The drug target genes show higher evolutionary conservation than non-target genes.

Journal: Oncotarget
Published Date: Jan 26, 2016

Abstract

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Authors

  • Wenhua Lv
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Yongdeng Xu
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Yiying Guo
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Ziqi Yu
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Guanglong Feng
    Department of Radiology, Second Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Panpan Liu
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Meiwei Luan
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Hongjie Zhu
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Guiyou Liu
    Genome Analysis Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
  • Mingming Zhang
    Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
  • Hongchao Lv
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Lian Duan
    Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
  • Zhenwei Shang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Yongshuai Jiang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Ruijie Zhang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

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