Long-Term Exposure to Interleukin-8, Lung Remodelling and Altered T Cell

Long-term exposure to interleukin-8, lung remodelling and altered T cell immunity in the context of chronic infection with Pseudomonas aeruginosa in chronic lung disease.

Kathryn Jane Quigley

A thesis submitted for the degree of Doctor of Philosophy

Lung Immunology Group

Infectious Diseases and Immunity

Department of Medicine

Imperial College London

August 2014

Declaration

I hereby declare that the research submitted in this thesis is my own and describes my own work under the supervision of Dr. Rosemary Boyton, at the Department of Medicine,

Imperial College, except where the work of others has been acknowledged at the point of use.

All sources of information have been appropriately cited and accompanied with a full list of references.

‘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.’

Abstract

In the lung, the fine balance between an appropriate immune response that defends against incoming pathogens while avoiding excessive inflammation leading to collateral tissue damage is dependent on appropriate and effective immune regulation. Lung disease can result in situations where the balance is tipped and excessive or inappropriate inflammation results in acute or chronic lung damage and impaired lung function. This is the case in non-CF bronchiectasis where enhanced inflammation and chronic lung infection are associated with irreversible tissue damage. Bronchiectasis is a pathological endpoint with several causes. It is often associated with chronic Pseudomonas aeruginosa infection and elevated levels of interleukin (IL)-8, a neutrophil chemoattractant, in bronchoalveolar lavage (BAL) and sputum. This thesis investigates the following hypotheses: 1) long-term exposure to high levels of IL-8 causes lung-remodeling and alters susceptibility to Pseudomonas aeruginosa infection; 2) individuals with bronchiectasis that are chronically infected with Pseudomonas aeruginosa have altered T cell immunity to the pathogen.

To investigate the impact of long-term exposure to IL-8 on lung-remodelling and susceptibility to Pseudomonas, a transgenic model made by the Boyton lab was used. IL-8 transgenic mice show enhanced mucus secretion, lung fibrosis and remodelling with leaky tight junctions, but effectively clear Pseudomonas aeruginosa compared to controls.

Differences in T cell immunity were tested in a cohort of patients with non-CF bronchiectasis, with or without Pseudomonas aeruginosa, to the outer membrane porin F

(OprF) protein. A narrowed repertoire of T cell epitopes and different adaptive immune responses were observed in patients chronically infected with Pseudomonas aeruginosa.

HLA-restricted OprF T cell epitopes were identified in HLA class II transgenics. The Boyton

3 lab has previously identified a genetic susceptibility to idiopathic bronchiectasis associated with the presence of the HLA-Cw*03. In this thesis, the association between HLA-Cw*03 and functional immunity was investigated.

Acknowledgements

I wish to start by thanking my supervisor Dr. Rosemary Boyton for giving me the opportunity to carry out a PhD project and for all her support and advice that has helped me drive my project forward over the past four years.

Several individuals have helped with a number of aspects of the work that went into this thesis. Many thanks must go to Dr. Michael Poidinger (Singapore Immunology Network) for help with the pathway analysis, Dr. Scott Brouilette for his time and advice on using Partek,

Ruhena Sergeant of the HLA-typing facility (Hammersmith Hospital) for efficiently processing all patient samples and to Ivan Andrew at the CSC genomics laboratory, Imperial

College for running the Affymetrix assay. A special thanks must go to Dr. Andrew Quigley and Dr. Rod Chalk at the University of Oxford for not only confirming my protein by mass spectrometry, but also for their guidance, help and willingness to discuss my ideas. In addition, thanks must be given to Chrysoula Panethymitaki and Bernadette Byrne for helping me in the initial stages of my protein work. I would also like to thank Dr. Stefan Worgall for providing the OprF plasmid. A thanks must also go to the CBS staff that maintained and looked after the transgenic lines.

This project would not have been possible without the dedication of Catherine Hennessy and

Dr. Michael Loebinger at the Royal Brompton Hospital for co-ordinating, recruiting and collecting blood samples for use in this project. I would also particularly like to thank all the patients that took part and donated blood.

I am very grateful for all the help, support and time that Dr. Catherine Reynolds gave me throughout my time at Imperial, but also to her never failing encouragement and for putting

5 up with my constant questioning. I would also like to thank all past and present members of the Lung Immunology Group and Human Disease and Immunogenetics Group. In addition thanks go to the members of the 8th floor that have helped me along the way. I would particularly like to thank Debs, Cheryl, Julia, Janet, Jitin and Malcolm. Janet, you are my rock!

Thanks are also extended to the MRC and Asthma UK Centre for the financial backing enabling me to do this PhD.

A big big big thank you to all my friends and family, in particular my parents and those that were phoned every few days for a chat during my write up to keep me sane. Lastly I am indebted to Chris who lived this thesis almost as much as I did but also for his love, continuous support and motivation throughout.

Contents Page

Declaration ...... 2 Copyright Declaration ...... 2 Abstract ...... 3 Acknowledgements...... 5 Table of Tables ...... 13 Table of Figures ...... 15

CHAPTER 1: Introduction ...... 29 1.1. Interleukin 8...... 31 1.2. IL-8 receptors and signalling ...... 35 1.3. Biological activity of IL-8 ...... 37 1.4. Neutrophils ...... 40 1.5. Characteristics of airway remodelling and lung damage associated with chronic respiratory disease...... 41 1.5.1. Mucus production ...... 42 1.5.2. Fibrosis ...... 43 1.5.3. Smooth muscle ...... 46 1.6. Tight junctions and epithelial permeability in respiratory disease ...... 47 1.7. hIL-8 transgenic model of chronic respiratory disease ...... 52 1.8. Chronic respiratory disease and infection ...... 52 1.9. Pseudomonas aeruginosa...... 53 1.9.1. The innate immune response to P. aeruginosa ...... 58 1.9.2. The adaptive immune response to P. aeruginosa ...... 62 1.10. Current treatment strategies for P. aeruginosa infections ...... 65 1.11. Outer membrane porin F (OprF) ...... 68 1.12. T cells ...... 70 1.12.1. T cell development ...... 71 1.12.2. Central and peripheral tolerance mechanisms ...... 72 1.12.3. CD4+ T cell Subsets ...... 74 1.12.4. Th1 and Th2 cells ...... 74 1.12.5. CD4+ T cell populations ...... 76 1.12.6. S1P and receptor mediated T cell trafficking ...... 77 1.12.7. Major histocompatibility complexes and antigen processing ...... 78 1.12.8. MHC class I and the endogenous processing pathway ...... 80

1.12.9. MHC class II and the exogenous processing pathway ...... 81 1.12.10. Major histocompatibility complexes associated with chronic respiratory disease ...... 82 1.13. Epitope mapping ...... 83 1.13.1. Computational epitope predictions ...... 83 1.13.2. Peptide design in CD4+ T cell epitope mapping ...... 88 1.13.3. Epitope mapping: CD4+ T cell mapping in humans and HLA transgenic mice ...... 88 1.14. Natural killer cells ...... 89 1.14.1. NK cell receptor expression ...... 91 1.14.2. Activating and inhibitory receptors ...... 92 1.14.3. NK cell responses...... 95 1.14.3.1. NK cell cytotoxicity ...... 96 1.14.3.2. Cytokine and chemokine production and interaction with other immune cells ...... 97 1.14.4. NK cells and bronchiectasis ...... 98 1.15. Aims and Outline of thesis ...... 101 1.15.1. Aims ...... 101 1.15.2. Outline ...... 102

CHAPTER 2: Materials and Methods ...... 104 2.1. Transgenic mice ...... 104 2.1.1. Human IL-8 (hIL-8) transgenic mice ...... 104 2.1.2. HLA-DR transgenic mice ...... 104 2.1.2.1. Human leukocyte antigen (HLA) ...... 104 2.2. Genotyping of transgenic mice ...... 105 2.2.1. High salt gDNA extraction ...... 105 2.2.2. Polymerase chain reaction (PCR) ...... 106 2.2.3. Agarose gel electrophoresis ...... 108 2.3. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) ...... 108 2.3.1. RNA extraction from murine lung tissue ...... 108 2.3.2. RNA extraction from human peripheral blood mononuclear cells (PBMCs) ...... 109 2.3.3. Complementary DNA (cDNA) synthesis...... 110 2.3.4. Real time-PCR for individual genes ...... 110 2.4. Murine fibrosis RT2 profilerTM PCR array ...... 113 2.4.1. RT2 first strand kit ...... 114 2.4.2. RT2 RNA QC PCR array ...... 114

2.4.3. Murine fibrosis RT2 profilerTM PCR array ...... 115 2.5. Differential cell counts ...... 119 2.5.1. Preparation of bronchoalveolar lavage (BAL) for differential cell counts ...... 119 2.5.2. Preparation of lung for differential cell counts ...... 119 2.5.3. Wright-Giemsa stain ...... 120 2.6. Immunohistochemistry ...... 121 2.6.1. Paraffin embedding and sectioning ...... 121 2.6.2. Hematoxylin and Eosin staining ...... 121 2.6.3. Periodic acid -Schiff staining ...... 122 2.6.4. Masson’s trichrome staining ...... 122 2.7. Immunofluorescence ...... 124 2.7.1. Frozen sections ...... 124 2.7.2. Immunofluorescent staining ...... 124 2.8. Enzyme-linked immunosorbent assay (ELISA) ...... 126 2.8.1. Preparation of BAL fluid for ELISA ...... 126 2.8.2. Preparation of lung tissue for ELISA ...... 126 2.8.3. Preparation of serum for ELISA ...... 127 2.8.4. Cytokine ELISA ...... 127 2.8.5. Murine albumin ELISA ...... 129 2.9. Labelled dextran ...... 130 2.10. Pseudomonas aeruginosa (P. aeruginosa) infection in vivo ...... 131 2.10.1. P. aeruginosa ...... 131 2.10.2. P. aeruginosa infection ...... 131 2.10.3. Bioluminescent imaging ...... 132 2.10.4. Thymidine proliferation assay ...... 132 2.11. Clinical measurements ...... 133 2.11.1. Ethical statement ...... 133 2.11.2. Blood collection and patient groups ...... 136 2.12. Cell isolation and processing ...... 137 2.12.1. PBMC isolation ...... 137 2.12.2. Assessment of cell viability (trypan blue stain) ...... 137 2.12.3. Cryopreservation of PBMCs ...... 138 2.12.4. Thawing of PBMCs ...... 138 2.13. Isolation of human gDNA ...... 138

2.13.1. HLA typing ...... 139 2.14. Flow cytometry ...... 140 2.14.1. Multiparameter FACS: Cell surface and intracellular cytokine staining ...... 140 2.14.1.1. Cell stimulation ...... 140 2.14.1.2. Surface antigen staining for human cells ...... 141 2.14.1.4. Intracellular cytokine staining ...... 142 2.15. Protein production and purification of OprF ...... 143 2.15.1. Growth of Escherichia coli strains ...... 143 2.15.1.1. Bacterial growth media ...... 143 2.15.2. DNA transformation of E. coli ...... 144 2.15.3. Preparation of glycerol stocks ...... 144 2.15.4. Plasmid dsDNA isolation and restriction digests...... 145 2.15.5. Protein expression and purification ...... 145 2.15.5.1. Protein expression...... 145 2.15.5.1.1. Small scale: Time course for OprF protein expression ...... 145 2.15.5.1.2. Small scale: Temperature optimisation for OprF protein expression ...... 146 2.15.5.1.3. Large scale: OprF protein expression ...... 146 2.15.5.1.4. Large scale: Preparation of crude cell lysates ...... 147 2.15.5.2. Purification: Affinity chromatography ...... 147 2.15.5.3. Protein analysis and detection ...... 148 2.15.5.3.1. SDS-PAGE ...... 148 2.15.5.3.2. Development of SDS-PAGE gels: Colloidal blue stain ...... 148 2.15.5.3.3. Development of SDS-PAGE gels: Western blot ...... 149 2.15.5.3.4. Determination of protein concentration ...... 150 2.15.5.3.5. Mass spectrometry ...... 150 2.16. OprF peptide panel ...... 150 2.17. Epitope mapping of OprF...... 152 2.17.1. In silico predictions ...... 152 2.17.2. Preparation of peptides and protein...... 152 2.17.3. Footpad immunisations ...... 153 2.17.4. Murine IFNγ enzyme-linked immunospot (ELISpots) ...... 153 2.17.5. Human IFNγ ELISpots ...... 154 2.18. 30 plex luminex assay ...... 156 2.19. Affymetrix microarray ...... 157

2.19.1. Preparation of RNA samples for Affymetrix whole transcriptome microarray analysis 158

CHAPTER 3: hIL-8 targeted expression in the lung leads to airway inflammation, remodelling and impaired tight junctions...... 162 3.1. Introduction ...... 162 3.2. Aims ...... 164 3.3. Results ...... 165 3.3.1. hIL-8 transgene and protein expression in the hIL-8 transgenic model...... 165 3.3.2. Investigating the impact of hIL-8 on lung disease pathology...... 167 3.3.2.1. Cellular inflammation in the airways of hIL-8 transgenics...... 167 3.3.2.2. Airway remodelling in the hIL-8 transgenics: Mucus production and mucin gene expression...... 172 3.3.2.3. Airway remodelling in the hIL-8 transgenics: Airway fibrosis...... 174 3.3.2.4. Airway remodelling in the hIL-8 transgenics: Epithelial integrity and tight junctions. 192 3.3.3. Investigating the impact of hIL-8 on P. aeruginosa infection...... 200 3.4. Discussion ...... 210 3.4.1. hIL-8 transgenics show chronic lung remodelling...... 210 3.4.2. Investigating the effects of hIL-8 on respiratory disease pathology ...... 212 3.4.3. Investigating the effects of hIL-8 on P. aeruginosa infection in vivo...... 222 3.5. Summary ...... 224

CHAPTER 4: CD4+ T cell epitope responses to OprF and identification of differential immunity between bronchiectasis patients with or without chronic P. aeruginosa infection...... 226 4.1. Introduction ...... 226 4.2. Aims ...... 229 4.3. Results ...... 229 4.3.1. Expression of OprF ...... 229 4.3.2. OprF purification by affinity chromatography ...... 233 4.3.3. Confirmation of a cellular response to OprF post P. aeruginosa infection...... 236 4.3.4. In silico predictions for HLA-DR restricted OprF epitopes...... 237 4.3.5. In vivo mapping of HLA-DR restricted OprF epitopes...... 241 4.3.6. Differences in the adaptive immune response to OprF protein in patients with bronchiectasis with and without chronic P. aeruginosa infection...... 246 4.4. Discussion ...... 262

4.4.1. Production and purification of OprF ...... 262 4.4.2. In silico and in vivo predictions of HLA-DR restricted OprF epitopes ...... 265 4.4.3. Differences in the adaptive immune response in bronchiectasis patients in the context of chronic P. aeruginosa infections ...... 268 4.5. Summary ...... 274

CHAPTER 5: An allele specific effect of HLA-Cw*03 expression and NK cell activation in patients with idiopathic bronchiectasis...... 276 5.1. Introduction ...... 276 5.2. Aims ...... 279 5.3. Results ...... 279 5.3.1. Increased activation of CD56+CD3- cells in idiopathic bronchiectasis patients not expressing the HLA-Cw*03 allele...... 279 5.4. Discussion ...... 298 5.5. Summary ...... 304

CHAPTER 6: General Discussion ...... 305 6.1. hIL-8 transgenic mice: a new tool to study chronic lung remodelling...... 305 6.2. Developing a peptide based vaccine for P. aeruginosa infections...... 308 6.3. Are NK cells dysfunctional in patients with idiopathic bronchiectasis? ...... 313 6.4. Summary ...... 313

References ...... 314

Appendix 1 ...... 431 1.1. IFNγ ELISpot data for each patient ...... 431 1.1.1. Individuals that were never culture positive for P. aeruginosa during 6 month study 431 1.1.2 Patients culture positive for P. aeruginosa in less than 50 % of sputum samples taken during the study ...... 439 1. 1.3 Patients culture positive for P. aeruginosa in more than 50 % of sputum samples taken during the study ...... 442 1.2 Permission Document ...... 446

Table of Tables

Chapter 2 Materials and Methods

Table 2. 1: PCR primer sequences and reaction conditions (Sigma-Aldrich)...... 107 Table 2.2 Primers for murine qRT-PCR genes obtained from Applied Biosystems...... 111 Table 2.3: Primer sequences for murine qRT-PCR genes obtained from Sigma-Aldrich ..... 112 Table 2.4 Primers for murine qRT-PCR genes obtained from Qiagen...... 112 Table 2.5 Primers for human qRT-PCR genes obtained from Qiagen ...... 113 Table 2.6 Conditions for TaqMan or SYBR Green detection systems with the Mx3000p real- time PCR machine...... 113 Table 2.7 Gene list for the murine fibrosis RT² profiler PCR array (Qiagen) ...... 116 Table 2.8 Masson’s trichrome scoring system (Ashcroft et al., 1988)...... 123 Table 2.9 Concentrations of primary and secondary antibodies used in cytokine ELISAs. .. 128 Table 2.10: Patient characteristics. Clinical characteristics for each patient diagnosed with pulmonary bronchiectasis...... 134 Table 2.11 Summary of the patient cohort diagnosed with bronchiectasis...... 136 Table 2.12 HLA-C group 1/group2 motifs and corresponding HLA-C alleles...... 140 Table 2.13 Human cell surface antigen antibodies ...... 141 Table 2.14 Human intracellular cytokine antibodies ...... 142 Table 2.15 E. coli strains used during this project...... 143 Table 2.16 Commercial plasmid vector used during this project...... 143 Table 2.17 Peptide panel sequences of each 20-amino acid peptide of OprF ...... 151 Table 2.18: Immunological protein panel in the 30-plex luminex kit (Invitrogen) ...... 156

Chapter 4 CD4+ T cell epitope responses to OprF and identification of differential immunity between bronchiectasis patients with or without chronic P. aeruginosa infection.

Table 4.1. In silico analysis of OprF with NetMHCIIpan and IEDB consensus methods. .... 239 Table 4.2: In silico and in vivo summary of HLA-DR1, HLA-DR4 and HlA-DR15 restricted OprF epitopes...... 245

Table 4. 3: Patient cohort used in the human IFNγ ELISpot assay were determined by positive sputum cultures for P. aeruginosa in patients with bronchiectasis...... 246 Table 4.4 Frequency table showing the number of patients responding to peptide depending on HLA-DR expression ...... 254 Table 4.5: Patient groups used in human Luminex assays were determined by positive sputum cultures for P. aeruginosa in patients with bronchiectasis...... 255 Table 4.6: Supernatants from OprF activated T cells obtained from IFNγ ELISpot assays. The immunological protein concentrations were determined using the 30-plex luminex assay for patients that never cultured positive or cultured positive >50% sputum samples...... 256 Table 4.7: Patient groups used in human qRT-PCR assays were determined by percentage of positive sputum cultures for P. aeruginosa in patients with idiopathic bronchiectasis or bronchiectasis due to other causes...... 258 Table 4.8: 12 outer membrane immunogenic proteins of P. aeruginosa identified by Montor and colleagues (Montor et al., 2009)...... 264

Chapter 5 An allele specific effect of HLA-Cw*03 expression and NK cell activation in patients with idiopathic bronchiectasis.

Table 5.1: The idiopathic bronchiectasis cohort used for human multiparameter FACS and qRT-PCR assays were classified by HLA-C group 1 or group 2 homozygous expression or by HLA-Cw*03 expression...... 280 Table 5.2: The idiopathic bronchiectasis cohort used for Affymetrix studies were classified by HLA-Cw*03 expression...... 290 Table 5.3: A summary of differentially regulated genes in PBMCs from patients with idiopathic bronchiectasis to compare the allele specific effect of HLA-Cw*03...... 291 Table 5.4: A summary of differentially regulated genes in PBMCs from patients with idiopathic bronchiectasis to compare the allele specific effect of HLA-Cw*03 ...... 296

Table of Figures

Chapter 1 Introduction

Figure 1.1: Schematic illustrating human IL-8...... 33 Figure 1.2: Schematic illustrating IL-8 signalling...... 37 Figure 1.3: Schematic illustrating epithelial to mesenchymal transition (EMT)...... 45 Figure 1.4: Epithelial tight junction components...... 48 Figure 1.5: Distinct stages of biofilm development...... 55 Figure 1.6: Candidate P. aeruginosa protein targets for vaccine design...... 67

Chapter 2 Materials and Methods

Figure 2.1 Recombinant OprF sequence...... 150

Chapter 3 hIL-8 targeted expression in the lung leads to airway inflammation, remodelling and impaired tight junctions.

Figure 3.1: hIL-8 transgene and protein expression in the hIL-8 transgenic model...... 166 Figure 3.2: Increased cellular infiltration in lung tissue of hIL-8 transgenics...... 168 Figure 3.3: Neutrophil infiltration into the BAL is increased in hIL-8 transgenics...... 170 Figure 3.4: Increased expression of murine CXC chemokines in the hIL-8 transgenics...... 171 Figure 3.5: Increased mucus production and mucin gene expression in hIL-8 transgenics. . 173 Figure 3.6: Evidence of airway remodelling in the hIL-8 transgenics...... 175 Figure 3.7: hIL-8 transgenic and non-hIL-8 transgenic littermate controls have differences in the relative expression of fibrotic genes in lung tissue at 10 weeks of age...... 177 Figure 3.8: hIL-8 transgenic and non-hIL-8 transgenic littermate controls have differences in the relative expression of fibrotic genes in lung tissue at 20 weeks of age...... 178 Figure 3.9: Ccl3 and Ifnγ transcripts are increased in hIL-8 transgenic mice...... 181 Figure 3.10: Fasl transcripts are increased in hIL-8 transgenic mice...... 182 Figure 3.11: CCL3 is increased in the lung tissue of younger hIL-8 transgenics...... 184

Figure 3.12: Pathway enrichment analysis to identify important pathways in the hIL-8 transgenic model...... 185 Figure 3.13: Pathway enrichment analysis to identify important pathways in the hIL-8 transgenic model...... 186 Figure 3.14: Occludin and fibronectin transcripts are significantly decreased in the hIL-8 transgenic mice...... 188 Figure 3.15: A summary of the direction of change of epithelial to mesenchymal transition markers in the hIL-8 transgenics compared to non-hIL-8 transgenic controls...... 189 Figure 3.16 Tight junction gene transcripts are decreased in the hIL-8 transgenic mice...... 191 Figure 3.17 A summary of the direction of change of tight junction components in the hIL-8 transgenics...... 192 Figure 3.18: Disrupted epithelial tight junctions in the lung of hIL-8 transgenics...... 194 Figure 3.19: Disrupted tight junctions and epithelial damage is observed in the lungs of hIL-8 transgenics...... 196 Figure 3.20: Positive claudin-18 staining in the intestine of hIL-8 transgenics...... 197 Figure 3.21: Integrity of the lung epithelium is disrupted in hIL-8 transgenics...... 198 Figure 3.22: Assessment of epithelial permeability by FITC-dextran (4 kDa) movement between BAL, lung and serum in hIL-8 transgenics...... 199 Figure 3.23: Determining the infection dose of P. aeruginosa in hIL-8 transgenics compared to non-hIL-8 transgenic controls...... 201 Figure 3.24: IVIS imaging of hIL-8 transgenics and non-hIL-8 transgenic control mice infected with bioluminescent P. aeruginosa (Xen41)...... 203 Figure 3.25: hIL-8 transgenics have reduced disease severity and enhanced bacterial clearance following infection with P. aeruginosa...... 205 Figure 3.26: 7 hours after intranasal inoculation with P. aeruginosa, hIL-8 transgenics have increased numbers of neutrophils in the BAL...... 207 Figure 3.27: 7 hours after intranasal inoculation with P. aeruginosa infection, hIL-8 transgenics have increased TNFα levels in BAL...... 209

Chapter 4 CD4+ T cell epitope responses to OprF and identification of differential immunity between bronchiectasis patients with or without chronic P. aeruginosa infection.

Figure 4.1: Confirmation of the correct pET SUMO.OprF plasmid...... 230 Figure 4.2: Time course of IPTG induction of the sumo fusion OprF protein to determine the optimal time point for production...... 231 Figure 4.3: OprF expression in soluble and insoluble samples at 27 ºC and 37 ºC...... 232 Figure 4.4: Purification of OprF...... 234 Figure 4.5: Mass spectrometry to assess purity of recombinant OprF...... 235 Figure 4.6: Systemic T cell responses to purified OprF after infection with P. aeruginosa. 236 Figure 4.7: The OprF amino acid sequence with epitopes predicted by TEPITOPE indicated for HLA-DR1, HLA-DR4 and HLA-DR15...... 240 Figure 4.8: Expression of human HLA-DR and absence of murine MHC (AβO) in HLA-DR1 AβO, HLA-DR4 AβO and HLA-DR15 AβO transgenic mice...... 242 Figure 4.9: HLA-DR1, DR4 and DR15 restricted epitope responses to the whole protein and peptide panel of OprF...... 243 Figure 4.10: Representative CD4+ T cell response to the whole protein and peptide panel of OprF in patients diagnosed with bronchiectasis...... 248 Figure 4.11: CD4+ T cell responses to OprF peptides in patients that never cultured positive for P. aeruginosa during the study...... 249 Figure 4.12: CD4+ T cell responses to OprF peptides in patients that cultured positive for P. aeruginosa during the study...... 250 Figure 4.13: Patients with more than 50 % of positive P. aeruginosa sputum cultures have a contracted repertoire of recognised epitopes...... 251 Figure 4.14: No difference is observed in the peptide response to mucoid or non-mucoid P. aeruginosa...... 252 Figure 4.15: Increased pro-inflammatory cytokines in patients culturing positive for P. aeruginosa in more than 50 % of sputum samples...... 257 Figure 4.16 Bronchiectasis (all causes except idiopathic) patients with > 50 % of P. aeruginosa positive sputum cultures had decreased Tbet and S1P1 expression in stimulated PBMCs...... 259

Figure 4.17 Tbet gene expression increases on activation while S1P1 decreases on activation in human PBMCs...... 260

Figure 4.18: No difference in expression of Tbet, GATA3, RORγt or S1P1 transcripts in patients with idiopathic bronchiectasis, with or without chronic P. aeruginosa infection. ... 261

Chapter 5 An allele specific effect of HLA-Cw*03 expression and NK cell activation in patients with idiopathic bronchiectasis.

Figure 5.1: Gating strategy to determine NK cell activation...... 282 Figure 5.2: IFNγ is increased in NK cells of HLA-Cw*03 negative patients compared to HLA-Cw*03 positive patients...... 284 Figure 5.3: No difference in IFNγ and granzyme A production by CD8+ T cells between HLA-C group 1 and 2 or with HLA-Cw*03 expression...... 285 Figure 5.4: No difference in IFNγ and granzyme A production by CD8-CD3+ T cells between HLA-Cw group 1 and or with HLA-Cw*03 expression...... 286 Figure 5.5: Increased expression of the RORγt transcript in stimulated PBMCs of HLA-C group 2 homozygous patients with idiopathic bronchiectasis...... 288

Figure 5.6: Decreased expression of GATA3 and S1P1 transcripts in stimulated PBMCs of HLA-Cw*03 positive patients with idiopathic bronchiectasis...... 289 Figure 5.7: Pathway enrichment analysis predominantly identified T cell related pathways in HLA-Cw*03 negative PBMCs with stimulation...... 292 Figure 5.8: Pathway enrichment analysis predominantly identified T cell related pathways in HLA-Cw*03 negative PBMCs with stimulation...... 293 Figure 5.9: 7 genes were differentially regulated in both HLA-Cw*03 negative and HLA- Cw*03 positive PBMCs with stimulation...... 295 Figure 5.10: IFNγ transcript expression is increased in HLA-Cw*03 negative patients compared to HLA-Cw*03 positive patients without stimulation...... 297

List of Abbreviations

µl microlitre

µM micromolar

3O-C12-HSL N-(3-oxododecanoyl)-L-homoserine lactone

ABPA Allergic bronchopulmonary aspergillosis

ADCC Antibody-dependant cellular cytotoxicity

ADP Adenosine diphosphate

AHL N-acyl-l-homoserine lactone

AICD Activation-induced cell death

AIRE Autoimmune regulator

Akt Protein kinase B

ALP Alkaline phosphatase

ANN Artificial neural networks

AP-1 Activator protein-1

APECED Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy

ARDS Acute respiratory distress syndrome

ASM Airway smooth muscle

BAL Bronchoalveolar lavage

BFA Brefeldin A bp Base pair

BSA Bovine serum albumin

C4-HSL N-butanoyl-L-homoserine lactone

CC10 Clara cell 10

CCL C-C Chemokine ligand

CCR C-C Chemokine receptor

CD Cluster of differentiation cDNA Complementary deoxyribonucleic acid

CFA Complete freund’s adjuvant

CFSE Carboxyfluorescein succinimidyl ester

CFU Colony-forming units

CLIP Class II-associated invariant chain peptide

ColI Collagen I

ColIII Collagen III

COPD Chronic obstructive pulmonary disease cpm counts per minute

Ct Cycle threshold

C-terminal Carboxy-terminus

CVID Common variable immunodeficiency

CXCL C-X-C Chemokine ligand

CXCR C-X-C Chemokine receptor

DAG Diacylglycerol

DAP DNAX activating protein dH2O Distilled water

DLN Draining lymph node

DMSO Dimethyl sulfoxide

DN Double negative

DNA Deoxyribonucleic acid

DNase Deoxyribonuclease dNTP Deoxyribonucleotide triphosphate

DP Double positive dsDNA double stranded deoxyribonucleic acid

DTT Dithiothreitol

E. coli Escherichia coli

EAE Experimental autoimmune encephalomyelitis

EDTA Ethylenediaminetetraacetic acid

EGF Epidermal growth factor

ELISA Enzyme linked immunosorbent assay

ELISpot Enzyme linked immunospot

ELR Glutamic acid, leucine and arginine motif

EMT Epithelial to mesenchymal transition

ER Endoplasmic reticulum

ERAP1 Endoplasmic reticulum aminopeptidase 1

ERL Glutamic acid, Leucine and Arginine sequence motif et al. et alia, and others

ETP Early T cell progenitors

FACS Fluorescence activated cell sorting

FCS Foetal calf serum

FDR False discovery rate

FEV1 Forced expiratory volume in 1 second

FGF Fibroblast growth factor

FITC Fluorescein isothiocyanate

FoxP3 Forkhead/winged-helix transcription factor box P3

FSC Forward scatter

FTY720 Fingolimod

21 g Centrifugal force

GAPDH Glyceraldehyde-3-phosphate dehydrogenase

G-CSF Granulocyte colony-stimulating factor gDNA Genomic deoxyribonucleic acid

GMCSF Granulocyte macrophage colony stimulating factor

HBSS Hanks balanced salt solution

HGF Hepatocyte growth factor hIL-8 Human interleukin 8

HLA Human leukocyte antigen

HLA-DR1 HLA-DRB1*0101

HLA-DR15 HLA-DRB1*1501

HLA-DR4 HLA-DRB1*0401

HPLC High-performance liquid chromatography

HRCT High resolution computed tomography

HRP Horseradish peroxidase

HSC hematopoietic stem cells

IC50 Half maximal inhibitory concentration

IEDB Immune Epitope Database

IFN- Interferon-

Ig Immunoglobulin

Ii Invariant Chain

IL- Interleukin

IL-872 Interleukin 8 - 72 amino acid variant

IL-877 Interleukin 8 - 77 amino acid variant iNOS Inducible nitric oxide synthase

IP-10 Interferon gamma-induced protein 10

IP3 Inositol 1, 4, 5-trisphosphate

IPF Idiopathic pulmonary fibrosis

IPTG Isopropyl β-D-1-thiogalactopyranoside

ITAM immunoreceptor tyrosin-based activation motif

ITIM immunoreceptor tyrosin-based inhibitory motif

IVIS In vivo imaging systems

JAM Junctional adhesion molecule

Kb Kilobase pair

KC Keratinocyte derived chemokine kDa Kilodaltons

KIR Killer immunoglobulin like receptor

KLF2 Kruppel-like factor 2

Ksr1 Kinase suppressor of Ras-1

L Litre

LB Lysogeny broth (Luria-Bertani bacterial growth media)

LFA-1 lymphocyte function-associated antigen-1

LPS Lipopolysaccharide

Mac-1 Macrophage-1 antigen

MAPK Mitogen activated kinase

MDNCF Monocyte-derived neutrophil chemotactic factor

MFI Mean fluorescence intensity

MHC Major histocompatibility complex

MIC MHC class I chain related gene

MIG Monokine induced by interferon gamma

MIP- Macrophage inflammatory protein- mM Millimolar

MMP- Matrix metalloproteinase

MONAP Monocyte derived neutrophil activating peptide

MPO Myeloperoxidase mRNA Messenger ribonucleic acid

NAF Neutrophil activating factor

NCR Natural cytotoxity receptor

NET Neutrophil extracellular traps

NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells

Ni-NTA Nickel-nitrilotriacetic acid

NK Natural killer cell

NKG2 Natural killer receptor

NKT Natural killer T cell

NLR Nod like receptors

NLRC4 NLR family CARD domain-containing protein 4 nm Nanometer nM Nanomolar

N-terminal Amino terminal

º C Degrees centigrade

O.C.T Optimal cutting temperature

OD Optical density

OprF Outer membrane porin F

ORMDL3 Orosomucoid-like

OVA Ovalbumin

P. aeruginosa Pseudomonas aeruginosa

PA1777 Locus tag P. aeruginosa protein OprF

PAI- P. aeruginosa autoinducer-

PAO1 P. aeruginosa strain

PBMC Peripheral blood mononuclear cells

PBS Phosphate buffered saline

PCD Primary Ciliary Dyskinesia

PCR Polymerase chain reaction

PDB Protein Database

PDZ PSD95-dig-ZO-1 domain

PE Phycoerythrin

PF Peptide Frame

PFA Paraformaldehyde pg Picograms

PGSC Pseudomonas Genetic Stock Center

+ pH -log10 [H ]

PI3Kγ Phosphatidylinositol 3 kinase γ

PIP2 Phosphatidylinositol 4, 5-bisphosphate

PIP3 Phosphatidylinositol 3, 4, 5-triphosphate

PKC Phosphokinase C

PL Peptide length

PLC-β Phospholipase C-β

PLD Phospholipase D pM Picomolar

PMA Phorbol myristate acetate

PRR Pattern recognition receptors

PS Peptide side chain effect

PSC Peptide score

PSGL-1 P-Selectin glycoprotein ligand-1

PVDF Polyvinylidene difluoride qRT-PCR Quantitative reverse transcription polymerase chain reaction

QS Quorum sensing

RAG Recombination activating gene

RANTES Regulated upon activation, normal T cell expressed, and secreted

RNA Ribonucleic acid

RNase Ribonuclease

RORγt Retinoic acid related orphan receptor γ T rpm Revolutions per minute

RPMI Roswell park memorial institute medium

S.O.C Super optimal broth with catabolise repression

S1P Sphingosine-1-phosphate

S1Pn Sphingosine-1-phosphate receptor

SD Standard deviation

SDS Sodium dodecyl sulphate

SDS-PAGE Sodium dodecyl sulphate-polyacrylamide gel electrophoresis

SEM Standard error of the mean

SFC Spot forming cells

SHP- Src homology domain-containing protein tyrosine phosphatase

SLP-76 SH2 domain-containing leukocyte protein of 76 kDa

SMA Smooth muscle actin

SMM-align Stabilisation matrix alignment method smt3 Ubiquitin-like protein Saccharomyces cerevisiae

SP Single positive sp. Species

SPAG-1 Sperm-associated antigen-1

SSC Side scatter

Stat Signal transducer and activator of transcription

SUMO Small ubiquitin-related modifier

Syk Spleen tyrosine kinase

T3SS Type 3 secretion system

TAE Tris-Acetate-EDTA

TAP Transporter associated with antigen processing

TBS Tris buffered saline

TCR T cell receptor

TER Transepithelial resistance

Tg Transgenic mice

TGEM Tetramer guided epitope mapping

TGFβ Transforming growth factor-β

Th T helper

TLR Toll like receptor

TMB 3,3′,5,5′-Tetramethylbenzidine

TNF- Tumour necrosis factor-

TRAIL TNF-related apoptosis-induced ligand

Treg Regulatory T cell

URT Upper respiratory tract flora

VEGF Vascular endothelial growth factor

WHO World Health Organisation

WT Wild type

Xen41 Bioluminescent strain of P. aeruginosa

ZAP-70 Zeta-chain-associated protein kinase 70

ZO- Zonula occluden

Other abbreviations are explained were appropriate in the text

For amino acid nomenclature, the standard three and one letter abbreviations are used throughout this thesis. Amino Acid Three Letter Abbreviation One Letter Abbreviation Alanine Ala A Arginine Arg R Asparagine Asn N Aspartic Acid Asp D Cysteine Cys C Glutamine Gln Q Glutamic Acid Glu E Glycine Gly G Histidine His H Isoleucine Ile I Leucine Leu L Lysine Lys K Methionine Met M Phenylalanine Phe F Proline Pro P Serine Ser S Theronine Thr T Tryptophan Trp W Tyrosine Tyr Y Valine Val V

CHAPTER 1: Introduction

Chronic respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and chronic lung infection are major global health issues. In 2005 the World Health

Organisation (WHO) estimated that chronic respiratory diseases account towards 4 % of the leading causes of global burden of disease (measured by disability adjusted life years) and 7

% of deaths worldwide (Beaglehole, 2005). Asthma affects approximately 300 million individuals worldwide and is estimated to rise by 100 million additional cases by 2025

(Masoli et al., 2004). COPD affects 5-15 % of adults in the westernised world and is associated with an irreversible reduction in lung function and enhanced deterioration in health status (Anto et al., 2001). Globally, the prevalence of bronchiectasis is unknown (Barker,

2002), however susceptibility is thought to be linked to geographic and genetic differences between populations (Boyton et al., 2013). The incidence of bronchiectasis is greater in indigenous populations including native Alaskans (Singleton et al., 2000) and Australian aborigines where 14.7 per 1000 children are reported to be affected (Chang et al., 2002). In addition the use of high resolution computed tomography (HRCT) scanning has increased the number of individuals diagnosed with bronchiectasis as the improved resolution provides evidence of bronchiectasis in those previously diagnosed with lung diseases such as COPD

(Patel et al., 2004).

Bronchiectasis is characterised by abnormally dilated bronchi, chronic infection and inflammation associated with chronic cough and sputum production (O'Donnell, 2008) and is described as an end stage disease often resulting from several pathological causes. These include genetic defects (Cystic fibrosis and Primary Ciliary Dyskinesia (PCD)), post infective processes (Mycobacterium tuberculosis), immune deficiencies and autoimmune diseases

(Boyton et al., 2013). However, a clinically distinct group of patients with idiopathic bronchiectasis for which no cause has been established, present with bilateral, predominantly lower lobe disease that is associated with sinusitis and adult onset (Boyton et al., 2006).

Evidence for the involvement of both the innate and adaptive immune response in the airways of individuals with bronchiectasis such as increased CD4+ T cells, CD8+ T cells, macrophages, neutrophils and IL-8 producing cells have been previously reported (Gaga et al., 1998; Silva et al., 1989).

Chronic inflammatory environments commonly result from the overproduction of proinflammatory mediators such as cytokines and chemokines that aid in the recruitment and activation of many inflammatory cells to a site of insult. Specifically, high levels of the chemokine Interleukin 8 (IL-8) is associated with several inflammatory disease states including rheumatoid arthritis (Seitz et al., 1991) and inflammatory bowel disease (Grimm et al., 1996). In particular, numerous studies have identified increased levels of IL-8 in the bronchoalveolar lavage (BAL), serum and sputum samples of patients diagnosed with chronic respiratory disease. These diseases with high IL-8 levels include bronchiectasis (Angrill et al., 2001), cystic fibrosis (Dean et al., 1993), COPD, asthma (Yamamoto et al., 1997), idiopathic pulmonary fibrosis (IPF) (Car et al., 1994) and allergic bronchopulmonary aspergillosis (ABPA) (Gibson et al., 2003). The high levels of this chemokine observed in patients with lung diseases contributes towards prolonged and increased neutrophil recruitment (Jatakanon et al., 1999; Richman-Eisenstat et al., 1993) and activation, leading to further progressive damage to the airways (Simpson et al., 2009).

1.1. Interleukin 8

IL-8 was first described in the late 1980s by several laboratories as a protein derived from lipopolysaccharide (LPS) induced human monocytes, demonstrating potent chemotactic and activating properties for neutrophils (Peveri et al., 1988; Schroder et al., 1987; Walz et al.,

1987; Yoshimura et al., 1987). Historically this protein was also known as monocyte-derived neutrophil chemotactic factor, MDNCF (Yoshimura et al., 1987), monocyte derived neutrophil activating peptide, MONAP (Schroder et al., 1987) and neutrophil activating factor, NAF (Peveri et al., 1988) before the unified name IL-8 was adopted (Westwick et al.,

1989). In addition to the established role of IL-8 in neutrophil chemotaxis, IL-8 has also been identified as an important chemoattractant for other cell types including eosinophils (Erger and Casale, 1995), T cells and basophils (Leonard et al., 1990).

The IL-8 gene is positioned at the chromosomal region 4q12-21 (Modi et al., 1990) and consists of four exons separated by three introns of non-coding deoxyribonucleic acid (DNA)

(Mukaida et al., 1989). On stimulation, transcription factors such as nuclear factor kappa- light-chain-enhancer of activated B cells (NF-κB) and activator protein-1 (Ap-1) bind to the promoter region of IL-8 and mediate its expression (Hobbie et al., 1997; Mastronarde et al.,

1998; Tal et al., 2010). The IL-8 gene is transcribed into a 1.8 Kb messenger ribonucleic acid

(mRNA) sequence that is subsequently translated into a 99 amino acid precursor protein

(Figure 1.1A). This precursor protein contains an N-terminal signal sequence that is cleaved extracellularly to yield a range of mature IL-8 proteins of variable lengths. The two major cleaved versions consist of either 77 (IL-877) or 72 (IL-872) amino acids (Gimbrone et al.,

1989; Matsushima et al., 1988) and both forms exhibit chemotactic properties, although IL-

872 has been shown to be more potent for neutrophils (Hebert et al., 1990). In addition to the

72 and 77 variants, other truncated versions of 71, 70 and 69 amino acids are produced that also exhibit biological activity (Lindley et al., 1988; Van Damme et al., 1990; Van Damme et al., 1989) (Figure 1.1A). Cleavage of the precursor IL-8 protein into its most abundant forms,

IL-877 and IL-872, has been shown to be dependent on the cell type producing it. A wide range of cells produce IL-8 including neutrophils (Padrines et al., 1994), T cells (Schroder et al., 1988), monocytes (Yoshimura et al., 1987), mast cells (Moller et al., 1993), endothelial cells (Strieter et al., 1989), epithelial cells (Eckmann et al., 1993), fibroblasts (Schroder et al.,

1990) and hepatocytes (Thornton et al., 1990). More specifically, T cells and monocytes predominantly produce the mature 72 amino acid cleaved protein whereas endothelial cells

(Gimbrone et al., 1989; Hebert et al., 1990; Lindley et al., 1988) and fibroblasts (Schroder et al., 1990) predominantly produce the mature 77 amino acid cleaved protein. Neutrophils produce both the 72 and 77 variants of IL-8, but also secrete proteolytic enzymes. Padrines and colleagues showed that neutrophil granule lysates and purified proteinase 3 cleaved IL-8 to form the 72 and 70 amino acid versions, resulting in enhanced neutrophil recruitment and activation (Padrines et al., 1994).

MTSKLAVALL AAFLISAALC EGAVLPRSAK ELRCQCIKTY 40

SKPFHPKFIK ELRVIESGPH CANTEIIVKL SDGRELCLDP 80

KENWVQRVVE KFLKRAENS 99

Figure 1.1: Schematic illustrating human IL-8. (A) The 99 amino acid sequence of human IL-8 contains an N-terminal signal sequence (blue). Mature versions of IL-8 include sizes of 77, 72, 71, 70 and 69 amino acids (arrow). Four cysteines (C) are involved in forming disulphide bonds between positions C-7 and C-34 and between position C-9 and C-50 (yellow). The characteristic ELR-CXC motif defining an ELR-CXC chemokine is underlined in green (Adapted from: (Baldwin et al., 1991; Roebuck,

1999)). (B) X-ray crystal structure of human IL-872 adapted from the Protein Data Bank (PDB) indicating N-and C-terminals and disulfide bonds (PDB ID; 3il8 (Baldwin et al., 1991)).

The structure for the mature 72 amino acid form of IL-8 was determined by X-ray crystallography and nuclear magnetic spectroscopy (Baldwin et al., 1991; Clore et al., 1990).

IL-8 has the ability to exist as both monomers and dimers. At high concentrations (>100 µM)

IL-8 exists as a homodimer stabilised by 6 hydrogen bonds whereas at lower concentrations that are more relevant to physiological conditions, IL-8 exists mainly as a monomer (Burrows et al., 1994; Paolini et al., 1994). Each monomer consists of a loop region at the N-terminus, three anti-parallel β-sheets and an α-helix at the C-terminus. In addition, four cysteine (C) residues form two disulphide bridges between positions C-7 to C-34 and C-9 to C-50 that stabilise the monomeric IL-8 structure (Baldwin et al., 1991) (Figure 1.1).

Chemokines are classified into subgroups dependant on the motif formed by the first two cysteines in the amino acid sequence and include C, CC, CXC and CX3C. The CXC subfamily is divided further depending on the presence of a specific ELR sequence motif positioned directly before the first N-terminal cysteine and consists of the three amino acids glutamic acid, leucine and arginine. IL-8 is a CXC chemokine (CXCL8) with an ELR-CXC motif (Figure 1.1A) (Murphy et al., 2000).

The expression of IL-8 is variable and although a wide range of cells are able to produce IL-

8, non-induced cells secrete very low levels. Only after some form of stimuli will cells produce elevated levels of IL-8. For example, pro-inflammatory cytokines IL-1 and TNF- induce human peripheral blood mononuclear cells (PBMCs) (Matsushima et al., 1988).

Additionally, cellular stress stimulated by environmental conditions such as hypoxia on endothelial cells (Karakurum et al., 1994) or air pollutants on lung epithelial cells induce IL-8

(Drumm et al., 1999; Tal et al., 2010). Furthermore, pathogens such as Salmonella

34 typhimurium on intestinal epithelial cells (Hobbie et al., 1997) or the respiratory syncytial virus on airway epithelium are capable of inducing IL-8 production (Mastronarde et al.,

1998).

1.2. IL-8 receptors and signalling

The G protein-coupled receptors, CXCR1 and CXCR2 bind to IL-8. These receptors are expressed by several cell types including neutrophils (Lee et al., 1992), monocytes, natural killer (NK) cells (Morohashi et al., 1995), CD8+ T cells (Takata et al., 2004), smooth muscle cells (Govindaraju et al., 2006) and endothelial cells (Addison et al., 2000). The CXCR1 receptor is more selective and binds IL-8 and CXCL6 whereas the CXCR2 receptor binds these as well as other ELR+ CXC chemokines such as CXCL1, 2, 3 and 7 (Murphy et al.,

2000). Following IL-8 binding, receptors are internalised before being recycled and re- expressed on the cell surface of neutrophils (Samanta et al., 1990). Interestingly, functional differences between the two IL-8 receptors have been demonstrated. Both receptors enable calcium mobilisation, release of granule enzymes and chemotaxis, but only CXCR1 has been shown to mediate phospholipase D (PLD) activation and respiratory burst indicating the important role for both receptors in regulating neutrophil activation and recruitment (Jones et al., 1996; Lee et al., 1992). In addition, IL-8 binds to CXCR2 with a higher affinity than

CXCR1 (Chuntharapai and Kim, 1995). Chuntharapai and colleagues hypothesised that this difference is vital in mediating the neutrophil’s response to IL-8. CXCR1 with low affinity may mediate IL-8 signalling in the inflammatory area where high IL-8 levels are present.

CXCR2 with high affinity may be more involved in neutrophil migration from distant sites where IL-8 levels are low (Chuntharapai and Kim, 1995). Once CXCR1 and CXCR2 receptors bind to ligands such as IL-8, the G proteins dissociate into α and βγ subunits that

35 subsequently regulate downstream signalling pathways. These involve phosphatidylinositol 3 kinase γ (PI3Kγ), mitogen activated kinase (MAPK) and phospholipase C-β (PLC-β) (Hirsch et al., 2000; Knall et al., 1996; Li et al., 2000) as shown in figure 1.2. PI3Kγ signalling involves the phosphorylation of phosphatidylinositol 4, 5-bisphosphate (PIP2). This produces phosphatidylinositol 3, 4, 5-triphosphate (PIP3) that in turn phosphorylates protein kinase B

(Akt), a regulator of cell migration. A PI3Kγ knockout model demonstrated a lack of Akt activation and impaired neutrophil migration in the presence of IL-8, but calcium mobilisation did occur (Hirsch et al., 2000; Li et al., 2000). PI3Kγ is also involved in signalling through MAPK and induces respiratory burst (Knall et al., 1996). In addition, the

βγ subunit can also activate signalling through phospholipase C- β (PLC-β). PLC-β generates inositol 1, 4, 5-trisphosphate (IP3) and diacylglycerol (DAG). IP3 is important in mobilisation of calcium and DAG is important for the activation of protein kinase C (PKC) that initiates respiratory burst. This was shown using a PLC- β knockout model in which calcium mobilisation and degranulation were impaired but chemotaxis in response to IL-8 remained

(Li et al., 2000). Therefore, IL-8 signalling is regulated through PI3Kγ / PLC- β dependent and independent pathways to mediate the neutrophil response (Hirsch et al., 2000; Knall et al., 1996; Li et al., 2000; Stillie et al., 2009).

