eLifeMicrobiologyPeer-reviewed journal article
The relationship between gut and nasopharyngeal microbiome composition can predict the severity of COVID-19
B. Martin-Castano, P. Diez-Echave, J. Garcia-Garcia, L. Hidalgo-Garcia, A. J. Ruiz-Malagon, J. A. Molina-Tijeras, M. J. Rodriguez-Sojo, A. Redruello-Romero, M. Martinez-Zaldivar, E. Mota, F. Cobo, X. Diaz-Villamarin, E. Fernandez-Varon, M. Alvarez-Estevez, F. Garcia, C. Morales-Garcia, S. Merlos, P. Garcia-Flores, M. Colmenero-Ruiz, A. Ruiz-Sancho, M. Nunez, M. E. Rodriguez-Cabezas, A. Carazo, J. Martin, R. Moron, , J. Galvez
Abstract
Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity, and microbiota could play a key role in the infection, progression, and outcome of the disease. SARS-CoV-2 infection has been associated with nasopharyngeal and gut dysbiosis and higher abundance of opportunistic pathogens. To identify new prognostic markers for the disease, a multicenter prospective observational cohort study was carried out in COVID-19 patients divided into three cohorts based on symptomatology: mild (n=24), moderate (n=51), and severe/critical (n=31). Faecal and nasopharyngeal samples were taken, and the microbiota was analyzed. Linear discriminant analysis identified M. salivarium, P. dentalis, and H. parainfluenzae as biomarkers of severe COVID-19 in nasopharyngeal microbiota, while P. bivia and P. timonensis were defined in faecal microbiota. Additionally, a connection between faecal and nasopharyngeal microbiota was identified, with a significant ratio between P. timonensis (faeces) and P. dentalis and M. salivarium (nasopharyngeal) abundances found in critically ill patients. This ratio could serve as a novel prognostic tool for identifying severe COVID-19 cases.
- organism: human
- assay: metagenomics
- research focus: host-pathogen & immune response
- approach: statistical model
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