Development of a Classification Taxonomy for Adipogenic Chemicals: A Novel Tool for Identifying Emerging Metabolic Health Threats
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ABSTRACT:
PROVIDER: PRJNA512513 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8384460_1.fastq.gz | Fastqsanger.gz | |||
| SRR8384460_2.fastq.gz | Fastqsanger.gz | |||
| SRR8384470_1.fastq.gz | Fastqsanger.gz | |||
| SRR8384470_2.fastq.gz | Fastqsanger.gz | |||
| SRR8384480_1.fastq.gz | Fastqsanger.gz |
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Project description:Growing evidence suggests that chemicals in disparate structural classes activate specific subsets of PPARγ’s transcriptional programs to generate adipocytes with distinct phenotypes. Our objectives were to 1) establish a novel classification method to predict PPARγ-interacting and modifying chemicals and 2) create taxonomy labels to group chemicals based on their effects on PPARγ’s transcriptome and downstream metabolic functions. We tested the hypothesis that an environmental ligand highly ranked by the taxonomy, but that segregated from the therapeutic ligands, would induce white but not brite adipogensis. 3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, synthetic nuclear receptor ligands known to influence adipocyte biology, suspected environmental PPARγ ligands). Differentiation was assessed by measuring lipid accumulation. mRNA expression was determined by highly multiplexed RNA-Seq and validated by RT-qPCR. A novel classification model was developed using an amended random forest procedure. A subset of environmental contaminants identified as strong PPARγ agonists were characterized further for lipid handling, mitochondrial biogenesis and cellular respiration in 3T3-L1 cells and primary human preadipocytes. The 76 chemicals generated a spectrum of adipogenic differentiation. We used lipid accumulation and RNA sequencing data to develop a classification system that 1) identified PPARγ agonists and 2) sorted agonists into likely white or brite adipocyte inducers. Expression of Cidec, was the most efficacious indicator for strong PPARγ activation. Two known environmental PPARγ ligands, tetrabromobisphenol A, and triphenyl phosphate, which sorted distinctly from therapeutic ligands, induced white but not brite adipocyte genes and induced fatty acid uptake but not mitochondrial biogenesis in 3T3-L1 cells. The highly ranked agonists, tonalid and quinoxyfen, also induced white adipogenesis in 3T3-L1 cells and primary human preadipocytes. A novel classification procedure accurately identified environmental chemicals as PPARγ-modifying chemicals distinct from known PPARγ-modifying therapeutics.
2021-08-01 | GSE124564 | GEO
Project description:BackgroundChemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-γ (PPARγ) to generate adipocytes with distinct phenotypes.ObjectivesOur objectives were to a) establish a novel classification method to predict PPARγ ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on PPARγ's transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPARγ ligands, would induce white but not brite adipogenesis.Methods3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPARγ 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 PPARγ agonists were analyzed by their effects on lipid handling, mitochondrial biogenesis, and cellular respiration in 3T3-L1 cells and human preadipocytes.ResultsWe used lipid accumulation and RNA-Seq data to develop a classification system that a) identified PPARγ agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPARγ activation. 3T3-L1 cells treated with two known environmental PPARγ 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 PPARγ agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes.DiscussionA novel classification procedure accurately identified environmental chemicals as PPARγ ligands distinct from known PPARγ-activating therapeutics.ConclusionThe 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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Project description:While the accumulation of bone marrow adipose tissue has been positively associated with aging and high fat diet, the physiological consequences are mostly unknown. It is well established that osteogenic and adipogenic progenitors share a common developmental origin.Unfavorable microenvironmental changes may bias these cell populations towards an adipogenic fate, which in turn could negatively influence bone homeostasis. Our previously collected data from mice reveal a high plasticity of the mesenchymal osteo-adipogenic stem cell population. Therefore, we now wish to perform by RNA-seq in defined cell population with varying adipogenic commitment within this heterogeneous stem cell pool.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
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2018-07-02 | GSE114475 | GEO
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