Examining the Characteristics of the Igg Antibody Response Generated During Early HIV-1 Infection Using Molecular Cloning

Examining the characteristics of the IgG antibody response generated during early HIV-1 infection using molecular cloning

techniques

Scarlett Eleanore Grace Turner

Imperial College London

Department of Medicine

Centre for Immunology and Vaccinology

Thesis Submitted For The Degree of Doctor of Philosophy

May 2018

Declaration of Originality

I, Scarlett Eleanore Grace Turner, declare that all the work presented in this thesis is my own work, and that any information used here from other published, unpublished sources or collaboration is correctly referenced.

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

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

Acknowledgments

Firstly, I would like to thank Dr Peter Kelleher for being a fantastic supervisor over the course of my

PhD, for giving me the opportunity to learn a wide range of lab techniques, and providing support through the twists and turns of the project. I would also like to thank Dominic Smith for all his help with lab orders and upgrading lab equipment, and Parisa Amjadi for her expertise on flow cytometry and spending hours sorting cells for me in the CL3 lab.

Secondly, I would like to thank Dr Juthathip Mongkolsapaya, Dr Wiyada Wongwiwat and Hantao Lou at Hammersmith, and Dr Lizzie Atkins for their expertise in Molecular Immunology, Dr Nesrina Imami for her many helpful tips and advice on doing a PhD, and Dr Katie McFaul for her role in setting up the Dean St Aries cohort and collecting blood samples for us to process in the lab. I have really enjoyed my PhD over the last four years, and a large part of that is down to all the other students and staff working in the CIV department under Professor Xiao-Ning Xu, who have all made it a great experience. Thanks to everyone!

Thirdly, thank you to Imperial College London for providing the funding for me to do this PhD, and also the British Society of Immunology (BSI) from who I received a travel award to present my work at their annual congress.

Last, but by no means least, a huge thank you to my Mum and Dad, my sister Helena and my grandparents for all their support and encouragement over the years.

Abstract

There is much interest in the possible use of broadly neutralising antibodies (BNAbs) isolated from chronically infected HIV-1 patients to reduce the HIV-1 latent reservoir and to serve as a tool in vaccine design. However, the RV144 trial, the only HIV vaccine efficacy trial to date to show any protection, has highlighted the potential importance of non-neutralising antibodies and IgG subclass in HIV-1 control, whilst anti-HIV-1 antibodies from early infection have been less well characterised. The aims of this project were to generate monoclonal antibodies from longitudinal samples of an early HIV-1 infected patient in multiple IgG subclasses, and to analyse their characteristics and functions.

Plasmablasts from an early HIV-1 infected patient at multiple time points were single cell sorted, and using molecular cloning techniques IgG1 monoclonal antibodies were isolated and tested for reactivity to HIV-1. Of 177 monoclonal antibodies, 2.82% were found to be HIV-1 specific. The distribution of heavy and light gene family usage was similar in HIV-1 specific and non-specific antibodies, whilst the number of nucleotide and amino acid mutations only increased over time in the non-specific antibodies. IgG1 expression vectors were modified to produce IgG2, IgG3, and IgG4 vectors, allowing patient derived anti-HIV-1 monoclonal antibodies and several well characterised BNABs to be generated in each of these subclasses. When tested using the RFADCC assay, one patient derived monoclonal antibody in the IgG1 and IgG3 subclasses, and one BNAb in the IgG3 subclass mediated weak ADCC activity.

Results have demonstrated that HIV-specific and non-specific antibodies isolated from early infection follow the same VH gene family usage trends, and that early infection anti-HIV IgG3 monoclonal antibodies may mediate weak ADCC activity. Furthermore, the IgG2-4 expression vectors now allow the rapid generation of antibodies in multiple subclasses, and may have the potential to generate more potent therapeutic monoclonal antibodies.

Table of Contents

Declaration of Originality ...... 2 Copyright Declaration ...... 3 Acknowledgments ...... 4 Abstract ...... 5 Table of Contents ...... 6 List of Tables ...... 12 List of Figures ...... 13 List of Abbreviations ...... 16 Chapter 1: Introduction ...... 19 1.1. Human Immunodeficiency Virus (HIV) ...... 20 1.1.1. Overview of HIV ...... 20 1.1.2. HIV Classification ...... 21 1.1.3. HIV-1 Viral Genome and Structure ...... 23 1.1.4. HIV-1 Replication Cycle ...... 29 1.1.5. The course of HIV-1 infection ...... 31 1.1.6. T and B cells during HIV-1 infection ...... 35 1.2. B Cells ...... 37 1.2.1. Immunoglobulin Genes ...... 37 1.2.2. B cell development and subsets ...... 40 1.2.3. B cell Activation ...... 44 1.3. Antibodies ...... 48 1.3.1. Antibody Classification ...... 48 1.3.2. Antibody Structure ...... 50 1.3.3. Antibody Functions ...... 52 1.4. Antibodies in HIV Infection ...... 57 1.4.1. Appearance of antibodies in response to HIV infection ...... 57 1.4.2. Broadly neutralising antibodies ...... 57 1.4.3. Non-neutralising antibodies...... 63 1.5. Monoclonal Antibody Production Technology ...... 65 1.5.1. Phage Display ...... 65 1.5.2. EBV Immortalisation ...... 67 1.5.3. Single cell expression cloning ...... 68 1.5.4. Summary of Technologies ...... 69

1.6. Aims and Hypotheses ...... 73 1.6.1. Aims ...... 73 1.6.2. Hypotheses...... 74 Chapter 2: Materials and Methods ...... 75 2.1. Lab consumables, reagents and equipment ...... 76 2.2. Ethics Approval ...... 79 2.3. Blood Processing for isolation and storage of PBMCs ...... 79 2.3.1. Blood Collection ...... 79 2.3.2. Blood Separation and PBMC Isolation ...... 79 2.3.3. Cell Counting ...... 80 2.3.4. Freezing Cells ...... 80 2.4. Monoclonal Antibody Production ...... 80 2.4.1. Overview ...... 80 2.4.2. Thawing Cells ...... 82 2.4.3. Plasmablast Cell Staining ...... 82 2.4.4. Single cell plasmablast sorting ...... 83 2.4.5. Polymerase Chain Reaction ...... 84 2.4.6. Gel Electrophoresis ...... 89 2.4.7. DNA Purification ...... 90 2.4.8. Digestions ...... 90 2.4.9. Vector-Insert Ligation ...... 93 2.4.10. Competent Cell Transformation ...... 93 2.4.11 Colony Selection and overnight growth ...... 94 2.4.12. Miniprep ...... 94 2.4.13. Digestion to check for insert ...... 95 2.4.14. Sequencing ...... 96 2.4.15. HEK293T cell line maintenance and transfection ...... 97 2.4.16. IgG ELISA to test for antibody ...... 100 2.5. Screening antibodies for HIV specificity ...... 103 2.5.1. CEM.NKR-CCR5 cell line ...... 104 2.5.2. HIV envelope proteins...... 104 2.5.3. Controls ...... 105 2.5.4. Coating CEM.NKR-CCR5 cells with gp140 ...... 105 2.5.5. Screening supernatants against HIV-1 gp140 ...... 106 2.5.6. Secondary Antibody ...... 107 2.5.7. Flow cytometric analysis ...... 107

2.6. IgG Subclass Vector Design ...... 107 2.6.1. Primer Design ...... 107 2.6.2. mRNA isolation...... 108 2.6.3. cDNA synthesis ...... 108 2.6.4. IgG Constant region PCR ...... 108 2.6.5. Cloning IgG2 constant region into IgG-Abvec vector ...... 109 2.6.6. Gibson Reaction ...... 109 2.6.7. Vector Mutagenesis ...... 112 2.7. Generating IgG2, IgG3 and IgG4 monoclonal antibodies ...... 112 2.7.1. Broadly Neutralising Antibody DNA ...... 112 2.7.2. Extraction of patient mAb variable regions ...... 114 2.7.3. IgG2, IgG3 and IgG4 Vector Digestion ...... 114 2.7.4. Molecular cloning of variable regions into IgG vectors ...... 115 2.7.5. BNAb Vector Mutagenesis ...... 115 2.7.6. IgG1-4 mAb production...... 116 2.8. Monoclonal antibody purification and quantification ...... 116 2.8.1. IgG Purification ...... 116 2.8.2. IgG Quantification ...... 117 2.8.3. Protein Gels ...... 117 2.9. RFADCC Assay ...... 118 2.9.1. PKH-26 Staining ...... 118 2.9.2. CFSE Staining ...... 120 2.9.3. HIV-1 gp140 coating ...... 120 2.9.4. Effector Cells ...... 120 2.9.5. RFADCC Assay ...... 120 2.9.6. RFADCC Flow cytometry analysis ...... 121 2.10. Analysis ...... 121 2.10.1. FACS DIVA and FlowJo ...... 122 2.10.2. Sequence Analysis ...... 122 2.10.3. Prism ...... 122 2.10.4. Phylogenetic Analysis ...... 123 Chapter 3: Examining the HIV-specific and non-specific antibodies generated from an acute/early infected HIV patient...... 124 3.1. Background ...... 125 3.1.1. B cell responses during HIV infection ...... 125 3.1.2. HIV antibodies ...... 126

3.1.3. Single B cell cloning for antibody production ...... 127 3.1.4. Aims...... 129 3.2. Monoclonal antibody production preliminary work...... 129 3.2.1. Flow cytometry antibody titrations ...... 129 3.2.2. Fresh vs Frozen PBMCs ...... 131 3.2.3. Preliminary mAb production ...... 132 3.3. Isolation of five HIV-specific monoclonal antibodies ...... 133 3.3.1. Visits 1-4 sorts ...... 134 3.3.2. HIV gp140 screening ...... 137 3.3.3. Index sorting analysis ...... 140 3.4 HIV-specific monoclonal antibodies ...... 143 3.5. Non-specific monoclonal antibodies...... 150 3.5.1. Heavy Chain Gene Usage ...... 150 3.5.2. Light Chain Gene Usage ...... 153 3.5.3. Heavy and Light Chain Nucleotide mutations ...... 155 3.5.4. Amino acid mutations ...... 161 3.5.5. CDRH3 and CDRL3s ...... 171 3.6. HIV-specific mAbs vs V1 Non-specific mAbs ...... 175 3.7. Molecular Phylogenetics Analysis ...... 177 3.8. Discussion ...... 180 3.8.1. Summary ...... 180 3.8.2. Isolating HIV-reactive mAb ...... 181 3.8.3. Gene family Usage ...... 184 3.8.4. Heavy and light chain mutations ...... 186 3.8.5. Complementarity determining regions ...... 188 3.8.6. Phylogenetic Analysis ...... 190 3.8.6. Limitations of work ...... 190 3.8.7. Future Work ...... 191 Chapter 4: Generating IgG subclass expression vectors and the production of IgG1-IgG4 monoclonal antibodies ...... 193 4.1. Introduction ...... 194 4.1.1. IgG subclasses ...... 194 4.1.2. Antibody mediated effector functions ...... 194 4.1.3. Single cell cloning techniques for antibody production...... 197 4.1.4. Aims...... 199 4.2. Construction of IgG2, IgG3 and IgG4 subclass expression vectors ...... 199

4.2.1 Amplification of IgG2, IgG3 and IgG4 constant regions ...... 200 4.2.2. Gibson reaction to generate IgG3 and IgG4 constant region vectors ...... 204 4.2.3. IgG2, IgG3 and IgG4 AgeI Mutagenesis ...... 208 4.3. Generation of patient derived monoclonal antibodies in different IgG subclasses ...... 210 4.3.1. Cloning heavy chain PCR products into IgG2, IgG3 and IgG4 expression vectors ...... 210 4.3.2. Extraction of heavy variable region DNA from IgG1 vector and ligation into IgG subclass expression vectors ...... 212 4.3.3. Production of IgG2, IgG3 and IgG4 monoclonal antibodies ...... 214 4.4. Production of broadly neutralising antibodies in multiple IgG subclasses ...... 215 4.4.1. Insertion of BNAb variable region fragments into heavy and light chain vectors ...... 217 4.4.2. BNAb Heavy Chain Vector 1st Mutagenesis ...... 219 4.4.3. BNAb Heavy Chain Vector 2nd Mutagenesis ...... 221 4.4.4. BNAb Lambda Chain Vector Mutagenesis ...... 222 4.4.5. BNAb variable regions in IgG1-IgG4 heavy and light chain vectors ...... 222 4.4.6. Generation of BNAbs in IgG1, IgG2, IgG3 and IgG4 subclasses ...... 225 4.4.7. Screening BNAbs against gp140 ...... 226 4.5 Discussion ...... 228 4.5.1. Summary ...... 228 4.5.2. Discussion ...... 228 Chapter 5: Examining functional responses of BNAbs and patient derived mAbs in different IgG subclasses...... 231 5.1. Introduction ...... 232 5.1.1. Effector Functions of antibodies ...... 232 5.1.2. Mechanism of ADCC...... 233 5.1.3. Assays to measure ADCC ...... 234 5.1.4. Aims and Hypothesis ...... 236 5.2. Purification and Quantification of IgG ...... 236 5.2.1. Purification of IgG ...... 236 5.2.2. Quantification of IgG ...... 238 5.2.3. Protein Gels for protein quality conformation ...... 239 5.3. Testing for ADCC activity using the RFADCC Assay ...... 240 5.3.1. Preliminary work ...... 240 5.3.2. RFADCC Assay Results ...... 243 5.4. VRC01 and V1X2C5 binding to different HIV clade B proteins...... 246 5.5. Discussion ...... 248 5.5.1. Summary ...... 248

5.5.2. Discussion ...... 248 5.5.3. Limitations ...... 254 5.5.4. Future work ...... 255 Chapter 6: Discussion ...... 256 6.1. Results Summary ...... 257 6.2. Discussion ...... 258 6.3. Final Conclusions ...... 265 References ...... 267 Appendices ...... 293

List of Tables

Table 1.1: Summary of HIV-1 structural, regulatory and accessory genes and the proteins they encode...... 23 Table 1.2: Advantages and Disadvantages of monoclonal antibody production techniques...... 70 Table 2.1: Reagents and consumables used for experiments...... 77 Table 2.2: Lab Equipment used for experiments...... 78 Table 2.3: Flow cytometry staining panel for the identification of plasmablasts...... 82 Table 2.4: Catch buffer reagents...... 83 Table 2.5: One-Step Reverse Transcriptase PCR Primers...... 85 Table 2.6: One-step RT PCR master mix components...... 85 Table 2.7: Kappa chain nested PCR primers...... 86 Table 2.8: Kappa chain variable region nested PCR reaction mixture...... 87 Table 2.9: Primers used for heavy, lambda and kappa cloning PCRs...... 88 Table 2.10: Variable region DNA digestion reaction mixtures...... 91 Table 2.11: Vector digestion reaction mixtures...... 92 Table 2.12: Digestion reaction mixtures to check for insert DNA within vector...... 95 Table 2.13: Seeding densities for 293T transfection in 24-, 6-well and 15cm tissue culture plates. ... 98 Table 2.14: HIV-1 clade B recombinant proteins...... 105 Table 2.15: Positive and negative controls used in screening assay...... 105 Table 2.16: IgG Constant region PCR components...... 108 Table 2.17: IgG3 and IgG4 Gibson Reaction Primers...... 111 Table 2.18: DNA sequences of VRC01, 3BNC117, PGT121 and 10E8 BNAb used for fragment synthesis...... 113 Table 2.19: Restriction digest of heavy, lambda and kappa DNA fragments...... 114 Table 2.20: BNAb vector mutagenesis primers...... 115 Table 3.1: Aries cohort patient B004 longitudinal cell populations...... 134 Table 3.2: Sort Summary for single plasmablasts sorts at visits 1-4...... 136 Table 3.3: Matching heavy and light chain variable region pairs from visits 1-4 sorts...... 137 Table 3.4: Comparison of CD19, CD27 and CD38 fluorescence intensity of different sorts...... 142 Table 3.5: Summary of HIV-specific monoclonal antibodies isolated from patient B004...... 143 Table 3.6: Wilcoxon matched-pairs test p values results comparing synonymous and non- synonymous nucleotide mutations of non-specific visit 1-4 monoclonal antibodies...... 158 Table 3.7: Kruskal-Wallis one way ANOVA test p values comparing synonymous or non-synonymous mutations between visits or FR/CDR regions...... 158 Table 3.8: One way ANOVA test p values for amino acid mutations between visits or FR/CDR regions...... 164 Table 4.1: Nucleotide alignment of miniprepped vectors with IgG2, IgG3 and IgG4 reference sequences...... 202 Table 4.2: Broadly neutralising antibodies selected for production in different isotypes...... 215 Table 5.1: Summary of direct and indirect assays used to measure ADCC...... 235 Table 5.2: Grouped statistical analysis of differences in ADCC activity of mAb and BNAb IgG isotypes...... 245 Table 5.3: Statistical analysis of IgG isotype ADCC results for individual mAb and BNABs...... 246

List of Figures

Figure 1.1: HIV-1 and HIV-2 classification...... 22 Figure 1.2: Gene organisation and structure of HIV-1...... 24 Figure 1.3: Overview of the HIV-1 replication cycle...... 30 Figure 1.4: The natural history of HIV-1 infection and the effect of ART...... 32 Figure 1.5: Immunoglobulin gene structure...... 38 Figure 1.6: The B cell developmental pathway...... 40 Figure 1.7: T cell dependent B cell activation...... 45 Figure 1.8: Generalised structure of IgG antibodies...... 51 Figure 1.9: Summary of antibody effector functions...... 52 Figure 1.10: BNAbs and their HIV-1 target sites...... 58 Figure 2.1: Overview of monoclonal antibody production protocol...... 81 Figure 2.2: Gating strategy used to identify and sort plasmablasts...... 84 Figure 2.3: Heavy, kappa and lambda cloning PCR example gels...... 90 Figure 2.4: Heavy, kappa and lambda miniprep vector example gels...... 96 Figure 2.5: Overview of HEK293 transfection for production of monoclonal antibodies...... 97 Figure 2.6: Plate Layout for IgG ELISA to confirm monoclonal antibody production...... 102 Figure 2.7: Flow cytometry based assay to test generated mAb for HIV reactivity...... 104 Figure 2.8: Plate Layout for screening transfection supernatants against HIV-1 gp140...... 106 Figure 2.9: Overview of Gibson reaction...... 110 Figure 2.10: Principle of RFADCC Assay...... 119 Figure 2.11: RFADCC assay 96 well plate layout...... 121 Figure 3.1: Flow cytometry B cell panel antibody titrations...... 130 Figure 3.2: Identifying the plasmablast population using flow cytometry...... 131 Figure 3.3: Concentration of trial run monoclonal antibodies...... 133 Figure 3.4: Acute HIV Patient B004 plasmablasts at visits 1-4...... 135 Figure 3.5: Patient B004 V1-4 serum screening against different clade B gp140 and gp120 proteins...... 138 Figure 3.6: HIV screening results of transfection supernatants of five identified HIV-specific monoclonal antibodies generated from a single patient...... 139 Figure 3.7: Index sorting FACS Plot for plasmablast CD19, CD27 and CD38 expression...... 140 Figure 3.8: CD19, CD27 and CD38 fluorescence intensity levels of sorted plasmablasts...... 141 Figure 3.9: Heavy chain gene family usage of anti-HIV patient monoclonal antibodies...... 144 Figure 3.10: Light chain gene family usage of anti-HIV patient monoclonal antibodies...... 144 Figure 3.11: Variable region nucleotide and amino acid mutations of the anti-HIV patient monoclonal antibodies...... 145 Figure 3.12: CDR lengths of the anti-HIV patient monoclonal antibodies...... 147 Figure 3.13: Heavy chain variable region nucleotide alignment of HIV specific antibodies...... 148 Figure 3.14: Amino acid sequences of the five HIV specific antibodies...... 149 Figure 3.15: Visit 1-4 non-specific antibody heavy chain variable (VH), joining (J) and diversity (D) gene family usage...... 151 Figure 3.16: Variable heavy (VH) and diversity (D) specific gene usage for V1-V4 non-specific antibodies...... 152 Figure 3.17: Light chain variable (VH) and joining (J) gene family usage for V1-V4 non-specific antibodies...... 154 Figure 3.18: Variable light (VL) specific gene usage for V1-V4 non-specific antibodies...... 155

Figure 3.19: Heavy chain variable region nucleotide mutations of the visit 1-4 non-specific monoclonal antibodies...... 156 Figure 3.20: Light chain variable region nucleotide mutations of the visit 1-4 non-specific monoclonal antibodies...... 160 Figure 3.21: Heavy chain variable region amino acid mutations of visit 1-4 non-specific monoclonal antibodies...... 162 Figure 3.22: Light chain variable region amino acid mutations of visit 1-4 non-specific monoclonal antibodies...... 163 Figure 3.23: Different types of heavy chain and light chain amino acid mutations of visit 1-4 non- specific monoclonal antibodies...... 165 Figure 3.24: Individual non-specific visit 1-4 mAb variable heavy chain amino acid mutations...... 167 Figure 3.25: Individual non-specific visit 1-4 mAb variable light chain amino acid mutations...... 168 Figure 3.26: WebLogo sequence image of heavy chain amino acid alignment...... 169 Figure 3.27: CDRH3 lengths of visit 1-4 non-specific monoclonal antibodies...... 171 Figure 3.28: CDRL3 lengths of visit 1-4 monoclonal antibodies...... 172 Figure 3.29: CDR3 lengths of V1-V4 non-specific monoclonal antibodies...... 172 Figure 3.30: Relative frequency of amino acids at CDRH1 and CDRH2 positions 1-8...... 173 Figure 3.31: Comparison of HIV-specific and visit 1 non-specific monoclonal antibodies in key characteristics...... 176 Figure 3.32: Heavy chain molecular phylogenetic analysis...... 178 Figure 3.33: Light chain molecular phylogenetic analysis...... 179 Figure 4.1: IgG-Abvec heavy chain expression vector used for production of IgG1 monoclonal antibodies...... 198 Figure 4.2: Schematic for generation on HIV specific antibodies in different isotypes...... 200 Figure 4.3: IgG subclass constant region PCR results...... 201 Figure 4.4: 1% agarose gel showing vectors which contained the IgG constant region insert...... 201 Figure 4.5: Clustal omega alignment of IgG2 vector sequence with IgG2_J00230 reference sequence...... 203 Figure 4.6: Clustal omega alignment of original IgG2 vector, mutated IgG2 vector sequence and IgG2_J00230 reference sequence...... 204 Figure 4.7: PCR products of IgG3 and IgG4 constant region PCR on 1% TBE-agarose gels...... 205 Figure 4.8: Digested vector minipreps from IgG3 and IgG4 Gibson reactions...... 205 Figure 4.9: Clustal omega alignment of IgG4 vector sequence and IgG4_ K01316 (IgG4*01) reference sequence...... 206 Figure 4.10: Clustal omega alignment of partial IgG3 vector protein sequences with IgG3 X03604 reference sequence...... 206 Figure 4.11: Clustal omega partial alignment of correct IgG3 vector sequence and reference sequence...... 207 Figure 4.12: Alteration of AgeI restriction site within constant region of IgG2, IgG3 and IgG4...... 208 Figure 4.13: Clustal omega alignment of IgG1, IgG2, IgG3 and IgG4 vectors...... 209 Figure 4.14: Clustal Omega Alignment of V1X3A2 in different isotypes...... 211 Figure 4.15: Clustal omega alignment of identical variable regions of five patient derived monoclonal antibodies in IgG1, IgG2, IgG3 and IgG4 subclasses...... 213 Figure 4.16: Semi-quantitative IgG ELISA results of HEK293 transfected cells using IgG1-4 expression vectors...... 214 Figure 4.17: Design of BNAb DNA fragments for insertion into heavy and light chain vectors...... 216 Figure 4.18: Clustal omega alignment of BNAB heavy chain vector protein sequences...... 217 Figure 4.19: Clustal omega alignment of heavy chain BNAb IgG1 vectors with IgG-Abvec...... 218

Figure 4.20: Nucleotide and amino acid sequences of BNAb heavy vectors to be mutated...... 220 Figure 4.21: VRC01 and 3BNC117 heavy chain vectors after 1st mutagenesis...... 221 Figure 4.22: Alignment of VHS-inserted VRC01 and 3BNC117 sequences...... 222 Figure 4.23: Alignment of L-part 2 inserted PGT121L and 10E8L sequences...... 222 Figure 4.24: Clustal omega alignment of IgG1-IgG4 heavy chain vector sequences...... 223 Figure 4.25: Clustal omega alignment of light chain vector sequences...... 224 Figure 4.26: Semi-quantitative IgG ELISA results of BNAb transfected 293T cells using IgG1-4 expression vectors...... 225 Figure 4.27: BNAb screening against HIV gp140 envelope protein...... 227 Figure 5.1: Summary of the mechanism of ADCC...... 233 Figure 5.2: ELISA results confirming IgG mAbs and BNAbs after concentration and purification...... 237 Figure 5.3: Quantification of purified mAbs and BNABs in each of different IgG subclasses...... 238 Figure 5.4: Protein gels indicating protein quality of mAbs and BNAbs in subclasses 1-4...... 240 Figure 5.5: RFADCC gating strategy and testing positive and negative controls...... 241 Figure 5.6: Selecting donor PBMCs for use as effector cells in the RFADCC assay...... 242 Figure 5. 7: ADCC activity of mAb and BNAb in different IgG isotypes...... 244 Figure 5.8: Reactivity of VRC01 and V1X2C5 IgG isotypes to different clade B envelope proteins. ... 247

List of Abbreviations

3’ 3 prime 5’ 5 prime ABA Assay Buffer A ADCC Antibody Dependent Cellular Cytotoxicity ADCD Antibody Dependent Complement Deposition ADCP Antibody Dependent Cellular Phagocytosis AHI Acute HIV-1 Infection AID Activation-induced cytidine deaminase AIDS Acquired Immune Deficiency Syndrome APC Allophycocyanin APC Antigen Presenting Cell APOBEC Apolipoprotein B mRNA Editing Catalytic Polypeptide-like ART Anti-Retroviral Treatment ASC Antibody Secreting Cell BCR B cell Receptor BER Base Excision Repair BNAb Broadly Neutralising Antibody bp Base Pairs BV421 Brilliant Violet 421 CD Cluster of Differentiation CD4bs CD4 binding site CD4i CD4 inducible site cDNA Complementary DNA CDR Complementarity Determining Region CFSE Carboxyfluorescein Diacetate Succinimidyl Ester CH Constant Heavy CMV Cytomegalovirus °C Degrees Celsius cpz Chimpanzee CRF Circulating Recombinant Form D Diversity dH20 Distilled Water DMEM Dulbecco's Modified Eagle Medium DMSO Dimethyl Sulfoxide DNA Deoxyribonucleic acid dNTP Deoxynucleotide EBV Epstein Barr Virus EDTA Ethylenediaminetetraacetic acid ELISA Enzyme Linked Immunosorbent Assay Env Envelope ER Endoplasmic Reticulum Fab Fragment Antigen Binding FACS Fluorescence Activated Cell Sorting FBS Fetal Bovine Serum

Fc Fragment Crystallisable FcR Fc Receptor FDC Follicular Dendritic Cell FITC Fluorescein Isothiocyanate Flu Influenza Virus FR Framework Region FSC Forward Scatter g Unit of gravity (for relative centrifugal force) ɣ Gamma GIT Gastrointestinal Tract gor Gorilla gp Glycoprotein H1-4 Hinge 1-4 HAT Histone Acetyl-transferase HDAC Histone Deacetylase HEK Human Embryonic Kidney Cells Hep A Hepatitis A HEPS Highly Exposed Persistently Seronegative HIV Human Immunodeficiency Virus HIVIG Anti-HIV Immune Globulin HRP Horseradish peroxidase HTLV Human T-Lymphotrophic Virus IFN Interferon Ig Immunoglobulin IMGT international immunogenetics information system ITAM Immunoreceptor Tyrosine-based Activation Motif ITIM Immunoreceptor Tyrosine-based Inhibitory Motif J Joining Kb Kilobase LAV Lymphadenopathy-associated Virus LPS Lipopolysaccharide LTR Long Terminal Repeat mAb Monoclonal Antibody MFI Median Fluorescence Intensity mg/ml Milligrams/ml MHC Major Histocompatibility Complex MMR Mismatch Repair MPER Membrane Proximal External Region mRNA Messenger RNA ng/ml Nanograms/ml NIBSC National Institute for Biological Standards NK Natural Killer NNRTI Non-nucleoside reverse transcriptase inhibitor NRTI Nucleoside/nucleotide reverse transcriptase inhibitor PBMC Peripheral Blood Mononuclear Cell PBS Phosphate Buffered Saline

PBS-T Phosphate Buffered Saline with Tween 20 PCR Polymerase Chain Reaction pDC Plasmacytoid dendritic cell PE Phycoerythrin PEI Polyethylenemine PerCP Peridinin Chlorophyll Protein PIC Preintegration complex pIgR Polymeric Immunoglobulin Receptor P-TEFb Positive Transcription Elongation Factor b RAG Recombination Activation Gene RFADCC Rapid Fluorometric Antibody Dependent Cellular Cytotoxicity Assay RNA Ribonucleic Acid RPMI Roswell Park Memorial Institute RSS Recombination Signal Sequence RT Reverse Transcriptase RT-PCR Reverse Transcriptase Polymerase Chain Reaction SARS Severe Acute Respiratory Syndrome scFv Single Chain Variable Fragment SHIV Simian/Human Immunodeficiency Virus sIgA Secretory IgA SIV Simian Immunodeficiency Virus SSC Side Scatter TAR Transactivator-Responsive Region TBE Tris-Borate EDTA Buffer TCR T cell receptor TdT Terminal Deoxynucleotide Transferase TFH Follicular Helper T Cell TGS Tris-Glycine-SDS Buffer Th T Helper Cell TLR Toll-like Receptor UCA Unmutated Common Ancestor V Variable V1-4 Visit 1-4 VH Variable Heavy WHO World Health Organisation α Alpha δ Delta ε Epsilon κ Kappa λ Lambda μ Mu μg/ml Micrograms/ml

Chapter 1: Introduction

1. Introduction

1.1. Human Immunodeficiency Virus (HIV)

1.1.1. Overview of HIV

In 2008, half of the Nobel Prize in Physiology or Medicine was awarded to Françoise Barré-Sinoussi and Luc Montagnier for the discovery of the Human Immunodeficiency Virus, or HIV as it is more commonly known. In the early 1980s, huge research efforts were ongoing to find the causative agent of the AIDS epidemic. In 1983, Barré-Sinoussi and Montagnier published a paper in Science detailing the isolation of a HTLV family retrovirus from a patient with symptoms of AIDS which they later called

Lymphadenopathy-associated Virus, or LAV, which was further characterised through the molecular cloning of its genome (1-3). Gallo et al also published a paper on the isolation of a virus from several

AIDS patients which was related to the HTLV-I family (4), which after several other isolation and characterisation experiments was termed HTLV-III (5, 6). It was later confirmed that the two groups had actually isolated the same virus, which was subsequently renamed and has since been known as

HIV.

According to the World Health Organisation (WHO), since the start of the HIV epidemic in 1981, over

70 million people worldwide have been infected with HIV, with around 35 million deaths attributed to the virus (7). Of the 36.7 million people currently living with HIV worldwide (as of the end of 2016),

17.8 million were women, 16.7 million were men, and 2.1 million were children under 15 years old

(7). Whilst there were 1.8 million people newly infected with HIV in 2016, there has been a 39% decrease in new infections since 2000, and anti-retroviral treatment (ART) usage has increased from just 2% of the infected population in 2000 to around 53% coverage in 2016 (20.9 million people by the middle of 2017) (7).

In the UK, there has been approximately 150,726 HIV diagnoses since the start of the epidemic and a total of 23,374 deaths, with 5164 new diagnoses and 442 deaths in 2016, (8). A 2016 report by Public

Health England indicated a UK HIV prevalence of 0.16%, and that of the people living with HIV in the

UK, 87% had been diagnosed, with 83% currently on treatment and 78% were virally repressed (both above the UNAIDS 90:90:90 target) (9). More recently, there have also been changes in when ART is first administered. The 2017 Public Health report states that 76% of newly diagnosed HIV infected individuals now start ART within 90 days of diagnosis, and a policy of ART which is started straight after diagnosis is now in place (10).

1.1.2. HIV Classification

HIV is a single stranded positive sense RNA virus classified into group VI of the Baltimore classification system. HIV can be classified as either HIV-1 or HIV-2, both of which belong to the Lentivirus genus within the subfamily Orthoretrovirinae, in the family of Retroviridae (11, 12). HIV-1 is genetically diverse and can be further classified phylogenetically to groups M, O or N, as well as the more recently identified group P, with each group originating from an independent transmission of simian immunodeficiency virus (SIV) from chimpanzees (SIVcpz), groups M and N, or gorillas (SIVgor), groups

O and P, to humans (13-19). For further classification of HIV-1, groups are followed by subtypes then sub-subtypes, with circulating recombinant forms, or CRFs, used to describe recombinant viruses (20) as illustrated in Figure 1.1.

Group M (major group) was the first group to be characterised after the isolation of the previously mentioned HIV virus in 1983 (1, 14). It is the largest HIV-1 group, and can be further divided into 9 subtypes; A, B, C, D, F, G, H, J and K, with subtypes A and F being further characterised to A1-4 and

F1-2 sub-subtypes respectively (21, 22). It is also group M which is responsible for the greatest number of infections worldwide (around 90%), with around 48% of HIV-1 infections caused by subtype C, 12% by subtype A, and 11% by subtype B (13, 23). Whilst subtype B accounts for only 11% of HIV-1 infections globally, a 2013 study which analysed 2730 HIV-1 sequences responsible for new infections in Europe found that subtype B was responsible for 66% of these infections (24). Originally, group M also included subtypes E and I, however upon genome sequencing these were actually found to be

CRFs which are now classified as CRF01_AE and CRF04_cpx (21), and as of the end of 2017, there were

90 CRFs in this group (25).

Figure 1.1: HIV-1 and HIV-2 classification. HIV-1 can be categorised into four groups; M, O, N and P. The main group (M) can be further grouped into 9 subgroups (A, B, C, D, F, G, H, J and K) and circulating recombinant forms (CRFs), with subgroups A and F having A1-A4 and F1 and F2 subtypes respectively. HIV-2 can be categorised into 8 subtypes (A-H).

Group O (outlier group) was the next group to be classified after the isolation of three distinct viruses termed ANT70 in 1990 (26), and MVP5180 (27) and VAU (28) in 1994. Since these isolations, group O

HIV-1 is estimated to have infected around 100,000 people, and is responsible for less than 1% of HIV-

1 infections worldwide (14, 18, 19), as it is generally confined to West and Central Africa, with dominance in Cameroon, Gabon and Equatorial Guinea (19, 23).

Group N (non-m/non-O) was the third group to be classified after the isolation of another type of HIV-

1 virus named YBF30 in 1998 from an AIDS patient from Cameroon (29). Sequence and phylogenetic analysis showed that the structural genes of the newly isolated virus were equidistant to both HIV-1 group M and the SIVcpz virus, and the envelope gene in particular was closely related to SIVcpz (14,

29). This HIV-1 group is much rarer, and since this first isolation, only 13 other patients have been identified as being infected with Group N HIV-1 and all have been from Cameroon (16, 30).

The most recent group of HIV-1 to be identified was group P (putative/pending) after the isolation of a HIV-1 virus in 2009, named the RBF168 strain, which through near-complete genome sequence was found to be closely related to SIVgor (31). Of all the groups identified for HIV-1, this is by far the rarest, with only two cases of this virus identified so far (16, 30, 32).

1.1.3. HIV-1 Viral Genome and Structure

Mature HIV particles are spherical and generally range from 110-128nm in diameter (33). The HIV genome is around 9.8kb in size (34, 35), with nine genes encoding 16 proteins. There are three structural genes; gag, pol and env, two regulatory genes; tat and rev, and four accessory genes; vpr, vpu, vif and nef, which are summarised in Table 1.1 and Figure 1.2.

Protein Classification Gene Protein Structural gag Pr55 Gag precursor protein p6 p7 nucleocapsid protein (NC) p17 matrix protein (MA) p24 capsid protein (CA) pol Pr160 GagPol precursor p10 protease (PR) p15 (66) RNase H p31 integrase (IN) p51 reverse transcriptase (RT) env gp160 Env precursor gp41 transmembrane protein (TM) gp120 surface glycoprotein (SU) Regulatory tat p14 transactivator protein rev p19 RNA splicing regulator Accessory vpr p15 virus protein r vpu p16 virus protein unique vif p23 viral infectivity protein nef p27 negative regulating factor

Table 1.1: Summary of HIV-1 structural, regulatory and accessory genes and the proteins they encode. The gag gene encodes a Pr55 Gag precursor protein which is cleaved to give the p7 nucleoprotein (NC), p17 matrix protein (MA), p24 capsid protein (CA) and p6. The pol gene encodes a Pr160 GagPol precursor protein which is cleaved to the p10 protease (PR), p15 RNase H, p31 integrase (IN) and p51 reverse transcriptase (RT). The env gene encodes a gp160 protein precursor which is subsequently cleaved to gp41 (TM) and gp120 (SU). The tat and rev regulatory genes encode p14 and p19 proteins respectively. The accessory genes vpr, vpu, vif and nef encode p15, p16, p23 and p27 proteins respectively. (12)

Figure 1.2: Gene organisation and structure of HIV-1. The organisation of the HIV-1 structural, regulatory and accessory genes in their open reading frames (a), and the HIV-1 virus structure (b). The HIV virus particle has at its centre the viral RNA genome, nucleocapsid, reverse transcriptase, integrase and accessory proteins contained within the capsid protein shell. This inner core is surrounded by matrix proteins, then the viral membrane on which the envelope protein trimer (gp120 and gp41) is expressed. Reprinted from Virology, Vol 251, Eric O. Freed, HIV-1 Gag Proteins: Diverse Functions in the Virus Life Cycle, Pages 1-15, Copyright (1998), with permission from Elsevier (36). Rights and permissions in Appendix 1.

Whilst there is a huge amount of literature on each of the structural, regulatory and accessory proteins, as these are not directly relevant to this work presented in this thesis, only a brief overview of each will be given here. However, the HIV-1 envelope will be discussed in more detail as it is the main target for neutralising and non-neutralising antibodies generated during HIV-1 infection.

1.1.3.1. Structural Proteins

As the name suggests, the structural proteins encoded by the gag, pol and env genes give the HIV virus its structure, which is demonstrated in Figure 1.2. The gag gene encodes proteins that are vital for correct virus assembly with the precursor being cleaved to give the nucleocapsid, matrix and capsid proteins. The pol gene encodes a GagPol precursor proteins which is cleaved to give the key enzymes required for viral replication; protease, RNase H, integrase and reverse transcriptase, and the env gene encode the gp160 protein which is cleaved to give the gp120 and gp41 components of the HIV-1 envelope. It is the gp120 component of the HIV-1 envelope protein that binds to the CD4 receptor on target cells initiating entry into the cells, and this as well as gp41 are key targets for neutralising and non-neutralising antibodies generated during HIV-1 infection. The HIV-1 envelope will therefore be discussed in more detail in section 1.1.3.4.

1.1.3.2. Regulatory Proteins

There are two regulatory proteins that are encoded by the regulatory genes tat (transcriptional transactivator) and rev (regulator of virion gene expression); the p14 transactivator protein, and the p19 RNA splicing regulator respectively. The 14kDa tat protein (37) recognises the transactivator- responsive region (TAR) of RNA located 3’ to the LTR of the HIV-1 genome when it undergoes a conformational change (38), and through this binding with the help of a several crucial cofactors such as P-TEFb simulates HIV-1 transcription and elongation (38). The rev protein on the other hand binds to the Rev response element (RRE) on unspliced viral mRNA in the nucleus, signalling the nuclear export of this mRNA (4 and 9kb mRNA encoding Gag, Pol, Env and accessory proteins) to the cell cytoplasm, and is therefore vital for the expression of the HIV-1 structural proteins (37, 39-43).

1.1.3.3. Accessory Proteins

During HIV-1 infection in humans, four accessory proteins vpr, vpu, vif and nef are encoded by accessory genes and have multiple roles in the HIV-1 infection of other cells as well as evasion from the cell mediated immune response (44).

Vpr is a 14kDa protein that is packaged into virus particles, and has been shown to be a regulator of

HIV-1 nuclear import in non-dividing cells through association with the preintegration complex (45-

49), modulate reverse transcription mutation frequency through association with uracil DNA glycosylase (50-52), halt the cell cycle in the G2 phase which may provide better conditions for HIV-1 transcription (45, 53-55), as well as several other functions.

Vpu is a 16kDa protein that is translated from the same intermediate sized mRNA, therefore termed bicistronic mRNA, that encodes the envelope protein, (56, 57). A key function of vpu is to downregulate CD4 expression through binding to CD4 in the endoplasmic reticulum (ER) and mediate its degradation, so that the gp160 envelope protein, which can also bind to CD4 and in doing so get stuck in the ER, can be transported to the cell surface for virion assembly (58-60). Another key function is the enhancement of virus release by antagonising tetherin activity, through direct binding of their transmembrane domains, which in the absence of vpu can prevent HIV-1 virion release by keeping the virions attached to the cell surface (61, 62).

Vif is a 23kDa protein that is essential for HIV-1 replication through its main role of preventing the inclusion of APOBEC3G, a cytidine deaminase that removes amine groups from cytidine molecules in single stranded DNA (63), in newly synthesised virions (64). It has been shown that in the absence of vif, APOBEC3G, an antiretroviral defence protein (65), is packaged into virions budding from the cell surface and leads to virions that are non-infectious (65, 66), through mutating the viral mRNA (64, 67).

Therefore to ensure virions are infectious for the HIV- replication cycle, vif prevents APOBEC3G from being incorporated into new virions by targeting them to the proteasome for degradation (68, 69).

Nef is around 27kDa in size and has several key functions including the downregulation of CD4 (70-72) and MHC class I molecules (73-76). The former, as previously discussed for the activities of the vpu protein, can bind to the HIV-1 envelope and prevent its incorporation into new virions, and therefore

CD4 downregulation can prevent this, whilst the down regulation of MHC molecules provides a way for the virus lying in infected cells to avoid being detected and killed by cytotoxic T cells (75). Nef has also been shown to downregulate the immune checkpoint receptor CTLA-4 through interactions in intracellular compartments, and targeting it towards lysosome degradation (77).

1.1.3.4. HIV-1 Envelope

One of the structural HIV-1 proteins essential for virus replication is the envelope glycoprotein that is encoded by the env gene. When transcribed, this gene leads to the production of a precursor protein called gp160, which is subsequently cleaved to gp120 and gp41. These units form a non-covalently associated heterotrimeric structure that is expressed on the surface of HIV-1 virions, with the three gp120 domains sitting on top of the three gp41 domains, and the gp41 domains connected to transmembrane proteins embedded in the viral membrane through the membrane proximal external region (MPER) (78). Studies have shown that each HIV particle has around 14 or 15 trimers (79, 80), or spikes as they are otherwise known, and it is within these trimers, more specifically gp120, that the

CD4 binding site is located.

The gp120 core consists of an inner domain, which has the V1/V2 stems at its distal end, and an outer domain, which has the V3 stem at its distal end and the V4 and V5 stems at its proximal end, parallel to each other and linked by the bridging sheet (81). After the initial binding of HIV virions to target cells, the second step in the entry of HIV-1 into host cells is the binding of the HIV-1 envelope to cell

CD4 receptors. This interaction takes place between 22 amino acid residues of CD4 domain 1 and 26 amino acid residues spread over six segments of the inner domain, outer domain and bridging sheet of the gp120 core (81, 82), known collectively as the CD4 binding site (CD4bs). The CD4:CD4bs interaction causes a conformational change in gp120 which among other structural alterations moves

the V1/V2 loops and closes the bridging sheet conformation creating the coreceptor (CXCR4 or CCR5) binding site with the V3 loop (81, 83-85). After these binding events, fusion of the virus membrane with that of the host target cell occurs through insertion into the target cell membrane of the now exposed gp41 fusion peptide, and this is followed by entrance of the viral genome into the host cell for replication (86).

As the HIV-1 envelope is the component responsible for binding to and entering host cells during the course of infection, it is a key target for neutralising and non-neutralising antibodies, examples of which will be discussed later. There are five key targets for antibodies on the HIV-1 envelope; the

CD4bs, the V1/V2 loops, the V3 loop, the gp120/gp41 bridging region, and the gp41 MPER. As all have roles in the processes leading to viral entry, binding of antibodies to these sites may prevent the interaction between gp120 and gp41 with the host cell membrane and may prevent fusion and entry into host cells.

On the other hand, as the HIV-1 envelope is a prime target for antibodies, the virus has several mechanisms employed by the envelope to avoid antibody responses. As previously described, the HIV-

1 envelope is composed of gp120 and gp41 subunits expressed in a heterotrimeric structure, and for the correct binding to host receptors and entry into target cells these proteins must be functional.

However, HIV-1 virions also display non-functional envelope proteins on their surface such as gp41

“stumps” that have shed the gp120 glycoproteins, or gp120 or gp41 monomers (87), so that any antibodies generated will be against these epitopes and therefore not effective against those of the functional proteins. Similarly the fact that the gp120/gp41 can have several different conformations during the process of cell attachment and entry can lead to the conformational masking of key epitopes targeted by antibodies (88, 89). Another mechanism that the envelope employs is a glycan shield consisting of branched carbohydrates that can cover the whole envelope, meaning that antibodies cannot reach their targets on gp120 (90-92). Whilst there are several other mechanisms that the HIV-1 envelope uses to avoid antibody detection, one of the most important is antigenic

variability of the gp120 subunit caused by the rapid mutation of the envelope gene (93-96), which means that by the time potent neutralising antibodies have been generated against the HIV-1 envelope it has usually mutated.

1.1.4. HIV-1 Replication Cycle

The HIV-1 replication cycle can be categorised into several key stages which are summarised in Figure

1.3. As previously described in section 1.1.3.4, it is the HIV-1 envelope that makes the contact with the target cell membrane and initiates the process of replication within the cell. The gp120 envelope subunit binds to the CD4 receptor on the cell surface triggering a conformational change that reveals the coreceptor binding site to which CCR5 (or CXCR4) binds. This leads to the release of the gp41 fusion peptide which inserts into the target cell membrane leading to fusion of the viral membrane and the target cell membrane (97-99).

Once the membranes have fused, the capsid enters the cell cytoplasm, where it is uncoated to release its contents; the viral genome RNA and other viral proteins. The process and timescale of uncoating is still not fully understood and several models have so far been proposed to occur between fusion and nuclear import (100). Recently, several papers have linked uncoating to the reverse transcription stage

(101-103), and Cosnefroy et al published results in 2016 with a hypothesis that uncoating occurs after first strand transfer during reverse transcription (104). This next stage is where the viral RNA is reverse transcribed to produce viral DNA in a reverse transcription complex, which is subsequently converted to the preintegration complex (PIC) (105, 106). Components of the PIC such as matrix proteins and integrase contain nuclear localisation signals which can recruit cellular transport proteins for nuclear import of the PIC (105, 107).

Figure 1.3: Overview of the HIV-1 replication cycle. HIV particles attach to host cells through non-specific or specific interactions and bind to the CD4 receptor followed by co-receptor binding (1). This signals a sequence of events leading to fusion of the viral membrane with the target cell membrane (2) and uncoating of the viral capsid in the cell cytoplasm (3). Viral RNA is reverse transcribed to DNA (4) and forms the preintegration complex (PIC) with other viral proteins such as integrase, which undergoes nuclear transport (5). In the cell nucleus, the viral DNA is integrated into host DNA (6), which is then transcribed into mRNA (7), and exported to the cytoplasm (8) where it is translated to HIV proteins (9). Following translation, HIV proteins are assembled (10) and packaged into the budding virus (11). The virus particle is then released from the cell membrane (12) where it matures into a fully infectious virus particle (13). Reprinted by permission from Springer Nature, Nature Reviews Microbiology. The structural biology of HIV-1: mechanistic and therapeutic, Alan Engelman and Peter Cherepanov, COPYRIGHT (2012) (108). Rights and Permissions in Appendix 2.

In the nucleus of the cell, the next step is the integration of the viral DNA into host DNA which is mediated by the PIC integrase protein which removes several nucleotides from the 3’ ends of viral

DNA (109-112). These 3’ ends then target phosphodiester bonds in the host DNA and join to the 5’ ends of the host DNA, leading to strand transfer to the host DNA where DNA polymerases finish the job of complete integration (113, 114). Following integration, the host DNA now containing viral DNA is transcribed and viral mRNA is exported from the nucleus to the cell cytoplasm where it is translated into proteins on cytosolic polysomes or the rough ER (115). The HIV-1 proteins (such as gag and pol) are then trafficked to the cell membrane and packaged into the new virus particles which start

budding from the surface. Upon release the new particles mature so that they can infected other cells and go through the replication cycle again.

As shown in Figure 1.3, ART can target several stages of the HIV-1 replication cycle, with examples of fusion inhibitors, CCR5 inhibitors and protease inhibitors indicated. These will be discussed in more detail in section 1.1.5.4.

1.1.5. The course of HIV-1 infection

HIV-1 is a blood borne virus that is primarily transmitted through sexual contact, with the following infection broadly divided into three phases; acute infection, which includes the eclipse phase and can be categorised by the Fiebig stages lasting until around 100 days after infection, early infection, and chronic infection which can progress to AIDS within several years without ART.

1.1.5.1. Natural History of HIV-1 Infection

The natural history of HIV-1 infection summarised in Figure 1.4 begins with the initial transmission of

HIV-1, which has been shown to be caused by a single transmitted founder virus in around 80% of infections (116). During the acute phase of infection, the viral load, or copies per ml of HIV RNA, increases to its peak around 6 weeks post infection. Conversely CD4 helper T cells in both the blood and gastrointestinal tract decrease, with the latter being severely depleted during the first few weeks of acute infection (117-119). Also during this acute phase of infection, there is widespread immune activation, an increase over time of the number of HIV-specific CD4 and CD8 T cells, and the appearance of antibodies specific for HIV which will be discussed later in section 1.4. It is also within the acute phase of infection that the latent reservoir is established (120).

Moving into the chronic phase of infection, the CD4 counts continue to decline, whilst the viral mRNA which had dropped after its peak around 6 weeks post infection gradually increases over time.

Immune activation generally remains high, with the HIV antibody levels also remaining fairly steady, whilst the HIV-specific CD4 and CD8 T cell counts also drop. Furthermore, over the course of HIV-1 infection, there is an accumulation of huge viral diversity of the virus in order to escape antibodies

and the CD8 cytotoxic T cell response, leading to subpopulations of virus with different genetic features termed quasispecies (118). After around 7-10 years post HIV-1 infection, the patient can progress to an AIDS diagnosis, defined as a CD4 count lower than 200 per mm3 and one or more opportunistic infections (121), leading ultimately to death.

Figure 1.4: The natural history of HIV-1 infection and the effect of ART. In untreated HIV infection, there is peak viral RNA during the acute phase of infection which then declines before increasing over time, and a decline in both the CD4 blood and CD4 gastrointestinal tract (GIT) counts (a). Also during the acute phase there is an increase in immune activation, anti-HIV antibodies and HIV-specific CD4 and CD8 T cells, which subsequently decline during chronic infection (b). After administration of ART, viral RNA rapidly decreases, whilst the CD4 blood count recovers with a slight increase in the CD4 GIT counts which level out over time (c), immune activation and HIV-specific CD8 counts also decrease over time whilst the anti-HIV antibody levels remains high (d). Reprinted from The Lancet, 384, Gary Maartens, Connie Celum, and Sharon R Lewin. HIV infection: epidemiology, pathogenesis, treatment, and prevention, Pages No. 258- 271., Copyright (2014), with permission from Elsevier (118). Rights and permissions in Appendix 3.

The natural history of infection discussed here however is in the absence of ART which has dramatically altered the progression of disease in infected individuals, and has been shown to suppress viral load and restore CD4 counts. Examples of currently used ART will be discussed in section

1.1.5.4.

1.1.5.2. Defining acute HIV Infection

Acute HIV-1 infection (AHI) has been difficult to define as it can vary among individuals, the line between acute infection and early infection can be difficult to distinguish, and many studies have used different defining features. The Fiebig stages however are commonly used to define acute HIV-1 infection through the use of different lab tests to determine what stage of infection a patient is in.

Before Fiebig stage I, is the eclipse phase which lasts for around 10 days post transmission (120, 122,

123). It is during this phase that the plasma viral RNA starts to increase and is first detectable during

Fiebig stage I by PCR (124). The viral load continues to rise until its peak generally 20-30 days post infection, corresponding to Fiebig stages III and IV, after which it declines to its set point around the end of stage V, which is the transitioning point to early chronic infection (120). In total there are 6

Fiebig stages, and as previously described stage I is determined by the presence of viral RNA. Stage II is determined by the presence of P24 antigen, and stage III is the first detection of antibodies by ELISA.

This is followed by stage IV which is determined by indeterminate western blot results, and stage V with western blot results positive for HIV-1 bands except p31, which only appears during Fiebig stage

VI (124).

1.1.5.3. Defining chronic HIV Infection

Defining chronic HIV infection is also difficult as this phase of the infection or disease has changed over time. When HIV was first identified in the 1980s, patients were rapidly progressing to AIDS and whilst there was treatment, it didn’t fully restore the immune functions in patients and was also toxic.

Since the advent of more modern ART drugs, patients lifespans have increased and the progression to

AIDS has slowed, however other illnesses linked to the heart, liver and kidneys are now more common

and known as non-AIDS morbidity (125). Now HIV-1 infected patients begin ART soon after diagnosis, which very quickly supresses the viral load and maintains the CD4 T cell count, which before was an indicator of when treatment should begin. A key hallmark of chronic HIV infection is the latent HIV reservoir which despite treatment remains in CD4 T cells, and this is a key barrier to a potential cure.

It is during chronic infection however, that neutralising antibodies to autologous and heterologous

HIV-1 are generated, with some patients also developing broadly neutralising antibodies which will also be discussed in section 1.4.

1.1.5.4. Antiretroviral Treatment

The antiretroviral treatment used for treating HIV-1 infection has advanced from the first use of a three drug treatment that blocked HIV-1 replication in 1996, through to the one pill a day treatment established in 2006, and it is now recommended that patients start ART as soon as possible after diagnosis (119). There are several key categories of HIV-1 drugs currently used to treat HIV-1 infection that can be grouped according to the stage of the replication cycle that they target; chemokine receptors antagonists, fusion inhibitors, integrase inhibitors and protease inhibitors (119). Another key class is the reverse transcriptase inhibitors of which there are two classes, nucleoside or nucleotide reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs).

Whilst these drugs target different stages of the HIV-1 replication cycle, the overall result is similar in that the viral load is supressed to below detectable levels, the CD4 T cell count is restored, and the widespread immune activation is decreased (118). When first diagnosed, in the UK it is recommended that patients commence treatment with a combination of three drugs, most often two NRTIs such as emtricitabine and tenofovir disproxil, with a third drug including an NNRTI boosted protease inhibitor or the integrase inhibitor raltegravir (126, 127).

