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A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens
BACKGROUND: Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor- [Formula: see text] ([Formula: see text]) to generate adipocytes with distinct phenotypes. OBJECTIVES: Our objectives were to a) establish a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Environmental Health Perspectives
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320370/ https://www.ncbi.nlm.nih.gov/pubmed/34323617 http://dx.doi.org/10.1289/EHP6886 |
Sumario: | BACKGROUND: Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor- [Formula: see text] ([Formula: see text]) to generate adipocytes with distinct phenotypes. OBJECTIVES: Our objectives were to a) establish a novel classification method to predict [Formula: see text] ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on [Formula: see text] transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong [Formula: see text] activation. 3T3-L1 cells treated with two known environmental [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] ligands distinct from known [Formula: see text]-activating therapeutics. CONCLUSION: The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals. https://doi.org/10.1289/EHP6886 |
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