Back

Zebrafish larvae as a model system for systematic characterization of drugs and genes in dyslipidemia and atherosclerosis

Bandaru, M. K.; Emmanouilidou, A.; Ranefall, P.; von der Heyde, B.; Mazzaferro, E.; Klingstroem, T.; Masiero, M.; Dethlefsen, O.; Ledin, J.; Larsson, A.; Brooke, H. L.; Wahlby, C.; Ingelsson, E.; den Hoed, M.

2019-06-11 genomics
10.1101/502674 bioRxiv
Show abstract

BackgroundHundreds of loci have been robustly associated with circulating lipids, atherosclerosis and coronary artery disease; but for most loci the causal genes and mechanisms remain uncharacterized.\n\nMethodsWe developed a semi-automated experimental pipeline for systematic, quantitative, large-scale characterization of mechanisms, drugs and genes associated with dyslipidemia and atherosclerosis in a zebrafish model system. We validated our pipeline using a dietary (n>2000), drug treatment (n>1000), and genetic intervention (n=384), and used it to characterize three candidate genes in a GWAS-identified pleiotropic locus on chr 19p13.11 (n>500).\n\nResultsOur results show that five days of overfeeding and cholesterol supplementation had independent pro-atherogenic effects, which could be diminished by concomitant treatment with atorvastatin and ezetimibe. CRISPR-Cas9-induced mutations in orthologues of proof-of-concept genes resulted in higher LDL cholesterol levels (apoea), and more early stage atherosclerosis (apobb.1). Finally, our pipeline helped identify putative causal genes for circulating lipids and early-stage atherosclerosis (LPAR2 and GATAD2A).\n\nConclusionsIn summary, our pipeline facilitates systematic, in vivo characterization of drugs and candidate genes to increase our understanding of disease etiology, and can likely help identify novel targets for therapeutic intervention.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.