLV-1 PVL and the frequencies of Tax-specific CD8+ T

EM cells expressing specific KIRs in ACs and HAM/TSP patients

In Chapter 3, several interesting inverse relationships were observed between the frequencies of specific KIR-expressing CD8+ T EM cells and the HTLV-1 PVL, particularly for the frequency of 2DL2+

CD8+ T EM cells. Therefore, the frequencies of 2DL1+, 2DS1+, 2DL2+, 2DS2+ and 2DL3+ functionally

Tax-specific CD8+ T cells were tested for correlations with PVL in ACs and HAM/TSP patients.

Relationships with PVL were not assessed for Tax-specific TCR+ CD8+ T EM cell frequencies because around half of the study subjects had no specific KIR+ Tax-specific TCR+ CD8+ T EM cells at all. Correlation analyses were performed separately on each donor group because of the highly significant difference found in PVLs between ACs and patients with HAM/TSP (see Figure 4.3 above).

The only trend with PVL that approached statistical significance was with 2DS1+ functionally Tax- specific CD8+ T EM cell frequencies in ACs (p=0.0881; Figure 4.8). Interestingly, this was a negative association and in fact most of the trend lines were inverse, with the notable exception of 2DL2

(Figure 4.8). While the direction of these relationships was similar to those observed between PVL and CD8+ T EM cell frequencies expressing inhibitory KIRs in Chapter 3 (Figure 3.9, Section 3.4.3), there were no statistically significant relationships between PVL and the frequencies of Tax-specific

CD8+ T EM cells expressing any of the five KIRs, in either ACs or patients with HAM/TSP (Figure 4.8).

157

Figure 4.8: Correlations of PVL with frequencies of functionally Tax-specific CD8+ T EM cells expressing specific KIR molecules in ACs and HAM/TSP patients. HTLV-1 PVL, as measured by q-PCR, is plotted against the frequencies of total IFNγ/CD107a+ CD3+ CD8+ CCR7– T cells (functionally Tax-specific CD8+ T EM cells) expressing 2DL1 (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2 (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3 (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1 (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2 (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–), stimulated with Tax peptides and analysed by flow cytometry, in each patient group. Fit lines were generated using linear regression. Correlations and statistical significance were determined using Spearman’s rank. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

158

4.4 Discussion

4.4.1 The Tax response of CD8+ T EM cells in ACs and HAM/TSP patients

Two well-established features of HTLV-1 infection and HAM/TSP disease are that both higher frequencies of Tax-specific CD8+ T cells and higher PVLs are observed in individuals with the inflammatory disease compared to healthy HTLV-1 carriers. Thus, my functional IFNγ/CD107a analysis showing that individuals with HAM/TSP had higher frequencies of functionally Tax-specific

CD8+ T EM cells than ACs is consistent with a number of studies using similar analyses, such as intracellular cytokine staining, ELISpot and target lysis assay (Daenke et al., 1996; Goon et al.,

2004a; Jacobson et al., 1990; Kubota et al., 2000a; Kubota et al., 2000b; Parker et al., 1992). I also showed that HAM/TSP patients had higher PVLs than ACs in this cohort, as has been previously reported (Kamihira et al., 2003; Kira et al., 1991; Manns et al., 1999; Nagai et al., 1998). Higher PVLs are thought to be maintained in patients with HAM/TSP, despite their increased frequencies of HTLV-

1-specific CD8+ T cells, because these cells are likely to be less effective at controlling the virus in individuals with inflammatory disease, that is, to have lower functional avidity (Kattan et al., 2009) and lower lytic efficiency (Asquith et al., 2005). At the same time, higher PVLs indicate that there is increased antigen expression in HAM/TSP patients, which would stimulate the expansion of HTLV-1- specific CD8+ T cells in these individuals. Thus, higher PVLs and higher frequencies of less effective

HTLV-1-specific CD8+ T cells drive each other in a cyclical manner in patients with HAM/TSP

(Bangham, 2009).

While the elevated frequencies of Tax-specific CD8+ T cells in HAM/TSP patients shown in the literature were not always statistically significantly different to those in ACs, I observed particularly high frequencies of functionally Tax-specific CD8+ T EM cells in HAM/TSP patients, which resulted in a highly significant difference between the two patient groups. This may be explained by my use of high concentrations of Tax peptides, which would have stimulated Tax-specific CD8+ T EM cells with a range of functional avidities and HAM/TSP patients are thought to have higher frequencies of low avidity Tax-specific CD8+ T EM cells. In contrast to the functional analysis of Tax-specific CD8+ T EM cells in donors with mixed HLA class I alleles, there was no difference in Tax-specific TCR+ CD8+ T

EM cell frequencies in HLA-A2+ ACs and patients with HAM/TSP. Similarly, Jeffery et al., (1999)

+ + found no statistically significant difference in HLA-A2/Tax11-19 tetramer CD8 T cell frequencies

159 between 19 ACs and 19 HAM/TSP patients in the Kagoshima cohort, however median Tax-specific

TCR+ CD8+ T cell frequencies were ~2-fold higher in HAM/TSP patients than in ACs. There could be several reasons why the functional IFNγ/CD107a analysis and the HLA-A2/Tax11-19 pentamer staining in the present study gave contrasting results for comparisons of Tax-specific CD8+ T EM cell frequencies in ACs and HAM/TSP patients. Firstly, the number of individuals that could be analysed by HLA-A2/Tax11-19 pentamer staining was much lower than for the functional IFNγ/CD107a analysis, which affects the power of the statistical analysis. Indeed, when greater numbers of HLA-A2+ individuals could be analysed, a difference between ACs and HAM/TSP patients was observed

(Jeffery et al., 1999). Secondly, there are obvious technical differences in the two assays which could influence the frequencies of Tax-specific CD8+ T EM cells they detect. The functional IFNγ/CD107a assay measures cells capable of making these responses following Tax peptides stimulation and covers multiple Tax epitopes across a mix of HLA class I alleles, whereas the HLA-A2/Tax11-19 pentamer detects the presence of an HLA-A2-restricted Tax11-19-specific TCR. Finally, inherent differences in the nature of anti-HTLV-1 CD8+ T cell immune responses between ACs and HAM/TSP patients could be influencing the frequencies of HTLV-1-specific CD8+ T EM cells detected by these two methods. Interestingly, the frequencies of Tax-specific CD8+ T EM cells detected in HLA-A2+ ACs

and HAM/TSP patients were very similar, whether they were measured by HLA-A2/Tax11-19 pentamer staining or by functional IFNγ/CD107a assay. This suggests that there may be fundamental differences in the anti-HTLV-1 CD8+ T cell immune responses of HLA-A2+ and HLA-A2– individuals, which is perhaps not surprising, given that possession of HLA-A*02 is protective in HTLV-1 infection

(Jeffery et al., 1999). These differences appear to have a strong influence on the detection of functionally Tax-specific CD8+ T EM cells in HAM/TSP patients, because the frequency distributions of these cells were very different in the HLA-A2+ cohort compared to the group with mixed HLA class I alleles. Therefore, any differences in the results observed using the two methods of measuring Tax- specific CD8+ T EM cells could be as much a feature of possessing an HLA-A2 allele as technical differences in the assays.

4.4.2 Very low frequencies of Tax-specific CD8+ T EM cells express specific KIRs

Specific KIR+ Tax-specific CD8+ T EM cells were detected in the majority of HTLV-1+ individuals.

However, in general the numbers of these cells were extremely low, no matter which method was

160 used for detecting Tax specificity, and they were at the absolute threshold of flow cytometric detection, particularly considering the functional markers are highly sensitive to background staining.

While a few individuals did have relatively high numbers of specific KIR+ Tax-specific CD8+ T EM cells, the frequencies of these cells within the functionally Tax-specific or Tax-specific TCR+ CD8+ T

EM cell populations were also very low in most HTLV-1 carriers. Three individuals had very high frequencies of specific KIR-expressing functionally Tax-specific CD8+ T EM cells, which were caused by their weak Tax responses, in the context of the short-term in vitro peptide stimulation assay used in this analysis. A low frequency of Tax-specific CD8+ T EM cells is one factor that may influence the detection of very low numbers of specific KIR-expressing Tax-specific CD8+ T EM cells in a particular individual. Another could be the fact that the specific KIR expression definitions are quite stringent and cells expressing them represent very small proportions of the total CD8+ T EM cell population in some donors. Since KIR expression has been reported to reduce the functional capacity of CD8+ T cells (van Bergen et al., 2010), this could result in under-detection of specific KIR+ Tax-specific CD8+

T EM cells when measuring them by their functional output. However, it was hoped that the 2uM concentrations of Tax peptide used in the present study would minimise this effect, since it has been reported that stimulation with 100nM of pp65 peptides can overcome KIR-mediated inhibition of effector function and proliferation in CMV-specific CD8+ T cell clones (van der Veken et al., 2009).

Supporting my observations of extremely low frequencies of Tax-specific CD8+ T EM cells expressing specific KIRs are two reports which demonstrated very low frequencies of virus-specific CD8+ T cells expressing global KIRs in some but not all individuals analysed (Anfossi et al., 2004; Bonorino et al.,

2007). In contrast with my findings, three other studies have shown that global KIR+ cells are not only detected amongst virus-specific CD8+ T cells but are enriched in this cell population (Alter et al., 2008;

Poon et al., 2005; van der Veken et al., 2009). Alter et al., (2008) reported that in four chronically HIV-

1-infected, untreated patients, on average 38.3% of Nef-specific CD8+ T cells expressed KIRs. High frequencies of KIR-expressing virus-specific CD8+ T cells were also found in individuals experiencing

CMV reactivation following SCT, whereas these cells were detected at low frequencies in a small number of healthy donors (van der Veken et al., 2009). KIR+ EBV-specific CD8+ T cell populations were also expanded in persistent infection compared to the primary phase (Poon et al., 2005). These

161 observations suggest that particular stages of infection or high levels of viral replication may be associated with the accumulation of virus-specific CD8+ T cells expressing KIRs.

Interestingly, although the higher PVLs in patients with HAM/TSP disease indicate increased viral replication in these individuals, my data suggested that ACs may have higher frequencies of 2DL1 or

2DL2-expressing functionally Tax-specific CD8+ T EM cells, rather than HAM/TSP patients. This observation would be consistent with the finding from Chapter 3 that AC status was associated with an expansion of 2DL2+ CD8+ T EM cells, which implied a protective role for 2DL2 expression on CD8+

T EM cells in HTLV-1 infection. However, the statistical analyses revealed only borderline significance. Furthermore, the HLA-A2/Tax11-19 pentamer analysis does not support ACs having higher frequencies of Tax-specific CD8+ T EM cells expressing any of the three inhibitory KIRs than patients with HAM/TSP. There are also some caveats to this result, concerning differences in the underlying

Tax responses of the two patient groups when measured by functional IFNγ/CD107a assay. Firstly, since ACs had markedly lower frequencies of functionally Tax-specific CD8+ T EM cells than

HAM/TSP patients, this could be elevating the percentages calculated for Tax-specific cells expressing 2DL1 or 2DL2 in ACs to a greater extent than in HAM/TSP patients. This effect was demonstrated in the extreme by the three individuals that had very high frequencies of specific KIR- expressing functionally Tax-specific CD8+ T EM cells, because of their very low Tax responses.

Secondly, because CD8+ T EM cells from individuals with HAM/TSP are likely to have lower functional avidity i.e. lower antigen sensitivity, if KIR expression is reducing CD8+ T cell functional output, this may result in greater under-detection of specific KIR+ functionally Tax-specific CD8+ T EM cells in

HAM/TSP patients than in ACs, as their cells may require higher antigen concentrations to become activated. This could explain why lower frequencies of functionally Tax-specific CD8+ T EM cells expressing specific KIRs were detected in HAM/TSP patients than in ACs, despite their high frequencies of functionally Tax-specific CD8+ T EM cells. However, it was hoped that the strong stimulation provided by high concentrations of Tax peptides was able to overcome KIR-mediated functional inhibition, as discussed above.

It is difficult to assess the impact that these differences in the underlying Tax responses of the two patient groups may be having on the frequencies of functionally Tax-specific CD8+ T EM cells

162 expressing 2DL1 or 2DL2 that are detected in ACs and HAM/TSP patients. It is possible that ACs may genuinely have expanded populations of HTLV-1-specific CD8+ T EM cells expressing 2DL2 or

2DL1. This would suggest that 2DL2, and indeed 2DL1, has a protective role in HTLV-1 infection via a direct effect of their expression on HTLV-1-specific CD8+ T EM cells, such as the KIR expression- induced CD8+ T cell survival mechanism proposed by Seich al Basatena et al., (2011). Thus, this finding would also provide further support for this hypothesis, in explaining how 2DL2 modulates HLA class I-mediated antiviral immune responses. However, on balance, given the limited accuracy with which it is possible to measure the very low numbers of specific KIR+ Tax-specific CD8+ T EM cells in these individuals by flow cytometry, together with the fact that the statistical analyses were only borderline significant, a firm conclusion cannot be drawn on this.

Despite the suggestion from the literature that high levels of viral replication may be associated with the accumulation of KIR-expressing virus-specific CD8+ T cells, there were no relationships between

PVL and the frequencies of Tax-specific CD8+ T EM cells expressing any of the five specific KIR definitions, either in ACs or HAM/TSP patients. This is in contrast to Chapter 3, where several inverse trends and correlations were observed between PVL and specific KIR+ CD8+ T EM cell frequencies, with the most notable being the strong association of higher frequencies of 2DL2+ CD8+ T EM cells with reduced PVLs in HAM/TSP patients and in individuals with PVLs greater than 1%. In line with the patterns of inhibitory KIR expression on total CD8+ T EM cells in Chapter 3, the inhibitory KIR repertoire expressed by Tax-specific CD8+ T EM cells was also independent of cognate HLA class I ligand possession. Thus, this finding similarly implies that the ligation of KIRs expressed on Tax- specific CD8+ T EM cells is not required for the maintenance of KIR-expressing Tax-specific CD8+ T

EM cell populations. Since there are many factors which could be influencing the accurate detection of specific KIR+ Tax-specific CD8+ T EM cells, this issue could consequently also be affecting the PVL correlation and HLA class I ligand possession analyses.

In order to detect the highest frequencies of HTLV-1-specific CD8+ T cells possible, the immunodominant Tax protein was the best HTLV-1 antigen to select for this analysis. However, Tax is only one of several HTLV-1 antigens that can be targeted by the CD8+ T cell immune response. Also,

HLA-A2/Tax11-19 pentamer analysis is even narrower in terms of antigen specificity, since it only

163 detects CD8+ T cells which recognise one epitope from Tax in the context of one HLA class I allele.

Just as there were a few individuals with higher numbers of specific KIR+ Tax-specific CD8+ T EM cells, it is possible that in different individuals, higher numbers of CD8+ T EM cells of other HTLV-1 specificities may express specific KIRs. Also, recognition of the HBZ protein, rather than the Tax protein by CD8+ T cell has been shown to be protective (Hilburn et al., 2011; Macnamara et al., 2010).

It would therefore be very interesting to analyse specific KIR expression on HBZ-specific CD8+ T cells in particular, though this will be technically challenging due to their very low frequency (Hilburn et al.,

2011; Suemori et al., 2009). It would also be interesting to determine specific KIR expression within the total HTLV-1-specific CD8+ T cell compartment, perhaps by using an HTLV-1 lysate to stimulate

CD8+ T cells. To avoid the potential issue of KIR expression-mediated inhibition of effector functions, it would be ideal to be able to use further pentamers to detect CD8+ T cells recognising other HTLV-1 epitopes, however these reagents are not currently available.

