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Physical Module Networks: an integrative approach for reconstructing transcription regulation.

Novershtern N, Regev A, Friedman N - Bioinformatics (2011)

Bottom Line: While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation.Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Hebrew University, Jerusalem 91904, Israel.

ABSTRACT

Motivation: Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.

Results: Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.

Availability: The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.

Contact: aregev@broad.mit.edu; nirf@cs.huji.ac.il.

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Related in: MedlinePlus

Viral infection in human host. Pathways reconstructed from H1N1 influenza virus proteins to responsive gene clusters. (a) Pathway connecting NP and PB2 viral proteins to cluster 2. (b) Pathway connecting NA viral protein to clusters 4 and 5 and HA viral protein to clusters 4 and 7. Color indicates protein categories (see legend).
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Figure 6: Viral infection in human host. Pathways reconstructed from H1N1 influenza virus proteins to responsive gene clusters. (a) Pathway connecting NP and PB2 viral proteins to cluster 2. (b) Pathway connecting NA viral protein to clusters 4 and 5 and HA viral protein to clusters 4 and 7. Color indicates protein categories (see legend).

Mentions: One intriguing example is the pathway that connects the viral polymerase subunits NP and PB2 to the human TF Interferon Regulatory Factor 2 (IRF2) (Fig. 6a), a known regulator of interferon-dependent gene expression, which binds 16 out of 57 genes in cluster #2, which is induced by interferon. The initial study identified a novel role for the viral polymerase in perturbing host signaling (Shapira et al., 2009), but its relation to the transcriptional program and mode of action remained unknown. Our PMN analysis suggests that the polymerase subunits act through a pathway that includes apoptotic proteins TRAF1, API1 and p53, and impact interferon-dependent gene expression, thus raising testable mechanistic hypotheses. Other interesting pathways (Fig. 6b) connect the NA and HA viral proteins to three clusters. All three pathways consist of (in order) CREBP, VCAF, MLL and CREBBP, but end in three different TFs: NFKB1 that binds cluster #7 (5 out of 60 genes, induced only in presence of whole virus), E2F1 that binds cluster #5 (5 out of 55 genes, induced by virus or viral RNA) and the interferon-dependent factor IRF1 that binds cluster #4 (23 out of 139 genes, induced by interferon alone), all major regulators in host response pathways. This suggests a novel role and mechanism for additional viral proteins in modulating the host transcriptional response.Fig. 6.


Physical Module Networks: an integrative approach for reconstructing transcription regulation.

Novershtern N, Regev A, Friedman N - Bioinformatics (2011)

Viral infection in human host. Pathways reconstructed from H1N1 influenza virus proteins to responsive gene clusters. (a) Pathway connecting NP and PB2 viral proteins to cluster 2. (b) Pathway connecting NA viral protein to clusters 4 and 5 and HA viral protein to clusters 4 and 7. Color indicates protein categories (see legend).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117354&req=5

Figure 6: Viral infection in human host. Pathways reconstructed from H1N1 influenza virus proteins to responsive gene clusters. (a) Pathway connecting NP and PB2 viral proteins to cluster 2. (b) Pathway connecting NA viral protein to clusters 4 and 5 and HA viral protein to clusters 4 and 7. Color indicates protein categories (see legend).
Mentions: One intriguing example is the pathway that connects the viral polymerase subunits NP and PB2 to the human TF Interferon Regulatory Factor 2 (IRF2) (Fig. 6a), a known regulator of interferon-dependent gene expression, which binds 16 out of 57 genes in cluster #2, which is induced by interferon. The initial study identified a novel role for the viral polymerase in perturbing host signaling (Shapira et al., 2009), but its relation to the transcriptional program and mode of action remained unknown. Our PMN analysis suggests that the polymerase subunits act through a pathway that includes apoptotic proteins TRAF1, API1 and p53, and impact interferon-dependent gene expression, thus raising testable mechanistic hypotheses. Other interesting pathways (Fig. 6b) connect the NA and HA viral proteins to three clusters. All three pathways consist of (in order) CREBP, VCAF, MLL and CREBBP, but end in three different TFs: NFKB1 that binds cluster #7 (5 out of 60 genes, induced only in presence of whole virus), E2F1 that binds cluster #5 (5 out of 55 genes, induced by virus or viral RNA) and the interferon-dependent factor IRF1 that binds cluster #4 (23 out of 139 genes, induced by interferon alone), all major regulators in host response pathways. This suggests a novel role and mechanism for additional viral proteins in modulating the host transcriptional response.Fig. 6.

Bottom Line: While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation.Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Hebrew University, Jerusalem 91904, Israel.

ABSTRACT

Motivation: Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.

Results: Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.

Availability: The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.

Contact: aregev@broad.mit.edu; nirf@cs.huji.ac.il.

Show MeSH
Related in: MedlinePlus