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Quantitative Trait Loci Mapping of Macrophage Atherogenic Phenotypes

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Quantitative Trait Loci Mapping of Macrophage Atherogenic Phenotypes QUANTITATIVE TRAIT LOCI MAPPING OF MACROPHAGE ATHEROGENIC PHENOTYPES BRIAN RITCHEY Bachelor of Science Biochemistry John Carroll University May 2009 submitted in partial fulfillment of requirements for the degree DOCTOR OF PHILOSOPHY IN CLINICAL AND BIOANALYTICAL CHEMISTRY at the CLEVELAND STATE UNIVERSITY December 2017 We hereby approve this thesis/dissertation for Brian Ritchey Candidate for the Doctor of Philosophy in Clinical-Bioanalytical Chemistry degree for the Department of Chemistry and the CLEVELAND STATE UNIVERSITY College of Graduate Studies by ______________________________ Date: _________ Dissertation Chairperson, Johnathan D. Smith, PhD Department of Cellular and Molecular Medicine, Cleveland Clinic ______________________________ Date: _________ Dissertation Committee member, David J. Anderson, PhD Department of Chemistry, Cleveland State University ______________________________ Date: _________ Dissertation Committee member, Baochuan Guo, PhD Department of Chemistry, Cleveland State University ______________________________ Date: _________ Dissertation Committee member, Stanley L. Hazen, MD PhD Department of Cellular and Molecular Medicine, Cleveland Clinic ______________________________ Date: _________ Dissertation Committee member, Renliang Zhang, MD PhD Department of Cellular and Molecular Medicine, Cleveland Clinic ______________________________ Date: _________ Dissertation Committee member, Aimin Zhou, PhD Department of Chemistry, Cleveland State University Date of Defense: October 23, 2017 DEDICATION I dedicate this work to my entire family. In particular, my brother Greg Ritchey, and most especially my father Dr. Michael Ritchey, without whose support none of this work would be possible. I am forever grateful to you for your devotion to me and our family. You are an eternal inspiration that will fuel me for the remainder of my life. I am extraordinarily lucky to have grown up in the family I did, which I will never forget. This work is also dedicated to my hometown of Mentor, Ohio and the greater Cleveland area. I was born and raised here and am proud to call this area my home. I have made lifelong friends here, and I am lucky to have grown up around such great people. ACKNOWLEDGEMENT I thank my advisor/mentor Dr. Jonathan D. Smith. I began my Ph.D. work with almost no laboratory experience. Dr. Smith took a chance on me, and I am forever grateful for the opportunity he provided me to come through, as I knew I would. Dr. Smith is an exemplary mentor. He is a genuinely compassionate individual, a true expert in every sense of the word, and one of the most intelligent individuals I have ever met. Without your expert and meticulous guidance, I would not be the person I am today. Thank you Greg Brubaker for your immeasurable assistance over the years and for making the lab a fun place to work. You are a modern day MacGyver and the type of person that makes the world go ‘round. Your creativity and handiness never cease to amaze me. Also, the numerous Cleveland sports talks helped keep things loose through thick and thin, and we finally got that championship!...hopefully with another to come before this thesis is formally submitted. Thank you Dr. Peggy Robinet for helping me get my feet on the ground when I first started in the lab, and for the countless scientific (and other) conversations in our office. Also, thank you for letting me use your cholesterol metabolism figure (Figure 1.1). You are better at drawing an endoplasmic reticulum than I am. A special thank you to Qimin Hai for performing CRISPR/Cas9 editing of genes related to my projects. You are a tireless worker, and a well-traveled “broke” man, in addition to being a fun guy to be around and work with. Your spirit and dedication are unique. Thank you to all my committee members who provided guidance on my Ph.D. quest. A special thank you to Dr. Stanley Hazen for your particularly helpful insight and suggestions, and to Dr. David Anderson and Dr. Aimin Zhou for your encouragement, guidance, and belief in me over the years. Thank you Dr. Renliang Zhang for the mass spectrometry tutelage. Finally, thank you to all Smith lab members, past and present, who probably didn’t get the recognition they deserve in this section. I appreciate and will never forget all of you. A special thank you to Dr. Patricia DiBello, a short time member of the Smith lab and former office mate. In addition to being an expert scientist/mentor, Pat’s compassion was critical in helping me through some tough times. The world could use more Pat DiBellos QUANTITATIVE TRAIT LOCI MAPPING OF MACROPHAGE ATHEROGENIC PHENOTYPES BRIAN RITCHEY ABSTRACT Macrophages are well established