1. The Genetic Architecture of Carbon Tetrachloride-Induced Liver Fibrosis in MiceSummary
- Author
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Iina Tuominen, Brie K. Fuqua, Calvin Pan, Nicole Renaud, Kevin Wroblewski, Mete Civelek, Kara Clerkin, Ashot Asaryan, Sara G. Haroutunian, Joseph Loureiro, Jason Borawski, Guglielmo Roma, Judith Knehr, Walter Carbone, Samuel French, Brian W. Parks, Simon T. Hui, Margarete Mehrabian, Clara Magyar, Rita M. Cantor, Chinweike Ukomadu, Aldons J. Lusis, and Simon W. Beaven
- Subjects
CCl4 ,Systems Genetics ,Liver Toxicity and Injury ,Genome-Wide Association Study ,Diseases of the digestive system. Gastroenterology ,RC799-869 - Abstract
Background & Aims: Liver fibrosis is a multifactorial trait that develops in response to chronic liver injury. Our aim was to characterize the genetic architecture of carbon tetrachloride (CCl4)-induced liver fibrosis using the Hybrid Mouse Diversity Panel, a panel of more than 100 genetically distinct mouse strains optimized for genome-wide association studies and systems genetics. Methods: Chronic liver injury was induced by CCl4 injections twice weekly for 6 weeks. Four hundred thirty-seven mice received CCl4 and 256 received vehicle, after which animals were euthanized for liver histology and gene expression. Using automated digital image analysis, we quantified fibrosis as the collagen proportionate area of the whole section, excluding normal collagen. Results: We discovered broad variation in fibrosis among the Hybrid Mouse Diversity Panel strains, demonstrating a significant genetic influence. Genome-wide association analyses revealed significant and suggestive loci underlying susceptibility to fibrosis, some of which overlapped with loci identified in mouse crosses and human population studies. Liver global gene expression was assessed by RNA sequencing across the strains, and candidate genes were identified using differential expression and expression quantitative trait locus analyses. Gene set enrichment analyses identified the underlying pathways, of which stellate cell involvement was prominent, and coexpression network modeling identified modules associated with fibrosis. Conclusions: Our results provide a rich resource for the design of experiments to understand mechanisms underlying fibrosis and for rational strain selection when testing antifibrotic drugs.
- Published
- 2021
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