1.1.6. T and B cells during HIV-1 infection

During HIV-1 infection, there is widespread immune dysregulation to cells involved in both the innate and adaptive immune responses to the virus such as NK cells and pDCs, and CD4+ T cells and B cells respectively (128), leading to a poor anti-HIV immune response and lack of control of the virus.

1.1.6.1. T cells in HIV infection

During a normal immune response to a pathogen, the CD4 and CD8 T cells comprise the major components of the cellular immune response. CD4+ T cells recognise through their T cell receptors

(TCRs) antigenic peptides displayed on the MHC class II molecules expressed on antigen presenting cells (APCs), and when activated can differentiate into Th1, Th2 or Th17 cells which can each mediate different functions. The CD8+ T cells on the other hand recognise peptides expressed on the MHC class

I molecules expressed on all nucleated cells, and are activated to kill infected cells.

A hallmark of HIV-1 infection is the decline in CD4+ T cells during the acute phase of infection. CD4+ T cells are the main target of HIV as they express the CD4 receptor on their surface which the gp120 component of the HIV-1 envelope binds to and triggers fusion and entry into the cell. Recent studies have shown that it is the cell to cell transmission of HIV through virological synapses from permissive

CD4+ T cells to non-permissive bystander CD4+ T cells that accounts for the mass loss of CD4+ T cells during HIV-1 infection, and not free virus particles, and that this transmission triggers innate immune detection of the infected cell and caspase 1- dependent pyroptosis (129-132). Furthermore, it is within a small population of memory CD4+ T cells that the latent reservoir is established fairly early after infection (133, 134) and removing this reservoir is a major obstacle for a HIV-1 cure. Moreover, the

CD8+ T cell response is ineffective in controlling or clearing HIV infection, in large part due to viral escape, with an inefficient response as early as during the acute phase of infection (135) and becomes exhausted during HIV-1 infection ultimately leading to advanced disease.

1.1.6.2. B cells in HIV Infection

The key role required of B cells during HIV-1 infection is to differentiate into antibody secreting cells

(ASCs) such as plasmablasts or plasma cells and produce antibody to target the infection through humoral immunity. However, HIV-1 infection has been shown to cause B cell dysregulation, with the hyperactivation of B cells being well characterised as leading to polyclonal B cell activation and hypergammaglobulinaemia, an increase in the memory B cell to plasmablast/plasma cell differentiation, and an increase in the number of autoantibodies produced (136) amongst others.

Polyclonal B cell activation was reported as early as 1983 by Lane et al who found that AIDS patients had high numbers of hyperactive B cells producing antibodies (137). This was followed up by a study by Schnittman et al who showed that purified B cells from HIV negative patients rapidly proliferated when cultured with virus from AIDS patients, peaking at four days after culture, with a response equal to that of known polyclonal B cell activators (138). Studies by Nagase et al in 2001 and De Milito et al in 2004 further examined the mechanisms of polyclonal B cell activation and hypergammaglobulinaemia in HIV infection. Nagase et al showed that the CD27+ memory B cell population was reduced in HIV infected patients compared to healthy controls, and that this was due to CD27:CD70 CD4 T cell interaction leading to activation and differentiation of memory B cells into plasma cells and higher antibody production (139). Similarly, De Milito et al showed that there was spontaneous production of IgG from the PBMCs of HIV infected individuals that was dependent on cell to cell contact likely to be CD4 T cells, and that naïve B cells were a key population of hyperactivated B cells and had undergone class switching from IgM to IgG (140). Moreover, it has been shown that HIV-1 nef upregulates B cell costimulatory receptors such as CD22 and CD80 through macrophage released CD23 and ICAM which is linked to polyclonal B cell activation. (141-143).

1.2. B Cells

In a paper published in Nature in 1965, Cooper et al demonstrated using a chicken model that there were two key lymphocyte lineages; one that developed in the thymus, now known as T cells, which the authors hypothesised to be for “immunological recognition”, and a lineage that arose in the bursa of fabricus (bone marrow in humans), now known to be B cells, that were described as the “production system for antibody” (144). Since this discovery B cells have been studied extensively, and the development of B cells and how they are activated, through to the immunoglobulin gene structure that dictates the antibodies produced by these cells and how they are affected in many diseases or infections are now known.

1.2.1. Immunoglobulin Genes

1.2.1.1. Gene Structure

B cells have three immunoglobulin gene loci used for production of the membrane bound B cell receptor and the antibodies secreted during an immune response. The heavy chain locus is around

1250kb in size and located on chromosome 14, the kappa chain locus is around 1820kb in size and located on chromosome 2, and the lambda chain locus which is around 1050kb is located on chromosome 22 (145). As will be discussed later, immunoglobulins are composed of 2 heavy chains, each consisting of three or four constant region domains depending on the immunoglobulin subclass, and one variable region domain, and 2 light chains (kappa or lambda) which are comprised of one constant region and one variable region domains.

On the heavy chain locus, nearest to the leader sequence at the 5’ end, are the variable (V) gene segments (Figure 1.5), of which there are just over 100 functional genes with multiple alleles in the population (146). These genes are followed by the diversity (D) gene segments, of which there are 23 functional genes, and then the Joining (J) genes of which there are 6 functional gene segments, (147).

During B cell development one of each of the variable, diversity and joining gene segments is randomly selected and joined together in the process of VDJ recombination, which will be discussed later, to

form the variable region which is responsible for recognising and binding to antigen. Further downstream from the V, D and J gene segments are the constant gene segments (ɣ1, ɣ2, ɣ3, ɣ4, α1,

α2, μ, ε, or δ), with the μ and δ expressed in early B cell development followed by class switching to

ɣ, α, or ε constant genes later on.

Figure 1.5: Immunoglobulin gene structure. The gene structures for the heavy (H), kappa (κ) and lambda (λ) loci are shown from 5’ to 3’ ends. At the start of each gene is the leader signal, followed by the variable (V) gene segments. The heavy chain variable genes are followed by diversity (D) genes then joining (J) genes, and then the constant gene segments. The kappa chain variable gene segments are followed by the joining gene segments and then a single constant gene. The lambda chain variable segments are followed by joining and constant genes interspersed among each other.

As will be discussed later, immunoglobulins can have either a kappa or a lambda light chain, and these loci are structurally similarly to the heavy chains with a variable, joining and constant gene segments, but light chains do not have diversity genes. The kappa chain locus has around 40 functional variable gene segments at the 5’ end, followed by 5 J gene segments, and one kappa constant gene segment

(Cκ) (148). The lambda chain locus is structured slightly differently, with around 35 variable gene segments (149), followed by the J gene and C gene segments interspersed with each other.

1.2.1.2. VDJ Recombination

As previously mentioned, variable regions of immunoglobulins consist of a variable gene segment, a diversity gene segment and a joining gene segment for heavy chains, and the variable and joining gene segments for light chains. Before the membrane bound BCR or antibody can be produced by the B cell, the genes present in the germ line DNA must be rearranged to give the VDJ segment can be transcribed with the constant region DNA before translation into the polypeptide heavy or light (VJ) chains. For this to happen VDJ recombination must occur.

As demonstrated previously in Figure 1.5, the gene order in the heavy chain locus is variable region segments, followed the diversity genes, and then the joining genes. Recombination signal sequences

(RSSs) consist of a conserved heptamer sequence, followed by a 12 or 23bp non-conserved spacer sequence, and then a conserved nonamer sequence, and are located 3’ to each variable gene segment, flank each diversity gene segment in both 5’ and 3’ directions, and are located 5’ of each joining gene segment (150-154) . Whilst the RSSs of the variable and joining gene segments both have the 23bp spacer sequence, the diversity gene segment RSSs have the 12bp spacer sequences, and this leads to correct VDJ recombination through use of the 12/23 rule. This rule means that recombination can only take place with two gene segments with different RSS spacer sequences, therefore the diversity gene segment can only join to the joining gene segment, and this combined section can then join to the variable gene segment through the diversity RSS sequence.

The actual mechanism of VDJ recombination begins with the looping of chromosomal DNA which brings together the diversity and joining gene segments, and later the DJ and variable gene segments.

Recombination-activation gene 1 and 2 (Rag-1 and Rag-2) proteins introduce double strand breaks into the DNA between the gene segment and its RSS, ultimately resulting in two gene segments with covalent hairpins at their 3’ (variable and diversity) or 5’ (joining and diversity) ends, and a discarded loop containing the RSS heptamer-spacer-nonamer sequences (145, 155-157). The hairpins of the now adjacent D-J or V-DJ gene segments are then opened by an protein complex called Artemis, and are joined by non-homologous end joining (145).

As these final processes within VDJ recombination are imprecise, a number of nucleotides are lost, with N (non-template encoded) and P (palindromic) nucleotides then added by the enzyme terminal deoxynucleotide transferase (TdT) to the D-J or V-DJ ends, leading to junctional diversity. Due to this, the random pairing of the variable, and joining regions (as well as the diversity in heavy chains) and the wide selection of genes present at each locus, there is a huge number of possible variable regions that could be encoded, giving rise to the hugely diverse antibody repertoire seen in humans.

1.2.2. B cell development and subsets

During the developmental or differentiation pathway of B cells from a pro-B cell in the bone marrow all the way through to antibody secreting plasma cells in the spleen and lymph nodes, there are a number of different types of B cell encountered (Figure 1.6).

Figure 1.6: The B cell developmental pathway. Pro-B cells undergo differentiation in the bone marrow (BM) to CD19 expressing large pre-B cells, small pre- B cells then immature B cells. These immature B cells can then migrate to the spleen where they can differentiate into follicular B cells, marginal zone B cells or B1 cells, the latter of which has so far not been well defined in humans. From here, follicular B cells can become activated by antigen and differentiate into short lived antibody producing plasma cells or form germinal centres where they undergo somatic hypermutation, class switching and affinity maturation leading to antigen specific memory B cells that can further differentiate into antibody producing plasmablasts or long lived plasma cells. Marginal zone B cells can also become activated and differentiate into plasmablasts and plasma cells to produce antibody. Adapted by permission from Springer Nature, Nature Reviews Rheumatology. CD19: a promising B cell target for rheumatoid arthritis, Thomas F. Tedder, COPYRIGHT (2009) (158). Rights and permissions in appendix 4.

Within the bone marrow, the B lymphocyte progenitor differentiates from a pre-pro B cell through several intermediate stages resulting in an immature B cell expressing the B cell receptor (BCR) in the

IgM class on its surface. This undergoes transition to the spleen (as a transitional B cell) where the naïve mature B cell can differentiate into marginal zone B cells, follicular B cells or B1 cells. It is important to note however, that the vast majority of work on B1 cells so far has been achieved through

mouse models, and this subset is still not well defined in humans. It is through the activation of the marginal or follicular B cells by recognition of antigen by the B cell receptor, with T cell help or through stimulation through TLRs and cytokines, that the cells can differentiate into short lived plasma cells, or go to the germinal centre and differentiate into plasmablasts or memory B cells which subsequently can differentiate into long lived plasma cells.

1.2.2.1. Memory B cells

Memory B cells can be generated in a T cell-dependent or T cell-independent manner, the former of which can take place either dependently or independently of a germinal centre. The majority of memory B cells are generated in germinal centres, and have therefore undergone class switch recombination, have a high affinity for antigen due to affinity maturation, and have high levels of variable region gene mutations due to the process of somatic hypermutation (159). These cells express antigen-specific B cell receptors on the cell surface ready to bind to antigen, and increased levels of co-stimulatory molecules such as CD80 and CD86 (160). Therefore, when upon a second exposure to a particular antigen the memory B cells are rapidly activated and differentiate into plasmablasts or plasma cells, resulting in high affinity and highly specific antibodies, providing a more effective antibody response.

Of course, memory B cells are also long lived, with studies showing they can survive within individuals for many years. For example, a paper by Yu et al in Nature in 2008 studied the antibody response of individuals born before 1915 that had survived the 1918 flu pandemic. In the study the authors generated B cell lines through EBV immortalisation, and found that in 7/8 donors these B cells secreted

1918 HA-specific antibodies (161), indicating that memory B cells responsible for these antibodies had been present in these individuals for almost 90 years. The smallpox vaccine has also been shown to result in antigen-specific memory B cells up to 50 years post vaccination (162).

In humans, it has been demonstrated that memory B cells can be IgM+, IgG+ or IgA+ (163-167). A 2011 paper by Berkowska et al characterised 6 memory B cell subsets based on their CD27 and Ig class

expression; CD27+IgM+, CD27+IgG+, CD27+IgA+, CD27-IgG+, CD27-IgA+ and the CD27+IgM+IgD+ natural effector memory cells, and that these memory B cells can arise through one of three maturation pathways (168). As memory B cells express the IgM, IgG or IgA B cell receptor on the cell surface, single cell sorting using antigen baiting can now be used to sort antigen-specific memory B cells, and can be used for single cell expression cloning to generate antigen-specific antibodies, and this will be discussed in more detail later.

1.2.2.2. Antibody secreting cells (ASCs)

Plasmablasts and plasma cells are terminally differentiated B cells known as ASCs. In the B cell developmental pathway, after the somatic mutation and class switching of B cells within the germinal centre, the germinal centre B cell differentiates into either a short lived plasma cell or a short lived plasmablast, which can then further differentiate into a long lived plasma cell. Whilst plasma cells remain in lymphoid organs or the bone marrow, plasmablasts are found in lymphoid organs and blood, and can migrate to sites of inflammation (169). In a 2015 Nature review, Nutt et al described how there is an activated B cell transcription network which maintains the activated B cell lineage, and an antibody-secreting cell transcription network which leads to plasmablast and plasma cells, and it is the switch from the former to the latter that triggers the differentiation to antibody secreting cells

(169). Whilst BACH2 and PAX5 transcription factors are key in activated B cells, ASCs predominantly express BLIMP1, IRF4 and XBP1 (169, 170).

Plasmablasts can be identified by the surface expression of CD19, high levels of CD27 and CD38, and the absence of CD20, and rapidly proliferate to produce large quantities of antibodies (171). These cells result from the differentiation of either naïve or memory B cells which have been activated by T cell dependent or independent mechanisms in response to antigen or cytokines such as IL-10 and IL-

21 (170). It has been well documented that there is a rapid “burst” of plasmablasts in the blood around

7 days post vaccination or infection (172), but the percentage of these cells that are antigen-specific can vary among different infections or vaccinations, as well as within individuals. For example, after

administration of the Influenza vaccine, up to 80% of plasmablasts are antigen-specific (172, 173), and

Tetanus vaccination has been shown to result in 97% of antigen-specific plasmablasts (174). Similarly

60-85% of plasmablasts from acutely infected Dengue patients are antigen-specific (175, 176), whilst on the other hand only 1.3% of plasmablasts from early infected HIV patients have been reported to be antigen-specific (177).

Normally in healthy individuals, or “steady state”, the plasmablast population is low in number, accounting for only 0.14% of total PBMCs (178), and has been shown to be predominantly IgA which account for 84% of plasmablasts, with IgG and IgM accounting for just 12% and 5% of plasmablasts respectively (178). Following primary infection, IgM and IgG plasmablasts increase in their percentage, and following secondary infection after class switching, somatic hypermutation and affinity maturation, the plasmablast response becomes predominantly IgG (171, 179).

Plasma cells are similar in their cell surface expression markers to plasmablasts, albeit with increased

CD27 and CD38 surface expression as well as CD138 and CXCR4 expression (169), and can exist as short lived or long lived cells. Grouped with plasmablasts as ASCs, they also follow the same trend of being stimulated 7 days after vaccination, and the same initial IgA steady state response, with a predominant IgG response after germinal centre reaction (178, 179). However, plasma cells can be distinguished from plasmablasts in that they do not proliferate, they lose mobility, and downregulate

MHC expression (170).

As previously mentioned, plasma cells can be short or long lived. Short lived plasma cells can be derived from the previously mentioned plasmablasts which have further differentiated to plasma cells in the extrafollicular foci and usually live for 7-14 days (180), or activated B cells at the sites of infection. On the other hand, long lived plasma cells can survive for months, and have been through affinity maturation and somatic hypermutation in the germinal centre, (181, 182). These cells produce high titres of antigen specific antibody and have differentiated from memory B cells or plasmablasts.

1.2.3. B cell Activation

B cells can be activated in one of two key ways; with T cell help known as T cell dependent activation, or in the absence of T cells known as T cell independent activation. The former is the predominant response to protein antigens, whilst the latter is generally against polysaccharide antigens (183). It is through these processes that memory B cells, plasma cells and plasmablasts are generated leading ultimately to the production of antigen-specific antibodies.

1.2.3.1. T cell dependent activation

The T cell dependent activation of B cells takes place within secondary lymphoid organs such as the lymph node, and can be either germinal centre dependent (i.e. taking place within a germinal centre) or germinal centre independent (also known as extrafollicular B cell activation), demonstrated in

Figure 1.7. Both mechanisms can lead to class switched differentiated B cells, but only the germinal centre reaction dependent B cell activation leads to B cells that have undergone high levels of somatic hypermutation and affinity maturation.

Both mechanisms of activation begin the same way with the entry of antigen to the lymph node which is subsequently internalised by dendritic cells and displayed using MHC class II proteins. CD4+ helper

T cells located within the T cell zone recognise the displayed peptides using the T cell receptor and are activated. Around the same time, B cells which are located within the B cell follicle recognise antigen through binding with the BCR and internalise it, displaying peptides on the cell surface using MHC class

II molecules. Through chemokine signalling, the B cells migrate to the B cell follicle/T cell zone interface where their MHC class II peptide displaying proteins are recognised by the TCRs of the T cells previously activated by the same antigen. Through this interaction, as well as those of costimulatory molecules such as the CD80-CD28 (otherwise known as B7-CTLA4) and CD40-CD40L interactions, the

B cells are activated and undergo proliferation, and it is at this point where the B cell has two options.

Either they can differentiate into an extrafollicular foci differentiating into memory B cells or short lived antibody producing plasmablasts/plasma cells, and thus have been activated by the germinal

centre independent reaction, or they can undergo further activation to become highly specific for antigen in the germinal centre dependent activation. The antibodies produced by the differentiated plasmablasts/plasma cells generated by the germinal centre independent activation are generally class switched (i.e. have changed their original IgM heavy chain gene to IgG or IgA), but have low levels of somatic hypermutation and affinity maturation.

Figure 1.7: T cell dependent B cell activation. The T cell dependent activation of B cells takes place within secondary lymphoid organs, and can be either germinal centre dependent or independent. Initially both naïve CD4+ T cells and B cells become activated by antigen in their respective T cell and B cell zones, and then interact at the interface of the T cell zone/B cell follicle, where the T cell provides the B cell with stimulation to undergo proliferation (a). From here the cells can either differentiate into short lived plasma cells and produce antibody in the extracellular foci (known as germinal centre independent B cell activation), or generate a germinal centre (c). In the germinal centre, the B cells first undergo somatic hypermutation and affinity maturation in the dark zone (d) and then move into

the light zone where they interact with follicular dendritic cells (FDCs) and follicular T helper (TFH) cells for affinity selection. These cells can then leave the germinal centre where they differentiate into antigen-specific memory B cells (e), or move back into the dark zone for further diversification. Reprinted by permission from Springer Nature: Nature, Nature Reviews Immunology. Memory B cells, Tomohiro Kurosaki, Kohei Kometani, and Wataru Ise, [COPYRIGHT] (2015) (167). Rights and Permissions in Appendix 5.

The other option for the activated B cell is to proliferate into a germinal centre. An antigen-specific B cell that has been activated can rapidly proliferate and form a germinal centre which can subsequently be divided into the dark zone and the light zone. It is within the dark zone that the B cells undergo proliferation, class switching and somatic hypermutation (184, 185), whilst the light zone is where affinity selection for antigen occurs.

The process of class switching rearranges the immunoglobulin DNA to change from the IgM/IgD constant gene to IgG, IgA, or IgE constant genes which will lead to the generation of a functional antibody response. As shown previously in Figure 1.5, the heavy chain constant regions genes are organised with the IgM and IgD constant regions first (μ and δ respectively), followed by the IgG, IgA and IgE constant region genes (γ, α, and ε respectively). The DNA 5 prime (5’) of each of these constant region genes (except the IgD gene) are regions of around 2-10kb in size, and consist of variations of pentameric repeats, making up the switch region (186, 187). During the process of class switch recombination, double stranded breaks are introduced into the switch regions through the activity of several enzymes. Activation-induced cytidine deaminase (AID) removes amine groups from cytosines in the switch regions converting them to uracil (188-191), and this is followed by base excision repair

(BER) and mismatch repair (MMR) enzymes converting the uracil to double stranded breaks (189, 192-

194). This process therefore leads to double stranded breaks in two places; before the IgM heavy chain

() constant region, and before either the , , or  heavy chain genes, and by looping the DNA between these breaks, these regions can be aligned and joined together by non-homologous end- joining recombination (191, 195).

The previously mentioned enzyme AID also has a key role in triggering somatic hypermutation of the

B cells within the germinal centre. This process introduces high levels of point mutations into the heavy and light chain variable regions, particularly within the complementarity determining regions, and allows the B cell to adapt its variable region to the antigen, resulting in a more specific antibody. As previously mentioned, in class switch recombination, AID deaminates cytosines, removing amine

groups and in the process converts the base to uracil, and this process is also the first step of somatic hypermutation which can target both C:G and A:T pairs. Therefore, when the cytosine in the C:G pair is deaminated and converted to uracil, a U:G pair is formed, and DNA repair or replication of this site can then lead to a transition from the original C to U to T, also meaning the G transitions to A, as well as transversions to A:T pairs (196). A:T pairs can also be mutated during the process of somatic hypermutation, and whilst the mechanisms through which this occurs have been less well characterised, it is known to be linked to the C:T mutations, as well as mismatch repair proteins MSH2 and MSH6, and uracil-N-glycolase (UNG) (196, 197).

The process of somatic hypermutation leads to affinity maturation of the BCRs expressed on the surface of the B cells, i.e. the BCRs and the antibodies produced by these B cells later on will recognise and bind to specific antigen with a greater affinity, and this coupled with the class switching to IgG subclasses for example will lead to a greater functional antibody response. After the somatic hypermutation within the dark zone of the germinal centre, the B cells migrate to the light zone where they undergo affinity selection against antigen through interactions with follicular dendritic cells

(FDCs) and follicular helper T cells (TFH). This results in antibodies with a high affinity for specific antigen being selected for differentiation into memory B cells or long lived plasma cells, or alternatively, the cells can go back to the dark zone and undergo further somatic hypermutation to further increase affinity (167, 184).

1.2.3.2. T cell independent activation

B-1 cells and marginal B cells can be activated without T cell help in a process termed T cell independent activation, and can be categorised into type I or type II responses. Type I B cell activation results from a non-specific polyclonal stimulation by B cell mitogens such as LPS or CpG, which bind to the BCRs expressed on the cell surface as well as other receptors such as complement receptors and toll like receptors (e.g. TLR4) (198-200). On the other hand, type II responses are stimulated by polysaccharide antigens which have multiple repeating antigenic units which subsequently bind to

BCRs. The cross linking of multiple B cell receptors stimulates the B cells to produce antigen-specific antibody (198), and this stimulation can be increased for further antibody production with the help of the cytokine BAFF which has been produced by activated dendritic cells (198, 200, 201). Whilst the mechanisms of activation of these two responses differ slightly, both produce similar responses that are largely of the IgM class with little class switching and have a low affinity for the antigen (202).

Whilst type I responses do not generate memory responses, there has been some evidence that type

II responses do generate memory B cells, though these cells have a different phenotype and characteristics to that of T-dependent generated B cells (183, 203).

1.3. Antibodies

1.3.1. Antibody Classification

In humans there are five different classes of antibodies; IgG, IgA, IgE, IgM and IgD. Whilst structurally similar they are each encoded by a different heavy chain gene leading to the different functions and properties associated with each class.

IgG is the main immunoglobulin found in serum accounting for around 78% of total Ig (204), has a gamma (ɣ) heavy chain, and is both expressed on the surface of B cells as a BCR or secreted during a humoral response to an infection. IgG exists as a monomer, and can be further classified into subclasses termed IgG1, IgG2, IgG3 and IgG4 which have ɣ1, ɣ2, ɣ3 and ɣ4 heavy chains respectively.

Whilst there is greater than 90% sequence homology between the heavy chain constant region sequences of the four subclasses (205, 206), different subclasses have different capabilities in performing Fc mediated effector functions.

IgA is the next most abundant class of antibody accounting for around 12% of total serum immunoglobulin (204), has an alpha (α) heavy chain, and is a key component of the mucosal immune response. IgA can be further classified into two subclasses; IgA1, which has an α1 heavy chain and accounts for around 90% of total serum IgA, and IgA2 which has an α2 heavy chain and accounts for around 10% of serum IgA (207). There are three main forms that IgA can take. In serum, the majority

of IgA exists in a monomeric form, whilst at mucosal sites the majority of IgA produced is dimeric, with a 15kDa polypeptide J chain linking the Fc regions of two IgA molecules together by disulphide bonds

(208). The third form is secretory IgA (sIgA), which as its name suggests is the predominant form of

IgA found in secretions. This form of IgA starts as dimeric IgA, which binds to the polymeric immunoglobulin receptor (pIgR) expressed on the basolateral surface of epithelial cells, and this complex is subsequently internalised and transported across the cell, after which the pIgR is cleaved leaving the secretory component attached to the now sIgA which is released into mucosal secretions

(207-209).

IgM makes up around 8-9% of serum immunoglobulins (204), has a mu (μ) heavy chain, and is a key component of the primary immune response. Whilst the general structure of IgM is similar to the other Ig classes, IgM lacks a hinge region, and instead has an additional heavy chain constant domain

(210). IgM can exist in two forms; as a monomer which is expressed on the surface of B cells as a BCR, or in a secreted form as a pentamer. Whilst the monomer form is the initial antibody class that a B cell produces and expresses on its surface during the B cell developmental process, the pentamer form is the key class of antibody that is secreted during a primary antibody response, comprising five IgM molecules linked by the J chain, and is a strong activator of complement (211).

Out of all the immunoglobulins, IgE accounts for the lowest percentage of serum immunoglobulins, making up just 0.0002% (204). IgE have epsilon (ε) heavy chains, are monomeric, and like IgM have an extra constant domain on the heavy chains and lack the hinge region. Whilst IgE doesn’t mediate antibody functions such as complement activation, IgE antibodies bind with high affinity to FcεRI on mast cells and basophils, and can trigger release of granules containing histamine, serine proteases and proteoglycans (212), hallmarks of an allergic response. IgE is also a key response to parasitic infections, namely helminths.

Finally, IgD makes up only around 0.23% of serum immunoglobulins (204), has a delta (δ) heavy chain and is produced as a monomer. The functions of IgD remain relatively unknown, however it is

expressed alongside IgM on the surface of mature B cells during the B cell developmental pathway, and has been shown to bind to several bacterial proteins through constant domain located sugar residues (213-215). It has been hypothesised that IgD acts as a modulator of the humoral immune response through modulating B cell selection (216).

1.3.2. Antibody Structure

Antibodies are 150kDa glycoproteins consisting of 4 polypeptide chains; two identical 50kDa heavy chains and two identical 25kDa light chains. As previously mentioned, there are five possible heavy chains (ɣ, α, δ, ε, μ), whilst light chains are either kappa (κ) or lambda (λ). Heavy chains are made up of four Ig domains; three constant regions termed CH1, CH2 and CH3, and one variable region called

VH (Figure 1.8). On the other hand, light chains have two Ig domains; one constant region, CL, and one variable region called VL. As the constant regions name suggests, the nucleotide and amino acid sequence of this region is standard among all antibodies of the same class and subclass, whilst it is the variable region through VDJ recombination, somatic hypermutation and affinity maturation is highly diverse and gives rise to the huge number of different epitopes the antibody population can recognise.

Antibodies can be further classified into two regions; the fragment antigen binding (F(ab)2) and the fragment crystallisable (Fc). The F(ab)2 region contain two Fabs which each consist of the CH1 and CL regions, and the VH and VL regions which together make the antigen binding site. The heavy and light chain variable regions can be broken down further to framework regions and complementarity determining regions. There are four framework regions, termed FR1, FR2, FR3 and FR4, and three complementarity determining regions termed CDR1, CDR2 and CDR3. Whilst the framework regions are more restricted in their sequence, the CDRs are the most variable parts of an antibody and contain high levels of mutations when compared to germline sequences. Furthermore, these CDRs are formed as hypervariable loops, and when the heavy and light chain variable regions fold correctly, the three heavy chain loops and the three light chain loops come together forming the antigen binding site, and it is therefore the CDRs that actually interact with the antigen.

Figure 1.8: Generalised structure of IgG antibodies. The general structure of IgG1-4 antibodies is illustrated with the heavy and light chain constant and variable regions indicated. The structures of IgG1, 2 and 4 are similar, whilst the IgG3 antibody has an elongated hinge region. The antibodies can be divided through enzymatic cleavage into the Fc region, containing the CH2 and CH3 regions, and the F(ab)2 region which contains two Fab regions, each consisting of the heavy and light chain variable regions, and the heavy chain CH1, and the variable constant region. It is within the variable region that the complementarity determining regions (CDR 1-3) are located with the antigen binding site.

The Fc region of an antibody contains the CH2 and CH3 regions of the two identical heavy chains.

Whilst this region doesn’t recognise or interact with antigen, this part of the antibody can mediate effector functions through binding to Fc receptors on the surface of other cells such as macrophages and natural killer (NK) cells, as well as binding to and activating complement, and the glycosylation site N297 is required for these interactions (217). It is the hinge which holds the Fab and Fc regions together through disulphide bonds (218), and gives the antibody its flexibility. Whilst the structure of the IgG1, 2 and 4 subclasses is relatively similar, the IgG3 antibody has an extended hinge region as illustrated in Figure 1.8, giving IgG3 a greater flexibility (219, 220).

1.3.3. Antibody Functions

As mentioned in section 1.3.2, antibodies can be divided into two key regions, the two Fab regions and the Fc region, and it is through these regions that the antibody can elicit a variety of functions summarised in Figure 1.9; neutralisation, complement mediated lysis, antibody dependent phagocytosis and antibody dependent cellular cytotoxicity. Whilst these functions use the Fab region for the recognition and binding of the antibody to the pathogen, it is through the Fc region of the antibody that ADCC, ADP and ADCD functions are mediated by binding to Fc receptors on effector cells such as Natural Killer (NK) cells or monocytes or complement components.

Figure 1.9: Summary of antibody effector functions. Antibodies can mediate four key effector functions (a) neutralisation of virus particles, (b) triggering the complement pathway leading to lysis of infected cells, (c) opsonisation and phagocytosis of virus particles through antibody-FcR binding, and (d) antibody dependent cellular cytotoxicity through antibody-FcR binding on NK cells. Reprinted from The Journal of Internal medicine, 262, M. Huber and A. Trkola. Humoral immunity to HIV-1: neutralization and beyond. Copyright (2007), with permission from John Wiley and Sons (221). Rights and Permissions in Appendix 6.

Fc receptors (FcRs) are proteins that are expressed on the surface of most cells from the hematopoietic cell lineage, (222-224), and can be classified as activatory or inhibitory depending on whether they carry or are associated with an ITAM or ITIM signalling component respectively (225,

226). FcRs can be further classified as to the subclass of immunoglobulins that they bind; FcɣR bind to

IgG antibodies, FcαR bind to IgA, and FcεR bind to IgE. There are three key families of FcɣR; FcɣRI,

FcɣRII (of which there are three subtypes FcɣRIIa, FcɣRIIb and FcɣRIIc) and FcɣRIII (of which there are two subtypes FcɣRIIIa and FcɣRIIIb). All of these receptors except FcɣRIIb are activatory, and whilst the

FcɣRI receptor is a high affinity receptor, all others are low affinity (222, 224). As the work in this thesis is done within the context of HIV, all following examples will be given in the context of viruses.

1.3.4.1. Neutralisation

Neutralisation can be simply defined as the blocking or inhibition of a pathogen by antibodies from infecting host cells. The simplest mechanism of doing this is for the antibody to bind to the ligand on the virus that binds to the host cell receptor, meaning the interaction between the virus and host cell cannot take place. For example, the PGT121 family of BNAbs have been shown to interact with the outer domain of HIV-1 gp120 which inhibits binding to the CD4 receptor through competition (227). If this interaction has taken place and the virus is bound to the cell receptor, neutralising antibodies can still prevent subsequent stages of cell entry by binding to further necessary epitopes for viral entry that have become exposed upon the initial virus binding to the receptor, or even prevent the fusion of virus to the host cell by the antibody binding to fusogenic proteins (228). For example in the context of HIV, antibody 17b is a CD4i antibody that binds to a region near the CD4bs exposed only after CD4 binding (229), whist BNAb 4E10 interacts with a conserved region of the gp41 transmembrane unit that is exposed during fusion when the gp41 is undergoing a conformational change (230-233)

Whilst the aim of the so far mentioned neutralising mechanisms is to prevent the virus from entering the cells, intracellular neutralisation is another option to prevent the virus from getting any further once it has entered the cell (233). An example of this is the blocking of viral epithelial cell transcytosis

by neutralising antibodies, and in the context of HIV-1 has been characterised by an anti-gp41 mucosal

IgA response in highly exposed persistently seronegative (HEPS) individuals that is associated with protection (234-238). Another form of intracellular neutralisation has been characterised more recently and involves recruitment of an intracellular cytosolic receptor called TRIM21. Using an

Adenovirus model, it was shown that antibodies coating virions remain attached to the virus after entry into the cell, and using the Fc region recruits TRIM21 which subsequently degrades the virus by targeting it to the proteasome, resulting in a decrease in the cells that are infected (239).

1.3.4.2. Antibody Dependent Cellular Cytotoxicity

Antibody dependent cellular cytotoxicity (ADCC) is an Fc mediated effector function of antibodies that recruits effector cells such as NK cells or monocytes to kill infected cells. This function was first described against tumour cells in the late 1960s/early 1970s in mouse models and leukaemia patients

(240-243), but has since been shown to be a key function utilised by antibodies against viruses (244).

As previously mentioned, there are several types of Fc receptors (or FcRs) found on immune cells that are used for antibody binding and mediating effector functions, and it is the low affinity FcɣRIIIa which facilitates ADCC (225, 243, 245-247). Upon recognition and binding to viral epitopes expressed on the surface of an infected cell, through the antigen binding site in the Fab region of an antibody, the Fc region can then bind to the FcɣRIIIa receptor on NK cells. The cross linking of multiple receptors can then trigger a signalling cascade beginning with the activation of the FcɣR associated ITAM chains by

Src family kinases (248), leading through to the activation of a MAPK family member called ERK which stimulates the degranulation of the NK cell (249). One of the key components of the cytotoxic granule is perforin, a glycoprotein which through binding to membrane phospholipids inserts into the target cell membrane, where it polymerises in the presence of calcium ions to create a pore, (250). This pore allows another cytotoxic granule component granzyme B, a serine protease, to enter the cells where it triggers the caspase-dependent signalling pathway leading to the death of the cell through apoptosis

(251).

Recently, ADCC has been shown to be an important Fc-mediated effector function of non-neutralising antibodies in the context of HIV infection. The RV144 vaccine trial showed that it was non-neutralising

IgG3 antibodies capable of mediating ADCC that were associated with protection, with elite controllers also having correlates to ADCC (252, 253). The role of ADCC in HIV infection will be further discussed in section 1.4.3.

1.3.4.3. Antibody Dependent Cellular Phagocytosis

Another Fc mediated effector function is antibody dependent cellular phagocytosis (ADCP), and this effector function recruits phagocytes such as macrophage or dendritic cells to phagocytose and destroy pathogens. The identification of this mechanism can be traced from the 1880s when Elie

Metchnikoff described the engulfment of particles by a variety of unicellular organisms, to present where the sequence of events from antibody recognition of antigen through to internalisation of antigen and subsequent destruction is known.

Cells that can perform phagocytosis can be categorised as professional phagocytes, such as macrophages, monocytes, dendritic cells and neutrophils, or non-professional phagocytes, such as epithelial cells and fibroblasts (254), and recently it has been demonstrated that professional phagocytes can direct non-professional phagocytes through the release of a soluble growth factor

(255). ADCP like ADCC is triggered through the binding of the Fc region of an antibody bound to an antigen to Fc receptors expressed on these cells, and whilst FcɣRI is the key high affinity receptor for phagocytosis, FcɣRII and FcɣRIIIa receptors have also been shown to mediate phagocytosis (256-260).

Antibodies recognise and bind to pathogens, or pathogen infected cells, through their antigen binding site, coating them and acting as opsonins. The Fc regions of the antibodies then bind to the FcRs expressed on the surface of the phagocytes, and it is the cross linking of these receptors which triggers the start of phagocytosis through ITAM activation. The signalling cascade triggered leads to the engulfment of the opsonised pathogen into phagosomes within the phagocyte, which subsequently fuse with lysosomes, forming a phagolysosome within which the pathogen is destroyed by superoxide

and hydrogen peroxide generated through NADPH-dependent oxidase activity termed the respiratory burst (261-264) or nitric oxide through the activity of inducible nitric oxide synthase (iNOS) (265-267).

1.3.4.4. Complement mediated lysis

Complement mediated lysis of virions or infected cells is another Fc mediated effector function, mediated through the activation of the classical complement pathway. The main complement component that the antibody binds to is the C1 complement molecule which consists of a hexamer with six globular heads called C1q and a tetramer containing two molecules each of two serine proteases termed C1r and C1s (268-271). The CH2 region of the antibody located within the Fc region binds to one of the six heads of the C1q component of complement (272), and when multiple antibodies bind to the multiple heads, the C1q component is activated leading to a conformational change and triggering the activation of the bound C1r, which subsequently triggers the activation of the C1s proteases (271, 273, 274). The C1s then cleaves the C4 complement component into C4a and

C4b, the latter of which covalently binds to the pathogen or infected cell surface, and is subsequently bound by C2 which is also then cleaved to give the C4b2a complex, otherwise known as the C3 convertase (271, 275).

It is at this point when the C3 convertase is activated that the other complement pathways (the alternative and mannose binding lectin pathways which are not discussed here) that initially followed different mechanisms of activation now follow the same pathway. The C3 convertase which is bound to the virion or cell surface then binds the C3 complement molecule, cleaving it to C3a and C3b, the latter of which binds to the cell/virion surface forming the C5 convertase (C4b2a3b) (275). The C5 convertase then cleaves C5 to C5a and C5b, the latter of which along with C6, C7, C8 and C9 complement components are classified as the terminal complement components and lead to the formation of the membrane attack complex (276). Briefly, C5b binds to the C6 and C7 complement components, the latter of which binds the complex to the virion/cell membrane. This is followed by binding to C8 which inserts into the membrane, with the final step of C9 binding to the complex and

polymerising and in doing so creating a channel or pore in the membrane (275, 276). This membrane attack complex then leads to lysis of the virus or infected cell.

1.4. Antibodies in HIV Infection

1.4.1. Appearance of antibodies in response to HIV infection

The first antibody responses to HIV-1 infection occur during acute infection, around 8 days after first detectable HIV-1 viraemia (100 copies/ml), and consists of IgM or IgG antibodies in a complex with

HIV-1 virions (277). This is followed 5 and 20 days later (13 and 28 days after detectable viraemia) by non-neutralising antibodies to gp41 and gp120 respectively (277). Neutralising antibodies against autologous virus takes several months to develop, and can vary among individual’s from 3-6 months to a year post infection (278-281). Neutralising antibodies against heterologous virus take further time still to develop, with detection of these antibodies taking place more than 1 year post infection (279,

281). Broadly neutralising antibodies can take several years to develop due to the high number of mutations through somatic hypermutation and affinity maturation (282-285), but these antibodies are generally rare and found in a small percentage of infected individuals.

1.4.2. Broadly neutralising antibodies

1.4.2.1. BNAb Overview

Broadly neutralising antibodies (BNAbs) are antibodies generally isolated from chronically infected

HIV patients that are able to neutralise a wide range of HIV isolates. These antibodies usually take several years to develop, and are only generated in around 15-35% of HIV infected individuals (286-

291). These antibodies have been shown to prevent HIV infection with passive immunisation of nonhuman primates (292-296), and more recently have become a major target for vaccine development and immunotherapy studies.

BNAbs target several key sites on the HIV envelope glycoprotein; the CD4 binding site, the V1/V2 loops, the V3 loop, and the gp41 MPER region, and many BNAbs that bind to these sites have now

been isolated and identified (Figure 1.10). The CD4bs monoclonal antibody b12 was one of the first

BNAbs isolated by Burton et al in 1991 using phage display library techniques, and was subsequently shown to neutralise a wide range of HIV-1 strains (297-301). Following on from this several other

BNAbs were isolated such as 2G12, 2F5 and 4E10 (302), and these and other BNAbs isolated around this time are now termed first generation BNAbs. More recently, thanks to key advances in single cell expression cloning, many more potent BNAbs are routinely being isolated such as PG9 (303), VRC01

(304) and 3BNC117 (305), and these are generally known as second generation BNAbs due to the more novel ways of isolating them, as well as generally being more potent.

Figure 1.10: BNAbs and their HIV-1 target sites. Broadly neutralising antibodies isolated from HIV-1 infected patients by a number of different mechanisms are grouped by their target sites; CD4 binding site,V1/V2 loops, V3 loop, the MPER and the gp120/gp41 bridging region. Reprinted from Trends in Immunology, Vol 35 /Issue 11, Hugo Mouquet. Antibody B cell responses in HIV-1 infection, Pages No. 549-561, Copyright (2014), with permission from Elsevier (93). Rights and Permissions in Appendix 7.

As well as being able to neutralise a wide range of HIV-1 viruses, BNABs have three further characteristics, the first being a high level of somatic mutation. This is driven by affinity maturation in the germinal centre which introduces the mutations into the variable regions, and it has been shown that whilst normal human antibodies have around 10 to 25 heavy chain variable region somatic nucleotide mutations (306), BNAbs can have twice as many if not triple the number of these mutations

(283, 284, 307-309). The second characteristic is a long heavy chain CDRH3. As previously described, the variable regions of antibodies can be divided into framework and complementarity determining regions, with the CDR hypervariable loops recognising the antigen. Normal human antibodies generally have CDRs of 16 amino acids in length (306), whilst BNAbs are generally have CDRs greater than 20 amino acids in length (310), even up to 34-39 amino acids long in the case of CAP256-VRC26 family of BNAbs (311). Thirdly, several BNAbs have been shown to be Polyreactive, with a study by Liu et al showing that of 22 BNAbs tested, 45% were Polyreactive compared to 11% of non-neutralising antibodies (312). The same paper also showed that a smaller percentage of BNAbs were also autoreactive.

1.4.2.2. Passive Vaccination of BNAbs

There have been many studies showing that BNAbs administered passively to animal models (nonhuman primates and mouse models) can provide protection from a chimeric simian/human immunodeficiency virus (SHIV) which have a HIV-1 envelope. For example, Mascola et al administered the BNAbs 2F5 and 2G12 intravenously to macaques alone as well as in combination with each other and the polyclonal HIVIG, and 24 hours later infected the macaques intravenously with 40 animal infectious doses of SHIV-89.6PD. Their results showed whilst individual BNAb or HIVIG provided no protection, the triple combination of 2F5, 2G12 and HIVIG provided complete protection for 50% of macaques infected, with the other 50% having lower viral load and better CD4 counts than controls despite no protection (293). The same authors later showed similar results after vaginal challenge with

SHIV, with 80% of macaques which were given the same triple combination as before completely

protected, and 40% and 50% protection in the 2F5 and 2G12 double combination and 2G12 groups respectively (296).

The BNAb b12 has also been shown to provide protection in macaques challenged with SHIV (313), as has 4E10 (295), and VRC01, PG9 and 10E8 (314), however the majority of these BNAbs have been shown to provide protection against SHIV challenges at high antibody doses (315). More recently, newer BNAbs such as PGT121, 3BNC117 and 10-1074 have also demonstrated their ability to provide protection against SHIV at much lower concentrations (292, 316-319).

Human clinical trials into passive vaccination with BNAbs have only commenced relatively recently, however VRC01 has already been through a phase 1 trial and been shown to be safe, well tolerated with a normal half-life when administered intravenously to healthy non-HIV infected individuals (320,

321). VRC01 was further shown to be safe and well tolerated when given intravenously to HIV patients both on and without ART, albeit with a slightly lower half life, and in the same study was shown to decrease plasma viral load by 12 to 59 fold in 80% of the non-ART HIV infected patients (322). Similarly, the BNAb 3BNC117 has also gone through a phase 1 clinical trial which has shown it to be safe and well-tolerated in humans, and decreased the viral load by up to 2.5 logs in HIV-1 infected patients

(323).

1.4.2.3. Vaccines to elicit BNAbs

BNAbs target various sites (summarised in Figure 1.9) on the HIV envelope protein to neutralise the virus, and therefore a vaccine that stimulates these types of antibodies would be incredibly beneficial.

If the sites and specific epitopes that BNABs with a high neutralisation breadth and potency bind to are known, then gp140 trimeric proteins that have the epitopes of a wide range of these antibodies can be used as a vaccine, with the hope that the vaccine will give rise to similar BNAbs. This type of vaccine has so far been unsuccessful, with a variety of issues contributing to this, such as stability issues of the gp41 and gp41-gp120 interactions on the soluble trimer (324, 325), the CD4 binding site region having both conserved and non-conserved regions intermixed and discontinuous epitopes (i.e

not linear), and the glycan dependent nature of the V1/V2 and V3 loops (326). More recently however, there have been advances in the stability of soluble gp140 envelope trimers that mimic the native envelope in structure, with papers by Sanders et al (325), Pugach et al (327), and Ahmed et al (328) showing the development of stable soluble trimers that mimic the native HIV- envelope, which may address this problem and could be used in vaccine design.

Whilst the above approach to generate BNAbs uses the design of the gp140 envelope trimer with known BNAb epitopes to stimulate production of these antibodies, another way to generate BNAbs through vaccination may be to actually target the germline variable genes that BNAbs use. Many studies are now tracing BNAbs back to their unmutated common ancestors (UCA), i.e. the original germline DNA, and using this information may provide better vaccine strategies. In a 2012 paper by

Haynes et al, the authors described a new approach to designing vaccines for HIV-1 called B-cell lineage vaccine design consisting of three key steps. Matching heavy and light chain variable region pairs that are clonally related are isolated, phylogenetics and computer analysis would then calculate the UCA of this antibody family, and then one immunogen (a soluble gp140 trimer, recombinant gp120 or high affinity peptides/proteins) would be designed to trigger stimulation of a particular gene family responsible for the UCA, whilst another would be designed to trigger the intermediate forms of the antibody to go down the lineage to get to the desired BNAb (329). Several studies have now examined

B cell lineages from acute to chronic infection and the appearance of BNAbs in patients with the hope that this can lead to design of different vaccine components (330-332). However, whilst a paper published at the end of 2017 by Williams et al showed that after sequential HIV envelope vaccination of macaques HIV neutralising B cell lineages developed, the neutralisation breadth was limited and therefore other vaccine components may be required (333).

The two approaches described above are not the only vaccine approaches to stimulate BNAbs that are currently being examined. Other options include; gene transfer or vectored immunoprophylaxis which has been studied in macaques and mice (334-341) with several human trials to examine the safety and

types of responses these vaccine elicit being performed (342-344), grafting BNAb epitopes onto scaffold proteins (326, 345-350) as well as HIV- gp120 outer domain fragments (326, 351, 352).

1.4.2.4. BNAbs for “shock and kill”

A more recent hypothesis is that BNAbs can be used alongside latency reversal agents to reduce the latent reservoir in HIV-1 infected patients. Whilst ART can be used to suppress HIV-1, this treatment must be taken continuously as halting ART leads to viral rebound with plasma viral loads of 500 copies/ml as soon as 10 days after stopping treatment (353-355). This is due to the integration of the

HIV genome into memory CD4+ T cells resulting in a latent reservoir which has been found to decay incredibly slowly with a half time of 3.7 years (356-360), and which can be reactivated after halting

ART treatment (361). Therefore removing this reservoir is key for any chance of curing HIV-1.

Originally, the “shock and kill” method for removing this reservoir used a combination of ART with viral transcription inducers or latency reversal agents, with the hypothesis the viral transcription inducers would lead to the RNA synthesis of HIV within the latent cell, leading to triggering the HIV replication cycle. This would result with the synthesis of key HIV proteins expressed on the cell surface marking the cell for destruction by the patient’s immune system (i.e. cytotoxic CD8 T cells), whilst the released viral particles would be prevented from infecting other cells by the ART (362).

Histone deacetylases (HDACs) are enzymes that tighten the interaction between histones and DNA through the removal of acetyl groups, and therefore prevent gene transcription as transcription factors cannot bind the DNA, and can lead to latency. In normal cells HDACs work in balance with histone acetyl-transferases (HATs), which add acetyl groups and therefore loosens the histone-DNA interaction so that gene transcription can take place, meaning normal regulation of gene transcription

(363). Therefore, used as a latency reversal agent for HIV, HDAC inhibitors would tip the balance in favour of HAT activity leading to gene transcription, which would provide the “shock” for cells to produce the HIV proteins which could lead to cell killing, and several key studies have shown that

HDAC inhibitors lead to an increase in HIV RNA expression in CD4+ T cells (364).

In recent years attention has turned to the combination of HDAC inhibitors with BNAbs as a way to reduce and clear the latent reservoir. BNAbs can generally neutralise a broad range HIV viruses, and as described in section 1.4.2.2 have been demonstrated to prevent infection in animal models and in early clinical studies have been shown to reduce plasma viral load. As many BNAbs can also mediate other Fc mediated effector functions such as ADCC and ADCP (365), they may be able to use these mechanisms to kill reactivated latent CD4+ T cells, as well as neutralising the newly released virus particles before it can infect other cells, providing an efficient “kill” for the method. In a 2014 paper,

Halper-Stromberg et al showed that administration of BNAbs to HIV infected mice after the combination of vorinostat (a HDAC inhibitor), with a BET protein inhibitor and T cell inhibitory pathway blocker prevented 57% of HIV infected mice from viral rebound (366). More recently, at the 2018 CROI conference, Barrouch et al presented results that showed that 45% of SHIV infected macaques given a TLR7 agonist and the BNAb PGT121 after the stop of ART did not have viral rebound after 6 months of end of treatment (with the study still ongoing), and whilst the other 55% did have viral rebound it was delayed to 112 days compared to the 21 days seen in control monkeys (367), demonstrating that the shock and kill method using BNAbs may have the potential to clear the latent reservoir.

1.4.3. Non-neutralising antibodies

As described in section 1.3, antibodies can perform functions other than neutralisation, termed Fc- mediated effector functions, which can include ADCC, ADCP and ADCD. In recent years there has been an increase in the number of studies examining these functions in HIV, largely due to the results of vaccine trials which have focussed the attention on non-neutralising antibodies.

In section 1.4.2.3, the current approach of designing vaccines to stimulate the generation of BNAb was described, due to the desire to produce antibodies in patients that may be able to prevent HIV-1 infection in humans. The RV144 trial has so far been the only HIV vaccine trial to shown any kind of protection from HIV-1, with results indicating a 31.2% efficacy (368). The vaccine itself consisted of a recombinant canarypox vector vaccine (ALVAC-HIV) administered at baseline, and weeks 4, 12 and 24,

with a recombinant gp120 subunit vaccine booster (AIDSVAX B/E) administered at weeks 12 and 24

(368). In follow up studies comparing the vaccine plasma samples of the RV144 trial to that of an earlier VAX003 trial, which had only the AIDSVAX B/E components and showed no protection, it was found that the RV144 trial induced a polyfunctional antibody response (shown by positive correlations between six antibody functional variables) whilst the VAX003 trial did not (252), highlighting the potential importance of non-neutralising antibodies against HIV-1.

Furthermore, the same paper showed that the different trials stimulated different IgG subclasses, with the RV144 trial stimulating predominantly IgG1 and IgG3 gp120-specific antibodies, whilst the VAX003, despite also stimulating IgG1 antibodies, had a large IgG2 and IgG4 response (252, 369). Similarly, these types of polyfunctional responses of Fc mediator effector functions have been shown to be present in elite controllers, with a paper by Ackerman et al showing that elite controllers had high correlations between different antibody mediated effector functions (ADCC, ADCP and ADCD) compared to viremic controllers and patients of treatment (253).

The mechanism of ADCC was previously described in section 1.3.4.2, and this effector function has been shown to be mediated by non-neutralising HIV antibodies. As previously described, polyfunctional antibody responses were shown in the RV144 trial results, when IgG3 antibodies were depleted from vaccine samples there was a significant loss of ADCC (252), and plasma samples from

RV144 vaccinated individuals showed significantly greater ADCC activity against gp120 proteins than placebo samples (370). Additionally, in a paper by Mayr et al published in 2017, results showed that non-neutralising antibodies targeting the V2 region of HIV actually led to a significantly higher percentage of target cell lysis by NK cells than a panel of BNAbs (371), and the non-neutralising A32 monoclonal antibody has been shown to mediate higher levels of ADCC activity than the BNAbs VRC01 and b12 (372). On the other hand Bruel et al showed that non-neutralising antibodies bound more weakly to primary HIV-1 isolates than BNAbs, mediated a lower percentage of ADCC activity, and had a more narrow ADCC epitope breadth (373).

Similarly, Chung et al showed that a depletion of IgG3 antibodies from RV144 samples led to a significant reduction in ADCP activity which had been part of the polyfunctional antibody response associated with HIV-1 protection (252), and results from an adenovirus vaccine candidate for HIV-1 showed that the non-neutralising antibodies mediated its greatest level of ADCP around 28 weeks after vaccination (342). Also, Ackerman et al demonstrated that spontaneous controllers and untreated HIV-1 progressors had a greater phagocytic potency than treated patients (374).

Despite the studies discussed here showing that non-neutralising antibodies can mediate effector functions that have been associated with control or HIV-1 protection, the majority of these studies have examined non-neutralising antibodies on a polyclonal level and have been isolated from chronically infected patients.

1.5. Monoclonal Antibody Production Technology

In recent years there has been key advances in the production of monoclonal antibodies from human samples which has led to this technology being increasingly used in the lab to study antibody responses to a variety of infections or vaccines. There are three key techniques that are currently used to generate human monoclonal antibodies; phage display, EBV immortalisation, and single cell expression cloning.

1.5.1. Phage Display

Phage display technology was first described by George Smith in 1985, where plasmid digests were inserted between the minor coat protein encoding gene III N- and C-terminals of a filamentous phage, and were subsequently displayed on the particle surface (375). Since the publication of this initial paper, the technology has been modified to generate phage display libraries that express single chain variable fragments (scFv) or Fab fragments of monoclonal heavy and light chain variable regions which can be screened for reactivity to specific antigens.

