Abstract

Genetic factors play an important role in contributing to variation in plasma lipid levels across the human population. Large genome‐wide association studies (GWAS) in humans have identified more than 100 loci significantly associated with plasma levels of low‐density lipoprotein (LDL) cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol (TC). To prioritize genes that are involved in cholesterol metabolism we developed a systematic approach to leverage genome‐wide liver transcriptomic and proteomic data from multiple mouse reference populations along with human lipid GWAS data. We constructed global co‐expression networks from 12 distinct mouse liver datasets and identified a conserved module of genes highly enriched for cholesterol biosynthetic processes. Based on replication across mutiple datasets we prioritized 112 unique genes. Intersection with human GWAS data for LDL, HDL, TC, and triglycerides identified 54 of these genes to be within 100 kilobases of a significant or suggestive significant single nucleotide polymorphism (SNP). 29 of the 54 prioritized genes have well documented biological roles in cholesterol metabolism, such as LDLR, PCSK9, and INSIG1. With the 25 identified genes with no documented role in cholesterol metabolism, we tested for sensitivity to cholesterol modulation and performed a functional screen by siRNA knockdown. From this analysis we identified, Sestrin1, as a candidate gene that is responsive to cholesterol manipulation and capable of modulating cellular cholesterol levels. Using Sestrin1 targeted mice we show that Sestrin1 heterozygous or homozygous knockout mice have elevated levels of plasma cholesterol when fed a diet enriched in cholesterol. Collectively, through a systematic approach we have identified 25 highly prioritized genes that have no documented role in cholesterol metabolism. One of these genes, Sestrin1, we validate in vivo and in vitro as a modulator of cholesterol metabolism.Support or Funding InformationBWP is supported by NIH R00‐HL123021This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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