Bioinformatic analysis of gene sets regulated by ligand-activated and dominant-negative peroxisome proliferator-activated receptor γ in mouse aorta

Henry L. Keen, Carmen M. Halabi, Andreas M. Beyer, Willem J. De Lange, Xuebo Liu, Nobuyo Maeda, Frank M. Faraci, Thomas L. Casavant, Curt D. Sigmund

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Objective-Drugs that activate peroxisome proliferator-activated receptor (PPAR) γ improve glucose sensitivity and lower blood pressure, whereas dominant-negative mutations in PPARγ cause severe insulin resistance and hypertension. We hypothesize that these PPARγ mutants regulate target genes opposite to those of ligand-mediated activation, and we tested this hypothesis on a genomewide scale. Methods and Results-We integrated gene expression data in aorta specimens from mice treated with the PPARγ ligand rosiglitazone with data from mice containing a globally expressed knockin of the PPARγ P465L dominant-negative mutation. We also integrated our data with publicly available data sets containing the following: (1) gene expression profiles in many human tissues, (2) PPARγ target genes in 3T3-L1 adipocytes, and (3) experimentally validated PPARγ binding sites throughout the genome. Many classic PPARγ target genes were induced by rosiglitazone and repressed by dominant-negative PPARγ. A similar pattern was observed for about 90% of the gene sets regulated by both rosiglitazone and dominant-negative PPARγ. Genes exhibiting this pattern of contrasting regulation were significantly enriched for nearby PPARγ binding sites. Conclusion-These results provide convincing evidence that the PPARγ P465L mutation causes transcriptional effects that are opposite to those mediated by PPARγ ligand, thus validating mice carrying the mutation as a model of PPARγ interference.

Original languageEnglish
Pages (from-to)518-525
Number of pages8
JournalArteriosclerosis, thrombosis, and vascular biology
Volume30
Issue number3
DOIs
StatePublished - Mar 1 2010
Externally publishedYes

Keywords

  • Bioinformatics
  • PPARγ microarray
  • Transcription factor
  • Vasculature

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