Phage display library for identification of antigen-specific monoclonal antibodies can be divided into three key stages; the construction of the phage display library itself, panning of the library against the

antigen, and then analysis of those phages which were selected against the antigen. The library construction starts with the isolation of mRNA from human tissue such as PBMCs, with bone marrow and splenic tissue also having successfully yielded phage display libraries (376, 377). After reverse transcriptase PCR for cDNA synthesis, random heavy and light chain variable region DNA is amplified and cloned into either a phage or phagemid vector which is then transformed into E.coli competent cells to produce the libraries. Phages such as M13KE can be used alone for library generation as the variable region DNA encoding the surface displayed ssFv or Fab fragment is inserted directly into the coat protein and therefore resides in the phage genome which contains all the other genes for phage particle production from the competent cells. On the other hand phagemid vectors, which while able to produce phage particles containing the variable region DNA to be expressed, do not contain all the genes necessary for phage release from the cells, and therefore require the addition of a helper phage to provide these additional proteins (378-382). These steps result in a library with a huge number of individual phages (106-1011 (383)) with different antibody specificities expressed both on their surface for screening against antigens and within their nucleotide sequence which can be used later for cloning purposes (384).

After generating the library, the phages are screened against the antigen of choice to detect those expressing antigen-specific scFv or Fab fragments in a process known as biopanning (385). This process incubates the phages with the selected antigen to allow the specific phages to bind, and then washes away the non-specific phages which have not bound to antigen. Those that did bind are then eluted and amplified, and the whole process is repeated several more times to ensure that only highly specific antibodies are selected (383, 385). By this point, there is an elute containing polyclonal phages expressing antibody on their surface, and the final analysis stage involves the transformation of competent cells with these phages so that monoclonal phages can be isolated. From here these monoclonals are then further characterised, with the heavy and light chain variable region sequences identified, and full size antibody, as opposed to scFv or Fab, can be produced through the use of IgG expression vectors.

More recently, this technology has been further advanced so that yeast and mammalian cells can be used in the generation of antibody display libraries, resulting in full length antibodies which have undergone the correct post translational modifications for folding and function (386-389).

1.5.2. EBV Immortalisation

Steinitz et al published a paper in Nature in 1977 showing that human lymphocytes could be infected with Epstein-Barr virus, collected from the supernatant of the B95-8 cell line, to generate permanent cell lines capable of secreting antibodies (390). Since this first paper, modifications of the protocol have increased transformation efficiency, and a wide range of studies have used this method to detect antigen-specific monoclonal antibodies.

The process of EBV immortalisation begins with the isolation of PBMCs from the patient(s) of interest, and the preparation of EBV for infection of the cells. As with the 1977 paper, the B95-8 cells, a cell line originating from a cotton-top tamarin monkey, is still commonly used as a source of the EBV, as it releases high titres of EBV into the cell culture supernatant which can be used to transform human B cells. After collection of culture supernatant, the EBV is added directly to the patient PBMCs in the presence of a polyclonal stimulant such as CpG to stimulate expansion of B cells (391), and the T cell suppressor cyclosporin A to prevent cytotoxic T cells from killing expanding EBV infected B cells, termed regression (392). After incubating the cells for a suitable amount of time, the supernatants can be screened against antigens of interest. If a particular supernatant shows the desired specificity, the cell culture that gave the supernatant can undergo limited dilution cloning, to obtain single cells from the polyclonal B cell culture, for cloning variable heavy and light chain genes into IgG expression vectors for monoclonal antibody production. It is also possible to cell sort B cells alone for transformation, and with this method there is little need for the use of the cyclosporin A as no T cells should be present in the culture.

1.5.3. Single cell expression cloning

More recently, single cell expression cloning has become a key method used to isolate monoclonal antibodies from patients, with a variety of protocols published. The basic concept of single cell expression cloning involves the single cell sorting of B cells from infected or vaccinated patients, from which variable heavy and light chain DNA is amplified through several rounds of PCR. Variable region

DNA is then cloned into immunoglobulin expression vectors containing the constant region genes, which are then transformed into competent cells. After several molecular cloning steps from selection of single colonies to miniprep of vector DNA, matching heavy and light chain vectors are transfected into a mammalian cell line, such as HEK293, which is cultured for several days after which the supernatant can be collected and screened against the antigen of choice. From here antibodies can be purified for further characterisation and testing.

There are generally two populations of B cells that are sorted for single cell expression cloning; memory B cells, or ASCs (plasmablasts or plasma cells), and the selection procedures for sorting these different populations are generally different. Memory B cells express the BCR on the cell surface, and therefore antigen specific memory B cells can be specifically targeted for sorting through antigen baiting. To do this the antigen of interest is biotinylated to a flurochrome and incubated with the cells before sorting. The memory B cells that are specific for the antigen bind to the antigen-flurochrome conjugate through the BCR, and can be selected for single cell sorting. This method has been widely used particularly in the context of HIV as a tool to isolate broadly neutralising antibodies (303-305,

393).

On the other hand, plasmablasts and plasma cells express little or no IgG BCR on their surface (394-

396) and so this technique is not possible. Therefore when sorting these types of cells, it is generally not known whether the cells will yield antigen-specific antibodies until the cloning procedure has finished with the screening of generated antibodies. However as antigen-specific plasmablasts have been shown to arise shortly after infection or vaccination, with a peak response around 7 days post

vaccination or infection (172, 173, 397, 398), these cells are often sorted during vaccine studies or after a known infection as they provide a picture of the current antibody response to the antigen.

Despite the basic concept of monoclonal antibody production being similar across different protocols, there are many variations of the techniques and a wide range of different expression vectors used, yet many of these protocols can be time consuming, especially when working with many samples.

However, a protocol published in Nature protocols by Smith et al in 2009 (based on previously published papers by Tiller et al and Wrammert et al (172, 399)) , demonstrated a protocol that yielded monoclonal antibodies fairly quickly from sorted plasmablasts, with antibodies generated as quick as

28 days after sorting plasmablasts (400). Whilst the authors of this paper stated that the protocol could be used to successfully generate recombinant human monoclonal antibodies against vaccine immunogens, it has since been used to generate antigen-specific antibodies from sorting plasmablasts from auto-immune or infected patients in the cases of Systemic lupus erythematosus (401), Vibrio cholerae (402), or Dengue virus (403), to name just a few examples.

1.5.4. Summary of Technologies

As described in sections 1.5.1-1.5.3 there are three key techniques that can be used to generate monoclonal antibodies from patient samples; phage display libraries, EBV immortalisation or single cell expression cloning. There are advantages and disadvantages associated with each technique which are summarised in Table 1.2.

Technique Advantages Disadvantages Examples

Phage Display - High throughput for antigen - Random pairing of VH and VL Rabies screening pairs SARS - Can be used to generate - Difficult to determine Hep A antibodies from a range of B relative frequencies of HIV cell subsets and different different specificities Ebola tissue - Depending on display system Measles - Huge diversity of generated used, post translational Flu mAbs modifications may be - Can be used for different different to mammalian cell immunoglobulin subtypes mAb production - Can isolate B cells generated - Further cloning required for many years previously in mAb production from ssFv or response to vaccine or Fab fragments infection - Potential selection bias

B cell - Matched heavy and light - Time consuming from EBV SARS Immortalisation chain variable regions transformation through to HIV - High throughput for antigen monoclonal antibody Dengue screening production RSV - Can isolate B cells generated - Transformation efficiency CMV many years previously in can be low Influenza response to vaccine or - Relatively low antigen infection Specificity - Can be used for different - High number of B cells immunoglobulin subtypes needed Single Cell - Matched heavy and light - Antigen baiting cannot be HIV Expression chain variable regions used for plasmablast sorting Rotavirus Cloning - High percentage of antigen- - Antigen baiting can Influenza specific mAbs from memory introduce selection bias into B cells selected using antigen results baiting - Antigen baiting may not - Potential for high percentage select for novel epitopes of antigen- specific mAbs - Antigen-specific from plasmablasts sorted 7 plasmablasts decrease over days post vaccination time from exposure to - Plasmablast sorting can give antigen overall picture of antibody - Currently majority of response at a particular time generated mAbs are IgG1 point - Plasmablast sorting can determine relative frequencies of different specificities

Table 1.2: Advantages and Disadvantages of monoclonal antibody production techniques. The advantages and disadvantages of phage display, B cell immortalisation and single cell expression cloning techniques are listed with references showing examples of infections that these techniques have been used to clone antibodies from (376, 385).

Despite the advantages of phage display including generation of antibodies of different isotypes from a range of B cell subsets and tissues, disadvantages include additional time required for further cloning of variable regions into full length antibodies, as well as a potential for selection bias caused by differences in phage growth rates (385). Furthermore, one aspect of phage display can be seen as both an advantage and disadvantage. The huge diversity of the antibodies generated by the random pairing of heavy and light chain variable regions can be an advantage for monoclonal antibody discovery for therapeutic use, but can be a disadvantage if the aim is to study the antibody repertoire produced in response to an infection or disease, as it cannot be guaranteed that the antibodies generated would have gone through the selection and self-tolerance processes that usually occur in antibody development in humans.

The B cell immortalisation method of generating monoclonal antibodies doesn’t have the problem of random heavy and light chain pairs, as it is from a single B cell obtained from limited dilution cloning that the matching heavy and light chain variable regions are cloned. Other advantages include the ability to isolate B cells generated many years before against a rare infection or vaccination, as well as isolating antibodies in different Ig subclasses. On the other hand, B cell transformation by EBV immortalisation can be low in efficiency, and generally a high number of B cells are screened in order to isolate a relatively low number of antigen-specific B cells and antibodies. Furthermore, this process can be time consuming due to the need to firstly infect the B cells, then grow up the cultures, followed by limited dilution to get the single cells, which can then undergo molecular cloning techniques to obtain monoclonal antibodies.

Single cell expression cloning is the most recent of the techniques described, also uses matching heavy and light chain pairs, and depending on the method used can produce many antibodies specific for the antigen of interest. Whilst antigen-baiting to select antigen-specific B cells is a highly efficient method to obtain a high percentage of antigen-specific monoclonal antibodies, this technique can only be used on B cells expressing a BCR, such as memory B cells, and can introduce a selection bias

into the results (376). For example, in the case of HIV, there is a huge diversity in the gp140 envelope both within the same, and among different clades, due to the high frequency of mutations to escape the neutralising antibody response. As different variable regions recognise different epitopes on an antigen, the use of a particular gp140 protein for antigen baiting may not select particular B cells through their specific BCR due to a different gp140 envelope structure or clade or the conformation of the protein used. Therefore antibodies generated through antibody baiting may not be representative of the true antibody response. By sorting plasmablasts with no pre-selection, this selection bias can be avoided.

Whilst sorting plasmablast provides an overall view of the antibody response of the patient at a particular time point, the main disadvantage is that as there is no pre-selection it is not known until after all the cloning steps and screening what percentage of monoclonal antibodies will be antigen- specific. However, if the plasmablasts from a vaccine study are sorted, the sort can be timed for optimal antigen-specific plasmablast percentages. Sorting plasmablasts is also useful for longitudinal analysis of antibodies generated in response to an infection or vaccination.

Therefore, depending for what purpose monoclonal antibodies are required, any of the above mentioned techniques could be used. As previously stated, the majority of antibodies against HIV are generally selected using antigen baiting and sorted for single cell expression cloning. Whilst this is highly efficient for this purpose, as the aim is to isolate antibodies capable of neutralising many strains of HIV, it does not provide an overall view of the antibody response arising in HIV infection. Therefore, the sorting of plasmablasts over multiple time points in early infection would allow the examination of both HIV-specific and non-specific antibodies over time and provide a snapshot into the antibodies that are being generated at this early stage of infection.

1.6. Aims and Hypotheses

1.6.1. Aims

As previously discussed in this chapter, the majority of studies on the HIV antibody response have examined antibodies isolated from chronic patients with the aim of discovering broadly neutralising antibodies. There have been studies on the antibody response in acute and early infection, though these generally examine the antibody response on a polyclonal level using serum, and the focus is generally on HIV antibodies not the non-specific antibodies that are generated. Furthermore, when non-neutralising or broadly neutralising antibodies are isolated and produced through single cell expression cloning, the antibodies are usually in the IgG1 subclass. More recently however, it has been demonstrated that the IgG3 subclass is important in mediating effector functions against HIV, and

IgG2 and IgG4 antibodies may also be relevant in other diseases. Therefore, several key aims were established for the work to be carried out for this thesis.

1.6.1.1. Chapter 3 Aims

1. Use single cell expression cloning of sorted plasmablasts to generate monoclonal antibodies

from an acute/early infected HIV patient over multiple time points.

2. Identify and characterise HIV-specific and non-specific monoclonal antibodies with regards

to their heavy and light chain gene family usage, nucleotide and amino acid mutations,

complementarity determining regions, and phylogenetic analysis.

1.6.1.2. Chapter 4 Aims

1. Modify currently used IgG1 heavy chain expression vectors to generate IgG2, IgG3 and IgG4

expression vectors.

2. Produce patient derived monoclonal antibodies and several well characterised broadly

neutralising antibodies in IgG1-IgG4 subclasses.

1.6.1.3. Chapter 5 Aims

1. Optimise the RFADCC, a well characterised flow based assay for testing ADCC activity of

monoclonal antibodies.

2. Perform antibody dependent cellular cytotoxicity experiments on monoclonal antibodies

and broadly neutralising antibodies in each of the IgG subclasses.

1.6.2. Hypotheses

The hypotheses for the work in this thesis can be broadly grouped into two key hypothesis;

1. There will be differences in the gene family usage, nucleotide and amino acid mutations, and

complementarity determining region lengths between HIV-specific and non-specific patient

derived monoclonal antibodies.

2. IgG1 and IgG3 antibodies (mAbs isolated from HIV infected patient and BNABs) will be better

mediators of ADCC than IgG2 and IgG4 antibodies.

Chapter 2: Materials and Methods

2. Materials and Methods

2.1. Lab consumables, reagents and equipment

Reagent Source Catalogue Number Histopaque Sigma Aldrich H8889 PenStrep Sigma Aldrich P4333 L-glutamine Sigma Aldrich G7513 Fetal Bovine Serum (FBS) Sigma Aldrich F4135 DMSO Sigma Aldrich D2650 Trypsin-EDTA Sigma Aldrich T4049 CD19 BV421 BD 562440 CD27 PE BD 555441 CD38 APC BD 555462 CD24 PerCP-Cy5.5 BD 561647 CD3 FITC BD 561806 CD14 FITC BD 555397 CD16 FITC eBioscience 11-0168-41 Aqua live/dead cell stain Invitrogen L34957 Compensation Beads BD 552843 FACS Tubes BD Falcon 352054 Trypan Blue Solution (0.4%) Sigma Aldrich T8154-100ML Reservoir Fisher Scientific 10320551 96 well PCR plates Appleton Woods BP001 PCR cap strips Appleton Woods BS261 96 well low profile skirted PCR plate ThermoFisher Scientific AB0800 RNasin Plus RNase Inhibitor Promega N2615 Tris 1M pH8 Life Technologies AM9855G RNase free water Qiagen 129112 Nuclease Free Water Qiagen 129115 Microseal Foil Biorad MSF1001 Falcon 96 well round bottom plates SLS Ltd 353077 Haemocytometer (slide glasstic 10 with counting grids) VWR 630-1506 One-step RT PCR Qiagen 210212 Taq DNA Polymerase Qiagen 201205 10mM dNTP mix Qiagen 201901 Ampicillin sodium salt Sigma Aldrich A9518 Agarose Sigma Aldrich A9539-50G 10x TBE Sigma Aldrich T4415-4L 100bp DNA quick load ladder NEB N0467S 1kb Quickload DNA ladder NEB N0468S Sybr Safe Invitrogen S33102 QIAquick Gel Extraction Kit Qiagen 28706 QIAquick PCR Purification Kit Qiagen 28106 MiniElute reaction clean up kit Qiagen 28204 1.5ml Microcentrifuge tube Fisher Scientific 3621 2ml microcentrifuge tubes Fisher Scientific 3453 AgeI-HF NEB R3552L SalI-HF NEB R3138S HindIII-HF NEB R3104S EcoRI-HF NEB R3101S BsiWi NEB R0553S

Reagent Source Catalogue Number BsiWi-HF NEB R3553S T4 DNA Ligase NEB M0202S XhoI NEB R3553S Dh5a competent cells Invitrogen 18265-017 SOC Media Invitrogen 15544-034 LB Broth with agar Sigma Aldrich L2897 LB Broth Sigma Aldrich L3022 Agar Plates Fisher Scientific 101R20 Terrific Broth Sigma Aldrich T5574-500ML 2.2ml deep well plate Starlab E2896-0220 Swann-Morton Scalpel (10A) VWR 233-5513 QIAprep Spin Miniprep Kit Qiagen 27106 PEI Sigma Aldrich 408727 DMEM (high glucose) Invitrogen 11965092 Ultradoma Serum Free Media Lonza 12-727F 100X MEM Non-essential Amino Acid Solution Sigma Aldrich M7145 RPMI Sigma Aldrich R0883 PBS Sigma Aldrich D8537 T75 flasks Corning 430641U 12 well tissue culture plates Grenier Bio-One 665180 6 well tissue culture plates Grenier Bio-One 657160 15cm tissue culture plates Sigma Aldrich CLS430599-60EA Total IgG ELISA kit eBioscience 88-50550-22 Amicon Ultra-15 Centrifugal Filter Unit (30kDa) Merck Millipore UFC903024 Amicon Ultra-15 Centrifugal Filter Unit (10kDa) Merck Millipore UFC901008 Ab Spin Trap GE Healthcare 28-4083-47 Ab buffer kit GE Healthcare 28-9030-59 50ml Syringe Grenier Bio-One SYR50LS 10% Mini-PROTEAN TGX Stain-Free Protein Gels Biorad 4568034S 2x Laemmli Sample Buffer Biorad 1610737 2-Mercaptoethanol Biorad 1610710 10x Tris/Glycine/SDS Buffer Biorad 1610732 IgG1 Sigma Aldrich I5154-1MG Human Serum Sigma Aldrich H4522-20ML gp120 (HIV-1 YU2) Immune Tech IT-001-0027p gp140 (Clade B) Immune Tech IT-001-0021p gp120 (SF162) Immune Tech IT-001-0028p gp140 (JRFL Clade B) Immune Tech IT-001-0024ΔTMp gp140 (Clade B/C consensus) Immune Tech IT-001-162p gp140 (BL10 Clade B) Immune Tech IT-001-171p gp140 (Bal.01 Clade B) Immune Tech IT-001-173Δ Goat anti-human IgG Fc Secondary Antibody, FITC Invitrogen H10101C CFSE ThermoFisher Scientific C34554 PKH-26 Sigma Aldrich MINI26-1KT 1.1ml microtubes ThermoFisher Scientific 15086 Q5 Site directed mutagenesis kit NEB E0554S 15ml centrifuge tube Grenier –Bio-One 188261 50ml centrifuge tube Grenier –Bio-One 227270 10ul, 20ul, 100ul, 1000ul pipette tips Starlabs - 5ml, 10ml, 25ml, 50ml serological pipettes VWR -

Table 2.1: Reagents and consumables used for experiments. The key reagents and their suppliers used in this experiments for this thesis are listed.

Equipment Name Supplier Incubators Heracell 150i C02 incubator Thermo Fisher Scientific Labnet 211DS (shaking) Labnet Centrifuges IEC Micromax Life Science International VWR microstar 17R VWR Sorvall Fresco Henderson Biomedical Heraeus Multifuge X3F Thermo Fisher Scientific Centrifuge MSE Mistral 3000i Henderson Biomedical Rotina 46 Hettich Zentrifigugen Vortex Vortex genie 2 Scientific Industries Heat Block Dri-Block DB3 Techne Water Baths VWB2 12 (unstirred bath) VWR JB Academy Series Grant Thermal Cycler UNO96 VWR Flow Cytometers FACS Aria III cell sorter BD Bioscience LSRII BD Bioscience Nanodrop Nanodrop 1000 Labtech Nanodrop 8000 Thermo Fisher Scientific Weighing Scales PB602-S Mettler Toledo Benchtop Autoclave Classical Media Autoclave Prestige Medical Gel Electrophoresis Biorad sub cell model-96 Biorad Wide mini sub cell GT Biorad Mini-PROTEAN Tetra cell Biorad PowerPac Basic Biorad Geldoc System Molecular Imager Gel Doc XR+ Biorad Plate Reader Anthos 2020 Biochrom Inverted Microscope Olympus CK2 Olympus Freezing Container Mr Frosty Freezing Container Thermo Fisher Scientific Vacuum Manifold QIAvac 24 Plus Qiagen

Table 2.2: Lab Equipment used for experiments. The key equipment and their suppliers used in experiments for the work shown in this thesis are listed.

Tables 2.1 and 2.2 provide a list of all the reagents, lab consumables and equipment used for experiments discussed in this thesis and their suppliers. The CEM.NKr-CCR5 cell line (ARP099), the A32 monoclonal antibody (#11438, lot 150330), and HIVIG (#3957, lot 140406) were all obtained from the

NIBSC reagent and NIH AIDS reagent programs. The IgG1 heavy chain expression vector, and the kappa and lambda chain expression vectors were a gift from Ju Mongkolsapaya. All primers were ordered from Invitrogen (ThermoFisher Scientific) using the custom DNA oligos tool, and ordered at 25nmole synthesis scale, desalted and 100uM in water.

2.2. Ethics Approval

An acute HIV infection cohort termed ARIES cohort was established at the Chelsea and Westminster

NHS Foundation Trust, 56 Dean Street, Soho, for the immunological analysis of HIV-1 acutely and early infected individuals. Ethics approval was obtained for this study from London Fulham Ethics with NRES

14/LO/2058.

2.3. Blood Processing for isolation and storage of PBMCs

2.3.1. Blood Collection

Whole blood was collected from cohort patients on site at the Dean St Clinic. A total of 11 blood tubes, comprising 7 lithium heparin, 2 SST serum tubes, and 2 plasma EDTA blood tubes were collected from each patient. Blood was then sent via courier to the Centre for Immunology and Vaccinology lab for processing.

2.3.2. Blood Separation and PBMC Isolation

Upon receipt of blood from HIV-1 infected patients, samples were transferred to the containment level 3 laboratory for processing. The 7 lithium heparin blood tubes were centrifuged at 500g for 10 minutes with break to collect plasma which was stored in 1ml aliquots in cryovials and stored in -80C freezer. Serum and plasma EDTA tubes were centrifuged for 10 minutes at 1200g, with serum and plasma stored in 1ml aliquots at -80°C. The remaining blood from the lithium heparin tubes for each patient was then transferred to a 50ml tube and diluted with PBS. For blood separation, 15ml of

Histopaque that had been adjusted to room temperature was dispensed into two 50ml tubes for each patient, with 25ml of blood layered over the hystopaque. Tubes were then centrifuged at 800g for 20 minutes with the brake set to zero. After density gradient centrifugation, PBMC layers were transferred to sterile 50ml tubes, with PBS added to 50ml. After centrifugation for 10 minutes at 500g with brake, the supernatant was poured off and the pellet resuspended in 10ml of PBS for counting.

2.3.3. Cell Counting

After mixing, 10ul of cell suspension was added to 10ul of trypan blue solution and mixed well. 10ul was then inserted into the haemocytometer chamber, and three of the nine squares (top left, centre and bottom right) were counted. The average number of cells was then multiplied by 10,000 and the dilution factor of 2, to get number of cells per ml, and then multiplied by 10 to get the total number of cells.

2.3.4. Freezing Cells

Cells were frozen in cryovials in 1ml aliquots between a concentration of 10 and 15 x106 cells/ml.

Counted cells were centrifuged at 500g for 10 minutes with brake, after which the supernatant was poured off and the cells resuspended. Freezing media was prepared with 90% FBS and 10% DMSO, and was added dropwise to the cell pellet. The cells were then mixed and 1ml aliquots were dispensed into labelled cryovials, which were then immediately transferred to “Mr Frosty” containers and placed in the -80C freezer. The following day samples were transferred to liquid nitrogen for long term storage.

2.4. Monoclonal Antibody Production

2.4.1. Overview

The nature protocol paper by Smith et al published in 2009 was used as a basis for monoclonal antibody production in the lab (404), along with the lab protocol of the Screaton lab (Molecular

Immunology, Hammersmith). An overview of the protocol is shown in Figure 2.1.

Figure 2.1: Overview of monoclonal antibody production protocol. Plasmablasts are single cell sorted and undergo several rounds of PCR to amplify variable region heavy and light ( and ) chain DNA which is subsequently cloned into constant region containing expression vectors. Vectors are then transformed in competent cells, after which single colonies are selected and grown overnight, and a miniprep performed to purify vector DNA. Matching heavy and light chain pairs are then used to transfect HEK293 cells and incubated for several days after which the supernatant is collected and tested for antibodies. Monoclonal antibodies can then be purified and tested for function.

2.4.2. Thawing Cells

RPMI media was supplemented with 10% FBS, 1% PenStrep (10,000U Penicillin and 10mg/ml streptomycin solution) and 1% L-glutamine (200mM solution) to give R10 media. R10 and 15ml falcon tubes were pre-chilled at 4°C, and the water bath heated to 37°C before thawing cells. PBMCs were retrieved from liquid nitrogen and transferred to the lab on dry ice. Vials were held in the water bath with shaking until a few ice crystals remained. The area around the cap was then wiped with 70% ethanol, the cap removed and the contents of the vial poured into the pre-chilled labelled 15ml falcon tube. As quickly as possible 6ml of pre-chilled R10 media was added to the cells drop by drop whilst swirling the tube. After a brief mix, another 5ml of R10 was added to the tube which was then centrifuged for 8 minutes at 300g. The supernatant was poured off, and the pellet resuspended in another 12ml of R10, followed by repeating the centrifuge step. The cells were resuspended in 10ml of R10 and counted as described before (section 2.3.3).

2.4.3. Plasmablast Cell Staining

For plasmablast cell staining, 5x106 PBMCs were transferred to FACS tubes, washed with PBS and

centrifuged at 300g for 5 minutes. The supernatant was poured off and the cell pellet resuspended.

Antibody/Dye Fluorchrome Clone Volume/5x106 Test (uL) CD19 BV421 HIB19 3.75 CD24 PerCP-Cy5.5 ML5 3.75 CD27 PE M-T271 30 CD38 APC HIT2 15 CD3 FITC UCHT1 30 CD14 FITC M5E2 30 CD16 FITC CB16 3.75 CD20 FITC 2H7 10

1ul in 1600ul dH20 -> Live/Dead Amcyam - 100ul/1x106 cells

Table 2.3: Flow cytometry staining panel for the identification of plasmablasts. PBMCs were stained with anti-CD19, anti-CD24, anti-CD27, anti-CD38, anti-CD3, anti-CD14, anti-CD16, anti- CD20 mouse anti-human fluorescent antibodies and the live/dead cell stain. All antibodies were titrated before use to determine the optimum staining volumes for 5 million PBMCs for sorting. As CD3, CD14, CD16 and CD20 were all being used to remove populations from gating they were all on the same channel (FITC).

The flow cytometry panel designed for the identification of plasmablasts for single cell sorting (Table

2.3) was added to each tube, briefly vortexed and incubated for 20 minutes at room temperature protected from the light. After staining, each tube was washed with PBS containing 2.5% FBS, and centrifuged for 5 minutes at 300g. After the supernatant was poured off, the cells were resuspended in 2.5ml of PBS (2.5% FCS) to give a final concentration of 2x106 cells/ml for sorting.

2.4.4. Single cell plasmablast sorting

Single cell sorting of plasmablasts was performed on a FACS Aria III cell sorter located within the containment level 3 laboratory. Before sorting, 96 well PCR plates were prepared, with 10ul of catch

buffer (prepared as in Table 2.4) aliquoted into each well, and stored at -20°C before use.

Reagent Volume per well (ul) Volume per plate (ul) RNase free water 9.6 1013.76 RNasin 0.24 25.34 1M Tris (pH8) 0.09 9.5

Table 2.4: Catch buffer reagents. Catch buffer was prepared by mixing the appropriate volumes of RNase free water, RNasin and 1M Tris (pH 8.0) together, and dispensing 10ul into each well of each catch plate. As catch buffer is a hypotonic solution, upon entry cell would lyse and immediate freezing would protect RNA.

Immediately before sorting, the stained cell suspension to be sorted was filtered through FACS tubes with filter caps (BD) to remove clumps and ensure a single cell suspension for sorting. Using an unstained control and a small volume of sample, voltages were checked for each population, followed by running the compensation controls and applying the compensation matrix to experiment. The catch plates were thawed just before sorting and placed on the 96 well sort plate module which was in cooling mode and had previously been homed to the right position. The sample was loaded on to the machine and the plasmablast population of interest was gated as in Figure 2.2.

Figure 2.2: Gating strategy used to identify and sort plasmablasts. Lymphocytes are first gated using SSC vs FSC, followed by FSC-H against FSC-A to remove doublets. CD19+DUMP- (CD3-CD14-CD16-CD20-) B cells were gated, followed by gating of plasmablasts as CD27hiCD38hi B cells.

CD19+CD27hiCD38hiCD3-CD14-CD16-CD20- plasmablasts were single cell sorted into each well of the 96 well catch plate, except for row H which was left blank to use as a negative control in the multiple

PCRs that would follow. As soon as the plate sort was complete, it was immediately sealed with aluminium foil cover, centrifuged at 100g for 1 minute, and then placed in the -80°C freezer.

2.4.5. Polymerase Chain Reaction

Several rounds of PCR were performed on the single sorted cells in order to amplify immunoglobulin variable region heavy and light chains for cloning and production of antibodies; a one-step reverse transcriptase (RT) PCR, nested PCR and cloning PCR.

2.4.5.1. RT-PCR

To amplify heavy and both kappa and lambda light chain variable region immunoglobulin genes, a one- step RT PCR was performed on the sorted plasmablasts using the primers listed in Table 2.5. All primers arrived at a concentration of 100uM in water, and forward and reverse primer stock solutions were prepared so that individual primers were at a 10uM concentration. A master mix using the One-Step

RT-PCR kit was prepared according to Table 2.6. All components were thawed at room temperature and added to the Eppendorf tube apart from the enzyme mix which was kept on ice and added last.

Name Sequence 5' - 3' 5' L VH1 ACAGGTGCCCACTCCCAGGTGCAG 5' L VH2 GACCATCCCTTCATCAGATCACC 5' L VH3 AAGGTGTCCAGTGTGARGTGCAG 5' L VH4/6 CCCAGATGGGTCCTGTCCCAGGTGCAG 5' L VH5 CAAGGAGTCTGTTCCGAGGTGCAG 5' L VH7 GGCAGCAGCAACAGCAGGTGCAGC 5' L VK1/2 ATGAGGSTCCCYGCTCAGCTGCTGG 5' L VK3 CTCTTCCTCCTGCTACTCTGGCTCCCAG 5' L VK4 ATTTCTCTGTTGCTCTGGATCTCTG 5' L Vλ1 GGTCCTGGGCCCAGTCTGTGCTG 5' L Vλ2 GGTCCTGGGCCCAGTCTGCCCTG 5' L Vλ3 GCTCTGTGACCTCCTATGAGCTG 5' L Vλ4/5 GGTCTCTCTCSCAGCYTGTGCTG 5' L Vλ6 GTTCTTGGGCCAATTTTATGCTG 5' L Vλ7 GGTCCAATTCYCAGGCTGTGGTG 5' L Vλ8 GAGTGGATTCTCAGACTGTGGTG

3' Cγ CH1 GGAAGGTGTGCACGCCGCTGGTC 3′ Cμ CH1 GGGAATTCTCACAGGAGACGA 3' CK 543 GTTTCTCGTAGTCTGCTTTGCTCA 3' Cλ CACCAGTGTGGCCTTGTTGGCTTG

Table 2.5: One-Step Reverse Transcriptase PCR Primers. Forward (5’) and reverse (3’) primers, and their nucleotide sequences, used in first one-step RT PCR to amplify heavy (H), kappa (K) and lambda (λ) chain variable regions. Forward primers target the 5’ end of the variable genes, whilst reverse primers target sequences located at the start of the constant region gamma, mu, kappa and lambda genes (Cγ, Cμ, CK and Cλ respectively), to ensure transcription of the entire variable region. Primers previously published by (399, 400, 405).

Master Mix Component Volume per well (ul) Volume per plate (ul) Forward Primer Mix 0.5 50 Reverse Primer Mix 0.5 50 dNTPs 0.5 50 5x One Step Enzyme Buffer 5 500 Nuclease free water 8.5 850 RT-Enzyme mix 0.5 50 Total 15.5ul 15ul/well

Table 2.6: One-step RT PCR master mix components. A master mix containing the one step RT PCR reaction components was prepared using the forward and reverse primer stocks from Table 2.4, dNTPs, nuclease free water and one-step RT PCR kit (Qiagen) components. Volumes of each reaction component was calculated for each plate and 15ul of master mix was added to each well.

Immediately before the enzyme mix was added to the master mix, the catch plate was retrieved from the -80°C freezer and thawed. Additionally before the enzyme mix was added to the master mix, the master mix tube was briefly vortexed, and then after adding the enzyme the tube was gently tapped to mix. 15ul of master mix was then added to each well of the 96 well plate, and the plate pulsed on the centrifuge to ensure all liquid was collected at the bottom of the tube. The plate was then placed in the thermal cycler and run on the following cycle; 30 minutes 50°C, 15 minutes 95°C, (30 seconds

94°C, 30 seconds 58°C, 1 minute 72°C) x50, 10 minutes 72°C. After completion of the cycle, the plate was removed from the PCR machine and placed in the -20°C freezer until use in following PCRs.

2.4.5.2. Nested kappa chain PCR

Whilst the heavy and lambda chain cloning PCRs could be carried out immediately following the RT-

PCR, a nested PCR was required to amplify the kappa chain before the subsequent cloning PCR. This was required to produce sufficient DNA to send for sequencing so that gene family specific primers could be used in the kappa cloning PCR, whilst a primer mixture was sufficient for heavy and lambda chain cloning PCRs.

Name Sequence 5' - 3' 5' Pan VK ATGACCCAGWCTCCABYCWCCCTG 3' CK 494 GTGCTGTCCTTGCTGTCCTGCTC

Table 2.7: Kappa chain nested PCR primers. Forward (5’) and reverse (3’) nested PCR primers used to amplify all kappa chain variable gene families. Forward primer targets sequences at the start of the variable region, whilst reverse primer targets the sequence at the start of the constant region to ensure the whole variable region is transcribed. Primers previously published by (399, 400, 405)

As before, primers (Table 2.7) arrived as 100uM in water, and 10uM stocks of forward and reverse primers were made. The master mix was prepared as in Table 2.8 in a PCR hood, once again with the enzyme added last. 23ul of master mix was then added to each well of a 96 well PCR plate and kept on ice. The plate was then transferred to a biological safety cabinet where 2ul of each well from the original catch plate that had undergone RT-PCR was transferred to the corresponding well in the new nested plate.

Master Mix Component Volume per well (ul) Volume per plate (ul) Forward Primer Mix 0.5 52.8 Reverse Primer Mix 0.5 52.8 dNTPs 0.5 52.8 10x Taq polymerase buffer 2.5 264 Nuclease free water 18.875 1993.2 Hot Star Taq Polymerase 0.125 13.2 One Step RT-PCR Product 2 - TOTAL 25 23ul/well

Table 2.8: Kappa chain variable region nested PCR reaction mixture. A master mix was prepared using the forward and reverse primer stocks from table 2.7, dNTPs, nuclease free water and Taq polymerase kit (Qiagen) components. Volumes of each reaction component was calculated for each plate and 23ul of master mix was added to each well.

After pulsing the plate on the centrifuge to collect all liquid in the bottom of each well, the plate was placed inside the thermal cycler and run on the following cycle: 15 minutes 95°C, (30 seconds 94°C, 30 seconds 58°C, 45 seconds 72°C) x50, 10 minutes 72°C. After the completion of the cycle, the plate was removed from the PCR machine, and PCR product from each well was loaded onto a 1% agarose gel and run to check for DNA bands (see section 2.4.6). Resulting kappa DNA was purified (see section

2.4.7) and sent for sequencing to Genewiz laboratories with the 3’ CK 494 primer (Table2.7).

2.4.5.3. Cloning PCRs

Cloning PCRs for heavy, lambda and kappa chains were performed with primers (Table 2.9) designed to add restriction enzyme sites to the 5 and 3 prime ends of variable regions for cloning. Three individual plates were prepared for cloning PCR; one for heavy chain, one for kappa chain, and one for lambda chain cloning. Master mixes were prepared as in Table 2.8, the only difference being the forward and reverse primers used for cloning PCR are those listed in Table 2.9. Template DNA produced from the RT-PCR (2.4.5.1) was added to the wells last before loading in thermal cycler with the same conditions as section 2.4.5.2.

Chain Name Sequence 5' - 3' 5' AgeI VH1/5 CTGCAACCGGTGTACATTCCGAGGTGCAGCTGGTGCAG 5' AgeI VH3 CTGCAACCGGTGTACATTCTGAGGTGCAGCTGGTGGAG 5' AgeI VH4 CTGCAACCGGTGTACATTCCCAGGTGCAGCTGCAGGAG 5' AgeI VH3-23 CTGCAACCGGTGTACATTCTGAGGTGCAGCTGTTGGAG 5' AgeI VH4-34 CTGCAACCGGTGTACATTCCCAGGTGCAGCTACAGCAGTG 5' AgeI VH 1–18 CTGCAACCGGTGTACATTCCCAGGTTCAGCTGGTGCAG 5' AgeI VH 1–24 CTGCAACCGGTGTACATTCCCAGGTCCAGCTGGTACAG 5' AgeI VH 3–9/30/33 CTGCAACCGGTGTACATTCTGAAGTGCAGCTGGTGGAG Heavy 5' AgeI VH 6–1 CTGCAACCGGTGTACATTCCCAGGTACAGCTGCAGCAG 5′ AgeI VH4–39 CTGCAACCGGTGTACATTCCCAGCTGCAGCTGCAGGAG 5′ AgeI VH1 CTGCAACCGGTGTACATTCCCAGGTGCAGCTGGTGCAG 5′ AgeI VH3–33 CTGCAACCGGTGTACATTCTCAGGTGCAGCTGGTGGAG

3’ SalI JH1/2/4/5 TGCGAAGTCGACGCTGAGGAGACGGTGACCAG 3’ SalI JH3 TGCGAAGTCGACGCTGAAGAGACGGTGACCATTG 3’ SalI JH6 TGCGAAGTCGACGCTGAGGAGACGGTGACCGTG 5' AgeI Vλ1 CTGCTACCGGTTCCTGGGCCCAGTCTGTGCTGACKCAG 5' AgeI Vλ2 CTGCTACCGGTTCCTGGGCCCAGTCTGCCCTGACTCAG 5' AgeI Vλ3 CTGCTACCGGTTCTGTGACCTCCTATGAGCTGACWCAG 5' AgeI Vλ4/5 CTGCTACCGGTTCTCTCTCSCAGCYTGTGCTGACTCA Lambda 5' AgeI Vλ6 CTGCTACCGGTTCTTGGGCCAATTTTATGCTGACTCAG 5' AgeI Vλ7/8 CTGCTACCGGTTCCAATTCYCAGRCTGTGGTGACYCAG

3' XhoI Cλ CTCCTCACTCGAGGGYGGGAACAGAGTG 5' AgeI VK1-5 CTGCAACCGGTGTACATTCTGACATCCAGATGACCCAGTC 5' AgeI VK1-9 TTGTGCTGCAACCGGTGTACATTCAGACATCCAGTTGACCCAGTCT 5' AgeI VK1D-43 CTGCAACCGGTGTACATTGTGCCATCCGGATGACCCAGTC 5' AgeI VK2-24 CTGCAACCGGTGTACATGGGGATATTGTGATGACCCAGAC 5' AgeI VK2-28 CTGCAACCGGTGTACATGGGGATATTGTGATGACTCAGTC 5' AgeI VK3-11 TTGTGCTGCAACCGGTGTACATTCAGAAATTGTGTTGACACAGTC Kappa 5' AgeI VK3-15 CTGCAACCGGTGTACATTCAGAAATAGTGATGACGCAGTC 5' AgeI VK3-20 TTGTGCTGCAACCGGTGTACATTCAGAAATTGTGTTGACGCAGTCT 5' AgeI VK4-1 CTGCAACCGGTGTACATTCGGACATCGTGATGACCCAGTC

3' BsiWI JK1/2/4 GCCACCGTACGTTTGATYTCCACCTTGGTC 3' BsiWI JK3 GCCACCGTACGTTTGATATCCACTTTGGTC 3' BsiWI JK5 GCCACCGTACGTTTAATCTCCAGTCGTGTC

Table 2.9: Primers used for heavy, lambda and kappa cloning PCRs. Forward and reverse cloning primers for heavy, lambda and kappa variable region DNA. Primers previously published by (399, 400, 405). All forward primers had AgeI restriction site attached to the front, whilst heavy lambda and kappa chain primers had SalI, XhoI and BsiWi restriction sites on the reverse primers respectively.

2.4.6. Gel Electrophoresis

2.4.6.1. Gel Preparation

Agarose gels were prepared to check for heavy, kappa and lambda variable region DNA resulting from the nested or cloning PCRs. A working 1X TBE 1L solution was prepared by diluting 100ml of 10X TBE stock with 900ml distilled water. Agarose gels were prepared at 1% concentration, with 2.5g of agarose added to 250ml of 1X TBE buffer in a conical flask. The flask was then heated for 3 minutes in the microwave to allow the agarose to dissolve. During heating, the Biorad sub cell model-96 gel tray was prepared by taping the open ends with autoclave tape. After cooling the solution, 25ul of SYBR

Safe was added to give a 1:10000 dilution and the flask gently swirled. The gel solution was then poured into the gel tray, and two 51 well combs (Biorad) were place at the top and mid-way over the gel. Gels were left to cool and solidify for 30-40 minutes before use.

2.4.6.2. Gel Loading and Running

The Biorad sub cell model-96 tank was filled with 1X TBE buffer, and the gel tray placed within the tank after removing autoclave tape. The gel combs were then removed and the buffer topped up to the maximum fill line. 3ul of the 25ul PCR product was added to 2ul of nuclease free water, and 1ul of 6X loading dye (NEB). After mixing the 6ul total volume for each well of the PCR plate was loaded into the wells of the gel. The first well of each row was left blank for the quick load 100bp DNA ladder. The voltage was set to 120 volts and the gel was run for 30-40 minutes. After running the gel, the gel tray was removed from the tank and the gel placed on a blue light filter within the Geldoc

XR+ imager. The filter was set to Sybr Safe and run to generate an image. Gel bands around 380bp,

540bp and 400bp indicated amplification of heavy, kappa and lambda variable regions respectively

(Figure 2.3).

Figure 2.3: Heavy, kappa and lambda cloning PCR example gels. 3ul of variable region cloning PCR product with 2ul of nuclease free water and 1ul of 6x NEB loading dye was mixed and loaded to each well of the gel. The first well of each row was used for the NEB 100bp quick load DNA ladder, and the gel run for 30-40 minutes at 120v.

2.4.7. DNA Purification

After analysing the gel image, all wells which contained matching heavy and light chain pairs were purified using the QIAquick PCR Purification Kit as per manufacturer’s instructions. Briefly, buffers were added to the remaining PCR sample and loaded into Qiagen spin columns, which were then inserted into a vacuum manifold. After washing, tubes were spun for 1 minute at 13000rpm in a benchtop microcentrifuge to remove residual buffer, and then eluted with 30ul elution buffer into a sterile 1.5ml microcentrifuge tube. Purified samples were stored at -20°C until later use.

2.4.8. Digestions

Both purified PCR product and heavy and light chain vectors were digested for cloning purposes so that the variable region heavy or light chain DNA could be ligated into their corresponding vectors.

2.4.8.1. Variable Region Digestions

Variable region DNA was digested with restriction enzymes for ligation into vectors, with the entire purified PCR product (30ul) being digested. Heavy chain DNA was digested with AgeI-HF and SalI-HF restriction enzymes, using Cutsmart buffer for 4 hours at 37°C in a 40ul reaction volume. Lambda chain

DNA was digested with AgeI-HF and XhoI restrictions, also with Cutsmart buffer for 4 hours at 37°C.

Kappa chain DNA was digested in a two-step protocol. The first step was digestion with AgeI-HF for 4 hours at 37°C in cutsmart buffer followed by purification with the QIAquick PCR purification kit as previously described (section 2.4.7), with elution in 30ul of elution buffer. The second step digested the 30ul elution product with the BsiWi restriction enzyme and buffer 3.1 for 4 hours at 55°C.

Heavy Chain Lambda Chain Kappa Chain 1 Kappa Chain 2 AgeI-HF 0.125ul 0.125ul 0.125ul - SalI-HF 2ul - - - XhoI - 0.5ul - - BsiWi - - - 0.5ul Cutsmart Buffer 4ul 4ul 4ul - Buffer 3.1 - - - 4ul NF Water 3.875ul 5.375ul 5.875ul 5.5 DNA 30ul 30ul 30ul 30ul TOTAL 40ul 40ul 40ul 40ul

Table 2.10: Variable region DNA digestion reaction mixtures. Heavy, lambda and kappa chain DNA digestion reaction mixtures were prepared with restriction enzymes AgeI-HF, SalI-HF, XhoI and BsiWi. Heavy and lambda chain DNA was double digested at 37°C for 4 hours, whilst kappa chain DNA was first digested with AgeI at 37°C for four hours, purified and then digested with BsiWi at 55°c for four hours. All restriction enzymes and buffers were from NEB. NF = nuclease free.

Digestions were prepared as master mixes in microcentrifuge tubes using the reagents listed in Table

2.10. The reagents for one heavy/lambda/kappa chain DNA digestion is shown, and this was multiplied by the number of samples to digest to create a master mix. 10ul of master mix dispensed into microcentrifuge tubes containing heavy, lambda or kappa chain DNA, and incubations at 37°C were carried out by placing the tube rack in an incubator set to 37°C, whilst incubations at 55°C were carried out using the heat block.

2.4.8.2. Vector Digestions

Heavy, lambda and kappa chain expression vectors (containing the heavy, lambda or kappa constant genes and the ampicillin resistance gene for colony selection) were digested with the same restriction enzymes as their corresponding DNA inserts. 1ug of each vector was digested for 4 hours using the reaction mixtures for each vector listed in Table 2.11. The heavy and lambda chain vectors were double digested at 37°C using cutsmart buffer, whilst the kappa vector was digested in a two-step process, as with the insert DNA, first with AgeI-HF in cutsmart buffer at 37°C, and then after purification with BsiWi at 55°C. All vector digestions were performed in a 40ul reaction volume.

Heavy Chain Lambda Chain Kappa Chain 1 Kappa Chain 2 AgeI-HF 1ul 1ul 1ul - SalI-HF 5ul - - - XhoI - 1.5ul - - BsiWi - - - 0.5ul Cutsmart Buffer 4ul 4ul 4ul - Buffer 3.1 - - - 4ul NF Water Up to 40ul Up to 40ul Up to 40ul 5.5ul Vector 1ug 1ug 1ug 30ul KC1 TOTAL 40ul 40ul 40ul 40ul

Table 2.11: Vector digestion reaction mixtures. Heavy, lambda and kappa vector digestion reaction mixtures were prepared with restriction enzymes AgeI- HF, SalI-HF, XhoI and BsiWi. Heavy and lambda chain vectors were double digested at 37°C for 4 hours, whilst the kappa chain vector was first digested with AgeI at 37°C for four hours, purified and then digested with BsiWi at 55°c for four hours. All restriction enzymes and buffers were from NEB. NF = nuclease free.

2.4.8.3. Gel Purification

After the digestions of variable region DNA and expression vectors (sections 2.4.8.1 and 2.4.8.2 respectively), each individual heavy, kappa or lambda digestion reaction volume was run on a 1% agarose gel for extraction and purification. The agarose gel was prepared as before (section 2.4.6), however 25-well combs were used instead of 51-well combs. 8ul of 6x loading dye was added to each

40ul reaction tube and then pulsed in the microcentrifuge to collect all liquid at the bottom of the tube. The entire 48ul of digest was loaded into each well of the gel, and the gel run for 40 minutes at

120V. The gel was visualised as before using the geldoc+ system. The blue light filter was then

transferred to a UV light box with the gel on top. Using glasses provided with the filter, the bands were visualised around 5.7kb, 5kb, and 5kb for heavy, lambda and kappa chains respectively. The bands were excised from the gel using a scalpel, and placed into labelled 1.5ml centrifuge tubes. Weighing scales were zeroed with an empty 1.5ml centrifuge tube, before weighing each tube containing the gel slices. The gel slices were then purified with QIAquick Gel Extraction Kit following manufacturer’s instructions, with the exception of eluting in 30ul elution buffer. Samples were stored at -20°C.

2.4.9. Vector-Insert Ligation

1ul of insert DNA was added to 1ul of vector and 6.5ul of nuclease free water in a 1.5ml microcentrifuge tube. The tube was placed in a heat block pre-heated to 65°C and incubated for 5 minutes to disrupt any vector-vector or insert-insert interactions. After brief cooling, 1.5ul of master mix containing 1ul T4 ligase buffer and 0.5ul T4 ligase was added to each tube, gently mixed and pulsed in the centrifuge to collect all liquid at the bottom of the tube. The ligation mixtures were incubated at room temperature for 3-4 hours before transforming.

2.4.10. Competent Cell Transformation

2.4.10.1. Agar Plate Preparation

Agar plates containing ampicillin were pre-prepared by adding 17.5g of LB agar to 500ml of deionised water in a sterile 500ml glass bottle. Lids were loosened with autoclave tape applied to the lid, and the bottle was autoclaved using a bench top autoclave. After cooling to less than 50°C in a water bath,

0.5ml of 100mg/ml ampicillin was added to the LB agar and gently mixed. Using a 50ml pipette, 12.5ml of agar was distributed to each agar plate and left at room temperature to solidify. Plates were stored at 4°C, and warmed to room temperature before use.

2.4.10.2. Transformation

Subcloning efficiency Dh5α cells were used for chemical competent cell transformation. Dh5α cells were retrieved from storage at -80°C and thawed on wet ice for approximately 15 minutes. During this time, 5ul of each ligation reaction was transferred to 1.5ml labelled microcentrifuge tubes and placed

on ice. After thawing, 25ul of competent cells were added to each microcentrifuge tube, with the tube gently tapped to mix. Each tube was then incubated on ice for 30 minutes, after which tubes were

“heat shocked” for 35 seconds at 42°C in a pre-heated water bath. Tubes were then immediately placed back on ice for a further 2 minutes. 950ul of SOC recovery media was then added to each tube, which were then placed inside a shaking incubator for 1 hour at 37°C, with shaking at 220rpm. After 1 hour recovery, microcentrifuge tubes were centrifuged for 10 minutes at 5500rpm to pellet bacterial cells. Supernatant was poured off into virkon, leaving around 100ul residual medium to resuspend the pellet in. This was then pipetted onto labelled agar plates, and spread until dry with disposable spreaders (VWR, UK). As vectors used in transformations contain the ampicillin resistance gene, only colonies of bacteria containing these vectors would grow on the ampicillin containing plates. Agar plates were then incubated overnight at 37°C, and the following morning plates were transferred to the fridge and stored at 4°C.

2.4.11 Colony Selection and overnight growth

For each agar plate, 2 individual colonies were selected for overnight growth and subsequent miniprep, on the basis that they were well isolated from other colonies. Sterile 2.2ml 96 well deep well plates were prepared for overnight culture by dispensing 1.2ml of terrific broth into each well using an Eppendorf stepper (Eppendorf). A p20 tip was used to pick individual colonies and was placed into each well. Once all colonies had been picked the plate was left for 1-2 minutes then each tip was removed using a multichannel pipette and disposed in virkon. The plate was sealed with aluminium foil cover and was incubated overnight at 37°C with shaking at 220rpm.

2.4.12. Miniprep

Each overnight culture was miniprepped using the Qiagen Spin Miniprep Kit according to manufacturer’s instructions. Before use, the provided RNase A solution and LyseBlue reagent were added to buffer P1 and stored at 4°C. The 1.2ml overnight cultures were first transferred to labelled

1.5ml microcentrifuge tubes and centrifuged for 3 minutes at 10000rpm. After this, the supernatant

was poured off and the pellet resuspended in 250ul buffer P1, with 250ul of buffer P2 then added to each tube. Tubes were subsequently inverted 6 times and incubated for 5 minutes at room temperature, after which 350ul of buffer N3 was added, and the tubes inverted 6 times to mix.

Following centrifugation at 130000rpm for 10 minutes, each supernatant was applied to a labelled

QIAprep spin column and placed in the QIAvac 24 plus vacuum manifold. After applying the vacuum,

0.5ml of buffer PB was added to each column to wash, and following vacuum 0.75ml buffer PE was added. Following a final vacuum turn on, the columns were placed in microcentrifuge tubes and centrifuged for 1 minute at 13000rpm. The column was then transferred to fresh labelled 1.5ml centrifuge tubes and 50ul buffer EB was applied to the column. After several minutes incubation at room temperature the column was centrifuged for 1 minute at 13000rpm, resulting in 50ul of elute containing the miniprep DNA.

2.4.13. Digestion to check for insert

In order to check that the vectors contained the insert DNA, a digestion was carried out on the minipreps. Heavy, lambda and kappa chain vectors were digested with EcoRI and SalI, AgeI and XhoI, and HindIII and AgeI restriction enzymes respectively (Table 2.12).

Heavy Chain Vector Lambda Chain Vector Kappa Chain Vector AgeI-HF - 0.125ul - EcoRI-HF 0.2ul - 0.2ul SalI-HF 0.2ul - - XhoI - 0.2ul - HindIII - - 0.2ul Custmart Buffer 2ul 2ul 2ul Nuclease free water 14.6ul 14.675ul 14.6ul Vector miniprep 3ul 3ul 3ul TOTAL 20ul 20ul 20ul

Table 2.12: Digestion reaction mixtures to check for insert DNA within vector. Reaction components for digestion of miniprepped vectors to confirm presence of insert. Master mixes using the reaction components were prepared, with 3ul of miniprep vector digested with restriction enzymes EcoRI- HF and SalI-HF for heavy chain vector, AgeI-HF and XhoI for lambda vectors, and EcoRI-HF and HindIII for kappa chain vectors.

All vectors were digested for 2 hours at 37°C, after which 4ul of 6x loading dye was added to each tube. 10ul of sample was then loaded into each well of a pre-prepared 1% agarose gel (as section

2.4.6), with 100bp and 1Kb DNA ladders used for reference, and run for 30-40 minutes at 120V. The gel was then placed on the geldoc+ imaging system on the blue light filter and image taken.

Figure 2.4: Heavy, kappa and lambda miniprep vector example gels. Heavy, kappa and lambda vectors were digested to check for the present of the variable region insert. 1kb DNA ladders were loaded in the first lane and 100bp DNA ladders were loaded in the second lane to check for the insert around 500bp in size.

Vectors that did contain the variable region insert had two bands, one at 500bp (insert) and one around 5-6kb (vector), whilst vectors without insert had just the one band around 5-6Kb (Figure 2.4).

2.4.14. Sequencing

Vectors that contained the insert were sent for sequencing to Genewiz laboratories (Genewiz, UK).

15ul of vector miniprep at a concentration of 100ng/ul was sent either in 1.5ml Eppendorf tubes labelled with cap markers obtained from genewiz, or a skirted 96 well plate. The AbVec Primer

(GCTTCGTTAGAACGCGGCTAC) was sent in a 1.5ml microcentrifuge tube at a concentration of 5uM in water, and was labelled with a primer cap marker also obtained from Genewiz. Using the genewiz online portal, cap markers and their corresponding vector label were uploaded and order created. On receipt of sequence results, sequences were first checked for clear sequencing results and peaks. The

FASTA files were then input into the V-Quest tool of the IMGT database where gene family usage,

CDR3 lengths and mutations could be analysed.

