Corticosteroid-regulated genes in rat kidney: Mining time series array data

Richard R. Almon, William Lai, Debra C. DuBois, William J. Jusko

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5′ GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.

Original languageEnglish (US)
Pages (from-to)E870-E882
JournalAmerican Journal of Physiology - Endocrinology and Metabolism
Volume289
Issue number5 52-5
DOIs
StatePublished - Nov 2005

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Physiology
  • Physiology (medical)

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