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4.4.3 Chapter 4 summary

In this Chapter, I analysed the frequencies of HTLV-1-specific CD8+ T cells expressing specific KIRs in ACs and patients with HAM/TSP and related this to HLA class I ligand possession and HTLV-1

PVL. The magnitude of the CD8+ T EM cell response to Tax was much greater in individuals with

HAM/TSP than in ACs by functional assay. The setup of my functional analysis, as well as differences in the nature of the Tax responses of these two patient groups likely contributed to this difference.

Conversely, there was no difference between HLA-A2+ ACs and patients with HAM/TSP in their

+ + frequencies of HLA-A2/Tax11-19 pentamer CD8 T EM cells, which could be due to differences in the assays or in the anti-HTLV-1 CD8+ T cell immune responses of HLA-A2+ and HLA-A2– individuals.

Specific KIR expression was detected on HTLV-1-specific CD8+ T EM cells in the majority of HTLV-1+ individuals, though mostly at extremely low frequencies. The frequencies of specific KIR-expressing

Tax-specific CD8+ T EM cells were independent of cognate HLA class I ligand possession and there were no relationships with HTLV-1 PVL. There was a suggestion that ACs may have expanded populations of functionally HTLV-1-specific CD8+ T EM cells expressing 2DL2 or 2DL1, compared to patients with HAM/TSP. However, there were several caveats to this result related to the underlying

Tax response of the two patient groups, thus with the limited accuracy of very low frequency cell measurements by flow cytometry, and the fact that statistical analysis was only borderline significant, it was not possible to draw a firm conclusion on this point.

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Chapter 5. Functional consequences of specific KIR expression on CD8+ T EM cells in HTLV-1 infection

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5.1 Introduction

The mechanism by which 2DL2 is proposed to influence HLA class I-restricted antiviral immunity is via

2DL2 expression on CD8+ T cells promoting their survival (Seich al Basatena et al., 2011; see also

Introduction, Section 1.8.2). Although inhibitory KIR expression on CD8+ T cells has been shown to disrupt TCR signalling and reduce effector function, it is thought that rather than preventing CD8+ T cell activation entirely, inhibitory KIR expression may raise their activation thresholds, thus protecting them from AICD (van Bergen et al., 2010; Vivier et al., 2004). I was therefore very interested to investigate the functional consequences of specific KIR expression on CD8+ T cells in HTLV-1 infection.

Previous studies in HIV, HCV and CMV infection have consistently shown reduced effector output from inhibitory KIR+ compared to inhibitory KIR– CD8+ virus-specific T cells (Alter et al., 2008; Anfossi et al., 2004; Bonorino et al., 2007; van der Veken et al., 2009). I analysed the functional effects of specific KIR expression on CD8+ T EM cells in the context of an HTLV-1 antigen by comparing the effector responses of specific KIR+ and specific KIR– CD8+ T cells to Tax peptide stimulation in ACs and HAM/TSP patients. However, as shown in Chapter 4 (see Table 4.2), the numbers of specific

KIR+ Tax-specific CD8+ T EM cells were mostly very low, which would have affected the reliability of this analysis. Thus, to be able to analyse higher numbers of functional cells, I also analysed the effect of specific KIR expression on the functional responses of CD8+ T EM cells to TCR-directed anti-

CD3/28 mAb stimulation, alongside responses to Tax peptide stimulation. Since the KIR-mediated reduction of CD8+ T cell effector function is thought to result from inhibitory KIR signalling disrupting activating signalling from the TCR, I also investigated the effect of specific KIR expression on the functional responses of CD8+ T EM cells to TCR-independent activation using PMA/CAI stimulation.

There are mixed reports as to whether the engagement of KIRs by cognate HLA class I ligands is required for KIR expression to mediate a reduction in CD8+ T cell effector output. Some studies found that the effector responses of KIR+ CD8 T cells were only reduced when cognate HLA class I ligand was present (Bonorino et al., 2007; van der Veken et al., 2009), while others have concluded that the reduction in CD8+ T cell functional responses mediated by KIRs is HLA class I ligand-independent

(Alter et al., 2008; Anfossi et al., 2004; Björkström et al., 2012). I therefore investigated the influence

167 of cognate HLA class I ligand possession on the functional responses of CD8+ T EM cells expressing the inhibitory KIRs 2DL1, 2DL2 and 2DL3 in HTLV-1 infection.

It has been proposed that KIR expression and inhibitory signalling raises the activation threshold of

CD8+ T cells, thus reducing their functional capacity but not inhibiting them completely (Anfossi et al.,

2004; Henel et al., 2006; Ugolini et al., 2001). Different CD8+ T cell effector functions require different activation thresholds to be reached in order to be initiated (Viola et al., 1996), such that the level of activation needed to trigger them increases as follows: degranulation -> cytokine production -> proliferation (Lim et al., 2000; Valitutti et al., 1995; Valitutti et al., 1996). Consequently, the polyfunctionality of CD8+ T cells also increases with stimulation strength, because more effector functions can be induced when activation levels are higher (Almeida et al., 2009). In line with this, KIR expression has been shown to uncouple effector functions, allowing degranulation but preventing cytokine release, which implies that by raising the activation threshold of CD8+ T cells, KIR expression alters the balance of CD8+ T cell effector responses in favour of functions triggered at lower activation levels (Henel et al., 2006). I therefore analysed the effect of specific KIR expression on the functional profile of CD8+ T cells in HTLV-1 infection by comparing specific KIR+ and specific KIR– CD8+ T cells for their levels of different effector functions in ACs and HAM/TSP patients. Again, I analysed the functional responses of CD8+ T EM cells to treatment with anti-CD3/28 mAb, alongside responses to

Tax peptide stimulation.

Prior to comparing the functional responses of specific KIR+ and KIR– CD8+ T cells, I wanted to establish which HTLV-1-infected individuals had Tax-specific cells in their specific KIR+ CD8+ T EM cell populations. Also, because in Chapter 3 ACs were found to have expanded 2DL2+ CD8+ T EM cell populations (see Figure 3.7A, Section 3.4.1), I was interested to determine the Tax specificity of specific KIR+ CD8+ T cells in ACs and HAM/TSP patients. Therefore, I analysed the frequencies of

Tax-specific cells within the different specific KIR+ CD8+ T EM cell populations in ACs and HAM/TSP patients. Finally, I was also interested to summarise all the different KIR+ and Tax-specific cell populations I have analysed, to understand the proportions they make up within the total CD8+ T cell parent population and how they relate to one another.

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All of the analyses comparing the functional responses of the different KIR+ and KIR– CD8+ T cell populations in this chapter were carried out used pairwise tests. This type of analysis allowed direct comparisons of KIR+ and KIR– CD8+ T cells within each donor, while controlling for differences between individuals in HLA class I genotype and PVL, both of which influence the functional responses of CD8+ T cells in HTLV-1-infected individuals. All of the cell frequencies presented in this

Chapter were calculated from processed absolute cell numbers generated in the functional analysis, as described in previous Chapters (see Chapter 4, Section 4.3.3 and Methods, Section 2.5.2).

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5.2 Aims

The aims of this chapter were:

 To determine the frequencies of functionally Tax-specific and Tax-specific TCR+ cells within the

different KIR+ CD8+ T EM cell populations and compare them in ACs and HAM/TSP patients

 To summarise the significant differences in cell frequencies between ACs and HAM/TSP patients

for all the KIR+ and Tax-specific cell populations analysed within total CD8+ T cells and how they

relate to one another

 To analyse the effect of specific KIR expression on the total IFNγ/CD107a response of Tax-

specific TCR+ CD8+ T EM cells following stimulation with Tax peptides

 To analyse the effect of specific KIR expression on the total IFNγ/CD107a response of CD8+ T

EM cells following TCR-dependent stimulation with anti-CD3/CD28 mAbs

 To analyse the effect of specific KIR expression on the total IFNγ/CD107a responses of CD8+ T

cells to TCR-independent activation using PMA/CAI stimulation

 To investigate the influence of cognate HLA class I ligand possession on the total IFNγ/CD107a

responses of CD8+ T EM cells expressing inhibitory KIRs

 To analyse the effect of specific KIR expression on the balance of IFNγ and CD107a responses

within the total IFNγ/CD107a responses of CD8+ T EM cells, following TCR-dependent stimulation

with Tax peptides or anti-CD3/CD28 mAbs

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5.3 Results: Specific KIR+ and Tax-specific cell frequencies within the total

CD8+ T cell population

5.3.1 Frequencies of functionally Tax-specific and Tax-specific TCR+ cells in specific

KIR-expressing CD8+ T EM cell populations

In Chapter 3, AC status was found to be associated with an expansion of 2DL2+ CD8+ T EM cells

(Figure 3.7A, Section 3.4.1), therefore, the Tax specificity of specific KIR+ CD8+ T EM cells was examined. The frequencies of specific KIR+ CD8+ T EM cells that were Tax-specific, either by functional IFNγ/CD107a assay or by HLA-A2/Tax11-19 pentamer staining, were determined for the five definitions of specific KIR expression and compared in ACs and patients with HAM/TSP to identify any changes that were associated with HAM/TSP. This analysis also aimed to establish which donors had Tax-specific cells in their specific KIR+ CD8+ T EM cell populations, so they could be selected for comparisons of functional responses in specific KIR+ and KIR– CD8+ T EM cell populations.

Frequencies of functionally Tax-specific cells within specific KIR+ CD8+ T EM cell populations were mostly very low (Figure 5.1A). Median frequencies in all specific KIR+ CD8+ T EM cell populations for both donors groups were around 1%, with the exception of 2DL3+ CD8+ T EM cells, which contained lower frequencies of functionally Tax-specific cells than the other specific KIR+ CD8+ T EM cell populations. However, most donors did have some functionally Tax-specific cells in their specific KIR+

CD8+ T EM cell populations and a few study subjects had relatively high frequencies, with up to 15% of 2DL1+ and 2DL2+ CD8+ T EM cells being functionally Tax-specific. Strikingly, there were no statistically significant differences between ACs and patients with HAM/TSP for frequencies of functionally Tax-specific cells within any of the specific KIR+ CD8+ T EM cell populations.

+ + + + Specific inhibitory KIR CD8 T EM cells that stained with HLA-A2/Tax11-19 pentamer in HLA-A2 donors were either at very low frequencies or were absent (Figure 5.1B). Median frequencies of Tax- specific TCR+ cells within 2DL2+ and 2DL3+ CD8+ T EM cells were 0.25% and 0.15% respectively, while Tax-specific TCR+ cells were mostly absent from 2DL1+ CD8+ T EM cells. The highest frequency value for Tax-specific TCR+ cells within specific inhibitory KIR+ CD8+ T EM cells was 2%

(for 2DL2+ cells), with this donor being an outlier. Again, there were no statistically significant

171 differences between ACs and patients with HAM/TSP for their frequencies of Tax-specific TCR+ cells within any of the specific inhibitory KIR+ CD8+ T EM cell populations. These findings of low frequencies of Tax-specific cells in specific KIR+ CD8+ T EM cells were expected, considering that very low numbers of specific KIR+ Tax-specific CD8+ T EM cells are detectable, either by functional

IFNγ/CD107a assay or HLA-A2/Tax11-19 pentamer staining, as shown in Chapter 4 (Section 4.3.3).

Figure 5.1: See over the page for legend.

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Figure 5.1: Frequencies of Tax-specific cells within specific KIR+ CD8+ T EM cell populations in ACs and HAM/TSP patients. Freshly thawed PBMC were stimulated with Tax peptides for 5 hours then stained with CD107a and + IFNγ mAbs, plus HLA-A2/Tax11-19 pentamer as necessary (HLA-A2 donors only) and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are (A) total IFNγ/CD107a+ cells (functionally Tax-specific cells) within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (specific KIR+ + + + + CD8 T EM cells) and (B) HLA-A2/Tax11-19 pentamer cells (Tax-specific TCR cells) within 2DL1 , 2DL2+ and 2DL3+ CD3+ CD8+ CCR7– T cells (inhibitory KIR+ CD8+ T EM cells), with the same specific KIR expression definitions. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between patient groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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5.3.2 Summary of the proportions of CD8+ T EM cells, specific KIR+ cells, Tax- specific cells and their overlap in total CD8+ T cells in HTLV-1+ individuals

In the two previous Chapters, several interesting changes were observed in the specific KIR+ and

Tax-specific CD8+ T EM cell frequencies that have been analysed in ACs and HAM/TSP patients.

These were namely, that (1) ACs had significantly higher frequencies of 2DL2+ cells within CD8+ T EM cells than HAM/TSP patients (Section 3.4.1, Figure 3.7), (2) HAM/TSP patients had higher frequencies of functionally Tax-specific cells within CD8+ T EM cells than ACs, which was highly significant (Section 4.3.1, Figure 4.2), and (3) there was a suggestion of higher frequencies of 2DL2+ and 2DL1+ cells within functionally Tax-specific CD8+ T EM cells in ACs compared to patients with

HAM/TSP (Section 4.3.3, Figure 4.6). To summarise and understand how the frequencies of all the different specific KIR+ and Tax-specific cell populations within the total CD8+ T cell population relate to one another, and observe how they change in ACs and HAM/TSP patients, proportional bar charts were created. In these proportional graphs, total CD8+ T cells are shown as 100% for both donor groups, since there was no significant difference in the frequencies of total CD8+ T cells per lymphocyte in ACs and patients with HAM/TSP and their median frequencies were very similar

(20.94% and 22.74% respectively; Section 3.3.1 Table 3.1). The median of each subpopulation of interest is represented as the proportion it makes up within the total CD8+ T cell median (Figure

5.2A&B).

The first graph of each panel shows that patients with HAM/TSP have higher frequencies of CD8+ T

EM cells within total CD8+ T cells than ACs (Figure 5.2A&B and, left-hand graphs). This difference was shown to be statistically significant in Chapter 3 (Section 3.4.1, Table 3.2). The analysis of specific KIR expression on CD8+ T EM cells revealed a statistically significant difference between the donor groups for only one KIR: ACs had higher frequencies of 2DL2+ cells within CD8+ T EM cells than patients with HAM/TSP, shown in Chapters 3 and 4 (p=0.0345, Section 3.4.1, Figure 3.7; p=0.0299, Section 4.3.2, Figure 4.5). Thus, ACs have significantly fewer CD8+ T EM cells within their total CD8+ T cell populations than HAM/TSP patients, but significantly more of their CD8+ T EM cell populations expressed 2DL2. The significantly higher frequencies of 2DL2+ cells within CD8+ T EM cells in ACs compared to HAM/TSP patients is also reflected in the proportions of 2DL2+ CD8+ T EM cells within total CD8+ T cells, which were slightly higher in ACs than in HAM/TSP patients in the

174 cohort with mixed HLA class I alleles and higher still in the HLA-A2+ cohort (Figure 5.2A&B, 2nd panel, middle graph). ACs also had slightly higher proportions of 2DS2+ CD8+ T EM cells within total

CD8+ T cells (Figure 5.2A, 5th panel, middle graph), and higher proportions of 2DL1+ and 2DL3+ CD8+

T EM cell populations within total CD8+ T cells in the HLA-A2+ cohort, compared to HAM/TSP patients

(Figure 5.2B, middle graphs). Conversely, in the cohort with mixed HLA class I alleles, HAM/TSP patients had slightly higher proportions of 2DL1+ and 2DL3+ CD8+ T EM cells within total CD8+ T cells than ACs, while proportions of 2DS1+ CD8+ T EM cells within total CD8+ T cells were essentially the same in both donor groups (Figure 5.2A, 4th panel, middle graph).

