Thesis - Open Access
Master of Science (MS)
Biology and Microbiology
Cholesterol metabolism, CRISPR-Cas9, Lipid droplets, Macrophages, Whole-genome screens
Macrophage foam cells contribute to atherosclerotic plaque formation, a pathology that underlies heart disease, peripheral arterial disease and stroke. Foam cells form when macrophages take up excessive amounts of low-density lipoprotein (LDL) leading to elevated cellular levels of neutral lipids, which are packaged into lipid droplet organelles. Despite the high public health priority motivating research of cardiovascular disease processes, current understanding of macrophage cholesterol metabolism including mechanisms of LDL uptake, cholesterol trafficking, lipid droplet biogenesis, lipid droplet degradation and cholesterol efflux is limited. Here, we implemented a CRISPR-Cas9 whole genome screening strategy to identify critical genes regulating macrophage cholesterol metabolism. Murine bone marrow derived macrophages (BMDM) were derived from transgenic mice expressing Cas9 protein and transduced with a pooled library of guide RNAs (sgRNA) that contained ~80,000 sgRNA targeting the majority of protein-coding genes in the mouse genome. BMDM were transduced with low viral multiplicity of infection to produce a single sgRNA insertion per cell. To identify gene disruptions conferring gain-of-function or loss-of-function effects on LDL uptake, cholesterol trafficking, or lipid droplet biogenesis, we exposed the mutant populations of BMDM to acetylated or oxidized LDL for 24 h, stained for cellular neutral lipid content using BODIPY 493/503 and sorted low- and high-fluorescence cells by flow cytometry. Similarly, to identify genes critical for lipid droplet degradation, cholesterol metabolism and cholesterol efflux, we exposed the BMDM to modified LDLs for 24 h followed by LDL removal and a 48-h chase. DNA from the sorted cells was deep sequenced to quantify sgRNA inserts. Bioinformatics and statistical analyses identified sgRNA inserts that were enriched in the low neutral lipid or high neutral lipid pools and generated ranked gene lists containing genes regulating cholesterol metabolism in macrophages. To validate the screen results, we made targeted gene disruptions in BMDM followed by staining and fluorescence microscopy of BODIPY and perilipin to verify the neutral lipid phenotype and evaluate lipid droplet morphology and cellular distribution. Of note, we highlight the identification of novel genes regulating neutral lipid levels in macrophages. Emc3 encodes an endoplasmic reticulum membrane complex protein, and when disrupted led to lower cellular neutral lipid content than wildtype cells. Whereas, Atg9a, an autophagy-related protein, when disrupted leads to higher neutral lipid due to inefficient neutral lipid clearance. In addition, gene set enrichment analysis (GSEA) analysis of screen results suggest that Golgi-conserved oligomeric complex and endoplasmic reticulum membrane complex proteins are critical for LDL processing. sgRNA for genes in these gene sets were enriched in the low fluorescence population in the 24-h oxidized or acetylated LDL conditions. Further, our screens identified many genes previously characterized in modified-LDL processing such as genes regulating receptor mediated clathrin-dependent endocytosis, Golgi-toendoplasmic reticulum trafficking, and autophagy-associated proteins as critical regulators for neutral lipids in BMDMs. Overall, these results provide new avenues for research to determine the mechanisms of cholesterol metabolisms in macrophages.
Library of Congress Subject Headings
Cholesterol -- Metabolism.
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Wanniarachchi, Kevin, "Identifying Critical Genes for Cholesterol Metabolism in Macrophages Using CRISPR-Cas9 Whole-Genome Screens" (2019). Electronic Theses and Dissertations. 3644.
Readme Supplemental table 1
707144_supp_4ADE0584-1713-11EA-B33A-8D8F4D662D30.xlsx (14956 kB)
Supplemental table 1
707144_supp_7D4506E4-1713-11EA-AFA0-C5904D662D30.csv (43 kB)
Supplemental table 2
707144_supp_9D197ED2-1713-11EA-8E54-EE904D662D30.csv (13 kB)
Supplemental table 3