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Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

Sun J, Zhao M, Jia P, Wang L, Wu Y, Iverson C, Zhou Y, Bowton E, Roden DM, Denny JC, Aldrich MC, Xu H, Zhao Z - PLoS Comput. Biol. (2015)

Bottom Line: The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival.The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects.Some results are supported by previous studies.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.

ABSTRACT
A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

No MeSH data available.


Related in: MedlinePlus

Functional comparison of the common genes, upstream network genes, and downstream network genes.The common genes were those found in both metformin upstream network and downstream network. The upstream network genes were those only belonging to the metformin upstream network. The downstream network genes were those only belonging to the metformin downstream network. A) Proportion of genes of interest in Gene Ontology (GO) molecular function domain. B) Comparison of proportion of enriched pathway in the three gene sets at the first-level category of KEGG annotation. C) The clustering of enriched pathways for the three gene sets at second-level category of KEGG annotation.
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pcbi.1004202.g004: Functional comparison of the common genes, upstream network genes, and downstream network genes.The common genes were those found in both metformin upstream network and downstream network. The upstream network genes were those only belonging to the metformin upstream network. The downstream network genes were those only belonging to the metformin downstream network. A) Proportion of genes of interest in Gene Ontology (GO) molecular function domain. B) Comparison of proportion of enriched pathway in the three gene sets at the first-level category of KEGG annotation. C) The clustering of enriched pathways for the three gene sets at second-level category of KEGG annotation.

Mentions: To further explore the functional characteristics of these bridge genes, we first compared them with upstream genes and downstream genes based on the GO Molecular Function domain using the online tool PANTHER Classification System [50] (Fig 4A). The proportion of genes in the following three GO terms were higher in the bridge genes than that in the upstream or downstream genes: binding (GO:0005488), receptor activity (GO:0004872), and transcription regulator activity (GO:0030528). However, for the following three GO terms, the proportion of upstream genes was significantly higher than that in other two gene sets: catalytic activity (GO:0003824), enzyme regulator activity (GO:0030234), and transporter activity (GO:0005215). For the downstream genes, only one GO term, structural molecule activity (GO:0005198), had a higher proportion.


Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

Sun J, Zhao M, Jia P, Wang L, Wu Y, Iverson C, Zhou Y, Bowton E, Roden DM, Denny JC, Aldrich MC, Xu H, Zhao Z - PLoS Comput. Biol. (2015)

Functional comparison of the common genes, upstream network genes, and downstream network genes.The common genes were those found in both metformin upstream network and downstream network. The upstream network genes were those only belonging to the metformin upstream network. The downstream network genes were those only belonging to the metformin downstream network. A) Proportion of genes of interest in Gene Ontology (GO) molecular function domain. B) Comparison of proportion of enriched pathway in the three gene sets at the first-level category of KEGG annotation. C) The clustering of enriched pathways for the three gene sets at second-level category of KEGG annotation.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4470683&req=5

pcbi.1004202.g004: Functional comparison of the common genes, upstream network genes, and downstream network genes.The common genes were those found in both metformin upstream network and downstream network. The upstream network genes were those only belonging to the metformin upstream network. The downstream network genes were those only belonging to the metformin downstream network. A) Proportion of genes of interest in Gene Ontology (GO) molecular function domain. B) Comparison of proportion of enriched pathway in the three gene sets at the first-level category of KEGG annotation. C) The clustering of enriched pathways for the three gene sets at second-level category of KEGG annotation.
Mentions: To further explore the functional characteristics of these bridge genes, we first compared them with upstream genes and downstream genes based on the GO Molecular Function domain using the online tool PANTHER Classification System [50] (Fig 4A). The proportion of genes in the following three GO terms were higher in the bridge genes than that in the upstream or downstream genes: binding (GO:0005488), receptor activity (GO:0004872), and transcription regulator activity (GO:0030528). However, for the following three GO terms, the proportion of upstream genes was significantly higher than that in other two gene sets: catalytic activity (GO:0003824), enzyme regulator activity (GO:0030234), and transporter activity (GO:0005215). For the downstream genes, only one GO term, structural molecule activity (GO:0005198), had a higher proportion.

Bottom Line: The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival.The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects.Some results are supported by previous studies.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.

ABSTRACT
A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

No MeSH data available.


Related in: MedlinePlus