A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens

• DOI: 10.1101/519629
• Publication Date: 2019-01-14T04:05:14Z
• Authors: Stephanie Kim; Eric Reed; Stefano Monti; Jennifer Schlezinger
• Keywords: RNA-Seq

Abstract Background Chemicals in disparate structural classes activate specific subsets of PPARγ’s transcriptional programs to generate adipocytes with distinct phenotypes. Objectives Our objectives were 1) establish a novel classification method predict PPARγ ligands and modifying chemicals, 2) create taxonomy group chemicals based on their effects transcriptome downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by taxonomy, but segregated from therapeutic ligands, would induce white not brite adipogenesis. Methods 3T3-L1 cells differentiated presence 76 (negative controls, nuclear receptor known influence adipocyte biology, potential ligands). Differentiation was assessed measuring lipid accumulation. mRNA expression determined RNA-Seq validated RT-qPCR. A model developed using an amended random forest procedure. subset contaminants identified as strong agonists analyzed handling, mitochondrial biogenesis cellular respiration human preadipocytes. Results used accumulation RNA sequencing data develop system agonists, sorted into likely or adipogens. Expression Cidec most efficacious indicator activation. Two tetrabromobisphenol triphenyl phosphate, which distinctly induced genes failed Pgc1a Ucp1 , fatty acid uptake cells. Moreover, two tonalide quinoxyfen, adipogenesis without concomitant health-promoting characteristics mouse Discussion procedure accurately PPARγ-activating therapeutics. The computational experimental framework has general applicability as-yet uncharacterized chemicals.