Juliana de Castilhos1,2, Eli Zamir1, Theresa Hippchen3, Roman Rohrbach1, Sabine Schmidt1, Silvana Hengler1, Hanna Schumacher1, Melanie Neubauer4, Sabrina Kunz5, Tonia Müller-Esch5, Andreas Hiergeist6, André Gessner6, Dina Khalid7, Rogier Gaiser1, Nyssa Cullin1, Stamatia M Papagiannarou1, Bettina Beuthien-Baumann8, Alwin Krämer9, Ralf Bartenschlager10,11, Dirk Jäger12, Michael Müller13, Felix Herth13, Daniel Duerschmied14, Jochen Schneider15, Roland M Schmid15, Johann F Eberhardt16, Yascha Khodamoradi16, Maria J G T Vehreschild16,17, Andreas Teufel18, Matthias P Ebert18, Peter Hau19, Bernd Salzberger20, Paul Schnitzler7, Hendrik Poeck5,21, Eran Elinav1,22, Uta Merle3, Christoph K Stein-Thoeringer1,12. 1. German Cancer Research Center (DKFZ), Research Division Microbiome and Cancer, Heidelberg, Germany. 2. Vale do Rio dos Sinos University (UNISINOS), Sao Leopoldo, Brazil. 3. Department of Gastroenterology and Infectious Diseases, University Clinic Heidelberg, Heidelberg, Germany. 4. Department of Medicine II, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. 5. Department of Internal Medicine III, University Clinic Regensburg, Regensburg, Germany. 6. Institute of Clinical Microbiology and Hygiene, University Clinic Regensburg, Regensburg, Germany. 7. Department of Virology, University Clinic Heidelberg, Heidelberg, Germany. 8. German Cancer Research Center (DKFZ), Research Division Radiology, Heidelberg, Germany. 9. German Cancer Research Center (DKFZ), Research Division Molecular Hematology/Oncology, Heidelberg, Germany. 10. Department of Infectious Diseases, Molecular Virology, Heidelberg University, Heidelberg, Germany. 11. German Cancer Research Center (DKFZ), Research Division Virus-associated Carcinogenesis, Heidelberg. 12. National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany. 13. Thoraxklinik and Translational Lung Research Center, Heidelberg University, Heidelberg, Germany. 14. Department of Internal Medicine III, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 15. Department of Internal Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. 16. Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Frankfurt, Germany. 17. German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany. 18. Department of Medicine II, Section of Hepatology, University Medical Center Mannheim, University of Heidelberg, Mannheim, and Center for Preventive Medicine and Digital Health Baden-Württemberg, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. 19. Wilhelm Sander-NeuroOncology Unit and Department of Neurology, University Clinic Regensburg, Regensburg, Germany. 20. Department of Infectious Disease, University Clinic Regensburg, Regensburg, Germany. 21. National Center for Tumor Diseases (NCT) WERA. 22. Weizmann Institute of Science, Rehovot, Israel.
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 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (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 multicenter, 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. CONCLUSIONS: 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.
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 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (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 multicenter, 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. CONCLUSIONS: 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.
Authors: Jillian H Hurst; Alexander W McCumber; Jhoanna N Aquino; Javier Rodriguez; Sarah M Heston; Debra J Lugo; Alexandre T Rotta; Nicholas A Turner; Trevor S Pfeiffer; Thaddeus C Gurley; M Anthony Moody; Thomas N Denny; John F Rawls; James S Clark; Christopher W Woods; Matthew S Kelly Journal: Clin Infect Dis Date: 2022-08-24 Impact factor: 20.999