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In Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

BACKGROUND : At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with Coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict COVID-19 illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders.

METHODS : To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multi-center, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate and severe COVID-19 (n=322 participants).

RESULTS : In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features.

CONCLUSION : In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.

de Castilhos Juliana, Zamir Eli, Hippchen Theresa, Rohrbach Roman, Schmidt Sabine, Hengler Silvana, Schumacher Hanna, Neubauer Melanie, Kunz Sabrina, Müller-Esch Tonia, Hiergeist Andreas, Gessner André, Khalid Dina, Gaiser Rogier, Cullin Nyssa, Papagiannarou Stamatia M, Beuthien-Baumann Bettina, Krämer Alwin, Bartenschlager Ralf, Jäger Dirk, Müller Michael, Herth Felix, Duerschmied Daniel, Schneider Jochen, Schmid Roland M, Eberhardt Johann F, Khodamoradi Yascha, Vehreschild Maria J G T, Teufel Andreas, Ebert Matthias P, Hau Peter, Salzberger Bernd, Schnitzler Paul, Poeck Hendrik, Elinav Eran, Merle Uta, Stein-Thoeringer Christoph K


COVID-19, SARS-CoV-2, dysbiosis, intensive medical care, machine learning, microbiome