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Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage. METHODS: In this study, clinical bioinformatics analysis was conducted on tr...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447736/ http://dx.doi.org/10.1097/EC9.0000000000000005 |
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author | Zhang, Haomin Chen, Haoran Zhang, Jundong Chen, Ximeng Guo, Bin Zhi, Peng Li, Zhuoyang Liu, Geliang Yang, Bo Chi, Xiaohua Wang, Yixing Cao, Feng Ren, Jun Lu, Xuechun |
author_facet | Zhang, Haomin Chen, Haoran Zhang, Jundong Chen, Ximeng Guo, Bin Zhi, Peng Li, Zhuoyang Liu, Geliang Yang, Bo Chi, Xiaohua Wang, Yixing Cao, Feng Ren, Jun Lu, Xuechun |
author_sort | Zhang, Haomin |
collection | PubMed |
description | BACKGROUND: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage. METHODS: In this study, clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection. RESULTS: Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways, including immune cells and toll-like receptor cascades. Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines (including IL6, NF-κB, and TNF-α) followed by concurrent inflammatory storm, which mediates the autoimmune response. Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection. CONCLUSIONS: This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19. |
format | Online Article Text |
id | pubmed-8447736 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-84477362021-09-20 Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 Zhang, Haomin Chen, Haoran Zhang, Jundong Chen, Ximeng Guo, Bin Zhi, Peng Li, Zhuoyang Liu, Geliang Yang, Bo Chi, Xiaohua Wang, Yixing Cao, Feng Ren, Jun Lu, Xuechun Emergency and Critical Care Medicine Original Article BACKGROUND: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage. METHODS: In this study, clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection. RESULTS: Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways, including immune cells and toll-like receptor cascades. Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines (including IL6, NF-κB, and TNF-α) followed by concurrent inflammatory storm, which mediates the autoimmune response. Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection. CONCLUSIONS: This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19. 2021-09-15 /pmc/articles/PMC8447736/ http://dx.doi.org/10.1097/EC9.0000000000000005 Text en Copyright © 2021 Shandong University, published by Wolters Kluwer, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | Original Article Zhang, Haomin Chen, Haoran Zhang, Jundong Chen, Ximeng Guo, Bin Zhi, Peng Li, Zhuoyang Liu, Geliang Yang, Bo Chi, Xiaohua Wang, Yixing Cao, Feng Ren, Jun Lu, Xuechun Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title | Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title_full | Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title_fullStr | Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title_full_unstemmed | Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title_short | Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19 |
title_sort | bioinformatics analysis of sars-cov-2 infection-associated immune injury and therapeutic prediction for covid-19 |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447736/ http://dx.doi.org/10.1097/EC9.0000000000000005 |
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