The middle graphs in each panel of Figure 5.2A clearly show that patients with HAM/TSP had markedly higher frequencies of functionally Tax-specific cells than ACs, either within CD8+ T EM cells, as described in Chapter 4 (p=0.0009; Section 4.3.1, Figure 4.2A) or within total CD8+ T cells. In contrast, there were no differences between HLA-A2+ ACs and HAM/TSP patients in their frequencies of Tax-specific TCR+ cells either within CD8+ T EM cells, as Chapter 4 showed (Section 4.3.1, Figure

4.2), or within total CD8+ T cells (Figure 5.2B, middle graphs). Thus, ACs had significantly fewer CD8+

T EM cells within their total CD8+ T cell populations than patients with HAM/TSP but significantly less of their CD8+ T EM cells responded to stimulation with Tax peptides. However, ACs and patients with

HAM/TSP had roughly the same frequencies of CD8+ T EM cells that had a Tax-specific TCR.

Analyses of KIR expression on functionally Tax-specific CD8+ T cells in Chapter 4 revealed trends towards ACs having higher frequencies of 2DL2+ and 2DL1+ cells within functionally Tax-specific CD8+

T EM cells than patients with HAM/TSP, both of which were very close to statistical significance

(p=0.0518 and p=0.0532, respectively; Section 4.3.3, Figure 4.6A). However, there were no differences between HLA-A2+ ACs and patients with HAM/TSP in their frequencies of inhibitory KIR+ cells within Tax-specific TCR+ CD8+ T EM cells. Thus, ACs had fewer CD8+ T EM cells that responded to stimulation with Tax peptides than HAM/TSP patients and there was a trend towards more of these functionally Tax-specific CD8+ T EM cells expressing 2DL2 or 2DL1 in ACs. However,

HLA-A2+ ACs and HAM/TSP patients had similar frequencies of CD8+ T EM cells with a Tax-specific

TCR and, similar frequencies of these Tax-specific TCR+ CD8+ T EM cells in ACs and HAM/TSP patients expressed KIRs.

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As shown above at the beginning of this Chapter, there were no differences between ACs and patients with HAM/TSP in the frequencies of functionally Tax-specific or Tax-specific TCR+ cells within specific KIR+ CD8+ T EM cells (Section 5.3.1, Figure 5.1). Thus, ACs had more CD8+ T EM cells that express 2DL2 than patients with HAM/TSP, but ACs and HAM/TSP patients had similar frequencies of these 2DL2+ CD8+ T EM cells that responded to stimulation with Tax peptides or that had a Tax- specific TCR.

Despite their very low frequencies, visual comparisons of the proportions of KIR+ Tax-specific CD8+ T

EM cells within total CD8+ T EM cells showed some interesting patterns (Figure 5.2A&B, right-hand graphs). Only proportions of 2DL2+ Tax-specific CD8+ T EM cells within total CD8+ T EM cells were higher in ACs compared to HAM/TSP patients by both measures of Tax-specificity (2nd panels). By functional assay, ACs had higher proportions of 2DL1+ functionally Tax-specific CD8+ T EM cells within total CD8+ T cells than HAM/TSP patients, but in HLA-A2+ individuals, the median frequencies of 2DL1+ cells within Tax-specific TCR+ CD8+ T EM cells were zero in both donor groups (1st panels).

Therefore, 2DL1+ Tax-specific TCR+ CD8+ T EM cells could not be represented as a proportion of total CD8+ T cells. Proportions of 2DL3+ Tax-specific TCR+ CD8+ T EM cells within total CD8+ T EM cells were greater in ACs than HAM/TSP patients, however HAM/TSP patients had proportions of

2DL3+ functionally Tax-specific CD8+ T EM cells within total CD8+ T EM cells compared to ACs (3rd panels). Proportions of 2DS1+ and 2DS2+ functionally Tax-specific CD8+ T EM cells within total CD8+

T EM cells were essentially the same in both donor groups (Figure 5.2A, 4th and 5th panels).

Strikingly, the analyses of frequency differences in cell populations expressing KIRs between ACs and HAM/TSP patients were consistently significant (or very nearly significant) for 2DL2 expression.

Therefore, the significant differences and relationships between the frequencies of the different cell populations in ACs and HAM/TSP patients are summarised here, in terms of 2DL2 expression. ACs had fewer CD8+ T EM cells within total CD8+ T cells than HAM/TSP patients but significantly more of their CD8+ T EM cells expressed 2DL2 than in HAM/TSP patients. Similarly, although ACs also had significantly fewer CD8+ T EM cells that respond to simulation with Tax peptides than HAM/TSP patients, there was a trend towards more of these functionally Tax-specific CD8+ T EM cells

176 expressing 2DL2 in ACs than in HAM/TSP patients as well. Finally, by visual comparison, ACs had more 2DL2+ Tax-specific CD8+ T EM cells as a proportion of the total CD8+ T cell population than

HAM/TSP patients.

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Figure 5.2: See following page for figure legend.

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Figure 5.2: Summary bar charts of the proportions of each specific KIR+ and Tax-specific cell population within total CD8+ T cells in ACs and patients with HAM/TSP. In each patient group, the median frequency of each specific KIR+ and Tax-specific subpopulation was calculated as the proportion it represents within the total CD8+ T cell population, which is depicted as 100% for both patient groups. Y-axis scales become progressively smaller left-to-right to accommodate the very low cell frequencies. Part (A) shows specific KIR+ and functionally Tax-specific (blue) cell proportions within total CD8+ T cells in all donors with mix HLA class I alleles, including HLA-A2+ donors, and part (B) shows inhibitory KIR+ and Tax-specific TCR+ (purple) cell proportions within total CD8+ T cells in HLA-A2+ donors only. The Y-axes for the right-hand graphs showing inhibitory KIR+ Tax-specific TCR+ CD8+ T EM cells in HLA-A2+ donors are different but both extent over a range of 0.1. KIR+ Tax-specific CD8+ T EM cell populations are depicted as the overlap between the Tax-specific CD8+ T EM cell population and the KIR+ CD8+ T EM cell population (blue or purple, with pink checks), so Tax-specific CD8+ T EM cell proportions (blue or purple) do not extent to the X-axis. KIR+ CD8+ T EM cell proportions (pink checks) extend from the X-axis to the top of the band representing KIR+ Tax-specific CD8+ T EM cell populations.

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Figure 5.2: See previous page for figure legend.

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5.4 Results: Effects of specific KIR expression on CD8+ T EM cell function

5.4.1 The functional responses of specific KIR+ compared to KIR– CD8+ T EM cells in

HTLV-1 infection

A reduction in CD8+ T cell effector function mediated by KIR expression is a key component of the

KIR-expression induced CD8+ T cell survival mechanism (Vivier et al., 2004). To investigate whether this occurs in HTLV-1 infection, I compared the functional responses of specific KIR+ and specific

KIR– CD8+ T cells to different stimuli in ACs and patients with HAM/TSP. PBMCs from ACs and

HAM/TSP patients were stimulated for 5 hours with a panel of overlapping Tax peptides, bead-bound anti-CD3/28 mAbs or phorbol myristate acetate and the calcium ionophore, calcimycin (PMA/CAI). As described previously, the CCR7 differentiation marker was included to focus the analysis on CCR7–

EM phenotype CD8+ T cells, because the majority of KIR expression is found on this CD8+ T cell subset. This ensured that the specific KIR+ and KIR– cells being compared had similar functional capacities (see Chapter 3, Section 3.4.1).

Total IFNγ/CD107a responses of specific KIR+ compared to KIR– Tax-specific TCR+ CD8+ T EM cells following stimulation with Tax peptides in ACs and HAM/TSP patients

The effect of specific KIR expression on the functional responses of CD8+ T EM cells was analysed in

+ + the context of an HTLV-1 antigen. HLA-A2/Tax11-19 pentamer CD8 T EM cells were analysed, in order to compare KIR+ and KIR– CD8+ T EM cells with known specificity for Tax. HLA-A2+ donors were selected for the analysis based on their possession of Tax-specific TCR+ cells within their inhibitory KIR+ CD8+ T EM cell populations, as determined above (Figure 5.1B, Section 5.3.1)

The frequencies of total IFNγ/CD107a+ cells in specific KIR+ and KIR– Tax-specific TCR+ CD8+ T EM cells following stimulation with Tax peptides were compared within the same donor for each specific

KIR using pairwise tests. This analysis was extremely low powered because of the low number of donors that could be assessed, which affects the reliability of the statistical analyses that were possible. However, there was a clear pattern of lower frequencies of total IFNγ/CD107a+ cells in KIR+ compared to KIR– Tax-specific TCR+ CD8+ T EM cells in both ACs and patients with HAM/TSP for all three inhibitory KIRs (Figure 5.3). In fact, the difference between the frequency of total IFNγ/CD107a+ cells in 2DL3+ and 2DL3– Tax-specific TCR+ CD8+ T EM cells was almost statistically significant in

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ACs (p=0.0625, Figure 5.3). This statistical result occurred despite the fact that in two ACs equal frequencies (~60%) of their 2DL3+ and 2DL3– Tax-specific TCR+ CD8+ T EM cells produced

IFNγ/CD107a. One outlying AC also had higher frequencies of total IFNγ/CD107a+ cells in their 2DL2+ than in their 2DL2– Tax-specific TCR+ CD8+ T EM cells. These three donors show that inhibition of

Tax-specific TCR+ CD8+ T EM cell effector responses by KIR expression does not necessarily occur in all donors.

Overall, in the majority of donors, expression of the three inhibitory KIRs mediated a consistent

+ pattern of inhibition of HLA-A2-restricted Tax11-19-specific CD8 T EM cell functional responses to stimulation with Tax peptides, for both ACs and patients with HAM/TSP, however there were no statistically significant results.

Figure 5.3: Frequencies of total functional cells in inhibitory KIR+ compared to KIR– Tax- specific TCR+ CD8+ T EM cell populations following stimulation with Tax peptides in HLA-A2+ ACs and HAM/TSP patients. Freshly thawed PBMC from HLA-A2+ patients were stimulated with Tax peptides for 5 hours then stained with CD107a and IFNγ mAbs, plus HLA-A2/Tax11-19 pentamer and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are total IFNγ/CD107a+ cells (total functional cells) within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–) + – +/– +/– +/– + + + – and 2DL3 (2DS2 2DL2 2DL1 2DS1 ) HLA-A2/Tax11-19 pentamer CD3 CD8 CCR7 T cells (inhibitory KIR+ Tax-specific TCR+ CD8+ T EM cells) paired with each corresponding inhibitory KIR– population. Data were not normally distributed, by D’Agostino and Pearson normality test. Therefore, statistical significance of pairwise comparisons between KIR+ and KIR– cell populations were determined using 2-tailed Wilcoxon matched pairs tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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Total IFNγ/CD107a responses of specific KIR+ compared to KIR– CD8+ T EM cells following anti-CD3/CD28 mAb stimulation in ACs and HAM/TSP patients

In order to analyse higher numbers of KIR+ functional cells in a larger group of donors, and for comparison with the Tax peptide stimulation analysis, the effect of specific KIR expression on the functional responses of CD8+ T EM cells to direct polyclonal stimulation of the TCR complex using anti-CD3/28 mAb treatment was analysed. Prior to this analysis, the frequencies of total

IFNγ/CD107a+ CD8+ T EM cells following stimulation with anti-CD3/28 mAb were compared in ACs and patients with HAM/TSP, to determine the nature of the responses to this polyclonal stimulus in the two donor groups. As Figure 5.4 shows, the two groups of HTLV-1 infected subjects had very similar frequencies of CD8+ T EM cells that produce IFNγ/CD107a in response to anti-CD3/28 mAb stimulation (Figure 5.4). The use of CCR7 to control for the differentiation stages of the KIR+ and KIR– cells being compared was particularly important for this analysis, because CD8+ T cells of all stages of maturation can respond to anti-CD3/28 stimulation.

Figure 5.4: Frequencies of total functional CD8+ T EM cells following stimulation with anti- CD3/28 mAb in ACs and HAM/TSP patients. Freshly thawed PBMC were stimulated with bead bound anti-CD3/28 mAbs for 5 hours then stained with CD107a and IFNγ mAbs analysed by flow cytometry. Frequencies shown are total IFNγ/CD107a+ cells within CD3+ CD8+ CCR7– T cells (CD8+ T EM cells). Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between patient groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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To compare the frequencies of total IFNγ/CD107a+ cells within specific KIR+ and KIR– CD8+ T EM cells following anti-CD3/28 mAb treatment within each donor, pairwise tests were carried out for each specific KIR definition. Consistently lower frequencies of total IFNγ/CD107a+ CD8+ T EM cells were observed in all five specific KIR+ populations, compared to their corresponding specific KIR– populations, within both ACs and patients with HAM/TSP (Figure 5.5). In ACs, the differences in total

IFNγ/CD107a+ CD8+ T EM cell frequencies between specific KIR+ and KIR– populations were all statistically significant, with the exception of 2DS1, which was close to significance (2DL1 p=0.0098,

2DL2 p=0.0031, 2DL3 p=0.002, 2DS1 p=0.0625, 2DS2 p=0.0156). In patients with HAM/TSP, differences in total IFNγ/CD107a+ CD8+ T EM cell frequencies were statistically significant for the three inhibitory KIR+ populations compared to their inhibitory KIR– counterparts (2DL1 p=0.0039,

2DL2 p=0.0003, 2DL3 p=0.0195). Although the analyses for the activating KIRs in HAM/TSP patients either lacked statistically significance (2DS2, p=0.25), or enough donors to perform a statistical test

(2DS1, n=2), the trend towards lower frequencies of total IFNγ/CD107a+ CD8+ T EM cells in specific

KIR+ compared to KIR– populations in HAM/TSP patients was maintained. Interestingly, the p values for the differences in frequencies of total IFNγ/CD107a+ CD8+ T EM cells in 2DL1+ and 2DL2+ populations, compared to their corresponding KIR– populations, were lower in patients with HAM/TSP than in ACs.

Overall, expression of the three inhibitory KIRs mediated a statistically significant reduction in the functional responses of CD8+ T EM cells to TCR-directed stimulation, for both ACs and patients with

HAM/TSP. A trend towards reduced CD8+ T EM cell effector function was also observed with the expression of activating KIRs, but this was only statistically significant for 2DS2 expression in ACs.

184

Figure 5.5: Frequencies of total functional cells in specific KIR+ compared to KIR– CD8+ T EM cell populations following stimulation with anti-CD3/28 mAb in ACs and HAM/TSP patients. Freshly thawed PBMC were stimulated with bead bound anti-CD3/28 mAbs for 5 hours then stained with CD107a and IFNγ mAbs, and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are total IFNγ/CD107a+ cells (total functional cells) within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (specific KIR+ CD8+ T EM cells) paired with each corresponding specific KIR– population. Data were not normally distributed, by D’Agostino and Pearson normality test. Therefore, statistical significance of pairwise comparisons between KIR+ and KIR– cell populations were determined using 2-tailed Wilcoxon matched pairs tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

185

Total IFNγ/CD107a responses of specific KIR+ compared to KIR– CD8+ T EM cells following

PMA/CAI stimulation in ACs and HAM/TSP patients

The effect of specific KIR expression on the functional responses of CD8+ T EM cells to TCR- independent stimulation was analysed. The total IFNγ/CD107a responses of KIR+ and KIR– CD8+ T

EM cells to treatment with PMA and CAI, which stimulate cells by increasing PKC signalling and increasing intracellular calcium flux, respectively, were compared. Pairwise tests within each donor revealed similar frequencies of total IFNγ/CD107a+ cells were observed in KIR+ compared to KIR–

CD8+ T EM cells for almost all the specific KIR definitions in ACs and HAM/TSP patients, despite a wide range of responses between different individuals (Figure 5.6). The exception was for 2DL3 expression in ACs, where significantly lower frequencies of 2DL3+ CD8+ T EM cells were observed to produce IFNγ/CD107a in response to PMA/CAI treatment than 2DL3– CD8+ T EM cells (p=0.0391).

Also, the difference between the frequencies of 2DL1+ and 2DL1– CD8+ T EM cells that were total

IFNγ/CD107a+ was close to statistical significance in ACs (p=0.0674).

