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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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A global read-out of the oxygen regulatory network learned by MEDUSA.By applying margin-based scoring to the full list of potential regulators for the up- and downregulated target genes in each experimental condition, we identified 54 predictive regulators in the oxygen regulatory network. For each condition, we show the state of the regulator in red (upregulated) or green (downregulated), where the brightness of the color indicates the significance of its contribution to up or down predictions for the targets, based on normalized margin score. Significance of the regulators to the up-regulated targets is shown in the left half of the column, while contribution to the down-regulated targets is shown in the right half. Some regulators contribute significantly to the prediction of both up- and down-regulated targets within a condition due to indirect regulation (e.g., a transcriptional activator that controls a repressor), combinatorial effects, and promoter sequence information. Regulators are ranked from top to bottom in order of overall predictive significance across experiments, computed by taking the larger of the normalized margin scores for up and down targets in each experiment and then averaging across experiments. The functional category for each regulator is indicated by an annotation given at the right of the figure and explained in the legend.
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pcbi-1000224-g006: A global read-out of the oxygen regulatory network learned by MEDUSA.By applying margin-based scoring to the full list of potential regulators for the up- and downregulated target genes in each experimental condition, we identified 54 predictive regulators in the oxygen regulatory network. For each condition, we show the state of the regulator in red (upregulated) or green (downregulated), where the brightness of the color indicates the significance of its contribution to up or down predictions for the targets, based on normalized margin score. Significance of the regulators to the up-regulated targets is shown in the left half of the column, while contribution to the down-regulated targets is shown in the right half. Some regulators contribute significantly to the prediction of both up- and down-regulated targets within a condition due to indirect regulation (e.g., a transcriptional activator that controls a repressor), combinatorial effects, and promoter sequence information. Regulators are ranked from top to bottom in order of overall predictive significance across experiments, computed by taking the larger of the normalized margin scores for up and down targets in each experiment and then averaging across experiments. The functional category for each regulator is indicated by an annotation given at the right of the figure and explained in the legend.

Mentions: To display the statistical importance of various regulators in the global oxygen and heme regulatory network, we summarized our results by using a global regulatory map (Figure 6). Figure 6 illustrates the significance of the regulators for predicting the up or down regulation of target genes under the tested six different experimental conditions, ranked by margin score. Several previously characterized regulators that are known to be important for oxygen and/or heme regulation, including Upc2, Rox1, Mga2, Hap4, and Hap1 [2], [27], [31]–[34], [46]–[51], ranked highly in this global regulatory map. Among the most significant regulators, six were previously known to be important for hypoxia response or oxygen regulation (Figure 6). Seven regulators known to be involved in cell cycle were identified by MEDUSA in this network. Intriguingly, six regulators known to be involved in pheromone response were identified (Figure 6). Likewise, several regulators known to regulate osmotic, salt and pseudohyphal growth were also identified. These results suggest that oxygen and heme regulation may share many regulators with pheromone and other signaling pathways. However, the regulators that are involved in general stress responses, such as Msn2, Msn4, and Hsf1 [52]–[55], were not identified by MEDUSA as significant regulators.


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)

A global read-out of the oxygen regulatory network learned by MEDUSA.By applying margin-based scoring to the full list of potential regulators for the up- and downregulated target genes in each experimental condition, we identified 54 predictive regulators in the oxygen regulatory network. For each condition, we show the state of the regulator in red (upregulated) or green (downregulated), where the brightness of the color indicates the significance of its contribution to up or down predictions for the targets, based on normalized margin score. Significance of the regulators to the up-regulated targets is shown in the left half of the column, while contribution to the down-regulated targets is shown in the right half. Some regulators contribute significantly to the prediction of both up- and down-regulated targets within a condition due to indirect regulation (e.g., a transcriptional activator that controls a repressor), combinatorial effects, and promoter sequence information. Regulators are ranked from top to bottom in order of overall predictive significance across experiments, computed by taking the larger of the normalized margin scores for up and down targets in each experiment and then averaging across experiments. The functional category for each regulator is indicated by an annotation given at the right of the figure and explained in the legend.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2573020&req=5

pcbi-1000224-g006: A global read-out of the oxygen regulatory network learned by MEDUSA.By applying margin-based scoring to the full list of potential regulators for the up- and downregulated target genes in each experimental condition, we identified 54 predictive regulators in the oxygen regulatory network. For each condition, we show the state of the regulator in red (upregulated) or green (downregulated), where the brightness of the color indicates the significance of its contribution to up or down predictions for the targets, based on normalized margin score. Significance of the regulators to the up-regulated targets is shown in the left half of the column, while contribution to the down-regulated targets is shown in the right half. Some regulators contribute significantly to the prediction of both up- and down-regulated targets within a condition due to indirect regulation (e.g., a transcriptional activator that controls a repressor), combinatorial effects, and promoter sequence information. Regulators are ranked from top to bottom in order of overall predictive significance across experiments, computed by taking the larger of the normalized margin scores for up and down targets in each experiment and then averaging across experiments. The functional category for each regulator is indicated by an annotation given at the right of the figure and explained in the legend.
Mentions: To display the statistical importance of various regulators in the global oxygen and heme regulatory network, we summarized our results by using a global regulatory map (Figure 6). Figure 6 illustrates the significance of the regulators for predicting the up or down regulation of target genes under the tested six different experimental conditions, ranked by margin score. Several previously characterized regulators that are known to be important for oxygen and/or heme regulation, including Upc2, Rox1, Mga2, Hap4, and Hap1 [2], [27], [31]–[34], [46]–[51], ranked highly in this global regulatory map. Among the most significant regulators, six were previously known to be important for hypoxia response or oxygen regulation (Figure 6). Seven regulators known to be involved in cell cycle were identified by MEDUSA in this network. Intriguingly, six regulators known to be involved in pheromone response were identified (Figure 6). Likewise, several regulators known to regulate osmotic, salt and pseudohyphal growth were also identified. These results suggest that oxygen and heme regulation may share many regulators with pheromone and other signaling pathways. However, the regulators that are involved in general stress responses, such as Msn2, Msn4, and Hsf1 [52]–[55], were not identified by MEDUSA as significant regulators.

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