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37 knockouts that demonstrate impaired rolling and extravasation of leukocytes (Mayadas et al.,

1993). Interactions between E-selectin and P-selectin glycoprotein ligand-1 (PSGL-1) induces a signalling cascade within the leukocyte that results in lymphocyte function- associated antigen-1 (LFA-1) activation and induces slow-rolling (Zarbock et al., 2008). In addition, initial tethering that enables the interaction of chemokines with their cell surface expressed receptors also induces neutrophil activation. Activation causes conformational changes in the surface expressed integrin molecules LFA-1 and macrophage-1 antigen (Mac-

1). This leads to increased avidity resulting in firm adhesion between the endothelium or epithelium and leukocyte (Constantin et al., 2000). This precedes extravasation through the paracellular pathway into surrounding tissue and is dependent on the integrity of the tight junctions and adheren junctions (Langer and Chavakis, 2009). IL-8 is an important chemokine involved in neutrophil migration across the epithelium (Carolan and Casale,

1996). The interaction between IL-8 and CXCR1 or CXCR2, expressed by neutrophils, induces the rapid loss of L-selectin (Smith et al., 1991) and upregulates the expression and avidity of β2 integrins (LFA-1) on the neutrophil surface. This enhances adhesion and enables migration into the surrounding tissue (Detmers et al., 1990). In addition to the role of

IL-8 in neutrophil extravasation, it is also important in neutrophil activation by inducing degranulation and respiratory burst. IL-8 acts indirectly through activating neutrophils that subsequently release mediators with chemotactic activity such as leukotriene B4 (Ford-

Hutchinson et al., 1980) or defensins that have shown chemotactic effects for T cells

(Chertov et al., 1996).

Not only is IL-8 directly involved in neutrophil activity, it is also important in regulating other cell types. Basophils have been shown to increase their chemotactic properties and adherence to endothelial cells in the presence of IL-8 (Bacon et al., 1994; Geiser et al., 1993).

IL-8 has also been shown to have a direct effect on the chemotaxis of B cells (Jinquan et al.,

1997) and suppression of IL-4 production by CD4+ T cells (Gesser et al., 1996).

IL-8 is also known to have an important role in angiogenesis and endothelial migration by increasing the permeability of the endothelium by inducing vascular endothelial growth factor receptor 2 phosphorylation and transactivation (Petreaca et al., 2007). Endothelial cells cultured in presence of IL-8 have also been shown to upregulate the production of matrix metalloproteinase (MMP)-2 and MMP9 that induce destruction of the basal lamina, enabling endothelial cells to migrate and proliferate in the surrounding connective tissue resulting in the formation of new blood vessels (Li et al., 2003; Petreaca et al., 2007).

Previous studies looking at the role of human (h) IL-8 in vivo by targeting hIL-8 expression to different organs in mice have been reported. Models include targeted expression in the liver (Simonet et al., 1994), intestines (Kucharzik et al., 2005) and the cornea (Oka et al.,

2006). hIL-8 expression in the intestines and cornea demonstrated increased neutrophilia and tissue infiltration. However, hIL-8 overexpression in the liver resulted in increased L-selectin shedding and no infiltration into tissue or tissue damage (Simonet et al., 1994). Models investigating the role of the murine chemokine keratinocyte-derived chemokine (KC) that has a similar function to hIL-8 has been reported. Transgenic mice overexpressing KC constitutively in the lung due to a Clara cell-specific promoter resulted in increased neutrophil migration into the lung tissue, although these cells were not activated. However, when infected with Klebsiella pneumoniae (K. pneumoniae), neutrophil influx was enhanced alongside increased bacterial clearance and improved survival (Tsai et al., 1998). The role of

KC in neutrophil recruitment has also been demonstrated in KC knockout models. This

39 model identified pathways involving NF-κB and MAPK signalling and chemokine induction

(CXCL2 and CXCL5) that are important in neutrophil recruitment. Depleted neutrophil recruitment in this model led to reduced survival when challenged with K. pneumoniae and poor bacterial clearance (Cai et al., 2010).

1.4. Neutrophils

Neutrophils are generally short lived immune cells that make up approximately 50-70 % of circulating leukocytes in humans but only 10-25 % in mice (Doeing et al., 2003; Mestas and

Hughes, 2004). Neutrophils originate and mature in the bone marrow and it is estimated that

2 x 1011 neutrophils are generated per day (Borregaard, 2010). Neutrophil production and blood neutrophil counts are controlled by a homeostatic mechanism. During an inflammatory response, the number of neutrophils increases. After a while these cells die and are cleared by macrophages. During this process macrophages down regulate their production of IL-23 that impacts on IL-17A production by specific T cells (γδ and natural killer T (NKT) cells).

Finally, IL-17A controls the production of Granulocyte colony-stimulating factor (G-CSF) that subsequently regulates neutrophil generation (Ley et al., 2006; Stark et al., 2005). During neutrophil maturation, three types of granules are formed separately and contain specific proteins. These include azurophilic, specific and gelatinase granules that contain proteins such as myeloperoxidase (MPO), lactoferrin and MMP9 respectively (Borregaard, 2010;

Borregaard et al., 1995). When stimulated neutrophils degranulate and release these proteins.

In addition neutrophils can also eliminate intra and extracellular pathogens by phagocytosis and form neutrophil extracellular traps (NETs) (Brinkmann et al., 2004). NETs consist of neutrophil DNA and other proteins such as histones, lactoferrin, MPO and neutrophil elastase. NETs can either enhance neutrophil phagocytosis of microbes or directly kill

40 microbes by antimicrobial proteins (Kolaczkowska and Kubes, 2013; Mocsai, 2013; Young et al., 2011). In addition to the well-established roles of neutrophils in the elimination of extracellular bacterial pathogens, recent work has reported a role for neutrophils in antiviral defence (Jenne et al., 2013), intracellular pathogen defence (Martineau et al., 2007) and in influencing the adaptive immune response (Puga et al., 2012).

Several approaches to study neutrophils in vivo have been reported. For example, cytotoxic agents such as cyclophosphamide have been used to induce neutropenia. However, cyclophosphamide is not specific to neutrophils and can also affect other leukocytes (Zuluaga et al., 2006). More specific depletion using antibodies targeted towards neutrophil markers such as granulocyte receptor -1 (Gr-1), a member of the ly6 marker family, have been commonly used. However, these antibodies are not always specific towards neutrophils and can also bind other leukocyte lineages (Daley et al., 2008). A more specific neutrophil antibody (anti-Ly6G) has been developed that has less specificity for non-neutrophil Gr-1+ populations (Daley et al., 2008). Several approaches to genetically deplete neutrophils such as G-CSF receptor knockouts (Liu et al., 1996) or models with mutated growth factor independence-1 transcriptional repressor (Ordonez-Rueda et al., 2012) have also been reported, but the effects on other leukocyte lineages in these models cannot be excluded.

1.5. Characteristics of airway remodelling and lung damage associated with chronic respiratory disease

Chronic respiratory disease pathology is typically characterised by an increase in inflammation and cellular recruitment to the lung parenchyma and airway lumen. Specific diseases induce the recruitment of different cell types that contribute to disease. Generally,

41 allergic asthma is characterised by an increase in eosinophils and T helper (Th)2 CD4+ T cells (Bousquet et al., 1990; Robinson et al., 1992) whereas steroidal resistant asthma is associated with an increase in neutrophils (Green et al., 2002) and COPD is associated with abundant neutrophils and CD8+ T cells (Peleman et al., 1999; Saetta et al., 1998). Excessive recruitment resulting from disrupted control mechanisms can result in tissue damage and subsequent airway remodelling (Simpson et al., 2009). Airway remodelling is often used to describe structural alterations that can occur in the airways of patients with chronic lung disease. The main characteristics associated with remodelling in the lung include: mucus hypersecretion and goblet cell hyperplasia, smooth muscle hypertrophy and hyperplasia, subepithelial fibrosis and angiogenesis. These observed changes contribute towards airway wall thickening and swelling resulting in the narrowing of the lumen and ultimately impaired lung function (Dunnill et al., 1969; Fahy et al., 2000; Hoshino et al., 1998; Ordonez et al., 2001;

Regamey et al., 2008).

1.5.1. Mucus production

An important aspect of the first line of defence in the respiratory tract is the presence of a mucosal layer that covers and helps protect the lung from respiratory pathogens and environmental toxins. This mucosal layer works alongside small epithelial protrusions known as cilia surrounded by a periciliary liquid layer that together help trap and remove pathogens from the lung (Bautista et al., 2009; Knowles and Boucher, 2002). The main constituents of mucus are the large highly O-glycosylated proteins known as mucins. Mucins in humans are encoded by MUC genes and expression of several mucins have been located to the respiratory tract (Higuchi et al., 2004; Kirkham et al., 2002; Reid et al., 1997; Williams et al.,

2001). The two main mucins identified in respiratory mucus are MUC5AC and MUC5B

(Kirkham et al., 2002). These two glycoproteins are known to be produced by either goblet cells (MUC5AC and MUC5B) or by sub mucosal glands (MUC5B) (Reid et al., 1997;

Wickstrom et al., 1998). However, the regulation of gene expression in terms of transcription has been reported to be controlled by components of the inflammatory response. During an insult such as infection, mediators including LPS, neutrophil elastase and cytokines such as

TNFα and IL-1β increase MUC5AC transcription (Koo et al., 2002; Song et al., 2003;

Voynow et al., 1999) while MUC5B transcription has been shown to be controlled by IL-6 and IL-17 (Chen et al., 2003). In addition, the stability of the gene post transcription is also regulated by certain inflammatory mediators. MUC5AC transcript stability is increased by

TNFα (Borchers et al., 1999) and neutrophil elastase (Voynow et al., 1999) whereas MUC5B transcript stability is less understood. A recent study by Bautista and colleagues demonstrated in vitro that the mRNA levels of both MUC5AC and MUC5B are both increased in human bronchial epithelial cell lines when in the presence of IL-8. In addition, IL-8 was also able to increase, at the post transcriptional level, the stability of MUC5AC (Bautista et al., 2009).

During lung disease inflammatory mediators are overproduced due to the dysregulated inflammatory response. This results in lung diseases such as asthma and COPD where mucus production and mucin expression are over produced, resulting in airway obstruction and impaired lung function (Ordonez et al., 2001; Vestbo et al., 1996).

1.5.2. Fibrosis

Dead or damaged tissue caused by infection, autoimmunity or mechanical insult requires repair mechanisms to reverse the damage and reinstate functioning cells. These repair mechanisms involve several stages including: (i) clotting and coagulation, (ii) inflammation,

(iii) fibroblast recruitment and proliferation and (iv) remodelling to resolve the normal tissue

43 structure (Wynn, 2011). However, if this process is dysfunctional due to constant injury or insult, then excessive tissue deposition and fibrosis occurs resulting in irreversible fibrotic scarring and loss of function within the tissue (Wynn, 2011). Fibrosis is an established remodelling characteristic of lung diseases (Hoshino et al., 1998) and a decline in lung function is associated with increased sub-epithelial fibrosis (Minshall et al., 1997). Causes of lung fibrosis are not always defined, such as in idiopathic pulmonary fibrosis, but several factors have been implicated to contribute towards fibrotic disease processes. These include: viral infections such as the Epstein-Barr virus (Egan et al., 1995; Kelly et al., 2002) and other related herpes viruses (Tang et al., 2003); environmental particles such as silica (Fubini and

Hubbard, 2003) and radiation therapy used in the treatment of cancer (Trask et al., 1985).

During a normal healing processs, damaged epithelial or endothelial cells produce mediators including chemokines that control repair processes. Initially, inflammatory cells such as macrophages and neutrophils are recruited to the site of damage to remove cell debris and foreign particles or pathogens. In addition, fibroblasts are recruited that deposit components of the extracellular matrix and proliferate into myofibroblasts that control wound contraction.

The wound subsequently closes and re-epithelialisation occurs over the newly established basal layer to provide a functioning epithelium (Wynn, 2011). An important but gradual process that takes place during normal wound healing is epithelial to mesenchymal transition

(EMT). Three types of EMT are described. Type 1 describes developmental EMT involved in processes such as embryogenesis. Type 2 describes EMT that occurs during wound healing and fibrosis and type 3 describes EMT related to cancer progression (Kalluri and Weinberg,

2009). Generally, EMT involves the transformation of epithelial cells into cells with a mesenchymal phenotype (Figure 1.3).

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45 demonstrated an increase in EMT in response to house dust mite in human bronchial epithelial cells primed with TGFβ (Heijink et al., 2010) and in vivo studies have identified active EMT in a house dust mite induced asthmatic model (Johnson et al., 2011). IL-8 has been implicated in inducing and maintaining tumour EMT (Fernando et al., 2011). However, the direct role of IL-8 in type 2 EMT, or indeed during fibrosis, is not fully elucidated.

Although, a study by Russo and colleagues demonstrated the potential importance of IL-8 signalling during fibrotic pathways via its receptor CXCR2. During bleomycin induced lung fibrosis, a CXCR2 antagonist demonstrated improved lung pathology and reduced collagen deposition alongside reduced neutrophil migration into the airways, but not the parenchyma and reduced IL-13 and TGFβ levels. This implicates the beneficial role that blocking the

CXCR2 receptor may have in the amelioration of lung fibrosis associated diseases (Russo et al., 2009).

1.5.3. Smooth muscle

Airway smooth muscle (ASM) is located in the trachea and bronchial tree and regulates the relaxation and contraction of the airways (bronchomotor tone). Although the role of ASM in healthy individuals is not fully understood, alterations such as an increase in ASM mass, hyper-reactivity or cytokine production can contribute towards airway constriction during respiratory disease (Amrani and Panettieri, 2003). An increase in ASM mass, in terms of hypertrophy and hyperplasia, contributes towards remodelling changes in the lung and is observed in many lung diseases including non-cystic fibrosis bronchiectasis, asthma and cystic fibrosis (Regamey et al., 2008; Woodruff et al., 2004). Changes in ASM can contribute to poor lung function (Benayoun et al., 2003). Non-specific ASM hyper-responsiveness has been demonstrated in the presence of certain cytokines or chemokines that contribute towards

46 airway narrowing. These include TNFα (Thomas et al., 1995), IL-1β (Tsukagoshi et al.,

1994), IL-5 (Rizzo et al., 2002) and IL-13 (Grunstein et al., 2002). Specifically, human airway smooth muscle cells have been shown to express both IL-8 receptors CXCR1 and

CXCR2 and that in the presence of IL-8, these cells were increasingly activated and demonstrated enhanced calcium flux leading to increased contraction and migration

(Govindaraju et al., 2006).

1.6. Tight junctions and epithelial permeability in respiratory disease

Epithelial and endothelial cells form a layer that covers the inner and outer surfaces of organs such as blood vessels, lung and intestine. These layers and in particular the epithelial layer of the respiratory tract, are vital in forming a barrier that protects the internal environment from the external environment. This barrier is formed of adjacent epithelial cells linked to neighbouring cells by paracellular junctional complexes made up of tight junctions, adheren junctions and desmosomes and are classified depending on function and location (Farquhar and Palade, 1963; Schneeberger and Lynch, 2004). Freeze fracture electron microscopy demonstrated tight junctions as continuous bands at the apical region of the epithelium

(Farquhar and Palade, 1963; Staehelin, 1973). Tight junctions are known to have two main functions. The first is to provide a barrier that seals the paracellular pathway between adjacent epithelial cells to prevent leakage or entry of environmental factors such as pathogen or noxious stimuli. The second is as a fence function that maintains the polarity of the cell and prevents proteins and lipids of the apical and basal regions from mixing, enabling two functionally and biochemically distinct regions (Dragsten et al., 1981; Sawada et al., 2003).

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(Ando-Akatsuka et al., 1996). This protein contains four transmembrane domains, a short N- terminal and a long C-terminal domain and is highly conserved between species (Ando-

Akatsuka et al., 1996). Occludin mediates adhesion between adjacent cells and overexpression of occludin results in an increase in the transepithelial resistance (TER), a measure of the general tightness of a monolayer (Balda et al., 1996). Occludin is not thought to be involved in the formation of morphologically intact tight junctions as knockout models have demonstrated normal tight junction morphology. However, evidence of chronic inflammation in the gut tissue was observed (Saitou et al., 2000). Furthermore, the N- terminal cytosolic region of occludin is important in regulating neutrophil migration across the paracellular pathway (Huber et al., 2000).

The claudin family are thought of as the building blocks of the tight junction. The first two members of the claudin family, claudin-1 and claudin-2, were identified by Furuse and colleagues (Furuse et al., 1998). There are now over 24 claudin members identified that are expressed by different tissues and serve different functions. For example, claudin-5 is expressed primarily by vascular endothelial cells (Morita et al., 1999b), while claudin-11 is expressed by oligodendrocytes and sertoli cells (Morita et al., 1999a). In addition, claudin-18 exists as two splice variants. Claudin-18.1 is localised to the lung and claudin-18.2 is localised to the gut (Niimi et al., 2001). Claudins consist of two extracellular loops that regulate binding to neighbouring claudins and cationic or anionic permeability. Of the claudins identified, several have been described with either a sealing (Claudin 4, 5, 8, 11, 14 and 18) or a pore forming function (claudins 2, 7, 10, 15 and 16) (Krause et al., 2008; Soini,

2011). In the lung several claudins have been identified (Coyne et al., 2003; Kaarteenaho et al., 2010) and of these lung residing claudin members, claudin-3, -4 and -18 have been identified as the most prominent in alveolar epithelial cells (LaFemina et al., 2010).

The third described group of tight junction proteins include the junctional adhesion molecules

(JAM) that are members of the immunoglobulin superfamily. JAM1 is the best studied JAM protein and is localised to and involved in the assembly of tight junctions (Liang et al., 2000;

Martin-Padura et al., 1998). It is also important in leukocyte transmigration (Martin-Padura et al., 1998; Ostermann et al., 2002) and acts as a receptor during microbial infections (Barton et al., 2001). JAM1 is capable of these functions by binding to various ligands such as itself

(Bazzoni et al., 2000), LFA-1 expressed by leukocytes (Ostermann et al., 2002) and the microbial antigen (σ-1) expressed by the reovirus (Barton et al., 2001).

The lung maintains this tight epithelial barrier between the internal and external environments to enable efficient gas exchange. Impaired regulation of this barrier results in characteristics associated with pulmonary diseases such as oedema in acute respiratory distress syndrome

(ARDS) (Matthay and Zemans, 2011). In addition, tight junctions provide a barrier to the entry of pathogens and noxious substances. However, studies have shown that air pollutants and tobacco smoke can make tight junctions leakier by disrupting tight junction components such as claudins and occludin (Caraballo et al., 2011; Shaykhiev et al., 2011). Pathogens can also influence tight junction permeability by down regulating the tight junction proteins and enabling access to interstitial tissues of the lung. Rhinovirus has been shown in nasal epithelial cells to downregulate the TER by decreasing the mRNA expression of claudin-1, occludin, ZO-1 and E-cadherin (Yeo and Jang, 2010). In addition, Pseudomonas aeruginosa

(P. aeruginosa) has been shown to decrease the TER by downregulating the tight junction protein ZO-1 (Rejman et al., 2007). Other virulence factors including type III toxins (ExoS) and rhamnolipids can enhance the invasiveness of P. aeruginosa by disrupting tight junction proteins ZO-1 and occludin (Soong et al., 2008) and altering tight junction structure

(Zulianello et al., 2006). Other inflammatory stimuli have been shown to impact on the tight

50 junction. The influence of cytokines on enhancing permeability in the intestines (Oshima et al., 2001; Youakim and Ahdieh, 1999), and in the lungs has been reported. Cytokines such as

TNFα and IFNγ downregulate tight junction proteins in the lung epithelium including occludin and JAM1 and subsequently upregulate epithelial permeability (Coyne et al.,

2002b). Murine studies have also focused on the impact of lung injury on tight junctions. For example, a bleomycin induced model of lung fibrosis demonstrated a decrease mainly in claudin-18 but also in claudin-5 that was associated with an upregulation in epithelial permeability (Ohta et al., 2012). In addition, lung alveolar cells cultured from septic rats showed a decrease in claudin-4, claudin-18 and occludin that was associated with a decrease in TER (Cohen et al., 2010). These studies demonstrate that during inflammatory lung diseases the increase in cytokines and pathogens present can alter permeability of the epithelium by regulating tight junction proteins.

Of particular interest in this thesis is the effect of the proinflammatory chemokine IL-8 on tight junction function of the lung epithelium. A direct role of hIL-8 has previously been reported in endothelial cells. In the presence of IL-8, an increase in endothelial permeability associated with a decrease in ZO-1, claudin-5 and occludin expression was observed in a dose-dependent manner (Yu et al., 2013). In addition, supernatants taken from dengue virus infected monolayers of endothelial cells were shown to contain high concentrations of IL-8, that when incubated with control endothelial monolayers, demonstrated the same effects. In particular, the tight junction protein occludin was down regulated (Talavera et al., 2004).

1.7. hIL-8 transgenic model of chronic respiratory disease

Mice do not have the gene for IL-8, but instead express keratinocyte derived chemokine (KC) and macrophage inflammatory protein (MIP)-2 that have similar functions to hIL-8 (Bozic et al., 1995; Wolpe et al., 1989). Mice also express homologues of the human CXCR1 and

CXCR2 receptors. Murine CXCR2 has been shown to induce chemotaxis of murine neutrophils in response to hIL-8 (Rot, 1991). More recently, the murine CXCR1 receptor was identified. Murine CXCR1 transfected cells demonstrated a chemotactic response to hIL-8

(Fan et al., 2007; Fu et al., 2005). Chronic respiratory diseases like bronchiectasis, COPD, cystic fibrosis and IPF have high IL-8 levels detectable in BAL, serum and sputum samples

(Angrill et al., 2001; Car et al., 1994; Dean et al., 1993; Yamamoto et al., 1997). To investigate the role that hIL-8 has upon lung remodelling processes such as mucus hypersecretion, fibrosis, inflammation and ASM a transgenic mouse model overexpressing the hIL-8 gene was developed in our lab at Imperial College by Dr. Catherine Reynolds and

Dr. Rosemary Boyton. This model specifically expresses hIL-8 protein in bronchial epithelial cells due to the presence of a Clara cell 10 (CC10) promoter. This hIL-8 transgenic model of chronic lung disease enables analysis of chronic lung disease pathology in the context of IL-

1.8. Chronic respiratory disease and infection

The lungs of healthy individuals were previously believed to be a sterile environment

(Baughman et al., 1987; Beck et al., 2012). However, with the advent of new technologies using culture independent techniques and metagenomics, diverse bacterial species colonising the lung at low levels have been identified. In addition, in healthy individuals, similar mircobiota profiles are now identified in both the lower and upper airways that previously

52 would not have been detected (Charlson et al., 2011; Hilty et al., 2010). Although culture based methods can identify certain bacterial species, culture independent methods, using for example 16S ribosomal RNA sequencing to determine the microbiome, have led to the ability to identify more complex and diverse communities. The microbiome of the lung has been observed to differ depending on geographic location, antibiotic usage and between healthy and diseased individuals (Hilty et al., 2010; Stressmann et al., 2011; Sze et al., 2012; Zhao et al., 2012). The microbiomes of patients with COPD, asthma, cystic fibrosis and bronchiectasis have been analysed by culture independent methods and have identified predominantly lactobacillus sp. in COPD (Sze et al., 2012) and members of the phylum proteobacteria including haemophilus sp., moraxella sp. and neisseria sp. in asthma (Hilty et al., 2010). In patients with cystic fibrosis, microbiome profiles reaffirm P. aeruginosa as the dominant species although others such as veillonella sp. and prevotella sp. were also prominent (Zhao et al., 2012). In addition, deep sequencing in patients with bronchiectasis reported P .aeruginosa as the most common organism detected and when present, was also the predominant organism detected. Other commonly associated species include haemophilus sp., streptococcus sp. and veillonella sp. (Duff et al., 2013). However, individuals with chronic P. aeruginosa infections associated with lung diseases like bronchiectasis, commonly present with enhanced disease progression, severe disease, poor quality of life and increased morbidity and mortality (Davies et al., 2006; Evans et al., 1996; Wilson et al., 1997).

1.9. Pseudomonas aeruginosa

P. aeruginosa was originally isolated by Carle Gessard in 1882. In 2000, the entire genome was sequenced and shown to be one of the largest bacterial genomes with 6.3 million base pairs (Stover et al., 2000).This large and diverse genome enables P. aeruginosa to survive in

53 a variety of environments with the ability to infect plants, animals and humans (Rahme et al.,

1995; Stover et al., 2000). P. aeruginosa is a common, gram-negative, rod shaped, opportunistic bacterium affecting individuals with impaired defence mechanisms. In particular high-risk groups include immunocompromised individuals (Afessa and Green,

2000; Vidal et al., 1999), patients with poor mucociliary clearance mechanisms (Rogers et al., 2013) and those with some form of epithelial damage due to injury or insult such as respiratory disease (Davies et al., 2006; Evans et al., 1996; Wilson et al., 1997) or severe burns (Tredget et al., 1992). Although P. aeruginosa is commonplace in the environment and exposure is frequent, infections are prevented in healthy individuals due to a robust immune response (Williams et al., 2010). This bacterium can exist as single planktonic cells or as a structured, surface bound community of cells that form a biofilm. During the development within a biofilm, P. aeruginosa alters the regulation of proteins that impact on motility, alginate production and quorum sensing (Sauer et al., 2002). The biofilm forms a protective layer for the microbial community residing within by providing resistance to antibiotics (Mah et al., 2003) and the host’s immune system. Biofilms evade the host by several mechanisms including: interfering with phagocytosis by macrophages (Simpson et al., 1988); inducing a slow oxidative response that is 25 % of the response induced by planktonic bacteria (Jensen et al., 1990) and the ability to scavenge oxygen free radicals produced by phagocytic cells

(Learn et al., 1987).

The biofilm forms in distinct stages (Figure 1.5). Initially planktonic P. aeruginosa cells attach reversibly to a surface. Following this, the cell undergoes gene modifications leading to irreversible attachment. Motility is lost and clusters of cells begin to form. These clusters start to mature and form towers made up of extracellular components and microbial colonies.

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55 alginate synthesis and the conversion to mucoidy (Hoffmann et al., 2005; Martin et al., 1993;

Mathee et al., 1999). However, alginate is not required in the formation of non-mucoid biofilms, there are instead other important polysaccharides identified that are synthesised by proteins encoded by the two operons psl and pel (Friedman and Kolter, 2004a, b; Jackson et al., 2004). The psl operon encodes 15 different transcripts that encode proteins involved in the synthesis of polysaccharides required for cell-surface and cell-cell adherence and maintenance of the biofilm structure of non-mucoid strains (Ma et al., 2006) while Pel induced polysaccharides are important in surface attachment and pellicle formation

(Friedman and Kolter, 2004a). Extracellular DNA is also an important component of biofilms, particularly in biofilms that are less than 60 hours old. This suggests a role for extracellular DNA in early biofilm development and initial cell-cell adhesion (Whitchurch et al., 2002).

Cell to cell communications via a mechanism known as quorum sensing (QS) is key to the development of biofilms and other virulence mechanisms. QS invokes the production of signalling molecules that direct the timing and production of several virulence factors in response to cell density. In P. aeruginosa, this is controlled by two main sensing systems classified as N-acyl-L-homoserine lactone (AHL) systems (las and rhl) (Pearson et al., 1994;

Pearson et al., 1995). AHL systems require an autoinducer and a transcriptional activator.

The autoinducer is produced at low levels in low populated colonies, but increases with cell density to a threshold concentration. At which point it binds to its transcriptional activator and subsequently activates the virulence genes that it regulates (Pesci et al., 1997). The las system involves LasI that directs the synthesis of the autoinducer N-(3-oxododecanoyl)-L- homoserine lactone (3O-C12-HSL) also known as P. aeruginosa autoinducer 1 (PAI-1) that binds to the transcriptional activator, LasR (Pearson et al., 1994). This interaction controls

56 the transcription of virulence genes for elastase (LasB), LasA protease (LasA) and alkaline protease gene (apr) (Gambello and Iglewski, 1991; Gambello et al., 1993; Pearson et al.,

1994; Toder et al., 1994). During the rhl system, RhlI directs the synthesis of the autoinducer

N-butanoyl-L-homoserine lactone (C4-HSL) also known as P. aeruginosa autoinducer 2

(PAI-2) that binds to the transcriptional activator RhlR to control the production of rhamnolipids (Ochsner et al., 1994; Ochsner and Reiser, 1995; Pearson et al., 1995).

Rhamnolipids are virulence factors that enhance the invasiveness of P. aeruginosa by disrupting epithelial tight junctions (Zulianello et al., 2006). The las and rhl systems are interlinked. The las QS system controls the rhl QS system transcriptionally and posttranslationally. LasR and PAI-1 can activate rhlR transcription while PAI-1 can block

PAI-2 from binding RhlR (Pesci et al., 1997).

Acute and chronic animal models of P. aeruginosa infections have previously been reported

(Carter et al., 2010; Starke et al., 1987). Acute infections, generally induced by intranasal or intratracheal administration, are either cleared over 48 hours or the animal dies within 24 hours from infection (Carter et al., 2010; Liu et al., 2011). To establish a chronic model, the bacterium is inoculated intratracheally with immobilising agents such as agarose beads that act as an artificial biofilm and helps to prevent mechanical clearing (Starke et al., 1987).

Mice infected with P. aeruginosa enmeshed in agarose beads had evidence of bacteria in the lungs lasting more than 21 days that was also accompanied with structural lung changes and neutrophil infiltration similar to that observed in bronchiectasis and cystic fibrosis (Starke et al., 1987). Animal models of chronic P. aeruginosa infection have been developed since that do not use artificial biofilms, but instead use clinical mucoid isolates. For example, a stable mucoid clinical isolate from cystic fibrosis patients established chronic infection in a murine model without the need of an artificial biofilm (Hoffmann et al., 2005).

1.9.1. The innate immune response to P. aeruginosa

The host’s defence mechanisms to bacterial infections in the lung require mechanical or barrier functions and innate and adaptive immune responses. The epithelial layer forms a tight barrier that prevents paracellular diffusion and entry into the basal lamina by invading pathogens. However, if breached, bacterial pathogens can gain entry and cause disease.

(Balkovetz and Katz, 2003). Virulence factors such as rhamnolipids expressed by P. aeruginosa are capable of disrupting this barrier and enabling tissue infiltration (Zulianello et al., 2006). The epithelial layer also contains goblet cells, important in the secretion of mucus and mucins, required to trap bacterial cells. The mucociliary elevator consists of tiny structures (cilia) present on the epithelial surface that move unidirectionally to remove mucus and trapped pathogens from the airways (Williams et al., 2010). Clearance of P. aeruginosa is enhanced in epithelial cells expressing the cystic fibrosis transmembrane conductance regulator that binds to and internalises P. aeruginosa (Pier et al., 1996; Schroeder et al.,

2001). The importance of mechanical clearance is demonstrated in individuals with PCD.

Mutations in genes such as sperm-associated antigen-1 (SPAG1) that encode components of the cilia lead to a defective mucociliary elevator, resulting in increased susceptibility to respiratory tract infections and bronchiectasis (Knowles et al., 2013).

The innate immune response is critical for the clearance of P. aeruginosa if the epithelial barrier is breached. A major aspect of innate immunity is the initial surveillance and detection of invading pathogens by pattern recognition receptors (PRR). PRRs are either expressed on epithelial or immune cell surfaces or are located within the cytosol of a cell and are able to detect microbial structures and subsequently induce the immune response. Toll-like receptors

(TLR) are one of the main families of transmembrane PRRs and each member differs in its

58 ability to recognise microbial structures (Takeda et al., 2003). Greater repertoires of TLRs are expressed by tissues continuously exposed to pathogens including the lung (Zarember and

Godowski, 2002). In addition, the downstream adaptor molecule MyD88 in TLR signalling is critical in the induction of an immune response to P. aeruginosa. Murine studies using

MyD88 deficient mice were unable to induce an inflammatory response or to control bacterial replication which highlights how essential functioning MyD88 signalling is in eliciting effective lung defences against P. aeruginosa (Skerrett et al., 2004). In defence against P. aeruginosa infections, an immune response is elicited by both TLR4 and 5 in response to LPS and flagellin respectively (Hajjar et al., 2002; Hayashi et al., 2001). Mice deficient in either one of these TLRs survive in a similar fashion to wild type mice in response to infection by a flagellated strain of P. aeruginosa. However, TLR4/5 double knockouts have decreased survival (Feuillet et al., 2006) and the importance of TLR4 is observed when a strain devoid of flagella is used as the immune response relies upon TLR4 signalling (Ramphal et al., 2008). TLR2 and TLR4 also initiate immune responses to alginate produced by mucoid strains of P. aeruginosa (Flo et al., 2002). Another class of PRRs include the Nod like receptors (NLR). The NLR family CARD domain-containing protein 4

(NLRC4) senses cytosolic microbial structures and activates the inflammasome and pro- caspase-1 complex. Activation leads to the generation of caspase 1 that initiates IL-1β and

IL-18 production as well as macrophage apoptosis (Franchi et al., 2006; Miao et al., 2010). In particular, NLRC4 recognises flagellin and type III secretion machinery of P. aeruginosa

(Franchi et al., 2007). Epithelial cells are also important during the innate response due to the secretion of anti-microbial products such as surfactant proteins. Surfactant A and D deficient models are more susceptible to P. aeruginosa and demonstrate reduced phagocytosis by alveolar macrophages (Giannoni et al., 2006). Surfactants are therefore important in the host immunity. However, in response to QS, P. aeruginosa can initiate the expression of virulence

59 factors that can destroy these surfactants such as elastase and protease IV (Alcorn and

Wright, 2004; Malloy et al., 2005).

Neutrophils are an important cell type involved in the clearance of P. aeruginosa. The ability to rapidly recruit neutrophils and for these neutrophils to efficiently clear P. aeruginosa infection is critical. Importantly, neutropenia leads to ineffective clearance of P. aeruginosa as demonstrated using neutrophil depleted murine models. Neutrophil depleted mice were more susceptible with increased mortality to inoculums containing low colony forming units

(CFU) of P. aeruginosa (Koh et al., 2009). The impact of impaired neutrophil recruitment during P. aeruginosa infection was demonstrated using antibodies specific for the CXCR2 receptor, required for recruitment and migration of neutrophils to sites of infection.

Subsequent infection with P. aeruginosa resulted in increased bacterial burden and reduced survival (Tsai et al., 2000). Activated neutrophils recruited to the site of an infection, degranulate and release mediators such as neutrophil elastase and nitric oxide that are important in bacterial clearance. A neutrophil elastase knockout model (Hirche et al., 2008) and inducible nitric oxide synthase (iNOS) inhibition models (Satoh et al., 2001; Zhang et al.,

2011), demonstrate increased susceptibility to P. aeruginosa with impaired bacterial clearance compared to wild type strains. In addition, a rat model of inhaled nitric oxide was associated with a decreased bacterial load of P. aeruginosa (Webert et al., 2000).

P. aeruginosa has also been shown to manipulate the host’s immune system by enhancing the production of chemokines like IL-8 to enhance neutrophil recruitment. This constant recruitment and activation of neutrophils results in enhanced lung damage and thus facilitates

P. aeruginosa infection. The flagellae, pili and autoinducer molecule, 3-O-C12-HSL,

60 expressed by P. aeruginosa are capable of directly stimulating IL-8 production in vitro by respiratory epithelial cells (DiMango et al., 1995; Smith et al., 2001). Furthermore, when stimulated with P. aeruginosa, human epithelial cells isolated from patients with cystic fibrosis had continued IL-8 production that may play a role in the ongoing inflammatory responses observed in patients with cystic fibrosis (Joseph et al., 2005).

Transgenic models for hIL-8 overexpression have previously been reported. However, these were not targeting hIL-8 expression specifically to the lung (Kucharzik et al., 2005; Oka et al., 2006; Simonet et al., 1994) as discussed in section 1.3. Other transgenic models targeting the murine chemokine KC, with similar functions to hIL-8, have been reported. The KC transgenic model demonstrated increased recruitment of neutrophils to the lung and an increased resistance to infection by the gram-negative bacterium, Klebsiella pneumonia (Tsai et al., 1998) or the fungal species, Aspergillus (Mehrad et al., 2002). In addition, the cytokines IFNγ and IL-12 were increased in the Aspergillosis KC transgenic model, suggesting an enhanced Th1 response contributing towards the observed increased recruitment of macrophages and towards a better outcome of disease compared to wild type strains (Mehrad et al., 2002). However, the overexpression of IL-10 in a murine model had increased susceptibility to infection by P. aeruginosa. Interestingly, this was associated with a decrease in KC and neutrophil recruitment resulting in inefficient clearance and enhanced mortality (Sun et al., 2009). These studies using transgenic mice indicate in vivo the role of neutrophil recruiting chemokines in improving the host response to P. aeruginosa and highlights the potential IL-8 may have in the pathogenesis of P. aeruginosa infections.

1.9.2. The adaptive immune response to P. aeruginosa

The role of lymphocytes in P. aeruginosa infections has been previously implicated using recombination activating gene (RAG) knockout models where increased susceptibly to P. aeruginosa and poor survival are associated with depleted lymphocytes (Koh et al., 2009).

Lymphocytes are therefore an important part of the adaptive immune response to clear P. aeruginosa and the role of CD4+ T cell subsets have been implicated in several studies using different murine strains with phenotypic differences associated with a Th1 or a Th2 bias.

Strains such as DBA/2 and C57BL/6 mice, with a Th1 phenotype, were more susceptible than

BALB/c mice to P. aeruginosa implicating a beneficial role of the Th2 response during chronic P. aeruginosa infection (Morissette et al., 1995; Tam et al., 1999). Further studies demonstrated increased TNFα levels 3 hours post infection and more efficient nitric oxide production during the first 24 hours of infection in the resistant BALB/c mice compared to

C57BL/6 mice (Gosselin et al., 1995). However, in contrast Moser and colleagues observed that the C3H/HeN strain (Th1 phenotype) was more resistant than BALB/c mice indicating a beneficial role of the Th1 response (Moser et al., 1999). Further experiments in which susceptible BALB/c mice were re-infected with P. aeruginosa identified a shift towards a

Th1 response associated with an improved outcome compared to mice infected once (Moser et al., 2002). These studies describe conflicting results. However, the differences in susceptibility and the favoured Th response in the clearance of P. aeruginosa could be explained by differences in the infection models used. Gosselin and colleagues used the mucoid PAO 579 strain, agar beads and a lower dose at 106 CFU/ml whereas Moser and colleagues used the clinical mucoid isolate 508, alginate and a much higher dose of 109

CFU/ml. In addition, Moser and colleagues, although not published, commented that in their chronic P. aeruginosa model, C57BL/6 mice were more susceptible than BALB/c mice at much lower doses. This implies an important difference in Th1 or Th2 response depending on

62 the dosage (Moser et al., 1999). Interestingly, clinical studies have identified a Th2 response, with increased IL-4, IL-5 and IL-13 and decreased IFNγ, favoured in cystic fibrosis patients with a P. aeruginosa infection compared to a more favoured Th1 response with increased

IFNγ and lower Th2 cytokines alongside improved lung function in cystic fibrosis patients without P. aeruginosa infections (Hartl et al., 2006; Moser et al., 2005; Moser et al., 2000).

The role of CD4+ T cells during an immune response elicited towards P. aeruginosa has been implicated. For example, CD4+ T cells isolated from rats immunised with killed P. aeruginosa provided protection and enhanced clearance in naive rats challenged with live P. aeruginosa (Dunkley et al., 1994) while mice immunised with an adenoviral based vector containing epitopes specific to the P. aeruginosa protein outer membrane porin F (OprF) demonstrated both a humoral and a cellular response determined by activated CD4+ and

CD8+ T cells secreting IFNγ (Worgall et al., 2005). Memory CD4+ T cell responses to P. aeruginosa in healthy controls (with no history of infection) and patients with cystic fibrosis have been previously demonstrated (Bayes et al., 2014). Further, a type three secretory protein (TTSS) of P. aeruginosa (PopB) has been shown to induce Th17 immunity and an antibody specific response. Interestingly, immunisation with PopB provides protection to P. aeruginosa in an IL-17-dependent and antibody-independent manner (Wu et al., 2012).

Early production of IL-17 occurs during P. aeruginosa infection in the murine lung.

Neutralisation of IL-17 demonstrated a decrease in bacterial clearance. Further to this, Th17 and γδ CD4+ T cells were shown to be important in IL-17 production during the early phase of infection (Liu et al., 2011; Liu et al., 2013). IL-17 and IL-23 production are important in neutrophil recruitment during chronic P. aeruginosa infections (Dubin and Kolls, 2007;

Dubin et al., 2012) and elevated IL-17A mRNA and protein levels are observed in the sputum isolated from patients with cystic fibrosis (Decraene et al., 2010; McAllister et al.,

2005). Th22 cells have been implicated during P. aeruginosa infections in both healthy individuals and patients with cystic fibrosis, however reduced Th17 cells were observed in the peripheral blood of this patient group (Bayes et al., 2014). The role of T regulatory (Treg) cells in P. aeruginosa infections has been shown to be controversial. Studies blocking IL-10, a main cytokine of Tregs, enhanced survival and bacterial clearance in a murine model of P. aeruginosa infection (Steinhauser et al., 1999). However, the direct role of Tregs during P. aeruginosa infection through anti-CD25 depletion has been shown not to have an effect on survival and clearance (Carrigan et al., 2009).

Other lymphocyte populations such as NKT cells are important cell types involved during P. aeruginosa infection. NKT cells are NK cells that express a T cell receptor (TCR) and are

CD1-d restricted. Once activated, NKT cells produce cytokines that contribute towards bacterial clearance. The role of NKT cells during P. aeruginosa infection was demonstrated using CD1-d deficient mice that had decreased neutrophil recruitment and survival and increased bacterial burden compared to controls. Further treatment with α-galactosylceramide lipid activated CD1-d restricted T cells and increased IFNγ production and P. aeruginosa eradication (Nieuwenhuis et al., 2002).

Although antibody responses are associated with P. aeruginosa infections, previous studies have shown that higher antibody titres are associated with poor prognosis and more severe disease (Hancock et al., 1984; Hoiby, 2001).

1.10. Current treatment strategies for P. aeruginosa infections

P. aeruginosa commonly infects individuals with impaired immune defences and is a common cause of infection in lung disease. In particular, patients with chronic lung disease such as cystic fibrosis, bronchiectasis and COPD are commonly associated with chronic infections (Burns et al., 2001; Murphy et al., 2008; Nicotra et al., 1995). Chronic P. aeruginosa infections are difficult to eradicate. Treatment strategies focus on preventing initial colonisation and rely mainly upon antibiotics. Rigorous antibiotic treatment during initial colonisation is sometimes an effective treatment option (Hansen et al., 2008).

Nevertheless, antibiotic resistance can occur in the context of chronic P. aeruginosa infection and the presence of P. aeruginosa enhances morbidity and mortality (Fothergill et al., 2012).

In 2000 the complete genome of P. aeruginosa was sequenced (Stover et al., 2000). This revealed a large and complex genome thought to be the basis of P. aeruginosa’s ability to not only infect a broad range of hosts (plants and animals) and survive in a variety of environmental conditions, but to also establish resistance mechanisms to both the host’s immune response and to antimicrobial agents (Mesaros et al., 2007; Stover et al., 2000). The resistance mechanisms that P. aeruginosa displays towards antibiotics can often occur simultaneously resulting in multidrug resistant strains and include the capacity to: (i) prevent penetration of the membrane, (ii) expression of efflux pumps, (iii) antibiotic inhibiting enzymes and (iv) the ability to develop new resistance mechanisms (Aaron et al., 2002;

Fothergill et al., 2012; Giwercman et al., 1991; Li et al., 1995; Mesaros et al., 2007).

As an infection persists, mature non-mucoid and mucoid biofilms form that enhance the resistance of P. aeruginosa to antibiotics (Aaron et al., 2002). Important components of the biofilm contribute towards the resistance properties of the biofilm and include the

65 exopolysaccharides, Psl and alginate (Billings et al., 2013; Nichols et al., 1988). Porins are structures in the outer membrane of P. aeruginosa through which large, hydrophobic antibiotics gain access to the cell. OprD is an important porin protein that mediates the entry of β-lactams such as carbapenems (Trias and Nikaido, 1990). A mutational loss of OprD is associated with reduced antibiotic effectiveness and resistant carbapenem strains (Livermore,

1992; Pai et al., 2001). In addition, P. aeruginosa expresses efflux pumps that in association with outer membrane proteins, transports a wide range of substrates including antibiotics out of a cell such as MexAB-OprM that can efflux antibacterial agents such as β-lactams (Li et al., 1995). Some porins and efflux pumps are co-regulated. OprD and MexEF-OprN are under the control of similar regulators that act with opposite effects. Thereby increased expression of the pump in response to an antibiotic would coincide with a decrease in the porin, thus increasing resistance to other antibiotics (Kohler et al., 1999; Kohler et al., 1997).

P. aeruginosa is also able to inactivate some antibiotics by producing enzymes such as β- lactamases that degrade the β-lactam ring of β-lactam antibiotics (Bagge et al., 2004).

The emergence of resistant strains highlights the need to identify other treatment strategies to prevent P. aeruginosa infections. These rely upon targeting other features of P. aeruginosa pathogenesis such as weakening the biofilm by targeting alginate with alginate lyase or DNA with DNase (Alipour et al., 2009; Alkawash et al., 2006) and targeting quorum sensing signalling (Smith et al., 2003). Theoretically, the ideal therapeutics would clear or prevent initial colonisation of P. aeruginosa. Another avenue to preventative treatment is the development of vaccine based therapies to enhance the immune system. Several types of vaccine exist that have both benefits and limitations. Inactivated and live attenuated vaccines induce strong immune responses yet pose risks if not produced properly, whereas rationally designed peptide based vaccines can induce specific immune responses and lack the

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The first vaccines developed for P. aeruginosa used LPS as the targeted antigen. However, this proved to have high toxicity and only targeted the particular serotype of P. aeruginosa that the LPS antigen was from, thus urging the need to combine LPS from different serotypes

(Pennington et al., 1975). During P. aeruginosa vaccine development, killed whole P. aeruginosa vaccines have been tested in phase I trials and were demonstrated to be safe.

However, no further studies have continued (Cripps et al., 2006). Outer membrane proteins are a logical candidate for vaccine development as they have been shown to have little side effects and are conserved throughout P. aeruginosa serotypes (Mansouri et al., 1999;

Mutharia et al., 1982).

1.11. Outer membrane porin F (OprF)

Surface expressed structural proteins constituting the outer membrane of gram-negative bacteria like P. aeruginosa include outer membrane proteins (Krishnan and Prasadarao,

2012). One of the most studied outer membrane proteins is outer membrane protein A

(OmpA). This protein is conserved through many species but is best known as an E. coli protein. OprF is the P. aeruginosa orthologue of OmpA that shares 56 % sequence similarity in the C-terminal domain (Brinkman et al., 2000). The major outer membrane proteins of P. aeruginosa include D, E, F, G, H and I (Mizuno and Kageyama, 1978). D, E and H have been further subgrouped as D1 and D2, E1 and E2 and H1 and H2 respectively (Hancock and

Carey, 1979; Yamano et al., 1990). OprF is a 33-39 kDa porin protein (Hancock and Carey,

1979) that functions to allow non-specific diffusion of small ionic and charged particles and has a critical role in the virulence of P. aeruginosa (Fito-Boncompte et al., 2011). OprF is an essential structural protein involved in P. aeruginosa cell morphology, growth in low osmotic conditions and attaches to the peptidoglycan layer (Hancock et al., 1981; Woodruff and

Hancock, 1989). OprF is also required in the adhesion to host cells such as pulmonary epithelial cells (Azghani et al., 2002) and is upregulated during biofilm growth under anaerobic conditions. Further, raised antibody titres specifically to OprF are observed in patients with cystic fibrosis and chronic P. aeruginosa infection (Yoon et al., 2002). The host’s immune response, in particular IFNγ production, is sensed by OprF. Binding of OprF to IFNγ results in the upregulation of two virulence factors of P. aeruginosa. These include lectin PA-1L and phenazine pyocyanin that consequently disrupt epithelial function (Wu et al., 2005). In addition, OprF is required for regulating QS in P. aeruginosa. A study using a mutant OprF strain (absent OprF protein) demonstrated, in addition to confirming the virulent roles previously discussed, impaired QS due to reduced levels of N-(3-oxododecanoyl)-L- homoserine lactone and N-butanoyl-L-homoserine lactone that are associated with a reduced capacity to produce QS virulence factors such as elastase, Lectin PA-1L and exotoxin A

(Fito-Boncompte et al., 2011).

