Prevalence of bacterial pathogens and potential role in COVID-19 severity in patients admitted to intensive care units in Brazil

  1. Fabíola Marques de Carvalho
  2. Leandro Nascimento Lemos
  3. Luciane Prioli Ciapina
  4. Rennan Garcias Moreira
  5. Alexandra Gerber
  6. Ana Paula C Guimarães
  7. Tatiani Fereguetti
  8. Virgínia Antunes de Andrade Zambelli
  9. Renata Avila
  10. Tailah Bernardo de Almeida
  11. Jheimson da Silva Lima
  12. Shana Priscila Coutinho Barroso
  13. Mauro Martins Teixeira
  14. Renan Pedra Souza
  15. Cynthia Chester Cardoso
  16. Renato Santana Aguiar
  17. Ana Tereza Ribeiro de Vasconcelos

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Abstract

Secondary bacterial and fungal infections are associated with respiratory viral infections and invasive mechanical ventilation. In Coronavirus disease 2019 (COVID-19), lung injury by SARS-CoV-2 and impaired immune response can provide a favorable environment for microorganism growth and colonization in hospitalized individuals. Recent studies suggest that secondary bacterial pneumonia is a risk factor associated with COVID-19. In Brazil, knowledge about microbiota present in COVID-19 patients is incipient. This work describes the microbiota of 21 COVID-19 patients admitted to intensive care units from two Brazilian centers. We identified respiratory, nosocomial and bacterial pathogens as prevalent microorganisms. Other bacterial opportunistic and commensal species are also represented. Virulence factors of these pathogenic species, metabolic pathways used to evade and modulate immunological processes and the interconnection between bacterial presence and virulence in COVID-19 progression are discussed.

Article Summary Line

We identified respiratory, nosocomial and bacterial pathogens as prevalent microorganisms in 21 Brazilian COVID-19 patients admitted to Intensive Care Units. Pathogen virulence factors and immune response evasion metabolic pathways are correlated to COVID-19 severity.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.22.20248501: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe study cohort included 12 males and 9 females and no age restrictions were applied (ranging from 37 to 89 years of age).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Species were identified from MG-RAST using RefSeq and functional abundances were obtained using SEED subsystem database.
    MG-RAST
    suggested: (MG-RAST, RRID:SCR_004814)
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    SEED
    suggested: (SEED, RRID:SCR_002129)
    Phyloseq R package was used to estimate the Shannon diversity index (20).
    Phyloseq
    suggested: (phyloseq, RRID:SCR_013080)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.12.22.20248501v2 on medRxiv
  3. Version published to 10.1101/2020.12.22.20248501v1 on medRxiv