遗传 ›› 2020, Vol. 42 ›› Issue (2): 172-182.doi: 10.16288/j.yczz.19-214

• 研究报告 • 上一篇    下一篇

脱发相关差异表达基因的生物信息学分析

向虹(), 阳小胡, 艾亮霞, 潘燕平, 胡勇()   

  1. 中国科学院深圳先进技术研究院,合成生物研究所,深圳 518055
  • 收稿日期:2019-10-02 修回日期:2019-12-13 出版日期:2019-12-20 发布日期:2019-12-23
  • 基金资助:
    深圳市科技创新委员会基础科学研究基金(JCYJ20180507182250795);中国博士后科学基金(2019M663173);深圳孔雀团队项目(KQTD20170331160605510)

Bioinformatics analysis of differentially expressed genes on alopecia

Xiang Hong(), Yang Xiaohu, Ai Liangxia, Pan Yanping, Hu Yong()   

  1. Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
  • Received:2019-10-02 Revised:2019-12-13 Online:2019-12-20 Published:2019-12-23
  • Supported by:
    the Shenzhen Science and Technology Innovation Committee Basic Science Research Grant(JCYJ20180507182250795);China Postdoctoral Science Foundation Grand(2019M663173);the Shenzhen Peacock Team Project(KQTD20170331160605510)

摘要:

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。

关键词: 脱发, 斑秃, GEO, 生物信息学

Abstract:

The molecular mechanism of alopecia areata (AA) is still elusive and here we utilized bioinformatics methods to analyze AA-related differentially expressed genes. In this study, GSE45512 and GSE45513 were downloaded from the NCBI sub-database Gene Expression Omnibus (GEO). The gene expressions of AA and normal samples were analyzed using the R package limma, which showed significant differences between AA and normal samples in two species. These genes were subject to functional annotation and protein interaction networks. At the same time, gene set enrichment analysis was conducted for all differentially expressed genes. The study revealed that a total of 225 differentially expressed genes were screened from human AA samples, and a total of 337 differentially expressed genes were screened from spontaneous AA skin samples in C3H/HeJ mice. There are 23 differentially expressed genes in the two species. GO and protein interaction network analysis shown gene enrichment in immune-related functions, and these proteins interact with each other. Gene set enrichment analysis showed that differential genes from both species were significantly enriched to chemokine signaling pathways, cytokine-cytokine receptor interactions, staphylococcus aureus infection, and antigen processing and presentation. Moreover, the human down-regulated differential gene not only maps to the alopecia in human phenotype ontology, but also maps to the pathologically relevant phenotype of the skin appendage. In brief, 23 significant differentially expressed genes were screened out coexisting in AA human and mouse by bioinformatics methods. In addition, the result demonstrated that AA is closely related to the immune process and skin appendage lesions. These results provide new ideas for the diagnosis and treatment of AA.

Key words: hair loss, alopecia areata, GEO, bioinformatics