Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis.
Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis.
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DOI：
10.1186/s12879-023-08784-x
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发表时间：
2023-11-29
影响因子：
3.7
通讯作者：
Huang, Jiegang
中科院分区：
文献类型：
--
作者：
Zhou, Jie;Yang, Xiping;Yang, Yuecong;Wei, Yiru;Lu, Dongjia;Xie, Yulan;Liang, Hao;Cui, Ping;Ye, Li;Huang, Jiegang
关于SARS-CoV-2和人类微生物群的研究在微生物群α多样性和关键微生物群方面产生了不一致的结果。为了解决这些问题并探索人类微生物群对SARS-CoV-2感染预后的预测能力，我们对现有研究进行了重新分析。我们回顾了Pubmed和Bioproject数据库中关于SARS-CoV-2和人类微生物群的现有研究（从开始到2021年10月29日），并提取了人类微生物群的原始16 S rRNA测序数据。首先，我们采用Meta分析和生物信息学方法对原始数据进行重新分析，评估SARS-CoV-2对人类微生物α多样性的影响。其次，采用机器学习（ML）来评估微生物群预测SARS-CoV-2感染预后的能力。最后，我们的目标是确定与SARS-CoV-2感染相关的关键微生物群。共纳入了20项与SARS-CoV-2和人体微生物群相关的研究，涉及肠道（n = 9），呼吸道（n = 11），口腔（n = 3）和皮肤（n = 1）微生物群。荟萃分析显示，在肠道研究中，当限制因素为排除抗生素影响的研究、横断面和病例对照研究、中国研究、美国研究和Illumina MiSeq测序研究时，SARS-CoV-2感染与微生物群α多样性下调相关（P < 0.05）。在呼吸系统研究中，当限制因子为V4序列时，SARS-CoV-2感染与α多样性下调相关（P < 0.05）。此外，SARS-CoV-2感染后多个时间点皮肤微生物群α多样性下调（P < 0.05）。然而，在SARS-CoV-2感染后，口腔微生物群α多样性没有观察到显著差异。基于基线呼吸（口咽）微生物群特征的ML模型显示出预测SARS-CoV-2感染后结局（存活和死亡，随机森林，AUC = 0.847，灵敏度= 0.833，特异性= 0.750）的能力。肠道、呼吸道和口腔中普雷沃菌和链球菌的共同差异与SARS-CoV-2感染的严重程度和恢复相关。SARS-CoV-2感染与人类肠道和呼吸道微生物群中α多样性的下调有关。呼吸道菌群有可能预测SARS-CoV-2感染者的预后。Prevotella和Streptococcus可能是SARS-CoV-2感染的关键菌群。在线版本包含补充材料，可通过10.1186/s12879-023-08784-x获得。
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. 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. 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. 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. The online version contains supplementary material available at 10.1186/s12879-023-08784-x.
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影响因子：
4.5
作者：
Kim HN;Joo EJ;Lee CW;Ahn KS;Kim HL;Park DI;Park SK
通讯作者：
Park SK
影响因子：
48
作者：
Callahan BJ;McMurdie PJ;Rosen MJ;Han AW;Johnson AJ;Holmes SP
通讯作者：
Holmes SP
microeco：用于微生物群落生态学数据挖掘的 R 包
影响因子：
4.2
作者：
Liu, Chi;Cui, Yaoming;Yao, Minjie
通讯作者：
Yao, Minjie
影响因子：
2.3
作者：
Fujita, Kiyoko;Takata, Iichiro;Mikamo, Hiroshige
通讯作者：
Mikamo, Hiroshige
DOI：
10.1007/978-981-13-2835-0_12
发表时间：
2018-01-01
期刊：
BIOCHEMISTRY AND CELL BIOLOGY OF AGEING, PT I: BIOMEDICAL SCIENCE
影响因子：
--
作者：
Maynard, Claire;Weinkove, David
通讯作者：
Weinkove, David