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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/101599

    Title: Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells
    Authors: 黃建宏;Chien-Hung Huang;邱金水;Jin-Shuei Ciou;陳順宗;Shun-Tsung Chen;郭集慶;Victor C. Kok;Yi Chung;蔡進發;Jeffrey J. P. Tsai;Nilubon Kurubanjerdjit;Chi-Ying F. Huang;*;吳家樂;Ka-Lok Ng;*
    Contributors: 生物資訊與醫學工程學系
    Date: 2016-09
    Issue Date: 2016-11-08 10:14:32 (UTC+8)
    Abstract: 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.


    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.


    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.


    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.
    Relation: PeerJ
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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