OprF is important in the virulence of P. aeruginosa. However, this protein is also a logical candidate for vaccine development because it is a major outer membrane protein that is surface exposed (Mutharia and Hancock, 1983) and is conserved throughout all strains of P. aeruginosa (Mutharia et al., 1982). In addition, it is also an immunogenic protein and OprF specific antibodies can be detected in the sera of cystic fibrosis patients (Moore et al., 2012) and it has shown a protective role in several murine models. In 1984, Gilleland and colleagues demonstrated the protective effect of OprF to subsequent challenge in a murine model of systemic infection. Immunisations enhance the humoral response observed in challenged mice (Gilleland et al., 1984). In a murine burn model, OprF was administered on day 1 and 14. On day 28 mice were subjected to burn and challenged with P. aeruginosa and demonstrated that OprF immunisations enhanced the humoral response and protected against

69 infection (Matthews-Greer and Gilleland, 1987). In a third study, a rat model of chronic lung infection had an enhanced ability to clear infection post immunisation with OprF (Gilleland et al., 1988). Since then, several other murine models demonstrating protection with an OprF immunisation in an acute pulmonary model have been reported, several of which used different methods for OprF delivery (Hughes and Gilleland, 1995; Krause et al., 2011; Price et al., 2001; Staczek et al., 1998; Worgall et al., 2005).

Previous studies have identified OprF as an ideal candidate for vaccine development.

However, knowledge of OprF epitopes linked to protection so far is generally limited to the identification of B cell epitopes (von Specht et al., 1995; Worgall et al., 2007; Worgall et al.,

2005). However, a role for CD4+ T cells in protecting against P. aeruginosa infections has been implicated (Dunkley et al., 1994; Worgall et al., 2005).

1.12. T cells

T cells are an important cell type of the lymphocyte population involved in the adaptive immune response. T cells are distinguished from other lymphocytes by the expression of a T cell receptor (TCR) on the surface of the cell. It is through the TCR that a T cell can recognise antigen and consequently become an activated effector T cell. The αβ TCR is restricted to recognising antigen in the form of peptide presented by major histocompatibility complex (MHC) class I or class II molecules. These T cells also express CD4 and CD8 cell markers and subsequently further define the T cell lineage. Cytotoxic or CD8+ T cells recognise the MHC class I: peptide bound complexes whereas helper or CD4+ T cells recognise MHC class II: peptide bound complexes that ‘help’ B cells to produce antibodies and enhance CD8+ T cell cytotoxicity (Wang and Reinherz, 2002).

1.12.1. T cell development

Immune cells mainly develop from pluripotent hematopoietic stem cells (HSC) in the bone marrow. T cells, however, require the unique microenvironment of the thymus. T cell development requires HSC progenitors to migrate from the bone marrow to the thymus by thymic homing mechanisms mediated by several cell surface expressed receptors such as

CCR7, CCR9 and PSGL-1 (Krueger et al., 2010; Rossi et al., 2005; Zlotoff et al., 2010). The thymus is critical for the development of mature and competent T cells that undergo lineage commitment, repertoire selection and functional maturation to enable T cells to ultimately recognise foreign antigen in the context of self-MHC. If the thymic microenvironment does not develop as seen in individuals with genetic defects such as DiGeorge syndrome or the

‘nude phenotype’ caused by mutations in the FOXN1 gene, reminiscent of the nude mouse, then T cells can’t mature resulting in a complete lack of mature T cells (Frank et al., 1999;

Hong, 2001).

Thymocytes progress through several developmental stages before committing to a specific T cell lineage that expresses either CD4 or CD8 surface markers. These stages in thymocyte development are associated with the expression of certain cell markers that occur in defined regions of the thymus (Godfrey et al., 1993; Lind et al., 2001; Love and Bhandoola, 2011). In addition, T cell lineage commitment and development is regulated by Notch (Maillard et al.,

2005; Radtke et al., 1999) and IL-7R (Peschon et al., 1994). Initially, thymus settling progenitors enter the thymus and generate early T cell progenitors (ETP) in the corticomedullary junction. ETPs do not express CD4 or CD8 and are described as being double negative (DN) cells. Thymocytes of the DN1 stage migrate to the cortex and DN2 thymocytes are generated by initiating the rearrangement of the D and J segments of the TCR

β chain with the help of RAG1 and RAG2 enzymes (Love and Bhandoola, 2011). At this point δ and γ chains can also begin to be rearranged (Livak et al., 1999). Transition from

DN2 thymocytes into DN3 thymoyctes occurs in the subcapsular zone where committed αβ

(or γδ) T cells undergo V and DJ segment rearrangement and the β chain protein is produced.

Following the completion of V(D)J recombination, DN3 thymocytes re-enter the cortex wherein the DN4 stage is initiated through β selection. Here, only successfully rearranged β chains are expressed with a surrogate α chain to generate the pre-TCR. In addition, CD4 and

CD8 cell markers are induced and double positive (DP) cells develop. During the DP phase, rapid proliferation occurs before the TCR α chain undergoes VJ recombination resulting in the expression of the αβ TCR complex (Love and Bhandoola, 2011).

1.12.2. Central and peripheral tolerance mechanisms

A large repertoire of functional αβ TCR complexes are formed during gene rearrangement and thymocyte development. The fates of DP thymocytes are decided during repertoire selection by thymic epithelial cells in the cortex. Positive selection will result if the interaction between the TCR and self-peptide is sufficient and induces a low intensity signal in the T cell. Some cells will be neglected or ‘non selected’ due to a non-existent or very weak interaction with self and therefore results in cell death. Only a small percentage of cells will pass the positive selection checkpoint. Those that do will become single positive (SP) T cells by regulating the expression of CD4 or CD8. Those that interacted with MHC class I down regulate the expression of CD4 and become CD8+ SP cells and those that interacted with MHC class II downregulate CD8 and become CD4+ SP cells. The positively selected SP

T cells migrate into the medulla. If the TCR binds with high affinity for self-peptide in the context of self-MHC it is negatively selected and undergoes apoptosis (Love and Bhandoola,

2011; Surh and Sprent, 1994). An important regulator that controls expression of tissue specific self-antigens by the thymic epithelial cells is the autoimmune regulator (AIRE)- dependant transcription molecule. Patients with mutated AIRE develop autoimmune disorders termed autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy

(APECED) due to the dysregulated expression of self-antigens (Anderson et al., 2002).

Following positive and negative selection, surviving T cells egress from the thymus into the circulation in a process regulated by sphingosine-1-phosphate (S1P) and its receptor sphingosine-1-phosphate receptor 1 (S1P1). S1P1 deficiencies result in an increase in SP cells in the medulla and subsequent reduction in peripheral T cells (Allende et al., 2004;

Matloubian et al., 2004). Mature SP T cells expressing the receptor migrate from the thymus into the circulation by the presence of a chemotactic gradient created by S1P in the corticomedullary junction and the circulation (Pappu et al., 2007; Zachariah and Cyster,

2010).

The mechanisms of central tolerance are not 100 % effective. Some autoreactive T cells can escape and enter the periphery. These autoreactive T cells are subsequently controlled by mechanisms described by peripheral tolerance (Mueller, 2010). Firstly, CD4+ T cells with specificity for self-antigens are deleted in the periphery due to persistent stimulation. This form of apoptotic cell death, known as activation-induced cell death (AICD), is due to the increased expression of Fas and FasL by T cells. Engagement of these proteins induces apoptosis and subsequent deletion of the autoreactive cell (Brunner et al., 1995; Singer and

Abbas, 1994). A second system in place that controls peripheral tolerance is the induction of anergy that is dependent on co-stimulation. If a co-stimulatory signal is non-existent due to,

73 for example, the upregulation of the co-inhibitory receptor, cytotoxic T lymphocyte antigen -

4, then a state of hyporesponsiveness within a cell is induced (Perez et al., 1997). Other mechanisms of peripheral tolerance include ignorance of antigen due to inability to access the location where the antigen is expressed (Ohashi et al., 1991) and suppression of T cells by

Tregs (Takahashi et al., 2000).

1.12.3. CD4+ T cell Subsets

CD4+ T cells have an important ‘helper’ role in the immune response. Naive CD4+ T cells are activated in response to their TCR interacting with foreign antigen in the context of self

MHC. The pattern of signals received during activation directs the specific differentiation of

Th populations. Different subpopulations include Th1, Th2, Th9, Th17 and Th22 cells that demonstrate distinct functions and cytokine production.

1.12.4. Th1 and Th2 cells

Differentiation of murine CD4+ T cells into two effector phenotypes on the basis of distinct cytokine profiles was identified in 1986 (Mosmann et al., 1986). Th1 differentiation is associated with the production of IFNγ, IL-2 and lymphotoxin (Mosmann et al., 1986) and are an essential cell type required to clear intracellular microbial infections (Mills et al.,

1993) whereas Th2 differentiation is associated with the production of IL-4 as well as IL-5 and IL-13 (Cherwinski et al., 1987; Mosmann et al., 1986) and are particularly important during helminth infections (Vella and Pearce, 1992). Dysregulation of either subset results in particular immune diseases: Th1 defects result in autoimmune associated diseases such as

Multiple Sclerosis (Olsson et al., 1990) while Th2 defects result in allergy such as asthma

(Robinson et al., 1992). The differentiation of CD4+ T cells into Th1 and Th2 cells is directed

74 by the signal pattern of cytokines present at the interaction between TCR and peptide: MHC- class II complex. IL-4 and IL-2 drive Th2 cell differentiation (Swain et al., 1990) and IL-12 drives Th1 differentiation (Hsieh et al., 1993; Manetti et al., 1993). In addition, the main cytokine, IFNγ associated with Th1 cells has also been shown to act in an autocrine manner to induce Th1 differentiation (Lighvani et al., 2001). Distinct differentiated CD4+ subpopulations can also be determined by the specific expression of certain cell surface markers. Th1 cells specifically express markers such as CXCR3 and CCR5 chemokine receptors whereas Th2 cells express markers such as CCR3 and CCR4 (Loetscher et al.,

1998; Sallusto et al., 1998; Sallusto et al., 1997). The transcription factor and downstream signalling transducer and activator of transcription (STAT) proteins determine CD4+ T cell differentiation. The transcription factor associated with inducing Th1 cell differentiation and

IFNγ production is T-bet. In particular, T-bet knockout models demonstrate defects in Th1 responses associated with reduced IFNγ production and increased IL-4 and IL-5 production

(Szabo et al., 2002). STAT4 is a protein activated by IL-12 signalling that enhances the activation of T-bet. The generation of Th1 cells in the response to IL-12 in a STAT4 knockout model was diminished, demonstrating the essential role of STAT4 in Th1 induction

(Kaplan et al., 1996). GATA3 is the transcription factor associated with Th2 cells and is upregulated during Th2 differentiation and downregulated during Th1 differentiation (Zhang et al., 1997; Zheng and Flavell, 1997). The important role of GATA3 in inducing the Th2 response was demonstrated using a GATA3 knockout model that completely impaired Th2 differentiation (Zhu et al., 2004). STAT6 is a protein activated by IL-4 that enhances GATA3 expression and subsequent Th2 phenotype while STAT5 is essential in maintaining GATA3 expression (Zhu et al., 2003; Zhu et al., 2001).

1.12.5. CD4+ T cell populations

In addition to Th1 and Th2 effector CD4+ T cell populations, naive CD4+ T cells can differentiate into other T helper subpopulations such as Th9, Th17 and Th22 depending on the cytokine milieu. Th9 CD4+ T cells were identified by Veldhoen and Dardalhon as a CD4+

T helper population differentiated from either naive T cells or Th2 cells in the presence of

TGFβ and IL-4 and produce the cytokine IL-9 (Dardalhon et al., 2008; Veldhoen et al.,

2008). The role of Th9 cells in the immune response is not fully understood although the role of IL-9 has been implicated in parasitic clearance (Licona-Limon et al., 2013) asthma

(Shimbara et al., 2000) and autoimmunity (Li et al., 2010).

A distinct CD4+ subset termed Th17 cells, differentiate from naive CD4+ T cells in the presence of TGFβ and IL-6. TGFβ induces foxp3 expression and subsequent Treg formation while IL-6 inhibits Tregs and favours IL-17 production (Bettelli et al., 2006; Mangan et al.,

2006; Veldhoen et al., 2006). In addition, IL-23 is required to maintain Th17 cells post differentiation of naive CD4+ T cells (Stritesky et al., 2008) and IL-21 acts in an autocrine manner to enhance Th17 differentiation. A deficiency in IL-21 contributes to an impaired

Th17 response and protects against experimental autoimmune encephalomyelitis (EAE)

(Korn et al., 2007; Nurieva et al., 2007). Th17 cells characteristically produce the cytokines

IL-17A, IL-17F, IL-21and IL-22 and express the surface receptors CCR6 and CCR4 (Acosta-

Rodriguez et al., 2007). IL-17A and F are proinflammatory cytokines that target a range of immune cells but are key in neutrophil recruitment (Hoshino et al., 1999; Laan et al., 1999).

The master regulator of Th17 cells, RORγt, is distinct from the regulators of other subpopulations of CD4+ T cells. RORγt knockout mice are deficient in Th17 cells and are associated with reduced incidence of autoimmune diseases (Ivanov et al., 2006).

Th22 cells are a CD4+ subset characterised by the production of IL-22 and TNFα. Naive T cells differentiate in the presence of IL-6 and TNFα to generate Th22 cells. Th22 cells express CCR4, CCR6 and CCR10 surface markers and are known to home to the skin and infiltrate the epidermis during inflammatory skin disorders where Th22 cells have been implicated to enhance wound repair (Duhen et al., 2009; Eyerich et al., 2009; Trifari et al.,

2009). The Th22 subset also upregulates a distinct transcription factor termed aryl hydrocarbon receptor that regulates IL-22 production (Trifari et al., 2009). IL-22 is a protective cytokine during gram-negative induced pneumonia but also can induce epithelial hyperplasia during psoriasis (Aujla et al., 2008; Boniface et al., 2007).

1.12.6. S1P and receptor mediated T cell trafficking

Sphinogosine-1-phosphate (S1P) is generated by an enzymatic cascade that converts sphingolipids via ceramide and sphingosine into S1P (Hannun and Obeid, 2008). In most tissues S1P levels are very low. However, erythrocytes contribute to higher levels in the circulation and lymphatic endothelial cells contribute to higher levels in the lymph (Pappu et al., 2007; Pham et al., 2010). S1P binds to five G protein-coupled receptors termed S1P1-5

(Rosen et al., 2009). The majority of immune cells express S1P1 including dendritic cells, eosinophils, macrophages, T cells, B cells and NKT cells (Allende et al., 2008; Czeloth et al.,

2007; Matloubian et al., 2004; Roviezzo et al., 2004; Singer et al., 2005) whereas other S1P receptor members are expressed by specific cells such as the expression of S1P5 limited to

NK cells (Walzer et al., 2007). The main function described for S1P interacting with S1P receptors is in the regulation of lymphocyte trafficking (Matloubian et al., 2004). T cells express S1P1 and S1P4 receptors (Matloubian et al., 2004; Wang et al., 2005) and as previously discussed in section 1.12.2., S1P and S1P1 interactions are required for the egress

77 of thymocytes from the thymus into the periphery but also in controlling lymphocyte migration and egress from lymph nodes (Matloubian et al., 2004). This critical role of S1P1 was demonstrated using S1P1 knockout models in which T cell egress was prohibited from lymph nodes and the thymus (Allende et al., 2004; Lo et al., 2005). Naive T cells, expressing

S1P1, enter the lymph node and become activated by dendritic cells presenting antigen.

Following activation, S1P1 is downregulated due to two mechanisms: (i) regulation by its transcription factor, Kruppel-like factor 2 (Carlson et al., 2006) or (ii) T cell activation increases CD69 expression that subsequently down regulates S1P1 expression (Shiow et al.,

2006). Activated T cells with reduced S1P1 expression are retained in the lymph node and proliferate into effector T cells. Newly generated effector T cells upregulate S1P1 expression and migrate out following the S1P gradient (Rivera et al., 2008). S1P1 expression can be reduced due to internalisation in response to environments with high S1P levels (Lo et al.,

2005). FTY720 (Fingolimod, GilenyaTM) is an immunosuppressant drug that downregulates

S1P receptor expression and retains lymphocytes within lymph nodes resulting in lymphopenia (Chiba et al., 1998; Graler and Goetzl, 2004). Subsequently, this compound reduced the pathogenesis of EAE and is now used in the treatment of Multiple Sclerosis

(Fujino et al., 2003; Kappos et al., 2006). During inflammation S1P levels are increased in tissues at site of injury or insult that impedes immune cell trafficking by promoting T cell retention within the tissue (Ledgerwood et al., 2008).

1.12.7. Major histocompatibility complexes and antigen processing

The TCR expressed by CD4+ and CD8+ T cells recognise the peptide bound major histocompatibility complex (MHC) expressed on the surface of antigen presenting cells resulting in T cell activation. Peptide antigens are presented to the TCR via two distinct

78 classes of MHC molecules termed MHC-class I and MHC class II. MHC class I molecules are expressed on nucleated cells and present processed antigenic peptides to CD8+ T cells whereas MHC-Class II is expressed primarily on professional antigen presenting cells such as dendritic cells and macrophages that both present processed antigenic peptides to CD4+ T cells (Blum et al., 2013).

The designation of human MHC is human leukocyte antigen (HLA). The HLA region, located on chromosome 6, is polygenic and in humans consists of three classical class I loci:

HLA-A, HLA-B and HLA-C and three class II loci: HLA-DR, HLA-DP and HLA-DQ for which multiple different alleles exist for each locus (Blum et al., 2013; Robinson et al.,

2013). The high polymorphism of the HLA alleles creates diversity within the general population that ensures a range of peptide binding specificities. MHC class I and class II loci in mice (H2 antigens) are located on chromosome 17. The classical class I molecules include

H2-D, H2-K and H2-L and class II include I-A and I-E (Blum et al., 2013). Both humans and mice express other MHC-class I molecules known as non-classical structures encoded on the same chromosomes. In humans these are designated HLA-E, HLA-F and HLA-G (Adams and Luoma, 2013).

Bjorkman and colleagues first reported the crystal structure for MHC-class I molecule HLA-

A2. This report demonstrated that class I molecules are composed of two polypeptide subunits consisting of an α chain with three extracellular domains and a β2 microglobulin chain (Bjorkman et al., 1987; Klein and Sato, 2000). The β2 microglobulin (β2M) chain does not have a transmembrane domain but binds the α chain to provide stability and is essential for MHC-class I formation (Hill et al., 2003). The first crystal structure for MHC-class II was

79 reported by Brown and colleagues in 1993. The structure of MHC-class II is composed of two non-covalently associated polypeptide α and β chains of which each chain is made up of two domains (Brown et al., 1993). For both class I and II molecules, the peptide binding structure contains a β sheet floor, framed by two anti-parallel α helixes. These structures form a groove or binding cleft in which the peptide can bind (Bjorkman et al., 1987; Blum et al.,

2013; Brown et al., 1993). The peptide binding groove exists between the polymorphic α1 and α2 domains of MHC-class I molecules, with closed ends and can bind peptides of 8-10 amino acids in length (Bjorkman et al., 1987; Jardetzky et al., 1991). Anchor residues at the

N- and C-terminal of the peptide interact with pockets within the binding groove and longer peptides are accommodated by bulging in the central portion of the peptide (Guo et al., 1992;

Klein and Sato, 2000). The peptide binding groove of MHC-class II consists of the polymorphic α1 and β1 domains, with open ends (Brown et al., 1993) and binds to longer peptides than MHC-class I that range between 13-25 amino acids in length with a peptide binding core of 9 amino acids (Chicz et al., 1993; Chicz et al., 1992). Amino acid side chains of the 9 amino acid peptide binding core, termed anchor residues, interact with polymorphic pockets within the binding groove and it is this interaction that determines binding and affinity of the peptide to the binding groove. In HLA-class II molecules, the anchor regions 1,

4, 6 and 9 beginning at the N-terminus of the peptide core are important for binding (Godkin et al., 1997); (Hammer et al., 1994a).

1.12.8. MHC class I and the endogenous processing pathway

HLA-class I molecules present endogenous antigenic peptides at the cell surface for recognition by, and activation of, CD8+ T cells. The process by which internal microbial peptides are processed and presented is described by the cytosolic transport pathway, the

80 purpose of which is to inform the immune response of an intracellular infection within the cell (Blum et al., 2013). Initially, microbial proteins are ubiquitin tagged for targeted transport to the proteasome where the proteins are cleaved into peptides of 9-15 amino acids

(Ciechanover, 1994). The cleaved peptides are transported into the endoplasmic reticulum

(ER) by membrane spanning transporters associated with antigen processing (TAP1 and 2) proteins. The enzyme, Endoplasmic reticulum aminopeptidase 1 (ERAP1), further cleaves the peptides before loading onto the MHC-class I complex (York et al., 2006). The MHC class I

α chain is synthesised and assembled with molecular chaperones (calnexin) to stabilise the molecule until it associates with the β2M chain within the ER. These chaperones are released and a second chaperone, calcineurin binds. This complex associates with TAP1/2 via tapasin to form the peptide loading complex. Once this complex binds to the peptide, it is then expressed on the cell surface (Sadasivan et al., 1996).

1.12.9. MHC class II and the exogenous processing pathway

HLA-class II molecules present exogenous antigenic peptides at the cell surface for recognition by, and activation of, CD4+ helper T cells. The process by which microbial peptides are processed and presented is described by the endocytic transport pathway in which pathogens are internalised by receptor mediated endocytosis or phagocytosis before degradation and subsequent expression via MHC class II (Blum et al., 2013). The pathogen once internalised is subjected to endocytic compartments of increasing acidity as well as exposure to multiple enzymes such as cathepsins (Bennett et al., 1992) that digest proteins ensuring peptides are ready to be loaded onto MHC-class II (Blum et al., 2013).

Newly synthesised MHC class II α and β chains assemble within the ER and form a complex with the invariant chain (Ii). The invariant chain is required to prevent premature binding to the peptide binding groove (Roche and Cresswell, 1990) and to direct the intracellular transport of the MHC class II complex (Lotteau et al., 1990). The MHC class II/li complex is progressively degraded until a small fragment known as the class II associated invariant chain peptide (CLIP) remains (Blum and Cresswell, 1988; Ghosh et al., 1995; Maric et al., 1994).

CLIP is removed and exchanged for peptide by HLA-DM, a MHC like molecule expressed within endosomal compartments (Denzin and Cresswell, 1995). A second MHC like molecule, HLA-DO, modulates HLA-DM by acting as a competitive inhibitor thus preventing its interaction with MHC-class II peptide complex and subsequent class II expression (Guce et al., 2013). The complete MHC-class II/peptide complex is then transported to the surface for TCR restricted CD4+ T cell activation (Blum et al., 2013;

Jensen, 2007).

1.12.10. Major histocompatibility complexes associated with chronic respiratory disease

MHC class I and II expression on the surface of infected cells or professional antigen presenting cells are essential in mediating the adaptive immune response. However, studies have demonstrated that specific HLA types are associated with chronic inflammatory disease

(Jones et al., 2006). Previous reports have identified that HLA-DR4 and DR7 predispose patients to cystic fibrosis and HLA-DR7 is associated with P. aeruginosa infection (Aron et al., 1999). The cause of idiopathic bronchiectasis is unknown, yet a role for the adaptive immune response has been implicated by the presence of CD4+ T cells in the lungs (Gaga et al., 1998). Evidence that further implicates the adaptive immune response is the association

82 of idiopathic bronchiectasis and HLA-class I and class II alleles. A genetic association between idiopathic bronchiectasis and HLA- class I expression was first reported by Boyton and colleagues in 2006. A genetic susceptibility was associated with the HLA-Cw*03 allele while the HLA-Cw*06 allele was protective. In addition, HLA-C group 1 homozygosity was associated with an increased susceptibility compared to HLA-C group 2 (Boyton et al.,

2006). HLA class II alleles DR1 and DQ5 have also been linked to idiopathic bronchiectasis and increased susceptibility (Boyton et al., 2008).

1.13. Epitope mapping

Identifying HLA restricted CD4+ T cell epitopes is an important step in understanding the cellular immune response towards P. aeruginosa. During the cellular immune response, antigen presenting cells efficiently process antigenic proteins and present them as linear peptides bound to self-MHC. Recognition of these processed peptides is determined by the

TCR expressed by CD4+ T cells. However, many regions on the protein do not bind to MHC and often those that do are not recognised by the T cell repertoire. Therefore, determining immunogenic sequences that can direct the T cell response towards an invading pathogen is clinically important and a vital step in the development of HLA-restricted CD4+ T cell peptide based vaccines (Berzofsky, 1988; Malherbe, 2009).

1.13.1. Computational epitope predictions

The initial concept that immunogenicity of protein sequences could be predicted by the pattern of amino acids was first implied in the 1980s (DeLisi and Berzofsky, 1985). Since then, the identification of HLA-restricted peptide binding motifs has led to advancements in computational predictive algorithms for identifying T cell epitopes and has proved beneficial

83 in peptide based vaccine development. In particular, in silico analysis of proteins from infectious agents can identify epitopes with predicted binding potential for MHC-class II. In silico predictions have become a favourable first line method in epitope discovery using T cell epitope prediction algorithm software. Several approaches for HLA based epitope predictions have been developed that include quantitative and virtual matrix based algorithms, artificial neural networks and consensus models. Computational analyses are generally built upon patterns identified in previous experimental peptide binding assays, HLA natural ligand sequencing and crystallographic studies of HLA and interaction with peptide

(Falk et al., 1991; Raddrizzani and Hammer, 2000).

The increased understanding of HLA class II interactions has greatly enhanced predictive capabilities. Matrix based prediction algorithms (quantitative and virtual matrices) are powerful tools in HLA class II ligand predictions (Marshall et al., 1995; Sturniolo et al.,

1999) and are based upon a computational analysis of the interaction between the side chains of the 9 amino acid peptide core and the pockets of the HLA binding groove. These pockets consist of polymorphic residues that determine the ability of peptide binding due to distinct characteristics such as size and chemical properties. Each pocket is given its own specific

‘pocket profile’ dependant on the amino acids that form the pocket and on quantifying the effects that natural amino acid substitutions have on the outcome of peptide binding

(Raddrizzani and Hammer, 2000; Sturniolo et al., 1999). In addition, the side chains of the peptide core can also determine binding affinity and either enhance (anchor residues) or reduce binding (inhibitory residues) to the pockets (Sinigaglia and Hammer, 1994). The quantification matrix is based on quantifying the contribution of each amino acid within the 9 amino acid core and the pocket profiles of a HLA molecule. A sum of these position specific values generates a numerical description or score that can be ranked for a number of query

84 peptides (Hammer et al., 1994b; Raddrizzani and Hammer, 2000; Sturniolo et al., 1999).

However, it is unlikely to be able to cover all HLA types with peptide binding assays due to the sheer number of polymorphic HLA class II molecules and the huge experimental HLA binding assays required. Sturniolo and colleagues demonstrated that pocket profiles were almost independent of the rest of the binding groove and so an individual pocket described for one HLA Class II allele could be shared with another HLA Class II allele as long as the amino acids that make up the pocket were identical. Ultimately, a smaller number of pocket profiles can be generated and assigned to a greater number of HLA alleles by sequence comparison of the pocket profiles. Subsequently, virtual matrices based upon identical pocket profiles were developed and are used in HLA-class II predictions (Raddrizzani and Hammer,

2000; Sturniolo et al., 1999).

The main virtual based prediction software programme used is TEPITOPE (Raddrizzani and

Hammer, 2000; Sturniolo et al., 1999). This predictive software can perform simultaneous analysis for several HLA Class II alleles that enables, at a glance, the identification of any promiscuous epitopes (Raddrizzani and Hammer, 2000). TEPITOPE has been used in the identification of several target proteins in infection (Panigada et al., 2002), allergy (de Lalla et al., 1999) and cancer (Schroers et al., 2002). Three parameters embedded in the

TEPTIOPE software required for effective predictions include peptide frame (PF), peptide side chain effects on binding (PS) and peptide length (PL) (Raddrizzani and Hammer, 2000).

The computational analysis achieved by TEPITOPE that includes these parameters involves identifying all PF in the input sequence. Position 1 of the PF always begins with an amino acid with a hydrophobic side chain. Then for each residue, a PS value is derived from the virtual matrix that is based on the type of amino acid and its position within a PF. Peptide length affects the binding of a peptide to an HLA Class II allele. However, in these same

85 studies it was shown that if a nonamer peptide binding core was flanked by at least two amino acids on either side then the PL did not need to be considered in a predictive model

(Hammer et al., 1994a; Raddrizzani and Hammer, 2000). Finally, each PF dependant on these parameters is given a numerical value or peptide score (PSC). The PSC is then ranked and the percentage threshold defined by the user either includes or excludes predicted PSC. The percentage threshold is described as the ‘percentage of best scoring natural peptides’ and it determines the stringency of the analysis. A low threshold of 1 % corresponds to a high stringency and all peptides identified would be in the top 1 % of predicted peptides with the best PSC (Raddrizzani and Hammer, 2000).

In addition to matrix based predictive algorithms, others exist that are based on artificial neural networks (ANNs). ANNs are self-training systems that can ‘learn’ and refine their ability to extract and retain patterns that can then be applied to unseen data sets. ANNs are slightly better at predicting epitopes than matrix based algorithms. However, ANN systems are limited by the requirement for large data sets that are not available for most HLA Class II alleles. ANNs also require alignment matrices to train the prediction systems (Raddrizzani and Hammer, 2000). NetMHCIIpan is an artificial neural network based epitope prediction algorithm that has been developed through several versions (Karosiene et al., 2013; Nielsen and Lund, 2009; Nielsen et al., 2010; Nielsen et al., 2008; Nielsen et al., 2007). Initially

NetMHCII based on the stabilisation matrix alignment method (SMM-align) was used that directly predicted peptide and MHC class II binding affinities based on 14 HLA-DR alleles

(Nielsen et al., 2007). Benchmark analysis identified NetMHCII to be superior to other available class II prediction software such as TEPTIOPE (Nielsen et al., 2007). This version was developed further using a HLA-DR pan specific method, known as NetMHCIIpan, to allow predictions of binding to any HLA-DR molecule in the absence of specific data for that

86 allele (Nielsen et al., 2008). Following on from this, a new version that incorporated neural network based methods (NN-align) was developed with predicative capabilities significantly outperforming both SMM-align and NetMHCIIpan methods. Computational analysis using the NN-align method considers the peptide binding core and binding affinity, residues flanking the peptide binding core and is trained using novel training algorithms that correct any bias caused by redundant binding core representation. Benchmarking of this version proved to outperform other MHC class II prediction algorithms (Nielsen and Lund, 2009). A novel pan-specific method that allows predictions for HLA-DR, DP and DQ is now available termed NetMHCIIpan-3.0 (Karosiene et al., 2013). This is an artificial neural network prediction based algorithm capable of predicting binding to all HLA class II molecules and is trained using IC50 values from over 50,000 quantitative peptide binding experiments covering all HLA class II. In addition, NetMHCIIpan-3.0 outperforms other methods (Karosiene et al.,

2013).

A third method developed by Wang and colleagues that is available through the Immune

Epitope Database (IEDB) website is the consensus method (Wang et al., 2008; Wang et al.,

2010). The consensus method combines three different prediction algorithms: NN-align,

SMM-align and the combinatorial peptide library. The combinatorial peptide library consists of a mixture of peptides of the same length that all share one residue at one position, the affinity of a panel of peptides to a HLA class II molecule is unbiased and gives a comprehensive assessment of its binding specificity (Wang et al., 2010). For each of the three methods the peptides are ranked and given a rank score. The consensus approach then finds the median score of the ranks from the three combined methods and generates the consensus rank score. The consensus rank score is then used in predicting MHC class II binding peptides (Wang et al., 2008; Wang et al., 2010).

Computational analysis identifies potential T cell epitopes that may be important HLA Class

II binding peptides. However, experimental evaluation of the predicted peptide and HLA allele is required to confirm the peptides are efficiently processed by endocytic antigenic processing by antigen presenting cells. Peptides can then subsequently bind to HLA class II alleles in vitro and those that bind can activate CD4+ T cells. The strategy used in this thesis to assess T cell epitopes utilised enzyme linked immunosorbent spot (ELISpots) assays with human PBMCs stimulated with peptide or cells obtained from draining lymph nodes of mice primed with whole OprF protein.

1.13.2. Peptide design in CD4+ T cell epitope mapping

Identification of immunodominant epitopes of an antigenic protein via T cell epitope mapping requires a peptide panel that spans the length of the protein. The binding groove of

MHC-class II molecules is open, allowing longer peptides to bind. Natural MHC class II peptides are longer than 14 amino acids (Chicz et al., 1993) and although the core binding region is 9 amino acids in length, synthetic peptides less than 12 amino acids or greater than

31 amino acids do not typically stimulate CD4+ T cells as well (Reece et al., 1994). Synthetic peptides, usually 12-20 amino acids, overlap to ensure no potential epitope is missed

(Malherbe, 2009; Musson et al., 2010).

1.13.3. Epitope mapping: CD4+ T cell mapping in humans and HLA transgenic mice

Memory CD4+ T cells are activated when the TCR encounters an immunodominant peptide presented by self-MHC. Activation results in the production of lineage specific cytokines

88 such as IFNγ and IL-4 (Sallusto et al., 1999). It is these cytokines that the ELISpot assay takes advantage of. This is one of several methods that can be utilised to identify human

CD4+ T cell responses to immunodominant epitopes. However, one of the major limitations of using human samples is that human antigen presenting cells express a range of HLA- molecules including HLA-DR, DP or DQ and for each type an individual inherits two alleles, one from each parent. Therefore, any immunodominant epitopes identified can’t be linked to a certain HLA type. One way to overcome this problem and to identify HLA restricted responses is to use transgenic murine models that express a single human HLA class II and lack any murine class II molecules. HLA-transgenic models are commonly used in T cell mapping studies for infections, allergy and autoimmunity. For example, HLA-DR1, DR4 and

DR15 transgenic mice were used to map the CD4+ T cell response in Burkholderia pseudomallei (Chu et al., 2011); HLA-DR1 transgenic mice were used to identify CD4+ T cell responses to the capsular F1 antigen of Yersinia pestis (Musson et al., 2010); HLA-DR1 transgenic mice were used to identify peptides in grass pollen allergens (Till et al., 2014);

HLA-DR3 and DQ8 transgenic mice were used to identify peptides associated with mycobacterial antigens (Geluk et al., 1998) and HLA-DQ6, DQ8 and DR3 transgenic mice were used to identify peptides in an acetylcholine receptor protein implicated in myasthenia gravis (Yang et al., 2002).

1.14. Natural killer cells

The MHC class I complex as described in section 1.12.7. and 1.12.8. is not only important in

CD8+ T cell recognition by the TCR, but is also critical for recognition by natural killer (NK) cells (Parham, 2005). NK cells are large granular lymphocytes constituting 5-15 % of the circulating lymphocytes (Lanier et al., 1986) and are essential for innate immune responses to

89 viral infections, tumours and their ability to direct and promote other immune cells (Vivier et al., 2011). The discovery of NK cells as a third distinct lymphocyte subset was determined during the 1970s that stemmed from the observation that lymphocytes from ‘normal’ individuals not primed by a tumour were able to rapidly kill it (Kiessling et al., 1975a;

Kiessling et al., 1975b; Takasugi et al., 1973). Initially, NK cells were identified with the capability to respond to cells lacking surface expressed MHC class I (Karre et al., 1986;

Storkus et al., 1987). However, further work revealed that NK cell activation is dependent on a balance between activating and inhibitory signals (Vivier et al., 2011). NK cells are now a well-established cell type participating in direct cytolysis of target cells, cytokine and chemokine production, antibody-dependant cellular cytotoxicity (ADCC) and regulation of other immune cell types involved in innate and adaptive immune responses (Vivier et al.,

2011).

NK cell development consists of several major steps and like other immune cells, NK cells are derived from HSC. The majority of NK cells are derived from HSC in the bone marrow, likely driven by IL-15 production by stromal cells (Mrozek et al., 1996). Studies using bone marrow depleted mouse models support NK cell dependant generation in the bone marrow

(Kumar et al., 1979), although NK progenitors have been located to other sites such as the thymus (Ikawa et al., 1999) and lymph nodes (Freud et al., 2005). However, the thymus is not an essential site of NK cell generation as studies using athymic nude mice demonstrate normal NK cell numbers in the periphery whereas T cells are depleted (Kiessling et al.,

1975a).

1.14.1. NK cell receptor expression

Unlike T and B cells, NK cells express receptors that do not undergo somatic recombination and indeed NK cells are present and functional in RAG-2 knockout mice (Shinkai et al.,

1992). NK cells express a wide range of receptors with the ability, depending on ligand expression, to induce activating or inhibitory signals within the cell (Chiesa et al., 2005).

Therefore, NK cell regulation is dependent on the balance between these signals. NK cells can become activated if target cells overexpress activation receptor ligands or if the inhibitory signal is reduced or abolished due to a reduction in self-MHC class I expression by the target cell. This process of NK activation is described as the ‘missing self’ hypothesis; a strategy adopted by stressed cells in response to tumours or viral infections that result in ineffective

CD8+ T cell immune surveillance. However, cells that do escape CD8+ T cell recognition are detected by NK cells capable of recognising depleted MHC expression (Karre et al., 1986;

Lanier, 2008). In addition, NK cells become functionally competent by self-tolerance or licensing that involves interactions between the inhibitory NK receptors with a self-MHC class I environment (Vivier et al., 2011).

NK cells express activating and inhibitory receptors from two evolutionary and structural distinct families of receptors, the immunoglobulin (Ig) like receptors and C-type lectin receptors (Chiesa et al., 2005). Activating receptors associate with adaptor proteins that contain immunoreceptor tyrosine-based activation motifs (ITAMs) and include: CD16;

Natural Cytotoxicity Receptors (NCR) such as NKp30, NKp44 and NKP46; Natural Killer

Receptors (NKR2) such as NKG2C and NKG2D and activating members of the killer immunoglobulin like receptors (KIR). In addition, NK cells express adhesion/ co-stimulatory molecules such as β1 and β2 integrins (Chiesa et al., 2005). Inhibitory receptors contain an

91 immunoreceptor tyrosine-based inhibitory motif (ITIM) and include NKG2A and inhibitory

KIRs (Long, 2008). Unlike inhibitory receptors, most NK activating receptors lack intrinsic signalling domains and thus require transmembrane co-receptor molecules. NKp44, activating KIRs and CD94/NKG2C associate with DNAX activating protein of 12 kDa

(DAP12) while CD16, NKp30 and NKp46 associate with CD3ζ and FcεRIγ and NKG2D associates with DAP10 (Chiesa et al., 2005). Interactions between activating receptor and ligand result in the activation of a downstream signalling cascade similar to that seen in T and

B cells. Tyrosine residues in the ITAM become phosphorylated by Src family kinases.

Phosphorylated ITAMs recruit and bind spleen tyrosine kinase (Syk) and zeta-chain- associated protein kinase of 70 kDa (ZAP-70) that subsequently induces signalling cascades resulting in the release of cytolytic granules, the production of cytokines and chemokines or

ADCC mediated destruction (Chiesa et al., 2005). Ligation of inhibitory receptors results in phosphorylation of the ITIM tyrosine residues leading to Src homology domain-containing protein tyrosine phosphatase (SHP-1 and SHP-2) recruitment. This subsequently targets and inhibits various components of the activating signal such as SH2 domain-containing leukocyte protein of 76 kDa (SLP-76) (Binstadt et al., 1998), the adaptor protein, crk (Liu et al., 2012) and the guanine nucleotide exchange factor, vav-1 (Stebbins et al., 2003).

1.14.2. Activating and inhibitory receptors

CD16 (FcγRIII) expressed by NK cells is an important Fc receptor involved in ADCC (Vivier et al., 2011). Infected cells coated in immunoglobulin G (IgG) are recognised by these Fc receptors expressed by NK cells. Cross-linking of CD16 results in the activation of CD3ζ

(O'Shea et al., 1991). This subsequently leads to downstream signalling in NK cells and in resting NK cells, CD16 activation is enough to direct cytotoxic killing of the infected cell

(Bryceson et al., 2006). NK cells are defined as CD3-CD56+ cells and are divided into two main populations dependant on the extent of expression of CD56 and the absence or presence of CD16. These populations include CD56brightCD16- and CD56dimCD16+ cells each with separate effector functions. Previous studies have shown that CD56dimCD16+ cells direct cytotoxic effects upon target cells, although this subset can also produce IFNγ early after stimulation (De Maria et al., 2011; Jacobs et al., 2001). CD56brightCD16- cells have low levels of perforin but upon stimulation produce vast amounts of cytokines and chemokines (Cooper et al., 2001; Jacobs et al., 2001). However, more recently, evidence indicating a more prominent role for CD56dim NK cells in cytokine production compared to CD56bright was reported (Fauriat et al., 2010).

NCRs are a group of potent activating receptors expressed on the surface of human NK cells.

This group of Ig-like receptors include NKp30, NKp44 and NKp46 discovered in the 1990s

(Pende et al., 1999; Sivori et al., 1997; Vitale et al., 1998). NKp30 and NKp46 are expressed by mature NK cells (Pende et al., 1999; Sivori et al., 1997), although some NKT cell populations have been shown to express NKp46 (Yu et al., 2011). NKp44, however, is only expressed by NK cells once activated (Vitale et al., 1998) and has been shown to be involved in direct recognition of bacterial pathogens such as P. aeruginosa and mycobacteria (Esin et al., 2008). Ligands for NCRs are not fully identified (Kruse et al., 2013) although recently, a specific cellular ligand for NKp44 was identified on the surface of tumour cells and is potentially a vital component in NK cell mediated cytotoxicity (Baychelier et al., 2013).

The C-type lectin receptor NKG2C is an activatory receptor that forms a heterodimer with

CD94 and recognises the non-classical MHC class I molecule, HLA-E, in humans (Braud et

93 al., 1998). NKG2A like NKG2C forms a heterodimer with CD94 and recognises HLA-E ligand but induces an inhibitory response (Lee et al., 1998; Natarajan et al., 2002). Another

NKG2 family activating receptor, expressed by NK cells, that recognises stressed cells is

NKG2D. However, this receptor is different to other NKG2 family receptors as it forms a homodimer and signals via the adaptor protein DAP10 to induce both a cytotoxic and cytokine mediated response (Wu et al., 1999). The ligands for NKG2D include MHC class I chain related gene (MIC)-A and MIC-B and UL16-binding proteins (Bauer et al., 1999;

Steinle et al., 2001). These ligands are expressed by healthy tissues at a baseline level maintained below the activating threshold of NK cells by microRNA (Stern-Ginossar et al.,

2008). However, cellular stresses such as viral infection and tumours upregulate NKG2D ligand expression (Draghi et al., 2007; Pende et al., 2002).

While the majority of NK cell receptors exhibit limited polymorphism, the KIRs are highly polymorphic (Middleton and Gonzelez, 2010) and have both inhibitory and activating members. The nomenclature denoted to KIR receptors includes either an L or an S that describes the length of the cytoplasmic tail. An L denotes a long tail belonging to inhibitory receptors and S denotes a short tail belonging to activating receptors. In addition, KIRs consist of two (KIR2D) or three (KIR3D) extracellular domains (Middleton and Gonzelez,

2010). An exception to this however, is KIR2DL4 that is in fact a stimulatory KIR that initiates cytokine and chemokine production in response to soluble HLA-G and is essential during the early weeks of pregnancy (Faure and Long, 2002; Rajagopalan and Long, 2012).

The ligands for KIR receptors are the HLA-class I molecules and thus KIRs are an important receptor type in regulating ‘missing self’. KIR3D receptors including KIR3DL1 recognise

HLA-A and HLA-B alleles containing the HLA-Bw4 epitope while KIR3DL2 recognises a minority of HLA-A alleles (Parham et al., 2012). KIR2DL1, 2 and 3 receptors recognise

HLA-C alleles. MHC-C was first recognisable in the orangutan and is thought to have arisen by gene duplication from MHC-B (Adams et al., 1999). With the separation of orangutans from chimpanzees and humans, the MHC-C evolved with a mutation that resulted in an amino acid dimorphism denoted as group 1 and group 2 (Parham, 2005). The differences between these two groups are defined by an amino acid mutation at position 80. Group 1 alleles contain an asparagine residue (Asn80) and group 2 alleles contain a lysine residue

(Lys80) (Colonna et al., 1993; Parham, 2005). KIRs like the TCR on T cells are able to interact with and bind to the HLA-class I molecules via the α helices and the exposed regions of the peptide. However, this interaction for KIRs is mainly located to the C-terminal of the

α1 helix, position 7 and 8 of the peptide and the N-terminal of the α2 helix (Boyington and

Sun, 2002; Peruzzi et al., 1996; Rajagopalan and Long, 1997). The dimorphism within HLA-

C alters the specificity of the KIR that binds. HLA-C group 1 alleles are specific towards inhibitory receptors: KIR2DL2 and KIR2DL3 and activating receptor KIR2DS2 whereas

HLA-C group 2 alleles are specific towards the inhibitory receptor: KIR2DL1 and activatory receptor KIR2DS1. Interestingly, differences exist in the strength of binding depending on the HLA-C mutation and ligand. HLA-C group 2 alleles binds to KIR2DL1 with a stronger interaction than HLA-C group 1 alleles with either KIR2DL2 or KIR2DL3 (Boyington and

Sun, 2002; Parham, 2005).

1.14.3. NK cell responses

For NK cells to acquire full effector function they require priming with IL-15 produced by dendritic cells (Lucas et al., 2007), but also IL-12 (Guia et al., 2008) and IL-18 (Chaix et al.,

2008). Activated NK cells then mediate their functions in several ways that include

95 cytotoxicity, cytokine and chemokine production and interacting directly or indirectly on other immune cells.

1.14.3.1. NK cell cytotoxicity

NK cells can kill target cells through several mechanisms. The most common is the ability to induce cell death by the directed secretion of cytotoxic proteins. If the interaction between activating NK receptors with target cell ligands overrides the inhibitory receptor signals, then the NK cell is stimulated and releases cytolytic granules (Long et al., 2013). NK cells contain cytolytic granules within secretory lysosomes enabling rapid release upon activation. With activation, secretory lysosomes polarise towards the target cell and subsequently release their components such as perforin, granzymes A and B and enzymes such as cathepsin B that protect NK cell from its own cytotoxic granules (Balaji et al., 2002; Long et al., 2013).

Perforin is vital for target cell induced apoptosis as shown by studies utilising perforin knockout mice that demonstrated impaired cytotoxicity in both NK and CD8+ T cells (Kagi et al., 1994). However, its actual function is not fully understood (Trapani and Smyth, 2002).

Originally, perforin was thought to induce the formation of pores within the target cell membrane that enables the entry of granzymes into the target cell, yet recent studies demonstrate perforin independent mechanisms of entry such as the granzyme B receptor, mannose-6-phosphate, that is involved in endocytic uptake of granzyme B (Motyka et al.,

2000). Recently, perforin has been shown to induce formation of small pores within the target cell membrane. This induces calcium flux which initiates endocytosis of perforin and granzymes (Thiery et al., 2010). Perforin then induces pores within the endosome allowing granzymes into the cytosol of the target cell and subsequent induced apoptosis (Thiery et al.,

2011). There are several granzyme enzymes released by NK cells. Granzyme A is involved in inducing DNA damage while granzyme B is a potent apoptotic inducer of target cells capable of inducing caspase dependant and independent mechanisms of cell death. Granzyme B directly cleaves the protein, BH3-interacting dominant death agonist (bid). This results in the increase in mitochondrial pro-apoptotic proteins such as cytochrome C that induces a signalling cascade via caspase 9 and caspase 3 to induce cellular apoptosis (Heibein et al.,

2000; Ida et al., 2005; Waterhouse et al., 2005).

NK cells can also induce cell death in the target cell by the interaction of death receptors such as FAS, TNF or TNF-related apoptosis-induced ligand (TRAIL). Interactions between these receptors results in adaptor protein recruitment that leads to caspase activation and apoptosis

(Scott et al., 2008).

1.14.3.2. Cytokine and chemokine production and interaction with other immune cells

NK cells are also capable of interacting with other immune cells whether directly through cell-cell contacts or via the production of cytokines and chemokines. NK cells are particularly renowned as being major producers of IFNγ and TNFα, but also produce a range of other cytokines and chemokines such as GM-CSF, IL-5, IL-8 and MIP1α (Cuturi et al., 1989;

Fauriat et al., 2010; Smyth et al., 1991; Warren et al., 1995).

IFNγ is classified as a type II interferon produced by several immune cells including NK cells, CD8+ T cells, Th1 CD4+ T cells, NKT cells, B cells and antigen presenting cells

(Schroder et al., 2004). IL-12 and IL-18 induce NK cells to produce IFNγ. Enhanced IFNγ production by activated NK cells influences many aspects of both the innate and adaptive immune responses. The cytokine milieu mediates naive CD4+ T cell differentiation. IFNγ is responsible for directing and amplifying the Th1 cell response (Lighvani et al., 2001) and inhibits Th2 differentiation (Gajewski and Fitch, 1988). IFNγ also influences MHC class I and class II antigen processing and presentation including the upregulation of both MHC class I (Shirayoshi et al., 1988) and class II (Chang and Flavell, 1995; Figueiredo et al.,

1989) expression and initiating the replacement of proteasome subunits with LMP2, LMP7 and MECL1 to form the immunoproteasome. The immunoproteasome efficiently enhances the quantity and quality of peptides that are loaded onto MHC (Hisamatsu et al., 1996). IFNγ also interacts with PMNs to reduce the production of neutrophil recruiting chemokines such as IL-8 (Cassatella et al., 1993) and enhances the production of chemokines such as

CXCL11, IP-10 and MIG (Gasperini et al., 1999) that recruit adaptive immune cells such as

Th1 and NK cells (Cole et al., 1998; Farber, 1997).

