ACE2 Shedding and Furin Abundance in Target Organs may Influence the Efficiency of SARS-CoV-2 Entry

Q3 Computer Science Open Bioinformatics Journal Pub Date : 2021-03-22 DOI:10.2174/1875036202114010001
Yuanchen Ma, Yinong Huang, Tao Wang, A. Xiang, Weijun Huang
{"title":"ACE2 Shedding and Furin Abundance in Target Organs may Influence the Efficiency of SARS-CoV-2 Entry","authors":"Yuanchen Ma, Yinong Huang, Tao Wang, A. Xiang, Weijun Huang","doi":"10.2174/1875036202114010001","DOIUrl":null,"url":null,"abstract":"\n \n Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a lineage B coronavirus, causing the worldwide outbreak of Corona Virus Disease 2019 (COVID-19). Despite genetically closed to SARS-CoV, SARS-CoV-2 seems to possess enhanced infectivity and subtle different clinical features, which may hamper the early screening of suspected patients as well as the control of virus transmission. Unfortunately, there are few tools to predict the potential target organ damage and possible clinical manifestations caused by such novel coronavirus.\n \n \n \n To solve this problem, we use the online single-cell sequence datasets to analyze the expression of the major receptor in host cells that mediates the virus entry, including angiotensin converting enzyme 2 (ACE2), and its co-expressed membrane endopeptidases.\n \n \n \n The results indicated the differential expression of ADAM10 and ADAM17 might contribute to the ACE2 shedding and affect the membrane ACE2 abundance. We further confirm a putative furin-cleavage site reported recently in the spike protein of SARS-CoV-2, which may facilitate the virus-cell fusion. Based on these findings, we develop an approach that comprehensively analyzed the virus receptor expression, ACE2 shedding, membrane fusion activity, virus uptake and virus replication to evaluate the infectivity of SARS-CoV-2 to different human organs.\n \n \n \n Our results indicate that, in addition to airway epithelia, cardiac tissue and enteric canals are susceptible to SARS-CoV-2 as well.\n","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Bioinformatics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1875036202114010001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 12

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a lineage B coronavirus, causing the worldwide outbreak of Corona Virus Disease 2019 (COVID-19). Despite genetically closed to SARS-CoV, SARS-CoV-2 seems to possess enhanced infectivity and subtle different clinical features, which may hamper the early screening of suspected patients as well as the control of virus transmission. Unfortunately, there are few tools to predict the potential target organ damage and possible clinical manifestations caused by such novel coronavirus. To solve this problem, we use the online single-cell sequence datasets to analyze the expression of the major receptor in host cells that mediates the virus entry, including angiotensin converting enzyme 2 (ACE2), and its co-expressed membrane endopeptidases. The results indicated the differential expression of ADAM10 and ADAM17 might contribute to the ACE2 shedding and affect the membrane ACE2 abundance. We further confirm a putative furin-cleavage site reported recently in the spike protein of SARS-CoV-2, which may facilitate the virus-cell fusion. Based on these findings, we develop an approach that comprehensively analyzed the virus receptor expression, ACE2 shedding, membrane fusion activity, virus uptake and virus replication to evaluate the infectivity of SARS-CoV-2 to different human organs. Our results indicate that, in addition to airway epithelia, cardiac tissue and enteric canals are susceptible to SARS-CoV-2 as well.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
靶器官中ACE2的脱落和Furin的丰度可能影响严重急性呼吸系统综合征冠状病毒2型的进入效率
严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)是一种乙型冠状病毒,导致2019冠状病毒病(COVID-19)在全球爆发。尽管与SARS-CoV基因接近,但SARS-CoV-2似乎具有增强的传染性和微妙的临床特征,这可能会阻碍早期筛查疑似患者和控制病毒传播。不幸的是,目前几乎没有工具可以预测这种新型冠状病毒引起的潜在靶器官损伤和可能的临床表现。为了解决这个问题,我们使用在线单细胞序列数据集来分析宿主细胞中介导病毒进入的主要受体的表达,包括血管紧张素转换酶2 (ACE2)及其共表达的膜内肽酶。结果表明,ADAM10和ADAM17的差异表达可能参与了ACE2的脱落,并影响了膜上ACE2的丰度。我们进一步证实了最近报道的在SARS-CoV-2刺突蛋白中推测的furin切割位点,该位点可能促进病毒与细胞融合。基于这些发现,我们建立了一种综合分析病毒受体表达、ACE2脱落、膜融合活性、病毒摄取和病毒复制的方法来评估SARS-CoV-2对人体不同器官的感染性。我们的研究结果表明,除了气道上皮,心脏组织和肠管也容易感染SARS-CoV-2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Open Bioinformatics Journal
Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)
CiteScore
2.40
自引率
0.00%
发文量
4
期刊介绍: The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.
期刊最新文献
Decision-making Support System for Predicting and Eliminating Malnutrition and Anemia Immunoinformatics Approach for the Design of Chimeric Vaccine Against Whitmore Disease A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease Early Prediction of Covid-19 Samples from Chest X-ray Images using Deep Learning Approach Electronic Health Record (EHR) System Development for Study on EHR Data-based Early Prediction of Diabetes Using Machine Learning Algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1