HIV Reservoirs During Suppressive Antiretroviral Therapy

HIV RESERVOIRS DURING SUPPRESSIVE

ANTIRETROVIRAL THERAPY

William Hey-Nguyen (né Hey-Cunningham) BMedSci (Hons)

A thesis submitted in total fulfilment of the requirements of the degree of Doctorate of Philosophy

Faculty of Medicine

August 2015 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet

Surname or Family name: Hey-Cunningham

First name: William Other name/s: Joseph

Abbreviation for degree as given in the University calendar: PhD

School: Kirby Institute Faculty: Medicine

Title: HIV reservoirs during suppressive antiretroviral therapy

Abstract 350 words maximum: (PLEASE TYPE) Viral reservoirs in diverse cell types and anatomical locations are considered the main barrier to a cure for HIV infection. How and where these reservoirs are established and maintained is therefore of great interest to HIV research. The initiation of therapy during early HIV infection can limit the size of viral reservoirs, and may also reduce chronic immune activation and inflammation, which are associated with non-AIDS co-morbidities and possibly the development of neurological complications.

In several clinical studies of HIV-infected individuals on suppressive therapy, we collected peripheral blood or lymph node specimens, purified various subsets of CD4+ T cells, then quantified HIV reservoirs within these cells by qPCR for total/integrated/2-LTR HIV DNA species, as well as HIV unspliced RNA.

The delineation of HIV reservoirs within antigen-specific CD4+ T cell subsets was challenging due to the very low frequency of these cells. The reduction in peripheral CD4+ T cell-associated HIV DNA levels (total/integrated) resulting from initiation of therapy during primary compared to chronic HIV infection was maintained for three years of therapy. Although HIV reservoir size was modestly but significantly associated with T cell activation, early ART did not reduce levels of chronic T cell activation. Viral reservoirs within lymph nodes were sampled using fine needle biopsies with no adverse events. This technique isolated lymph node resident cells sufficient for the quantification of HIV DNA/RNA, and results indicated that HIV DNA/RNA levels were higher in CD4+ T cells of the lymph node compared to peripheral blood. In a cohort with long-term virally suppressed HIV infection, HIV DNA levels in peripheral blood mononuclear cells did not correlate with neurocognition.

The early initiation of therapy provides long-term benefits by limiting the formation of viral reservoirs. Further investigations are needed to identify the role HIV reservoirs have in chronic T cell activation and the development of neurocognitive disorders. Fine needle biopsies allow for the non-invasive sampling of lymph node resident cells and the quantification of HIV reservoirs, and may be used to improve our understanding of viral reservoirs as efforts to achieve a cure for HIV infection are expanded

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‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

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TABLE OF CONTENTS

ORIGINALITY STATEMENT ...... II

LIST OF FIGURES ...... X

LIST OF TABLES ...... XIII

LIST OF ABBREVIATIONS ...... XIV

ABSTRACT ...... XXII

ACKNOWLEDGEMENTS ...... XXIII

PUBLICATIONS & CONFERENCE PROCEEDINGS ...... XXV

CHAPTER 1: LITERATURE REVIEW ...... 1

1.1 INTRODUCTION: ...... 2

1.2 HUMAN IMMUNODEFICIENCY VIRUS ...... 4 1.2.1 The virus ...... 4 1.2.2 Virion structure ...... 4 1.2.3 The viral genome ...... 5 1.2.4 The viral life cycle ...... 7 1.2.4.1 Cell entry ...... 7 1.2.4.2 Reverse transcription ...... 8 1.2.4.3 Integration ...... 9 1.2.4.4 Production of viral proteins ...... 10 1.2.4.5 Virion production and release ...... 11 1.2.5 HIV DNA species and the life cycle: ...... 11 1.2.5.1 Unintegrated HIV DNA ...... 12 1.2.5.1.1 Linear unintegrated HIV DNA ...... 12 1.2.5.1.2 Episomal unintegrated HIV DNA ...... 12 1.2.5.2 Integrated proviral HIV DNA ...... 14

1.3 HIV INFECTIONS IN THE HUMAN POPULATION ...... 14 1.3.1 Transmission & establishment of HIV infection ...... 14 1.3.2 The immune response ...... 16 1.3.3 CD4+ T cells in HIV infection ...... 19 1.3.3.1 CD4+ T cell depletion ...... 19 iii

1.3.4 Antigen-specific CD4+ T cells ...... 20 1.3.4.1 HIV specific CD4+ T cells ...... 20 1.3.4.2 CMV-specific CD4+ T cells ...... 20 1.3.4.3 TT-specific CD4+ T cells ...... 21 1.3.4.4 Measurement of antigen-specific CD4+ T cells ...... 22 1.3.5 Antiretroviral therapy ...... 24 1.3.5.1 Restoration of CD4+ T cells ...... 25 1.3.5.2 Limitations of ART ...... 26 1.3.5.2.1 Low-level plasma viral load ...... 26 1.3.5.2.2 Chronic immune activation ...... 28 1.3.5.2.3 Serious non-AIDS events ...... 29 1.3.5.3 When to initiate ART ...... 29

1.4 HIV RESERVOIRS DURING ART ...... 31 1.4.1 CD4+ T cells ...... 31 1.4.2 Monocytes, macrophages & dendritic cells ...... 32 1.4.3 Gastrointestinal tract ...... 34 1.4.4 Lymph nodes ...... 35 1.4.5 Central nervous system ...... 36 1.4.6 Reservoir establishment ...... 37 1.4.7 Reservoir latency...... 38 1.4.8 Reservoir maintenance ...... 39 1.4.9 Reservoir evolution ...... 40 1.4.10 Measuring HIV reservoirs ...... 41 1.4.10.1 Replication competent HIV ...... 42 1.4.10.2 PCR measurement of cell-associated HIV nucleic acids ...... 43 1.4.10.2.1 DNA ...... 43 1.4.10.2.2 RNA...... 44

1.5 THE SEARCH FOR A CURE ...... 45 1.5.1 Eradicating HIV reservoirs ...... 45 1.5.1.1 ART intensification ...... 45 1.5.1.2 Bone marrow transplantation ...... 46 1.5.1.3 Shock/kick & kill strategies ...... 47 1.5.2 Inducing a functional cure ...... 49 iv

1.5.3 Analytic treatment interruptions ...... 51

1.6 CONCLUSIONS AND AIMS OF THIS THESIS ...... 51

CHAPTER 2: MATERIALS & METHODS ...... 55

2.1 BUFFERS & SOLUTIONS ...... 56

2.2 SAMPLE DONORS & COLLECTION ...... 58

2.3 SAMPLE PROCESSING ...... 58 2.3.1 Collection of plasma ...... 58 2.3.2 Isolation of peripheral blood mononuclear cells ...... 58 2.3.3 Cryopreservation of PBMC...... 59 2.3.4 Recovery of peripheral blood mononuclear cells from cryostorage ...... 59 2.3.5 Assessment of cell viability & concentration ...... 59 2.3.5.1 Haemocytometer ...... 59 2.3.5.2 BD TrucountTM tubes ...... 60 2.3.6 Isolation of CD4+ T cells from peripheral blood mononuclear cells ...... 60 2.3.6.1 Negative bead selection...... 60 2.3.6.2 Fluorescence activated cell sorting ...... 61 2.3.7 Isolation of CD4+ T cells by fluorescence activated cell sorting...... 62 2.3.7.1 Culture conditions ...... 62 2.3.7.2 Monoclonal antibody staining ...... 63 2.3.7.3 Gating strategy ...... 64 2.3.7.4 Methodological optimization for the purification of antigen-specific CD4+ T cell subsets ...... 65 2.3.7.4.1 Antigenic stimulation of peripheral blood mononuclear cells recovered from cryostorage...... 66 2.3.7.4.2 Antigenic stimulation of fresh whole blood ...... 71 2.3.7.5 Antigen preparation & titration ...... 73 2.3.7.5.1 HIV Subtype B Gag peptide pool ...... 73 2.3.7.5.2 CMV lysate ...... 73 2.3.7.5.3 Tetanus Toxoid ...... 75 2.3.7.5.4 Staphlyococcal enterotoxin B ...... 76

2.4 EXTRACTION OF NUCLEIC ACIDS ...... 76 2.4.1 Cell-associated DNA ...... 77 2.4.1.1 Direct lysis buffer ...... 77 v

2.4.1.2 Qiagen DNeasy blood & tissue kit ...... 77 2.4.2 Co-extraction of cell-associated DNA & RNA ...... 78 2.4.2.1 Qiagen Allprep DNA/RNA kit ...... 78 2.4.2.2 Trizol® reagent ...... 79 2.4.3 Plasma RNA ...... 80 2.4.4 DNase treatment of RNA ...... 81 2.4.4.1 Plasma RNA ...... 81 2.4.4.2 Cell-associated RNA ...... 81 2.4.5 Assessment of nucleic acid quantity & quality ...... 81 2.4.5.1 Spectrophotometry ...... 81 2.4.6 Comparison of nucleic acid extraction techniques ...... 82

2.5 QUANTIFICATION OF HIV DNA SPECIES ...... 83 2.5.1 Total HIV DNA...... 86 2.5.1.1 Total HIV pol DNA qPCR ...... 86 2.5.1.2 Total HIV gag DNA qPCR ...... 86 2.5.1.3 Standards & controls ...... 87 2.5.1.4 Methodological optimization of total HIV DNA Quantification with limited cell numbers ...... 88 2.5.1.5 Performance of qPCR for total HIV gag & pol DNA ...... 91 2.5.2 Integrated HIV DNA ...... 93 2.5.2.1 Integrated HIV DNA qPCR ...... 95 2.5.2.2 Standards & controls ...... 95 2.5.3 Episomal 2-LTR HIV DNA ...... 96 2.5.3.1 2-LTR HIV DNA qPCR ...... 96 2.5.3.2 Standards & controls ...... 97 2.5.4 β-actin qPCR ...... 98 2.5.4.1 Standards & controls ...... 98

2.6 QUANTIFICATION OF HIV RNA ...... 98 2.6.1 Plasma HIV RNA ...... 98 2.6.2 Cell-associated HIV RNA...... 99

2.7 CLONAL SEQUENCING OF THE HIV VIRAL GENOME ...... 99 2.7.1 cDNA synthesis ...... 100 2.7.2 Amplification of HIV DNA ...... 100 vi

2.7.2.1 HIV gag nested PCR ...... 101 2.7.2.2 HIV pol nested PCR ...... 101 2.7.3 Agarose gel electrophoresis ...... 101 2.7.4 PCR product purification ...... 102 2.7.4.1 Wizard® purification kit ...... 102 2.7.4.2 Qiagen QIAquick gel extraction kit ...... 102 2.7.5 PCR product cloning ...... 103 2.7.6 Amplification of PCR product clones ...... 103 2.7.7 Purification of amplified PCR product clones ...... 104 2.7.8 Sequencing reaction ...... 104

CHAPTER 3: HIV DNA IN ANTIGEN-SPECIFIC CD4+ T CELLS ...... 107

3.1 INTRODUCTION...... 108

3.2 MATERIALS & METHODS ...... 110 3.2.1 Sample collection & processing ...... 110 3.2.2 Quantification of total HIV DNA & clonal sequencing ...... 111 3.2.1 Sequence editing & analyses ...... 113

3.3 RESULTS ...... 114 3.3.1 Study design & patient screening ...... 114 3.3.2 Study participant characteristics ...... 114 3.3.3 Isolation of antigen-specific CD4 T cell subsets ...... 115 3.3.3.1 Antigen-specific CD4+ T cell responses, cell yields & purity ...... 115 3.3.4 HIV DNA in antigen-specific CD4+ T cell subsets ...... 117 3.3.5 Clonal HIV DNA sequences from antigen-specific CD4 T cells ...... 118

3.4 DISCUSSION ...... 123

3.5 CONCLUSIONS ...... 127

3.6 ACKNOWLEDGEMENTS ...... 127

CHAPTER 4: HIV DNA IN CD4+ T CELLS ...... 129

4.1 INTRODUCTION...... 130

4.2 AIMS ...... 131

4.3 METHODS ...... 131 4.3.1 Plasma HIV RNA quantification ...... 131 4.3.2 CD4+ T cell isolation & DNA extraction ...... 132

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4.3.3 Quantification of HIV DNA species ...... 132 4.3.4 Calibrating the measurement of HIV DNA species ...... 133 4.3.5 T Cell immunological phenotyping ...... 135 4.3.6 Statistical analyses ...... 136

4.4 RESULTS ...... 136 4.4.1 Study design & patients...... 136 4.4.2 ART, CD4+ T cell-associated HIV DNA, & plasma viral load ...... 138 4.4.3 ART & T cell activation ...... 140 4.4.4 Plasma viral load, HIV DNA & T cell activation ...... 141 4.4.5 Viral rebound in one study participant ...... 142

4.5 DISCUSSION ...... 143

4.6 CONCLUSIONS ...... 146

4.7 ACKNOWLEDGEMENTS ...... 146

CHAPTER 5: HIV RESERVOIRS IN LYMPH NODES ...... 149

5.1 INTRODUCTION ...... 150

5.2 MATERIALS & METHODS ...... 152 5.2.1 Study participants ...... 152 5.2.2 Sample collection ...... 152 5.2.3 Cellular yields from lymph node fine needle biopsy specimens ...... 152 5.2.4 Immunophenotypic analysis of whole blood & lymph node fine needle biopsy specimens ...... 154 5.2.5 Isolation of CD4+ T cells from whole blood & lymph node fine needle biopsy specimens ...... 156 5.2.6 Extraction and quantification of HIV nucleic acids...... 157 5.2.7 Statistical analyses ...... 158

5.3 RESULTS ...... 158 5.3.1 Study participant characteristics ...... 158 5.3.2 Lymph node fine needle biopsies ...... 160 5.3.3 Cellular yields in lymph node fine needle biopsy specimens ...... 160 5.3.4 Immune phenotype of cells in lymph node fine needle biopsies ...... 162 5.3.5 Activated T & B cells in lymph node fine needle biopsies ...... 163 5.3.6 CD4+ T cell-associated total HIV DNA & unspliced RNA ...... 164

5.4 DISCUSSION ...... 168 viii

5.5 CONCLUSIONS ...... 170

5.6 ACKNOWLEDGEMENTS ...... 170

CHAPTER 6: PBMC-ASSOCIATED HIV DNA AND HAND ...... 173

6.1 INTRODUCTION...... 174

6.2 MATERIALS & METHODS ...... 176 6.2.1 Study design, ethics & patients ...... 176 6.2.2 Neurocognitive examination ...... 176 6.2.3 DNA extraction & total HIV DNA quantification ...... 177 6.2.4 Neurocognitive functioning and peripheral blood mononuclear cell- associated HIV DNA levels ...... 178 6.2.5 Statistical analyses ...... 178

6.3 RESULTS ...... 179 6.3.1 Neurocognitive performance at study visits ...... 181 6.3.2 PBMC-associated total HIV DNA at study visits ...... 183 6.3.3 Total HIV DNA, HIV disease factors & neurocognitive performance...... 184 6.3.4 Change in total HIV DNA levels & neurocognitive performance...... 186

6.4 DISCUSSION ...... 187

6.5 CONCLUSIONS ...... 190

6.6 ACKNOWLEDGEMENTS ...... 191

CHAPTER 7: GENERAL DISCUSSION ...... 193

REFERENCES ...... 202

LIST OF FIGURES

Figure 1.1: Numbers of people living with HIV/AIDS worldwide...... 2

Figure 1.2: Global estimates of HIV infections & deaths by year...... 3

Figure 1.3: Simplifed structure of the HIV virion...... 5

Figure 1.4: The HIV genome...... 6

Figure 1.5: The HIV viral life cycle...... 7

Figure 1.6: The formation of circular episomal HIV DNA species...... 13

Figure 1.7: T cell dynamics & viral kinetics during untreated HIV infection...... 18

Figure 1.8: Decay of plasma HIV RNA following the initiation of ART...... 25

Figure 1.9: CD4+ T cell reconstitution during ART...... 26

Figure 1.10: Memory CD4+ T cell subsets & HIV reservoirs...... 32

Figure 2.1: Gating strategy for the purification of CD4+ T cells...... 62 Figure 2.2: Gating strategy for the isolation of antigen-specific CD4+ T cells and control populations...... 65

Figure 2.3: Dead or dying lymphocytes in the CD25/CD134 co-expression assay...... 67

Figure 2.4: Dead or dying lymphocytes as identified by changes in FSC and SSC...... 68 Figure 2.5: Overnight resting of recovered peripheral blood mononuclear cells in the CD25/CD134 co-expression assay...... 69 Figure 2.6: Density of peripheral blood mononuclear cell cultures in the CD25/CD134 co-expression assay...... 71 Figure 2.7: Comparison of whole blood & peripheral blood mononuclear cells in the CD25/CD134 co-expression assay...... 72 Figure 2.8: Titration of CMV lysate for the stimulation of CMV-specific CD4+ T Cells...... 75 Figure 2.9: Titration of Tetanus Toxoid (TT) for the stimulation of TT-specific CD4+ T Cells...... 76 Figure 2.10: Summary DNA/RNA yields for the various extraction methods utilized to conduct the experiments comprising this thesis...... 83 Figure 2.11: HIV DNA species & the location of PCR amplicons amplified in this thesis...... 85

Figure 2.12: Validation of the total HIV pol DNA qPCR standard curve...... 89 x

Figure 2.13: Comparison of DNA extraction methods...... 90 Figure 2.14: Quantification of total HIV pol DNA in DNA extracted using the direct lysis buffer (DLB) or the Qiagen DNeasy blood and tissue kit...... 91 Figure 2.15: Total HIV DNA, and β-actin qPCR positive controls throughout the thesis...... 92 Figure 2.16: Summary standard curves for the total HIV pol and gag DNA qPCR assays...... 93

Figure 2.17: Schematic of the integrated HIV DNA qPCR...... 94

Figure 3.1: Positive controls for the total HIV pol DNA & β-actin qPCR...... 112

Figure 3.2: Screening of potential study participants...... 114

Figure 3.3: Antigen-specific CD4+ T cell responses...... 116 Figure 3.4: Cellular yields (a) and purity (b) following fluorescence activated cell sorting of antigen-specific CD4+ T cell subsets...... 117 Figure 3.5: Total HIV pol DNA levels in antigen-specific CD4+ T cell subsets and control populations...... 118

Figure 3.6: Phylogenetic analysis of all clonal gag (a) and pol (b) viral sequences. .... 119 Figure 3.7: Baseline numbers of PCR-induced synonymous changes between pairs of clonal sequences...... 120 Figure 3.8: Phylogenetic analysis of gag (a/c) & pol (b/d) viral sequences from study participants 2 (a/b) & 9 (c/d)...... 121 Figure 3.9: Sequence diversity of viral populations derived from pre-ART plasma & CD4+ T cell subsets during suppressive ART...... 123

Figure 4.1: Positive controls for HIV DNA species & β-actin qPCR...... 133 Figure 4.2: Calibrating the measurement of HIV DNA species over the two phases of the PINT study...... 135

Figure 4.3: Plasma HIV RNA & CD4+ T cell-associated HIV DNA species...... 138

Figure 4.4: CD4+ & CD8+ T cell activation levels...... 140 Figure 4.5: Correlations between plasma viral load & CD4+ T cell-associated HIV DNA species during suppressive ART (week 24 to 156 of this study)...... 142 Figure 4.6: HIV nucleic acid species & T cell phenotypes for the one participant who developed integrase resistance & developed plasma viremia at the final study visit. .. 143

Figure 5.1: Gating strategy for Trucount® assessment of absolute cell counts...... 154 Figure 5.2: Gating strategy for germinal centre (GC) B cells, plasmablasts & activated T cells...... 155

Figure 5.3: Gating strategy for follicular T helper cells (TFH) and regulatory T cells (Treg)...... 156

Figure 5.4: Positive controls for the total HIV pol DNA & β-actin qPCR...... 158

Figure 5.5: Ultrasound image of the inguinal lymph node (LN) being sampled...... 160

Figure 5.6: Lymphocyte counts in lymph node fine needle biopsy specimens...... 161 Figure 5.7: Minimal levels of neutrophils, natural killer (NK) cells & monocytes in lymph node (LN) fine needle biopsy (FNB) specimens...... 161

Figure 5.8: Follicular T helper cells (TFH) & germinal centre (GC) B cells in lymph node fine needle biopsy specimens...... 162 Figure 5.9: Activated T cells in lymph node (LN) fine needle biopsy (FNB) specimens...... 164 Figure 5.10: Purification of CD4+ T cells from lymph node (LN) fine needle biopsy (FNB) specimens by fluorescence activated cell sorting (FACS)...... 165 Figure 5.11: Cell-associated total HIV pol DNA & HIV gag usRNA in CD4+ T cells purified from whole blood (WB) and lymph node (LN) fine needle biopsy (FNB) specimens...... 166

Figure 5.12: Correlations between T cell activation & HIV DNA/RNA levels...... 167

Figure 6.1: Positive controls for the total HIV pol DNA & β-actin qPCR...... 178

Figure 6.2: Baseline & follow-up total HIV pol DNA levels in PBMC...... 183

Figure 6.3: Total HIV pol DNA levels in PBMC at the baseline visit...... 184 Figure 6.4: Association between changes in total HIV pol DNA levels & decline in motor- coordination or semantic fluency...... 187

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LIST OF TABLES

Table 2.1: Monoclonal antibodies used to isolate antigen-specific CD4+ T cell subsets & control populations...... 63

Table 2.2: Primers & probes used to quantify HIV DNA species by qPCR...... 84

Table 2.3: Primers used to amplify HIV DNA for clonal sequencing...... 100

Table 3.1: Timing for collection of pre-ART plasma samples...... 113

Table 3.2: Characteristics of study participants...... 115

Table 4.1: Study participant characteristics, serious adverse & new AIDS events...... 137 Table 4.2: Associations between CD4+ or CD8+ T cell activation & HIV DNA species or plasma viral load during suppressive ART (week 24 to 156 of this study)...... 141

Table 5.1: Monoclonal antibodies for cellular yield & immunophenotyping...... 153

Table 5.2: Characteristics of HIV-infected study participants...... 159 Table 6.1: Baseline & follow-up study participant demographics, & laboratory & clinical characteristics...... 180 Table 6.2: Study participant demographics by American Academy of Neurology categories...... 182

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LIST OF ABBREVIATIONS

3’ LTR 3’ long terminal repeat 3TC Lamivudine 5’ LTR 5’ long terminal repeat 6-FAM 6-Carboxylfluorescein g Microgram

L Microlitre

m Micromolar A Adenine aa Amino acids AAN American Academy of Neurology ABC Abacavir Ag Antigen Ag-sp-CD4+ Antigen-specific CD4+ (T cells) AGRF Australian Genome Research Facility AIDS Acquired immune deficiency syndrome ANI Asymptomatic neurocognitive impairment ANOVA Analysis of variance APC Allophycocyanin APC-Cy7 Allophycocyanin-Cyanin dye 7 tandem conjugate APOBEC3 Apolipoprotein B mRNA-editing enzyme catalytic polypeptide like 3 ART Antiretroviral therapy ARV Antiretroviral (drugs) ATI Analytic treatment interruption ATZ Atazanavir AZT Zidovudine BEB Back extraction buffer BHQ-1 Black hole quencher 1 BMT Bone marrow transplantation xiv

bNAb Broadly neutralizing antibodies bp Base pair bs Bootstrap C Cytosine Ca Capsid CA Cell-associated cART Combination antiretroviral therapy CCR5 C-C chemokine receptor 5 CD Cluster of differentiation cDNA Complementary deoxyribonucleic acid CFSE Carboxyfluorescein diacetate succinimidyl ester CHI Chronic HIV infection CI Confidence interval

CO2 Carbon dioxide cm Centimetre CMV Cytomegalovirus CMV-sp-CD4+ CMV-specific CD4+ (T cells) CNS Central nervous system CpG Cytosine-phostphate-guanine CRP C-reactive protein CSF Cerebrospinal fluid

Cq Quantitative threshold cycle CXCR4 C-X-C type receptor 4 DC Dendritic cells DCP Dried cell pellet dH2O Distilled H2O DLB Direct lysis buffer DMEM Dulbecco’s Modified Eagle Medium DMSO Dimethyl sulphoxide DNA Deoxyribonucleic acid

xv dNTPs Deoxyribonucleoside triphosphates DPBS Distilled phosphate buffered saline DRV Darunavir dsDNA Double stranded DNA DTG Dolutegravir ECD Energy coupled dye EDTA Ethylenediaminetetra-acetic acid EFV Efavirenz EI Entry inhibitor ELISA Enzyme-linked immunosorbent assay ELISPOT Enzyme-linked immunospot assay FACS Fluorescence activated cell sorting FCS Foetal calf serum FDC Follicular dendritic cell FI Fusion inhibitor FITC Fluoroscein isothiocyanate FNB Fine needle biospy FSC -A Forward scatter area FSC-W Forward scatter width FSPV Fosamprenavir FTC Emtracitabine G Guanine Gag-sp-CD4+ Gag-specific CD4+ (T cells) GALT Gut-associated lymphoid tissue GAPDH Glyceraldehye 3-phosphate dehydrogenase GC Germinal centre GIT Gastrointestinal tract hAB Human AB Serum HAD HIV-associated dementia HAND HIV-associated neurodegenerative disorders xvi

HAART Highly active antiretroviral therapy HC Healthy controls HDAC Histone deacetylation HDACi Histone deacetylase inhibitor HIV Human immunodeficiency virus HIV-sp-CD4+ HIV specific CD4+ (T cells) HLA Human lymphocyte antigen HNRC-IADL The HIV Neurobehavioral Research Center Instrumental of Activity of Daily Living HREC Human Research Ethics Committee IBAC Immunology and infectious diseases ambulatory care unit ICC Intracellular cytokine IDU Injecting drug user IFN Interferon Ig Immunoglobulin IL Interleukin IMDM Iscove’s Modified Dulbecco’s Medium IN Integrase Infl-sp-CD4+ Influenza specific CD4+ (T cells) INI Integrase inhibitor INSIGHT International network for strategic initiatives in global HIV trials IQR Interquartile range IUPM Infectious units per million kb Kilobases LB Luria broth LC Langerhans cells LEDGF Lens epithelium-derived growth factor ln Natural logarithm LN Lymph node

LN2 Liquid nitrogen LOD Limit of detection xvii

LOQ Limit of quantification LRA Latency reversal agent LTNP Long-term non-progressor LTR Long terminal repeat M Molar MA Matrix mAb Monoclonal antibodies MCP-1 Monocyte chemoattractant protein-1 MFI Mean fluorescence intensity mg Milligram

MgCl2 Magnesium chloride

MgSO4 Magnesium sulfate MHC Major histocompatibility complex MIP-1 Macrophage inflammatory protein-1 mL Millilitre mM Millimolar MND Mild neurocognitive disorder mRNA Messenger RNA MSM Men who have sex with men msRNA multiply spliced RNA MVC Maraviroc MVOA Murine viral outgrowth assay ND Not detected NA Not applicable/not available NART National Adult Reading Test NC Nucleocapsid NCN Neurocognitively normal NF-κB Nuclear factor kappa B NIAID US National Institute of Allergy and Infectious Diseases NIH National Institute of Health (USA) xviii

ng Nanogram NK Natural killer nm Nanometre nM Nanomolar NNRTI Non-nucleoside reverse transcriptase inhibitor NRTI Nucleoside reverse transcriptase inhibitor NVP Nevirapine PAOFI Personal Assessment for Own Functioning PB Pacific Blue PBMC Peripheral blood mononuclear cells PCR Polymerase chain reaction PE Phycoerythrin PE-Cy7 Phycoerythrin-Cyanin dye 7 tandem conjugate PerCP Peridinin-chlorophyll-protein PerCP-Cy5.5 Peridinin-chlorophyll-protein-Cyanin dye 5.5 tandem conjugate PFA Paraformaldehyde PHAEDRA Primary HIV and early disease research in Australia PHI Primary HIV infection PI Protease inhibitor PIC Pre-integration complex PINT Pilot integrase inhibitor trial Pro Protease PTC Post-treatment control(lers) P-TEFb positive transcription elongation factor pVL Plasma viral load qPCR Quantitative real-time polymerase chain reaction QVOA Quantitative viral outgrowth assay RAL Raltegravir RNA Ribonucleic acid RNAPII RNA polymerase II

xix rpm Revolutions per minute RPMI Roswell Park Memorial Institute medium rt Room temperature RT Reverse transcriptase RT-qPCR Reverse transcriptase quantitative real time polymerase chain reaction RTI Reverse transcriptase inhibitor RTV Ritonavir SAMHD1 SAM domain and HD domain-containing protein 1 sCD14 Soluble CD14 SD Standard deviation SEB Staphylococcal enterotoxin B SIV Simian immunodeficiency virus SMART Strategies for management of antiretroviral therapy SNAEs Serious non-AIDS events SSC-A Side scatter area Sp1 Specificity protein 1 SPARTAC Short pulse ART treatment at seroconversion START Strategic timing of antiretroviral therapy SQV Saquinavir T Thymine

T1/2 Half-life TAE Tris-acetate EDTA buffer TAMRA Tetramethylrhodamine

TCM Central memory CD4+ T cells TCR T cell receptor TDF Tenofovir

TEM Effector memory CD4+ T cells

TFH T follicular helper (cell) TILDA Tat/rev induced limiting dilution assay TNF Tumor necrosis factor xx

Treg T regulatory cell/Regulatory T cell

TSCM Stem cell like memory CD4+ T cells TT Tetanus toxoid TT-sp-CD4+ TT-specific CD4+ (T cells)

TTD Terminally differentiated memory CD4+ T cells

TTM Transitional memory CD4+ T cells UHC Uninfected healthy controls UNAIDS Joint United Nations programme on HIV/AIDS USA United States of America usRNA Unspliced RNA VISCONTI Viro-immunological sustained control after treatment interruption WB Whole (peripheral) blood WHO World Health Organization x g Gravitational force

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ABSTRACT

Viral reservoirs in diverse cell types and anatomical locations are considered the main barrier to a cure for HIV infection. How and where these reservoirs are established and maintained is therefore of great interest to HIV research. The initiation of therapy during early HIV infection can limit the size of viral reservoirs, and may also reduce chronic immune activation and inflammation, which are associated with non-AIDS comorbidities and possibly the development of neurological complications.

ACKNOWLEDGEMENTS

The work described in this thesis was conducted in the HIV Immunovirology and Pathogenesis Research Laboratory at St Vincent’s Centre for Applied Medical Research (AMR) Darlinghurst, Australia, and the Kirby Institute for infection and immunity, UNSW Australia. Two research institutes under the directorship of Professor David Cooper.

A PhD is a test of patience and perseverance, not only for the candidate, but for those supporting them. I’d like to express my gratitude and admiration for all those who patiently persevered with me throughout the 4 & ½ years of this candidature.

In particular, I would like to thank my primary supervisor Anthony Kelleher, for giving me the opportunity to complete this thesis, and for creating the environment for PhD candidates like myself to succeed. I appreciate and admire the time and dedication you invested in helping me, and all those within your laboratory. I am very grateful for, and appreciate the support provided by my co-supervisor Kersten Koelsch. Thank you for the many conversations we had discussing the details of HIV reservoirs, as well as life beyond science. Your door was always open, even when Arsenal played Borussia Dortmund. To John Zaunders, also a co-supervisor, thank you for your constant shedding of wisdom and jokes. It was great learning from you, and having a good time doing so. The three of you guided me through this thesis, and I am forever grateful and indebted.

There are several people within the laboratory who deserve a special mention for helping me substantially over the course of this thesis. None more so than Michelle Bailey, who spent many days and nights in charge of some marathon cell sorting experiments, for not 1 or 2, but 3 chapters of this thesis. It’s been great working with, and getting to know you. I also would like to thank Yin Xu, who deputized for Michelle with some of the marathon sort experiments, and also helped significantly during the study of fine needle biopsies. Of course there is Chester, who I hope learnt as much from me as I did from him. Although it was a lengthy process, I really enjoyed troubleshooting experiments with you and your unique sense of humour. I also want to thank, and owe a great deal to Kristin

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McBride, who handed over the HIV DNA assays, and was always willing to discuss the finer details of these techniques.

I would like to express my gratitude to all of the senior laboratory members who encouraged debate and discussion, sharing their knowledge with the laboratory; Mee- Ling Munier, Kazuo Suzuki, Tanya Applegate, David van Bockel, Stuart Turville, and all those who contributed by providing seminars and the discussion that followed.

There are many people that made my time as a PhD student so rewarding and fun. To all the PhD students, past and present; Chan ‘Dr lots of fun’ Phetsouphanh, Daniel ‘free lunch’ Murray, Catalina Mendez, Brendan Jacka, Scott ‘Andy Murray’ Ledger, Sam McAllery, Laura Cook, Ivy Shih, Winnie Tong, Denise Hsu, Andrew Wong, Kanin Salao, Tina Iemma; you’ve all not only helped through many discussions about science and experimental techniques, but also made my time in the laboratory really enjoyable. Of course you are not the only ones; Kat Marks, Tracey Barret, Pat Grey, Susanna Ip, Lucette Cysique, Bertha Fsadni, Maria Piperias, Julie Yeung, Mel Lograsso, and Kim Grassi, plus anyone else I apologize for forgetting. Together through all tea-time chats, crosswords, soccer and the karaoke, you’ve helped more than you probably realise.

A special thanks must go to my family and friends that patiently supported me along the way. To my now wife Christina Hey-Nguyen, in hindsight I can always appreciate your encouragement and support, driving me to work harder and achieve more. I don’t need hindsight however, to appreciate the love and care you provide to my life as a whole. Thanks are well deserved for my mum and dad, who happily supported me through providing love, food and shelter for much of this candidature. Speaking of food, I cannot forget to thank my mother- and brother-in law, Anh and Tommy, who, through cooking at home and at Rice Paper, provided plenty of delicious nourishment. And to the rest of my friends, thanks for always believing I could succeed in this PhD, and for the many forms of distraction that allowed me to continue living a normal life, for the most part.

Financial support for my candidature was from the Australian government in the form of an Australian Postgraduate Award, a top-up award from St Vincent’s AMR, and the generous extension of this scholarship by Anthony Kelleher. xxiv

PUBLICATIONS & CONFERENCE PROCEEDINGS

Publications WJ Hey-Cunningham, JM Murray, V Natarajan, J Amin, CL Moore, S Emery, DA Cooper, J Zaunders, AD Kelleher & KK Koelsch for the PINT study team. Early Antiretroviral Therapy With Raltegravir Generates Sustained Reductions in HIV Reservoirs but not Lower T Cell Activation Levels. AIDS, 2015, 29:911-919.

LA Cysique*, WJ Hey-Cunningham*, N Dermody, P Chan, BJ Brew, and KK Koelsch. Peripheral Blood Mononuclear Cells HIV DNA Levels Impact Intermittently on Neurocognition. PLoS One, April 8, 2015, DOI:10.1371/journal.pone.0120488. *Joint first-authorship.

OS Søgaard, ME Graversen, S Leth, R Olesen, CR Brinkmann, SK Nissen, AS Kjaer, PW Denton, WJ Hey-Cunningham, KK Koelsch, G Pantaleo, K Krogsgaard, M Sommerfelt, R Fromentin, N Chomont, TA Rasmussen, L Østergaard, and M Tolstrup. The Depsipeptide Romidepsin Reverses HIV-1 Latency in vivo. PLoS Pathogens, manuscript accepted and in preparation.

Publications - submitted / in preparation WJ Hey-Cunningham*, Y Xu*, CF Pearson, M Bailey, K Suzuki, R Tantau, S Obeid, B Milner, A Field, A Carr, M Bloch, DA Cooper, AD Kelleher, JJ Zaunders and KK Koelsch. Lymph Node Fine Needle Biopsies to Quantify Germinal Centre Activity and HIV Reservoirs During Antiretroviral Therapy. *Joint first-authorship.

Conference Proceedings WJ Hey-Cunningham, N Dermody, P Chan, KK Koelsch and LA Cysique (2015). HIV DNA peripheral reservoirs have a non-linear impact on brain pathology. 22nd Conference on Retroviruses and Opportunistic Infections, Seattle, WA, USA.

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WJ Hey-Cunningham, K McBride, J Murray, J Zaunders, Sean Emery, D Cooper, AD Kelleher, KK Koelsch and for the PINT Study Group (2014). Continued decay of HIV DNA species during 3 years of ART is not reflected by decay of HIV RNA in plasma. 20th International AIDS Conference 2014 and IAS Towards an HIV Cure Symposium, Melbourne, Victoria, Australia. Also presented at the 22nd St. Vincent’s Campus Research Symposium, Sydney, NSW, Australia, with updated results and analysis.

WJ Hey-Cunningham, M Bailey, Y Xu, AD Kelleher, KK Koelsch and J Zaunders (2014). Activation of antigen-specific CD4+ T cells impacts on the establishment of the HIV DNA reservoir. 2014 IAS Towards an HIV Cure Symposium, Melbourne, Victoria, Australia.

KK Koelsch, WJ Hey-Cunningham, SC Sasson, CF Pearson, KH Marks, Y Xu, M Bailey, BM Hiener, S Palmer, JJ Zaunders, JJ Post, ST Milliken, AD Kelleher, DA Cooper (2014). Allogeneic bone marrow transplantation in two HIV-1 infected patients shows no detectable HIV-1 RNA or DNA, and a profound reduction in HIV-1 antibodies. 20th International AIDS Conference 2014 and IAS Towards an HIV Cure Symposium, Melbourne, Victoria, Australia.

WJ Hey-Cunningham, N Dermody, BJ Brew, KK Koelsch and LA Cysique (2014). When does the viral reservoir size in PBMC matter for brain pathology in persons with chronic HIV infection? 3rd Annual NHMRC Research Translation Faculty Symposium, Melbourne, Victoria, Australia.

KK Koelsch, WJ Hey-Cunningham, JM Murray, KL McBride, J Zaunders, S Emery, DA Cooper and AD Kelleher, for the PINT Study Group (2013). Ongoing decay of HIV 2-LTR circles after treatment initiation with raltegravir in treatment naive patients. Sixth International Workshop on HIV Persistence During Therapy, Miami, Florida, USA

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1 CHAPTER 1: LITERATURE

REVIEW

CHAPTER 1: LITERATURE REVIEW

1.1 Introduction:

The human immunodeficiency virus (HIV) infects and kills cells integral to the human immune system, leading to the development of acquired immunodeficiency syndrome (AIDS), and leaving those affected susceptible to opportunistic infections and malignancies which are often fatal.

The HIV/AIDS pandemic is a major global health issue. In 2013, approximately 35 million people were infected with HIV, and 1.5 million people died due to AIDS [1]. A large proportion of HIV infections are in sub-Saharan Africa (Figure 1.1), where the pandemic has decimated workforces and destroyed communities, leading the International Monetary Fund to label it a ‘major development crisis’ [2]. Around the globe, HIV/AIDS affects some of the world’s most marginalized populations; including sex workers, men who have sex with men (MSM), and injecting drug users (IDU).

Figure 1.1: Numbers of people living with HIV/AIDS worldwide. Obtained from www.unaids.org in March 2015.

The development of antiretroviral therapy [ART; also referred to as combination ART (cART) or highly active ART (HAART)] is one of modern medicine’s great success

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stories. ART has turned the tide on the global HIV pandemic, with both the number of new global HIV infections and deaths due to AIDS declining in recent years (Figure 1.2).

Figure 1.2: Global estimates of HIV infections & deaths by year. Dotted lines show upper and lower estimates. New HIV infections and deaths due to AIDS are plotted on the right y-axis. Generated using data obtained from www.aidsinfoonline.org in July 2015.

With successful administration of ART, HIV infection transforms into a manageable chronic disease, by preventing disease progression and the development of AIDS. Unfortunately, the costs of ART, and difficulty distributing ART in resource-poor settings, have limited its reach to many of the World’s most vulnerable and in need populations. Due to the success of ART, a significant movement is currently underway to scale up the distribution and use of ART, with the joint United Nations programme on HIV/AIDS (UNAIDS) recently setting ambitious targets of ‘90 90 90’ by 2020; 90% diagnosed, 90% receiving ART, and 90% virally suppressed [3]. ART however, is significantly limited by its inability to cure HIV, leaving those infected with HIV reliant on ART for the duration of their life.

The growing HIV-infected population is subject to a several significant complications associated with their HIV status and continual adherence to ART. HIV-infected people may be subject to legal and social discrimination driven by the stigma associated with HIV/AIDS and the practices that transmit the virus. The life-long reliance of HIV-infected 3

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individuals on ART places their immune system under chronic stress, and is associated with a range of complications termed serious non-AIDS events (SNAEs).

Overcoming the HIV/AIDS epidemic will require not only the prevention of new infections, but also the development of a cure to clear the HIV-infected population. Social interventions and the upscaling of ART will play an extremely important role in preventing HIV transmission and disease progression, however, the study of potential strategies to cure HIV infection remains an important target of research.

1.2 Human Immunodeficiency Virus

1.2.1 The virus

There are two major subtypes of HIV, type 1 (HIV-1), and type 2 (HIV-2), from here on this thesis will refer to HIV-1 as HIV, unless otherwise specified. HIV is a member of the Lentivirus genus, a subgroup of the Retroviridae family of viruses. Lentivirus literally means ‘slow virus’, so named due to the lengthy incubation period before the development of disease. Other members of the Lentivirus genus include the simian-, feline-, and bovine- immunodeficiency viruses, all characterized by their deleterious effect on the host animals immune system. Lentiviruses are single-stranded positive sense, enveloped ribonucleic acid (RNA) viruses. Upon infection of permissive cells, the viral RNA genome is converted into double stranded (ds) deoxyribonucleic acid (DNA) by the virally encoded reverse transcriptase (RT), integrated into the host genome, then the host cell machinery is exploited to produce viral proteins and generate new virions for export and infection of new target cells.

1.2.2 Virion structure

The HIV virion (Figure 1.3) is approximately 100-120nm in diameter [4, 5], of non- uniform morphology, and contains 2 copies of the viral RNA genome which are usually identical. These RNA copies are coupled with the viral RT and integrase (IN) enzymes, and are encapsulated in a cone shaped capsid or core [6]. The core is comprised of the viral protein p7/Nucleocapsid (NC), which interacts with the viral RNA, and p24/Capsid (Ca) which forms the structure of the core. The viral core is further enveloped in a

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myristoylated matrix of the viral protein p17/Matrix (MA) [6]. The two viral proteins Vif and Nef are closely associated with this core structure, and the third viral accessory protein Vpf is most likely found outside the core. The core structure is further enveloped by a host cell derived lipid membrane which contains the trans-membrane viral protein gp41 [7], the viral protein gp120 [5, 8], and other host cell molecules such as major histocompatibility complex (MHC) proteins [9, 10].

Figure 1.3: Simplifed structure of the HIV virion. Reproduced from commons.wikimedia.org, accessed in March 2015.

1.2.3 The viral genome

The HIV genome is approximately 9.7 kilobases (kb) in length and consists of 46 open reading frames encoding for 16 viral proteins (Figure 1.4). The genome consists of 3 primary genes; gag, pol, and env, flanked by two identical long terminal repeats (LTR) at both the 5’ and 3’ ends [11]. The HIV LTR is approximately 640 nucleotides in length and is segregated into the U3, R, and U5 regions. The LTR regions are integral to the regulation of gene expression, reverse transcription of viral RNA into DNA, integration into the host genome and viral replication [12]. The genome can be read in three separate reading frames, and overlapping between the gene-coding regions occurs [13]. The 3 main genes are translated into the Gag, Gag-Pol and Env polyproteins that form the structure of the HIV virion.