1.14.4. NK cells and bronchiectasis

NK cells are important innate immune cells that are recruited rapidly to sites of insult and contribute towards inflammatory processes including the recruitment of other cell types such as T cells (Somersalo et al., 1994) and resolution of disease such as through neutrophil mediated apoptosis via NKp46 and Fas dependant mechanisms (Thoren et al., 2012). The

KIRs and their HLA class I ligands are highly polymorphic and many studies have linked an association between disease and/or a combination of the HLA class I molecule and KIR expressed (Boyton and Altmann, 2007; Parham, 2005). In particular, associations have been

98 implicated in infection (Khakoo et al., 2004), reproduction (Hiby et al., 2004) and autoimmunity (Martin et al., 2002).

Bronchiectasis is a disease often associated with progressive lung damage and chronic bacterial infections. A role for NK cells during chronic P. aeruginosa colonisation of patients with bronchiectasis has previously been shown. Phenotypic differences in NK cell responses to chronic P. aeruginosa infection showed that NK cells from P. aeruginosa resistant mice had greater IFNγ, GM-CSF and surface expression of LFA-1 levels but lower Fc receptor expression compared to more susceptible mice (Calum et al., 2003). The importance of HLA- class I expression and regulation of NK cells has been demonstrated in patients with a rare disease known as TAP deficiency syndrome. Defective TAP expression in either TAP1 or

TAP2 proteins results in the impaired translocation of processed peptides into the ER. This consequently results in an absence of peptide ligands available for loading onto HLA class I molecules. Without peptide, HLA-class I molecules are unstable and lead to reduced HLA- class I expression on the cell surface. Patients with TAP deficiency syndrome typically suffer from recurrent bacterial infection, skin lesions and ultimately bronchiectasis and respiratory failure. NK cells from TAP deficient individuals have been shown to be more highly activated which may contribute towards the progressive lung damage observed (Gadola et al.,

2000; Zimmer et al., 1999; Zimmer et al., 1998).

A genetic association between HLA-Cw*03 and the presence of idiopathic bronchiectasis has been previously reported. Boyton and colleagues demonstrated an increase in HLA-Cw*03 in those with idiopathic bronchiectasis while HLA-Cw*06 was reduced. An increased susceptibility was also associated with HLA-C group 1 homozygosity. Group 1 homozygous

99 patients expressing the activating receptors KIR2DS1 and/or KIR2DS2 are increased in individuals with idiopathic bronchiectasis. This combination may lead to a higher level of NK cell activation due to the lack of HLA-C group 2 alleles to interact with the KIR2DL1 inhibitory receptor (Boyton et al., 2006). Previous findings that NK cells are dysregulated in

TAP deficiency syndrome and the association between HLA-C group 1 homozygosity and

KIR interactions contribute to the hypothesis that NK cells in idiopathic bronchiectasis patients could be inappropriately activated.

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1.15. Aims and Outline of thesis

1.15.1. Aims

Chronic respiratory diseases have a variety of causes such as environmental insults and genetic influences as well as unknown origins and affect many people worldwide. Increased levels of proinflammatory mediators such as IL-8 are enhanced in addition to pathological changes such as inflammation and structural alterations. Damage and structural changes occurring during the course of disease often provide ideal conditions for bacterial infections.

P. aeruginosa is a common pathogen associated with respiratory disease and leads to chronic infections resulting in the subsequent decline in lung function, morbidity and death.

However, not all patients with respiratory disease become infected potentially due to differences in the immune response to infection. However, of those chronically infected, current treatment strategies are ineffective due to the rise in antibiotic resistant strains. This highlights the need for novel therapies to both treat and prevent P. aeruginosa. At present no vaccine targeted towards P. aeruginosa infections is licensed for use in humans.

In this thesis the following issues will be addressed:

1. What are the consequences of constant hIL-8 exposure on the lung in terms of

inflammation and remodelling processes?

2. How does chronic IL-8 exposure and subsequent remodelling processes impact on

infection with a common pathogen associated with chronic lung disease such as P.

aeruginosa?

3. Can CD4+ T cell epitopes restricted to HLA-DR molecules for a specific

immunogenic protein of P. aeruginosa be identified to (i) Increase our knowledge on

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CD4+ T cell epitopes for peptide-based vaccine design and (ii) Identify differences in

the immune responses in patients with a chronic respiratory disease who do or do not

become chronically infected with P. aeruginosa?

4. Can NK cell dysfunction be used to explain the causes of idiopathic bronchiectasis?

1.15.2. Outline

Chapter 3 begins by characterising a previously generated hIL-8 transgenic model to understand, in the context of a specific proinflammatory mediator, how it impacts on lung remodelling processes and subsequently how the increased level of hIL-8 and chronic remodelling processes associated with chronic respiratory disease influences bacterial infections in particular for infection with P. aeruginosa.

Following on from this in chapter 4, a major protein of P. aeruginosa known as OprF is produced. This protein is used to map specific HLA restricted CD4+ T cell epitopes by utilising available HLA-DR transgenic mice and ELISpot techniques. In addition, the differences in the adaptive immune response in terms of IFNγ producing CD4+ T cells are investigated in patients with bronchiectasis to determine any differences that may exist between those able to clear chronic P. aeruginosa infections and those that become chronically infected.

Lastly chapter 5 focuses on improving our understanding of how idiopathic bronchiectasis originates. A genetic link identifying the susceptibility of the HLA-C group 1 homozygous patients and more specifically HLA-Cw*03 patients with developing idiopathic

102 bronchiectasis has been previously reported (Boyton et al., 2006). This genetic susceptibility was proposed to be linked to dysregulated NK cell activation. This chapter is directed at identifying differences in NK cell function in patients with idiopathic bronchiectasis with this genetic association.

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CHAPTER 2: Materials and Methods

2.1. Transgenic mice

2.1.1. Human IL-8 (hIL-8) transgenic mice hIL-8 transgenic mice were generated by Dr. Catherine Reynolds and Dr. Rosemary Boyton of the Lung Immunology Group. This transgenic line was generated on a C57BL/6 background that expresses hIL-8 continuously and specifically in the lungs due to the use of the Clara cell 10 (CC10) promoter that directs expression in bronchial epithelial cells.

2.1.2. HLA-DR transgenic mice

2.1.2.1. Human leukocyte antigen (HLA)

HLA alleles are highly polymorphic molecules that are classified systematically by nomenclature defined by the World Health Organisation nomenclature committee for factors of the HLA system. Currently there are over 10,000 alleles described (http://hla.alleles.org/).

HLA alleles studied in this thesis include HLA-DR1 (DRA*0101/DRB1*0101), HLA-DR4

(DRA*0101/DRB1*0401) and HLA-DR15 (DRA*0101/DRB1*1501).

Human MHC Class II transgenic mice expressing HLA-DR1 (Altmann et al., 1995), HLA-

DR4 (Ito et al., 1996) and HLA-DR15 (Ellmerich et al., 2005) were used in this thesis. These transgenic lines express specific human HLA and lack any endogenous mouse MHC class II

(H2-Aβ) by successful breeding of the HLA transgenic mice with C57BL/6 Aβo mice. All animals were sex, age and weight matched. All animal procedures and regulations for animal

104 welfare were approved by the UK Home Office and based on the Animals (Scientific procedures) Act 1986.

2.2. Genotyping of transgenic mice

Polymerase chain reaction (PCR) was used to verify the genotype of the transgenic mice used in this thesis. Initially mice were allocated specific ear marks for identification and tail biopsies were taken for genomic deoxyribonucleic acid (gDNA) extraction.

2.2.1. High salt gDNA extraction

Tail biopsies were digested in 400 µl of digestion buffer (50 mM Tris HCl pH 8.0, 100 mM ethylenediaminetetraacetic acid (EDTA), 100 mM NaCl, 1 % sodium dodecyl sulphate

(SDS), all from Sigma-Aldrich, Dorset, UK) containing 0.25 mg/ml proteinase K (Sigma-

Aldrich) and incubated at 56 ºC overnight. 200 µl of 6 M saturated NaCl was added and samples were mixed vigorously for 30 seconds. Tissue samples were centrifuged for 15 minutes at 15700 g and the supernatant was removed and transferred to a clean tube. To precipitate the DNA, 600 µl of isopropanol was added and samples were mixed by inversion before centrifuging for 10 minutes at 15700 g. The supernatant was discarded and the pellet was washed with 800 µl of 70 % ethanol (Sigma-Aldrich) and centrifuged for 5 minutes at

15700 g. Excess ethanol was removed, the pellet was air dried and dissolved in 40 µl of molecular biological water (DNase, RNase and protease free, Sigma-Aldrich) at 56 ºC for 15 minutes. The concentration of DNA in each sample was determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, UK).

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2.2.2. Polymerase chain reaction (PCR)

A generic PCR protocol is detailed below, however the oligonucleotide primer sequences, product size and reaction conditions are subject to change depending on the gene of interest

(Table 2.1).

All PCR reactions were carried out in a reaction volume of 25 µl containing 1 µl of diluted gDNA (1:25 dilution, approximately 50-100 ng/ml). In addition reaction mixes also contained 200 µM deoxyribonucleotide triphosphate (dNTP) mix (Bioline Reagents Ltd,

London, UK), 1X reaction buffer (16 mM (NH4)2SO4, 67 mM Tris-HCl (pH 8.8 at 25 ºC),

0.01 % tween-20, Bioline), 1.5 mM MgCl2 (Bioline), 0.5 µM forward primer (Sigma-

Aldrich), 0.5 µM reverse primer (Sigma-Aldrich) and 0.6 units of BIOTAQ DNA polymerase

(Bioline). For the negative control, template DNA was replaced with water. Samples were loaded onto the G-Storm GS1 thermal cycler PCR machine (G-Storm, Somerton, UK) and run under specific conditions depending on the primer pair used (Table 2.1). Generally conditions for DNA amplification by PCR began with 5 minutes at 95 ºC, 35 cycles of 1 minute at 95 ºC, 1 minute at 55 ºC and 1 minute at 72 ºC, with a final elongation step of 8 minutes at 72 ºC.

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Table 2. 1: PCR primer sequences and reaction conditions (Sigma-Aldrich).

Annealing Size Gene Primer Sequence (5’-3’) temperature (bp)

GTTCTTAACCTGTTGGTC RBPWT1 (F) GGAA RBP 55 ºC 500 GCTTGAGGCTTGATGTTC RBPWT2 (R) TGTATTGC

AGGAATTCTGTAAACAT IL-8F (F) GACTTCCAAGC IL-8 55 ºC 319 GCGAATTCTTATGAGTTC IL-8R (R) TCAGCCCTCTTC

CTCCAAGCCCTCTCCCA humanDRαF (F) GAG 55 ºC 150 ATGTGCCTTACAGAGGC humanDRαR (R) CCC HLA-DR TTCAATGGGACGGAGCG HumanDRβF (F) GGTG 55 ºC 200 CTGCACTGTGAAGCTCT humanDRβR (R) C

TCCGCAGGGCATTTCGT Murine DRAβOF (F) MHC GTA 68 ºC 250 class II AGGGAGGTGTGGGTCTC DRAβOR (R) WT CGG

TCCGCAGGGCATTTCGT Murine DRAβOF (F) MHC GTA 60 ºC 500 class II DRAβO-KO3 (R) GAGGATCTCGTCGTGAC KO CCA

(F) = Forward Primer (R) = Reverse Primer

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2.2.3. Agarose gel electrophoresis

A 1 % solution of agarose (Sigma-Aldrich) was prepared by dissolving 1 g of agarose in 100 ml 1X Tris-acetate-EDTA (TAE) buffer by heating in a microwave for 3 minutes. Once cool to touch, 5 µl of 10,000x SybrSafeTM (Invitrogen, Life Technology, Paisley, UK) was added.

The gel was poured into a gel cast, left to set and then submerged in a gel tank containing 1X

TAE solution. 2 µl of 1X loading dye (40 % sucrose, (Sigma-Aldrich) and 0.25 % bromophenol blue (Sigma-Aldrich)) was added to each DNA sample and 12 µl of sample were loaded into the gel alongside 5 µl of DNA HyperladderII (Bioline) to enable size determination of DNA samples. Samples were run at 100 volts for 45 minutes and PCR product bands were visualised with BioDoc-It® Imaging System (UVP, Cambridge, UK) and

LabWorks™ image capture and analysis software.

2.3. Quantitative reverse transcriptase polymerase chain reaction (qRT-

PCR)

RNA was extracted from several tissues including murine lung tissue (section 2.3.1.) and human PBMCs (section 2.3.2.). cDNA was synthesised from RNA samples (section 2.3.3.) for use in qRT-PCR assays (section 2.3.4.).

2.3.1. RNA extraction from murine lung tissue

Harvested lung tissue was immediately immersed in RNAlater (Qiagen, Manchester, UK), and stored overnight at 4 ºC. The following day, lung samples were homogenised in 1 ml of

TRIzol® (Invitrogen) with an Omni THTM homogeniser (Camlab, Cambridge, UK). 200 µl of chloroform (Sigma-Aldrich) was added and shaken vigorously for 30 seconds before

108 incubating on ice for 5 minutes and centrifuging at 13400 g for 15 minutes at 4 ºC. The upper clear aqueous phase was reserved, to which 500 µl of isopropanol was added and incubated at room temperature for 10 minutes. Samples were centrifuged at 13400 g for 5 minutes at 4 ºC and the supernatant discarded. The pellet was washed with 1 ml of 75 % ethanol and centrifuged at 5200 g for 5 minutes at 4 ºC. Ethanol was removed and the pellet was air dried at room temperature for 10 minutes before dissolving in 20 µl of molecular biological water.

Total RNA concentration and purity were measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific, UK).

2.3.2. RNA extraction from human peripheral blood mononuclear cells

(PBMCs)

RNA extraction from human peripheral PBMCs was carried out using the Absolutely RNA

Microprep kit (400805, Agilent Technologies, Wokingham, UK). PBMCs previously isolated and stored (section 2.12.) were thawed (section 2.12.4.) and were either unstimulated or stimulated (section 2.14.1.1.). PBMCs where resuspended in 900 µl RNAlater overnight at 4

ºC and frozen at -40 ºC. To extract RNA from PBMCs, cells were thawed and removed from

RNAlater by centrifugation at 5000 g for 5 minutes. The supernatant was discarded and the pelleted cells were lysed in 100 µl of lysis buffer containing β-mecaptoethanol by vortexing.

100 µl of 70 % ethanol was added and vortexed to ensure sufficient mixing. Samples were transferred to a RNA-binding spin cup seated in a 2 ml collection tube and centrifuged for 30 seconds at 13000 g to capture RNA onto the matrix of the spin cup. The filtrate was removed and RNA was washed in 600 µl of 1X low-salt wash buffer by centrifuging for 30 seconds at

13000 g. The filtrate was removed and centrifuged for a further 2 minutes at 13000 g to dry the matrix. 30 µl of DNase solution (5 µl RNase-free DNaseI and 25 µl DNase digestion

109 buffer) was added directly to the matrix and incubated at 37 ºC for 15 minutes. RNA samples were washed with 500 µl of 1X high salt wash buffer by centrifuging for 30 seconds at 13000 g. The filtrate was removed and samples were washed a second time with 600 µl of 1X low- salt wash buffer by centrifugation for 30 seconds at 13000 g and the filtrate was disregarded.

A further 300 µl 1x low-salt wash buffer was added for 30 seconds at 13000 g before an additional spin for 2 minutes to dry the matrix. The spin cup was transferred to a fresh eppendorf and 30 µl of heated elution buffer was added. Samples were incubated at room temperature for at least 2 minutes before a final spin for 1 minute at 13000 g to collect RNA samples.

2.3.3. Complementary DNA (cDNA) synthesis cDNA was synthesised using reagents from Invitrogen and 300 ng of extracted RNA (section

2.3.1. and 2.3.2.). 300 ng of RNA, 50 ng of random primers and 1 µl 10 mM dNTP mix in a total of 13 µl RNase free water were heated to 65 ºC for 5 minutes and incubated on ice for at least 1 minute. The denatured RNA samples were added to the cDNA synthesis master mix (4

µl of 5X first-strand buffer, 1 µl 0.1 mM dithiothreitol (DTT), 1 µl (40 units) of RNase OUT and 1 µl (200 units) of Superscript III Reverse Transcriptase) and run in the G-Storm PCR machine for 5 minutes at 25 ºC, 1 hour at 50 ºC and 15 minutes at 70 ºC.

2.3.4. Real time-PCR for individual genes qRT-PCR reactions using primers obtained from Sigma-Aldrich or Applied Biosystems (Life

Sciences, UK) used the TaqMan® Universal PCR Master Mix (Applied Biosystems) and gene specific probes for the amplification and detection of DNA whereas primers purchased from

Qiagen used the RT2 SYBR Green/ ROX qPCR master mix (Qiagen). Reactions using gene

110 specific probes were carried out in a total reaction volume of 20 µl and consisted of 1 µl cDNA, 10 µl TaqMan® Universal PCR Master Mix, 1 µl of primer/probe mix and 8 µl RNase free H2O. Reactions using SYBR Green were carried out in a total reaction volume of 25 µl and consisted of 12.5 µl RT2 SYBR Green master mix, 1 µl cDNA, 1 µl RT2 qPCR primer

(10 µM) and 10.5 µl RNase-free water. Samples were run in triplicate on a Stratagene

Mx3000p qRT-PCR machine (Agilent Technologies, Berkshire, UK), using the standard filter set FAM/SYBR Green (excitation: 492 nm; emission: 516 nm) and under the conditions in table 2.6. Ct values were acquired (Cycle threshold: The cycle number where the fluorescence signal crosses a set threshold) and the relative quantification of data was determined using standard curves. Standard curves for each murine (Table 2.2, 2.3 and 2.4) and human (Table 2.5) gene of interest and housekeeping genes analysed were generated using a 1 in 5 serial dilution of cDNA. The standard curve generated an efficiency equation that was used to convert Ct values to numerical values. Differences in the amount of RNA between samples were controlled for by normalising to the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) for each sample. For each gene of interest analysed, a value of 1 was assigned to the sample with the lowest gene expression and the other samples were expressed as a value relative to 1.

Table 2.2 Primers for murine qRT-PCR genes obtained from Applied Biosystems. Gene Catalogue number KC Mm00433859_m1 MIP-2 Mm00436450_m1 Ocln Mm00500912_m1 Cdh1 Mm01247357_m1 Fn1 Mm01256744_m1 Vim Mm01333430_m1 F11r Mm00554113_m1 Cldn18 Mm00517321_m1 Tjp1 Mm00493699_m1 GAPDH 4352932E

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Table 2.3: Primer sequences for murine qRT-PCR genes obtained from Sigma-Aldrich Gene Forward Primer Reverse primer Probe (5’ – 3’) (5’ – 3’) (5’ – 3’)

Muc5ba GCACGTAAATG ATGGACCTTGC [6FAM]TATCCAAGTACTC CGACTGTCT TCTCCTGAC CATGGAGGCCC[TAM]

Muc5aca TCCCTTACCTA GGGAGTACATG [6FAM]GAGGGCCCAGTG ACCAGCAGAA GAGATGCTGT AGCATCTCCTACT[TAM]

Collagen TTCTACACCTG AGACCTGGTTG [6FAM]TCCGGGTCCTCCT IIIb CTCCTGTGC TCCTGGAAG GGCATTC[TAM]

Collagen Ib TAAGGGTACCG GTTCACCTCTCT [6FAM]AGAGCGAGGCCT CTGGAGAAC CACCAGCA TCCCGGAC[TAM]

Smooth AAACGAACGCT GATGCCCGCTG [6FAM]CCAGAGACTCTC Muscle TCCGCTG ACTCCAT TTCCAGCCTCTTTCATTG Actinc [TAM]

a Sequence for primers from (Tetaert et al., 2007) b Sequences designed in lab c Sequence for primers from (Horan et al., 2005)

Table 2.4 Primers for murine qRT-PCR genes obtained from Qiagen Gene Catalogue number Ccl3 PPM02949F Ifnγ PPM03121A Fasl PPM02926E Smad2 PPM04430C Sp1 PPM04585F Tgfb3 PPM02993A Mmp14 PPM03617D Timp3 PPM03453F Edn1 PPM05274B GAPDH PPM02946E

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Table 2.5 Primers for human qRT-PCR genes obtained from Qiagen Gene Catalogue number Granzyme A PPH00314F Granzyme B PPH02594A S1P1 PPH01350F IL12RB1 PPH00594A IL12RB2 PPH00595C Ifnγ PPH00380C Perforin PPH07126A IL27 PPH18307B IL-7R PPH00607G Tbet PPH00396A Rorc PPH05877A Gata3 PPH02143A Hprt1 PPH01018C GAPDH PPH00150F

Table 2.6 Conditions for TaqMan or SYBR Green detection systems with the Mx3000p real-time PCR machine TaqMan detection system SYBR Green detection system

1 cycle of 50 ºC for 2 minutes 1 cycle of 95 ºC for 10 minutes

1 cycle of 95 ºC for 10 minutes 40 cycles of (95 ºC for 15 seconds, 60 ºC for 1 minute)

50 cycles of (95 ºC for 15 seconds, 60 ºC Automated dissociation curve for 1 minute)

2.4. Murine fibrosis RT2 profilerTM PCR array

RT2 profiler arrays (Qiagen) are capable of investigating disease states by analysing the expression of many genes simultaneously by qRT-PCR. In this thesis, the murine fibrosis

RT2 ProfilerTM PCR array (PAMM-120A, Qiagen) was used to study 84 key fibrotic genes

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(Table 2.7) and 5 housekeeping genes (GAPDH, Actb, Hprt, Hsp90ab1 and Gusb) to help establish important pathways contributing to remodelling processes in the lung.

2.4.1. RT2 first strand kit

The RT2 first strand kit (Qiagen) provides efficient first strand cDNA synthesis with genomic

DNA elimination and external RNA controls for use with RT2 profilerTM PCR array (Qiagen).

700 ng murine lung RNA (section 2.3.1.) was added to 2 µl of 5X gDNA elimination buffer and nuclease-free water up to 10 µl before incubating at 42 ºC for 5 minutes. Samples were immediately chilled on ice for at least 1 minute. 10 µl of the first strand RT-Cocktail (4 µl

BC3 (5X RT buffer 3), 1 µl P2 (Primer and external control mix), 2 µl RE3 (RT enzyme mix

3) and 3 µl nuclease free water) were added to each sample and incubated at 42 ºC for exactly

5 minutes followed by heating to 95 ºC for 5 minutes to immediately stop the reaction before adding 91 µl of nuclease-free water.

2.4.2. RT2 RNA QC PCR array

The RT2 RNA QC PCR array (PAMM-999A, Qiagen) assesses the quality of a cDNA sample before it is used on the RT2 ProfilerTM PCR array. This quality control check tests for RNA integrity, the presence of inhibitors of reverse transcription and PCR amplification and for gDNA contamination that can be problematic for SYBR Green based qRT-PCR experiments.

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Rows A through E of the 96-well RT2 RNA QC PCR plate were loaded with 25 µl of experimental cocktail 1 (6 µl cDNA from section 2.4.1., 75 µl RT2 SYBR Green PCR master mix and 69 µl ddH2O). Rows G and H were loaded with 25 µl of experimental cocktail 2 (45

2 µl RT SYBR Green PCR master mix and 45 µl ddH2O) and row F was loaded with 25 µl of experimental cocktail 3 (1 µl of 1:100 dilution of original RNA sample and 24 µl experimental cocktail 2). Samples were run on the Mx3000p under the conditions in table

2.6. Ct values were inputted into the online Qiagen/ SABiosciences excel-based RT2 RNA

QC PCR array data analysis template that automatically interprets the quality of RNA samples (http://www.sabiosciences.com/pcrarraydataanalysis.php).

2.4.3. Murine fibrosis RT2 profilerTM PCR array

Following a successful quality control assay, 25 µl of experimental cocktail 4 (1350 µl 2X

2 SABiosciences RT PCR master mix, 102 µl cDNA from section 2.4.1. and 1248 µl H2O) was added to each well of the 96-well array plate and run on the Mx3000p system, under conditions in table 2.6. Ct values were obtained and analysed using Partek Genomics Suite version 6.6 with help from Scott Brouilette (Partek technical support) to identify genes with a fold change greater than ± 1.2 and a p value <0.05 using Anova statistical testing. Differential genes identified were confirmed by individual qRT-PCR assays (Section 2.3.4.). Pathway analysis was performed using Metacore (GeneGo Inc.) and provided by Dr Michael

Poidinger, Singapore Immunology Network.

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Table 2.7 Gene list for the murine fibrosis RT² profiler PCR array (Qiagen) Symbol Description Acta2 Actin, alpha 2, smooth muscle, aorta Angiotensinogen (serpin peptidase inhibitor, clade A, Agt member 8) Akt1 Thymoma viral proto-oncogene 1 Bcl2 B-cell leukemia/lymphoma 2 Bmp7 Bone morphogenetic protein 7 Cav1 Caveolin 1, caveolae protein Ccl11 Chemokine (C-C motif) ligand 11 Ccl12 Chemokine (C-C motif) ligand 12 Ccl3 Chemokine (C-C motif) ligand 3 Ccr2 Chemokine (C-C motif) receptor 2 Cebpb CCAAT/enhancer binding protein (C/EBP), beta Col1a2 Collagen, type I, alpha 2 Col3a1 Collagen, type III, alpha 1 Ctgf Connective tissue growth factor Cxcr4 Chemokine (C-X-C motif) receptor 4 Dcn Decorin Edn1 Endothelin 1 Egf Epidermal growth factor Eng Endoglin Fasl Fas ligand (TNF superfamily, member 6) Grem1 Gremlin 1 Hgf Hepatocyte growth factor Ifng Interferon gamma Il10 Interleukin 10 Il13 Interleukin 13 Il13ra2 Interleukin 13 receptor, alpha 2 Il1a Interleukin 1 alpha Il1b Interleukin 1 beta Il4 Interleukin 4 Il5 Interleukin 5 Ilk Integrin linked kinase Inhbe Inhibin beta E

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Itga1 Integrin alpha 1 Itga2 Integrin alpha 2 Itga3 Integrin alpha 3 Itgav Integrin alpha V Itgb1 Integrin beta 1 (fibronectin receptor beta) Itgb3 Integrin beta 3 Itgb5 Integrin beta 5 Itgb6 Integrin beta 6 Itgb8 Integrin beta 8 Jun Jun oncogene Lox Lysyl oxidase Ltbp1 Latent transforming growth factor beta binding protein 1 Mmp13 Matrix metallopeptidase 13 Mmp14 Matrix metallopeptidase 14 (membrane-inserted) Mmp1a Matrix metallopeptidase 1a (interstitial collagenase) Mmp2 Matrix metallopeptidase 2 Mmp3 Matrix metallopeptidase 3 Mmp8 Matrix metallopeptidase 8 Mmp9 Matrix metallopeptidase 9 Myc Myelocytomatosis oncogene Nuclear factor of kappa light polypeptide gene enhancer in Nfkb1 B-cells 1, p105 Pdgfa Platelet derived growth factor, alpha Pdgfb Platelet derived growth factor, B polypeptide Plat Plasminogen activator, tissue Plau Plasminogen activator, urokinase Plg Plasminogen Serpina1a Serine (or cysteine) peptidase inhibitor, clade A, member 1a Serpine1 Serine (or cysteine) peptidase inhibitor, clade E, member 1 Serpinh1 Serine (or cysteine) peptidase inhibitor, clade H, member 1 Smad2 MAD homolog 2 (Drosophila) Smad3 MAD homolog 3 (Drosophila) Smad4 MAD homolog 4 (Drosophila) Smad6 MAD homolog 6 (Drosophila) Smad7 MAD homolog 7 (Drosophila)

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Snai1 Snail homolog 1 (Drosophila) Sp1 Trans-acting transcription factor 1 Stat1 Signal transducer and activator of transcription 1 Stat6 Signal transducer and activator of transcription 6 Tgfb1 Transforming growth factor, beta 1 Tgfb2 Transforming growth factor, beta 2 Tgfb3 Transforming growth factor, beta 3 Tgfbr1 Transforming growth factor, beta receptor I Tgfbr2 Transforming growth factor, beta receptor II Tgif1 TGFB-induced factor homeobox 1 Thbs1 Thrombospondin 1 Thbs2 Thrombospondin 2 Timp1 Tissue inhibitor of metalloproteinase 1 Timp2 Tissue inhibitor of metalloproteinase 2 Timp3 Tissue inhibitor of metalloproteinase 3 Timp4 Tissue inhibitor of metalloproteinase 4 Tnf Tumor necrosis factor Vegfa Vascular endothelial growth factor A

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2.5. Differential cell counts

Immune cells in the BAL and lung were obtained as described in section 2.5.1. and 2.5.2. respectively. To determine cell types based on cellular morphology, samples were stained with the Wright Giemsa stain (section 2.5.3.).

2.5.1. Preparation of bronchoalveolar lavage (BAL) for differential cell counts

Following terminal anaesthesia, the trachea was canulated and 0.4 ml of PBS was administered three times to lavage the lung. The BAL fluid was pooled for each mouse and then centrifuged for 8 minutes at 260 g. Live cell counts of pelleted cells from BAL samples were counted using KOVA® Glasstic slides with counting grids (HYCOR Biomedical Ltd,

Edinburgh, UK) and 0.1 % trypan blue solution (Sigma-Aldrich). Following this, the cells were resuspended in complete media (25 µg/ml streptomycin sulphate (Gibco, Life

Technologies, UK), 25 U/ml penicillin G (Gibco) and 2 mM L-glutamine (Gibco) in RPMI

1640 (Gibco) medium, pH 7.2-7.4) to a concentration of 5 x 105 cells/ml.

2.5.2. Preparation of lung for differential cell counts

Lung tissue was finely chopped and added to 5 ml of complete media containing 0.15 mg/ml

Collagenase D and 25 µl DNase 1 (Roche, Basel, Switzerland). Lung samples were incubated at 37 ºC for 1 hour with shaking at 120 rpm before being passed through a 70 μM BD

FalconTM nylon cell strainer (BD Biosciences, Oxford, UK). Samples were centrifuged for 5 minutes at 260 g and the supernatant was removed. All samples were resuspended in 500 µl of complete media and live cell counts were determined as described in section 2.12.2.

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Following this, the cells were resuspened in complete media to a concentration of 5 x 105 cells/ml.

BAL and lung cytospins were prepared from 100 µl of cell sample previously prepared in section 2.5.1. and 2.5.2. for a final concentration of 5 x 104 cells per slide. Cells were spun onto polysine Superfrost plus glass microscope slides (VWR International, Lutterworth, UK) using a Shandon Cytospin® 3 cytocentrifuge at 400 rpm for 4 minutes before being left at room temperature to air dry for 5 minutes. The cells were then fixed in methanol (Sigma-

Aldrich) for 3 minutes at room temperature.

2.5.3. Wright-Giemsa stain

To stain cells for differential cell counting, BAL and lung cytospins were immersed in

Wright-Giemsa stain (Sigma-Aldrich) for 1 minute followed by 6 minutes without agitation in dH2O. Slides were then rinsed in dH2O and left to dry before mounting with DPX (VWR).

Cell counts of at least 300 cells per slide were counted and the cell type was determined by specific cell characteristics including cell size, cytoplasmic staining and morphology of the nucleus.

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2.6. Immunohistochemistry

Murine lung tissue was prepared for sectioning and staining as described in section 2.6.1.

Sections were stained with either the hematoxylin and eosin stain to determine cellular inflammation (2.6.2.), the periodic acid-Schiff stain to determine mucus production (2.6.3.) or the Masson’s trichrome stain to determine the level of fibrosis (2.6.4.).

2.6.1. Paraffin embedding and sectioning

Lungs post-harvest were inflated with an intra-tracheal infusion of phosphate buffered saline

(PBS, Invitrogen) and then immediately fixed in 10 % formalin (Sigma-Aldrich) at 4 ºC overnight. Following this, lung tissue was paraffin embedded and tissue sections were cut at 4

µm thick and mounted on Superfrost plus microscope slides. Three histology stains were used on paraffin embedded lung sections. Before staining, sections were deparaffinised (2 x 5 minutes in histoclear (National diagnostics, East Yorkshire, UK) and then hydrated through decreasing concentrations of ethanol (100 %, 90 % and 70 %) to dH2O. After staining, lung sections were dehydrated through increasing concentrations of ethanol (70 %, 90 % and 100

%) followed by 3 x 5 minutes in histoclear before mounting with a coverslip and DPX.

Histology images were obtained using a Leica light microscope 2500 and Leica Qwin version

3 software.

2.6.2. Hematoxylin and Eosin staining

Following section 2.6.1. tissue sections were immersed in hematoxylin for 3 minutes and rinsed in running tap water before differentiating with three brief dips in acid/alcohol solution

(1 % HCl /70 % ethanol). Sections were rinsed in running tap water and then placed in 1 %

121 eosin (Raymond A Lamb Ltd, East Sussex, UK) for 1 minute followed with a rinse in running tap water before dehydrating and mounting. Cellular inflammation surrounding each bronchi and blood vessel per section were scored based on a numerical scale from 0 to 3 previously described by McMillan and colleagues (0 = Bronchi or blood vessel not surrounded by inflammatory cells; 1 = Bronchi or blood vessel surrounded by occasional inflammatory cells; 2 = Inflammation completely surrounds and is 1-4 cells in diameter from the edge of the bronchi or blood vessel; 3 = Inflammation completely surrounds and is >5 cells in diameter from the edge of the bronchi or blood vessel) (McMillan et al., 2002).

2.6.3. Periodic acid -Schiff staining

Following section 2.6.1. tissue sections were oxidised in 0.5 % periodic acid for 5 minutes and rinsed in dH2O before immersing in Schiff’s Reagent (Fisher Scientific, Loughborough,

UK) for 15 minutes. Sections were rinsed in dH2O and then washed in running tap H2O for 5 minutes. Sections were counterstained with Mayers Haematoxylin (VWR) for 20 seconds before washing with running tap water followed by dehydrating and mounting. The percentage of mucus present per bronchi was scored using a numerical scale from 0 to 4 previously described by McMillan and colleagues (0= <5 %, 1= 5-25 %, 2 = 25-50 %, 3= 50-

75 %, 4= >75 %; the mucus score was calculated from the total score divided by the number of airways examined) (McMillan et al., 2002).

2.6.4. Masson’s trichrome staining

Following section 2.6.1 tissue sections were incubated overnight at room temperature in

Bouin’s fixative solution (Sigma-Aldrich). Afterwards, sections were rinsed in running tap water, placed in Weights iron haematoxylin solution (Sigma-Aldrich) for 5 minutes, washed

122 in running tap water for 5 minutes and then rinsed in dH2O. Following this, sections were stained for 5 minutes in Biebrich scarlet-acid fuchsin solution (Sigma-Aldrich) and rinsed in dH2O before differentiating in phosphotungstic/ phosphomolybdic acid solution (Sigma-

Aldrich) for 5 minutes. Sections were immediately immersed in aniline blue (Sigma-Aldrich) solution for 5 minutes before transferring to 1 % acetic acid for 2 minutes. Afterwards, sections were rinsed in tap water and then dehydrated and mounted. A scoring system previously described by Ashcroft and colleagues was used to systematically score the level of fibrosis in all airways per field and an average score was obtained for each section. Scoring was based on a numerical scale from 0-8 (Table 2.8, (Ashcroft et al., 1988)).

Table 2.8 Masson’s trichrome scoring system (Ashcroft et al., 1988). Grade of fibrosis Histological features

0 Normal Lung 1 Minimal fibrosis and thickening of bronchiolar walls 2-3 Moderate thickening to bronchiolar walls without damage to lung structure 4-5 Increased fibrosis with damage to lung structure. Formation of fibrous bands or small fibrous masses 7 Severe damage to lung structure and large fibrosis areas 8 Total fibrosis, obliteration in the field

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2.7. Immunofluorescence

Murine lung and gut tissue were frozen and prepared for sectioning as described in section

2.7.1. Sections were then stained with secondary fluorophore-labelled antibodies and visualised using confocal microscopy (section 2.7.2.).

2.7.1. Frozen sections

Lungs, post-harvest, were inflated with 50 % optimal cutting temperature (O.C.T) compound

(VWR) in PBS and then immersed in a base mould containing 100 % O.C.T compound. Gut tissue post-harvest was directly immersed in a base mould containing 100 % O.C.T compound. Base moulds were immediately placed in isopentane (VWR) on dry ice and stored at -80 ºC. To cut tissue sections, tissue blocks were trimmed and sectioned at 5 µM thick on a cryostat (Leica, CM1850). Sections were then mounted onto Superfrost plus microscope slides and stored at -80 ºC until required for immunofluorescence.

2.7.2. Immunofluorescent staining

All steps were carried out at room temperature. Frozen sections were thawed for 5 minutes before fixing with 4 % paraformaldehyde (PFA) for 10 minutes. Sections were washed three times in PBS for 5 minutes each and blocked with blocking buffer (3 % bovine serum albumin (BSA, Sigma-Aldrich), 10 % mouse serum (Sigma-Aldrich) and 10 % donkey serum

(Sigma-Aldrich) in PBS) for 30 minutes. After blocking, sections were washed three times as described above and incubated overnight with goat anti-human IL-8 polyclonal antibody at

10 µg/ml (Abcam, Cambridge, UK) and rabbit anti-mouse claudin-18 monoclonal antibody at

5 µg/ml (Invitrogen) diluted in blocking buffer. Control sections were incubated in blocking

124 buffer with no primary antibody. The next day sections were washed in PBS and then incubated for 1 hour in blocking buffer containing appropriate secondary antibodies at 10

µg/ml (donkey anti-rabbit IgG conjugated to alexa fluor 546 (Invitrogen) and donkey anti- goat IgG conjugated to alexa fluor 680 (Invitrogen)). Stained sections were washed and mounted with ProLong Gold Antifade mountant with 4', 6-diamidino-2-phenylindole (DAPI,

Invitrogen) to stain nuclei. Sections were left to dry and viewed using a Leica TCS-SP5

Confocal microscope (Leica Microsystems, Milton Keynes, UK). Images were taken with

Leica Microsystems software (LAS AF lite version 2.6.0). Approximately 9 images per mouse were captured and epithelial/ tight junction damage was scored by 4 blinded independent individuals using a numerical scale from 0 to 3 to determine epithelial damage

(0=normal, organised epithelium with intact tight junctions, 1= less organised epithelium, 2= damaged epithelium, 3= extensive damage to epithelium, little or no tight junctions).

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2.8. Enzyme-linked immunosorbent assay (ELISA)

Cytokine concentrations in lung homogenate, BAL and serum samples were quantitatively measured by ELISA. ELISA plates coated with capture antibodies form complexes with cytokines within a sample. Biotin-conjugated detection antibodies and enzyme mediated substrate development detect these complexes. Cytokine concentration was determined against a linear standard curve of known concentration.

2.8.1. Preparation of BAL fluid for ELISA

Following terminal anaesthesia, the trachea was canulated and 0.4 ml of PBS was administered three times to lavage the lung. The BAL fluid was pooled for each mouse and then centrifuged for 8 minutes at 260 g. The supernatant was removed and frozen for subsequent ELISA analysis.

2.8.2. Preparation of lung tissue for ELISA

Lung homogenate for ELISA analysis was prepared from snap frozen lung samples. 100 mg/ml of lung tissue was homogenised in Hanks balanced salt solution (HBSS, Gibco) containing a cOmplete ULTRA mini protease inhibitor tablet (Roche). Lung homogenates were centrifuged at 16000 g for 5 minutes. Following this the supernatant was removed and stored at -20 ºC.

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2.8.3. Preparation of serum for ELISA

Blood samples extracted from terminally anesthetised mice were left to coagulate at room temperature. Samples were centrifuged at 1000 g for 20 minutes after which the serum was removed and stored at -20 ºC.

2.8.4. Cytokine ELISA

The concentrations of hIL-8 (R&D Systems Europe Ltd, Abingdon, UK), mIFNγ (R&D

Systems), mCCL3 (R&D Systems), mIL-6 (BioSourceTM, Life Technologies, UK), mIL-17

(Mabtech AB, Nacka, Sweden) and mTNFα Duoset® (R&D Systems) were determined by

ELISA in one or more of BAL, lung and serum samples. 100 µl of capture antibody (Table

2.9) was added to the wells of a 96-well ELISA plate (Maxisorp, Nunc, SLS). The plates were sealed and incubated over night at room temperature. After incubation, plates were washed three times with 300 µl wash buffer (0.05 % Tween®20 (Sigma-Aldrich) in PBS, pH

7.2-7.4) per well and subsequently blocked for 1 hour at room temperature with 300 µl blocking buffer (IL-8 cytokine: 1 % BSA/ 5 % Sucrose /0.05 % NaN3 in PBS, pH 7.2 – 7.4 and for all other cytokines 1 % BSA/PBS). Blocking buffer was removed and plates were washed three times. Standards were prepared from stock recombinant proteins diluted with diluent buffer (0.1 % BSA in PBS) to 4000 pg/ml and subsequent serial dilutions of 1 in 2 to

15.125 pg/ml. 50 µl of standard or sample prepared in diluent buffer were added in duplicate, sealed and incubated for 2 hours at room temperature. After incubation, plates were washed three times and 100 µl of biotinylated secondary antibody (Table 2.9) was transferred to each well and incubated at room temperature for 2 hours. After incubation plates were washed three times and 100 µl of streptavidin-horseradish peroxidase (HRP, R&D systems) diluted

1:200 with diluent buffer was added. Plates were incubated in the dark for 20 minutes at

127 room temperature. After incubation, plates were washed three times and 100 µl/well of

3,3’,5,5’-tetramethylbenzidine (TMB) substrate reagent (Sigma-Aldrich) was added and incubated in the dark at room temperature to allow the enzymatic colour reaction to develop.

The reaction was stopped by the addition of 100 µl/well of 1 M H2SO4 (Sigma-Aldrich) stop solution.

The optical densities of each well were determined at a wavelength of 450 nm on a microplate reader (SunriseTM-Tecan, Reading, UK). ELISA data was analysed using

Graphpad version 5.01 software. The concentration of each sample was determined from the linear portion of the standard curve and any sample dilutions were taken into account in the analysis.

Table 2.9 Concentrations of primary and secondary antibodies used in cytokine ELISAs. Cytokine Primary Antibody Secondary antibody

Mouse anti human diluted Biotinylated goat anti-human diluted hIL-8 1:1000 in PBS to 0.5 µg/ml 1:2500 in 0.1 % BSA/ PBS to 20 ng/ml

Biotinylated goat anti-mouse diluted mIFNγ Goat anti-mouse diluted 1:125 in 1:125 in 0.1 % BSA/ PBS to 0.2 0.1 M NaHCO to 0.4 µg/ml 3 µg/ml

Biotinylated goat anti-mouse diluted mCCL3 Goat anti-mouse diluted 1:1000 1:125 in 0.1 % BSA/ PBS to 0.1 in 0.1 M NaHCO to 0.2 µg/ml 3 µg/ml

mIL-6 Biotinylated anti-mouse diluted Anti-mouse diluted 1:800 0.1 M cytosetTM 1:2000 in 0.1 % BSA/ PBS to 0.1 NaHCO to 1.25 µg/ml 3 µg/ml

Biotinylated anti-mouse diluted Anti-mouse diluted 1:500 in mIL-17A 1:1000 in 0.1 % BSA/ PBS to 0.5 PBS to 1 µg/ml µg/ml

mTNFα Anti-mouse diluted 1:180 in Biotinylated anti-mouse diluted 1:90 Duoset® PBS to 0.8 µg/ml in 0.1% BSA/ PBS to 0.4 µg/ml

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2.8.5. Murine albumin ELISA

The concentration of murine albumin was determined in BAL samples using a mouse albumin ELISA quantisation set from Bethyl Laboratories, Inc (Montgomery, Texas, USA).

Anti-mouse albumin capture antibody was diluted in coating buffer (0.05 M NaHCO3, pH

9.6) to 10 µg/ml and 100 µl transferred to the appropriate wells of a 96-well ELISA plate

(Maxisorp, Nunc, SLS). The ELISA plates were sealed and incubated for 1 hour at room temperature. After incubation, plates were washed five times with 200 µl wash buffer (50 mM Tris, 0.14 M NaCl, 0.05 % Tween®20, pH 8.0) per well and subsequently blocked with

200 µl per well of blocking buffer (50 mM Tris, 0.14 M NaCl, 1 % BSA, pH 8.0) for 30 minutes at room temperature. Blocking buffer was removed and plates were washed five times. Mouse reference serum containing 45 mg/ml albumin was diluted with diluent buffer

(50 mM Tris, 0.14 M NaCl, 0.1 % BSA, 0.05 % Tween®20, pH 8.0) to 10,000 ng/ml. This was then diluted to 500 ng/ml and subsequently diluted 1 in 2 to generate the standard concentration range from 500 ng/ml to 7.8 ng/ml from which the standard curve was plotted.

100 µl of standard or sample (diluted 1:5000 with diluent buffer) were added in duplicate.

Plates were sealed and incubated for 1 hour at room temperature. After incubation the plates were washed five times and 100 µl of 1 mg/ml HRP detection antibody was diluted in diluent buffer to 0.02 µg/ml, transferred to each well and incubated at room temperature for 1 hour.

After incubation the plates were washed five times and 100 µl/well of TMB substrate reagent was added and incubated in the dark at room temperature to allow the enzymatic colour reaction to develop. The reaction was stopped by the addition of 100 µl/well of 1M H2SO4 stop solution. The optical densities of each well were determined at a wavelength of 450 nm on a microplate reader (Sunrise-Tecan). ELISA data was analysed using Graphpad version

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5.01 software. The concentration of each sample was determined from the linear portion of the standard curve and any sample dilutions were taken into account in the analysis.

2.9. Labelled dextran

FITC-Dextran was used to assess permeability changes and epithelial damage of tight junctions. Mice received 40 µl of 4 kDa FITC-Dextran (50 mg/ml in PBS, Sigma-Aldrich) by intranasal administration. 30 minutes later mice were culled and lung, BAL and blood samples were obtained and processed as described earlier in section 2.8.1, 2.8.2, 2.8.3 for generating lung homogenate, BAL and serum samples respectively. The amount of FITC-

Dextran present in these samples was measured in duplicate by FLUOstar Galaxy software

(MTX lab systems, Virginia, USA) at an excitation wavelength of 490 nm and an emission wavelength of 525 nm. The sample concentrations were determined against a standard curve of known FITC-Dextran diluted in PBS (standard curve range from 1 mg/ml to 7.8 µg/ml).

The BAL: lung ratio was used to measure pulmonary epithelial permeability. A high ratio determines a lower permeability.

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2.10. Pseudomonas aeruginosa (P. aeruginosa) infection in vivo

2.10.1. P. aeruginosa

The parental strain of P. aeruginosa (PAO1) was obtained from the Pseudomonas Genetic

Stock Centre, from which the P. aeruginosa (Xen41) strain was derived by Caliper Life

Sciences (PerkinElmer, Cambridge, UK) for bioluminescence imaging. The Xen41 bacterial chromosome contains a single copy of the luxCDABE operon of Photorhabdus luminescens.

This bioluminescence system emits visible light at approximately 490 nm.

2.10.2. P. aeruginosa infection

P. aeruginosa (from glycerol stocks) was plated on LB plates and incubated overnight at 37

ºC and 5 % CO2. The next day 5 ml of LB broth was inoculated with a single colony and incubated overnight at 37 ºC and 220 rpm. Overnight cultures were washed three times in 50 ml PBS and centrifuged for 10 minutes at 3000 g. After the final wash PBS was removed and the cell pellet was resuspended in 500 µl PBS. This stock solution was diluted to 1-3 x 106

CFU in PBS and 50 µl in single droplets were delivered to the nostrils of an anesthetised mouse (by inhalation of isoflurane gas) and allowed to completely inhale. The bacterial inoculum was quantified by plating serial dilutions of the stock solution onto LB agar. At pre- determined experimental end points (loss of movement, piloerection, >20 % weight loss, a hunched posture or a definitive time point) mice were culled and tissues harvested for analysis.

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2.10.3. Bioluminescent imaging

For imaging of bioluminescent P. aeruginosa, mice were shaven and anesthetised with isoflurane before placing on the heated mat inside the IVIS Lumina II instrument

(PerkinElmer). Mice were checked every 4 hours or if required every hour depending on the disease severity. Images were taken with Living Image version 4.2 software at approximately

0, 4, 8, 10, 24, 28 and 32 hour time points. At predefined end-points (defined as loss of >20

% body weight, hunched posture, piloerection or lack of mobility) lung, blood and BAL samples were harvested.

