Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.
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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4470683?pdf=render |
| _version_ | 1818503680992739328 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Jingchun Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T21:27:14Z |
| format | Article |
| id | doaj.art-f383be40c6d943a9bedaa0637ff5d4f5 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T21:27:14Z |
| publishDate | 2015-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f383be40c6d943a9bedaa0637ff5d4f52022-12-22T01:32:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-06-01116e100420210.1371/journal.pcbi.1004202Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.Jingchun SunMin ZhaoPeilin JiaLily WangYonghui WuCarissa IversonYubo ZhouErica BowtonDan M RodenJoshua C DennyMelinda C AldrichHua XuZhongming ZhaoA 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.http://europepmc.org/articles/PMC4470683?pdf=render |
| spellingShingle | 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 Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. PLoS Computational Biology |
| title | Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. |
| title_full | Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. |
| title_fullStr | Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. |
| title_full_unstemmed | Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. |
| title_short | Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. |
| title_sort | deciphering signaling pathway networks to understand the molecular mechanisms of metformin action |
| url | http://europepmc.org/articles/PMC4470683?pdf=render |
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