2.4.15. HEK293T cell line maintenance and transfection

The human embryonic kidney (HEK) 293T cell line was passaged and maintained in culture in T75 flasks for production of monoclonal antibodies. D10 media was prepared with DMEM media with high L- glutamine, with added 10% FCS, 1% Penstrep and 1x non-essential amino acids. Media was stored at

4°C, and was pre-heated to 37°C using the water bath before use. An overview of the transfection procedure is illustrated in Figure 2.5.

Figure 2.5: Overview of HEK293 transfection for production of monoclonal antibodies. HEK293T cells were passaged twice weekly until confluent. The day before transfection (Day 1) cells were counted and seeded at the appropriate density in 6 well, 12 well or 15cm tissue culture plates, and incubated overnight at 37°C. 1 hour before transfection the cell culture media is replaced with fresh D10 and incubated. Matching heavy and light chain vectors were added at the appropriate concentration to neat DMEM media in a microcentrifuge tube, vortexed and then PEI was added. After incubating at room temperature for 15 minutes, the entire volume was added to the tissue culture wells/plate and incubated overnight at 37°C. The following day, media was replaced with serum free media and cells were incubated for 4-5 days at 37°C. Supernatant was then aspirated and tested for IgG antibodies.

2.4.15.1. HEK293T Cell Passage

Cells were passaged twice weekly with a 1:10 split. Before passage the flask was checked under the microscope for confluency. The culture media was then aspirated using a 10ml stripette, and disposed of in virkon. The cell layer was gently washed by adding 10ml PBS to the side of the flask and then carefully tilting the flask back and forth several times to wash the cells. The PBS was then aspirated and disposed of in virkon. 1ml of trypsin-EDTA solution was then added to the flask and tilted back and forth to cover the surface and then incubated at 37°C for several minutes. The flask was then tapped to dislodge any remaining attached cells and 9ml of D10 media was added to the flask. The cells were mixed vigorously with 9ml being removed and disposed of, with 1ml remaining. 19ml of

D10 was then added to the flask, with the flask gently moved back and forth to ensure even distribution of cells. The flask was then checked under the microscope and then placed flat in the incubator at 37°C.

2.4.15.2. Seeding Cells for Transfection

The day before transfection the cells were seeded into either 6 or 24 well plates, or 15cm tissue culture plates. Cells were checked under the microscope and were seeded when around 80% confluent. Cell culture media was removed, cells trypsinised and resuspended in 10ml D10 as before (section

2.4.15.1). However the whole cell suspension was transferred to a 50ml falcon tube (2 T-75 flasks per

50ml falcon tube) where it was topped up to 50ml with D10 media. The tube was then centrifuged for

5 minutes at 300g and resuspended in 10ml D10 for counting (as in section 2.3.3).

24-well Plates 6-well Plates 15cm Plates Cells/well 1.75x105 8x105 - Total Cells/Plate 4.2x106 4.8x106 11x106 Seeding Volume/well (ml) 0.5ml 2ml - Seeding Volume/Plate (ml) 12ml 12ml 20ml Concentration (cells/ml) 3.5x105 4x105 5.5x105

Table 2.13: Seeding densities for 293T transfection in 24-, 6-well and 15cm tissue culture plates. The numbers of cells per well and plate required for each transfection was used to calculate overall number of wells/plates and therefore transfections that could be performed with the calculated number of cells.

The cells were then resuspended at particular concentrations to get the correct cell density for seeding the plates (Table 2.13), and dispensed into the appropriate number of wells/plates. Tissue culture plates were then rocked back and forth to get even distribution of cells, checked under the microscope and placed in the incubator until the following day for transfection.

2.4.15.3. Polyethylenemine Preparation

Polyethylenemine (PEI) was prepared before transfection and stored at -20°C. PEI solution was weighed to get 50mg in 1ml of nuclease free water. This was then diluted to get a 1mg/ml solution, which was then filter sterilised and aliquoted in 0.5ml aliquots.

2.4.15.4. HEK 293 Transfection

Approximately 1 hour before transfection, the tissue culture plates were checked under the microscope to ensure they were confluent enough (around 70%) for transfection. Cell culture media was carefully aspirated from the tissue culture plate using a 10ml stripette, and disposed of in virkon.

Fresh D10 media was then gently added to the side of the plate gradually lowering the plate to be horizontal and the plate gently rocked back and forth to cover the whole plate with media.

Transfection mixes were then prepared as follows; for 24-well plates 300ng of matching heavy and light chain vector DNA (measured using nanodrop) was aliquoted into a 1.5ml microcentrifuge tube with 60ul of DMEM media (without supplement) and mixed using a vortex. PEI at 1mg/ml was thawed from previously made stock and 3ul was added to each tube and mixed vigorously. The transfection reagent was then incubated at room temperature for 15 minutes before adding drop by drop to the labelled well of the 24 well plate. For the 6-well plate and 15cm2 tissue culture plates the same process was followed but with different quantities of DNA and volumes of media and PEI. For 6-well plate 1ug of heavy and light chain DNA was added to 250ul of DMEM with 9ul PEI, and for the tissue culture plates 15ug of DNA was added to 2ml of DMEM and 100ul PEI. The plates were then gently rocked back and forth and then placed in the incubator (37°C, 5%CO2) overnight.

2.4.15.5. Serum Free Media

Around 18-24 hours after transfection, the cells were briefly checked under the microscope and then the cell culture media was removed as before. Serum free Ultradoma media had been pre-heated to

37°C, and was then added to the side of the well/tissue culture plate (0.5ml, 2ml and 25ml for 26, 6 well and 15cm tissue culture plates respectively), with the plate gently rocked to cover the whole plate with media. The plates were then incubated for 5 days at 37°C.

2.4.15.6. Harvesting Antibody

On the 5th day after changing transfection media to serum free media, the antibody containing media for each antibody transfection was harvested. Depending on the volume of media to be collected, either 2ml, 15ml or 50ml centrifuge tubes were pre-labelled for each antibody. Using a p1000 tip for the 24- or 6-well plates, or a 20ml stripette, the media from each plate was collected and dispensed into the corresponding labelled tube. This transfection supernatant was then stored at 4°C until testing or purification.

2.4.16. IgG ELISA to test for antibody

Transfection supernatant was tested for presence of antibodies using a commercial IgG ELISA kit from eBioscience and Thermofisher called IgG Ready Set Go. The first ELISA tests were semi-quantitative in that at this point the aim of the ELISA was simply a screening method to determine whether antibodies had been generated by the transfection or not. Manufacturer’s instructions were followed to perform the ELISA. All instructions here were followed for 1 plate, if more were used reagents were scaled up accordingly.

2.4.16.1 Plate Coating

The day before the ELISA was carried out, the ELISA plate provided with the kit was coated with capture antibody. Following kit instructions, coating buffer was prepared by diluting PBS 10 times in deionised water; 1ml PBS and 9ml dH20. A 1:250 dilution of capture antibody was then made in the coating buffer by adding 40ul capture antibody to 9960ul coating buffer. The buffer was poured into

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a 25ml reservoir, mixed with a 200ul multichannel pipette, and 100ul was added to each well of the

96 well plate provided. The plate was then sealed using an adhesive plate seal (VWR, UK) and incubated at 4°C overnight.

2.4.16.2 Wash Buffer

Wash buffer was prepared by adding 250ul of Tween-20 (Sigma Aldrich, UK) to 500ml of PBS to give

PBS-T (0.05% Tween-20). During each wash 250ul of wash buffer was added to each well of the 96 well plate using a 300ul multichannel pipette (ThermoFisher) and 300ul tips (Starlab). The first two wash steps consisted of 2 washes, all other wash steps consisted of four washes. During the first two wash steps, liquid within the wells was aspirated using multichannel pipette followed by adding the wash buffer and repeating. As per manufacturer’s instructions all other washes were performed by flicking off the buffer within the well, adding the PBS-T, incubating for 1 minute to soak, flicking off the buffer and blotting on absorbent towel, and repeating this 3 times.

2.4.16.3. Blocking Buffer

After the first wash, blocking buffer was prepared by diluting 2.5ml assay buffer A (ABA) provided with the kit 1:10 with 22.5ml of deionised water. The buffer was mixed by inverting the tube several times, and was then poured into a 25ml reservoir. Using a 300ul multichannel pipette, 250ul of blocking buffer was added to each well. The plate was then sealed using an adhesive plate seal and incubated at room temperature for 2 hours.

2.4.16.4. Preparing Standard

The recombinant IgG standard provided with the IgG ELISA kit was prepared just before washing the blocking buffer off the plate to allow 10-30 minutes reconstitution before adding to the plate. The standard was provided as a powder/tablet within a glass vial with the reconstitution volume of deionised water to add labelled on the vial. By adding the correct volume of water and allowing to reconstitute at room temperature the concentration would be 200ng/ml.

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2.4.16.5. Testing Supernatants

After blocking, the plate was washed twice with PBS-T as described. Residual wash buffer was removed by blotting the plate on paper towel until there was no visible liquid remaining. 100ul of neat transfection supernatant was added to each well referring to a generalised plate layout prepared beforehand (Figure 2.6).

Figure 2.6: Plate Layout for IgG ELISA to confirm monoclonal antibody production. IgG standards were added in duplicate to the first two columns of the 96 ELISA well plate, with a 1:2 dilution series carried out to row G. Wells H1 and H2 were left blank, and transfection supernatants were added in duplicate to the remaining wells.

Columns 1 and 2 were left for the IgG standard. 100ul of assay buffer A at 1x concentration (prepared by diluting ABA provided 1:20 with deionised water) was added to each well of the allocated wells for the standards as well as any blank wells. 100ul of IgG standard, prepared as described, was added to wells A1 and A2 and mixed well. Two-fold serial dilutions of the replicate standards were made in each column by transferring 100ul of A1 and A2 to B1 and B2, mixing and so on to and including G1 and G2.

H1 and H2 were left as blank. The plate was then sealed and incubated at room temperature for 2 hours.

2.4.16.6. Secondary Antibody

After 2 hours incubation at room temperature, the plates were washed four times as described previously. The secondary antibody was prepared by diluting the provided IgG/HRP antibody 1:250 in

1x assay buffer A, 0.5ml of assay buffer A was added to 9.5ml of deionised water. 40ul of buffer was

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then removed, and 40ul of horseradish peroxidase conjugated IgG antibody was added. After inverting the tube several times to mix, the antibody was poured into a 25ml reservoir, and 100ul of secondary antibody was added to each well using a multichannel pipette. The plate was then sealed and incubated at room temperature for 1 hour.

2.4.16.7. TMB Substrate and Stop Solution

After 1 hour incubation, the plate was washed as before. 100ul of TMB-substrate (as provided) was then added to each well, with the plate being sealed and incubated for 15 minutes at room temperature in the dark. During this incubation period the stop solution was prepared. A 2N H2SO4

(sulphuric acid) solution was prepared by adding 2ml of 10N H2SO4 (Sigma Aldrich) to 8ml of deionised water in a 15ml centrifuge tube. After 15 minutes of incubation, 100ul of stop solution was added to each well of the 96 well plate.

2.4.16.8. Plate Reader and Analysis

The plate was then placed inside the plate reader, and the absorbance read at 450nm. The absorbance at this wavelength was noted for each well and was used to determine whether antibody was present in the transfection supernatant sample. Any absorbance greater than the lowest standard results was determined as being positive for antibody, providing that the lowest standard OD value was greater than the blank plus 3 standard deviations.

2.5. Screening antibodies for HIV specificity

A flow cytometry based assay was used to screen transfection supernatants and patient serum for reactivity to HIV gp140 using the CEM.NKr-CCR5 cell line that would be used in functional assays. The assay here is described for screening transfection supernatants, however the same protocol was followed for screening patient serum, with serum being diluted to a final concentration of 1:1000. A general overview of the assay is illustrated in figure 2.7.

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Figure 2.7: Flow cytometry based assay to test generated mAb for HIV reactivity. CEM.NKr-CCR5 cells were coated with 50μg/ml HIV-1 clade B gp140 (or gp120) and incubated for 1 hour at 37°C. After washing cells, transfection supernatant containing mAbs was incubated with the cells for 1 hour at 37°C. After multiple washes to remove excess antibody, a 1:100 dilution of IgG-FITC antibody was added to cells and incubated at room temperature for 30 minutes. Cells were fixed and analysed on a LSRII and FlowJo, with FITC+ cells indicating binding of antibody to HIV gp140. Diagrams of expected flow cytometry plots for negative and positive samples (see controls listed in section 2.5.3) are shown.

2.5.1. CEM.NKR-CCR5 cell line

The CEM.NKr-CCR5 cell line (obtained from NIBSC) was received on dry ice and stored in liquid nitrogen until use. The cells were thawed as described previously (section 2.4.2) using R10, and initially seeded in a T25 flask. Cells were monitored until around 80% confluent and expanded to a T75 flask, after which cells were passaged twice weekly when confluent, seeding flasks with 10X106 cells

(generally a 1:4 split).

2.5.2. HIV envelope proteins

A variety of HIV envelope proteins were used in different assays. All proteins were purchased from

Immune Technologies and were available at 1mg/ml concentrations. Upon arrival of proteins, they were aliquoted into 10ul aliquots and stored at -80°C.

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Recombinant Protein Stock Concentration HIV-1 gp120 HIV-1 YU2 1mg/ml HIV-1 gp120 SF162 1mg/ml HIV-1 gp140 Clade B 1mg/ml HIV-1 gp140 JRFL Clade B 1mg/ml HIV-1 gp140 Clade B/C consensus 1mg/ml HIV-1 gp140 BL10 Clade B 1mg/ml HIV-1 gp140 Bal.01 Clade B 1mg/ml

Table 2.14: HIV-1 clade B recombinant proteins. A variety of gp120 and gp140 recombinant HIV-1 proteins were purchased from Immune Technologies and stored in 10ul at 1mg/ml aliquots.

2.5.3. Controls

The A32 monoclonal anti-HIV antibody and HIVIG (polyclonal purified IgG from HIV infected donors) were used as positive controls, whilst IgG1 and human serum were used as negative controls. Controls were prepared at 2x concentration (Table 2.15) to account for 1:2 dilution when added to CEM.NKr-

CCR5 cells.

Control Stock Conc. Dilution for 2X Conc. Final Conc. IgG1 negative control 1mg/ml 1:500 : 2ul IgG1 + 998ul PBS 1μg/ml Serum negative control Neat 1:500 : 2ul IgG1 + 998ul PBS 1:1000 A32 positive control 1.65mg/ml 1:825: 1ul A32 + 824ul PBS 1μg/ml HIVIG positive control 50mg/ml 1:50: 10ul HIVIG + 490ul PBS, 1μg/ml then 1:500: 2ul 1:50 HIVIG + 998ul PBS

Table 2.15: Positive and negative controls used in screening assay. IgG1 and serum from healthy individuals were used as negative controls, whilst the anti-HIV mAb A32 and purified IgG from HIV donors (HIVIG) were sued as positive controls. Stock concentrations of antibodies/serum is shown, with dilution to get 2x concentration.

2.5.4. Coating CEM.NKR-CCR5 cells with gp140

CEM.NKr-CCR5 cells were transferred from the T75 flask using a 10ml stripette to a 50ml falcon tube with R10 added to 50ml. After centrifugation at 350g for 5 minutes, the supernatant was poured off, the pellet resuspended in 10ml of R10, and cells were counted as described before (section 2.3.3).

Cells were mixed and then 2.5x106 cells were aliquoted into a labelled FACS tube and centrifuged at

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350g for 5 minutes. The supernatant was carefully aspirated with a 1000ul pipette tip and the pellet resuspended in a small volume (50ul) of R10. HIV-1 gp140 or gp120 recombinant proteins were thawed, and 90ul of R10 was added to the 10ul aliquot of 1mg/ml HIV gp140 and mixed. 50ul of this was then added to the FACS tube containing CEM.NKr-CCR5 cells, briefly vortexed and incubated for

1 hour at 37°C.

2.5.5. Screening supernatants against HIV-1 gp140

After incubation, 3ml of R10 was added to the cells and were then centrifuged for 5 minutes at 350g.

This was repeated and then the cells were counted. Cells were resuspended at 6.5x105 cells/ml, and

100ul of cells (65000 cells) were aliquoted into each well of a 96 well tissue culture plate.

1 2 3 4 5 6 7 8 9 10 11 12

A FITC Secondary Ab IgG1 Negative Control Serum Negative Control A32 Positive Control

B HIVIG Positive Control 1 2 3

C 4 5 6 7

D 8 9 10 11

E 12 13 14 15

F 16 17 18 19

G 20 21 22 23

H 24 25 26 27

Figure 2.8: Plate Layout for screening transfection supernatants against HIV-1 gp140. 100ul of CEM.NKr-CCR5 cells were dispensed into each well of the 96 well plate. A1-A3 wells were left blank until secondary antibody step for FITC-IgG control. Positive and negative controls were allocated in triplicate to wells A4-B3, whilst remaining wells were used for screening transfection supernatants in triplicate.

100ul of neat supernatant was added to triplicate wells from B4 onwards, whilst 100ul of positive and negative controls (Table 2.15) were added in triplicate to wells A1-B3 as in the layout in Figure 2.8.

The plate was then incubated at 37°C for 1 hour. After incubation, the plate was washed with PBS and centrifuged at 350g for 5 minutes. The supernatant was flicked off, and the plate briefly vortexed

(gently) to resuspend cells. The PBS wash and centrifugation was repeated, once again with the supernatant flicked off and the plate vortexed.

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2.5.6. Secondary Antibody

IgG-FITC was used as a secondary antibody. A 1:100 dilution was prepared by adding 100ul of IgG-FITC antibody to 9900ul PBS, which was briefly vortexed, with 100ul added to each well. The plate was then incubated at room temperature protected from light for 30 minutes. The plate was then washed with

PBS and centrifuged at 350g for 5 minutes and the supernatant flicked off. After briefly vortexing the plate, 100ul of BD cytofix (BD) was added to each well, and each well transferred to individual 1.1ml microtubes which were stored at 4°C until analysis.

2.5.7. Flow cytometric analysis

Microtubes were placed inside FACS tubes and run on the LSRII. Analysis was performed using FlowJo v10 (FlowJo, BD). Lymphocytes were gated using FSC vs SSC, followed by FSC-A vs FSC-H to exclude doublets. Using positive and negative controls, a gate to detect FITC+ cells was set on SSC vs FITC plots.

Percentage of cells that were FITC+ indicated secondary antibody binding to anti-HIV antibodies which were bound to the gp140 coated on the surface of the CEM.NKr-CCR5 cells, and thus indicated HIV positive supernatants.

2.6. IgG Subclass Vector Design

The IgG1 vector (kindly given as a gift by Juthathip Mongkolsopaya) was modified to produce IgG2,

IgG3 and IgG4 vectors to enable all the different IgG subclasses to be studied. Modification of the vector was achieved through collaboration with the Screaton lab.

2.6.1. Primer Design

Primers to amplify the constant regions of the IgG2, IgG3 and IgG4 genes were designed using IgG2,

IgG3, and IgG4 reference cDNA sequences (J00230, X03604 and K01316 respectively) obtained from the IMGT repertoire resource on the IMGT database (406). The sequences were aligned and as the 3’ and 5’ end sequences were similar, the same forward and reverse primers could be used for each IgG.

Primers were designed with SalI restriction sites added to the forward primer -

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ACGGTCGACCAAGGGCCCATCGGTCTTCCCCCTG, and the HindIII restriction site added to the reverse primer - ATTAAGCTTTCATTTACCCRGAGACAGGGAGAGGCTCTTCTG, and were ordered as before.

2.6.2. mRNA isolation

The Oligotex mRNA mini kit (Qiagen) was used to extract mRNA from 1x106 donor PBMCs, using the

“Isolation of PolyA+ mRNA from animal cells” protocol as instructed.

2.6.3. cDNA synthesis

The superscript III reverse transcriptase kit (ThermoFisher Scientific) was used for cDNA synthesis as per manufacturer’s instructions using the mRNA isolated from the previous step (2.6.2). Thermal cycler was set to 65°C for 5 minutes, 4°C for 3 minutes, 50°C for 60 minutes, 85°C for 5 minutes. 1ul of RNase H was then added, with a final step set for 20 minutes at 37°C.

2.6.4. IgG Constant region PCR

Platinum Pfx DNA polymerase (ThermoFisher Scientific) was used for amplification of IgG constant region DNA. The reaction mixture component in Table 2.16 was used.

Reaction Component Volume Forward Primer 1.5ul Reverse Primer 1.5ul 10x Buffer 10ul dNTPs 4ul Nuclease Free Water 13.5ul MgSO4 1ul 10x enhancer 15ul Pfx 0.5ul Template 3ul

Table 2.16: IgG Constant region PCR components. Platinum Pfx DNA polymerase reaction mixture used to amplify the IgG2, 3 and 4 constant regions of human cDNA.

After preparing the reaction mixture, the template cDNA was added last to the PCR tube and loaded into the thermal cycler. The thermal cycler was set to 5 minutes 94°C, [15 seconds 94°C, 30 seconds

55°C, 1min/Kb 68°C]x35 with storage set to 4°C.

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2.6.5. Cloning IgG2 constant region into IgG-Abvec vector

The PCR product from step 2.6.4 was run on a 1% agarose gel, extracted from the gel and purified as described before (section 2.4.6 and 2.4.8.3), eluting in 45ul of elution buffer. The whole 45ul of eluted

IgG constant region DNA was then digested with 1ul SalI-HF, 1ul HindIII-HF and 5ul cutsmart buffer overnight at 37°C. The IgG-Abvec vector was also digested overnight with the same vectors in a 100ul volume – 1ug vector (6.32ul), 10ul cutsmart buffer, 1ul SalI-HF, 1ul HindIII-HF and 81.68ul nuclease free water. The following day the QIAquick PCR purification kit was used as per manufacturer’s instructions to purify digested IgG constant region and IgG vector. The PCR IgG insert was then ligated into the vector at a 1:3 vector insert ratio; 1.8ul vector, 13ul PCR insert, 1.7ul 10x T4 ligase buffer, 0.5l

T4 ligase and incubated at room temperature overnight. Transformations, picking colonies, overnight cultures, minipreps and sequencing were then performed as previously described (sections 2.4.10-

2.4.14). When sequencing results were retrieved they were aligned with IgG2, IgG3 and IgG4 reference sequences to find matches.

2.6.6. Gibson Reaction

As only IgG2 constant region had been amplified using the steps previously, the Gibson reaction was used to amplify and clone IgG3 and IgG4 constant region DNA. This technique uses PCR to amplify the gene of interest into two fragments with an overlapping section and then ligate these directly into the vector (Figure 2.9). Therefore both the IgG3 and IgG4 each had a forward primer with the SalI restriction site and a reverse primer targeting midway through the sequence which when used together would generate the first fragment, with a forward primer, that was the complement reverse of the reverse primer for fragment one, and a reverse primer with the HindIII restriction site which when used together would generate fragment 2. Primers (Table 2.17) were designed and ordered as before.

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Figure 2.9: Overview of Gibson reaction. The full DNA region of interest cannot be amplified properly, so the Gibson reaction is used to amplify the DNA in two shorter fragments which can then be ligated into the vector. Fragment 1 is amplified by a forward primer with a restriction site matching the vector cloning site, and a reverse primer. Fragment 2 is amplified by a forward primer that is complementary reverse to the fragment 1 reverse primer, in other words the target the same region as the fragment 1 reverse primer, and a reverse primer with a restriction site matching the vector cloning site. After PCR to amplify first and second fragments, a 5’ exonuclease removes the 5’ ends of each fragment, which are then added with DNA polymerase, to extend the 3’ ends, and DNA ligase to ligate the fragments into the digested vector.

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IgG3 Gibson Reaction Primers F_SalI_IgG3 gtgtacactcgagcgtacggtcgaccaagggcccatcggtcttccccctggcgccctg IgG3_F caaggatacccttatgatttc IgG3_R gaaatcataagggtatccttg R_HindIII_IgG3 caagttgggccatggcggccaagctttcatttacccggagacagggagaggctcttctgc IgG4 Gibson Reaction Primers F_SalI_IgG4 gtgtacactcgagcgtacggtcgaccaagggcccatccgtcttccccctggcgccctg IgG4_F tcatgcccagcacctgagttcctggggggacca IgG4_R tggtccccccaggaactcaggtgctgggcatga R_HindIII_IgG4 caagttgggccatggcggccaagctttcatttacccagagacagggagaggctcttctgt

Table 2.17: IgG3 and IgG4 Gibson Reaction Primers. Forward and heavy primers were designed to amplify IgG3 and IgG4 constant region genes in two fragments for insertion in heavy chain vectors using the Gibson reaction. SalI restriction sites were added to forward primers (F_) of first fragment primers whilst HindIII restriction sites were added to reverse primers (R_) of second fragment primers.

Several PCR reactions were carried out on the cDNA previously synthesised using several different kits in order to get correct DNA bands. Pfx DNA polymerase was used as before, as well as Platinum Taq polymerase (ThermoFisher Scientific) and AccuTaq DNA polymerase (ThermoFisher Scientific) using manufacturer’s instructions. PCR products were run on 1% agarose gels as described before with gel extraction and purification as described before.

For the Gibson reaction, a 15ul enzyme master mix was aliquoted from a pre-prepared master mix

(320ul 5x ISO reaction buffer, 0.64ul 10U/ul T5 exonuclease, 20ul 2U/ul Phusion polymerase, 160ul of

40U/ul Taq ligase and water to 1.2ml). Vector was digested as previously described (section 2.21.5).

0.04pmols of both fragments were added to 0.4pmols of vector and made up to 5ul with nuclease free water. For IgG3, 1.71ul of IgG3F was added to 0.81ul of IgG3R, 1.58ul of vector and 0.9ul of nuclease free water. For IgG4, 0.92ul of IgG4F was added to 0.34ul of IgG4R, 1.58ul of vector and 2.16ul of water. The 5ul of fragments and vector was added to the 15ul enzyme master mix and incubated in a thermal cycler for 1 hour at 50°C. Following this 5ul of Gibson reaction product was added to 100ul of

Dh5α competent cells and transformation steps through to sequencing results were carried out as previously (sections 2.4.10-2.4.14), with sequencing results aligned with IgG3 and IgG4 reference sequences, using clustal omega and transeq emboss as before, to find matches.

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2.6.7. Vector Mutagenesis

The IgG2, IgG3 and IgG4 vectors all had an AgeI restriction site within the IgG2, IgG3 and IgG4 constant regions. As the vector is digested with this enzyme for cloning purposes, this site had to be changed using mutagenesis. Forward and reverse primers (forward - gactacttccccgagcctgtgacggtgtcgtg and reverse – cacgacaccgtcacaggctcggggaagtagtc) were designed and ordered as before. Reaction volume for PCR was set up in 25ul total volume for each IgG using the Pfx kit and manufacturer’s instructions;

5ul 10x buffer, 2ul dNTP, 0.5ul forward and reverse primers, 0.5ul MgSO4, 7.5ul 10X enhancer, 0.25ul

Pfx, 8.25ul nuclease free water and 0.5ul vector. The thermal cycler was set to the following cycles; 5 minutes 94°C, [30 sec, 94°C, 30 sec 58°C, 6 min 68°C]x16, 68°C 10 minutes. After PCR, as described previously gels were run, DNA was purified, followed by transformation steps through to sequencing.

2.7. Generating IgG2, IgG3 and IgG4 monoclonal antibodies

Using the newly generated IgG2, IgG3 and IgG4 heavy chain expression vectors, monoclonal antibodies derived from the sorted patient plasmablasts and synthesised BNAbs were generated.

2.7.1. Broadly Neutralising Antibody DNA

Several broadly neutralising antibodies were selected to be generated in the IgG1-IgG4 vectors. Heavy and light chain sequences for VRC01, 3BNC117, PGT121 and 10E8 BNAbs were retrieved from the Los

Alamos HIV sequence database antibody information tables (CATNAP), and were modified to include restriction sites at the 5’ and 3’ ends for cloning into the IgG expression vectors (Table 2.18).

Sequences were sent to ThermoFisher and were synthesised as fragments using their Gene Art Strings

DNA fragment service. Upon receipt of fragments, they were resuspended at 20ng/ul and left to reconstitute at room temperature for 1 hour. Following this, 200ng of each fragment was digested with their corresponding restriction enzymes; AgeI-HF and SalI for heavy chain, AgeI-HF and XhoI for lambda chain, and AgeI and BsiWi for kappa chain fragments as per table 2.19. Digestion was carried out at 37°C for 1 hour, then digests were purified using the Qiagen mini elute reaction clean up kit.

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VRC01 Heavy: tcgagtctgaccggtcaggtgcagctggtgcagtctgggggtcagatgaagaagcctggcgagtcgatgagaatttcttgtcgggcttctggatatgaat ttattgattgtacgctaaattggattcgtctggcccccggaaaaaggcctgagtggatgggatggctgaagcctcgggggggggccgtcaactacgcac gtccacttcagggcagagtgaccatgactcgagacgtttattccgacacagcctttttggagctgcgctcgttgacagtagacgacacggccgtctacttt tgtactaggggaaaaaactgtgattacaattgggacttcgaacactggggccggggcaccccggtcatcgtctcatcagcgtcgacgtcaggtc Kappa: tcgagtctgaccggtgaaattgtgttgacacagtctccaggcaccctgtctttgtctccaggggaaacagccatcatctcttgtcggaccagtcagtatgg ttccttagcctggtatcaacagaggcccggccaggcccccaggctcgtcatctattcgggctctactcgggccgctggcatcccagacaggttcagcggc agtcggtgggggccagactacaatctcaccatcagcaacctggagtcgggagattttggtgtttattattgccagcagtatgaattttttggccagggga ccaaggtccaggtcgacattaagcgacgtacggtcaggtc 3BNC117 Heavy: tcgagtctgaccggtcaggtccaattgttacagtctggggcagcggtgacgaagcccggggcctcagtgagagtctcctgcgaggcttctggatacaac attcgtgactactttattcattggtggcgacaggccccaggacagggccttcagtgggtgggatggatcaatcctaagacaggtcagccaaacaatcct cgtcaatttcagggtagagtcagtctgactcgacacgcgtcgtgggactttgacacattttccttttacatggacctgaaggcactaagatcggacgaca cggccgtttatttctgtgcgcgacagcgcagcgactattgggatttcgacgtctggggcagtggaacccaggtcactgtctcgtcagcgtcgaccaaggg ccca Kappa: tcgagtctgaccggtgacatccagatgacccagtctccatcctccctgtctgcctctgtgggagataccgtcactatcacttgccaggcaaacggctactt aaattggtatcaacagaggcgagggaaagccccaaaactcctgatctacgatgggtccaaattggaaagaggggtcccatcaaggttcagtggaaga agatgggggcaagaatataatctgaccatcaacaatctgcagcccgaagacattgcaacatatttttgtcaagtgtatgagtttgtcgtccctgggacca gactggatttgaaacgtacggtggctgcacca PGT121 Heavy: tcgagtctgaccggtcagatgcagttacaggagtcgggccccggactggtgaagccttcggaaaccctgtccctcacgtgcagtgtgtctggtgcctcca taagtgacagttactggagctggatccggcggtccccagggaagggacttgagtggattgggtatgtccacaaaagcggcgacacaaattacagcccc tccctcaagagtcgagtcaacttgtcgttagacacgtccaaaaatcaggtgtccctgagccttgtggccgcgaccgctgcggactcgggcaaatattatt gcgcgagaacactgcacgggaggagaatttatggaatcgttgccttcaatgagtggttcacctacttctacatggacgtctggggcaatgggactcagg tcaccgtctcctcagcgtcgacgtcaggtc Lambda: tcgagtctgaccggttccgatatatctgtggccccaggagagacggccaggatttcctgtggggaaaagagccttggaagtagagctgtacaatggtat caacacagggccggccaggccccctctttaatcatatataataatcaggaccggcccagcgggatccctgagcgattctctggctcccctgactcccctt ttgggaccacggccaccctgaccatcaccagtgtcgaagccggggatgaggccgactattactgtcatatatgggatagtagagttcccaccaaatggg tcttcggcggagggaccacgctgaccgtgttacgtcagcccaaggctgccccctcggtcactctgttcccgccctcgaggtcaggtc 10E8 Heavy: tcgagtctgaccggtgaggtgcagctggtggagtctgggggaggcttggtgaagcctggaggatcccttagactctcatgttcagcctctggtttcgactt cgataacgcctggatgacttgggtccgccagcctccagggaagggcctcgaatgggttggtcgtattacgggtccaggtgaaggttggtcagtggacta tgctgcacccgtggaaggcagatttaccatctcgagactcaattcaataaatttcttatatttggagatgaacaatttaagaatggaagactcaggccttt acttctgtgcccgcacgggaaaatattatgatttttggagtggctatccgccgggagaagaatacttccaagactggggccggggcaccctggtcaccg tctcctcagcgtcgacgtcaggtc Lambda: tcgagtctgaccggttcctatgagctgactcaggagactggtgtctctgtggccctgggacggacagtcacaatcacgtgccggggagacagcctcaga agtcattatgcaagttggtaccaaaagaagccaggacaggcccctatacttctcttctatggtaaaaataatcgtccttcaggggtcccagaccgattct ctggctccgcctcaggaaacagagcttccttgaccatctctggggctcaggcggaagacgacgcggaatattattgtagttctcgggacaagagtggca gccgtctgtcggtcttcggcggggggaccaaactgaccgtcctcagtcagcccaaggctgccccctcggtcactctgttcccgccctcgaggtcaggtc

Table 2.18: DNA sequences of VRC01, 3BNC117, PGT121 and 10E8 BNAb used for fragment synthesis. Heavy and light chain sequences were retrieved from the Los Alamos HIV sequence database antibody information tables (CATNAP). AgeI restriction sites were added to the 5’ ends of all heavy and light chain sequences, whilst SalI, BsiWi and XhoI restriction sites were added to the 3’ ends of heavy, kappa and lambda chain sequences respectively.

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Heavy Lambda Kappa DNA 10ul (200ng) 10ul (200ng) 10ul (200ng) AgeI-HF 0.2ul 0.2ul 0.2ul SalI 2ul - - XhoI - 1ul - BsiWi-HF - - 0.2ul Cutsmart buffer 2.5ul 2.5ul 2.5ul Nuclease free water 10.3ul 11.3ul 12.1ul

Table 2.19: Restriction digest of heavy, lambda and kappa DNA fragments. Gene Art strings DNA fragments for BNAbs were digested with restriction enzymes before ligation into corresponding vectors.

2.7.2. Extraction of patient mAb variable regions

The IgG1 heavy chain vectors (V1X1B5, V1X2B12, B1X2C5, V1X3A2 and V2X1F6) that were used to generate the gp140 specific monoclonal antibodies were digested to extract the variable region for cloning into the IgG2, IgG3 and IgG vectors. 1ug of heavy chain vector DNA was digested in a total volume of 50ul. 1ul of AgeI-HF and 5ul of SalI-HF was added to a 1.5ml microcentrifuge tube with 5ul cutsmart buffer. Using the concentration of the heavy chain miniprep calculated by nanodrop, 1ug of miniprep vector was then added to the tube, and was topped up to 50ul with nuclease free water. The tubes were then pulsed on the centrifuge to collect all volume in the bottom of the tube and incubated for 2 hours at 37°C. After the 2 hours the entire digestion volume was loaded onto a 1% agarose gel

(prepared and loaded as described before) and run for 1 hour at 120V. The band around 500bp

(variable region) in size was extracted from the gel (as described before) and purified by gel extraction kit (as described before).

2.7.3. IgG2, IgG3 and IgG4 Vector Digestion

The IgG2, IgG3 and IgG4 vectors were digested with the same enzymes in the same reaction volumes as with the IgG1 vector (Table 2.11). IgG1 vectors, and kappa and lambda vectors were also digested as before so that BNAbs in IgG1 could be produced.

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2.7.4. Molecular cloning of variable regions into IgG vectors

The heavy chain variable regions of the five patient derived monoclonal antibodies (V1X1B5, V1X2B12,

B1X2C5, V1X3A2 and V2X1F6) were ligated into the IgG2, 3 and 4 vectors using T4 ligase as described before (section 2.4.9). The BNAb DNA heavy chain fragments were ligated into the IgG1, IgG2, IgG3 and IgG4 vectors, and the light chain DNA was ligated into corresponding kappa or lambda chain vectors. Cloning steps from Dh5 transformation to minipreps and sequencing were followed as before (section 2.4.10-2.4.14). After sequencing, IgG1-4 vectors were aligned using Clustal Omega for each antibody or BNAb to ensure all variable regions were 100% matches.

2.7.5. BNAb Vector Mutagenesis

All heavy chain vectors, and 10E8 and PGT121 lambda chain vectors required mutagenesis. The VRC01 heavy chain vector required a 2 base pair insertion before the SalI restriction site, whilst the 3BNC117 heavy chain vector needed a single base pair substitution around the SalI restriction site. All heavy chain vectors also required a 9 nucleotide/3 amino acid insertion (L-part 2 sequence) at the start of the variable regions, as did the PGT121 and 10E8 light chains.

Mutagenesis Forward Primer Reverse Primer VRC01H 1bp Insertion gcgtcgaccaagggcccatc tgatgagacgatgaccggg VRC01H VHS Insertion ttcccaggtgcagctggtgcag tgtacaccggttgcagttgctac 3BNC117H 1bp Substitution cgtcagcgtcgaccaagggcc agacagtgacctgggttcc 3BNC117 VHS Insertion ttcccaggtccaattgttacagtctg tgtacaccggttgcagttgctac PGT121 VHS Insertion ttcccagatgcagttacaggagtc tgtacaccggttgcagttgctac 10E8 VHS Insertion ttccgaggtgcagctggtggag tgtacaccggttgcagttgctac 10E8L SVV Insertion ggtttcctatgagctgactcagg acagaaccggttgcagttgctac PGT121L SVT Insertion gacctccgatatatctgtggcc acagaaccggttgcagttgctac

Table 2.20: BNAb vector mutagenesis primers. Forward and reverse primers designed to introduce insertions or substitutions into the variable regions of VRC01, 3BNC117, PGT121 and 10E8 heavy chain vectors, and 10E8 and PGT121 lambda chain vectors.

Primers to insert these mutations were designed (Table 2.20) using the NEBase Changer tool v1.2.7

(NEB) and were ordered as before. The NEB Q5 site directed mutagenesis kit was used for mutagenesis with reaction mixture prepared as per manufacturer’s instructions; 12.5ul Q5 Hot start 2x master mix,

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1.25ul vector specific 10uM forward and reverse primers, 1ul template DNA and 9ul nuclease free water. The reaction was then placed in the thermal cycler with the following protocol; 30 seconds

98°C, [10 seconds 98°C, 30 seconds 67°C, 2 minutes 72°C]x25, 2 minutes 72°C. After PCR, 1ul of PCR product underwent KLD treatment; 1ul PCR product, 5ul 2x KLD reaction buffer, 1ul KLD enzyme mix and 3ul nuclease free water. This was incubated at room temperature for 5 minutes, followed by Dh5α transformations with the ultra-competent cells provided in the kit and subsequent steps to sequencing competed as described before (section 2.4.10-2.4.14).

2.7.6. IgG1-4 mAb production

Using minipreps of heavy and light chain vectors for all five patient-derived mAbs, and all four BNAbs monoclonal antibodies, with alignments showing exact matches for variable regions for all subclasses,

IgG1-IgG4 monoclonal antibodies were produced. Transfection protocols previously described as before (section 2.4.15) were followed by seeding 15cm tissue culture plates for antibody production.

Protocols for ELISA were followed as in section 2.4.16 to confirm mAb, and screening against HIV1- gp140 as in section 2.5 before proceeding with IgG purification, quantification and further testing.

2.8. Monoclonal antibody purification and quantification

2.8.1. IgG Purification

15ml of supernatant was collected for each transfection. Before purification it was necessary to concentrate the supernatant as the purification system to be used had a capacity for 600ul. Amicon

Ultra-15 Centrifugal Filter Units were labelled, and supernatants were applied to each tube, which were then centrifuged for 20 minutes at maximum speed. The small volume of supernatant remaining

(≤1ml) containing the concentrated antibodies was then applied to equilibrated protein G HP spin trap columns. The kit instructions were followed for purification procedure, using buffers from the Ab buffer kit (GE Healthcare), resulting in purified monoclonal antibodies.

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2.8.2. IgG Quantification

IgG ELISAs were carried out as before (section 2.4.16). Whilst the same plate layout was used as before

(Figure 2.6) with regards to the standards, each antibody had a series of dilutions with each dilution in triplicate so that the readings within the standard range of the IgG standards could be used to calculate the correct concentrations. After reading the absorbance at 450nm, blank well measurements were subtracted from all other wells, and a standard curve was plotted using the IgG standard range from 1.56ng/ml to 100ng/ml in Microsoft excel. The equation of the line was reversed/inversed, and then used to calculate the concentration of each antibody. Dilution factors were taken into account, and only absorbances which fell within the standard range could be used for calculating IgG.

2.8.3. Protein Gels

10% Mini-PROTEAN TGX Stain-Free Protein Gels were used to check antibody purity and quality. The gels were unpackaged with the plastic comb removed from the top of the gel, and the green tape removed from bottom of the gel. 1L of running buffer was prepared by making a 1 in 10 dilution of

10x TGS running buffer using deionised water. A 50ml syringe was filled with running buffer and used to rinse the wells of the gel before securely placed inside the running module and inserting into the

Mini-PROTEAN Tetra cell. The running buffer was then poured into the inner and outer chambers. For sample preparation, 0.25ul of β-mercaptoethanol was added to 4.75ul of 2x laemmli sample buffer in a labelled PCR tube for each antibody to be tested. 5ul of purified antibody was then added to corresponding labelled tubes, with the tube placed in the thermal cycler for 5 minutes and heated to

95°C. The antibody samples were then mixed and loaded into each well of the gel, leaving the 1st and

6th wells blank for the precision plus protein 10-250kDa unstained standard (Biorad) which was then also added. The gel was then run for 35 minutes at 200v. After running, the gels were removed from the tank and running module, and the plastic slides either side of the gel removed. The gel was then placed inside the Geldoc+, and the program for stain free protein gels was selected and run. Resulting gels were saved and analysed.

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2.9. RFADCC Assay

The RFADCC first described by Gomez-Roman et al in 2006 (407) was optimised in the lab to test generated antibodies for ADCC activity. The CEM.NKr-CCR5 cell line was cultured as described previously (section 2.5.1), double stained with PKH-26 (Sigma Aldrich) and CFSE (Invitrogen), coated with HIV-1 gp140 and incubated with effector cells and purified antibodies. A brief overview of the

RFADCC is illustrated in Figure 2.10.

2.9.1. PKH-26 Staining

CEM.NKr-CCR5 cells were transferred from the T-75 flask to a labelled 50ml falcon tube using a 25ml stripette and filled to 50ml with R10. The tube was centrifuged for 5 minutes at 350g with the supernatant then poured off and the cells resuspended in 10ml R10. The cells were then counted as before (section 2.3.3). 10x106 cells were transferred to a new sterile 15ml tube and topped to 15ml with R10, with centrifugation as before. The supernatant was aspirated leaving just the cell pellet, which was then resuspended by gentle pipetting in 0.5ml of 2x diluent C of the PKH-26 dye kit. In a fresh FACS tube, the cell dye was prepared by adding 2ul of PKH-26 ethanolic dye to 0.5ml of 2x diluent

C and was mixed well. This cell dye solution was then added to the CEM.NKr-CCR5 cells and immediately mixed through pipetting. The cells were incubated at room temperature for 5 minutes with periodic mixing, with the staining stopped by adding 2ml of neat serum and incubated for 1 minute. The cells were then centrifuged at 400g for 10 minutes, and the supernatant poured off. The cells were resuspended in 10ml of complete R10 medium and transferred to a new sterile tube and centrifuged at 400g for 5 minutes. The wash was repeated twice more, with the cells then resuspended in 10ml and counted.

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Figure 2.10: Principle of RFADCC Assay. CEM.NKr-CCR5 (target) cells are double stained with PKH-26 cell membrane dye and CFSE intracellular dye, and then coated with HIV-1 gp140. Target cells are then incubated with PBMCs (effector) cells and purified monoclonal antibodies for four hours, then analysed on the flow cytometer. If ADCC has occurred cells will retain PKH-26 cell membrane dye but would have lost the intracellular CFSE and the percentage of PKH- 26+CFSE- indicated percentage of ADCC. Based on RFADCC assay by Gomez-Roman et al (407).

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2.9.2. CFSE Staining

A 5mM stock solution of CFSE was prepared by adding 18ul of DMSO to the CFSE tube (Invitrogen) and making 1ul aliquots and freezing at -20°C until use. For staining, the counted cells from the previous

PKH-26 staining (2.9.1) were resuspended at 1x106 cells/ml in PBS, with 1x106 cells transferred to a new FACS tube (multiple tubes were used to ensure enough cells for experiment). A 1ul aliquot of

CFSE was thawed and diluted with 4ul of PBS (to give a 1:5 dilution) and mixed well. 1ul of the CFSE was then added to 1x106 CEM.NKr-CCR5 cells and incubated at room temperature for 20 minutes protected from light. After staining, 3ml of cold R10 was added to the cells and incubated for 5 minutes to stop staining. Cells were centrifuged at 400g for 5 minutes, and supernatant poured off, with the wash repeated another two times. The cells were then counted as before for coating with HIV-1 gp140.

2.9.3. HIV-1 gp140 coating

Cells were coated under the same conditions as described before (section 2.5.4), followed by washing twice with R10 media and counting.

2.9.4. Effector Cells

PBMCs from healthy individuals were used as effector cells for the RFADCC assay. Whole blood was collected from donors with PBMCs isolated and frozen as described before (sections 2.3). The day before the assay, the PBMCs were retrieved from liquid nitrogen and thawed (as in section 2.4.2). The cells were counted and resuspended at 2x106 cells/ml in a 15ml centrifuge tube, and incubated overnight at 37°C with 5% C02.

2.9.5. RFADCC Assay

Coated CEM.NKr-CCR5 cells (target cells) were resuspended at 2x105 cells/ml, with 50ul of cells (10000 cells) dispensed into each well of a 96 well round bottom plate. Effector cells thawed the day previously were counted and resuspended at 2x106 cells/ml, with 50ul of effector cells (100,000

PBMCs) added to each well. Positive and negative controls were prepared as previously (Table 2.15) and distributed in triplicate to wells A1-A12 as per Figure 2.11. The remaining wells were used for

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samples which were plated in triplicate. The plate was then centrifuged for 3 minutes at 400g to cluster cells, and incubated for four hours at 37°C. The plate was then washed with PBS and centrifuged at 400g for 5 minutes twice, with supernatant flicked off and 100ul of BD cytofix was added. Cells were then transferred to microtubes and stored at 4°C.

1 2 3 4 5 6 7 8 9 10 11 12

A IgG1 Negative Control Serum Negative Control A32 Positive Control HIVIG Positive Control

B V1X2B12 IgG1 V1X2B12 IgG2 V1X2B12 IgG3 V1X2B12 IgG4

C V1X2C5 IgG1 V1X2C5 IgG2 V1X2C5 IgG3 V1X2C5 IgG4

D 3BNC117 IgG1 3BNC117 IgG2 3BNC117 IgG3 3BNC117 IgG4

E VRC01 IgG1 VRC01 IgG2 VRC01 IgG3 VRC01 IgG4

Figure 2.11: RFADCC assay 96 well plate layout. 96 well plates were used to run all samples for RFADCC testing on the same plate. Row A was used for positive and negative controls, which were all run in triplicate. Each mAb/BNAb to be tested in each subclass were added in triplicate, with each IgG subclass of a particular antibody all on the same row.

2.9.6. RFADCC Flow cytometry analysis

Microtubes were placed inside FACS tubes and run on the LSRII. Analysis was performed using FlowJo v10. A wide gate was set to include PBMCs and CEM.NKr-CCR5 cells on FSC vs SSC, followed by FSC-A vs FSC-H to exclude doublets. Using positive and negative controls, a gate to include CFSE+PKH26-,

CFSE-PKH26+ and CFSE+PKH-26+ cells and exclude the PBMCs (double negative population) was set on the CFSE vs PKH-26 plots. A quadrant gate was then set to separate single positive and double positive populations, with the percentage of PKH-26+CFSE- cells as an indicator of ADCC activity.

2.10. Analysis

Several key software were used for analysing flow cytometry files, designing graphs, and performing statistical analysis and phylogenetic analysis.

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2.10.1. FACS DIVA and FlowJo

For flow cytometry, the LSRII (BD Bioscience) was used to run all samples for analysis. Samples were briefly vortexed and loaded on to the cytometer arm, with samples run on “high”. FACS Diva software was used to collect events, and FCS files were saved and exported for analysis in FlowJo V10. For sorting plasmablasts for monoclonal antibody production, the FACS Aria III (BD Biosciences) located in a biosafety hood within a containment level 3 laboratory was used. FACS Diva was once again used to collect and sort events, with the index sort function used for later analysis, and FCS files were saved for analysis in FlowJo.

2.10.2. Sequence Analysis

The international immunogenetics information system (IMGT) was used for analysing antibody heavy and light chain sequences. Heavy and light chain sequences were input to the V-Quest function on the

IMGT website (408), which returned database results on a variety of information including closest germ line matches, gene family usage and mutations.

The Clustal Omega online multiple sequence alignment tool by the European Bioinformatics Institute

(EMBL-EBI) (409-411) was used to align nucleotide and protein sequences for analysis. Sequences were input to the online tool and submitted, with results showing alignments and conserved nucleotides or amino acids between sequences.

For nucleotide sequences that needed to be converted to protein sequences before analysis, the

EMBOSS Transeq online tool also by EMBL-EBI was used (410-412). Here the sequence was input to the online tool, with the frame selection changed to 6 (all six frames). After results the correct frame was chosen to give the correct protein sequence for analysis.

2.10.3. Prism

All graphs and statistical analysis were generated using GraphPad Prism 7.

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2.10.4. Phylogenetic Analysis

For phylogenetic analysis, all heavy chain sequences were input into MEGA 7, and DNA sequences were aligned. The resulting alignment was then used to compute a Phylogeny Reconstruction analysis, or phylogenetic tree using the Maximum Likelihood statistical method and Phylogeny test with using the Bootstrap method (500 bootstrap replications).

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Chapter 3: Examining the HIV-specific and non-specific antibodies generated from an acute/early infected HIV patient.

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3. Examining the HIV-specific and non-specific antibodies generated from an acute/early infected HIV patient. 3.1. Background

3.1.1. B cell responses during HIV infection

During acute HIV-1 infection, the first B cell responses to HIV-1 appear initially as antigen-antibody complexes around 8 days after plasma viremia is first detected. Non-neutralising anti-gp41 antibodies appear around 5 days following this, with anti-gp120 antibodies 14 days later (277). It is then several weeks to months later when autologous neutralising antibodies appear, and can be years before broadly neutralising antibodies are generated in only a small percentage of infected individuals (413).

It has been well documented that HIV infection impacts B cells causing B cell dysregulation and non- specific immune activation, leading to an ineffective antibody response. For example, hypergammaglobulinaemia, an increase in the generation of autoantibodies, and an increase in B cell to plasmablast differentiation have all been characterised during HIV infection (136). A study by Moir et al in 2010 looked at the differences in B cell responses in early, as well as chronic, HIV infection compared to uninfected controls. The results showed that the average CD19+ B cell counts of the early infected patients was around half that of the HIV-negative group at baseline, and that the percentage of the plasmablast population of total B cells was significantly greater in the early infected patients at baseline compared to the same patients 12 months later after being on ART as well as the HIV negative controls (414).

Two key methods are currently used to measure the percentage of B cells or antibodies specific for

HIV in patient samples; ELISPOTs can measure the percentage of antibody secreting cells (ASCs) that generate HIV-specific antibodies, whilst flow cytometry is used to measure the percentage of B cells that are specific for and bind to fluorescently labelled gp120/gp140. Using these methods, it has been shown that around 1.3% of ASCs are gp140-specific in early infected HIV viremic patients, and the percentage of HIV-specific B cells ranged between 0.5-3.4% of total B cells (177). On the other hand,

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the percentages of anti-HIV antibodies from ASCs, and HIV-specific B cells in chronic HIV infection patients are reduced when compared to early infected patients. Using ELISPOTS it has been shown that just 0.00025% of PBMC from chronic patients are ASCs that secrete gp120-specific IgG, and when further classified into specific cell populations, only 0.0031% of total B cells and 0.047% of plasmablasts secrete these HIV-specific antibodies (286). The same study also found that 0.09% of B cells from chronic patients were gp140-specific, whereas another study on an HIV-1 elite controller found that 2% of B cells bound to gp140 (283).

3.1.2. HIV antibodies

As previously mentioned, the first antibodies that develop during HIV infection are non-neutralising.

These antibodies whilst not able to neutralise virus may be able to mediate other functions such as antibody dependent cellular cytotoxicity (ADCC). It has been demonstrated previously that ADCC mediating antibodies from HIV vaccinated individuals preferentially used the VH1 gene family (415).

Neutralising and broadly neutralising antibodies (BNAbs) have also been shown to preferentially use this gene family (310, 416). Furthermore, several studies have compared the antibody repertoires of

HIV-1 infected patients to healthy individuals and other chronic or acute infections, and have found that during HIV-1 infection the VH1 gene family usage increases whilst VH3 usage decreases (417,

418). Further work by Li et al showed that even within an infected individual the HIV-1 antibodies predominantly used VH1 family genes, whilst the non-HIV-1 antibodies in the same individual preferentially used VH3 (419).

HIV antibodies also generally have longer CDRH3 regions and somatic mutations than non-HIV antibodies. CDRH3 lengths of human antibodies generally range from 2-31 amino acids long, with an average of around 12 amino acids in length (420, 421), whilst HIV antibodies generally have CDRH3s greater than this, and neutralising antibodies and BNAB having CDRH3s usually greater than 20 amino acids, even up to 33 amino acids in V1/V2 specific antibodies (310, 422).

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Due to viral escape, the antibody response is always evolving to try and catch up with the virus, and does this through somatic hypermutation within the variable regions. HIV Neutralising antibodies that are generated through these processes can have around twice the number of variable heavy chain mutations than the 10-20 mutations seen in standard human antibodies, whilst bNAbs which take further to develop and are more rare than autologous neutralising antibodies have been shown to have more than 80 mutations in this region (307, 423).

3.1.3. Single B cell cloning for antibody production

There are three key methods that are currently used to isolate monoclonal antibodies; phage display,

B cell immortalisation, and single cell expression cloning, (376). The technology used for phage display was first described in 1985 (375), and has evolved and improved over time for creating large antibody phage display libraries which are routinely used for the selection of antigen specific antibodies such as those against HIV. Despite the huge diversity of antibodies that can be generated, it cannot be certain that antigen-specific antibodies isolated through this method would have actually been present in the antibody repertoire of the infected individual, as the pairing of the heavy and light chain

DNA is random. Therefore whilst this technology is incredibly useful in platforms for antibody discovery for treatment, it is less accurate for studying the antibody response generated within individuals to an antigen.