2.10.4. Thymidine proliferation assay

Mice were inoculated via intranasal administration with 50 µl of 1-3 x 106 CFU P. aeruginosa. 96 hours later the spleen was harvested and splenocytes were removed and placed in RPMI media. Cells were counted and resuspended to 4 million cells/ ml from which

2 x 105 cells were added per well containing increasing concentrations of OprF protein at 0,

1.56, 3.13, 6.25, 12.5, 25, 50 µg/ml in triplicate and incubated with cells for 48 hours.

Staphylococcal enterotoxin B was incubated with cells as a positive control. Following the 48 incubation, 10 µl of 1:10 dilution of tritiated thymidine (3H-thymidine) was added overnight, following which cells were harvested using the Harvester96 (TomTec Life Sciences, UK) and thymidine incorporation was measured using a Wallac TriLux MicroBeta 1450 liquid scintillation counter and MicroBeta windows workstation. Results were expressed as counts per minute (cpm).

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2.11. Clinical measurements

2.11.1. Ethical statement

Patients were recruited by Catherine Hennessy, Dr Rosemary Boyton and Dr Michael

Loebinger from the Royal Brompton Hospital with full patient consent under the ethics code

10/H0801/53. Table 2.10 summarises patients recruited during this study.

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Table 2.10: Patient characteristics. Clinical characteristics for each patient diagnosed with pulmonary bronchiectasis.

P. aeruginosa status (positive sputum Lab Sex Age samples) Diagnosis Other infections ID Non- mucoid mucoid BO1 F 60 0 0 PCD H.Influ, S. pneum, BO2 F 81 YES 4/8 YES 4/8 Idiopathic M. catarr BO3 F 69 0 0 CVID H. influ, M. catarr, S. pneum BO4 F 35 0 0 Post Infective - BO5 F 67 0 0 ABPA S. aureus, H. influ BO6 F 43 YES 2/8 0 Idiopathic Aspergillus, S. maltophillia BO7 M 61 0 YES 2/2 Idiopathic - BO8 F 56 0 0 Idiopathic - BO9 F 64 0 0 Idiopathic - B10 F 73 0 0 Post Infective URT flora, S. aureus B11 F 19 0 0 Idiopathic H.influ B12 M 70 YES 3/7 0 ABPA S. aureus B13 M 72 0 YES 7/7 Post Infective - B14 M 67 0 YES 7/7 Post Infective - B15 M 66 YES 6/8 0 Idiopathic - B16 M 66 0 YES 5/7 ABPA S. aureus, H.influ B17 F 74 YES 1/7 0 Post Infective URT flora B18 M 78 0 0 Post Infective - B19 M 42 0 0 Idiopathic - B20 F 60 0 0 Idiopathic - B21 F 63 0 YES 7/7 Post Infective - B22 M 73 0 0 Idiopathic M. catarr, S. maltophillia B23 M 32 0 YES 1/1 PCD - B24 M 62 0 YES 7/7 Other S. aureus B25 M 77 0 0 Idiopathic - B26 F 79 0 0 Idiopathic URT flora B27 F 58 0 0 ABPA URT flora, Aspergillus B28 F 66 0 0 Idiopathic URT flora B29 F 33 0 0 Post Infective - B30 F 51 0 0 Idiopathic - B31 F 74 YES 1/7 YES 2/7 Idiopathic URT flora, P. vulgaris B33 M 45 0 0 Idiopathic URT flora B35 F 42 0 0 PCD S. aureus B36 F 61 0 0 PCD URT flora, S. maltophillia B37 M 54 0 YES 2/2 Idiopathic - M. catarr, S. pneum, H. influ, B38 F 24 0 0 PCD S. aureus B39 F 53 0 0 Other URT flora, S. aureus B40 M 66 YES 1/7 YES 5/7 ABPA Candida B41 F 67 YES 4/7 0 Idiopathic URT flora, B42 M 64 0 0 Idiopathic - B43 F 63 0 YES 3/8 Idiopathic URT flora, B44 F 43 0 0 Idiopathic URT flora, 134

B45 M 61 0 0 Other (YS) H. influ B46 F 31 0 0 Post Infective S. pneum B47 F 74 YES 4/4 0 Post Infective - B48 F 83 YES 1/7 0 Post Infective URT flora B49 F 52 0 0 Idiopathic Candida URT flora, M. catarr, S. B50 F 66 YES 1/7 0 Idiopathic maltophillia B51 F 76 YES 1/7 0 Idiopathic URT flora, S. aureus URT flora, Coliform, S. B52 F 54 YES 1/9 0 Idiopathic maltophillia, S. aureus B53 F 47 YES 3/7 0 Idiopathic URT flora, Candida B54 F 61 0 0 Post Infective URT flora, H. influ B55 M 62 0 0 ABPA URT flora B56 F 43 0 0 CVID URT flora, H.influ B57 F 78 0 YES 4/7 Idiopathic - B58 M 77 0 YES 3/7 Idiopathic URT flora, S. aureus B59 F 65 YES 1/1 0 Idiopathic S. aureus B60 M 68 0 0 Idiopathic - B61 F 59 0 YES 5/8 Idiopathic URT flora B62 F 54 0 0 Post Infective H. influ B63 F 29 0 0 ABPA S. aureus, URT flora B64 F 64 0 0 Other URT flora, Candida B67 F 68 YES 4/7 0 Post Infective URT flora, Aspergillus B68 F 55 YES 3/8 0 PCD URT flora B69 F 77 0 0 Idiopathic M. catarr, S. pneum B70 F 56 0 YES 7/7 ABPA - B71 F 69 0 YES 1/7 Post Infective S. aureus URT flora, S. aureus, S. B72 F 31 0 0 PCD pneum URT flora, S. pneum, B73 M 61 0 0 Post Infective Coliform B74 F 62 0 0 Idiopathic URT flora, S. aureus B75 F 66 0 0 Post Infective URT flora, H. influ URT flora, β- Haemo B76 F 71 YES 1/9 0 Idiopathic Streptococcus group C B77 M 65 0 0 Idiopathic URT flora, M. catarr B78 F 73 0 YES 2/7 Post Infective URT flora, Candida B79 F 34 0 0 PCD URT flora, M. catarr, H. influ URT flora, S. aureus, P. B80 F 75 0 0 Idiopathic vulgaris, Coliform B81 F 65 YES 2/7 0 Post Infective H. influ, S. aureus, URT flora B82 M 61 YES 3/7 YES 6/7 ABPA - B83 M 57 0 0 Idiopathic URT flora B84 F 61 0 0 Idiopathic URT flora B85 F 40 YES 6/7 0 PCD - PCD, Primary Ciliary Dyskinesia; ABPA, allergic bronchopulmonary aspergillosis; CVID, Common variable immunodeficiency; YS, Young’s Syndrome; URT flora, upper respiratory tract flora; H. influ, Haemophilus influenzae; S. pneum, Streptococcus pneumoniae; M.catarr, Moraxella catarrhalis; S. aureus, Staphylococcus aureus; P. vulgaris, Proteus vulgaris; S. Maltophillia, Stenotrophomonas maltophilia

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2.11.2. Blood collection and patient groups

Patients clinically diagnosed with bronchiectasis were recruited for this study. The cause of bronchiectasis is summarised in table 2.11. Blood samples for PBMC isolation, serum collection and genotyping were collected from each patient. Sputum samples were taken from each patient over the 6 month study period and were sent for microscopy and culture to establish the presence or absence of P. aeruginosa. The P. aeruginosa status for each patient was then used to determine patient groups for this study. Sputum samples were either classified during the study as: never culture positive for P. aeruginosa; positive for P. aeruginosa on less than 50 % of samples and positive for P. aeruginosa on more than 50 % of samples.

Table 2.11 Summary of the patient cohort diagnosed with bronchiectasis. Non-Idiopathic Bronchiectasis Idiopathic Other (Young’s Disease, Bronchiectasis PCD Post Infective ABPA CVID α1-antitripsin deficiency)

9 (21%) 20 (45%) 9 (21%) 2 (5%) 4 (10%) 38

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2.12. Cell isolation and processing

PBMCs were isolated from human peripheral blood samples as described in section 2.12.1.

PBMCs were frozen until used in several assays including multiparameter FACS, ELISpots, qRT-PCR and Affymetrix assays.

2.12.1. PBMC isolation

Approximately 20 ml of venous blood was collected into heparinised tubes (heparin sulphate,

1000 U/ml, BD) from which blood PBMCs were extracted by density gradient centrifugation.

Initially blood was diluted in an equal volume of PBS before carefully layering 20 ml onto 15 ml of warmed histopaque-1077 (Sigma-Aldrich). Samples were then centrifuged for 20 minutes at 800 g and room temperature with no brake to separate PBMCs, serum, histopaque and red blood cells into distinct layers. PBMCs formed a layer at the interface between the plasma and histopaque and was carefully aspirated with a Pasteur pipette. PBMCs were then washed three times with PBS for 10 minutes at 350 g, 300 g and finally at 200 g. The cell pellet was resuspended in 10 ml PBS and cell viability was determined by trypan blue staining (section 2.12.2.).

2.12.2. Assessment of cell viability (trypan blue stain)

20 µl of suspended cells was diluted 1:1 with trypan blue (Sigma-Aldrich). This stain is taken up by dead cells turning them blue while it is excluded from live cells leaving them colourless. Only viable cells were counted on a Neubauer hemocytometer under x40 magnification to determine the cell density.

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2.12.3. Cryopreservation of PBMCs

Pelleted PBMCs were resuspended in 500 µl human AB serum (PAA Laboratories, GE

Healthcare Life Sciences, Buckinghamshire, UK). Human AB serum containing 20 % DMSO

(Sigma-Aldrich) was added dropwise to this cell suspension in a 1:1 dilution. Immediately cells were transferred to a NalgeneTM Cryo 1 ºC container (Nalgene) and frozen at -80 ºC for

48 hours. Following this, cells were moved to liquid nitrogen for long term storage.

2.12.4. Thawing of PBMCs

Cryovials were removed from liquid nitrogen and thawed quickly in a 37 ºC water bath. Cells were then resuspended in 10 ml of RPMI 1640 medium. Cells were washed twice by centrifugation at 338 g for 10 minutes at room temperature. The pellet was resuspended in complete media and the viability of the cells was determined by trypan blue exclusion

(section 2.12.2.).

2.13. Isolation of human gDNA

Approximately 2 ml of whole blood per patient was collected and frozen at -80 ºC. To extract gDNA, whole blood samples were thawed at room temperature and 2 ml of whole blood was added to 2 ml of buffer A (0.32 M sucrose, 10 mM Tris HCl, 5 mM MgCl2, 0.75 % Triton-X-

100 (Sigma-Aldrich) to lyse red blood cells and made up to 6 ml with cold, sterile dH2O.

Samples were inverted and incubated for 3 minutes on ice before centrifuging at 3500 rpm for 15 minutes. The supernatant was removed and the pellet was resuspended in 6 ml of diluted (1:4) buffer A in dH2O by vortexing. This wash step was repeated once more before 5 ml of digestion buffer (20 mM Tris-HCl, 4 mM Na2EDTA and 100 mM NaCl) and 500 µl of

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10 % SDS was added to the pellet and resuspended by vigorous vortexing for 1 minute. To this 50 µl of 20 mg/ml proteinase K was added and incubated for 2 hours at 55 ºC.

Afterwards samples were removed and placed on ice for 3 minutes before adding 4 ml of 5.3

M NaCl. Samples were vortexed for 15 minutes and then centrifuged at 3500 rpm for 30 minutes. The supernatant was transferred to a fresh tube to which an equal volume of cold isopropanol was added and inverted. The gDNA appeared as white strands within the isopropanal and was transferred to a clean eppendorf. gDNA was washed with 70 % ethanol and centrifuged. The ethanol was removed and the gDNA pellet was air dried. Once dried the gDNA was resuspended in 300 µl water and left to dissolve overnight at 4 ºC. gDNA was reserved for HLA-typing.

2.13.1. HLA typing

HLA-typing was performed by the Histocompatibility and Immunogenetics department,

Hammersmith Hospital, Imperial College Healthcare, National Health Service Trust on gDNA extracted from whole blood described in section 2.13. Low resolution typing for human HLA-Class I (HLA-C) and class II (HLA-DR and DQ) was performed using PCR sequence specific primers. High resolution typing for HLA-C was performed by direct sequencing. High resolution of HLA-C was required to determine HLA-C group 1 and group

2 motifs (Table 2.12) to identify homozygous patients for further analysis.

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Table 2.12 HLA-C group 1/group2 motifs and corresponding HLA-C alleles. (Boyton et al., 2006; Vairo et al., 2013). HLA-C group 1 (Asn-80) HLA-C group 2 (Lys-80) Cw*01(02,03) Cw*02 (02) Cw*03 (02,03,04) Cw*04 (01) Cw*07(01-06) Cw*05 (01) Cw*08 (01-03) Cw*06 (02) Cw*12(02,03,06) Cw*07(07) Cw*14 (03) Cw*12(04,05) Cw*16(01,03,04) Cw*15 (02, 03, 04, 05) Cw*16 (02) Cw*17 (01, 02) Cw*18 (01, 02)

2.14. Flow cytometry

The principle of flow cytometry involves the analysis of cell size, granularity and expression of cell surface markers and/or intracellular molecules by the specific labelling with antibodies conjugated to fluorochromes. Labelled single cells pass by a laser, exciting the conjugated fluorochrome that subsequently emits fluorescence that can be detected at its specific wavelength. In the following experiments, data was acquired using the FACSAriaII flow cytometer (BD Biosciences) and analysed using Flowjo version 10 software.

2.14.1. Multiparameter FACS: Cell surface and intracellular cytokine staining

2.14.1.1. Cell stimulation

Human PBMCs prepared as in section 2.12. were resuspended to 10 x 106 cells/ml. 100 µl of cells were added to corresponding wells of a U-bottomed plate (VWR) (1 x106 cells per well) and were either stimulated with 100 µl of stimulation mix (final concentration per well: 50

140 ng/ml phorbol myristate acetate (PMA, Sigma-Aldrich), 1 µg/ml ionomycin (Sigma-Aldrich) and 10 µg/ml brefeldin A (BFA, GolgiPlug, BD Biosciences) or unstimulated with 100 µl of unstimulation mix (final concentration per well: 10 µg/ml BFA) and incubated for 4 hours at

37 ºC in 5 % CO2.

2.14.1.2. Surface antigen staining for human cells

Following the 4 hour incubation, PBMCs were centrifuged at 285 g for 5 minutes. The supernatants were removed and PBMCs were washed twice with PBS. 200 µl of

LIVE/DEAD® fixable red stain (Invitrogen) diluted 1:3000 in PBS was added to each well and incubated at room temperature for 30 minutes in the dark. Cells were then washed twice in PBS by centrifuging at 285 g for 5 minutes before resuspending in FACS buffer for 20 minutes on ice in the dark. PBMCs were incubated in 200 µl of surface monoclonal antibodies or isotype controls (Table 2.13) diluted in FACS buffer for 30 minutes on ice and in the dark.

Table 2.13 Human cell surface antigen antibodies Working Specificity Fluorochrome Clone Isotype Company concentration BD CD3 V500 UCHT1 Mouse IgG1, κ 5 µl/test Biosciences RPA- BD CD8 AlexaFluor700 Mouse IgG1, κ 5 µl/test T8 Biosciences BD CD56 PE-Cy7 B159 Mouse IgG1, κ 5 µl/test Biosciences BD NKp44 AlexaFluor647 P44-8.1 Mouse IgG1, κ 5 µl/test Pharmingen

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2.14.1.4. Intracellular cytokine staining

Cells were washed in FACS buffer and resuspended in 1X cytofix/ cytoperm (BD

Biosciences) solution for 30 minutes in the dark and on ice. Cells were then washed in 200 µl of 1X perm/wash buffer (10X diluted 1:10 in dH2O) and resuspended with intracellular anti- cytokine antibodies or isotype controls (Table 2.14) diluted in 1X perm/ wash buffer and incubated for 30 minutes on ice and in the dark. Following a final wash with 200 µl 1X perm/wash, cells were resuspended in 1 % PFA/PBS and data was acquired on the FACS

Aria II (BD). The gating strategy for human cells is shown in figure 5.1.

Table 2.14 Human intracellular cytokine antibodies Working Specificity Fluorochrome Clone Isotype Company concentration BrilliantViolet BD IFNγ B27 Mouse IgG1, κ 5 µl/test 421 Biosciences Granzyme BD FITC CB9 Mouse IgG1, κ 20 µl/test A Biosciences

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2.15. Protein production and purification of OprF

2.15.1. Growth of Escherichia coli strains

2.15.1.1. Bacterial growth media

Lysogeny broth (LB) (Bertani, 1951) was prepared by adding 20 g of LB powder (composed of 10 g Tryptone, 5 g Yeast Extract, 5 g Sodium Chloride; Sigma-Aldrich) to one litre of dH2O and autoclaved. The broth was cooled to 50 ºC and kanamycin was added to a final concentration of 50 µg/ml.

LB agar plates were prepared by adding 35 g of LB agar powder to 1 litre of dH2O and autoclaved. The media was cooled to 50 ºC and kanamycin was added to a final concentration of 50 µg/ml. LB agar was poured into sterile Petri dishes, cooled and stored at 4 ºC.

2.15.1.2. Bacterial strains

Table 2.15 E. coli strains used during this project. Strain Genotype Reference F- Φ80lacZΔM15 Δ(lacZYA-argF) U169 recA1 (Woodcock et DH5α endA1 hsdR17(r -, m +) phoA supE44 thi-1 gyrA96 k k al., 1989) relA1 λ- (Studier and BL21 (DE3) F– ompT hsdS (r - m -) gal dcm λ(DE3) B B B Moffatt, 1986)

Table 2.16 Commercial plasmid vector used during this project.

Vector Purpose Antibiotic Selection Supplier Smt3 Protein expression Kanamycin Invitrogen

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2.15.2. DNA transformation of E. coli

An smt3 vector (Table 2.16) containing OprF with a N-terminal polyhistidine tag was previously constructed by Worgall et al, 2005 and was kindly given as a gift by Dr. Stefan

Worgall, Weill Cornell Medical College, USA for work in this thesis (Worgall et al., 2005).

This plasmid was transformed into both DH5α (Invitrogen) and BL21(DE3) cells (Invitrogen)

(Table 2.15). DH5α cells were used to efficiently express the OprF plasmid and BL21(DE3) cells were used for protein expression. DH5α and BL21(DE3) cells were thawed on ice and

20 µl of chemically competent cells were added to 5 ng of plasmid for 30 minutes on ice.

Cells were heat shocked at 42 ºC for 30 seconds and incubated on ice for at least 1 minute.

250 µl of sterile super optimal broth with catabolise repression (S.O.C) media (Invitrogen) was added and cells were incubated at 37 ºC, with shaking at 220 rpm for 1 hour. Using aseptic techniques, cells were plated and grown overnight at 37 ºC on LB agar (Sigma-

Aldrich) containing 50 µg/ml of kanamycin (Sigma-Aldrich) for antibiotic selection of the transformed plasmid.

2.15.3. Preparation of glycerol stocks

An individual transformed colony was picked and inoculated into 6 ml of sterile LB broth and grown in the presence of 50 µg/ml kanamycin, for plasmid selection, overnight at 37 ºC with shaking at 220 rpm. 0.8 ml of overnight culture was added to 0.2 ml of sterile analytical reagent grade 100 % glycerol in a corning cryo-vial and stored at -80 ºC.

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2.15.4. Plasmid dsDNA isolation and restriction digests.

The GenElute plasmid mini-prep kit (Sigma-Aldrich) was used for the rapid isolation of plasmid dsDNA from recombinant E. coli overnight cultures. The restriction enzymes

BamH1 (10 U/µl, Promega, Southampton, UK) and HindIII (10 U/µl, Promega) were used to digest the isolated plasmids. 1 µl of each enzyme was added to 2 µl of 10X buffer E

(Promega), 11 µl water and 5 µl of plasmid and incubated at 37 ºC for 2 hours. The digests were then run on a 1 % agarose gel (Section 2.2.3.) to show 2 bands of approximately 5.6 kb and 1 kb that correspond to the size of the digested plasmid fragments of the smt3-OprF plasmid.

2.15.5. Protein expression and purification

Expression and purification of the recombinant OprF protein followed the protocol previously published by S. Worgall and colleagues with minor modifications (Worgall et al.,

2005). Briefly recombinant OprF was over-expressed in the BL21 E. coli strain (Table 2.15) using Isopropyl β-D-1-thiogalactopyranoside (IPTG, Sigma-Aldrich) induction and purified using affinity chromatography with Ni-NTA resin (Qiagen).

2.15.5.1. Protein expression

2.15.5.1.1. Small scale: Time course for OprF protein expression

10 ml of fresh LB media containing 50 µg/ml kanamycin was inoculated with 100 µl of an overnight culture of BL21 transformed cells. Cells were grown for two hours with shaking at

220 rpm, 27 ºC until an OD600 of 0.6 (mid-log) was reached. Protein induction followed the addition of 0.5 mM IPTG and 1 ml samples were taken every hour for 6 hours. Samples were

145 centrifuged at 13000 g for 10 minutes and run by SDS-PAGE (Novex, Life Technologies,

UK) as described in section 2.15.5.3.1.

2.15.5.1.2. Small scale: Temperature optimisation for OprF protein expression

10 ml of fresh LB media containing 50 µg/ml kanamycin was inoculated with 100 µl of an overnight culture of BL21 transformed cells. Cells were grown for two hours with shaking at

220 rpm, 27 ºC or 37 ºC until an OD600 of 0.6 (mid-log) was reached. Protein production was induced by 0.5 mM IPTG. 1 ml samples were taken before and after IPTG induction. 3 hours post induction cells were centrifuged at 13000 g for 10 minutes. Insoluble and soluble fractions were obtained following the procedure described in section 2.15.5.1.4. for large scale production. Insoluble (pellet) and soluble fractions (supernatant) were run on SDS-

PAGE gel (Novex, Life Technologies, UK) as described in section 2.15.5.3.1.

2.15.5.1.3. Large scale: OprF protein expression

BL21-OprF transformed cells from glycerol stocks were plated and grown overnight (37 ºC and 5 % CO2) on LB agar plates containing 50 µg/ml kanamycin. One colony was used to inoculate 10 ml of fresh LB broth containing 50 µg/ml kanamycin and incubated overnight at

37 ºC, 220 rpm. 5 ml of overnight culture were used to inoculate 1 litre (1:200 dilution) of LB broth and grown up at 27 ºC and 220 rpm until an OD600 reading of 0.8 nm. Cells were induced with IPTG (final concentration of 0.5 mM) and incubated for 3 hours. Cells were then harvested at 800 g and 4 ºC for 20 minutes. The supernatant was removed and the pellets were flash frozen in liquid nitrogen until required.

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2.15.5.1.4. Large scale: Preparation of crude cell lysates

Cells were thawed on ice for 15 minutes and resuspended for every 1 mg of wet cell pellet in

2 ml of tris buffered saline (TBS) buffer 1 (50 mM Tris, 0.5 EDTA, 50 mM NaCl, 20 mM

Imidazole (Sigma-Aldrich), pH 7.4). Chicken egg white lysozyme (Sigma-Aldrich) to a final concentration of 1 mg/ml and Pefabloc inhibitor (Sigma-Aldrich) to a final concentration of

0.25 mM was added to the cell suspension. Cells were incubated on ice for 30 minutes and then sonicated on ice for 3 bursts of 10 seconds followed by a 1 minute rest. Cells were then centrifuged at 14,000 g and 4 ºC for 30 minutes to pellet the cell debris and the supernatant was retained.

2.15.5.2. Purification: Affinity chromatography

The OprF protein was fused to a SUMO fusion protein and a polyhistidine tag. The polyhistidine tag was used to perform affinity chromatography using Ni-NTA beads.

1 ml bed volume of Ni-NTA (Qiagen) was pre-equilibrated in TBS buffer 2 (50 mM Tris, 0.5

EDTA, 300 mM NaCl, 20 mM Imidazole (Sigma-Aldrich), pH 7.4). This was added to thawed lysate (section 2.15.5.1.4.) and incubated at 4 ºC for 2 hours. Resin/lysate mix was then added to a polyethylene column (Qiagen). After the cap was removed the flow through was collected. The column was washed with 30 column volumes of TBS buffer 2 and protein was eluted with an increasing concentration gradient of imidazole containing 300 mM NaCl.

2x 500 µl of 50 mM Imidazole, 2x 500 µl of 150 mM Imidazole and 3x 500 µl of 300 mM

Imidazole followed by 4x 500 mM Imidazole. The samples from 300 mM and 500mM

Imidazole elution steps were pooled together and dialysed into SUMO protease buffer (50 mM Tris, 1 mM DTT, 150 mM NaCl, pH 8.0) with stirring overnight. SUMO protease

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(Invitrogen) was added to the protein sample and incubated at 30 ºC for 2 hours. This was followed by an additional incubation with Ni-NTA resin for 2 hours at 4 ºC and added to a fresh polyethylene column. Ni-NTA beads remained bound to the SUMO fusion protein and the polyhistidine tag. The flow through contained the purified mature OprF protein. This was dialysed using a 10,000 molecular weight cut off slide-A-lyzer dialysis cassette (Fisher

Scientific) into PBS with stirring at 4 ºC overnight. The protein sample was then concentrated to 1 ml using an Amicon Ultra-4 centrifugal filter with 10,000 kDa molecular weight cut-off

(Merck Millpore Ltd, Carrigtwohill, Ireland) at 12,000 g and 4 ºC for 7 minutes.

2.15.5.3. Protein analysis and detection

2.15.5.3.1. SDS-PAGE

4-12 % NuPAGE Novex Bis-Tris mini gels (Novex) were loaded with protein sample.

Protein samples were prepared in a volume of 10 µl consisting of 2.5 µl 4X NuPAGE LDS sample buffer (Novex), 2 µl protein sample, 1 µl 1 M DTT (Sigma-Aldrich) and 4.5 µl dH2O.

Samples were denatured at 70 ºC for 10 minutes in a heat block. NuPAGE gel was inserted into XCell surelockTM Mini-Cell apparatus containing 1X MOPS SDS running buffer

(Novex). The gel comb was removed and wells washed with MOPS running buffer. 10 µl of sample and 6 µl of Seeblue® Plus2 pre-stained standard (Novex) for determining protein size were pipetted into wells and run at 200 V for 50 minutes.

2.15.5.3.2. Development of SDS-PAGE gels: Colloidal blue stain

The colloidal blue staining kit (Invitrogen) was used for the staining and visualisation of proteins resolved by SDS-PAGE. Initially the SDS-PAGE gel was removed from the gel

148 cassette and incubated with gentle mechanical shaking for 10 minutes at room temperature in

100 ml of fixing solution (40 ml dH2O, 50 ml methanol and 10 ml acetic acid (Sigma-

Aldrich)). The fixing solution was removed and replaced with 95 ml of staining solution (55 ml dH2O, 20 ml methanol and 20 ml stain A) for 10 minutes at room temperature with gentle mechanical shaking. 5 ml of stain B solution was then added and incubated at room temperature with gentle mechanical shaking for a minimum of 3 hours. The staining solution was removed and replaced with 200 ml dH2O overnight to destain the gel.

2.15.5.3.3. Development of SDS-PAGE gels: Western blot

Proteins separated via SDS-PAGE were transferred to a PVDF (Polyvinylidene difluoride) membrane (Hybond-P, GE Healthcare Life sciences) using the Xcell IITM Blot module and

NuPAGE 1X Transfer buffer (50 ml NuPAGE 20X transfer buffer (Invitrogen), 1 ml

NuPAGE antioxidant (Invitrogen), 100 ml methanol and 849 ml dH2O) at 30 V for 1 hour and 4 °C. The PVDF membrane was then rinsed in PBS and incubated in blocking solution (5

% milk powder (Marvel) in 0.05 % tween/PBS) for 1 hour at room temperature with gentle mechanical shaking. The PVDF membrane was washed three times in 0.05 % tween/PBS for

15 minutes each before incubating overnight at 4 °C with gentle shaking in anti-polyhistidine

(monoclonal) IgG antibody produced in mouse (Sigma-Aldrich) diluted 1:3000 in 1%

BSA/PBS. The following day the primary antibody solution was removed and the membrane was washed as previously described before incubating for 1 hour with anti-mouse IgG- alkaline phosphatase antibody produced in goat (Sigma-Aldrich) diluted 1:50,000 in 0.05 %

Tween, TBS, 5 % milk. The membrane was then washed as before and then incubated with

BCIP/NBT substrate for 5 minutes or until a purple colour developed. dH2O was added to stop colour change.

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2.15.5.3.4. Determination of protein concentration

The concentration of the OprF protein solution was determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific). Solutions were directly measured at 280 nm and the extinction coefficient of the protein was assumed to be 1.0 cm-1mg-1ml from which the protein concentration was directly calculated.

2.15.5.3.5. Mass spectrometry

The purified OprF protein sample was resolved by SDS-PAGE and the protein bands were analysed by the Structural Genomics Consortium, Oxford University. Briefly the resolved protein bands were digested with trypsin. The peptides generated were then analysed by liquid chromatography electrospray ionisation tandem mass spectrometry. A sequence for each peptide was generated and subjected to database searches. Identified peptide sequences were used to map the full-length of the protein sequence.

2.16. OprF peptide panel

The OprF derived peptide panel consisting of 34 peptides was synthesised from the 350 amino acid precursor sequence of OprF (Accession number NP_250468) (Figure 2.1).

. OrpF sequence

MKLKNTLGVVIGSLVAASAMNAFAQGQNSVEIEAFGKRYFTDSVRNMKNA50 DLYGGSIGYFLTDDVELALSYGEYHDVRGTYETGNKKVHGNLTSLDAIYH100 FGTPGVGLRPYVSAGLAHQNITNINSDSQGRQQMTMANIGAGLKYYFTEN150 FFAKASLDGQYGLEKRDNGHQGEWMAGLGVGFNFGGSKAAPAPEPVADVC200 SDSDNDGVCDNVDKCPDTPANVTVDANGCPAVAEVVRVQLDVKFDFDKSK250 VKENSYADIKNLADFMKQYPSTSTTVEGHTDSVGTDAYNQKLSERRANAV300 RDVLVNEYGVEGGRVNAVGYGESRPVADNATAEGRAINRRVEAEVEAEAK350

Figure 2. 1 Recombinant OprF sequence.

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Lyophilised peptides were purchased from GL Biochem Ltd (Shanghai, China) and were reconstituted in dimethyl sulfoxide (DMSO, Sigma-Aldrich) at 25 mg/ml. For use in epitope mapping assays, peptides reconstituted in DMSO were further diluted in complete media.

Peptides were designed to cover the sequence of OprF. Each peptide was 20 amino acids long and overlapped by 10 amino acids (Table 2.17).

Table 2.17 Peptide panel sequences of each 20-amino acid peptide of OprF Peptide Peptide Position Peptide Sequence Identification 1 [1-20] MKLKNTLGVVIGSLVAASAM 2 [11-30] IGSLVAASAMNAFAQGQNSV 3 [21-40] NAFAQGQNSVEIEAFGKRYF 4 [31-50] EIEAFGKRYFTDSVRNMKNA 5 [41-60] TDSVRNMKNADLYGGSIGYF 6 [51-70] DLYGGSIGYFLTDDVELALS 7 [61-80] LTDDVELALSYGEYHDVRGT 8 [71-90] YGEYHDVRGTYETGNKKVHG 9 [81-100] YETGNKKVHGNLTSLDAIYH 10 [91-110] NLTSLDAIYHFGTPGVGLRP 11 [101-120] FGTPGVGLRPYVSAGLAHQN 12 [111-130] YVSAGLAHQNITNINSDSQG 13 [121-140] ITNINSDSQGRQQMTMANIG 14 [131-150] RQQMTMANIGAGLKYYFTEN 15 [141-160] AGLKYYFTENFFAKASLDGQ 16 [151-170] FFAKASLDGQYGLEKRDNGH 17 [161-180] YGLEKRDNGHQGEWMAGLGV 18 [171-190] QGEWMAGLGVGFNFGGSKAA 19 [181-200] GFNFGGSKAAPAPEPVADVC 20 [191-210] PAPEPVADVCSDSDNDGVCD 21 [201-220] SDSDNDGVCDNVDKCPDTPA 22 [211-230] NVDKCPDTPANVTVDANGCP 23 [221-240] NVTVDANGCPAVAEVVRVQL 24 [231-250] AVAEVVRVQLDVKFDFDKSK 25 [241-260] DVKFDFDKSKVKENSYADIK 26 [251-270] VKENSYADIKNLADFMKQYP 27 [261-280] NLADFMKQYPSTSTTVEGHT 28 [271-290] STSTTVEGHTDSVGTDAYNQ 29 [281-300] DSVGTDAYNQKLSERRANAV 30 [291-310] KLSERRANAVRDVLVNEYGV 31 [301-320] RDVLVNEYGVEGGRVNAVGY 32 [311-330] EGGRVNAVGYGESRPVADNA 33 [321-340] GESRPVADNATAEGRAINRR 34 [331-350] TAEGRAINRRVEAEVEAEAK

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2.17. Epitope mapping of OprF

Immunodominant epitopes to the OprF protein can be identified in several ways. In addition to computational analysis by in silico prediction software, the frequencies of IFNγ producing

CD4+ T cells in HLA-DR transgenic animals or patients with bronchiectasis were determined using IFN γ Enzyme-Linked ImmunoSpot (ELISpots).

2.17.1. In silico predictions

Computational prediction software was used to define potential MHC class II restricted CD4+

T cell epitopes. In silico analysis was performed using software programmes including

TEPITOPE, NetMHCIIpan (Centre for Biological sequence analysis server,

(http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0) (Karosiene et al., 2013) and the consensus method available from the IEDB website (http://tools.immuneepitope.org/mhcii/)

(Wang et al., 2008).

2.17.2. Preparation of peptides and protein

Stock peptides (25 mg/ml) and stock OprF protein (1 mg/ml) were diluted to a working concentration of 0.1 mg/ml or 0.05 mg/ml in complete media with 10 % FCS for human and murine ELISpots respectively. 50 µl of each peptide or protein were added in triplicate to corresponding wells for a final concentration at 0.05 mg/ml or 0.025 mg/ml for human or murine ELISpots respectively.

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2.17.3. Footpad immunisations

Purified OprF protein (section 2.15.) was used for immunisations of HLA-DR transgenic animals. Immunisations consisted of a 1:1 ratio of protein to Complete Freund’s Adjuvant

(CFA, Sigma-Aldrich) prepared by vortexing the emulsion for 30 minutes followed by sonication for 45 minutes in an ice cold water bath. 50 µl of the emulsion, containing 25 µg of protein in CFA, was injected subcutaneously into the left footpad of each animal. 10 days following footpad immunisations with the protein emulsion, mice were culled by cervical dislocation. The left popliteal draining lymph node (DLN) was harvested into plain RPMI

1640 and passed through a 70 µM nylon cell strainer. Cells were centrifuged at 1200 rpm for

10 minutes and the supernatant removed. Cells were resuspended in HL-1 medium

(BioWhittaker, Loughborough, UK) supplemented with 2 mM L-glutamine, 25 µg/ml streptomycin sulphate and 25 U/ml penicillin G and counted using a Neubauer haemocytometer. Cell viability was determined by trypan blue staining and viable cells were resuspended to 5 million cells/ml.

2.17.4. Murine IFNγ enzyme-linked immunospot (ELISpots)

The Diaclone murine IFNγ ELISpot kit (Gen-Probe Diaclone SAS, Besançon, France) was used for all murine ELISpots.

Wells of a 96-well PVDF flat bottomed plate (MSIP45, Millipore Ltd, Watford, UK) were washed with 25 µl of 35 % ethanol for 30 seconds, at room temperature. Plates were emptied, washed once with 100 µl PBS/well and coated with 100 µl/well of capture antibody (diluted

1:100 in PBS) overnight at 4 ºC. The following day, the plates were emptied and washed three times with PBS. 100 µl of blocking buffer (2 % dry skimmed milk in PBS) was added to each well and incubated for 2 hours at room temperature. Plates were washed once with

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100 µl/well PBS and 50 µl/well of stimulator at appropriate concentration was added in triplicate to each corresponding well. To this, 50 µl/well of DLN cells (section 2.17.3.) were added resulting in a total of 2.5 x 105 cells per well. The negative control, to determine background, consisted of media and cells only. Cells were incubated, undisturbed for 72 hours at 5 % CO2 and 37 ºC. The cells were then removed and the wells washed with 100

µl/well of washing buffer (0.1 % Tween®20 in PBS) for 10 minutes at 4 °C. Wells were washed 3 more times to ensure full removal of cells. Lyophilised detection antibody was reconstituted with 0.55 ml of distilled water and then diluted 1:1000 in 1 % BSA/PBS and

100 µl/ml was added per well and incubated at room temperature for 90 minutes. Plates were washed three times with 100 µl/well of washing buffer and incubated with Steptavidin-AP conjugate (diluted 1:10,000 in 1 % BSA/PBS) for 1 hour at room temperature. Plates were washed three times with 100 µl/well washing buffer followed with 1 wash with 100 µl/well of dH2O. 100 µl of ready-to-use BCIP/NBT buffer was added to each well and developed over a 10 minute period. Wells were emptied, the backing removed and washed excessively with water. Plates were left to dry before reading on the ELISPOT reader (AID Diagnostika

GmbH, Strassberg, Germany). Responses were considered positive if greater than 2SD above the mean of the negative control.

2.17.5. Human IFNγ ELISpots

PBMCs stored in liquid nitrogen were thawed as in section 2.12.4. PBMCs were added to 10 ml of complete media and centrifuged at 350 g for 10 minutes. The media was removed and

10 ml of fresh media was added and again centrifuged at 350 g for 10 minutes. Cells were then counted (section 2.12.2.) and resuspended in complete media with 10 % FCS to a concentration of 4 million cells/ml.

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The ELISpotPRO Human IFNγ Kit (Mabtech, Nacka Strand, Sweden) was used for all human

ELISpots.

The pre-coated ELISpotPRO plates were removed from their packaging and washed four times at 200 µl/well with sterile PBS. Plates were blocked with complete media containing 10 %

FCS for a minimum of 30 minutes at room temperature. The media was removed and 50 µl of peptide or OprF was added in triplicate to the appropriate wells. In addition to the OprF positive control, anti-CD3 (mAb CD3-2) diluted 1:1000 in complete media containing 10 %

FCS was used as an additional positive control for cytokine production. 50 µl/well of cell suspension was added to each well resulting in 2 x105 cells per well. To determine the number of cells spontaneously producing IFNγ, a media and cell only well was also prepared.

The plates were incubated, undisturbed in a humidified incubator at 37 ºC and 5 % CO2 for

24 hours. Following the incubation, supernatants were removed and frozen for Luminex assays. Plates were washed five times with PBS (200 µl/well). 100 µl/well of detection antibody (7-B6-ALP, diluted 1:200 in 0.5 % FCS in PBS and sterile filtered through 0.2 µM filter) was added and incubated for two hours at room temperature. Prior to removal of the detection antibody the ready-to-use BCIP/NBT plus reagent was filtered through a 0.45 µM filter and subsequently 100 µl/well was added and developed over 10 minutes. Colour development was stopped by the removal of the backing plastic and extensive washing with water. Plates were left to dry and read using the ELISPOT reader (AID Diagnostika GmbH,

Strassberg, Germany). Data was analysed using Graphpad version 5.01 and R version 3.0.0 analysis. (R Core Team (2013); URL http://www.R-project.org/).

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2.18. 30 plex luminex assay

Immunological proteins such as cytokines, chemokines and growth factors can be analysed in a single sample on a Luminex platform by multiplex bead immunoassays. The human 30-plex panel (Invitrogen) was used to simultaneously and quantitatively determine multiple proteins

(Table 2.18) in tissue culture supernatants obtained from PBMCs stimulated with OprF protein or media only controls during the human IFNγ ELISpot assays (section 2.17.5.).

Table 2.18: Immunological protein panel in the 30-plex luminex kit (Invitrogen) Cytokines Chemokines Growth factors

G-CSF, GM-CSF, IFN-α, IFN- Eotaxin, IP-10, MCP-1, EGF, FGF-Basic, HGF, γ, IL-1β, IL-1RA, IL-2, IL-2R, MIG, MIP-1α, MIP-1β, VEGF IL-4, IL-5, IL-6, IL-7, IL-10, RANTES, IL-8 IL-12(p40⁄p70), IL-13, IL-15,

IL-17 and TNF-α

The 96-well filter plate provided with the kit was pre-wet with 200 µl of 1X wash buffer

(20X concentrate diluted in dH2O) and incubated at room temperature for 30 seconds before removal by a vacuum manifold. Human cytokine 30-plex antibody bead solution underwent vortexing and sonication for 30 seconds before 25 µl was added to each well. Plates were washed by adding 200 µl 1X wash buffer and allowed to soak for 30 seconds before removal by a vacuum manifold and repeated. 50 µl of incubation buffer was added to each well.

Standards were made by reconstituting the human 16-plex standard and the human 14-plex standard in 500 µl of 50 % assay diluent and 50 % RPMI (10 % FCS). 300 µl from each standard were combined and then serially diluted 1 in 3 with 50 % assay diluents and 50 %

RPMI (10 % FCS). 100 µl of standards and 50 µl of assay diluents and 50 µl of sample were added to corresponding wells and incubated for 2 hours at room temperature on an orbital

156 shaker (500 rpm). After incubation the plates were washed as previously described and 100

µl of 1X biotinylated detector antibody (10X diluted in biotin diluent) was added to each well and incubated at room temperature for 1 hour with shaking (500 rpm). After incubation the plates were washed and 100 µl of 1X Streptavidin-RPE (10X diluted in Streptavidin-RPE diluent) was added to each well and incubated for 30 minutes at room temperature with shaking (500 rpm). After incubation the plates were washed three more times and a further

100 µl of 1X wash solution was added and placed on the orbital shaker for 2 minutes to resuspend beads. The plate was then placed on the Bio-plexTM 200 system and fluorescence intensities were determined using the software Bio-plex manager version 4.1.1. The concentrations of 30 immunological proteins in each sample were determined from the linear portion for each standard curve and any sample dilutions were taken into account in the analysis.

2.19. Affymetrix microarray

Total RNA was isolated from human PBMCs as described in section 2.3.2. RNA quality and integrity was assessed using an Agilent Bioanalyser RNA 6000 Nano chip and the Agilent

2100 Bioanalyser (Agilent Technologies). Samples were prepared for Affymetrix whole transcriptome microarray analysis using kits obtained from Applied Biosystems and following subsequent steps detailed in 2.19.1. The Ambion® WT expression kit ultimately synthesises cDNA that is fragmented and labelled using the Affymetrix GeneChip® WT

Terminal Labelling Kit (PN 900671) for use in Affymetix® GeneChip® Whole Transcript

(WT) Expression Arrays (Genechip human gene ST 2.0 array) run by the Clinical Sciences

Centre genomics laboratory, Medical Research Centre. An Affymetrix GeneChip® Poly-A

RNA control kit (PN 900433) was utilised in the Affymetrix assay to provide exogenous

157 controls to monitor the entire target labelling process. Data was analysed in Partek Genomics

Suite version 6.6. The Anova test was used for statistical analysis. Stringency settings included a fold change > ±1.5 and FDR adjusted p value <0.05 or a fold change > ± 2.5, unadjusted p value <0.01. Pathway analysis was performed using Metacore from GeneGo

Inc. by Dr Michael Poidinger, Singapore Immunology Network. MicroRNA targets were predicted using MirDB (http://mirdb.org/miRDB/) and mircoRNA.org online programs.

2.19.1. Preparation of RNA samples for Affymetrix whole transcriptome microarray analysis

Sense strand cDNA generated from high quality total RNA samples was fragmented and labelled for Affymetrix whole transcriptome microarray analysis.

Step 1: Synthesis of first-strand cDNA

100 ng of total RNA in a volume not exceeding 3 µl was added to 2 µl of pre diluted poly A

RNA control and made up to 5 µl with RNase free water. 5 µl of first strand master mix (4 µl first strand buffer mix and 1 µl first strand enzyme mix) was added to each RNA sample and run on a thermocycler (pre-heated lid at 50 ºC, 25 ºC for 60 minutes, 42 ºC for 60 minutes and 4 ºC for at least 2 minutes). Samples were then placed on ice before proceeding immediately to step 2.

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Step 2: Synthesis of second strand cDNA

50 µl of second strand master mix (32.5 µl nuclease free H2O, 12.5 µl second strand buffer mix and 5 µl second strand enzyme mix) was added to each sample and run on a thermocycler with heated lid disabled at 16 ºC for 60 minutes, 65 ºC for 10 minutes and 4 ºC for at least 2 minutes and placed on ice before proceeding immediately to step 3.

Step 3: Synthesis and amplification of antisense cRNA using in vitro transcription (IVT)

30 µl of IVT master mix (24 µl IVT buffer mix and 6 µl IVT enzyme mix) was added to each sample and run on a thermocycler with a heated lid at 50 ºC followed by 40 ºC for 16 hours and held at 4 ºC.

Step 4: Purifying cRNA

60 µl of cRNA binding mix (10 µl Nucleic acid binding magnetic beads and 50 µl nucleic acid binding buffer concentrate) was added to each sample in a U-bottomed plate with an equal volume of isopropanol. The plate was sealed and gently shaken for 2 minutes on a vortex genie 2 with plate adapter. Beads were captured with a magnet for 5 minutes and the supernatant was removed and discarded. Beads were shaken with 100 µl nucleic acid wash solution for 1 minute and then captured again. The supernatant was removed and the beads washed a second time. After supernatant removal the plate was shaken vigorously for 1 minute to evaporate ethanol and 40 µl of pre-heated elution buffer was added to each sample

159 and incubated for 2 minutes. The plate was shaken for a further 3 minutes and the beads were captured. The supernatant containing the sample was placed on ice.

Step 5: Determining the amount and quality of cRNA

The quantity and quality of the cRNA was determined by measuring the absorbance at 260nm and 280 nm by a NanoDrop spectrophotometer. To progress to the next step a 260/280 ratio between 1.9 to 2.0 and a cRNA yield > 455 ng/ml is required.

Step 6: Synthesis of second-cycle cDNA

10 µg of cRNA in a volume of 22 µl was added to 2 µl random primers and run on the thermocycler with a heated lid at 75 ºC followed by 70 ºC for 5 minutes, 25 ºC for 5 minutes and 4 ºC for 2 minutes. To this, 16 µl of second cycle master mix (8 µl second cycle buffer mix and 8 µl second cycle enzyme mix) was added and run with a heated lid at 75 ºC followed by 25 ºC for 10 minutes, 42 ºC for 90 minutes, 70 ºC for 10 minutes and 4 ºC for at least 2 minutes.

Step7: Degradation of cRNA with RNase H to yield single stranded cDNA

2 µl of RNase H is added to the second cycle cDNA samples and run on the thermocycler with a heated lid at 75 ºC followed by 37 ºC for 45 minutes, 95 ºC for 5 minutes and 4 ºC for

2 minutes.

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Step 8: Purifying single stranded cDNA

The protocol used in step 4 for the purification of cRNA was also used for the purification of single stranded cDNA except for the following points. Initially 18 µl RNase free water was added to 60 µl of cDNA sample that was added to 60 µl of binding buffer in a U-bottomed plate to which 120 µl of 100 % ethanol was added. Elution of cDNA samples required the addition of 30 µl of pre-heated elution buffer. The supernatant containing cDNA was kept on ice and the concentration of was determined on the NanoDrop. A small aliquot was reserved for a bioanalyser chip to compare the quality of cDNA before and after fragmentation.

Step 9: Fragmenting and labelling single stranded cDNA

Fragmentation and labelling of cDNA uses the Affymetrix GeneChip WT terminal labelling kit (PN900671). 5.5 µg of single stranded cDNA is added to RNase free water in a total of

31.2 µl. To this 16.8 µl of fragmentation mater mix is added (10 µl RNase free water, 4.8 µl

10X cDNA fragmentation buffer, 1 µl UDG (10 U/Bl) and 1 µl APE (1,000 U/Bl)). The samples are then run under thermocycler conditions consisting of 37 ºC for 60 minutes, 93 ºC for 2 minutes, 4 ºC for 2 minutes and then held at 4 ºC. A 1.5 µl aliquot is removed for analysis, alongside the un-fragmented sample retained in step 8, using the Agilent 2100 bioanalyser. 45 µl of fragmented cDNA is added to 15 µl of labelling master mix (12 µl 5X

TdT buffer, 2 µl TdT and 1 µl 5 mM DNA labelling reagent). Samples were run at 37 ºC for

60 minutes, 70 ºC for 10 minutes, 4 ºC for 2 minutes and then held at 4 ºC. Samples were stored at -80 ºC until required for the Affymetrix array.

161

CHAPTER 3: hIL-8 targeted expression in the lung leads to airway inflammation, remodelling and impaired tight junctions.

