Signatures of COVID-19 Severity and Immune Response in the Respiratory Tract Microbiome
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Abstract
COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the respiratory tract, results in highly variable outcomes ranging from minimal illness to death, but the reasons for this are not well understood. We investigated the respiratory tract bacterial microbiome and small commensal DNA viruses in hospitalized COVID-19 patients and found that each was markedly abnormal compared to that in healthy people and differed from that in critically ill patients without COVID-19.
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SciScore for 10.1101/2021.04.02.21254514: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Subjects: Following informed consent (IRB protocol #823392), samples were collected beginning a median of 4 days after hospitalization (generally within one week of hospitalization or identification of COVID+ status if post-admission). Randomization For analyses comparing groups with different numbers of samples per subject, PERMANOVA testing used specimens randomly subsampled 1000 times to one sample per patient and mean p values reported. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Random forest classification was implemented using the randomForest … SciScore for 10.1101/2021.04.02.21254514: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Subjects: Following informed consent (IRB protocol #823392), samples were collected beginning a median of 4 days after hospitalization (generally within one week of hospitalization or identification of COVID+ status if post-admission). Randomization For analyses comparing groups with different numbers of samples per subject, PERMANOVA testing used specimens randomly subsampled 1000 times to one sample per patient and mean p values reported. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Random forest classification was implemented using the randomForest package (v4.6-14) in R. randomForestsuggested: (RandomForest Package in R, RRID:SCR_015718)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. Many patients presented quite ill, and when initially sampled were already intubated or had reached high WHO scores. Thus, the ability to distinguish clinical course based on microbiome markers may not necessarily reflect prior predictive power. Our patients were heterogenous, with extensive use of antibiotics that could influence the bacterial microbiome. We enrolled subjects early in the COVID-19 pandemic when clinical management and outcomes may not have been optimal. We did not analyze local lung mucosal immune/inflammatory markers, and systemic immune profiling was available for only a subset of patients. There is no gold standard for diagnosis of bacterial pneumonia superinfection in this population, limiting the ability to definitively link ETA findings. Finally, lower respiratory tract microbiome information was only available from the patients who were intubated. In summary, we report profound dysbiosis of the respiratory tract bacterial and viral microbiome in hospitalized COVID-19 patients, which differs from that of non-COVID patients, exhibits accelerated destabilization over time, and associates with disease severity and systemic immune profiles. In intubated patients the lung microbiome is dysbiotic with frequent enrichment of Staphylococcus. The small commensal viruses, Anelloviridae and Redondoviridae, were the strongest discriminators of patient intubation. This work provides a basis for further studies to delineate mechani...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
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