Overall, the functional responses of CD8+ T EM cells to TCR-independent stimulation were not affected by specific KIR expression. This effect was observed for all five specific KIR expression definitions in ACs and patients with HAM/TSP, with the exception of 2DL3+ CD8+ T EM cells in ACs, whose effector function was reduced by 2DL3 expression. Together, the findings above show a clear inhibitory KIR-mediated reduction in the functional responses of CD8+ T EM cell to TCR-directed stimulation with anti-CD3/28 mAb treatment and, to a weaker extent, with antigen-specific stimulation of HTLV-1-specific CD8+ T EM cells. TCR-independent activation appears capable of bypassing this

KIR-mediated reduction of CD8+ T EM cell functional responses to stimulation via the TCR.

186

Figure 5.6: Frequencies of total functional cells in specific KIR+ compared to KIR– CD8+ T EM cell populations following stimulation with PMA/CAI treatment in ACs and HAM/TSP patients. Freshly thawed PBMC were stimulated with PMA and CAI for 5 hours then stained with CD107a and IFNγ mAbs, and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are total IFNγ/CD107a+ cells (total functional cells) within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (specific KIR+ CD8+ T EM cells) paired with each corresponding specific KIR– population. Data were not normally distributed, by D’Agostino and Pearson normality test. Therefore, statistical significance of pairwise comparisons between KIR+ and KIR– cell populations were determined using 2-tailed Wilcoxon matched pairs tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

187

5.4.2 Influence of cognate HLA class I ligand on the functional responses of inhibitory KIR+ CD8+ T EM cells in HTLV-1 infection

The influence of HLA class I ligand possession on the total IFNγ/CD107a responses of 2DL1+, 2DL2+ and 2DL3+ CD8+ T EM cells to anti-CD3/28 mAb stimulation was investigated in ACs and patients with

HAM/TSP. This analysis was carried out using the anti-CD3/28 mAb stimulation data only, because

HLA-A2+ donor numbers were too low to perform the analyse using this cohort. Also, activating KIRs were not analysed because the exact ligand specificity of 2DS1 and 2DS2 is uncertain, the interactions that are known are weak and donor numbers were much lower than for their inhibitory counterparts. To compare the frequencies of total IFNγ/CD107a+ cells within inhibitory KIR+ CD8+ T

EM cell populations in the presence and absence of HLA class I ligands, ACs and patients with

HAM/TSP were stratified according to their possession of the highest affinity cognate HLA class I ligands for each of the three inhibitory KIRs tested: group HLA-C2 for 2DL1 and group HLA-C1 for

2DL2 and 2DL3.

Interestingly, ACs in possession of a HLA-C2 group ligand had significantly higher frequencies of total

IFNγ/CD107a+ cells within their 2DL1+ CD8+ T EM cell populations than ACs without an HLA-C2 group allele (p=0.0424, Figure 5.7). This was not the case in patients with HAM/TSP, for whom similar frequencies of total IFNγ/CD107a+ cells were observed in 2DL1+ CD8+ T EM cells for donors with or without an HLA-C2 group ligand. There was also a trend towards higher frequencies of total

IFNγ/CD107a+ cells within 2DL2+ CD8+ T EM cell populations in ACs that had an HLA-C1 group ligand than ACs that did not, however this was not statistically significant. A similar trend was also observed for frequencies of total IFNγ/CD107a+ 2DL2+ CD8+ T EM cells in HAM/TSP patients, although this was not reliable because there was only one individual in the HLA-C1– group. Frequencies of total

IFNγ/CD107a+ cells within 2DL3+ CD8+ T EM cell populations seemed to show a slight trend in the opposite direction to total IFNγ/CD107a+ cell frequencies in 2DL2+ and 2DL1+ CD8+ T EM cells, in that there were higher frequencies in donors that did not have an HLA-C1 group ligand for 2DL3 than those that did. This was observed for both ACs and HAM/TSP patients however, the differences were not statistically significant. Therefore, there was an overall pattern of higher frequencies of total

IFNγ/CD107a+ cells within inhibitory KIR+ CD8+ T EM cells in individuals that possess a cognate HLA class I ligand than in those individuals that do not, although the statistical results were not strong.

188

KIR HLA class I ligand

2DL1 Group HLA-C2lys80

asn80 2DL2 Group HLA-C1 asn80 2DL3 Group HLA-C1

Figure 5.7: The effect of cognate HLA class I ligand possession on the frequencies of total functional cells in specific KIR+ CD8+ T EM cell populations following anti-CD3/28 mAb stimulation in ACs and HAM/TSP patients. Frequencies of total IFNγ/CD107a+ cells (total functional cells) within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–) and 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–) CD3+ CD8+ CCR7– T cells (inhibitory KIR+ CD8+ T EM cells), stimulated with bead bound anti-CD3/28 mAbs and analysed by flow cytometry, are shown in HTLV-1+ study subjects with and without at least one copy of the cognate HLA class I ligand for each inhibitory KIR analysed. Data were not normally distributed in both study groups, by D’Agostino and Pearson normality test. Therefore, medians are shown and statistical significance between study groups was determined using 2-tailed Mann-Whitney tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

189

5.4.3 The functional profile of specific KIR+ compared to specific KIR– CD8+ T EM cells in HTLV-1 infection

It is thought that the expression of inhibitory KIRs on T cells raises their activation threshold and in line with this inhibitory KIR expression has been shown to alter the balance of T cell effector responses in favour of functions triggered at lower activation levels (Henel et al., 2006). Following the observation above, that inhibitory KIR expression mediated a reduction in the functional responses of

CD8+ T EM cell to TCR-directed stimulation in HTLV-1 infection, the ability of specific KIR expression to modify the profile of CD8+ T EM cell effector functions in ACs and HAM/TSP patients was investigated. The frequencies of IFNγ single+, CD107a single+ or IFNγ/CD107a double+ cells in total functional (total IFNγ/CD107a+) CD8+ T EM cells were compared in specific KIR+ and KIR– populations within the same donor using pairwise tests. Responses to stimulation with Tax peptides and anti-CD3/28 mAb were assessed in all donors whose specific KIR+ CD8+ T EM cell populations contained functional cells, as determined above (see Figure 5.1A, Section 5.3.1).

IFNγ single+, CD107a single+ and IFNγ/CD107a double+ effector responses in specific KIR+ compared to KIR– total functional CD8+ T EM cells following Tax peptide stimulation

The effect of specific KIR expression on the balance of CD8+ T EM cell effector functions was analysed in the context of an HTLV-1 antigen by stimulating with Tax peptides. Comparisons of

IFNγ/CD107a double+ cell frequencies in KIR+ and KIR– total functional CD8+ T EM cell populations showed striking results. There were significantly lower frequencies of IFNγ/CD107a double+ cells in

KIR+ compared to KIR– total functional CD8+ T EM cells in both ACs and patients with HAM/TSP for all inhibitory KIRs, except for 2DL2 in patients with HAM/TSP. Most p values were highly significant and the 2DL2 analysis in patients with HAM/TSP was almost statistically significant (Figure 5.8, top 3 panels, right-hand graph sections; 2DL1 ACs p=0.004 HAM/TSP patients p=0.0006, 2DL2 ACs p=0.0003 HAM/TSP patients p=0.0523, 2DL3 ACs p=0.0371 HAM/TSP patients p=0.001). Similar effects were observed for activating KIR+ and KIR– total functional CD8+ T EM cells (Figure 5.8, 4th and 5th panels, right-hand graph sections). Significantly lower frequencies of IFNγ/CD107a double+ cells were observed in 2DS2+ compared to 2DS2– total functional CD8+ T EM cells in ACs (5th panel, p=0.0156). The same trend was observed for the 2DS2 analysis in patients with HAM/TSP and for the

2DS1 analysis in ACs, although neither quite reached formal statistical significance (4th and 5th

190 panels, both p=0.0625). The analyses for the activating KIRs were hampered by low donor numbers, none more so than the 2DS1 analysis in patients with HAM/TSP, which is shown for completeness but as only one donor could be analysed it was not informative.

Analyses comparing IFNγ single+ and CD107a single+ cell frequencies in KIR+ and KIR– total functional CD8+ T EM cells also showed some interesting effects of KIR expression on the balance of these effector functions. Strikingly, there were no statistically significant differences in IFNγ single+ cell frequencies in KIR+ and KIR– total functional CD8+ T EM cells in ACs or patients with HAM/TSP for any of the five KIRs analysed (Figure 5.8, left-hand graph sections). However, significantly higher frequencies of CD107a single+ cells were observed in 2DL2+ compared to 2DL2– total functional CD8+

T EM cells in ACs but not in HAM/TSP patients (Figure 5.8, 2nd panel, middle graph sections; ACs p=0.0184). In the 2DL3 analysis, HAM/TSP patients had significantly higher frequencies of CD107a single+ cells in 2DL3+ compared to 2DL3– total functional CD8+ T EM cells, whereas there was no difference in ACs (Figure 5.8, 3rd panel, middle graph sections; HAM/TSP patients p=0.0273). For the

2DL1 analysis, there was a trend towards higher frequencies of CD107a single+ cells in 2DL1+ compared to 2DL1– total functional CD8+ T EM cells, which was close to statistical significance in

HAM/TSP patients, but not in ACs (Figure 5.8, 1st panel, middle graph sections, HAM/TSP patients p=0.0872). Comparisons of CD107a single+ cell frequencies in activating KIR+ and KIR– total functional CD8+ T EM cell populations showed no statistically significant differences for either ACs or patients with HAM/TSP (Figure 5.8, 4th and 5th panels, middle graph sections).

Together, these data show that the proportion of the total CD8+ T EM cell effector response to stimulation with Tax peptides composed of cytokine production alone was not affected by expression of any of the five KIRs. However, there appeared to be a shift in the functional profile away from bifunctional capacity when inhibitory KIRs and potentially activating KIRs were expressed, towards degranulation alone, particularly when 2DL2 or 2DL3 were expressed.

191

Figure 5.8: See following page for figure legend.

192

Figure 5.8: Frequencies of IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells in specific KIR+ compared to KIR– total functional Tax-specific CD8+ T EM cells in ACs and patients with HAM/TSP. Freshly thawed PBMC were stimulated with Tax peptides for 5 hours then stained with CD107a and IFNγ mAbs, and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) total IFNγ/CD107a+ CD3+ CD8+ CCR7– T cells (specific KIR+ total functional Tax-specific CD8+ T EM cells) paired with each corresponding specific KIR– population. Data were not normally distributed, by D’Agostino and Pearson normality test. Therefore, statistical significance of pairwise comparisons between KIR+ and KIR– cell populations were determined using 2-tailed Wilcoxon matched pairs tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

193

IFNγ single+, CD107a single+ and IFNγ/CD107a double+ effector responses of specific KIR+ compared to KIR– total functional CD8+ T EM cells following anti-CD3/28 mAb stimulation

To allow the analysis of higher numbers of KIR+ functional cells, and for comparison with the results from the Tax peptide stimulation analysis, the effect of specific KIR expression on the balance of

CD8+ T EM cell effector functions was analysed following polyclonal TCR stimulation with anti-CD3/28 mAb treatment. The overall pattern of differences between the frequencies of KIR+ and KIR– total functional CD8+ T EM cells within each functional output category was broadly similar to those observed following Tax peptide stimulation. Comparisons of IFNγ/CD107a double+ cell frequencies in

KIR+ compared to KIR– total functional CD8+ T EM cells again showed consistently lower frequencies of these cells in KIR+ than in KIR– populations in both donor groups for all five KIRs (Figure 5.9, right- hand graph sections). Statistical significance was achieved for both ACs and HAM/TSP patients in the

2DL2 analysis (2nd panel; ACs p=0.0102 HAM/TSP patients p=0.0294), for ACs but not patients with

HAM/TSP in the 2DL3 analysis (3rd panel; ACs p=0.002) and similarly in the 2DS2 analysis (5th panel; ACs p=0.0156), and for patients with HAM/TSP but not ACs in the 2DL1 analysis (1st panel;

HAM/TSP patients p=0.0179). There were no statistically significant differences for comparisons of

IFNγ/CD107a double+ cell frequencies in 2DS1+ and 2DS1– total functional CD8+ T EM cell populations in either ACs or HAM/TSP patients.

Comparisons of KIR+ and KIR– total functional CD8+ T EM cells for their IFNγ single+ and CD107a single+ cell frequencies following anti-CD3/28 mAb treatment also showed similar results to those observed after stimulation with Tax peptides. Again, a striking lack of any statistically significant differences in frequencies of IFNγ single+ cells in KIR+ compared to KIR– total functional CD8+ T EM cells was observed in both ACs and HAM/TSP patients for the five KIRs (Figure 5.9, left hand graph sections). The general pattern of higher CD107a single+ cell frequencies in KIR+ compared to KIR– total functional CD8+ T EM cells observed with Tax peptide stimulation was also evident with anti-

CD3/28 mAb stimulation but there were some differences as to which comparisons were statistically significant. Significantly higher frequencies of CD107a single+ cells were observed in 2DL2+ compared to 2DL2– total functional CD8+ T EM cells in patients with HAM/TSP but not in ACs (Figure 5.9, 2nd panel, middle graph sections, HAM/TSP p=0.002). 2DL3+ total functional CD8+ T EM cells in ACs contained significantly higher frequencies of CD107a single+ cells than their 2DL3– counterparts, but

194 this comparison was not statistically significant in patients with HAM/TSP (Figure 5.9, 3rd panel, middle graph sections; HAM/TSP patients p=0.0137). Both ACs and HAM/TSP patients had significantly higher frequencies of CD107a single+ in 2DL1+ compared to 2DL2– total functional CD8+

T EM cells and both comparisons were highly statistically significant (Figure 5.9, 1st panel, middle graph sections; AC p=0.0098 HAM p=0.0007). There were also significantly higher frequencies of

CD107a single+ cells in 2DS2+ compared to 2DS2– total functional CD8+ T EM cells in ACs (Figure

5.9, 5th panel, middle graph sections; ACs p=0.0156). This was the only statistically significant result for the analysis of the activating KIRs, however in general the directions of the trends in the CD107a single+ data were similar to those observed for the analysis of the inhibitory KIRs.

In summary, KIR expression also modified the balance of CD8+ T EM cell effector responses to polyclonal TCR-directed stimulation, in essentially the same way as with Tax peptide stimulation. That is, the profile of CD8+ T EM cell functional output was shifted away from bifunctional capacity and towards degranulation, while cytokine production was maintained at similar levels. These changes were observed when inhibitory KIRs and to some extent activating KIRs were expressed, therefore they were not specific to a particular KIR molecule or patient group.

195

Figure 5.9: See following page for figure legend.

196

Figure 5.9: Frequencies of IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells in specific KIR+ compared to KIR– total functional CD8+ T EM cells following stimulation with anti- CD3/28 mAb in ACs and patients with HAM/TSP. Freshly thawed PBMC were stimulated with bead bound anti-CD3/28 mAbs for 5 hours then stained with CD107a and IFNγ mAbs, and the anti-KIR mAb clone combinations and analysed by flow cytometry. Frequencies shown are IFNγ single+, CD107a single+ and IFNγ/CD107a double+ cells within 2DL1+ (2DS1– 2DL2+/– 2DS2+/– 2DL3+/–), 2DL2+ (2DS2– 2DL3– 2DL1+/– 2DS1+/–), 2DL3+ (2DS2– 2DL2+/– 2DL1+/– 2DS1+/–), 2DS1+ (2DL1– 2DL2– 2DS2– 2DL3–) and 2DS2+ (2DL3– 2DL2+/– 2DL1+/– 2DS1+/–) total IFNγ/CD107a+ CD3+ CD8+ CCR7– T cells (specific KIR+ total functional CD8+ T EM cells) paired with each corresponding specific KIR– population. Data were not normally distributed, by D’Agostino and Pearson normality test. Therefore, statistical significance of pairwise comparisons between KIR+ and KIR– cell populations were determined using 2-tailed Wilcoxon matched pairs tests. AC, asymptomatic carrier; HAM, patient with HAM/TSP.