3.1. Introduction

Airway inflammation and remodelling are characteristics associated with the pathology of chronic respiratory diseases (Dunnill et al., 1969; Fahy et al., 2000; Hoshino et al., 1998;

Ordonez et al., 2001; Regamey et al., 2008). Chronic inflammatory environments commonly result from the overproduction of proinflammatory mediators such as cytokines and chemokines that aid in the recruitment and activation of many inflammatory cells to a site of insult. In particular, several studies have shown high levels of the chemokine IL-8 in BAL, serum and sputum samples from patients diagnosed with chronic inflammatory lung disease

(Angrill et al., 2001; Car et al., 1994; Dean et al., 1993; Gibson et al., 2003; Yamamoto et al., 1997). A major function of IL-8 is its ability to act as a chemoattractant for several cell types, but in particular much work has focused on its potent role in the recruitment and activation of neutrophils (Leonard et al., 1990; Yoshimura et al., 1987). The high levels of

IL-8 observed in patients with chronic lung diseases like cystic fibrosis, bronchiectasis and asthma have been shown to contribute towards an increase in neutrophil infiltration into the airways (Jatakanon et al., 1999; Richman-Eisenstat et al., 1993) that are also increasingly activated. Both these effects can lead to further damage to the airways (Simpson et al., 2009).

Airway remodelling is often used to describe structural alterations that can occur in the airways of patients with chronic respiratory disease. The main characteristics associated with remodelling in the lung include: mucus hypersecretion and goblet cell hyperplasia, smooth

162 muscle hypertrophy and hyperplasia, subepithelial fibrosis and angiogenesis. These changes in the airways induce the thickening of the airway wall and narrowing of the lumen leading to decreased lung function (Dunnill et al., 1969; Fahy et al., 2000; Hoshino et al., 1998;

Ordonez et al., 2001; Regamey et al., 2008). In addition to the role that IL-8 has in the recruitment of neutrophils and other immune cells, evidence exists that implicates a direct role for IL-8 in several aspects of airway remodelling. A protective layer of mucus, made up of mucins, forms a barrier in the airways important in trapping pathogens, mucociliary clearance and airway hydration (Knowles and Boucher, 2002). During chronic respiratory disease, the amount of mucus and mucin expression increases resulting in airway obstruction and poor lung function (Ordonez et al., 2001; Vestbo et al., 1996). hIL-8 has been reported to impact on the regulation of airway mucin genes in vitro in human bronchial epithelial cells

(as described in section 1.5.1.). Cells incubated with IL-8 had an increase in expression of mRNA for the two main mucin lung genes, MUC5AC and MUC5B (Bautista et al., 2009).

IL-8 has also been implicated in airway smooth muscle (ASM) function. Govindaraju and colleagues demonstrated that not only do human ASM cells express the IL-8 receptors

CXCR1 and CXCR2, but in response to IL-8, ASM cells become more activated and migrate

(as described in section 1.5.3.) (Govindaraju et al., 2006). A potential role of IL-8 in fibrosis has been demonstrated by Russo and colleagues (as described in section 1.5.2.). In this study, a bleomycin model of lung fibrosis demonstrated improved lung pathology after the addition of a CXCR2 (IL-8 receptor) antagonist (Russo et al., 2009).

In this chapter, the role of hIL-8 in lung inflammation and remodelling is investigated using an in vivo model of lung targeted hIL-8 expression. This model was developed by Dr.

Catherine Reynolds and Dr. Rosemary Boyton in the Lung Immunology Group at Imperial

College. Using a construct containing hIL-8 on a Clara cell 10 (CC10) promoter, hIL-8

163 expression is targeted specifically to bronchial epithelial cells in the lung of the transgenic mice. This results in chronic and constitutive hIL-8 protein expression. The potential impact of hIL-8 on the murine lung in terms of cellular inflammation and airway remodelling was investigated.

Patients with chronic lung disease, such as bronchiectasis or cystic fibrosis, commonly present with chronic lung infections. Typical lung infections in these patients include respiratory pathogens such as P. aeruginosa, H. influenzae, S. aureus and S. pneumoniae

(Duff et al., 2013; Hilty et al., 2010; King et al., 2007). P. aeruginosa is an opportunistic, extracellular pathogen that commonly infects individuals with damaged lung tissue as a result of chronic respiratory disease or in those who are immunocompromised (Afessa and Green,

2000; Davies et al., 2006; Evans et al., 1996). Chronic infection with P. aeruginosa is associated with enhanced disease progression and increased morbidity and mortality (Davies et al., 2006). The hIL-8 transgenic model provides an opportunity to explore the role of hIL-8 during P. aeruginosa infection.

3.2. Aims

 To identify the impact of long term exposure to elevated levels of hIL-8 in the lung in

terms of lung inflammation and structural changes.

 To explore the impact of hIL-8 on immunity to P. aeruginosa infection.

164

3.3. Results

3.3.1. hIL-8 transgene and protein expression in the hIL-8 transgenic model.

A transgenic murine model with targeted hIL-8 expression in bronchial epithelial cells was previously generated by Dr. Catherine Reynolds and Dr. Rosemary Boyton. This established transgenic line was genotyped by PCR analysis of gDNA to confirm hIL-8 transgene expression. Figure 3.1A depicts a representative PCR gel confirming the presence of the control gene, RBP (500 bp) and target gene, hIL-8 (319 bp). In addition, to verify that the hIL-8 transcripts were translated into protein, a hIL-8 cytokine ELISA was used to detect the protein concentration of hIL-8 in the BAL, lung tissue and serum of the transgenic animals compared to non-hIL-8 transgenic littermate controls (Figure 3.1B). Approximately 2000 pg/ml was detectable in the BAL and lung tissue and 1000 pg/ml was detected in the serum of the hIL-8 transgenics while the transgene and protein were absent in the non-hIL-8 transgenic littermate controls.

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3.3.2. Investigating the impact of hIL-8 on lung disease pathology.

3.3.2.1. Cellular inflammation in the airways of hIL-8 transgenics. hIL-8 is an established chemokine involved in cellular recruitment. In particular, hIL-8 is a potent neutrophil chemoattractant (Yoshimura et al., 1987), but is also important in the recruitment of other cell types such as lymphocytes and basophils (Geiser et al., 1993; Larsen et al., 1989). During respiratory disease an increase in inflammatory recruitment is observed alongside increased hIL-8 levels (Jatakanon et al., 1999; Yamamoto et al., 1997). To determine the impact targeted hIL-8 expression in bronchial epithelial cells has on immune cell recruitment and infiltration into the lung tissue, lung sections from hIL-8 transgenics and non-hIL-8 transgenic littermate controls were stained with a hematoxylin and eosin stain

(Figure 3.2A and 3.2B). Cellular nuclei are stained blue by hematoxylin and non-specific proteins are stained pink with eosin (Fischer et al., 2008). Once stained, cellular infiltration into lung tissue surrounding the bronchi and blood vessels was scored using an established scoring method of cellular inflammation (McMillan et al., 2002). Data was combined from 3 independent experiments from both my MSc and PhD projects for this analysis. Inflammation was increased in areas surrounding the bronchi, but was significantly increased in areas surrounding the blood vessels of hIL-8 transgenic mice compared to non-hIL-8 transgenic littermate controls (Figure 3.2C). In addition to hematoxylin and eosin staining, the total number of inflammatory cells from BAL and whole lung tissue samples was obtained (Figure

3.3A). Total inflammatory cell numbers were increased in the BAL of hIL-8 transgenics.

There was no significant difference observed in whole lung tissue.

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Previous studies have demonstrated cross species reactivity between murine neutrophils and hIL-8 (Rot, 1991). To investigate the effects of hIL-8, specifically on different immune cell types and in particular on neutrophils, differential cell counts were carried out from BAL and lung cytospins (Figure 3.3B). Data was combined from 2 independent experiments from both my MSc and PhD projects for this analysis. Total neutrophil numbers and the percentage of neutrophils in the BAL were significantly increased in the hIL-8 transgenics (50 %) compared to non-hIL-8 transgenic littermate controls (8 %, p<0.001). In the lung there was no significant difference in the total number of neutrophils present, although the percentage of neutrophils in the lung tissue was significantly reduced in the hIL-8 transgenics (57 %) compared to non-hIL-8 transgenic littermate controls (71 %, p<0.005). This may indicate that neutrophils are moving from the lung into the BAL in the hIL-8 transgenics.

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3.3.2.2. Airway remodelling in the hIL-8 transgenics: Mucus production and mucin gene expression.

The overproduction of mucus is a common feature associated with structural changes in the lungs of patients with chronic respiratory disease and contributes towards a decline in lung function (Kim and Criner, 2013; Shimura et al., 1996; Vestbo et al., 1996). The role that hIL-

8 may have on mucus production in the airways has previously been demonstrated in vitro and discussed in section 1.5.1. In the presence of hIL-8, human bronchial epithelial cells increased their production of the two main lung mucin genes MUC5AC and MUC5B

(Bautista et al., 2009). To investigate the impact hIL-8 may have on the lung in vivo, lung sections from non-hIL-8 transgenic littermate controls compared to hIL-8 transgenics (Figure

3.5A and 3.5B) were stained with a periodic acid-Schiff (PAS) stain to visualise mucus production. Although there were not large amounts of mucus produced by bronchial epithelial cells, there was an increase in the amount of mucus in the hIL-8 lungs compared to non-hIL-8 transgenic littermate controls, for which very little mucus, if any, was observed in the majority of the non-hIL-8 transgenic littermate controls (Figure 3.5C). In addition to this observation, the impact of hIL-8 on the relative expression of the two main mucin genes present in lung mucus was investigated by qRT-PCR as part of my MSc (Quigley,2010). The data showed that the relative expression of Muc5b transcripts (Figure 3.5D) were increased by almost 6 fold (p< 0.001) and transcripts for Muc5ac (Figure 3.5E) were increased by almost 10 fold (p<0.005) in the hIL-8 transgenics compared to non-hIL-8 transgenic littermate controls.

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3.3.2.3. Airway remodelling in the hIL-8 transgenics: Airway fibrosis.

Another structural change, in addition to mucus hypersecretion and goblet cell metaplasia, that often takes place in the lungs of patients with chronic respiratory disease is airway fibrosis. Fibrosis is caused by the increased deposition of extracellular matrix proteins and smooth muscle hypertrophy and hyperplasia. The degree of fibrosis in terms of extracellular deposition was visualised using Masson’s trichrome staining of lung sections (Figure 3.6A and 3.6B) that stains collagen blue. The level of fibrosis in the lungs of the hIL-8 transgenics was compared to non-hIL-8 transgenic littermate controls at several time points including 10,

15, 20 and 27 weeks of age. Stained sections were scored using the Ashcroft method

(Ashcroft et al., 1988). No difference was seen in the younger age groups between the hIL-8 transgenics and controls, however at 27 weeks the score was significantly increased (p<0.05) in the hIL-8 transgenics implicating enhanced fibrosis in older hIL-8 transgenics. To further investigate the impact of hIL-8 on fibrotic processes in the murine lung, three markers of fibrosis including collagen I (ColI; Figure 3.6D), collagen III (ColIII; Figure 3.6E) and smooth muscle actin (SMA; Figure 3.6F) were analysed by qRT-PCR as part of my MSc

(Quigley, 2010). The relative expression of two extracellular matrix protein transcripts, collagen I and collagen III (Figure 3.6D, E) were both increased in the hIL-8 transgenics with a significant increase in collagen III (p<0.005). SMA was also increased in the hIL-8, although it did not achieve a significantly different result (Figure 3.6F).

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3.8A respectively. Two way Anova statistical testing using Partek software showed 17 transcripts at 10 weeks (Figure 3.7B and 3.7C) and 12 transcripts at 20 weeks (Figure 3.8B and 3.8C) with a fold change either greater than 1.2 or less than -1.2 and a p value <0.05.

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& Gene Fold change p value Fasl 2.43 0.0149 Thbs2 2.00 0.0133 Ifnγ 1.96 0.0051 Stat1 1.52 0.0002 Itgα2 1.50 0.0026 Mmp14 1.38 0.0119 Mmp13 1.34 0.0366 Ccl3 1.33 0.0157 Tgif1 1.30 0.0166 Smad2 1.24 0.0201 Sp1 1.21 0.0005 Plau 1.21 0.0296 Timp3 -1.23 0.0040 Edn1 -1.46 0.0007 Plat -1.74 0.0029 Il1α -1.75 0.0044 Itgβ6 -1.80 0.0113 )LJXUH K,/ WUDQVJHQLF DQG QRQK,/ WUDQVJHQLF OLWWHUPDWH FRQWUROV KDYH

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& Gene Fold change p value Ccl3 3.02 0.0047 Fasl 2.05 0.0371 Ifnγ 2.02 0.0118 Il1β 1.79 0.0464 Cxcr4 1.56 0.0078 Bcl2 1.39 0.0256 Tgfb3 1.35 0.0378 Tgfbr1 1.34 0.0395 Tgfbr2 1.32 0.0381 Sp1 1.20 0.0444 Ltbp1 -1.21 0.0486 Timp1 -1.55 0.0046

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Of the genes shown by qRT-PCR array analysis to be significantly different, 9 were chosen for confirmation by qRT-PCR (Figure 3.9 and 3.10). The genes chosen to be validated were either shown to be differentially regulated at both age groups analysed or were genes linked in specific fibrotic pathways. The genes chosen for validation were Ccl3, Ifnγ, Smad2, Sp1,

Tgfβ3, Fasl, Edn1, Timp3 and Mmp14. Of these 9 genes Ccl3, Ifnγ and Fasl transcripts were all upregulated in the hIL-8 transgenics compared to non-hIL-8 transgenic littermate controls

(Figure 3.9A and 3.10A). More specifically, Ccl3 showed an increase in fold change in hIL-8 transgenics compared to controls with age. Figure 3.9A shows in the hIL-8 transgenics that at

10 weeks Ccl3 had a fold increase of 1.5 (p>0.05), at 20 weeks a fold increase of 2 (p<0.05) and at 27 weeks a fold increase of 2.5 (p<0.05). At all ages, Ifnγ transcript expression remained at an approximate 2 fold increase in the hIL-8 transgenics (Figure 3.9A).

Transcripts associated with the TGFβ pathway including Smad2 and Sp1 were shown to be significantly increased at 10 weeks (Figure 3.7B and 3.7C) and Tgfβ3 at 20 weeks (Figure

3.8B and 3.8C) in the hIL-8 transgenics by the qRT-PCR array. However, qRT-PCR confirmations only validated a significant increase for Sp1 at 10 weeks with no differences at

20 or 27 weeks. Although not significant, Smad2 was increased at 10 weeks in the hIL-8 transgenics compared to non-hIL-8 transgenic littermate controls, but there was little difference at 20 and also 27 weeks for Smad2 and 10, 20 and 27 weeks for Tgfβ3 (Figure

3.9B). FasL encodes the Fas ligand protein that binds to the receptor Fas/CD95 and induces apoptosis (Nagata, 1999). This interaction has been implicated during lung fibrosis (Kuwano et al., 1999a). Fasl was confirmed by individual qRT-PCR assays to be increased at all age groups in the hIL-8 transgenics with a significant difference at 10 and 27 weeks (Figure

3.10A). The Edn1 gene encodes the protein, endothelin-1 involved in vasoconstriction (Stow et al., 2011). qRT-PCR confirmations did not confirm the decrease identified in the array analysis at 10 weeks (Figure 3.7), although at 27 weeks expression was decreased (Figure

179

3.10B). TIMP3 is an inhibitor of MMP14 (Will et al., 1996). No significant differences in

Timp3 transcript expression were seen except for at 27 weeks where expression was significantly decreased. Mmp14 expression was not significantly different between groups at any age.

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CCL3 and IFNγ in lung tissue and BAL, cytokine ELISAs were used. CCL3 is increased at both 10 and 20 weeks in the hIL-8 transgenics compared to controls. By 27 weeks CCL3 was decreased in the hIL-8 transgenics and interestingly the concentration of CCL3 was reduced with age (Figure 3.11A). At the transcript level, Ifnγ had an approximate 2 fold increase in the hIL-8 transgenics at all age groups, however the concentration of IFNγ was only significantly increased at 20 weeks with no differences observed at 10 or 27 weeks (Figure

3.11B). No differences in concentration for either CCL3 or IFNγ were seen in the BAL

(Figure 3.11C and D).

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The qRT-PCR array used in this thesis was chosen to specifically analyse genes involved in

fibrotic processes. hIL-8 transgenics and non-hIL-8 transgenic littermate controls at 10 weeks

were further studied by pathway analysis using the software program, Metacore (Genego Inc.

with help from Michael Poidinger, Singapore Immunology Network) (Figure 3.12 and 3.13).

The top 10 enrichment pathways identified from this analysis are listed in figure 3.12 with the

top three enriched pathways involving epithelial to mesenchymal transition (EMT).

Figure 3.12: Pathway enrichment analysis to identify important pathways in the hIL-8 transgenic model.

RNA isolated from 10 week old hIL-8 transgenic and littermate controls was converted into cDNA and gene expression was determined using a RT2 profiler PCR array for murine fibrotic genes. The top 10 enrichment pathways were identified by pathway analysis performed using Metacore from GeneGo Inc (Pathway analysis provided by Dr Michael Poidinger, Singapore Immunology Network). The enrichment p values per pathway were calculated based on a hypergeometric distribution with significant enrichment defined using the false discovery rate (FDR) corrected p value. Data presented as -log(p value).

185

Figure 3.13: Pathway enrichment analysis to identify important pathways in the hIL-8 transgenic model. RNA isolated from 10 week old hIL-8 transgenic and littermate controls were converted into cDNA and gene expression was determined using a RT2 profiler PCR array for murine fibrotic genes. The top enrichment pathway identified using the metacore database (GeneGo, Inc. with help from Michael Poidinger, Singapore Immunology Network) was the regulation of epithelial to mesenchymal transition (EMT). Red and blue bars represent genes where expression was either up- or downregulated respectively in the hIL-8 transgenics compared to non-hIL-8 transgenic controls. The amount of colour within each bar represents the magnitude of gene expression. Red, green, and grey lines represent negative, positive, and unspecified effects.

186

Several signalling genes involved in the EMT pathway were analysed in the murine fibrosis array such as Tgfβ3, Smad2, Sp1 and Tgif1. However, downstream markers of EMT such as claudin-1, occludin, E-cadherin, N-cadherin, vimentin and fibronectin were not included.

During EMT, epithelial cells convert into mesenchymal cells resulting in a decrease in epithelial markers and an increase in mesenchymal markers (Kalluri and Weinberg, 2009). To confirm if the changes observed may be leading to differences in EMT between hIL-8 transgenics and non-IL-8 controls, transcripts for two epithelial (E-cadherin and occludin) and two mesenchymal (vimentin and fibronectin) markers were analysed by qRT-PCR at 10,

20 and 27 weeks (Figure 3.14). Occludin transcripts where shown to decrease with age and were significantly decreased 2 fold by 27 weeks (Figure 3.14B). However, fibronectin, a marker of mesenchymal cells, also significantly decreased by approximately 2 fold at 20 and

27 weeks in the hIL-8 transgenics (Figure 3.14D). E-cadherin (Figure 3.14A) and vimentin

(Figure 3.14C) did not significantly change. These changes are summarised in figure 3.15.

The decrease in both epithelial and mesenchymal markers in hIL-8 transgenics suggests that

EMT was not driving the fibrotic process forward in the hIL-8 transgenics.

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Epithelial Cells Transitioning Cells Mesenchymal Cells

Epithelial Markers Induction of EMT Mesenhymal Markers E-Cadherin Cytokines (TGFβ) N-Cadherin

ZO-1 Growth factors Fibronectin Occludin Vimentin

Figure 3.15: A summary of the direction of change of epithelial to mesenchymal transition markers in the hIL-8 transgenics compared to non-hIL-8 transgenic controls.

Interestingly, occludin is one of the three main proteins located at tight junctions that form between epithelial and endothelial cells. Tight junctions situated at the apical side of the epithelium, regulate the movement of molecules, proteins and cells through the paracellular pathway (Dragsten et al., 1981; Sawada et al., 2003). qRT-PCR analysis of occludin transcripts showed a decrease in expression in the hIL-8 transgenics (Figure 3.14B). It has been previously shown in vitro that endothelial cells exposed to IL-8 in a dose-time dependant manner decreased occludin, claudin-5 and ZO-1 mRNA and protein expression.

This was associated with an increase in endothelial permeability (Yu et al., 2013). To further investigate tight junctions in the hIL-8 transgenic model, other tight junction gene transcripts were analysed. In addition to occludin, the other main transmembrane proteins present at tight junctions include claudins and JAMs. Claudins are made up of over 25 family members that differ in their location and either have a ‘sealing’ or ‘leaky’ role in terms of the paracellular permeability (Krause et al., 2008). Claudin-18 is an important claudin present in the lungs (Niimi et al., 2001) and claudin-18 transcripts were shown to be decreased in the hIL-8 transgenics at all age groups with a significant difference at both 20 and 27 weeks

(Figure 3.16A). JAM1 did not differ at 10 or 20 weeks, but at 27 weeks it was significantly

189 decreased (Figure 3.16B). In addition to the transmembrane proteins, cytosolic proteins such as ZO-1 link tight junction proteins to the actin cytoskeleton and regulate the opening of tight junctions (Fanning et al., 1998). ZO-1 transcripts remained at a similar level at 10 and 20 weeks with a trend to decrease at the older age group, although this was not significant

(Figure 3.16C).

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To quantify the degree of epithelial damage and tight junction disruption, images obtained from 6 hIL-8 transgenics and 6 non-hIL-8 transgenic controls were systematically scored by

4 blinded individuals (Figure 3.19). The degree of epithelial damage and loss of integrity of the tight junctions was more apparent in areas of hIL-8 staining (average score of 2) than to areas without hIL-8 staining (average score of 1) in the hIL-8 transgenics and also in comparison to non-hIL-8 transgenic controls (average score of 0.7) (Figure 3.19A). In addition figure 3.19B depicts the data as a percentage of the number of images scoring either a 0-1 or a 2-3 in the hIL-8 transgenics compared to non-hIL-8 transgenic controls. Almost 80

% of hIL-8 transgenics scored a 2-3 (in areas with hIL-8 staining) while 88 % of non-hIL-8 transgenic controls scored a 0-1 (Figure 3.19B). This shows that hIL-8 impacts primarily on the epithelium where it is produced, but also has the ability to affect other areas to a lesser degree.

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3.3.3. Investigating the impact of hIL-8 on P. aeruginosa infection.

Initially, an optimal infection dose for P. aeruginosa lung infection in the hIL-8 transgenic model was established. This dose was determined by differences in disease severity observed between the hIL-8 transgenic model and non-hIL-8 transgenic controls (C57BL/6 mice). In figure 3.23A, 100 % of non-hIL-8 transgenic controls infected with 50 µl of a 1.5 x 106 CFU inoculum reached the pre-determined disease severity end points at 8 hours and were culled, whereas 100 % of hIL-8 transgenics survived to 32 hours. At lower doses, 100 % of both hIL-8 transgenics and non-hIL-8 transgenic littermate controls survived with little or no observed differences in disease severity between the two groups. The CFU/mg of lung tissue was determined at the experimental end point (Figure 3.23B). With a decreasing dose the bacterium was cleared, although no difference was seen between groups.

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201 in the infectious protocol. Representative IVIS images of a hIL-8 transgenic and a non-hIL-8 transgenic controls are shown during the course of the 32 hour experiment. Images were taken before infection and at 8 hours, 24 hours and 32 hours post infection. Bioluminescence was not visualised at any time point after intranasal injections in the whole mouse (Figure

3.24B). Non-hIL-8 transgenic controls receiving the 1.5 x 106 CFU/ 50 µl inoculum dose were culled at 8 hours and immediate ex vivo imaging of the lung and subsequent determination of lung CFU demonstrated detectable bioluminescence within the lung. Post- harvest determination of CFU/mg lung tissue observed colonisation of the lungs at 3 x 103

CFU/mg lung tissue. Therefore, an established P. aeruginosa infection was present, but due to factors affecting light propagation through tissue, bioluminescence could not be detected in live mice on a C57BL/6 background.

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To validate the differences in disease severity observed in the hIL-8 transgenics (Figure

3.23), a second experiment with greater numbers per group (Figure 3.25) was carried out.

Enhanced disease severity was observed in non-hIL-8 transgenic controls (C57BL/6) compared to hIL-8 transgenics with 20 % survival compared to 80 % respectively (p<0.05) at

32 hours. Disease severity end points are defined at loss of movement, piloerection, >20 % weight loss, abnormal breathing and cold to touch. In addition, the disease severity correlated with CFU/mg of lung tissue (Figure 3.25B). The reduced disease severity and CFU/mg lung tissue indicates that the hIL-8 transgenics could clear the infection easier than the non-hIL-8 transgenic controls which suggests that in this in vivo model, hIL-8 has a protective role against infection.

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(Figure 3.27A), TNFα (Figure 3.27B) and IFNγ (Figure 3.27C) in the BAL of the hIL-8 transgenics compared to non-hIL-8 transgenic littermate controls either infected with P. aeruginosa or administered PBS as a control. With infection, the concentration of IL-6 was significantly increased in both infected groups compared to PBS controls, but there was no difference in the concentration between the two infected groups. TNFα also showed a significant increase in concentration in the infected groups compared to PBS controls, but interestingly the infected hIL-8 transgenics had a significant increase (p<0.01) in the concentration of TNFα (~2300 pg/ml) compared to infected non-hIL-8 transgenic littermate controls (~1400 pg/ml). However, it was surprising that IFNγ decreased with infection compared to PBS controls. To investigate this further, qRT-PCR for IL-6 and IFNγ in the lung tissue were analysed (Figure 3.27D and E). IL-6 increased with infection in both groups and thus supports the differences observed for IL-6 concentration in the BAL. IFNγ in the

PBS control hIL-8 transgenic group was significantly increased compared to the PBS non- hIL-8 transgenic littermate control group which supports the baseline data observed in figure

3.9. However, with infection, IFNγ is increased in both groups to a similar level (Figure

3.27E). To determine differences in neutrophil activation, MPO transcripts were investigated although no difference was observed (Figure 3.27F).

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3.4. Discussion

The purpose of this chapter was to characterise the impact, in vivo, of expressing hIL-8 in the lung using a hIL-8 lung targeted transgenic model. The experiments from this chapter have demonstrated that the constitutive expression of hIL-8 was associated with increased cellular infiltration and a significant increase in the number of neutrophils in the BAL. hIL-8 was also shown to induce an increase in mucus production and mucin gene expression, lung fibrosis associated with an increase in CCL3 and IFNγ, and an increase in tight junction permeability.

In addition, the presence of hIL-8 is protective against infection with P. aeruginosa.

3.4.1. hIL-8 transgenics show chronic lung remodelling.

Murine models have been developed to study human chronic respiratory diseases including

IL-13, MMP-1 and Orosomucoid-like (ORMDL) 3 transgenics (D'Armiento et al., 1992;

Miller et al., 2014; Zhu et al., 1999b). However, due to the complex and diverse nature of respiratory disease, most only model specific traits of human disease rather than mimicking the entire disease phenotype (Kips et al., 2003). Therefore, the majority of in vivo models are designed to address specific features associated with human lung diseases like COPD and asthma and include models that overexpress or lack specific proteins, express natural genetic mutations or undergo lung destruction induced by the inhalation of tobacco smoke and other noxious stimuli. For example, the effects of certain cytokines such as those associated with an asthmatic phenotype have been reported. Transgenics over expressing IL-13 demonstrated tissue inflammation, mucus hypersecretion and metaplasia, airway fibrosis, tissue eosinophilia and airway hyperresponsiveness. These are all characteristics associated with the pathogenesis of asthma and thus identify the effects of IL-13 during disease (Zhu et al.,

1999b). Other models overexpressing proteins such as MMP-1, degrade components of the

210

ECM such as collagen I. This results in airway wall disruption that has similarities to human emphysema but doesn’t display any inflammatory or fibrotic changes (D'Armiento et al.,

1992). Inducible transgenics that express cytokines, such as IFNγ in the lungs, demonstrated features such as alveolar enlargement that are similar to COPD and increased MMP12 and cathepsin expression (Wang et al., 2000), while MMP12 knockout models don’t develop emphysema or recruit macrophages in response to tobacco smoke (Hautamaki et al., 1997).

The IL-8 transgenic model described in this thesis demonstrates lung inflammation and neutrophilia, mucus hypersecretion, fibrosis and poor epithelial integrity associated with enhanced permeability. These are characteristics associated with chronic respiratory diseases such as bronchiectasis and COPD. No well-established murine model, specifically for bronchiectasis, has been reported and models such as those mentioned above don’t fully display all pathological aspects of respiratory disease. Therefore, this hIL-8 transgenic model described in this thesis demonstrates some remodelling processes similar to those observed during respiratory disease and provides a good model that can contribute to further study.

Previous studies have reported transgenic models of hIL-8 overexpression. However, hIL-8 expression was targeted to other tissues such as the intestines, liver and cornea rather than the lung (Kucharzik et al., 2005; Oka et al., 2006; Simonet et al., 1994). Consistent with observations in the hIL-8 transgenic model described in this thesis, studies by Kucharzik and

Oka observed neutrophilia with infiltrates observed in tissues expressing hIL-8, such as the lamina propria (Kucharzik et al., 2005) or cornea (Oka et al., 2006). In addition, hIL-8 targeted expression to the cornea resulted in activated neutrophils and subsequent tissue damage (Oka et al., 2006). The chemokine KC, displays similar properties to hIL-8 and is expressed naturally in the mouse. Transgenic models targeting KC expression to the lungs have previously been reported and are described in section 1.3. This model demonstrates

211 increased neutrophil migration to the lungs. However, no tissue damage was observed and neutrophils were not markedly activated (Tsai et al., 1998). Whereas the lung targeted hIL-8 transgenic model, characterised in this thesis, demonstrated airway remodelling and epithelial disruption in addition to neutrophil infiltration in the BAL.

Although IL-8 is not a gene expressed by mice, in vitro studies have shown that hIL-8 has cross-species reactivity with neutrophils isolated from a range of animal species including the mouse. However, the potency of hIL-8 in the mouse for neutrophil chemotaxis was reduced compared to humans and other animal species (Rot, 1991). In the hIL-8 transgenic model used in this thesis, hIL-8 levels were detected at approximately 2000 pg/ml in the lung and

BAL. In the KC transgenic model, levels of KC in the lung at baseline were higher than the hIL-8 model (3800 pg/ml) (Tsai et al., 1998). In comparison, physiologic hIL-8 levels from the bronchoalveolar lavage in patients with chronic respiratory diseases such as COPD (100-

3000 pg/ml) (Pesci et al., 1998), bronchiectasis (0-5520 pg/ml) (Angrill et al., 2001) and asthma (0-110 pg/ml) (Hollander et al., 2007), are at a similar if not lower level. The relative potency of hIL-8 in recruiting murine neutrophils is lower compared to human neutrophils

(Rot, 1991) therefore, the effective concentration of hIL-8 in the mouse would be similar to that observed in human disease.

3.4.2. Investigating the effects of hIL-8 on respiratory disease pathology

Impaired lung function during chronic respiratory disease is commonly associated with increased numbers of neutrophils (Stanescu et al., 1996). The data presented in this thesis indicate that lung targeted hIL-8 expression is associated with large numbers of neutrophils into the BAL. Once activated, neutrophils degranulate and release mediators that can impact

212 on the characteristics observed in the hIL-8 transgenic murine model. For example, activated neutrophils produce neutrophil elastase that impairs mucociliary clearance by damaging the lung epithelium and resulting in an accumulation of mucus, ideal for pathogens and consequently leading to an enhanced immune response (Amitani et al., 1991). Neutrophil elastase has also been shown to enhance Muc5ac mRNA stability thus increasing mRNA and protein expression in lung epithelial cells (Voynow et al., 1999). In addition, neutrophil oxidative burst results in the release of reactive oxygen species that can act on epithelial and endothelial adhesion molecules causing an increase in permeability (Usatyuk and Natarajan,

2005). Potentially the effects observed in the hIL-8 transgenic model could be a direct effect of the increased level of resident lung neutrophils rather than hIL-8. An ideal step to further understand the impact of recruited neutrophils in the murine lung of the hIL-8 transgenics would be to deplete neutrophils. Several models targeting neutrophils have been described but have limitations as described in section 1.4. Furthermore a viable, neutrophil knockout model has not yet been reported. Although active neutrophils are known to be involved in remodelling processes in the lung, strong evidence exists that implicate a role for hIL-8 acting independently of neutrophils in the pathology of lung remodelling.

Firstly, an increase in mucus and the two mucin genes Muc5ac and Muc5b were observed in the hIL-8 transgenic model. Evidence that hIL-8 can affect mucus or mucin expression independently of neutrophils was shown in an in vitro study by Bautista and colleagues as previously discussed in section 1.5.1. Two lung epithelial cells lines incubated with increasing concentrations of hIL-8, increased the expression of the two main mucin genes

MUC5AC and MUC5B (Bautista et al., 2009). A study connecting IL-8 with mucin gene regulation in a disease state, although less direct, implicates the role IL-8 may have on mucus production. Nagarkar and colleagues investigated the role of the IL-8 receptor CXCR2 on

213 mucin gene regulation in a model of human rhinovirus induced asthma. Asthma induced

CXCR2-/- mice had decreased Muc5b transcript expression compared to wild type controls.

This demonstrates the significance of the CXCR2 receptor and implicates the importance of the IL-8 pathway in regulating mucin expression in a disease state (Nagarkar et al., 2009).

Although not significant, mRNA levels of SMA were increased in the hIL-8 transgenics.

Evidence that hIL-8 can influence smooth muscle independently of neutrophils has previously been shown and is discussed in section 1.5.3. Govindaraju and colleagues showed that human airway smooth muscle cells (HASMC) expressed both IL-8 receptors CXCR1 and

CXCR2 and that these cells in the presence of hIL-8 in vitro had increased calcium flux resulting in increased HASMC contraction and migration (Govindaraju et al., 2006).

The expression of two markers of fibrosis, collagen I and collagen III, were both increased in the hIL-8 transgenics. In addition, Masson’s trichrome staining identified visual differences in the level of fibrosis in older hIL-8 transgenic mice. This data indicates that hIL-8 may be affecting fibrotic processes in the hIL-8 transgenic lung. The impact of IL-8 on fibrotic pathways independently of neutrophils has also been described in the literature and introduced in section 1.5.2. In a bleomycin induced lung fibrotic model the CXCR2 receptor was blocked with an antagonist. It was observed in those with the inability to now signal through CXCR2 that there was a decrease in neutrophil infiltration and collagen deposition which resulted in improved lung pathology (Russo et al., 2009). As IL-8 is an agonist for

CXCR2 this suggests the potential of IL-8 in contributing to fibrosis. Further implications of hIL-8 and its role in fibrosis have also been observed in studies of irradiation induced fibrosis. Here, an irradiated epithelial cell line co-incubated with fibroblasts resulted in

214 enhanced collagen and ECM deposition. With the addition of anti-IL-8, collagen deposition was significantly reduced demonstrating a direct role of hIL-8 produced by irradiated epithelial cells on mediating the contribution of fibroblasts in fibrotic pathways (Kuhlmann et al., 2009).

To investigate the role of hIL-8 on fibrotic processes further in the hIL-8 transgenic model a fibrotic array, individual qRT-PCR validations and subsequent pathway analysis were carried out that demonstrated changes to several markers of fibrosis. Transcripts of Ccl3, Ifnγ and

Fasl and in particular the concentration of CCL3 were shown to be significantly increased in lung tissue of the hIL-8 transgenic mice. CCL3 is a chemokine expressed by numerous cell types and in particular by alveolar epithelial cells (Olszewska-Pazdrak et al., 1998) and alveolar macrophages (Standiford et al., 1993) in the lung. CCL3 is regarded as a proinflammatory chemokine and as such, is an important chemoattractant for a variety of immune cell types such as: neutrophils (Zeng et al., 2003), fibroblasts (Wu et al., 2008),

CD8+ T cells (Castellino et al., 2006), NK cells (Zeng et al., 2003) and NKT cells (Kim et al., 2002). The importance of CCL3 in the pathology of fibrosis has been indicated in several clinical and murine studies. Elevated levels of this chemokine have been observed in patients with chronic respiratory diseases such as IPF (Standiford et al., 1993), asthma (Alam et al.,

1996) and cystic fibrosis (Brennan et al., 2009). In addition to these human studies, several groups have demonstrated an important role of CCL3 in fibrotic pathways in the murine lung.

It was observed by Smith and colleagues that CCL3 was increased in the lungs of bleomycin treated mice. Production was located in the bronchial epithelial cells and alveolar macrophages and antibodies towards CCL3 resulted in reduced cellular recruitment and fibrosis (Smith et al., 1994). Other models such as knockout models of CCL3 support a role for CCL3 in fibrotic processes. CCL3 depletion results in decreased collagen accumulation,

215 fibroblast recruitment, TGFβ levels and an overall decrease in the amount of fibrosis observed in the lung (Ishida et al., 2007). CCL3 binds two G protein-coupled receptors,

CCR1 and CCR5 (Murphy et al., 2000). Knockout models for CCR1 and CCR5 demonstrated the importance of CCR1 and not CCR5 in neutrophil recruitment in response to inflammation induced by OVA exposure (Ramos et al., 2005). Whether hIL-8 works directly or indirectly to increase levels of CCL3 is unknown, but these chemokines are linked and have important roles in fibrotic processes. Depletion of CCL3 using anti-CCL3 antibodies in the murine model would help to clarify the impact CCL3 is having in the hIL-8 transgenic model.

Ifnγ mRNA levels were also increased in the hIL-8 transgenics, although the concentration of

IFNγ in the lung was only increased at 20 weeks of age. Increased Ifnγ mRNA levels are seen in epithelial cells of patients with respiratory disease like cystic fibrosis (Wojnarowski et al.,

1999). IFNγ is renowned for its role as a Th1 cytokine in the inflammatory response.

However, it has also been shown to be linked to CCL3. Neutrophils express receptors for

IFNγ (Hansen and Finbloom, 1990) and it has been shown that IFNγ coordinates CCL3 induced neutrophil migration (Bonville et al., 2009). The mechanism for this is unknown, but previous studies have shown that human neutrophils increase their expression of CCR1 when in the presence of IFNγ and subsequently enhance their chemotactic response (Bonecchi et al., 1999). Increased CCL3 and IFNγ have been observed in the lung tissue and not in the

BAL of the hIL-8 transgenics. How these are linked is not fully understood. Further work to identify the specific cell types involved in the enhanced production of CCL3 and IFNγ would establish the role of immune cells in fibrotic pathways in this model.

216

FasL is expressed predominantly by T cells (Suda et al., 1993) and interacts with its receptor

Fas expressed by several cell types. Interactions between these two surface molecules suppress immune responses by inducing apoptosis of target cells (Ju et al., 1995).

Dysregulated Fas/FasL interactions ultimately result in tissue destruction and can contribute towards disease. For example, upregulated soluble FasL is detected in BAL of patients with respiratory disease (Kuwano et al., 1999b) and studies using a murine model of bleomycin induced lung fibrosis demonstrated enhanced FasL expression and associated apoptosis of epithelial cells (Hagimoto et al., 1997). In addition, the effects of FasL were abolished when administered alongside a soluble form of FasL antigen or antibody (Kuwano et al., 1999a).

An increased level of fibrosis and Fasl mRNA levels were observed in the hIL-8 transgenic model. This suggests that potentially excessive FasL could induce enhanced apoptosis of epithelial cells in the lung. The identification of cell types expressing FasL by multiparameter

FACS and quantitation of apoptosis by TUNEL staining would determine the level of apoptosis in this model.

Pathway analysis of fibrotic genes using metacore software analysis indicated the potential importance of the EMT pathway in contributing to fibrosis observed in the hIL-8 transgenic model. During airway fibrosis, epithelial cells can undergo a transition into cells with a mesenchymal phenotype. Although the fibroblast pool in the lung consists of either resident lung fibroblasts or those recruited from the circulation, a good proportion have been shown to be derived from the epithelium (Tanjore et al., 2009). In addition, a role for EMT during respiratory diseases in which IL-8 levels are increased has been implicated in patients with

COPD (Sohal et al., 2010) and asthma (Hackett et al., 2009). Analysis of E-cadherin, occludin, vimentin and fibronectin transcripts to determine if EMT is induced in the hIL-8 transgenics, demonstrated that the expression of these markers did not fit the expected profile

217 of established EMT. Instead occludin and fibronectin transcripts were decreased while E- cadherin or vimentin did not change. A direct role for IL-8 on fibronectin has not been extensively reported but has been implicated in inducing and maintaining type III tumour related EMT. Blocking IL-8 signalling in tumour cells had no effect on E-cadherin protein expression. However, fibronectin mRNA and protein expression were decreased, demonstrating that IL-8 can directly enhance fibronectin production. In addition, in vitro experiments incubating breast epithelial tumour cell lines with IL-8 resulted in a reduction in

E-cadherin and an increase in fibronectin protein expression (Fernando et al., 2011). This data contradicts the impact that constitutive hIL-8 expression may have on fibronectin mRNA levels in the murine lung in this thesis. Interestingly, transcript expression of another ECM component, collagen III, was demonstrated to be increased in the hIL-8 model and deposition around the airways in older mice was observed. It would therefore be interesting to identify the impact of hIL-8 in the lung on the protein level, with a specific focus on fibronectin by western blotting or immunohistochemistry. In addition, a role for hIL-8 on ECM protein production, although not directly fibronectin, has been demonstrated in vitro. Irradiated epithelial cells cultured with fibroblasts enhanced collagen and ECM protein deposition. This was reduced by anti-IL-8 treatment and indicates a function of IL-8, post epithelial damage, that can regulate fibroblast deposition of ECM proteins (Kuhlmann et al., 2009).

Occludin is an important protein of tight junctions (Furuse et al., 1993). Tight junctions are located on the apical side of the epithelium and endothelium and are essential in the regulation of the barrier and fence functions of the paracellular pathway (Dragsten et al.,

1981; Farquhar and Palade, 1963). Therefore, tight junctions are vital in regulating epithelial and endothelial permeability and changes to tight junctions are associated with several diseases including pulmonary oedema (Wray et al., 2009), IBD (Mazzon et al., 2002) and

218 brain oedema associated with the blood brain barrier (Wolburg et al., 2003). In this thesis, a decrease in occludin transcript expression was observed in the hIL-8 transgenic model. This potentially implies that hIL-8 could be affecting the integrity of the tight junctions. Other tight junction proteins were investigated and the mRNA level for claudin-18 and JAM1 were also decreased. Although differences for ZO-1 were not significant, the mRNA levels were reduced in the hIL-8 transgenics. Interestingly, immunofluorescence studies for claudin-18 in the bronchial epithelium demonstrated damaged epithelium in hIL-8 transgenics. This poor integrity of the tight junctions observed in the hIL-8 transgenics suggests dysregulated tight junctions and functional studies demonstrated enhanced permeability in the hIL-8 transgenics. Previous studies have reported the impact that cytokines have on tight junction proteins within the epithelium and endothelium that contribute to changes in permeability. In vitro studies investigating endothelial cells exposed to IL-8 in a dose-time dependant manner demonstrated decreased expression of occludin, claudin-5 and ZO-1 that was associated with an increase in endothelial permeability (Yu et al., 2013). Although the impact of IL-8 has not been reported for epithelial cells, other cytokines have been shown to affect the tight junctions of epithelial cells in different tissues including IL-1β, IL-10, IFNγ and TNFα (Abe et al., 2003; Boivin et al., 2009; Mankertz et al., 2000; Oshima et al., 2001; Youakim and

Ahdieh, 1999). IFNγ and TNFα, in particular, have been studied in intestinal cell lines and are associated with a decrease in occludin, claudin-2 and ZO-1, an increase in claudin-1 and an increase in epithelial permeability (Boivin et al., 2009; Mankertz et al., 2000; Youakim and Ahdieh, 1999). Not only do cytokines cause the down regulation of tight junctions, but

IFNγ has also been demonstrated to induce the endocytosis of tight junction transmembrane proteins in intestinal cell lines. These proteins include occludin, JAM-1 and claudin-1 and results in a leakier epithelial barrier (Bruewer et al., 2005). Studies investigating the effect on lung epithelial tight junctions have demonstrated similarities in the effects of IFNγ and TNFα

219 on mRNA and protein expression levels. Airway cell lines incubated with IFNγ and TNFα decrease occludin and JAM1 expression and this is associated with an increase in permeability (Coyne et al., 2002a). In this thesis, in addition to a decrease in occludin expression, claudin-18 and JAM1 were also significantly decreased in the hIL-8 transgenics and particularly in older mice. Older hIL-8 transgenics were also observed to have an increased level of fibrosis, therefore indicating an association between fibrosis and tight junction integrity. The importance of claudin-18 during fibrosis has been previously shown in a murine model of bleomycin induced fibrosis. With enhanced fibrosis, claudin-18 expression along with claudin-5 expression was decreased as well as an increase in permeability (Ohta et al., 2012). Claudin-18 is located in two areas and in two alternative forms that differ by the first exon. Claudin-18.1 is expressed in the lung while claudin-18.2 is expressed in the gut

(Niimi et al., 2001). A knockout model for the gut variant of claudin-18 reported enhanced paracellular H+ permeability, structural defects and loss of tightly packed tight junctions. The

H+ leak resulted in tissue damage and enhanced proinflammatory mediators in particular IL-

1β. This consequently leads to an influx in neutrophils that contributed to further tissue destruction and ultimately gastritis (Hayashi et al., 2012). This study demonstrates the importance of claudin-18 and its role as a sealing claudin member that reduces the level of permeability of the epithelium. This report also supports the enhanced permeability and associated reduction in claudin-18 in the hIL-8 model observed in this thesis. A claudin-18 knockout model specifically for claudin-18 in the lungs would be an ideal model to investigate the effects of claudin-18 on lung epithelial permeability.

The claudin family consists of many family members and in this thesis only one member has been investigated. Other claudins such as claudins-1, -5 and -4 are expressed in lung tissue with different effects. Fibrosis induction and IL-8 exposure has demonstrated decreased

220 expression of claudin-5 in endothelial cells (Ohta et al., 2012; Yu et al., 2013). Similarly, claudin-4 has been previously shown to be important in the lung. Claudin-4 is increased during lung injury and is associated with increased fluid clearance and reduced disease severity in acute lung injury (Rokkam et al., 2011; Wray et al., 2009). Therefore, further analysis of other tight junction proteins would be invaluable to the analysis of tight junction and their effect on lung remodelling pathology in the context of hIL-8.

Permeability studies in vitro often use small molecular tracers or TER measurements across a monolayer of cells (Boivin et al., 2009; Coyne et al., 2002b; Mankertz et al., 2000; Oshima et al., 2001; Youakim and Ahdieh, 1999). In vivo, however, disruption of the epithelium can result in a two way effect in which environmental particles can penetrate the lung interstitial tissue from the airways, but serum proteins can also be transported out and into the airways such as albumin (Jamieson et al., 2013). Therefore, to fully understand the epithelial permeability both aspects need to be addressed. In this thesis, to investigate changes in the permeability of the epithelial-capillary barrier in vivo in the IL-8 model, both albumin concentration in the BAL and the diffusion of the labelled small molecule FITC-dextran

(4kda) were measured. Although a small amount of albumin was detected in the BAL of the control mice, much higher levels of albumin were observed in the hIL-8 transgenics demonstrating enhance permeability in the hIL-8 transgenics. To measure the diffusion of a small labelled tracer from the airways into the lung tissue and subsequently the blood, a second method using FITC labelled dextran was used. However, this system for detecting epithelial permeability is limited as it did not appear to be sensitive enough to detect the subtle differences observed in the tight junctions of the hIL-8 transgenics.

221

3.4.3. Investigating the effects of hIL-8 on P. aeruginosa infection in vivo.

P. aeruginosa infections are commonly associated with chronic lung disease and contribute to a long term poor prognosis, morbidity and death (Davies et al., 2006). In this thesis, the hIL-8 transgenic model of chronic lung remodelling provided an ideal model to investigate the impact of lung remodelling processes induced by long term expression of hIL-8 on P. aeruginosa infection. The disease severity and survival of hIL-8 transgenics was significantly improved compared to non-hIL-8 transgenic controls. In addition, investigating the underlying differences in the immune response contributing to this difference demonstrated higher total number of neutrophils and TNFα levels in the hIL-8 transgenics. Neutrophils are essential in P. aeruginosa clearance (Koh et al., 2009; Wisplinghoff et al., 2003) and in this model it is clear that neutrophils play an important role. At baseline hIL-8 transgenics have a higher percentage of neutrophils in the BAL than control mice which provides a source of neutrophils that are more activated and mature and already present in the lungs.

Subsequently, when exposed to P. aeruginosa, neutrophils already reside within the BAL that can promptly clear infection before an infection can be established, whereas non-hIL-8 transgenic controls require the initiation of an immune response by resident macrophages and dendritic cells and chemokine production leading to increased recruitment of neutrophils. In addition, higher levels of TNFα were detected in the BAL of the hIL-8 transgenics with infection potentially demonstrating a protective role of TNFα. An increase in TNFα has previously been demonstrated during lung infections with several other bacterial species including Legionella pneumophila (Blanchard et al., 1987), M. tuberculosis (Allie et al.,

2013) and Pneumocystis carinii (Kolls et al., 1993). A role for increased TNFα levels during

P. aeruginosa infections has also been previously reported. The early production of TNFα provided a protective role in more resistant mice. Subsequent experiments in which anti-

TNFα antibodies were administered alongside P. aeruginosa enhanced infection (Gosselin et

222 al., 1995). In addition, the administration of recombinant TNFα alongside P. aeruginosa improves clearance in the lungs (Buret et al., 1994).

