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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

Overview of DSPathNet, a novel computational framework to construct a drug-specific signaling pathway network (SPNetwork): metformin as a case.Step 1: we collected the metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We also compiled one human SPNetwork. Step 2: we utilized the metformin upstream and downstream genes as seeds to generate a metformin-specific SPNetwork from the human SPNetwork. The process involved longitudinal and lateral movements. Step 3: we utilized disease genes and genome-wide association studies (GWAS) data to evaluate if the metformin-specific SPNetwork was enriched with disease genes for type 2 diabetes (T2D) and cancer, genes associated with metformin action. Furthermore, we derived a crosstalk network of metformin action for T2D and cancer in order to identify key components in the metformin signal transduction via network topological and functional analysis. The nodes in orange correspond to the drug-related upstream genes, the nodes in green to the drug-related downstream genes, and the nodes in red to the nodes common to the upstream and downstream gene networks.
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pcbi.1004202.g001: Overview of DSPathNet, a novel computational framework to construct a drug-specific signaling pathway network (SPNetwork): metformin as a case.Step 1: we collected the metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We also compiled one human SPNetwork. Step 2: we utilized the metformin upstream and downstream genes as seeds to generate a metformin-specific SPNetwork from the human SPNetwork. The process involved longitudinal and lateral movements. Step 3: we utilized disease genes and genome-wide association studies (GWAS) data to evaluate if the metformin-specific SPNetwork was enriched with disease genes for type 2 diabetes (T2D) and cancer, genes associated with metformin action. Furthermore, we derived a crosstalk network of metformin action for T2D and cancer in order to identify key components in the metformin signal transduction via network topological and functional analysis. The nodes in orange correspond to the drug-related upstream genes, the nodes in green to the drug-related downstream genes, and the nodes in red to the nodes common to the upstream and downstream gene networks.

Mentions: Fig 1 outlines the framework to build the metformin-specific SPNetwork and S1 Table summarizes the data sources, software and evaluation data used in the study. Briefly, we first collected metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We compiled a human SPNetwork from two databases, Pathway Commons [35] and TRANSFAC [36], as a background pathway system for all signal transduction processes in humans. To weight the association of each node with metformin action, we assigned a functional similarity score to each node based on their Gene Ontology (GO) annotations and metformin upstream genes. The human SPNetwork included 37,881 edges and 4,367 nodes. Then, we utilized the metformin upstream and downstream genes as seeds to produce the metformin-specific SPNetwork from the human SPNetwork via random walk approaches. In this process, we applied a crossing network strategy to generate the drug-specific SPNetwork from background human SPNetwork by longitudinal and lateral movements. Finally, we computationally evaluated the metformin-specific SPNetwork by examining the enrichment of genes in the network using two types of data. The first includes the disease genes of type 2 diabetes (T2D) and cancer, the two diseases in which metformin has been actively studied. The second contains the individual genotyping data from five GWAS datasets: one T2D GWAS dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D treated by metformin. Our evaluation results indicated that the metformin-specific SPNetwork was significantly enriched with genes with mutations that could contribute to the pathology of T2D and cancer, and genes that may be associated with metformin-associated cancer survival (Table 1). To further investigate the molecular mechanisms underlying metformin action, we built a crosstalk subnetwork based on common genes to T2D and cancer, network topology, and functional analyses. We revealed several critical components, modules, and pathways that might be involved in metformin action.


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)

Overview of DSPathNet, a novel computational framework to construct a drug-specific signaling pathway network (SPNetwork): metformin as a case.Step 1: we collected the metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We also compiled one human SPNetwork. Step 2: we utilized the metformin upstream and downstream genes as seeds to generate a metformin-specific SPNetwork from the human SPNetwork. The process involved longitudinal and lateral movements. Step 3: we utilized disease genes and genome-wide association studies (GWAS) data to evaluate if the metformin-specific SPNetwork was enriched with disease genes for type 2 diabetes (T2D) and cancer, genes associated with metformin action. Furthermore, we derived a crosstalk network of metformin action for T2D and cancer in order to identify key components in the metformin signal transduction via network topological and functional analysis. The nodes in orange correspond to the drug-related upstream genes, the nodes in green to the drug-related downstream genes, and the nodes in red to the nodes common to the upstream and downstream gene networks.
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Related In: Results  -  Collection

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

pcbi.1004202.g001: Overview of DSPathNet, a novel computational framework to construct a drug-specific signaling pathway network (SPNetwork): metformin as a case.Step 1: we collected the metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We also compiled one human SPNetwork. Step 2: we utilized the metformin upstream and downstream genes as seeds to generate a metformin-specific SPNetwork from the human SPNetwork. The process involved longitudinal and lateral movements. Step 3: we utilized disease genes and genome-wide association studies (GWAS) data to evaluate if the metformin-specific SPNetwork was enriched with disease genes for type 2 diabetes (T2D) and cancer, genes associated with metformin action. Furthermore, we derived a crosstalk network of metformin action for T2D and cancer in order to identify key components in the metformin signal transduction via network topological and functional analysis. The nodes in orange correspond to the drug-related upstream genes, the nodes in green to the drug-related downstream genes, and the nodes in red to the nodes common to the upstream and downstream gene networks.
Mentions: Fig 1 outlines the framework to build the metformin-specific SPNetwork and S1 Table summarizes the data sources, software and evaluation data used in the study. Briefly, we first collected metformin upstream genes from multiple sources and inferred metformin downstream genes from metformin-induced gene expression data. We compiled a human SPNetwork from two databases, Pathway Commons [35] and TRANSFAC [36], as a background pathway system for all signal transduction processes in humans. To weight the association of each node with metformin action, we assigned a functional similarity score to each node based on their Gene Ontology (GO) annotations and metformin upstream genes. The human SPNetwork included 37,881 edges and 4,367 nodes. Then, we utilized the metformin upstream and downstream genes as seeds to produce the metformin-specific SPNetwork from the human SPNetwork via random walk approaches. In this process, we applied a crossing network strategy to generate the drug-specific SPNetwork from background human SPNetwork by longitudinal and lateral movements. Finally, we computationally evaluated the metformin-specific SPNetwork by examining the enrichment of genes in the network using two types of data. The first includes the disease genes of type 2 diabetes (T2D) and cancer, the two diseases in which metformin has been actively studied. The second contains the individual genotyping data from five GWAS datasets: one T2D GWAS dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D treated by metformin. Our evaluation results indicated that the metformin-specific SPNetwork was significantly enriched with genes with mutations that could contribute to the pathology of T2D and cancer, and genes that may be associated with metformin-associated cancer survival (Table 1). To further investigate the molecular mechanisms underlying metformin action, we built a crosstalk subnetwork based on common genes to T2D and cancer, network topology, and functional analyses. We revealed several critical components, modules, and pathways that might be involved in metformin action.

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