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5.5 Discussion

5.5.1 Inhibitory KIR expression reduces the functional response of CD8+ T EM cells to TCR-dependent but not TCR-independent activation

The expression of inhibitory KIRs was associated with a reduction in the effector responses of CD8+ T

EM cells to TCR-directed stimulation in HTLV-1 infected individuals. This effect of inhibitory KIR expression has been previously reported in studies analysing viral antigen-specific responses of

PBMC or virus-specific T cell clones from infected individuals (Alter et al., 2008; Anfossi et al., 2004;

Bonorino et al., 2007; van der Veken et al., 2009), as well as those analysing responses to polyclonal

TCR-directed stimulation such as SEB, pools of immunodominant viral peptides, anti-CD3/28 mAbs or

PHA by PBMC and inhibitory KIR+ T cell clones from uninfected donors (Alter et al., 2008; Björkström et al., 2012; Henel et al., 2006). Effector responses measured include proliferation, cytokine production and cytotoxic degranulation. Thus, although these studies used diverse cell types, assay techniques and readouts they all consistently report that effector responses of CD8+ T cells are reduced when inhibitory KIRs are expressed.

I observed a reduction in CD8+ T EM cell effector function associated with inhibitory KIR expression in the context of HTLV-1 antigen stimulation, in the majority of ACs and patients with HAM/TSP.

Although this is an interesting result, it was not reliable because very low numbers of cells were analysed. As discussed in Chapter 4 (see Section 4.4.2), the numbers of inhibitory KIR+ Tax-specific

TCR+ CD8+ T EM cells were already at the absolute threshold of flow cytometric detection, so the

+ number of functional cells within this HLA-A2/Tax11-19 pentamer population was even smaller, frequently zero. Also, the number of individuals that could be analysed was low and unsurprisingly there were no statistically significant results. However, the analysis of CD8+ T EM cell responses to anti-CD3/28 mAb stimulation was much more robust, in terms of numbers of donors and KIR+ functional cells, and this analysis also revealed a reduction in effector responses when inhibitory KIRs were expressed with highly significant p values, supporting the same finding for stimulation with Tax peptides. There was also a pattern of reduced CD8+ T EM cell effector responses to anti-CD3/28 mAb treatment when activating KIRs were expressed, which was statistically significant for 2DS2 expression and almost significant for 2DL1 expression in ACs. This could be explained by the co- expression of inhibitory KIRs, since the expression definition for 2DS2 includes potential co-

198 expression of 2DL2, as well as other inhibitory KIRs that could not be excluded from the analysis.

Interestingly, the p values for comparisons of the functional responses of 2DL1+ and 2DL2+ CD8+ T

EM cells with their 2DL1– and 2DL2– counterparts, were lower in patients with HAM/TSP than in ACs.

It is tempting to speculate that this more consistent inhibitory effect of 2DL1 and 2DL2 expression in

HAM/TSP patients may result from the ability of these KIRs to confer a greater reduction in the effector function of CD8+ T EM cells from HAM/TSP patients than ACs, especially given there is no difference in the underlying functional response of CD8+ T EM cells to anti-CD3/28 mAb stimulation between the two patient groups. However, the differences in the p values could result from more statistical power in the HAM/TSP patient group which contained more individuals. Also, it is difficult to know the relevance of CD8+ T EM cell responses to artificial polyclonal stimulation in terms of meaningful immunological differences between ACs and HAM/TSP patients.

In contrast to stimulation via the TCR, the functional responses of CD8+ T EM cells to TCR- independent stimulation were generally not affected by the expression of specific KIR in ACs and

HAM/TSP patients. There was a reduction in CD8+ T EM cell effector function in ACs when 2DL3 was expressed. However, this was the only statistically significant result and the p value was higher than for the effect observed with TCR-directed simulation, indicating the inhibitory effect of 2DL3 expression on CD8+ T EM cell function was not as consistent when these cells were activated independently of their TCR. Therefore, this general lack of effect of specific KIR expression on CD8+ T

EM cell responses to PMA/CAI treatment suggests that TCR-independent stimulation is able to bypass KIR-mediated interference with CD8+ T EM cell activation and that specific KIR+ CD8+ T cells do not have an intrinsic functional defect. Using a very similar experimental design, Alter et al., (2008) reached the same conclusions. Together, my observations support two components of the KIR expression-induced CD8+ T cell survival mechanism which is proposed to explain how 2DL2 can influence HLA class I-restricted antiviral immunity, (1) that KIR expression reduces the effector responses of CD8+ T cells, and (2) that it does so by interfering with TCR-mediated CD8+ T cell activation.

The presence of cognate HLA class I ligand was not required for the observed inhibitory KIR- mediated reduction in CD8+ T EM cell effector responses to anti-CD3/28 mAb stimulation. In fact quite

199 the contrary, there was a trend towards an association of HLA class I ligand possession with increased functional responses. ACs with an HLA-C2 group ligand had significantly higher frequencies of responding 2DL1+ CD8+ T EM cells than ACs without this ligand, and there was a similar pattern of higher frequencies of responding 2DL2+ CD8+ T EM cells in ACs and HAM/TSP patients with an HLA-C1 group ligand. This observation was unexpected, since the literature reports either that KIR engagement is necessary in order to observe a reduction in the effector function of

KIR+ CD8+ T cells (Bonorino et al., 2007; van der Veken et al., 2009), or that KIR+ CD8+ T cell function was equally repressed whether HLA ligand was present or not (Alter et al., 2008; Anfossi et al., 2004;

Björkström et al., 2012). This latter observation implying constitutive inhibition of CD8+ T cell function by KIR expression has been attributed to the fact that KIRs can recruit the phosphatases SHP-1 and

SHP-2 (Alvarez-Arias et al., 2007) and can bind to and inhibit PKC (Chwae et al., 2002) independently of HLA class I ligand interaction.

In NK cell biology, the expression of KIRs that are able to interact with cognate HLA class I ligand was shown to increase NK cell functionality in response to stimulation with target cells, compared to NK cells expressing KIRs specific for non-self-HLA class I (Anfossi et al., 2006; Kim et al., 2008). This observation provides support for KIR:HLA class I ligand interactions in the education of NK cells, in which their functional responsiveness to target cells is fine-tuned. Thus, the observed trend towards increased functionality of KIR+ CD8+ T EM cells from individuals with a cognate HLA class I ligand could suggest that the responsiveness of KIR+ CD8+ T EM cells may be tuned by an educational influence of HLA class I, as has been described for NK cells. Very little is known about this effect in

KIR+ CD8+ T cells in humans. One study of PBMC from healthy humans has concluded that this tolerance mechanism probably does not occur in human CD8+ T cells (Björkström et al., 2012). In mice, intraepithelial CD8+ T cells expressing inhibitory NK cell receptors were not functionally educated by interactions with cognate MHC class I (Taveirne et al., 2011). KIR expression on NK cells and T cells is initiated at very different developmental stages; during maturation in the bone marrow, and post thymic maturation and acquisition of memory phenotype, respectively. Also, while the KIRs are one of the major families of receptors through which NK cell activation is controlled, the activation of T cells is primarily dependent on TCR triggering by engagement with peptide:HLA class I complexes. As discussed in Chapter 3 (Section 3.5.2), there is mounting evidence that the function of

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NK cells continues to be influenced by interactions with cognate HLA class I in the periphery, implying that the education of NK cells may be a continuous process over the lifespan of an NK cell, rather than being limited to a maturation phase in the bone marrow (Sun, 2010). While it could be possible that T cells expressing KIRs may also be functionally influenced by interactions with cognate HLA class I in the periphery, it is difficult to reconcile the major differences in the function of KIRs on NK cells and T cells with an educational role for cognate HLA class I ligand on the functionality of KIR+

CD8+ T cells, as is proposed for NK cells. There are also a number of limitations to this observation of increased functionality in KIR+ CD8+ T EM cells from individuals with a cognate HLA class I ligand which affect its reliability. Firstly, there is only a statistically significant result for 2DL1 and the p value is not strong. Secondly, numbers of study subjects was low, particularly for those lacking a cognate

HLA class I ligand. Thirdly, individuals with HLA-C2/C2 genotypes can ligate 2DL2 and 2DL3 to a degree (Moesta et al., 2008), thus these individuals are not necessarily without an HLA class I ligand, particularly for 2DL2. Finally, the potential for co-expression of multiple KIRs included in the KIR expression definitions may make it possible for other KIR:HLA class I ligand pairs to influence the functional response of these specific KIR+ CD8+ T EM cells. It would be interesting to repeat this analysis in a much bigger cohort of individuals, and preferably using physiological stimulation of the

TCR, to determine whether interactions with cognate HLA class I in the periphery are capable of tuning the functional responsiveness of KIR+ CD8+ T EM cells.

5.5.2 The functional responses of specific KIR+ CD8+ T EM cells are biased towards a cytotoxic phenotype and have reduced bifunctional capability

The KIR-mediated reduction in CD8+ T EM cell functional responses to TCR-directed stimulation discussed above was not a generalised effect on all effector functions rather, KIR expression was associated with a particular shift in the functional profile of CD8+ T EM cells in HTLV-1 infected individuals. The functional responses of KIR+ CD8+ T EM cells were biased towards degranulation alone, while bifunctional capability was reduced compared to KIR– CD8+ T EM cell responses but cytokine production alone was unchanged. This effect was observed for functional responses to Tax peptide stimulation and to anti-CD3/28 mAb treatment, thus the TCR-directed polyclonal analysis with higher levels of stimulation and greater numbers of responding cells supports the same observations made in the context of HTLV-1 antigen stimulation. Similarly, the uncoupling of degranulation and

201 cytokine production has been reported in 2DL2+ CD4+ T cell clones (Henel et al., 2006) and also in

CD8+ T cells expressing a variety of inhibitory NK cell receptors in humans and mice (Cambiaggi et al., 1999; Gu et al., 2003; Ince et al., 2004).

The lack of an effect of specific KIR expression on cytokine release alone was consistent for all KIR molecules, both patient groups and both types of stimulation. The reduction in the bifunctional capability of CD8+ T EM cells when specific KIRs were expressed was also clear, with almost all comparisons being statistically significant and the same trend towards a reduction in those that were not, including the activating KIRs. Again, this could be explained by the potential for co-expression of inhibitory KIRs that are not excluded from the definitions of 2DS1 and 2DS2 expression. It also indicates that expression of any KIR seems to modify the balance of CD8+ T EM cell effector functions to some extent and this is not a specific effect of one KIR in particular such as 2DL2. Interestingly though, when 2DL2 was expressed, the shift in the functional profile of CD8+ T EM cells towards degranulation alone observed with Tax peptide stimulation was statistically significant in ACs but not in HAM/TSP patients. It is tempting to suggest that this indicates a protective cytotoxic CD8+ T EM cell phenotype is being preserved by 2DL2 expression in ACs but not in HAM/TSP patients. However, when 2DL3 was expressed, a statistically significant increase in degranulation alone was observed in

HAM/TSP patients and not in ACs. While it is interesting that the only statistically significant differences for an effect of KIR expression on degranulation alone were observed when 2DL2 or 2DL3 was expressed, the reliability of the Tax peptide stimulation analysis is low because, as discussed in

Chapter 4 (Section 4.4.2), many of the study subjects had numbers of specific KIR+ functionally Tax- specific CD8+ T EM cells that were at the absolute threshold of flow cytometric detection. Also, these differentials between ACs and patients with HAM/TSP for the effect of 2DL2 and 2DL3 expression on degranulation alone from CD8+ T EM cells were not supported by the analysis of CD8+ T EM cell responses to anti-CD3/28 mAb stimulation. In fact, the statistically significant results for the analysis of these two KIRs in ACs and HAM/TSP patients were the opposite to Tax peptide stimulation.

Overall, these data show that there is a consistent pattern of KIR expression modifying the balance of

CD8+ T EM cell effector responses to TCR-directed stimulation, which is not specific to one particular

KIR molecule or to a particular patient group. The observation of statistically significant effects may be

202 more related to factors affecting the reliability of the analysis i.e. donor numbers and numbers of functional cells available to analyse, which in turn is related to the level of stimulation. Nevertheless, the general observation that the functional profile of KIR+ CD8+ T EM cells was shifted away from bifunctional capacity and towards a cytotoxic phenotype supports the idea that KIR expression raises the activation threshold of T cells, as it does for NK cells and, in doing so, alters the qualitative balance of activation-dependent T cell function (Anfossi et al., 2004; Henel et al., 2006; Ugolini et al.,

2001). Since polyfunctionality requires higher levels of activation in order for multiple effector functions to be triggered, whereas cytotoxicity is initiated readily upon antigen-induced activation, my findings are consistent with the hierarchy in which different effector functions have been shown to sequentially reach their activation threshold (Almeida et al., 2009; Betts et al., 2004; Valitutti et al.,

1996). Thus, my observations are consistent with the component of the KIR expression-induced CD8+

T cell survival mechanism which proposes that KIR expression raises the activation threshold of CD8+

T cells and thus provides further support for this mechanism in explaining how 2DL2 influences HLA class I-restricted antiviral immunity.

Importantly, a KIR expression-induced increase in activation thresholds may not only be exerting a protective immune effect by reducing AICD in CD8+ T cells. In modifying the CD8+ T cell effector profile, rather than causing a generalised reduction in function, KIR expression on CD8+ T cells may also have important qualitative implications for protective immunity in the context of the chronic antigen stimulation and immune activation observed in persistent infection with HTLV-1. By focussing the CD8+ T cell response on killing infected cells and limiting cytokine production, KIR expression could preserve protective anti-HTLV-1 immune responses while preventing chronically activated CD8+

T cells from exacerbating inflammatory processes and contribute to HAM/TSP immunopathogenesis.

Therefore, this mechanism provides a further rationale for the apparent paradox of KIR expression reducing CD8+ T cell effector function but potentially having a beneficial impact on the immune response to HTLV-1 and protection from inflammatory disease. It would be interesting to analyse the effector responses of KIR+ and KIR– CD8+ T cells over a gradient of antigenic stimulation to determine the activation thresholds of the different effector functions and the effect that KIR expression has on this more directly. This would require either the selection of individuals with very high frequencies of

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KIR+ HTLV-1-specific CD8+ T cells or the generation of KIR+ and KIR– CD8+ T cell clones, ideally with identical HTLV-1-specific TCRs.

5.5.3 Proportional summary: 2DL2+ Tax-specific CD8+ T EM cells make up a greater proportion of the total CD8+ T cell population in ACs

In the two previous Chapters, the comparisons of KIR+ cell population frequencies between ACs and

HAM/TSP patients revealed that statistically significant differences were almost exclusively observed for 2DL2 expression. This is consistent with finding that HLA class I-associated antiviral immunity was enhanced by possession of a 2DL2 gene, but not by any of the other KIR genes tested (Seich al

Basatena et al., 2011). The proportional summary analysis showed that while ACs had significantly lower frequencies of CD8+ T EM cells and functionally Tax-specific CD8+ T EM cells than HAM/TSP patients, they also had higher frequencies of 2DL2+ cells within both of these parent cell populations than HAM/TSP patients, and by statistical analysis, these comparisons were significant and borderline significant, respectively. The proportional summary also revealed that all of these frequency differentials between ACs and HAM/TSP patients were reflected in the proportions these 2DL2+ and/or functionally Tax-specific cell populations made up within total CD8+ T cells. This included the very low proportions of 2DL2+ functionally Tax-specific CD8+ T EM cells within total CD8+ T cells, which nevertheless were slightly higher in ACs compared to HAM/TSP patients by visual comparison.

