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Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.
Li, Sijia; Yang, Siyuan; Zhou, Yuzheng; Disoma, Cyrollah; Dong, Zijun; Du, Ashuai; Zhang, Yongxing; Chen, Yong; Huang, Weiliang; Chen, Junru; Song, Deqiang; Chen, Zongpeng; Liu, Pinjia; Li, Shiqin; Zheng, Rong; Liu, Sixu; Razzaq, Aroona; Chen, Xuan; Tao, Siyi; Yu, Chengping; Feng, Tianxu; Liao, Wenyan; Peng, Yousong; Jiang, Taijiao; Huang, Jufang; Wu, Wei; Hu, Liqiang; Wang, Linghang; Li, Shanni; Xia, Zanxian.
Afiliación
  • Li S; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Yang S; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Zhou Y; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Disoma C; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Dong Z; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Du A; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Zhang Y; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Chen Y; The First Hospital of Changsha, Changsha, China.
  • Huang W; The First Hospital of Changsha, Changsha, China.
  • Chen J; Suzhou Geneworks Technology Co., Ltd., Suzhou, China.
  • Song D; Suzhou Geneworks Technology Co., Ltd., Suzhou, China.
  • Chen Z; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Liu P; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Li S; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Zheng R; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Liu S; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Razzaq A; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Chen X; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Tao S; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Yu C; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Feng T; Xiangya School of Medicine, Central South University, Changsha, China.
  • Liao W; Department of Gynaecology and Obstetrics, The First Affiliated Hospital of University of South China, Hengyang, China.
  • Peng Y; Hunan Provincial Key Laboratory of Medical Virology, Bioinformatics Center, College of Biology, Hunan University, Changsha, China.
  • Jiang T; Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Huang J; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Wu W; Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Hu L; The First Hospital of Changsha, Changsha, China.
  • Wang L; Beijing Key Laboratory of Emerging Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Li S; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
  • Xia Z; Hunan Key Laboratory of Animal Models for Human Diseases, Department of Cell Biology, School of Life Sciences, Central South University, Changsha, China.
Front Microbiol ; 12: 712081, 2021.
Article en En | MEDLINE | ID: mdl-34707577
ABSTRACT
COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Microbiol Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Microbiol Año: 2021 Tipo del documento: Article País de afiliación: China