CHAPTER 1: LITERATURE REVIEW

Figure 1.4: The HIV genome. Adapted from [13].

The Gag polyprotein is cleaved by the viral protease (Pro) into p24/Ca, p17/MA and the p7/NC proteins. The NC protein (p13) is further cleaved into the proteins p7 and p6. The Gag-Pol polyprotein, also processed by Pro, is translated into additional Pro (p10), IN (p31), RNase H (p15), and RT (p50). The Env (gp160) polyprotein is cleaved by the host cell endoprotease furin into the surface glycoprotein (gp120) and the trans-membrane glycoprotein (gp41). Other proteins Rev, Tat, Nef, Vif, Vpr, Vpu, Vpx are translated from sub-genomic messenger RNA (mRNA) generated by splicing events, with multiple splicing events critical to the production of Tat, Nef and Rev [14]. The function of relevant proteins will be described in the appropriate section of the viral life cycle.

CHAPTER 1: LITERATURE REVIEW

1.2.4 The viral life cycle

Figure 1.5: The HIV viral life cycle. The essential steps are discussed in 1.2.4. Reproduced from [15].

1.2.4.1 Cell entry

HIV enters host cells via interactions between viral glycoproteins on the virion surface and cell surface molecules. HIV entry requires target cell expression of CD4, plus one of either CCR5 or CXCR4, termed the two co-receptor molecules (reviewed in [16]). Entry is initiated when the surface viral protein gp120 binds with CD4 on the host cell (reviewed in [17]). This binding causes a conformational change of gp120 exposing the co-receptor binding site or V3 loop [18, 19]. The V3 region of gp120 is the primary determinant of viral co-receptor tropism (CCR5 or CXCR4) and is highly mutable allowing for evasion of neutralization by antibodies [19, 20]. Cells expressing these cell surface molecules are 7

CHAPTER 1: LITERATURE REVIEW

therefore permissive to HIV infection. Individuals lacking the CCR5 molecule, due to homozygous ∆32 deletions in the CCR5 gene (around 1% of Caucasians) are resistant to infection by CCR5-tropic strains [21-23]. CD4+ T lymphocytes, by definition expressing high levels of CD4, are the primary target of HIV, while dendritic cells (DC) and macrophages, which express approximately 10-fold lower levels of CD4, can also be infected [24].

After the binding events described above, gp41 undergoes conformational changes allowing for fusion of the viral envelope with the host cell membrane, and release of the viral core into the cytoplasm [25].

1.2.4.2 Reverse transcription

Following entry into the cytoplasm, the HIV capsid is uncoupled to release the viral RNA genome that is then converted into single then dsDNA by the viral enzyme RT. Also included within the sequence of pol that codes for RT, is the coding sequence for ribonuclease (RNase) H, a non-specific endonuclease that degrades the RNA strand when in duplex with DNA [26]. Due to the absence of a proofreading mechanism, the process of reverse transcription introduces high rates of DNA base pair (bp) mis-incorporations, contributing to the high rate of viral evolution which is characteristic of HIV and other retroviruses [27, 28].

Several host cell factors influence HIV at this stage of the life cycle. For example, SAM domain and HD domain-containing protein 1 (SAMHD1) can inhibit reverse transcription by depleting the pool of available deoxyribonucleoside triphosphates (dNTPs), the building blocks of DNA. SAMHD1 is expressed in CD4+ and CD8+ T cells, as well as myeloid cells, and its higher expression in quiescent CD4+ T cells and myeloid cells is linked to restricted HIV reverse transcription and infection [29, 30]. The apolipoprotein B mRNA-editing enzyme catalytic polypeptide like 3 (APOBEC3) family of proteins are cellular cytidine deaminases that can also interfere with reverse transcription. APOBEC3 proteins induce numerous deoxycytidine to deoxyuridine mutations in the complementary DNA (cDNA) synthesized during RT, resulting in hypermutation of guanine (G) to adenine (A) (G to A hypermutation) in the DNA product [31], and may be involved in

CHAPTER 1: LITERATURE REVIEW

the adaptation and evolution of natural HIV infections [32]. This process often renders the integrated viral genome incapable of producing infectious viral particles [33-35]. However, the HIV regulatory protein Vif can restore HIV infectivity by binding to APOBEC3 proteins leading to their polyubiquitylation and proteasomal degradation [36].

1.2.4.3 Integration

Following reverse transcription, viral DNA is integrated into the host cell genome by a two-step process mediated by the viral protein IN (reviewed in [37, 38]). The first step, which occurs in the cytoplasm and is termed 3’-processing, primes the viral DNA for integration. During this step IN trims the 3’ ends of the viral DNA, exposing a conserved CA dinucleotide motif and generating 3’-hydroxyl ends required for the next stage of integration [37, 39-41]. Following 3’-processing, the viral DNA binds to IN complexes with other host cell and viral proteins to form what is termed the ‘pre-integration complex’ (PIC). The PIC, which includes the viral proteins RT, MA, NC and Vpr, is then transferred into the nucleus (reviewed in [42]). Here, the PIC catalyses the second step of the integration process, known as ‘strand transfer’, which involves the ligation of the viral 3’-hydroxyl DNA ends to the 5’-phosphate DNA of the host chromosome [43]. The integration process is completed by host cell DNA repair mechanisms [44]. Once integrated into the host cell genome HIV DNA is referred to as a provirus.

The integration process is not entirely efficient. Aborted HIV DNA integration events that do not support viral replication are also found within the nucleus of HIV-infected cells [45], and will be discussed further in section 1.2.5.1. The integrated forms of HIV DNA are stable and persist for the life-span of the host cell, and may be passed on to daughter cells during cell division.

The selection of HIV DNA integration sites is not an entirely random process. Various analyses of HIV DNA integration sites, in both CD4+ T cell derived cell lines and primary CD4+ T cells, have consistently found that HIV DNA preferentially integrates into intronic regions of actively transcribed genes [46-51]. These studies have further identified a range of host cell DNA characteristics associated with HIV DNA integration

CHAPTER 1: LITERATURE REVIEW

including: gene density, CpG islands, G/C enriched areas and histone methylation patterns.

Cellular host factors may be involved in the targeted nature of integration. The protein lens epithelium-derived growth factor (LEDGF/p75) is likely to assist in the direction of the PIC to certain areas of the chromosomal DNA [52-56], as such, LEDGF may offer a novel therapeutic target for anti HIV molecules [57].

Recent elegant studies have confirmed HIVs affinity for integration into actively transcribed genes, and provided evidence that the HIV PIC simply targets the nearest available gene once inside the nucleus. Cohn et al. studied 6,719 unique integration sites in CD4+ T cells from 13 individuals both on and off ART, and again identified a preference for actively transcribed genes [58]. Work by Marini et al. supports a model by which the 3-dimensional structure of the nucleus influences integration, with actively transcribed genes with open chromatin at the nuclear periphery identified as the main targets of HIV integration [59].

The selection of HIV DNA integration sites may influence the persistence of viral reservoirs and will be discussed further in section 1.4.8.

1.2.4.4 Production of viral proteins

Once integrated into the host cell genome, translation of HIV genes is controlled by cellular transcription factors and the viral regulatory proteins Tat and Rev. Transcription of HIV RNA is initiated by binding of host cell transcription factors including, but not limited to, nuclear factor kappa B (NF-κB) and specificity protein 1 (Sp1), to the HIV promoter region within the 5’ LTR [60]. The viral protein Tat also plays a vital role in promoting rapid and efficient elongation of HIV transcripts [61]. Tat achieves this by recruiting the positive transcription elongation factor (P-TEFb) and binding to the transactivation-responsive region (TAR) located in the 5’ LTR downstream of NF-κB and Sp1 binding sites [61].

CHAPTER 1: LITERATURE REVIEW

Transcription of HIV mRNA is carried out by the host cell RNA polymerase II (RNAPII). During the initial or early phase, HIV transcripts are multiply spliced (ms) to form tat, rev and nef mRNA, which are specifically transported to the cytoplasm for translation into viral proteins (reviewed in [13]). Once translated into proteins, Tat and Rev return to the nucleus where Tat increases the rate of transcription by promoting elongation [61]. Following the accumulation of Rev within the nucleus, transcription of singly and unspliced (us) gag, gag-pol, env, vif, vpr and vpu mRNA occurs and these mRNA are transported out of the nucleus for translation into viral proteins or incorporation into new virions [13]. Similar to during reverse transcription (1.2.4.2), the host cell RNAPII does not contain a proof-reading mechanism, leading this process to also contribute the characteristically high rate of viral evolution for HIV [27, 28].

This stage of the HIV life cycle has been intensively investigated with the hopes of developing a cure. Strategies aimed at both suppressing HIV transcription to prevent virus production, or promoting HIV transcription to assist in the clearance of latent reservoirs, will be discussed in detail in section 1.5.

1.2.4.5 Virion production and release

Following the translation of viral proteins, new virions are assembled at the plasma membrane and released from the cell for the infection of new target cells. The Gag and Gag-Pol polyproteins congregate at the plasma membrane to form immature virions which are encapsulated by the lipid membrane containing viral Env proteins during budding. Following budding, maturation of the virion occurs as the viral enzyme Pro cleaves Gag and Gag-Pol polyproteins into their functional subunits (p7/NC, p24/C p17/MA, and p6) which are then arranged into the characteristic cone shaped viral core (reviewed in [62]).

1.2.5 HIV DNA species and the life cycle:

During different stages of the HIV life cycle, HIV DNA is present in distinct forms (illustrated in Figure 1.5 and Figure 1.6). The specific identification of the various forms of HIV DNA allows for sophisticated investigations of the HIV life cycle.

CHAPTER 1: LITERATURE REVIEW

1.2.5.1 Unintegrated HIV DNA

Unintegrated forms comprise the majority of HIV DNA in untreated patients [63, 64], however are likely to represent the minority of HIV DNA during ART [65].

1.2.5.1.1 Linear unintegrated HIV DNA

The product of RT, linear HIV DNA exists briefly prior to integration into the host genome [66, 67]. It is thought linear unintegrated HIV DNA is degraded by host cell defence mechanisms targeting dsDNA, with in vitro studies indicating this form of HIV DNA is stable for only 1-6 days [68, 69]. Given some viral proteins can be generated from linear unintegrated HIV DNA, and that this form of HIV DNA can be rescued by superinfection or cellular activation [70], its existence can be considered a form of latency termed pre-integration latency.

1.2.5.1.2 Episomal unintegrated HIV DNA

Circular episomal forms of HIV DNA arise from aborted integration events (Figure 1.6). Unintegrated circles can be generated through a process called ‘autointegration’ whereby linear HIV DNA is processed by IN and attempts to integrate into another HIV DNA molecule [67, 71]. Circular HIV DNA molecules containing one copy of the LTR (1-LTR circles) are formed by either homologous recombination of the viral LTR or incomplete reverse transcription events [72]. Circular HIV DNA molecules containing two copies of the LTR gene (2-LTR circles) are the products of non-homologous end joining orchestrated by host cell DNA repair mechanisms as a protective response to the presence of dsDNA [71, 73]. These circular forms of HIV DNA are exclusively found within the host cell nucleus, cannot sustain replication, and are considered ‘dead end products’ of the HIV life cycle [67, 74, 75].

CHAPTER 1: LITERATURE REVIEW

Figure 1.6: The formation of circular episomal HIV DNA species. Reproduced from [76].

Because of the unique sequence in the junction between the 5’ and 3’ LTR of 2-LTR circles, this form of unintegrated HIV DNA can be specifically measured by polymerase chain reaction (PCR) techniques, however this technique is complicated by high levels of sequence diversity [63, 77].

Original observations of rapid decreases in 2-LTR circles following the initiation of ART [78, 79], led to the hypothesis that the presence of 2-LTR circles is indicative of nascent infection [80-85]. However, several other studies have shown that 2-LTR circles are in fact more stable than initially thought [68, 86-91], leading researchers to hypothesize that the initial observations of 2-LTR decay are in fact due to rapid cell death and not the degradation of 2-LTR circles. The stability of 2-LTR circles and their use as a marker of nascent infection remains controversial, and will be discussed further in Chapter 4.

Although many groups have focused on 2-LTR circular forms of episomal HIV DNA, 2- LTR circles appear to make up a very small proportion of the total pool of episomal HIV DNA, which is in fact dominated by 1-LTR circles [45, 91]. This bias of attention is most likely due to the difficulty in identifying and quantifying 1-LTR circles or autointegrants. 13

CHAPTER 1: LITERATURE REVIEW

1.2.5.2 Integrated proviral HIV DNA

The presence of integrated proviral HIV DNA is a hallmark of retroviral infections. It allows for the rapid production of new virions, and importantly for HIV in the context of ART, provides a mechanism for persistence through the copying of viral DNA during cell division (discussed in 1.4). HIV integrates primarily into the genome of CD4+ T cells, the main target of HIV infection, but can also integrate into DCs [92], macrophages [93- 96], and monocytes [97-99]. The majority of infected cells contain only one provirus [100, 101], however in certain circumstances, for example in the spleen, multiple copies per cell are common, corroborated by the occurrence of recombination [101-104].

1.3 HIV infections in the human population

1.3.1 Transmission & establishment of HIV infection

HIV is transmitted between hosts through bodily fluids containing either infectious virus or HIV-infected cells. The most common route of transmission is through sexual contact, both vaginal and anal [105]. In this context, HIV in genital fluids, such as semen or vaginal and rectal fluids, crosses a mucosal membrane to establish infection in a susceptible host. HIV can also be transmitted through contaminated blood, such as during needle sharing by IDU and by transfusion with blood products or organ donation, and from a mother to her child during pregnancy, childbirth and breastfeeding, referred to as vertical transmission. HIV transmission is an inefficient process with approximately 1 in 1000 sexual contacts resulting in a transmission event [106], however, this value varies significantly depending on the type of sexual contact, circumcision status, and the presence of concurrent inflammation or ulceration of the genital mucosa. This review will focus on events occurring during the mucosal acquisition of HIV during sexual contact.

During transmission via sexual contact, HIV enters the new host through the foreskin, vaginal, cervical or rectal mucosal layer. At this stage, HIV encounters several physical barriers that contribute to the observed low efficiency of transmission. The acidic cervicovaginal mucus (pH 4) combined with normal flora and the epithelium prevent HIV virions from crossing the mucosal membrane to infect target cells [107]. The various mucosal membranes involved are differentially susceptible to HIV infection and contribute to the difference efficiency of transmission for the individual types of sexual 14

CHAPTER 1: LITERATURE REVIEW

contact. The stratified squamous epithelium of the male foreskin and glans provides a very strong barrier to HIV infection, while the multilayered squamous epithelium of the vaginal mucosa is less protective, but still provides a greater barrier than the single layer of columnar epithelium guarding the endocervix and the rectum, the latter of which also contains higher levels of CD4+ T cells the primary target cell of HIV (reviewed in [108]).

The protective effects inferred by such physical barriers are believed to contribute to a phenomenon known as the ‘transmission’ or ‘genetic bottleneck’. Several studies analysing HIV sequences shortly after transmission have identified a single ‘founder’ virus [109, 110], indicating that one single virion is responsible for establishing the majority of infections. The occurrence of a genetic bottleneck is influenced by the susceptibility of the membrane involved in transmission. Infections across rectal mucosa encounter fewer barriers than transmission events involving vaginal mucosa, and indeed single founder virus accounts for approximately 60% of rectal [109, 111, 112], compared to 80% of vaginal transmissions [109, 111] and 40% of IDU [113]. As a consequence, the sequence diversity of HIV is relatively low during primary infection, with important implications that will be discussed further in section 1.4.9.

After HIV crosses the epithelial barrier, the cells found within these mucosal layers are the first exposed to HIV infection. These include Langerhans cells (LC), myeloid DCs, intraepithelial CD4+ T cells, and resting CD4+ T cells in the lamina propria [114, 115]. Once infected, these cells quickly disseminate the infection to the draining lymph node (LN) and establish a systemic infection [115-117]. In a simian immunodeficiency virus (SIV) model of rhesus macaques HIV RNA and DNA were detected as early as 24 hours in DCs and CD4+ T cells in the endocervical mucosa, and within the LN after 2 days [118-120], preceding the appearance of plasma viremia, which is detected around 10 days post infection [121, 122]. If the transmission occurs by IDU or blood transfusion, the primary cellular targets are most likely CD4+ T cells in the blood, LN or spleen [116].

Once the infection is systemic, the abundance of target cells and rapid replication of HIV results in widespread infection of a large range of tissues and cells. CD4+ T cells, due to the expression of CD4, CCR5 and CXCR4 (see 1.2.4.1) are the primary target of HIV infection, however many other cell types are also infected, and in a variety of anatomical 15

CHAPTER 1: LITERATURE REVIEW

locations and tissues. The targeting by HIV of lymphocytes including CD4+ T cells heavily involves lymphatic tissues in the spread and amplification of HIV infection.

Monocytes and macrophages are involved in the spread of HIV infection, as they traffic to a range of tissues including the brain. Within the brain monocytes may transmit infection to, or differentiate into a range of cell types found to be infected by HIV despite expressing low levels of CD4 (reviewed in [123]), including perivascular macrophages, parenchymal microglia, meningeal macrophages, choroid plexus macrophages and microglia and astrocytes [124-127]. While the central nervous system (CNS) is largely devoid of CD4+ T cells, the cerebrospinal fluid (CSF) of a healthy individual contains low levels of memory CD4+ T cells susceptible to HIV infection. This rapid dissemination of HIV infection throughout the human body allows HIV to quickly establish viral reservoirs which will be discussed in detail in section 1.4.

1.3.2 The immune response

The immune system responds to this rapid and massive spread of HIV infection, however while initially able to partially control viral replication, in the majority of cases the immune system unable to prevent disease progression and the development of AIDS. The immune response to HIV characteristically involves the innate component, providing a non-specific response to foreign pathogens, as well as stimulating the adaptive component, that specifically and strongly targets HIV and HIV-infected cells. HIV hijacks the immune system by primarily targeting cells integral to the immune response, allowing HIV to efficiently replicate and causing severe disruptions to the immune response that contribute to disease pathogenesis.

The innate immune response to HIV infection involves DC and natural killer (NK) cells (reviewed in [128] and [129]). Situated within the foreskin, vaginal, cervical or rectal mucosal layers, DCs including LC and plasmacytoid DCs encounter HIV very early during infection. These cells respond to the presence of HIV RNA or the binding of gp120 to CD4 molecules on their surface, and secrete type 1 interferons (IFN) and tumor necrosis factor (TNF) which are both involved in shutting down viral replication and stimulating NK cells [130-134]. NK cells exert antiviral activity through direct lysis of

CHAPTER 1: LITERATURE REVIEW

target cells or antibody-dependent-cell-mediated-cytotoxicity. DCs also play an important role by processing and presenting antigen (Ag) to stimulate cells of the adaptive immune response, and through the secretion of cytokines such as interleukin (IL)-12, IL- 15 and IL-18, the former of which induces CD4+ T cell help [129].

The adaptive immune response involves T and B lymphocytes that generate complex and highly specific cellular and humoral (antibody-mediated) responses towards invading pathogens. CD8+ T cells recognize and kill infected cells by either lysing cells harbouring HIV through the secretion of perforin and granzymes, or by engaging death receptors on the surface of infected cells [135]. They play an important role having been associated with the initial control of viral replication [136, 137], and the level of plasma viral load (pVL) at set-point and subsequent CD4+ T cell loss [138].

CD4+ T cells play a vital role in the immune response to pathogens such as HIV, assisting both the CD8+ T cell response and the generation of antibody producing B cells. CD4+ T cells are activated by interactions between T cell receptors (TCR) and Ag-MHC complexes to differentiate into T cell subtypes depending on the cytokine milieu: Th1,

Th2, Th9, Th17, regulatory T cells (Treg), and T follicular helper cells (TFH) (reviewed in [139, 140]). These subtypes mount the initial adaptive response to pathogens, and a small proportion of these cells further differentiate into memory CD4+ T cells (reviewed in [141]). As the primary target of HIV, CD4+ T cells, in particular activated CD4+ T cells, play multiple and contradictory roles during HIV infection; they act as viral factories pumping out HIV virions; they contribute importantly to the immune response (discussed further in 1.3.3); and during ART they are the major component of persistent HIV reservoirs (discussed in 1.4.1).

B cell mediated antibody responses to HIV infection are first present around 8 days post viral RNA detection in the form of immune complexes [122]. Antibodies to the individual viral proteins appear over time: Env (gp41), Gag (p24 and p55), RT (p66), Env (gp120), MA (p17) then IN (p31) specific antibodies, become detectable respectively at approximately 13, 18, 21, 28, 33 and 53-89 days after the detection of viral RNA [122, 142]. Importantly, this sequential appearance of HIV antibodies, along with tests for HIV RNA and p24 Ag, allows for the identification of primary HIV infection (PHI) and an 17

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estimation of the time of infection [142]. In a small subset of patients, broadly neutralizing antibodies (bNAb) capable of conferring protection against HIV infection are present [143, 144], and are a prime candidate for vaccination strategies.

The immune response described above somewhat controls HIV infection after 12-20 weeks of infection. Viral load peaks after approximately 20-40 days post infection, then decreases and stabilizes at what is termed the viral set-point after several months [142, 145-147] (Figure 1.7). This state of balance between viral production and the immune response may be sustained for several years [148-151], however in the vast majority of cases is eventually overcome by HIV and subsequent CD4+ T cell decline. A small proportion (<5%) of HIV-infected individuals are able to control their infection for an extended period of time (>8 years), likely through either host genetic factors or viral factors, or both (reviewed in [152]). These individuals termed long-term non-progressors (LTNP) are divided into elite controllers with a pVL <50 copies/mL, and viremic controllers (pVL<2,000 copies/mL), making up around 0.55% and 3.88% of HIV- infected individuals respectively [153]. However, as our experience with ART-treated HIV infection progresses, evidence has emerged suggesting the majority of those previously defined as LTNP eventually do progress to AIDS without therapy [151].

Figure 1.7: T cell dynamics & viral kinetics during untreated HIV infection. Reproduced from [154].

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1.3.3 CD4+ T cells in HIV infection

The CD4+ T cell response to HIV is present during acute infection and contributes importantly to the immune response, having been associated with improved viral control [155-159]. However, as the key target of HIV infection, the progressive depletion of CD4+ T cells and development of AIDS is a hallmark of chronic untreated HIV infection [160-163].

1.3.3.1 CD4+ T cell depletion

Following the establishment of infection, HIV initially expands locally, then rapidly spreads throughout the body to other lymphoid tissues (see 1.3.1). During acute infection, HIV infects and significantly depletes CD4+ T cells in a range of tissues, primarily within the gastrointestinal tract (GIT) and LNs [116, 120, 164-173]. One study of acute SIV infection measured an initial loss of around 80% of memory CD4+ T cells, with up to 60% of memory CD4+ T cells infected at the peak of infection [169]. At this stage of infection, the main cause of CD4+ T cell death appears to be cytolysis and apoptosis induced by HIV infection [169, 174-176], while the HIV-specific immune response also contributes by killing infected CD4+ T cells [177, 178]. This massive infection also induces the homing of CD4+ T cells to lymphoid tissue [179], presenting an additional component explaining the depletion of CD4+ T cells from peripheral blood.

While HIV infection of CD4+ T cells is likely the primary cause of CD4+ T cell death during acute or PHI, the relatively low frequency of HIV/SIV infected CD4+ T cells (0.01-1%) during chronic infection appears insufficient to account for the observed ongoing CD4+ T cell depletion [66, 169, 180-182]. Instead, CD4+ T cell activation and death is likely driven by the chronic activation of the immune system (causes are discussed in 1.3.5.2.2) [183-185]. In addition, HIV infection progressively diminishes the ability of the immune system to compensate for this continual death of CD4+ T cells. The thymus, responsible for the generation of naïve CD4+ T cells, becomes increasingly atrophic [186, 187] with reduced lymphopoiesis observed in HIV infection [188, 189]. The ability of LN to expand peripheral CD4+ T cells is also impaired [190, 191], the result of tissue fibrosis caused by increased Treg activity [192, 193] and the induction of collagen deposition by fibroblasts [190, 194, 195]. Thus, over time the continual death of

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CD4+ T cells overcomes the homeostatic capabilities of the immune system resulting in the progressive depletion of CD4+ T cells.

1.3.4 Antigen-specific CD4+ T cells

1.3.4.1 HIV specific CD4+ T cells

HIV-specific CD4+ (HIV-sp-CD4+) T cells are generated early during infection, and are present in the majority of patients, albeit at a low frequency [156, 157]. Although several studies have observed a rapid decline of HIV-specific responses during acute infection [196-198], HIV-sp-CD4+ T cells remain detectable during untreated chronic HIV infection (CHI) [104, 403], with the majority specific for Gag and Nef [199-202]. HIV- sp-CD4+ T cells may be more likely to be infected by HIV as they are recruited to sites of viral replication and remain in close proximity to their cognate Ag for extended periods of time [203].

HIV-sp-CD4+ T cells are also present in subjects receiving ART, however at very low frequencies (<0.1%) [198, 204, 205], and presumably decrease due to reduced exposure to HIV Ag [206, 207], and/or immune reconstitution and the increased proportion of the CD4+ T cell pool that are naïve (see 1.3.5.1). After the initiation of ART, the proliferative HIV-sp-CD4+ T cell response returns in a few patients [208-211], and the level of cytokine producing HIV-sp-CD4+ T cells transiently increases [211], and functionally switches from IFN-γ to IL-2 production [212].

1.3.4.2 CMV-specific CD4+ T cells

Cytomegalovirus (CMV) co-infection is very high (>80%) in HIV-infected individuals, particularly in MSM [213, 214], and is associated with immune activation and an increased risk of mortality and SNAEs (discussed in 1.3.5.2.3) [215, 216]. CMV-specific CD4+ (CMV-sp-CD4+) T cells are found in the circulation of individuals with untreated HIV infection, and can reach levels up to and greater than 10% of total CD4+ T cells [217]. The impact of ART on CMV-sp-CD4+ T cells is not entirely clear, however probably generates some level of reconstitution.

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CMV-sp-CD4+ T cells are believed to be somewhat resistant to HIV infection. The majority of CMV-sp-CD4+ T cells are CD57+ and produce macrophage inflammatory protein-1 (MIP-1)β, a CCR5 ligand that blocks the binding of gp120 to CCR5, and are therefore less likely to be infected by HIV [218-220]. Possibly due to this protective effect, several studies have indeed found HIV-infected subjects have a higher proportion of CD4+ T cells specific to CMV than uninfected controls [221-223].

Following the initiation of ART, several signs of an improved CMV-sp-CD4+ T cell response have been observed. Two longitudinal analyses of CMV-sp-CD4+ T cells during ART measured increases in the proliferative response to CMV after 3-4 years of ART, which was then followed by a modest but significant decrease [224, 225].

Several cross-sectional studies found lower numbers of CMV-sp-CD4+ T cells in untreated compared to ART-treated HIV infection [223, 226], indicating that CMV-sp- CD4+ T cells decrease during ART. However, a larger cross-sectional study found the % of total CD4+ T cells specific to CMV remained high during suppressive ART [224].

Hsu et al. followed a cohort of 50 HIV-infected individuals initiating ART for 96 weeks of therapy [227]. In this study, the proportion of patients with detectable CMV-sp-CD4+ T cell responses increased from 80% at baseline to 96% at the end of the study. Interestingly, the numbers of CMV-sp-CD4+ T cells increased early and remained significantly higher than baseline for the majority of the study. However, after an initial and transient increase after 4 weeks, the % of CD4+ T cells specific to CMV decreased slowly and was significantly below baseline at the end of the study.

1.3.4.3 TT-specific CD4+ T cells

Infection by Clostridium tetani, a bacterium that produces tetanus toxin, causes tetanus also known as lockjaw, an infection characterized by severe muscle spasms and fatal in approximately 10% of cases [228]. A vaccine containing formaldehyde inactivated tetanus toxin (Tetanus Toxoid; TT) was first produced in 1924 [228], introduced as part of the Diphtheria-tetanus-whole-cell pertussis vaccine in 1953 in Australia, then included in the national vaccination schedule in 1975 [229]. Australian national immunization

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reports found very high rates of tetanus antitoxin, >90% for all age groups up to 50, and greater than 70% in those >50 years old [230]. Based on the waning of antitoxin titres, TT booster immunizations are recommended after 10 years [228].

Given successful tetanus vaccination strategies, cases of tetanus are extremely low; around 0.01 per 100,000 people in the United States of America (USA) in 2009 [228] and Australia in 2007 [231]. The immune response to TT is therefore considered, and has been used as, a model for immune memory in the absence of Ag exposure [232, 233]. As such, TT-specific CD4+ (TT-sp-CD4+) T cells once generated by vaccination are maintained by the homeostatic mechanisms of the immune system.

Due to a scarcity of literature, it is currently unclear how long TT-sp-CD4+ T cells may persist for, however TT-sp-CD4+ T cell responses are found in a significant subset of individuals, including HIV-infected subjects [232, 233]. In addition, one study showed that ART recovered the CD4+ T cell proliferative response to TT, raising the proportion with detectable responses from 14-28% to 50-59% of the study population [234].

1.3.4.4 Measurement of antigen-specific CD4+ T cells

Several assays have been developed to study Ag-specific CD4+ (Ag-sp-CD4+) T cell responses, each with their own advantages and disadvantages.

The use of MHC-tetramer complexes and antibody staining can identify and live cell sort Ag-sp-CD4+ T cell populations [235, 236], however requires the laborious development of specific Human lymphocyte Ag (HLA)-restricted reagents, and is complicated by TCR affinity and avidity, among other issues [237, 238].

Proliferative responses to antigenic stimulation can be detected by measuring [3H] thymidine incorporation into DNA [239], or carboxyfluorescein diacetate succinimidyl ester (CFSE) labelling of cells [240, 241]. [3H] thymidine incorporation is labour intensive, involves working with radioactive material, and cannot delineate responses by individual cells unless the population of interest is isolated beforehand. CFSE labelling can be combined with cell surface marker staining and flow cytometry to investigate 22

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unique cell populations of interest, however may be toxic to cells and influence the expression of cell surface activation markers [242]. New dyes for example “Cell Trace Violet” have been developed, are being validated, may overcome some of these issues [243].

Ag-sp-CD4+ T cells can also be identified by the induced production of cytokines using the intracellular cytokine (ICC) assay or enzyme-linked immunospot (ELISPOT). The latter method only provides information regarding the total cell population being examined [244]. The ICC assay can be combined with flow cytometry and used as a high through-put assay analysing multiple subpopulations at the same time [245]. ‘Cytokine capture’ kits involving dual antibody conjugates that bind to cell surface molecules (often CD45) and capture the induced expression of cytokines as they are released from stimulated cells [246], allow for the isolation of cytokine positive cells by fluorescence activated cell sorting (FACS) or magnetic bead selection. The major limitation of assays based on cytokine expression, is their reliance on predicting the cytokines involved in the Ag-specific response being examined. Other limitations include the fact that given the variety of cytokine producing CD4+ T cell subsets, these assays may only identify a proportion of the total Ag-sp-CD4+ T cell response.

An assay developed by Zaunders et al. identifies Ag-sp-CD4+ T cell responses by the induced up-regulation and co-expression of two T cell activation markers, CD25 and CD134 (OX40), following a 44-48 stimulation with Ag [217]. The authors found this method to be highly sensitive and specific [217, 247], and while results correlated with the ICC assay, they were near 10-fold higher than IFN-γ producing CD4+ T cells when measuring the vaccinia specific response. To confirm bystander activation did not contribute to the observed high levels of Ag-sp-CD4+ T cells, the authors cultured whole blood (WB) from pairs of donors with respective known positive and negative responses in Transwell® permeable supports (Corning; New York, USA). On average, while the known Ag responsive cultures measured 2.1% of CD4+ T cells co-expressing CD25 and CD134, only 0.12% of CD4+ T cells were CD25+CD134+ in bystander wells. Furthermore CD4+CD25+CD134+ cells stimulated by the superantigen toxic shock syndrome-1, known to interact exclusively with the human TCR β-chain variable domain 2.1 (TCR Vβ2), were found to be dominated by TCR Vβ2-expressing cells [248], 23

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confirming that responses to the CD25/CD134 co-expression assay are generated through TCR stimulation and not bystander activation.

1.3.5 Antiretroviral therapy

ART successfully prevents viral replication by targeting various proteins and enzymes specific to the HIV life cycle. Current classes of antiretroviral (ARV) drugs include: reverse transcriptase inhibitors (RTI), both nucleoside (NRTI) and non-nucleoside (NNRTI); Pro inhibitors (PI); fusion inhibitors (FI); entry inhibitors (EI); and integrase inhibitors (INI). Combinational ART was introduced to account for the failure of mono or dual therapies that resulted from antiviral selective pressure and the development of resistance [249-255]. Current regimens usually contain a combination of ARV from different categories, usually a backbone of 2 NRTIs with either a PI, NNRTI or INI [256].

Following the initiation of ART, pVL rapidly decreases to below the limit of detection (LOD; 20-50 copies/mL plasma depending on the assay used) of conventional assays within approximately 3-4 months (Figure 1.8). Additional assays have been developed to improve sensitivity and are capable of measuring down to 0.3 copies/mL, however are not suitable for high throughput analysis and consequently not used in clinical practice [257]. These assays have allowed for a more sophisticated analysis of low-level pVL during ART, and have indeed revealed that the majority of patients are likely to continue to produce virions during ART [258-263]. While there are several phases of pVL decay after initiation of ART that corresponding with the loss of different infected cell types with different half-lives (T1/2), pVL levels appear to stabilize after approximately 2 years [264]. In many patients low-level pVL persists indefinitely during ART, with important implications for chronic treated HIV infections.

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Figure 1.8: Decay of plasma HIV RNA following the initiation of ART. Reproduced from [264].

The relatively new class of ARV, INI including raltegravir (RAL) and dolutegravir (DTG), has generated much interest given the role of integrated HIV DNA in persistent HIV reservoirs. INI prevent integration by binding to IN and chelating divalent metal ions [265-268]. Several studies have investigated the impact of RAL-containing therapy on pVL decay and HIV DNA reservoirs and will be discussed in detail in section 1.4.

1.3.5.1 Restoration of CD4+ T cells

The suppression of viral replication by ART facilitates a partial restoration of the immune system. Following the initiation of ART, CD4+ T cell counts increase as the viral burden and immune activation levels decrease. This increase in CD4+ T cells occurs in the majority of patients, and is generally described to occur in 3 phases (Figure 1.9). The 1st phase occurs rapidly, with an approximate rise of 100-200 cells/mL WB within one month [269-272], likely reflecting the redistribution of memory CD4+ T cells from lymphoid tissue into the circulation [208, 273]. The 2nd and more gradual increase results from increased thymic output [188, 274], and peripheral expansion of naïve CD4+ T cells [208, 271, 275-277]. The rise in CD4+ T cell numbers then often plateaus or slows down 3-5 years after ART initiation [278-281], but CD4+ T cells can continue to rise for up to 10 years [269].

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Figure 1.9: CD4+ T cell reconstitution during ART. Reproduced from [282].

1.3.5.2 Limitations of ART

While ART successfully inhibits viral replication, prevents disease progression and facilitates the reconstitution of the immune system, there are several important limitations associated with its use. ART appears unable to completely eradicate HIV from infected individuals (discussed in 1.4), with HIV reservoirs and low-level pVL persisting in the majority of patients receiving ART. HIV-infected individuals are therefore reliant on ART for the duration of their life, or at least until a curative intervention is developed (discussed in 1.5). The long-term use of ARV is complicated by moderate but manageable toxicity, (reviewed in [283]), the development of resistance which is also manageable (detailed in [284]), and may play a role in the development of SNAEs (discussed in 1.3.5.2.3). Furthermore, the persistence of HIV reservoirs and low-level pVL have also been linked to the chronic activation of the immune system, which may play a role in the development of SNAEs.

1.3.5.2.1 Low-level plasma viral load

The existence of low-level pVL raises several important and controversial questions about chronic treated HIV infection. What is the source of this ‘residual’ or low-level pVL? Does low-level pVL reflect the incomplete suppression of HIV replication by ART? If so, is there ongoing replication of HIV during ART, and is this involved in the maintenance of viral reservoirs (discussed in 1.4.8).

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The source of low-level pVL during suppressive ART remains unclear. Most [285-288], but not all [289, 290] studies have observed phylogenetic clustering of viral sequences derived from plasma and CD4+ T cells during ART, indicating pVL results from the intermittent activation of CD4+ T cells containing HIV proviruses. This inconsistency may result from sampling error as plasma and lymphocytes isolated from WB are unlikely to reflect the entire viral load in the human body. Alternate explanations include the ongoing replication of HIV in anatomical/pharmacological sanctuaries such as lymphoid tissue or the CNS, and the slow release of low levels of HIV into the circulation, or the persistent and stable release of virus from an unknown cell type(s) (reviewed in [291]).

ART intensification, the addition of another ARV of a different class or increasing dosage, has been studied in an attempt to suppress any residual viral replication that may occur. Again results have been inconsistent (discussed in 1.5.1), with the majority of studies observing no impact on low-level pVL [81, 292-299]. Several investigations have however observed some changes that may reflect residual viral replication. Yukl et al. observed a decrease in CA HIV gag usRNA in the ileum that was not accompanied by changes in pVL [293]. A study investigating the impact of ART intensification with the CCR5 antagonist maraviroc (MVC) observed a transient increase in pVL, decreased T cell activation and some signs of a reduced latent viral reservoirs [300], however further studies of MVC have failed to reproduce these results [301]. Other groups investigating intensification with the INI RAL have identified indirect signs of changes in viral replication including increases in 2-LTR circles and decreases in D-dimer levels [81, 302], however this was not accompanied by any changes to low-level pVL.

Other studies have tested the hypothesis that, if low-level pVL reflects ongoing rounds of replication, viral sequences from both the plasma and HIV reservoirs would evolve over time (due to errors introduced by RT; see 1.2.4.2) and potentially lead to the deveopment of drug resistance. In general, these studies have not identified signs of evolution, however, it is possible that viral evolution and ongoing replication occurs in a subset of patients (discussed in detail in 1.4.9).

An important implication of low-level pVL, is the role this may play in activating the immune system. Regardless of the source, the presence of HIV virions, viral proteins and 27

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nucleic acids may contribute to the chronic immune activation experienced by HIV- infected individuals receiving ART. The causes of chronic immune activation are discussed below (1.3.5.2.2), and the relationship between viral reservoirs, low-level pVL, and T cell activation analysed in more detail in Chapter 4.

1.3.5.2.2 Chronic immune activation

Despite the suppressive effect of ART, HIV-infected individuals with undetectable pVL display several signs of immune activation and inflammation including increased levels of the activation markers CD38 and HLA-DR on CD4 and CD8 T cells [303, 304] and higher than normal levels of serum biomarkers for immune activation and inflammation such as; Immunoglobulin (Ig)A, IgG, IFN-α, monocyte chemoattractant protein-1 (MCP- 1), soluble TNF receptor II, soluble TNF-related apoptosis-inducing ligand, soluble CD163, C-reactive protein (CRP), high-sensitivity CRP, IL-6, D-dimer, soluble CD27, B-cell activating factor, soluble CD14 (sCD14), and IL-8 [305-308].

Given the multifactorial nature of chronic immune activation and inflammation, it is likely there are several contributing causes. Persistent HIV reservoirs and low-level pVL correlate with, and may contribute to, CD4+ or CD8+ T cell activation levels [309-311]. However this relationship may also be driven by immune activation playing a role in the maintenance of HIV reservoirs [312]. The movement of microbes or microbial products across the gastro-intestinal mucosa, referred to as microbial translocation, and as measured by circulating levels of lipopolysaccharide, sCD14 or bacterial 16S ribosomal DNA [313], is also associated with elevated T cell activation and other markers of immune activation and inflammation [314-316]. Additional factors such as the general dysregulation of the immune system and co-infections are also likely to contribute towards chronic immune activation [216, 317].

This persistent state of chronic immune activation in treated HIV infection is believed to prematurely age the immune system, a phenomenon termed ‘immunosenescence’ (reviewed in [318, 319]). HIV-infected individuals with chronic but treated infection also display elevated markers of immune exhaustion, such as the upregulation of negative

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regulatory markers on CD4 and CD8 T cells [203, 320-322], and the deficiency of positive regulatory molecules such as CD28 and BB-1 [323, 324].

1.3.5.2.3 Serious non-AIDS events

The term SNAEs refers to a variety of conditions such as cardiovascular disease, renal disease, liver disease, neurocognitive disorders and non-AIDS malignancies. In general, these conditions are associated with ageing and lead to increased morbidity and mortality in chronic treated HIV infection [325, 326]. The existence of SNAEs and their clinical impact highlight the importance of developing a cure for HIV infection.

Chronic immune activation and immunosenescence are widely believed to drive the development of SNAEs [318, 327]. The size of persistent HIV reservoirs and low-level pVL, given their potential role in chronic immune activation, may also play a role in the pathogenesis of SNAEs. HIV reservoirs and chronic immune activation may also be involved in the development of HIV-associated neurodegenerative disorders (HAND), although technically an AIDS related condition, and will be discussed further Chapter 6.

The long-term toxicity of the different ARVs may also be involved in the development of SNAEs. Metabolic complications and lipodystrophy are a common adverse effect of ART, thought to result from mitochondrial toxicity and insulin resistance, and is associated with PI use [328, 329]. There are concerns over abacavir (ABC) use and the development of cardiovascular disease [330-332], subtle renal impairment has been observed following extended tenofovir (TDF) and atazanavir (ATZ), and bone disease may be linked with prolonged TDF use or continuous ART in general [333-335]. The risks of ARV associated side effects are far outweighed by the positive benefits provided by ART, but nevertheless should be considered when selecting ARV regimens in order to reduce potential side effects.

1.3.5.3 When to initiate ART

The question of when to initiate ART is currently, and has long been, the subject of intense debate [336-340]. The benefits of ART, through suppressing viral replication, preserving immune function and preventing disease progression, need to be weighed up against the 29

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lifelong dependence on ART and the potential risk of developing drug resistance and ARV related toxicities.

ART initiation is recommended for all symptomatic individuals, however a consensus has not been achieved on when to initiate ART in asymptomatic individuals. The initial strategy was to hit hard and hit early, with ART recommended for asymptomatic individuals with HIV RNA >10,000 copies/mL or a CD4+ T cell count <500 cells/µL, covering a large proportion of asymptomatic individuals [341]. The appearance of long- term ARV-related toxicities caused guidelines to shift in 2001 by the World Health Organization (WHO) to recommend postponing therapy until CD4+ T cells decline to <350 cells/µL [342]. Recent clinical trials have for the first time identified a clinical benefit for those initiating ART with a CD4+ T cell count around 350-500 cells/µL as opposed to <350 cells/µL [343-345], and WHO guidelines were again recently changed to ART initiation at a CD4+ T cell count ≤500 cells/µL [256].

