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. 2021 Oct 25:ciab902. doi: 10.1093/cid/ciab902

Severe dysbiosis and specific Haemophilus and Neisseria signatures as hallmarks of the oropharyngeal microbiome in critically ill COVID-19 patients

Juliana de Castilhos 1,22,#, Eli Zamir 1,#, Theresa Hippchen 2,#, Roman Rohrbach 1,#, Sabine Schmidt 1, Silvana Hengler 1, Hanna Schumacher 1, Melanie Neubauer 3, Sabrina Kunz 4, Tonia Müller-Esch 4, Andreas Hiergeist 5, André Gessner 5, Dina Khalid 6, Rogier Gaiser 1, Nyssa Cullin 1, Stamatia M Papagiannarou 1, Bettina Beuthien-Baumann 7, Alwin Krämer 8, Ralf Bartenschlager 9,10, Dirk Jäger 11, Michael Müller 12, Felix Herth 12, Daniel Duerschmied 13, Jochen Schneider 14, Roland M Schmid 14, Johann F Eberhardt 15, Yascha Khodamoradi 15, Maria J G T Vehreschild 15,16, Andreas Teufel 17, Matthias P Ebert 17, Peter Hau 18, Bernd Salzberger 19, Paul Schnitzler 6, Hendrik Poeck 4,20, Eran Elinav 1,21,#, Uta Merle 2,#, Christoph K Stein-Thoeringer 1,11,#,
PMCID: PMC8586732  PMID: 34694375

Abstract

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.

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

Supplementary Material

ciab902_suppl_Supplementary_Materials_1

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ciab902_suppl_Supplementary_Materials_1

Articles from Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America are provided here courtesy of Oxford University Press

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