de Castilhos, Juliana and Zamir, Eli and Hippchen, Theresa and Rohrbach, Roman and Schmidt, Sabine and Hengler, Silvana and Schumacher, Hanna and Neubauer, Melanie and Kunz, Sabrina and Mueller-Esch, Tonia and Hiergeist, Andreas and Gessner, Andre and Khalid, Dina and Gaiser, Rogier and Cullin, Nyssa and Papagiannarou, Stamatia M. and Beuthien-Baumann, Bettina and Kraemer, Alwin and Bartenschlager, Ralf and Jaeger, Dirk and Mueller, Michael and Herth, Felix and Duerschmied, Daniel and Schneider, Jochen and Schmid, Roland M. and Eberhardt, Johann F. and Khodamoradi, Yascha and Vehreschild, Maria J. G. T. and Teufel, Andreas and Ebert, Matthias P. and Hau, Peter and Salzberger, Bernd and Schnitzler, Paul and Poeck, Hendrik and Elinav, Eran and Merle, Uta and Stein-Thoeringer, Christoph K. (2022) Severe Dysbiosis and Specific Haemophilus and Neisseria Signatures as Hallmarks of the Oropharyngeal Microbiome in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients. CLINICAL INFECTIOUS DISEASES, 75 (1). E1063-E1071. ISSN 1058-4838, 1537-6591
Full text not available from this repository. (Request a copy)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. Coronavirus disease 2019 (COVID-19) infections can affect the architecture of the oropharyngeal microbiome in severe cases. Neisseriaor Haemophilusspp. can predict poor outcomes in hospitalized patients, but antibiotic treatments, ventilation, or sampling timepoints are major confounders when considering microbiome features as biomarkers.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; SARS-CoV-2; COVID-19; microbiome; dysbiosis; machine learning |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Innere Medizin III (Hämatologie und Internistische Onkologie) Medicine > Lehrstuhl für Medizinische Mikrobiologie und Hygiene Medicine > Lehrstuhl für Neurologie Medicine > Abteilung für Krankenhaushygiene und Infektiologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 22 Feb 2024 14:50 |
| Last Modified: | 22 Feb 2024 14:50 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57772 |
Actions (login required)
 |
View Item |