Another potential determinant of when to initiate ART, is the impact the timing of ART initiation has on HIV reservoirs. The early initiation of ART limits the formation of HIV reservoirs, which may have clinical benefits (to be discussed in 1.4.6). One study identified 14 patients (around 15% of the study population) who were able to control their infection without therapy, after 3 years of ART initiated during PHI [346]. Limiting the size of HIV reservoirs may also provide clinical benefits by reducing low-level pVL and chronic T cell activation, however the links between these findings are not clearly understood (discussed further in chapter 4). Early ART also limits the genetic diversity within HIV DNA reservoirs [347, 348], and combined with restricting the size of HIV DNA reservoirs, this may be important for the introduction of curative strategies (discussed further in 1.5).

The question of when to initiate ART requires careful consideration. In some contexts with a high risk of transmission, for example in infected and pregnant women [349] or sero-discordant couples [350], ART should be initiated as soon as possible to prevent transmission. Results from the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT) sponsored Strategic Timing of ART (START) study were released in the final stages of writing this thesis. Findings of this study have important 30

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implications for recommendations on when to initiate ART and on HIV research in general and will be discussed in more detail in Chapter 7.

1.4 HIV reservoirs during ART

The presence of a latent reservoir was first documented in 1997 by Chun et al., Finzi et al., and Wong et al. who stimulated viral production from peripheral resting CD4+ T cells isolated from ART treated individuals with undetectable pVL [351-353]. The integration of viral genomic material into the host cell DNA allows HIV to exist as a provirus (see 1.2.4.3), and, through the suppression of viral expression by mechanisms which are poorly understood (discussed in 1.4.7), avoid clearance by the immune system. HIV reservoirs are widely considered the major barrier to a cure, as they persist indefinitely (discussed in 1.4.6), and rapidly give rise to viral rebound in plasma following any interruptions to ART [354].

HIV infects a wide variety of cells and tissues (see 1.3.1), consequently HIV reservoirs are diverse, and can be viewed conceptually as either cellular or anatomical. Those best described consist primarily of lymphocytes containing integrated provirus, and given these cells exist in and circulate through the tissues and vasculature of infected individuals, the two conceptualizations are not mutually exclusive.

1.4.1 CD4+ T cells

As the key target of HIV infection, CD4+ T cells, in particular memory CD4+ T cells are the largest contributor to HIV reservoirs and the most well characterized reservoir for HIV [66, 312, 352, 353, 355].

Memory CD4+ T cells comprise a heterogeneous population of cells that can be subdivided into various compartments based on their memory status and functional phenotype. Individual memory CD4+ T cell subsets differ in their contribution to HIV reservoirs (reviewed in [356] and illustrated in Figure 1.10). Central (TCM), transitional

(TTM), and effector (TEM) memory CD4+ T cells contain integrated HIV DNA at higher frequencies than the more differentiated terminally differentiated memory CD4+ T cells

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(TTD). A recently identified subset of CD4+ T cells with stem cell like properties (TSCM) has also been shown to harbour relatively high levels of HIV DNA [357, 358]. Naïve CD4+ T cells also contain HIV DNA, albeit at a lower frequency than other CD4+ T cell subsets [219, 312, 347, 357, 359-362]. The study of HIV reservoirs within these subsets has provided several important insights into the establishment and persistence of HIV reservoirs (discussed in 1.4.6 and 1.4.8).

Figure 1.10: Memory CD4+ T cell subsets & HIV reservoirs. Reproduced from [356].

Other CD4+ T cell subtypes, for example Treg and TFH may contribute to long-lived reservoirs for HIV, and will be discussed in section 1.4.4. Memory CD4+ T cells as long- lived reservoirs for HIV, and considered the major barrier to a cure for HIV, will be examined in more detail throughout this thesis in Chapters 3, 4, and 5.

1.4.2 Monocytes, macrophages & dendritic cells

Although the primary focus of HIV reservoir studies has been CD4+ T cells, other cell types, namely of myeloid lineage may be involved. Monocytes that upon stimulus are rapidly recruited to tissue sites, differentiate into macrophages and DC (reviewed in [363]). Monocytes, macrophages, and DC are infected during early/acute HIV infection, and are involved in spreading HIV infection to tissues including the brain, gut, LN, spleen, and lung (discussed in 1.3.1). Within tissues there are thought to be two populations of macrophages, those derived from circulating monocytes, and another population maintained by local proliferation (reviewed in [364]).

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Whether or not these cell types are significant contributors to long-lived viral reservoirs, in particular in the context of virus rebound, for example after cessation of ART, is still debated (reviewed in [365]), however several characteristics make them potential contributors to long-lived reservoir for HIV. Macrophages are resistant to viral cytolysis [366, 367], with various cellular restriction factors contributing to the suppression of viral replication (discussed in 1.2.4.2). This allows HIV-infected macrophages to be relatively long-lived cells that can produce low levels of virus, leading several groups to hypothesize these cells may be important viral reservoirs, and a potential source of low- level pVL during suppressive ART [98, 368-370].

However, relative to CD4+ T cells, monocytes/macrophages have received little attention as a potential barrier to a cure for HIV, most likely due to the difficulty in accessing tissue resident macrophages. Several studies have found circulating monocytes harbour HIV DNA in patients receiving ART [98, 371-373], however others have failed to detect HIV DNA in myeloid cells [348, 374]. Tissue resident macrophages may also act as a reservoir for HIV. Yukl et al. detected HIV DNA in non-CD4+ T cells from the gut [374], and further identified the source to be macrophages [375]. Macrophages in other tissues have also been found to contain HIV DNA during suppressive ART, including mucosal macrophages in the GIT [347, 375, 376], and alveolar macrophages [377, 378]. The identification of tissue resident macrophages containing HIV DNA by bronchoalveolar lavage may represent an improvement in our ability to investigate these cells as HIV reservoirs [377], however this study did not further purify macrophages from bronchoalveolar lavages that are known to also contain CD4+ T cells [379].

A caveat to studies finding HIV DNA in macrophages, is the possibility that HIV DNA signals result from the phagocytosis of HIV-infected CD4+ T cells undergoing apoptosis. This possibility may be considered unlikely in patients receiving suppressive ART, as latently infected CD4+ T cells generally do not undergo apoptosis, and during the process of phagocytosis, DNA is rapidly degraded to fragments ~0.5-2 kb within the first few hours [380]. Confirmation that macrophages harbour inducible replication competent virus is required to establish this cell type as a long-lived reservoir for HIV.

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The trafficking of monocytes to various tissues throughout the body and their capacity to secrete pro-inflammatory cytokines has implicated these cells in the pathogenesis of a number of HIV-associated complications. Macrophages are understood to play an important role in the pathogenesis of HAND (reviewed in [381, 382]), and are involved in the progression of atherosclerosis (reviewed in [383]). Importantly, the trafficking if monocytes/macrophages to tissues such as the CNS also offers a potential mechanism by which they evade the effects of ART in a privileged site (discussed further in 1.4.5).

It is similarly unclear whether DC contribute to long-term viral reservoirs. Circulating DC probably do not act as a reservoir for HIV [384], however tissue resident DC such as follicular DC (FDC) found in the LN, may act as a reservoir and will be discussed further in section 1.4.4 and Chapter 5.

1.4.3 Gastrointestinal tract

Gut-associated lymphatic tissue (GALT) of the gastrointestinal system is an important anatomical reservoir for HIV. The GALT harbours approximately 5-20% of the total body lymphocytes [385], presenting this site as an important contributor to the overall pool of latently infected CD4+ T cells during treated HIV infection [386]. In addition, biopsies sampling the gastrointestinal tract of virally suppressed individuals have revealed in some [386, 387] but not all [388] studies, that HIV DNA and RNA concentrations are higher in GALT than WB.

Several groups have suggested that GALT provides a unique site for HIV reservoir persistence during ART [389, 390]. In the gut, high frequencies of activated CD4+ T cells [391], the propensity of these cells to express CCR5 [164, 392], and persistent immune activation may support low levels of HIV replication during ART. In a study of RAL intensification in virally suppressed individuals, Yukl et al. found that although pVL did not change, CD4+ T CA HIV gag usRNA in the ileum decreased, and T cell activation levels in multiple gut sites trended towards a decrease [293]. Furthermore, the same group observed that in contrast with WB, where the majority of CA HIV DNA is in TCM cells, in the ileum and rectum most CA HIV DNA and RNA is found in TEM cells [374]. These

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studies indicate the compartmentalization of HIV reservoirs in GALT that are distinct to those observed in WB.

However, the importance of GALT as a unique reservoir for HIV is still debated. In WB, the majority of HIV DNA is in fact found in non-gut homing CD4+ T cells [355]. Furthermore, similar and minimal decay rates have been observed in both GALT and WB [312, 389, 393, 394].

1.4.4 Lymph nodes

Much like GALT, several factors potentially make LN a unique and important location for the persistence of HIV reservoirs.

A recent study found concentrations of ARV were lower in LN than WB, and correlated with continued virus production as measured by CA HIV RNA [395]. However, as this study examined LN samples collected prior to and after only 6 months of ART, it is also possible that the concentrated viral replication found within the LN diminished ARV concentrations to drive the observed link. As such, these results should be confirmed in studies of long-term virally suppressed individuals.

Germinal centres (GC) within LN may provide a sanctuary site for HIV reservoirs. In an SIV model, Fukazawa et al. found that cytotoxic virus-specific CD8+ T cells were relatively excluded from B cell follicles within the GC [396]. Further, and possibly secondary to the exclusion of CD8+ T cells from GC, GC contain unique cell types that may harbour HIV virions or provirus during ART. Both TFH cells and FDC are found within GC and may act as viral reservoirs (discussed further in Chapter 5).

While LN are known to be a major site of replication during untreated infection [397], there is a little evidence regarding HIV reservoirs in LN during suppressive ART. Chomont et al. and Stockenstrom et al. compared the frequency of latently infected CD4+ T cells from LN and WB and found no statistical difference [312, 348]. The role LN may play as a reservoir for HIV during ART deserves further investigation and will be discussed further in Chapter 5. 35

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1.4.5 Central nervous system

The CNS offers another unique anatomical site that may allow for the persistence of HIV during ART. HIV invades the brain early during untreated infection and targets perivascular macrophages, microglia and astrocytes [398, 399]. The long T1/2 of brain resident cells which can survive for months to decades [370, 400, 401], and their ability to resist the cytolytic impact of HIV infection (discussed in 1.4.2), suggests a mechanism by which HIV-infected cells could persist in the CNS. In addition, suboptimal penetration of ARV in the CNS may provide an environment permissive to HIV persistence [402- 405].

However, whether the CNS is the location of viral reservoirs capable of contributing significantly to virus recrudescence is very controversial, and definitive evidence is lacking (reviewed in [406, 407]). The inability to access and sample the CNS of otherwise healthy HIV-infected individuals, essentially prohibits the direct quantification of viral reservoirs in this anatomical site. Animal models of SIV infection have found that viral DNA persists in the brains of macaques despite the suppression of HIV RNA in the CSF and plasma [408-411]. Studies of CSF have only indirectly indicated the latent infection of the brain and CNS in HIV-infected individuals on suppressive ART. However, the phenomenon known as CSF viral escape, wherein HIV RNA remains detectable in the CNS despite undetectable pVL in the blood, suggests a distinct viral reservoir may be present in the CNS [412, 413]. Comparisons of HIV RNA in the plasma and CSF further found significant genetic compartmentalization in the CNS [414].

It is relatively well established that in untreated HIV infection, the infection of brain resident macrophages and microglia is heavily involved in the pathogenesis of HAND. It is unclear however, whether the infection of CNS resident cells and induced inflammation continues to contribute to neuropathogenesis during suppressive ART (reviewed in [381, 407, 415]). Several studies have found signs of inflammation in the brain during ART that may be linked to CNS reservoirs [412, 413, 416, 417]. Given the role of circulating macrophages in transmitting HIV infection to the CNS, and by contributing to immune activation, it has been hypothesized that the measurement of HIV reservoirs in peripheral blood monocytes/macrophages may act as a surrogate marker of the environment within the CNS. One group has in fact found a relationship between PBMC- or monocyte- 36

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associated HIV DNA levels and HAND [418-421], however this relationship has not been studied extensively in the context of long-term treated HIV infection.

1.4.6 Reservoir establishment

Although HIV reservoirs are diverse and dynamic, this thesis will primarily focus on the HIV reservoir in memory CD4+ T cells, considered the major barrier to a cure. Two pathways are hypothesized by which HIV establishes a pool of latently HIV-infected memory CD4+ T cells: 1) through the infection of activated CD4+ T cells that survive and revert to a memory phenotype; and 2) via direct infection and integration into the genome of resting memory CD4+ T cells.

Given the massive infection of activated CD4+ T cells during PHI, it appears likely the 1st pathway is the primary mechanism, and indeed HIV-sp-CD4+ T cells have been found to contain higher levels of HIV DNA than other memory CD4+ T cells [203]. Initial evidence suggested that resting CD4+ T cells were resistant to integration by HIV [70, 422-425], however, subsequent in vitro studies have shown they are permissive to HIV DNA integration, albeit at a lower efficiency than activated cells [426-428]. Furthermore, low levels of HIV DNA are regularly detected in naïve CD4+ T cells [312, 357], which by definition have not been activated, although could potentially have been infected during thymopoiesis.

Given the rapid spread of HIV during acute infection (discussed in 1.3.1), reservoirs are likely established throughout the body very early during infection. A small pool of latently infected memory CD4+ T cells can be found during acute or early HIV infection [429], and in a recent study using an SIV model, reservoirs capable of fuelling viral rebound after a period of ART were established within 3 days of intra-rectal infection, and prior to the emergence of plasma viremia [121].

Furthermore, the size of HIV DNA reservoirs, or the number of latently HIV-infected memory CD4+ T cells grows over time during untreated HIV infection [430]. The initiation of ART can to some degree effectively limit the establishment of HIV reservoirs, with the implications of this approach discussed earlier (1.3.5.3).

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1.4.7 Reservoir latency

Believed integral to the maintenance of HIV reservoirs is the control of viral latency, preventing virus production and clearance by the immune system. Several mechanisms have been proposed by which HIV maintains a latent state. The integration of HIV into long-lived lymphocytes such as memory CD4+ T cells, microglia and other macrophages provides a favourable environment for the maintenance of HIV latency. Given the diversity of HIV reservoirs, multiple cell types, anatomical locations, and integration sites, the control of latency is likely to be complex and involve a variety of mechanisms.

The integration of HIV into actively transcribed genes may counterintuitively lead to transcriptional interference and promote latency. The elongation of RNAPII and transcription through the viral promoter can physically exclude the transcriptional machinery from binding to the 5’ LTR to initiate viral transcription [431].

The establishment of a repressive chromatin environment appears to contribute to the restriction of viral transcription. Epigenetic modifications, including DNA methylation and histone deacetylation (HDAC) or methylation, alter the physical structure of chromatin and affect transcription levels, and have been linked to HIV latency in cellular models (reviewed in [356]). Manipulating levels of HDAC of DNA has emerged as the primary candidate for interventions aimed at reversing latency, with histone deacetylase inhibitors (HDACi) being tested in the clinical setting (discussed in 1.5.1).

As the transcription of HIV requires the specific recruitment of host factors including NF- κB, Sp1 and nuclear factor of activated T cells (see 1.2.4.4), the sequestering of these factors also contributes to the establishment of HIV latency [432, 433]. Similarly, the incorporation of P-TEFb into an inactive complex can restrict its availability for efficient transcriptional elongation and limit the synthesis of Tat [434, 435]. Interestingly, several studies have indicated that Tat, important for transcriptional elongation of HIV (see 1.2.4.4), also functions as a regulation mechanism that mediates viral latency [436, 437]. These studies found that Tat expression was able to control virus expression irrespective of cellular activation status, indicating an important role for Tat in maintaining latency.

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These molecules, or the viral factors they interact with, provide additional targets for interventions aimed at manipulating viral latency.

Although HIV reservoirs during ART are often referred to as latent, this characterization is not entirely true. Viral RNA, albeit at very low levels is often detected in the plasma of ART-treated individuals, and can also be found in other locations including the CSF. In addition, viral RNA appears often transcribed in cells from virally suppressed individuals as reflected by the presence of CA HIV RNA. This raises the possibility that viral rebound upon suppression of ART may not necessarily originate solely from latently infected cells, but that cells which are somewhat activated and capable of low-level virus production may spark the recrudescence of virus.

1.4.8 Reservoir maintenance

Few, many, or all of the HIV reservoirs described above persist indefinitely during suppressive ART [438]. The process by which these reservoirs are maintained is of great interest as it likely holds the key for successfully curing HIV infection. Two main theories have been proposed: 1) the long T1/2, homeostatic capacity, and Ag-driven proliferation of memory CD4+ T cells ensures reservoir maintenance with minimal to no disruption of latency [312, 439-441]; and 2) low-level viral replication and the infection of new target cells persists despite ART to continually replenish HIV reservoirs.

The contribution of long-lived memory CD4+ T cells and homeostatic proliferation is evidenced by the higher levels, and increasing proportion over time, of HIV DNA found within long-lived (TSCM and TCM) when compared with short-lived subsets (TEM) of memory CD4+ T cells in ART-treated individuals [312, 358]. Additional evidence supporting a primary role for homeostatic cell proliferation is the recently reported expansion of clonal HIV sequences in the more differentiated subsets of memory CD4+ T cells [58, 348, 442]. It is hypothesized that this results from the antigenic stimulation and proliferation of latently infected memory CD4+ T cells, however clonal expansions have been found to include defective virus, somewhat undermining the importance of this pathway [347, 348].

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Both pathways for reservoir maintenance may give rise to residual viremia, however it is expected that only ongoing rounds of replication would generate viral evolution, as HIV goes through the viral life cycle. Investigations of HIV sequences during ART have mostly failed to detect signs of viral evolution in plasma, WB, GALT, and LN [347, 348, 443-448], arguing against this pathway, and will be discussed in more detail below (1.4.9).

Several recent reports have offered evidence that HIV integrates into certain genes implicated in the development of cancer, prompting hypotheses that this targeted integration is involved in the maintenance of latently infected CD4+ T cells [449-451]. However, Cohn et al. recently performed a comprehensive analysis of HIV integration sites [58], and found that the preferential integration by HIV into cancer-related genes is no different to HIVs well known affinity for actively transcribed genes (see 1.2.4.3). Furthermore this study did not identify an association between HIV integration into cancer-related genes and persistence or clonal expansion, and, in a longitudinal analysis of three participants, the proportion of integrations into these genes in fact decreased over time.

While there is currently more evidence suggesting homeostatic proliferation is the primary driver of HIV reservoir persistence, current models are unable to definitively explain the source of low-level pVL, and hence do not rule out a role for ongoing replication. Further investigations of how and where HIV reservoirs persist despite ART may improve our understanding of these processes.

1.4.9 Reservoir evolution

Evolutionary studies of HIV plasma viral sequences and DNA reservoirs offer insight into their establishment and maintenance during suppressive ART. However, the interpretation of these studies must be carefully considered, as the presence of replication deficient provirus [347, 452], and difficulty in sampling all relevant reservoirs [453], represent significant limitations.

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Analyses of pVL and HIV reservoirs during ART have in general revealed viral sequences to be remarkably stable over time. Earlier studies performed shortly after the introduction of cART provided mixed results. Signs of evolution were observed in viral sequences from the plasma of some but not all patients, however, this evolution may have resulted from the incomplete suppression of pVL [446, 454, 455]. Subsequent studies have since observed minimal to no viral evolution in plasma [287, 443, 448], WB CD4+ T cells [347, 447], and GALT CD4+ T cells [347, 456].

The majority of transmission events involve a single founder virus, resulting in a genetic bottleneck (see 1.3.1). In concordance with the accumulation of HIV DNA reservoirs over time during untreated infection (see 1.4.6), the diversity observed within HIV reservoirs appears to result from the successive archiving of circulating plasma viruses [457]. Indeed, viral diversity in HIV DNA reservoirs is greater in those initiating ART during CHI compared to PHI [347, 348].

1.4.10 Measuring HIV reservoirs

The assessment of HIV reservoirs during ART is significantly complicated by their diverse nature. Secondary to the ease of sampling, the majority of studies analysing HIV reservoirs have focused on WB. However, given the potential compartmentalization of HIV reservoirs within a range of sites and tissues (see 1.4), WB may inaccurately reflect total body reservoir load. The circulatory nature of long-lived memory CD4+ T cells [458] could explain why they are often measured as the greatest contributors to HIV DNA reservoirs in WB, whereas activated T cells, TFH, and macrophages that also contribute to HIV reservoirs are mostly sequestered in tissues where the bulk of lymphocytes in the body reside, are less accessible to measure, and therefore less likely to be considered as a source of the reservoir [385].

As such the analysis of HIV reservoirs requires sampling of a variety of tissues. While in animal models, the majority of tissues can be accessed, obtaining ex vivo samples from humans is far more difficult. GALT (see 1.4.3) and LN (see 1.4.4) have been sampled by excisional biopsies for the study of HIV reservoirs, however the invasiveness of this technique limits its application. Other techniques are available for sampling LN including

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surgical dissection and fine needle biopsy (FNB) however have not been systematically investigated for the measurement of HIV reservoirs. Sampling the CNS in otherwise healthy HIV-infected individuals on ART, is highly difficult and usually relies on indirect measurement of the CSF by lumbar puncture.

A variety of assays are available for detecting and quantifying HIV reservoirs, however, our understanding of what they reflect and the importance of these different methods is not entirely clear. The low frequency of cells containing HIV in patients receiving ART means highly sensitive assays are required for their analysis.

1.4.10.1 Replication competent HIV

Latent HIV reservoirs were first identified using what is referred to as the quantitative viral outgrowth assay (QVOA) [351-353]. Resting CD4+ T cells isolated from infected donors are stimulated ex vivo to produce virus, which is amplified by the addition of allogeneic irradiated peripheral blood mononuclear cells (PBMC) or CD4+ T cells from an HIV negative donor, and the presence of virus determined by the detection of the HIV protein p24. A limiting dilution series and Poisson statistics are used to calculate the minimum frequency of latently infected cells, and the assay readout expressed as infectious units per million (IUPM) resting CD4+ T cells. Using this technique, the majority of patients on long-term ART harbour latently infected resting CD4+ T cells at the very low frequency of 0.1-10 IUPM [453].

The value of the QVOA is that it only detects cells containing replication competent virus. However, recent studies have indicated the QVOA likely underestimates, representing only 10-30% of, the replication competent HIV reservoir [453, 459]. Other disadvantages are that it is expensive, labour intensive, requires large volumes of blood (120-180mL), and is difficult to standardize between laboratories. The Siliciano group responsible for originally designing the assay have recently updated the QVOA, improving the sensitivity and cost effectiveness [460], however, it remains more time consuming and costly than PCR based methods discussed below.

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Another group have modified the QVOA by performing the viral outgrowth step in a murine model, naming this version the murine viral outgrowth assay (MVOA) [461]. The authors, and another group [462], have each identified one patient who, while undetectable using the original QVOA, was positive by the MVOA. While the MVOA may be more sensitive than QVOA, it only provides a qualitative result, and its sensitivity is yet to be compared with other PCR based methods, which are far more sensitive than the QVOA.

1.4.10.2 PCR measurement of cell-associated HIV nucleic acids

Highly sensitive real time real-time quantitative PCR (qPCR) assays are commonly used to measure HIV DNA and RNA in nucleic acids extracted from cells. While PCR based methods cannot easily distinguish between HIV RNA and DNA, this can be done by the selective isolation of DNA or RNA.

With HIV DNA levels in lymphocytes from individuals on suppressive ART ranging between 1-1,000 copies per million cells [453], PCR assays must not only be highly sensitive, but accurate at low levels to detect modest differences or changes to HIV reservoirs. Digital PCR, which has been around since 1992 [463], following advances in technology is now presenting as a method that may improve sensitivity and accuracy at low copy numbers [464]. Importantly, this technique also allows for the absolute quantification of target nucleic acids without the need for standards [464, 465], and is being validated for use in HIV DNA reservoir studies [466, 467].

1.4.10.2.1 DNA

Real-time qPCR assays for HIV DNA, capable of discerning unique forms of HIV DNA (discussed in 1.2.5), have been developed to identify the total amount of HIV DNA, or specifically either integrated [468, 469] or 2-LTR circular [86] forms of HIV DNA. These assays target specific areas of HIV or genomic DNA to discern the various forms of HIV DNA (detailed in section 2.5 and illustrated in Figure 2.11).

Studies have utilized these specific assays to significantly progress our understanding of HIV latency and viral reservoirs (discussed in 1.4). While these assays are more sensitive 43

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than the QVOA [453], they may overestimate the size of the relevant reservoir by detecting both replication competent and deficient provirus [347, 452].

Another complication of PCR based measures for HIV reservoirs is their dependency on high sequence homology between assay primers/probes and patient HIV strains, which is undermined by the high levels of genetic variability of HIV strains (reviewed in [28]). This issue can be overcome by individually sequencing patient HIV DNA sequences and redesigning specific primers and probe sets, however, this process is time consuming, costly and difficult to standardize. Several groups have somewhat overcome this issue by designing assays targeting highly conserved regions of the HIV genome [470-472].

1.4.10.2.2 RNA

PCR based methods have long been used to quantify CA HIV RNA in patients receiving suppressive ART [473-484], and in recent years are being increasingly used as a marker for HIV reservoir persistence and activity (reviewed in [15]). In a fashion similar to HIV DNA qPCR, RT-qPCR assays for HIV RNA can differentiate between different forms of CA HIV RNA, including us and ms transcripts (see Figure 1.5 for an illustration of HIV RNA species during the viral life cycle).

The quantification of CA HIV usRNA may be a more sensitive measure of changes to HIV reservoirs than pVL or HIV DNA. In one study under conditions of reduced ARV suppression, CA HIV gag usRNA increased in the absence of any changes to pVL [485]. However the number of CA HIV usRNA transcripts does not always correlate with virion production [486]. In light of studies assessing the ability of HDACi to induce viral production from latent reservoirs (discussed in 1.5.1), one study elegantly showed that the assays measuring CA HIV usRNA do not differentiate between bona fide HIV transcription and the read-through transcription of genomic RNA [487], questioning their use as a marker of HIV transcription. Therefore, the measurement of CA HIV usRNA while a potentially advantageous technique, needs to be further investigated.

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The requirement of splicing events to generate msRNA suggests that this form of CA HIV RNA reflects virally initiated transcription, leading several groups to focus on ms forms of CA HIV RNA. Procopio et al. recently designed an assay combining the traditional T cell activation and limiting dilution aspects of the QVOA with the detection of HIV msRNA by RT-qPCR [488, 489]. They named this assay tat/rev Induced Limiting Dilution Assay (TILDA), and found that it correlated reasonably well with the QVOA and PCR based assays for HIV DNA. Finally, they found this assay quantified HIV reservoirs at a value larger than QVOA, which may underestimate (1.4.10.1), and below PCR based DNA assays that may overestimate (1.4.10.2.1) the size of relevant HIV reservoirs. Further research into this assay is required to confirm whether or not the measurement of msRNA truly reflects replication competent virus, and to assess the impact of genetic variability within the region being amplified, on the reproducibility of this assay across different HIV clades and laboratories.

1.5 The search for a cure

A cure for HIV infection is a major goal of research, and is focused on the manipulation of latent reservoirs. Currently, two approaches are being pursued: (1) a sterilizing cure, whereby HIV reservoirs are completely eradicated, curing individuals of HIV infection; and (2) a functional cure, in which viral transcription from latent reservoirs is suppressed, allowing HIV-infected individuals to cease ART yet remain free of disease. Both approaches have seen encouraging progress in recent years, however still face significant hurdles and involve important ethical and logistical considerations.

1.5.1 Eradicating HIV reservoirs

1.5.1.1 ART intensification

Following on from the success of ART in controlling HIV infections, there has been much interest in the use of ART intensification to reduce or clear HIV reservoirs. If low-level pVL reflects ongoing production of HIV and this is involved in the maintenance of latent reservoirs during ART (discussed in 1.3.5.2.1), or, if HIV reservoirs exist in sites with limited ART penetrance, the intensification of ART may be able to reduce HIV replication and clear HIV reservoirs. Studies of ART intensification have met with some, but limited success. 45

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As discussed in section 1.3.5.2.1 the majority of studies investigating ART intensification have had little impact on low-level pVL, however several studies have indeed observed some changes in other measurements that may reflect decreases in low-level viral replication. Furthermore while there is some evidence that ART intensification can reduce the size of viral reservoirs, again the majority of studies indicate that ART intensification cannot clear HIV reservoirs. One study did observe an increased rate of HIV reservoir decay following ART intensification, however this occurred in only 3 of 5 patients, and the 2 patients with no change had the most virologically controlled infection [490]. A study investigating the impact of ART intensification with the MVC observed a decrease in the IUPM levels [300], however further studies of MVC have failed to reproduce these results [301].

1.5.1.2 Bone marrow transplantation

Spurred on by the only current case of HIV infection being cured, there is significant interest in bone marrow transplantation (BMT) and the clearance of HIV reservoirs. The one cured individual known as the ‘Berlin patient’ was receiving suppressive ART when he underwent two allogeneic hematopoietic stem cell transplants as treatment for acute myeloid leukemia [491]. The donor for the second transplant was homozygous for the CCR5 Δ32 mutation, in effect replacing the recipient’s immune system with cells resistant to HIV infection (see 1.2.4.1). Following BMT the ‘Berlin patient’ also developed graft versus host disease [491]. The ‘Berlin patient’ has remained off ART with no signs of HIV since 2008 [491]. Unfortunately, follow-up studies of BMT using donors homozygous for the CCD5 Δ32 have failed to reproduce this finding, with the majority of patients dying within one year of transplantation [492], and one case of viral rebound [493]. Two cases of BMT with wild-type CCR5 termed the ‘Boston BMT patients’, again raised hopes of a cure with extended periods of ART-free virologic remission [494], however these too were followed unfortunately by viral rebound 12 and 32 weeks of virologic remission [495]. The use of BMT to cure HIV is faced by several significant challenges: 1) the apparent dependency on CCR5 Δ32 homozygous donors and the associated difficulty of identifying HLA matched donors; 2) the morbidity and mortality associated with BMT; 3) a lack of understanding of how this works; 4) the inability to scale this type of curative practice to the wider HIV-infected population; and 5) a lack of any assay capable of predicting viral rebound. Nevertheless, the study of HIV-infected 46

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patients undergoing BMT may provide important insight into HIV reservoir persistence and inform the development of curative strategies.

The success of the ‘Berlin patient’ has also spurred on studies of gene editing of CCR5 in autologous CD4+ T cells to generate cells resistant to HIV infection [496]. This technique also faces significant challenges: 1) the difficulty of achieving global CCR5 gene editing and infusion back into recipients; 2) the likely need for chemotherapy to condition recipients of autologous infusions and the associated morbidity; and 3) the complex nature of HIV viral reservoirs preventing a cure.

1.5.1.3 Shock/kick & kill strategies

A large proportion of studies exploring possible approaches to a cure follow what is referred to as the ‘shock and kill’ or ‘kick and kill’ approach. The aim of this approach is to ‘shock’ or ‘kick’ latently infected cells into producing virus, allowing for killing or clearance of these cells by the immune system or other adjunctive therapies. This approach faces several significant challenges, and is currently shifting towards combining multiple ‘shock’ interventions in collaboration with multiple ‘kill’ strategies.

Various latency reversal agents (LRA) that target the cellular and viral mechanisms that control viral latency (discussed in 1.4.7) are being investigated. The most well characterized category/class of LRA includes HDACi. These agents attempt to stimulate HIV transcription through modulating the epigenetic mechanisms underpinning latency by preventing the acetylation of histones which is associated to condensation and transcriptional silencing of chromatin [497]. Several HDACi have been tested clinically with mixed results. Vorinostat, also known as suberanilohydroxamic acid, showed some clinical evidence of latent HIV reactivation, however there appeared to be a blunting of response upon repeat administration [498-500]. Other HDACi, Romidepsin and Panobinostat appear more potent than Vorinostat [501, 502], with the latter recently showing promise by inducing HIV transcription, pVL and a transient decrease in total HIV DNA [503, 504]. There are, however, several issues associated with the use of HDACi as an LRA. The impact of HDACi on DNA is non-specific, and one study observed a prolonged and general increase in gene expression for up to 84 days post

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administration of Vorinostat [500]. Furthermore, HDACi alone do not appear capable of the ‘kill’ step, with no sustained decreases in the size of latent HIV reservoirs, and may in fact actively suppress the ability of cytotoxic T cells to kill HIV-infected cells [505].

A variety of other LRA are being tested including DNA methylation inhibitors, bromodomain inhibitors, protein kinase C activators, and immune checkpoint blockers. In light of this influx, several groups have developed models to test prospective agents prior to the clinical setting. Through this work several important observations have been made that may explain some of the above results, and further highlight the challenges faced by this approach. In these models, while treatment with LRA induced viral transcription in some circumstances, this increase in viral transcripts generally did not translate into the production of viral proteins and release of virions [487, 506]. Further investigation using these models indicated there may be a ‘post-transcriptional block’ to reactivating HIV production [507], and, perhaps more significantly, one study found that the measured increase in CA HIV gag usRNA resulted from a generalized increase in gene expression and read-through transcription of viral RNA, rather than bona fide transcription of HIV RNA [487].

The second step of this approach, to ‘kill’ the latently infected cells ‘shocked’ into producing virus, has received less attention, but is now seen as an important target of HIV cure research. It was initially believed that once latently infected cells were shocked into producing HIV, concurrent ART and the immune system would clear these cells. However, as discussed above, although several studies have shown increases in CA HIV RNA and pVL in response to LRA, none have observed sustained decreases in the number of latently infected cells.

Again a variety of approaches are being investigated. One strategy involves the introduction of autologous immune effector cells. In an ex vivo model, cytotoxic CD8+ T cells stimulated with Gag peptides were capable of killing CD4+ T cells carrying latent virus following reactivation with Vorinostat [508], but this has only been explored in vitro. Another proposed immune effector cell type are cytokine-stimulated NK cells that following stimulation may act to kill reactivated cells producing virus [509]. An advantage of these approaches could be the ability of immune effector cells to gain access 48

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to the majority of sites contributing to HIV reservoirs, however the potential exclusion of cytotoxic CD8+ T cells may prevent this in LN GC [396]. As discussed earlier (1.4.6 and 1.4.9), the prompt initiation of ART limits the size and viral diversity of HIV DNA reservoirs in CD4+ T cells. Recent evidence points to this as an important consideration for the use of cytotoxic CD8+ T cells, suggesting that unless ART was initiated within the first 3 months, the vast majority of latent viruses carry cytotoxic T lymphocyte escape mutations [510].

Other agents being investigated include: bNAb specific to HIV, that have been studied ex vivo [511], in bone-marrow-liver-thymus humanized mice [512], and in a simian HIV model [513] (reviewed in [514]); bi-specific antibodies that theoretically may bind to and activate CD4+ T cells while also recruiting cytotoxic CD8+ T cells [509]; and radiolabelled antibodies to viral proteins [515-517]. An important caveat to all of these approaches is that they require expression of viral proteins on reactivated latently infected cells, meaning they will only be successful in conjunction with an effective LRA that induces virion production or the expression of viral proteins.

In response to the mixed results of ‘shock and kill’ strategies, current approaches are shifting towards a combination of therapies, including one or more LRA and killing agent. In vitro studies indicate combining LRA may indeed improve the reactivation of virus from latent reservoirs and should be investigated clinically [518]. Using a humanized mouse model, Halper-Stromberg et al. showed that combining multiple LRA with bNAb may impact on latent reservoir size and increased the time to viral rebound [519]. Several trials are currently underway combining LRA with kill agents such as therapeutic vaccination or bNAb (discussed in [520]).

1.5.2 Inducing a functional cure

The second aim of research into a cure for HIV infection is to generate a functional cure allowing for extended periods of ART-free HIV remission. This approach may be in fact more feasible than a sterilizing cure with several examples already described. Again, however, the generation of a functional cure faces significant challenges.

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As discussed earlier (1.3.5.3), the initiation of ART during early HIV infection may allow for ART-free periods in a subset of patients. The Viro-Immunological Sustained CONtrol after Treatment Interruption (VISCONTI) study followed 14 post-treatment controllers (PTC), representing ~15% of the entire study population, who, after approximately 3 years of ART initiated during PHI, had controlled their viremia for a median of 89 months at the time of publication [346]. It was observed that these patients had relatively small HIV reservoirs, however, no immune correlates of protection have been identified. Another study, the Short pulse ART treatment at seroconversion (SPARTAC) trial has also linked viral reservoir size to PTC. In this study, the amount of total HIV DNA at the time of analytic treatment interruption (ATI) was predictive of the length of ART-free remission prior to viral rebound [521]. Together, these studies indicate the size of the viral reservoir may predict the potential for PTC, and places early ART as a potential strategy for generating a functional cure. However, caution must be taken when following this logic. In several recent cases, including the ‘Boston BMT patients’ and a perinatally infected infant who initiated ART very early following infection (referred to as the ‘Mississippi baby’), extended periods of ART-free virologic remission were followed by viral rebound [495, 522, 523], questioning the ability of measuring HIV DNA to predict PTC.

Other approaches to a functional cure act to prevent HIV transcription by influencing the molecular pathways controlling HIV latency. Examples include: short inhibitory RNA [524], or other molecules that prevent HIV transcription such as p-TEFb (CDK9/Cyclin T1) [525, 526]; inhibitors that block PIM-1, a “gatekeeper” kinase thought needed to reactivate HIV from latency [527]; and heat-shock protein 90 that potentially controls HIV reactivation from latency [528]. Another approach at the proof of concept stage is the use of genome editing technologies that cut out or inactivate/suppress latent provirus [529, 530]. Many of these approaches would benefit from improving our understanding of the diverse HIV reservoirs that are important to the persistence of HIV, and of the mechanisms controlling HIV latency.

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1.5.3 Analytic treatment interruptions

The testing of curative strategies for HIV will ultimately require interruptions to treatment. Analytic treatment interruptions (ATI) are therefore currently being reassessed in the context of testing curative strategies. Given the success of ART in controlling HIV infection, it is important to consider the potential harm ATI may introduce into a generally healthy population.

With careful planning and close monitoring ATI can pose minimal health risks to study participants. Patients with well controlled pVL in the SPARTAC trial underwent ATI that were not linked with any adverse, including cardiovascular, events [521]. The current consensus is that two strategies should be taken, either a monitored ART pause, or a structured 16-week interruption [509], both nevertheless require close monitoring and measurement of markers reflecting HIV reservoirs and replication.

Another important consideration for the design and use of ATI is the ability of current assays for predicting undesired (viral rebound) and desired outcomes (a sterilizing or functional cure). While the measurement of HIV DNA may have predictive role in the time to viral rebound, there are no current assays that appear capable of predicting a cure [531, 532]. It is therefore important to assess the assays currently available in the context of a cure, and to develop new assays to detect and quantify all relevant HIV reservoirs.

1.6 Conclusions and aims of this thesis

Due to the presence of viral reservoirs, the eradication of HIV infection is currently not possible. Consequently, lifelong ART is required. Given the continued transmission of HIV and efforts to scale up ART distribution, the numbers of HIV-infected people receiving ART will continue to grow, posing significant global human suffering and financial burden.

While there has been some progress regarding how and where HIV reservoirs persist during ART, there are still several significant challenges ahead for the development of strategies to combat the HIV/AIDS pandemic. A lack of understanding of the diverse cell types and anatomical locations contributing to persistent HIV reservoirs has impeded 51

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progress with strategies to clear HIV infection. Further studies of viral reservoirs and an improved sampling of the various cell types and anatomical locations will assist in advancing research into a cure for HIV infection.

The chapters comprising this thesis focus around the measurement of HIV reservoirs in patients receiving long-term suppressive ART. The general aim is to develop our understanding of HIV reservoirs: how and where they persist during ART; how they are measured and what those measurements mean; and their relationship with low-level pVL, chronic T-cell activation, and the development of SNAEs, or other clinical complications, namely HAND.

Chapter 3 aimed to advance the understanding of how HIV DNA reservoirs persist during ART. Using the CD25/CD134 co-expression assay, Ag-sp-CD4+ T cell subsets with different Ag exposure patterns were isolated to compare and contrast their contribution to the HIV DNA reservoir in circulating memory CD4+ T cells. By selecting populations of Ag-sp-CD4+ T cell populations likely to be chronically activated by persistent co- infections (CMV; see 1.3.4.2), or populations maintained by homeostatic mechanisms due to the absence of Ag (TT; see 1.3.4.3), we hypothesized these cellular subsets would act as models for the two potential pathways by which HIV reservoirs are maintained (see 1.4.8).

The aims of Chapter 4 were also to investigate how HIV DNA reservoirs in CD4+ T cells persist during ART, and further looked into some of the potential implications of persistent HIV reservoirs. This study measured CD4+ T CA HIV DNA reservoirs, low- level pVL and T cell activation markers in two cohorts who initiated therapy during either PHI or CHI, and continued therapy for three years. This study allowed us to measure the decay of HIV DNA reservoirs in peripheral CD4+ T cells, assess the importance of initiating therapy during PHI, and analyse the relationship between HIV DNA reservoirs, pVL and T cell activation.

The objectives of Chapter 5 were to; firstly, investigate FNB as a technique for sampling LN and measuring HIV reservoirs, and secondly to analyse the importance of LN as a site for HIV reservoir persistence. The purpose of these experiments was to improve our 52

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ability to measure the diverse viral reservoirs for HIV during long-term ART, and to develop our understanding of LN as a reservoir for HIV during ART.

Chapter 6 shifted the focus towards the role persistent HIV reservoirs may have in the development of HAND. In a large cohort of patients with long-term treated HIV infection, we searched for a relationship between the size of circulating cellular HIV DNA reservoirs and a range of neurological tests. Through this study we aimed to extend our understanding of HAND in the setting of long-term suppressive ART, and to assess the use of measuring HIV DNA in PBMC as a surrogate marker of CNS damage, as direct measurement of reservoirs in the CNS is largely infeasible.

2 CHAPTER 2: MATERIALS &

METHODS

CHAPTER 2: MATERIALS & METHODS

2.1 Buffers & solutions

The following is a list of buffers and solutions used in the experiments of this thesis. Reagents with no supplier provided were made in house.

