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Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

Gao F, Foat BC, Bussemaker HJ - BMC Bioinformatics (2004)

Bottom Line: These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs.We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region.Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA. fg2037@columbia.edu

ABSTRACT

Background: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data.

Results: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

Conclusion: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

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Assigning directionality to divergently transcribed promoters. For pairs of divergently transcribed genes sharing a single promoter region occupied by one or more transcription factors, our method can be used to determine which gene is regulated by which factor. In the diagrams, genes are represented as squares with arrows showing the transcription direction; transcription factors are shown as ovals. The numbers shown are significance scores for the coupling between the transcription factor and the gene, equal to the negative 10-based logarithm of the P-value. Significant regulatory relationships are shown as arrows to colored boxes. In (A), the cell cycle transcription factor Mbp1p regulates the recombinase RAD51 but not the endopeptidase PUP3, while in (B), the putative rRNA processing regulator Fhl1p regulates the ribosomal subunit RPL40B but not the protein kinase MLP1. In the scenario illustrated in (C), both Nrg1p and Hap6p bind to the intergenic upstream region of FSP2 and HXT9. The coupling analysis shows that Nrg1p in this case works bi-directionally and regulates both genes, while Hap6p regulates neither gene.
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Figure 3: Assigning directionality to divergently transcribed promoters. For pairs of divergently transcribed genes sharing a single promoter region occupied by one or more transcription factors, our method can be used to determine which gene is regulated by which factor. In the diagrams, genes are represented as squares with arrows showing the transcription direction; transcription factors are shown as ovals. The numbers shown are significance scores for the coupling between the transcription factor and the gene, equal to the negative 10-based logarithm of the P-value. Significant regulatory relationships are shown as arrows to colored boxes. In (A), the cell cycle transcription factor Mbp1p regulates the recombinase RAD51 but not the endopeptidase PUP3, while in (B), the putative rRNA processing regulator Fhl1p regulates the ribosomal subunit RPL40B but not the protein kinase MLP1. In the scenario illustrated in (C), both Nrg1p and Hap6p bind to the intergenic upstream region of FSP2 and HXT9. The coupling analysis shows that Nrg1p in this case works bi-directionally and regulates both genes, while Hap6p regulates neither gene.

Mentions: Taken together, the results mentioned above convincingly demonstrate that the use of a coupling factor threshold as a novel additional criterion leads to significantly improved specificity in the prediction of functional TF targets. The biological implications of our analysis are highlighted in the case of divergently transcribed genes that share a common promoter region, represented as a single microarray probe. There are 1592 such probes out of the total 4532 probes in the ChIP experiments of Lee et al. [4]. When the ChIP data indicate that a TF binds to the intergenic region, nothing can be said about whether it regulates one of the genes or both based on that information alone. By contrast, our regulatory coupling analysis naturally allows us to distinguish between these different scenarios and make precise statements about which genes are controlled by each of the factors that occupy the promoter region (see Fig. 3). Both uni- and bi-directional control by TFs is observed. Indeed, we found the functional annotation of the protein encoded by the coupled targets to be consistent with what was known about the function of the bound TF in most cases analyzed [20].


Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

Gao F, Foat BC, Bussemaker HJ - BMC Bioinformatics (2004)

Assigning directionality to divergently transcribed promoters. For pairs of divergently transcribed genes sharing a single promoter region occupied by one or more transcription factors, our method can be used to determine which gene is regulated by which factor. In the diagrams, genes are represented as squares with arrows showing the transcription direction; transcription factors are shown as ovals. The numbers shown are significance scores for the coupling between the transcription factor and the gene, equal to the negative 10-based logarithm of the P-value. Significant regulatory relationships are shown as arrows to colored boxes. In (A), the cell cycle transcription factor Mbp1p regulates the recombinase RAD51 but not the endopeptidase PUP3, while in (B), the putative rRNA processing regulator Fhl1p regulates the ribosomal subunit RPL40B but not the protein kinase MLP1. In the scenario illustrated in (C), both Nrg1p and Hap6p bind to the intergenic upstream region of FSP2 and HXT9. The coupling analysis shows that Nrg1p in this case works bi-directionally and regulates both genes, while Hap6p regulates neither gene.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC407845&req=5

Figure 3: Assigning directionality to divergently transcribed promoters. For pairs of divergently transcribed genes sharing a single promoter region occupied by one or more transcription factors, our method can be used to determine which gene is regulated by which factor. In the diagrams, genes are represented as squares with arrows showing the transcription direction; transcription factors are shown as ovals. The numbers shown are significance scores for the coupling between the transcription factor and the gene, equal to the negative 10-based logarithm of the P-value. Significant regulatory relationships are shown as arrows to colored boxes. In (A), the cell cycle transcription factor Mbp1p regulates the recombinase RAD51 but not the endopeptidase PUP3, while in (B), the putative rRNA processing regulator Fhl1p regulates the ribosomal subunit RPL40B but not the protein kinase MLP1. In the scenario illustrated in (C), both Nrg1p and Hap6p bind to the intergenic upstream region of FSP2 and HXT9. The coupling analysis shows that Nrg1p in this case works bi-directionally and regulates both genes, while Hap6p regulates neither gene.
Mentions: Taken together, the results mentioned above convincingly demonstrate that the use of a coupling factor threshold as a novel additional criterion leads to significantly improved specificity in the prediction of functional TF targets. The biological implications of our analysis are highlighted in the case of divergently transcribed genes that share a common promoter region, represented as a single microarray probe. There are 1592 such probes out of the total 4532 probes in the ChIP experiments of Lee et al. [4]. When the ChIP data indicate that a TF binds to the intergenic region, nothing can be said about whether it regulates one of the genes or both based on that information alone. By contrast, our regulatory coupling analysis naturally allows us to distinguish between these different scenarios and make precise statements about which genes are controlled by each of the factors that occupy the promoter region (see Fig. 3). Both uni- and bi-directional control by TFs is observed. Indeed, we found the functional annotation of the protein encoded by the coupled targets to be consistent with what was known about the function of the bound TF in most cases analyzed [20].

Bottom Line: These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs.We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region.Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, Columbia University, New York, New York 10027, USA. fg2037@columbia.edu

ABSTRACT

Background: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data.

Results: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.

Conclusion: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Show MeSH
Related in: MedlinePlus