Frontiers in Microbiology (Nov 2022)

Gut microbiota profile of COVID-19 patients: Prognosis and risk stratification (MicroCOVID-19 study)

  • José Guilherme Nobre,
  • José Guilherme Nobre,
  • José Guilherme Nobre,
  • Mariana Delgadinho,
  • Carina Silva,
  • Carina Silva,
  • Joana Mendes,
  • Vanessa Mateus,
  • Edna Ribeiro,
  • Diogo Alpuim Costa,
  • Diogo Alpuim Costa,
  • Miguel Lopes,
  • Ana Isabel Pedroso,
  • Frederico Trigueiros,
  • Maria Inês Rodrigues,
  • Cristina Lino de Sousa,
  • Miguel Brito

DOI
https://doi.org/10.3389/fmicb.2022.1035422
Journal volume & issue
Vol. 13

Abstract

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BackgroundGut microbiota is intrinsically associated with the immune system and can promote or suppress infectious diseases, especially viral infections. This study aims to characterize and compare the microbiota profile of infected patients with SARS-CoV-2 (milder or severe symptoms), non-infected people, and recovered patients. This is a national, transversal, observational, multicenter, and case–control study that analyzed the microbiota of COVID-19 patients with mild or severe symptoms at home, at the hospital, or in the intensive care unit, patients already recovered, and healthy volunteers cohabiting with COVID-19 patients. DNA was isolated from stool samples and sequenced in a NGS platform. A demographic questionnaire was also applied. Statistical analysis was performed in SPSS.ResultsFirmicutes/Bacteroidetes ratios were found to be significantly lower in infected patients (1.61 and 2.57) compared to healthy volunteers (3.23) and recovered patients (3.89). Furthermore, the microbiota composition differed significantly between healthy volunteers, mild and severe COVID-19 patients, and recovered patients. Furthermore, Escherichia coli, Actinomyces naeslundii, and Dorea longicatena were shown to be more frequent in severe cases. The most common COVID-19 symptoms were linked to certain microbiome groups.ConclusionWe can conclude that microbiota composition is significantly affected by SARS-CoV-2 infection and may be used to predict COVID-19 clinical evolution. Therefore, it will be possible to better allocate healthcare resources and better tackle future pandemics.

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