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Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis
BACKGROUND: The studies on SARS-CoV-2 and human microbiota have yielded inconsistent results regarding microbiota α-diversity and key microbiota. To address these issues and explore the predictive ability of human microbiota for the prognosis of SARS-CoV-2 infection, we conducted a reanalysis of exi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685584/ https://www.ncbi.nlm.nih.gov/pubmed/38031010 http://dx.doi.org/10.1186/s12879-023-08784-x |
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author | Zhou, Jie Yang, Xiping Yang, Yuecong Wei, Yiru Lu, Dongjia Xie, Yulan Liang, Hao Cui, Ping Ye, Li Huang, Jiegang |
author_facet | Zhou, Jie Yang, Xiping Yang, Yuecong Wei, Yiru Lu, Dongjia Xie, Yulan Liang, Hao Cui, Ping Ye, Li Huang, Jiegang |
author_sort | Zhou, Jie |
collection | PubMed |
description | BACKGROUND: The studies on SARS-CoV-2 and human microbiota have yielded inconsistent results regarding microbiota α-diversity and key microbiota. To address these issues and explore the predictive ability of human microbiota for the prognosis of SARS-CoV-2 infection, we conducted a reanalysis of existing studies. METHODS: We reviewed the existing studies on SARS-CoV-2 and human microbiota in the Pubmed and Bioproject databases (from inception through October 29, 2021) and extracted the available raw 16S rRNA sequencing data of human microbiota. Firstly, we used meta-analysis and bioinformatics methods to reanalyze the raw data and evaluate the impact of SARS-CoV-2 on human microbial α-diversity. Secondly, machine learning (ML) was employed to assess the ability of microbiota to predict the prognosis of SARS-CoV-2 infection. Finally, we aimed to identify the key microbiota associated with SARS-CoV-2 infection. RESULTS: A total of 20 studies related to SARS-CoV-2 and human microbiota were included, involving gut (n = 9), respiratory (n = 11), oral (n = 3), and skin (n = 1) microbiota. Meta-analysis showed that in gut studies, when limiting factors were studies ruled out the effect of antibiotics, cross-sectional and case–control studies, Chinese studies, American studies, and Illumina MiSeq sequencing studies, SARS-CoV-2 infection was associated with down-regulation of microbiota α-diversity (P < 0.05). In respiratory studies, SARS-CoV-2 infection was associated with down-regulation of α-diversity when the limiting factor was V4 sequencing region (P < 0.05). Additionally, the α-diversity of skin microbiota was down-regulated at multiple time points following SARS-CoV-2 infection (P < 0.05). However, no significant difference in oral microbiota α-diversity was observed after SARS-CoV-2 infection. ML models based on baseline respiratory (oropharynx) microbiota profiles exhibited the ability to predict outcomes (survival and death, Random Forest, AUC = 0.847, Sensitivity = 0.833, Specificity = 0.750) after SARS-CoV-2 infection. The shared differential Prevotella and Streptococcus in the gut, respiratory tract, and oral cavity was associated with the severity and recovery of SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection was related to the down-regulation of α-diversity in the human gut and respiratory microbiota. The respiratory microbiota had the potential to predict the prognosis of individuals infected with SARS-CoV-2. Prevotella and Streptococcus might be key microbiota in SARS-CoV-2 infection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08784-x. |
format | Online Article Text |
id | pubmed-10685584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106855842023-11-30 Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis Zhou, Jie Yang, Xiping Yang, Yuecong Wei, Yiru Lu, Dongjia Xie, Yulan Liang, Hao Cui, Ping Ye, Li Huang, Jiegang BMC Infect Dis Research BACKGROUND: The studies on SARS-CoV-2 and human microbiota have yielded inconsistent results regarding microbiota α-diversity and key microbiota. To address these issues and explore the predictive ability of human microbiota for the prognosis of SARS-CoV-2 infection, we conducted a reanalysis of existing studies. METHODS: We reviewed the existing studies on SARS-CoV-2 and human microbiota in the Pubmed and Bioproject databases (from inception through October 29, 2021) and extracted the available raw 16S rRNA sequencing data of human microbiota. Firstly, we used meta-analysis and bioinformatics methods to reanalyze the raw data and evaluate the impact of SARS-CoV-2 on human microbial α-diversity. Secondly, machine learning (ML) was employed to assess the ability of microbiota to predict the prognosis of SARS-CoV-2 infection. Finally, we aimed to identify the key microbiota associated with SARS-CoV-2 infection. RESULTS: A total of 20 studies related to SARS-CoV-2 and human microbiota were included, involving gut (n = 9), respiratory (n = 11), oral (n = 3), and skin (n = 1) microbiota. Meta-analysis showed that in gut studies, when limiting factors were studies ruled out the effect of antibiotics, cross-sectional and case–control studies, Chinese studies, American studies, and Illumina MiSeq sequencing studies, SARS-CoV-2 infection was associated with down-regulation of microbiota α-diversity (P < 0.05). In respiratory studies, SARS-CoV-2 infection was associated with down-regulation of α-diversity when the limiting factor was V4 sequencing region (P < 0.05). Additionally, the α-diversity of skin microbiota was down-regulated at multiple time points following SARS-CoV-2 infection (P < 0.05). However, no significant difference in oral microbiota α-diversity was observed after SARS-CoV-2 infection. ML models based on baseline respiratory (oropharynx) microbiota profiles exhibited the ability to predict outcomes (survival and death, Random Forest, AUC = 0.847, Sensitivity = 0.833, Specificity = 0.750) after SARS-CoV-2 infection. The shared differential Prevotella and Streptococcus in the gut, respiratory tract, and oral cavity was associated with the severity and recovery of SARS-CoV-2 infection. CONCLUSIONS: SARS-CoV-2 infection was related to the down-regulation of α-diversity in the human gut and respiratory microbiota. The respiratory microbiota had the potential to predict the prognosis of individuals infected with SARS-CoV-2. Prevotella and Streptococcus might be key microbiota in SARS-CoV-2 infection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08784-x. BioMed Central 2023-11-29 /pmc/articles/PMC10685584/ /pubmed/38031010 http://dx.doi.org/10.1186/s12879-023-08784-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Jie Yang, Xiping Yang, Yuecong Wei, Yiru Lu, Dongjia Xie, Yulan Liang, Hao Cui, Ping Ye, Li Huang, Jiegang Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title | Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title_full | Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title_fullStr | Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title_full_unstemmed | Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title_short | Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis |
title_sort | human microbiota dysbiosis after sars-cov-2 infection have the potential to predict disease prognosis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685584/ https://www.ncbi.nlm.nih.gov/pubmed/38031010 http://dx.doi.org/10.1186/s12879-023-08784-x |
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