The B cell immortalisation method to generate monoclonal antibodies solves the problem of guaranteeing naturally occurring matching heavy and light chain pairs in antibodies that actually went through selection, as memory B cells isolated from patients are used to generate “immortal” cell lines from which antigen-specific antibodies can be screened and cloned. Despite these advantages and the technology being able to isolate and clone rare B cells, there are two key disadvantages. First, a high number of B cells need to be screened in order to find a relatively few number of antigen-specific monoclonal antibodies, and second, this technique works best on memory B cells. Therefore if the

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ongoing immune response, or the response in a patient at particular time points are being studied, the antibody generated may be from the memory B cell repertoire and not have arisen more recently.

More recently, single cell expression cloning has been used extensively for the isolation and cloning of antigen-specific antibodies. There are generally two B cell populations sorted for antibody isolation via this technique; memory B cells or plasmablasts. With memory B cells, antigen baiting can be used to selectively target B cells with a B cell receptor binding to fluorescently labelling antigens, such as the gp120 protein, meaning that the majority of cells sorted should give rise to antigen specific antibodies. As plasmablasts express very low levels/no B cell receptor on the surface however, antigen baiting cannot be used for screening these cells, and therefore the percentage of cells that will give rise to antigen-specific antibodies is generally unknown at the point of sorting. On the other hand, plasmablasts generally arise shortly after infection and provide a good indicator of the antibody responses generated at a particular time point, and are especially useful for vaccine trials.

Within the context of HIV research, single cell expression cloning is now routinely used to isolate HIV- specific antibodies for the purposes of finding broadly neutralising antibodies that may potentially be used in treatment, or testing the efficacy and what types of antibodies trial vaccinations yield. The vast majority of the studies to find broadly neutralising antibodies use memory B cells with antigen baiting to specifically sort HIV-specific cells, whilst vaccine trials sort plasmablasts 7 days post vaccination when the percentage of antigen-specific plasmablasts is at its peak. However, in order to examine the overall antibody response generated longitudinally in an HIV-infected patient, plasmablasts provide a way to look at the circulating antibodies present at a specific time point, and therefore would allow longitudinal analysis.

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3.1.4. Aims

There were three key aims for this part of the project:

1. Establish plasmablast sorting and single B cell cloning technology in the lab.

2. Generate HIV-specific monoclonal antibodies from the plasmablasts of an acute/early

infected HIV patient sorted at four time points using frozen PBMC samples and no antigen

baiting.

3. Study both the HIV-specific and non-specific monoclonal antibody heavy and light chain gene

family usage, nucleotide and amino acid mutations, CDR lengths and phylogenetics.

3.2. Monoclonal antibody production preliminary work

In order to generate monoclonal antibodies from PBMCs, the first step in the process is to stain the cells for plasmablasts, identify them using flow cytometry, and subsequently sort them using a cell sorter. Preliminary work was carried out to titrate the antibodies used for identifying plasmablasts, decide whether fresh or frozen samples should be used for sorting, and to ensure the molecular cloning techniques used for monoclonal antibody production were working efficiently in the lab.

3.2.1. Flow cytometry antibody titrations

Antibodies for CD19, CD27, CD38, CD3, CD16 and CD20 were selected on the appropriate fluorochrome and used in the design of a B cell staining panel to identify plasmablasts for FACS sorting.

Using the MFI and standard deviation of the negative control and positive and negative populations for each test volume, the stain index for each antibody test volume was calculated (Figure 3.1). The volume that gave the best stain index whilst not being excessive was used for staining 1x106 cells;

1.25ul of CD19 BV421, 10ul of CD27 PE, 5ul of CD38 APC, 10ul of CD3 FITC, 10ul of CD16 FITC and 20ul of CD20 FITC.

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Figure 3.1: Flow cytometry B cell panel antibody titrations. The antibodies used for staining PBMCs for isolation of plasmablasts were tested using 2 fold titrations to determine the best staining volumes. Flow cytometry plots (top panels) showing separation of negative and positive populations were used to calculate MFI and SD, which were then used to calculate the stain indices (bottom panel) of each antibody. The antibodies that were tested; (a) CD19 BV421, (b) CD27 PE, (c) CD38 APC, (d) CD3 FITC, (e) CD16 FITC and (f) CD20 FITC. Samples were run on the LSRII and analysed in flowjo v10.

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3.2.2. Fresh vs Frozen PBMCs

Due to the processing and sorting of infectious samples, the FACS Aria III used for sorting in this project was located within a biological safety cabinet within the containment level 3 laboratory and operated by a facility manager. As longitudinal samples of acute/early infected HIV patients would be sorted, as well as constrictions with containment level 3 sorting work, it was determined that frozen samples may be better suited for sorting to allow visit 1-4 PBMCs to be stained and sorted at the same time.

Figure 3.2: Identifying the plasmablast population using flow cytometry. (a) The gating strategy used to identify plasmablasts from PBMC sample. Lymphocytes were gated first, followed by exclusion of doublets, then gating of CD19+ but CD3-CD16- (DUMP channel) B cells. The CD27hiCD38hi population was then identified as plasmablasts. (b) FACS plots of plasmablast populations from fresh, fresh overnight rested (ONR), and frozen PBMCs taken day 7 post flu vaccination. (c) Comparison of matched fresh and frozen plasmablast populations from five healthy donors. All samples were run on LSRII and analysed in flowjo v10.

Before work on the HIV samples could begin, healthy and flu vaccinated donor PBMCs were used to test whether there was any difference in the plasmablast population between using fresh and frozen samples. Three different conditions were tested; freshly isolated PBMCs from day 7 post flu vaccination immediately stained, the same fresh day 7 PBMCs rested overnight and stained, and day

7 PBMCs that had been frozen, thawed and then stained. Using the antibodies previously titrated

(except for CD20 which was not used until staining HIV samples), plasmablasts were identified as

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CD27hiCD38hi cells within the CD19+ B cell population (Figure 3.2a). Whilst the plasmablast population completely disappeared in the day 7 fresh overnight rested sample, the frozen PBMCs showed no difference compared to the fresh sample (Figure 3.2b and 3.2c). Therefore, it was concluded that as the plasmablast population was preserved in the frozen sample, these samples would be suitable for sorting.

3.2.3. Preliminary mAb production

Before work could commence on the generation of monoclonal antibodies from an acute/early HIV infected patient (of which frozen samples were limited in number), several test sorts and the subsequent molecular cloning work was carried out on healthy, flu vaccinated, and chronic HIV donors to ensure the protocol was working correctly in the lab. Briefly, after sorting single plasmablasts into individual wells of a 96 well PCR plate, samples were flash frozen and stored at -80°C until use. Each catch plate then underwent several rounds of PCR to amplify variable region heavy and light chain

DNA, which was then purified, digested and ligated into heavy and light chain expression vectors containing the immunoglobulin constant regions. These vectors were then transformed into chemically competent cells and grown on agar plates overnight, with single colonies subsequently selected for overnight culture and miniprep. Matching heavy and light chain minipreps were then used to transfect a mammalian cell line for antibody production.

After implementing the protocol in the lab, total IgG ELISAs were used to test the cell culture supernatants of the transfected cells for the presence of IgG to confirm that the all the cloning procedures had worked. Figure 3.3 demonstrates that the single cell sorting and molecular cloning techniques implemented in the lab resulted in varying concentrations of monoclonal antibodies at the end of the protocol. Therefore, as these results confirmed that monoclonal antibodies could be generated in the lab from sorting single plasmablasts from frozen samples, the acute/early HIV infected samples could now be used for antibody production.

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Figure 3.3: Concentration of trial run monoclonal antibodies. Plasmablasts from several different HIV donors were used to generate monoclonal antibodies, with the supernatant of the transfected mammalian cell lines tested for IgG antibodies. Samples from patients C2 (chronic HIV patient not on ART), D1 and D8 (chronic HIV patients on ART) and E12 (HIV infected within 1 year of infection) were sorted with resulting antibodies confirmed by IgG ELISA.

3.3. Isolation of five HIV-specific monoclonal antibodies

An acute/early infected HIV patient classified as patient B004 was selected from the ARIES cohort samples based on the following criteria; the patient having longitudinal samples covering visits 1-4, an initial high viral load at visit 1, patient is infected with a clade B virus, and a good plasmablast response at most time points (tested by pre-screening). The aim was to generate monoclonal antibodies from this patient at all time points, and from those generated, identify HIV specific antibodies. The patient whose plasmablasts were sorted and from which monoclonal antibodies were generated was a white

British male, and on enrolment to the ARIES cohort study whilst not HIV-1 positive (Visit 0), in other words before infection, a blood sample was taken to establish baselines for CD3 lymphocytes, CD4+ and CD8+ T cells, NK cells and B cells (Table 3.1).

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Parameter Visit 0 Visit 1 Visit 2 Visit 3 Visit 4 % 81.05 82.771 83.4 81.4 80.701 CD3+ Lymphocytes Cells/uL 2185 1683 1791 1950 1855 % 81.06 82.491 83.4 81 80.355 Total T cells Cells/uL 2216 1686 1756 1982 1930 % 39.21 25.324 32.4 30.8 34.828 CD4+ Helper T cells Cells/uL 1087 519 696 745 807 % 40.90 56.295 48.8 48 43.388 CD8+ Cytotoxic T cells Cells/uL 1133 1153 1049 1161 1006 CD4:CD8 Ratio 0.96 0.45 0.7 0.6 0.803 % 13.19 12.497 11.1 13.4 12.793 CD3-CD56+ NK cells Cells/uL 356 254 229 330 318 % 5.90 4.357 5.2 5.8 7.167 CD19+ B cells Cells/uL 159 89 107 143 178

Table 3.1: Aries cohort patient B004 longitudinal cell populations. The percentage and number of cells/uL of key cell populations pre-HIV-1 infection (visit 0) and after infection (visit 1-4) were determined using NHS facilities. CD3+ Lymphocytes, Total T cells, CD4+ Helper T cells, CD8+ Cytotoxic T cells, CD56+ NK cells and CD19+ B cell populations were measured, with percentages given of total lymphocytes.

At each subsequent visit the same cells were measured (Table 3.1). The visit 1 sample was taken 8 days after the first HIV positive test results, the visit 2 sample taken 36 days later, the visit 3 sample taken 39 days after that, and the visit 4 sample was taken 34 days following this.

3.3.1. Visits 1-4 sorts

The FACS Aria III cell sorter was used to sort individual plasmablasts from four one-monthly visits (visits

1-4) using the gating strategy previously described, with the CD27hiCD38hi population being sorted

(Figure 3.4a). At each time point, the plasmablast percentage of CD19+CD20- B cells and therefore percentage of total lymphocytes decreased (Figure 3.4b).

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Figure 3.4: Acute HIV Patient B004 plasmablasts at visits 1-4. (a) FACS plots showing the plasmablast population identified by CD27 high and CD38 high expression that was sorted from frozen samples taken at 1, 2, 3 and 4 months (V1-4) after diagnosis. Percentage of CD19+CD27hiCD38hiCD20- B cells is indicated to the left of the gated plasmablasts. (b) Summary of viral load (red) and plasmablast percentage of total lymphocytes (blue) in relation to days since diagnosis (first positive HIV test). Initiation of ART is indicated by the dashed line.

Initially at day 0, i.e. the day of the first HIV positive test result, the log viral load of patient B004 was very high at 5.72 (528964 copies/ml). The plasmablast percentage was also at its highest point at the date of the first blood sample for storing PBMCs being taking several days later (0.59% of total lymphocytes). The patient started ART 15 days after the positive HIV test, as indicated by the dotted line, and from this point onwards both the viral load and plasmablast percentage decreased. As indicated by Figure 3.4b, the viral load dropped by almost 5 logs within 1 month after starting

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treatment and became undetectable in under four months. The same declining trend is seen with the plasmablast percentage after treatment (0.12%, 0.03% and 0.008% of total lymphocytes at V2-4 respectively), and a correlation statistic of r=0.8455 (r2=0.7148) between the viral load and the plasmablast percentage shows that there is a fairly strong correlation between the two.

Because of the decrease in the percentage of plasmablasts of total lymphocytes over time, this meant that the number of full 96 well catch plates that could be sorted into also decreased. In total for visits

1-4, 15 catch plates were sorted; 5 plates for both V1 and V2, whilst V3 produced 3 plates, and V4 produced 1.5 plates. In order to try and obtain equal numbers of antibodies from each visit, 3 sort plates from each of visits 1-3 and the 1.5 sort plates from visit 4 were thawed, followed by multiple rounds of PCR to amplify heavy and light chain variable region DNA.

V1 V2 V3 V4 SW PW SY SW PW SY SW PW SY SW PW SY (#) (#) (%) (#) (#) (%) (#) (#) (%) (#) (#) (%) Plate 1 84 74 88.1 84 59 70.2 84 76 90.5 84 74 88.1 Plate 2 84 74 88.1 84 77 91.7 84 62 73.8 59 43 72.8 Plate 3 84 70 83.3 84 47 56 84 54 60.7 - - - Average 84 72.7 86.5 84 61 72.6 84 64 75 71.5 58.5 80.5

Table 3.2: Sort Summary for single plasmablasts sorts at visits 1-4. The number of sorted wells (SW) according to index sort file on FACS Aria III, number of positive wells (PW) for heavy and/or light chain DNA after PCR, and sort yield (SY) calculated by percentage of sorted wells which had a heavy and/or light chain DNA band for each sorted plate is indicated.

The sort yield was determined at each time point by measuring the yield of heavy and/or light chain

DNA bands using gel electrophoresis for each sort plate, as the presence of either a heavy or a light chain DNA band would mean that a cell was indeed sorted into a particular well, despite whether or not an antibody could be produced from it. Despite the very low plasmablast percentages generally observed for all time points during the sort (Figure 3.4), the yield or efficiency of the sort was good.

For example, the V1 sort which sorted a plasmablast population accounting for 0.059% of total lymphocytes gave 86.5% positive wells for the heavy and/or light chain DNA after PCR, whereas at a much lower plasmablast percentage of 0.008% at V4 the yield was still 80.5% (Table 3.2). These results

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indicate that it is possible to sort plasmablasts at a very low percentage from frozen PBMC samples and still achieve good yield of heavy and light chain DNA for generating antibodies. For proceeding further with monoclonal antibody generation however, only matching heavy and light chain pairs could be used to ensure that the subsequent antibodies produced were indeed ones that would have circulated during the individual.

V1 V2 V3 V4 Plate 1 60 23 54 37 Plate 2 32 45 26 13 Plate 3 30 17 15 - Total 122 85 95 50

Table 3.3: Matching heavy and light chain variable region pairs from visits 1-4 sorts. The matching variable heavy and light chain pairs from each sort plate from visits 1-4 were identified with the total potential antibodies for each visit indicated.

V1 yielded the most matching heavy and light chain pairs (122) and thus potential monoclonal antibodies, followed by V3, V2 and then V4 (95, 85, and 50 matching pairs respectively) a shown in

Table 3.3. Each matching heavy and light chain pair were then individually cloned into heavy and light chain expression vectors which were then used to transfect a HEK293 cell line for antibody production.

After testing the transfection supernatants for IgG by ELISA, all IgG positive supernatants were screened against HIV gp140 in order to detect any HIV-specific antibodies using a flow cytometry- based assay.

3.3.2. HIV gp140 screening

As there are a variety of commercial recombinant HIV envelope proteins available, 5 clade B gp140 and 2 clade B gp120s were purchased for testing reactivity to patient B004 serum, with the view that that gp140/gp120 with the best response would be the best protein for testing generated IgG monoclonal antibodies against. Briefly, the CEM.NKr-CCR5 cell line was coated with the different gp140/gp120s purchased and then incubated with the V1-4 serum. A FITC conjugated secondary IgG antibody was then added. Therefore, the percentage of cells that were FITC+ indicated the level of serum antibody binding to the different proteins.

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Figure 3.5: Patient B004 V1-4 serum screening against different clade B gp140 and gp120 proteins. CEM.NKR-CCR5 cell lines were coated with different gp140/120 proteins, incubated with visit 1-4 serum, and then incubated with a secondary FITC conjugated antibody. (a) Representative FACS plots showing gating of FITC positive cells (indicative of binding of serum antibodies to HIV gp140/gp120) for positive and negative controls (first row) and V1-V4 serum (second row). (b) Percentage of cells FITC positive, and (b) FITC MFI of cells for V1-V4 serum tested against different clade B HIV gp140/gp120. FITC-Ab control was secondary antibody only (no patient serum), HS = healthy serum (HIV negative), and HIVIG positive control (pooled IgG from HIV donors).

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At all time points the greatest percentage of CEM.NKr-CCR5 cells that were FITC positive were cells that were coated with the clade B/C consensus gp140, with 87.85%, 95.1%, 87.85% and 90.8% at V1-

4 respectively (Figure 3.5b). The MFI results also confirmed these results with MFIs for FITC of 2519,

3888, 2745.5 and 2958 for V1-4 respectively (Figure 3.5c). Therefore, the clade B/C consensus gp140 was used for screening the monoclonal antibodies generated from patient B004 at V1-4.

Figure 3.6: HIV screening results of transfection supernatants of five identified HIV-specific monoclonal antibodies generated from a single patient. The IgG ELISA (a), flow cytometry based gp140 screening assay (b), and gp140 ELISA experiments (c) showing that V1X1B5, V1X2B12, V1X2C5, V1X3A2 and V2X1F6 monoclonal antibodies are reactive to HIV gp140. For IgG ELISA, supernatants with OD450 greater than the lowest IgG ELISA kit standard (1.56ng/ml) were classed as being IgG positive, with the greatest IgG standard (100ng/ml) being a strong positive control. For gp140 screening (ELISA and flow cytometry assay), non-specific IgG1 and A32 (both at 1μg/ml) were used as monoclonal negative and positive controls respectively, with results greater than the IgG1 negative control plus 2 standard deviations classed as positive. Flow experiments measured percentage of gp140-coated CEM.NKr-CCR5 that were FITC positive for each supernatant, whilst ELISA experiments measured luminescence at 450nm wavelength.

Using the flow cytometry CEM.NKR-CCR5 coating assay, four transfection supernatants (V1X1B5,

V1X2B12, V1X2C5 and V1X3A2) were found to bind to the clade B/C consensus gp140 (Figure 3.6b).

All monoclonal antibodies were also tested by ELISA for binding to the same clade B/C consensus gp140, and here it was found that mAb V2X1F6 also had a response, albeit small, to the clade B/C consensus protein (Figure 3.6c). The IgG ELISA results for each of the five transfection supernatants that had HIV reactive antibodies are also shown in Figure 3.6a. At this stage in the antibody production,

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the IgG ELISA was purely used to detect whether IgG was present before screening for HIV reactivity.

Concentration of these antibodies would be determined at a later time point.

These results therefore demonstrate that HIV-specific monoclonal antibodies can be generated through sorting the plasmablasts of an acute/early infected HIV patient from frozen PBMC samples, with no B cell enrichment, bulk sorting or pre-selecting/antigen baiting.

3.3.3. Index sorting analysis

Once the antibodies that were HIV specific were identified, the original index sort files generated during the FACS Aria III sorting were examined for any differences in expression profiles of the main markers used to identify plasmablasts (CD19, CD27, CD38) between the HIV-specific and the other plasmablasts.

Figure 3.7: Index sorting FACS Plot for plasmablast CD19, CD27 and CD38 expression. The individual index file FACS plots of the sorted plasmablasts that resulted in visit 1 and 2 non-specific antibodies and the five HIV-specific monoclonal antibodies were combined and colour coded. The CD19 vs DUMP channel plot and the CD27 and CD38 plots are shown, with non-specific plasmablasts in grey, V1X1B5 in blue, V1X2B12 in red, V1X2C5 in purple, V1X3A2 in green and V2X1F6 in orange.

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As shown in Figure 3.7, there was no real clustering of the plasmablasts which gave rise to the HIV – specific antibodies. In terms of CD27 expression these plasmablasts were quite diverse or spread out within the gated sorted cells, whereas they were closer to each other in terms of CD38 expression.

Using the index sort files, the levels of CD19, CD27 and CD38 expression on the plasmablasts responsible for the HIV specific monoclonal antibodies were compared to the expression levels of the visit 1, visit 2, visit 3 and visit 4 sorted plasmablasts.

Figure 3.8: CD19, CD27 and CD38 fluorescence intensity levels of sorted plasmablasts. The CD19 (a), CD27 (b), and CD38 (c) fluorescence levels of individual plasmablasts from visits 1, 2, 3 and 4 were compared to the HIV-specific plasmablasts. Results of a 1 way ANOVA tested using the non-parametric Kruskal-Wallis test are indicated on each graph, with p values of p<0.0001, p=0.0078 and p<0.0001 for CD19, CD27 and CD38 fluorescence intensity respectively. HIV mAbs n=5, Visit 1 n=252, Visit 2 n= 252, Visit 3 n=251, Visit 4 n=144.

As expected, because the plasmablasts are identified as being CD27hiCD38hi, the fluorescence intensity of these markers were greater than the level of CD19 positive expression as indicated by the difference in range of y-axis (Figure 3.8). There were significant statistical differences in the mean CD19, CD27 and CD38 fluorescence intensity between the five groups (HIV-specific and visits 1-4) of plasmablasts

(p<0.0001, p=0.0078 and p<0.0001 respectively). Whilst the CD19 mean fluorescence intensity increased over time (peaking at visit 3 before a small decline at visit 4), the opposite trend was seen with CD38 expression which declined over time (Figure 3.8).

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Both the CD27 and CD38 fluorescence intensity of the HIV specific plasmablasts were wide ranging, whereas the CD19 fluorescence intensity was less varied.

CD19 Fluorescence Intensity Visit 1 Visit 2 Visit 3 Visit 4 HIV mAbs 0.4680 ns 0.1226 ns 0.0160 * 0.0305 * Visit 1 0.0002 *** <0.0001 **** <0.0001 **** Visit 2 <0.0001 **** 0.0570 ns Visit 3 0.0778 ns CD27 Fluorescence Intensity Visit 1 Visit 2 Visit 3 Visit 4 HIV mAbs 0.9833 ns 0.6058 ns 0.9387 ns 0.7786 ns Visit 1 0.0007 *** 0.6957 ns 0.2626 ns Visit 2 0.0030 ** 0.0632 ns Visit 3 0.4241 ns CD38 Fluorescence Intensity Visit 1 Visit 2 Visit 3 Visit 4 HIV mAbs 0.2429 ns 0.0167 * 0.0076 ** 0.0020 ** Visit 1 <0.0001 **** <0.0001 **** <0.0001 **** Visit 2 0.1218 ns 0.0021 ** Visit 3 0.1105 ns

Table 3.4: Comparison of CD19, CD27 and CD38 fluorescence intensity of different sorts. Two-tailed Mann Whitney test results comparing each of the five groups (HIV-mAbs, visits 1-4) to each other for CD19, CD27 and CD38 fluorescence intensity for individual plasmablasts. P values are given in the first column of each visit, with the statistical significance given in the second represented as asterisks.

Whilst the ANOVA tests showed that there were differences between the groups in general, it seems that the visit 1 plasmablasts in particular were significantly different from the other groups (Table 3.4).

The visit 1 plasmablasts had a significantly lower average CD19 fluorescence intensity compared to visit 2 (p=0.0002) and visit 3 and 4 (p<0.0001) plasmablasts, but a significantly higher average CD38 fluorescence intensity than visits 2, 3 and 4 (p<0.0001). The average CD38 fluorescence intensity of the plasmablasts that yielded the HIV specific antibodies was also significantly higher than the visit 2,

3 and 4 plasmablasts.

These results show that there were differences between the CD19, CD27 and CD38 fluorescence intensity and thus expression levels between the plasmablasts that were cloned to give HIV specific antibodies and the other visit 1-4 plasmablasts.

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3.4 HIV-specific monoclonal antibodies

Of the five HIV-specific monoclonal antibodies identified from patient B004, four were from plasmablasts sorted from the visit 1 sample, and one was from the visit 2 sample. A brief summary of the key characteristics of these antibodies are in table 3.5

Heavy Chain Light Chain CDR mAb CDR3 Sequences Gene Family Gene Family Lengths IGHV1-46*01 IGLV2-11*01 H: 8.8.19 ARLSSRIMITFGGVMASDI V1X1B5 IGHJ3*02 IGLJ2*01 L: 9.3.10 CSYAGSYTSV IGHD3-16*01 IGHV3-7*03 IGKV3-20*01 H: 8.8.7 ARDGGEY V1X2B12 IGHJ4*02 IGKJ3*01 L: 7.3.9 QQYGSSRVT IGHD7-27*01 IGHV3-7*03 IGKV1-5*03 H: 8.8.7 ARDGGDY V1X2C5 IGHJ4*02 IGKJ1*01 K: 6.3.9 QQYNSYSET IGHD6-19*01 IGHV4-59*08 IGLV2-11*01 H: 8.7.12 ARQRLWRVGFDP V1X3A2 IGHJ5*02 IGLJ1*01 L: 9.3.10 CSYAGSYTNV IGHD4-23*01 IGHV4-34*01 IGKV1-39*01 H: 8.7.23 ARARLIPHPRLLYAQALYYGMDV V2X1F6 IGHJ6*02 IGKJ4*01 K: 6.3.9 QQSYTTPLT IGHD2-2*02

Table 3.5: Summary of HIV-specific monoclonal antibodies isolated from patient B004. Heavy and light chain variable (V), joining (J) and diversity (D) gene usage, CDR lengths (number of amino acids) and CDR3 sequences for heavy and light chains are shown.

Of the five mAbs, 2 used the IGHV3 gene family, 2 used the IGHV4 gene family and 1 used the IGHV1 gene family (Table 3.5 and Figure 3.9). For joining gene usage, 2 used the IGHJ4 gene, with each of the remaining three using the IGHJ3, IGHJ5 and IGHJ6 genes. All the mAbs used different diversity genes, but 4/5 of the mAbs had their D genes in the first reading frame (diversity genes can potentially be read from any of the three reading frames (424)). For the light chain variable regions, 3 had kappa light chains whilst 2 had lambda light chains (Figure 3.10). Of the kappa chain antibodies, IGKV1 and

IGKV3 were the only variable gene families used, whilst the IGKJ1, IGKJ3 and IGKJ4 joining genes were all used in equal measure. Of the lambda antibodies, both used the IGLV2-11 variable region genes, but different joining genes (IGLJ1 and IGLJ2).

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Figure 3.9: Heavy chain gene family usage of anti-HIV patient monoclonal antibodies. The VH (a), VJ (b), and D gene (c) family usage, and D reading frame (d) of the five patient derived HIV monoclonal antibodies were analysed using the IMGT database, with the percentage of total patient HIV monoclonal of each gene family indicated.

Figure 3.10: Light chain gene family usage of anti-HIV patient monoclonal antibodies. The percentage of kappa and lambda light chains used by HIV monoclonal antibodies (a), the VK (b), VKJ (c), VL (d) and VLK (e) gene family usage of the five patient derived HIV monoclonal antibodies were analysed using the IMGT database, with the percentage of kappa or lambda patient HIV monoclonal of each gene family indicated.

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Figure 3.11: Variable region nucleotide and amino acid mutations of the anti-HIV patient monoclonal antibodies. The variable region heavy chain (a) and light chain (b) nucleotide mutations as a percentage of total variable region nucleotides, and the heavy chain (c) and light chain (d) amino acid mutations as a percentage of total variable region amino acids for each of the HIV-specific monoclonal antibodies were calculated. For nucleotide mutations both the synonymous (S) and non-synonymous (NS) mutations are shown, with all mutations were identified using the IMGT database.

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In order to examine the synonymous and non-synonymous nucleotide mutations of the heavy and light chain variable regions, the number of these mutations retrieved from the IMGT database was calculated as a percentage of the total variable region nucleotide count on an individual antibody basis to account for the variation in variable region length among antibodies. This was also done for the amino acid mutation percentage, as shown in Figure 3.11.

Whilst the majority of the five HIV monoclonal antibodies had few heavy chain mutations, a common theme was that of the nucleotide mutations present in the heavy chain, there was a greater percentage of non-synonymous than synonymous mutations (Figure 3.11). For example, mAb V1X1B5 had synonymous and non-synonymous mutations accounting for around 0.68% and 2.04% respectively of the total variable region nucleotides, and mAb V2X1F6 had synonymous and non- synonymous mutations accounting for around 1.37% and 1.71% of the total variable region nucleotides respectively. Of course, a single non-synonymous mutation can lead to an amino acid mutation, and therefore the percentages of amino acid mutation for the antibodies are generally greater than the nucleotide mutations. The mAb V1X1B5 had amino acid mutations accounting for

6.12% of total variable region amino acids, V2X1F6 had around 5.15% mutations, whilst the other three antibodies had 2% or lower. Despite the antibodies being examined individually here, the average heavy chain amino acid mutation for the HIV monoclonal antibodies was 3.28%.

For the variable region light chains, two of the antibodies (V1X2B12 and V1X2C5) had no synonymous or non-synonymous mutations, and therefore no amino acid mutations (Figure 3.11). On the other hand, mAb V2X1F6 had synonymous and non-synonymous mutations of around 2.46% and 5.28% of total nucleotides respectively, leading to this antibody having around 13.83% of its variable chain amino acids mutated from the germline sequence. The average light chain amino acid mutation percentage of total variable region amino acids for the five anti-HIV monoclonal antibodies was calculated to be 3.17%.

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Figure 3.12: CDR lengths of the anti-HIV patient monoclonal antibodies. The heavy chain (a) and light chain (b) complementarity determining region lengths for CDR1, CDR2 and CDR3 for each of the five HIV-1 patient monoclonal antibodies were determined using the IMGT database.

Whilst the CDRH1 and CDRH2 lengths of the five HIV monoclonal antibodies were fairly consistent, there was a range of lengths of the CDRH3s. Antibodies V1X2B12 and V1X2C5 both had short CDRH3s of only 7 amino acids, V1X3A2 had an average CDRH3 length of 11 amino acids, whilst V1X1B5 and

V2X1F6 monoclonal antibodies had above average or long CDRH3 lengths of 19 and 23 amino acids respectively (Figure 3.12). On the other hand, there was slightly more variation in the CDRL1 lengths of the five monoclonals, whilst all CDRL2s were three amino acids long. Furthermore, despite a huge amount of variation generally being associated with the CDRH3 regions of HIV monoclonal antibodies in particular, and a range of results for the five antibodies described here, there was much less variation in the CDRL3s (Figure 3.12) which were all either 9 or 10 amino acids in length.

Upon aligning the heavy chain nucleotide sequences (Figure 3.13), it was clear that overall there was little nucleotide conservation between the 5 monoclonal antibodies. For example, the CDR1 and CDR2 regions only have 10/24 and 5/21 conserved nucleotides. The CDR3 region is much more difficult to compare due to the different lengths of this region in each antibody and the high diversity generally associated with this region. Furthermore, there were many differences between the antibodies within the framework regions which generally have less variability than the CDRs, however FR4 was much more conserved between the antibodies.

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Figure 3.13: Heavy chain variable region nucleotide alignment of HIV specific antibodies. Clustal omega alignment of heavy chain nucleotide sequences of the five identified HIV-specific monoclonal antibodies V1X1B5, V1X2B12, V1X2C5, V1X3A2 and V2X1F6 (A-E respectively). FR-IMGT and CDR-IMGT regions are highlighted, with IMGT numbering indicating the three nucleotide code at each amino acid position. Asterisks indicate conserved nucleotides.

These results are mirrored in the amino acid sequences (Figure 3.14), with the generated monoclonal antibodies consisting of a wide range of amino acids. There are several positions however within each

FR and CDR where amino acids seem to be conserved among the five monoclonal antibodies, such as leucine (L) at position 4 and glycine (G) at position 8 both within FR1, as well as isoleucine (I) at position

56 and glycine at position 62 within CDR2. The FR4 region in particular is conserved among the five antibodies with only one amino acid position varying out of the 11 amino acid sequence. However, at most positions the amino acids vary among the HIV-specific antibodies.

Furthermore, at these positions, the colours of the contributing amino acids are different. Therefore, at corresponding positions the monoclonal antibodies have amino acids that are different chemically.

For example, at position 84, which falls within CDRH2, 3 monoclonal antibodies had lysine (K) which is

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a basic amino acid, another mAb has glutamic acid (E) which is an acidic amino acid, and one antibody has threonine (T) which is a polar amino acid. Once again the CDRH3 region cannot be truly compared among the antibodies due to the varying lengths.

Figure 3.14: Amino acid sequences of the five HIV specific antibodies. WebLogo image showing frequency plot of distribution of amino acids for the variable heavy chain of five HIV- specific monoclonal antibodies. Polar amino acids are coloured green (G,S,T,Y,C), basic amino acids are blue (K,R,H), acidic amino acids are red (D,E), neutral amino acids (Q,N) are pink and hydrophobic amino acids (A,V,L,I,P,W,F,M) are black. Protein sequence is from the N-terminus to C-terminus with position number and FR-IMGT and CDR-IMGT numbering labelled below.

These results show that the HIV specific antibodies generated from patient B004 vary in the heavy and light chain genes that they use, the lengths of their CDRs and their nucleotide and amino acid sequences.

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3.5. Non-specific monoclonal antibodies

Now that the HIV antibodies had been identified, the gene family usage, mutations and CDR lengths of the remaining non-specific visit 1-4 monoclonal antibodies was examined to test whether the non- specific antibodies being generated at each time point were different in these aspects.

3.5.1. Heavy Chain Gene Usage

There seemed to be a preferential heavy chain gene family usage at all time points, with around half of the monoclonal antibodies at visits 1-4 generally using the VH3 gene family; 50.7%, 43.9%, 52.1% and 66.7% for visits 1-4 respectively (Figure 3.15). The VH4 family was the second most used subgroup for visits 1-4, with 32.3%, 39.0%, 29.2% and 16.7% respectively, and this was followed by VH1 then

VH5 for visits 1-3, but conversely VH5 then VH1 for visit 4 antibodies. The VH2 subgroup of genes were not used at any visits.

Similarly, with the JH gene usage, there was one gene in particular that was used more frequently than the others for the non-specific antibodies. The IGHJ4 gene was used by 46.2% of visit 1 antibodies,

48.8% of visit 2 antibodies, 54.2% of visit 3 antibodies, and 72.2% of visit 4 antibodies, and therefore actually increased in usage over time. IGHJ6 and IGHJ5 genes were the next most frequently used at visits 1-3, whilst IGHJ1, 2, 3, 5 and 6 were used in equal measure at visit 4. The results for D gene usage was more varied at each visit, although D3 and D6 gene families were generally the most used at each time point, and the D reading frame also varied at different time points.

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Figure 3.15: Visit 1-4 non-specific antibody heavy chain variable (VH), joining (J) and diversity (D) gene family usage. VH (a), VJ (b) and D (c) gene family usage, and D reading frame (d) for visits 1-4 monoclonal antibodies that were generated from patient B004 were analysed using the IMGT database. The results are expressed as a percentage of total monoclonal antibodies from that visit. Visit 1 n=65, Visit 2 n=41, Visit 3 n= 48, visit 4 n=18.

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Figure 3.16: Variable heavy (VH) and diversity (D) specific gene usage for V1-V4 non-specific antibodies. The percentage usage of specific VH (a) and D (b) genes of the total antibodies for each visit are plotted with their corresponding gene family indicated under each gene for visit 1-4 antibodies, with the key on the right hand side.

Looking more specifically at the VH genes used by the visit 1-4 antibodies generated, the IGHV3-23 and IGHV3-30 genes were used at fairly high percentages at most time points (Figure 3.16). The IGHV4-

31, 4-39, and 5-51 genes are also used at every time point, whilst most other genes are used at one or two time points in particular. Out of all the D genes used by the antibodies generated, 9/23 specific D genes were used at each visit, but there were no real preferential gene usage. With both the VH and

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D usage there were no specific trends where a particular gene for example was increasingly or decreasingly used over time.

3.5.2. Light Chain Gene Usage

All groups except the visit 1 antibodies had a greater percentage of kappa chain usage than lambda chain antibodies; 43.1% of visit 1 antibodies, 53.7% of visit 2 antibodies, 58.3% of visit 3 antibodies, and 100% of visit 4 antibodies (Figure 3.17). These results also indicate that the percentage of antibodies that were kappa chain actually increased at each visit.

Of the kappa light chains, the IGKV1 gene family accounted for greater than 50% of all kappa chains at visits 2, 3 and 4, and just below 40% of visit 1 antibodies. Whilst the IGKV3 gene family was the second best used gene family at each visit, usage declined slightly over time accounting for 35.71%,

31.82%, 28.57% and 27.78% respectively. Of the kappa joining genes, the IGKJ1 and IGKJ4 genes were generally the most used at visits 1-4, but the IGKJ2 gene also increased to similar percentages at later visits 3 and 4. Out of all the visits, the greatest gene family usage was 40% for IGKJ4 at visit 2, whilst at the other visits the highest gene usage was generally around 30% and varied at different visits.

Only the visits 1, 2 and 3 sorts yielded monoclonal antibodies with lambda light chains. At visit 1, the

IGLV1 gene family was the most used, accounting for 45.95% of the visit 1 lambda chain antibodies.

This gene family subsequently decreased in gene usage at visit 2 and visit 3 with 31.57% and 25% respectively. Conversely, the IGLV2 gene family increased in usage over the three visits with 35.14%,

36.84% and 40% for visits 1-3 respectively. Whilst the IGLV3 gene family was also used up to around

20% at most visits, the other gene families were used at much lower percentages, with the IGLV5 and

IGLV6 genes not used at all at visits 2 and 3. Whilst at visit 1-3 there were varying levels of usage of the different J genes, visit 1 was the only time that the IGLJ7 gene was used. The greatest gene family usage was at visit 2, where 47.37% of lambda monoclonal antibodies used the IGLJ3 gene.

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Figure 3.17: Light chain variable (VH) and joining (J) gene family usage for V1-V4 non-specific antibodies. Gene family usage for visit 1, visit 2, visit 3, and visit 4 monoclonal antibody light chains generated from patient B004 are shown. (a) Distribution of kappa and lambda light monoclonal antibodies, (b) VK gene family usage, (c) VKJ gene family usage, (d) VL gene family usage and (e) VLJ gene family usage. There were no lambda antibodies isolated from the visit 4 sort. For kappa chain antibodies, V1 n=28, V2 n=22, V3 n=28, V4 n=18. For lambda chain antibodies, V1 n=37, V2 n=19, V3 n=20, V4 n=0.

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Figure 3.18: Variable light (VL) specific gene usage for V1-V4 non-specific antibodies. The usage of variable region light chain specific genes as a percentage of all monoclonal antibodies are plotted with their corresponding gene family indicated under each gene for visit 1-4 antibodies, with the key on the right hand side.

The percentage usage of specific kappa and lambda genes of total light chain usage was examined

(Figure 3.18). There were three key peaks for the visit 4 antibodies; the IGKV1-39, IGKV1-5 and

IGKV3-11 genes were used by 16.7%, 22.2% and 16.7% of antibodies from visit 4 respectively, and also used by antibodies generated from visits 1-3. The antibodies from other visits however didn’t seem to use any specific VL genes, however the IGKV1-39 and IGKV3-11 genes both increased in their usage over visits 1-4.

3.5.3. Heavy and Light Chain Nucleotide mutations

The heavy and light chain nucleotide sequences were analysed using the IMGT website, with synonymous and non-synonymous nucleotide mutations examined among the visit 1, 2, 3 and 4 antibodies. As previously described in section 3.4, nucleotide and amino acid mutations were calculated as a percentage of the total variable region nucleotide and amino acid lengths on an individual antibody basis to account for variation in nucleotide/amino acid lengths. For longitudinal analysis of non-specific monoclonal antibodies examined in this section, antibodies were then grouped to visit numbers 1, 2, 3 and 4 for analysis.

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Figure 3.19: Heavy chain variable region nucleotide mutations of the visit 1-4 non-specific monoclonal antibodies. An overall summary of the visit 1-4 heavy chain nucleotide mutations is given (a), followed by visit 1 (b), visit 2 (c), visit 3 (d) and visit 4 (e), with mutations for the FR1-3 and CDR1-2 regions shown. For each graph the synonymous (S) and non-synonymous mutations (NS) as a percentage of total variable region nucleotides for the antibodies for each visit are shown, with all mutations were identified using the IMGT database. Statistical analysis was carried out in GraphPad prism, with the Wilcoxon matched-pairs test used to compare matched synonymous and non-synonymous mutations in V1-4 (a) and in FR and CDR regions (b-e), whilst the Kruskal- Wallis one way ANOVA test was used to compare synonymous or non-synonymous mutations over visits 1-4 (a), or between different regions (b-e). Statistical results are indicated by asterisks indicating p value summary (**** p value < 0.0001), with the bar showing the groups the test has been performed on. Visit 1 n=65, Visit 2 n=41, Visit 3 n= 48, visit 4 n=18.

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At all time points there was a statistically significant greater average percentage of heavy chain non- synonymous mutations than synonymous mutations (Figure 3.19a and Table 3.6). The average percentage of heavy chain nucleotide non-synonymous mutations appeared to increase over visits 1-

4, and a one way ANOVA test showed that there were significant differences between these types of mutations over time (p<0.0001) , as well as the synonymous mutations (p=0.0001) shown in figure

3.19a and table 3.7. The monoclonal antibodies generated from visit 4 had the greatest average mutations of around 3.15% and 6.73% of their total nucleotides for synonymous and non-synonymous mutations respectively, whilst visit 1 antibodies had lower average nucleotide mutations of 0.79% and

2.81% for synonymous and non-synonymous nucleotide mutations respectively.

The heavy chain nucleotide mutations were then further broken down at each visit to framework and complementarity determining regions (Figure 3.19b-e), with the number of mutations per FR/CDR calculated as a percentage of the total nucleotides in their respective regions. As with overall synonymous and non-synonymous mutations, there was a significantly greater average percentage of non-synonymous mutations than synonymous mutations in each variable region FR and CDR, with the majority of statistical tests indicating p values <0.0001 (Table 3.6). When the matched results for each region were compared using a one way ANOVA, there were statistically significant differences between the non-synonymous mutations over the different regions at visits 1, 2, 3 and 4 with p values of 0.0006, 0.0007, <0.0001 and <0.0001 respectively (Table 3.7). There was also a significant statistical difference between the synonymous mutations found in each of the FR and CDR regions of the visit 2 heavy chain variable region (p value of 0.0036)

The visit 4 monoclonal antibodies had the greatest average percentage of heavy chain mutations within the complementarity determining regions (CDR1 and CDR2) with greater than 10% of total nucleotides in these regions being mutated non-synonymously, and over all four visits, it was generally the CDRs that had the greatest mutation frequency.

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Test Visit 1 Visit 2 Visit 3 Visit 4 Heavy Nt Summary <0.0001 <0.0001 <0.0001 0.0019 Heavy FR1 <0.0001 0.0002 0.0005 0.0029 Heavy CDR1 0.0001 <0.0001 <0.0001 <0.0001 Heavy FR2 0.0002 <0.0001 0.0017 0.0229 Heavy CDR2 <0.0001 <0.0001 <0.0001 0.0008 Heavy FR3 <0.0001 <0.0001 <0.0001 0.0004 Light Nt Summary <0.0001 <0.0001 <0.0001 <0.0001 Light FR1 <0.0001 0.0173 <0.0001 0.0021 Light CDR1 0.0002 <0.0001 <0.0001 0.0001 Light FR2 0.7199 0.0068 0.3258 0.4585 Light CDR2 0.0372 0.0070 0.0003 >0.9999 Light FR3 0.0025 <0.0001 <0.0001 0.0786

Table 3.6: Wilcoxon matched-pairs test p values results comparing synonymous and non-synonymous nucleotide mutations of non-specific visit 1-4 monoclonal antibodies. The heavy chain and light chain summary tests were carried out on the overall matched synonymous and non-synonymous results for each visit corresponding to figures 19a and 20a. All other tests were performed on matched synonymous and non-synonymous mutations in different framework or complementarity determining regions of heavy and light chain visit 1-4 non-specific monoclonal antibodies corresponding to Figures 19b-e and 20b-e.

Test Synonymous Non-Synonymous Heavy Chain V1-4 Summary 0.0001 <0.0001 Heavy Chain Visit 1 0.1254 0.0006 Heavy Chain Visit 2 0.0036 0.0007 Heavy Chain Visit 3 0.0739 <0.0001 Heavy Chain Visit 4 0.0559 <0.0001 Light Chain V1-4 Summary <0.0001 <0.0001 Light Chain Visit 1 <0.0001 <0.0001 Light Chain Visit 2 <0.0001 <0.0001 Light Chain Visit 3 <0.0001 0.0103 Light Chain Visit 4 0.1062 <0.0001

Table 3.7: Kruskal-Wallis one way ANOVA test p values comparing synonymous or non-synonymous mutations between visits or FR/CDR regions. The heavy chain and light chain summary tests were performed on overall V1-4 synonymous mutations and overall V1-4 non-synonymous mutations (corresponding to figures 19a and 20a),. All other tests were performed on synonymous or non-synonymous mutations between the different framework or complementarity determining regions of heavy and light chain visit 1-4 non-specific monoclonal antibodies (corresponding to Figures 19b-e and 20b-e).

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The light chain nucleotide amino acid mutations for the visit 1-4 monoclonal antibodies were also examined. Similar results to the heavy chain nucleotide mutations were observed, with a significantly greater percentage of non-synonymous than synonymous light chain mutations at each time point

(Figure 3.20a and Table 3.6). On the other hand, whilst the heavy chain nucleotide non-synonymous mutations had increased in percentage at each subsequent visit to around 7% at visit 4, the light chain non-synonymous mutations increased over visit 1-3 from 2.46% to 3.56%, but then slightly dropped at visit 4 to 3.42%, with the synonymous mutations also following this trend (0.65% to 1.8% from visit

1 to 3 followed by drop to 1.24% at visit 4). There were significant differences between the percentages of overall synonymous mutations, as well as overall non-synonymous mutations at each time point with p<0.0001 for both (Table 3.7).

Furthermore, when mutations were broken down at each specific time point, the average percentage of non-synonymous nucleotide mutations in each light chain FR and CDR region was greater than the synonymous mutations (Figure 3.20b-e). Whilst visits 1-3 generally had significant differences between the two types of mutations in the majority of framework and complementarity determining regions, the visit 4 antibodies only had significant differences between the synonymous and non- synonymous mutations in the FR1 and CDR1 (Table 3.6). On the other hand, when matched mutations in the different FR and CDR regions were compared, there were significant differences in the percentage of non-synonymous mutations at each time point (Table 3.7), as well as for the synonymous mutations at visits 1-3 but not visit 4.

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Figure 3.20: Light chain variable region nucleotide mutations of the visit 1-4 non-specific monoclonal antibodies. An overall summary of the visit 1-4 light chain nucleotide mutations is given (a), followed by visit 1 (b), visit 2 (c), visit 3 (d) and visit 4 (e), with mutations for the FR1-3 and CDR1-2 regions shown. For each graph the synonymous (S) and non-synonymous mutations (NS) as a percentage of total nucleotides for the antibodies for each visit are shown, with all mutations were identified using the IMGT database. Statistical analysis was carried out in GraphPad prism, with the Wilcoxon matched-pairs test used to compare matched synonymous and non-synonymous mutations in V1-4 (a) and in FR and CDR regions (b-e), whilst the Kruskal-Wallis one way ANOVA test was used to compare synonymous or non-synonymous mutations over visits 1-4 (a), or between different regions (b-e). Statistical results are indicated by asterisks indicating p value summary (**** p value < 0.0001), with the bar showing the groups the test has been performed on. Visit 1 n=65, Visit 2 n=41, Visit 3 n= 48, visit 4 n=18.

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3.5.4. Amino acid mutations

The percentage of amino acids in each antibody that were mutated compared to germline sequences was also examined with the results shown in Figures 3.21 and 3.22. Visit 1 monoclonal antibodies had the lowest average percentage of amino acids mutations with 6.72% of total variable region amino acids being mutated, followed by visit 3 and visit 2, with 9.11% and 9.65% respectively, and then visit

4 with the greatest amino acid mutation percentage of 13.5%. Results of a one way ANOVA showed that the percentage of heavy chain amino acids that were mutated was significantly different over visits 1-4 (Figure 3.21a and Table 3.8).

As with the nucleotide mutations, the amino acid variable region mutations were then broken down into the FR1, CDR1, FR2, CDR2 and FR3 regions. It is clear from this that it is the CDR1 and CDR2 regions that have the greatest percentage of heavy chain amino acid mutations at each visit, with greater than

25% of the amino acids in CDR1 and CDR2 being mutated compared to germline at visit 4 in particular.

Matched one way ANOVA tests showed that there were significant differences between the percentage of heavy chain mutations across the different FR and CDR regions for each visit (Figure

3.21b-e and Table 3.8), with p values of <0.0001 for all visits.

Similar results were seen with the light chain amino acid mutations, with a significant difference in the percentage of light chain amino acids over visits 1 to 4, as well as significant differences between the percentage of light chain amino acid mutations in the different FR and CDR regions at each visit (Figure

3.22 and table 3.8). As with the heavy chain amino acid mutations, the CDR1 and CDR2 regions of the light chain had the greatest percentage of mutations at each visit. Interestingly while it was the CDR2 region that had a higher amino acid mutation percentage than the CDR1 in the heavy chain, the opposite was the case for the light chain. Also parallel to the heavy chain results, at visit 4 the light chain monoclonal antibody CDR1 region mutations accounted for around 25% of total amino acids, conversely the CDR2 region mutations averaged around 5% of amino acids.

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Figure 3.21: Heavy chain variable region amino acid mutations of visit 1-4 non-specific monoclonal antibodies. An overall summary of the visit 1-4 heavy chain amino acid mutations is given (a), followed by visit 1 (b), visit 2 (c), visit 3 (d) and visit 4 (e), with mutations for the FR1-3 and CDR1-2 regions shown. The Kruskal-Wallis one way ANOVA test was used to compare amino acid mutations over visits 1-4 (a), whilst the Friedman test was used to compare matched results between different regions (b-e). Statistical results are indicated by asterisks indicating p value summary (**** p value < 0.0001). Visit 1 n=65, Visit 2 n=41, Visit 3 n= 48, visit 4 n=18.

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Figure 3.22: Light chain variable region amino acid mutations of visit 1-4 non-specific monoclonal antibodies. An overall summary of the visit 1-4 light chain amino acid mutations is given (a), followed by visit 1 (b), visit 2 (c), visit 3 (d) and visit 4 (e), with mutations for the FR1-3 and CDR1-2 regions shown. The Kruskal-Wallis one way ANOVA test was used to compare amino acid mutations over visits 1-4 (a), whilst the Friedman test was used to compare matched results between different regions (b-e). Statistical results are indicated by asterisks indicating p value summary (**** p value < 0.0001). Visit 1 n=65, Visit 2 n=41, Visit 3 n= 48, visit 4 n=18.

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Test Heavy Chain Light Chain AA Summary 0.0001 <0.0001 Visit 1 <0.0001 0.0007 Visit 2 <0.0001 0.0012 Visit 3 <0.0001 0.0012 Visit 4 <0.0001 <0.0001

Table 3.8: One way ANOVA test p values for amino acid mutations between visits or FR/CDR regions. The Kruskal-Wallis test was used for heavy chain and light chain visit 1-4 summary tests, whilst the Friedman test was used to analyse differences in mutations between the FR/CDR regions for each visit.

After studying the overall amino acid heavy and light chain mutations within the different framework and complementarity regions, the actual types of mutations that were taking place in the visit 1-4 monoclonal antibodies was examined. Using the IMGT database to analyse heavy and light chain sequences, the amino acid mutations within each monoclonal antibody were classified into seven groups covering four types of mutation; mutation leading to amino acid that was very similar (+ + +), similar (+ + - and + - +), dissimilar (+ - -, - + - and - - +), or very dissimilar (- - -) compared to the germline amino acid. The plus or minus sign at each of the three position refers to whether the mutated amino acid belongs to the same hydropathy, volume, and chemical characteristics classes in that order.

When the antibodies from each visit were examined as a population, it was clear that the amino acid mutations resulted in mostly dissimilar or very dissimilar amino acids when compared to germline sequences (Figure 3.23). The visit 4 monoclonal antibodies generally had the greatest mutation percentage for each type of mutation of heavy chain variable region amino acids, whilst the visit 1 antibodies had the lowest percentages for each type. For example, the visit 4 antibodies had mutations that meant an average of 0.74% heavy chain variable region amino acids had mutated to very similar amino acids, whilst an average of 5.51% heavy chain variable region amino acids had mutated to very dissimilar amino acids. On the other hand, the visit 1 antibodies had lower average mutation percentages of 0.44% and 1.79% of heavy chain variable region amino acids mutating to very similar and very dissimilar amino acids respectively.

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There were statistical differences between the visit 1-4 monoclonal antibodies heavy chain variable regions for the similar mutations (+ - +), the dissimilar mutations (+ - - and - + -) and the very dissimilar mutation (- - -) with p values of 0.0198, 0.0133, 0.0037 and <0.0001 respectively.

There were similar results for the light chain variable regions with the visit 1 monoclonal antibodies having the lowest light chain amino acid mutations for each type of mutations, and visit 3 or visit 4 antibodies having the greatest amino acid mutation percentage for each mutation type. There were also statistical differences between the visit 1-4 light chain variable regions for the similar mutations

(+ + - and + - +), the dissimilar mutations (+ - - and - + -) and the very dissimilar mutation (- - -) with p values of 0.401, 0.0002, 0.0020, <0.0001 and 0.0015 respectively. Interestingly, for both the heavy and light chain amino acid mutations, the percentage of mutations leading to amino acid that were dissimilar (- + -) to the original germline amino acids actually increased over visits 1-4, the only mutation category to do this.

After looking at the amino acid mutations on a population level for each visit, heat maps showing the individual antibodies generated from visit 1-4 were also created to examine the antibodies on a monoclonal level. As illustrated by Figure 3.24, the majority of monoclonal antibodies had multiple types of heavy chain mutations when compared to their germline variable regions, with the vast majority of mutations leading to amino acids that were different from the original in at least one of the hydropathy, volume or chemical characteristics. It is also clear that each antibody has a fairly consistent level or intensity across all types of mutation, i.e. if a monoclonal antibody has a low percentage of amino acids that mutate to a similar amino acid, they also generally have a low percentage of amino acids that mutate to a dissimilar amino acid, and vice versa. Similar patterns were seen for the light chain amino acid mutations indicated by the heat map in Figure 3.25, although generally there were lower levels of light chain amino acid mutations than heavy chain amino acid mutations.

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Figure 3.24: Individual non-specific visit 1-4 mAb variable heavy chain amino acid mutations. Heat map of different types of heavy chain amino acid mutations classified as being very similar (+ + +), similar [(+ + -),(+ - +)], dissimilar [(+ - -),(- + -),(- - +)], or very dissimilar (- - -) compared to the germline amino acid. Individual monoclonal antibodies generated from patient B004 are shown, grouped into visit 1, 2, 3 and 4 monoclonal antibodies. The intensity of colour for each monoclonal refers to the number of amino acid mutations as indicated by the key on the right hand side, with the maximum value ≥15.

