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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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Experimental confirmation of the oxygen regulators identified by MEDUSA.MEDUSA identified Mdg1, Met28, Upc2, Pig1 and Rme1 as specific regulators of the hypoxia-inducible OLE1 gene. To detect the effects of these regulators on the OLE1 gene, the full-length OLE1 promoter-lacZ reporter [39] was transformed into the wild type or mutant cells with one of the indicated genes deleted. β-galactosidase activities were measured in cells grown in air or in hypoxic chamber. Data plotted here are averages from at least three independent transformants. The arrows indicate the effects of hypoxia on the expression levels of Mdg1, Met28, Upc2, Pig1 and Rme1. That is, Mdg1 was downregulated whereas the rest were upregulated in hypoxic cells.
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pcbi-1000224-g007: Experimental confirmation of the oxygen regulators identified by MEDUSA.MEDUSA identified Mdg1, Met28, Upc2, Pig1 and Rme1 as specific regulators of the hypoxia-inducible OLE1 gene. To detect the effects of these regulators on the OLE1 gene, the full-length OLE1 promoter-lacZ reporter [39] was transformed into the wild type or mutant cells with one of the indicated genes deleted. β-galactosidase activities were measured in cells grown in air or in hypoxic chamber. Data plotted here are averages from at least three independent transformants. The arrows indicate the effects of hypoxia on the expression levels of Mdg1, Met28, Upc2, Pig1 and Rme1. That is, Mdg1 was downregulated whereas the rest were upregulated in hypoxic cells.

Mentions: We then used margin-based scoring for regulators to identify Mdg1, Met28, Upc2, Pig1 and Rme1 as potential regulators for the OLE1 promoter. Only Upc2 was previously known to be involved in oxygen regulation. The expression of all these regulators but Mdg1 was upregulated by hypoxia. Note that the MEDUSA model does not assert that these regulators directly bind the OLE1 promoter but does predict that they regulate OLE1 expression, perhaps through indirect interactions. Conceptually, the margin score for a regulator is similar to “knocking out” the regulator from the regulatory program and computing whether the effect is predicted to be significant for specific targets and conditions (see Methods). This connection suggests a direct approach for validation of these regulators using the corresponding deletion mutants. Namely, to determine the effects of these regulators on the OLE1 promoter-lacZ reporter activity, we measured β-galactosidase activities in wild type and mutant cells with one of the regulator genes deleted (Figure 7). Except for Δmdg1 cells, the reporter activity in the hypoxic mutant cells were all reduced, compared to that in wild type cells (Figure 7). Because hypoxia suppressed Mdg1 expression, indicating its negative role in OLE1 induction, it is conceivable that its deletion would not affect the reporter activity in hypoxic cells. In contrast, because hypoxia induced the expression of other regulators, indicating their positive role in OLE1 induction, their deletion would cause the reporter activity to decrease in hypoxic cells. Deletion of MET28, UPC2, and PIG1 also significantly reduced the fold induction of the OLE1 reporter activity by hypoxia (Figure 7). These experimental results strongly support the power of the MEDUSA analysis to predict regulators for specific targets.


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)

Experimental confirmation of the oxygen regulators identified by MEDUSA.MEDUSA identified Mdg1, Met28, Upc2, Pig1 and Rme1 as specific regulators of the hypoxia-inducible OLE1 gene. To detect the effects of these regulators on the OLE1 gene, the full-length OLE1 promoter-lacZ reporter [39] was transformed into the wild type or mutant cells with one of the indicated genes deleted. β-galactosidase activities were measured in cells grown in air or in hypoxic chamber. Data plotted here are averages from at least three independent transformants. The arrows indicate the effects of hypoxia on the expression levels of Mdg1, Met28, Upc2, Pig1 and Rme1. That is, Mdg1 was downregulated whereas the rest were upregulated in hypoxic cells.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2573020&req=5

pcbi-1000224-g007: Experimental confirmation of the oxygen regulators identified by MEDUSA.MEDUSA identified Mdg1, Met28, Upc2, Pig1 and Rme1 as specific regulators of the hypoxia-inducible OLE1 gene. To detect the effects of these regulators on the OLE1 gene, the full-length OLE1 promoter-lacZ reporter [39] was transformed into the wild type or mutant cells with one of the indicated genes deleted. β-galactosidase activities were measured in cells grown in air or in hypoxic chamber. Data plotted here are averages from at least three independent transformants. The arrows indicate the effects of hypoxia on the expression levels of Mdg1, Met28, Upc2, Pig1 and Rme1. That is, Mdg1 was downregulated whereas the rest were upregulated in hypoxic cells.
Mentions: We then used margin-based scoring for regulators to identify Mdg1, Met28, Upc2, Pig1 and Rme1 as potential regulators for the OLE1 promoter. Only Upc2 was previously known to be involved in oxygen regulation. The expression of all these regulators but Mdg1 was upregulated by hypoxia. Note that the MEDUSA model does not assert that these regulators directly bind the OLE1 promoter but does predict that they regulate OLE1 expression, perhaps through indirect interactions. Conceptually, the margin score for a regulator is similar to “knocking out” the regulator from the regulatory program and computing whether the effect is predicted to be significant for specific targets and conditions (see Methods). This connection suggests a direct approach for validation of these regulators using the corresponding deletion mutants. Namely, to determine the effects of these regulators on the OLE1 promoter-lacZ reporter activity, we measured β-galactosidase activities in wild type and mutant cells with one of the regulator genes deleted (Figure 7). Except for Δmdg1 cells, the reporter activity in the hypoxic mutant cells were all reduced, compared to that in wild type cells (Figure 7). Because hypoxia suppressed Mdg1 expression, indicating its negative role in OLE1 induction, it is conceivable that its deletion would not affect the reporter activity in hypoxic cells. In contrast, because hypoxia induced the expression of other regulators, indicating their positive role in OLE1 induction, their deletion would cause the reporter activity to decrease in hypoxic cells. Deletion of MET28, UPC2, and PIG1 also significantly reduced the fold induction of the OLE1 reporter activity by hypoxia (Figure 7). These experimental results strongly support the power of the MEDUSA analysis to predict regulators for specific targets.

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