Nasopharyngeal Microbial Communities of Patients Infected With SARS-CoV-2 That Developed COVID-19

  1. Maria Paz Ventero
  2. Rafael R. C. Cuadrat
  3. Inmaculada Vidal
  4. Bruno G. N. Andrade
  5. Carmen Molina-Pardines
  6. Jose M. Haro-Moreno
  7. Felipe H. Coutinho
  8. Esperanza Merino
  9. Luciana C. A. Regitano
  10. Cynthia B. Silveira
  11. Haithem Afli
  12. Mario López-Pérez
  13. Juan Carlos Rodríguez

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

SARS-CoV-2 is an RNA virus causing COVID-19. The clinical characteristics and epidemiology of COVID-19 have been extensively investigated, however, only one study so far focused on the patient’s nasopharynx microbiota. In this study we investigated the nasopharynx microbial community of patients that developed different severity levels of COVID-19. We performed 16S ribosomal DNA sequencing from nasopharyngeal swab samples obtained from SARS-CoV-2 positive (56) and negative (18) patients in the province of Alicante (Spain) in their first visit to the hospital. Positive SARS-CoV-2 patients were observed and later categorized in mild (symptomatic without hospitalization), moderate (hospitalization), and severe (admission to ICU). We compared the microbiota diversity and OTU composition among severity groups and built bacterial co-abundance networks for each group.

Results

Statistical analysis indicated differences in the nasopharyngeal microbiome of COVID19 patients. 62 OTUs were found exclusively in SARS-CoV-2 positive patients, mostly classified as members of the phylum Bacteroidota (18) and Firmicutes (25). OTUs classified as Prevotella were found to be significantly more abundant in patients that developed more severe COVID-19. Furthermore, co-abundance analysis indicated a loss of network complexity among samples from patients that later developed more severe symptoms.

Conclusion

Our study shows that the nasopharyngeal microbiome of COVID-19 patients showed differences in the composition of specific OTUs and complexity of co-abundance networks. Taxa with differential abundances among groups could serve as biomarkers for COVID-19 severity. Nevertheless, further studies with larger sample sizes should be conducted to validate these results.

Article activity feed

  1. Version published to 10.3389/fmicb.2021.637430
  2. ScreenIT

    SciScore for 10.1101/2020.12.01.407486: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The research project was conducted under the written approval of the Ethic Committee of Clinical Research with Drug (In Spanish, CEIm) of the “Hospital General Universitario de Alicante (Spain)”, and in collaboration with the Biobank of Clinical and Biomedical Research Institute of Alicante (ISABIAL), which are included in the Valencian Network of Biobanks.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The quality of raw sequences was assessed by FastQC software.
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Sequences were trimmed using trimmomatic [12] and the resulting paired reads were merged using casper [13], generating individual fragments of about 460 bp.
    trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Merged amplicon sequences were grouped in operational taxonomic units (OTUs) using cd-hit [14] with an identity of 97%.
    cd-hit
    suggested: (CD-HIT, RRID:SCR_007105)
    Sequences were queried against small subunits (16S) rRNA genes by the SILVA database [15] for taxonomic classification.
    SILVA
    suggested: (SILVA, RRID:SCR_006423)
    The network matrices were loaded in the Cytoscape 3.8 software, and connections filtered by p-value (≤ 0.05) and correlation (≤ −0.6 or ≥ 0.6).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

  3. Version published to 10.1101/2020.12.01.407486v1 on bioRxiv