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Figure 3.25: Individual non-specific visit 1-4 mAb variable light chain amino acid mutations. Heat map of different types of light chain amino acid mutations classified as being very similar (+ + +), similar [(+ + -),(+ - +)], dissimilar [(+ - -),(- + -),(- - +)], or very dissimilar (- - -) compared to the germline amino acid. Individual monoclonal antibodies generated from patient B004 are shown, grouped into visit 1, 2, 3 and 4 monoclonal antibodies. The intensity of colour for each monoclonal refers to the number of amino acid mutations as indicated by the key on the right hand side, with the maximum value ≥15.

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Interestingly, when comparing the specific heavy and light chain amino acid mutations for a specific antibody, it is evident that a higher heavy chain mutation frequency doesn’t necessarily mean a higher light chain mutation frequency. For example, as indicated by the heat map in Figure 3.24, antibody

V1X1D3 from the visit 1 group has a fairly high intensity of heavy chain amino acids that have mutated to very similar, similar, dissimilar and very dissimilar amino acids when compared to the germline sequences, whereas the heat map in figure 3.25 shows that this antibody has no light chain mutations at all. Conversely, antibody V1X1G2 which has a very low level intensity of heavy chain mutations has a much higher intensity of light chain mutations.

Figure 3.26: WebLogo sequence image of heavy chain amino acid alignment. Visit 1, visit 2, visit 3 and visit 4 monoclonal antibody heavy chain protein sequences were aligned using Clustal omega, with the resulting alignment entered into the WebLogo internet program. Total height (measured in bits) of each column represents how conserved the amino acid position is among all sequences, with the different letters in each column representing frequency of that particular amino acid at that position over all sequences. Numbering refers to amino acid positioning using IMGT numbering, with FR and CDRs labelled underneath.

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After observing the number and types of amino acid mutation in the generated monoclonal antibodies, all visit 1 to visit 4 heavy chain sequences were aligned, with the alignment entered into the WebLogo program. Using the sequence image that the program creates, it could be determined whether there were regions of conserved amino acids among the heavy chains.

In general, the framework regions (FR1-4) were fairly conserved over all the visit 1-4 antibodies. In particular, the FR2 and FR4 regions were highly conserved, with the majority of amino acid positions occupied by one amino acid, (Figure 3.26). For example, in FR2, at positions 41 and 43, tryptophan (W) and arginine (R) reach a height on the y-axis of 4 bits, meaning that the vast majority of visit 1-4 heavy chain sequences have these amino acids at these positions. Likewise in FR4, nearly the whole region was conserved among heavy chains. The CDRs on the other hand were much more variable. The CDR1 did have some positions that were conserved among antibody heavy chains such as positon 27, glycine

(G), and positions 31 and 34 of which a large proportion of the sequences had serine (S). The CDR3 region was a lot more difficult to compare due to the varying lengths in different antibodies. However it is clear that there were no conserved positions other than at the very 5’ and 3’ end.

These results show that monoclonal antibodies generated from visits 1-4 had varying levels of nucleotide mutation, with a higher percentage of non-synonymous to synonymous mutations therefore leading to multiple amino acid mutations, and the greatest levels of mutations were found to be in the CDR regions of each antibody. Despite the variability in the different visit 1-4 generated antibodies, there were conserved amino acids in the variable heavy chain, in particular the FR2 and

FR4 regions.

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3.5.5. CDRH3 and CDRL3s

The visit 1, 2, 3 and 4 antibodies were examined to see if there were any differences in the lengths of the CDRH3 and CDRL3 in the monoclonals generated at each visit.

Figure 3.27: CDRH3 lengths of visit 1-4 non-specific monoclonal antibodies. Relative proportions of different CDRH3 lengths of monoclonal antibodies generated at visit 1 (a), visit 2 (b), visit 3 (c), and visit 4 (d). CDRH3 lengths are on the x-axis, with the percentage of heavy chains for that visit on the y-axis.

Despite differences in the relative percentages of each CDRH3 lengths for each visit, the overall distribution of CDRH3 lengths at each visit looked fairly similar, with the bulk of the CDRH3s being between 10 and 20 amino acids long, (Figure 3.27). There was a key peak at each visit showing the

CDRH3 length with the greatest percentage of IGH sequences for that visit; 12 amino acids for visit 1

(16.9%), 13 and 14 amino acids at visit 2 (both 15.4%), 13 amino acids at visit 3 (18.75%) and 16 amino

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acids at visit 4 (27.8%). Despite this differences, there was no significant difference between the

CDRH3 lengths for the different groups of antibodies (Figure 3.29).

Figure 3.28: CDRL3 lengths of visit 1-4 monoclonal antibodies. Relative proportions of different CDRL3 lengths of monoclonal antibodies generated at visit 1 (a), visit 2 (b), visit 3 (c), and visit 4 (d). CDRL3 lengths are on the x-axis, with the percentage of heavy chains for that visit on the y-axis.

Figure 3.29: CDR3 lengths of V1-V4 non-specific monoclonal antibodies. Box and whisker plots showing CDRH3 (a) and CDRL3 (b) amino acid lengths of visit 1, visit 2, visit 3 and visit 4 generated monoclonal antibodies. Statistical results of Kruskal-Wallis indicated on figure, with p=0.0027 for CDRL3s.

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The CDRL3 lengths of the visit 1-4 antibodies were overall shorter than the CDRH3s, and were less variable in length (Figures 3.28 and 3.29). Also similar to the CDRH3s was that the distribution of

CDRL3s at each visit looked similar. While the CDRH3s with the greatest percentage of sequences differed across the visits for the heavy chain, it was the same for the light chain CDRL3s. At all visits, the CDRL3 length of the greatest percentage of generated antibodies was 9 amino acid long; 39.0% at visit 1, 47.5% at visit 2, 44.7% at visit 3, and 72.2% at visit 4. There was however a significant difference in the lengths of CDRL3s of the different groups (p=0.0027).

Figure 3.30: Relative frequency of amino acids at CDRH1 and CDRH2 positions 1-8. The relative frequency of each different amino acid at each of the 8 positions at CDRH1 and CDRH2 was calculated for the visit 1, 2, 3 and 4 non-specific antibodies.

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Whilst the CDRH3 is extremely variable and differs in length for different antibodies, thus making it difficult to compare the composition of these regions in different antibodies, the CDRH1 and CDRH2 are more restricted in the number of amino acids of each region (between 8-10 amino acids in each

CDR, however most are 8). The amino acid composition at each of the 8 positions in each complementarity determining region of the visit 1-4 generated antibodies was examined (figure 3.30).

For the CDRH1, the amino acid make up of positions 1-4 were largely the same for visits 1-4 monoclonal antibodies. Glycine (G) was the amino acid present at position 1 for 100% of all the antibodies generated at each time point, expect for visit 2 at which it was 97.6%. At position 2, there were three key amino acid options; phenylalanine (F), glycine (G) and tyrosine (Y) with around 50, 25, and 15% respectively at each visit. Around 50% of the amino acids at position 3 for each visit were threonine (T), and around 70% of the amino acids at position 4 at each visit were phenylalanine (F).

Positions 5 and 6 were also fairly similar over the five groups, but with more varied percentages of each amino acid option, whilst positions 7 and 8 were the most varied across the different groups. For example, of the HIV-specific antibodies, all had tyrosine (Y) at position 7, whereas at the other visits there was a range of 4-9 possible different amino acids at this position. At position 8, the visit 1-4 antibodies varied among 6-12 amino acids. The CDRH2 amino acid compositions at each of the visit 1-

4 generated antibodies were largely similar to each other in pattern, with small differences in relative frequency.

These results show that the CDRH3 and CDRL3 lengths of the non-specific visit 1-4 monoclonal antibodies generally follow the same distribution. The CDRH1 and CDRH2 of the non-specific visit 1-4 antibodies also largely followed the same pattern of amino acid distribution at each position within the CDR, albeit with differences in percentage at each time point.

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3.6. HIV-specific mAbs vs V1 Non-specific mAbs

Until this point, the five HIV-specific monoclonal antibodies have been analysed separately from the visit 1-4 non-specific monoclonal antibodies. Out of the five HIV-specific monoclonal antibodies, four were isolated from total visit 1 sort generated monoclonal antibodies, therefore these antibodies can only be compared to the non-specific antibodies of the same time point i.e. visit 1.

The heavy chain gene family usage was similar for both the HIV monoclonal antibodies and the non- specific visit 1 antibodies, with around 50% of both using the IGHV3 gene family (Figure 3.31). Similarly it is the IGKV1 and IGKV3 genes which were the most frequently used by both the HIV specific and visit

1 non-specific monoclonal antibodies. The synonymous and non-synonymous nucleotide mutations were compared for the HIV specific and non-specific visit 1 monoclonal antibodies, and for both the heavy and light chain variable regions the visit 1 non-specific antibodies had greater percentage mutations (Figure 3.31e and f). The same trend was seen for the amino acid mutations, with the V1 non-specific antibodies having a greater percentage of amino acid mutation compared to the HIV specific monoclonal antibodies. Furthermore, the visit 1 non-specific antibodies overall had longer

CDRH3s and CDRL3s than the HIV-specific monoclonal antibodies.

However, when statistical tests were carried out between these groups, there were no statistical differences between the HIV specific and non-specific antibodies, and this may be down to the fact that the HIV group only contained 4 monoclonal antibodies.

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and CDRL3

synonymous

, heavy chain nucleotide

clonal antibodies (V10). HIV antibodies HIV (V10). antibodies clonal

specific mono specific

specific monoclonal antibodies in key characteristics.

specific V1 mAbs n=65.

specific and 1 non visit

specific visit 1 antibodies are the grey bars. Nucleotide mutations were compared for synonymous (S) and non

HIV

hainaminomutationschain lightacid (e), nucleotide lightacid mutations(f),chainamino mutations (g),CDRH3lengths(h)

: Comparison of

The The variable heavy (VH) gene family usage (a), variable kappa heavy(d), mutations c (VK) gene family non usage 1 visit the (b), to (HIV) variable 1 visit from lambda isolated antibodies monoclonal (VL) HIV four for gene compared were (i) usage lengths (c) are the black bars, whilst non (NS) mutations. HIV specific mAbs n=4, non

Figure

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3.7. Molecular Phylogenetics Analysis

After comparing gene family usage, nucleotide and amino acid mutations, and CDR lengths of both the HIV specific and non-specific visit 1-4 monoclonal antibodies, all heavy and light chain sequences were aligned and used to create phylogenetic trees for analysis of the relatedness of all the antibodies generated.

The heavy chain sequences seemed to be split into three clades groups which then further differentiated to more closely related groups and then the individual antibodies (Figure 3.32). The heavy chain of antibody V4X1B10 was on its own in a completely separate outgroup and was unrelated to all the other antibody heavy chains. On further analysis it was found that this heavy chain had a very large number of mutations compared to germline and other heavy chain sequences. When looking for differences in the closeness of the antibodies from different visits, it was found that the visit numbers of the antibodies were widely distributed around the phylogenetic tree. In other words, the antibodies aren’t all grouped into visit 1 antibodies in one clade, visit 2 antibodies in another, they are spread all over the tree. Furthermore, the HIV-specific antibodies are also widely distributed on the phylogenetic tree. The heavy chain of V1X1B5 is in one large clade with 31 other heavy chain sequences, V1X2B12 and V1X2C5 are more related being together in another of the large clades, and the V1X3A2 and V2X1F6 are in the final of the three large clades.

There was similar results when the light chain sequences of the HIV-specific, visit 1, visit 2, visit 3 and visit 4 antibodies were aligned and used to create a phylogenetic tree (Figure 3.33). This time there were two distinct clades further divided into smaller groups/clades, as well as two outliers within a single outgroup, and once again antibodies from different visits were widely distributed. On the other hand, the distribution of the HIV-specific light chains are grouped differently to the heavy chains. For example whilst the heavy chains of V1X2B12 and V1X2C5 were very closely related (same node), the light chains of these antibodies whilst in the same large clade, are much more removed from each

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other. Conversely, whilst the V1X1B5 and V1X3A2 heavy chains were distant from each other, their light chains are much more closely related.

Figure 3.32: Heavy chain molecular phylogenetic analysis. All heavy chain variable region sequences from visit 1, 2, 3, 4 and HIV-specific antibodies were aligned using ClustalW, with the alignment used for phylogenetic analysis by maximum likelihood method using Mega 7 software to create an unrooted phylogenetic tree. Branch lengths are measured by the number of substitutions per site (scale shown). Red circles indicate the anti-HIV antibodies.

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Figure 3.33: Light chain molecular phylogenetic analysis. All light chain variable region sequences from visit 1, 2, 3, 4 and HIV-specific antibodies were aligned using ClustalW, with the alignment used for phylogenetic analysis by maximum likelihood method using Mega 7 software to create an unrooted phylogenetic tree. Branch lengths are measured by the number of substitutions per site (scale shown). Red circles indicate the HIV specific antibodies.

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These results show that the HIV-specific antibodies are more closely related to other antibodies from visits 1-4 than they are each other in both their heavy and light chain variable regions. Also, antibodies aren’t grouped within the same clades with other antibodies only from the same time point, they are related to antibodies from multiple time points. Furthermore, the heavy and light chains of the same individual antibodies are different in their relatedness to the heavy and light chains of other individual antibodies.

3.8. Discussion

3.8.1. Summary

The aim of this project was to generate monoclonal antibodies from the sorted plasmablasts of an early infected HIV individual at four time points, identify HIV-specific antibodies, and compare the characteristics of these to the other non-HIV specific antibodies generated. In this study, 5 HIV-specific antibodies were identified from the antibodies cloned from visit 1, 2, 3 and 4 plasmablasts (4 from V1 and 1 from V2), with the heavy and light chains compared to those of 61 visit 1, 41 visit 2, 48 visit 3 and 18 visit 4 non-HIV specific monoclonal antibodies.

It was found that the HIV antibodies followed the same variable heavy and joining gene family usage pattern as the non-specific visit 1-4 antibodies, with preferential usage of the VH3 and J4 gene families.

Both HIV monoclonal antibodies and the visit 1-4 non-specific monoclonal antibodies had significantly higher percentages of non-synonymous nucleotide than synonymous mutations, and there were significant differences in the percentage of these mutations (as well as amino acid mutations) over time for the non-specific antibodies. Despite the visit 1 non-specific antibodies having a greater frequency of nucleotide and amino acid mutations than the HIV monoclonal antibodies, this did not reach statistical significance.

Furthermore, both the HIV monoclonal antibodies and the non-specific antibodies had a range of

CDRH3 lengths, but there were no significant differences between CDRH3 lengths of the anti-HIV antibodies and the non-specific visit 1 antibodies. Finally, phylogenetic analysis of HIV monoclonal

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antibodies as well as visit 1-4 non-specific antibodies showed no distinctive clustering, indicating that both heavy and light chain nucleotide sequences were randomly distributed.

3.8.2. Isolating HIV-reactive mAb

There are currently two key B cell populations that are sorted for single cell cloning and production of antibodies from HIV infected or vaccinated individuals. Memory B cells are often sorted for the purposes of isolating broadly neutralising antibodies and are selected through gp120/gp140 antigen baiting, whilst antibody secreting cells (ASCs) such as plasmablasts or plasma cells have no pre- screening or selection and are often sorted 7 days after trial vaccines to test efficacy and the types of antibodies that are stimulated. In this study, the aim was to sort plasmablasts at multiple time points from an acute/early infected HIV patient and generate HIV antibodies from these sorts, and this was achieved. Visit 1 (first sample after diagnosis) yielded 69 antibodies of which four were reactive to HIV

(5.8%), visit 2 (one month later and after the induction of ART) yielded just one HIV reactive antibody out of 42 (2.4%), whilst visit 3 and visit 4 yielded no HIV-reactive antibodies.

A study by Liao et al in 2011 using sorted plasma cells from five acutely infected HIV-1 patients isolated

67 HIV-1 reactive monoclonal antibodies (6.9%) from 977 generated mAbs (425). Therefore the percentage of visit 1 antibodies generated in this study that were HIV-reactive is fairly consistent with the figures published in the Liao paper. It is important to note however that the percentage of HIV- reactive antibodies from each of the individual five donors studied in this paper was variable, ranging from 0.8% to 14.4% of total antibodies generated, and that one of the donors had received 7 days of

ART after exposure before blood samples were collected (425). Although visit 2 yielded a percentage of HIV-reactive antibodies within the range of those patients in the above mentioned paper, as these antibodies were generated after ART had started, the results are not truly comparable due to the effect of ART on ASC populations.

These results are somewhat different to the results published in other papers however for percentage of HIV-specific ASCs or memory B cells. As 5.8% of visit 1 antibodies were HIV-reactive, it can be

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roughly estimated that around 5.8% of plasmablasts or ASCs at this time point were HIV-reactive, equating to around 0.92% of total B cells, and 0.0342% of total lymphocytes. In the literature on the other hand, the results are generally lower than this. For example, Buckner et al showed using

ELISPOTs that 1.3% of ASC were HIV-specific in early infected HIV viremic patients, with the percentage of HIV-specific B cells ranging from 0.5 to 3.4% of total B cells (177), whilst Moir et al found an average of 0.0025% and 0.021% of ASC B cells were specific for immobilised HIV gp120 and soluble HIV gp120 respectively in 6 early infected HIV infected individuals (414). However, these statistics were obtained through different methods (ELISPOTs) than the results in this and the Liao study which may account for the differences, as ELISPOTs may underestimate responses as only ASC actively secreting antibody are measured,(426).

As previously mentioned, visit 1 had the greatest plasmablast percentage of total lymphocytes which then decreased at each subsequent visit, and there were no HIV-reactive antibodies generated from visit 3 or 4 samples. A key factor that may have influenced these results was the initiation of ART just

15 days after diagnosis and 8 days after the first sample was taken for sorting. It has been well characterised in the literature that ART has an impact on B cells, more specifically it reduces the plasmablast population, (177, 414, 427). The hypothesis is that as viral replication is driving the stimulation of the plasmablast population (177), if ART reduces viral load and therefore means less virus replication, there will be a smaller plasmablast response whether specific or non-specific. If the plasmablast population is smaller, the chance of isolating HIV-specific antibodies from these sorts is also much lower.

It could be argued that more HIV-specific antibodies weren’t isolated from visit 2, or from visits 3 and

4 because the numbers of plasmablasts sorted were lower at these time points, and therefore by sorting more cells HIV-reactive antibodies could have been generated. From a more technical point of view however, it is unlikely that this would have made much of a difference. When staining the cells for sorting, the maximum number of cells from each frozen sample were stained to try and ensure a

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good yield. Due to the small number of vials of each of the time point samples being used, and their need in other studies, it would have been difficult to use more cells with not much added benefit due to the low percentage of HIV-specific plasmablast/ASCs. Furthermore, to try and ensure the results were comparable, the same number of cells were sorted at each time point (except visit 4). In terms of actually sorting, with the plasmablast percentage of total lymphocytes that could be sorted being generally very low, it was very difficult for the FACS sorter to actually detect the plasmablasts and sort them. This resulted in long sorts for the later time points, which may have affected the quality of RNA in each well, and therefore capability to generate antibodies through cloning, as it led to longer time periods between starting the sort and flash freezing the sorted plates.

As previously discussed in the introduction and summarised in this section, plasmablasts haven’t been able to be sorted using antigen baiting due to the low or no surface expression of IgG on the cell surface required for antigen baiting techniques as with memory B cells. Very recently however, a paper was published by Pinder et al in the Journal of Immunology which describes a new Ig capture assay method where this may be possible. This new method detects antigen-specific IgG that has been secreted by plasmablasts, and uses an affinity matrix of several different components to fluorescently label the specific plasmablast for cell sorting and subsequent cloning (428). The authors showed that using this new screening method, 94.12% of monoclonal antibodies generated from sorted plasmablasts were antigen specific, as opposed to the 1.37% of antibodies generated through traditional plasmablast sorting, as used in the results shown in this thesis (428). Whilst this technology/protocol was published after the work presented in this thesis was completed, it would be incredibly beneficial in future work to increase the number of HIV-specific antibodies generated from patients to study the types of antibodies being generated at longitudinal time points. It is important to note however that the Pinder study vaccinated healthy volunteers with a HIV-1 trial vaccine and sorted plasmablasts at days when peak plasmablasts responses would be expected (7 days post vaccine). The yields or how successful this approach would be in an early HIV-1 infection setting would therefore be unknown and would need further testing.

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Despite the fact that plasmablasts have been sorted from acute/early HIV infection before and studied over the course of an immunisation (Liao 2011), this is the first time to our knowledge that plasmablasts from monthly samples within early infection have been sorted with both HIV-reactive and non-reactive monoclonal antibodies generated and compared before and after ART.

3.8.3. Gene family Usage

Several key studies have previously shown that the VH3 gene family is preferentially used by antibodies from healthy individuals (429, 430). Therefore it was expected that the non-HIV reactive antibodies generated from visits 1-4 would preferentially use the VH3 gene family, and these results were observed, with around 50% of V1-V4 antibodies using this gene family. These results are also consistent with those in a study by Li et al, in which the gene family usage of 10 HIV-specific monoclonal antibodies, generated from sorted memory B cells selected through antigen baiting, was compared to that of non-HIV specific antibodies from the same donor. This paper found that the VH3 gene family was used by the non-HIV antibodies from the HIV infected patient, followed by VH4, VH1 then VH5, (419) with this pattern also observed in the results found in this thesis.

On the other hand, the results here do differ from the Li paper with regards to the HIV-specific antibodies generated. In the Li paper it is the VH1 gene family which were predominantly used by the

HIV-specific antibodies, followed by VH3, VH4 and VH5 gene families (419), whilst the majority of HIV- reactive antibodies generated in this study used VH3 and VH4 in equal measure, with only one antibody using the VH1 gene family. Similarly, other studies looking at gene family usage in HIV infected individuals have also found that VH1 usage is increased whilst VH3 usage is decreased not only in monoclonal antibodies isolated from patients (417, 431), but also in total lymphocytes in AIDS patients when compared to antibodies from healthy individuals (418). Furthermore, many of the

CD4bs bNAbs that have been isolated use genes from the VH1 gene family (310, 416)

A paper by Breden et al examined the heavy chain gene families used (as well as the CDR lengths and somatic hypermutation) by 425 monoclonal antibodies broadly categorised into four groups; mAbs

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associated with HIV infection, chronic infections excluding HIV, acute infections, and systemic autoimmune disease. This paper found 39% of HIV antibodies used VH1, 29% used VH3 and 24% used

VH4, whilst other chronic infections predominantly used VH3, then VH4 and VH1 with usage at 44%,

26% and 21% respectively (417). The acute infections (non-HIV) and systemic autoimmune diseases on the other hand both had VH3 usage greater than 50%. Therefore the results generated in this project do not fall in with the groups analysed in the Breden paper.

It is important to note however, that the studies of the gene family usage in HIV infected individuals mentioned so far, and in fact the vast majority of studies looking at gene family usage, usually examine patients that are in the chronic phase of infection. A paper by Liao et al mentioned previously did however briefly look at the gene family usage of antibodies vs non-specific antibodies in acutely infected individuals, and the results in this thesis more closely match these results. In the Liao paper the gene family usage of the HIV-specific and non-specific antibodies generated from several donors followed the same gene family usage pattern with very similar statistics for each, with preferential

VH3 usage for both types of antibodies with 61.2 and 55.7% usage for HIV and non-HIV antibodies respectively, followed by VH1 and then VH4 (425). The results in this study therefore are consistent with these previously reported gene pattern usage trend results, albeit with different percentages of usage for each gene family.

The decline in the VH3 family usage in HIV-1 antibodies described previously for those studies with antibodies generated from the chronic phase of infection has been attributed to HIV gp120 being a superantigen (432-434). It has been shown that a number of residues dispersed across the heavy chain variable region of VH3 antibodies interact non-conventionally with the HIV gp120 (435), and 16 VH positions in particular, of which 13 are located in framework regions as opposed to the CDRs, have been correlated with gp120 binding (436). As a superantigen, gp120 therefore causes high stimulation of VH3 B cells and antibody production in early HIV infection, but overtime causes depletion of B cells or antibodies with this heavy chain gene family (434, 437). As the five HIV antibodies generated in this

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project were from a patient in the early stages of infection (1 and 2 months after diagnosis) the effect of gp120 acting as a superantigen may be linked to 40% of these antibodies being VH3, although another 40% of the antibodies used VH4. A key limitation here is that only five HIV-specific antibodies were generated, and if more had been generated the results may be more conclusive, this will be discussed more fully later.

The Liao paper also looked at the gene family usage of the light chain genes, and once again the results are fairly similar to the results found in this study. In both cases, VK1 was the kappa chain gene that had the greatest usage, followed by VK3 in both HIV and non-HIV antibodies. The lambda chain gene usage was slightly different, in this study both HIV antibodies used VL2, and the non-HIV antibodies had the greatest usage of VL1 and VL2 gene families at each time point (except for visit 4 which had no lambda chain antibodies), whilst in the Liao paper VL3 family usage was greatest, followed by VL2 then VL1 (425).

The results of gene family usage found in this project therefore correspond with the already published data on heavy chain and light chain gene usage in HIV. Most papers published thus far have focussed on either HIV antibodies or “healthy” antibodies in their respective donors, and whilst there have been comparisons of the two within the same patient in the Li paper, this was from a patient during chronic infection. Here the HIV-specific and non-specific antibody response over time in the same patient has been examined. Unfortunately due to low specificity of HIV-specific plasmablasts in early infection,

HIV-specific antibodies could not be generated at later time points, though the results do allow a comparison of total HIV-specific antibodies to non-HIV antibodies that are from visits 1-4.

3.8.4. Heavy and light chain mutations

It has been demonstrated extensively that neutralising and broadly neutralising antibodies have high levels of mutations driven by viral escape, somatic hypermutation and affinity maturation (416, 438).

For example, around 20% of variable heavy chain amino acids are mutated in bNAbs such as b12, 10E8 and PGT121, increasing to 48% in VRC01 family antibodies (439), and are capable of neutralising a

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broad range of viruses due to this high mutation rate, whilst antibodies from acute or early HIV-1 infection have fewer mutations (440).

In this study, the heavy and light chain synonymous and non-synonymous nucleotide mutations and subsequent amino acid mutations were examined for the five HIV-specific monoclonal antibodies, and the visit 1 to 4 non-specific monoclonal antibodies. The previously discussed Liao paper compared the mutations of HIV-antibodies and non-HIV antibodies in both the heavy chain and light chain, and these results were comparable to the results found in this thesis. Liao et al found that acute HIV antibodies had an average mutation frequency of 5%, 3.6% and 3.1% for heavy chain, kappa chain and lambda chain variable genes (425). The overall average of HIV monoclonal heavy chain results was lower

(3.28%) than those published in the Liao paper, while the grouped light chain results were more similar to the published results (3.17%).

The non-specific antibodies results are quite different however. In the Liao paper, the non-HIV antibodies have VH and VL (kappa and lambda) frequency mutations of 6.1%, and 4.9% and 4.5% respectively. Whilst the visit 1 non-HIV antibodies were comparable to this (6.7% and 5.1% for VH and

VL respectively), the visit 2-4 non-HIV antibodies had higher numbers/percentages of mutations (9.5-

13.4% and 7.6-8.3% for VH and VL respectively). Of course, comparing the longitudinal results obtained from the patient in this thesis to the Liao paper has some drawbacks. The monoclonal antibodies generated in the Liao paper were pooled from five donors an average of 21.4 days after infection, whilst the antibodies generated as part of this project was from one patient over four one month time points with the first visit known to be within four months of infection. Therefore it may be possible to compare visit 1 antibodies and HIV antibodies to the Liao paper results, but not the visit

2-4 non-specific monoclonal antibodies.

Interestingly, the non-HIV antibodies generated from visits 1-4 seemed to increase in the number of nucleotide and amino acid mutations over time, and these differences were statistically different when tested by ANOVA. This may have been expected in HIV monoclonal antibodies over time if HIV

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antibodies had been generated at each time point as it is would have been likely that over time somatic hypermutation would have been occurring and therefore the HIV antibodies would have higher levels of mutations. As the antibodies from visits 1-4 are not specific or reactive against HIV, it was expected that these antibodies at each time point would be part of the non-specific immune response seen in early HIV infection, and therefore be fairly consistent over time in terms of their sequence data. In the case of gene family usage and CDRH3 lengths this was true, but there were significant differences in the heavy and light chain mutations of the visit 1, 2, 3 and 4 mAbs, with the visit 1 antibodies having the lowest percentage of mutations.

The rate of somatic hypermutation in normal human immunoglobulin genes is usually between 10-5 and 10-3 mutations per base pair per generation (441, 442), equating to around 15-20 amino acid mutations in a normal human antibody (282, 306). Therefore despite the increasing number of mutations at each visit in the non-HIV antibodies, the results shown here for non-HIV antibodies generated in early HIV infection are still lower than normal.

3.8.5. Complementarity determining regions

The complementarity determining regions (CDRs) are hypervariable domains of an antibody located in the heavy and light chain variable regions, making up the antigen binding site. There are three hypervariable loops or CDRs in each chain, and the CDRH3 in particular is the most variable, due to the presence of V, D and J genes, and can have varying lengths. It has been shown for example that the CDRH3 of bNAbs are very long, up to 33 amino acids, (310, 422), and this provides a functional advantage for the antibody in allowing the antibody to penetrate the glycan shield of HIV or reach the gp41 hydrophobic residues (443-445).

In this study it was found that the five early infection HIV monoclonal antibodies had a range of CDRH3 lengths from very short (7 amino acids) to fairly long (23 amino acids), whilst the visit 1-4 non-specific monoclonal antibodies were fairly uniform in their CDRH3 distribution. When the HIV monoclonal

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antibodies that had been isolated from visit 1 were compared to the non-specific visit 1 antibodies, there was no significant difference in the average length of heavy chain variable region CDRH3.

Normal CDRH3 lengths vary among humans but are typically around 12 amino acids in length (306,

420, 421), and whilst the HIV mAbs have a CDRH3 average length similar to this (13.6), the average

CDRH3 lengths of the non-HIV visit 1-4 antibodies isolated in this study is greater than this. In the previously mentioned study by Liao et al into antibodies in acute infection, they found that the average

CDRH3 length of the HIV-1 and non-HIV antibodies was 15.1 and 14.9 amino acids respectively (425).

Whilst this paper showed that the HIV antibodies had on average longer CDRH3s than the non-HIV antibodies, the results in this thesis are the opposite.

The results also differ to those reported in the Breden paper. In the Breden paper, the HIV non-broadly neutralising antibodies had an average CDRH3 length of 17.7, chronic infection antibodies (not including HIV) had CDRH3s of 17.6 amino acids, and acute infection antibodies had CDRH3s of 14.7 amino acids long (417). Once again however it is important to note that this paper did examine antibodies isolated from chronic HIV infection, so the results here are not completely comparable.

Although the acute infection antibodies studied were not specific for HIV, the results for CDRH3 lengths for these antibodies were closer to the acute/early isolated HIV antibodies seen in this study.

While the CDRH3 lengths of the HIV and non-HIV antibodies in this study differed to those results published in the literature, the CDRL3 lengths of the antibodies examined in this study were more similar. The average CDRL3 length for the HIV mAbs in this study was 9.4, whilst visit 1-4 non-HIV mAbs were 9.8, 9.8, 9.5 and 8.8 amino acids long respectively. This was comparable to those in the Liao paper, which had kappa and lambda chains of 9.41 and 10.5 respectively for HIV mAbs, and the non-

HIV mAbs also had similar lengths (425). In this thesis, the HIV mAbs haven’t been categorised into kappa or lambda light chains, they are just classified as light chains in general as there was only five

HIV-specific antibodies to compare.

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3.8.6. Phylogenetic Analysis

The phylogenetic trees of the heavy and light chain HIV-specific and non-specific antibodies show that there was a diverse range of antibodies generated from the patient studied in this project. The phylogenetic analysis is based on sequence data, with the scale indicating distance between different sequences based on nucleotide differences. As previously mentioned, HIV infection triggers a non- specific activation of plasmablasts in early HIV infection (177). The phylogenetic analysis seemed to show a random distribution of heavy and light chain antibodies in their respective phylogenetic trees in the sense that some visit 1 non-specific antibodies were more closely related to visit 3 antibodies than other visit 1 antibodies for example. As there is this non-specific immune activation during acute/early HIV infection, this may explain the results seen here. Furthermore, antibodies in which the heavy chains were closely related were much more spread out in the light chain trees and vice versa. This can be explained by the random pairing of heavy and light chains in antibody development.

3.8.6. Limitations of work

There were several key limitations to the work carried out in this project. A main limitation was the number of HIV monoclonal antibodies that were isolated from the patient for analysis. As only five

HIV-reactive mAbs were isolated from the patient across the time points sorted, it made comparisons between the HIV mAbs and visit 1-4 non-HIV antibodies difficult with an obvious impact on testing statistical significance. The reason for only five HIV-specific monoclonals was linked to another key limitation, plasmablast percentage of the infected donor.

Whilst the plasmablast percentage at visit 1 was suitable for sorting, the percentages at visits 2-4 were much lower. This impacted the sort as it was more difficult for the machine to actually find the plasmablasts and sort them. This meant longer sorts per plate sorted, and fewer plates could be sorted, meaning the number of potential antibodies that could be generated and therefore the number of HIV-specific antibodies that could be isolated was decreased. Furthermore, as PBMC

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samples for acute/early patients were limited due to requirement in other studies, only 1 frozen vial could realistically be used per time point, also limiting the number of potential antibodies.

Another limitation was that only one early infected patient was studied. Due to the aim to study the longitudinal antibody response to HIV, it was decided that time and resources should be focussed on one patient at multiple time point instead of more patients at one or two time points. Ideally if time had allowed, samples from another patient at the same time points would have been used to generate antibodies and analyse the antibody response over time. Plasmablast responses can be quite variable among donors so studying another patient may have given different results.

Furthermore, it could be seen as a limitation that the patient studied started ART just days after the first sample was taken. Due to a change in NHS guidelines about initiation of antiretroviral treatment all patients in the UK now start this soon after diagnosis. Originally this study had planned to examine the antibody response over time in early infection from patients that were not on ART over the time period studied. However because of the change in ART guidelines this couldn’t be achieved. Whilst this did then enable before and after ART to be examined, it did mean that viral load was reduced at later time points and therefore the chance of antigen-specific antibodies also reduced.

3.8.7. Future Work

As mentioned in the limitations, longitudinal samples from only one patient were used to generate antibodies due to time and resources, and as plasmablast percentage was low only five HIV-specific antibodies were isolated. Therefore two key aims for future work would be to increase the number of

HIV-specific antibodies from the patient studied so far, as well as generate antibodies from other patients. This should increase the validity of the results, and would also mean more confidence in statistical analysis. Furthermore, to use the new protocol by Pinder et al described in section 3.8.2 would increase the yields of HIV-specific antibodies generated from infected patients, aiding in this.

Another aim for future work would be to further study the antibodies generated, determining exactly where the antibodies generated bind to on the HIV envelope, and find the exact binding site. This

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could be possible through epitope mapping, competitive ELISAs or using a yeast based library system currently under development in our lab.

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Chapter 4: Generating IgG subclass expression vectors and the production of IgG1-IgG4 monoclonal antibodies

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Chapter 4. Generating IgG subclass expression vectors and the production of IgG1-IgG4 monoclonal antibodies

4.1. Introduction

4.1.1. IgG subclasses

IgG antibodies can be categorised into four subclasses which are determined by their heavy chain gene; IgG1 (ɣ1), IgG2 (ɣ2), IgG3 (ɣ3) and IgG4 (ɣ4), which account for around 60%, 30%, 6% and 4% of total serum IgG (219, 446, 447). Whilst there are differences in the amino acid sequences of different isotypes, all IgG subclasses are similar in structure with two identical heavy chains consisting of three constant region domains (CH1, CH2, CH3) and one variable region domain (VH), and two identical light chains comprising one constant region (CL) and one variable region (VL). The light chain can be either kappa (κ) or lambda (λ) with the general ration 2:1 in humans respectively.

In 1959, Rodney Porter performed a key experiment to examine the structure of antibodies where the enzyme papain was used to cleave the IgG into three fragments (I, II, III) of the same 50kDa size. One of the fragments produced crystals which were generally homogenous (around 90%) among different antibodies, and was termed the fragment crystallisable or Fc region, whilst the other two fragments which had different charges to the Fc region did not crystallise and were heterogeneous (differences up to 100%) among different antibodies, and were termed antigen binding fragment or Fab (448, 449).

It is through the Fc region of the antibody that different effector functions are mediated such as antibody dependent cellular cytotoxicity (ADCC) and antibody dependent cellular phagocytosis (ADCP) through binding to the Fc receptor on effector cells, and antibody dependent complement deposition

(ADCD) through C1q binding. Due to the differences in Fc region among the different IgG subclasses,

IgG1, IgG2, IgG3 and IgG4 antibodies mediate different levels of effector functions.

4.1.2. Antibody mediated effector functions

Antibody dependent cellular cytotoxicity, or ADCC, is the mechanism by which an antibody recognises and binds to antigen displayed on the surface of an infected cell, and through the Fc region binds to

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and recruits an effector cell such as natural killer (NK) cells to activate the release of cytotoxic granules.

More specifically it is the crosslinking of the low-affinity FcɣRIIIa (CD16a) on NK cells that triggers ADCC

(219, 450-452). A key paper by Michaelsen et al testing chimeric mouse-human antibodies for ADCC showed that IgG3 antibodies was the best subclass for mediating ADCC, followed by IgG1, IgG4 then

IgG2 (453), with the same hierarchical pattern is seen for IgG binding affinity to the FcɣRIIIa receptor that mediates ADCC (219, 454).

Within the context of HIV, similar results showing that IgG3 and IgG1 antibodies are the best mediators of ADCC activity against HIV-1 have been described. Experiments examining the antibody responses of the RV144 vaccine trial which showed a modest protection of 31.2% (368) showed that there were polyfunctional IgG3 and IgG1 responses associated with protection (252). When IgG3 antibodies were depleted from the trial patient samples, there was a significant loss of ADCC activity when tested, and moreover loss of correlation between functional responses in general (252). In another study,

Ackerman et al examined the antibody responses of HIV-1 elite controllers compared to viremic patients and those on ART, with results showing that whilst the viremic and ART patients had greater

IgG2 and IgG4 responses associated with poor correlations of antibody effector functions, the elite controllers had polyfunctional IgG3 and IgG1 antibody responses that correlated with functional activity and viral control (253).

Antibody dependent cellular phagocytosis, or ADCP, is the process where antibodies opsonise pathogens, and through crosslinking of FcRs on phagocytic cells, such as macrophages or neutrophils, activate engulfment of the pathogen which is subsequently destroyed. The role of phagocytosis in

HIV-1 infection has been less well characterised compared to other Fc mediated effector function such as ADCC and ADCD, however the two studies examining antibody polyfunctionality by Chung and

Ackerman which were previously mentioned also studied phagocytosis as one of the effector functions induced in the polyfunctional response (252, 253).

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Antibody dependent complement deposition (ADCD) is the mechanism whereby antibodies bind to and activate the C1q component of complement, triggering the classical pathway of complement cascade which ultimately results in the formation of a membrane attack complex leading to cell lysis.

In the 1960s and 70s it was shown through a number of studies using myeloma proteins that IgG3 has the greatest binding affinity to C1q, followed by IgG1, IgG2 and then IgG4 (455-457). Bindon et al went on to further examine antibody the mediation of the whole complement pathway in order to study overall lysis of each of the IgG subclasses and not just C1q binding. Their 1988 paper showed results that agreed with those previously published that IgG3 had the best binding to C1q, but that it was the presence of IgG1 that resulted in the greatest cell lysis (458).

As described in these examples, IgG3 in particular is a potent mediator of different effector functions, and this has been attributed to its longer hinge region. Whilst IgG1, IgG2 and IgG4 antibodies have hinge regions of 12-15 amino acids, the IgG3 hinge region is around 4 times this, up to 62 amino acids long (459), giving it a greater flexibility as well as greater binding to Fc receptors and complement as a longer hinge means less shielding of FcR/C1q binding sites on the Fc regions by Fab regions (219).

On the other hand, whilst IgG2 and IgG4 have shown to be poor mediators of effector functions, several studies in monoclonal therapy fields have shown that this may not completely be the case. For example a 2010 study by Schneider-Merck et al compared the ADCC activity of an IgG1 mAb

(zalutumumab) and an IgG2 mAb (panitumumab) that both target and have similar affinities for the epidermal growth factor receptor (EGF-R), and are both approved for use in immunotherapy. Whilst only the IgG1 antibody was capable of mediating ADCC activity through NK cells, the results showed that the IgG2 mAb was as effective as the IgG1 mAb in mediating ADCC through recruiting myeloid cells, such as monocytes and neutrophils, using the FcɣRIIa (460). More recently, Konitzer et al published results of a study on the anti-CD20 monoclonal antibody Rituximab which had been adapted from IgG1 heavy chain to IgG2 and IgG4 subclasses. The results showed that whilst ADCC activity did decrease in the IgG2 and IgG4 Rituximab antibodies, direct apoptosis of Burkitt’s lymphoma cell lines

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increased from 10-11% in the IgG1 version, to almost 50% in the IgG2 version, and around 40% in the

IgG4 version of the antibody (461). Similarly, by altering the IgG1 Rituximab hinge region through deletion of a two adjacent amino acids in the upper hinge of IgG1 to be more similar to that of the

IgG2 and IgG4 hinge, there was around a threefold increase in apoptosis (461).

Of course the vast majority of these studies mentioned have examined bulk IgG1/2/3/4 from serum in testing effector functions, or when monoclonals have been used, they are often of different specificities. Therefore, the ability to generate monoclonal antibodies in a range of IgG subclasses but with the same variable regions would allow a comparison of individual monoclonal antibodies with the same specificity but different mediation of effector functions.

4.1.3. Single cell cloning techniques for antibody production

Single cell cloning techniques are now routinely used for antibody production, with a number of various techniques used as discussed in previous chapter. All however follow a similar protocol of single cell sorting the B cell population of interest, amplifying variable heavy and light chains and ligating them into a constant region vector.

This technology has become a crucial tool for the isolation of broadly neutralising antibodies within the field of HIV, so not only that they may be used therapeutically, but knowledge gained from these antibodies could be used in aiding the design of a vaccine to stimulate the desired antibody response.

The vast majority of monoclonal antibodies generated are of the IgG1 subclass, however monoclonal antibodies of other isotypes do exist, such as the IgG3 MPER BNAb 2F5 (302) , but these have generally been generated through other techniques such as phage display, hybridoma technology or EBV immortalisation, which each have disadvantages associated with them highlighted in a review by

Wilson et al (376).

Single B cell cloning on the other hand is a newer technology and is now routinely used. A key advantage to this technology is that as single B cells are sorted into 96 well plates for cloning, each well contains one matching heavy and light chain pair, and therefore it can be certain that the antibody

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that is generated at the end of the process is an actual pairing that went through selection in the individual from which cells were isolated.

A widely followed protocol for monoclonal antibody production using single cell cloning techniques was published in Nature Protocols by Smith et al, and uses an IgG constant region heavy chain vector

(Figure 4.1) and light chain vectors (kappa or lambda) to produce antibodies (404). The PBR322 based heavy chain vector contains the variable region cloning site, flanked by the AgeI and SalI restriction sites, into which the amplified heavy chain variable region DNA from single cell sorting is cloned, and is followed downstream by the IgG1 constant region gene. When this heavy chain vector is paired with its matching light chain vectors containing light chain variable region DNA and used to transfect

HEK293T cells, IgG1 antibodies are produced.

Figure 4.1: IgG-Abvec heavy chain expression vector used for production of IgG1 monoclonal antibodies. The 5750bp PBR322 based vector contains a human CMV promotor for initiation of transcription, followed by a murine immunoglobulin signal peptide sequence. The variable domain cloning site flanked by AgeI and SalI restriction enzymes is then followed by the human IgG1 constant region gene, after which a HindIII restriction site is located. The vector also contains a SV40 polyadenylation sequence after the constant region, and the ampicillin resistance gene for selection of clones during the cloning procedure. Adapted by permission from Springer Nature, Nature Protocols. Rapid generation of fully human monoclonal antibodies specific to a vaccinating antigen, Kenneth Smith, Lori Garman, Jens Wrammert, Nai-Ying Zheng, J Donald Capra et al, COPYRIGHT (2009). Supplementary Figure 1, (404). Rights and permissions in Appendix 8.

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However, as described in section 4.1.1, other IgG subclasses mediate effector functions at different levels and therefore may be better at a particular function than the IgG1 antibody. Therefore, modifying this vector to contain the constant region genes of the IgG2 (Cɣ2), IgG3 (Cɣ3) and IgG4 (Cɣ4) subclasses would be very beneficial so that antibodies of the same specificity but different Fc regions can be generated through single cell cloning techniques. There have been several studies before where antibodies have been isotype switched to get antibodies of different IgG subclasses in both HIV research as well as against other pathogens, (461-465), though this usually involves adapting already characterised IgG1 antibodies, more laborious single cell cloning protocols and techniques, and different vectors and cell lines for antibody production than those in the Smith protocol. By modifying this vector to generate IgG2, IgG3 and IgG4 vectors, amplified heavy chain variable region DNA will be able to be immediately cloned into readily available vectors in all IgG subclasses to allow efficient production of the same antibody in multiple subclass for testing in functional assays.

4.1.4. Aims

There were three key aims for this chapter. Firstly, to construct IgG2, IgG3 and IgG4 constant region containing expression vectors through modification of current IgG1 (Abvec) expression vector.

Secondly, to generate IgG2, IgG3 and IgG4 monoclonal antibodies derived from early infected HIV patient sorted plasmablasts, and finally to produce well characterised broadly neutralising monoclonal antibodies in IgG1, IgG2, IgG3 and IgG4 isotypes.

4.2. Construction of IgG2, IgG3 and IgG4 subclass expression vectors

In order to be able to produce antibodies of different IgG subclasses in the lab through single cell expression cloning, it was necessary to modify the IgG-Abvec expression vector currently used in monoclonal antibody production (404). This PBR322 based vector contains the IgG1 constant region and variable region cloning site to enable IgG1 antibody production (previously shown in Figure 4.1).

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Figure 4.2: Schematic for generation on HIV specific antibodies in different isotypes. The current IgG1 vector would be digested with enzymes flanking the IgG1 constant region so that the constant region could be removed through gel extraction. IgG2, IgG3 and IgG4 constant regions would be amplified from human cDNA and ligated into the vector. The variable regions of those IgG1 antibodies that were specific or reactive to gp140 would then be ligated into the IgG2-4 vectors, enabling production of antibodies in all IgG subclasses.

The proposed method for adapting this vector so that IgG2, IgG3 and IgG4 antibodies could be produced involved amplifying these different constant regions from human cDNA, extracting the IgG1 constant region from the IgG-Abvec vector, and then ligating in the other isotype constant regions

(Figure 4.2).

4.2.1 Amplification of IgG2, IgG3 and IgG4 constant regions

Using primers designed for the IgG2, IgG3 and IgG4 constant regions, PCR was performed on cDNA synthesised from isolated human polyA+ mRNA. In order to maximise chances of resulting PCR product, two annealing temperatures of 55°C and 65°C were used.

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1 2 3 4

Figure 4.3: IgG subclass constant region PCR results. PCR to amplify IgG from human cDNA using two different annealing temperatures. Lane 1: 100bp DNA ladder, lane 2: PCR with 55°C annealing temperature, lane 3: PCR with 65°C annealing temperature, lane 4: negative control. PCR products were run on a 1% TBE agarose gel.

Figure 4.4: 1% agarose gel showing vectors which contained the IgG constant region insert. Vectors that had been ligated with PCR amplified IgG constant regions were transformed, single colonies were picked from transformation agar plates and grown overnight at 37°C (with 220rpm shaking). Cultures were miniprepped and a small volume of elute was digested with SalI and HindIII restriction enzymes to check for the presence of the PCR product insert from the IgG2/3/4 constant region PCR. Lane 1: 100bp DNA ladder, lanes 7,8 12, 14 and 22 show vector that contains IgG constant region insert (indicated by arrow).

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There was a prominent band around 1Kb in size for the PCR using the higher annealing temperature of 65°C (Figure 4.3). The IgG2, IgG3 and IgG4 constant regions were expected to be 980bp, 1549bp, and 983bp in size respectively, thus it was likely that this band was either the amplified constant region of either IgG2 or IgG4. After gel extraction and purification, the PCR product was ligated into the IgG1

Ab-Vec vector, previously digested to remove the IgG1 constant region, and transformed into Dh5a competent cells. Colonies were then picked, cultured overnight, and miniprepped, followed by restriction enzyme digestion to check for the insert (Figure 4.4).

Only 5 out of the 23 minipreps digested contained an insert around 1000bp in size (indicating IgG2 or

IgG4), and these minipreps were sent for sequencing. Sequences for each miniprepped vector were input into the NCBI “Align Sequences Nucleotide Blast” tool and aligned with the IgG2, IgG3 and IgG4 reference sequences. Table 4.1 shows that all minipreps were most closely related to the IgG2 constant region (≥95%) and therefore this region had been amplified, not the IgG3 or IgG4 constant regions.

Vector IgG2_J00230 IgG3_X03604 IgG4_K01316 No. (% Identity) (% Identity) (% Identity) 1 97 92 92 2 95 89 90 3 97 92 92 4 96 93 91 5 95 89 91

Table 4.1: Nucleotide alignment of miniprepped vectors with IgG2, IgG3 and IgG4 reference sequences. Constant regions within vector minipreps were aligned with IgG2, IgG3 and IgG4 reference sequences. Given figures indicate percentage identity to the reference sequence.

As none of the vectors contained a 100% match for the IgG2 constant region, colonies were repeatedly picked, cultured and miniprepped until a near to 100% match was found. A complete match to the reference sequences was required for each subclass vector, however if vectors were short of this by just one or two amino acids, mutagenesis could be performed to get a 100% match.

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<----- IgG2_Vec2 GDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGVHSSVRSTKGPS IgG2_J00230 ------STKGPS ******

------CH1 Region ------IgG2_Vec2 VFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSS IgG2_J00230 VFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSS ************************************************************

------> <- Hinge -> <------IgG2_Vec2 VVTVTSSNFGTQTYTCNVDHKPSNTKVDKTVERKCCVECPPCPAPPVAGPSVFLFPPKPK IgG2_J00230 VVTVPSSNFGTQTYTCNVDHKPSNTKVDKTVERKCCVECPPCPAPPVAGPSVFLFPPKPK **** *******************************************************

------CH2 Region ------IgG2_Vec2 DTLMISRTPEVTCVVVDVSHEDPEVQFNWYVDGMEVHNAKTKPREEQFNSTFRVVSVLTV IgG2_J00230 DTLMISRTPEVTCVVVDVSHEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTFRVVSVLTV *********************************:**************************

------> <------IgG2_Vec2 VHQDWLNGKEYKCKVSNKGLPAPIEKTISKTKGQPREPQVYTLPPSREEMTKNQVSLTCL IgG2_J00230 VHQDWLNGKEYKCKVSNKGLPAPIEKTISKTKGQPREPQVYTLPPSREEMTKNQVSLTCL ************************************************************

------CH3 Region ------IgG2_Vec2 VKGFYPSDIAVEWESNGQPENNYKTTPPMLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVM IgG2_J00230 VKGFYPSDIAVEWESNGQPENNYKTTPPMLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVM ************************************************************

------> IgG2_Vec HEALHNHYTQKSLSLSLGK*KLGRHGPTCLLQLIMVTNKAIASQISQIKPFFSQX IgG2_J00230 HEALHNHYTQKSLSLSPGK*------**************** ***

Figure 4.5: Clustal omega alignment of IgG2 vector sequence with IgG2_J00230 reference sequence. Multiple IgG2 vectors were aligned with the reference sequence (IgG2_J00230), with the closest sequence to the reference shown. There were two amino acid residues that differed from the reference sequence, but match other IgG2 isotypes (highlighted in grey), whilst one amino acid differed from all isotypes (highlighted in red).

Now that it was established that the amplified constant region from the PCR was the IgG2 constant region, all nucleotide sequences obtained from sequenced minipreps were converted to protein sequences and aligned with the protein IgG2 reference sequence. The closest match found had 2 amino acid variations compared to the reference sequence in the CH1 and CH2 regions (highlighted grey in Figure 4.5), however upon further analysis it was found that these matched another IgG2 isotype (IgG2*02 – accession number AJ250170). There was however one amino acid change at the very 3’ end of the CH3 region (highlighted red in Figure 4.5) that did not match any other reference sequences.

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In order to have a protein IgG2 constant region sequence that was a 100% match to the reference sequences, a mutagenesis PCR to mutate the leucine (L) residue to the correct proline (P) residue of the IgG2_J00230 sequence was performed.

IgG2_Vec HEALHNHYTQKSLSLSLGK*KLGRHGPTCLLQLIMVTNKAIASQISQIKPFFSQX IgG2_mTVec HEALHNHYTQKSLSLSPGK*KLGRHGPTCLLQLIMVTNKAIASQIHK*SIYA IgG2_J00230 HEALHNHYTQKSLSLSPGK*------**************** ***

Figure 4.6: Clustal omega alignment of original IgG2 vector, mutated IgG2 vector sequence and IgG2_J00230 reference sequence. The mutated IgG2 vector (IgG2_mTVec) protein sequence was aligned with the original IgG2 vector sequence and the IgG2 protein reference sequence, and showed that vector now had the correct amino acid.

This mutagenesis successfully corrected the L to P, (Figure 4.6) and therefore the IgG2 constant region within the vector was now a complete match for reference sequences, among all regions – CH1, CH2,

CH3 and the hinge region. As the original PCR with designed primers for IgG2, IgG3 and IgG4 had only amplified the IgG2 constant region, another method was necessary to amplify IgG3 and IgG4 constant regions.

4.2.2. Gibson reaction to generate IgG3 and IgG4 constant region vectors

Despite repeated attempts to obtain the IgG3 and IgG4 constant region DNA from PCR with different primers, only the IgG2 constant DNA was being amplified. Therefore, an alternate method known as the Gibson reaction was used. Briefly, this method uses multiple primers to amplify the DNA fragment of interest in two parts, with an overlapping section at the end of the first fragment and the start of the second fragment, and then combines them directly into the vector. Primers to target the IgG3 and

IgG4 constant regions were designed and PCR performed.

Initially, only the second fragment of both IgG3 and IgG4 was correctly amplified by the PCR reaction, visualised by bands at 720bp and 660bp respectively (Figure 4.7a). However, after repeating the PCR with the same primers but different PCR polymerase kits, the first fragment for both IgG3 and IgG4 were both seen on the agarose gel in lanes 3 and 4 for IgG3, and lanes 6 and 7 for IgG4 (Figure 4.7b).

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The bands for the first and second fragments for each IgG were extracted and purified, before proceeding with the Gibson reaction to ligate the two together into the vector. After transformation, picking colonies, overnight culture and miniprep as before, vectors were digested to check for the insert of the right size.

