人支气管上皮细胞SARS-CoV感染到COVID-19的单细胞转录组学数据扩增

IF 3.7 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Biological Procedures Online Pub Date : 2020-07-23 eCollection Date: 2020-01-01 DOI:10.1186/s12575-020-00127-3
Reza Zolfaghari Emameh, Hassan Nosrati, Mahyar Eftekhari, Reza Falak, Majid Khoshmirsafa
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引用次数: 3

摘要

背景:严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)是2019年12月在中国武汉出现的冠状病毒新成员冠状病毒19 (COVID-19)的病原体,随后在各大洲传播。由于SARS-CoV-2与SARS-CoV的相似性约为77.5%,因此预计SARS-CoV-2的转录组和免疫调控与SARS-CoV有很高的重叠百分比。结果:本研究利用感染SARS-CoV的人支气管上皮细胞(2B4细胞系)的单细胞转录组学数据,在Expression Atlas数据库中进行了注释,将该数据扩展到COVID-19。此外,我们采用系统生物学方法,包括基因本体(GO)和Reactome通路分析来确定SARS-CoV感染细胞的功能基因和通路。对表达图谱数据库的转录组学分析显示,感染SARS-CoV的2B4细胞系的大部分基因下调,导致免疫系统过度激活,诱导信号通路,从而引发细胞因子风暴。此外,GO:0016192(囊泡介导的转运)、GO:0006886(细胞内蛋白转运)和GO:0006888 (ER到高尔基囊泡介导的转运)是SARS-CoV感染细胞本体网络中排名前三位的GO。同时,在SARS-CoV感染细胞网络中,R-HAS-6807070(磷酸酶和紧张素同源物或PTEN调控)与其他Reactome通路的相关性最高。PTEN在树突状细胞、B细胞和t细胞的激活以及促炎细胞因子的分泌中发挥关键作用,并与下调基因协同促进COVID-19患者的细胞因子风暴。结论:基于SARS-CoV转录组与SARS-CoV-2的高相似性,SARS-CoV感染的免疫调控、信号通路和促炎因子数据可扩展到COVID-19,为未来的药物和疫苗研究提供有效平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV.

Results: In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients.

Conclusions: Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.

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来源期刊
Biological Procedures Online
Biological Procedures Online 生物-生化研究方法
CiteScore
10.50
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: iological Procedures Online publishes articles that improve access to techniques and methods in the medical and biological sciences. We are also interested in short but important research discoveries, such as new animal disease models. Topics of interest include, but are not limited to: Reports of new research techniques and applications of existing techniques Technical analyses of research techniques and published reports Validity analyses of research methods and approaches to judging the validity of research reports Application of common research methods Reviews of existing techniques Novel/important product information Biological Procedures Online places emphasis on multidisciplinary approaches that integrate methodologies from medicine, biology, chemistry, imaging, engineering, bioinformatics, computer science, and systems analysis.
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