Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells
| Autor: | Jeffrey J. P. Tsai, Yi Chung, Victor C. Kok, Nilubon Kurubanjerdjit, Chi Ying F. Huang, Ka-Lok Ng, Chien-Hung Huang, Jin-Shuei Ciou, Shun-Tsung Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Drugs and Devices Gene set enrichment analysis Vascular smooth muscle Mechanical stress Bioinformatics Time-course microarray lcsh:Medicine Computational biology 030204 cardiovascular system & hematology Biology General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 0302 clinical medicine Clonogenic assay Gene Molecular Biology Microarray analysis techniques General Neuroscience Stress induced Drug repositioning lcsh:R Topological parameters General Medicine Cardiovascular disease Phenotype 030104 developmental biology Gaussian graphical model Vascular smooth muscle cell General Agricultural and Biological Sciences DrugBank |
| Zdroj: | PeerJ, Vol 4, p e2478 (2016) PeerJ |
| ISSN: | 2167-8359 |
| Popis: | BackgroundAbnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear.MethodsIn this study, we examined how VSMC responds under mechanical stress by using time-course microarray data. A three-phase study was proposed to investigate the stress-induced differentially expressed genes (DEGs) in VSMC. First, DEGs were identified by using the moderated t-statistics test. Second, more DEGs were inferred by using the Gaussian Graphical Model (GGM). Finally, the topological parameters-based method and cluster analysis approach were employed to predict the last batch of DEGs. To identify the potential drugs for vascular diseases involve VSMC proliferation, the drug-gene interaction database, Connectivity Map (cMap) was employed. Success of the predictions were determined using in-vitro data, i.e. MTT and clonogenic assay.ResultsBased on the differential expression calculation, at least 23 DEGs were found, and the findings were qualified by previous studies on VSMC. The results of gene set enrichment analysis indicated that the most often found enriched biological processes are cell-cycle-related processes. Furthermore, more stress-induced genes, well supported by literature, were found by applying graph theory to the gene association network (GAN). Finally, we showed that by processing the cMap input queries with a cluster algorithm, we achieved a substantial increase in the number of potential drugs with experimental IC50 measurements. With this novel approach, we have not only successfully identified the DEGs, but also improved the DEGs prediction by performing the topological and cluster analysis. Moreover, the findings are remarkably validated and in line with the literature. Furthermore, the cMap and DrugBank resources were used to identify potential drugs and targeted genes for vascular diseases involve VSMC proliferation. Our findings are supported by in-vitro experimental IC50, binding activity data and clinical trials.ConclusionThis study provides a systematic strategy to discover potential drugs and target genes, by which we hope to shed light on the treatments of VSMC proliferation associated diseases. |
| Databáze: | OpenAIRE |
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