Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome

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作者
Qin Liu
Qi Su
Fen Zhang
Hein M. Tun
Joyce Wing Yan Mak
Grace Chung-Yan Lui
Susanna So Shan Ng
Jessica Y. L. Ching
Amy Li
Wenqi Lu
Chenyu Liu
Chun Pan Cheung
David S. C. Hui
Paul K. S. Chan
Francis Ka Leung Chan
Siew C. Ng
机构
[1] Microbiota I-Center (MagIC),Department of Medicine and Therapeutics, Faculty of Medicine
[2] The Chinese University of Hong Kong,Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, Faculty of Medicine
[3] Hong Kong,Center for Gut Microbiota Research, Faculty of Medicine
[4] The Chinese University of Hong Kong,The Jockey Club School of Public Health and Primary Care, Faculty of Medicine
[5] The Chinese University of Hong Kong,Stanley Ho Centre for Emerging Infectious Diseases, Faculty of Medicine
[6] The Chinese University of Hong Kong,Department of Microbiology, Faculty of Medicine
[7] The Chinese University of Hong Kong,undefined
[8] The Chinese University of Hong Kong,undefined
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摘要
Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.
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