Back Extraction Buffer (BEB)  4M Guanidine Thiocyanate (Sigma-Aldrich; Missouri, USA)  50mM Sodium Citrate (Sigma-Aldrich)  1M Tris (free base) (Sigma-Aldrich)

D10 Media  Dulbecco’s Modified Eagle Medium (DMEM) + glutamax (Life Technologies; California, USA) supplemented with 10% Human AB (hAB) serum (Lonza; Basel, Switzerland)

Direct Lysis Buffer (DLB)  10mM Tris HCl (pH 8)  0.1% Polyoxyethylene (10) Lauryl Ether (Sigma-Aldrich)

DNA Binding Buffer  4M guanidine-HCl (Sigma-Aldrich)  0.75M Potassium acetate, pH 4.6

DNA Wash Buffer  40mM Tris (Sigma-Aldrich)  8mM ethylenediaminetetraacetic acid (EDTA)  0.16M NaCl (pH 7.5)  60% ethanol (Sigma-Aldrich)

I5/I10 Media  Iscove’s Modified Dulbecco’s Medium (IMDM) + glutamax (Life Technologies) supplemented with 5% (I5) or 10% (I10) hAB serum (Lonza) 56

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Luria Broth (LB) Agar  1% Bacto Tryptone  0.5% Yeast extract  0.5% Sodium Chloride  0.08% Sodium hydroxide  1.5% Agar

MACS Buffer  Distilled phosphate buffered saline (DPBS; Life Technologies)  2mM EDTA  1% fetal calf serum (FCS: Bovogen Biologicals; Victoria, Australia)

Methyl Violet Stain  3% acetic acid and 1% Methyl Violet

PBA Buffer  DPBS (Life Technologies) supplemented with 0.1% Bovine Serum Albumin (Sigma-Aldrich)

R10 Media  Roswell Park Memorial Institute medium (RPMI; Life Technologies) containing 10% FCS (Bovogen Biologicals)

Cryopreservation Media  FCS (Bovogen Biologicals) containing 10% dimethyl sulphoxide (DMSO; Sigma-Aldrich)

Tris-Acetate-EDTA (TAE)  24.2% Tris  5.71% Acetic Acid  5% EDTA 57

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2.2 Sample donors & collection

Donors participating in the various studies are described within the individual chapters. All donors provided written and informed consent prior to the collection of samples. WB was collected from donors by venepuncture into vacutainers containing an anti-coagulant specified in each individual chapter. LN samples were collected from inguinal LN by fine needle biopsy FNB, described in detail in the Chapter 5.

2.3 Sample processing

The methods in this section describe how WB or LN tissue samples were processed to isolate the cell populations investigated in the experiments of this thesis. Unless otherwise specified, all centrifugation steps were performed on an Allegra® X-15R (Beckman Coulter, California, USA) or Heraeus Multifuge 3 S-R (Thermo-Fischer Scientific; Massachusetts, USA) bench-top centrifuges.

2.3.1 Collection of plasma

WB was centrifuged at 400 gravitational force (x g) for 15 minutes at rt with no brake. Plasma was collected and centrifuged at 1,350 x g for a further 15 minutes at room temperature (rt). Clarified plasma was stored at -80°C.

2.3.2 Isolation of peripheral blood mononuclear cells

PBMC were isolated from WB by Ficoll-Paque separation [533]. When required, plasma was collected as described above, and 6-15mL buffy coat layer was mixed with RPMI (Life Technologies) to a final volume of 35-40mL, then underlaid with 10mL Ficoll- Paque (Healthcare Biosciences; Little Chalfont, UK). For samples where plasma was not collected, WB was mixed at a 1:1 ratio with distilled phosphate buffered saline (DPBS) or RPMI (Life Technologies), and 25-35mL of the resulting mixture was overlaid onto 12.5mL Ficoll-Paque (Healthcare Biosciences). The tube was centrifuged at 400 x g for 23 minutes at rt with no brake. The layer containing PBMC was collected and transferred into a new tube, and RPMI (Life Technologies) was added to a volume of 45mL. The tube was centrifuged at 400 x g for 15 minutes at rt, the supernatant decanted, and the cell pellet re-suspended in 10mL R10 media. The concentration of the cell suspension was 58

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measured by methyl-violet staining and haemocytometer count (BOECO; Hamburg, Germany; 2.3.5.1).

2.3.3 Cryopreservation of PBMC

When required, PBMC isolated by Ficoll-Paque separation were preserved for later use by cryostorage in vapour-phase liquid nitrogen (LN2) tanks. Following isolation, PBMC were pelleted by centrifugation at 250 x g for 10 minutes at rt. Cell pellets were gently re-suspended in cryopreservation media (pre-cooled to 4°C) at a concentration of 5- 10x106 cells per mL. The solution was dispensed in 1 mL aliquots into NUNC cryotubesTM (Thermo-Fischer Scientific), placed in a Mr. FrostyTM container (Thermo- Fischer Scientific) or CoolCellTM (Biocision; California, USA), then immediately transferred to a -80°C freezer, or directly placed in a controlled rate freezer. Samples were stored for 24 hours to allow for gentle cooling (-1°C per minute), then transferred to a vapour-phase LN2 tank for long-term cryostorage.

2.3.4 Recovery of peripheral blood mononuclear cells from cryostorage

PBMC samples were collected from cryostorage in an esky containing dry ice then thawed quickly in a 37°C water-bath. 12mL pre-warmed (37°C) media (I5 media for the stimulation of Ag-sp-CD4+ T cells, or R10 media for all other samples) was added to wash off dimethyl sulphoxide (DMSO; Sigma-Aldrich). Cells were pelleted by centrifugation at 200 x g for 7 minutes at rt. The wash step with pre-warmed media was repeated once, and the cell suspension assessed for cell viability and concentration by trypan blue staining and a haemocytometer count (2.3.5.1). Cells were pelleted once more by centrifugation at 200 x g for 7 minutes at rt, and recovered PBMC were re-suspended in the required media.

2.3.5 Assessment of cell viability & concentration

2.3.5.1 Haemocytometer

The concentration and viability of cell suspensions was determined using a Neubauer haemocytometer (BOECO). 20-50µL of cell suspension was diluted in 150-180µL of 59

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either trypan blue (Sigma-Aldrich) or methyl violet stain. 20µL of the resulting mixture was applied to a haemocytometer and the number of cells in each quadrant counted. The average stained and unstained cell count per quadrant was use to estimate cell concentration and viability.

2.3.5.2 BD TrucountTM tubes

The concentration of cell suspensions was determined using BD TrucountTM tubes (BD Biosciences; New Jersey, USA). 50µL of cell suspension and antibody (when required) were added to a TrucountTM tube and samples incubated at rt for 10 minutes. 500µL 0.5% paraformaldehyde (PFA: Proscitech; Queensland, Australia) was then added to the tube. Samples were acquired on an LSR II flow cytometer (BD Biosciences) and analysed using FlowJo (Tree Star; Ashland, OR, USA). TrucountTM tubes contain a predefined number of fluorescent beads, and when mixed with a known volume of sample then analysed by flow cytometry, can be used to calculate the concentration of cell suspensions [534]. When combined with antibody staining of cell surface molecules this technique can be used to determine sample purity and the concentration of particular cell populations.

2.3.6 Isolation of CD4+ T cells from peripheral blood mononuclear cells

2.3.6.1 Negative bead selection

In the experiments required for Chapter 4, CD4+ T cells were isolated from PBMC using the Human CD4+ T Cell Isolation kit II for magnetic bead based negative selection (Miltenyi Biotech; Cologne, Germany). Cells were pelleted by centrifugation at 200 x g for 10 minutes at rt. The supernatant was decanted and the cell pellet re-suspended in 80µL 4°C MACS Buffer. Cells were incubated with 20µL antibody cocktail containing biotin-conjugated monoclonal antibodies (mAb) against CD8, CD14, CD16, CD19, CD36, CD56, CD123, TCRγ/δ and Glycophorin A, for 10 minutes at 4°C. 60µL of MACS Buffer and 40µL of Anti-Biotin MicroBeads was then added and samples incubated for a further 15 minutes at 4°C. Cells were then washed by addition of 4mL MACS buffer and centrifugation at 200 x g for 10 minutes at rt. The supernatant was aspirated by pipetting, and the cells re-suspended in 300µL of MACS Buffer.

CHAPTER 2: MATERIALS & METHODS

The LD column was inserted into the magnetic MidiMACS separator and washed once by applying 2mL MACS Buffer. The cell suspension was applied to the column followed by 1.8mL MACS Buffer, and the effluent containing CD4+ cells collected. The column was removed from the MidiMACS Separator and 1.8mL of MACS Buffer forced through the column using a plunger to collect the magnetically labelled CD4- cells. Cell suspensions were assessed for cell concentration and purity by antibody staining [CD4 phycoerythrin (PE) and CD45 Peridinin-chlorophyll-protein (PerCP)] and TrucountTM analysis (2.3.5.2). Tubes containing CD4+ and CD4- cells were centrifuged at 16,060 x g for 5 minutes to pellet cells, the supernatant carefully aspirated, and cells stored as dried cell pellets (DCP) at -80°C.

2.3.6.2 Fluorescence activated cell sorting

In the experiments required for Chapter 5, CD4+ T cells were isolated from LN FNB or PBMC samples by fluorescence activated cell sorting (FACS). Briefly, PBMC or FNB cell suspensions were incubated with mAb, CD3 pacific blue (PB), CD4 PE and CD8 allophycocyanin-cyanin dye 7 tandem conjugate (APC-Cy7) (all BD Biosciences) for 15 minutes at rt. Excess antibody was washed off by addition of 5mL R10 media and centrifugation at 400 x g for 7 minutes at rt. Stained cells were re-suspended in 1mL R10 media for purification by FACS on a FACSARIATM II cell sorter (BD Biosciences).

CHAPTER 2: MATERIALS & METHODS

Figure 2.1: Gating strategy for the purification of CD4+ T cells. After gating on lymphocytes (SSC-A/FSC-A profile), and excluding doublets (FSC-A/FSC-W profile) CD4+ T cells were identified as CD3+CD4+CD8- events.

2.3.7 Isolation of CD4+ T cells by fluorescence activated cell sorting.

In the experiments required for Chapter 3, Ag-sp-CD4+ T cell subsets were isolated from WB or PBMC samples using the CD25/CD134 co-expression assay [217] and FACS. Briefly, WB or PBMC samples are incubated with individual Ag, activated CD4+ T cells then identified by the induced up-regulation and co-expression of CD25 and CD134, and purified by FACS. To isolate sufficient numbers of Ag-sp-CD4+ T cells for the analysis of HIV DNA within these cells, large scale cultures were set up in tissue culture flasks (Corning or BD Biosciences). For all other experiments, samples were cultured in 24- well tissue culture plates (Corning of BD Biosciences).

2.3.7.1 Culture conditions

For 24-well plate cultures, 250µL WB mixed with 250µL IMDM (Life Technologies), or 1.5x106 PBMC suspended in 500µL I10 media, was cultured per well. For large scale cultures WB mixed with equal volumes of IMDM (Life Technologies), or PBMC

CHAPTER 2: MATERIALS & METHODS

suspended in I10 media, were incubated in tissue culture flasks. WB/IMDM (Life Technologies) samples were cultured at a density of 0.25-0.35mL/cm2. PBMC samples were cultured at a concentration between 1.5-6x106 PBMC/mL, and a density between 0.375-1.5x106 PBMC/cm2. PBMC recovered from cryostorage were rested for 16 hours at 37°C in a humidified atmosphere with 5% CO2 in air, before the addition of Ag. Ag were added at the pre-determined concentrations (2.3.7.5), and reactions incubated for

44-48 hours at 37°C in a humidified atmosphere with 5% CO2 in air.

2.3.7.2 Monoclonal antibody staining

Following the 44-48 hour incubation samples were stained with the mAb listed in Table 2.1. Antibodies specific to CD3, CD4, CD25 and CD134 were required for all cultures to identify Ag-sp-CD4+ T cells. Antibodies specific to CD20 and were added when staining large-scale cultures to identify and isolate additional control populations.

Table 2.1: Monoclonal antibodies used to isolate antigen-specific CD4+ T cell subsets & control populations.

Target Fluorescent Volume Manufacturer Cat # Molecule Conjugate (µL) / test CD3 PerCP-Cy5.5 10 BD Biosciences 340949 CD4 PE-Cy7 2.5 BD Biosciences 348789 CD20 APC-Cy7 5 BD Biosciences 335794 CD25 APC 2.5 BD Biosciences 340939 CD45RA ECD 10 Beckman Coulter PNIM2711U CD134 PE 20 BD Biosciences 340420 CD, cluster of differentiation; PerCP, Peridinin Chlorophyll Protein; PE, phycoerythrin; APC, allophycocyanin; APC-Cy7, allophycocyanin-cyanin dye 7 tandem conjugate; ECD, energy coupled dye (phycoerythrin-Texas Red conjugate). n/a = not available.

For 24-well plate cultures, 100-150µL WB/IMDM or PBMC cultures were incubated with 1 test of mAb mix (Table 2.1) for 15 minutes at rt. Then for PBMC samples 2mL PBA was added. For WB/IMDM cultures 500µL Optilyse® C Lysis Solution (Beckman Coulter) was added, samples vortexed immediately, incubated for 10 minutes at rt, and

CHAPTER 2: MATERIALS & METHODS

then 2mL DPBS (Life Technologies) was added. All samples were centrifuged at 200 x g for 7 minutes at rt and cell pellets re-suspended in 200µL 0.5% PFA (Proscitech).

For large scale PBMC cultures, cells in suspension were collected by aspirating culture media, and cells adhered to the culture flask scraped off in additional IMDM (Life Technologies), then resulting cell suspensions aspirated. Large scale WB/IMDM cultures were collected by aspiration, and residual WB/IMDM washed off and collected with additional IMDM (Life Technologies). WB/IMDM cultures were processed to isolate PBMC by Ficoll-Paque separation (2.3.2). All PBMC suspensions were pelleted by centrifugation at 200 x g for 7 minutes at rt. Cell pellets were re-suspended in 1 test of mAb mix (Table 2.1) per 107 PBMC, then incubated for 15 minutes at rt. To wash off unbound antibodies, 2mL PBA was added per 107 PBMC, and cells pelleted by centrifugation at 200 x g for 7 minutes at rt. Supernatant was removed and cells re- suspended in 1mL 0.5% PFA (Proscitech) per 107 cells.

Samples were acquired or purified by flow cytometry using an LSR II flow cytometer or FACSARIATM II cell sorter respectively (both BD Biosciences), and analysed using FlowJo software (Tree Star Inc.).

2.3.7.3 Gating strategy

The gating strategy for Ag-sp-CD4+ T cells and control populations is shown in Figure 2.2. Dead cells and doublets were excluded by analysis of side scatter area (SSC-A), forward scatter area (FSC-A), and forward scatter width (FSC-W) parameters. Ag-sp- CD4+ T cells were identified and isolated as CD3+CD20-CD4+CD25+CD134+ cells. Additional control populations of total lymphocytes (FSC-A and SSC-A profile), B Cells (CD20+CD3-) and memory (CD3+CD4+CD45RA-) and naïve CD4+ T cells (CD3+CD4+CD45RA+) were also purified.

CHAPTER 2: MATERIALS & METHODS

Figure 2.2: Gating strategy for the isolation of antigen-specific CD4+ T cells and control populations. Lymphocytes were gated by FSC-A/SSC-A profile. Doublets were excluded by comparing FSC-A with FSC-W profile. B cells were identified as CD20+CD3dim/neg. CD4+ T cells were divided into memory (CD45RA-) and naïve (CD45RA+) subsets. Antigen- specific CD4+ T cells were identified as CD3+CD4+CD25+CD134+ cells.

2.3.7.4 Methodological optimization for the purification of antigen- specific CD4+ T cell subsets

To complete the aims of Chapter 3, Ag-sp-CD4+ T cells were isolated from PBMC or WB samples for the analysis of the HIV DNA reservoir within these populations. To perform this analysis, we initially planned on using cryopreserved PBMC samples collected during previous clinical studies. Before beginning, various parameters were investigated to ensure the efficient recovery of Ag-sp-CD4+ T cells from cryopreserved PBMC. After the initial analysis it became apparent the PBMC repository available was not sufficient. To improve our ability to isolate sufficient numbers of Ag-sp-CD4+ T cells, ethics approval was obtained for the collection of 200mL WB from appropriate HIV-infected individuals. Before beginning the analysis using WB samples, experiments

CHAPTER 2: MATERIALS & METHODS

were performed to determine the most efficient methods for isolating Ag-sp-CD4+ T cells and quantifying total HIV pol DNA by qPCR.

2.3.7.4.1 Antigenic stimulation of peripheral blood mononuclear cells recovered from cryostorage

PBMC recovered from cryostorage are susceptible to death during in vitro processing. Dead cells may interfere with flow cytometric analysis due to auto-fluorescence [535] or non-specific binding with antibodies [536-538]. Both scenarios may result in the inaccurate identification and isolation of cell populations by FACS. To exclude this possibility, experiments comparing the identification of Ag-sp-CD4+ T cells with and without a viability marker were performed. PBMC from HIV-infected individuals (n=4) were recovered from cryostorage (2.3.4) and stimulated (2.3.7.1) separately with the Ag: CMV, TT or Gag (2.3.7.5). After stimulation, PBMC cultures were stained with the LIVE/DEAD® Violet Dye (Life Technologies) according to the manufacturer’s protocol, before incubation with cell surface mAb (2.3.7.2), and analysis by flow cytometry using FlowJo (Tree Star Inc.). Briefly, 1mL DPBS (Life Technologies) was added to stimulated PBMC suspensions and cells pelleted by centrifugation at 200 x g for 7 minutes at rt. The wash step with DPBS (Life Technologies) was repeated once, and cell pellets re- suspended in 1mL DPBS (Life Technologies). 1µL LIVE/DEAD® Violet Dye was added and samples incubated in the dark for 30 minutes at rt. 1mL DPBS (Life Technologies) was added and cells pelleted by centrifugation at 200 x g for 7 minutes at rt. Stained cells were then re-suspended in 0.5% PFA (Proscitech) for analysis by flow cytometry using FlowJo (Tree Star Inc.).

To determine the contribution of dead cells to Ag-sp-CD4+ T cell populations identified using the CD25/CD134 co-expression assay, gating strategies with (Figure 2.3a), and without (Figure 2.3b) the exclusion of cells stained with the LIVE/DEAD® Violet Dye were compared. Differences were virtually non-existent for both the proportion of total CD4+ T cells (Figure 2.3c), and the absolute event count (Figure 2.3d) of CD25+CD134+ T cells. These data demonstrated the inclusion of a viability dye was not necessary for the isolation of Ag-sp-CD4+ T cells from cryopreserved PBMC samples.

CHAPTER 2: MATERIALS & METHODS

Figure 2.3: Dead or dying lymphocytes in the CD25/CD134 co-expression assay. Gating strategies with (a), and without (b) the inclusion of a viability marker. Panels (c) and (d) show the proportion of total CD4+ T cells, and total event count respectively, of CD25+CD134+ cells gated with (purple) and without (green) the inclusion of a viability dye.

Previous studies have found that resting PBMC recovered from cryostorage in an overnight culture prior to in vitro manipulations may increase responsiveness to various stimuli [539]. Experiments were therefore performed to compare the stimulation of Ag- sp-CD4+ T cells using PBMC recovered from cryostorage with and without a 16 hour resting incubation. PBMC from HIV-infected individuals (n=4) were recovered from cryostorage (2.3.4), and cell concentration and viability determined by haemocytometer count with trypan blue staining (2.3.5.1). 0.75x106 PBMC suspended in 500µL I10 media

CHAPTER 2: MATERIALS & METHODS

was added per well in a 24-well plate. Separately, no Ag (negative control), staphylococcal enterotoxin B (SEB; 2.3.7.5.4; positive control) CMV lysate (2.3.7.5.2), TT (2.3.7.5.3) or Gag peptides (2.3.7.5.1) were added to cultures, either immediately or after a 16 hour incubation at 37°C in humidified atmosphere with 5% CO2 in air. Following a 44-48 hour incubation, samples were stained with mAb (2.3.7.2), acquired on an LSR-II flow cytometer (BD Biosciences) and analysed by using FlowJo (Tree Star Inc.). Rested and un-rested cultures were compared by measuring: the size of the Ag-sp- CD4+ T cell populations (CD25+CD134+ as a % of total CD4+); the signal to noise ratio (Ag-sp-CD4+ T cell responses divided by the negative control response); the CD134 PE mean fluorescence intensity (MFI) of CD25+CD134+ cells; and the culture viability (live and dead lymphocytes were identified by the FSC-A/SSC-A profile as described in Figure 2.4), as calculated by determining the proportion of total lymphocytes (live + dead) with an FSC-A/SSC-A profile indicating they were live.

Figure 2.4: Dead or dying lymphocytes as identified by changes in FSC and SSC. Live lymphocytes were identified by the low SSC-A and intermediate FSC-A, while dead or dying lymphocytes were identified by reducing FSC-A and increasing SSC-A. Gated live or dead/dying populations of lymphocytes were then assessed for viability by staining with the LIVE/DEAD® Violet Dye (Life Technologies).

CHAPTER 2: MATERIALS & METHODS

According to the measurements described above, the rested and un-rested PBMC cultures performed similarly in the CD25/CD134 co-expression assay (Figure 2.5). The signal to noise ratio however, was significantly improved in the rested PBMC cultures (Figure 2.5d), probably the result of reduced background stimulation. Subsequent experiments using cryopreserved PBMC were therefore rested 16 hours prior to the addition of Ag.

Figure 2.5: Overnight resting of recovered peripheral blood mononuclear cells in the CD25/CD134 co-expression assay. Brown (rested PBMC) and grey (un-rested PBMC) box and whisker plots display median, interquartile range, and maximum and minimum values. (a) The % of CD4+ T cells co-expressing CD25/CD134; (b) the signal to noise ratio (Ag-sp- responses divided by background responses in the absence of Ag); (c) the CD134 PE MFI of CD25+CD134+ CD4+ T cells; and (d) the viability of PBMC cultures post stimulation. Data were compared using a paired t test; *p<0.05.

Experiments were also performed to determine the optimal concentration for PBMC cultures in the CD25/CD134 co-expression assay. We hypothesized that culturing PBMC at a higher density may improve viability and facilitate contact between memory CD4+ T cells and Ag presenting cells, therefore enhancing the activation of memory CD4+ T

CHAPTER 2: MATERIALS & METHODS

cells. PBMC from HIV-infected individuals (n=4) were recovered from cryostorage (2.3.4), and cell concentration enumerated and viability determined by trypan blue staining and haemocytometer count (2.3.5.1). PBMC samples were prepared at concentrations of 1.5x106, 3x106, and 6x106 cells per mL in I10 media. 500µL of each cell concentration was added to a well of a 24-well plate (2cm2 per well) resulting in cell densities of 0.375x106, 0.75x106 and 1.5x106 PBMC per cm2. Cultures were rested overnight then stimulated by the separate addition of either; no Ag (negative control), staphylococcal enterotoxin B (SEB; 2.3.7.5.4; positive control) CMV lysate (2.3.7.5.2), TT (2.3.7.5.3) or Gag peptides (2.3.7.5.1). Following the 44-48 hour incubation, samples were stained with mAb (2.3.7.2) then analysed by flow cytometry.

The assay outcomes described previously were compared for the different concentrations and densities of PBMC cultures (Figure 2.6). Cell concentration did not appear to impact on the stimulation of Ag-sp-CD4+ T cells; the % of CD4+ T cells expressing CD25/CD134 and the signal to noise ratio was unchanged across all 3 concentrations tested (Figure 2.6a/b). The CD134PE MFI of CD25+CD134+ Ag-sp-CD4+ T cells was decreased in PBMC cultures at a density of 1.5x106 compared to 0.375x106 cells per cm2 (Figure 2.6c), and culture viability was significantly improved in PBMC cultures at the highest density (Figure 2.6d). The CD134 PE MFI was considered important as the purification of CD25+CD134+ Ag-sp-CD4+ T cells requires clear expression of the two markers used, however, the cell viability was also considered crucial, as losses due to cell death could significantly reduce cell yields. Given there was no clear concentration that would be advantageous to this study, and that cell density may be difficult to control when culturing in tissue culture flasks, all subsequent experiments PBMC samples were cultured within the range tested by these optimization experiments.

CHAPTER 2: MATERIALS & METHODS

Figure 2.6: Density of peripheral blood mononuclear cell cultures in the CD25/CD134 co-expression assay. PBMC cultures were incubated at a density of 0.375x106 (purple), 0.75x106 (green) and 1.5x106 (black) cells per cm2. (a) The % of CD4+ T cells co- expressing CD25/CD134; (b) the signal to noise ratio; (c) the CD134 PE MFI of CD25+CD134+ CD4+ T cells; and (d) the viability of PBMC cultures post stimulation. Data were compared using a paired t test; *p<0.05.

2.3.7.4.2 Antigenic stimulation of fresh whole blood

The efficiency of isolating Ag-sp-CD4+ T cell subsets using CD25/CD134 co-expression assay was compared when stimulating either WB/IMDM or freshly isolated PBMC cultures. Previous studies within our laboratory found that Gag-sp-CD4+ T cells were more efficiently isolated following the stimulation of PBMC. WB was collected from uninfected healthy controls (UHC), and the Ags CMV and TT used for this comparison. PBMC were isolated from WB by Ficoll-Paque separation (2.3.2) and cell yield determined by haemocytometer count (2.3.5.1). Parallel cultures were set up in T25 flasks containing either 9mL WB/IMDM or 27x106 PBMC in 9mL I10 media. Cultures were stimulated by the separate addition either no Ag SEB (2.3.7.5.4), CMV lysate (2.3.7.5.2) or TT (2.3.7.5.3), then stained with mAb and analysed by flow cytometry (2.3.7.1 and 2.3.7.2). Total PBMC counts were calculated in mAb stained samples using BD 71

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TrucountTM tubes (2.3.5.2). As an additional set of controls, WB/IMDM and PBMC cultures were also stimulated and processed in 24-well plates as described in sections 2.3.7.1 and 2.3.7.2. Post stimulation, lymphocyte yields were significantly higher in WB/IMDM relative to PBMC cultures (Figure 2.7b). The size of the Ag-sp-CD4+ T cell populations for the positive control (SEB) and CMV lysate, but not TT, was significantly larger in WB/IMDM cultures (Figure 2.7). As a result, large-scale WB/IMDM cultures yielded greater numbers of CMV-sp-CD4+ T cells, but smaller numbers of TT-sp-CD4+ T cells (Figure 2.7c). In subsequent experiments, CMV-sp-CD4+ T cell populations were isolated from stimulated cultures of WB/IMDM, while Gag- and TT-sp-CD4+ T cells were isolated from stimulated cultures of PBMC.

Figure 2.7: Comparison of whole blood & peripheral blood mononuclear cells in the CD25/CD134 co-expression assay. Blue (PBMC) and red (WB) box and whisker plots display median, interquartile range, maximum and minimum values. (a) The % of CD4+ T cells co-expressing CD25/CD134. (b) Lymphocyte yields pre (WB and PBMC) and post (WB or PBMC separately) stimulation. (c) Yields of Ag-sp-CD4+ T cells for CMV and TT from large scale cultures. Data were compared by paired t test; *p<0.05.

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2.3.7.5 Antigen preparation & titration

The Ags used during this thesis were selected and tested to ensure highly specific stimulation of memory CD4+ T cell subsets.

2.3.7.5.1 HIV Subtype B Gag peptide pool

The HIV Consensus B Gag peptides - Complete Set reagent consists of 123 peptides, 15 amino acids (aa) in length, overlapping by 11aa, and spanning the entire HIV subtype B Gag protein. This reagent was obtained through the AIDS Research and Reference Reagent Program (catalogue number 8117), Division of AIDS, NIAID, NIH. Individual peptides were provided as 1mg of lyophilized peptide. Each peptide was carefully dissolved in 5-30µL DMSO (Sigma-Aldrich), then slowly diluted to a final volume of 100µL in DPBS (Life Technologies). Equal volumes of each 123 individual peptides were pooled to a final concentration of 0.081mg/mL for each peptide. Titration experiments were performed previously by members of our laboratory. For the stimulation of Gag-sp- CD4+ T cells, 24.6µL HIV Gag peptide pool was added per mL of culture, corresponding to a final concentration of 2µg/mL for each peptide.

2.3.7.5.2 CMV lysate

CMV lysate stocks were generated by: propagating CMV on Human Foreskin Fibroblasts (MRC-5 cells); collecting, purifying and concentrating MRC-5 culture supernatant; then lysing viral particles by freeze-thaw cycles. This work was a collaborative effort between myself, Professor Anthony Kelleher, and Dr. Stuart Turville. Mycoplasma negative MRC-5 cells (gift from Bill Rawlinson), were grown in D10 media at 37°C in humidified air with 5% CO2. At a confluency of 80-90%, MRC-5 cells were infected with CMV isolate AD169 (gift from Philip Cunningham): for each T150 flask of MRC-5 cells, 125µL virus stock was mixed with 25mL D10 media, and the resulting solution used to replace culture supernatant. For the next 3 weeks, culture supernatant was collected once per week, stored at -20°C, and replaced with fresh D10 media. Culture supernatant was clarified by centrifugation at 2,500 x g for 20 minutes at 20°C, and pellets containing cell debris discarded. Clarified supernatant was pooled then concentrated using a Millipore Pellicon tangential flow filtration unit (Merck Millipore; Massachusetts, USA) from 700mL down to 25mL. The concentrated supernatant was then subject to high speed 73

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centrifugation through sucrose to pellet CMV virions: 1mL 20% sucrose was underlaid below the 25mL concentrated CMV+ supernatant, and the tube centrifuged at 75,000 x g for 1 hour at 4°C. The supernatant was aspirated, and the pellet re-suspended in 1mL RPMI (Life Technologies); the pellet was incubated overnight at 4°C to allow for complete re-suspension. The resulting CMV stocks were dispensed in 50µL aliquots and stored at -80°C. A negative control of clarified and concentrated uninfected MRC-5 culture supernatant was generated in parallel.

Titration experiments were performed to determine the optimal concentration of CMV lysate for the stimulation of CMV-sp-CD4+ T cells. CMV lysate stocks were diluted 1 in 10 in IMDM (Life Technologies) and freeze-thawed 5 times. Additional dilutions of the CMV lysate equating to 1 in 31 and 1 in 100 of the original stock were generated in IMDM (Life Technologies). WB was collected from CMV IgG seropositive and seronegative donors and cultured in 24-well plates with 10µL separately of one of each CMV lysate dilution and the negative control. Cultures were processed as described in sections 2.3.7.1 and 2.3.7.2.

In WB from individuals positive for CMV serology, the 1 in 31 dilution of the CMV lysate induced the largest population of CMV-sp-CD4+ T cells (Figure 2.8a). No discernible Ag-sp-CD4+ T cell responses were observed in WB from individuals with negative CMV serology stimulated with all CMV lysate dilutions (Figure 2.8a). Similarly, no responses were observed when WB from both CMV seropositive and seronegative individuals when stimulated with the negative control (Figure 2.8b). These results confirmed that the CMV lysate specifically stimulates CMV-sp-CD4+ T cells. For all subsequent experiments, 20µL of the CMV lysate diluted 1 in 31 in IMDM (Life Technologies) was added per mL of culture. To prepare the remaining 50µL aliquots of CMV lysate stocks; 1.5mL IMDM (Life Technologies) was added, samples freeze thawed 5 times, then stored in 1.2mL aliquots at -80°C.

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Figure 2.8: Titration of CMV lysate for the stimulation of CMV-specific CD4+ T Cells. (a) Mean % of CD4+ T cells co-expressing CD25/CD134 following stimulation of WB from CMV IgG seropositive (red; median of n=4) and seronegative (blue; n=1) individuals with CMV lysate dilutions (1:10, 1:31, and 1:100) or no Ag (0). (b) Ag-sp-CD4+ T cell responses in 5 CMV IgG seropositive (red) and seronegative (blue) individuals after stimulation with the concentrated, CMV uninfected MRC-5 culture supernatant.

2.3.7.5.3 Tetanus Toxoid

Highly purified TT from Clostridium tetani was acquired from the Statens Serum Institut (Copenhagen, Denmark). This Ag was supplied at a concentration of 762 Lf/mL (Limit of Flocculation units), corresponding to a protein concentration of 1.905mg/mL. The optimal concentration for the stimulation of TT-sp-CD4+ T cells was determined by titration experiments. WB was collected from 3 individuals with a history of TT vaccination and 1 individual with no previously recorded TT immunization. Cryopreserved PBMC from HIV-infected individuals were recovered from cryostorage (2.3.4). Dilutions of TT Ag equating to 10, 4, 2, 0.2 and 0.1Lf/mL were added to stimulate WB or recovered PBMC cultures as described in sections 2.3.7.1 and 2.3.7.2.

The optimal concentration of the TT Ag for the specific stimulation of TT-sp-CD4+ T cells was determined to be 2LF/mL (5µg/mL) or 0.4LF/mL (1µg/mL) for WB and PBMC cultures respectively (Figure 2.9). TT-sp-CD4+ T cell responses to TT were not present

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in the WB collected from an individual with no history of TT vaccination, indicating that this Ag specifically activates TT-sp-CD4+ T cells.

Figure 2.9: Titration of Tetanus Toxoid (TT) for the stimulation of TT-specific CD4+ T Cells. Mean % of CD4+ T cells co-expressing CD25/CD134 for WB (red; n=3) and recovered PBMC (blue; n=3) from individuals vaccinated with TT, and WB (black; n=1) from an individual with no recorded TT vaccination.

2.3.7.5.4 Staphlyococcal enterotoxin B

SEB (Sigma-Aldrich) was used as a positive control for the stimulation of Ag-sp-CD4+ T cells. SEB is a super-Ag that activates CD4+ T cells by cross-linking MHC class II molecules with specific Vb regions of a subset of T-cell receptors. To generate SEB stocks lyophilized Ag was dissolved in DPBS (Life Technologies) to a final concentration of 1mg/mL, dispensed in 10µL aliquots, and stored at -20°C. For use, 10µL stocks were thawed and diluted in 90µL DPBS (Life Technologies) and 10µL was added per mL of culture, resulting in a final concentration of 1µg/mL.

2.4 Extraction of nucleic acids

Nucleic acids were extracted from purified populations of cells using a variety of techniques. Centrifugation steps were performed using a Heraeus® Biofuge® Pico

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microcentrifuge (Thermo-Fischer Scientific) or Eppendorf Refrigerated Microcentrifuge 5417R (Eppendorf; Hamburg, Germany).

2.4.1 Cell-associated DNA

2.4.1.1 Direct lysis buffer

For the quantification of total HIV pol DNA copies in purified Ag-sp-CD4+ T cell subsets in Chapter 3, DNA was extracted using a ‘direct lysis’ buffer (DLB) [471]. This technique allows for the quantification of HIV DNA by qPCR using a cellular lysate containing un- purified DNA, and was selected to reduce DNA losses when extracting DNA from small populations of cells. This technique was tested and optimized for the conditions specific to this thesis (2.5.1.4). To ensure the complete removal of all buffers and proteins that may inhibit the lysis reaction, isolated cells (2.3.7) were washed twice by: the addition of DPBS (Life Technologies) to a total volume of 1mL, and centrifugation at 6,000 x g for 2 minutes. After the 2nd DPBS (Life Technologies) wash, the supernatant was carefully removed and cells lysed by the addition of DLB (a minimum volume of 36µL, and increased proportionately to ensure a maximum of 105 cells per 9µL buffer) and 1µL proteinase K (0.1mg/mL; Sigma-Aldrich) per 9µL DLB. Lysis reactions were incubated at 65°C overnight then at 95°C for 15 minutes. Lysates were stored at -80°C until quantification of total HIV pol DNA by qPCR 2.5.1.1.

2.4.1.2 Qiagen DNeasy blood & tissue kit

DNA was extracted from CD4+ T cells purified by magnetic bead separation (2.3.6.1) in Chapter 4 using the DNeasy Blood and Tissue Kit (Qiagen; Hilden, Germany), according to the manufacturer’s protocol. Each DNeasy column has the capacity to bind DNA from a maximum of 5x106 cells. For CD4+ T cell samples containing greater than 5x106 cells, reagent volumes were adjusted proportionately, and lysed samples with added ethanol were split over multiple DNeasy columns.

CD4+ T cell DCP were re-suspended in 200µL DPBS (Life Technologies) and 20µL proteinase K (provide with the kit). Cells were lysed by the addition of 200µL AL buffer, vortexing, and incubation at 56°C for between 30 minutes to 16 hours. 200µL of 100%

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ethanol (Sigma-Aldrich) was added and samples thoroughly mixed by pulse vortexing for 15 seconds. The resulting mixture was applied to a DNeasy column and samples centrifuged at 6,000 x g for 1 minute. 500µL AW1 buffer was applied to the column and samples centrifuged at 6,000 x g for 1 minute. 500µL AW2 buffer was applied to the column and samples centrifuged at 16,060 x g for 3 minutes. The column was centrifuged for an additional 2 minutes at 16,060 x g to remove residual traces of AW2 buffer. 30-

100µL RNase/DNase free distilled H2O (dH2O) was applied to the membrane and samples incubated for 30 minutes at rt. DNA was eluted by centrifugation at 6,000 x g for 1 minute at rt. The DNA elution step was repeated by addition of another 30-100µL dH2O, a 30 minute incubation then centrifugation, and DNA eluates pooled. DNA quantity and quality was assessed by spectrophotometry (2.4.5) and samples stored at - 80°C until further analysis.

2.4.2 Co-extraction of cell-associated DNA & RNA

2.4.2.1 Qiagen Allprep DNA/RNA kit

In the experiments required for Chapter 6, DNA and RNA were extracted from cryopreserved PBMC using the Allprep DNA/RNA Kit (Qiagen), according to the manufacturer’s protocol. Unless specified, all centrifugation steps were performed at 8,000 x g for 1 minute at rt. Cryopreserved PBMC were thawed quickly in a 37°C water- bath, and cryopreservation media washed off by the addition of 5mL pre-warmed (37°C) R10 media then centrifugation for 15 minutes at 200 x g at rt. Cell pellets were re- suspended in 1mL DPBS (Life Technologies) and a maximum of 5 x 106 PBMC were removed for DNA/RNA extraction. Cells were lysed by the addition of 600µL RLT buffer containing β-mercaptoethanol (Sigma-Aldrich). The resulting cellular lysate was homogenized by passing through a blunt 18 gauge needle 10 times, and the homogenized lysate applied to an Allprep DNA column. The DNA column was centrifuged and the flow-through collected for RNA extraction. The DNA column was stored at 4°C during the RNA extraction steps.

The flow through from the DNA column containing RNA was mixed with an equal volume of 70% ethanol (Sigma-Aldrich), then applied to an Allprep RNA column. The RNA column was centrifuged and the flow-through discarded. The RNA column was 78

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then washed by the addition of 700µL RW1 buffer and centrifugation. The RNA column was washed a further 2 times by addition of 500µL RPE buffer and centrifugation. Residual RPE buffer was removed by an additional centrifugation step at 16,060 x g for 3 minutes at rt. RNA was eluted from the column by addition of 50µL RNase/DNase free dH2O, incubation for 1 minute at rt, and centrifugation. The elution step was repeated once and RNA eluates pooled.

The DNA column was washed by addition of 500µL of AW1 buffer and centrifugation, followed by addition of 500µL of AW2 buffer and centrifugation. DNA was eluted from the column by addition of 75µL RNase/DNase free dH2O, incubation for 1 minute at rt and centrifugation. The elution step was repeated once and DNA eluates pooled.

DNA and RNA quantity and quality were assessed by spectrophotometry (2.4.5), and stored at -80°C until further analysis.

2.4.2.2 Trizol® reagent

In the experiments required for Chapter 5, DNA and RNA were extracted from purified CD4+ T cells (2.3.6.2) using the Trizol® Reagent (Life Technologies). Isolated cells were pelleted by centrifugation at 8,000 x g for 2 minutes. The supernatant was carefully removed and cells re-suspended in 50µL DPBS (Life Technologies). Cells were lysed by the addition of 950µL Trizol® Reagent and samples mixed by pipetting. The homogenized lysate was incubated at rt for 5 minutes then stored at -80°C until nucleic acid extraction.

CD4+ T cells homogenized in Trizol® were thawed then incubated at rt for an additional 30 minutes. 200µL chloroform was added and samples mixed by 15 seconds of vigorous shaking by hand. Samples were incubated for 2-3 minutes then centrifuged at 12,000 x g for 15 minutes at 4°C. The aqueous phase was carefully removed using a pipette and transferred to a new tube. The remaining phases were stored at 4°C for subsequent DNA extraction. The aqueous layer was mixed with 2µL glycogen (20mg/mL: Hoffman-La Roche; Basel, Switzerland) and 500µL isopropanol (Sigma-Aldrich). Samples were incubated at -20°C overnight to facilitate precipitation of RNA. RNA was pelleted by

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centrifugation at 12,000 x g for 30 minutes at 4°C. The supernatant was carefully removed and RNA pellets washed by addition of 1mL ice-cold 75% ethanol (Sigma-Aldrich), followed by centrifugation at 12,000 x g for 5 minutes. The RNA pellet wash step was repeated once. The ethanol supernatant was carefully removed, and RNA pellets air dried for 2-5 minutes, then re-suspended in 30µL DNase/RNase free dH2O. RNA samples were stored at -80°C for batched analysis.

DNA was extracted from the remaining interphase and organic phase using a back extraction buffer (BEB). 500µL BEB was added and samples mixed by vigorous shaking for 15 seconds. Samples were incubated at rt for 10 minutes then centrifuged at 12,000 x g for 15 minutes at 4°C. The aqueous layer containing DNA was transferred to a clean tube, then 400µL isopropanol (Sigma-Aldrich) and 2µL glycogen (20mg/mL; Hoffman- La Roche) added. Samples were mixed by shaking, and incubated overnight at 4°C to facilitate DNA precipitation. DNA was pelleted by centrifugation at 12,000 x g for 5 minutes at 4°C. The supernatant was carefully removed and the DNA pellet washed by addition of 1mL ice-cold 75% ethanol (Sigma-Aldrich) and centrifugation at 12,000 x g for 5 minutes at 4°C. The DNA pellet wash step was repeated twice. The ethanol supernatant was carefully removed and the pellet allowed to air dry for 2-5 minutes. The DNA pellet was re-suspended in 35µL 8mM sodium hydroxide (NaOH). Samples were centrifuged at 12,000 x g for 10 minutes at rt to pellet any insoluble material. The supernatant was transferred to a new tube and 1.12µL 1M Hepes added to neutralize sample pH. 50mM ethylenediaminetetraacetic acid (EDTA) was added to a final concentration of 1mM. DNA and RNA quality and quantity were assessed by spectrophotometry (2.4.5) and samples stored at -80°C for batched analysis.

2.4.3 Plasma RNA

To examine pre-ART plasma viral populations in Chapter 3, RNA was extracted from plasma (2.3.1) using the QIAamp Viral RNA Mini kit (Qiagen), according to the manufacturer’s protocol. Unless specified, all centrifugation steps were performed for 1 minute at 6,000 x g at rt. Plasma samples were clarified by centrifugation, then 140µL of plasma was mixed with 560µL AVL buffer containing carrier RNA. Samples were mixed thoroughly by pulse vortexing for 15 seconds, then incubated for 10 minutes at rt. The

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plasma lysate was mixed with 560µL 100% ethanol (Sigma-Aldrich) then applied to a QIAamp Mini column. The column was centrifuged and the flow-through discarded. The RNA column was washed by the addition of 500µL of AW1 buffer and centrifugation, followed by the addition of 500µL of AW2 buffer and centrifugation. Residual AW2 buffer was removed by an additional centrifugation step at 16,060 x g for 3 minutes at rt. RNA was eluted by the addition of 30µL AVE buffer, incubation for 1 minute at rt, and centrifugation. RNA eluates were stored at -80°C until further analysis.

2.4.4 DNase treatment of RNA

2.4.4.1 Plasma RNA

Plasma RNA (2.4.3) was treated with TURBOTM DNase (Life Technologies) to remove residual DNA, according to the manufacturer’s protocol. Briefly, reactions containing up TM TM to 44µL RNA, 5µL 10X TURBO DNase Buffer, 1µL TURBO DNase, and dH2O to a final volume of 50µL, were incubated at 37°C for 30 minutes. DNase was removed from RNA by re-extraction using the Qiagen Viral Mini Kit (2.4.3), and DNA free RNA stored at -80°C until further analysis.

