tetano
Editor, Senior Moderator
Clin Infect Dis
. 2021 Oct 25;ciab902.
doi: 10.1093/cid/ciab902. Online ahead of print.
Severe dysbiosis and specific Haemophilus and Neisseria signatures as hallmarks of the oropharyngeal microbiome in critically ill COVID-19 patients
Juliana de Castilhos[SUP] 1 2 [/SUP], Eli Zamir[SUP] 1 [/SUP], Theresa Hippchen[SUP] 3 [/SUP], Roman Rohrbach[SUP] 1 [/SUP], Sabine Schmidt[SUP] 1 [/SUP], Silvana Hengler[SUP] 1 [/SUP], Hanna Schumacher[SUP] 1 [/SUP], Melanie Neubauer[SUP] 4 [/SUP], Sabrina Kunz[SUP] 5 [/SUP], Tonia Müller-Esch[SUP] 5 [/SUP], Andreas Hiergeist[SUP] 6 [/SUP], André Gessner[SUP] 6 [/SUP], Dina Khalid[SUP] 7 [/SUP], Rogier Gaiser[SUP] 1 [/SUP], Nyssa Cullin[SUP] 1 [/SUP], Stamatia M Papagiannarou[SUP] 1 [/SUP], Bettina Beuthien-Baumann[SUP] 8 [/SUP], Alwin Krämer[SUP] 9 [/SUP], Ralf Bartenschlager[SUP] 10 11 [/SUP], Dirk Jäger[SUP] 12 [/SUP], Michael Müller[SUP] 13 [/SUP], Felix Herth[SUP] 13 [/SUP], Daniel Duerschmied[SUP] 14 [/SUP], Jochen Schneider[SUP] 15 [/SUP], Roland M Schmid[SUP] 15 [/SUP], Johann F Eberhardt[SUP] 16 [/SUP], Yascha Khodamoradi[SUP] 16 [/SUP], Maria J G T Vehreschild[SUP] 16 17 [/SUP], Andreas Teufel[SUP] 18 [/SUP], Matthias P Ebert[SUP] 18 [/SUP], Peter Hau[SUP] 19 [/SUP], Bernd Salzberger[SUP] 20 [/SUP], Paul Schnitzler[SUP] 7 [/SUP], Hendrik Poeck[SUP] 5 21 [/SUP], Eran Elinav[SUP] 1 22 [/SUP], Uta Merle[SUP] 3 [/SUP], Christoph K Stein-Thoeringer[SUP] 1 12 [/SUP]
Affiliations
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: COVID-19; SARS-CoV-2; dysbiosis; intensive medical care; machine learning; microbiome.
. 2021 Oct 25;ciab902.
doi: 10.1093/cid/ciab902. Online ahead of print.
Severe dysbiosis and specific Haemophilus and Neisseria signatures as hallmarks of the oropharyngeal microbiome in critically ill COVID-19 patients
Juliana de Castilhos[SUP] 1 2 [/SUP], Eli Zamir[SUP] 1 [/SUP], Theresa Hippchen[SUP] 3 [/SUP], Roman Rohrbach[SUP] 1 [/SUP], Sabine Schmidt[SUP] 1 [/SUP], Silvana Hengler[SUP] 1 [/SUP], Hanna Schumacher[SUP] 1 [/SUP], Melanie Neubauer[SUP] 4 [/SUP], Sabrina Kunz[SUP] 5 [/SUP], Tonia Müller-Esch[SUP] 5 [/SUP], Andreas Hiergeist[SUP] 6 [/SUP], André Gessner[SUP] 6 [/SUP], Dina Khalid[SUP] 7 [/SUP], Rogier Gaiser[SUP] 1 [/SUP], Nyssa Cullin[SUP] 1 [/SUP], Stamatia M Papagiannarou[SUP] 1 [/SUP], Bettina Beuthien-Baumann[SUP] 8 [/SUP], Alwin Krämer[SUP] 9 [/SUP], Ralf Bartenschlager[SUP] 10 11 [/SUP], Dirk Jäger[SUP] 12 [/SUP], Michael Müller[SUP] 13 [/SUP], Felix Herth[SUP] 13 [/SUP], Daniel Duerschmied[SUP] 14 [/SUP], Jochen Schneider[SUP] 15 [/SUP], Roland M Schmid[SUP] 15 [/SUP], Johann F Eberhardt[SUP] 16 [/SUP], Yascha Khodamoradi[SUP] 16 [/SUP], Maria J G T Vehreschild[SUP] 16 17 [/SUP], Andreas Teufel[SUP] 18 [/SUP], Matthias P Ebert[SUP] 18 [/SUP], Peter Hau[SUP] 19 [/SUP], Bernd Salzberger[SUP] 20 [/SUP], Paul Schnitzler[SUP] 7 [/SUP], Hendrik Poeck[SUP] 5 21 [/SUP], Eran Elinav[SUP] 1 22 [/SUP], Uta Merle[SUP] 3 [/SUP], Christoph K Stein-Thoeringer[SUP] 1 12 [/SUP]
Affiliations
- PMID: 34694375
- DOI: 10.1093/cid/ciab902
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: COVID-19; SARS-CoV-2; dysbiosis; intensive medical care; machine learning; microbiome.