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

Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, Zhang L, Leslie C - PLoS Comput. Biol. (2008)

Bottom Line: 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.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.Supplemental data are included.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Columbia University, New York, New York, United States of America.

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 Co2+. 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.

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Expression signatures identified by perturbation of the oxygen regulatory network.(A) Heat maps showing real-valued expression profiles of genes that are members of the 16 signatures identified. The expression values are in log2. The rows represent genes and the columns represent the 6 experimental conditions. Bright red indicates strong upregulation, bright green indicates strong downregulation, and black indicates no change in expression. Each signature is labeled with statistically significant functional annotations. (B) Each block displays the average real-valued expression (stem plot in dark blue) and discrete expression profile (bar plot in light blue) for each signature over the 6 experimental conditions. The real-valued expression values are in log2.
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pcbi-1000224-g001: Expression signatures identified by perturbation of the oxygen regulatory network.(A) Heat maps showing real-valued expression profiles of genes that are members of the 16 signatures identified. The expression values are in log2. The rows represent genes and the columns represent the 6 experimental conditions. Bright red indicates strong upregulation, bright green indicates strong downregulation, and black indicates no change in expression. Each signature is labeled with statistically significant functional annotations. (B) Each block displays the average real-valued expression (stem plot in dark blue) and discrete expression profile (bar plot in light blue) for each signature over the 6 experimental conditions. The real-valued expression values are in log2.

Mentions: Prior to performing more integrative computational analysis, we also examined the broad patterns of gene expression in our dataset. We identified 16 distinct discretized co-expression signatures (see Methods) to which we assigned the differentially expressed genes, including 5 pairs of antagonistic signatures whose patterns of expression are nearly the same but opposite in direction (Figure 1). For example, we found a pair of expression signatures consisting of genes that are exclusively induced by heme deletion (signature 11) or exclusively suppressed in this condition (signature 14). Signature 14 contains the ergosterol biosynthesis genes, which are known to be heme regulated, while most of the genes in signature 11 (354 out of 500) are functionally uncharacterized and may include novel heme-regulated genes. While most of these expression signatures contain several hundred genes, a few sets are smaller and functionally more coherent. In particular, signature 16 consists of 34 Hap1-dependent genes that are strongly suppressed in all conditions including the Δhap1 experiment. These genes include the COX and QCR genes and are involved in aerobic respiratory processes, electron transport, and heme-dependent oxidoreducatase activity. In most cases, the expression signatures could be assigned significant functional terms, though in general only a smaller subset of the genes in a signature belong to the enriched category (see Text S1 for details).


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

Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, Zhang L, Leslie C - PLoS Comput. Biol. (2008)

Expression signatures identified by perturbation of the oxygen regulatory network.(A) Heat maps showing real-valued expression profiles of genes that are members of the 16 signatures identified. The expression values are in log2. The rows represent genes and the columns represent the 6 experimental conditions. Bright red indicates strong upregulation, bright green indicates strong downregulation, and black indicates no change in expression. Each signature is labeled with statistically significant functional annotations. (B) Each block displays the average real-valued expression (stem plot in dark blue) and discrete expression profile (bar plot in light blue) for each signature over the 6 experimental conditions. The real-valued expression values are in log2.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2573020&req=5

pcbi-1000224-g001: Expression signatures identified by perturbation of the oxygen regulatory network.(A) Heat maps showing real-valued expression profiles of genes that are members of the 16 signatures identified. The expression values are in log2. The rows represent genes and the columns represent the 6 experimental conditions. Bright red indicates strong upregulation, bright green indicates strong downregulation, and black indicates no change in expression. Each signature is labeled with statistically significant functional annotations. (B) Each block displays the average real-valued expression (stem plot in dark blue) and discrete expression profile (bar plot in light blue) for each signature over the 6 experimental conditions. The real-valued expression values are in log2.
Mentions: Prior to performing more integrative computational analysis, we also examined the broad patterns of gene expression in our dataset. We identified 16 distinct discretized co-expression signatures (see Methods) to which we assigned the differentially expressed genes, including 5 pairs of antagonistic signatures whose patterns of expression are nearly the same but opposite in direction (Figure 1). For example, we found a pair of expression signatures consisting of genes that are exclusively induced by heme deletion (signature 11) or exclusively suppressed in this condition (signature 14). Signature 14 contains the ergosterol biosynthesis genes, which are known to be heme regulated, while most of the genes in signature 11 (354 out of 500) are functionally uncharacterized and may include novel heme-regulated genes. While most of these expression signatures contain several hundred genes, a few sets are smaller and functionally more coherent. In particular, signature 16 consists of 34 Hap1-dependent genes that are strongly suppressed in all conditions including the Δhap1 experiment. These genes include the COX and QCR genes and are involved in aerobic respiratory processes, electron transport, and heme-dependent oxidoreducatase activity. In most cases, the expression signatures could be assigned significant functional terms, though in general only a smaller subset of the genes in a signature belong to the enriched category (see Text S1 for details).

Bottom Line: 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.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.Supplemental data are included.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Columbia University, New York, New York, United States of America.

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 Co2+. 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.

Show MeSH
Related in: MedlinePlus