(a) (b) 1 2 3 4 5 1 2 3 4 5 6 7

IgG3 IgG4

Figure 4.7: PCR products of IgG3 and IgG4 constant region PCR on 1% TBE-agarose gels. PCR products of IgG3 and IgG4 first and second fragments for Gibson reaction were run on a gel. (a) PCR resulted in IgG3 and IgG4 second fragments but not first fragments. Lane 1: 100bp DNA ladder, Lane 2: IgG3 first fragment PCR, Lane 3: IgG3 second fragment PCR, Lane 4: IgG4 first fragment PCR and Lane 5: IgG4 second fragment PCR. (b) PCR to amplify first fragments of IgG3 (lanes 2,3 and 4) and IgG4 (lanes 5,6 and 7) using Pfx (lanes 2 + 5), Platinum Taq (lanes 3 + 6) and AccuTAq (lanes 5 and 7) DNA polymerase. Both gels have 100bp DNA ladder in lane 1.

1 2 3 4 5 6 7 8 9 10

Figure 4.8: Digested vector minipreps from IgG3 and IgG4 Gibson reactions. Miniprepped vectors with IgG3 and IgG4 first and second fragment insertion using Gibson reaction were digested to check for an insert of the correct overall constant region size, around 1000bp. Lane 1 shows the 100bp DNA ladder, lanes 2-5 are the IgG3 vectors, and lanes 6-10 are the IgG4 miniprepped vectors.

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<----- IgG4_Vec GDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGVHSSVRSTKGPS IgG4_K01316 ------STKGPS ******

------CH1 Region ------IgG4_Vec VFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSS IgG4_K01316 VFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSS ************************************************************

------> <- Hinge -> <------IgG4_Vec VVTVPSSSLGTKTYTCNVDHKPSNTKVDKRVESKYGPPCPSCPAPEFLGGPSVFLFPPKP IgG4_K01316 VVTVPSSSLGTKTYTCNVDHKPSNTKVDKRVESKYGPPCPSCPAPEFLGGPSVFLFPPKP ************************************************************

------CH2 Region ------IgG4_Vec KDTLMISRTPEVTCVVVDVSQEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTYRVVSVLT IgG4_K01316 KDTLMISRTPEVTCVVVDVSQEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTYRVVSVLT ************************************************************

------> <------IgG4_Vec VLHQDWLNGKEYKCKVSNKGLPSSIEKTISKAKGQPREPQVYTLPPSQEEMTKNQVSLTC IgG4_K01316 VLHQDWLNGKEYKCKVSNKGLPSSIEKTISKAKGQPREPQVYTLPPSQEEMTKNQVSLTC ************************************************************

------CH3 Region ------IgG4_Vec LVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSRLTVDKSRWQEGNVFSCSV IgG4_K01316 LVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSRLTVDKSRWQEGNVFSCSV ************************************************************

------> IgG4_Vec MHEALHNHYTQKSLSLSLGK*KLGRHGPTCLLQLIMVTNKAIASQIS IgG4_K01316 MHEALHNHYTQKSLSLSLGK*------*********************

Figure 4.9: Clustal omega alignment of IgG4 vector sequence and IgG4_ K01316 (IgG4*01) reference sequence. Multiple IgG4 vectors were aligned with the reference sequence, with a complete matching vector sequence shown. CH1, CH2, CH3 and hinge regions are indicated.

<----- H1 -----> <---- IgG3_X03604 YSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGDTTHTCPRCPEPKSCDT IgG3Vec1 YSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGD------IgG3Vec2 YSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGD------IgG3Vec3 YSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGD------IgG3Vec4 YSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGD------********************************************

H2 ----> <---- H3 ----> <---- H4 ----> IgG3_X03604 PPPCPRCPEPKSCDTPPPCPRCPEPKSCDTPPPCPRCPAPELLGGPSVFLFPPKPKDTLM IgG3Vec1 ------TPPPCPRCPAPELLGGPSVFLFPPKPKDTLM IgG3Vec2 ------TPPPCPRCPAPELLGGPSVFLFPPKPKDTLM IgG3Vec3 ------TPPPCPRCPAPELLGGPSVFLFPPKPKDTLM IgG3Vec4 ------TPPPCPRCPAPELLGGPSVFLFPPKPKDTLM *******************************

Figure 4.10: Clustal omega alignment of partial IgG3 vector protein sequences with IgG3 X03604 reference sequence. Four examples of IgG3 vectors developed in the lab were aligned with the IgG3 reference sequence The hinge regions are indicated and highlighted on the reference sequence.

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All of the miniprepped vectors contained the insert of around 1000bp for both IgG3 and IgG4, resulting from the 300bp and 720bp fragments used for IgG3 and 300bp and 660bp fragments for IgG4, (Figure

4.8). These vectors were sent for sequencing, and one of the IgG4 vectors was found to be a complete match for the reference sequence, (Figure 4.9).

The IgG3 vector was more problematic, and after repeating the picking of colonies, miniprep and sequencing, there still wasn’t a complete match to the IgG3_X03604 reference sequence. Whilst the sequence matched for the CH1, CH2, and CH3 regions, it was the hinge region that failed to match the reference sequence. This region was either only partially present, i.e. the first part of hinge 1 and the last part of hinge 4 (Figure 4.10), or missing the entire hinge region of H1-H4 altogether.

As the full hinge was not present, this region was not being amplified correctly through the original

PCR, new primers were designed to repeat the Gibson reaction. Whilst the forward primer for the first fragment, and the reverse primer for the second fragment remained the same, the forward and reverse primer for the middle overlapping section was changed to aim to incorporate the hinge region.

After repeating the PCR with these new primers, and the Gibson reaction and cloning procedures as before, the resulting miniprepped vectors were sent for sequencing.

<----- H1 -----> IgG3_Vec VLQSSGLYSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGDTTHTCPRCP IgG3_X03604 VLQSSGLYSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVELKTPLGDTTHTCPRCP ************************************************************

<---- H2 ----> <---- H3 ----> <---- H4 ----> IgG3_Vec EPKSCDTPPPCPRCPEPKSCDTPPPCPRCPEPKSCDTPPPCPRCPAPELLGGPSVFLFPP IgG3_X03604 EPKSCDTPPPCPRCPEPKSCDTPPPCPRCPEPKSCDTPPPCPRCPAPELLGGPSVFLFPP ************************************************************ Figure 4.11: Clustal omega partial alignment of correct IgG3 vector sequence and reference sequence. Clustal omega alignment of IgG3 vector and IgG3_X03604 reference sequence highlighting the now correctly amplified full hinge region consisting of hinge 1 (H1) followed by three repeating hinge sections (H2-4), flanked by the last 43 amino acid residues of the CH1 region and the first 14 amino acid residues of CH2 region.

This time, the full hinge section, along with the CH1, CH2 and CH3 regions, was fully amplified in the

PCR reaction (Figure 4.11), and was a complete match for the reference sequences.

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4.2.3. IgG2, IgG3 and IgG4 AgeI Mutagenesis

Now that the vectors for IgG2, IgG3 and IgG4 had matching protein sequences with the reference sequences, the vectors were almost ready to be used for antibody production. However, upon checking the sequences for restriction enzyme sites, it was found that all three had an AgeI restriction enzyme site within the CH1 region (Figure 4.12a). As an AgeI restriction enzyme sites is used for cloning the variable region DNA from sorted plasmablasts into to the vectors, this additional site would have to be removed, as otherwise when digesting the vector for ligation, the vector would also be cut at this site as opposed to just the designated cloning site.

(a)

IgG2 ATCATCCTTTTTCTAGTAGCAACTGCAACCGGTGTACACTCGAGCGTACGGTCGACCAAG IgG3 ATCATCCTTTTTCTAGTAGCAACTGCAACCGGTGTACACTCGAGCGTACGGTCGACCAAG IgG4 ATCATCCTTTTTCTAGTAGCAACTGCAACCGGTGTACACTCGAGCGTACGGTCGACCAAG ************************************************************ G2/3/4 I I L F L V A T A T G V H S S V R S T K

IgG2 GGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCGGCC IgG3 GGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCTGGGGGCACAGCGGCC IgG4 GGCCCATCCGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCGGCC ******** *********************************** * * *********** G2/4 G P S V F P L A P C S R S T S E S T A A G3 G G

IgG2 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAACCGGTGACGGTGTCGTGGAACTCAGGC IgG3 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAACCGGTGACGGTGTCGTGGAACTCAGGC IgG4 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAACCGGTGACGGTGTCGTGGAACTCAGGC ************************************************************ G2/3/4 L G C L V K D Y F P E P V T V S W N S G

(b)

IgG2 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAGCCTGTGACGGTGTCGTGGAACTCAGGC IgG3 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAGCCTGTGACGGTGTCGTGGAACTCAGGC IgG4 CTGGGCTGCCTGGTCAAGGACTACTTCCCCGAGCCTGTGACGGTGTCGTGGAACTCAGGC ************************************************************ G2/3/4 L G C L V K D Y F P E P V T V S W N S G

Figure 4.12: Alteration of AgeI restriction site within constant region of IgG2, IgG3 and IgG4. (a) Nucleotide sequences of 180bp region of IgG2, IgG3 and IgG4 vectors containing cloning site and start of constant region containing extra AgeI site is shown. Variable region cloning site (yellow) is flanked by AgeI (grey) and SalI (green), with the additional AgeI site highlighted (row 3). (b) 60bp region of IgG2, IgG3 and IgG4 vectors containing mutated AgeI restriction site to alternate 6bp nucleotide sequence, but yielding the same protein sequence. For both (a) and (b), corresponding protein sequence is underneath the nucleotide sequences, and asterisks indicate conserved nucleotide sequence.

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A mutagenesis PCR to convert two nucleotides in this six nucleotide AgeI sequence to an alternative sequence that still yields the same amino acid code but no longer an AgeI restriction site was designed and performed, once again followed by competent cell transformation, selection of colonies and miniprep. Sequence analysis showed the nucleotide sequence was indeed altered, but still resulted in the same amino acid sequence and thus same constant region for each IgG subclass (Figure 4.12b).

<------CH1-- IgG1_Vec MGWSCIILFLVATATGVHSSVRSTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTV IgG2_Vec MGWSCIILFLVATATGVHSSVRSTKGPSVFPLAPCSRSTSESTAALGCLVKDYFPEPVTV IgG3_Vec MGWSCIILFLVATATGVHSSVRSTKGPSVFPLAPCSRSTSGGTAALGCLVKDYFPEPVTV IgG4_Vec MGWSCIILFLVATATGVHSSVRSTKGPSVFPLAPCSRSTSESTAALGCLVKDYFPEPVTV **********************************.*:*** .******************

------> IgG1_Vec SWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKRVE IgG2_Vec SWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVTSSNFGTQTYTCNVDHKPSNTKVDKTVE IgG3_Vec SWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYTCNVNHKPSNTKVDKRVE IgG4_Vec SWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTKTYTCNVDHKPSNTKVDKRVE ******************************** **.:**:** ***:********** **

<------Hinge------IgG1_Vec ------PKSCDKTHTCPPC IgG2_Vec RKCC------VECPPC IgG3_Vec LKTPLGDTTHTCPRCPEPKSCDTPPPCPRCPEPKSCDTPPPCPRCPEPKSCDTPPPCPRC IgG4_Vec SK------YGPPCPSC ** * > <------CH2 ------IgG1_Vec PAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKT IgG2_Vec PAPP-VAGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVQFNWYVDGMEVHNAKT IgG3_Vec PAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVQFKWYVDGVEVHNAKT IgG4_Vec PAPEFLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSQEDPEVQFNWYVDGVEVHNAKT *** :.*******************************:*****:*:*****:*******

------> <------IgG1_Vec KPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVY IgG2_Vec KPREEQFNSTFRVVSVLTVVHQDWLNGKEYKCKVSNKGLPAPIEKTISKTKGQPREPQVY IgG3_Vec KPREEQFNSTFRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKTKGQPREPQVY IgG4_Vec KPREEQFNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKGLPSSIEKTISKAKGQPREPQVY ******:***:********:*****************.**: *******:**********

------CH3 ------IgG1_Vec TLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSK IgG2_Vec TLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPMLDSDGSFFLYSK IgG3_Vec TLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESSGQPENNYNTTPPMLDSDGSFFLYSK IgG4_Vec TLPPSQEEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSR *****:****************************.*******:****:***********:

------> IgG1_Vec LTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK* IgG2_Vec LTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK* IgG3_Vec LTVDKSRWQQGNIFSCSVMHEALHNRSTQKSLSLSPGK* IgG4_Vec LTVDKSRWQEGNVFSCSVMHEALHNHYTQKSLSLSLGK* *********:**:************: ******** ***

Figure 4.13: Clustal omega alignment of IgG1, IgG2, IgG3 and IgG4 vectors. Protein sequences of original IgG1 and new IgG2, IgG3 and IgG4 vectors were aligned, with CH1, Hinge, CH2 and CH3 regions indicated.

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After the mutagenesis step for each subclass, each IgG vector was realigned with their corresponding

IgG2, IgG3 and IgG4 reference sequence to reconfirm the sequences were still 100% matched. The original IgG-Abvec (IgG1), and generated IgG2, IgG3 and IgG4 vectors were then aligned (Figure 4.13).

The final result of these experiments was the construction of IgG2, IgG3 and IgG4 expression vectors through modification of the original IgG-Abvec vectors described in the literature. The construction of these vectors will allow antibodies with difference constant regions but the same variable regions to be produced to allow to test them for function.

4.3. Generation of patient derived monoclonal antibodies in different IgG subclasses

As previously discussed in results chapter 1 of this thesis, five monoclonal antibodies specific for HIV gp140 were generated from sorted plasmablasts from an early infected HIV patient. These antibodies were all generated as IgG1 antibodies as per the protocol followed. However, now that vectors for the other IgG isotypes have been generated, these monoclonal antibodies can also be adapted to express the other constant regions, enabling the function of antibodies with the same variable region but different constant regions to be compared. The light chains (kappa or lambda) remain unchanged.

4.3.1. Cloning heavy chain PCR products into IgG2, IgG3 and IgG4 expression vectors

Initially, to clone the heavy chain variable region of each monoclonal antibody into each of the newly generated isotype expression vectors, the original cloning PCR product that was previously used to generate the IgG1 monoclonal antibody (from work described in chapter 3) was used. As previously described, the digested PCR product was ligated with the digested heavy chain vectors, transformed, colonies selected for overnight culture and then miniprepped. Vector minipreps were then sent for sequencing. Upon receiving the vector sequences, the nucleotide sequences were translated to protein sequences and aligned with the IgG1 “consensus” sequence for examining sequence similarity.

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V1X3A2HG1 QLQLQESGPGLVKPSETLSLTCTVSGGSISSYYWSWIRQPPGKGLEWIGYIYFSGSTNYN V1X3A2H2G2 QVQLQESGPGLVKPSETLSLTCTVSGGSISSYYWSWIRQPPGKGLEWIGYIYFSGSTNYN V1X3A2H4G3 QLQLQESGPGLVKPSETLSLTCTVSGGSISSYYWSWIRQPPGKGLEWIGYIYFSGSTNYN V1X3A2H3G4 QLQLQESGPGLVKPSETLSLTCTVSGGSISSYYWSWIRQPPGKGLEWIGYIYFSGSTNYN

*:**********************************************************

V1X3A2HG1 PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARQRLWRVGFDPWGQGTLVTVSS V1X3A2H2G2 PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARQRLWRVGFDPWGQGTLVTVSS V1X3A2H4G3 PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARQRLWRVGFDPWGQGTLVTVSS V1X3A2H3G4 PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARQRLWRVGFDPWGQGTLVTVSS

**********************************************************

Figure 4.14: Clustal Omega Alignment of V1X3A2 in different isotypes. Original IgG1 (italic bold), IgG2, IgG3 and IgG4 expression vector variable region amino acid sequences for mAb V1X3A2 were aligned. Mutations compared to IgG1 sequence are highlighted (grey), and matching or conserved amino acids identified by asterisks (*) under the sequence alignment. The variable region only is shown.

It was incredibly important that the variable regions in each isotype expression vector were 100% identical to each other so that when it came to testing antibodies for function, any differences could be attributed specifically to different constant regions. When the IgG3 and IgG4 vector variable sequences for mAb V1X3A2 were aligned with the IgG1 sequence, they were 100% match (Figure

4.14). The IgG2 sequence however contained a single amino acid mutation (L>V) and therefore still needed work before it could be used for antibody production. For all other antibody variable regions and isotype sequences however there was a problem.

When the other four monoclonal antibody variable regions were ligated into the IgG subclass vectors using the original PCR product that had been used to generate the IgG1 antibodies, there was multiple mutations in each subclass compared to the IgG1 sequence likely due to original PCR. If a single mutation had been present, it may have been possible to mutate this back to the correct sequence, however as multiple mutations within each variable region were found, another method using the variable regions from the original IgG1 vector was used.

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4.3.2. Extraction of heavy variable region DNA from IgG1 vector and ligation into IgG subclass expression vectors

In order to ensure the variable regions of each IgG subclass were the same for each of the five patient isolated antibodies, the variable region from the IgG1 vector that yielded the gp140 specific monoclonal antibody was extracted through digestion with AgeI and SalI restriction enzymes, and ligated into the IgG2-4 vectors. After transformation through to miniprep protocols as before, miniprepped vectors were sent for sequencing and aligned to IgG1 variable region sections. Upon alignment, the variable regions for each mAb in each subclass were complete matches (Figure 4.15).

As shown previously in figure 4.14, whilst the IgG3 and IgG4 variable regions of V1X3A2 matched the original IgG1 sequence, the IgG2 variable region sequence had one mutation. Using the digested variable region from the IgG1 vector and ligating this into the IgG2 subclass vector for V1X3A2H now meant the IgG2 vector had the same variable region as the previously successful IgG3 and IgG4 vectors

(Figure 4.15).

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IgG4 subclasses.IgG4

V2X1F6 V2X1F6 (e) in each IgG subcass vector were aligned to

4 subclasses for each mAb had variable complete region matches.

: Clustal omega alignment of identical variable regions patient of five derived monoclonal IgG2, antibodies in IgG1, IgG3 and

Figure 4.

The The heavy chain variable regions protein sequences of mAb V1X1B5 (a), V1X2B12 (b), V1X2C5 (c), V1X3A2 (d), an allensure antibodies of the IgG1

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4.3.3. Production of IgG2, IgG3 and IgG4 monoclonal antibodies

Now that the IgG1-IgG4 vectors were confirmed to be exact matches in terms of the variable regions, each IgG subclass vector was transfected into the 293T cell line with the same light chain vector for each IgG subclass. After 5 days the culture supernatant was harvested and tested by IgG ELISA for the presence of antibody.

Figure 4.16: Semi-quantitative IgG ELISA results of HEK293 transfected cells using IgG1-4 expression vectors. Monoclonal antibodies V1X1B5, V1X2B12, V1X2C5, V1X3A2 and V2X1F6 were generated in IgG1-4 subclasses using the IgG1-4 expression vectors. ELISA was used to measure IgG in transfection supernatant of each antibody at a wavelength of 450nm.Highest and lowest standard IgG concentration (100ng/ml and 1.56ng/ml respectively) were used as controls, with any OD450 greater than the lowest IgG standard classed as being IgG positive.

The supernatant of each monoclonal in each subclass was found to contain IgG and thus the generation of monoclonal antibodies in each of the four IgG subclasses had worked. The purpose of this ELISA was purely to test whether the newly designed IgG2-4 vectors would actually generate

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antibodies. Both V1X1B12 and V1X2C5 showed good levels of antibody in the supernatant as indicated by the OD values (Figure 4.16). All antibodies seemed to have good IgG4 production, whilst IgG3 generally seemed to be lower among each antibody. Overall these results show that the developed

IgG2, IgG3 and IgG4 vectors can be successfully used to generate monoclonal antibodies of same specificity but different subclasses.

4.4. Production of broadly neutralising antibodies in multiple IgG subclasses

In the past decade, much focus within HIV vaccine research has been the identification and isolation of broadly neutralising antibodies. These antibodies are now well characterised, however the vast majority of these antibodies are of the IgG1 subclass, largely due to the methods used to generate these antibodies requiring the use of an IgG1 backbone vector. As previously shown in this chapter,

IgG2, IgG3 and IgG4 constant region containing vectors have now been generated to enable the production of IgG2, IgG3 and IgG4 monoclonal antibodies. Therefore 4 well characterised BNAbs from previous studies were selected to generate in different isotypes to test for not only the change in function among different isotypes with the same variable region and binding specificity, but also to compare to the five gp140-specific patient derived monoclonal antibodies generated previously.

HIV Binding Heavy Chain Gene Light Chain Gene BNAb Original Isolation Site Family Family IGHV3-15*05 IGLV3-19*01 Huang et al, 2012 10E8 MPER IGHD3-3*01 IGLJ3*02 (Nature) IGHJ1*01 IGHV4-59*01 IGLV3-21*02 Walker et al, 2011 PGT121 V3 Loop IGHD3-3*02 IGLJ3*02 (Nature) IGHJ6*03 IGHV1-02*02 IGKV3-11*01 Wu et al, 2010 VRC01 CD4bs IGHD3-16*01 (or *02) IGKJ2*01 (Science) IGHJ1*01 or IGHJ2*01 IGVH1-2 IGKV1D-33 Schied et al, 2011 3BNC117 CD4bs IGHD6-25/2-8 IGKJ3 (Science) IGHJ2/6

Table 4.2: Broadly neutralising antibodies selected for production in different isotypes. BNAbs 10E8, PGT121, VRC01 and 3BNC117 selected for generation of IgG1, IgG2, IgG3 and IgG4 monoclonal antibodies, along with their corresponding heavy and light chain variable gene families, and original isolation paper.

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The four BNAb selected for generation in IgG1-4 subclasses were 10E8, PGT121, VRC01 and 3BNC117.

As shown in table 4.2, 2 BNAbs were kappa light chain (VRC01 and 3BNC117) and the other two lambda light chain (10E8 and PGT121). Of the four selected, two were specific to the CD4bs, one to the MPER and one to the V3 loop.

(a) VRC01

H: 5’-TCGAGTCTGACCGGTcaggtgca………gtctcatcaGTCGACGTCAGGTC-3’ κ: 5’- TCGAGTCTGACCGGTgaaattgt………attaagcgaCGTACGGTCAGGTC-3’

(b) 3BNC117

H: 5’-TCGAGTCTGACCGGTcaggtcca………aagggcccaGTCGACGTCAGGTC-3’ κ: 5’-TCGAGTCTGACCGGTgacatcca………gctgcaccaCGTACGGTCAGGTC-3’

H: 5’-TCGAGTCTGACCGGTgaggtgca………gtctcctcaGTCGACGTCAGGTC-3’ Λ: 5’-TCGAGTCTGACCGGTtcctatga………ccgtcctcaCTCGAGGTCAGGTC-3’

(d) PGT121

H: 5’-TCGAGTCTGACCGGTcagatgca………gtctcctcaGTCGACGTCAGGTC-3’ Λ: 5’-TCGAGTCTGACCGGTtccgatat………accgtgttaCTCGAGGTCAGGTC–3’

Figure 4.17: Design of BNAb DNA fragments for insertion into heavy and light chain vectors. 5 prime and 3 prime 23bp regions of variable heavy and light chain VRC01 (a), 3BNC117 (b), 10E8 (c), and PGT121 (d) broadly neutralising antibody nucleotide sequences for DNA synthesis. AgeI restriction sites are located at the 5’ end of all heavy and light chain sequences, SalI restriction sites were added to the 3’ end of the heavy chain sequences, and XhoI or BsiWi added to lambda or kappa 3’ ends respectively.

For each BNAb to be generated, the Los Alamos HIV laboratory database was used to identify accession numbers for matching heavy and light chain sequences on the NCBI genbank database. To the 5 prime end of each heavy chain nucleotide sequence an AgeI restriction enzyme site was added, with a SalI restriction enzyme site added to the 3 prime end (Figure 4.17) to allow insertion into the

IgG1 vector. Likewise, an AgeI restriction site was added to the 5 prime end of each of the light chain

(both lambda and kappa) variable region sequence, and the restriction site added to the 3 prime end was either XhoI or BsiWi for lambda and kappa chain variable regions respectively.

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4.4.1. Insertion of BNAb variable region fragments into heavy and light chain vectors

After digestion of DNA fragments and vectors, ligation into the IgG1 vector and subsequent transformation and colony selection steps, resulting minipreps that were shown to contain the insert of the correct size were sent for sequencing. Clustal omega alignments were performed with the retrieved sequences aligned with the corresponding variable region protein sequence of each BNAb heavy or light chain that was synthesised.

(a)

VRC01_P ------SSLTGQVQLVQSGGQMKKPGESMRISCRAS VRC01HG1 HRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGQVQLVQSGGQMKKPGESMRISCRAS :: *************************** VRC01_P GYEFIDCTLNWIRLAPGKRPEWMGWLKPRGGAVNYARPLQGRVTMTRDVYSDTAFLELRS VRC01HG1 GYEFIDCTLNWIRLAPGKRPEWMGWLKPRGGAVNYARPLQGRVTMTRDVYSDTAFLELRS ************************************************************ VRC01_P LTVDDTAVYFCTRGKNCDYNWDFEHWGRGTPVIVSSASTSGX------VRC01HG1 LTVDDTAVYFCTRGKNCDYNWDFEHWGRGTPVIVSSVDQGPIGLPPGTLLQEHLWGHSGP ************************************.. (b)

10E8_P ------SSLTGEVQLVESGGGLVKPGGSLRLSCSAS 10E8HG1 HRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGEVQLVESGGGLVKPGGSLRLSCSAS :: *************************** 10E8_P GFDFDNAWMTWVRQPPGKGLEWVGRITGPGEGWSVDYAAPVEGRFTISRLNSINFLYLEM 10E8HG1 GFDFDNAWMTWVRQPPGKGLEWVGRITGPGEGWSVDYAAPVEGRFTISRLNSINFLYLEM ************************************************************ 10E8_P NNLRMEDSGLYFCARTGKYYDFWSGYPPGEEYFQDWGRGTLVTVSSASTSGX------10E8HG1 NNLRMEDSGLYFCARTGKYYDFWSGYPPGEEYFQDWGRGTLVTVSSVDQGPIGLPPGTLL **********************************************.. . (c)

3BNC117_P ------SSLTGQVQLLQSGAAVTKPGASVRVSCEAS 3BNC117HG1 HRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGQVQLLQSGAAVTKPGASVRVSCEAS :: *************************** 3BNC117_P GYNIRDYFIHWWRQAPGQGLQWVGWINPKTGQPNNPRQFQGRVSLTRHASWDFDTFSFYM 3BNC117HG1 GYNIRDYFIHWWRQAPGQGLQWVGWINPKTGQPNNPRQFQGRVSLTRHASWDFDTFSFYM ************************************************************ 3BNC117_P DLKALRSDDTAVYFCARQRSDYWDFDVWGSGTQVTVSSASTKGPVDVRS------3BNC117HG1 DLKALRSDDTAVYFCARQRSDYWDFDVWGSGTQVTVSSASTKGPVDQGPIGLPPGTLLQE ********************************************** (d)

PGT121_P ------SSLTGQMQLQESGPGLVKPSETLSLTCSVS PGT121HG1 HRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATGQMQLQESGPGLVKPSETLSLTCSVS :: *************************** PGT121_P GASISDSYWSWIRRSPGKGLEWIGYVHKSGDTNYSPSLKSRVNLSLDTSKNQVSLSLVAA PGT121HG1 GASISDSYWSWIRRSPGKGLEWIGYVHKSGDTNYSPSLKSRVNLSLDTSKNQVSLSLVAA ************************************************************ PGT121_P TAADSGKYYCARTLHGRRIYGIVAFNEWFTYFYMDVWGNGTQVTVSSASTSGX------PGT121HG1 TAADSGKYYCARTLHGRRIYGIVAFNEWFTYFYMDVWGNGTQVTVSSVDQGPIGLPPGTL ***********************************************

Figure 4.18: Clustal omega alignment of BNAB heavy chain vector protein sequences. Vector sequences (HG1)of BNAb VRC01 (a), 10E8 (b), 3BNC117 (c), and PGT121 (d), were aligned with the original published (_P) heavy chain sequences retrieved from the Los Alamos database.

217

For each one of the four BNAb heavy chain IgG1 vectors, the protein sequences were a 100% match with the original heavy chain variable region (Figure 4.18). However, on alignment with the IgG1 Ab-

Vec vector sequence, two key problems were noted. Firstly, the IgG1 constant region sequence within each IgG subclass heavy chain vector did not match the IgG-Abvec sequence at all, and secondly all four heavy chain vectors were missing a “VHS” amino acid sequence seen in the IgG AbVec sequence

(Figure 4.19).

hIgG_1 ------MGWSCIILFLVATATGVHSSV---- PGT121HG1 ---XDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATG---QMQLQE 10E8HG1 XXLGDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATG---EVQLVE VRC01HG1 YDLGDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATG---QVQLVQ 3BNC117HG1 -XLGDTIE*HPLCLSLHRCPLPGPTAPRFYRLNSTMGWSCIILFLVATATG---QVQLLQ **************** .: hIgG_1 ------PGT121HG1 SGPGLVKPSETLSLTCSVSGASISDSYWSWIRRSPGKGLEWIGYVHKSG---DTNYSPSL 10E8HG1 SGGGLVKPGGSLRLSCSASGFDFDNAWMTWVRQPPGKGLEWVGRITGPGEGWSVDYAAPV VRC01HG1 SGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMGWLKPRG--GAVNYARPL 3BNC117HG1 SGAAVTKPGASVRVSCEASGYNIRDYFIHWWRQAPGQGLQWVGWINPKT--GQPNNPRQF

hIgG_1 ------PGT121HG1 KSRVNLSLDTSK----NQVSLSLVAATAADSGKYYCARTLHGRRIYGIVAFNEWFTYFYM 10E8HG1 EGRFTISRLNSI----NFLYLEMNNLRMEDSGLYFCARTGKYYDFWSGYP----PGEEYF VRC01HG1 QGRVTMTRDVYS----DTAFLELRSLTVDDTAVYFCTRGKNCDYNWD------F 3BNC117HG1 QGRVSLTRHASWDFDTFSFYMDLKALRSDDTAVYFCARQRS--DYWD------F

hIgG_1 ------RSTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSW---N PGT121HG1 DVWGNGTQVTVSS------VDQGPIGL------PPGTLLQE------HLWGHSG 10E8HG1 QDWGRGTLVTVSS------VDQGPIGL------PPGTLLQE------HLWGHSG VRC01HG1 EHWGRGTPVIVSS------VDQGPIGL------PPGTLLQE------HLWGHSG 3BNC117HG1 DVWGSGTQVTVSSASTKGPVDQGPIGL------PPGTLLQE------HLWGHSG * *::: * . Figure 4.19: Clustal omega alignment of heavy chain BNAb IgG1 vectors with IgG-Abvec. Protein sequences of BNAb vectors were aligned with the original IgG1 Abvec vector sequence. Variable regions are highlighted in grey with the correct start of the constant region highlighted in red on the heavy chain IgG1 vector, whilst mismatching constant regions are highlighted in blue.

Alignments were also made between the four IgG1 heavy chain BNAb and several previously generated monoclonal in the lab to confirm the required presence of the VHS sequence. Therefore, the next two steps before the vectors could be used was to correct the constant region in each vector, and insert the “VHS” sequence which is the L-part2 sequence of the leader peptide. The leader peptide is present at the N terminus of synthesised proteins, in this case IgG, and is vital for translocation to the cell surface for secretion. Therefore, whilst the IgG1-4 vectors contain the longer L-part1 of the leader peptide, if L-part2 of this sequence is missing, proper antibody production may not be achieved.

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At this point, the BNAb variable regions had only been inserted into the IgG1 vector, so therefore the issues could be amended in this vector alone.

There were similar issues with the light chain for the L-part2 sequence. While the variable and constant regions within the two lambda vectors, PGT121 and 10E8, were correct in sequence, a 3 amino acid sequence between the end of the signal peptide leader and the start of the variable region was missing as in the heavy chains. The kappa chain variable DNA fragments had previously been resynthesized to incorporate this region, and so the vector sequences matched.

4.4.2. BNAb Heavy Chain Vector 1st Mutagenesis

As previously mentioned, the IgG1 constant region needed correcting in each of the BNAb heavy chain vectors. It was found that for three of the four BNAbs (10E8, VRC01 and PGT121) this alteration in sequence had been down to a frameshift mutation in the amino acid sequence due to the lack of an additional 2 nucleotide base pairs between the end of the variable region and the SalI restriction enzyme site (Figure 4.20). As the BNAb vectors did not have these 2 extra base pairs before the addition of restriction sites, although the variable sequence matched perfectly, the absence of these nucleotides caused a frame shift mutation downstream in the constant region. This resulted in a different protein sequence that did not match the IgG1 constant region in any way and would therefore not have produced antibody.

For 3BNC117 however, there was a slightly different issue. When the sequence had been obtained from the NCBI genbank sequence, the restriction site had been added to the 3’ end as with the others, and a check of each sequence for restriction sites that would disrupt the cloning process was performed. Towards the 3’ end of the sequence another AgeI restriction site was found, and the nucleotide sequence was thus changed to an alternative one that yielded the same protein sequence.

On analysing the vector sequence however, it became clear that this sequence was actually part of the constant region and not just the variable region as had been implied. Therefore, there was the same issue of the constant region being wrong, but to correct this a mutagenesis PCR could be

219

designed that mutated the altered AgeI restriction site back to the correct version, after which the variable region could be cut out of the vector and ligated into another new IgG1 vector giving the correct sequence.

Therefore to restore the constant region to its correct sequence for all BNAB heavy chain except

3BNC117, an insertion mutagenesis to add in 2 nucleotides between the variable region and restriction enzyme site was designed and performed to cause a frameshift mutation restoring the original constant region sequence. To correct the 3BNC117 vector, a substitution PCR was performed to revert the altered SalI restriction site back to the correct sequence.

(a) VRC01

accccggtcatcgtctcatcagTCGACCAAGGGCCCATCGGTCTTCCCCCTGGCACCCTC T P V I V S S V D Q G P I G L P P G T L

(b) 10E8

accctggtcaccgtctcctcagTCGACCAAGGGCCCATCGGTCTTCCCCCTGGCACCCTC T L V T V S S V D Q G P I G L P P G T L

actcaggtcaccgtctcctcagTCGACCAAGGGCCCATCGGTCTTCCCCCTGGCACCCTC T Q V T V S S V D Q G P I G L P P G T L

(d) 3BNC117

acccaggtcactgtctcgtcagcgtctaccaagggcccagTCGACCAAGGGCCCATCGGT T Q V T V S S A S T K G P V D Q G P I G

(e) IgG1 Vector

accggtgtacactcgagcgtacggTCGACCAAGGGCCCATCGGTCTTCCCCCTGGCACCC T G V H S S V R S T K G P S V F P L A P

Figure 4.20: Nucleotide and amino acid sequences of BNAb heavy vectors to be mutated. Partial heavy chain vector sequences of the BNAb VRC01 (a), 10E8 (b), PGT121 (c), 3BNC117 (d), and uncloned heavy chain IgG1 (e), expression vectors. The 3’end of the inserted heavy chain variable region is shown (grey), followed by SalI restriction enzyme site (green) and 5’ section of the IgG1 constant region (bold).

Of these heavy chain mutagenesis PCRs carried out, the VRC01 and 3BNC117 mutations worked

(Figure 4.21), and so the next insertion (L-part 2 VHS sequence) could be made. PGT121 and 10E8 on

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the other hand did not, and therefore rather than repeating these until they worked, if at all, the decision was made to get these DNA fragments synthesised again with the correct insertions.

(a)

VRC01_Var ------QVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMGWLK VRC01H_Ori FLVATATGQVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMGWLK VRC01HGC1 FLVATATGQVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMGWLK IgG1_Vec FLVATATG------

VRC01_Var PRGGAVNYARPLQGRVTMTRDVYSDTAFLELRSLTVDDTAVYFCTRGKNCDYNWDFEHWG VRC01H1_Ori PRGGAVNYARPLQGRVTMTRDVYSDTAFLELRSLTVDDTAVYFCTRGKNCDYNWDFEHWG VRC01HGC1 PRGGAVNYARPLQGRVTMTRDVYSDTAFLELRSLTVDDTAVYFCTRGKNCDYNWDFEHWG hIgG_1 ------. VRC01_Var RGTPVIVSS------VRC01H1_Ori RGTPVIVSSVDQG-PIGLPPGTLLQEHLWGHSGPGLPGQGLLPRTCDGLVELRRPDQRRA VRC01HGC1 RGTPVIVSSASTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSW---NSGALTS hIgG_1 ----VHSSVRSTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSW---NSGALTS .* * * (b)

3BNC117Ori GTGGAACCCAGGTCACTGTCTCGTCAGCGTCTACCAAGGGCCCAGTCGACCAAGGGCCCA 3BNC117HI GTGGAACCCAGGTCACTGTCTCGTCAGCGTCGACCAAGGGCCCAGTCGACCAAGGGCCCA 3BNC117H GTGGAACCCAGGTCACTGTCTCGTCAGCGTCGACCAAGGGCCCA------******************************* ************ G T Q V T V S S A S T K G P V D Q G P

Figure 4.21: VRC01 and 3BNC117 heavy chain vectors after 1st mutagenesis. (a) Clustal omega protein sequence alignment of VRC01 vector that has undergone mutagenesis (VRC01HGC1) with the heavy chain variable region of VRC01 (VRC01_Var), the original unmutated VRC01 vector (VRC01H_Ori) and the original IgG expression vector (hIgG_1), and the now corrected constant region highlighted grey. (b) Clustal omega alignment of 3BNC117 vector that has undergone mutagenesis (3BNC117HI) with the original 3BNC117 vector that required mutagenesis (3BNC117Ori) and the 3BNC117 heavy chain variable region (3BNC117H). The nucleotide that required substitution is highlighted grey, with the correct nucleotides highlighted green. Asterisks indicate conserved nucleotides.

4.4.3. BNAb Heavy Chain Vector 2nd Mutagenesis

Now that the IgG1 constant region amino acid sequence has been corrected for VRC01 and 3BNC117, the next step was to do another insertion mutagenesis PCR to insert a nine nucleotide/three amino acid sequence between the end of the amino acid leader sequence and the start of the variable region.

It was shown previously that the peptide leader sequence is actually comprised of two parts; L-part1 and L-part2. As when the DNA fragments were designed this wasn’t included, they would have to be inserted here. Figure 4.22 shows that this mutagenesis PCR worked and that the sequences were now complete, and ready for ligation into other subclass vectors.

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(a)

VRC01H_Var ------QVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMG VRC01H_Ori FLVATATG---QVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMG VRCO1H_Ins FLVATATGVHSQVQLVQSGGQMKKPGESMRISCRASGYEFIDCTLNWIRLAPGKRPEWMG ************************************************* (b)

3BNC117H_Var ------QVQLLQSGAAVTKPGASVRVSCEASGYNIRDYFIHWWRQAPGQGLQWVG 3BNC117H_Ori FLVATATG---QVQLLQSGAAVTKPGASVRVSCEASGYNIRDYFIHWWRQAPGQGLQWVG 3BNC117H_Ins FLVATATGVHSQVQLLQSGAAVTKPGASVRVSCEASGYNIRDYFIHWWRQAPGQGLQWVG *************************************************

Figure 4.22: Alignment of VHS-inserted VRC01 and 3BNC117 sequences. The VRC01 (a) and 3BNC117 (b) mutated vectors (_Ins) were aligned with the variable regions (_Var) and the original unmutated vectors (_Ori) using clustal omega. Asterisks indicate conserved nucleotides.

4.4.4. BNAb Lambda Chain Vector Mutagenesis

As previously mentioned, there was also an amino acid leader insertion required for the lambda chain for two of the broadly neutralising antibodies (PGT121 and 10E8).

(a) PGT121L_Var ------SDISVAPGETARISCGEKSLGSRAVQWYQHRAGQAPSLIIYNNQDRPSG PGT121L_Ori FLVATATG---SDISVAPGETARISCGEKSLGSRAVQWYQHRAGQAPSLIIYNNQDRPSG PGT121L_Ins FLVATATGSVTSDISVAPGETARISCGEKSLGSRAVQWYQHRAGQAPSLIIYNNQDRPSG ************************************************* (b) 10E8L_Ori FLVATATG---SYELTQETGVSVALGRTVTITCRGDSLRSHYASWYQKKPGQAPILLFYG 10E8L_Var ------SYELTQETGVSVALGRTVTITCRGDSLRSHYASWYQKKPGQAPILLFYG 10E8L_Ins FLVATATGSVVSYELTQETGVSVALGRTVTITCRGDSLRSHYASWYQKKPGQAPILLFYG *************************************************

Figure 4.23: Alignment of L-part 2 inserted PGT121L and 10E8L sequences. The PGT121 (a) and 10E8 (b) lambda chain mutated vectors (_Ins) were aligned with the variable regions (_Var) and the original unmutated vectors (_Ori) using clustal omega. Asterisks indicate conserved nucleotides.

The L-part 2 of the leader sequences for lambda chain were more variable and so the 9 nucleotide/ 3 amino acid sequence of the closest germline sequence was inserted using the same protocol as the heavy chain. The sequence results were aligned (Figure 4.23) and the mutated lambda chains now had the correct sequence.

4.4.5. BNAb variable regions in IgG1-IgG4 heavy and light chain vectors

After all the IgG1 BNAb vector sequences were correct, the variable regions from each were cut from the vector using restriction enzymes, and ligated into each of the IgG2, IgG3 and IgG4 vectors.

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Figure 4.24: Clustal omega alignment of IgG1-IgG4 heavy chain vector sequences. The IgG1 - IgG4 heavy chain vector and variable reference sequences of VRC01 (a), PGT121 (b), 10E8 (c), and 3BNC117 (d), were aligned. Sequence shown is from L-part1 onwards, and includes the first 12 constant region amino acids.

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Resulting minipreps were then sent for sequencing, and IgG1, IgG2, IgG3 and IgG4 sequences were aligned. The variable region for each BNAb was a 100% match for each IgG1-IgG4 vector and the original variable region sequence from the NCBI genbank database (Figure 4.24).

(a) VRC01K ------EIVLTQSPGTLSLSPGETAIISCRTSQYGSLAWYQQRPGQA VRC01K1 MGWSCIILFLVATATGTTGEIVLTQSPGTLSLSPGETAIISCRTSQYGSLAWYQQRPGQA *****************************************

VRC01K PRLVIYSGSTRAAGIPDRFSGSRWGPDYNLTISNLESGDFGVYYCQQYEFFGQGTKVQVD VRC01K1 PRLVIYSGSTRAAGIPDRFSGSRWGPDYNLTISNLESGDFGVYYCQQYEFFGQGTKVQVD ************************************************************

VRC01K IKR------VRC01K1 IKRRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVT ***

(b) PGT121L ------SDISVAPGETARISCGEKSLGSRAVQWYQHRAGQAPSLIIY PGT121L1 MGWSCIILFLVATATGSVTSDISVAPGETARISCGEKSLGSRAVQWYQHRAGQAPSLIIY *****************************************

PGT121L NNQDRPSGIPERFSGSPDSPFGTTATLTITSVEAGDEADYYCHIWDSRVPTKWVFGGGTT PGT121L1 NNQDRPSGIPERFSGSPDSPFGTTATLTITSVEAGDEADYYCHIWDSRVPTKWVFGGGTT ************************************************************

PGT121L LTVLRQPKAAPSVTLFPP------PGT121L1 LTVLRQPKAAPSVTLFPPSSEELQANKATLVCLISDFYPGAVTVAWKADSSPVKAGVETT ******************

(c) 10E8L ------SYELTQETGVSVALGRTVTITCRGDSLRSHYASWYQKKPGQ 10E8L1 MGWSCIILFLVATATGSVVSYELTQETGVSVALGRTVTITCRGDSLRSHYASWYQKKPGQ *****************************************

10E8L APILLFYGKNNRPSGVPDRFSGSASGNRASLTISGAQAEDDAEYYCSSRDKSGSRLSVFG 10E8L1 APILLFYGKNNRPSGVPDRFSGSASGNRASLTISGAQAEDDAEYYCSSRDKSGSRLSVFG ************************************************************

10E8L GGTKLTVLSQPKAAPSVTLFPP------10E8L1 GGTKLTVLSQPKAAPSVTLFPPSSEELQANKATLVCLISDFYPGAVTVAWKADSSPVKAG **********************

(d) 3BNC117K ------DIQMTQSPSSLSASVGDTVTITCQANGYLNWYQQRRGKAPK 3BNC117K1 MGWSCIILFLVATATGARCDIQMTQSPSSLSASVGDTVTITCQANGYLNWYQQRRGKAPK *****************************************

3BNC117K LLIYDGSKLERGVPSRFSGRRWGQEYNLTINNLQPEDIATYFCQVYEFVVPGTRLDLK-- 3BNC117K1 LLIYDGSKLERGVPSRFSGRRWGQEYNLTINNLQPEDIATYFCQVYEFVVPGTRLDLKRT **********************************************************

3BNC117K ------3BNC117K1 VAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSK

Figure 4.25: Clustal omega alignment of light chain vector sequences. The light chain vector and variable reference sequences of VRC01 (a), PGT121 (b), 10E8 (c), and 3BNC117 (d), were aligned. Sequence shown is from L-part1 onwards and includes part of the light chain constant region.

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The light chain vectors were also aligned with the original variable region sequence obtained from the

NCBI genbank database and were complete matches (Figure 4.25). The light chain would remain the same for each subclass. Now that all vectors were correct, the antibodies could be produced through

293T transfection.

4.4.6. Generation of BNAbs in IgG1, IgG2, IgG3 and IgG4 subclasses

After ligation of BNAb variable region DNA into the IgG1, IgG2, IgG3 and IgG4 vectors, the resulting minipreps were used to transfect the HEK293T cell line. After 5 days the supernatant was collected and tested for IgG by ELISA, and then screened for reactivity against gp140. Trial runs were initially performed in order to confirm that it was possible to generate each antibody of each isotype. From this, preliminary results confirmed the presence of IgG in the supernatants (Figure 4.26).

Figure 4.26: Semi-quantitative IgG ELISA results of BNAb transfected 293T cells using IgG1-4 expression vectors. BNAbs PGT121, 10E8, VRC01 and 3BNC117 were generated in IgG1-4 subclasses using the IgG1-4 expression vectors. ELISA was used to measure IgG in transfection supernatant of each antibody at a wavelength of 450nm. Highest and lowest standard IgG concentration (100ng/ml and 1.56ng/ml respectively) were used as controls, with any OD450 greater than the lowest IgG standard classed as being IgG positive.

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All monoclonal antibodies in each subclass showed IgG in the ELISA results. Both VRC01 and 3BNC117 showed good levels of antibody for each of the IgG subclasses as indicated by the high OD, whilst

PGT121 and 10E8 had lower ODs and therefore lower levels of antibody. At this point, the ELISA was purely to check that antibody of each subclass was produced.

4.4.7. Screening BNAbs against gp140

As these BNAb in different subclasses were synthesised as DNA fragments and inserted into the subclass vectors, a gp140 CEM.NKr-CCR5 flow cytometry based experiment was performed to check that these generated antibodies in each subclass did actually recognise the gp140 envelope protein.

Briefly, the CEM.NKr-CCR5 cell line was coated with gp140, the broadly neutralising antibodies added, followed by a secondary FITC antibody which was then run on an LSRII and analysed in Flowjo.

In terms of the percentage of cells that were FITC positive, i.e. had antibody bound to it, nearly all the antibodies showed binding (Figure 4.27). VRC01 in particular showed nearly 100% cells were FITC positive for all IgG subclasses, as did PGT121 except for the IgG2 subclass antibody. Likewise 3BNC117 showed greater than 60% FITC positive cells for all IgG subclasses. On the other hand 10E8 showed a lower percentage of FITC positive cells, though this was likely to be due to the low antibody titre within the harvested supernatant that was previously indicated by IgG ELISA.

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Figure 4.27: BNAb screening against HIV gp140 envelope protein. A flow cytometry screening assay was used to confirm the binding of the BNAbs in different isotypes to HIV gp140. Supernatant from HEK293T transfection was screened against CEM.NKR-CCR5 cells coated with HIV gp140, with a secondary IgG FITC antibody used to measure binding. (a) Percentage of cells that were FITC+ (i.e. cells that have BNAb bound to gp140), and (b) FITC MFI of cells.

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4.5 Discussion

4.5.1. Summary

The original aim of this part of the project was to generate vectors capable of producing antibodies in each of the four different IgG subclasses; IgG1 IgG2, IgG3 and IgG4. Whilst the IgG1 vector was already available and used in many studies, including generating antibodies in the first results chapter of this thesis, the IgG2-4 expression vectors would be created by modifying the current IgG1 vector. In this chapter, it has been shown that the IgG1 constant region was removed from the original IgG vector, and replaced with the IgG2, IgG3 and IgG4 constant regions amplified from human cDNA. These vectors were then used to produce patient isolated HIV-specific monoclonal antibodies as well as well characterised BNAb in IgG1-IgG4 subclasses.

4.5.2. Discussion

There have been many studies examining the effect of different IgG subclasses on antibody effector functions such as ADCC, ADCVI, ADP and complement dependent cytotoxicity. It has been well characterised that IgG1 and IgG3 antibodies are better at mediating effector functions such as ADCC and activating the c1q component of complement than the IgG2 and IgG4 subclasses (453, 458).

Recently, much research has been focussed on generating neutralising monoclonal antibodies from

HIV infected individuals through use of single cell expression cloning, and the vast majority of antibodies generated this way are IgG1. Non-neutralising antibodies have recently been shown to be just as important, for example polyfunctional responses have been correlated with the 31.2% efficacy of the RV144 trial (252, 368), as well as strong polyfunctional response correlations in elite controllers

(253). As these antibodies mediate functions such as ADCC, the ability to generate these antibodies in a variety of IgG subclasses for enhanced mediator activity would be highly beneficial.

Whilst IgG1 and IgG3 in particular have been singled out as the key subclasses for use in treatment or for stimulation by a vaccine against HIV, IgG2 and IgG4 subclasses may also be important in other infections. IgG2 for example is the greatest responder to polysaccharide antigens, and therefore

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makes up the key response to encapsulated bacteria (219, 466-468), whilst IgG4 have been shown to be the predominant IgG response to Schistosoma mansoni, a causative agent of schistosomiasis, (451,

469). Moreover, several studies have shown that therapeutic antibodies in IgG2 and IgG4 IgG subclasses may be just as effective in mediating some functions as the IgG1 (460, 461).

Therefore, the three key aims set out at the start of this chapter were to construct IgG2-4 constant region expression vectors, to generate IgG1-4 monoclonal antibodies from an early infected patient, and to produce broadly neutralising antibodies in IgG1-4 subclasses. This would allow the monoclonal antibody production protocol originally published by Smith et al in Nature protocols (404) and used in our labs to generate antibodies not just in IgG1 subclass, but also in IgG2, IgG3 and IgG4 subclasses.

This would be beneficial not only to work in the context of HIV, for both neutralising and non- neutralising antibodies, but other vectors such as the IgG2 and IgG4 vectors may be beneficial in studying the antibody responses to other diseases. As the results in this chapter have shown, all three of these aims were achieved.

A key obstacle that was encountered when modifying the IgG vector to get different IgG subclasses was the full hinge region of the IgG3 vector not being amplified. Whilst the IgG1, IgG2 and IgG4 all have similar hinge regions consisting of 15 (IgG1) and 12 (IgG2 and IgG4) nucleotides, the IgG3 hinge is much longer, comprising of four hinge sections (or exons) termed hinge 1 to hinge 4 (H1-H4) and

62bp total in length (219, 470, 471). The IgG3 gene is also polymorphic, with up to 15 different allotypes currently known, each of which can have different hinge region lengths (206, 219, 447).

Whilst all allotypes described in the literature have the same H1 exon, H2, H3 and H4 exons are repeating units of the same nucleotide sequence, and the number of these repeating units varies among different allotypes. For example, IGH3*01 and IGH3*05 both have all four hinge regions (H1-

H4), whilst IGH3*12 is missing H2, and IGH3*04 lacks both H2 and H3 (219). As the majority of IgG3 allotypes have all four hinge regions, and the concern that missing a particular repeating hinge section could potentially affect function due to a shorter hinge, it was important to ensure that a full hinge

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region containing H1-H4 was used, and this was achieved by altering primers used to amplify this whole region.

The work in this thesis has therefore led to four broadly neutralising antibodies (10E8, 3BNC117,

PGT121 and VRC01) and five patient B004 derived monoclonal antibodies (V1X1B5, V1X2B12, V1X2C5,

V1X3A2 and V2X1F6) being generated in all four IgG subclass with the aim of testing effector function for each of these antibodies. The broadly neutralising antibodies were selected on the basis that they are currently in the process of being tested in clinical trials (307, 472), and therefore results suggesting that one IgG subclass might be better for a particular antibody in mediating effector functions may be useful for this purpose.

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Chapter 5: Examining functional responses of BNAbs and patient derived mAbs in different IgG subclasses.

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5. Examining functional responses of BNAbs and patient derived mAbs in different IgG subclasses 5.1. Introduction

5.1.1. Effector Functions of antibodies

As previously discussed in the introduction chapter of this thesis, antibodies mediate several key functions. Whilst broadly neutralising antibodies have been and continue to be a key focus in HIV-1 research for design of effective HIV-1 vaccines as well as potential uses in immunotherapy and clearing the latent HIV-1 reservoir, it has recently been demonstrated in a number of papers that non- neutralising antibodies may be just as important. For example, the Fc mediated effector function of

ADCC mediated by non-neutralising antibodies has been shown to be a correlate of protection in the

RV144 trial results (252, 473), and elite controllers have been shown to have greater levels of this activity than viremic patients (253)

However, the majority of studies examining the ADCC activity of non-neutralising antibodies from HIV-

1 infected patients have either been isolated from chronic infection (371, 373), or have generally been tested on a polyclonal level, i.e. testing serum or plasma (474, 475). A 2011 paper by Chung et al tested the ADCC activity of serum samples taken from early infected HIV-1 patients a median of 100 days and

148 days post infection. The results showed that 50% of the patients had serum that mediated ADCC responses against recombinant gp140 at each time point, and that already by 100 days post infection there was ADCC activity, albeit at a low level, in the patients (476). Interestingly, the authors showed that ADCC responses to linear peptides pools of the HIV-1 envelope were much lower and only observed in one patient by 100 days post infection, indicating that the antibodies mediating these early ADCC response recognise conformational epitopes (476). It is important to note however that this study tested ADCC indirectly through the measurement of CD107a and IFNɣ expression on NK cells and not a direct cell lysis protocol. The assays used to determine ADCC will be discussed in more detail later (section 5.1.3).

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Furthermore, several key studies have demonstrated that IgG1 and IgG3 antibodies are the best mediators of ADCC activity, as well as other Fc mediator effector activity, with the best binding to Fc receptors (453, 454, 458). However, once again these studies have generally used purified polyclonal

IgG1 and IgG3 to test these functions. Several studies have used IgG3 monoclonal antibodies in these types of experiments, though these monoclonals have generally been produced through EBV immortalisation or phage display, have undergone mutagenesis to exchange IgG1 constant regions for

IgG3, or have a IgG1/3 hinge variant where the constant region remain IgG1, but the hinge region has been replaced by that of an IgG3 constant region hinge.