TNFα is a cytokine that has an important role in the chemotaxis of monocytes and neutrophils

(Newman and Wilkinson, 1989; Wang et al., 1990) potentially by upregulating adhesion molecules expressed by the epithelium and neutrophils (Tosi et al., 1992). TNFα is therefore an important cytokine in recruitment and one would assume to have an important role in inflammatory cell recruitment during P. aeruginosa. However, Gosselin and colleagues through blocking TNFα did not observe changes to macrophage, neutrophil or lymphocyte recruitment into the BAL. Rather, the protective role initiated by TNFα during P. aeruginosa infections was suggested to increase the production of nitric oxide by recruited neutrophils

(Gosselin et al., 1995). To investigate the role of TNFα further, in the hIL-8 transgenic model, anti-TNFα antibodies could be administered at the same time as P. aeruginosa infection to determine if this affects the outcome of infection.

Although transgenic models expressing hIL-8 have been reported (Kucharzik et al., 2005;

Oka et al., 2006; Simonet et al., 1994), none were lung specific. However, murine models with similar lung specific expression, but of the chemokine KC, have been reported. The KC transgenic model specifically expresses KC in the lung and demonstrated enhanced neutrophil recruitment and increased resistance to the gram-negative bacterium K. pneumoniae (Tsai et al., 1998). In addition, a IL-10 transgenic model demonstrated increased susceptibility to P. aeruginosa infection. In this model, the increased level of IL-10 resulted in a reduction in KC and neutrophil recruitment in the lungs (Sun et al., 2009). These models investigating chemokines with a similar function to hIL-8 and infections with gram-negative

223 bacterial infections support the observation in this thesis that the clearance of the gram- negative bacterium, P. aeruginosa, is enhanced in a murine model that expresses hIL-8.

The model characterised in this chapter demonstrates that increased levels of the hIL-8 chemokine is beneficial for bacterial clearance, however long term exposure results in lung damage. P. aeruginosa has previously been demonstrated to impact on the tight junctions localised to the epithelium in the airways. Virulence factors such as rhamnolipids, and exotoxins dysregulate tight junctions enabling enhanced entry. Although enhanced clearance is observed in the murine model in this thesis, experiments were carried out in young mice

(~8 weeks). Potentially, enhanced lung damage and loss of tight junction integrity observed in the murine model in older mice in this thesis may enhance P. aeruginosa infection. Thus further work investigating P. aeruginosa infections in older hIL-8 transgenic mice would be beneficial.

3.5. Summary

The purpose of this chapter was to characterise the hIL-8 transgenic model in terms of the impact that the over expression of hIL-8 may have on airway remodelling processes. It was observed that these mice present with increased lung inflammation and more specifically enhanced neutrophil infiltrates into the BAL, mucus hypersecretion and mucin gene upregulation, lung fibrosis in terms of increased collagen III and Ifnγ transcripts and Ccl3 expression and a disrupted epithelium resulting in enhanced permeability. In addition, this model demonstrated increased resistance to infection by P. aeruginosa that was associated with enhanced neutrophil infiltration and TNFα levels. This model of chronic lung remodelling therefore helps in our understanding, in the context of IL-8, complex pathways

224 associated with the pathology of human chronic lung diseases and acute P. aeruginosa infections. This greater understanding of the role of IL-8 will aid in targeting pathways to potentially develop novel treatment strategies for chronic respiratory diseases.

225

CHAPTER 4: CD4+ T cell epitope responses to OprF and identification of differential immunity between bronchiectasis patients with or without chronic P. aeruginosa infection.

4.1. Introduction

P. aeruginosa is a common bacterium associated with respiratory disease. Chronic P. aeruginosa infections can result in lung damage and reduced lung function that contributes towards poor long term prognosis, morbidity and death (Burns et al., 2001; Murphy et al.,

2008; Nicotra et al., 1995). Treatment strategies in place require the use of antibiotics.

Rigorous treatment with antibiotics at the time of infection is sometimes successful (Hansen et al., 2008). However, due to the emergence of antibiotic resistant strains, treatments are becoming less effective and once established, P. aeruginosa is very hard to eradicate (Li et al., 1995). This highlights the urgency for other avenues of protection. Vaccination is regarded as an effective strategy in which an individual is deliberately exposed to non- pathogenic forms to induce a memory immune response, that activates upon subsequent exposure to the pathogenic form, for rapid clearance of infection. Most existing vaccines consist of attenuated or inactivated pathogens or subunit vaccines consisting of antigenic proteins. More recently, vaccines are being designed that are based on specific components of an antigenic protein that potentially focuses the immune response to induce safer yet more potent immune responses to conserved regions (Sette and Fikes, 2003).

Pathogens encode a large range of potential antigenic targets for the immune response, however, only a select few will be recognised and capable of inducing a response. Those

226 specific amino acid sequences capable of initiating a response are described as immunodominant epitopes (Berzofsky, 1988). Factors that impact on the immunodominance of epitopes include endosomal processing of antigen, capability of peptides generated to bind to MHC and the ability of a TCR to recognise and bind to the MHC:peptide complex

(Berzofsky, 1988). T cells are a major component of adaptive immunity and vital in providing specific and memory driven responses. In particular, the role of CD4+ T cells is essential in shaping the adaptive immune response to provide protection towards pathogens.

Not only does this cell type, once activated, produce cytokines such as IFNγ or TNFα that influence other innate cells and also develop into memory CD4+ T cells, but they are also essential in promoting and maintaining CD8+ T cell effector and memory cells via dendritic cell licensing (Novy et al., 2007; Smith et al., 2004). In addition, CD4+ T cells are also required to promote B cell differentiation into plasma cells and subsequent production of antibodies and development of memory B cells (Smith et al., 2000). Studies using CD4+ depleted models demonstrate an impaired ability to clear pathogens (Caruso et al., 1999; Xu et al., 2002), but in particular a role for CD4+ T cells in immunity to P. aeruginosa has been described (Dunkley et al., 1994; Worgall et al., 2001; Worgall et al., 2005; Wu et al., 2012).

CD4+ T cells are therefore critical in Infect Immun. and the identification of CD4+ T cell immunodominant epitopes for a specific antigen that primes the immune response and provides protection upon exposure to pathogens, is fundamental in rational epitope-based vaccine design. In addition, regions that are weakly immunogenic or could potentially cause harm can be excluded (Rosa et al., 2010).

Currently no vaccine has been licensed for P. aeruginosa and there is insufficient data published to design a T cell epitope-based vaccine for use in murine or human models. P. aeruginosa consists of a large genome (Stover et al., 2000) that encodes many potential

227 proteins that could be included for rational epitope-based vaccine design. In particular, the outer membrane proteins are regarded as logical targets. Outer membrane porin F (OprF) is a major outer membrane protein that is conserved and surface exposed (Mutharia and Hancock,

1983; Mutharia et al., 1982). In addition, several studies have demonstrated the immunogenicity of OprF (Gilleland et al., 1988; Gilleland et al., 1984; Krause et al., 2011;

Matthews-Greer and Gilleland, 1987; Price et al., 2001; Worgall et al., 2005). The majority of these studies however, report on B cell epitopes and the induction of OprF antibodies post

OprF immunisation rather than T cell epitopes. As discussed above, CD4+ T cells have a critical role in driving both the cellular and humoral responses. Therefore the identification of

CD4+ T cell epitopes is a beneficial step in epitope-based vaccine development.

The identification of CD4+ T cell epitopes can be approached in several ways. Computational prediction software such as TEPITOPE, NetMHCIIpan and the consensus method utilise

HLA-restricted peptide binding motifs and epitope-based binding studies to develop algorithms that can be used to help predict important HLA restricted epitopes (Nielsen et al.,

2007; Sturniolo et al., 1999; Wang et al., 2008). Usually in silico analysis is the first stage in the identification of important epitopes. However, epitopes predicted on HLA-binding in silico do not always generate true epitopes. False positives arise due to in vivo peptide processing and presentation that determines the immunogenicity of a peptide. It is vital that in vivo studies are used. Humanised HLA transgenic mice express individual human HLA class

II molecules and lack any endogenous murine MHC class II. These mice are then primed with whole protein from which peptide responses are determined. Epitope mapping such as this enables the identification of important epitopes that are restricted to a single HLA class II molecule.

228

4.2. Aims

 To produce and purify an immunodominant antigen of P. aeruginosa (OprF)

 To identify HLA-DR1, HLA-DR4 and HLA-DR15 restricted CD4+ T cell epitopes

within the OprF protein of P. aeruginosa.

 To understand the differences in immunity to infection to P. aeruginosa in individuals

with a diagnosis of bronchiectasis (a lung disease associated with chronic lung

damage).

4.3. Results

4.3.1. Expression of OprF

A pET SUMO expression vector (Invitrogen) containing the gene for mature OprF (pSUMO-

OprF) was kindly donated by Stefan Worgall (Weill Cornell Medical College, USA) (Figure

4.1A). This expression system contains a N-terminal polyhistidine (6xHis) tag for purification and detection and a small ubiquitin related modifier (SUMO) protein from

Saccharomyces cerevisiae (smt3) that is a homologue of mammalian SUMO-1 (Saitoh et al.,

1997). The SUMO protein is fused to OprF and this enhances the production and solubility of

OprF. In addition, the SUMO fusion protein can be cleaved by the SUMO protease enzyme

(Ulp) for purification (Li and Hochstrasser, 1999; Mossessova and Lima, 2000). Restriction sites for HindIII and BamH1 at the 3’ and 5’ end of the OprF gene respectively, were utilised to confirm the correct insert in the expression vector. The combination of these two restriction enzymes in a restriction digest yielded two bands at approximately 5.6 kb and 1 kb that corresponded with the expected sizes for the pET SUMO vector without the OprF insert and the OprF insert respectively (Figure 4.1B). This therefore confirmed the correct pSUMO-

OprF plasmid. To maintain the construct, the correctly identified plasmid was transformed

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The conditions determined from these small scale pilot experiments confirmed conditions used by Worgall and colleagues at 27 ºC for 3 hours post IPTG induction and were used for further large scale, 1 litre expression.

4.3.2. OprF purification by affinity chromatography

The recombinant 6xHis-SUMO-OprF fusion protein was purified using nickel-chelating affinity chromatography (Worgall et al., 2005). Cell lysates (prepared as described in section

2.15.5.1.4.) were applied to pre-equilibrated and washed Ni-NTA agarose beads (as described in section 2.15.5.2.). The wash and incubation buffers contain a low concentration of imidazole (20 mM) to help prevent binding of non-specific proteins. Lysate incubated with

Ni-NTA samples were loaded onto a 5 ml disposable column from which the recombinant fusion protein was eluted. Eluted fractions were resolved by SDS-PAGE and protein bands were visualised by colloidal blue staining and western blotting (Figure 4.4). Protein was initially eluted with an imidazole gradient from 150 mM to 500 mM to help remove non- specific proteins. Eluted fractions using 300 mM imidazole (lane 1-3 of Figure 4.4) and 500 mM imidazole (lane 4-7 of Figure 4.4) were pooled and the recombinant fusion protein was digested with SMUO protease enzymes (lane 8 of Figure 4.4). The cleaved OprF protein (~37 kDa) (Worgall et al., 2005)) was further purified with Ni-NTA beads and reapplied to the column. Recombinant OprF protein was eluted in the flow through (Lane 9 of Figure 4.4). In addition, a western blot using anti-polyhistidine tag antibodies demonstrated the lack of staining at the position of the purified OprF band confirming the cleavage of the polyhistidine tag and SUMO protein (~13 kda).

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4.3.4. In silico predictions for HLA-DR restricted OprF epitopes.

The first step to predict immunodominant epitopes of OprF required in silico analysis software programs including NetMHCIIpan, the IEDB consensus method and TEPITOPE.

The sequences of all 34 peptides that span the length of the OprF protein (as described in table 2.17. of section 2.16.) were inputted into the in silico online prediction programs

NetMHCIIpan and the consensus method for MHC class II HLA-DR including: DR1, DR3,

DR4, DR7, DR8, DR9, DR10, DR11, DR13, DR14 and DR15 (Table 4.1). NetMHCIIpan uses binding algorithms to predict the binding affinity of peptide sequences to HLA-DR and represents this by generating an IC50 score per 15 amino acid peptide (Karosiene et al., 2013;

Nielsen et al., 2008). IC50 binding scores are defined as: high binding = a low IC50 score <50 nM (shown in green); intermediate binding = moderate IC50 score <500 nM (shown in blue) and low binding = a high IC50 score <5000 nM (not shown). The IEDB consensus score, known as the consensus rank score, combines three different prediction algorithms including

NN-align, SMM-align and combinatorial library. The lower the consensus rank score, the higher the predicted binding affinity. In silico predictions were also investigated using the software program, TEPITOPE, specifically for HLA-DR1, HLA-DR4 and HLA-DR15 restricted alleles (Figure 4.7). The OprF sequence was imported into TEPITOPE and epitopes for these HLA alleles were predicted at a stringency predictive threshold of 3 %. The 3 % threshold parameter predicts the top 3 % of predicted peptides for a given protein and is determined by the user. A low % threshold corresponds to a high stringency prediction and vice versa. For this study, a 3 % threshold was chosen as this level of stringency has been previously shown to be sensitive enough to detect up to 80% of HLA-DR restricted epitopes

(Sturniolo et al., 1999). Comparing important peptides identified by NetMHCIIpan and the consensus method with TEPITOPE predictions for HLA-DR1, HLA-DR4 and HLA-DR15 show similar predicted outcomes. NetMHCIIpan identifies 8 peptides for HLA-DR1, 2

237 peptides for HLA-DR4 and 1 peptide for HLA-DR15 containing at least one epitope with high affinity binding (Table 4.1) whereas TEPITOPE identified 7 predicted epitopes for

HLA-DR1, 5 for HLA-DR4 and 5 for HLA-DR15 (Figure 4.7).

238

Table 4.1. In silico analysis of OprF with NetMHCIIpan and IEDB consensus methods. To determine MHC-class II HLA-DR peptide binding of OprF, 34 peptides were inputted into NetMHCIIpan and IEDB Consensus software programs. HLA- DR1, DR3, DR4, DR7, DR8, DR9, DR10, DR11, DR13, DR14 and DR15 alleles were investigated and the highest score recorded per peptide. The predicted affinity by NetMHCIIpan was measured using an IC50 score (nM). Peptides predict ed strong binding have a low IC50 (<50 nM) score (green) or moderate binding have an intermediate IC50 (<500 nM) (blue). For these peptides the IEDB consensus method was also used to predict affinity using the consensus rank score. A lower Consensus rank score predicts stronger affinity for HLA allele. 239

Figure 4.7: The OprF amino acid sequence with epitopes predicted by TEPITOPE indicated for HLA-DR1, HLA-DR4 and HLA-DR15. The 350 amino acid sequence of OprF was imported into TEPITOPE HLA-class II prediction software. 9 amino acid epitopes were predicted for HLA-DR1, HLA-DR4 and HLA-DR15 and are indicated in blue with position 1 of each epitope indicated in red. 7 epitopes were predicted for HLA-DR1, 5 epitopes predicted for HLA-DR4 and 5 epitopes predicted for HLA-DR15 at a stringency threshold for prediction set at 3 %.

240

4.3.5. In vivo mapping of HLA-DR restricted OprF epitopes.

In silico analysis identified several potentially important OprF epitopes for HLA-DR1, HLA-

DR4 and HLA-DR15 restricted molecules. However, natural processing of peptides during an immune response, the ability for that peptide to bind HLA class II and the ability for a TCR to recognise the peptide/MHC complex may limit some of these predicted epitopes.

Therefore, to investigate CD4+ T cell responses to OprF in vivo, transgenic mice expressing human MHC class II whilst lacking endogenous murine MHC class II (H2-Aβo) were used

(Altmann et al., 1995; Ellmerich et al., 2005; Ito et al., 1996). Humanised models used in this project included transgenic mice expressing human MHC class II molecules restricted to

HLA-DR1, HLA-DR4 and HLA-DR15 alleles only. Prior to use for in vivo experiments, transgene expression for human HLA-DRα, HLA-DRβ chains and the murine MHC knock out (Aβº) phenotype were confirmed by PCR analysis (Figure 4.8). These HLA-DR transgenic mice were footpad primed with OprF. Ten days later, cells from the draining lymph node (DLN) were removed and stimulated by whole protein or individual peptides as described in section 2.17.

In vivo mapping studies identified important epitopes that were HLA-DR restricted (Figure

4.9). HLA-DR1 transgenics identified peptide 4 (EIEAFGKRYFTDSVRNMKNA) with n=15 combined from 4 separate experiments (Figure 4.9A). HLA-DR4 transgenics identified three peptides: peptide 6 (DLYGGSIGYFLTDDVELALS), peptide 14

(RQQMTMANIGAGLKYYFTEN) and peptide 15 (AGLKYYFTENFFAKASLDGQ) with n=9 from 2 separate experiments (Figure 4.9B). HLA-DR15 transgenics identified peptide 11

(FGTPGVGLRPYVSAGLAHQN) with n=10 from 2 separate experiments (Figure 4.9C).

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The data obtained from in silico analysis of predicted epitopes and the peptides identified in vivo studies are summarised in table 4.2. A peptide with a positive result is indicated by (+) for TEPITOPE and in vivo studies. For NetMHCIIpan, positive results are identified with either (S+) for strong binders or (I+) for intermediate binders. Peptides that were shown to be important in these studies are highlighted, in particular, peptide 6 and 15 for HLA-DR4 and peptide 11 for HLA-DR15. Although no peptide was identified in all three analyses for HLA-

DR1, peptide 4 was identified in the murine studies and was shown to be an intermediate binder from NetMHCIIpan. In addition, TEPITOPE identified a potential epitope present in peptide 5 and seven positions of peptide 5 are also covered by peptide 4. TEPITOPE and

NetMHCIIpan both predicted promiscuity for several peptides for HLA-DR1, -DR4 and -

DR15. However, in vivo analysis did not identify any promiscuous peptides, implicating the limitations of in silico predictions.

244

Table 4.2: In silico and in vivo summary of HLA-DR1, HLA-DR4 and HlA-DR15 restricted OprF epitopes. OprF peptides identified by in silico analysis (NetMHCIIpan and TEPITOPE) and in vivo murine studies restricted to HLA-DR1, HLA-DR4 and HLA-DR15 alleles are shown for

TEPITOPE and in vivo analysis with a (+) or for NetMHCIIpan predictions with a (S+) for strong binders and a (I+) for intermediate binders. Peptides identified from in silico and in vivo studies are indicated in blue.

245

4.3.6. Differences in the adaptive immune response to OprF protein in patients with bronchiectasis with and without chronic P. aeruginosa infection.

A cohort of patients diagnosed with bronchiectasis were used to investigate the differences in the adaptive immune response to help understand why some patients are able to clear P. aeruginosa infections, whereas others can’t and become chronically infected (Table 2.10).

Patients with an established diagnosis of bronchiectasis were classified into one of three groups based on bacterial cultures from sputum samples obtained during the study period.

Each patient entered into the study had sputum collected for microscopy and culture every month for the 6 month study period. Groups were defined as: (i) those that never cultured positive for P. aeruginosa during the study, (ii) those that cultured positive for P. aeruginosa on less than 50 % (<50 %) of sputum samples or (iii) those that cultured positive for P. aeruginosa on more than 50 % (>50 %) of sputum samples.

To investigate the adaptive immune response in these patient groups, differences in the CD4+

T cell responses stimulated by OprF or synthesised peptides were assessed in patient PBMCs by human IFNγ ELISpot assays (Table 4.3).

Table 4. 3: Patient cohort used in the human IFNγ ELISpot assay were determined by positive sputum cultures for P. aeruginosa in patients with bronchiectasis.

Group Classification Age ±SD Sex (F/M) n number Never culture positive for P. aeruginosa 55 ±16 22F/9M 31 <50 % cultures positive for P. aeruginosa 68 ±8 11F/1M 12 >50 % cultures positive for P. aeruginosa 63 ±13 8F/7M 15

246

Representative ELISpot data for each of the three groups for responses to peptide 1-34, OprF whole protein and anti-CD3 stimulation are shown in figure 4.10 (ELISpot data for all patients are presented in appendix 1). A positive epitope was determined when 2 or more spots for each peptide were above the baseline (Defined as 2 standard deviations above the mean of the negative control for each patient). Positive epitope responses were converted to a percentage of the highest responding peptide per individual and visualised as a heat map for all patients in each group. Never culture positive during study (Figure 4.11), cultured positive

<50 % of sputum samples (Figure 4.12A) and cultured positive >50% of sputum samples

(Figure 4.12B).

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To assess the HLA allele effect on responding epitopes, the frequencies of HLA-DR1, DR4 and DR15 patients with the number of responding peptides was determined (Table 4.4).

Previous in silico and in vivo studies investigating specifically one HLA-allele demonstrated peptide 4 for HLA-DR1, peptide 6 and 15 for HLA-DR4 and peptide 11 for HLA-DR15 to be important in eliciting a T cell response. Analysis of responding peptides in the patient groups is not quite as straight forward, as human antigen presenting cells express a range of HLA- molecules (HLA-DR, DP and DQ). However, patterns are observed that implicate associations between peptide-HLA complexes and P. aeruginosa infections. Interestingly, for patients with at least one HLA-DR15 allele that had never cultured positive during the study, peptide 11 was in the top 6 responding peptides. Of the 15 HLA-DR15 positive individuals that never cultured P. aeruginosa, 7 responded to peptide 11 (47 %) while only 1 out of 9 (11

%) HLA-DR15 positive individuals responded in those that cultured positive for P. aeruginosa.

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To further investigate differences in the immune responses between patients never infected compared to those with >50 % of sputum samples culturing positive for P. aeruginosa, immunological protein profiles were determined using a 30-plex luminex assay. Supernatants collected from patient PBMCs stimulated with OprF or from unstimulated PBMCs in media only (negative control) from the IFNγ ELISpots (Table 4.5), were utilised to determine differences with protein specific stimulation. Table 4.6 shows the concentrations of selected immunological proteins detected in supernatants from PBMCs isolated from patients that either never cultured positive for P. aeruginosa or cultured positive in > 50 % of sputum samples during the study. Those that were seen to have a significant difference are denoted by *. The T cell driven immune response to OprF stimulation elicited a significant increase in production of innate cytokines and chemokines in PBMCs from patients with >50 % of P. aeruginosa positive cultures including IL-6, IL-12, MIP-1α and MIP-1β. This implicates an enhanced innate response in these patients (Figure 4.15.A-D). In addition, the concentration of IL-8 in these patients may show a significant increase if larger sample sizes are used in further experiments (Figure 4.15E)

Table 4.5: Patient groups used in human Luminex assays were determined by positive sputum cultures for P. aeruginosa in patients with bronchiectasis. Group Classification Luminex protein Age ±SD Sex (F/M) n number Never culture positive for P. IL-6/IL-8/MIP-1α 53 ±17 11F/2M 13 aeruginosa All other proteins 52 ±15 18F/6M 24 >50 % cultures positive for P. IL-6/IL-8/MIP-1α 64 ±11 7F/5M 12 aeruginosa All other proteins 56 ±14 4F/4M 8

255

Table 4.6: Supernatants from OprF activated T cells obtained from IFNγ ELISpot assays. The immunological protein concentrations were determined using the 30-plex luminex assay for patients that never cultured positive or cultured positive >50% sputum samples.

Never >50% b Immunological protein a a P value (pg/ml) (pg/ml)

Cytokines GM-CSF 136 ±25 240 ±50

G-CSF 348 ±77 779 ±258

TNF-α 517 ±96 1027 ±319

IFN-α 3.9 ±2 10.7 ±6

IL-1β 1671 ±150 2315 ±276

IL-2 0.2 ±0.05 0.2 ±0.1

IL-4 1.2 ±0.4 3.6 ±0.9 **

IL-5 0.5 ±0.1 0.8 ±0.3

IL-6 5339.1 ±1508 12772 ±3401 *

IL-7 15.1 ±2 21.6 ±5.8

IL-8 35230 ±17683 95584 ±34319

IL-10 88.3 ±21 159 ±68

IL-12 12.4 ±2 24.9 ±5 *

IL-13 1.1 ±0.3 2.2 ±0.7

IL-15 52.8 ±10 60.6 ±28

IL-17 0.1 ±0.05 0.3 ±0.2

IL-1RA 0 0

IL-2R 53.8 ±11 83.5 ±14

Chemokines MIP-1α 7338 ±2465 18963 ±4145 * MIP-1β 1215 ±230 4038 ±1040 **

RANTES 161 ±36 202 ±46

EOTAXIN 0.05 ±0.02 0.05 ±0.02

IP-10 1.9 ±0.7 2.7 ±2

MCP1 0 0

MIG 2.5 ±1 2.3 ±2

Growth Factors VEGF 72.1 ±13 78.3 ±32

EGF 1.3 ±0.3 0.6 ±0.3

HGF 3.1 ±1 3.0 ±2

FGF 6.6 ±1 7.8 ±2 a Protein concentrations in pg/ml and presented as mean ± SEM. b Statistical significance determined with a Mann-Whitney U statistical test (*, p<0.05; **, p<0.01) 256

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257 disease associated with sinusitis and chronic infection (Boyton et al., 2006). Individual qRT-

PCR on activated PBMCs was carried out for patients diagnosed with idiopathic bronchiectasis or with bronchiectasis due to other underlying causes (Table 4.7) for genes listed in table 2.5. No differences were seen for most genes analysed. Interestingly, activated

PBMCs from patients never culturing positive for P. aeruginosa had increased Tbet expression compared to patients with > 50 % of P. aeruginosa culture positive sputum samples (Figure 4.16A). Other T cell associated transcription factors, Gata3 and RORγt, were not different (Figure 4.16B and C respectively). However, an additional transcript investigated known as S1P1, important in lymphocyte migration, was shown to be significantly decreased in those chronically infected with P. aeruginosa (Figure 4.16D). A second group of patients diagnosed with idiopathic bronchiectasis demonstrated with stimulation an increase in Tbet (Figure 4.17A) and a decrease in S1P1 (Figure 4.17D).

However, no differences in Tbet or S1P1 transcripts were observed between never culture positive compared to > 50 % of cultures positive (Figure 4.18A and D). In addition, Gata3 was unchanged and RORγt although increased, in those that do not culture positive for P. aeruginosa, was not significant (Figure 4.18 B and C respectively).

Table 4.7: Patient groups used in human qRT-PCR assays were determined by percentage of positive sputum cultures for P. aeruginosa in patients with idiopathic bronchiectasis or bronchiectasis due to other causes.

Total number of Average % of sputum samples patients age ±SD positive for P. aeruginosa (M/F) (Years) Never (0%) >50%

Bronchiectasis due 11 to underlying 52 ±16 7 4 (3M/8F) cause

15 Idiopathic 64 ±11 10 5 (5M/10F) bronchiectasis

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4.4. Discussion

In this chapter the outer membrane protein of P. aeruginosa, OprF, was produced and purified for use in murine and human studies. In silico analysis and in vivo murine studies using HLA transgenic mice identified several HLA class II restricted immunodominant epitopes. Furthermore, epitope studies in patients with bronchiectasis identified differences in the adaptive immune response that could potentially contribute to the clearance of infection.

4.4.1. Production and purification of OprF

Recombinant mature OprF from P. aeruginosa was expressed in E. coli BL21 (DE3) cells at

27 ºC and purified using a polyhistidine tag and nickel affinity chromatography. SDS-PAGE and mass spectrometry analysis demonstrated the presence of OprF at the expected size of approximately 37 kDa (Worgall et al., 2005) (Band 2, Figure 4.5A). A band of approximately

51 kDa (Band 4, Figure 4.5A) was confirmed by mass spectrometry as the uncleaved OprF-

SUMO fusion protein. An additional band was also confirmed as the OprF-SUMO fusion protein although of a slightly lower size, potentially due to premature cleaving of the OprF

(Band 3, Figure 4.5A). Although faint, a fourth band at approximately 34 kDa was also observed. Mass spectrometry analysis confirmed this as OprF and a soluble E. coli contaminant protein known as ribose-phosphate pyrophosphokinase. A focus for future work would be directed towards enhancing the purity of the OprF protein. To do this, a two-step chromatographic procedure that uses size exclusion chromatography, in addition to nickel affinity chromatography, to remove the contaminating protein could be used. However, this would need to take into account the differences in size of the proteins. A second method to increase the protein purity would be to separate the insoluble membrane preparation from the soluble proteins in the supernatant. After the initial slow spin to remove cell debris generated

262 from the sonication step, a second high speed ultracentrifugation step to pellet the cell membranes containing the OprF protein could be included in the protocol. The soluble contaminating protein would be removed with the supernatant resulting in a purer protein preparation.

OprF is not the only immunogenic candidate for immune studies. Other proteins such as exotoxin A, proteases and outer membrane proteins E, H2 and I are important virulence factors that have previously been shown to be immunogenic (Hancock et al., 1984; Klinger et al., 1978). Microarray analysis of outer membrane proteins of P. aeruginosa identified 12 proteins, in addition to previously known immunogenic proteins, as strong immunogens and included exoenzyme and flagellum related proteins (Table 4.8) (Montor et al., 2009). Further, a type three secretory system protein, PopB has been identified as an inducer of Th17 immunity and provides protection to subsequent challenge to P. aeruginosa in an antibody- independent manner (Wu et al., 2012)

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Table 4.8: 12 outer membrane immunogenic proteins of P. aeruginosa identified by Montor and colleagues (Montor et al., 2009).

PA Size Symbol Protein name and function Reference Number (kDa) Exoenzyme T (Sundin et PA0044 exoT 49 T3SS effector. Disrupts actin microfilaments al., 2001) ADP-ribosylating activity Beta lactam resistance. (Juan et al., PA0807 ampDh3 29 Regulation of ampC expression 2006) Peptidoglycan associated lipoprotein OprL (Hancock et PA0973 OprL 18 Structural, lipoprotein al., 1990) Flagella hook protein (Choi et al., PA1080 FlgE 48 Flagellum assembly 2011) (Song and Flagellin type B PA1092 fliC 34 Yoon, Subunit protein of bacterial flagella 2014) Chitinase (Folders et PA2300 chic 53 Extracellular enzyme that degrades chitin al., 2001) (Letoffe et PA3407 hasAp 21 Heme acquisition protein al., 1998) Exoenzyme S T3SS effector. (Rocha et PA3841 exoS 48 ADP-ribosyltransferase and GTPase- al., 2003) activating protein activity. PA3931 28 Conserved hypothetical protein Chromosomal cephalosporinase (Juan et al., PA4110 ampC 43 Beta lactamase - antibiotic resistance 2006) Azurin (Murphy et PA4922 azu 16 Mediates electron transfer al., 1993) Periplasmic phosphate-binding protein (Zaborina et PA5369 pstS 34 Adherence al., 2008) T3SS, Type 3 secretion system; ADP, adenosine diphosphate.

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4.4.2. In silico and in vivo predictions of HLA-DR restricted OprF epitopes

Antigenic proteins are processed via the exogenous pathway and presented via HLA-class II molecules in humans to TCRs expressed by CD4+ T cells (Blum et al., 2013). This is an essential step in the immune response towards pathogens and identification of these immunogenic peptides that elicit CD4+ T cell responses are potential HLA restricted CD4+ T cell epitopes that can be used for future rational epitope-based vaccine design. Several computational software programs incorporating different predicting algorithms such as matrix based algorithms utilised by TEPITOPE or learning algorithms utilised by

NetMHCIIpan and the consensus method were selected to determine potential epitopes in this thesis (Karosiene et al., 2013; Sturniolo et al., 1999; Wang et al., 2008). These in silico programs for HLA-class II analysis were selected over other programs due to previous performance benchmark analysis (Wang et al., 2008). Based on high and intermediate affinity binding determined by NetMHCIIpan and TEPITOPE analysis of OprF peptides, 5 similar predicted peptides were identified for HLA-DR1, 5 for HLA-DR4 and 5 for HLA-

DR15 in this thesis.

Epitopes predicted to bind specific HLA molecules by in silico analysis do not always identify functional epitopes. Further in vivo analysis is required to ensure the epitope is processed for presentation by HLA and that T cells, expressing a TCR with specificity to the bound peptide and HLA molecule, exist in the T cell population. Investigating immunodominant restricted epitopes in humans is particularly hard due to the low numbers of

CD4+ T cells specific to an antigenic protein (Malherbe, 2009), HLA polymorphism and expression of multiple HLA alleles (HLA-DR, DP and DQ). Currently, the number of HLA- class II alleles documented in the IMGT/HLA database include 7 DRα, 1512 DRβ, 51 DQα,

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509 DQβ, 37 DPα and 248 DPβ (Robinson et al., 2013). Therefore, humanised HLA- transgenic mice that express a single HLA molecule are paramount to identifying restricted

HLA epitopes. In addition, mice primed in the footpad with either protein or peptide of interest, generate cells in the DLN that present naturally processed peptides and can be used in epitope mapping experiments. The T cell mapping studies in this thesis utilised synthetic peptides of 20 amino acids that overlapped every 10 amino acids. Typically short peptides (8-

10 amino acids) don’t stimulate CD4+ T cells as well as longer peptides (12 amino acids or more) (Reece et al., 1994). The core consists of a 9 amino acid sequence (Chicz et al., 1993;

Chicz et al., 1992). To reduce the possibility of missing an epitope, each peptide overlaps by

10 amino acids (Malherbe, 2009). Although proteins contain many potential immunodominant sequences, in reality the response is usually limited and only a few epitopes can elicit an immune response. In this thesis the CD4+ T cell response was seen to be focused on only a few epitopes. HLA-DR1 expressing mice responded to peptide 4. HLA-DR4 expressing mice responded to peptide 6, 14 and 15 and HLA-DR15 expressing mice responded to peptide 11.

A comparison of in silico and in vivo analysis for epitope predictions clearly match for studies using HLA-DR4 and DR15 transgenic mice (Table 4.2). Both avenues of analysis identified peptide 6 and 15 in HLA-DR4 and peptide 11 in HLA-DR15 mice. The limitations of in silico analysis were highlighted with HLA-DR1 predictions. Many more epitopes were identified than were functional epitopes and of those predicted, functional epitopes were missed. This confirms the importance of in vivo studies.

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Earlier studies have identified three B cell epitopes of OprF capable of eliciting high titres of antibody (Gilleland et al., 1995; Hughes et al., 1992). These B cell epitopes are denoted as peptide 9 (TDAYNQKLSERRAN), peptide 10 (NATAEGRAINRRVE) and peptide 18

(NEYGVEGGRVNAVG) and have been further utilised in protective infection studies in rodents (Gilleland and Gilleland, 1995; Hughes and Gilleland, 1995). Peptide 10 (also denoted as Epi8) was further utilised in an adenoviral vector and demonstrated both a humoral and cellular response for CD4+ and CD8+ T cells (Worgall et al., 2005). The sequences of peptide 9 and 10 are located to peptide 29 and 33/34 respectively in this thesis.

In silico predictions used in this thesis such as NetMHCIIpan identified intermediate binding for peptide 29 with HLA-DR1, DR8, DR10 and DR11 and no binding for peptide 33 or 34

(Table 4.1). In addition in vivo studies in this thesis did not identify these as important peptides (Figure 4.9). This indicates that these peptides are not HLA-DR restricted. In addition, although Epi8 was able to induce both cellular and humoral responses, the studies reported, utilised CBA (H-2k), BALB/c (H-2d) and C57BL/6 (H-2b) mice that express wild type MHC-class II and not human HLA-class II as in this thesis (Worgall et al., 2005).

In this thesis, epitope mapping was limited to HLA-DR1, DR4 and DR15 mice. Further mapping of epitope responses in other HLA-DR and DQ restricted models would be beneficial. Experiments so far do not suggest promiscuous epitopes. However, in silico predictive mapping does identify several potential epitopes for other HLA-DR restricted molecules including DR3, DR7, DR8, DR9, DR10, DR11, DR13 and DR14.

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4.4.3. Differences in the adaptive immune response in bronchiectasis patients in the context of chronic P. aeruginosa infections

Patients diagnosed with bronchiectasis are characterised by abnormally dilated bronchi, damaged lungs and poor lung function, usually as a result of a known cause (O'Donnell,

2008). In addition, patients are often chronically infected by respiratory pathogens such as P. aeruginosa (Duff et al., 2013). However, some patients with bronchiectasis are susceptible and become chronically infected, while others do not. The purpose of this section was to identify if differences exist in the adaptive immune response between patients in particular by mapping the CD4+ T cell responses to OprF.

Approximately half of the individuals with a diagnosis of bronchiectasis recruited for this study were classified as never sputum culture positive for P. aeruginosa, while the rest were sputum culture positive for P. aeruginosa. Those infected with P. aeruginosa were sub divided into two groups comprising <50 % positive sputum cultures and >50 % positive sputum cultures. Interestingly, those with >50 % sputum cultures positive with P. aeruginosa also had a significantly contracted repertoire of IFNγ responding CD4+ T cells and enhanced proinflammatory cytokine production (IL-6, Mip1α, MIP-1β and IL-8) than patients classified as never sputum positive during the study. The lack of a Th1 response in relation to

P. aeruginosa infections has been previously reported in both murine and human studies.

Mouse models of chronic P. aeruginosa infections have established that mice with a Th1 phenotype demonstrate enhanced bacterial clearance, with milder lung inflammation and better disease outcomes than mice with a Th2 phenotype (Moser et al., 1999; Moser et al.,

2002). Human studies have also shown a correlation between disease outcome and Th1/Th2 associated phenotype. For example, PBMCs obtained from cystic fibrosis patients with or

268 without P. aeruginosa were stimulated with outer membrane proteins isolated from P. aeruginosa. An enhanced IFNγ response was observed in those without P. aeruginosa infection, whereas a higher IL-4 response was observed in those with P. aeruginosa infections. The increased IFNγ levels also correlated with improved lung function, thus implicating potential benefits in shifting from a Th2 to a Th1 response in P. aeruginosa infected patients that may improve lung function and disease outcome (Moser et al., 2000). In a separate study, Th1 and Th2 differences in BAL samples obtained from patients with cystic fibrosis, with or without P. aeruginosa infection, was investigated. Th2 (CCR4+CD4+) cells and associated production of Th2 cytokines IL-4, IL-13 and TARC were enhanced in those infected with P. aeruginosa and correlated with increased disease severity while IFNγ was increased in those not infected (Hartl et al., 2006). In summary, data presented in this thesis may help to explain the differences seen in both murine and human models of P. aeruginosa infection. Patients with chronic P. aeruginosa infection lack Th1 responses due to a lack in

CD4+ T cells capable of recognising and being stimulated by peptide bound to MHC class II.

However, this does not address the Th2 response. Potentially, a greater repertoire of Th2

CD4+ responding cells may be present in the infected patients. Indeed, an increase in IL-4 production (although very low concentrations) in chronically infected patients in this thesis was observed in the supernatants obtained from OprF stimulated CD4+ T cell driven cellular responses. A direction for future work would be to investigate the Th2 and specific antibody responses in a cohort of bronchiectasis patients using IL-4 ELISpots or OprF IgG specific

ELISAs to determine if patients with P. aeruginosa infections favour a Th2 responding repertoire of CD4+ T cells and an enhanced antibody response. The reduced repertoire of responding IFNγ+ CD4+ T cells could contribute to a lack of T cell ‘help’ and may subsequently lead to reduced CD8+ T cell responses, macrophage activation and poor bacterial clearance.

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Interestingly, in addition to a contracted repertoire of responding CD4+ T cells in chronically infected patients, innate associated cytokines and chemokines (IL-6, MIP-1α, MIP-1β, IL-12 and IL-8) were enhanced in supernatants obtained from OprF stimulated PBMCs. This increase in the proinflammatory cytokines is supported by previous in vivo and in vitro studies. Proinflammatory mediators such as IL-8 are induced in response to P. aeruginosa infections. This is thought mainly to be regulated via the recognition of the P. aeruginosa structures such as flagella via TLR5 (Adamo et al., 2004). TLR signalling through MyD88 in response to P. aeruginosa regulates phosphorylation of downstream phosphoproteins and transcription factors that upregulate IL-8 expression (Bezzerri et al., 2011). The mechanisms of cell activation induced by porins is not well established, particularly the direct stimulation by OprF on proinflammatory mediators. A study investigating general P. aeruginosa porins and the immune response identified the production of TNFα and IL-6 (Cusumano et al.,

1997), while other studies investigating several other gram-negative bacterial porins have demonstrated the involvement of TLR1, TLR2 and TLR6 and the induction of cytokines such as TNFα, MIP-1α, MIP-1β, IL-6 and IL-12 (Banerjee et al., 2008; Galdiero et al., 2004;

Massari et al., 2006; Ray et al., 2003). IL-12 directs Th1 polarisation (Athie-Morales et al.,

2004) and MIP-1α and MIP-1β are expressed by Th1 promoting dendritic cells (Lebre et al.,

2005). These cytokines are increased in the chronically infected patients compared to those that never cultured positive during the study. This suggests the immune response is potentially trying to direct a Th1 response, although the number of Th1 responding T cells is significantly contracted in this group. The enhanced production of proinflammatory mediators in response to P. aeruginosa is supported in a study investigating cystic fibrosis and P. aeruginosa infection. Elevated levels of proinflammatory cytokines and chemokines in the BAL tended to be higher compared to non-infected patients for GM-CSF, MCP-1 and

MIP-1β and reached significance for IL-6, IL-1β, and TNFα (Hartl et al., 2006). However,

270 although this increase in proinflammatory cytokines is observed in chronically infected patients, an increase in these mediators is observed in a general comparison between cystic fibrosis patients (with or without P. aeruginosa) and healthy controls (Bonfield et al., 1995;

Hartl et al., 2006). It was therefore hypothesised that this increase is due to inflammation rather than changes to the Th response, but this stronger proinflammatory response in those chronically infected may impede an efficient Th2 response (Hartl et al., 2006).

An interesting direction to further this work would be to confirm in silico and in vivo analysis and to determine the characteristics of HLA-class II restricted CD4+ T cells that respond to peptides identified in vivo studies by using HLA-Class II tetramers and CSFE labelling. A focus for the direction of future work to further confirm in silico and in vivo analysis is to use tetramer guided epitope mapping (TGEM) in human cells (Novak et al., 2001). TGEM uses

HLA class I or class II tetramers loaded with peptide to identify and isolate specific CD8+ or

CD4+ T cells respectively. Previous studies using TGEM technology have identified several

HLA restricted epitopes in bacterial (James et al., 2007), viral (Novak et al., 2001), allergic

(Till et al., 2014) and tumour (Molldrem et al., 1999) studies. Another step to validate the epitopes identified by in silico and in vivo studies would be to identify proliferative responses to peptides using carboxyfluorescein succinimidyl ester (CFSE) labelled PBMCs obtained from HLA-DR1, -DR4 or -DR15 positive individuals (Shiomi et al., 2010).

HLA-class II tetramers are a tool that consist of four biotinylated HLA molecules tetramised by fluorochrome conjugated streptavidin and loaded with a peptide of interest (Novak et al.,

2001). HLA class I tetramers were initially reported to identify CD8+ T cell responses in patients infected with human immunodeficiency virus (Altman et al., 1996). Since then,

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HLA-class II tetramers have been used to investigate CD4+ T cell responses including bacterial and viral infections (Meyer et al., 2000) and allergy (Kwok et al., 2010; Till et al.,

2014). To characterise CD4+ T cells specific for HLA-DR1, HLA-DR4 or HLA-DR15 restricted epitopes of OprF, tetramers consisting of the correct HLA type and loaded peptide

(highlighted in section 4.3.5) would identify CD4+ T cells specific to these complexes. The percentage of tetramer positive cells between disease groups or the differences in the phenotype of these cells can then be characterised using flow cytometry and specific cell markers that determine CD4+ T cell subtypes (e.g. Th1 or Th2 cells) and the activation status of a cell (e.g. Naive, effector or memory). Cell markers to determine subtype would include:

Th1-CXCR3 (Sallusto et al., 1998); Th2 – CCR4, CCR3 (Sallusto et al., 1998) and to determine activation status markers would include: naive-CD45RA and CXCR4 (Akbar et al., 1988; Sallusto et al., 1998); activated-CD69 and CD40L (Howland et al., 2000; Testi et al., 1989) and memory- CCR7, CD62L (Sallusto et al., 1999). In addition functional differences of tetramer positive cells can also be analysed via identifying the cytokines produced (e.g. IFNγ or IL-4).

The ELISpot data generated from patients with bronchiectasis demonstrated a reduced repertoire of IFNγ+ CD4+ T cell responses in those with chronic P. aeruginosa infection compared to those never infected during the study. To further investigate differences in the adaptive immune response, PBMCs obtained from non-idiopathic patients were stimulated and the main CD4+ T cell subset transcription factors Tbet, Gata3 and RORγt in addition to sphingosine-1-phosphate receptor 1 (S1P1) were investigated. Patients never culture positive during the study had increased expression of Tbet and S1P1 transcripts compared to those that were chronically culture positive. Tbet is a transcription factor mainly associated with the differentiation of Th1 cells (Szabo et al., 2000), although it is also expressed by several other

272 cell types located in PBMC samples including dendritic cells (Lighvani et al., 2001), NK cells (Townsend et al., 2004) and activated γδ T cells (Yin et al., 2002). S1P1 is a surface receptor expressed by endothelial and epithelial cells, but also by PBMCs such as T cells

(Matloubian et al., 2004), macrophages (Singer et al., 2005), NKT (Allende et al., 2008) and dendritic cells (Czeloth et al., 2007) and is important in several physiological pathways.

These include limb formation during embryonic development (Chae et al., 2004) and immune cell trafficking of dendritic cells, macrophages and T cells (Czeloth et al., 2007; Matloubian et al., 2004; Singer et al., 2005). In particular, S1P1 is essential in lymphocyte egress from the thymus and secondary lymphoid organs in response to its ligand sphingosine-1-phosphate

(S1P) (Matloubian et al., 2004) that is more concentrated in the blood and lymph (Pappu et al., 2007; Pham et al., 2010). S1P1 downregulation is observed upon stimulation with antigen

TCR interactions within the lymph node (Matloubian et al., 2004). Activated T cells downregulate S1P1 and are transiently retained within the lymph node allowing proliferation.

S1P1 is re-expressed approximately 3 days later and effector T cells exit the lymph node and enter the circulation (Matloubian et al., 2004); (Rivera et al., 2008). Identification of cell types within PBMC samples expressing S1P1 is essential to understand the differences observed in patients able to clear P. aeruginosa compared to those chronically infected. In addition, S1P1 is vital in cellular migration. To investigate this further, studies looking at the chemotaxis potential of the different cell types expressing S1P1 would identify effects of migration in response to S1P.

T cells deficient in S1P1 have been shown in mice to have impaired migration into the blood

(Allende et al., 2004; Matloubian et al., 2004) while T cells from S1P1 transgenics preferentially migrate to the blood instead of lymphoid tissues (Chi and Flavell, 2005; Graler et al., 2005). In addition, treatment with fingolimod (FTY720), a S1P1 agonist, inhibits

273 lymphocyte egress into the blood resulting in immunosuppressive effects (Brinkmann et al.,

2002; Mandala et al., 2002). Therefore, S1P1 is crucial in the migration of effector T cells and this decrease in S1P1 transcripts in patients culturing positive for P. aeruginosa in more than

50 % of sputum samples in this thesis could implicate impaired T cell migration. A recent study demonstrated that Th2 cell migration out of the lymph nodes and spleen was inhibited when a specific protein, extracellular matrix protein-1 (ECM1), was removed (Li et al.,

2011). ECM1 was shown to be specific for Th2 cells, upregulated during the late phase response of Th2 cell differentiation and subsequently upregulates S1P1 re-expression via IL-2 receptor inhibition. In addition, ECM1 expression is regulated by Stat6 and the Th2 transcription factor, Gata3. Mice deficient in ECM1 did not re-express S1P1 resulting in the continued retention of effector Th2 cells in the secondary lymphoid tissues and a significant reduction in the blood (Li et al., 2011). Potentially, a similar mechanism could be hypothesised to explain the reduction in S1P1 transcripts observed in samples taken from the blood of patients culturing positive for P. aeruginosa in more than 50 % of sputum samples that could potentially be restricted to Th1 cells. Hypothetically, chronically infected patients with a reduction in S1P1 may retain T cells (Th1) within the lymph node resulting in reduced effector Th1 cells in the circulation and consequently result in the contracted repertoire of

CD4+ T cell responses and poor clearance of P. aeruginosa observed in these patients.

4.5. Summary

The aim of this chapter was to produce and purify a major immunodominant protein associated with P. aeruginosa that could be used to map HLA-DR restricted CD4+ T cell epitopes as well as identifying differences in the adaptive immune response in patients with bronchiectasis that either enable P. aeruginosa clearance or contribute towards chronic

274 infection. HLA-DR1, -DR4 and -DR15 restricted epitopes were identified using in silico epitope predictions and humanised HLA-DR transgenic mice. Analysis of these three restricted HLA-DR molecules implies CD4+ T cell epitope restriction is specific for each

HLA molecule. Further, individuals with chronic P. aeruginosa lung infections present with a significantly contracted repertoire of responding IFNγ+ CD4+ T cells. This suggests a reduced level of Th1 responding cells in these patients that could potentially be due to mechanisms involving lymphocyte trafficking from the lymphoid tissues to the circulation.

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CHAPTER 5: An allele specific effect of HLA-Cw*03 expression and NK cell activation in patients with idiopathic bronchiectasis.

