Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells

PeerJ

article

Chien-Hung Huang¹  Jin-Shuei Ciou²  Shun-Tsung Chen²  Victor C. Kok²  Yi Chung²  Jeffrey J. P. Tsai²  Nilubon Kurubanjerdjit⁴  Chi-Ying F. Huang⁵  Ka-Lok Ng² 

[1] Department of Computer Science and Information Engineering, National Formosa University;Department of Bioinformatics and Medical Engineering, Asia University;Division of Medical Oncology, Kuang Tien General Hospital Cancer Center;School of Information Technology, Mae Fah Luang University;Institute of Biopharmaceutical Sciences, National Yang-Ming University;Department of Medical Research, China Medical University Hospital, China Medical University

关键词: Drug repositioning;    Cardiovascular disease;    Gaussian graphical model;    Vascular smooth muscle cell;    Gene set enrichment analysis;    Topological parameters;    Time-course microarray;    Mechanical stress;   

DOI  :  10.7717/peerj.2478

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background Abnormal 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. Methods In 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. Results Based 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. Conclusion This 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.

【 授权许可】

CC BY