ACs also had very slightly higher frequencies of 2DL1+ functionally Tax-specific CD8+ T EM cells within total CD8+ T cells than HAM/TSP patients by visual comparison, also reflecting the borderline statistically significant increase in the frequency of functionally Tax-specific CD8+ T EM cells expressing 2DL1 in ACs compared to HAM/TSP patients. As discussed in Chapter 4 (Section 4.4.2), the HLA-A2/Tax11-19 pentamer analysis did not support ACs having higher frequencies of Tax-specific

CD8+ T EM cells expressing any of the three inhibitory KIRs than patients with HAM/TSP. However, the proportional summary showed that HLA-A2+ ACs did also have higher frequencies of 2DL2+ Tax- specific TCR+ CD8+ T EM cells within total CD8+ T cells than HLA-A2+ HAM/TSP patients by visual comparison. This further demonstrates that the frequencies of functionally Tax-specific CD8+ T EM cells expressing 2DL1 or 2DL2 that are detected in ACs and HAM/TSP patients are likely to be influenced by (1) differences in the nature of the underlying Tax responses of the two patient groups, in terms of frequency and potentially functional avidity, as discussed in Chapter 4 (Section 4.4.2), and

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(2) whether the Tax response is measured by HLA-A2/Tax11-19 pentamer staining or functional assay and/or potential differences in the anti-HTLV-1 CD8+ T cell immune responses of HLA-A2+ and HLA-

A2– individuals, as discussed in Chapter 4 (Section 4.4.1).

Although it is potentially interesting that ACs had higher frequencies of 2DL2+ Tax-specific CD8+ T EM cells within total CD8+ T cells than patients with HAM/TSP by both measures of Tax specificity, the numbers of these cells were at the absolute threshold of flow cytometric detection and therefore the accuracy with which they can be detected remains a considerable issue. As discussed in Chapter 4

(Section 4.4.2), there may be several reasons why the numbers of 2DL2+ functionally Tax-specific

CD8+ T EM cells measured were so low, one of which was their under-detection by functional assay due to KIR expression-induced reduction in the functional capacity of CD8+ T cells. While it was hoped that the use of 2uM concentrations of Tax peptides would minimise this effect, as discussed above, inhibitory KIR expression was found to mediate a reduction in CD8+ T EM cell effector responses to a strong polyclonal TCR-directed stimulus, i.e. anti-CD3/28 mAb treatment. Thus, it seems likely that in this cohort, an inhibitory effect of KIR expression could be a factor in the detection of low numbers of 2DL2+ Tax-specific CD8+ T EM cells by functional assay. As discussed in Chapter 4

(Section 4.4.2), this may have resulted in greater under-detection of 2DL2+ functionally Tax-specific

CD8+ T EM cells in HAM/TSP patients than in ACs, because CD8+ T EM cells from individuals with

HAM/TSP are likely to have lower functional avidity i.e. lower antigen sensitivity, thus requiring higher antigen concentrations to activate. It could be therefore that KIR expression can only be detected on high avidity Tax-specific CD8+ T EM cells, which may not be completely inhibited by their expression of KIRs and are thought to be more abundant in ACs. Finally, these caveats of KIR+ antigen-specific

CD8+ T cell detection by functional assay demonstrate that where possible peptide:HLA class I pentamer staining is probably the better method to measure these cells. While these reagents also have their own limitations, such as the requirement for particular HLA class I alleles, which reduces the number of donors that can be tested, and the detection of specificity for a single epitope, there are less factors complicating the interpretation of their staining on KIR+ CD8+ T cells.

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5.5.4 Chapter 5 summary

In this Chapter, I analysed the effects of specific KIR expression on the functional responses of CD8+

T EM cells in ACs and patients with HAM/TSP and related this to HLA class I ligand possession.

Inhibitory KIR expression was associated with a reduction in effector responses of CD8+ T EM cells to both HTLV-1 antigen and polyclonal TCR-directed stimulation, but not to TCR-independent stimulation, showing it was possible to bypass KIR-mediated interference with CD8+ T EM cell activation. Furthermore, specific KIR+ CD8+ T EM cells showed a shift in effector profile, towards a cytotoxic phenotype with reduced bifunctional capability, which is consistent with a raised activation threshold in these cells. These effects were observed with expression of any KIR to some extent and were not specific to a particular patient group, though it was tempting to speculate that there was a differential between ACs and HAM/TSP patients when 2DL2 was expressed. Together, my observations are consistent with the KIR expression-induced CD8+ T cell survival mechanism, which proposes that by disrupting TCR-mediated activation, inhibitory KIR expression reduces CD8+ T cell effector responses and protects them from AICD but, rather than inhibiting them completely, KIRs raise the activation threshold of CD8+ T cells and alter their effector profile. Therefore my findings provide further support for this hypothesis in explaining how 2DL2 influences HLA class I-restricted antiviral immunity. Unexpectedly, there was a trend towards increased functionality in KIR+ CD8+ T

EM cells in the presence of cognate HLA class I ligand, suggesting that the responsiveness of KIR+

CD8+ T EM cells may be tuned by an educational effect of HLA class I interactions, as has been described for NK cells. However, it is difficult to reconcile this with the major differences in the timing and functional role of KIR expression on T cells compared to NK cells.

I also summarised the proportions of all the different specific KIR+ and Tax-specific cell populations analysed in previous Chapters within the total CD8+ T cell population. This demonstrated visually how differences in the measurement of Tax specificity were likely to have influenced the frequencies of

Tax-specific CD8+ T EM cells expressing specific KIRs that were detected. It also highlighted how small the specific KIR+ Tax-specific CD8+ T EM cell populations are within the total CD8+ T cell population and the issue of their accurate detection by functional assay, which is likely to be influenced by KIR-mediated reduction in CD8+ T EM cell function.

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Chapter 6. General discussion and summary

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In HTLV-1 infection, it remains largely unknown why a minority of infected individuals develop the inflammatory disease HAM/TSP, while the majority are lifelong asymptomatic carriers of the virus.

However, there are multiple lines of evidence that the quality or effectiveness of the CD8+ T cell response has a key protective role to play in determining the PVL and consequently the risk of

HAM/TSP (Bangham, 2009). Host genes, particularly the HLA class I genes, have a strong influence on the effectiveness of the CD8+ T cell response to HTLV-1. Indeed, specific HLA class I alleles are associated with protection from, or susceptibility to, HAM/TSP. Seich al Basatena et al., (2011) recently showed that these positive and negative HLA class I-mediated effects on the outcome of infection with HTLV-1 were enhanced in individuals that had a 2DL2 gene, alongside these particular

HLA class I molecules. The mechanism proposed to explain this effect is a model of KIR expression- induced CD8+ T cell survival. This model postulates that 2DL2 expression on CD8+ T cells disrupts

TCR-mediated activation, which raises their activation thresholds and reduces their functional response, but would also protect CD8+ T cells restricted by protective or detrimental HLA class I alleles from AICD, thus enhancing their effects. Furthermore, I speculate that rather than inhibiting the functional responses of CD8+ T cells completely, 2DL2 expression may modify the effector profile of

CD8+ T cells, as well as prolong their survival, as reviewed in Vivier et al., (2004).

This project investigated the patterns and functional effects of KIR expression on T cells and NK cells in HTLV-1 infection for the first time, focussing mainly on CD8+ T cells and using newly characterised anti-KIR mAbs to analyse the expression of 2DL2 and four other 2-domained KIRs at a level of detail that has not been possible previously.

I present the following main findings:

(1) AC status was associated with an expansion of 2DL2+ CD8+ T EM cells.

(2) Higher frequencies of 2DL2+ CD8+ T EM cells were associated with reduced PVLs in HTLV-1

carriers with PVLs greater than 1%.

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(3) Higher frequencies of 2DL3+ CD8+ T EM cells were also associated with reduced PVLs in ACs

across the full range of PVLs

(4) The expression of specific KIR molecules was detected on HTLV-1-specific CD8+ T EM cells in

the majority of HTLV-1+ individuals, though at very low frequencies.

(5) Inhibitory KIR expression was associated with a reduction in effector responses of CD8+ T EM

cells to TCR-dependent but not TCR-independent stimulation, showing it was possible to bypass

KIR-mediated interference with CD8+ T EM cell activation.

(6) Rather than mediating a generalised reduction in function, specific KIR expression biased the

effector profile of CD8+ T EM cells towards a cytotoxic phenotype with reduced bifunctional

capability, which is consistent with a raised activation threshold in these cells.

(7) Global KIR+ CD4+ T cell populations were expanded in HAM/TSP patients and accumulated with

increasing donor age in ACs, suggesting a role for KIR expression on CD4+ T cells in HAM/TSP

pathogenesis.

(8) Reductions in the frequency of 2DL1+ and 2DS1+ NK cells were observed in HTLV-1+ individuals,

which are consistent with previous observations of reduced frequency and function of NK cells in

HTLV-1 infection.

Findings (1) and (2) are consistent with a protective role for 2DL2 expression on CD8+ T cells in

HTLV-1 infection, and together with finding (3), suggest a protective effect of expressing gene products from the 2DL2/3 locus on CD8+ T EM cells. Findings (4) – (6) provide evidence in support of the KIR expression-induced CD8+ T cell survival hypothesis in explaining how 2DL2 (and potentially other KIRs) can influence HLA class I-mediated antiviral immunity. Findings (7) and (8) are additional observations of some potential importance and both deserve further research. HTLV-1-infected and

HTLV-1-specific CD4+ T cells are strongly implicated in HAM/TSP disease development, but the precise pathogenic mechanisms remain unknown. Similarly, relatively little is known about NK cell

209 function in HTLV-1 infection but there are many indications that these cells play an important role in controlling the virus. Thus, further analysis of the KIR-related effects reported here may provide valuable insight into protective and pathogenic immune responses to HTLV-1.

Several aspects of the KIR expression-induced CD8+ T cell survival mechanism and the findings relating to the influence of 2DL2 on the outcome of HTLV-1 infection, from the genetic analysis and from my work, initially seem paradoxical – for example, the fact that reducing the functional responses of CD8+ T cells could have a protective immune effect. I suggest that the basis of this effect of 2DL2 expression is two-fold: (1) it mediates protection of CD8+ T cells from AICD, and (2) it fine-tunes the effector profile of CD8+ T cells. Protection from AICD is proposed to prolong the survival of CD8+ T cells. In ACs, which are thought to have more effective CD8+ T cells than HAM/TSP patients, i.e. the cells have greater functional avidity and lytic efficiency, it is thus proposed that 2DL2 expression prolongs the survival of more effective CD8+ T cells in ACs and less effective CD8+ T cells in patients with HAM/TSP. The question of the survival or longevity of 2DL2+ CD8+ T cells was not addressed in this thesis and it will be extremely important for the on-going testing of the KIR expression-induced

CD8+ T cell survival hypothesis to quantify the impact of 2DL2 on CD8+ T cell survival by direct experimental means. The ideal approach would be an in vivo analysis, preferably in humans, since typical laboratory animals such as rodents do not have KIR genes. It would be useful initially to establish the lifespan of 2DL2+ compared to 2DL2– CD8+ T cells in healthy individuals, before attempting more complex analyses of their dynamics during persistent infection. This would be possible in human volunteers, using the technique of deuterated glucose labelling combined with flow cytometry to monitor 2DL2+ CD8+ T cells in PBMC; this technique has been successfully employed previously in our department to analyse the lifespan of HTLV-1-infected cells in vivo (Asquith et al.,

2007b).

Based on my finding that specific KIR expression biased the effector profile of CD8+ T EM cells towards a cytotoxic phenotype and reduced bifunctional capacity, as well as similar reports in the literature, I speculate that KIRs may fine-tune the functional responses of CD8+ T cells as a consequence of raising their activation thresholds, and in addition to protecting them from AICD. This is an attractive hypothesis, not only because it further explains how reducing the functional responses

210 of CD8+ T cells could have a protective immune effect, but also because it could explain how KIRs may optimise the effectiveness of the CD8+ T cell response in a manner that is particularly relevant to disease development in HTLV-1 infection. KIR expression may allow CD8+ T cells to continue killing

HTLV-1-infected cells, while at the same time limiting the production of inflammatory mediators which could contribute to HAM/TSP immunopathogenesis. Indeed, a similar hypothesis has been suggested previously, to explain the differential effect of a TNFα promoter polymorphism in HAM/TSP patients compared to ACs (Asquith et al., 2000). It is tempting to speculate that KIR expression may tune the

CD8+ T cell responses of ACs to be even more effective and that this does not occur in HAM/TSP patients, but in my experiments I saw no consistent evidence of a difference in the effector function profile between ACs and HAM/TSP. This lack of difference could have arisen because my ex vivo analysis cannot replicate in vivo effects on CD8+ T cell function which may occur over time, especially given the prolonged survival aspect of the proposed mechanism. I did, however, observe a higher frequency of 2DL2+ CD8+ T EM cells in ACs compared to HAM/TSP patients: this higher frequency could be due to improved functional tuning of CD8+ T cells by 2DL2 in ACs, together with protection from AICD enabling 2DL2+ CD8+ T EM cells to accumulate over time or even proliferate at a low level.

Conversely, in HAM/TSP patients, the higher level of HTLV-1 antigen expression and CD8+ T cell activation may result in AICD despite functional tuning by 2DL2, and therefore lower frequencies of

2DL2+ CD8+ T EM cells in these individuals. This could also explain why I observed that higher frequencies of 2DL2+ CD8+ T EM cells were associated with reduced PVLs in HAM/TSP patients and in individuals with PVLs greater than 1%, but there was no correlation when ACs over the full range of

PVLs were analysed. There could be a stronger relationship between 2DL2+ CD8+ T EM cell frequency and PVL when HTLV-1 antigen expression is higher, because this creates more opportunity for AICD.

A second paradoxical aspect of the findings relating to the influence of 2DL2 on the outcome of

HTLV-1 infection is that in the immunogenetic analysis, the influence of 2DL2 gene possession on

HLA class I-restricted antiviral immunity can be either positive or negative, since it enhanced both the protective and detrimental effects associated with possession of specific HLA molecules. Thus, the effect 2DL2 has on infection outcome seems to be dependent on the HLA class I background. The model of KIR expression-induced CD8+ T cell survival explains this HLA class I context dependence

211 of 2DL2 by proposing that 2DL2 expression protects CD8+ T cells restricted by either protective or detrimental HLA class I alleles from AICD, thus both survive for longer and their effects on the outcome of infection are enhanced. I further speculate that functional tuning of CD8+ T cell responses could also contribute to explaining the context dependence of the influence of 2DL2 on antiviral immunity. It could be that functional tuning of CD8+ T cells has more of an influence or is more helpful in some contexts compared to others. 2DL2 could fine-tune protective HLA-C*08-restricted CD8+ T cells to function even more effectively. However for HLA-B*54-restricted CD8+ T cells, if they are detrimental due to a low level of functionality, 2DL2 could tune down their effector responses to the point that they are non-functional or, if HLA-B*54-restricted CD8+ T cells are detrimental because they are over-activated by the high antigen loads observed in individuals with this allele, these cells may die by AICD despite their 2DL2 expression and fine-tuning will not be able to influence their function.

