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.

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.

Project description:Even though urothelial cancer (UC) is the fourth common tumor type among males, progress in treatment development has been deficient. Pathological assessment provides the urologists with only a broad classification, complicated by frequent disagreement among pathologist and the co-existence of different grading systems. Consequently, there is a great need for an objective, reproducible and biologically relevant classification system to make treatment more efficient. In the present investigation we present a molecular taxonomy for UC stratification based on integrated genomics. We used gene expression profiles from 308 UC to define seven molecular subtypes using step-by-step partitions and a bootstrap approach. Results were validated in three independent and publically available data sets. The subtypes differ significantly with respect to expression of cell cycle genes, of receptor tyrosine kinases particularity FGFR3, ERBB2 (HER2), and EGFR, of an FGFR3 associated gene expression signature, of cytokaratins, and of cell adhesion genes. The subtypes also differ significantly with respect to FGFR3, PIK3CA, and TP53 mutations. The expression of key proteins was validated by IHC on TMA. A further inspection indicated that the subtypes could be reduced to four major types of UC; Urobasal/D-driven, Genomically unstable/E-driven, Evolved urobasal, and Basal/SCC like, with characteristic and highly divergent molecular phenotypes. We show that the molecular subtypes cut across pathological classification and that tumors classified as one subtype maintain their characteristic molecular phenotype irrespective of pathological stage and grade. Available data from the Drugbank database and the Cochrane central registry of controlled trials indicate that susceptibility to specific drugs is more likely to be associated with the molecular stratification than with pathological classification. The presented molecular taxonomy stratifies UC into subtypes with distinct molecular phenotypes and biological properties. We anticipate that the molecular taxonomy will be a useful tool in future clinical investigations. Total RNA from fresh-frozen resection samples of 308 urothelial carcinomas was hybridized to the Illumina HumanHT-12 V3.0 expression beadchip arrays (Illumina Inc) at the SCIBLU Genomics Centre at Lund University Sweden (http://www.lth.se/sciblu). Supplementary files: GSE32894_non-normalized_308UCsamples.txt file = Raw intensity values for 308 UC (urothelial tumor) samples subjected only to background correction. GSE32894_reps_normals_preprocess*.txt files = Descriptive details and non-normalized data for technical replicates and normal samples that were used only in the preprocessing of the data. This dataset partly overlaps with Series GSE32549. Names of the overlapping sample names are the same, but the title of each sample is unique to the hybridization.

Project description:Even though urothelial cancer (UC) is the fourth common tumor type among males, progress in treatment development has been deficient. Pathological assessment provides the urologists with only a broad classification, complicated by frequent disagreement among pathologist and the co-existence of different grading systems. Consequently, there is a great need for an objective, reproducible and biologically relevant classification system to make treatment more efficient. In the present investigation we present a molecular taxonomy for UC stratification based on integrated genomics. We used gene expression profiles from 308 UC to define seven molecular subtypes using step-by-step partitions and a bootstrap approach. Results were validated in three independent and publically available data sets. The subtypes differ significantly with respect to expression of cell cycle genes, of receptor tyrosine kinases particularity FGFR3, ERBB2 (HER2), and EGFR, of an FGFR3 associated gene expression signature, of cytokaratins, and of cell adhesion genes. The subtypes also differ significantly with respect to FGFR3, PIK3CA, and TP53 mutations. The expression of key proteins was validated by IHC on TMA. A further inspection indicated that the subtypes could be reduced to four major types of UC; Urobasal/D-driven, Genomically unstable/E-driven, Evolved urobasal, and Basal/SCC like, with characteristic and highly divergent molecular phenotypes. We show that the molecular subtypes cut across pathological classification and that tumors classified as one subtype maintain their characteristic molecular phenotype irrespective of pathological stage and grade. Available data from the Drugbank database and the Cochrane central registry of controlled trials indicate that susceptibility to specific drugs is more likely to be associated with the molecular stratification than with pathological classification. The presented molecular taxonomy stratifies UC into subtypes with distinct molecular phenotypes and biological properties. We anticipate that the molecular taxonomy will be a useful tool in future clinical investigations.

			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

Dataset Information

Development of a Classification Taxonomy for Adipogenic Chemicals: A Novel Tool for Identifying Emerging Metabolic Health Threats

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets