Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis
Abstract 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 reanalysi...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-023-08784-x |
| _version_ | 1827604225387921408 |
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| author | Jie Zhou Xiping Yang Yuecong Yang Yiru Wei Dongjia Lu Yulan Xie Hao Liang Ping Cui Li Ye Jiegang Huang |
| author_facet | Jie Zhou Xiping Yang Yuecong Yang Yiru Wei Dongjia Lu Yulan Xie Hao Liang Ping Cui Li Ye Jiegang Huang |
| author_sort | Jie Zhou |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T05:56:01Z |
| format | Article |
| id | doaj.art-2bd02db7665849ddb3ffd0738a12703f |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-03-09T05:56:01Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-2bd02db7665849ddb3ffd0738a12703f2023-12-03T12:13:32ZengBMCBMC Infectious Diseases1471-23342023-11-0123111210.1186/s12879-023-08784-xHuman microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosisJie Zhou0Xiping Yang1Yuecong Yang2Yiru Wei3Dongjia Lu4Yulan Xie5Hao Liang6Ping Cui7Li Ye8Jiegang Huang9Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityGuangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical UniversityAbstract 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.https://doi.org/10.1186/s12879-023-08784-xSARS-CoV-2/COVID-19Human microbiotaα-diversityKey microbiotaMachine learning |
| spellingShingle | Jie Zhou Xiping Yang Yuecong Yang Yiru Wei Dongjia Lu Yulan Xie Hao Liang Ping Cui Li Ye Jiegang Huang Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis BMC Infectious Diseases SARS-CoV-2/COVID-19 Human microbiota α-diversity Key microbiota Machine learning |
| 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 | SARS-CoV-2/COVID-19 Human microbiota α-diversity Key microbiota Machine learning |
| url | https://doi.org/10.1186/s12879-023-08784-x |
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