Furthermore, context dependent functional tuning by 2DL2 may explain why Seich al Basatena et al.,

(2011) found that the protective effect of HLA-A*02 possession was not enhanced in the presence of

2DL2. As they are thought to be strongly protective, HLA-A*02-restricted CD8+ T cells may be functioning effectively at a high level, which could result in them being maximally protective and fine- tuning by 2DL2 then many not further influence their function. It is also possible that the enhancement of the detrimental effects of HLA-B*54 possession by the presence of a 2DL2 gene are specific to this particular HLA class I allele. Indeed, unlike the immunogenetic study, my data suggest a protective role for 2DL2 expression on CD8+ T cells rather in than any detrimental effects. Furthermore, I found that these protective effects may extend to other KIRs, particularly 2DL3, which suggests there could be a general protective effect of expressing inhibitory KIRs on CD8+ T cells in infection. It is important to note that my findings may differ from the immunogenetic study not only because my analysis investigated the impact of KIRs at the cellular rather than genetic level but also because my cohort was not stratified by HLA class I genotype. Only six HTLV-1 carriers in my cohort had HLA-C*08 and none had HLA-B*54, which was not surprising given that these alleles tend to be found in east Asian populations, such as Japanese, rather than in Afro-Caribbean populations. These protective and detrimental HLA class I molecules can be viewed as markers of CD8+ T cell effectiveness however, in the general population, HLA class I molecules will be on a spectrum of effectiveness to a particular infection and many factors in addition to HLA class I genotype will influence the effectiveness of a particular individual’s CD8+ T cell response. As my analysis encompassed all HLA class I alleles and

212 studies a different ethnic group to the immunogenetic analysis, my findings could be more relevant to the population as whole and they also strengthen the evidence that the expression of 2DL2 (and potentially other KIRs) on CD8+ T cells has important protective effects on the outcome of HTLV-1 infection.

In a similar manner to the context dependence of the influence of 2DL2 possession, the frequencies of KIR+ total CD8+ and virus-specific CD8+ T cells and their relationships with viral load that are reported in the literature vary according to the viral infection and types of patients analysed. These alterations mostly appeared to be related to either particular stages of infection or patient groups associated with higher levels of viral replication, particularly for KIR-expressing virus-specific CD8+ T cells. The fact that differences in the dynamics of KIR+ CD8+ T cell populations are observed between different viral infections, patient groups and viral loads suggests that the impact of KIR+ CD8+ T cells on the outcome of persistent viral infection differs according to the context, just as the influence of

2DL2 on antiviral immunity was dependent on the underlying HLA class I-mediated effects.

Particularly, it seems that the consequences of KIR expression on the CD8+ T cell response might differ according to the antigen load. Furthermore, since the correlation I observed between PVL and the frequency of 2DL2+ CD8 T EM cells was in HTLV-1 carriers with high PVLs, whereas the relationship between PVL and 2DL3+ CD8 T EM cells was with the full range of PVLs in ACs, this suggests that different KIRs can also have protective effects in the context of different viral loads.

Thus, 2DL2 and potentially 2DL3, could be context-dependent modulators of CD8+ T cell function.

Although it is a very interesting theory, the proposal that 2DL2 expression modifies the function of

CD8+ T cell responses by fine-tuning them is very speculative. It may be that the changes I observed in effector function profile simply reflect the functional reduction and raised activation threshold needed to prolong survival via reduced AICD, rather than indicating any tuning effect. Also, all five of the KIRs I analysed were able to modify the effector profile of CD8+ T EM cells in both ACs and

HAM/TSP patients towards a cytotoxic phenotype to some extent. Thus, in terms of the present analysis of the frequency of functional KIR+ CD8+ T cells, there is no functional evidence for a specific effect of 2DL2 expression on CD8+ T cells resulting in tuning or raised activation thresholds. This

213 remains a difficulty of ex vivo analyses when trying to relate them to what occurs in vivo, as many more complex effects and interactions will be occurring in vivo that cannot be examined ex vivo.

Conflicting conclusions have been drawn in the literature as to whether KIR+ CD8+ T cells have a positive or negative influence on the outcome of viral infection. Studies analysing global KIR expression and total functional responses have proposed that KIR+ CD8+ T cells may be exhausted and could therefore contribute to a lack of control of the viral infection. Conversely, more detailed studies of specific effector functions and expression of particular KIRs have suggested that KIR expression may preserve antigen-specific CD8+ T cells or prevent autologous damage caused by persistent stimulation of inflammatory responses. Similarly, I observed changes in KIR expression on

CD8+ T EM cells and NK cells and an influence of KIR+ CD8+ T EM cells on PVL, when specific rather than global KIR expression was analysed. Although all functional studies consistently report that inhibitory KIR expression reduces CD8+ T cell responses, and the function of inhibitory KIRs on NK cells is to render them unresponsive in the presence of cognate HLA class I, a model of complete inhibition of antiviral CD8+ T cell function by KIRs is inconsistent with the major control of T cell activation via TCR triggering. It seems clear that the effects of KIR expression on CD8+ T cell function are subtle. They can also be different in different contexts and may be specific to particular KIR molecules. Therefore, it will be important for future analyses of the expression and function of KIRs on

CD8+ T cells to be sufficiently detailed and to focus on specific KIR expression. These studies would be greatly assisted by the development of reagents to allow detection of individual KIR expression.

One explanation for the variation in the expression and function of KIRs on CD8+ T cells observed between different persistent viral infections could be that since the host:pathogen dynamics vary, different types of immune responses are needed and KIRs have the potential to influence these responses in a number of different ways. This is demonstrated to some extent by the various immunogenetic studies showing associations of KIR genes with the outcome of viral infection and the differing functional mechanisms they imply. In both HTLV-1 and HCV infection, 2DL2 enhanced immune effects proposed to be mediated by HLA class I-restricted CD8+ T cells, whereas in HCV and

HIV-1 infection, 2DL3/HLA-C1, and 3DS1 or 3DL1 alongside HLA-Bw480Ile alleles, respectively, mediated protection attributed to improved NK cell function. In contrast, no genetic associations of

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KIRs with the outcome of HTLV-1 infection that implicate NK cells as the effector cell type have been demonstrated. However, the strongest protective effect of KIR genotype in HIV-1 infection was afforded by 3DL1/HLA-B*57, which suggested the involvement of 3DL1+ NK cells and HLA-B*57- restricted CD8+ T cells. It is important to consider that, since KIRs can be expressed on both CD8+ T cells and NK cells, multiple KIR-mediated mechanisms could occur simultaneously or in synergy.

Thus, KIRs could contribute to controlling the delicate balance of protective responses in both cell types. The immune effects mediated could vary in different persistent viral infections and, as the immunogenetic studies imply, different KIRs may have different functional tuning effects, further increasing the diversity of these mechanisms. Other more complex interplay between NK cells, CD8+

T cells, DCs and/or CD4+ T cells may be involved in mediating the effects of KIR genes on antiviral immunity. Such alternative mechanisms proposed to explain the influence of 2DL2 in HTLV-1 infection (Cook et al., 2013) involve CD8+ T cells restricted by protective or detrimental HLA class I alleles interacting directly or indirectly with 2DL2+ NK cells, which in turn influences their survival.

However, there would be no element of functional tuning of CD8+ T cell responses with these mechanisms. Although KIR-mediated functional tuning is an attractive hypothesis, it requires further testing.

A further challenge for the KIR-mediated functional tuning of CD8+ T cells and the KIR expression- induced CD8+ T cell survival mechanisms in explaining how 2DL2 influences HLA class I-mediated

HTLV-1 immunity via expression on CD8+ T cells, is that very low frequencies of HTLV-1-specific

CD8+ T EM cells expressed 2DL2 or any other specific KIR molecules. Is it possible that such a small proportion of cells could influence the outcome of HTLV-1 infection? 2DL2+ CD8+ T EM cells accumulated in ACs but not HAM/TSP patients, which suggests a protective role for 2DL2 expression on CD8+ T EM cells in HTLV-1 infection, rather than the induction of 2DL2 expression by HTLV-1 infection per se. Although I did not observe an expansion of Tax-specific CD8+ T EM cells expressing

2DL2 in ACs, it could be that the expanded total CD8+ T EM cell populations expressing 2DL2 are specific for HTLV-1 antigens, other than Tax. Interestingly, HBZ-specific CD8+ T cells are unusually protective, despite their very low frequency, which shows that low frequency cells are indeed able to influence the outcome of HTLV-1 infection. Although it would be technically challenging, it would be extremely interesting to determine whether 2DL2 expression is associated with specificity for HBZ in

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CD8+ T cells. Another way in which very low frequencies of 2DL2-expressing HTLV-1-specific CD8+ T

EM cells may be able to influence the outcome of HTLV-1 infection would be if these cells were long- lived. If these cells could survive and proliferate at very low levels, they may modulate immune responses to persistent infection over many decades, thus mediating subtle effects over a long time period. This highlights the importance of the proposed survival mechanism and the fact that the longevity of 2DL2+ CD8+ T cells is key question for future work, as discussed above. An alternative explanation for the accumulation of 2DL2+ CD8+ T cells in ACs could be that some of these cells are specific for other pathogens, if the ability of 2DL2 to influence HLA class I-restricted responses is a general mechanism, which seems likely given that 2DL2 also influences anti-HCV immunity in this way. There may also be a potential protective role for increased frequencies of bystander 2DL2+ CD8+

T cells in HTLV-1 infection but the mechanism for this is less obvious.

A final question concerning the findings relating to the influence of 2DL2 on the outcome of HTLV-1 infection is the following: in the immunogenetic analysis, why is 2DL2 in particular able to influence

HLA class I-restricted antiviral immunity, whereas other KIR genes do not? In contrast to the immunogenetic study, I observed that all five of the KIRs I analysed were able to modify the effector profile of CD8+ T EM cells towards a cytotoxic phenotype to some extent and that higher frequencies of 2DL3+ CD8+ T EM cells were strongly associated with reduced PVLs in ACs. This suggests that there may be a protective effect of expressing gene products from the 2DL2/3 locus on CD8+ T EM cells and furthermore, that all KIRs could potentially tune CD8+ T cell functional responses. However, in keeping with the immunogenetic findings, I found that only 2DL2+ CD8+ T EM cells accumulated in

ACs and only 2DL2 gene possession resulted in a lower PVL in HAM/TSP patients. Also, the fact that the relationship between PVL and 2DL2+ CD8+ T EM cell frequencies was in HTLV-1 carriers with high loads, who have a high risk of disease development, whereas the relationship between PVL and

2DL3+ CD8+ T EM cell frequencies was across the full range of PVLs in ACs suggests that 2DL2 is influencing a crucial tipping point for HAM/TSP risk. This could explain why 2DL2 but not 2DL3 was identified by the immunogenetic study to influence HTLV-1 infection outcome. Together, these observations point towards a particularly strong protective effect of 2DL2 but do not rule out the fact that other inhibitory KIRs may be having similar effects. There could be a particular property of 2DL2 expression or function that distinguishes it from other inhibitory KIRs, which perhaps acts subtly in

216 vivo over time and cannot be replicated in my ex vivo analysis. In terms of its function, it could be that

2DL2 provides optimal functional tuning to CD8+ T cells. Several aspects of 2DL2 biology provide a functional basis for this proposal. Firstly, its ligands are effectively ubiquitous because it can interact with both HLA-C1 and HLA-C2 group alleles, thus in all 2DL2+ individuals, this KIR can bind a ligand.

Secondly, the 2DL2/HLA-C1 interaction is of intermediate inhibitory strength, compared to the stronger 2DL1/HLA-C2 combination, and the weaker 2DL3/HLA-C1 pairing. This intermediate inhibitory strength could deliver an optimum balance of functional tuning. Finally, since the 2DL2/HLA-

C2 interaction is functional but is weaker than the 2DL2/HLA-C1 interaction, a combination of the latter two points could result in a gradient of functional tuning provided by 2DL2. It is also important to note however that specifically in relation to the immunogenetics study, 2DL2 was present at the most informative frequencies, particularly in the HCV cohort, in which several inhibitory KIRs could be analysed which could have strengthened the statistical results for this KIR in particular. In terms of a particular property of 2DL2 expression which could distinguish it from other inhibitory KIRs, it could be that 2DL2 expression is induced more readily on CD8+ T cells than other inhibitory KIRs. It is not currently known how KIR expression is initiated on T cells. Since higher frequencies of CD8+ T EM cells expressing 2DL2 are consistent with protective immune effects it will be important to determine how this occurs, particularly if it becomes desirable to induce KIR expression for therapeutic purposes.

The evidence summarised herein suggests that 2DL2 (and potentially 2DL3) can modulate CD8+ T cell responses, and so influence their effectiveness or quality and thus, the risk of HAM/TSP.

Alongside the observations of Seich al Basatena et al., (2011), my findings add to our understanding of the genetic determinants of protective CD8+ T cell immunity in HTLV-1 infection. There are many factors that will be able to influence CD8+ T cell functional effectiveness and a balance of these will in turn influence infection outcome; 2DL2 is only one of these factors. The protective immune characteristics of HTLV-1-specific CD8+ T cells can be summarised as follows: possession of protective HLA class I alleles, higher per-cell expression of cytotoxic genes, higher lytic efficiency, higher functional avidity, possession of 2DL2 and higher frequencies of 2DL2 expression. The ability of 2DL2 and potentially other KIRs to modulate CD8+ T cell function may also be a relevant protective mechanism in other persistent viral infections, particularly those that induce high viral loads, such as

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HIV and HCV. In these infections, high levels of viral antigen expression result in chronic immune activation and inflammation, leading to damage to lymph node architecture and cirrhosis respectively; it appears that these viruses can overcome immune defences by replicating faster than they can be eliminated. KIR expression could counter virus-induced inflammation in such cases by enabling virus- specific CD8+ T cells to use cytotoxic functions without causing collateral damage. Since attributes of the quality rather than the quantity of the CD8+ T cell response are associated with effective viral containment, it would be very interesting to analyse the effect of 2DL2 expression on CD8+ T cell functional properties, such as the lytic efficiency or functional avidity. It would be ideal to perform stimulations with the natural antigen of the CD8+ T cells; therefore, given the very low frequency of antigen-specific KIR+ CD8+ T cells in PBMC, and the necessity for cell sorting and antigen titrations to carry out these types of experiments, the best system to use would be CD8+ T cell clones. The generation of a bank of clones with differing antigen sensitivities or lytic efficiencies, which could either be screened for natural KIR expression or transfected with 2DL2 and other KIRs for comparison, would provide an excellent resource to analyse the effects of KIRs on a range of CD8+ T cell functional properties.

Persistent infection with HTLV-1 establishes a complex dynamic between the host immune response and the virus, making it difficult to decipher protective immune processes in this context.

Nevertheless, we are beginning to understand more about the requirements of an effective CD8+ T cell response to HTLV-1. Protracted antigen expression chronically activates CD8+ T cells, which can lead to their exhaustion or AICD and can stimulate inflammatory responses which could exacerbate

HAM/TSP immunopathology. Thus, increasing evidence suggests that an effective CD8+ T cell response contains the virus without exacerbating inflammation. Based on my findings and together with those of Seich al Basatena et al., (2011), I propose that 2DL2 contributes to the effectiveness of the CD8+ T cell response, perhaps as a context-dependent functional modulator, by optimally tuning the responses of CD8+ T cells, as well as prolonging their survival in the face of sustained antigen exposure. Genetic analyses have been invaluable to our understanding of the importance of the CD8+

T cell response to HTLV-1, not least because the direction of causality for disease manifestation is unequivocal. The influence that possession of a 2DL2 gene has on HLA class I-restricted immunity

218 not only adds to our knowledge of the host genetic factors that determine the outcome of HTLV-1 infection but it also inspired this project to investigate the underlying cellular mechanism. However, the complexity of the interactions between the KIRs and HLA class I at the cellular level means the genetic studies in this area currently precede the functional work. Given that our findings potentially diversify the function of KIRs into adaptive immunity and suggest a mechanism that could be relevant to other persistent viral infections, further work to determine the exact underlying mechanism is important and may provide interesting insights into previous unknown aspects of antiviral immune responses.

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Appendices

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Appendix 1: KIR and HLA genotypes and allotypes of all donors.

