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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

Common genes and a crosstalk subnetwork between T2D and cancer.(A) The four-way Venn diagram summarizes the number of shared genes among the four gene sets with smallest P-value less than 0.05 in the T2D GWAS and the three types of cancer GWAS data sets (breast, pancreatic, prostate) in metformin-specific SPNetwork. (B) Degree comparison of common genes among the four gene sets in A, common genes’ direct interactors, and all genes in metformin-specific SPNetwork. (C) A crosstalk subnetwork of metformin action for T2D and cancer with three modules and enriched pathways. The legends for orange nodes, red nodes, and green nodes are same as in Fig 3. The nodes with underlines are key components in the metformin signal transduction process.
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pcbi.1004202.g006: Common genes and a crosstalk subnetwork between T2D and cancer.(A) The four-way Venn diagram summarizes the number of shared genes among the four gene sets with smallest P-value less than 0.05 in the T2D GWAS and the three types of cancer GWAS data sets (breast, pancreatic, prostate) in metformin-specific SPNetwork. (B) Degree comparison of common genes among the four gene sets in A, common genes’ direct interactors, and all genes in metformin-specific SPNetwork. (C) A crosstalk subnetwork of metformin action for T2D and cancer with three modules and enriched pathways. The legends for orange nodes, red nodes, and green nodes are same as in Fig 3. The nodes with underlines are key components in the metformin signal transduction process.

Mentions: From above analyses, we observed that the metformin-specific SPNetwork was enriched with genes associated with T2D and cancer, and genes associated with metformin-associated cancer survival. To gain more insights into how metformin act in T2D and cancer treatment, we generated a subnetwork to synopsis the crosstalk between T2D and cancer based on the common genes with nominal significance (P-value < 0.05) among the four GWAS data sets (T2D, CGEMS breast cancer, pancreatic cancer, and prostate cancer). There were 25 genes common to all the four gene sets (Fig 6A), and there were only five edges in the metformin-specific SPNetwork (S8 Fig). By further examining degree distributions of the common 25 genes and their direct interactors (71 genes), we found that their interactors had significantly more interactions than the 25 genes as well as all the genes in the metformin-specific SPNetwork (Wilcoxon’s test P-value: 2.1 × 10–4 and 2.4 × 10–9, respectively) (Fig 6B). The 25 genes included one hub (PPARG) while the 71 genes included 21 of the 38 hub nodes in the metformin-specific SPNetwork. Similarly, the 25 genes contained three bridge nodes while the 71 genes contained 15 of the 41 bridge nodes between metformin upstream and downstream network. These observations indicate that the interactors of the 25 common nodes were more likely to play important roles for signal transduction.


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)

Common genes and a crosstalk subnetwork between T2D and cancer.(A) The four-way Venn diagram summarizes the number of shared genes among the four gene sets with smallest P-value less than 0.05 in the T2D GWAS and the three types of cancer GWAS data sets (breast, pancreatic, prostate) in metformin-specific SPNetwork. (B) Degree comparison of common genes among the four gene sets in A, common genes’ direct interactors, and all genes in metformin-specific SPNetwork. (C) A crosstalk subnetwork of metformin action for T2D and cancer with three modules and enriched pathways. The legends for orange nodes, red nodes, and green nodes are same as in Fig 3. The nodes with underlines are key components in the metformin signal transduction process.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004202.g006: Common genes and a crosstalk subnetwork between T2D and cancer.(A) The four-way Venn diagram summarizes the number of shared genes among the four gene sets with smallest P-value less than 0.05 in the T2D GWAS and the three types of cancer GWAS data sets (breast, pancreatic, prostate) in metformin-specific SPNetwork. (B) Degree comparison of common genes among the four gene sets in A, common genes’ direct interactors, and all genes in metformin-specific SPNetwork. (C) A crosstalk subnetwork of metformin action for T2D and cancer with three modules and enriched pathways. The legends for orange nodes, red nodes, and green nodes are same as in Fig 3. The nodes with underlines are key components in the metformin signal transduction process.
Mentions: From above analyses, we observed that the metformin-specific SPNetwork was enriched with genes associated with T2D and cancer, and genes associated with metformin-associated cancer survival. To gain more insights into how metformin act in T2D and cancer treatment, we generated a subnetwork to synopsis the crosstalk between T2D and cancer based on the common genes with nominal significance (P-value < 0.05) among the four GWAS data sets (T2D, CGEMS breast cancer, pancreatic cancer, and prostate cancer). There were 25 genes common to all the four gene sets (Fig 6A), and there were only five edges in the metformin-specific SPNetwork (S8 Fig). By further examining degree distributions of the common 25 genes and their direct interactors (71 genes), we found that their interactors had significantly more interactions than the 25 genes as well as all the genes in the metformin-specific SPNetwork (Wilcoxon’s test P-value: 2.1 × 10–4 and 2.4 × 10–9, respectively) (Fig 6B). The 25 genes included one hub (PPARG) while the 71 genes included 21 of the 38 hub nodes in the metformin-specific SPNetwork. Similarly, the 25 genes contained three bridge nodes while the 71 genes contained 15 of the 41 bridge nodes between metformin upstream and downstream network. These observations indicate that the interactors of the 25 common nodes were more likely to play important roles for signal transduction.

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