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Expression time course data HEY2 KO and WT MASMC treated with PDGF

ABSTRACT: The cardiovascular restricted transcription factor CHF1/Hey2 has been previously shown to regulate the smooth muscle response to growth factors. To determine how CHF1/Hey2 affects the smooth muscle response to growth factors, we performed a genomic screen for transcripts that are differentially expressed in wild type and knockout smooth muscle cells after stimulation with platelet derived growth factor. We screened 45101 probes representing more than 39,000 transcripts derived from at least 34,000 genes, at eight different time points. We analyzed the expression data utilizing an algorithm based on Bayesian statistics to derive the best polynomial clustering model to fit the expression data. We found that in a total of 9827 transcripts the normalized ratio of knockout to wild type expression diverged more than 3 fold from baseline in at least one time point, and these transcripts separated into 17 distinct clusters. Further analysis of each cluster revealed distinct alterations in gene expression patterns for immediate early genes, transcription factors, matrix metalloproteinases, signaling molecules and other molecules important in vascular biology. Keywords: time course

ORGANISM(S): Mus musculus

PROVIDER: GSE6526 | GEO | 2007/03/01

SECONDARY ACCESSION(S): PRJNA98801

REPOSITORIES: GEO

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Transcription profiling of wild type and Hey2 knockout mouse aortic smooth muscle cells treated with PDGF for different durations to identify differentially expressed transcripts.

Project description:The cardiovascular restricted transcription factor CHF1/Hey2 has been previously shown to regulate the smooth muscle response to growth factors. To determine how CHF1/Hey2 affects the smooth muscle response to growth factors, we performed a genomic screen for transcripts that are differentially expressed in wild type and knockout smooth muscle cells after stimulation with platelet derived growth factor. We screened 45101 probes representing more than 39,000 transcripts derived from at least 34,000 genes, at eight different time points. We analyzed the expression data utilizing an algorithm based on Bayesian statistics to derive the best polynomial clustering model to fit the expression data. We found that in a total of 9827 transcripts the normalized ratio of knockout to wild type expression diverged more than 3 fold from baseline in at least one time point, and these transcripts separated into 17 distinct clusters. Further analysis of each cluster revealed distinct alterations in gene expression patterns for immediate early genes, transcription factors, matrix metalloproteinases, signaling molecules and other molecules important in vascular biology. Experiment Overall Design: WT and Hey2 knockout mouse aortic smooth muscle cells were treated in parallel with PDGF and harvested at successive time points for RNA extraction and hybridization on Affymetrix microarrays. We sought to identify differentially expressed transcripts through Bayesian statistical methods that would explain the differential response to PDGF observed in vitro.

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