Global Specific KIR/CD8 HLA-A HLA-B HLA-C HLA-C Donor KIR KIR T cell 2DL1 2DS1 2DL2 2DS2 2DL3 2DL1*004-like 2DL3*005 ligand 1 2 1 2 1 2 expression expression function group UAF 1 0 2 1 1 2 0 + ND *0201 *0301 *3501 *4102 *1703 *0401 C2/C2 UAH 1 0 2 1 0 0 2 - - *0201 *0301 *0702 *1401 *0802 *0702 C1/C1 UAM1 1 0 1 0 1 1 1 ND - *0101 *3002 *3801 *3502 *0401 *1203 C2/C1 UAM2 1 0 2 0 0 0 1 ND - *2301 *3002 *4402 *5801 *0401 *0701 C2/C1 UAM3 1 0 2 0 0 0 2 ND - *0301 *3201 *1501 *3501 *0303 *0401 C1/C2 UAP 1 0 2 0 1 1 1 ND + *0201 *1101 *1501 *3906 *0401 *1203 C2/C1 UAR 1 0 2 1 0 0 2 - - *0201 *0201 *4402 *1501 *0304 *0501 C1/C2 UAW 1 0 1 0 1 1 1 ND - *2501 *3201 *4402 *1501 *0303 *0501 C1/C2 UBA 1 0 2 0 0 0 2 ND - *3201 *6801 *5101 *3503 *1502 *0401 C2/C2 UBB 1 0 2 0 0 0 2 ND - *0101 *2902 *0801 *4501 *0602 *0701 C2/C1 UCA 1 0 0 0 2 2 0 ND ND *0301 *1101 *4402 *3501 *0401 *0501 C2/C2 UCB 1 0 2 0 0 0 2 ND - *0101 *3002 *0702 *4402 *0501 *0702 C2/C1 UGL 1 0 1 0 1 1 1 ND - *0301 *1101 *5501 *3501 *0303 *0401 C1/C2 UGT 1 0 1 0 1 1 1 ND - *0101 *0201 *0801 *1501 *0701 *0303 C1/C1 UHN 1 0 2 0 0 0 2 ND - *0101 *0201 *0801 *1302 *0602 *0701 C2/C1 UJD 1 0 2 0 0 0 2 ND - *0301 *1101 *5101 *5501 *0102 *0303 C1/C1 UKG 1 0 2 1 0 0 2 - - *0101 *3201 *0801 *2705 *0102 *0701 C1/C1 UKT 1 0 2 0 0 0 2 ND - *0201 *0301 *5201 *3503 *0401 *1601 C2/C1 ULC 1 0 2 0 0 0 2 ND - *0301 *2501 *4402 *1402 *1601 *0802 C1/C1 UME 1 0 2 1 0 0 2 - - *0201 *0201 *1501 *1801 *0401 *0701 C2/C1 UMS 1 0 1 1 1 1 1 - - *0101 *2601 *3801 *5701 *0602 *1202 C2/C1 UN 1 0 2 1 0 0 2 - - *2902 *3201 *3906 *5501 *0501 *0702 C2/C1 UNL 1 0 2 1 0 0 2 - - *0201 *6801 *4402 *3503 *0704 *0401 C1/C2 UO 1 0 2 0 0 0 2 ND - *0101 *0201 *0801 *4001 *0304 *0701 C1/C1 UP 0 0 2 0 0 0 2 ND - *0201 *3101 *4001 *4001 *0304 *0304 C1/C1 URA 1 0 0 1 2 2 0 ND ND *2402 *6601 *3503 *4102 *1703 *1203 C2/C1 USM 1 0 2 2 2 2 0 + ND *2402 *6801 *5201 *1402 *0802 *1202 C1/C1 UST 1 0 2 0 0 0 2 ND - *0101 *0301 *0801 *3501 *0401 *0701 C2/C1 UT 1 0 2 0 1 1 1 ND - *0301 *2301 *0702 *1503 *0210 *1504 C2/C2 UYS 1 0 2 1 0 0 2 - - *3101 *3101 *4002 *4002 *0304 *0304 C1/C1

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Global Specific KIR/CD8 HLA-A HLA-B HLA-C HLA-C Donor KIR KIR T cell 2DL1 2DS1 2DL2 2DS2 2DL3 2DL1*004-like 2DL3*005 ligand expression expression function 1 2 1 2 1 2 group HAE 1 1 2 0 1 1 1 ND + *2402 *3001 *0702 *5701 *0602 *0701 C2/C1 HAI 1 1 2 0 1 1 1 ND - *0101 *3001 *4201 *1510 *1701 *0701 C2/C1 HAY 1 1 1 1 1 1 1 - + *1101 *0301 *1501 *2702 *0202 *0401 C2/C2 HBA 1 1 2 1 0 0 2 - - *0201 *1101 *0702 *3501 *0401 *0702 C2/C1 HBE 1 1 2 0 0 0 2 ND - *0205 *3001 *0705 *5301 *0401 *0702 C2/C1 HBK 1 1 2 0 0 0 2 ND + *2601 *3201 *0801 *1503 *0202 *0304 C2/C1 HBL 1 2 0 0 0 2 ND - *0201 *2601 *0801 *5801 *0304 *0701 C1/C1 HBT 1 0 2 0 0 0 2 ND - *0201 *1101 *5101 *3503 *1203 *1502 C1/C2 HBV 0 1 1 0 1 1 1 ND - *0201 *0201 *4501 *1501 *0304 *0602 C1/C2 HCA 1 1 1 0 2 2 0 ND ND *2601 *3002 *1302 *1801 *0501 *0602 C2/C2 HCE 1 1 2 0 1 1 1 ND - *0201 *3201 *1401 *4402 *0501 *0802 C2/C1 HCI 1 1 2 0 0 0 2 ND + *0202 *6601 *1801 *4102 *0501 *1701 C2/C2 HCS 1 1 1 1 2 2 0 + ND *2301 *6810 *0702 *5702 *0702 *1801 C1/C2 HCT 1 1 2 0 1 1 1 ND - *3002 *8001 *4403 *5301 *0401 *0401 C2/C2 HDB 1 2 0 0 0 2 ND - *0201 *2301 *5301 *5703 *0304 *0701 C1/C1 HDC 1 1 2 1 0 0 2 - - *0101 *6603 *1516 *4101 *1601 *1701 C1/C2 HDD 1 1 1 0 2 + 0 ND ND *2301 *6802 *5301 *8101 *0401 *0802 C2/C1 HDF 1 1 2 1 1 1 1 + - *0101 *1101 *3701 *3802 *0602 *0702 C2/C1 HDN 1 1 0 2 3 2 0 ND ND *3001 *3601 *1403 *5301 *1701 *0401 C2/C2 HDO 1 1 2 0 0 0 2 ND - *3001 *7401 *4403 *5301 *0401 *0401 C2/C2 HDS 1 1 2 1 1 1 1 + - *0201 *0201 *0702 *3503 *0401 *0702 C2/C1 HDY 1 1 2 1 0 0 2 - - *3402 *3402 *4403 *4901 *0401 *0701 C2/C1 HER 1 1 2 0 1 1 1 ND - *0301 *6802 *0705 *5801 *0701 *0702 C1/C1 HEY 0 1 0 1 2 2 0 ND ND *0202 *2301 *4403 *5001 *0602 *0401 C2/C2 HFG 0 1 2 0 0 0 2 ND + *0201 *6901 *4001 *5501 *0303 *0303 C1/C1 HFL 1 1 2 0 0 0 2 ND - *0201 *3301 *5001 *5301 *0401 *0602 C2/C2 HGA 1 1 1 0 2 1 0 ND ND *0201 *2902 *4501 *3501 *1601 *1601 C1/C1 HGB 1 1 0 0 2 2 0 ND ND *0301 *2402 *4402 *4403 *1601 *0501 C1/C2 HGP 1 1 1 0 1 1 1 ND + *0201 *3101 *5101 *3503 *0401 *1402 C2/C1

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HGR 0 1 1 0 2 2 0 ND ND *2301 *2301 *5802 *1510 *0304 *0602 C1/C2 HHD 1 1 2 0 0 0 2 ND + *2301 *3301 *5801 *5801 *0302 *0701 C1/C1 HHI 1 1 2 0 0 0 2 ND - *0201 *3601 *4402 *5301 *0501 *0401 C2/C2 HHP 1 1 2 0 0 0 2 ND - *0101 *2902 *4402 *4001 *1601 *0304 C1/C1 HHQ 1 1 2 0 1 1 1 ND - *0202 *3001 *5802 *5801 *0602 *0701 C2/C1 HHR 0 1 0 0 2 2 0 ND ND *2301 *3002 *0705 *0705 *1504 *0702 C2/C1 HHZ 1 1 2 0 0 0 2 ND + *0201 *3001 *3501 *4201 *1701 *0401 C2/C2 HID 0 1 1 1 1 1 1 - - *0101 *0211 *5701 *3503 *0401 *0602 C2/C2 HIG 1 1 2 0 0 0 2 ND - *0201 *2402 *5201 *1518 *1202 *0801 C1/C1 HIP 1 1 2 0 1 1 1 ND - *0101 *0301 *0702 *5501 *0602 *0702 C2/C1

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Global Specific KIR/CD8 HLA-A HLA-B HLA-C HLA-C Donor KIR KIR T cell 2DL1 2DS1 2DL2 2DS2 2DL3 2DL1*004-like 2DL3*005 ligand expression expression function 1 2 1 2 1 2 group TAA 0 1 2 2 1 1 1 + - *0201 *2301 *4002 *5211 *1502 *1601 C2/C1 TAC 1 1 2 0 2 1 1 ND - *0201 *0201 *5211 *5211 *0302 *1601 C1/C1 TAN 1 2 1 1 1 1 + - *2601 *8001 *1401 *3501 *0801 *0401 C1/C2 TAQ 1 1 2 0 0 0 2 ND - *0201 *2301 *0702 *4501 *0702 *1601 C1/C1 TAS 1 1 2 0 2 2 0 ND ND *3001 *8001 *4201 *5301 *0401 *1701 C2/C2 TAT 1 1 2 0 1 1 0 ND ND *0201 *6801 *1503 *3501 *0202 *1601 C2/C1 TAZ 1 1 2 1 0 0 2 - - *2402 *2601 *5101 *5211 *1202 *1402 C1/C1 TBG 1 1 2 0 0 0 2 ND - *7401 *2402 *5301 *5602 *0401 *0401 C2/C2 TBP 1 1 1 0 1 1 1 ND - *2402 *6801 *1801 *3906 *0501 *0702 C2/C1 TBW 1 1 2 0 1 1 1 ND - *0201 *0301 *0702 *4402 *0501 *0702 C2/C1 TBZ 0 1 2 0 0 0 2 ND - *2901 *6802 *1503 *5301 *0202 *0401 C2/C2 TCD 1 1 1 1 2 2 0 + ND *3402 *6802 *3501 *5802 *0304 *0304 C1/C1 TCF 1 1 0 0 2 2 0 ND ND *0301 *3301 *4501 *5301 *0401 *1601 C2/C1 TCH 1 1 1 0 1 1 1 ND - *3402 *6802 *1801 *5301 *0202 *0401 C2/C2 TCI 1 1 2 0 1 1 1 ND + *3601 *6802 *1510 *5301 *0401 *0802 C2/C1 TCJ 1 1 2 0 1 1 1 ND - *0301 *2301 *4403 *4501 *0401 *1601 C2/C1 TCK 1 1 1 0 1 2 1 ND - *2901 *3002 *1302 *5703 *0602 *0701 C2/C1 TCL 1 1 2 0 1 0 1 ND - *0201 *2301 *0705 *4501 *0702 *1601 C1/C1 TCP 0 1 2 1 1 1 1 + - *0205 *2301 *1516 *5301 *0401 *1402 C2/C1 TCQ 1 1 1 0 2 2 0 ND ND *0301 *3001 *4201 *5801 *0701 *1701 C1/C2 TCR 1 1 2 0 0 0 1 ND - *3301 *3601 *1516 *5703 *0701 *1402 C1/C1 TCV 0 1 2 0 0 0 2 ND - *0301 *3201 *4402 *4402 *0501 *0704 C2/C1 TCX 0 1 2 0 0 0 2 ND - *3002 *3601 *3501 *5701 *0401 *1801 C2/C2 TCY 1 0 1 0 1 0 1 ND - *0201 *2301 *0801 *1501 *0202 *0304 C2/C1 TCZ 1 1 2 0 1 1 1 ND - *6602 *3001 *1503 *1801 *0202 *0210 C2/C2 TDB 1 1 1 1 1 1 1 - - *0212 *2402 *5101 *3543 *1502 *0102 C2/C1 TDK 1 1 1 0 1 1 1 ND + *0101 *2902 *0801 *3501 *0401 *0701 C2/C1 TDL 0 1 3 0 1 0 1 ND + *0301 *3303 *4403 *1302 *0401 *0602 C2/C2 TDR 1 1 2 1 1 1 1 + - *0201 *2402 *0702 *2705 *0702 *0102 C1/C1

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TDT 1 1 2 2 1 1 1 + - *2402 *3303 *5801 *3501 *1203 *0302 C1/C1 TDV 1 1 1 0 1 1 1 ND - *3301 *7401 *1402 *1503 *0210 *0802 C2/C1 TDW 1 1 4 0 0 0 1 ND - *3001 *7401 *4403 *4201 *1701 *0401 C2/C2 TDX 1 1 2 0 1 0 0 ND ND *3303 *3601 *5301 *5301 *0401 *0413 C2/C2 TW 1 1 2 0 0 0 2 ND - *2301 *6601 *4201 *4201 *1701 *1701 C2/C2

Grey highlight indicates HTLV-1 carriers with HLA class I molecules investigated for their interaction with KIR genes by Seich al Basatena et al., (2011)

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Appendix 2: Summary of permissions for third party copyright works.

Page Type of Source work Copyright Work out Permission Permission permission Orphan Number work: text, holder & of to re-use requested refused work figure, year copyright map, etc. Page 23 Figure 1.1 Matsuoka M, Jeang K-T. Human T-cell © 2011 leukemia virus type 1 (HTLV-1) and Macmillan leukemic transformation: viral infectivity, Publishers Tax, HBZ and therapy. Oncogene Limited (2011) Vol 30, pp 1379–1389  Page 40 Figure 1.2 IPD-KIR database, release 2.5.0: © IPD-KIR www.ebi.ac.uk/ipd/kir/ 10/11 Robinson J, Halliwell JA, McWilliam H, Lopez R, Marsh SGE. IPD - the Immuno Polymorphism Database. Nucleic Acids Research (2013), 41: D1234-40  Page 42 Table 1.1 Ivarsson MA ,Michaëlsson J and Fauriat © 2014 C. (2014) Activating killer cell Ig-like Ivarsson, receptors in health and disease. Front. Michaëlsson Immunol.5:184. and Fauriat doi:10.3389/fimmu.2014.0018  Page 44 Figure 1.3 Middleton D, Gonzelez F (2010). The © 2009 extensive polymorphism of KIR genes. Blackwell Immunology 129(1): 8-19. Publishing Ltd  Page 46 Figure 1.4 IPD-KIR database, As above. © IPD-KIR 10/11  Page Table 1.2 Kulkarni, S Martin, MP and Carrington, © 2008 58-9 M. The Yin and Yang of HLA and KIR in Elsevier Ltd human disease. Seminars in Immunology (2008) vol 20 p343–352. 

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Figure 1.1. Permission to reuse figure in a thesis granted by licence agreement from Nature Publishing Group, as shown below.

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Figure 1.2 and 1.4. http://www.ebi.ac.uk/ipd/licence.html States “We have chosen to apply the Creative Commons Attribution-NoDerivs Licence to all copyrightable parts of our databases”

Table 1.1. Source article states “This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).”

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