Suppr超能文献

感染 SARS-CoV-2 后人类微生物群落失调有可能预测疾病预后。

Human microbiota dysbiosis after SARS-CoV-2 infection have the potential to predict disease prognosis.

机构信息

Guangxi Key Laboratory of AIDS Prevention and Treatment & School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.

Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Nanning, Guangxi, China.

出版信息

BMC Infect Dis. 2023 Nov 29;23(1):841. doi: 10.1186/s12879-023-08784-x.

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.

摘要

背景

关于 SARS-CoV-2 和人类微生物组的研究在微生物组 α 多样性和关键微生物组方面得出了不一致的结果。为了解决这些问题并探索人类微生物组对 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。

方法

我们在 Pubmed 和 Bioproject 数据库中检索了现有的关于 SARS-CoV-2 和人类微生物组的研究(从成立到 2021 年 10 月 29 日),并提取了人类微生物组的可用原始 16S rRNA 测序数据。首先,我们使用荟萃分析和生物信息学方法重新分析了原始数据,并评估了 SARS-CoV-2 对人类微生物 α 多样性的影响。其次,使用机器学习 (ML) 评估了微生物组预测 SARS-CoV-2 感染预后的能力。最后,我们旨在确定与 SARS-CoV-2 感染相关的关键微生物组。

结果

共纳入 20 项关于 SARS-CoV-2 和人类微生物组的研究,涉及肠道(n=9)、呼吸道(n=11)、口腔(n=3)和皮肤(n=1)微生物组。荟萃分析表明,在肠道研究中,当限制因素为排除抗生素影响、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 感染与微生物组 α 多样性下调相关(P<0.05)。在呼吸道研究中,当限制因素为 V4 测序区域时,SARS-CoV-2 感染与 α 多样性下调相关(P<0.05)。此外,SARS-CoV-2 感染后皮肤微生物组的 α 多样性在多个时间点下调(P<0.05)。然而,SARS-CoV-2 感染后口腔微生物组的 α 多样性没有显著差异。基于 SARS-CoV-2 感染后基线呼吸道(口咽)微生物组谱的 ML 模型表现出预测结局(生存和死亡,随机森林,AUC=0.847,敏感性=0.833,特异性=0.750)的能力。肠道、呼吸道和口腔中共同的差异普雷沃氏菌和链球菌与 SARS-CoV-2 感染的严重程度和恢复有关。

结论

SARS-CoV-2 感染与人类肠道和呼吸道微生物组的 α 多样性下调有关。呼吸道微生物组有可能预测感染 SARS-CoV-2 的个体的预后。普雷沃氏菌和链球菌可能是 SARS-CoV-2 感染的关键微生物组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9abf/10685584/9ffd9c32628d/12879_2023_8784_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验