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

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

Authors :
Jingchun Sun
Min Zhao
Peilin Jia
Lily Wang
Yonghui Wu
Carissa Iverson
Yubo Zhou
Erica Bowton
Dan M Roden
Joshua C Denny
Melinda C Aldrich
Hua Xu
Zhongming Zhao
Source :
PLoS Computational Biology, Vol 11, Iss 6, p e1004202 (2015)
Publication Year :
2015
Publisher :
Public Library of Science (PLoS), 2015.

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.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.f383be40c6d943a9bedaa0637ff5d4f5
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1004202