2.4.4.2 Cell-associated RNA

Before quantifying CA HIV gag usRNA by RT-qPCR the experiments of Chapter 5, RNA samples were treated with DNase I Amplification Grade (Life Technologies) to remove residual DNA. Briefly, reactions containing 12µL RNA, 1.4µL 10x DNase I Reaction Buffer and 1µL DNase I, were incubated at 37°C for 20 minutes, 75°C for 10 minutes, then held at 4°C until addition to the RT-qPCR reaction (2.6.2).

2.4.5 Assessment of nucleic acid quantity & quality

2.4.5.1 Spectrophotometry

DNA and RNA quantity and quality were assessed by spectrophotometry using either a NanoPhotometer® (Implen; California, USA) or NanoDrop 2000c (Thermo-Fischer TM Scientific). The apparatus was cleaned with 70% ethanol then dH2O using a Kimwipe (American MasterTech; California, USA), and the appropriate settings applied for sample measurements. 1-3µL of the DNA/RNA elution buffer was applied to the lid to blank the 81

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machine. The lid was again cleaned, then 1-3µL of RNA/DNA sample was applied and measurements collected. The above process was repeated for each sample, with the lid cleaned in-between measurements of samples.

2.4.6 Comparison of nucleic acid extraction techniques

For the studies described in this thesis, we strategically selected DNA/RNA extraction techniques based on the experimental requirements and aims of the individual studies. While methods were optimized and compared (2.5.1.4) before beginning the experiments required for this thesis, a summary of experimental results further justified the selection of DNA/RNA extraction techniques.

As a measure of the efficiency of each nucleic extraction method, we calculated yields based on the number of cells being analysed for: DNA, expressed as the actual yield of DNA as a percentage of the amount expected, assuming that 80,000 cells contains 500ng DNA, and by calculating the total ng of DNA in samples using the β-actin qPCR (2.5.4); or RNA, by estimating the total ng of RNA/106 cells, based on spectrophotometry readings (2.4.5.1). The DLB method (2.4.1.1), selected following experimental optimization and validation for the analysis of HIV DNA reservoirs in very small subsets of CD4+ T cells (0) in Chapter 3, provided the greatest DNA yield, however was accompanied by increased variability relative to kit based methods (Figure 2.10). For the completion of experiments in Chapter 4, the QIAGEN DNeasy blood and tissue kit (2.4.1.2) was chosen, as cell numbers were not limited (~3-5x106 cells), and to remain consistent with previous analyses. The co-extraction of DNA/RNA from the same population of cells was introduced in later studies to allow for a more comprehensive analysis of HIV reservoirs by measuring both CA HIV DNA and RNA. The Trizol® reagent (2.4.2.2) was selected for analysis in Chapter 5 as cell numbers were limited (~50,000-1,000,000). This technique provided greater yields of DNA and RNA compared to kit based methods (QIAGEN Allprep DNA/RNA kit), however, was more time- consuming and technically challenging, as evidenced by the higher levels of variability (Figure 2.10). A kit based method (QIAGEN Allprep DNA/RNA kit was; 2.4.2.1) was selected in Chapter 6 as cell numbers were not restricted (~3-5x106).

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Figure 2.10: Summary DNA/RNA yields for the various extraction methods utilized to conduct the experiments comprising this thesis. DNA/RNA extraction yields of different techniques were compared using unpaired t tests with Welch’s correction.

2.5 Quantification of HIV DNA species

Real-time qPCR assays were used to amplify and quantify HIV DNA species in DNA extracted from the various cell populations studied in this thesis. The primers and probes used are detailed in Table 2.2, and the location of the various amplicons relative to the HIV genome is illustrated in Figure 2.11. The assays used for Chapter 4 [The Pilot integrase inhibitor trial (PINT) study] were developed within our lab primarily by a previous PhD student Kristin McBride. A great deal of work was required to optimize these assays, and involved the development of patient specific primer and probe sets and plasmid standards to account for mismatches within patient viral sequences. Custom oligonucleotide primers and probes were synthesized by Sigma-Aldrich (Missouri, USA), for all but the β-actin qPCR. For the measurement of all HIV DNA species, calculated copy numbers were normalized for cellular input by measurement of total DNA using the β-actin qPCR described in 2.5.4. A negative control of qPCR mastermix with no template added was included for all qPCR reactions.

CHAPTER 2: MATERIALS & METHODS Table 2.2: Primers & probes used to quantify HIV DNA species by qPCR. qPCR Primer / Probe Sequence (5’ → 3’) 5’ mod. 3’ mod. bp Binding site* SK145 AGT GGG GGG ACA TCA AGC AGC C 22 1359-1380 SKLNA2 AT[C] A[A]T [G]AG GAA [G]CT [G]C 6-FAM BHQ-1 17 1402-1418 Total HIV SKCC1B TAC TAG TAG TTC CTG CTA TGT CAC TTC C 28 1486-1513 gag DNA 11001 Forward GGT GGG GGG ACA TCA AGC AGC CAT GCA AAT 30 1359-1388 11001 TG Probe AT[T] A[A]T [G]AG GAG [G]CT [G]C 6-FAM BHQ-1 17 1402-1418 11001 Reverse TAC TAG TGG TTC CTG CTA TGT CAC TTC C 28 1486-1513 mf299 GCA CTT TAA ATT TTC CCA TTA GTC CTA 27 2536-2562 Total HIV ri15 CAG [G]A[A] T[G]G [A]TG G 6-FAM BHQ-1 13 2590-2602 pol DNA ri16 CTG [T]C[A] A[T]G [G]CC A 6-FAM BHQ-1 13 2619-2631 mf302 CAA ATT TCT ACT AAT GCT TTT ATT TTT TC 29 2634-2662 2-LTR J F GCT AAC TAG GGA ACC CAC TGC TTA AG 26 9582-9607 2-LTR J F3 GCT AGC TAG AGA ACC CAC TGC TTA AG 26 9582-9607 2-LTR 2-LTR Probe 1 ACA [C]A[C] A[A]G [G][T]T 6-FAM BHQ-1 12 58-69 HIV DNA 2-LTR Probe 2 ACA [C]G[C] A[A]G [G][C]T 6-FAM BHQ-1 12 58-69 2-LTR J R TGG GTG GTG CCT CAA ACT AGT ACC AGT 27 128-154 HIV reverse TGG ATG GTG CTA CAA GCT AGT ACC AGT 27 128-154 Alu 1 TCC CAG CTA CTG GGG AGG CTG AGG 24 n/a Alu 2 GCC TCC CAA AGT GCT GGG ATT ACA G 25 n/a L-M667 ATG CCA CGT AAG CGA AAC TCT GGC TAA CTA GGG AAC CCA CTG 42 9579-9607 L-M667 #2 ATG CCA CGT AAG CGA AAC TCT GGC TAA CTA GTG AAC CCA CTG 42 9579-9607 Integrated AA55M GCT AGA GAT TTT CCA CAC TGA CTA A 25 9694-9718 AA55M 2 GCT AGA GAT TTT TCT ACT TCG ATT A 25 9694-9718 HIV DNA AA55M 3 GCT AGA GAT TTT CCA CAC TGA CTA TC 26 9694-9719 LTR-FL CAC AAC AGA CGG GCA CAC ACT ACT TGA 6-FAM TAMRA 27 9634-9660 LTR-FL 2 AAC AAC AGA CGG GCA CAC ACT ACT TGA 6-FAM TAMRA 27 9634-9660 LTR-FL 3 CAC AAC AGA TGG GCA CAC ACT ACT GA 6-FAM TAMRA 26 9634-9659 Lambda T1 ATG CCA CGT AAG CGA AAC T 19 n/a Red letters in nucleotide sequences represent mismatched bases for patient specific qPCR; Brackets indicate locked nucleic acids; mod = modification; 6-FAM = 6- Carboxyfluorescein; BHQ-1 = Black Hole Quencher 1; TAMRA = Tetramethylrhodamine; bp = base pairs. *Binding sites are reported relative to HIV B reference genome (HXB2). 1Binds to the underlined heel of L-M667/LM667 #2.

CHAPTER 2: MATERIALS & METHODS

Figure 2.11: HIV DNA species & the location of PCR amplicons amplified in this thesis. Locations are shown relative to the HIV subtype B reference genome HXB2. Shaded blue areas represent genomic DNA. 85

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2.5.1 Total HIV DNA

Total HIV DNA was quantified by qPCR using two different sets of primers and probes targeting the HIV pol and gag genes. As is evident in Figure 2.11, these primer and probe sets amplify a region of the HIV genome present in all species of HIV DNA. A previously published qPCR specific to the HIV pol gene [472] was utilized in Chapters 3, 4 and 6. This qPCR was originally optimized to amplify a broad range of HIV subtype B variants and was introduced with the aim of reducing mismatches between primers and probes with patient viral sequences. The qPCR targeting the HIV gag gene was used in Chapter to remain consistent with the original work examining the PINT cohort [430, 540, 541]. For patient 11001 of the PINT cohort, a specific set of primers, probe and standards were designed and optimized to compensate for differences in this individual’s viral sequences. All samples were assayed in duplicate, and total HIV DNA copies determined using a set of plasmid standards. All total HIV DNA qPCR assays were performed on an iQTM5 or CFX96 TouchTM real-time PCR Detection System (Bio-Rad Laboratories; California, USA).

2.5.1.1 Total HIV pol DNA qPCR

Reactions contained 25µL iQ Supermix (Bio-Rad Laboratories), 1µM mf299 and mf302,

100nM ri15 and ri16, 10µL DNA template, and dH2O to a final volume of 50µL. Reactions were incubated at 95°C for 3 minutes, followed by 45 cycles of 95°C for 15 seconds then 60°C for 1 minute.

2.5.1.2 Total HIV gag DNA qPCR

Reactions contained 12.5µL iQ Supermix, 800nM SK145 and SKCC1B, 200nM

SKLNA2, 5µL DNA template and dH2O to a final volume of 25µL. Reactions were incubated at 95°C for 3 minutes, followed by 45 cycles of 95°C for 15 seconds then 60°C for 1 minute.

For patient 11001 reactions contained 12.5µL iQ Supermix, 800nM 11001 Forward and

11001 Reverse, 300nM 11001 TG Probe, 5µL DNA template, and dH2O to a final volume

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of 25µL. Reactions were incubated at 95°C for 3 minutes, followed by 45 cycles of 95°C for 15 seconds then 64.5°C for 1 minute.

2.5.1.3 Standards & controls

Standard curves were generated using the pNL4-3 plasmid, obtained through the AIDS Research and Reference Reagent Program (catalogue number 114), Division of AIDS, NIAID, NIH from Dr Malcolm Martin [542]. The pNL4-3 plasmid was linearised by EcoRI (New England Biolabs; Massachusetts, USA) restriction enzyme digestion, and linearisation confirmed by agarose gel electrophoresis (2.7.3). Plasmid DNA concentration was determined by spectrophotometry (2.4.5), and plasmid copy number calculated based on the nucleotide length of the pNL4-3 plasmid, and the weight of the individual nucleotides. 10-fold serial dilutions were made from 3 x 106 to 3 copies per 10µL, or 107 to 10 copies per 5µL, for the total HIV DNA pol and gag qPCR respectively. Plasmid dilutions were stored as 2lµ1 and 11µL aliquots at -80°C for use in individual qPCR, for the total HIV DNA pol and gag qPCR respectively.

For patient 11001 a standard curve was designed by generating a plasmid containing a patient specific PCR product spanning the region of the HIV gag gene targeted by the primer and probe set. 10-fold serial dilutions were made from 107 to 10 copies per 5µL, and dilutions stored as 11µL aliquots at -80°C for use in individual qPCR.

For use a positive control in all total HIV DNA qPCR, PBMC were isolated from an HIV- infected individual, and DNA extracted using DLB (2.4.1.1) or the Qiagen DNeasy Blood and Tissue Kit (2.4.1.2). Different extraction techniques were used to match with the methods used in each chapter. Extracted DNA was dispensed in 31µL and 21µL aliquots for the Total HIV DNA pol and gag qPCR respectively, and stored at -80°C for use in individual qPCR.

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2.5.1.4 Methodological optimization of total HIV DNA Quantification with limited cell numbers

The experiments involved in Chapter 3 required the sensitive and precise quantification of total HIV DNA levels in small numbers of Ag-sp-CD4+ T cells isolated from HIV- infected individuals with undetectable pVL. The small size of CMV-, TT- and Gag-sp- CD4+ T populations (0.5-5% of total CD4+ T cells; [217]), and low frequencies of total HIV DNA within memory CD4+ T cells in this type of cohort (1-1000 integrated HIV DNA copies/106 PBMC; [543]), posed a significant hurdle to the successful completion of this project. Before beginning, steps were taken to optimize our ability to accurately quantify total HIV DNA in small populations of cells, which we anticipated to contain approximately 50,000 to 500,000 cells. Multiple DNA extraction techniques were investigated and the total HIV pol DNA PCR was optimized.

To quantify total HIV DNA levels in DNA samples using the methods described in this theses, 4 replicates are required: 2 each for the total HIV DNA and β-actin qPCR. Given the relative copy numbers of total HIV DNA (low) and β-actin (high) in typical DNA samples, the proportion of the sample being used to measure total HIV DNA was increased by doubling the reaction volume (50µL instead of 25µL) and DNA input (10µL instead of 5µL) for this qPCR. We also hypothesized this may decrease the impact of sampling error when measuring low copy number samples. To further increase the sensitivity of the total HIV pol DNA qPCR we validated a standard curve with a lower dynamic range; 3x106 to 3 copies instead of 107 to 10 copies of pNL4-3 plasmid (Figure 2.12).

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Figure 2.12: Validation of the total HIV pol DNA qPCR standard curve. Summary standard curve from 10 repeats of the total HIV pol DNA qPCR using 10-fold serial dilutions of pNL4-3 plasmid from 3x106 down to 3 copies per 10µL. Error bars represent inter-assay standard deviation.

To investigate HIV DNA reservoirs in Ag-sp-CD4+ T cells in Chapter 3, 40µL of DNA was required: 30µL to quantify total HIV pol DNA by qPCR, and 10µL to examine viral sequences by nested PCR and clonal sequencing. The majority of DNA extraction kits are optimized for larger volumes (100-200µL), therefore we compared a range of DNA extraction techniques to identify the most appropriate method for this project.

The Qiagen DNeasy Blood and Tissue Kit (2.4.1.2), DLB (2.4.1.1), Qiagen QIAamp DNA Micro kit, and the NucliSENS® easyMAG® platform (BioMérieux; Crappone, France) were all investigated. Upon initial testing, both the Qiagen QIAamp DNA Micro Kit and the NucliSENS® easyMAG® resulted in the least efficient extraction of DNA and were therefore not tested any further (data not shown). Additional experiments were performed to compare the efficiency and suitability of the DLB method and Qiagen DNeasy Blood and Tissue Kit.

Firstly, the efficiency of DNA extraction for the DLB and Qiagen DNeasy Blood and Tissue Kit were compared. Cryopreserved PBMC isolated from healthy individuals were recovered (2.3.4) and cell viability and concentration enumerated by trypan blue staining and haemocytometer count (2.3.5.1). Cell suspensions were dispensed in volumes containing 106, 105 and 5x104 PBMC. DNA was extracted using the two techniques, and

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DNA concentration measured by β-actin qPCR (2.5.4). DNA yields were calculated by multiplying the measured DNA concentration by the total volume of sample and expressed as a percentage of the expected DNA yield based on the number of cells in the sample (cellular input). The DLB method isolated significantly higher yields of DNA from 105 and 5x104 PBMC (Figure 2.13a). Furthermore, DNA yields did not differ significantly when lowering buffer volume from 50µL to 25µL (Figure 2.13b).

Figure 2.13: Comparison of DNA extraction methods. (a) DNA yields of extractions using the DLB (black) and Qiagen DNeasy Blood and Tissue Kit (grey). (b) DNA yields from extractions using 50µL (purple) and 25µL (green) of DLB. Data were compared using a paired t test: *p<0.05.

As the DLB technique does not involve a DNA purification step, additional experiments were performed to confirm that the DNA extracted using this technique could be used to accurately quantify total HIV DNA by qPCR. PBMC from 3 HIV-infected individuals were recovered from cryostorage (2.3.4) then cell viability and concentration measured by trypan blue staining and a haemocytometer count (2.3.5.1). HIV-infected PBMC samples were dispensed in aliquots containing 106, 105 and 5x104 PBMC (6 aliquots each) and DNA extracted from 3 aliquots per technique using the DLB (2.4.1.1) or Qiagen DNeasy Blood and Tissue Kit (2.4.1.2). Total HIV DNA was quantified in extracted DNA samples using the total HIV pol DNA qPCR (2.5.1.1). Median total HIV pol DNA values generated by DLB and Qiagen DNeasy Blood and Tissue Kit were similar, however less variability was observed in the DNA extracted using the DLB (Figure 2.14). These results

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confirmed that DNA extracted from small cell numbers using the DLB was suitable for the quantification of total HIV DNA.

Figure 2.14: Quantification of total HIV pol DNA in DNA extracted using the direct lysis buffer (DLB) or the Qiagen DNeasy blood and tissue kit. Blue (DLB) and green (Qiagen DNeasy Blood and Tissue Kit) data points display values for 106 (circles), 105 (squares), or 5x104 (triangles) PBMC. Box and whisker plots display median, interquartile range and minimum and maximum values.

2.5.1.5 Performance of qPCR for total HIV gag & pol DNA

Over the course of the experiments in this thesis, qPCR methods of quantifying total HIV DNA levels were largely reproducible, as evidenced by the quantification of the positive control (Figure 2.15). In Chapters 3 and 5, additional controls of the same patient derived DNA, but at lower DNA concentrations, were included to approximate the concentration of the samples being tested, and revealed that the total HIV pol qPCR was less accurate at the lower DNA concentrations being tested (Figure 2.15).

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Figure 2.15: Total HIV DNA, and β-actin qPCR positive controls throughout the thesis. The quantification of the total HIV DNA positive control was compared between qPCR assays using a Mann-Whitney test.

Another issue facing the use of PCR based methods is reproducibility and standardization across qPCR assays and laboratories. Included in this thesis were two qPCR assays for total HIV DNA, with primers and probes targeting the HIV gag and pol genes. Over the course of this thesis, the two qPCR assays quantified the positive control at significantly different levels (Figure 2.15). In, contrast, quantification of the plasmid standards was reproducible, and highly similar for the two assays (Figure 2.16).

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Figure 2.16: Summary standard curves for the total HIV pol and gag DNA qPCR assays. Red and blue data points represent the mean (error bars show standard deviation) cycle threshold of the plasmid standard curves for the total HIV DNA pol and gag qPCR respectively from all qPCR reactions performed during the experiments of this thesis.

2.5.2 Integrated HIV DNA

Integrated HIV DNA levels were quantified by a nested qPCR [430, 469]. This assay specifically amplifies integrated HIV DNA by utilizing the Alu repeat element of the human genome (Figure 2.17). The 1st round primers target the HIV ltr (L-M667) and human genomic Alu repeat elements (Alu 1 and Alu 2) to specifically amplify integrated HIV DNA. The two Alu-specific primers allow for the amplification of integrated HIV DNA regardless of its orientation relative to the Alu repeat elements. The 2nd round primers target the heel of L-M667 (Lambda T) and the HIV ltr (AA55M) to specifically amplify 1st round products. The 2nd round PCR includes a probe (LTR-FL) to quantify HIV DNA by qPCR.

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Figure 2.17: Schematic of the integrated HIV DNA qPCR. Reproduced from [468]. 1st round primers target the HIV ltr region (L-M667) and genomic Alu repeat elements (Alu 1 and Alu 2). 2nd round primers target the heel of L-M667 (Lambda T) and HIV ltr (AA55M) specifically amplifying 1st round products.

Test samples were assayed in quadruplicate during 1st round reactions, and each quadruplicate then assayed in duplicate by the 2nd round nested qPCR. Serial dilutions of ACH-2 (an HIV-infected T cell clone) DNA were used as a standard curve to calculate integrated HIV DNA copies in test samples. To complete the experiments of Chapter 4, individual primers, probes, and standard curves were designed and optimized for participants 5004 and 11004 to compensate for viral sequence mismatches in these patients. 1st round reactions were performed on a Veriti® Thermal Cycler (Life Technologies) and 2nd round reactions were performed on an iQTM5 real-time PCR Detection System (Bio-Rad Laboratories).

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2.5.2.1 Integrated HIV DNA qPCR

For the standard integrated HIV DNA qPCR, 1st round reactions contained 12.5µL iQ

Supermix, 300nM Alu 1 and Alu 2, 100nM L-M667, 1µM magnesium chloride (MgCl2), st 5µL DNA template, and dH2O to a final volume of 25µL. 1 round reactions were incubated at: 94°C for 7 minutes; followed by 12 cycles of 94°C for 30 seconds, 60°C for 30 seconds then 72°C for 3 minutes; then 72°C for 7 minutes. 2nd round reactions contained 12.5µL iQ Supermix, 300nM Lambda T and AA55M, 200nM LTR-FL, 1µM st nd MgCl2, 5µL 1 round product, and dH2O to a final volume of 25µL. 2 round reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute.

For patient 5004 1st round reactions contained 12.5µL iQ Supermix, 300nM Alu 1 and

Alu 2 primers, 100nM L-M667, 1µM MgCl2, 5µL DNA template, and dH2O to a final volume of 25µL. 1st round reactions were incubated at: 94°C for 7 minutes; followed by 12 cycles of 94°C for 30 seconds, 60°C for 30 seconds and 72°C for 3 minutes; then 72°C for 7 minutes. 2nd round reactions contained 12.5µL iQ Supermix, 300nM Lambda T and st AA55M 2, 200nM LTR-FL 2, 1µM MgCl2, 5µL 1 round product, and dH2O to a final volume of 25µL. 2nd round reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute.

For patient 11004 1st round reactions contained 12.5µL iQ Supermix, 300nM Alu 1 and

Alu 2 primers, 100nM L-M667 2, 1µM MgCl2, 5µL DNA template, and dH2O to a final volume of 25µL. 1st round reactions were incubated at: 94°C for 7 minutes; followed by 12 cycles of 94°C for 30 seconds, 60°C for 30 seconds and 72°C for 3 minutes; then 72°C for 7 minutes. 2nd round reactions contained 12.5µL iQ Supermix, 300nM Lambda T and st AA55M 3, 200nM LTR-FL 3, 1µM MgCl2, 5µL 1 round product, and dH2O to a final volume of 25µL. 2nd round reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute.

2.5.2.2 Standards & controls

For use as a standard curve for the integrated HIV DNA qPCR, DNA was extracted using the Qiagen DNeasy Blood and Tissue kit (2.4.1.2) from ACH-2 cells, obtained 95

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through the AIDS Research and Reference Reagent Program (catalogue number 349), Division of AIDS, NIAID, NIH from Dr. Thomas Folks [544, 545]. DNA quantity and quality were assessed by spectrophotometry (2.4.5). ACH-2 cells contain 1 copy of integrated HIV DNA per cell, therefore, assuming that 50ng of DNA is equal to the DNA content of 8,000 cells, 50ng DNA contains 8,000 copies of integrated HIV DNA. 4-fold serial dilutions were made from 8,000 (50ng DNA) to 2 (0.012ng DNA) copies of HIV DNA per 5µL, and dispensed in 6µL aliquots, then stored at -80°C for use in individual qPCR.

For patients 5004 and 11004 standard curves were designed by generating plasmids containing patient specific PCR products spanning the HIV 3’ ltr region targeted by the primer and probe sets. 10-fold serial dilutions were made from 107 to 10 copies per 5µL, and stored as 11µL aliquots at -80°C for use in individual qPCR.

The positive control generated for the total HIV DNA qPCR (2.5.1) was also used as the positive control for integrated HIV DNA qPCR.

2.5.3 Episomal 2-LTR HIV DNA

Episomal 2-LTR HIV DNA circles were quantified using a primer and probe set specifically targeting the junction between the 5’ ltr and 3’ ltr regions unique to 2-LTR HIV DNA circles (Figure 2.11) [86]. To complete the experiments require for Chapter 4, individual primer and probe sets and plasmid standards were designed and optimized to compensate for mismatches with viral sequences for study participants 1005, 11003 and 11005. All samples were assayed in duplicate and 2-LTR HIV DNA copies determined using a set of plasmid standards. Reactions were performed on an iQTM5 real-time PCR Detection System (Bio-Rad Laboratories).

2.5.3.1 2-LTR HIV DNA qPCR

Standard 2-LTR HIV DNA qPCR reactions contained 12.5µL iQ Supermix, 280nM 2-

LTR J F and 2-LTR J R, 200nM 2-LTR Probe 1, 5µL DNA template, and dH2O to a final volume of 25µL. Reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute. 96

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For patient 1005 reactions contained 12.5µL iQ Supermix, 280nM 2-LTR J F3 and HIV reverse, 200nM 2-LTR Probe 2, 5µL DNA template, and dH2O to a final volume of 25µL. Reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 62.8°C for 1 minute.

For patient 11003 reactions contained 12.5µL iQ Supermix, 280nM 2-LTR J F and HIV reverse, 200nM 2-LTR Probe 1, 5µL DNA template, and dH2O to a final volume of 25µL. Reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 65.1°C for 1 minute.

For patient 11005 reactions contained 12.5µL iQ Supermix, 280nM 2-LTR J F and 2-

LTR J R2, 200nM 2-LTR Probe 1, 5µL DNA template and dH2O to a final volume of 25µL. Reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 67.2°C for 1 minute.

2.5.3.2 Standards & controls

A standard curve was generated using a plasmid containing a PCR product spanning the 2-LTR junction. HUT78 cells, obtained through the AIDS Research and Reference Reagent Program (catalogue number 89), Division of AIDS, NIAID, NIH from Dr. Robert Gallo [546] were infected using pNL4-3 virus. DNA was extracted from pNL4-3 infected HUT78 cells using the Qiagen DNeasy Blood and Tissue Kit (2.4.1.2), and a PCR product spanning the 2-LTR junction amplified, then inserted into a plasmid. For patients 1005, 11003 and 11005, specific standard curves were designed using patient specific 2-LTR junction PCR products. 10-fold serial dilutions of the various plasmid standards were made from 107 to 10 copies per 5µL. Dilutions were dispensed in 11µL aliquots and stored at -80°C for use in individual qPCR.

DNA was extracted from HIV (pNL4-3) infected HUT78 cells, using the Qiagen DNeasy Blood and Tissue kit (2.4.1.2), and used as a positive control for the 2-LTR HIV DNA qPCR. DNA quantity and quality was assessed by spectrophotometry (2.4.5), DNA dispensed in 21µL aliquots, and stored at -80°C for use in individual qPCR.

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2.5.4 β-actin qPCR

The quantity of total genomic DNA in test samples was measured by qPCR quantification of the reference gene β-actin using the TaqMan β–actin detection kit (Life Technologies). The TaqMan β–actin detection kit contains the forward (CGG AAC CGC TCA TTG CC) and reverse (ACC CAC ACT GTG CCC ATC TA) primers, and probe (6-FAM/TAMRA labelled) that target a 289bp conserved region of the β-actin exon 3 [547]. Reactions contained 12.5µL of iQ Supermix, 180nM of Forward and Reverse primers, 120nM probe, 5µL DNA template, and dH2O to a final volume of 25µL. Reactions were performed on an iQTM5 or CFX96 TouchTM real-time PCR Detection System (Bio-Rad Laboratories). Reactions were incubated at 95°C for 3 minutes followed by 45 cycles of 95°C for 15 seconds and 60°C for 1 minute. All samples were assayed in duplicate and DNA concentration determined using a set of DNA standards.

2.5.4.1 Standards & controls

DNA was extracted from pooled HIV negative PBMC and serial dilutions made to generate a standard curve. PBMC were isolated from HIV negative pooled buffy coats (provided by the Australian Red Cross Blood Service) by Ficoll-Paque separation (2.3.2), DNA extracted using the Qiagen DNeasy Blood and Tissue Kit (2.4.1.2), and DNA quality and quantity assessed by spectrophotometry (2.4.5). 10-fold serial dilutions were made from 100ng to 0.01ng per µL, dispensed in 11µL aliquots, and stored at -80°C for use in individual qPCR.

A positive control of pooled human DNA at a concentration of 10ng/µL was provided with the TaqMan Beta-Actin Detection Reagents Kit. The provided DNA solution was dispensed in 11µL aliquots and stored at -80°C for use in individual qPCR.

2.6 Quantification of HIV RNA

2.6.1 Plasma HIV RNA

Plasma HIV RNA was measured using the single-copy assay [257]. Briefly, 7mL of patient plasma was centrifuged to pellet virus for RNA isolation, cDNA synthesis, and qPCR. Primer and probe sequences for four patients were modified to adjust for sequence

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variability present in these individuals. The lower LOD of the single copy assay was 0.3 copies/mL. If a sample contained less than one 1 copy/mL it was assigned a value of 1 copy/mL, while those samples with a negative result were designated undetectable.

2.6.2 Cell-associated HIV RNA

CA HIV gag usRNA was quantified by RT-qPCR [257]. Reactions contained 0.6µL RT, 1.2µL RNase Inhibitor, 30µL SensiFASTTM Probe No-Rox 2x Mastermix (Bioline Reagents Ltd; London, UK), 250nm primers SK145 and SKCC1B, and 75nm SKLNA-2

(see Table 2.2 for details), 6µL RNA template, and dH2O to a final volume of 60µL. HIV gag usRNA copies were normalized for RNA input by measurement of Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) by RT-qPCR. Reactions contained 0.2µL RT, 0.4µL RNase Inhibitor, 10µL SensiFASTTM Probe No-Rox 2x Mastermix (Bioline Reagents Ltd), 250nm primers mf45 (TCG ACA GTC AGC CGC ATC TT) and mf46 (GGC AAC AAT ATC CAC TTT ACC AG), 75nm mf70 (6-FAM-AAG GTC GGA GTC

AAC GGA TTT GGT CGT-BHQ1), 2µL of a 1:10 dilution of RNA template in dH2O, and dH2O to a final volume of 20µL. Reactions were incubated at 45°C for 10 minutes, then 94°C for 2 minutes, followed by 45 cycles of 95°C for 7 seconds and 60°C for 20 seconds. Reactions were performed on a CFX96 TouchTM real-time PCR Detection System (Bio-Rad Laboratories). All samples were assayed in duplicate. HIV gag usRNA copies were determined using a set of plasmid standards described earlier for the total HIV DNA gag qPCR (2.5.1.3). GAPDH copies were determined by comparison with a dilution series of a plasmid containing the target sequence (kindly provided by Dr. Kazuo Suzuki).

2.7 Clonal sequencing of the HIV viral genome

To genetically characterize HIV DNA derived from purified Ag-sp-CD4+ T cells in Chapter 3, clonal viral sequences were generated by nested PCR, TOPO TA cloning, and sequencing of individual PCR clones.

CHAPTER 2: MATERIALS & METHODS

2.7.1 cDNA synthesis

To examine viral sequences from patient plasma samples in Chapter 3, cDNA was synthesized from DNase-treated RNA (2.4.4) using the SuperScript® VILOTM cDNA Kit (Life Technologies), according to the manufacturer’s protocol. Briefly, a reaction containing 5µL RNA, 2µL 10X SuperScript® Enzyme Mix, 4µL VILOTM Reaction Mix, and dH2O to a final volume of 20µL, was gently mixed and incubated at 25°C for 10 minutes, 42°C for 1 hour, then 85°C for 5 minutes. Synthesized cDNA was stored at - 80°C until further analysis.

2.7.2 Amplification of HIV DNA

Two nested PCR assays were used to amplify HIV DNA for TOPO TA cloning and sequencing. Primers are detailed in Table 2.3 and the location of PCR amplicons relative to the HIV genome is illustrated in Figure 2.11. Reactions were performed on a Veriti® Thermal Cycler (Life Technologies) or T100TM Thermal Cycler (Bio-Rad Laboratories).

Table 2.3: Primers used to amplify HIV DNA for clonal sequencing.

PCR Primer Sequence (5’ → 3’) bp Binding site*

GP1A CCC TTC AGA CAG GAT CAG 18 989-1006

HIV gag GP1B CCA CAT TTC CAA CAG CCC 18 2022-2039 Nested PCR GP2A GCA CAG CAA GCA GCA GCT 18 1132-1149

GP2B GTG CCC TTC TTT GCC ACA 18 1972-1989

IBF1 TGA TGA CAG CAT GYC ARG GAG T 22 1826-1847

HIV pol RT-21 CTG CTA TTA ADT CTT TTG CTG GG 23 3509-3531 Nested PCR CWF1 GAA GGA CAC CAA ATG AAA GAY TG 23 2044-2066

Frenkel 2 GTA TGT CAT TGA CAG TCC AGC 21 3301-3321

M13F GTA AAA CGA CGG CCA G 16 n/a Colony PCR M13R CAG GAA ACA GCT ATG AC 17 n/a Degenerate bases: Y = C or T; R = A or G; D = A, G or T. bp = base pair. *Relative to HIV B reference genome (HXB2).

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2.7.2.1 HIV gag nested PCR

An 858bp product in the HIV gag gene covering nucleotides 1,132-1,989 of the HXB2 reference genome (Figure 2.11) was amplified by nested PCR [548]. 1st round reactions contained 1µM GP1A and GP1B, 0.14µL Platinum® Taq DNA Polymerase High Fidelity and 2µL 10X High Fidelity PCR Buffer (Life Technologies), 250µM dNTPs, 2.5mM magnesium sulfate (MgSO4), 5µL cDNA (2.7.1) or DNA (2.4.1.1), and dH2O to a final volume of 20µL. Reactions was incubated at: 94°C for 5 minutes; followed by 35 cycles of 94°C for 1 minute, 52°C for 1 minute and 72°C for 2 minutes; then 72°C for 10 st nd minutes. The 1 round product was diluted 1 in 10 in dH2O before addition to a 2 round reaction identical to the first except with the primers GP2A and GP2B. Nested PCR products were analysed by agarose gel electrophoresis (2.7.3).

2.7.2.2 HIV pol nested PCR

A 1,277bp product from the HIV pol gene covering nucleotides 2,044-3,321 of the HXB2 reference genome (Figure 2.11) was amplified by nested PCR. 1st round reactions contained 0.25µM IBF1 and RT-21, 0.14µL Platinum® Taq DNA Polymerase High Fidelity and 2µL 10X High Fidelity PCR Buffer (Life Technologies), 250µM dNTPs,

1.8mM MgSO4, 5µL cDNA (2.7.1) or DNA (2.4.1.1), and dH2O to a final volume of 20µL. Reactions were incubated at: 94°C for 2 minutes; followed by 50 cycles of 94°C for 15 seconds, 55°C for 30 seconds and 72°C for 90 seconds; then 72°C for 10 minutes. st nd The 1 round product was diluted 1 in 10 in dH2O before addition of 5µL to a 2 round reaction containing 0.2µM of primers CWF1 and Frenkel 2, 0.14µL Platinum® Taq DNA Polymerase High Fidelity and 2µL 10X High Fidelity PCR Buffer (Life Technologies), nd 250µM dNTPs, 2mM MgSO4, and dH2O to a final volume of 20µL. 2 round reactions were incubated at: 94°C for 2 minutes; followed by 40 cycles of 94°C for 30 seconds, 55°C for 1 minute and 72°C for 90 seconds; then 72°C for 10 minutes. Nested PCR products were analysed by agarose gel electrophoresis (2.7.3).

2.7.3 Agarose gel electrophoresis

Amplified PCR products were visualised by agarose gel electrophoresis and the appropriate bands excised for PCR product purification. Products were loaded onto a 1- 2% agarose gel (Amresco; Ohio, USA) containing SYBR safe (Life Technologies) or 101

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GelRed (Biotium; California, USA) in tris-acetate EDTA (TAE) buffer, and electrophoresed at 7 volts per cm of gel for 45-60 minutes. Electrophoresed products were visualised using a GelDoc gel imaging system (Bio-Rad Laboratories). Appropriate bands were excised using the x-tracta Gel Extraction Tool (Sigma-Aldrich) and PCR products purified using the methods described below.

2.7.4 PCR product purification

Amplified PCR products were purified using gel extraction PCR purification kits. Due to changes in available products, two different kits were used during this thesis.

2.7.4.1 Wizard® purification kit

Excised PCR products were purified using the Wizard® SC Gel and PCR Clean-Up System (Promega; Madison, USA), according to the manufacturer’s protocol. Briefly, 1µL Membrane Binding Solution was added per mg of agarose gel slices containing PCR products. Samples were mixed by vortexing and incubated at 50-65°C for 10 minutes to dissolve the agarose gel. The resulting solution was applied to a Wizard® SC column, incubated at rt for 1 minute, and centrifuged at 16,060 x g for 1 minute at rt. The membrane was then washed by addition of 700µL DNA Wash Buffer and centrifugation at 16,060 x g for 1 minute at rt. This wash step was repeated using 500µL Membrane Wash Buffer. To remove remaining wash buffer, the membrane was centrifuged with the lid off for an additional 1 minute at 16,060 x g at rt. DNA was then eluted from the membrane by addition of 30µL DNase/RNase free dH2O, a 1 minute incubation, and centrifugation for 1 minute at 16,060 x g at rt.

2.7.4.2 Qiagen QIAquick gel extraction kit

Excised PCR products were purified using the QIAquick Gel Extraction Kit (Qiagen), following the manufacturer’s protocol. All centrifugation steps were performed for 1 minute at 16,060 x g at rt. Briefly, 3µL of Buffer QG was added per mg of gel, samples were mixed by vortexing and incubated at 50°C for 10 minutes. 1µL isopropanol was then added per mg gel, and the sample mixed by vortexing. The mixture was applied to the QIAquick spin column, and samples centrifuged. To wash the membrane, 500µL

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Buffer QG was added and samples centrifuged, then 750µL Buffer PE added and samples centrifuged. The QIAquick column was centrifuged for an additional 1 minute to remove residual PE buffer. DNA was eluted in 30µL DNase/RNase free dH2O by application to the membrane, incubation for 1 minute at rt then centrifugation. Purified PCR products were stored at -80°C until further analysis.

2.7.5 PCR product cloning

Purified PCR products were cloned using the TOPO® TA Cloning® Kit for Sequencing (Life Technologies), according to the manufacturer’s protocols. For the ligation of PCR products into the pCR4-TOPO® vector, the following reagents were added to a reaction in the order listed: 1-2µL purified PCR product, 1µL Salt Solution, 2-3µL dH2O, and 1µL pCR4-TOPO® vector. The reaction was gently mixed then incubated at rt for 30 minutes. 2µL of the ligation mix was added to a vial of One Shot® TOP10 cells, and reactions incubated on ice for 20 minutes. Reactions were heat-shocked for precisely 30 seconds in a 42°C water-bath then immediately quenched on ice. 250µL of rt SOC medium was added, and samples incubated horizontally in a shaking incubator, either an Ecotron (Infors HT; Bottmingen, Switzerland) or OM15 Orbital Shaking Incubator (Ratek; Victoria, Australia), at 37°C for 1 hour at 200rpm. 20-80µL of transformed cells were then spread on luria broth (LB) agar plates containing 100µg/mL ampicillin. LB agar plates were incubated at 37°C overnight, or at rt for 48-72 hours.

2.7.6 Amplification of PCR product clones

Successfully transformed clones (white colonies) were selected and amplified by a PCR using primers specific to the cloning vector, and flanking the inserted PCR product (Table 2.3). Colonies were transferred using a pipette tip into a reaction mix containing 200nM M13F and M13R (Table 2.3), 0.14µL Platinum® Taq DNA Polymerase High Fidelity and 2.5µL 10x High Fidelity PCR Buffer (Life Technologies), 200µM dNTPs, 2mM

MgSO4, and dH2O to a final volume of 25µL. Reactions were incubated at: 94°C for 5 minutes; followed by 35 cycles of 94°C for 15 seconds, 52°C for 30 seconds and 72° for 90 seconds; then 72°C for 10 minutes. Amplified PCR products were stored at -20°C until purification.

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2.7.7 Purification of amplified PCR product clones

Amplified PCR clones were purified using 96-well DNA binding plates. Due to changes in available products, two different plates were used. 50µL DNA binding buffer was added to PCR products (2.7.7). Samples were mixed and the entire volume applied to either a Whatman® 96-well PCR Clean-Up UNIFILTER® filter plate (Sigma-Aldrich) or AcroPrepTM Advance 96-well Filter Plates (Pall Corporation; NY, USA), and plates centrifuged at 1,800 x g for 4 minutes at rt using an Allegra® X-15R centrifuge (Beckman Coulter). 400µL DNA wash buffer was applied to the membrane and plates centrifuged at 1,800 x g for 6 minutes at rt. To elute purified PCR products 30µL DNase/RNase free dH2O was applied to the membrane, the plate incubated for 2 minutes at rt, and then centrifuged at 1,800 x g for 4 minutes at rt. Purified PCR products were stored at -20°C until further analysis.

2.7.8 Sequencing reaction

Purified clonal PCR products were prepared for sequencing using a BigDye® Terminator v3.1 Cycle Sequencing Kit (Life Technologies). Reactions contained 50-100nM M13F or M13R primer, 4µL 5x Sequencing Buffer and 0.1-0.5µL BigDye® Terminator v3.1 Ready Reaction Mix (Life Technologies), 1-4µL purified clonal PCR products (2.7.7), and dH2O to a final volume of 20µL. Reactions were performed on a Veriti® Thermal Cycler (Life Technologies) or T100TM Thermal Cycler (Bio-Rad Laboratories). Reactions were incubated at 96°C for 2 minutes followed by 25 cycles of 96°C for 10 seconds, 50°C for 5 seconds and 60°C for 4 minutes. BigDye® Terminator v3.1 reaction products were sent to the Australian Genome Research Facility (AGRF) for reaction clean-up and sequencing.

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3.1 Introduction

Understanding the mechanisms by which a pool of memory CD4+ T cells containing integrated HIV DNA are maintained during suppressive ART, is believed crucial to the development of strategies aimed at purging this reservoir. As discussed in section 1.4.8, two main hypotheses are currently present in the literature: 1) the long T1/2, homeostatic capacity, and Ag driven proliferation of memory CD4+ T cells ensures reservoir maintenance with little to no disruption of latency; and/or 2) low levels of ongoing viral replication and the infection of new target cells persists despite ART to continually replenish HIV reservoirs.

Several studies have established an important role for memory CD4+ T cells with the capacity for self-renewal and/or a long T1/2 in HIV reservoir maintenance [312, 348, 358]. These studies have primarily investigated the contribution of different CD4+ T cell subsets based on memory status to HIV reservoirs (see 1.4.1). Chomont et al. found that the TCM, cells characterized by extremely low levels of cellular proliferation and an intrinsic ability to survive for decades [440, 441], provided the largest contribution to the total pool of CD4+ T cells harbouring integrated HIV DNA [312, 549]. In a large cross- sectional study, Jaafoura et al. modelled the decay of integrated HIV DNA in various memory CD4+ T cell subsets, finding the long-lived subsets decayed at the slowest rates;

TSCM>TCM>TEM [358]. Chomont et al. further identified a viral reservoir in TTM cells that appeared maintained by homeostatic proliferation [312]. Similarly, several studies have found that over time the proportion of identical viral sequences in TEM increased, suggesting a role for cell proliferation [348, 550]. Together these studies strongly suggest the long T1/2 of memory CD4+ T cells and cellular proliferation are involved in the maintenance of HIV reservoirs during suppressive ART.

