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PLoS Comput Biol. 2015 Jun 17;11(6):e1004202. doi: 10.1371/journal.pcbi.1004202. eCollection 2015 Jun.

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

Author information

1
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.
2
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
3
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
4
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
5
Department of Thoracic Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America.
6
National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China.
7
Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee, United States of America.
8
Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
9
Department of Thoracic Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America; Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
10
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America; Department of Cancer Biology, 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.

PMID:
26083494
PMCID:
PMC4470683
DOI:
10.1371/journal.pcbi.1004202
[Indexed for MEDLINE]
Free PMC Article

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