癌变·畸变·突变 ›› 2020, Vol. 32 ›› Issue (5): 355-362.doi: 10.3969/j.issn.1004-616x.2020.05.005

• 论著 • 上一篇    下一篇

PM2.5染毒HBE细胞前后差异表达的microRNAs及其生物信息学分析

蔡颖1,2, 李闰冰1,2, 郑凯1,2, 秦双建1,3, 李柏茹1,3, 张朝晖2, 徐新云1   

  1. 1. 深圳市疾病预防控制中心, 广东 深圳 518055;
    2. 南华大学公共卫生学院, 湖南 衡阳 421001;
    3. 中南大学湘雅公共卫生学院, 湖南 长沙 410078
  • 收稿日期:2020-05-20 修回日期:2020-09-03 出版日期:2020-10-01 发布日期:2020-10-12
  • 通讯作者: 徐新云,E-mail:xyxu2008@163.com E-mail:xyxu2008@163.com
  • 作者简介:蔡颖,E-mail:1479288556@qq.com。
  • 基金资助:
    深圳市科技研发项目(JCYJ20190807102205480,JCYJ2017413101713324)

Differential microRNAs expression and bioinformatics analyses in HBE cells exposed to PM2.5

CAI Ying1,2, LI Runbing1,2, ZHENG Kai1,2, QIN Shuangjian1,3, LI Boru1,3, ZHANG Zhaohui2, XU Xinyun1   

  1. 1. Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, Guangdong;
    2. School of Public Health, University of South China, Hengyang 421001;
    3. Xiangya School of Public Health, Central South University, Changsha 410078, Hunan, China
  • Received:2020-05-20 Revised:2020-09-03 Online:2020-10-01 Published:2020-10-12

摘要: 目的:采用microRNA(miRNA)测序和生物信息学方法,分析大气细颗粒物(PM2.5)染毒人支气管上皮HBE细胞前后差异表达的miRNAs,预测差异miRNAs的生物学功能和信号转导途径。方法:用50 μg/mL的PM2.5混悬液染毒HBE细胞,以未染毒组作为对照组,处理24 h后提取总RNA样品,Small RNA样品预处理试剂盒构建文库,并利用illumina HiSeqTM 2500/MiSeq测序平台进行高通量测序。以调整后P < 0.05的筛选标准得到差异miRNAs,使用miRanda和RNAhybrid两个软件共同预测差异miRNAs的靶基因并进行GO和KEGG功能富集分析,利用STRING数据库和Cytoscape软件对miRNAs和靶基因及靶基因之间的相互作用关系进行可视化分析。结果:高通量测序方法共检测到PM2.5染毒前后的包含1 137个miRNAs的表达谱,27个为差异表达的miRNAs,其中7个上调,20个下调。GO和KEGG富集分析发现,这些差异miRNAs主要参与细胞代谢、酶活性调节等生物学过程,聚集于胞内如细胞质、细胞器等,涉及代谢途径、PI3K-Akt信号通路、Ras信号通路、MAPK信号通路等与细胞增殖、分化、凋亡及癌症发生等相关的重要信号通路。此外,通过构建相互作用网络图,鉴定了miR-371b-3p、miR-371a-5p、miR-27a-3p、miR-7-5p、miR-372-3p等相互作用数量前11位的核心miRNAs,以及3个核心靶基因(VEGFAMAPK3CCR5)。结论:PM2.5染毒HBE细胞后引起了27个miRNAs的差异表达,涉及PI3K-Akt信号通路、Ras信号通路及MAPK信号通路等与癌症发生发展相关的重要信号通路,其中筛选了11个核心miRNAs和3个核心基因,为深入研究PM2.5致癌毒理作用机制提供了实验依据。

关键词: 大气细颗粒物, 人支气管上皮细胞, microRNA, 生物信息学, 信号通路

Abstract: OBJECTIVE: To investigate effects of PM2.5 on HBE cells using microRNA (miRNA) sequencing and bioinformatics methods. METHODS: HBE cells were treated with 50 μg/mL PM2.5 suspension and untreated cells were used as control. Total RNA samples were extracted 24 hours later. The Small RNA sample pretreatment kit was used to construct a library and the illumina HiSeqTM 2500/MiSeq sequencing platform was used for high-throughput sequencing. The differentially expressed miRNAs were obtained according to the screening criteria of adjusted P < 0.05,and the target genes of the differentially expressed miRNAs were predicted by miRanda and RNAhybrid software,and then GO and KEGG function enrichment analyses were performed. The STRING database and the Cytoscape software were used to visualize interactions between miRNAs and target genes and between target genes. RESULTS: Using high-throughput sequencing methods,a total of 1 137 miRNAs were detected before and after PM2.5 exposure in HBE cells. Among them,27 miRNAs were differentially expressed,including 7 up-regulated and 20 down-regulated. The enrichment analyses using GO and KEGG show that these differential miRNAs were mainly involved in biological processes such as cell metabolism and enzyme activity regulation. They are mainly involved with metabolic,PI3K-Akt signaling,cancer,Ras signaling,MAPK signaling pathways,etc. related to cell proliferation,differentiation,apoptosis and cancer. By constructing an interaction network diagram,the core miRNAs with the number of TOP 11 interactions such as miR-371b-3p,miR-371a-5p,miR-27a-3p,miR-7-5p,miR-372-3p,and 3 core target genes (VEGFA,MAPK3,CCR5) were identified. CONCLUSION: 27 miRNAs were differentially expressed after HBE cells were exposed to PM2.5. These miRNAs are involved with the PI3K-Akt signaling,Ras signaling,MAPK signaling pathways,etc. which are involved with the development of cancer. Among them,11 core miRNAs and 3 core genes were screened,our results provide the scientific basis for further study of the carcinogenic mechanism of PM2.5.

Key words: PM2.5, human bronchial epithelial cells, microRNA, bioinformatics, signal pathway

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