The evidence supporting a role for ongoing replication in HIV reservoir maintenance is somewhat less convincing. As discussed in 1.3.5.2.1 and 1.5.1.1, several studies have observed changes that may reflect HIV replication following ART intensification; increased 2-LTR HIV DNA and decreased D-dimer levels [81, 302]; a transient increase in pVL and modestly reduced IUPM [300]; or decreases in CA HIV usRNA in the ileum [293]; however, in general ART intensification has not impacted on the size of viral

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reservoirs. In addition, investigations of whether or not HIV reservoirs evolve over time have mostly revealed they are in fact genetically stable, arguing against a role for ongoing replication in the maintenance of HIV reservoirs (discussed in 1.4.9).

Several groups have made observations that may imply a role for ongoing viral replication in the maintenance of viral reservoirs during ART. Some have hypothesized that cell-to- cell transmission of HIV through virological synapses is less sensitive to, and may facilitate low levels of viral replication to occur during, suppressive ART [551]. However, this mechanism of reservoir maintenance would likely result in the evolution of HIV reservoirs, and is yet to be confirmed in vivo. Chun et al. found higher levels of total HIV DNA in activated compared to resting CD4+ T cells, and identified cross-over events between the two compartments by phylogenetic analysis [552]. They hypothesized that this reflected ongoing replication and the cycling of HIV between the two CD4+ T cell subsets, however it is also possible that their observations stem from events that occurred as HIV reservoirs were seeded pre-ART. Another study found that the decay of total and 2-LTR HIV DNA was slower over the 1st year of ART in activated (CD38+) relative to resting (CD38-) CD4+ T cells, indicating that T cell activation is involved in reservoir maintenance during the early stages of ART [540]. However these findings have not been confirmed in follow-up studies of patients with long-term undetectable pVL.

Although a role for ongoing replication in HIV reservoir maintenance has not been established, studies are yet to definitively identify the source of low-level pVL (discussed in 1.3.5.2.1). Furthermore, it is possible that ongoing replication occurs in a subset of patients, and that HIV reservoirs found in diverse anatomical locations/cellular subsets are maintained by different mechanisms.

Although measuring HIV DNA levels in CD4+ T cells separated by memory status has significantly improved our understanding of HIV reservoirs (discussed in 1.4.1 and 1.4.8), few studies have investigated HIV DNA reservoirs in memory CD4+ T cells based on their functional capacity. One study observed higher levels of total HIV DNA in influenza-specific CD4+ (infl-sp-CD4+) T cells compared to Gag- and TT-sp-CD4+ T cells, in patients receiving annual influenza vaccinations [553]. The authors hypothesized that the regular activation of infl-sp-CD4+ T cells stimulated virus production and led to 109

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the accumulation of HIV DNA within these cells, however this study was limited by the low purity of Ag-sp-CD4+ T cell subsets isolated using cytokine capture and magnetic bead separation. Following a similar hypothesis, we aimed to extend on this initial study by utilizing the CD25/CD134 co-expression assay and FACS to isolate Ag-sp-CD4+ T cells to high levels of purity, and through the genetic characterization of the viral populations they contain. By strategically selecting Ag-sp-CD4+ T cells present in the majority of HIV-infected individuals (discussed in 1.3.4), we hypothesized that comparing HIV DNA reservoirs within CMV-sp-CD4+ memory CD4+ T cells (as a model of chronic Ag exposure and the regular activation and turnover of memory CD4+ T cells; discussed in 1.3.4.2) and TT-sp-CD4+ T cells (as a model for homeostatic proliferation maintaining this particular subset of memory CD4+ T cells; discussed in 1.3.4.3) may help elucidate the pathways involved in HIV reservoir maintenance. More specifically, we hypothesized that the regularly activated CMV-sp-CD4+ T cell subset would contain HIV DNA at a higher frequency than the predominantly resting TT-sp- CD4+ T cell subset, as was the interpretation of the study of infl-sp-CD4+ T cells [553]. We also analysed Gag-sp-CD4+ T cells as, although they appear preferentially infected in untreated infection and the initial stages of ART [203], a single study of Gag-sp-CD4+ T cells in long-term treated HIV infection found no signs of an increased contribution to persistent HIV reservoirs [553].

3.2 Materials & Methods

3.2.1 Sample collection & processing

Study participants provided written and informed consent prior to the collection of 200mL WB, approved by the St. Vincent’s Hospital Sydney Human Research Ethics Committee (HREC/12/SVH77). WB was collected by venepuncture into vacutainers containing sodium heparin (BD Biosciences). Samples were split for either antigenic stimulation of WB, or for the isolation (2.3.2) and antigenic stimulation of PBMC. WB or PBMC cultures were incubated with one of the following: no Ag (negative control), SEB (positive control; 2.3.7.5.4), Gag peptides (2.3.7.5.1), CMV lysate (2.3.7.5.2), or TT (2.3.7.5.3). Cultures were stimulated for 44-48 hours under the conditions detailed in 2.3.7.1, then stained with mAb (2.3.7.2). Ag-sp-CD4+ T cell subsets and control populations were isolated by FACS on a FACSAriaTM II cell sorter (BD Biosciences)

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following the gating strategy shown in Figure 2.2, and described in section 2.3.7.3. The purity of sorted samples was assessed by acquiring a small proportion of the purified sample on a FACSAriaTM II cell sorter (BD Biosciences) and flow cytometric analysis in FlowJo (Tree Star Inc.). Lymphocytes, and memory (CD45RA-) or naïve (CD45RA+) CD4+ T cells were purified to use as a positive control for the differential measurement of HIV DNA levels within these cells (published data demonstrate that memory CD4+ T cells contain HIV DNA at the highest frequency; discussed in 1.4.1). B cells were purified for use as a negative control as they are not infected by HIV.

3.2.2 Quantification of total HIV DNA & clonal sequencing

Genomic DNA was extracted from isolated cell populations using the DLB (2.4.1.1), and total HIV pol DNA quantified by qPCR (2.5.1.1). Average qPCR efficiencies [mean ± standard deviation (SD)] were 95.3±6.1% and 87.4±4.4% (with R2 values all >0.99) for the total HIV pol DNA and β-actin qPCR respectively. The dynamic range of the total HIV pol DNA assay as determined by the plasmid (pNL4-3) standard curve (mean±SD) 6 was 3x10 copies [quantitative threshold cycle (Cq)=17.70±0.47] to 3 copies

(Cq=37.22±1.00) per reaction. Mean (±SD) values for the β-actin positive control were 9.07±1.6ng/µL (Figure 3.1). Normalised total HIV DNA copies (mean±SD) for two positive controls, DNA extracted from PBMC isolated from an HIV-infected individual (see 2.5.1.3), diluted to two different DNA concentrations of ~35ng/µL and ~3.5ng/µL, were 749.1±426.2 and 768.1±741.3 copies per 106 cells respectively (Figure 3.1).

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Figure 3.1: Positive controls for the total HIV pol DNA & β-actin qPCR. Box plots display the median, interquartile range, and minimum and maximum values for the β-actin (2.5.4.1) and total HIV pol DNA (2.5.1.3) qPCR positive controls.

Clonal HIV sequences covering portions of the gag and pol genes were generated using DNA extracted from purified cell populations, and three historic pre-ART plasma samples for each patient (Table 3.1). Plasma RNA was extracted using the QIAGEN viral RNA mini kit (2.4.3), treated with DNase (2.4.4.1), and cDNA synthesized by RT (2.7.1). Viral gag and pol genes were amplified by nested PCR (2.7.2.1 and 2.7.2.2 respectively), using extracted DNA or cDNA generated from pre-ART plasma samples. Nested PCR products were visualized by agarose gel electrophoresis (2.7.3), and purified using either the Wizard® purification kit (2.7.4.1) or QIAGEN QIAquick Gel Extraction kit (2.7.4.2). Purified PCR products were cloned using the TOPO TA cloning kit (2.7.5), individual clones amplified by PCR (2.7.6), then resulting PCR products purified using 96-well plates with DNA binding silica membranes (Sigma-Aldrich or Pall Corporation; 2.7.7). PCR products were prepared for sequencing by performing a sequencing reaction using the BigDye® Terminator v3.1 kit (2.7.8), then sent to the AGRF for reaction clean-up and sanger sequencing. As a negative control to account for PCR-induced nucleotide substitutions introduced during the generation of clonal PCR products, pNL4-3 plasmid was amplified, cloned, and sequenced following the methods described above.

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Statistical comparisons were performed in GraphPad Prism version 6 software (GraphPad Software; San Diego, CA, USA).

Table 3.1: Timing for collection of pre-ART plasma samples.

Estimated months since HIV infection

Study participant Pre-ART plasma Sorted cell populations 1 2 3 2 0.7 39.7 64.5 161.2 9 0.6 11.7 22.3 111.3 10 3.3 20.3 35.0 106.1

3.2.1 Sequence editing & analyses

Raw sequence files were edited to create consensus contigs using the freely available BioEdit software version 7.19 for Windows [554]. Sequences were tested for APOBEC induced G to A hypermutation (see 1.2.4.2) using Hypermut 2.0 software, freely available at www.hiv.lanl.gov [555]. For the assessment of G to A hypermutation, the most prominent plasma RNA sequence from the earliest pre-ART sample was used as the reference sequence. Sequences with significant APOBEC3G mediated G to A hypermutation were excluded from estimations of viral diversity, as editing by APOBEC3 does not reflect viral replication.

Phylogenetic analysis was performed in MEGA software version 6.0 [556]. Sequences were aligned as codons using MUSCLE [557]. Evolutionary history was inferred using the Maximum Likelihood method based on the General Time Reversible model [558], with 1000 bootstrap (bs) replicates. Codon-based evolutionary divergence between clonal viral sequences was estimated using the Nei-Gojobori model [559].

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3.3 Results

3.3.1 Study design & patient screening

Participants were selected from a previous study cohort investigating primary HIV and early disease research in Australia (PHAEDRA). Eligible participants were required to have commenced ART and have maintained a pVL <50 copies/mL for at least 2 years (measurements taken every 3-8 months). Prospective study participants were screened using the most recently collected cryopreserved PBMC samples for: total HIV pol DNA copy numbers in PBMC by qPCR (2.5.1.1); and the frequency of Gag-, CMV-, and TT- sp-CD4+ T cell subsets using the CD25/CD134 co-expression assay. Based on these results (Figure 3.2) patients were prioritized then approached for recruitment into the study.

Figure 3.2: Screening of potential study participants. Unique symbols represent individual study participants (see Table 3.2).

3.3.2 Study participant characteristics

11 Caucasian males were recruited into the study through the Immunology and Infectious Diseases Ambulatory Care Unit (IBAC) at St. Vincent’s Hospital, and Taylor Square Private Clinic, both located in Darlinghurst, Australia, after providing written informed consent (Table 3.2). Prior to the collection of samples, study participants had maintained

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an undetectable pVL for a median of 278 (range 129-587 weeks, with pVL measurements made on average once every 26 (range 13-39) weeks.

Table 3.2: Characteristics of study participants.

Weeks of Proximal CD4 pVL<50 Study Age ART regime pVL <50 T cell count copies/mL participant (years) copies/mL (cells/mm3) (n)

1 (▼) 54 3TC/ABC/EFV 400 525 16 2 (■) 39 EFV/FTC/TDF 162 468 7 3TC/ABC/RTV/ 3 ( ) 48 331 429 10 SQV 3TC/ABC/DRV/ 4 (●) 38 129 1860 10 RTV 5 (○) 52 3TC/ABC/NVP 587 539 23

3TC/ABC/DRV/ 6 ( ) 58 278 850 7 RAL/RTV 3TC/ABC/EFV/ 7 ( ) 37 436 907 16 FSPV/MVC/RTV 3TC/ABC/EFV/ 8 ( ) 56 455 1034 18 MVC 9 ( ) 59 FTC/TDF/RAL 241 930 11

10 ( ) 34 EFV/FTC/TDF 155 819 6

11 ( ) 48 FTC/NVP/TDF 277 676 13 Median 48 278 819 11 (range) (34-59) (129-587) (429-1860) (6-23) 3TC, lamivudine; ABC, abacavir; DRV, darunavir; EFV, efavirenz; FSPV, fosamprenavir; FTC, emtricitabine; MVC, maraviroc; NVP, nevirapine; RAL, raltegravir; RTV, ritonavir; SQV, saquinavir; TDF, tenofovir; pVL, plasma viral load.

3.3.3 Isolation of antigen-specific CD4 T cell subsets

3.3.3.1 Antigen-specific CD4+ T cell responses, cell yields & purity

Ag-sp-CD4+ T cell responses were measured as the percentage of CD4+ T cells induced to co-express CD25/CD134 (CD25+CD134+ % of CD4; see Figure 2.2 and 2.3.7.3). Results are displayed in Figure 3.6. Median (range) responses for the assay negative 115

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controls (no Ag) were; 0.031% (0.009-0.068%) for PBMC, and 0.033 (0.007-0.077%) for WB. Median (range) responses for the positive control (SEB) were 4.74% (3.0-13.6%) for PBMC, and 12.45% (5.2-19.6%) for WB. A positive response was defined as the observed response (CD25+CD134+ % of CD4) of the negative control analysed for each individual, plus two standard deviations for the combined responses of all negative controls during the study. Median (range) responses for Ag stimulated cultures were: all 11 participants had detectable CMV-sp-CD4+ T cells; 2.46% (0.903-6.03%); 9/11 participants had a discernible HIV Gag-sp-CD4+ T cell response, 0.61 (0.097-1.58%); and all 11 participants had a detectable TT-sp-CD4+ T cell response, 0.78% (0.102- 5.44%).

Figure 3.3: Antigen-specific CD4+ T cell responses. Unique symbols represent individual study participants (see Table 3.2). Box plots display median with interquartile range, minimum, and maximum values. PBMC, peripheral blood mononuclear cells. WB, whole blood.

Median (range) cellular yields of FACS purified Ag-sp-CD4+ T cell subsets are displayed in Figure 3.4a: 184,700 (24,540-487,800) for CMV-sp-CD4+ T cells; 57,800 (16,580- 235,600) for Gag-sp-CD4+ T cells; and 66,630 (12,320-641,700) for TT-sp-CD4+ T cells. Approximately half (15/31) of the isolated populations yielded less than 100,000 cells, the number hypothesized sufficient for quantification of total HIV DNA levels by qPCR. Ag-sp-CD4+ T cell subsets were isolated to high levels of purity, with the majority (26/31) greater than 90% pure (Figure 3.4b). 116

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Figure 3.4: Cellular yields (a) and purity (b) following fluorescence activated cell sorting of antigen-specific CD4+ T cell subsets. Unique symbols represent individual study participants (see Table 3.2). Box plots display median with interquartile range, minimum, and maximum values.

3.3.4 HIV DNA in antigen-specific CD4+ T cell subsets

For 5 of the 11 study participants, total HIV pol DNA levels were quantified in 2/3 or more Ag-sp-CD4+ T cell subsets (Figure 3.5). Interestingly, for patient 8, despite relatively high yields of cells, HIV DNA was not detected in some patient samples but at very low levels. In agreement with our current understanding of HIV DNA reservoirs, 6 total HIV pol DNA levels (log10 copies per 10 cells; median, range) were elevated in memory (CD45RA-) CD4+ T cells (2.629, 2.225-4.095) compared to naïve (CD45RA+) 117

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CD4+ T cells (2.375, 1.427-3850, p=0.06) and lymphocytes (1.134, 0-1.904, p=0.06). HIV DNA was detected in one of the five purified B cell samples, albeit at low levels; 6 6 0.6 log10 copies per 10 B cells. Levels of total HIV pol DNA (log10 copies per 10 cells; median, range) did not differ significantly between the three Ag-sp-CD4+ T cell subsets: CMV (2.385, 1.892-3.734); Gag (2.504, 1.775-3.7); and TT (2.549, 1.961-3.921).

Figure 3.5: Total HIV pol DNA levels in antigen-specific CD4+ T cell subsets and control populations. Unique symbols represent individual study participants (see Table 3.2). Box plots display median with interquartile range, minimum, and maximum values. ND, not detected. Paired values were compared using Wilcoxon signed rank tests. Open symbols indicate samples that were detected below the dynamic range of the plasmid standard curve.

3.3.5 Clonal HIV DNA sequences from antigen-specific CD4 T cells

To further delineate the contribution of Ag-sp-CD4+ T cells to HIV reservoirs, we characterized the viral gag and pol sequences derived from these particular memory CD4+ T cell subsets. Viral sequences were successfully amplified, cloned, and sequenced in at least 2/3 Ag-sp-CD4+ T cell subsets from 3 and 2 patients for the gag and pol genes respectively. An initial phylogenetic analysis confirmed the absence of cross- contamination between patient samples during the generation of sequence data, by demonstrating distinct clustering of clonal sequences from study participants and the plasmid pNL4-3 (Figure 3.6). 118

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Figure 3.6: Phylogenetic analysis of all clonal gag (a) and pol (b) viral sequences. Unique symbols represent individual study participants (see Table 3.2). Evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model [558], with 1000 bootstrap replicates. Trees are drawn to scale, with branch length measured in the number of substitutions per site.

To account for potential PCR-induced nucleotide substitutions, a high fidelity Taq polymerase was used, and pNL4-3 plasmid was analysed to establish a baseline number of PCR-induced nucleotide substitutions. We generated sets of viral sequences (gag n=33, and pol n=23) following the nested PCR amplification of the HIV plasmid pNL4-3. As the majority of in vivo nucleotide substitutions are synonymous, we estimated the number of synonymous changes between each pair of clonal sequences using the Nei-Gojobori model (Figure 3.7) [559]. Nested PCR amplification of the pol gene generated low levels of synonymous nucleotide substitutions, consistent with the estimated nucleotide substitution rates provided by the manufacturer for the high fidelity Taq polymerase used (Life Technologies). Nested PCR amplification of the gag gene introduced a significant number of synonymous nucleotide substitutions. This may be explained by DNA damage and PCR errors induced by PCR reaction conditions, for example extended incubations at a high temperature (94°C) [560], and excess MgSO4 [561]. To determine whether the nucleotide substitutions observed in viral populations derived from study participant 119

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samples were greater than PCR-induced levels, the number of synonymous changes relative to that observed for pNL4-3 control sets, were compared by Mann-Whitney tests.

Figure 3.7: Baseline numbers of PCR-induced synonymous changes between pairs of clonal sequences. Sets of clonal sequences were generated following nested PCR amplification of pNL4-3 plasmid. The number of synonymous changes between each pair of clonal sequences was estimated using the Nei-Gojobori model [559].

To assess whether viral sequences in Ag-sp-CD4+ T cells could be linked to historic plasma viral sequences, we also characterized gag and pol sequences from plasma viral populations collected at three time-points pre-ART (Table 3.1). Two hypotheses drove this part of the study: 1) genetic similarities between HIV DNA sequences derived from cellular HIV reservoirs during long-term suppressive ART and pre-ART plasma sequences indicate that minimal to no evolution occurs over time during suppressive ART; and 2) after establishing an evolutionary history with sequential pre-ART plasma samples, genetic similarities with viral sequences derived from cellular HIV reservoirs may be used to date the seeding of HIV reservoirs. We observed some signs of clustering between pre-ART plasma sequences and HIV reservoir sequences derived from lymphocytes (Figure 3.8c, bs 0.54), memory CD4+ T cells (Figure 3.8c, bs 0.41), and CMV-sp-CD4+ T cells (Figure 3.8a, bs 0.14). However, considering background nucleotide substitution rates and bs values, this clustering was most likely not significant.

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Figure 3.8: Phylogenetic analysis of gag (a/c) & pol (b/d) viral sequences from study participants 2 (a/b) & 9 (c/d). Evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model [558]. Trees are drawn to scale, with branch length measured in the number of substitutions per sites. Open symbols indicate replication deficient sequences as determined by the presence of stop codons or significant APOBEC3 mediated G to A hypermutation. Red circles highlight clusters of pre-ART plasma with viral sequences derived from cellular HIV reservoirs during long-term suppressive ART. The green circle highlights a cluster of replication deficient clones primarily derived from TT-sp- CD4+ T cells.

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Previous studies have identified the clonal expansion of viral sequences in TEM cells [348, 550], indicating a role for cell proliferation in maintaining HIV DNA reservoir size, however these findings have been underscored by the presence of replication deficient clones [347]. During this study, we identified a group of viral gag sequences derived primarily from TT-sp-CD4+ T cells, with clear signs of APOBEC3 mediated G to A hypermutation (Figure 3.8c), indicating they are replication deficient. Unfortunately viral pol sequences were not available from TT-sp-CD4+ T cells for this individual to confirm this pattern.

As a further comparison of viral populations derived from distinct cellular reservoirs during long-term suppressive ART, we compared the levels of viral diversity within the clonal sequences we generated (Figure 3.9). Two pathways may lead to differential levels of sequence diversity: firstly, the seeding of viral reservoirs in cell subsets may occur repetitively throughout HIV infection prior to ART; and secondly, ongoing viral replication during ART may generate increasing levels of diversity in particular subsets. As expected, viral diversity increased over time prior to the initiation of ART, reflected by changes in viral diversity of clonal sequences derived from the three pre-ART plasma samples. A pattern of higher sequence diversity also emerged in viral populations derived from memory CD4+ T cells relative to naïve CD4+ T cells, and CMV-sp-CD4+ T cells compared to Gag- and TT-sp-CD4+ T cells. Although we established baseline levels of sequence diversity introduced by the methods used, as samples contained low copy numbers of HIV DNA, we were unable to distinguish samples with multiple viral clones and low diversity, and samples with a single clone. Viral populations with diversity indistinguishable (an insignificant p value by Mann-Whitney test) from the negative control (pNL4-3) are marked by an asterisk in Figure 3.9.

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Figure 3.9: Sequence diversity of viral populations derived from pre-ART plasma & CD4+ T cell subsets during suppressive ART. Column and whisker graphs display median, interquartile range, minimum and maximum values for study participant 2 (green), 9 (maroon), and 10 (blue). n/a, not available. *Indicates populations with genetic diversity indistinguishable from the negative control (pNL4-3) as compared by Mann-Whitney tests.

3.4 Discussion

This study aimed to compare and contrast HIV DNA reservoirs within Ag-sp-CD4+ T cell subsets, and improve our understanding of HIV DNA reservoirs that prevent a cure. We hypothesized that Ag-sp-CD4+ T cell subsets with different Ag exposure patterns may act as models for the two primary hypotheses by which HIV reservoirs are maintained during suppressive ART, and, that differences in HIV DNA levels or viral populations genetics within these subsets, may provide insight into the establishment and maintenance of HIV reservoirs. The key results of this study were; total HIV DNA levels 123

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were similar in CMV-, TT-, and Gag-sp-CD4+ T cells; viral sequences derived from pre- ART plasma and immune cell subsets during long-term suppressive ART clustered together during phylogenetic analysis; replication deficient viral sequences were found in TT-sp-CD4+ T cells; and viral diversity appeared elevated in CMV relative to TT- and Gag-sp-CD4+ T cells.

However, we encountered several technical limitations which made parts of the analyses difficult if not impossible to interpret. Low cellular yields limited our ability to quantify total HIV DNA levels in Ag-sp-CD4+ T cell subsets. Furthermore, the low frequency of HIV DNA within memory CD4+ T cells derived from individuals with long-term undetectable pVL, coupled with the low cellular yields, restricted our ability to genetically characterize viral populations in Ag-sp-CD4+ T cell subsets. The use of nested PCR and TOPO TA cloning resulted in PCR-induced nucleotide substitutions that clouded our interpretation of the observed sequence diversity for the viral populations studied. In light of the above limitations we cannot rule out that with larger samples sizes, both in terms of the number of patients, and the number of Ag-sp-CD4+ T cells harvested, one might find different observations.

Nevertheless, several of the observations made in this study agree with literature indicating that cellular proliferation is the primary mechanism driving the maintenance of HIV reservoirs during ART. HIV DNA reservoirs did not appear to accumulate within regularly activated subsets of memory CD4+ T cells, indicating that antigen driven proliferation does not expand viral reservoirs through stimulating virus production. Furthermore, replication deficient HIV DNA reservoirs were found in TT-sp-CD4+ T cells, adding to evidence that homeostatic proliferation is involved in the maintenance of HIV reservoirs. Finally, viral sequences derived from pre-ART plasma were phylogenetically similar to viral sequences derived from cellular reservoirs during long- term suppressive ARTs, indicating minimal viral evolution occurs during long-term suppressive ART. However as indicated above, the absolute validity of these observations is unclear.

The finding that HIV DNA levels were similar in all Ag-sp-CD4+ T cell subsets is in contrast with the original study that found an increased total HIV DNA burden in infl-, 124

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compared to Gag- or TT-sp-CD4+ T cells [553]. It is possible that, as CMV-sp-CD4+ T cells are somewhat resistant to HIV infection (discussed in 1.3.4.2), this prevents the accumulation of HIV reservoirs within this cellular subset despite regular activation. This difference may also result from the limitations faced by both studies. The present study was limited by a small sample size due to low yields of Ag-sp-CD4+ T cells and our ability to accurately quantify HIV DNA levels. Although we were able to detect the expected differences in total HIV DNA burden in lymphocytes and memory or naïve CD4+ T cell subsets [453], the qPCR controls included demonstrated that at low HIV DNA copy numbers the accuracy of the qPCR used decreased (Figure 3.1). The study originally describing increased HIV DNA burden in infl-sp-CD4+ T cells also faced several methodological limitations. Firstly, the methods used (cytokine secretion detection and magnetic bead isolation) are susceptible to the non-specific isolation of CD4+ T cells producing cytokines due to ongoing in vivo immune responses, and a negative control with no antigenic stimulation was not described [553]. Secondly, the levels of purity observed by Jones et al. were substantially lower than in the present study; with mean (range) purities of; Gag- 71.6% (43.3-92.3%), TT- 55.3% (48.6-78.4%), and infl-sp-CD4+ T cells 94.5% (90.8-96.6%). It is plausible that, if the cells contaminating Gag- and TT-sp-CD4+ T cell populations contain HIV DNA at a lower frequency than memory CD4+ T cells, the relatively high purity of infl-sp-CD4+ T cell populations explains why HIV DNA levels were the greatest in this subset. Of note however, median levels of HIV DNA measured by Jones et al. for Gag- and TT-sp-CD4+ T cells 6 (approximately log10 2.7 copies / 10 CD4+ T cells) were similar to that observed for all 6 Ag-sp-CD4+ T cell subsets in this study (approximately log10 2.4-2.6 copies / 10 CD4+ T cells). The use of differing methods in these two studies highlights the difficulty of selecting techniques to balance specificity and efficiency when designing studies investigating HIV reservoirs in small subsets of CD4+ T cells. It is our belief that high levels of specificity are required to accurately delineate reservoirs within CD4+ T cell subsets.

In agreement with the study by Jones et al., HIV DNA reservoirs were not disproportionately found in HIV/Gag-sp-CD4+ T cells. This finding is in contrast to the elevated levels of proviral DNA observed by Douek et al. in untreated infection or patients who had recently initiated ART [203], and probably reflects the fact that while

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HIV-sp-CD4+ T cells are preferentially infected, they may also be more susceptible to cell death, and therefore less likely to seed long-lived viral reservoirs.

The observed trend towards increased sequence diversity in CMV- relative to Gag- and TT-sp-CD4+ T cells is difficult to interpret. As discussed above this study was limited by a small number of study participants with detectable HIV reservoirs within Ag-sp-CD4+ T cell subsets, and even further limited for patients in which viral sequences were successfully characterized. To examine whether the observed levels of viral diversity were driven by the low numbers of HIV DNA copy numbers within test samples, we assessed the relationship between total HIV DNA copy numbers and the level of viral diversity, finding this relationship to not be significant (Pearson correlation coefficient of 0.18, p=0.52). There are also potential biological explanations for the observed differences. The increased diversity within CMV-sp-CD4+ T cells may reflect the Ag driven proliferation and induced viral replication that occurs exclusively within this subset of cells during suppressive ART, however this hypothesis was not supported by relatively high levels of total HIV DNA. It is also possible that during untreated HIV infection, as HIV reservoirs are seeded, regularly activated CD4+ T cells, such as those specific to CMV are more susceptible to repeated rounds of reservoir seeding. Although we observed limited viral diversity in Gag-sp-CD4+ T cells, which are also highly activated during untreated HIV infection, as discussed previously, these cells may be more susceptible to HIV induced death, and therefore be less likely to repeatedly seed HIV reservoirs prior to the initiation of ART.

There are significant difficulties faced when investigating HIV reservoirs in very small subsets of cells. The low frequency of cells containing HIV DNA during suppressive ART necessitates the isolation of cell numbers that are difficult to obtain with current methods. Leukapheresis is a potential method for improving cell yields, but this would pose a significantly higher burden on patients, and it is not clear that this method would isolate lymphocytes sufficient for the analysis attempted by this study. The use of digital PCR that improves accuracy when quantifying samples with low copy numbers may assist in these types of studies (discussed in 1.4.10.2). The genetic characterization of HIV from various sources requires comprehensive sampling of viral populations to confidently draw conclusions through evolutionary analyses. Increasing cell numbers would partially 126

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address this concern, while other methods including single-proviral sequencing, which has been successfully used to genetically characterise viral populations of HIV [100, 347, 348, 550], and single-cell analysis techniques, D may be considered for future studies. However each of these techniques has its own advantages and disadvantages, with some of these techniques and their use in the study of HIV reservoirs discussed further in Chapter 7.

3.5 Conclusions

While this study yielded some interesting results by delineating the cellular subsets contributing to HIV reservoirs, the limitations of this study highlight the difficulties faced when investigating very small subsets of cells. Nevertheless, several observations of this study agreed with the growing evidence that cellular proliferation, whether Ag driven or homeostatic, appears to be the key driver of HIV reservoir persistence.

3.6 Acknowledgements

We would like to express our gratitude to: all the patients who volunteered their time for this study; the physicians who assisted in patient recruitment, Dr Kersten Koelsch, Professor Anthony Kelleher, Professor David Cooper, Dr Mark Bloch, Dr Dick Quan, and Professor Andrew Carr; and Karen McRae for the collection of patient samples. The purification of cell subsets by FACS was performed by Michelle Bailey and Yin Xu. Dr David van Bockel provided assistance with the methods for genetically characterizing viral populations. Finally, we would like to thank Professor Andrew Leigh Brown for assistance in the phylogenetic analysis of viral sequences.

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CD4+ T CELLS

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4.1 Introduction

Due to the persistence of viral reservoirs [562, 563], HIV-infected individuals are reliant on life-long ART. Long-term treated HIV infections are further characterized by elevated levels of immune activation and inflammation, as well as increased rates of SNAEs and mortality (discussed in 1.3.5.2) [318]. Memory CD4+ T cells harbouring integrated HIV DNA, the largest contributor to persistent HIV reservoirs, are thought to give rise to low- level pVL [438], which may contribute to chronic T cell activation [309, 310]. The early initiation of ART is currently being heavily scrutinized as a potential strategy for improving the management of HIV infection (discussed in 1.3.5.3) [336, 564]. Multiple studies have shown that early ART limits, but does not prevent the formation of HIV DNA reservoirs in CD4+ T cells [121, 429, 565, 566], preserves immune function [566- 569], and has been associated with lower levels of T cell activation [311, 569, 570]. The long-term benefits of early ART in relation to HIV reservoirs, low-level pVL and chronic immune activation requires further investigation.

Persistent HIV DNA reservoirs are measured by a variety of techniques (discussed in 1.4.10). The assessment of replication competent HIV by ex vivo stimulation and the detection of virus production [66] is time consuming, difficult to standardize, requires large volumes of blood, and is unsuitable for high throughput analysis. Nested real-time PCR assays measuring integrated HIV DNA appear to act as a surrogate marker for replication competent provirus [453] but may overestimate the relevant reservoir due to the presence of replication incompetent proviruses [459]. Episomal 2-LTR circular forms of HIV DNA arising from aborted integration events may reflect the ongoing production of virus and infection of new cells during ART (discussed in 1.2.5). Several studies have identified increases in 2-LTR circles following intensification with the INI RAL [81, 302], however interpretation of these results is complicated by controversy surrounding the stability of 2-LTR circles [88, 89, 296, 571]. Investigating the dynamics of total, integrated, and 2-LTR HIV DNA species during long-term ART will help clarify the relevance of their measurement in this context.

Here we present results from the extension phase of the PINT study [430, 540, 541]. The PINT study investigated the impact of RAL-containing ART on pVL, CD4+ T CA HIV

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DNA reservoirs, and T cell activation in two cohorts initiating therapy during either PHI or CHI. Initial findings, over 52 weeks of follow up, demonstrated those initiating ART during PHI experienced a more rapid decline in pVL and had reduced total and integrated HIV DNA reservoirs in CD4+ T cells. Interestingly however, no significant differences in 2-LTR HIV DNA levels at week 52 compared to baseline were observed between the 2 groups, and the rapid decline of pVL in the PHI cohort did not lead to significantly lower pVL compared to the CHI group after 1 year of ART.

4.2 Aims

Therefore, the PINT study was extended for an additional 2 years to investigate longer- term dynamics of CD4+ T CA HIV DNA, pVL and T cell activation during extended ART with RAL. Primarily this allowed us to further assess the longitudinal effects of early ART initiation on HIV DNA reservoir decay, pVL dynamics and T cell activation levels. These concurrent measurements also enabled investigations of the contribution persistent HIV reservoirs may have towards chronic T cell activation during suppressive ART. Co-incidentally, the development of resistance to INI and subsequent viremia in one study participant provided a unique opportunity to evaluate the assays used for predicting viral rebound, which will be of increasing importance as the utility and safety of ATI is reassessed. The specific aims of this chapter were therefore: 1) to assess the long-term benefits of the early initiation of ART, in particular relating to HIV DNA reservoirs size, pVL and T cell activation; 2) to monitor the dynamics of 2-LTR episomal forms of HIV DNA during long-term ART with RAL; 3) to assess the relationship between, and potential contribution of, persistent HIV reservoirs to low-level pVL and T cell activation levels.

4.3 Methods

4.3.1 Plasma HIV RNA quantification

WB collected from study participants by venepuncture into vacutainers containing EDTA (BD Biosciences). WB was processed to obtain plasma (2.3.1), then isolate PBMC by ficoll-paque separation (2.3.2). Plasma HIV RNA levels were measured using the single

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copy assay (2.6.1). This work was performed by Ven Natarajan at Leidos Biomedical Research Inc., Frederick, MD, USA.

4.3.2 CD4+ T cell isolation & DNA extraction

CD4+ T cells were isolated from 20x106 PBMC by negative bead selection and stored as DCP at -80°C (2.3.6.1). DNA was extracted from CD4+ DCP using the QIAGEN DNeasy Blood and Tissue Kit (2.4.1.2). DNA purity was assessed by spectrophotometry (2.4.5.1). All samples had an A260/A280 ratio between 1.8 and 2.0, indicating a high level of purity.

4.3.3 Quantification of HIV DNA species

Total (2.5.1.2), integrated (2.5.2), and 2-LTR (2.5.3) HIV DNA species were quantified by qPCR. When required, HIV DNA qPCR assays were redesigned to account for mismatches between primers and probes with individual patient sequences: total n=1, integrated n=2, 2-LTR n=3.

HIV DNA copy numbers were calculated in reference to standard curves and were normalized for DNA input by measurement of β-actin by qPCR, then expressed as HIV DNA copies per 106 CD4+ T cells. Approximately 500ng DNA was assayed per replicate for each qPCR which equates to 80,000 cells [572]. All assays had a sensitivity of 1 copy per replicate, the LOD was therefore 1 copy per 80,000 cells, equating to 12.5 copies per 106 cells. The limit of quantification (LOQ) for each HIV DNA qPCR was calculated by multiplying the LOD by the minimum value of the standard curve used to quantify test samples. For total and 2-LTR HIV DNA the minimal standard curve concentration was 10 copies per replicate, therefore the LOQ was 125 copies per 106 cells. For the integrated HIV DNA qPCR, the minimum standard curve concentration was 8 copies per replicate, therefore the LOQ was 100 copies per 106 cells.

Average qPCR efficiencies were: 95±2% (total), 94±4.5% (2-LTR), 88.4±5.8% (integrated) and 89.9±5% (β-actin). The dynamic range of the total HIV gag DNA assay 7 as determined by the plasmid (pNL4-3) standard curve was 10 copies (Cq=16.95±0.41) to 3 copies (Cq=37.49±0.67) per reaction. Mean (±SD) values for the β-actin positive 132

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control were 8.24±0.9ng/µL. Normalised HIV DNA copies (mean±SD) for qPCR positive controls analysed in each qPCR assay were: total, 1,168±484; integrated 61±51; and 2-LTR 5,490,589±499,471 copies per 106 cells (Figure 4.1).

Figure 4.1: Positive controls for HIV DNA species & β-actin qPCR. Box plots display the median, interquartile range, and minimum and maximum values for the β-actin (2.5.4.1) and total HIV gag DNA (2.5.1.3), integrated HIV DNA (2.5.2.2) and 2-LTR HIV DNA (2.5.3.2) qPCR positive controls.

4.3.4 Calibrating the measurement of HIV DNA species

As the analysis performed in this chapter formed the extension phase of the PINT study, steps were taken to ensure the measurement of HIV DNA species was consistent with the 1st year of this study. Kristin McBride optimized the various qPCR used for this study, and performed the experiments comprising the 1st year of the study. Although the techniques used were consistent over the two phases, variability may have been introduced by several factors: time, experiments were conducted approximately 3 years apart; batch to batch variation of the reagents used; differences introduced during the quantification of plasmid standards, for example spectrophotometry measurements to calculate copy number; and manual handling by the two users. Several steps were implemented to ensure consistency. Firstly, practice qPCR runs were performed to compare the quantification of plasmid standards, positive controls, and qPCR efficiency

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with results from the 1st year of the study (data not shown). Secondly, 2 time-points (week 24 and 52) from the 1st year of the study were included in the analysis of the extension phase. Leftover DNA from the stored samples at -80°C was used for this purpose and thus allowed a direct comparison of data generated during the two phases of the study.

Over the two phases of the study, the measurement of total (R2=0.79, p<0.01) and integrated (R2=0.73, p<0.01) HIV DNA species correlated strongly and significantly (Figure 4.2a/b). While the measurement of 2-LTR circles (R2=0.32, p<0.01) also correlated significantly (Figure 4.2c), the relationship was not as strong. Mean non-log- transformed data were similar from the 1st year of the study and the extension phase, with respective HIV DNA copies per 106 CD4+ T cells of: total 1,803 and 2,810; integrated 527 and 499; and 2-LTR 555 and 556 (log-transformed data are shown in Figure 4.2d/e/f respectively). To combine the two sets of data, results from the extension phase were adjusted according to the ratio of the mean values described above: total 0.64; integrated 1.06; and 2-LTR 1.00. As weeks 24 and 56 were assayed for HIV DNA copies in two separate qPCR, the average was taken for the combined 3 year analysis.

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Figure 4.2: Calibrating the measurement of HIV DNA species over the two phases of the PINT study. The limit of detection (dashed and dotted lines) and limit of quantification (dotted lines) are indicated for each assay. Correlations were assessed by linear regression analyses.

4.3.5 T Cell immunological phenotyping

T cell phenotype and activation levels were assessed by mAb staining and flow cytometric analysis of fresh anti-coagulated WB. The following subsets were monitored throughout the study: (1) Naïve CD4+ T cells (CD4+CD45RO-CD62L+), (2) TCM

(CD4+CD45RO+CD62L+), (3) TEM (CD4+CD45RO+CD62L-), (4) TTD (CD4+CD45RO-CD62L-), (5) activated CD4+ T cells (CD4+CD38+HLA-DR+), (6) activated CD8+ T cells (CD8+CD38+HLA-DR+), (7) Ag activated CD4+ T cells (CD4+CD38++CCR5+, [573]), (8) Ag activated CD8+ T cells (CD8+CD38++CCR5+), (9) adaptive Treg (CD4+CD45RO+CD25+CD127-), (10) natural Treg (CD4+CD45RO- CD25+CD127-), (11) gut-homing memory CD4+ T cells (CD4+CD45RO+Integrin-β7+), (12) non-gut-homing memory CD4+ T cells (CD4+CD45RO+Integrin-β7-). Stained cells were acquired on a LSR II flow cytometer (BD Bioscience), and data analysed using FlowJo (Tree Star Inc.). This work was performed by John Zaunders and Michelle Bailey

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4.3.6 Statistical analyses

Results are presented separately for the PHI and CHI study populations as medians with interquartile range (IQR). Comparisons between PHI, CHI and UHC cohorts were made using two-sided Mann-Whitney U-tests in GraphPad Prism version 6 software (GraphPad Software). Decay of HIV nucleic acid species was assessed by mixed linear effects modelling using Matlab (The Mathworks; Natick, NA, USA). Patient data used in decay calculations were right-censored relative to the particular assay LOQ. The relationship between HIV DNA species, pVL, and T cell activation measurements was assessed by linear regression. Mathematical modelling was conducted by John Murray.

4.4 Results

4.4.1 Study design & patients

The PINT trial was an open-label study consisting of two cohorts of treatment naïve patients initiating RAL-containing ART during either PHI (n=8) with an estimated HIV- infection duration of less than 6 months, or CHI (n=8) with a documented established HIV infection of at least 12 months. Baseline characteristics are described in Table 4.1. Primary infections were identified as either acute (n=3; ≤3 bands on a Western Blot, and one or both of either a positive p24 Ag or proviral DNA) and early [n=5; positive detuned HIV subtype B, E and D (BED) Enzyme-linked immunosorbent assay (ELISA), or previously negative serology within 6 months of confirmed positive serology]. The PINT trial is registered at ClinicalTrials.gov (NCT00641641). One individual from the CHI cohort dropped out after week 52 of the study. To establish normal levels of T cell activation, UHC were recruited from St. Vincent’s Hospital and UNSW Australia, Sydney, Australia (HREC\13\SVH\145).

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Table 4.1: Study participant characteristics, serious adverse & new AIDS events.

Primary HIV Chronic HIV Study participant characteristics infection (n=8) infection (n=8)

Male (n) 8 (100%) 8 (100%)

Age in years at day 0 (median and range) 44.3 (21.3-59.5) 38.3 (27.3-50.3)

Likely mode of transmission (n) 8 8 Homosexual intercourse

Duration of infection in months at day 0 2.7 (1.4-6.8) 47.7 (16.8-277.1) (median and range)

Current AIDS (n) 0 0

Primary infection (n)

Acutea 3 0

Earlyb 5 0

Patients with primary infection symptoms (n) 7 0

Serious adverse events (n) 0 0

New AIDS on study 0 0 a≤3 band on western blot and one or both of either positive p24 Ag or positive proviral DNA. bPositive detuned or BED ELISA result or previously negative serology within 6 months of confirmed positive serology.

Results from the 1st year of the trial have been described in detail [430, 541]. This chapter focused on HIV nucleic acids and T cell dynamics during successful viral suppression, defined as from week 24 onwards when pVL was relatively stable (Figure 4.3a).