5.1.2. Mechanism of ADCC

The mechanism or process of antibody dependent cellular cytotoxicity involves three components; a target cell, an effector cell and an antigen-specific antibodies (summarised in Figure 5.1).

Figure 5.1: Summary of the mechanism of ADCC. An infected cell displays antigen on the cell surface to which antigen specific antibodies bind. Effector cells such as NK cells which express FcɣRIIIa (CD16 – coloured red) are recruited to the site and the Fc receptor binds the antibody. This interaction leads to NK cell activation and release of cytotoxic granules containing perforin and granzyme B which lead to cell lysis and death.

In the context of HIV-1 infection, the infected CD4+ T cell displays HIV-1 proteins (i.e. the gp140 envelope protein) on its cell surface, which is recognised by the Fab region of antigen-specific antibodies. These antibodies then bind through their Fc region to an Fc receptor on an effector cell

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such as NK cells. The cross linking of several receptors triggers the release of cytotoxic granules from the NK cell which then kill the cell.

5.1.3. Assays to measure ADCC

There are numerous assays that can be used to measure the ADCC activity of monoclonal antibodies, and they can largely be grouped into direct or indirect assays. Whilst direct assays measure target cell death or lysis, indirect assays generally examine markers of activation on the effector cells used in the assays, and these approaches are summarised in Table 5.1.

The majority of assays that are now used to measure ADCC activity of antibodies are flow cytometry based, either measuring NK cell activation markers as an indicator of ADCC activity, or measuring cell death or loss of the HIV-1 infected of gp140/gp120 coated target cells themselves. However, due to the wide number of assays used, variations in similar protocols, and different HIV-1 proteins or infecting viruses used to test ADCC, it is difficult to compare results. For example, a 2011 paper by

Ferarri et al used granzyme B as an indicator of ADCC showing that the BNAb VRC01 resulted in less than 10% of granzyme B activity for multiple concentrations of antibody against HIV-1 BaL infected target cells (372). Smalls-Mantey showed similar results with VRC01 showing less than 10% ADCC activity at multiple concentration against infected cells, though the highest concentration (100μg/ml) resulted in greater than 20% ADCC (477). On the other hand, Bruel et al showed that VRC01 mediated no ADCC activity against HIV-1 NLAD8 infected cells, but around 10% ADCC activity against HIV-1 NL4.3 infected cells (365).

The RFADCC assay uses the CEM.NKr-CCR5 CD4 T cell line, resistant to normal natural killer cell killing, as a target cell which is double stained with the intracellular dye CFSE and a cell membrane dye called

PKH-26. PBMCs or isolated NK cells are used as effector cells which are incubated with the target cells and antibodies for 4 hours, after which the cells are analysed on a flow cytometer. The percentage of

ADCC can be calculated as the percentage of cells which retain the PKH-26 cell membrane dye but have lost the intracellular CFSE indicating that these cells have been lysed. Despite the previously

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mentioned difficulties in comparing antibodies when using different assays and viruses or proteins, the RFADCC assay has been used successfully to determine the ADCC activity of a wide range of antibodies in a number of studies (253, 474, 478).

Type of Assay Overview Ref Assay - Chromium labelled target cells (HIV infected or gp140/gp120 pulsed) are incubated with effector cells Chromium release (479, and antibodies/serum. assay 480) - Loss of chromium is measured and is indicator of ADCC activity. - Target cells (HIV infected or coated) are double stained with a cell membrane dye and intracellular dye and incubated with antibodies/serum and effector cells. RFADCC (407) -ADCC is measured as the percentage of target cells that have lost the intracellular dye but still have the cell membrane dye. - CD4 cell line transduced with luciferase gene used as target cells, CD16 expressing NK cell line used as Direct Loss of luciferase effector cells. (481) activity - Target cell line infected with HIV-1, then incubated with effector cells and antibodies/serum. - RLUs used to calculate ADCC activity - gp120 coated target cells are labelled with a viability dye and a cell surface dye, whilst effector cells are labelled with a different cell surface dye. - Target cells, are then incubated with effector cells and Flow Cytometry antibodies, after which they are fixed with a known (482) Based Assay number of count particles for standardisation. -Using flow cytometry, target cells are identified based on their cell surface and viability dyes, and ADCC can be calculated. - Effector cells (NK cells) are incubated with CD4 target cells (HIV-1 infected or coated with gp120/gp140) and antibodies/serum. Intracellular (476, Indirect - After incubation cells are stained with variety of cytokine staining 483) markers to identify NK cells, as well as staining for CD107a (marker of degranulation), IFNɣ (marker of NK cell activation) and granzyme B.

Table 5.1: Summary of direct and indirect assays used to measure ADCC. Direct (chromium release assay, the RFADCC assay, luciferase assay and flow cytometry based assays) and indirect (intracellular cytokine staining) assays used to measure ADCC are listed with a brief overview of the assay methods, and examples of several key papers that have used these assays.

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5.1.4. Aims and Hypothesis

There were two key aims for this part of the thesis:

1. Purify and quantify monoclonal and broadly neutralising antibodies in each IgG isotype

2. Test the ADCC activity of each purified antibody

The key hypothesis was that IgG1 and IgG3 antibodies would mediate higher levels of ADCC than the

IgG2 and IgG4 isotypes of each antibody.

5.2. Purification and Quantification of IgG

In the previous results chapter, five HIV-1 reactive monoclonal antibodies from an early infected patient and four well characterised broadly neutralising antibodies were generated in IgG1-4 subclasses and shown to bind to HIV-1 gp140. In order to test these antibodies for function, large numbers of HEK293T cells were transfected in order to produce sufficient antibody which could then be purified, quantified by IgG ELISA, and protein quality confirmed by protein gels.

5.2.1. Purification of IgG

After transfection of 293T cells with matching pairs of heavy and light chain mAb or BNAbs DNA, supernatants were harvested and tested for IgG using IgG ELISA. After confirming presence of IgG, the supernatants were concentrated. Due to large numbers of cells being transfected to get as high titre of antibody as possible for use in functional assays, supernatant volume also increased. As purification would be achieved using Ab spin trap purification system which could only take smaller volumes of supernatant as opposed to larger volumes when using protein G or A agarose columns for example, the supernatants must first be concentrated to achieve this.

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R 2

O 1

0 l) l) 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G /m /m g g Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig Ig n n 6 0 . 0 (1 1 ( in x V1X1B5 V1X2B12 V1X2C5 V1X3A2 V1X2C5 VRC01 PGT121 10E8 3BNC117 M a M G Ig G Ig B004 mAbs BNAbs

Figure 5.2: ELISA results confirming IgG mAbs and BNAbs after concentration and purification. Purified antibodies were diluted 1:10 with assay buffer A (ELISA kit component) and run in duplicate on the 96 well ELISA plate. IgG standards ranging from 100ng/ml to 1.6ng/ml were also run on the plate to act as controls, with the highest standard concentration used as a positive control (100ng/ml), and the lowest standard concentration (1.6ng/ml) used as a cut off for determining IgG positive samples. Absorbance was measured at 450nm with the reading indicating presence of IgG.

After concentration, the supernatants were purified using the Ab spin trap purification system with another IgG ELISA performed using 1:10 dilutions of purified antibodies to check the concentration and purifications had worked (Figure 5.2). The IgG standard provided in the commercial ELISA kit was used as a control, with the lowest standard concentration through series dilution (1.6ng/ml) used as a cut-off for determining IgG positive samples, and the highest standard concentration as a positive control.

All but two purified supernatants were positive for IgG (anything greater than the OD450 reading of

0.097 for the lowest IgG standard was classed as having IgG). However at this point of checking the antibodies, it was estimated that around 50% of antibodies would have concentrations lower than

100ng/ml and therefore it was unlikely that these would be able to be tested for ADCC activity.

Furthermore as antibodies with the same variable region but different Fc regions were to be compared, only those with all IgG isotypes at a suitable concentration would be tested.

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5.2.2. Quantification of IgG

After confirming that the concentration and purification of each supernatant resulted in antibody, each monoclonal antibody in each IgG subclass was tested by ELISA to determine actual IgG concentration.

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c 100

0 0 1 2 3 4 1 2 3 4 G G G G G G G G Ig Ig Ig Ig Ig Ig Ig Ig Subclass Subclass

Figure 5.3: Quantification of purified mAbs and BNABs in each of different IgG subclasses. ELISAs were performed on 2 patient B004 derived antibodies; V1X2B12 (a) and V1X2C5 (b), and two BNABs; VRC01 (c) and 3BNC117 (d), in IgG1, IgG2, IgG3 and IgG4 isotypes to determine concentration. Antibodies were run in triplicate, with results shown an average of three repeat ELISAs.

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On performing the first IgG ELISAs it was evident that about 50% of the purified antibodies would not have a sufficient concentration to be tested in the ADCC assays despite concentrating the antibody.

Of all the antibodies, there was 2 mAbs (V1X2B12 and V1X2C5) and 2 BNABs (VRC01 and 3BNC117) with high enough concentrations in each IgG subclass that could be tested for ADCC activity, and so repeated ELISAs were performed on these antibodies to determine concentration. The concentration of each of the purified antibodies varied among subclass and between different antibodies, however it was interesting to note that for each antibody the IgG3 had the lowest concentration (Figure 5.3).

For example, V1X2C5 IgG1 had a concentration of 1079.01μg/ml whereas IgG3 isotype was

267.23μg/ml. Similarly 3BNC117 had concentrations of 1030.57μg/ml and 134.91μg/ml for IgG1 and

IgG3 respectively. Overall, the concentrations ranged from 41.20μg/ml (VRC01 IgG3) to a much higher

1376.99μg/ml (3BNC117 IgG2).

5.2.3. Protein Gels for protein quality conformation

Now that the concentrations of each of the antibodies had been calculated, samples were loaded into protein gels and run to confirm that the IgG heavy and light chains were the correct size for each antibody subclass of the two mAbs and two BNAbs that would be tested for ADCC. The light chain and heavy chain bands were expected to be around 25kD and 50kDa in size respectively, with a slightly higher size for IgG3 due to the longer hinge region, and these results were observed in

Figure 5.4. Therefore it was concluded that antibodies of the correct size and subclass were generated, and therefore suitable to be tested for ADCC function.

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Figure 5.4: Protein gels indicating protein quality of mAbs and BNAbs in subclasses 1-4. Early infection monoclonal antibodies V1X2B12 and V1X2C5 (a), and broadly neutralising antibodies VRC01 and 3BNC117 (b) were run on a 10% Mini-PROTEAN TGX Stain-free protein gel under reducing conditions for 35 minutes at 200V. Light chain bands are around 25kD in size, and heavy chain bands are around 50kD in size. Protein standard from 10-250kD is on the left hand side of each antibody.

5.3. Testing for ADCC activity using the RFADCC Assay

The RFADCC assay by Gomez-Roman et al is a flow cytometry based assay to measure ADCC, and has been widely used to measure responses by HIV antibodies. Therefore this assay was implemented into the lab to test the ADCC responses of monoclonal antibodies and the generated BNAbs in each of the

IgG subclasses.

5.3.1. Preliminary work

The assay was optimised in the lab using monoclonal A32 (HIV specific monoclonal antibody shown to mediate ADCC activity) and polyclonal HIVIG (purified IgG from pooled HIV donor serum) positive

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controls, and human serum and non-specific IgG negative controls. The CEM.NKr-CCR5 cell line (target cells) was double stained with the cell membrane dye PKH-26 and intracellular dye CFSE, coated with

HIV gp120, and incubated with PBMCs from a healthy donor (effector cells) and positive and negative controls for four hours before fixing and running on the flow cytometer.

(a)

(b)

Figure 5.5: RFADCC gating strategy and testing positive and negative controls. (a) Gating strategy for calculating ADCC using the RFADCC assay; dead cells are excluded with a gate set to include PBMCs and CEM.NKr-CCR5 cells, using positive and negative controls the gate is set to include CFSE+, PKH26+ and CFSE+PKH26+ target cells, a quadrant gate is then set. The percentage of cells that are PKH26+CFSE- is defined as the % ADCC. (b) Flow plots showing target cell population distribution under IgG1 and healthy serum negative controls, and HIVIG and A32 positive control conditions.

Dead cells were excluded from the gating, with a wide gate to include PBMCs and the CEM.NKr-CCR5 cell line on FSC versus SSC parameters. Using single stained controls, appropriate gates were set for determining PKH-26 and CFSE positive and double positive populations (Figure 5.5a). The negative and positive controls were then tested using the RFADCC assay (Figure 5.5b), to confirm both gating strategy and that they would be suitable controls for use in the assay. As shown in the flow plots in

Figure 5.5b, there are few cells that have lost CFSE but still express PKH-26 for the two negative controls (therefore very little ADCC), whereas there is a significant population of cells that are CFSE-

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PKH26+ for the HIVIG and IgG1 positive controls indicating ADCC. Therefore these controls were found to be good controls for the assay.

Figure 5.6: Selecting donor PBMCs for use as effector cells in the RFADCC assay. (a) Flow cytometry plots of gated CD3-CD56+ NK cells (first row) and CD3-CD56+CD16+ NK cells (second row) from PBMCs of four healthy donors. (b) Calculated percentage of lymphocytes from four healthy donors that are CD56+CD16+ NK cells. (c) Percentage of ADCC (%) under 5 conditions; target and effector cells only, IgG1 negative control, serum negative control, HIVIG positive control, and A32 positive control carried out by PBMC effector cells from the four healthy donors (D1-D4).

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Total PBMCs were used as effector cells to provide a source of NK cells to kill the target cells through

ADCC. As NK cell percentage and activity can be varied among donors, it was important to test several donors to find the best responder to positive controls to provide a pool of PBMCs that could be used in each repeat assay to reduce variation in results.

Donor PBMCs from four healthy individuals were stained with anti-CD3, CD56 and CD16 flow cytometry antibodies and analysed using FlowJo software. Gates were set to identify total NK cells and NK cells expressing CD16, which is the FcɣR that triggers ADCC through binding antibody Fc region

(Figure 5.6a), and the percentage of lymphocytes that were CD3-CD56+CD16+ NK cells was then calculated. Donor 4 was found to have by far the highest NK cell percentage of total lymphocytes at

44.3% (Figure 5.6b), and this donor also had one of the best HIVIG and A32 positive control responses,

37.15 and 44.55% respectively, when tested using the RFADCC assay (Figure 5.6c). Therefore, as donor

4 also had a fairly low negative control or background response, this donor was selected to provide the effector cells for all future RFADCC assays.

5.3.2. RFADCC Assay Results

In total 16 antibodies were tested for ADCC activity using the RFADCC assay; V1X2B12 in G1-G4,

V1X2C5 in G1-G4, VRC01 in G1-G4 and 3BNC117 in G1-G4 isotypes. Using the 95th percentile of the results for the IgG negative control as an indicator of ADCC activity, three antibodies were found to mediate significant activity when compared to the control; V1X2C5 in the IgG1 isotype with 9.23%

ADCC activity, V1X2C5 in the IgG3 isotype with 11.41% ADCC activity, and VRC01 in the IgG3 isotype with 8.46% ADCC activity (Figure 5.7). One other antibody, V1X2B12 in the IgG3 subclass, was classed as borderline for ADCC activity, with a result of 8.05% activity. Whilst this monoclonal had an ADCC percentage greater than the mean plus standard deviation of the negative control, it fell just below the 95th percentile cut off of 8.26% and was not statistically greater than the negative control, and therefore has not been classed as a mediator of ADCC activity.

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Figure 5. 7: ADCC activity of mAb and BNAb in different IgG isotypes. Two patient B004 derived antibodies V1X2B12 (b) and V1X2C5 (c) and two BNABs VRC01 (d) and 3BNC117 (e) were tested for ADCC activity using the RFADCC assay. Each antibody was tested in IgG1, IgG2, IgG3 and IgG4 isotypes in triplicate, with the assay repeated three times for each antibody. % ADCC was defined as the percentage of CEM.NKr-CCR5 cells that had lost CFSE expression but still had PKH-26 expression. Any results greater than the 95th percentile of the IgG1 negative control results was classed as posiive for ADCC (indicated by dotted line on figures b-e), whilst the monoclonal antibody A32 and the polyclonal HIVIG were used as postiive controls for the assay. Statistical analysis of differences in ADCC activity were carried out using Prism, with two-tailed unpaired T-tests (Mann-Whitney tests) used to test differences between individual IgG isotypes, and a one way ANOVA (Kruskal Wallis test) used to test variance among the four groups. Asterisks indicate statistical results, with the top bold result indicating ANOVA results (b-d), with the T-test results underneath. The three antibodies found to mediate ADCC activity significantly greater than the negative control are shown in (f), with p values of 0.0009 (***), 0.0002 (***) and 0.0188 (*) for V1X2C5 G1, V1X2C5 G3 and VRC01 G3.

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As indicated by Figure 5.7, the majority of antibodies did not mediate significant ADCC activity, with all of the 3BNC117 subclass antibodies in particular well below the cut off for significant ADCC activity.

However, for the V1X2B12, V1X2C5 and VRC01 antibodies, there were variations in the levels of activity among the different subclasses, with IgG3 giving the greatest response for each monoclonal.

V1X2B12 V1X2C5 VRC01 p Value 0.0033 0.0006 0.124 Significance ** *** *

Table 5.2: Grouped statistical analysis of differences in ADCC activity of mAb and BNAb IgG isotypes. Kruskal-Wallis (ANOVA) tests were carried out on IgG1-IgG4 ADCC results for V1X2B12, V1X2C5 and VRC01 antibodies to examine whether the results were statistically different from each other. The p value and significance of each test is indicated.

As there seemed to be differences in the ADCC activity of different subclasses of the same monoclonal or BNAb, ANOVA tests were performed on the data sets to confirm if the results were statistically different as a group. As Table 5.2 demonstrates, there were statistical differences in the ADCC activity of the different IgG subclasses for the V1X2B12, V1X2C5 and VRC01 antibodies. The patient derived monoclonal antibody V1X2C5 showed the greatest difference (p=0.0006) in ADCC activity between subclasses. As all results for the BNAb 3BNC117 were well below the threshold for significant ADCC activity, as expected there were no differences among different subclasses. Statistical differences between individual isotypes was then further examined using T-tests (Table 5.3).

Of all the antibodies tested, the patient derived monoclonal antibody V1X2C5 showed the greatest differences between individual IgG isotypes as shown by the results in Table 5.3. Both the IgG1 and

IgG3 isotypes showed a significantly greater ADCC activity than the IgG2 isotype (p=0.0040 and p=0.0002 respectively), with the IgG3 isotype also mediating significantly greater ADCC than the IgG1 and IgG4 isotypes (p=0.0053 for both). The monoclonal antibody V1X2B12 also had statistically different levels of ADCC activity, with the IgG1, IgG2 and IgG3 isotypes mediating significantly greater

ADCC activity than the IgG4 isotype (p=0.0180, 0.0042 and 0.0475 respectively). On the other hand,

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the only significantly statistical difference for VRC01 was that the IgG3 isotype had a significantly higher level of ADCC activity than the IgG1 isotype (p=0.0490).

IgG Isotype mAb G2 G3 G4 P value Sig. P value Sig. P value Sig. G1 0.7773 ns 0.2137 ns 0.0132 * V1X2B12 G2 0.1615 ns 0.0028 ** G3 0.0012 ** G1 0.0040 ** 0.0053 ** 0.2581 ns V1X2C5 G2 0.0002 *** 0.7477 ns G3 0.0053 ** G1 0.2562 ns 0.0490 * 0.7607 ns VRC01 G2 0.0750 ns 0.8013 ns G3 0.3296 ns

Table 5.3: Statistical analysis of IgG isotype ADCC results for individual mAb and BNABs. The ADCC results for each antibody isotype were compared to the other isotypes for that particular antibody with t-tests performed to examine statistical differences between isotypes. Two-tailed unpaired T-tests were performed, with p value and significance (sig.) indicated in the table. ns = not significance.

It is of course important to note though that the scales of the y-axis the antibodies tested is up to 15%

ADCC activity, and therefore although significant ADCC activity was observed for several antibodies, this is still at a low level.

5.4. VRC01 and V1X2C5 binding to different HIV clade B proteins.

It was previously shown that the serum from patient B004 reacted best to the clade B/C consensus gp140 (Figure 3.5, section 3.3.2) and therefore this envelope protein was used in all screening and

RFADCC experiments. After examining the results of the RFADCC assay, the patient B004 derived mAb

V1X2C5 and the BNAb VRC01 both had antibodies that mediated ADCC, albeit at low levels, and had statistical differences between the IgG isotypes. Therefore these antibodies in each of the IgG1, 2, 3 and 4 isotypes were tested against a range of different gp140/gp120 proteins to see if there were variations in binding to different proteins, and whether different isotypes of the same antibody also bound differently.

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Figure 5.8: Reactivity of VRC01 and V1X2C5 IgG isotypes to different clade B envelope proteins. The BNAb VRC01 and patient B004 mAb V1X2C5 were tested against a range of different clade B gp120 and gp140 proteins coated on CEM.NKr-CCR5 cells; gp140 clade B, gp140 JRFL, gp140 B/C consensus, gp140 BL10, gp140 Bal.01, gp120 SF162 and gp120 YU2. They were also tested against CEM.NKr-CCR5 cells that were not coated with any envelope protein (uncoated – UC). All antibodies and positive and negative controls (HIVIG and non-specific IgG control respectively) were tested at 1μg/ml final concentration. Cells were coated with 50μg/ml envelope proteins, incubated with antibodies, followed by a secondary IgG-FITC antibody (1:100 dilution) and run on LSRII. The percentage of FITC positive cells indicates binding.

The VRC01 antibody reacted strongly to the gp140 Bal.01 protein in all IgG1-IgG4 isotypes (around

100% binding for all isotypes), whilst all isotypes reacted very poorly to gp140 BL10 (all below 10% binding). The IgG1 and IgG2 isotypes of VRC01 bound more strongly to gp140 clade B, gp140 JRFL, and the gp140 B/C consensus protein than the IgG3 and IgG4 VRC01 isotypes. This was also seen with mAb

V1X2C5 with the IgG1 and IgG2 isotypes binding better to the different envelope proteins than the

IgG3 and IgG4 isotypes. The V1X2C5 IgG1 antibody also matched the HIVIG control for all but one of the gp140 proteins. It was interesting to note that for each of the IgG isotypes for both the VRC01 and

V1X2C5 antibodies there was low binding to the gp120 proteins.

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5.5. Discussion

5.5.1. Summary

In this chapter, it has been demonstrated how monoclonal antibodies generated from an early infected HIV donor and broadly neutralising antibodies synthesised that have been generated in IgG1-

4 isotypes were purified, tested for protein quality and tested using ELISA to determine concentration.

These antibodies were then tested for ADCC activity using the RFADCC assay. Overall, three antibodies were found to mediate ADCC activity; V1X2C5 in the IgG1 subclass, V1X2C5 in the IgG3 subclass and

VRC01 in the IgG3 subclass.

5.5.2. Discussion

Antibodies can mediate several non-neutralising effector functions such as antibody dependent cellular cytotoxicity (ADCC), antibody dependent cellular viral inhibition (ADCVI), antibody dependent phagocytosis (ADP), and complement dependent cytotoxicity (CDC). Whilst much focus has been directed at isolating BNAbs from HIV infected donors with the aim of using these antibodies therapeutically or designing vaccines that stimulate these types of antibodies, in recent years it has been shown that non neutralising antibodies may be just as important.

The RV144 trial was the first and so far only trial that has shown a promising result for a vaccine, with a protection of 31.2% (368). This vaccine contained a recombinant gp120 AIDSVAX B/E boost component that was also present in the earlier VAX003 trial (484), yet the latter showed no protection.

It was found that the two vaccines actually induced different immune responses. Whilst the VAX003 vaccine predominantly stimulated an IgG4 response, as well as an IgG2 response in more than 50% of patients, leading to a monofunctional antibody response, the RV144 vaccine stimulated an IgG3 response that was linked to a polyfunctional antibody response (252). This polyfunctional response was defined by Chung et al as ADCC and ADCP activity, and in their 2014 paper the authors showed that depletion of IgG3 antibodies from the RV144 vaccine trial patient samples led to significant reductions and loss of ADCP and ADCC activity respectively, (252). They also showed that while the

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RV144 samples had a positive correlation between different effector functions, such as ADCC and

ADCP, indicating polyfunctionality, this correlation was also lost when IgG3 was depleted (252). The results that Chung et al demonstrate emphasise the importance of IgG3 in mediating non-neutralising effector functions.

Several studies examining the antibody responses produced by elite controllers have also shown the importance of IgG1 and IgG3, and polyfunctional antibody responses. A paper in 2010 by Banerjee et al showed that patients who could control HIV-1 without antiretroviral therapy had higher anti-gp120

IgG1 titres and more frequent anti-gp120 IgG3 than chronic progressors (485). Whilst in a 2016 paper,

Ackerman et al showed that elite controllers display higher quality polyfunctional antibody responses when compared to viremic controllers and infected subjects either on or not on ART, with positive correlations between IgG1 and IgG3 and effector functions (253). Therefore, the aim here was to purify monoclonal antibodies generated from an early infected donor and several synthesised BNABs that were generated in each of the IgG isotypes (Chapter 4), and to test each of these antibodies for

ADCC activity. It was hypothesised that IgG1 and IgG3 antibodies would mediate higher levels of ADCC than the IgG2 and IgG4 isotypes of each antibody.

The results described in this chapter do match the hypothesis to some extent. Firstly, the results of the ANOVA tests to determine whether there was a statistical difference between the different IgG isotypes of the same antibody showed that for the 2 patient mAbs (V1X2B12 and V1X2C5) and 1 BNAb

(VRC01) the level of ADCC mediated by the different isotypes was statistically different. Secondly, for three of the four antibodies tested (V1X2B12, V1X2C5 and VRC01), it was the IgG3 subclass which had the best results, with the IgG4 subclass generally having the lowest results, whilst the 3BNC117 BNAb had negative results for all subclasses. Thirdly, the patient derived mAb V1X2C5 showed the best response for supporting the hypothesis, with both the IgG1 and IgG3 isotypes showing significantly greater ADCC activity than the negative control, the IgG1 mAb showing significantly greater activity than the IgG2 isotype, and the IgG3 mAb being the best overall mediator of ADCC activity, with

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significantly greater ADCC activity than the IgG2, IgG4 as well as IgG1 isotypes. The two BNAbs also had significantly greater ADCC by the IgG3 isotypes than the IgG1 and IgG2, and IgG2 and IgG4 isotypes for VRC01 and 3BNC117 respectively.

A key matter to take into consideration however, is that despite the differences in percentages of

ADCC between the different isotypes when tested statistically, the actual percentages overall for ADCC are fairly low (less than 15%). However, as the ADCC activity of the IgG1 and IgG3 isotypes of V1X2C5 and the IgG3 isotype of VRC01 were statistically greater than the negative control, it could be concluded that these antibodies are weak mediators of ADCC. Several studies have shown that ADCC responses can arise fairly early in infection. For example, Chung et al showed that 50% of early infected patients in their study had anti-gp140 ADCC mediating antibodies capable of activating NK cells by a mean of 111 days (around 3 and a half months or 15 weeks) after infection (476). More recently, through measuring NK cell activation markers such as CD107a, IFNɣ and TNF-α, Dugast et al demonstrated that purified IgG from acutely infected HIV-1 patients could activate NK cells, indicating

ADCC activity when tested against native envelope proteins as early as 4 months post infection (475).

When patient B004 tested positive for HIV-1, test results gave a RITA score of 1, meaning that the patient had been infected within the last four months. Therefore, patient B004 fits into a similar timeframe as those tested in the studies mentioned above. However, several key differences exist between those and this study which make it difficult to compare results. Firstly, the studies used either purified plasma or serum antibodies to test for ADCC, therefore polyclonal or total IgG ADCC activity was being examined compared to monoclonals tested in this project. Secondly the assays used to determine ADCC were different. Whilst this project used the RFADCC assay to measure cells that had lost intracellular CFSE but retained cell membrane dye PKH-26 indicating lysed cells and thus ADCC, both of the afore mentioned studies measured NK cell activation markers such as CD107a and IFN therefore measuring ADCC indirectly. Thirdly, the actual HIV envelope proteins that each assay uses are different. In this study, patient serum was screened against a range of different clade B gp140

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proteins, with the clade B/C consensus protein which gave the best responses used in the assay to test the monoclonals. In the Chung and Dugast papers on the other hand, IgG was tested against either overlapping peptide pools of clade B consensus or purified gp140 protein (476) or target cells infected with pseudotyped virus (475).

Furthermore, in the literature, it is very difficult to find examples of monoclonal antibodies that have been isolated from acute or early infection that have been tested for and can mediate ADCC, with

PubMed searches for monoclonal antibodies from acute/early HIV-1 infection that mediate ADCC returning very few results. There have been several studies as previously mentioned that look at ADCC activity of serum or purified bulk IgG from early or acute infected patients (475, 476), however most studies performed have examined the effector functions of previously isolated BNABs or non- neutralising antibodies isolated from patients in chronic stages of infection.

Whilst one could argue that the monoclonal antibodies generated from patient B004 might not have been expected to mediate high levels of ADCC due to them being isolated from early infection, it was expected that there be some ADCC activity by the BNABs selected. 3BNC117 is a tier 2 neutralising

CD4bs IgG1 BNAb that was first isolated by Scheid et al in 2011 (305), and has been shown to mediate

ADCC activity in previous studies. Bruel et al showed that 3BNC117 mediated between 35-40% ADCC against NLAD8 and NL4.3 infected cell lines (365), whilst results from von Bredow et al showed that

3BNC117 was a potent ADCC mediator against HIV with up to 80% ADCC measured through reduction in relative light units (RLU) (486). Furthermore, in experiments examining clearance of HIVYU2 infected cells in mice, Lu et al demonstrated that mice treated with wild type 3BNC117 had fewer infected cells than those mice treated with a Fc mutant variant of 3BNC117 which could not perform ADCC and

ADCP but could perform neutralisation, indicating that 3BNC117 can mediates ADCC (487).

On the other hand, results in the literature have been more variable for ADCC activity for VRC01. This

BNAB was first isolated by Wu et al in 2010, and like 3BNC117 is also a tier 2 neutralising IgG1 CD4bs monoclonal (304). Whereas the previously mentioned Bruel et al paper showed 3BNC117 mediated

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ADCC activity, the same paper indicated that VRC01 was a weak ADCC mediator with only 0-10% ADCC against HIV NLAD8 and NL4.3 (365). Similarly a paper by Ferrari et al showed that incubation of HIVBaL infected cells with VRC01 lead to less than 10% granzyme B activity which can be used as a marker for

ADCC (372), and work by Smalls-Mantey et al studying ADCC using infected T cell elimination (or ICE) experiments also showed VRC01 to mediate weak ADCC activity at below 10% (477). Conversely, the von Bredow paper results indicated that VRC01 did mediate ADCC against HIV infected cells with up to 50% ADCC observed (486).

Therefore, as results published so far in the literature show that 3BNC117 in particular does seem to mediate ADCC activity, it was expected that the 3BNC117 tested in this project may also show similar levels of ADCC, while it was not expected that VRC01 would show much ADCC activity. Therefore, the results here do differ. Still, as previously mentioned, the wide variety of different ADCC assays used in different studies, and the range of HIV gp140 or gp120 proteins or infected cells that antibodies are tested against make it difficult to compare results. Very recently however, a paper was published by

Richard et al (March 2018) comparing several different ADCC assays for different antibodies to examine the effect of bystander cells on these assays. In one of the tests performed, 3BNC117 was tested using the RFADCC assay and was shown to mediate no ADCC activity against gp120 coated target cells. The authors came to the conclusion that performing ADCC assays with gp120 coated target cells favours the detection of ADCC responses mediated by anti-CD4i antibodies, and not BNAbs that generally recognise the functional trimer (478). Therefore, this could be a key reason as to why the BNAbs tested in this thesis did not show ADCC activity, however it is also important to note that whilst the paper used gp120 coated target cells, the work in this chapter used gp140-coated target cells.

In this project, the aim was to examine the ADCC responses of the patient isolated monoclonal antibodies as well as the BNABs in each of the four IgG subclasses; IgG1, IgG2, IgG3 and IgG4. As previously discussed, IgG1 and IgG3 antibodies are better mediators of Fc mediated effector functions

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such as ADCC. In the studies previously published the vast majority of envelope BNABs or non- neutralising mAbs tested are of the IgG1 subclass. This may be due to the techniques used to generate mAbs using single cell cloning techniques using the IgG1 vector (as discussed in chapter 4), as well as

IgG1 being the most dominant response in infected patients. Whilst there are now large numbers of

IgG1 BNABs, very few BNABs actually have different IgG subclass Fc regions. Despite several IgG2 mAb identified using the Los Alamos compendium, these antibodies did not have broad neutralising capabilities. The BNAb 10E8 is IgG3, but there are few others, and there are no IgG4 BNAbs.

Several BNAbs have been modified to express IgG3 hinge variants to increase the flexibility of the antibodies and potentially enhance ADCC activity. For example, Bournazos et al generated bispecific broadly neutralising antibodies with an IgG3 hinge region (488). Bispecific antibodies are able to recognise two epitopes and through their Fc region can mediate effector functions such as ADCC and

ADCP as per normal monoclonals (489) and would therefore be incredibly useful in HIV therapy. In their study, Bournazos et al combined several different combinations of BNAbs to make a variety of

IgG3 hinge variant bispecific antibodies which were then tested for neutralisation breadth (488).

Whilst they found that individual monoclonal BNAb showed no difference in neutralisation breadth between IgG1 and IgG3 hinge variant antibodies, several bispecific antibodies, including

3BNC117/PGT121, with the IgG3 hinge variant did have synergistic activity, with increased breadth and lower IC50 titres. However, these antibodies still have the CH1, CH2 and CH3 regions of the IgG1 constant region gene, and this paper did not test ADCC function.

Despite the majority of antibodies examined in this chapter not showing significant ADCC activity when compared to the negative control, the results generally show that the IgG3 version of the monoclonal antibody had the highest ADCC activity percentage, which corresponds to the previously published studies showing that IgG3 antibodies are generally better at mediating Fc mediated effector functions than IgG1, IgG2 and IgG4 antibodies. It is also interesting to note that of the three

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monoclonal antibodies shown to have significant ADCC activity, two were from a patient derived monoclonal antibody from early infection, and were in the IgG1 and IgG3 isotypes.

5.5.3. Limitations

As previously mentioned in this discussion, the RFADCC assay was used to determine the ADCC activity of 2 monoclonal antibodies isolated from an early infected HIV-1 patient and 2 BNAbs that were in each of the IgG subclasses (IgG1-IgG4). There were several limitations to these experiments.

Firstly, of the 9 antibodies (four BNABs and five mABs) generated in chapter 4, only 2 of the mAbs and

2 of the BNAbs had high enough concentrations after purification in each of the IgG subclasses to be tested using the RFADCC assay, despite large scale antibody production. In the original protocol followed for monoclonal antibody production by Smith et al, the authors describes how some antibodies do not transfect well into HEK293T cells, therefore resulting in a low concentration (400), and the fact that several antibodies were generated in large quantities demonstrates the transfection method itself works. Whilst more cell culture plates could be transfected to try to increase the antibody yield, as the purification system used for purifying antibodies can only deal with small volumes, this would not necessarily work.

Another limitation was that the RFADCC assay used in the study used the CEM.NKR-CCR5 cell line which was coated with the HIV envelope proteins. Despite one of the main advantages of this assay being that gp120 or gp140 proteins can be used, and therefore the higher biosafety containment levels are not required, it would be more biologically relevant to use infected cells as targets. Using primary isolates or pseudovirus to infect the cells would mean the display of gp140 on the surface of the target cells would be more like natural infection and therefore be better for testing ADCC. This would however have been difficult to achieve within the timeframe of this thesis, but could be recommended as future work.

A further limitation was that PBMCs were used as effector cells as opposed to isolated natural killer cells. In the original RFADCC paper, the effector cells used were PBMCs, however a later paper by

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Kramski et al showed that when using PBMCs the ADCC activity observed was actually down to monocyte phagocytosis (490). Therefore, more recently a number of studies have used purified NK cells to test ADCC activity. The main issue with this for this project was that for all the antibodies to be tested in triplicate in multiple tests a large number of PBMCs would be needed to isolate the number of NK cells required for these experiments, and therefore when first testing mAbs this would not be possible. It was planned however that if any significant ADCC activity was observed for any of these antibodies, then the assay would be repeated with purified NK cells to confirm ADCC activity.

5.5.4. Future work

If future work were to be carried out to continue this work, several experiments could be performed.

Firstly, some of the other monoclonal and broadly neutralising antibodies could be tested for ADCC activity, this would require high scale antibody production and purification. Secondly, protocols to establish infection of the CEM.NKr-CCR5 cell line used in the assay with primary isolates or pseudovirus could be implemented in the lab to test the antibodies against a more biologically relevant gp140.

Thirdly, as previously mentioned, there are a variety of ADCC assays, and therefore others such as

CD107a and IFNɣ assays could also be used to test ADCC. Furthermore, ADCC is not the only Fc effector function mediated by antibodies, and therefore assays to test phagocytosis as well as antibody dependent complement deposition could also be tested.

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Chapter 6: Discussion

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6. Discussion

6.1. Results Summary

The overall aim of the work presented in this thesis was to isolate monoclonal antibodies from longitudinal samples of an acute/early infected HIV-1 infected patient, generate these antibodies in multiple IgG isotypes, and to test these and several BNAbs for ADCC activity.

In chapter 3 of this thesis, it was shown that 5 anti-HIV antibodies were isolated from patient B004 (4 from visit 1 and 1 from visit 2), with 172 non-specific antibodies also isolated (65 from visit 1, 41 from visit 2, 48 from visit 3 and 18 from visit 4). Gene family usage followed similar patterns for the HIV-1 specific and non-specific antibodies with the VH3 heavy chain variable gene predominantly used. The five HIV-1 antibodies had low levels of mutations, whilst the non-specific antibodies had mutations that increased in percentage of total nucleotides and amino acids over time. Phylogenetic analysis also showed the anti-HIV-1 antibodies were not related and were more closely related to the non- specific antibodies than each other. Furthermore, there wasn’t really any relationship between the non-specific antibodies generated at each visit, and the distribution seemed fairly random.

In chapter 4 of this thesis, the IgG1 vector currently used for monoclonal antibody production was modified to generate IgG2, IgG3 and IgG4 vectors so that antibodies with these subclasses could be generated. During this part of the project there were several issues that required mutagenesis and several PCR attempts, however these issues were rectified resulting in the IgG subclass vectors.

Subsequently the five anti-HIV antibodies generated from patient B004 in the IgG1 subclass were also generated in IgG2, IgG3 and IgG4, as were four BNABs; VRC01, PGT121, 10E8 and 3BNC117.

Chapter 5 of this thesis showed that these antibodies were purified and quantified, and then tested for ADCC activity using the RFADCC assy. Two BNAbs (VRC01 and 3BNC117) and two patient derived mAbs (V1X2B12 and V1X2C5) were tested for ADCC in each of the IgG subtypes (IgG1-4), with results showing that of the 16 antibodies tested, 3 exhibited weak ADCC activity. The patient derived

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monoclonal antibody V1X2C5 showed ADCC activity in the IgG1 and IgG3 isotypes, with the BNAb

VRC01 also showing activity in the IgG3 isotype, however this activity was at a low level (<15%). Whilst the other antibodies did not show significant ADCC activity, there was differences between the activity of different isotypes of the same antibody.

6.2. Discussion

As discussed in previous chapters, BNAbs arise in around 20-30% of HIV-1 infected patients but develop only after several years of infection, due to the prolonged exposure to antigen required for the high levels of somatic hypermutation that leads to the high neutralising ability of these antibodies.

More recently isolated BNAbs known as second generation antibodies such as VRC01 and 3BNC117 are very potent neutralisers which have been shown to prevent HIV-1 infection in non-human primate models after passive transfer, and are therefore a key goal of HIV-1 vaccines. Despite efforts to achieve

BNAbs through vaccination with gp140 or gp120 subunit vaccines, vaccine candidates have so far been unable to stimulate these antibodies and have generally been non-neutralising. In recent years, these vaccine strategies have often traced the evolution of BNAbs back to their unmutated common ancestor (UCA), or germ line DNA, with the identification of intermediate ancestors or gene families identified along this pathway used to design the vaccines. Using this information, it has now been hypothesised that multiple boosters of slightly different gp120/gp140 subunits targeting different stages of these antibody pathways will likely have to be used in order to steer the antibody response towards BNAbs.

However, as discussed previously, non-neutralising antibodies may be just as important in protection from HIV-1. For example, both the RV144 trial results, which showed a 31.2% efficacy linked to non- neutralising IgG3 antibodies, and the polyfunctional antibody responses observed in elite controllers, have been linked to ADCC activity. The majority of studies examining ADCC function of antibodies have either been performed on polyclonal serum or plasma, or when studying monoclonals they have generally been isolated from chronic infection or vaccine candidates. If antibodies from early HIV-1

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infection could mediate ADCC activity and potentially provide protection from HIV-1, vaccine candidates that stimulate these antibodies could be designed. Moreover, as antibodies from early infection generally have lower levels of somatic hypermutation, they may be more easily triggered through a single vaccine as opposed to the addition of various boosters required for BNAb development. Whilst a paper by Chung et al did show that antibodies from early infection can mediate

ADCC (476), these results were obtained from patient serum and not monoclonal antibodies.

Of the three antibodies that showed significant ADCC activity compared to the negative control, two were the IgG1 and IgG3 isotypes of the patient derived antibody V1X2C5. Antibody V1X2C5 in the IgG1 and IgG3 isotypes mediated an average of 9.22% and 11.41% ADCC respectively, which despite being weak activity was statistically greater than the monoclonal negative control. Furthermore, both were statistically greater than the IgG2 isotype, with the IgG3 isotype also significantly greater than the IgG4 as well as IgG1 isotypes, and as a group there was a statistical difference in the activity between the different isotypes. The V1X2C5 antibody was isolated from the early infected patient B004 at visit 1

(sample taken 8 days after HIV-1 diagnosis), had a VH3 gene family usage, an amino acid mutation frequency of 2% and a CDRH3 length of 7 amino acids. A paper by Bonsignori et al found that 74%, of anti-HIV ADCC mediating antibodies (determined by a granzyme B assay) isolated from HIV-1 vaccine recipients preferentially used the VH1 gene family, and had low levels of somatic mutation, between

0.5 and 1.5% (415). Whilst the V1X2C5 mAb used a different gene family from the majority of the

ADCC mediating antibodies in the Bonsignori paper, 13% of the ADCC antibodies in the Bonisgnori paper did used the VH3 gene family like the V1X2C5 antibody isolated from patient B004. The mutation frequency was also similar, but the CDRH3 lengths did differ. Whilst the CDRH3 of V1X2C5 was just 7 amino acids long, those isolated in the Bonsignori paper generally ranged from 11 to 19 amino acids long.

It has been well documented that IgG3 antibodies are the best mediators of ADCC activity, and therefore the fact that the V1X2C5 and VRC01 IgG3 antibodies tested in this thesis showed significant

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ADCC activity when compared to the negative control complements these published results. IgG1 antibodies have also been shown to be good mediators of ADCC activity, and the V1X2C5 IgG1 antibody tested here also showed ADCC activity. Another interesting observation with the results for the V1X2C5 antibodies was that the antibody subclasses showed that pattern expected for ADCC activity, in that IgG3>IgG1>IgG2>IgG4, with the IgG2 and IgG4 antibodies showing no ADCC activity when compared to the negative control.

As discussed in chapter 5, the other tested patient derived monoclonal antibody V1X2B12 was borderline for significant ADCC activity when compared to the negative control, but did not quite reach the threshold set for positive ADCC activity. It is interesting to note that this antibody uses the same heavy chain variable and joining genes families even down to the same specific genes as the

V1X2C5 mAb, but differ in their heavy chain diversity genes. Moreover, they also share a very similar

CDRH3 sequence of 7 amino acids, with the only difference being position 6 which is glutamic acid (E) and aspartic acid (D) for V1X2B12 and V1X2C5 respectively.

Of the two BNAbs tested, whilst only the IgG3 subclass monoclonal of the VRC01 antibody showed significant ADCC activity when compared to the control, all the 3BNC117 IgG subclasses showed negative results well below the threshold for determining ADCC activity. As discussed in chapter 5, the

3BNC117 antibody has been shown in several published papers to mediate ADCC, and both the VRC01 and 3BNC117 BNAbs use the VH1 heavy chain gene family which has been linked to ADCC (415).

However, it is important to reiterate that it is difficult to compare many of the results for ADCC with those published due to the huge range of variations of assays used, and the wide variety of HIV-1 proteins or virus used in these assays. Despite the majority of antibodies in different isotypes did not show significant ADCC activity when compared to the negative control, it was interesting to note that it was the IgG3 isotype that had the best results for each antibody. If more time had been available, these antibodies would have also been tested for other functions such as antibody mediated

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phagocytosis and antibody mediated complement deposition, and this would be a key aim for future work.

Originally, it was planned that 5 mAbs and 4 BNAbs in each of the IgG subclasses and therefore a total of 36 antibodies would be tested for ADCC activity, however issues with the antibody yields prevented this. Due to the number of antibodies required to be purified, standard purification techniques with protein A agarose columns were not a viable option due to the high expense for this number of antibodies. Furthermore, protein A agarose purification techniques are not compatible with IgG3 purification and therefore an alternative protein G spin trap antibody purification column from GE healthcare was used for all antibodies. These columns however had much smaller volume capacities, and therefore antibody containing supernatant from the transfection of HEK293T cells that had tested positive by an IgG ELISA were first concentrated and then purified. Around 50% of the antibodies

(patient derived and BNAb) simply did not have high enough concentrations to be tested at the 2μg/ml concentration set for the RFADCC assay used. In the nature protocol paper by Smith et al followed for antibody production, the authors state that some antibodies are poor expressers and do not transfect well resulting in lower yields (404). A way to combat this would be to carry out larger volumes of transfections, however this would not have been possible in the time available in this project, but could be done in future work.

Not only did the purification methods limit the number of antibodies that could be tested, but the number of HIV specific antibodies in the first place that was isolated was a limiting number in both the numbers of antibodies that could be tested for ADCC, as well as examining the characteristics such as gene family usage, nucleotide mutations and CDRH3 lengths. As the longitudinal antibody response during early HIV-1 infection was to be examined, plasmablasts were selected for sorting as they are indicative of the current antibody response. A disadvantage of using plasmablasts however was that antigen baiting could not be used due to low surface expression of the B cell receptor on plasmablasts, and therefore it was not known until going through all the cloning steps and antibody production

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protocols whether the antibodies were HIV-1 specific. Memory B cells are an alternative as they can be selected using antigen baiting and therefore the yield should be high for HIV-1 specific antibodies.

However this introduces a bias into the antibodies that are isolated, and as the memory B cell population is being sorted there is an uncertainty as to when exactly the B cells with that particular B cell receptor and therefore later antibody was generated.

As shown in chapter 3, of 177 monoclonal antibodies isolated over four visits, only 5 (2.8%) were found to be HIV-1 specific. Despite being comparable with the relatively few other studies sorting plasmablasts from HIV-1 infection, this is still a low percentage and has an impact on the number of antigen-specific antibodies that can be tested for function. Since the end of experiments for this thesis, a paper has been published by Pinder et al where they have designed a scaffold system as a method of antigen baiting plasmablasts and showed that their percentage of HIV-1 specific antibodies after vaccination increased from 1.37% to 94.12% using this method (428). Therefore, for further work, a key aim would be to increase the number of antibodies from early infection to be tested for function, and using a similar method to the one described by Pinder et al, a higher yield of HIV-1 antibodies should be isolated. Using a method such as this may also improve the chances of isolating anti-HIV antibodies from later longitudinal samples which have a reduced plasmablast percentage. However, it is important to note that as this technique was used for a vaccinating antigen, the same antigen was used in design of the scaffold to isolate plasmablasts producing antigen-specific antibodies. As the work presented in this thesis examined acute and early infection, selecting a suitable gp140 or gp120 protein may prove more difficult, and may introduce a selection bias into sorting, excluding a large number of HIV-specific plasmablasts.

Because so few anti-HIV monoclonal antibodies were isolated from the early infected patient, more focus was placed on analysing the gene family usage the non-specific antibodies isolated from visits

1, 2, 3 and 4 to examine whether these characteristics changed over time during early HIV infection.

As shown in chapter 3, the VH3 gene family usage was at 50.7%, 43.9%, 52.1% and 66.7% at visits 1,

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2, 3 and 4 respectively, and this dominant usage of the VH3 gene family corresponds to the results in published literature that show that antibodies non-specific for HIV or from healthy individuals preferentially use this gene family. CDRH3 lengths were also similar over time showing similar length distributions. On the other hand the percentages of synonymous and non-synonymous mutations increased in time over the four visits for the non-specific antibodies. Whilst it was expected that anti-

HIV antibodies would have an increase in mutations over time due to somatic hypermutation and affinity maturation, this was not expected for non-specific antibodies.

Somatic hypermutation requires antigen exposure to drive the B cells into the dark zone in the germinal centre for this process to occur, leading to B cells with higher levels of mutation so that they are more specific for the antigen, and can then leave the germinal centre and differentiate into antibody secreting cells. As plasmablasts are not long lived, they indicate the current antibody response at the time the sample was taken for sorting. Therefore one could conclude that the antibodies isolated at visit 4 which had a higher percentage of both synonymous and non-synonymous mutations than those isolated at visit 1 originated from B cells that had undergone more somatic hypermutation in the germinal centre. B cells can differentiate into short lived plasmablasts before the germinal centre reaction, however these are often not class switched and are unmutated (491), and therefore it is unlikely that these plasmablasts producing antibodies with mutations up to 10% by visit 4 were activated independently of the germinal centre reaction. Furthermore, as these antibodies were not specific for HIV, it leads to the question as to what is driving the somatic hypermutation of these antibodies. Therefore, a key plan for future work would be to test these antibodies against different antigens for flu, CMV or tetanus to determine what antibodies are stimulated following HIV infection. It has been well documented that there is a mass non-specific immune activation during HIV infection and therefore testing the cognate antigens of these antibodies may potentially provide key information.

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The results in chapter 4 showed that IgG2, IgG3 and IgG4 expression vectors were generated in the lab through modification of the IgG1-Abvec vector. There were several issues in the development of these vectors such as having to use alternative methods to obtain the constant region DNA of the IgG3 and IgG4 subclasses for cloning into the vector through the Gibson reaction, as well as several mutagenesis steps to remove restriction sites and correct amino acid sequence, however the biggest issue was the IgG3 hinge region. As previously discussed, whilst the different IgG subclasses all have a similar structure, IgG3 antibodies have an extended hinge region, consisting of a hinge 1 (H1) section followed by three repeating nucleotide sequences termed H2, H3 and H4. It has been shown in several papers that whilst individuals have the H1 and H4 segments, different populations of individuals can lack H2, H3 or both segments (219). When trying to amplify the IgG3 constant region gene, the hinge region was not being fully amplified, and after repeated PCR and picking of single clones was missing the middle segments. It therefore took several attempts, a change of PCR primers, and using the

Gibson reaction until the full hinge was amplified. Following from this however, the IgG3 vector was a complete match for the constant gene reference sequence.

The generation of these vectors allowed monoclonal antibodies with the same variable regions but different IgG constant regions to be compared for function, more specifically ADCC as discussed earlier. Whilst the IgG subclass vectors have different gamma heavy chains, they all have the same construct with the same restriction enzyme sites for cloning in the variable region. This makes it incredibly easy to efficiently generate multiple monoclonal antibodies in multiple IgG isotypes at the same time, and could be used in a wide range of applications or disease models. As the vast majority of BNAbs as well as monoclonal antibodies currently being tested in the context of HIV are of the IgG1 subclass, these vectors could be used to generate the antibodies in IgG3 which has been shown in published literature to be the best mediators of antibody mediated effector functions, and may be beneficial for therapeutic use.

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6.3. Final Conclusions

In this thesis, the characteristics of the early antibody response were examined using the single cell sorting of plasmablasts and molecular cloning techniques to generate monoclonal antibodies which were characterised as either HIV-specific or non-specific.

The HIV-specific monoclonal antibodies were similar in their gene family usage to the non-specific antibodies, had a range of CDRH3 lengths and had very low levels of mutation. Whilst the non-specific antibodies predominantly used the VH3 gene family longitudinally, there was an increase in the mutation percentage over time, indicating that the plasmablasts from which these antibodies were derived originated from B cells that had undergone increasingly more rounds of somatic hypermutation in the germinal centre over time. The original hypothesis that there would be a difference in the gene family usage, nucleotide and amino mutations and CDR lengths between the

HIV-specific and non-specific antibodies is therefore not completely correct, as while the HIV antibodies isolated did have a lower percentage of mutations than the non-specific antibodies, this was not statistically significant (probably down to the low number of anti-HIV antibodies isolated), and the fact that HIV-specific and non-specific antibodies followed similar gene family usage trends.

IgG subclass expression vectors were generated from the IgG1 expression vector currently used in monoclonal antibody generation so that the anti-HIV antibodies isolated as well as several well characterised BNAbs could be produced in the lab in the IgG1, IgG2, IgG3 and IgG4 subclasses. This technology could now be used in other disease or infection models, as well as within the HIV-1 research field, to examine the responses of different IgG subclasses in functional tests.

Following the testing of these IgG subclass antibodies, it was found that one patient derived monoclonal antibody in the IgG1 and IgG3 subclasses, and one BNAb in the IgG3 subclass mediated weak ADCC. With much focus now on using BNAbs for therapeutic treatment, shock and kill methods, and use in a potential vaccine, the results observed in this thesis shows that monoclonal antibodies

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from early infection may be able to mediate weak ADCC activity, with the IgG3 isotype giving the best results. Therefore, IgG3 antibodies could potentially be used in these applications, however more work would be needed to strengthen these conclusions. The hypothesis that IgG1 and IgG3 antibodies would be better mediators of ADCC than IgG2 and IgG4 was therefore correct, with the only antibodies found to have ADCC activity were in IgG1 or IgG3 subclasses. It is of course important to note however that only several antibodies isolated from the early infected patient could be tested, and therefore further work would be required to confirm whether IgG2 or IgG4 antibodies truly do not mediate any

ADCC activity.

Future work would include isolating anti-HIV monoclonal antibodies from a number of early infected

HIV patients at multiple time points during early infection to examine the antibody response more fully, using a scaffold system such as that published by Pinder et al to achieve a higher yield of HIV- specific antibodies from patients, and producing these antibodies in IgG1, IgG2, IgG3 and IgG4 subclasses for testing. These and those antibodies already produced as part of the work discussed in this thesis would be tested for ADCC activity, with assays for antibody mediated phagocytosis and complement deposition also used to assess polyfunctionality of antibodies. Finally, it would be useful to test a number of monoclonal antibodies with weak ADCC activity together to determine whether multiple antibodies with weak activity could provide a synergistic effect when used simultaneously, on the basis that polyclonal antibodies such as the HIVIG used as a control in work in this thesis generally provides a much stronger response.

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