A predictive model of the oxygen and heme regulatory network in yeast Journal Article


Authors: Kundaje, A.; Xin, X.; Lan, C.; Lianoglou, S.; Zhou, M.; Zhang, L.; Leslie, C.
Article Title: A predictive model of the oxygen and heme regulatory network in yeast
Abstract: Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co 2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included. © 2008 Kundaje et al.
Keywords: unclassified drug; gene sequence; promoter region; gene deletion; genetics; nonhuman; validation process; accuracy; metabolism; gene; gene expression; biological model; biology; gene expression profiling; computational biology; models, biological; oxygen; protein; protein binding; down-regulation; transcription factor; pathology; algorithms; physiology; hypoxia; dna; algorithm; messenger rna; saccharomyces cerevisiae; binding site; gene control; down regulation; upregulation; gene regulatory network; yeast; cobalt; up-regulation; dna binding; multigene family; hot temperature; gene regulatory networks; heme; machine learning; protein hap1; protein hap4; protein mga2; protein upc2; medusa; ole1 gene; oxygen sensing; heat; nucleic acid database; databases, nucleic acid
Journal Title: PLoS Computational Biology
Volume: 4
Issue: 11
ISSN: 1553-7358
Publisher: Public Library of Science  
Date Published: 2008-11-01
Start Page: e1000224
Language: English
DOI: 10.1371/journal.pcbi.1000224
PUBMED: 19008939
PROVIDER: scopus
PMCID: PMC2573020
DOI/URL:
Notes: --- - "Cited By (since 1996): 14" - "Export Date: 17 November 2011" - "Source: Scopus"
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MSK Authors
  1. Christina Leslie
    106 Leslie