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Figure 4.3: Plasma HIV RNA & CD4+ T cell-associated HIV DNA species. Medians and interquartile ranges for the primary HIV infection (black squares and solid lines) and chronic HIV infection (red triangles and dashed lines) cohorts. Time points in which medians were significantly different (p<0.05 by Mann-Whitney U test) are indicated by an asterisk. The limit of detection (dashed and dotted lines) and limit of quantification (dotted lines) are indicated for each assay.

4.4.2 ART, CD4+ T cell-associated HIV DNA, & plasma viral load

We first compared decay of HIV nucleic acids between the two cohorts by Mann-Whitney tests of patient specific decay rates determined by linear regression. No significant differences were observed: pVL p=0.16, total p=0.23, integrated p=0.64, and 2-LTR p=0.32. Comparisons between cohorts of 2-LTR and integrated HIV DNA decay were complicated by limited numbers in the PHI cohort (2-LTR n=4, integrated n=3) of

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quantifiable values above the assay LOQ. Therefore, decay rates were measured for the entire study population using mixed linear effects modelling.

Plasma HIV RNA (Figure 4.3a)

Decay of pVL was not significantly different from no decay, with a T1/2 of 1,008 days [95% confidence interval (CI), 152 to 2,168]. Plasma viral load levels were higher in the

CHI than PHI cohort, reaching significance at weeks 78 (log10 1.32 vs 0.78, p=0.02) and

104 (log10 1.51 vs 0.81, p=0.03), and trending at weeks 32 (log10 1.32 vs 0.67, p=0.07),

52 (log10 1.48 vs 0.83, p=0.12), 130 (log10 1.32 vs 0.15, p=0.06) and 156 (log10 1.26 vs 0.39, p=0.08). One study participant in the CHI group developed resistance to RAL, most likely due to incomplete adherence to the ARV regimen, and exhibited plasma viremia (12,383 copies/mL plasma) at the final study visit.

Total & integrated HIV DNA in peripheral CD4+ T cells (Figure 4.3b/d) Decay of total and integrated HIV DNA species was significantly different from no decay, with a respective T1/2 of 1,337 days (95% CI, 779 to 1,895) and 828 days (95% CI, 590 to 1,066). For the entire duration of the study, the CHI cohort continued to harbour significantly higher levels of total and integrated HIV DNA than the PHI cohort. From week 52 onwards, for the majority of the PHI cohort (6/7), detectable integrated HIV DNA levels were below the LOQ.

Episomal 2-LTR HIV DNA in peripheral CD4+ T cells (Figure 4.3c) Episomal 2-LTR HIV DNA was intermittently detectable in 12/15 study participants (5/8 PHI; 7/7 CHI). Specific primer and probe sets were redesigned for 3 of the 12 study participants with detectable levels. 2-LTR circular HIV DNA decayed significantly with a T1/2 of 772 days (95% CI 320 to 1,224). The difference in 2-LTR HIV DNA levels between the CHI and PHI cohort was less apparent than for total or integrated HIV DNA, reaching significance only at weeks 78 (log10 2.74 vs 2.50, p=0.03) and 104 (log10 2.52 vs 1.10, p=0.04), and trending at week 130 (log10 2.61 vs 2.19, p=0.10). At week 104 of ART, 2-LTR HIV DNA was undetectable in the majority (3/5) of the PHI cohort.

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4.4.3 ART & T cell activation

For both cohorts, the proportion of CD4+ (Figure 4.4a/c) and CD8+ (Figure 4.4b/d) T cells co-expressing CD38+HLA-DR+ or CD38++CCR5+ decreased rapidly following ART initiation. From week 24 onwards, both measurements of T cell activation remained mostly stable on CD8+ T cells but increased on CD4+ T cells in the final study visits. Total activated (CD38+HLA-DR+), but not Ag activated (CD38++CCR5+) CD4+ and CD8+ T cells trended towards a modest but often significant increase relative to the UHC. After 24 weeks of ART, no significant differences were observed between the CHI and PHI cohorts for both measures of T cell activation.

Figure 4.4: CD4+ & CD8+ T cell activation levels. Medians with interquartile range for the primary HIV infection (PHI; black squares and solid lines) and chronic HIV infection (CHI; red triangles and dashed lines) cohorts. Mann-Whitney U tests were used to compare PHI, CHI, and uninfected healthy controls (UHC). A significant p value (<0.05) is indicated by; PHI⃰ vs CHI, CHI vs UHC, and +PHI vs UHC. The dashed (median) and dotted (25th/75th percentile) lines represent values from the UHC cohort. 140

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4.4.4 Plasma viral load, HIV DNA & T cell activation

During suppressive ART (week 24 onwards), low-level pVL and CD4+ T CA total HIV gag DNA respectively, correlated weakly but significantly with the proportion of CD8+ T cells co-expressing HLA-DR+CD38+ (rho=0.34, p<0.01 and rho=0.20, p=0.046) and CD38++CCR5+ (rho=0.23, p=0.02 and rho=0.21, p=0.04). No other significant correlations were observed (Table 4.2).

Table 4.2: Associations between CD4+ or CD8+ T cell activation & HIV DNA species or plasma viral load during suppressive ART (week 24 to 156 of this study).

CD4+ T cell-associated HIV DNA levels Plasma viral Measurement of T cell load activation Integrated Total 2-LTR

rho p value rho p value rho p value rho p value

Total Activated CD4+ T Cells 0.07 0.48 -0.04 0.73 0.04 0.68 0.06 0.59 (HLA-DR+CD38+ % of CD4+)

Ag Activated CD4+ T Cells -0.01 0.89 -0.08 0.55 <0.01 0.98 <0.01 0.97 (CD38++CCR5+ % of CD4+)

Total Activated CD8+ T Cells 0.34 <0.01 0.12 0.30 0.20 0.046 0.21 0.07 (HLA-DR+CD38+ % of CD8+)

Ag Activated CD8+ T Cells 0.23 0.02 0.19 0.11 0.21 0.04 0.13 0.25 (CD38++CCR5+ % of CD8+)

Associations were assessed using Spearman’s rank correlation coefficient test.

All species of HIV DNA correlated significantly with each other from week 24 onwards of this study (Figure 4.5a/b/c). The strongest association was between total and integrated HIV DNA (rho=0.83, p<0.01). Total (rho=0.46, p<0.01) and integrated (rho=0.53, p<0.01) but not 2-LTR (rho=0.24, p=0.051) HIV DNA correlated significantly with pVL (Figure 4.5d/e/f).

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Figure 4.5: Correlations between plasma viral load & CD4+ T cell-associated HIV DNA species during suppressive ART (week 24 to 156 of this study). The limit of detection (dashed and dotted line) and limit of quantification (dotted line) of the HIV RNA and DNA assays are indicated on the individual graphs. Unique symbols represent individual study participants from chronic HIV-infected (open symbols) and primary HIV-infected (closed symbols) cohorts. Correlations were assessed by Spearman’s rank correlation coefficient test.

4.4.5 Viral rebound in one study participant

At the final time point the development of plasma viremia in one CHI participant was not reflected by increases in CD4+ T cell HIV DNA reservoirs (Figure 4.6a). As expected, total activated (CD38+HLA-DR+) CD8+ T cells and the CD8+ T cell count increased concurrently with the development of viremia (Figure 4.6b/d).

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Figure 4.6: HIV nucleic acid species & T cell phenotypes for the one participant who developed integrase resistance & developed plasma viremia at the final study visit.

TN, naïve CD4+; TCM, central memory CD4+; TEM, effector memory CD4+; TTD, terminally differentiated CD4+; nTreg, natural T regulatory CD4+; aTreg, adaptive T regulatory CD4+; TGH, gut-homing CD4+; TNGH, non-gut-homing CD4+.

4.5 Discussion

ART has significantly transformed the course of the HIV pandemic, controlling disease progression and preventing the development of AIDS. While current WHO guidelines recommend ART initiation at a CD4+ T cell count <500 cells/µL [256], several countries encourage initiating treatment as soon as possible after diagnosis [564], and recent findings from the INSIGHT START study will likely lead to a change in WHO ARV guidelines (discussed in 1.3.5.3 and Chapter 7). Multiple groups, including ourselves, have shown that early ART limits the formation of the HIV DNA reservoir [430, 541, 565, 566, 570]. Here we demonstrated, in a longitudinal study, that the reduction in peripheral HIV DNA reservoirs resulting from early initiation of ART is maintained for 3 years of therapy. Furthermore, the limited and similar decay rates of HIV DNA 143

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reservoirs in both cohorts, observed during the later stages of this study, indicates the timing of ART is an important determinant of reservoir size. Although other groups sampling a larger study population have observed a more rapid decay rate of total HIV DNA reservoirs in patients receiving long-term ART initiated in PHI compared to CHI [574-576], both data sets suggest the difference in HIV DNA reservoir size generated by early ART may not be reduced by extended periods of therapy. Long-term studies of HIV DNA reservoir decay in cohorts initiating ART at different stages of HIV infection will further assist evaluation of the full benefits of early ART.

While the identification of patients in the very earliest stages of HIV infection is overwhelmingly impractical, initiating ART within 3 days of SIV infection in a non- human primate model was still unable to completely block formation of DNA reservoirs [121]. Although reservoir formation is not fully inhibited by early ART initiation, this approach may still provide clinical benefits. Recent results from the SPARTAC trial linked reductions in HIV DNA reservoirs with a prolonged time to viral rebound following treatment interruptions [521]. The PTC group from the VISCONTI cohort, a subset of patients initiating ART during PHI with small viral reservoirs were able to control their infection following treatment interruption [346]. Despite evidence of such a potential link, caution must be taken given the eventual rebound observed in several recent cases involving undetectable reservoirs [495, 577]. During this study, the lack of, or delay to, increases in HIV DNA species following the development of resistance and plasma viremia in one study participant questions the use of HIV DNA reservoirs for predicting viral rebound. Additional assays for the measurement of all HIV DNA reservoirs capable of fuelling viral rebound need to be investigated, and, along with research of those initiating ART during PHI, should be considered when designing studies assessing ATIs.

Also important for evaluating the benefits of early ART is the impact it has on chronic T cell activation, which is believed to result from several factors, including microbial translocation and low-level HIV transcriptional activity (discussed in 1.3.5.2.2). In contrast to previous studies, we did not observe reduced levels of CD8+ T cell activation in those initiating ART during PHI relative to CHI [570, 578]. While in untreated infections HIV DNA levels are linked with pVL and T cell activation, the scenario during 144

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suppressive ART is much less clear. During this study HIV DNA reservoirs were associated with pVL, a relationship previously identified [309], and, while we also observed a weak link between pVL and CD8+ T cell activation, previous studies have indicated this correlation disappears as pVL decreases to very low levels [579]. Others have linked HIV DNA reservoirs directly with T cell activation [310, 311], yet again, while we identified a weak relationship between these parameters, this correlation may disappear after extended periods of highly successful ART (10 years of undetectable pVL in [580]). Taken together, these data indicate that the contribution made by persistent reservoirs to chronic T cell activation is small, and this may be accounted for by extended periods of highly suppressive ART. It should be noted however, that the ability of this study to identify significant correlations between T cell activation and measures of HIV, and the strength of these correlations, is limited by the relatively low numbers of patients, and the low levels of pVL and HIV DNA species. Finally, other facets of the immune system such as the inflammatory response, as reflected by the bio-markers IL-6 and D- dimer, appear to play a stronger role in the pathogenesis leading to SNAEs and mortality [581].

The stability of 2-LTR HIV DNA circles and their relevance for monitoring viral reservoirs during suppressive ART remains unclear. In the context of INI containing ART, increases in 2-LTR circles may reflect recent HIV replication and new infection of target cells. The absence of a significant association between 2-LTR circles and pVL during this study argues against such a link, however, again this study was limited by the low levels of pVL and 2-LTR circles observed. Measurement of 2-LTR circles during ART appears complicated by the low frequency and high variability of this HIV DNA species. Repeat qPCR measurements using the same DNA samples were less consistent for 2-LTR circles compared to total or integrated HIV DNA. Towards the later stage of our study, 2-LTR circles approached the LOD of the assays used, and, as reported by others, were frequently undetectable [453, 582]. Interestingly, similar proportions of patients with undetectable 2-LTR circles was recently observed in the absence of an INI [583]. Furthermore, the decay rates of total, integrated, and 2-LTR were indistinguishable once pVL was relatively stable. We hypothesise this reflects a common cause, perhaps decay of the cells harbouring these molecules. While 2-LTR HIV DNA circles do not appear to provide additional information when monitoring persistent HIV DNA reservoirs

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during long-term ART, their measurement may elicit unique information during interventions designed to perturb the steady state of HIV reservoirs during suppressive ART.

4.6 Conclusions

The initiation of ART during primary, compared to CHI, generated sustained reductions in peripheral CD4+ T CA HIV DNA reservoirs and moderately reduced pVL. The impact this may have on chronic T cell activation is less clear: although low-level pVL was associated with CD8+ T cell activation, initiation of ART during PHI or CHI did not impact differentially on T cell activation. The role that restricting the early formation of HIV DNA reservoirs may have in the management of HIV infections, and the potential clearing of HIV reservoirs during the testing of curative strategies, deserves further investigation.

4.7 Acknowledgements

Other members of the PINT study team are: Kristin McBride, Michelle Bailey, Katherine Marks, Nabila Seddiki at the Centre for Applied Medical Research, St Vincent’s Hospital Sydney, Sydney, New South Wales, Australia; Linda Gelgor, Christoph Boesecke and Claudia Mische at the Kirby Institute, UNSW Australia, Sydney, New South Wales, Australia; Mark Danta at St Vincent’s Hospital Sydney, David Baker at East Sydney Doctors, Mark Bloch at Holdsworth House Medical Practice, and Robert Finlayson at Taylor Square Private Clinic, Sydney, New South Wales, Australia; and Yunden Baldramaa at Leidos Biomedical Research Inc., Frederick, Maryland, USA.

The quantification of plasma HIV RNA by single copy amplification was conducted by Ven Natarajan at SAIC-Frederick, USA. The immunophenotyping work was performed by John Zaunders and Michelle Bailey, and John Murray modelled the decay of HIV nucleic acids. We would also like to thank Tracey Barrett for her fantastic administrative support and Maria Piperias, Bertha Fsadni, Kate Merlin and Julie Yeung for laboratory support. Finally, our gratitude goes to the patients who volunteered their time for this study.

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5.1 Introduction

As discussed in section 1.4.4, several factors make LN a unique and potentially important location for the persistence of HIV reservoirs. In addition, LN are involved in all stages of HIV infection, making them important sites for studies of HIV pathogenesis.

HIV infection is characterized by the systemic, progressive depletion of CD4+ T cells and dysregulation of the immune system (discussed in 1.3.3). CD4+ T cells, the main contributors to HIV reservoirs (see 1.4.1), are present at the greatest concentration within LN, however, CD4+ T cell loss in LN is only moderate in rhesus macaques infected with SIV, in contrast to the reported massive depletion in the GALT [164, 169]. Some studies even suggested that the number of CD4+ T cells in LN may actually increase [584, 585], and that this is associated with T cell proliferation [586]. To date, cross-sectional studies of HIV reservoirs have mostly identified a higher frequency of HIV DNA and RNA in CD4+ T cells from GALT compared to WB [387, 453, 531]. However, studies of HIV reservoirs within LN are limited and have found similar levels of HIV DNA in cells derived from LN and WB [312, 347]. The relative preservation of CD4+ T cells in the LN may make this compartment a major cellular reservoir for HIV [587], and the importance of HIV reservoirs in LN deserves further investigation.

HIV and SIV infections are notable for their induction of hyperplastic GC, both the site of production of anti-virus antibodies, and potentially a sanctuary for viral persistence.

TFH, a CD4+ T cell subset found exclusively within lymphoid tissues, are concentrated within GC [588]. During HIV/SIV infection, not only are TFH cells increased in number [589-593] along with increased levels of GC B cells and hypergammaglobulinaemia, but

TFH also harbor very high levels of HIV [589, 590] or SIV DNA [593]. HIV virions are also detected on the processes of FDC within GC, in numbers that exceed all other anatomical sources [587, 594-596]. In experimental models, infectious virions can be recovered from FDC for months after initial infection [597], and there is evidence these FDC associated viral particles are not completely eliminated by ART [598]. Finally, a recent study found that anti-viral CD8+ T cells were excluded from GC [396]. Coupled with evidence that ARV may be partially excluded from LN [395], these findings present

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the LN as a distinct site that contains high levels of cell types that are important contributors to HIV reservoirs during ART.

One key problem of treated HIV infection is the persistently elevated levels of immune activation and chronic inflammation, which may be associated with SNAEs (discussed in 1.3.5.2). Such ongoing immune activation may play a role in the maintenance of the HIV reservoir [540, 599], while persistent low reactivation of latent HIV probably contributes to activation (discussed in 1.3.5.2.2). If LN act as a sanctuary for viral persistence, they will be an important site to study in relation to chronic immune activation.

Although the characterization of lymphoid tissue may generate important information in regards to HIV pathogenesis and persistence during ART, prospective immunological and virological studies of HIV infection within lymphoid tissue are limited due to the difficulty of accessing these tissues. FNB is a quick, inexpensive, and minimally invasive method for accessing lymphoid tissue [600]. Several studies have utilized FNB to investigate LN at the beginning of the ART era during HIV infection [600-603], however this was prior to the identification of TFH, and it is currently unclear whether FNB isolate lymphoid cells sufficient for analysis of cellular immunophenotypes and HIV reservoirs using up-to-date methodologies. We recently demonstrated the feasibility of using FNB to study TFH and GC B cells in peripheral LN in a non-human primate model of SIV infection [604]. During our SIV study, we showed FNB isolate lymphoid cells that were representative of the total LN, were not contaminated by blood, and could be repeatedly performed on the same subject.

The aim of this study was therefore to investigate the feasibility of using ultrasound- guided FNB of inguinal LN to study HIV pathogenesis and persistence. We hypothesized FNB would yield LN resident lymphocytes sufficient for simultaneous quantification of HIV reservoirs and transcriptional activity by qPCR, and the analysis of complex T and B cell phenotypes by polychromatic flow cytometry.

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5.2 Materials & Methods

5.2.1 Study participants

Study participants were recruited through IBAC at St. Vincent’s Hospital, Darlinghurst, Australia. Exclusion criteria were: CD4+ T cell count <200 cells/mm3, anaemia, bleeding diathesis, allergy to local anaesthetics, suspected or diagnosed malignancy, pregnancy or pVL in the ART group >20 copies/ml by COBAS®AmpliPrep / COBAS®TaqMan® HIV Test, v2.0. This study was approved by the St Vincent’s HREC (12/SVH/232). All participants provided written, informed consent after the nature and possible consequences of the study was explained.

5.2.2 Sample collection

Ultrasound-guided FNB was performed under local anaesthesia by a trained radiologist and assisted by a fully trained ultrasound technician, and involved 5 passes of a 25-gauge needle into a single LN in the inguinal chain. A portion of the first pass was taken for cytological examination, and the remaining passes transferred into R10 media. Cells were pelleted by centrifugation at 200 x g for 7 minutes then re-suspended in 1mL R10 media.

WB was collected into vacutainer tubes containing acid-citrate-dextrose anti-coagulant (BD Biosciences), and PBMC isolated by density-gradient centrifugation on Ficoll-paque (Bovogen; 2.3.2).

5.2.3 Cellular yields from lymph node fine needle biopsy specimens

40µL of the LN FNB cell suspension was counted for the cellular yield of lymphocyte subsets by mAb staining (using mAb in panel A of Table 5.1) in TrucountTM tubes (BD Biosciences; 2.3.5.2). In parallel, 100 L of WB was stained in a 5mL flow tube as above. Gating strategies are detailed in Figure 5.1.

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Table 5.1: Monoclonal antibodies for cellular yield & immunophenotyping.

Channel A B C

R670 CD56-APC PD-1-AF647 Bcl6-AF647

R710 CD3-AF700 CD4-AF700 CD3-AF700

R780 CD20-APC-Cy7 CD8-APC-Cy7 CD20-APC-Cy7

B525 CD8-FITC CD25-FITC Ki67-FITC

B695 CD45-PerCP CXCR5-PerCP-Cy5.5 CD45-PerCP

V450 CD16-PB CD127-BV421 IgD-BV421

V550 CD15-V500

G525 CD4-PE ICOS-PE CD19-PE

G610 CD14-PE-CF594 CD45RA-PE-CF594 CD27-PE-CF594

G780 CD33-PE-Cy7 CD3-PE-Cy7 CD38-PE-Cy7

CD, cluster of differentiation; PD-1 programmed cell death protein 1; Bcl6, B-cell lymphoma 6; IgD, Immunoglobulin D; ICOS, Inducible T-cell co-stimulator; APC, allophycocyanin; APC- Cy7, allophycocyanin-cyanin dye 7 tandem conjugate; AF647, Alexa Fluor 647; AF700, Alexa Fluor 700; FITC, fluorescein isothiocyanate; PerCP, Peridinin Chlorophyll Protein; PB, Pacific Blue; BV, Brilliant Violet ; PE, phytoerythrin.

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Figure 5.1: Gating strategy for Trucount® assessment of absolute cell counts. (a) Gating strategy to Trucount® beads. (b) Lymphocytes were identified as SSClowCD45+, and further subdivided into B cells (CD20+CD3-), CD4+ T cells (CD3+CD20-CD4+), and CD8+ T cells (CD20-CD3+CD8+). Monocytes/macrophages were identified as SSC- AmedCD45+CD33+CD14+. (c) Neutrophils were identified as a CD15+SSC-Amid-high population.

5.2.4 Immunophenotypic analysis of whole blood & lymph node fine needle biopsy specimens

WB or 120µL of the LN FNB cell suspension was stained with mAb, acquired on an LSR II flow cytometer (BD Biosciences), and data analysed using FlowJo software (Tree Star Inc.). For the phenotyping of T cell subsets: 40L of LN FNB cell suspension or 100L of WB was incubated with mAb listed in panel B (Table 5.1) for 15 minutes in the dark at rt; RBC lysed by addition of 500µL Optilyse® C lysing solution (Beckman Coulter) and incubation at rt for 10 minutes; then samples washed and fixed by resuspension in 0.5% PFA (Proscitech). For the phenotyping of B cell subsets: 80L of LN FNB cell suspension or 100L of WB was stained with the mAb listed in panel C (Table 5.1) as above; RBC lysed by addition of 2mL 1× FACS Lysing Solution (BD Biosciences) and incubation at rt for 10 min. Cells were washed then treated with the Forkhead box P3 Buffer set (BD Biosciences) to permeabilize the cellular and nuclear membranes

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according to manufacturer’s instructions. Permeabilized cells were stained with the intracellular/intranuclear antibodies Ki-67-FITC (10L) and Bcl6-AF647 (2.5L) in dark at rt for 30 min, then washed and fixed as above. Gating strategies are detailed in Figure 5.2 and Figure 5.3.

Figure 5.2: Gating strategy for germinal centre (GC) B cells, plasmablasts & activated T cells. Lymphocytes were identified as SSC-AlowFSC-Alow-mid, then CD45+ events. Activated T cells were identified as CD3+CD19- then CD38+Ki67+ events. Total B cells were identified as CD19+CD3- events, then further subdivided into plasmablasts (CD38++CD27++) and germinal centre (GC) B cells (CD38+Ki67+ then Bcl6+CD20+).

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Figure 5.3: Gating strategy for follicular T helper cells (TFH) and regulatory T cells (Treg). CD4+ T cells were identified as CD3+CD4+ then subdivided into Treg

neg-mid (CD25+CD127 ) and TFH (CD45RA- then PD1+CXCR5+ then ICOS+CD127-).

5.2.5 Isolation of CD4+ T cells from whole blood & lymph node fine needle biopsy specimens

CD4+ T cells were isolated from the remaining LN FNB cell suspension (84% of the total sample) or PBMC by FACS on a FACSAria II cell sorter (BD Bioscience). mAb to CD3- Pacific Blue, CD4-PE and CD8-APC-Cy7 (all BD Biosciences) were used to identify CD3+CD4+CD8- T lymphocytes (Figure 2.1). Isolated cells were resuspended in 50µL DPBS (Life Technologies), lysed by mixing with 950µL Trizol® (Life Technologies), and stored at -80°C until downstream processing. 156

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5.2.6 Extraction and quantification of HIV nucleic acids

Total DNA and RNA were extracted from sorted CD4+ T cells lysed in Trizol (2.4.2.2). Total HIV pol DNA levels were quantified by qPCR (2.5.1.1). Residual DNA was removed from extracted RNA by treatment with DNase I (Promega; 2.4.4.2). HIV gag usRNA was quantified by RT-qPCR (2.6.2).

PBMC samples in which HIV DNA or RNA was not detected were assigned a LOD value. This value was calculated assuming a sensitivity of 3 copies of HIV DNA/RNA per reaction, and determined for each individual sample based on the total DNA/RNA as measured by β-actin qPCR or GAPDH RT-qPCR. While in other studies (Chapters 4 and 6) described in this thesis, the LOD for the total HIV DNA qPCR was assumed to be 1 copy per reaction, the LOD was raised for this study as cell numbers were restricted, increasing the likelihood of samples approaching the assay LOD, and therefore the impact this may have on the overall interpretation of results. A LOD of 3 copies per reaction was selected as this value corresponds to the lowest concentration of the plasmid standard curve, which was consistently detected. LN FNB samples with undetectable HIV DNA/RNA were excluded from analysis as they likely resulted from low cell yield and insufficient nucleic acid material, and therefore may introduce a systematic bias. PBMC samples with undetectable HIV DNA/RNA were included as these samples had sufficient nucleic acid. Individuals with values determined for both PBMC and LN FNB were included in the final analysis.

Average qPCR efficiencies were 94.1±2.2% and 86.5±1.3% (with R2 values all >0.99) for the total HIV (pol) DNA and β-actin qPCR respectively. Mean (±SD) values for the β-actin positive control were 8.06±0.7ng/µL. The dynamic range of the total HIV pol DNA assay as determined by the plasmid (pNL4-3) standard curve was 3x106 copies

(Cq=18.62±0.68) to 3 copies (Cq=38.92±0.47) per reaction. Normalised total HIV pol DNA copies (mean±SD) for the assay positive control (2.5.1.3), at DNA concentrations of ~67ng/µL and ~3.9ng/µL and analysed in each qPCR assay, were 366.3±111.8 and 734.3±254.0 copies per 106 cells respectively. Average RT-qPCR efficiencies were 99.6±2.7% and 97.8±2.1% (with R2 values all >0.99) for the HIV gag usRNA and GAPDH RT-qPCR respectively.

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Figure 5.4: Positive controls for the total HIV pol DNA & β-actin qPCR. Box plots display the median, interquartile range, and minimum and maximum values for the β-actin (2.5.4.1) and total HIV pol DNA (2.5.1.3) qPCR positive controls.

5.2.7 Statistical analyses

Unpaired data between groups were compared using the Mann-Whitney test. Paired data between specimens were compared using the Wilcoxon matched-pairs signed rank test. The relationship between two parameters was analysed using a Spearman’s rank correlation coefficient test. All statistical tests were performed in GraphPad Prism version 6 software (GraphPad Software).

5.3 Results

5.3.1 Study participant characteristics

10 healthy controls (HC) and 21 HIV-infected individuals, 10 of whom had received ART for at least 1 year (ART group), and 11 who had never received ART (ART-naïve group) were recruited into the study. The characteristics of HIV-infected study participants are detailed in Table 5.2. Of the 10 HC, 5 were male and 5 were female, and the median (range) age was 43 (27-58) years.

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Table 5.2: Characteristics of HIV-infected study participants.

Age Months Since CD4 Count Viral Load Sex ART Regime (years) Diagnosis (cells/mm3) (copies/mL) ART group m 69 348 RAL, ETR, ABC 882 <20 m 46 11 RAL, TDF, FTC 333 <20 m 32 18 RAL, TDF, FTC 583 <20 m 51 92 RAL, TDF, FTC 706 <20 m 39 20 RPV, TDF, FTC 700 <20 m 42 24 RPV, TDF, FTC 1009 <20 m 31 27 TDF, FTC, DRV/r 348 <20 m 36 36 TDF, FTC, DRV/r 537 <20 TDF, FTC, NVP, m 55 173 550 <20 RAL, RTV

TPV, RTV, MVC, m 58 104 360 <20 TDF, FTC Median 44 32 557 (range) (31-69) (11-348) (333-1,009) ART-naïve group m 32 2 N/A 852 41,200 m 27 4 N/A 622 38,200 m 62 5 N/A 830 22,000 m 39 9 N/A 486 15,500 m 80 11 N/A 383 25,000 m 34 17 N/A 870 31,000 m 34 108 N/A 326 6,567 m 71 108 N/A 419 680 m 64 143 N/A 470 4,500 m 59 154 N/A 328 680 m 37 170 N/A 740 300 Median 39 17 486 15,500 (range) (27-80) (2-170) (326-870) (300-41,200)

ABC, abacavir; DRV/r, ritonavir-boosted darunavir; ETR, etravirine; FTC, emtricitabine; MVC, maraviroc; NVP, nevirapine; RAL, raltegravir; RTV, ritonavir; TDF, tenofovir.

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5.3.2 Lymph node fine needle biopsies

All 31 LN FNB procedures were performed without any adverse events. The LN in the inguinal region were clear and easily identified in the ultrasound image (Figure 5.5). All subjects reported minimal to no discomfort both during and after the procedure, and were able to engage in their usual daily activities immediately after the procedure.

Figure 5.5: Ultrasound image of the inguinal lymph node (LN) being sampled. The LN area is circled by the red oval.

5.3.3 Cellular yields in lymph node fine needle biopsy specimens

Total lymphocyte yields of all 21 LN FNB ranged between 141,112 and 17,155,960 cells with a median of 1,607,352 cells, and were not significantly different for all subject groups (Figure 5.6a). There were no significant differences in the number of CD4+ T cells recovered from LN FNB samples between each of the subject groups (Figure 5.6b). However, relative to the HC group, CD8+ T cell counts were increased in the ART group, and even higher in ART-naive group (Figure 5.6c). As a result, the ratio of CD4+ to CD8+ T cells was significantly lower in the ART group relative to HC, and was further reduced in the ART-naive group (Figure 5.6d).

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Figure 5.6: Lymphocyte counts in lymph node fine needle biopsy specimens. Box and whisker graphs show median, interquartile range, and minimum and maximum values of: (a) lymphocytes; (b) CD4+ T cells; (c) CD8+ T cells; and (d) the ratio of CD4+ to CD8+ T cells. HC; healthy controls (open circles). ART; HIV-infected individuals on therapy (black squares). ART- naive; HIV-infected individuals who have never received therapy (black triangles). Statistical comparisons between groups were performed using Mann-Whitney tests.

The vast majority of CD45+ leukocytes found in LN FNB samples were lymphocytes: the ratio of neutrophils (Figure 5.7a), NK cells (Figure 5.7b SSCloCD45+CD3-CD20- CD16+ and/or CD56+), and monocytes (Figure 5.7c) to lymphocytes were significantly lower in LN FNB compared to WB specimens.

Figure 5.7: Minimal levels of neutrophils, natural killer (NK) cells & monocytes in lymph node (LN) fine needle biopsy (FNB) specimens. Ratios of (a) neutrophils, (b) NK cells, and (c) monocytes to lymphocytes, in LN FNB and whole blood specimens. Lines represent median with interquartile range for the three groups combined: healthy controls (HC, open circles); and HIV-infected individuals either on (ART, black squares), or off ART (ART-naïve, black triangles). Statistical comparisons were performed using Wilcoxon signed rank tests. 161

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5.3.4 Immune phenotype of cells in lymph node fine needle biopsies

TFH cells were readily identified as a distinct population in LN FNB samples, but not in WB (Figure 5.8a). Compared to the HC group (median 0.67%, IQR 0.4-2.7%) the proportion of CD4+ T cells that were TFH trended to an increase in the ART group (median 3.7%, IQR 0.4-8.6%, p=0.19) and were significantly elevated in the ART-naïve group (median 3.3%, IQR 2.4-4.1%, p=0.02). Similarly, GC B cells were also identified exclusively in LN FNB samples (Figure 5.8b). Again, compared to the HC group (median 0.49%, IQR 0.01-1.05%), the proportion of CD3-CD19+ B cells that were GC B cells trended towards an increase in the ART group (median 3.52, IQR 0.67-26.08%, p=0.052), and were significantly elevated in the ART-naïve group (median 12.5%, IQR 5.87-21.9%, p<0.01). The majority of samples from the HC group had low levels of GC B cells in LN FNB (median frequency: 0.49%). In contrast, GC B cells were clearly present in the majority of LN FNB samples from both HIV-infected groups (median frequency: ART 3.52%; ART-naïve 12.5%: Figure 5.8).

Figure 5.8: Follicular T helper cells (TFH) & germinal centre (GC) B cells in lymph node fine needle biopsy specimens. Box and whisker plots display medians with interquartile range, and minimum and maximum values of: (a) the frequency of TFH cells as a percentage of total CD4+ T cells and (b) the frequency of GC B cells as a percentage of total CD3-CD19+ B cells. HC; healthy controls (open circles). ART; HIV-infected individuals on therapy (black squares). ART-naive; HIV-infected individuals who have never received therapy (black triangles). Statistical comparisons between groups were performed using Mann-Whitney tests.

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Several other immune cell subtypes were identified however will not be discussed further as they are not pertinent to the aims of this thesis: Treg (CD3+CD4+CD25+CD127low), both memory (CD45RA-) and naïve (CD45RA+) subsets (Figure 5.3); and plasmablasts (CD3-CD19+CD38++CD27++, Figure 5.2). We were unable to identify FDC in FNB specimens, using mAb targeting CD35 and CD21 (data not shown), probably due to FDC processes attaching to LN connective tissue, preventing their aspiration by FNB.

5.3.5 Activated T & B cells in lymph node fine needle biopsies

Compared to the HC group (median 0.52%, IQR 0.42-0.80%) the proportion of CD3+ T cells that expressed the activation markers CD38/Ki-67 were significantly increased in the ART group (median 1.14%, IQR 0.91-1.78%, p<0.01), and were further elevated in the ART-naïve group (median 4.04%, IQR 2.28-6.16%, p<0.01: Figure 5.9a). CD3+ T cell activation levels (CD38+Ki-67+ % of CD3+) in WB and LN FNB specimens correlated strongly (rho=0.77, p<0.01, Figure 5.9b). Numbers of activated T cells also correlated strongly with both TFH cell numbers (rho=0.66, p<0.01: Figure 5.9c) and GC B cell numbers (rho=0.66, p<0.01: Figure 5.9d).

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Figure 5.9: Activated T cells in lymph node (LN) fine needle biopsy (FNB) specimens. (a) Box and whisker plots display median with interquartile range, and minimum and maximum, of the proportion of CD3+ T cells expressing CD38+Ki-67+. Correlations between: (b) T cell activation levels (CD38+Ki-67+ % of CD3+) in LN FNB and WB specimens; (c) absolute T follicular helper cell and activated T cell numbers; and (d) absolute GC B cell and activated T cell numbers. HC; healthy controls (open circles). ART; HIV-infected individuals on therapy (black squares). ART-naive; HIV-infected individuals who have never received therapy (black triangles). Statistical comparisons between groups were performed using Mann-Whitney tests. Correlations were assessed using Spearman’s rank correlation coefficient test.

5.3.6 CD4+ T cell-associated total HIV DNA & unspliced RNA

A median of 2.95×105 CD4+ T cells (range; 1.81×104 to 2.23×106: Figure 5.10a) were purified from 860 µL of the washed and resuspended LN FNB specimens by FACS, with a sort recovery rate of 52.0±13.8% (Figure 5.10b). 1×106 CD4+ T cells were purified 164

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from PBMC for analysis in parallel with LN FNB samples. One individual from both the ART and ART-naive groups was excluded from further analysis due to incorrect sample storage. Two participants from the ART group were excluded from HIV gag usRNA analysis as purified CD4+ T cells had to be fixed in 0.5% PFA from these patients only for safety/biocontainment reasons.

Figure 5.10: Purification of CD4+ T cells from lymph node (LN) fine needle biopsy (FNB) specimens by fluorescence activated cell sorting (FACS). (a) CD4+ T cell yields following FACS purification from LN FNB specimens from the ART-treated (ART, black squares) and ART-naïve (black triangles) HIV-infected groups. (b) FACS recovery rate (efficiency) of CD4+ T cell purification from LN FNB specimens. Efficiency was calculated by expressing the CD4+ T cell yield as a percentage of the absolute CD4+ T cell count in the volume of specimen assayed (860µL). Statistical comparisons between groups were performed using Mann-Whitney tests.

Total HIV pol DNA was detected in CD4+ T cells purified from 19/19 LN FNB (ART- naive 10/10, ART 9/9) and 18/19 PBMC (ART-naive 9/10, ART 9/9) samples. HIV gag usRNA was detected in CD4+ T cells purified from 13/17 LN FNB (ART-naive 8/10, ART 5/7) and 13/17 PBMC (ART-naive 8/10, ART 5/7) samples. For one individual in the ART-naïve group, HIV gag usRNA was undetectable in both LN FNB and PBMC CD4+ T cell samples, possibly due to primer/probe mismatches, and was therefore excluded from further analysis. PBMC samples with undetectable HIV DNA (n=1) or RNA (n=3) were assigned a LOD value and are indicated by open symbols in Figure 5.11. 165

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Figure 5.11: Cell-associated total HIV pol DNA & HIV gag usRNA in CD4+ T cells purified from whole blood (WB) and lymph node (LN) fine needle biopsy (FNB) specimens. Total HIV (pol) DNA (a) and HIV gag usRNA (b) copies per 106 CD4+ T cells purified from WB (black circles) and LN FNB (grey squares) specimens. (c) The ratio of HIV gag usRNA to HIV pol DNA. (d) Correlation between HIV DNA and RNA copies as assessed using Spearman’s rank correlation coefficient test. Open symbols indicate when HIV DNA or RNA was not detected and samples were assigned a limit of detection value (see methods for more details). Paired WB and LN FNB samples were compared using Wilcoxon signed rank tests. Unpaired WB or LN FNB samples from ART and ART-naïve groups were compared using Mann- Whitney tests.

HIV DNA and RNA levels in CD4+ T cells from both LN and WB correlated significantly with each other (rho=0.41, p=0.01: Figure 5.11d). Log10 total HIV pol DNA copies per 106 CD4+ T cells were higher in CD4+ T cells purified from LN FNB compared to WB in both the ART-naive (3.22 vs 2.70, p=0.02) and ART (3.21 vs 3.08, p=0.13) groups 6 (Figure 5.11a). Log10 HIV gag usRNA copies per 10 CD4+ T cells were similarly, if not

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more clearly, elevated in LN FNB samples from both the ART-naïve (3.97 vs 3.05, p<0.01) and ART (3.66 vs 2.95, p=0.06) groups (Figure 5.11b). Furthermore, the ratio of HIV gag usRNA to HIV pol DNA was also higher in LN FNB samples relative to WB in the ART-naive group (1.26 vs 1.09, p=0.03), however the analysis of the ART group was limited to 3 patients as samples with either undetected HIV DNA or RNA were excluded from this analysis (Figure 5.11c).

No significant correlations were identified between total HIV pol DNA or HIV gag usRNA levels and the proportion of CD3+ T cells that were activated (CD38+Ki-67+), in both LN FNB and WB specimens (Figure 5.12).

Figure 5.12: Correlations between T cell activation & HIV DNA/RNA levels. Total HIV pol DNA and HIV gag usRNA and activated T cells (CD38+Ki-67+ % of CD3) in lymph node fine needle biopsies (a/b respectively) and whole blood (c/d respectively).

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5.4 Discussion

The results derived from this study demonstrate that ultrasound-guided FNB of inguinal LN is a safe and well-tolerated technique for sampling peripheral LN, isolating sufficient lymphocytes for both extensive flow cytometric immunophenotyping as well as HIV DNA/RNA quantification in the majority of samples. Compared to our previous study on macaques [604], the use of ultrasound and increasing the number of passes from 2 to 5, yielded over ten times more lymphocytes. Isolated cell suspensions were not contaminated by blood as indicated by the very low levels of granulocytes and NK cells that do not reside in LN, and contained LN specific lymphocytes (TFH and GC B cells) similar to previous studies of LN in macaques and humans [604-606]. Together, these results confirm that the cells within our LN FNB material were indeed derived from lymphoid tissue.

The systemic loss of CD4+ T cells is a hallmark of HIV infection with WB CD4+ T cell counts being routinely used to monitor disease progression during untreated infection. Although profound CD4+ T cell loss has been previously reported in GALT during primary SIV [164] or HIV infections [166, 170], and in LN during acute SIV infection [169], we found that in our subjects the number of CD4+ T cell numbers recovered in LN FNB were not decreased in HIV-infected patients compared to HC, even in those not receiving ART. FNB, do not necessarily reflect lymphocyte numbers in the total LN, and provide only a sample of the entire LN resident lymphocyte pool, but as expected CD8+ T cell numbers were higher in the HIV-infected subject groups, and the ratio of CD4+ to CD8+ T cells was lower in both HIV-infected groups, such that the apparent contribution of CD4+ T cells to the total T cell pool is indeed less in HIV-infected patients. Note that in our macaque study, the proportions of lymphocyte subsets determined by FNB showed good concordance with the results of whole LN biopsies in the same animals [604]. Previous reports of CD4+ T cell depletion were based on decreases in the CD4+ % of CD3+ cells [166, 169], which will be affected by a significant increase in CD8+ T cell numbers. Therefore, our results do not contradict previous studies, but suggest that CD4+ T cell numbers are relatively preserved in LN, consistent with being potentially important sites for HIV reservoir during treated infection.

Our observations confirm and extend previous studies of LN FNB for the assessment of HIV reservoirs and transcriptional activity in LN [600-603]. In both the ART and ART- 168

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naïve groups, the majority of CD4+ T cell preparations purified from LN FNB samples were sufficient for the assessment of total HIV pol DNA levels (19/19), and to a lesser extent HIV gag usRNA (13/17) levels. A low CD4+ T cell yield (<5x104 cells) and primer probe mismatches may explain three quarters of instances in which HIV gag usRNA was undetectable. Increasing the number of passes performed during the LN FNB has to be weighed against increasing chance of bleeding within the LN undergoing FNB and therefore contamination of LN tissue with WB. Omitting the cytological examination step may further improve cell yields for flow cytometry and the ability to analyse HIV transcription, although the inclusion of cytological examination allows for the exclusion of occult infections or malignancy.

The observed trends towards increased HIV DNA/RNA copy numbers and the ratio of HIV gag usRNA to HIV pol DNA in CD4+ T cells purified from LN FNB relative to WB, indicate that LN act as a significant contributor to both HIV replication during untreated infection, and HIV reservoirs during treated infection. Expanding the use of LN FNB during HIV infections will allow for a more comprehensive analysis of LN as a distinct site for HIV reservoirs. As emerging therapies aimed at generating a cure for HIV work by either reactivating and clearing, or transcriptionally silencing HIV reservoirs, LN FNB offers a highly valuable tool for future studies assessing these therapies, and in particular for longitudinal analyses, however, although the longitudinal application of LN FNB was successful in our previous SIV study [604], this has not been tested in humans.

