A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens.
| Autor: | Kim, Stephanie1,2, Reed, Eric1,3,4, Monti, Stefano1,3,4 smonti@bu.edu, Schlezinger, Jennifer J.1,2 jschlezi@bu.edu |
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| Předmět: |
*RNA
Lipid metabolism Reverse transcriptase polymerase chain reaction Sequence analysis Classification Oxygen consumption Peroxisome proliferator-activated receptors Random forest algorithms Fat cells Messenger RNA Enzyme-linked immunosorbent assay Adiponectin Polymerase chain reaction Ligands (Biochemistry) Phenotypes |
| Zdroj: | Environmental Health Perspectives. Jul2021, Vol. 129 Issue 7, p1-18. 18p. 2 Diagrams, 2 Charts, 9 Graphs. |
| Abstrakt: | BACKGROUND: Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-? (PPAR?) to generate adipocytes with distinct phenotypes. OBJECTIVES: Our objectives were to a) establish a novel classification method to predict PPAR? ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on PPAR?’s transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPAR? ligands, would induce white but not brite adipogenesis. METHODS: 3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPARc ligands). Differentiation was assessed by measuring lipid accumulation. mRNA expression was determined by RNA-sequencing (RNA-Seq) and validated by reverse transcription–quantitative polymerase chain reaction. A novel classification model was developed using an amended random forest procedure. A subset of environmental contaminants identified as strong PPAR? agonists were analyzed by their effects on lipid handling, mitochondrial biogenesis, and cellular respiration in 3T3-L1 cells and human preadipocytes. RESULTS: We used lipid accumulation and RNA-Seq data to develop a classification system that a) identified PPAR? agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPAR? activation. 3T3-L1 cells treated with two known environmental PPAR? ligands, tetrabromobisphenol A and triphenyl phosphate, which sorted distinctly from therapeutic ligands, had higher expression of white adipocyte genes but no difference in Pgc1a and Ucp1 expression, and higher fatty acid uptake but not mitochondrial biogenesis. Moreover, cells treated with two chemicals identified as highly ranked PPAR? agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes. DISCUSSION: A novel classification procedure accurately identified environmental chemicals as PPAR? ligands distinct from known PPAR?-activating therapeutics. CONCLUSION: The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals. [ABSTRACT FROM AUTHOR] |
| Databáze: | GreenFILE |
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