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

数据驱动的脂肪生成化学物质转录分类法,用于识别白色和亮白色脂肪生成物

阅读:10
作者:Stephanie Kim, Eric Reed, Stefano Monti, Jennifer J Schlezinger

Background

Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-γγ<math><mi>γ</mi></math> (PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math>) to generate adipocytes with distinct phenotypes. Objectives: Our objectives were to a) establish a novel classification method to predict PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on PPARγ'sPPARγ's<math><mrow><mtext>PPAR</mtext><mi>γ</mi><mo>'</mo><mi>s</mi></mrow></math> transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> ligands, would induce white but not brite adipogenesis.

Conclusion

The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals. https://doi.org/10.1289/EHP6886.

Discussion

A novel classification procedure accurately identified environmental chemicals as PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> ligands distinct from known PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math>-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.

Methods

3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> 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 PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> 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 PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> activation. 3T3-L1 cells treated with two known environmental PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> 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 PPARγPPARγ<math><mrow><mtext>PPAR</mtext><mi>γ</mi></mrow></math> agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。