Similar to in the periphery, levels of activated CD4+ T cells were elevated in LN FNB from both the ART and ART-naïve group, and were related to germinal centre activity

(TFH and GC B cells). We did not however identify any correlations between HIV DNA or RNA and T cell activation in either WB or LN FNB specimens. As this was a pilot study, this may well be explained by our small sample size, and/or the identification of generalized T cell activation as opposed to CD4+ and CD8+ specific T cell activation. Nevertheless, this study demonstrates that FNB can be used to measure and compare immune activation, HIV reservoirs, and transcriptional activity. Further studies will be useful for investigating potential relationships.

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5.5 Conclusions

This study demonstrates that FNB of peripheral LN is a safe and minimally invasive means of accessing lymphoid tissue, which allows for the extensive analysis of the immune response to HIV, and viral reservoirs. Understanding the regulation of HIV transcription in vivo, in subjects on ART, and its relationship to residual immune activation, is essential to rational development of curative therapies that target latent reservoirs [607]. Currently there is much interest in circulating TFH-like CD4+ T cells

[608], however, their relationship to LN TFH is unclear [609]. As such, access to LN is still necessary to better understand TFH/GC B cell interactions, particularly for the generation of HIV-specific bNAb [610] and for human antibody responses in general [611]. The application of this technique holds significant wider promise for the systematic monitoring of immune responses within LN during other pathogenic infections, or post vaccination, particularly with the recent availability of single cell techniques to study immune cell differentiation [612].

5.6 Acknowledgements

The study described in this chapter was a collaborative effort. Study participants were recruited by Dr Kersten Koelsch, Professor Anthony Kelleher, Dr Andrew Field, Professor Andrew Carr, Dr Mark Block, and Professor David Cooper. WB specimens were collected by Karen McRae. LN FNB were performed by Dr Robyn Tantau, Dr Solange Obeid, and Dr Brad Milner. Laboratory processing a joint effort by: Dr John Zaunders, Michelle Bailey, Yin Xu, Chester Pearson and myself; with Chester Pearson and myself performing the HIV DNA/RNA quantification.

We would like to thank all individuals enrolled into this study for their generous participation. We would like to thank Karen MacRae for expert nursing support, and Bertha Fsadni, Kim Grassi, Melanie Lograsso and Kate Merlin for their laboratory support. Also, we are grateful to Steve Yukl for his valuable contributions in the optimization of the Trizol® DNA and RNA extraction protocol, and Dr Kazuo Suzuki for his assistance with the quantification of HIV gag usRNA by RT-qPCR. Finally we would like to acknowledge Tracey Barrett for outstanding administrative support.

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ASSOCIATED HIV DNA

AND HAND

CHAPTER 6: PBMC-ASSOCIATED HIV DNA AND HAND

6.1 Introduction

The introduction of ART has not seen a significant decrease in the proportion of HIV- infected patients with HAND [613-615]. While the prevalence of HIV-associated dementia (HAD), the most severe form of neurocognitive impairment has in fact reduced, other milder forms remain very common, with approximately 50% of HIV-infected individuals presenting with asymptomatic neurocognitive impairment (ANI) or mild neurocognitive disorder (MND) [614, 616], rates similar to pre-ART levels [617, 618]. Researchers have proposed several possible explanations: 1) irreparable brain damage caused by HIV infection occurs prior to the initiation of ART; 2) during ART, chronic immune activation and an inflammatory environment drives neuropathogenesis, possibly fuelled by viral reservoirs; and 3) antiretroviral drugs have neurotoxic effects that may lead to cognitive impairment (reviewed in [619]).

This chapter will focus on the role of HIV reservoirs in the pathogenesis of HAND. Evidence that HIV reservoirs are involved in the pathogenesis of HAND during ART stems from observations that neuro-inflammation continues in patients with treated HIV infection. For example, microglial activation appears to continue during treated HIV infection. One study using positron emission tomography found elevated levels of [11C]- PK11195, a marker of activated microglia, in HIV-infected individuals receiving suppressive ART, and further found that elevated [11C]-PK11195 was also associated with decreased executive function [416]. In addition, a post-mortem autopsy study has also found increased levels of activated microglia in virologically suppressed patients [417]. Investigations of the CSF have also found higher than normal levels of a marker of immune activation, in this case neopterin that is secreted by activated macrophages, and further linked elevated neopterin levels with the presence of HIV RNA in the CSF, indicating that persistent HIV reservoirs within the CNS are involved in driving immune activation [412, 413].

The mechanisms by which HIV reservoirs, both within the CNS and, likely more important, outside the CNS, may contribute to cellular activation, inflammation, and neuronal dysfunction/damage, are complex and yet to be fully elucidated (reviewed in [407, 620]). Many causes identified in the context of untreated infection are being

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investigated; for example the production and secretion of viral proteins including gp120 and Tat by infected cells, pro-inflammatory signals from infected macrophages, and the production and release of neurotoxic products including free radicals and the oxidative stress pathway and quinolinic acid [620]. As the production of viral proteins is not inhibited by ART, there is much interest in the viral protein Tat in this context, which has been detected in the CSF of a subset of patients receiving ART [407, 621], and is known to have neurotoxic effects.

Given the difficulty of accessing CNS tissue samples and the role circulating monocytes/macrophages are likely to play, it is hypothesized that HIV reservoirs in circulating monocytes/macrophages, that are far more easily accessible, may to some extent reflect the situation within the CNS [407]. The frequency of total HIV DNA in PBMC has been associated with cognitive dysfunction in ART-naïve HIV-infected individuals who have otherwise advanced HIV infection (AIDS) and a high prevalence of current HAD [420]. In the same cohorts, the authors have shown that total HIV DNA levels in PBMC were also associated with cognitive dysfunction in ART-treated subjects [418], however, these study participants initiated ART relatively recently, with approximately 50% of the study cohort maintaining undetectable pVL for 6-12 months. The same group has further identified a relationship between the level of total HIV DNA within activated CD14+ monocytes and the occurrence of persistent HAND, and showed that CD14+ monocytes from HIV-infected patients with HAD produced more inflammatory cytokines, including TNF-α and IL-6, than monocytes from patients with normal cognition [622]. Peripheral HIV reservoirs may also contribute to the non- dementia forms of HAND, in particular ANI and MND, but the evidence for such a process is less robust than in patients with HAD [415, 419]. Even in studies focusing on the activated CD14+ monocytes, it is not clear how many patients had mild chronic and treated HAND [382, 622]. Therefore, it is unclear how peripheral HIV DNA levels are related to this increasingly common HAND category.

The relationship between peripheral HIV reservoirs and HAND is yet to be investigated in a population with well managed ART-treated HIV infection, such as that in Australia [613]. Moreover, the effect of the duration of HIV infection and long-term clinical stability, also typical of well-managed cohorts, has not yet been rigorously explored. The 175

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primary aim of our study was to investigate the association of HIV DNA levels within PBMC to HAD and non-dementia HAND in patients with long-term successfully treated HIV infection. The second aim was to determine if changes in the levels of total HIV DNA over time were associated with declining neurocognition.

6.2 Materials & Methods

6.2.1 Study design, ethics & patients

All individuals provided informed consent before participating in the study, which was approved by the St. Vincent’s Hospital (08/SVH/90) and the University of New South Wales (08380-08/SVH/90) HRECs. The main criteria for inclusion were; >45 years of age; stable ART for at least 6 months; a nadir CD4 count ≤350 cells/mm3; and no active opportunistic disease. Exclusion criteria included; a history of neurological disorders predating HIV diagnosis; psychiatric disorders on the psychotic axis (e.g. schizophrenia); current substance use disorders (within 12 months of study enrolment using a formal psychiatric screen); and being non-proficient in English (see [613, 623] for more details).

The study consisted of two visits approximately 18 months apart.

6.2.2 Neurocognitive examination

Neurocognitive examinations were completed by Dr Lucette Cysique and colleagues, with detailed procedures reported in [613, 623]. A standard neuropsychological test battery covering seven ability domains that is in widespread use for NeuroAIDS research in the US was used [614]. At follow-up alternate versions of tests were used as appropriate (i.e., Hopkins Verbal Learning Test-Revised and the Verbal Fluency). In addition to the neuropsychological functions’ tests, all participants were assessed with: the National Adult Reading Test (NART, 2nd Edition [624]); The HIV Neurobehavioral Research Center Instrumental of Activity of Daily Living (HNRC-IADL) [625]; and the Personal Assessment for Own Functioning (PAOFI) [626]. The HNRC-IADL, the PAOFI, clinical history and standard neuropsychological examination scores enabled the classification of each into the three categories of the American Academy of Neurology (AAN) 2007 HAND criteria: ANI; MND and HAD [616]. As reading skills are uniquely preserved in HIV-infected individuals [627], the NART error score and education level (years) were

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transformed into an Australian standardized index (with a mean of 100 and SD of 15) to reflect premorbid cognitive ability (see [613] for more details).

6.2.3 DNA extraction & total HIV DNA quantification

PBMC were recovered from cryostorage (2.3.4), and DMSO washed off by addition of R10 and centrifugation in an Allegra® X-15R (Beckman Coulter) at 400 x g for 7 minutes at rt. DNA was extracted from 3-5x106 PBMC using the Qiagen AllPrep DNA/RNA kit

(2.4.2.1). DNA samples were eluted in DNase/RNase free dH2O and stored at -80°C for batched analysis. DNA quantity and purity was assessed by spectrophotometry (2.4.5.1). DNA concentrations in extracted samples were on average (±SD) 67.6±24.1ng/µL. A260/A280 ratios were all between 1.8 and 2.0 indicating a high level of purity.

Total HIV pol DNA levels were quantified by qPCR (2.5.1.1). Average qPCR efficiencies were 92.8±3% and 86.6±2.6% (with R2 values all >0.99) for the total HIV pol DNA and β-actin qPCR respectively. The dynamic range of the total HIV pol DNA assay as 6 determined by the plasmid (pNL4-3) standard curve was 3x10 copies (Cq=17.63±0.30) to 3 copies (Cq=38.04±0.78) per reaction. The LOD for total HIV pol DNA was assumed to be 1 copy per reaction, which contained on average 676ng of genomic DNA, the equivalent of 108,160 cells. Mean (±SD) values for the β-actin qPCR positive control were 8.46±0.6ng/µL. Normalised total HIV pol DNA copies for the positive control analysed in each qPCR assay was 701±203 copies per 106 cells.

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Figure 6.1: Positive controls for the total HIV pol DNA & β-actin qPCR. Box plots display the median, interquartile range, and minimum and maximum values for the β-actin (2.5.4.1) and total HIV pol DNA (2.5.1.3) qPCR positive controls.

6.2.4 Neurocognitive functioning and peripheral blood mononuclear cell-associated HIV DNA levels

To analyse potential relationships between the level of total HIV DNA in PBMC and neurocognition, three main neuropsychological outcomes were used: 1) the overall neurocognitive performance, defined as the battery-wide demographically uncorrected scaled score (mean of 10 and SD of 3), a higher score indicates better performance; 2) the presence of non-dementia forms of HAND defined as the AAN categories ANI, MND, and HAD; 3) the overall change score (regression-based change z-score [628] with a mean of 0 and SD of 1), which represents neurocognitive performance change across the test battery between baseline and follow-up visits. Furthermore, changes in total HIV DNA levels were compared with changes to individual test scores, which represent change in specific neurocognitive functions (also regression-based change z-scores).

6.2.5 Statistical analyses

Total HIV pol DNA levels were log10 transformed to approximate a normal distribution, however, the percentage change between total HIV pol DNA levels at baseline and

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follow-up was calculated using natural log (ln) transformed values, as recommended in [629].

Univariate analyses were used to investigate associations between baseline total HIV pol DNA and a variety of HIV disease factors or measures of neurocognitive performance: premorbid cognitive ability, ART initiated during the 1st year of infection, HIV duration (years of infection), CD4+ T cell counts, ART Central Nervous System Penetrance score, ART duration at baseline (months), and whether or not pVL remained undetectable for both study visits. Appropriate statistical methods included Pearson correlation, chi- square, t-test, or analysis of variance (ANOVA) test. Multiple regression analyses were performed to adjust for potential confounding factors, and are described in the results section with the relevant analyses. Statistical analyses were conducted using the statistical package JMP software version 10 (SAS Institute Inc; Cary, NC, USA), or in GraphPad Prism version 6 software (GraphPad Software).

6.3 Results

The study included 80 adults with chronic HIV infection (Table 6.1) enrolled between 2009 and 2011 into the HIV and Brain Aging Research Program. Over the course of the study there were no new AIDS defining illnesses, however 1 case had a CD4+ T cell count that dropped below 200 copies/mm3.

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Table 6.1: Baseline & follow-up study participant demographics, & laboratory & clinical characteristics.

Baseline Follow-up

Age (years) 55.08±7.53 56.74±7.61

Education (years) 14.06±2.75 14.11 ± 3.20 79 males / Gender (count) - 1 female Ethnicity (% Anglo-Australian) 98.7% -

Premorbid cognitive ability 108.97±7.22 -

HIV risk groups (% MSM) 86.2% -

Current HAND (%) 53.7% - 36.2% / 10.0% / ANI / MND / HAD (%) - 7.5% History of HAND (%) 15.0% -

Nadir CD4+ T cell count (cells/mL; median and IQR) 194 (59294) -

CD4+ T cell count (cells/mL; median and IQR) 556 (319-722) 614 (413-862)

Plasma HIV RNA (% with <50 copies/mL) 97.5% 87.5%

Plasma HIV RNA always undetectable (%) - 85%

CSF HIV RNA (% with <50 copies/mL; n=34) 97% -

Median duration of HIV infection at baseline (years) 19.1 -

AIDS (% of total; CDC 1993) 67.5% No new

Median Current ART duration (months)1 29 37

ART initiated during 1st year of HIV infection (%) 20.0% -

6 Total HIV pol DNA (log10 copies / 10 PBMC) 2.26 ± 0.62 2.22 ± 0.61

Mean±SD unless otherwise indicated. HAND, HIV-associated neurocognitive disorder; ANI, Asymptomatic neurocognitive impairment; IQR, interquartile range; MND, Mild Neurocognitive Disorder; HAD, HIV-associated dementia; CSF, cerebrospinal fluid. 111.3% changed ART between baseline and follow-up.

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6.3.1 Neurocognitive performance at study visits

At baseline, 46% of the patients had non-dementia HAND (36% ANI and 10% MND) and 7.5% had HAD, the rest were neurocognitively normal (NCN). Demographics and laboratory measurements of study participants, grouped as NCN, ANI, MND and HAD, are shown in Table 6.2. At follow-up, neurocognitive function had declined significantly in 14% of study participants (as defined in [613]). As analysed by ANOVA with Control Dunnett’s, neurocognitive decline was more likely in those with baseline HAD (global change score mean±SD of -0.55±0.51, p=0.02) which was significantly lower than the NCN group (global change score of -0.06±0.06), but was no different from those with non-dementia HAND (ANI+MND; p>0.95). Premorbid cognitive ability was also lower in those with HAD (102.07±9.37, p<0.05) compared to the NCN group (109.68±5.87), but did not differ from non-dementia HAND (ANI+MND; p>97), as analysed by ANOVA with Control Dunnett’s. Moreover, current HAD was associated with a history of HAD (chi-squared test, χ2=7.2, p<0.03), as opposed to non-dementia HAND. That is, 50% of those with a history of HAND currently had HAD, compared to 16% of non- dementia HAND (14% of ANI, 25% of MND), and 8% of NCN.

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Table 6.2: Study participant demographics by American Academy of Neurology categories.

NCN (n=37) ANI (n=29) MND (n=8) HAD (n=6)

Age (years) 51.8±5.4 58.1±6.3‡ 57.1±9.7 58.0±13.2

Education (years) 14.3±2.4 14.5±3.1 13.3±2.3 12.0±2.8

Premorbid cognitive ability 109.7±5.8 109.1±8.4 110.5±4.6 102.1±9.4*

History of HAND (%) 8 14 25 50† Neurocognitive performance -0.06±0.06 -0.06±0.38 -0.01±0.49 -0.55±0.51* global change score Nadir CD4+ T cell count 199 180 189 124 (cells/mL; median and IQR) (80-288) (60-300) (98-238) (35-314) Baseline CD4+ T cell count 595 448 321 681 (cells/mL; median and IQR) (408-726) (266-672) (291-554) (326-795) Plasma HIV RNA 97.3 96.6 100 100 (% with <50 copies/mL) CSF HIV RNA 92.9 (14) 100 (11) 100 (4) 100 (5) [% with <50 copies/mL (n)] Median duration of HIV 18.9±6.1 20.4±8.0 16.3±6.4 13.8±5.5‡ infection at baseline (years) AIDS at baseline visit? 45.9 37.9 25 50 (% of total; CDC 1993) Median Current ART 37.8±29.7 42.3±39.7 31.9±23.1 46.7±39.1 duration (months) ART initiated during 1st 24.3 10.3 12.5 50 year of HIV infection (%) Baseline total HIV pol DNA 6 2.26±0.55 2.35±0.64 1.95±0.80 2.26±0.61 (log10 copies / 10 PBMC) Follow-up total HIV pol DNA 6 2.17±0.54 2.30±0.65 1.92±0.70 2.51±0.53 (log10 copies / 10 PBMC) Mean±SD unless otherwise indicated. Demographics of patients with American Academy of Neurology HAND categories were compared with the NCN group: *p<0.05 by ANOVA with Control Dunnett’s; †p<0.03 by chi-squared test (χ2=7.2); ‡p<0.01 by Mann-Whitney test. NCN, neurocognitively normal; ANI, asymptomatic neurocognitive impairment; MND, mild neurodegenerative disorder; HAD, HIV-associated dementia; HAND, HIV-associated neurocognitive disorder; IQR, interquartile range; CSF, cerebrospinal fluid; CDC, centre for disease control.

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6.3.2 PBMC-associated total HIV DNA at study visits

Total HIV pol DNA levels in PBMC were mostly stable between study visits, with mean 6 (±SD) log10 copies / 10 PBMC of 2.26 (±0.62) at baseline and 2.22 (±0.61) at follow-up (Figure 6.2a), and were correlated strongly across visits (rho=0.73; p<.01; Figure 6.2b). The mean change in total HIV pol DNA levels (the % difference between follow-up and baseline levels of ln total HIV DNA) was -4.44±26.88%.

Figure 6.2: Baseline & follow-up total HIV pol DNA levels in PBMC. Statistical comparisons between ART and no ART during the 1st year of HIV infection were performed using Mann-Whitney tests. Correlations were assessed using Spearman’s rank correlation coefficient test. The limit of detection (dashed and dotted lines) and limit of quantification (dotted lines) are indicated for the total HIV pol DNA qPCR assay.

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6.3.3 Total HIV DNA, HIV disease factors & neurocognitive performance

On univariate analysis, baseline total HIV pol DNA levels in PBMC were higher in those individuals with: a longer duration of infection (rho=0.28, p<0.02; Figure 6.2d); a lower premorbid cognitive ability (rho=-0.24, p<0.04; Figure 6.3b); or in those who did not st 6 receive ART during the 1 year of infection (mean±SD log10 copies / 10 PBMC of 2.33±0.58 vs. 1.97±0.68, p=0.03 by t test; Figure 6.3c). There were no other significant associations between total HIV pol DNA levels and HIV disease factors.

Figure 6.3: Total HIV pol DNA levels in PBMC at the baseline visit. PBMC associated total HIV DNA levels and: a) neurocognitive score; b) premorbid cognitive ability. c) impaired vs unimpaired study participants; d) study participants separated by the American Academy of Neurology (AAN) HIV-associated neurocognitive disorder (HAND) categories of Asymptomatic neurocognitive impairment (ANI), HIV-associated dementia (HAD) and mind neurocognitive disorder (MND). Correlations were assessed by Spearman’s rank correlation coefficient test. Total HIV DNA levels in PBMC from those with normal cognition and with the AAN HAND categories were compared by t tests. The limit of detection (dashed and dotted lines) and limit of quantification (dotted lines) are indicated for the total HIV pol DNA qPCR assay. 184

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Multiple regression analyses showed that at baseline total HIV pol DNA levels in PBMC were not associated with the overall neurocognitive performance (Figure 6.3a shows the unadjusted model). Models were first adjusted for factors associated significantly with baseline total HIV pol DNA levels by univariate analyses, including the initiation of ART during the 1st year of infection and premorbid cognitive ability. Models were then adjusted for current CD4+ T cell count, age, duration of ART, in a second step to account for any residual effects. Importantly, the initiation of ART during the 1st year of infection and duration of HIV infection at baseline were highly associated (p<0.01), and the models were run without duration of HIV infection to avoid co-linearity. The same statistical rationale was followed for all other regression analyses that found no significant associations between baseline levels of total HIV pol DNA in PBMC and neurocognitive performance: impaired (any HAND) vs unimpaired (NCN; Figure 6.3c); or NCN vs ANI, MND, or HAD individually (Figure 6.3d).

Multiple regression analyses found that baseline total HIV pol DNA levels in PBMC (all p>0.40) were not associated with the overall change in neurocognitive score in unadjusted and adjusted models. The latter models were adjusted for baseline CD4+ T cell count; a persistently undetectable pVL (yes/no); baseline ART duration; and with or without initiation of ART during the 1st year of infection. Demographics were not entered in these models as they are corrected for in the overall change score.

Several individuals developed viral blips (<1000 copies/mL), and one individual at each study visit presented with plasma viremia. Those individuals with a persistently undetectable pVL during the study period had lower baseline PBMC-associated total HIV pol DNA levels compared to those who did not always have undetectable pVL (mean±SD 6 log10 copies / 10 PBMC of 2.56±0.41 vs. 2.21±0.63; p<0.05). The same pattern was present for follow-up total HIV pol DNA levels, although did not reach statistical 6 significance (mean±SD log10 copies / 10 PBMC of 2.54±0.59 vs. 2.17±0.57; p=0.06). Importantly however, there was no relationship between the % change of total HIV pol DNA and pVL status (p>0.60). There were two patients with detectable pVL at baseline (60 and 13,000 copies/mL), and 10 patients with detectable pVL at follow-up (two patients with 70 copies/mL; four patients with 110-150 copies/mL, three patients with 160-460 copies/mL and one patient with 13,000 copies/mL). 185

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6.3.4 Change in total HIV DNA levels & neurocognitive performance

The % change in total HIV pol DNA levels between study visits was associated with a decline in both motor-coordination (Grooved Pegboard dominant hand; rho=-0.26, p<0.03; Figure 6.4a), and semantic fluency (animal category; rho=-0.25, p<0.03; Figure 6.4c). In multivariate analyses, adjusted for relevant factors as determined in previous models, the % change in total HIV pol DNA remained associated with declining motor coordination (ß=-0.26, p<0.02), and declining semantic fluency (ß=-0.25; p=0.02). In control analyses we excluded one patient with an unusually low value for the change in total HIV pol DNA levels to test whether this datum influenced the statistical significance of the observed relationships. Analyses remained significant for semantic fluency (rho=- 0.28, p=0.01, Figure 6.4b) and trended for motor-coordination (rho=-0.19, p<0.10, Figure 6.4d). We repeated the quantification of total HIV pol DNA levels in PBMC at both study visits for this one individual, and confirmed that the original datum was most likely real. Analyses are reported with (Figure 6.4a/c) and without (Figure 6.4b/d) the outlier.

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Figure 6.4: Association between changes in total HIV pol DNA levels & decline in motor-coordination or semantic fluency. The relationship between the % change in total HIV pol DNA levels between baseline and follow-up visits, and motor-coordination or semantic fluency, with (a/c) and without (b/d) an outlying datum, were assessed by Spearman’s rank correlation coefficient test.

6.4 Discussion

This study was characterized by three main findings. First, at study entry, in HIV-infected adults on long-term suppressive ART, the level of total HIV pol DNA in PBMC was not associated with overall neurocognitive performance or HAND clinical categories. Second, while the frequency of total HIV pol DNA in PBMC was relatively stable over the 18-month study period, an increase in reservoir size, although very subtle, was modestly but significantly associated with a decline in two specific tests of neurocognitive function (including semantic fluency and motor-coordination) that have been associated with progression to HAND [630]. Finally, higher levels of PBMC associated total HIV pol DNA were correlated with a lower premorbid cognitive ability, and the latter was also 187

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uniquely associated with baseline HAD and historical HAND. This is the first time that an association between premorbid cognitive ability (an estimate of cognitive performance prior to HIV infection or the development of HAND; see 6.2.2) and total HIV pol DNA levels in PBMC has been observed.

There are key differences between our study cohort and another cohort in which a correlation between HIV DNA levels in peripheral reservoirs and HAD was initially detected [419-421, 622]. This study examined a cohort of high functioning and optimally treated individuals, with the majority of patients (97% at baseline, and 85% for both visits) maintaining undetectable pVL, compared to 50-60% in [419-421, 622]. However, this research group also performed separate analyses of only those participants with undetectable pVL, and found the relationships between HAD and total HIV DNA levels in PBMC were maintained [419-421]. Another potentially important difference to our study cohort was the proportion of study participants that presented with HAD, that in our study was relatively low (7.5%) compared to 55% [421], 52% [420] and 11% [419] in other cohorts. The cohort within our study was older (median age of 55 vs 45-40), with a longer duration of HIV infection (19 years vs ~9 years), and likely had an undetectable pVL for a longer period of time (data that was not included in previous studies [419-421, 622]), providing a plausible explanation for the reduced levels of HAD detected in our study population. It is therefore possible that the relatively low prevalence of HAD in this study limited our power to identify a significant relationship between HAD and total HIV DNA levels within PBMC.

If in cohorts with well managed HIV infection such as that investigated in this study, the proportion presenting with HAD is relatively low, and the link between peripheral HIV DNA reservoirs and HAND subtle, more sensitive approaches than those used in this study may be required to identify any relationship between peripheral HIV reservoirs and HAND. Of note during this study, we found that an increase in PBMC associated total HIV pol DNA levels modestly correlated with a decrease in neurocognitive function (semantic fluency and motor coordination), potential indicating a role for peripheral HIV reservoirs in the pathogenesis of HAND during ART. These signals are very subtle and will require confirmation in other cohorts. For future investigations one should consider focusing on activated monocytes/macrophages, however this approach is more time 188

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consuming and costly, and the detection of HIV DNA within monocytes/macrophages is not yet clearly understood (discussed in 1.4.2). The measurement of the transcriptional activity of HIV reservoirs by quantifying CA HIV RNA transcripts, may also improve this analysis by quantifying a potential marker of the production of viral proteins or virus (discussed in 1.4.10.2.2). In future investigations it will also be important that studies record detailed information regarding cohort demographics, the timing and length of ART, and treatment regimens, as these may impact on the magnitude of any correlation if present, and may help to delineate other potential causes of neuropathogenesis, such as the neurotoxic effects of ARV.

Several findings of this study fit with the hypothesis that irreversible HIV-induced brain damage, which occurs prior to the initiation of ART, drives the pathogenesis of HAND [614, 631-633]. We observed links between previous HAND, and current HAD or neurocognitive decline, indicating that previous brain damage leaves those affected predisposed to further neurological deterioration and complications. Those with current HAD also had a reduced premorbid cognitive ability, an estimate of cognitive functioning prior to HIV infection. The estimation of premorbid cognitive ability was based on the level of education and reading performance, as the latter neurocognitive test has been found to be uniquely preserved in HIV-infected individuals [627]. However in the study finding reading performance was preserved in HIV infection, it was unclear how severe the neurocognitive impairment was in the population studied [627]. It is possible that, as is the case in other neurological conditions, for example Alzheimer’s disease [634], reading performance declines with the severity of neurological impairment. This would invalidate the use of reading performance to predict premorbid cognitive functioning in patients with more severe forms of HAND. A unique finding of this study was the significant correlation between total HIV DNA levels in PBMC and premorbid cognitive ability (rho=-0.24, p=0.034). It is difficult to surmise a logical explanation for higher levels of PBMC-associated total HIV DNA in those with a poorer cognitive ability prior to HIV infection. However, it is possible that the observed relationship in this study was driven by the patients with HAD, who also had a lower premorbid cognitive ability. To test this, we excluded the participants with HAD, and found that the association between total HIV DNA levels in PBMC and premorbid cognitive ability remained significant (rho=-0.23, p=0.044). Future studies clarifying the preservation of reading performance

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in HIV-infected individuals across the whole spectrum of NCN and HAND would aid in the clarifying the interpretation of this observation.

In studies assessing the relationship between HIV DNA levels and neurocognition, it may be difficult to separate the roles that HIV-induced damage prior to ART, and persistent HIV reservoirs during ART, play in neuropathogenesis. As previously discussed, HIV DNA levels increase over time during untreated HIV infection (see 1.4.6), and although HIV DNA levels decrease following ART initiation, they are relatively stable after 1 year of therapy (see Chapter 4). These dynamics mean that to some extent, the size of HIV DNA reservoirs is a record of events that occurred during untreated infection. Indeed, in Chapter 4 we found that even after 3 years of ART, total HIV gag DNA levels in CD4+ T cells were elevated in those initiating therapy during CHI as opposed to PHI. Therefore, as untreated HIV infection progresses, it is possible damage to the CNS and the size of peripheral HIV reservoirs (and potentially HIV reservoirs within the CNS), accumulate in parallel. In either scenario the pathogenesis of HAND may be restricted by the prompt initiation of ART, however the protective effect of early ART on the CNS is largely unknown. Future studies should record detailed information on the timing of ART initiation and assess any impact on HAND. The Neurological sub-studies of the INSIGHT sponsored START study [635] may soon shed light on this question.

6.5 Conclusions

In the context of long-term controlled HIV infection, the role of HIV reservoirs, as reflected by total HIV pol DNA levels in PBMC, appears limited with a relatively weak link to neurocognition. Further investigations into HIV reservoirs and immune activation within the brain will be important to improve our understanding of HIV neuropathogenesis in these types of cohorts. The collection of in depth information regarding the length of HIV infection and ART initiation/regimens will assist in the delineating the neuropathogenesis of HAND.

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6.6 Acknowledgements

Firstly, we would like to thank the study participants who gave their time for this study. This study was a collaboration with Lucette Cysique at Neuroscience Research Australia, who along with Nadene Dermody, Phillip Chan, and Bruce Brew conducted neurological examinations and collated HIV infection data. We would also like to thank St. Vincent’s Applied Medical Research HIV Laboratory staff for providing outstanding services, in collecting and storing PBMC samples, and assisting with general laboratory duties: Bertha Fsadni, Maria Piperias, Julie Yeung, Kim Grassi, Melany Lograsso, and Kate Merlin.

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DISCUSSION

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The study of viral reservoirs is an important focus of HIV research. The work undertaken as part of this thesis was primarily aimed at improving our understanding of how and where viral reservoirs are established prior to, and maintained during suppressive ART. With the focus of HIV reservoir research shifting towards the testing of strategies to cure HIV infection, it is also highly important to understand, improve, and expand the quantitative and qualitative techniques available for measuring HIV reservoirs. The recent findings of the INSIGHT START study [635] have provided what many believe is the final piece of the ‘when to initiate ART’ puzzle [636], and will have significant and far-reaching impacts on HIV research, with the implications discussed throughout the final chapter of this thesis.

The question of when an HIV-infected individual should begin ART has long been debated. The INISGHT START study has for the first time in the context of a randomised clinical trial, strongly demonstrated that initiating ART at a CD4+ count in the normal range (>500 cells.mm3) as opposed to following a CD4+ T cell count dropping below 350 cells/mm3, significantly improved clinical outcomes by reducing the incidence of serious AIDS related events, SNAEs, and death from any cause [635]. Together with the positive impact early ART provides by limiting the formation of HIV reservoirs, preserving immune function, and preventing the transmission of HIV (discussed in 1.3.5.3), this evidence is likely to shift worldwide guidelines to recommend immediate initiation of ART for all HIV-infected individuals [636]. The scaling up of ART distribution to provide access for the entire HIV-infected population will face significant challenges [637]. For translational research into HIV infection, a change in guidelines will make it ethically challenging to design studies directly comparing early versus delayed initiation of ART, as was conducted in Chapter 4. Similarly, the inclusion of an ART-naïve group of subjects in Chapter 5 will become more complicated. Although EC and LTNP have been vigorously studied in the hopes of finding immune correlates for protection against HIV, these cohorts will likely become even rarer, as patients from these groups may now consider starting ART, and in the future may be identified less frequently if the vast majority of patients initiate ART soon after diagnosis.

Since the first identification of HIV reservoirs in HIV-infected individuals with undetectable pVL [351-353], the remarkable stability of these viral reservoirs has been 194

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confirmed by many studies ([438, 583, 638] and discussed in 1.4.8). Researchers have found that viral reservoirs are established very early during HIV infection, (discussed in 1.4.6), and have identified a range of cell types and anatomical locations involved (discussed in 1.4). Investigations of how viral reservoirs are maintained have begun to delineate the mechanisms involved but are yet to clearly define the important pathways (see 1.4.8).

There is significant interest in the ability of the prompt initiation of ART to limit the formation of HIV reservoirs. It has been hypothesized that the very early initiation of ART may prevent the formation of HIV reservoirs, facilitating the clearance of HIV infection by ART, or the development of PTC following a period of ART. Following the disappointments of the ‘Mississippi baby’ [522, 523], and several other case studies of infants infected and treated in a similar manner [639, 640], it appears that HIV reservoirs capable of fuelling viral rebound are established very early during infection. Furthermore, in an SIV model, it was found that ART initiation within 3 days of infection was unable to prevent reservoir formation [121]. Together these examples strongly indicate that very early ART cannot prevent the formation of HIV reservoirs. More recently however, another perinatally infected child treated very early during HIV infection has been followed for 11 years of ART-free virologic remission [641]. While the investigation of infants initiating therapy very early during infection garners great interest, the examples described above must remain the exception rather than the rule. Appropriate prophylactic measures (e.g. effective ART for the prevention of mother to child transmission) are highly efficient for preventing perinatal infection, and are recommended for all pregnant, HIV-infected women. A further limitation of very early ART (i.e. during acute infection) as a strategy to prevent the formation of viral reservoirs is the overwhelming impracticality of scaling up this approach as extremely few individuals are identified at this stage of infection.

Nevertheless, early initiation of ART, where possible, may play a part in future strategies to either clear HIV reservoirs or the development of a functional cure. As discussed and evaluated in Chapter 4, the early initiation of ART is strongly linked to reducing HIV reservoir size. While multiple groups have identified benefits for early initiation of ART in terms of HIV reservoir size, primarily in cross-sectional studies [121, 429, 565, 566], 195

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the power of the PINT study was the prospective and longitudinal study design, generating data indicating that extended periods of ART cannot replicate the impact of early ART on HIV reservoir size. Although (as mentioned above) the early initiation of ART is also limited by the required identification of individuals infected during PHI, this may increase as ARV guidelines change and rapid testing for HIV infection is scaled up. Through limiting the size and diversity of HIV reservoirs (discussed in 1.4.6), early ART may facilitate: a sterilizing cure, by minimizing viral reservoirs and therefore the barrier to a cure; or the development of PTC, by allowing for presumably the immune system to establish control of viral reservoirs. While in recent years the field has witnessed a small but growing number of cases of PTC [346, 641-643], for the future it will be important to assess the clinical benefits experienced by these individuals, and compare them to those on suppressive ART.

The debate surrounding the mechanism of HIV reservoir persistence is dominated by two main hypotheses, HIV reservoirs are maintained by either: 1) the long T1/2, homeostatic capacity, and Ag driven proliferation of memory CD4+ T cells that contain integrated HIV DNA, and/or 2) low levels of ongoing viral replication and the infection of new target cells persists despite ART to continually replenish HIV reservoirs. Many other factors such as host cell nuclear factors, CD8+ T cell responses during ART, innate immune mechanisms, and HIV transcription factors are also likely to play a significant role in HIV reservoir maintenance. In Chapter 3, we attempted to advance our understanding how HIV reservoirs are maintained in circulating memory CD4+ T cells. Levels of HIV DNA and genetic characteristics of the virus were compared in Ag-sp- CD4+ T cell subsets as models for the two hypothesized pathways of reservoir persistence. Although this study was limited by sample size and our ability to purify cell numbers sufficient for the intended analysis, the study generated several observations consistent with cellular proliferation playing the primary role in persistence of HIV reservoirs. Yet we, as previously observed [347, 348], also identified replication deficient provirus in CD4+T cell subsets that are maintained by cellular proliferation, in our case TT-sp-CD4+ T cells. This finding somewhat undermines the importance of cellular proliferation as a mechanism for reservoir persistence, however recent studies by the research group around Sarah Palmer group found that clonal expansion indeed also occurs for replication competent clones [348, 550].

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While there is significant evidence of cellular proliferation replenishing viral reservoirs during long-term ART, the interpretation of these data is limited by a lack of understanding of the finer details of memory T cell generation and homeostasis. Firstly, how are resting memory CD4+ T cells generated and how do activated TEM transform into TCM? Secondly, what signals do memory CD4+ T cells require (e.g. IL-7), and how are these provided in vivo? Finally, do the same cells persist for years or are they replaced by the occasional cell division to produce new cells of the same clone? While these questions were not the focus of this thesis, we believe that answering them will assist in our interpretation of data indicating a role for cellular proliferation in the maintenance of HIV reservoirs, and potentially in the development of strategies to clear viral reservoirs.

For studies of HIV reservoir persistence, it is also important to consider the possibility that HIV reservoirs in different anatomical locations or cell types are maintained by different mechanisms. This notion in part drove the aims of Chapter 5. There is still much to be learnt regarding the maintenance of HIV reservoirs, with investigations of circulating memory CD4+ T cells falling short of answering the important questions definitively.

Although the successful administration of ART controls HIV replication and significantly improves the health of HIV-infected individuals, several clinical complications remain associated with chronic treated HIV infection (discussed in 1.3.5.2.3). In Chapter 4 we found a small but significant relationship between HIV reservoirs in peripheral CD4+ T cells, low-level pVL and T cell activation. This finding indicates that HIV reservoirs may play a role in the pathogenesis of SNAEs through low-levels of viral production that stimulates the immune system, however, the relationship was weak and the causal direction of this correlation is currently unclear. In Chapter 6 we investigated the potential association of peripheral circulating HIV reservoirs with HAND. While we found no direct association between HIV DNA levels in PBMC and HAND, we observed some very subtle signs that HIV reservoirs contribute to neuropathogenesis during suppressive ART. These observations, the high incidence of HAND, and the continued presence of chronic immune activation and SNAEs warrant further investigations into the underlying causes, and the study of agents to limit these processes in patients receiving suppressive ART. 197

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Current techniques do not appear sensitive enough, nor capable of sampling all the relevant viral reservoirs. In addition, it is still unclear which viral reservoirs are responsible for rebound following the cessation of ART. Improving the understanding of assays measuring HIV reservoirs, and our ability to sample diverse viral reservoirs is important to furthering HIV reservoir and cure research. However, it is important to consider that the search for an assay to predict viral rebound may remain elusive, and therefore controlled and focussed ATI studies may be conducted in the future in this context.

The aim of Chapter 5 was to improve on techniques capable of sampling peripheral LN tissue as a potential site for HIV reservoir persistence. While this was a pilot study with limited numbers, the results indicated that FNB of LN is a promising technique for characterizing HIV reservoirs in LN tissue. Furthermore, the comparison of HIV DNA and RNA levels suggested that HIV reservoirs are found at relatively high levels in LN tissue compared to WB. Finally, FNB were minimally invasive, and although the study in Chapter 5 did not involve repeated procedures on the same individual, this was previously performed in our study on non-human primates [604], which suggests that this approach may also be feasible during longitudinal studies. The implementation of FNB to evaluate HIV reservoirs in LN may be useful for: improving our understanding of viral reservoirs; identifying the viral reservoirs responsible for viral rebound; assessing interventions aimed at clearing HIV reservoirs; and monitoring individuals undergoing ATI.

Currently available methods are also limited by our incomplete understanding of what they reflect (discussed in 1.4.10). While PCR based assays are highly sensitive, they are limited by their inability to differentiate between replication deficient and competent virus. Sequencing PCR clones offers one potential method for identifying replication deficient clones, as was the case in Chapter 3. Combining high throughput sequencing techniques with PCR based assays may offer a potential method of qualitatively and quantitatively measuring HIV reservoirs.

The quantification of HIV nucleic acids in the experiments comprising this thesis relied on PCR based methods, with the overall monitoring of qPCR highlighting some of the 198

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advantages and disadvantages of this technique. In Chapter 4, we were able to show that the measurement of total and integrated HIV DNA were strongly correlated, consistent with other observations from studies comparing available methods for quantifying HIV reservoirs [453]. In addition, the quantification of the total HIV DNA positive control was largely reproducible, albeit less accurate at lower DNA concentrations (Figure 2.15). These findings indicate that qPCR quantification of HIV reservoirs is reproducible and accurate, however, this accuracy was reduced in samples with low DNA concentrations. Another issue facing the use of PCR based methods is reproducibility and standardization across different qPCR assays and laboratories. Included in this thesis were two qPCR assays for total HIV DNA, with primers and probes targeting the HIV gag and pol genes. A comparison of the performance of these two qPCR over the course of this thesis revealed that the quantification of the plasmid standards (Figure 2.16), but not the positive control (Figure 2.15), were consistent and reproducible. The apparent variation of HIV DNA measurements across two qPCR assays highlights the need for standardization between laboratories that may use unique PCR based assays for the measurement of HIV reservoirs. Here, important advances can be provided by digital PCR technologies, through both the improved accuracy and reproducibility of measuring low copy number samples, and through the generation of standards with defined numbers of target molecules following absolute quantification [453, 464-467, 644].

Finally, the development of new techniques for the quantification and characterisation of HIV reservoirs could significantly aid HIV research. The majority of methods currently available rely on the indirect measurement of HIV. The direct identification of heterogeneous and scarce cellular HIV reservoirs could vastly advance the field, but is extremely challenging. Flow cytometry easily achieves adequate throughput, however there is no published method for identifying latently HIV-infected cells. There are several assays in development that detect intracellular expression of p24 Ag [644], or HIV RNA by FISH [645, 646], that may be applicable in the context of suppressive ART, but will likely be complicated by low levels of HIV DNA/RNA expression and the potential absence of p24 Ag. Time of flight mass cytometry (CyTOF) may improve the resolution of flow cytometry analysis, and be able to identify latently infected cells if they can be distinguished by other factors such as phosphorylation patterns [644]. Other single-cell platforms including and microengraving [644] and microfluidics combined with PCR

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based detection of HIV nucleic acids may allow for the specific identification of cells containing HIV reservoirs, and should be further investigated. Also being investigated is antibody-targeted positive emission tomography, which is a technique for non-invasively visualizing whole-body HIV reservoirs [647, 648], however these methods are likely to face the same challenges associated with the low frequency and latent nature of viral reservoirs.

While the ultimate goal of research into a cure for HIV is the eradication of viral reservoirs, there is much to be learned. This body of work furthered our understanding of HIV reservoirs by extending evidence regarding the impact of early ART on reservoir formation, and the role of cellular proliferation in the maintenance of HIV reservoirs. The evaluation of FNB provided additional evidence to suggest that LN significantly contribute to persistent HIV reservoirs, and presented FNB as a technique that may be a useful for future studies of HIV reservoirs. The development of a strategy to cure HIV infection is an extremely difficult and complex task, while the field has seen significant progress in recent years, it is currently apparent that an improved understanding of the viral reservoirs preventing a cure, is essential to success in this endeavour.

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