The BCAR1 Locus in Carotid Intima-Media Thickness and Atherosclerosis Freya Boardman-Pretty University College London Thesis submitted for the degree of Doctor of Philosophy 1 Declaration of work I, Freya Boardman-Pretty, confirm that the work presented in this thesis is my own and has been generated by me as the result of my own original research. Where information has been derived from other sources, this has been indicated in the text. My role in the work presented in each chapter is outlined below. In chapter 3 I carried out all bioinformatics analysis, including identification of SNPs in strong LD, analysis of regulatory marks associated with these SNPs using UCSC Genome Browser, HaploReg and ElDorado, and selection of candidate SNPs. I used the Gene-Tissue Expression (GTEx) browser and Gilad/Pritchard eQTL browser to investigate eQTLs. In chapter 4 I was responsible for genotyping the PLIC cohort for the SNP rs4888378. Genotype data and an analysis plan were sent to Danilo Norata and Andrea Baragetti (University of Milan), who carried out the statistical tests that I requested in PLIC and provided test results and the necessary data for the meta-analysis. I carried out the sex-stratified meta-analysis and additional event rate analysis. In chapter 5 I carried out electrophoretic mobility shift assays (EMSAs) on candidate SNPs, multiplex competitor EMSAs and supershift EMSAs. For the luciferase assay, I designed reporter fragments of interest and carried out the cloning experiments to create the four sets of reporter vectors, and carried out the luciferase assays. I also carried out transfection experiments. In chapter 6 I carried out all experiments involved in design of the 4C protocol. My understanding and design of the protocol was developed with the advice of contacts carrying out similar experiments: Johanna Fischer (Crick Institute), Marjon Verstegen (Hubrecht Institute, Utrecht) and Michal Mokry (UMC Utrecht). In chapter 7 I used exome sequencing data to identify the SNP rs1035539 for study, and carried out genotyping in the IMPROVE and PLIC cohorts. Genotyping data and analysis plan were sent to Danilo Norata and Andrea Baragetti, who carried out the analysis I requested and provided test results. I carried out statistical analysis in IMPROVE. In chapter 8, the pEGFP-C2/BCAR1 and pENTR3C-BCAR1 plasmids had previously been created by the Cardiovascular Biology and Medicine group, and were provided to me for further use. I carried 2 out side-directed mutagenesis, cloning, virus production and assays on transfected and infected cells, with help and guidance from Ian Evans. 3 Acknowledgments Many people gave invaluable help and advice throughout my PhD. I would first like to thank my supervisor Steve Humphries, who from the moment of offering me the PhD position to helping me with the final details of my thesis, has been hugely supportive. His guidance and encouragement have helped me to understand and enjoy new areas of genetics, and motivated me to persevere when things were less easy. Equal thanks go to my secondary supervisor Andrew Smith, whose expertise and knowledge have made a great difference to my PhD, helping me to shape my research and construct much of my thesis. He has always been willing to help me with long and difficult experiments, or to know when I needed to go and complain about them at the pub. Thank you also to Philippa Talmud who has been there for support many times throughout my PhD, and to Ann Walker for the encouraging words during long writing sessions. I am also greatly appreciative towards Jackie Cooper and Fotios Drenos, who have given me a great deal of advice and insight into statistical methods, and were always happy to answer my questions. Thank you to all past and present members of CVG who have made the lab such an enjoyable environment to work in; for being a welcoming and friendly group and for joining in with the bake- offs, plank-offs, cocktail parties, away days and more that make the group such a unique place to be. Thank you to Katherine for always being there to talk about topics both work-related and more inconsequential, Marta for being a constant friend and source of advice, and to Dauda for being my go-to for a chat or for advice on all things technology-related. I am also very grateful to Jutta for all the lab expertise she has shared with me over the years, and to Jay and Jacquie for their help and support. I would like to thank the British Heart Foundation for providing valuable funding to the group to support our research. I would also like to thank the members of the Cardiovascular Biology and Medicine Group, particularly Ian Evans, who has taught me many new methods and has always been on hand to answer my many questions. His tireless support and assistance with both lab work and writing have helped shape a large portion of my thesis. I am very grateful to my collaborators Andrea Baragetti and Danilo Norata, who worked with me on analysis of the PLIC cohort; big thanks to Andrea who was always willing to run new analyses for me and answer all my questions. Thank you too to Karl Gertow and the Karolinska group, who have been excellent collaborators and without whom the basis of this thesis would be very different. 