5.1. Introduction

Bronchiectasis is a common structural endpoint. Causes include: post infectious processes

(M. tuberculosis), ABPA, genetic disorders (PCD, cystic fibrosis and Young’s syndrome),

CVID and immunodeficient or autoimmune diseases (Systemic Lupus Erythematosus (SLE) and Rheumatoid Arthritis) (Cohen and Sahn, 1999; Pasteur et al., 2000). Structural changes result in irreversible and abnormal dilation of the bronchi. This is associated with excessive inflammation and recurrent infections leading to airway obstruction and reduced lung function (Barker, 2002). The occurrence of bronchiectasis is unknown. In the developed world less people suffer from severe disease, however with more advanced technology

(HRCT), that can detect with higher resolution, more individuals are being diagnosed who previously may not have been identified (Boyton, 2012; Perez et al., 1998). Bronchiectasis is more common among remote populations such as native Alaskans and Australian aborigines where the incidence, particularly in children, is reported to be much higher (Chang et al.,

2002; Singleton et al., 2000). The changes that occur within the lung are thought to be two fold. First, initiation as a result of some form of structural damage or insult that second, results in a ‘vicious cycle’ involving bacterial infection and increased airway inflammation

(Boyton et al., 2013). In approximately half of patients diagnosed with bronchiectasis, no known underlying cause is identified and these patients are defined with idiopathic bronchiectasis (Pasteur et al., 2000). In idiopathic bronchiectasis bilateral, predominantly lower lobe disease with associated sinusitis is observed (Boyton et al., 2006)

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Dysregulated lung inflammation in patients with bronchiectasis has been observed including increased levels of proinflammatory mediators such as TNFα, IL-8 and elastase along with increased neutrophils and macrophages (Gaga et al., 1998; Tsang et al., 2000). NK cells have also been implicated in contributing towards bronchiectasis. Individuals with TAP deficiency syndrome fail to express HLA-class I that consequently leads to impaired NK cell function and development of bronchiectasis (Zimmer et al., 1999; Zimmer et al., 1998). An association between the adaptive immune response and idiopathic bronchiectasis is suggested due to evidence indicating the presence of CD4+ and CD8+ T cells in the lungs of idiopathic bronchiectasis patients (Gaga et al., 1998; Silva et al., 1989) and the implication of HLA-

DR1 and HLA-DQ5 expression in disease (Boyton et al., 2008).

HLA-class I molecules have also been associated with idiopathic bronchiectasis (Boyton et al., 2006). The HLA-class I molecule HLA-C, can be classified into two groups based on the amino acid located in position 77 and 80 of the α1 domain. HLA-C molecules with an asparagine at position 80 and a serine at position 77 are defined as group 1, while group 2

HLA-C molecules contain a lysine at position 80 and an asparagine at position 77 (Colonna et al., 1993; Colonna et al., 1992). Importantly, these two HLA-C groups bind specific activating and inhibitory ligands presented by NK cells, known as killer immunoglobulin receptors (KIR). HLA-C group 1 molecules bind the inhibitory receptors KIR2DL2 and

KIR2DL3 and activating receptor KIR2DS2, while HLA-C group 2 molecules bind the inhibitory receptors KIR2DL1 and the activating receptor KIR2DS1. This interaction between HLA-C and KIRs regulates NK cell activation. Interestingly, in a cohort of 96 UK patients diagnosed with idiopathic bronchiectasis compared to 101 controls, susceptibility towards idiopathic bronchiectasis was observed in HLA-C group 1 homozygous patients. In particular, an allele specific effect was observed, with an increased susceptibility in HLA-

277

Cw*03 patients. In addition, a decrease in HLA-C group 2 homozygosity was observed in the idiopathic patients that importantly demonstrated increased protection with HLA-Cw*06 expression (Boyton et al., 2006).

A hierarchy of inhibition based on binding studies and identification of structure has been developed whereby inhibition is regulated by KIR2DL receptors binding to HLA-C (Parham,

2005). KIR2DL1 has been shown to induce the strongest inhibition in a HLA-C group 2 homozygous population while KIR2DL3 demonstrates the weakest level of inhibition in

HLA- C group 1 homozygous population with heterozygous interactions in between (Parham,

2005). A relationship between HLA-C and KIR expression was implied in a cohort of idiopathic patients (Boyton et al., 2006). In the idiopathic group an increase in patients expressing stimulatory KIR in HLA- C group 1 homozygous patients was observed, implicating susceptibility. Whereas, HLA-C group 1 and group 2 heterozygous individuals expressing one or both stimulatory receptors (KIR2DS1 and KIR2DS2) is considered protective and is a combination that few idiopathic patients expressed. HLA-C group 1 homozygous individuals don’t bind to the strongest inhibitory receptor KIR2DL1. This may overall lead to fewer NK cells under inhibitory control resulting in a greater impact of stimulatory KIRs that effect the level of NK cell activation (Boyton et al., 2006). It was therefore hypothesised that idiopathic bronchiectasis patients are more susceptible if HLA-

Cw*03 positive or homozygous for HLA-C group 1 due to dysregulated NK cell activation

(Boyton et al., 2006). In this chapter the function of NK cells from HLA-C group 1 and

HLA-C group 2 and HLA-Cw*03 positive individuals with a diagnosis of idiopathic bronchiectasis were studied.

278

5.2. Aims

To investigate the impact of HLA-C on the immune response:

 To determine the effect of group 1 homozygosity compared to group 2 homozygosity

on NK cell activation in a cohort of patients with idiopathic bronchiectasis.

 To determine the effect of HLA-Cw*03 allele expression on NK cell activation in a

cohort of patients with idiopathic bronchiectasis.

 To further identify differences between HLA-Cw*03 positive patients and HLA-

Cw*03 negative at the transcript level.

5.3. Results

5.3.1. Increased activation of CD56+CD3- cells in idiopathic bronchiectasis patients not expressing the HLA-Cw*03 allele.

Previously, a genetic association indicating the susceptibility of HLA-C group 1 homozygosity and more specifically the presence of the HLA-Cw*03 allele with idiopathic bronchiectasis has been reported (Boyton et al., 2006). It was also suggested in this study that this association with susceptibility to bronchiectasis with HLA-C group 1 homozygosity could be due to HLA-C/ KIR interactions, resulting in a highly activated and dysregulated

NK cell response that contributes towards the disease pathology of bronchiectasis (Boyton et al., 2006).

279

In this chapter, the impact of HLA-C group 1 and group 2 homozygosity or HLA-Cw*03 allele specificity has on the activation of NK cells in a cohort of patients diagnosed with idiopathic bronchiectasis was investigated (Table 5.1). NK cell activation was determined by multiparameter flow cytometry analysis of IFNγ and granzyme A production and expression of the natural cytotoxicity receptor NKp44, expressed by activated NK cells (Vitale et al.,

1998). In addition, expression of transcription factors by qRT-PCR and Affymetrix analysis between patients expressing or lacking HLA-Cw*03, with or without stimulation of PBMCs were investigated.

Table 5.1: The idiopathic bronchiectasis cohort used for human multiparameter FACS and qRT-PCR assays were classified by HLA-C group 1 or group 2 homozygous expression or by HLA-Cw*03 expression.

Total number of patients Average age ± SD (M/F) (Years) HLA-C group 1 homozygous 17 (4M/13F) 65 ±10 HLA-C group 2 homozygous 4(1M/3F) 51 ±8 HLA-Cw*03 negative 13 (2M/11F) 60 ±10 HLA-Cw*03 positive 8 (3M/5F) 64 ±12

Frozen PBMCs from an idiopathic bronchiectasis cohort were thawed, washed and then incubated for 4 hours (with or without stimulation). Cells were stained for surface and intracellular markers and analysed by flow cytometry (as described in section 2.14.). Gates were set dependant on isotype controls. The rationale used for the gating strategy is shown in figure 5.1. Initially forward (FSC) and side (SSC) scatter plots were used to determine the lymphocyte gate (Figure 5.1A). The PBMCs used were frozen which results in a higher proportion of cellular death compared to fresh PBMCs. Therefore, to determine live from dead cells, a LIVE/DEAD® fixable red dead cell stain was used (Figure 5.1B). The

LIVE/DEAD® stain is a fluorescent dye that binds to proteins. The dye cannot penetrate the

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No differences in the percentage of CD56+CD3- cells producing IFNγ or granzyme A between group 1 and group 2 homozygous patients (Figure 5.2A and 5.2E) or between HLA-

Cw*03 positive and HLA-Cw*03 negative patients (Figure 5.2C and 5.2G) was observed.

However, the mean fluorescence of intensity (MFI) measuring the average amount of IFNγ produced by each stimulated cell in patients expressing HLA-Cw*03 was significantly decreased compared to HLA-Cw*03 negative patients (Figure 5.2D). In this experiment, granzyme A was seen to be constantly produced in both unstimulated and stimulated cells with no differences between groups (Figure 5.2E-H). In addition to NK cells, CD8+ cells when activated produce increased levels of IFNγ and so IFNγ production by this cell type was also investigated. However, figure 5.3 A-D demonstrates that there was no difference in

IFNγ expression with stimulation. Figure 5.3E-H demonstrates that there is no difference in granzyme A expression in CD8+CD3+ cells. There was also no difference in expression of

IFNγ or granzyme A in CD3+CD8- cells (Figure 5.4).

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Although no functional differences were observed between HLA-C group 1 or group 2 homozygous idiopathic patients, qRT-PCR of mRNA isolated from activated PBMC samples from these patients demonstrated an increase in RORγt expression in group 2 homozygous patients (Figure 5.5C), but no difference in the other transcription factors Tbet (Figure 5.5A) and Gata3 (Figure 5.5B) or the transcript S1P1 were observed (Figure 5.5D). Functional data comparing HLA-Cw*03 positive patients with HLA-Cw*03 negative patients shows a decrease in IFNγ production by NK (CD56+CD3-) cells from patients expressing HLA-

Cw*03 allele (Figure 5.2D). Further investigations into transcript differences between stimulated PBMCs of HLA-Cw*03 negative patients compared to HLA-Cw*03 positive patients showed no differences for Tbet or RORγt expression, although GATA3 and S1P1 transcripts were both increased (Figure 5.6).

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To further investigate differences between patients expressing HLA-Cw*03 at the mRNA level, mRNA was extracted from unstimulated and stimulated PBMCs (Table 5.2) and prepared for Affymetrix analysis. Groups for analysis included: unstimulated compared to stimulated HLA-Cw*03 negative patients; unstimulated compared to stimulated HLA-Cw*03 positive patients; unstimulated HLA-Cw*03 negative patients compared to unstimulated positive patients and finally stimulated HLA-Cw*03 negative compared to stimulated HLA-

Cw*03 positive patients. A summary of this data analysis is shown in table 5.3 with the stringency settings defined as > ±1.5 fold change and an adjusted FDR P value <0.05 (Table

5.3). A clear difference was observed in the number of differentially regulated genes in the

HLA-Cw*03 negative patients with stimulation (105 genes) compared to HLA-Cw*03 positive patients with stimulation (13 genes). However, at baseline (unstimulated) or with stimulation between HLA-Cw*03 negative and HLA-Cw*03 positive patients, no genes were differentially regulated (Table 5.3).

Table 5.2: The idiopathic bronchiectasis cohort used for Affymetrix studies were classified by HLA-Cw*03 expression.

Total number of patients Average age ±SD (M/F) (Years) HLA-Cw*03 negative 5 (1M/4F) 63 ± 11 HLA-Cw*03 positive 4 (0M/4F) 68 ± 11

290

Table 5.3: A summary of differentially regulated genes in PBMCs from patients with idiopathic bronchiectasis to compare the allele specific effect of HLA-Cw*03. Frozen PBMCs from idiopathic bronchiectasis patients (n=9) were either unstimulated or stimulated with PMA/ionomycin. RNA was extracted and samples were prepared for Affymetrix analysis. Data was acquired by the CSC genomics laboratory, Imperial College. Data was subsequently analysed using Partek and differentially regulated genes were identified by statistical testing using Anova. Statistical testing between groups for genes with > ±1.5 fold change and a false discovery rate (FDR) adjusted p value to correct for multiple comparisons, p<0.05.

>1.5 fold change Comparison p<0.05 (with FDR) HLA-Cw*03 negative 105 (unstimulated vs stimulated) HLA-Cw*03 Positive 13 (unstimulated vs stimulated) Unstimulated 0 (HLA-Cw*03 negative vs positive) Stimulated 0 (HLA-Cw*03 negative vs positive)

Differential genes identified in HLA-Cw*03 negative patients with stimulation were inputted into Metacore, Genego software for pathway analysis with help from Michael Poidinger

(Figure 5.7 and 5.8). Of the 105 genes differentially regulated, pathways were identified that predominantly highlighted the importance of T cells and in particular CD4+ T cells and their differentiation into the T cell subsets: Th1, Th2, Th9, Th17 and Th22. In comparison, HLA-

Cw*03 positive patients upregulated the cytokines IFNγ and IL-22, potentially indicating only Th1 and Th22 cells. Genes differentially regulated are shown by either a red or blue bar next to the gene in the pathway map (Figure 5.8).

291

Figure 5.7: Pathway enrichment analysis predominantly identified T cell related pathways in HLA-Cw*03 negative PBMCs with stimulation. PBMC RNA isolated from idiopathic patients was converted into cDNA and gene expression was determined by Affymetrix array. The top 10 enrichment pathways were identified by pathway analysis performed using Metacore from GeneGo Inc (Pathway analysis provided by Dr Michael Poidinger, Singapore Immunology Network). The enrichment p values per pathway were calculated based on a hypergeometric distribution with significant enrichment defined using the False Discovery Rate (FDR) corrected p value. Data presented as -log(p value).

292

Figure 5.8: Pathway enrichment analysis predominantly identified T cell related pathways in HLA-Cw*03 negative PBMCs with stimulation. PBMC RNA isolated from idiopathic patients was converted into cDNA and gene expression was determined by Affymetrix array. The top enrichment pathways identified using the Metacore database (GeneGo, Inc. with help from Michael Poidinger, Singapore Immunology Network) involved T cells and CD4 differentiation related genes. Red and blue bars represent genes where expression was either up- or downregulated respectively in stimulated PBMCs compared to unstimulated PBMCs. The amount of colour within each bar represents the magnitude of gene expression. Red, green, and grey lines represent negative, positive, and unspecified effects.

293

Of the genes shown to be differentially regulated, 7 are common between unstimulated and stimulated PBMCs for HLA-Cw*03 negative patients and unstimulated and stimulated

PBMCs for HLA-Cw*03 positive patients as shown in the Venn diagram in figure 5.9. IFNγ production as expected, is increased with stimulation. HLA-Cw* 03 negative patients have an

11 fold increase and HLA-Cw*03 positive patients have a 42 fold increase in IFNγ. Despite this difference in fold change between the two groups, no significant difference with FDR adjustments was seen between these groups with stimulation (Table 5.3). However, genes differentially regulated at baseline between HLA-Cw*03 negative and HLA-Cw*03 positive patients were identified when FDR adjustments were removed. Defined settings for this analysis included a fold change > ± 2.5 and a p value <0.01. 34 genes were identified to be differentially regulated under these settings (Table 5.4). Of these genes, a vast majority are classified as IFN inducible genes. In addition, although just out of the defined settings, IFNγ demonstrated an almost 4 fold increase in HLA-Cw*03 negative patients at baseline (p value

= 0.014) compared to HLA-Cw*03 positive patients. The dot plot in figure 5.10 represents the distribution at baseline and with stimulation of IFNγ transcript expression for each individual patient. In addition, the box and whisker plots indicate data spread. At baseline the difference between the two groups can be visualised. Patients not expressing HLA-Cw*03 generally had a greater level of IFNγ transcripts than those expressing HLA-Cw*03. With stimulation however, IFNγ expression increases, but the difference in the distribution between the two groups is lost. This highlights the importance that differences at baseline have in these patient groups. Interestingly, a difference in expression of several microRNAs

(miRNA) was observed at baseline. In particular, the primary miRNA known as mir-125b-1 was shown to be downregulated in HLA-Cw*03 negative patients. Of the genes identified to be differentially regulated, one specific gene known as N-myc integrator (NMI) was upregulated in HLA-Cw*03 negative patients. Interestingly, NMI is a predicted mRNA target

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Table 5.4: A summary of differentially regulated genes in PBMCs from patients with idiopathic bronchiectasis to compare the allele specific effect of HLA-Cw*03 Frozen PBMCs from idiopathic bronchiectasis patients (n=9) were either unstimulated or stimulated with PMA/ionomycin. RNA was extracted and samples were prepared for Affymetrix analysis. Data was acquired by the CSC genomics laboratory, Imperial College. Data was subsequently analysed using Partek and differentially regulated genes were identified by statistical testing using Anova. Statistical testing between groups for genes with a > ± 2.5 fold change and an unadjusted p value, p<0.01.

>2.5 Fold change Comparison p<0.01(Unadjusted p value) Unstimulated 34 (HLA-Cw*03 negative vs positive) Stimulated 29 (HLA-Cw*03 negative vs positive)

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5.4. Discussion

The purpose of this chapter was to determine the functionality of NK cells in a cohort of idiopathic patients. Although no difference in IFNγ production was observed between HLA-

C group 1 compared to group 2 patients, an allele specific difference was observed with

HLA-Cw*03 expression. Individuals that were HLA-Cw*03 negative made more IFNγ. In addition, differences at baseline between HLA-Cw*03 positive patients and negative patients was observed with a broader lymphocyte and more activated response in individuals not expressing HLA-Cw*03.

In the cohort of patients recruited for this study, about half were diagnosed with idiopathic bronchiectasis. Approximately 50 % of these were homozygous for HLA-C group 1 while 10

% were homozygous for HLA-C group 2. The greater percentage of HLA-C group 1 homozygous patients observed, in this cohort of idiopathic patients, supports the previous genetic association reported for HLA-C group 1 homozygosity with idiopathic bronchiectasis

(Boyton et al., 2006). Individuals diagnosed with bronchiectasis are commonly chronically infected with bacterial pathogens including: P. aeruginosa, H. Influenzae, non-tuberculosis mycobacterium and aspergillosis for which innate immunity and NK cells are important in rapid cytotoxic killing of infected target cells and the production of cytokines that impact on other immune cells. NK cells bridge the gap between innate and adaptive immunity by secreting chemotactic factors such as IL-8 that recruit CD4+ and CD8+ lymphocytes

(Somersalo et al., 1994). NK cell activity also regulates the effector function of CD8+ T cells to help clear pathogens such as M. tuberculosis. Once activated, NK cells secrete IFNγ that act on monocytes to secrete other cytokines like IL-15 and IL-18 which enhance CD8+ proliferation and lysis of infected cells (Vankayalapati et al., 2004). A difference in NK cell

298 phenotype influenced by the Th1/Th2 balance altered the outcome of P. aeruginosa infections (Calum et al., 2003). NK cells therefore play a critical role in the innate immune response to infection, but also shape the adaptive immune response, in particular in the clearance of bacterial species associated with bronchiectasis. The role of dysfunctional NK cells have been associated with the development of bronchiectasis in individuals diagnosed with TAP deficiency syndrome. This is a rare disease that accounts for a low proportion of patients diagnosed with bronchiectasis. However, it does result in the loss of either one or both TAP proteins resulting in impaired HLA-class I expression and enhanced activation of

NK cells, thus implicating a role for NK cells in bronchiectasis development (Zimmer et al.,

1999; Zimmer et al., 1998). In addition, a genetic association with HLA-C group 1 homozygous patients and KIR expression implicates a role for dysregulated NK cells with patients diagnosed with idiopathic bronchiectasis (Boyton et al., 2006). In this chapter, differences in NK cell functionality was compared between idiopathic patients of HLA-C group 1 homozygous or group 2 homozygous phenotype. No difference between group 1 or group 2 homozygous patients with IFNγ production by NK cells was observed. However, both groups were diagnosed with bronchiectasis so although no difference was seen between groups, a functional difference may exist when compared to healthy controls. Therefore, further experiments would be required including individuals without bronchiectasis to fully establish the functional level of NK cell activation without disease. However, an allele specific difference was observed with HLA-Cw*03 expression. Patients positive for HLA-

Cw*03 expression demonstrated a reduced level of IFNγ production by CD56+CD3- cells compared to patients negative for HLA-Cw*03. Potentially, this implies that the NK cell function of HLA-Cw*03 negative patients is greater than HLA-Cw*03 positive patients.

299

Although no difference was observed between HLA-C group 1 homozygous and group 2 homozygous individuals functionally, a difference in RORγt transcript expression in mRNA samples from stimulated PBMCs was observed between these groups. RORγt is the main transcription factor of Th17 cells but is also expressed by group 3 innate lymphoid cells

(ILC3) (Walker et al., 2013). Previous studies investigating the impact of NK cells on Th17 cells observed a suppression of Th17 cells, although this was related to the CNS and pregnancy (Fu et al., 2013; Hao et al., 2010). IL-17 is the main cytokine associated with

Th17 cells and is also important in bacterial clearance. In particular, a role for IL-17 in the clearance of P. aeruginosa has been previously reported (Liu et al., 2011; Liu et al., 2013). In addition, reduced Th17 cells are observed in the peripheral blood of cystic fibrosis patients compared to controls (Bayes et al., 2014). The increased expression of RORγt in HLA-C group 2 patients potentially suggests that these patients may be able to clear infections more efficiently.

Interestingly, Affymetrix analysis of stimulated PBMCs compared to unstimulated PBMCs from HLA-Cw*03 positive and HLA-Cw*03 negative patients implicate a difference not only in the number of genes, but also in the T cell subsets activated. In particular, HLA-

Cw*03 negative patients upregulate genes associated with CD4+ T cell subsets including:

IFNγ (Th1), IL-4 (Th2), IL-9 (Th9), IL-21 (Th17) and IL-22 (Th22), while HLA-Cw*03 positive patients only upregulated IFNγ (Th1) and IL-22 (Th22) with stimulation. Previous studies have implicated an increase in CD4+ T cells in bronchiectasis, although the subtype of the infiltrating CD4+ T cells was not reported (Gaga et al., 1998). This broader range of potentially activated CD4+ T cell subsets implicates a more diverse immune response in these patients. In addition, qRT-PCR demonstrates increased GATA3 transcripts in the HLA-

Cw*03 negative patients implicating an enhanced Th2 response, although no difference in

300

Tbet or RORγt was observed. However, these transcription factors are expressed by other cell types within PBMC samples so differences in transcription factors can’t be assumed to be solely caused by CD4+ T cell subsets.

Of the genes identified to be differentially regulated when stimulated, for HLA-Cw*03 negative patients and HLA-Cw*03 positive patients, 7 genes were observed in both groups to be upregulated. Early growth response genes (Egr)-1, 2 and 3 are transcription factors involved in regulating proliferation and activation of lymphocytes (Safford et al., 2005). Egr2 and Egr3 are negative regulators of T cell activation and are required for anergy induction in

CD4+ T cells (Harris et al., 2004; Safford et al., 2005) while Egr1enhances T cell function

(Decker et al., 2003). In this study Egr2 and 3 were both increased by a similar level while

Egr1 was upregulated 2 fold more in HLA-Cw*03 positive patients. IFNγ was upregulated by approximately 4 fold greater in HLA-Cw*03 positive patients compared to HLA-Cw*03 negative patients. However, as figure 5.10 shows, there is a difference at baseline between these two groups that appears to be lost with stimulation that could account for this difference. At baseline IFNγ demonstrated a 4 fold difference in expression between these two groups (p = 0.014). In addition, the HLA-Cw*03 negative patients also had an increased difference in regulation of several interferon inducible genes that are commonly implicated in viral immunity. For example, the IFN type 1 gene interferon-alpha inducible protein (IFI44) is upregulated. Over expression of IFI44 has an antiproliferative effect thus resulting in controlled cellular proliferation (Hallen et al., 2007). However, IFI44 is only induced by type

1 interferons (Kitamura et al., 1994). BTN3A3 is also upregulated in HLA-Cw*03 negative patients that has been shown to be important in T cell and NK cell stimulation (Messal et al.,

2011). CXCL11 is another gene that is upregulated in response to interferons including IFNγ

(Cole et al., 1998) and is important in the chemotaxis of NK and T cells expressing CXCR3

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(Robertson, 2002). The upregulation of these genes suggest that HLA-Cw*03 negative patients are potentially at a higher level of activation than HLA-Cw*03 positive patients.

Thus, the presence of HLA-Cw*03 is associated with increased susceptibility to idiopathic bronchiectasis (Boyton et al., 2006), but also with a lower level of activated immune cells that may potentially contribute to a less effective bacterial/ pathogen clearance.

Interestingly, other genes that were differentially regulated in the HLA-Cw*03 negative patients included several microRNA (miRNA) genes. miRNAs are short (~22bp), highly conserved, non-coding single stranded RNA molecules that target mRNA sequences and control expression (O'Connell et al., 2010). miRNA involvement has been described for many diseases including cancer (Volinia et al., 2006), inflammation (Chaudhuri et al., 2011) and viral infections (Jopling et al., 2005). RNA polymerase II enzymes transcribe primary miRNA (pri-miRNA) transcripts from gDNA. The pri-miRNA is then cleaved by Drosha and

DiGeorge syndrome critical region gene 8 (DGCR8) enzymes into pre-miRNA. Pre-miRNA is exported into the cytoplasm by exportin 5 where the enzyme Dicer further cleaves the pre- miRNA into a miRNA duplex that binds to the 3’ untranslated region of the target mRNA in the RNA-induced silencing complex (RISC). The targeted mRNA sequence is then either degraded or translation is prevented (O'Connell et al., 2010). miRNAs are essential in the adaptive immune response. Dicer knockout models have shown the critical role that the miRNA pathway has in T cell development (Muljo et al., 2005) while miR-181 has been shown to control the sensitivity of the TCR during T cell development (Li et al., 2007) and miR-155 regulates CD4+ T cell differentiation (Banerjee et al., 2010). In this chapter HLA-

Cw*03 negative patients had reduced expression of pri-miR125b1 compared to HLA-Cw*03 positive patients at baseline. This is the precursor form of two mature miRNAs (miR-125b1-

3p or miR-125b-5p). miR-125b has previously been shown to be involved in macrophage

302 activation (Chaudhuri et al., 2011), B cell upregulation of transcriptional repressor Blimp-1

(Malumbres et al., 2009) and murine miR-125b transgenic models overexpressing this miRNA in HSC results in leukaemia (Bousquet et al., 2010). More importantly, miR-125b has been shown to control naive CD4+ T cells by suppressing CD4+ T cell differentiation and thus reduced the generation of effector cells (Rossi et al., 2011). This miRNA is downregulated in the HLA-C*w03 negative patients compared to HLA-Cw*03 positive patients suggesting that there is enhanced differentiation of CD4+ T cells. This would support the effects identified with stimulation as potentially a larger repertoire of CD4+ T cell subset are induced in the HLA-Cw*03 negative patients. In addition, the predictive software programs available online including MIRDB and microRNA.org were used for predicting miRNA targets of mature miRNA. The gene N-myc interactor (NMI), involved in enhancing

STAT mediated transcription induced by IL-2 or IFNγ (Zhu et al., 1999a) was identified as a potential target of miR-125b-1-3p. Interestingly, NMI was upregulated in HLA-Cw*03 negative patients while pri-mir125b1 was downregulated implicating a potential target of miR-125b1 in these patients.

To fully understand the impact and functional role of miRNA expression in HLA-Cw*03 negative patients compared to HLA-Cw*03 positive patients, further study that first confirms the differential expression of the miRNA in patient samples by qRT-PCR or northern blotting is required. Following this, the identification of cell types expressing the specific miRNA is essential as miRNA expression is specific to certain cell types (O'Connell et al., 2010). Once the cell type is identified, the miRNA can then be targeted by Antagomirs. Antagomirs are synthetic oligonucleotides generated to disrupt and silence a specific miRNA of interest that consequently results in alterations to downstream mRNA targets that can be identified

(Krutzfeldt et al., 2005; Trajkovski et al., 2011). Potentially this would lead to the

303 identification of pathways involved in the development of allele specific associated bronchiectasis.

5.5. Summary

In this chapter, investigating the functionality of NK cells in idiopathic bronchiectasis patients revealed no difference between HLA-C group 1 homozygous and group 2 homozygous patients. However, there is a very interesting allele specific observation.

Individuals with idiopathic bronchiectasis expressing HLA-Cw*03 appear to have a lower level of general immune activation, in particular a reduced range of CD4+ T cell subsets and less NK cell IFNγ production than HLA-Cw*03 negative patients. Previous studies have shown that the presence of HLA-Cw*03 is associated with increased susceptibility to idiopathic bronchiectasis, but those not expressing HLA-Cw*03 have a heightened immune response that may contribute to improved pathogen clearance and therefore less non-specific innate driven inflammatory damage.

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CHAPTER 6: General Discussion

This section aims to address the issues highlighted in section 1.15 of the introduction using the findings identified in this thesis and draws attention to areas where further investigation is required.

6.1. hIL-8 transgenic mice: a new tool to study chronic lung remodelling.

Inflammatory cell infiltration into the lung results as a response to injury or insult and is essential to protect and restore normal lung function. However, continued or repeated exposure resulting in long-term airway inflammation, can contribute towards remodelling processes in the lung. Chronic lung remodelling encompasses a range of processes associated with structural change. These include: mucus hypersecretion, smooth muscle hypertrophy and hyperplasia, sub-epithelial fibrosis and angiogenesis. These all contribute towards the narrowing of the lumen and dilation of bronchial walls leading to airway obstruction and impaired lung function (Dunnill et al., 1969; Fahy et al., 2000; Hoshino et al., 1998; Ordonez et al., 2001; Regamey et al., 2008).

These remodelling processes are characteristic of many lung diseases. However, the inflammatory cells present and anatomical sites of structural changes differ between diseases.

Asthma is generally associated with airway wall thickening as a result of fibrosis and smooth muscle changes that causes a decrease in the size of the bronchial lumen. Parenchymal destruction doesn’t occur, whereas in COPD, damage to the lung parenchyma resulting in emphysema is found (Jeffery, 2001). Characteristic inflammatory cells in allergic asthma include activated CD4+ T cells and eosinophils (Bousquet et al., 1990; Robinson et al., 1992),

305 whereas in steroid resistant asthma enhanced neutrophil numbers are seen (Green et al.,

2002). COPD is associated with CD8+ T cells and neutrophils (Peleman et al., 1999; Saetta et al., 1998) while the prominent cell types seen in bronchiectasis are neutrophils, macrophages and CD4+ T cells (Gaga et al., 1998). However, enhanced inflammatory cell infiltration into lungs of patients with respiratory diseases like COPD, bronchiectasis and asthma are all associated with enhanced proinflammatory mediators and in particular, the chemokine IL-8 is elevated in all these lung diseases (Angrill et al., 2001; Dean et al., 1993; Yamamoto et al.,

1997). IL-8 is a potent neutrophil chemoattractant that has never been studied in vivo in the lung.

Investigating the in vivo effects of hIL-8 in the murine lung is an exciting endeavour, but it does have limitations. Firstly, IL-8 is not a natural gene encoded in mice and secondly, although previous studies have demonstrated chemotactic activity of hIL-8 for murine neutrophils, they are not as efficient at migrating as human neutrophils (Rot, 1991). The concentration of hIL-8 detected in the BAL of hIL-8 transgenics is similar, if not higher than, levels detected in the BAL obtained from patients with respiratory disease (as discussed in section 3.4.1.). However, as hIL-8 is not as efficient at recruiting murine neutrophils, a lower effective concentration of hIL-8 is present in the mouse model. A comparison of the hIL-8 model and its ability to recruit neutrophils compared to a transgenic model over expressing the functional homologue of hIL-8, KC, demonstrated that the total neutrophil number recruited is similar (Tsai et al., 1998). An approximate 12 fold increase in total neutrophil count was observed in the hIL-8 transgenic model in this thesis, while an approximate 15 fold increase in total neutrophil numbers in the KC transgenic model was observed thus implicating the effective role of hIL-8 in the transgenic model.

306

The pathogenesis of human respiratory disease is complex and no model truly imitates all aspects associated with lung disease, however they do provide an increased understanding into the effects of specific proteins on remodelling processes in the lung. Transgenic models expressing IL-13, IFNγ, ORMDL3 and MMP-1 or knockout models demonstrate this and identify specific roles in the context of each protein (D'Armiento et al., 1992; Miller et al.,

2014; Wang et al., 2000; Zhu et al., 1999b). The hIL-8 model, characterised in this thesis, provides an insight into the role of hIL-8 in vivo on lung remodelling processes including mucus secretion, fibrosis and epithelial damage and is therefore an essential tool in understanding pathways leading to remodelling processes in patients with respiratory diseases in the context of IL-8.

Respiratory diseases like bronchiectasis are commonly associated with bacterial infections such as P. aeruginosa, H. influenzae and S. pneumoniae (Duff et al., 2013). The hIL-8 transgenics that demonstrate characteristics of lung remodelling are a useful model to investigate the impact of bacterial infection. In this thesis, the impact of chronic hIL-8 expression on acute P. aeruginosa infection was investigated. The outcome of this was twofold. Either (i) the hIL-8 transgenics would be more susceptible due to the enhanced lung remodelling processes observed or (ii) the hIL-8 model would be more protective due to the enhanced level of neutrophil infiltrates in the lung. It was observed in this thesis that the hIL-

8 transgenics had increased resistance to P. aeruginosa compared to wild type mice.

Investigations into immune differences between the two groups demonstrated elevated total neutrophil cell counts and TNFα levels. Increased TNFα levels have previously been shown to increase resistance to P. aeruginosa infection (Gosselin et al., 1995). However, differences observed were limited to certain features of innate immunity and as of yet, no analysis into differences in the adaptive immune response have been investigated in this model. Different

307 structures expressed by P. aeruginosa, particularly during acute infection as the planktonic form, are recognised by different receptors of innate immunity. In particular, TLR receptors such as TLR4 and TLR5 are important in recognising LPS and flagella (Hajjar et al., 2002;

Hayashi et al., 2001). A role for cytokines impacting on TLR expression has previously been established for IFNγ, TNFα and IL-6 (Romieu-Mourez et al., 2009; Zarember and Godowski,

2002). Although the role of IL-8 on TLR has not been established, the effect that cytokines can modulate TLR expression may be important in this model. Lymphocytes and in particular

CD4+ T cells, have been implicated in P. aeruginosa clearance and enhanced numbers are associated with diseases like bronchiectasis (Gaga et al., 1998). Further, a Th1 CD4+ T cell response is associated with a protective role and enhanced clearance of P. aeruginosa (Moser et al., 1999; Moser et al., 2002; Moser et al., 2000). Therefore, further analysis into other innate and adaptive aspects in the hIL-8 model is required and studies using innate and adaptive qRT-PCR arrays investigating this are ongoing in the Boyton laboratory at Imperial

College.

6.2. Developing a peptide based vaccine for P. aeruginosa infections.

The most successful vaccines in use today generally consist of live attenuated pathogens such as the Varicella virus vaccine (Varis and Vesikari, 1996) and inactivated pathogens such as the inactivated polio vaccine (Shahzad and Kohler, 2009). However, vaccines using these approaches can be problematic due to unwanted side effects, inflammation, reversion to virulence and storage issues (Black et al., 2010). With the increased understanding of the immune response and interactions between adaptive immune cells to enable effective antibody production, approaches towards vaccine design are being directed with a peptide based focus utilising peptides that induce CD4+ T cell help to ensure more efficient vaccines

308 are developed. For example, a recent study identified peptides specific for tetanus and diphtheria toxoid that focused on initiating a robust CD4+ T cell response in humans, mice and non-human primates, which enhanced the efficiency of the vaccine targeted towards nicotine addiction (Fraser et al., 2014).

Multiple candidates have been considered in the development of a vaccine towards P. aeruginosa. However, at present a vaccine directed towards P. aeruginosa is unavailable.

Vaccines utilising several approaches have been assessed including whole cell killed and live attenuated vaccines or vaccines targeted towards certain structural components such as: outer membrane proteins, alginate, flagella and pilin (Baumann et al., 2004; Cripps et al., 2006;

Doring et al., 2007; Hertle et al., 2001; Krause et al., 2011; Pennington et al., 1975; Pier et al., 1994; Priebe et al., 2002). While these candidate vaccine targets have shown potential, they also have limitations resulting in the lack of an effective vaccine being developed further than phase III for therapy in humans (Sharma et al., 2011). An understanding of the interplay between the host’s immune response and pathogen are required to aid in the development of a successful vaccine. P. aeruginosa is an extracellular pathogen for which the humoral and antibody dependent opsonisation have important roles in the immune response (Sharma et al.,

2011). Further, evidence from numerous studies have also implicated the importance of the T cell mediated response in protection to P. aeruginosa (Dunkley et al., 1994; Porwoll et al.,

1983; Powderly et al., 1986) and in particular the more protective role of a Th1 mediated response (Moser et al., 2002; Moser et al., 2000). The identification of CD4+ T cell epitopes required to induce a robust immune response is essential, as CD4+ T cells are required to

‘help’ CD8+ T cell and B cell effector and memory responses (Novy et al., 2007; Smith et al.,

2004; Smith et al., 2000).

309

Outer membrane proteins have received increased attention as promising vaccine candidates.

Previous studies have focused mainly on immunisations with the two main antigenically conserved outer membrane proteins OprF and OprI. Indeed, several reports in which human volunteers were immunised with OprI or a combination of OprI/OprF proved it to be safe and immunogenic with the induction of IgG and IgA antibodies (Baumann et al., 2004; von

Specht et al., 1996). The majority of rodent studies using outer membrane proteins demonstrated elevated IgG antibody responses to whole protein and to specific B cell epitopes with subsequent protection (Finke et al., 1990; Finke et al., 1991; Gilleland et al.,

1988; Hughes et al., 1992; Matthews-Greer and Gilleland, 1987; von Specht et al., 1995).

This suggests class switching to IgG and thus implicates a role for helper CD4+ T cells.

Indeed, epitope 8 (OprF311-341) has been identified as an important epitope in the protection against P. aeruginosa that mounts both humoral and cellular responses in wild type mice

(Worgall et al., 2007; Worgall et al., 2005). Nonetheless, published data identifying specific

T cell epitopes to the protein OprF of P. aeruginosa is limited for peptide vaccine design.

Several strategies can be utilised in the identification of immunodominant T cell epitopes. In this thesis, CD4+ T cell epitopes were identified using computational HLA-class II predictions and humanised transgenic mice specifically expressing HLA-DR class II molecules.

Epitope mapping using in vivo or in vitro methods is a costly and labour intensive process.

Often to aid in the identification of epitopes in silico, software programs are used that utilise algorithms to predict the binding of potential epitopes. The predictive methods selected in this thesis were chosen due to previous benchmark studies identifying TEPITOPE, Propred and NetMHCIIpan as the best performing methods along with the implementation of the consensus method (Karosiene et al., 2013; Lin et al., 2008; Wang et al., 2008; Wang et al.,

310

2010). However, due to peptide processing, presentation by MHC class II and recognition by specific TCR in vivo results in limitations in the capabilities of in silico predictions. As observed in this thesis, not all epitopes predicted result in binding and even some peptides that can bind are missed in the predictions. This strengthens the need for in vivo and in vitro experiments to confirm true immunodominant epitopes. However, epitope mapping studies previously reported need to be interpreted with caution if mice expressing wild type murine

MHC class II are used. Previous epitope mapping for OprF utilised wild type animals

(Hughes et al., 1992; Worgall et al., 2005), thus the specificity of these epitopes in humans is unknown. Therefore, the use of transgenic strains expressing human HLA-class II can be used to map T cell responses that would relate to human HLA-class II. In addition, the murine model provides a source of antigen specific T cells following OprF immunisation.

Targeting specific CD4+ T cell responses in a single vaccine is limited due to the highly polymorphic binding grooves of HLA-class II molecules and each individual within the population has a specific set of HLA- class II alleles each for HLA-DR, DP and DQ that display different binding potentials for certain peptides. This results in a large repertoire of alleles within the population that potentially bind different peptides. To circumvent this, a universal peptide with promiscuity to bind several alleles would be beneficial in vaccine design however, promiscuity doesn’t always occur. Therefore, to develop a peptide-based vaccine to encompass the general population, vaccines would either require a small number of promiscuous epitopes or a large number of epitopes with the ability to bind different alleles. Studies in this thesis focusing on HLA-DR alleles were limited to available transgenic strains. However, preliminary mapping demonstrates that a range of specific epitopes are required for at least HLA-DR1, DR4 and DR15 which puts emphasis on a OprF peptide- based vaccine requiring a range of epitopes to cover a greater spread of the population.

311

In this thesis, functional differences in patients with idiopathic bronchiectasis with or without

P. aeruginosa were observed. Patients culturing positive in more than 50 % of sputum samples demonstrated a contracted repertoire of responses to OprF peptides as compared to those not infected with P. aeruginosa. This enhanced number of IFNγ producing CD4+ T cells in those not infected supports the concept that Th1 cells are protective (Moser et al.,

2002; Moser et al., 2000). However, due to HLA-DR polymorphism and the expression of

HLA-DR, DP and DQ class II molecules it is difficult to draw firm conclusions about HLA-

DR expression frequencies and responding peptides between the two groups.

Whether the HLA-DR restricted epitopes identified using in silico and in vivo studies would be able to induce an effective and consequently a protective immune response, in terms of activated CD4+ T cells and subsequent activation of B cells and antibody production, to P. aeruginosa is unknown. Previous studies have shown that the whole OprF protein is able to induce a sufficient total IgG antibody response and specific IgG isotypes including IgG1,

IgG2a, IgG3 and IgA in response to P. aeruginosa post OprF immunisations indicating class switching (Krause et al., 2011). In addition, B cell epitopes identified previously were able to induce high IgG antibody titres implicating the effectiveness of OprF epitopes (Hughes et al.,

1992; Worgall et al., 2005) that later demonstrated a protective role in mice (Hughes and

Gilleland, 1995). However, to further investigate if the OprF peptides identified in this thesis are capable of inducing an immune response, several experiments are required. These include in vitro studies utilising CFSE T cell proliferation and characterisation of CD4+ T cell phenotypes following tetramer binding. In addition, in vivo analysis of T cell proliferative responses using increasing doses of the corresponding peptide following administration of peptide 4 to HLA-DR1, peptide 6 or 15 to HLA-DR4 or peptide 11 to HLA-DR15 transgenic mice. To further these experiments, analysis of disease severity and immune responses

312 following immunisations with confirmed peptides in the appropriate HLA-restricted line and subsequent challenge with P. aeruginosa would determine their protective capabilities.

6.3. Are NK cells dysfunctional in patients with idiopathic bronchiectasis?

The cause of idiopathic bronchiectasis is unknown and previous studies have implied a genetic susceptibility and subsequent potential role of dysregulated NK cells (Boyton et al.,

2006). The functional role of NK cells in patients diagnosed with idiopathic bronchiectasis between HLA-C group 1 and group 2 homozygous patients were investigated. However, no difference in NK activity was observed. Further studies are required to compare the activation state between these HLA-C group 1 and 2 homozygous idiopathic patients with healthy controls. In addition, more patients are required to make firm conclusions about the data.

Increased susceptibility to idiopathic bronchiectasis is associated with the presence of HLA-

Cw*03 and from this thesis a lower level of lymphocyte activation has been implicated.

Further studies are required to investigate the level of activation of the different T cell subsets to confirm if this is a true result. In addition, the identification of the role of miRNAs at baseline and identification of potential mRNA targets may increase our understanding of pathways that could lead to this enhanced level of activation observed in the HLA-Cw*03 negative patients.

6.4. Summary

In this thesis, a model of chronic lung remodelling in the context of hIL-8 has been characterised, implicating the role of hIL-8 in vivo on inflammation, mucus production, fibrosis and epithelial damage that has not been previously reported in the lung. This model of chronic respiratory disease also demonstrates enhanced resistance to P. aeruginosa

313 infection. Following on from this, the work in this thesis has also identified HLA-DR1, -DR4 and -DR15 restricted CD4+ T cell epitopes to a major outer membrane protein of P. aeruginosa, OprF in in vivo studies that can be used to direct future work in the development of peptide based vaccines for P. aeruginosa. The observed contracted repertoire of IFNγ responding CD4+ T cells in chronically infected patients suggests a reduced CD4+ T cell memory population in the peripheral blood. Identification of immune differences in these patients with the chronic respiratory disease, bronchiectasis, increases our understanding of differences in the immune response that enables clearance of P. aeruginosa. The presence of

HLA-Cw*03 is associated with increased susceptibility to idiopathic bronchiectasis.

Individuals with idiopathic bronchiectasis who are HLA-Cw*03 negative have enhanced lymphocyte activation which may result in improved bacterial clearance.

The hIL-8 transgenic model provides an exciting tool that will further enhance our understanding of the role IL-8 has on lung remodelling processes and contribution to lung disease. Further investigations using this IL-8 model could lead to targeted pathways and novel treatment strategies for chronic respiratory disease. CD4 T cell responder epitopes identified in this thesis require further testing to identify the protective ability in HLA- restricted models and ultimately the incorporation into epitope based vaccine design. This thesis has potentially identified a link between a genetic susceptibility and subsequent NK cell dysregulation in patients with idiopathic bronchiectasis. This could lead to further studies in NK cell activation status in comparison to healthy controls that could enhance our understanding of the involvement of the immune response and infection in idiopathic bronchiectasis.

314

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Appendix 1 1.1. IFNγ ELISpot data for each patient

1.1.1. Individuals that were never culture positive for P. aeruginosa during 6 month study

B1 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B3 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B9 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B10 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

431

B18 3000 2000 1000

pbmc cells pbmc 40

6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B19 3000 2000 1000

pbmc cells pbmc 100 6 80 60 40 sfc per 10 per sfc  20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B25 2000 1000

50 40 pbmc cells pbmc 6 30 20

10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B26 3000 2000 1000

cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

432

B27 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B28 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B29 3000 2000 1000 pbmc cells pbmc 6 60 40 sfc per 10 per sfc

 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B33 3000 2000 1000

pbmc cells pbmc 40 6 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

433

B35 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B36 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B38 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B39 3000 2000 1000

pbmc cells pbmc 40 6 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

434

B45 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B46 2500 2000 1500 1000 500

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B54 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B55 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20

10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

435

B56 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B62 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B63 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B64 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

436

B72 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B73 3000 2000 1000

pbmc cells pbmc 30 6

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B74 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B75 3000 2000 1000 60 pbmc cells pbmc 6 40

10 per sfc  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

437

B77 3000 2000 1000

pbmc cells pbmc 40

6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B79 3000 2000 1000

pbmc cells pbmc 30 6

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B83 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

438

1.1.2 Patients culture positive for P. aeruginosa in less than 50 % of sputum samples taken during the study

B12 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34) B17 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B31 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B43 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

439

B48 3000 2000 1000

pbmc cells pbmc 60 6 40

10 per sfc 20  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B50 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B52 3000 2000 1000

pbmc cells pbmc 100 6 80 60 40 sfc per 10 per sfc  20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B68 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

440

B71 2500 2000 1500 1000 500

pbmc cells pbmc 50 6 40 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B76 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B78 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B81 2000 1500 1000 500 40 pbmc cells pbmc 6 30 20 sfc per 10 per sfc  10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

441

1. 1.3 Patients culture positive for P. aeruginosa in more than 50 % of sputum samples taken during the study

B2 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B13 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B14 2000 1500 1000 500

pbmc cells pbmc 40 6 30 20

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B16 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

442

B21 2000 1500 1000 500 pbmc cells pbmc 6 40

20 sfc per 10 per sfc  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B23 3000 2000 1000

pbmc cells pbmc 30 6 20

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B24 2000 1500 1000 500 100 pbmc cells pbmc 6 80 60 40 sfc per 10 per sfc

 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B40 3000 2000 1000

pbmc cells pbmc 40 6 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

443

B47 3000 2000 1000

pbmc cells pbmc 40 6 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B57 3000 2000 1000

pbmc cells pbmc 50 6 40 30 20 sfc per 10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B61 3000 2000 1000

pbmc cells pbmc 100 6 80 60 40 sfc per 10 per sfc  20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B67 3000 2000 1000 50 40 pbmc cells pbmc 6 30 20

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

444

B70 3000 2000 1000

pbmc cells pbmc 40 6 30 20

10 per sfc

 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B82 3000 2000 1000

pbmc cells pbmc 30 6 20

sfc per 10 per sfc 10  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

B85 2000 1500 1000 500

pbmc cells pbmc 60 6

sfc per 10 per sfc 20  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 CD3 OPRF PA1777 peptide (1-34)

445

1.2 Permission Document

Permission attained from Michael Poidinger for use of pathway anlysis figures in figure 3.12, 3.13, 5.7 and 5.8

From: [email protected] star.edu.sg [[email protected]] on behalf of Michael Poidinger (SIgN) [[email protected]] Sent: Wednesday, September 26, 2012 3:55 AM To: Reynolds, Catherine J Subject: RE: Pathway analysis favour

No problem. Results attached. Note I assumed this was mouse. If it’s human, let me know and I will re-run, though I expect similar results.

You are allowed to use everything, including the images, in the paper. If you do use it, please add

“Pathway analysis performed using Metacore from GeneGo Inc” to the methods sections

And

“Pathway analysis provided by Dr Michael Poidinger, Singapore Immunology Network”

To the acknowledgements.

Mike

446