as central players in the pathogenesis of atherosclerosis. The aim of my work was to identify genes that play causal roles in macrophage cholesterol metabolism and macrophage inflammasome activation; phenotypes thought to be relevant to atherosclerosis pathology. Quantitative trait loci (QTL) mapping was performed to investigate these phenotypes using bone marrow- derived macrophages (BMDMs) from AKR x DBA/2 intercross mice. DBA/2 aopE-/- mice develop ~ 10 times larger aortic root lesions than AKR apoE-/- mice, and DBA/2 BMDMs store more cholesterol esters (CEs) and have greater inflammasome activity than AKR BMDMs. To study cholesterol metabolism, F4 BMDMs were loaded with 50g/mL of acetylated LDL (acLDL). Total and free cholesterol (FC) levels were measured and normalized to cellular protein. QTL mapping analysis revealed 10 distinct loci. A chromosome 1 QTL had the highest logarithm of the odds (LOD) scores for the cholesterol phenotypes measured. Soat1, which codes for ACAT1, an enzyme that esterifies FC to CEs, was our top candidate gene. AKR mice are known to harbor a 33 amino acid N-terminal ACAT1 truncation. CRISPR/Cas9 editing of DBA/2 embryonic stem cells was performed to create this truncation. Cells were then differentiated into macrophages and loaded with 50g/mL of acLDL. The results unequivocally verified Soat1 as the causal modifier gene vii at this QTL, as edited macrophages accumulated significantly more FC, and significantly less CE. To study inflammasome activity, F4 BMDMs were primed with 1g/mL lipopolysaccharide (LPS) for 4 hours and subsequently treated with 5mM adenosine triphosphate (ATP) for 30 minutes. IL-1 levels in the culture media were measured via an ELISA assay and normalized to cellular protein. QTL mapping revealed a highly significant association at chromosome 7, and the Pycard gene, which codes for the inflammasome adaptor protein ASC, was our top candidate. AKR vs. DBA/2 mice have a 3’UTR SNP (untranslated region single nucleotide polymorphism) in the Pycard gene. Pycard mRNA and ASC were shown to be expressed at ~ threefold higher levels in DBA/2 BMDMs vs. AKR BMDMs. CRISPR/Cas9 editing of Pycard is in progress, and will determine if the 3’UTR SNP is causal at this QTL. viii TABLE OF CONTENTS Page ABSTRACT ………………………………………………………………………………………………………………. vii LIST OF TABLES ……………………………………………………………………………………………………….. xiii LIST OF FIGURES ……………………………………………………………………………………………………… xiv ABBREVIATIONS ……………………………………………………………………………………………………… xvi CHAPTER INTRODUCTION ……………………………………………………………………………………….. 1 1.1. Part: Macrophage Atherogenic Phenotypes ……………………………………… 1 1.1.1. Atherosclerosis ……………………………………………………………………. 1 1.1.2. Macrophage Foam Cells ………………………………………………………. 3 1.1.2.1 Soat1/ACAT1 ………………………………………………………… 5 1.1.3. Inflammasomes …………………………………………………………………… 7 1.1.3.1 Pycard/ASC ………………..……………………………….…….…. 10 1.2. Part Quantitative Trait Loci (QTL) Mapping ……………………………………… 11 1.2.1. Genetic Association Mapping ………………………………………………. 11 1.2.2. R/qtl …………………………………………………………………………………….. 15 ix 1.2.3. Identification of Causal Modifier Genes ……………………………….. 19 . EXPERIMENTAL SECTION ………………………………………………………………………… 20 2.1. Generation and genotyping of F4 mice ………………………………… 20 2.2. Harvesting and culturing bone marrow-derived macrophages 21 2.3. Acetylated LDL preparation and cholesterol mass assay ………. 21 2.4. Fluorescent staining and imaging of neutral lipids ……………….. 22 2.5. R/qtl analysis of cholesterol metabolism phenotypes ………….. 22 2.6. CRISPR/Cas9 Soat1 editing of DBA/2 embryonic stem cells ….. 23 2.7. Embryonic stem cell differentiation into macrophages ………… 26 2.8. IL-1 secretion assay ……………………………………………………………. 27 2.9. R/qtl analysis of cholesterol IL-1 secretion …………………………. 27 2.10. Pycard mRNA and ASC expression assays …………………………….. 27 2.11. Pycard mRNA turnover assay …………………………………………....... 28 2.12. ASC speck imaging and quantification ………………………………….. 29 2.13. Bioinformatic analysis ………………………………………………………….. 29 . RESULTS ………………………………………………………………………………………………… 31 3.1. Genetic map for F4 mice ……………………………………………………... 31 x 3.2. QTL mapping of cholesterol metabolism phenotypes ………….. 32 3.3. Confirmation of Soat1 as the causal Mcmm1 gene ………………. 37 3.4. Mcmm2 – Mcmm10 candidate genes ………………………………….. 45 3.5. QTL mapping of macrophage IL-1 secretion ………………………. 56 3.6. AKR vs. DBA/2 strain divergent Pycard and ASC expression …. 63 3.7. More ASC specks are formed in DBA/2 vs. AKR macrophages 63 3.8. Pycard mRNA is turned over faster in AKR vs. DBA/2 macrophages ………………………………………………………………………………………………. 66 3.9. Irm4-Irm6 candidate genes ………………………………………………….. 66 VDISCUSSION ……………………………………………………………………………………………. 73 4.1. ACAT1 N-terminal truncation significantly alters macrophage cholesterol metabolism ………………………………………………………………………………. 74 4.2. Mcmm candidate genes ………………………………………………………. 77 4.3. Irm3 and
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