4 I would also like to thank the organisers of the IMPROVE, WHII, EAS and MDC cohorts used in this thesis, and the individuals who participated in these studies. Finally, thank you to my family and friends, who have given me a great deal of support throughout the last few years. I appreciate the patience and encouragement they have shown me and without their help I’m sure I would not be where I am today. 5 Abbreviations AMD: age-related macular degeneration ANOVA: analysis of variance ASAP: Advanced Study of Aortic Pathology BCAR1: breast cancer antiestrogen resistance 1 BiKE: Biobank of Karolinska Endarterectomies BMI: body mass index bp: base pairs BSA: bovine serum albumin CAD: coronary artery disease CCA-IMT: common-carotid intima-media thickness CFDP1: craniofacial development protein 1 CHARGE: Cohorts for Heart and Aging Research in Genomic Epidemiology CHD: coronary heart disease ChIP: chromatin immunoprecipitation ChIP-seq: chromatin immunoprecipitation sequencing CHST6: carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 6 CIP: calf intestinal alkaline phosphatase COS-7: CV-1 (simian) in origin, carrying SV40 CRISPR: clustered regularly-interspaced short palindromic repeats CRP: C-reactive protein CTRB1: chymotrypsinogen B1 CTRB2: chymotrypsinogen B2 DMEM: Dulbecco's modified eagle medium DMSO: dimethyl sulphoxide dNTPs: deoxynucleoside triphosphates EAS: Edinburgh Artery Study EGF: epidermal growth factor EMEM: Eagle's minimal essential medium ENCODE: Encyclopaedia of DNA Elements ER: oestrogen receptor EVS: exome variant server FBS: foetal bovine serum FDR: false discovery rate FH: familial hypercholesterolaemia 6 GAPDH: Glyceraldehyde-3-Phosphate Dehydrogenase GFP: green fluorescent protein GTEx browser: Gene-Tissue Expression browser GWAS: genome-wide association study HDL: high density lipoprotein HEK293: human embryonic kidney 293 HR: hazard ratio HR: homologous recombination HUVEC: human umbilical vein endothelial cell IBC: ITMAT-Broad_CARe iCOGS: iSelect Collaborative Oncological Gene-environment Study IMPROVE: European Carotid IMT and IMT-Progression as Predictors of Vascular Events IMT: intima-media thickness KDR: kinase insert domain receptor (aka vascular endothelial growth factor receptor 2) LD: linkage disequilibrium LDHD: lactate dehydrogenase D LDL: low density lipoprotein MAF: minor allele frequency MDC: Malmö Diet and Cancer Study MDS: multi-dimensional scaling NHEJ: non-homologous end joining NRP1: neuropilin-1 nt: nucleotides NTC: no-template control OR: odds ratio PBS: phosphate-buffered saline PBST: phosphate-buffered saline with 0.1% Tween 20 PDGF: platelet-derived growth factor PLIC: Progressione della Lesione Intimale Carotidea SDS: sodium dodecyl sulphate SDS: Sodium dodecyl sulphate SH3: src homology 3 siRNA: short interfering RNA SNAP: SNP Annotation and Proxy Search SOC: super optimal broth with catabolite repression TBE: Tris/Borate/EDTA 7 TE: tris-EDTA TEMED: Tetramethylethylenediamine TF: transcription factor TMEM170A: transmembrane protein 170A VEGF: vascular endothelial growth factor VSMC: vascular smooth muscle cell WHII: Whitehall II ZFP1: zinc finger protein 1 ZNRF1: zinc and ring finger 1 8 Abstract Carotid intima media thickness (IMT) is a marker of subclinical atherosclerosis that can predict cardiovascular events over traditional risk factors. This thesis focused on a chromosome 16 locus associated with IMT and coronary artery disease, in order to identify functional variation affecting protein structure or gene expression, and investigate the role of novel genes in atherosclerosis. Bioinformatics tools were used to identify variants in strong LD with the lead SNP and to filter these to a shortlist of potential regulatory variants. Electrophoretic mobility shift assays on these 6 SNPs detected allele-specific protein binding to the lead SNP rs4888378 in CFDP1, and implicated the protein FOXA. Luciferase reporter assays showed a 35-92% decrease in gene expression with the A allele. Expression-QTL analysis confirmed associations of the protective allele of rs4888378 with higher expression of BCAR1 in vascular tissues. Genotyping and analysis of the lead SNP in the PLIC cohort of 2144 healthy men and women suggested a sex-specific effect of the SNP on IMT progression. Meta-analysis of five cohort studies supported a protective effect of the A allele on common-carotid IMT in women only. As illustrated by this locus, functional variation often lies in enhancers far from its target promoter. Circular chromosome conformation capture (4C) was explored to investigate interactions between enhancers and promoters at this locus, with the aim of capturing regions interacting with rs4888378 and the BCAR1 promoter. Analysis of exome sequencing data identified a SNP in BCAR1 (coding amino acid change P76S), which was genotyped in two cohorts (IMPROVE and PLIC).