Profiling of Oral Microbiota and Cytokines in COVID-19 Patients

  1. Valerio Iebba
  2. Nunzia Zanotta
  3. Giuseppina Campisciano
  4. Verena Zerbato
  5. Stefano Di Bella
  6. Carolina Cason
  7. Roberto Luzzati
  8. Marco Confalonieri
  9. Anna Teresa Palamara
  10. Manola Comar

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Abstract

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recently demonstrated in the sputum or saliva, suggesting how the shedding of viral RNA outlasts the end of symptoms. Recent data from transcriptome analysis show that the oral cavity mucosa harbors high levels of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease, serine 2 (TMPRSS2), highlighting its role as a double-edged sword for SARS-CoV-2 body entrance or interpersonal transmission. Here, we studied the oral microbiota structure and inflammatory profile of 26 naive severe coronavirus disease 2019 (COVID-19) patients and 15 controls by 16S rRNA V2 automated targeted sequencing and magnetic bead-based multiplex immunoassays, respectively. A significant diminution in species richness was observed in COVID-19 patients, along with a marked difference in beta-diversity. Species such as Prevotella salivae and Veillonella infantium were distinctive for COVID-19 patients, while Neisseria perflava and Rothia mucilaginosa were predominant in controls. Interestingly, these two groups of oral species oppositely clustered within the bacterial network, defining two distinct Species Interacting Groups (SIGs). COVID-19-related pro-inflammatory cytokines were found in both oral and serum samples, along with a specific bacterial consortium able to counteract them. We introduced a new parameter, named CytoCOV, able to predict COVID-19 susceptibility for an unknown subject at 71% of power with an Area Under Curve (AUC) equal to 0.995. This pilot study evidenced a distinctive oral microbiota composition in COVID-19 subjects, with a definite structural network in relation to secreted cytokines. Our results would be usable in clinics against COVID-19, using bacterial consortia as biomarkers or to reduce local inflammation.

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  1. Version published to 10.3389/fmicb.2021.671813
  2. ScreenIT

    SciScore for 10.1101/2020.12.13.422589: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All patients provided informed consent for the use of their data and clinical samples for the purposes of the present study.
    RandomizationIntranetwork communities (here called Species Interacting Groups - “SIGs” 18,25) were retrieved using the Blondel community detection algorithm 26 by means of randomized composition and edge weights, with a resolution equal to 1 27.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableStudy Cohort and Samples: A total of 26 patients, 6 women (mean age 66±16 years) and 20 men (mean age 66±15 years) hospitalized at the Infectious Diseases Unit, University of Trieste, Italy, between April 10th 2020 and May 5th 2020, tested positive for COVID-19, were selected for this study.

    Table 2: Resources

    Antibodies
    SentencesResources
    Briefly, the undiluted samples (50 μl) were mixed with biomagnetic beads in 96-well flat-bottom plates, and after incubation for 30 min at room temperature followed by washing plate with Bio-Plex wash buffer, 25 μl of the antibody–biotin reporter was added.
    antibody–biotin
    suggested: None
    Software and Algorithms
    SentencesResources
    Soluble Immune Mediators Quantification: The profile of a panel of 27 cytokines including chemokines and growth factors was assessed in duplicate, in oral swabs of positive and negative subjects for SARS-CoV-2 using magnetic bead-based multiplex immunoassays (Bio-Plex Pro™ human cytokine 27-plex panel, Bio-Rad Laboratories, Milan, Italy) according to the pre-optimized protocol 15.
    Bio-Rad Laboratories
    suggested: (Bio-Rad Laboratories, RRID:SCR_008426)
    United States) and Bio-Plex Manager software (v.6, Bio-Rad).
    Bio-Plex Manager
    suggested: None
    Microbiota characterization: Raw FASTQ files were analyzed with DADA2 pipeline v.
    DADA2
    suggested: (dadasnake, RRID:SCR_019149)
    Bioinformatic and statistical analyses on recognized ASV were performed with Python v.3.8.2.
    Bioinformatic
    suggested: (QFAB Bioinformatics, RRID:SCR_012513)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Network analysis was performed on unified datasets 18 taking care of an optimized visual representation with Gephi v.
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    For microbiota analysis, measurements of α diversity (within sample diversity) such as Richness and Shannon index, were calculated at species level using the SciKit-learn package v.0.4.1.
    SciKit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    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:
    Even if with intrinsic limitations due to the number of subjects involved, and the missing point of a shotgun implementation to ascertain gene and/or pathways differences among controls and COVID-19, our study would give a hint to the significance of the oral microbiota restoration during pandemic as a public health intervention 31,39–42.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.12.13.422589v1 on bioRxiv