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Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

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Pathway packaging of the 'biopolymer metabolic process' module. As an assembler, this co-regulated module organized eight divergent metabolic pathways. Cancer pathways are in purple, and cancer-related pathways are in blue.
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Figure 6: Pathway packaging of the 'biopolymer metabolic process' module. As an assembler, this co-regulated module organized eight divergent metabolic pathways. Cancer pathways are in purple, and cancer-related pathways are in blue.

Mentions: To further investigate the assembling power of co-regulated modules on metabolic pathways, we performed KEGG annotation analysis for each module using DAVID (Count > = 2; EASE < = 0.05) [38,39], a useful tool that integrates different sources of biological information to obtain biological annotations, and ranks them by statistical significance. We found that 79 (82%) modules had significant annotated pathway information (Additional file 7). A sample module ('biopolymer metabolic process') assembled eight divergent metabolic pathways (Figure 6). We discovered two cancer-related TFs (RPA1 and RPA2) that function as hub TFs, forming focal nodes in information exchange between eight metabolic pathways. These two TFs and their binding proteins in the module work in a complementary manner to rewire the mismatch repair, cell cycle, and homologous recombination pathways leading to the dysfunction of different cancer pathways [40-42]. In our prior studies, we found that genes in cancer development and progression are distributed sparsely among different metabolic pathways. According to pathway analysis, we concluded that our modules had the functionality of organizing multiple biological pathways and controlling numerous cell behaviours, which eventually contribute to cancer pathogenesis.


Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Pathway packaging of the 'biopolymer metabolic process' module. As an assembler, this co-regulated module organized eight divergent metabolic pathways. Cancer pathways are in purple, and cancer-related pathways are in blue.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914056&req=5

Figure 6: Pathway packaging of the 'biopolymer metabolic process' module. As an assembler, this co-regulated module organized eight divergent metabolic pathways. Cancer pathways are in purple, and cancer-related pathways are in blue.
Mentions: To further investigate the assembling power of co-regulated modules on metabolic pathways, we performed KEGG annotation analysis for each module using DAVID (Count > = 2; EASE < = 0.05) [38,39], a useful tool that integrates different sources of biological information to obtain biological annotations, and ranks them by statistical significance. We found that 79 (82%) modules had significant annotated pathway information (Additional file 7). A sample module ('biopolymer metabolic process') assembled eight divergent metabolic pathways (Figure 6). We discovered two cancer-related TFs (RPA1 and RPA2) that function as hub TFs, forming focal nodes in information exchange between eight metabolic pathways. These two TFs and their binding proteins in the module work in a complementary manner to rewire the mismatch repair, cell cycle, and homologous recombination pathways leading to the dysfunction of different cancer pathways [40-42]. In our prior studies, we found that genes in cancer development and progression are distributed sparsely among different metabolic pathways. According to pathway analysis, we concluded that our modules had the functionality of organizing multiple biological pathways and controlling numerous cell behaviours, which eventually contribute to cancer pathogenesis.

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Show MeSH
Related in: MedlinePlus