Nosocomial Pseudomonas aeruginosa regulates alginate biosynthesis and Type VI secretion system during adaptive and convergent evolution for coinfection in critically ill COVID-19 patients

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Abstract

COVID-19 pandemic has caused millions of death globally and caused huge impact on the health of infected patients. Shift in the lung microbial ecology upon such viral infection often worsens the disease and increases host susceptibility to secondary infections. Recent studies have indicated that bacterial coinfection is an unignorable factor contributing to the aggravation of COVID-19 and posing great challenge to clinical treatments. However, there is still a lack of in-depth investigation on the coinfecting bacteria in COVID-19 patients for better treatment of bacterial coinfection. With the knowledge that Pseudomonas aeruginosa is one of the top coinfecting pathogens, we analyzed the adaptation and convergent evolution of nosocomial P. aeruginosa isolated from two critical COVID-19 patients in this study. We sequenced and compared the genomes and transcriptomes of P. aeruginosa isolates longitudinally and parallelly for its evolutionary traits. P. aeruginosa overexpressed alginate and attenuated Type VI secretion system (T6SS) during coinfection for excessive biofilm formation and suppressed virulence. Results of bacterial competition assay and macrophage cytotoxicity test indicated that P. aeruginosa reduced its virulence towards both prokaryotic competitors and eukaryotic host through inhibiting its T6SS during evolution. P. aeuginosa T6SS is thus one of the reasons for its advantage to cause coinfection in COVID-19 patients while the attenuation of T6SS could cause a shift in the microecological composition in the lung. Our study will contribute to the development of therapeutic measures and the discovery of novel drug target to eliminate P. aeruginosa coinfection in COVID-19 patient.

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  1. Joanna Goldberg

    Review of "Nosocomial Pseudomonas aeruginosaregulates alginate biosynthesis and Type VI secretion system during adaptive and convergent evolution for coinfection in critically ill COVID-19 patients"

    Reviewer: Joanna Goldberg (Emory) | 📕 ◻️◻️◻️◻️

  2. SciScore for 10.1101/2021.04.09.439260: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This work is approved by the Ethics Committee of Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology [2020-184] and filed with the Ethics Committee of Southern University of Science and Technology [20200069].
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Cytotoxicity assay: The cytotoxicity of the isolates was assayed by using murine RAW 264.7 macrophages.
    RAW 264.7
    suggested: CLS Cat# 400319/p462_RAW-2647, RRID:CVCL_0493)
    Experimental Models: Organisms/Strains
    SentencesResources
    Bacterial competition assay: Competition between P. aeruginosa and Escherichia coli was carried out following the steps described previously by Hachani.et.al[32].
    P. aeruginosa
    suggested: None
    Software and Algorithms
    SentencesResources
    Rearrangements of draft genomes were checked using Mauve software v.
    Mauve
    suggested: (Mauve, RRID:SCR_012852)
    Circular plot was constructed using BLAST Ring Image Generator [26]
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    Genomic loci were visualized using Easyfig package[27].
    Easyfig
    suggested: (Easyfig, RRID:SCR_013169)
    Prediction of protein functions of the genes were performed using NCBI Prokaryotic Genome Annotation Pipeline (PGAP) [29].
    NCBI Prokaryotic Genome Annotation Pipeline
    suggested: None
    Total read counts of each sample were normalized and compared using DESeq2 R package.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    GO enrichment analysis of DEGs was performed on DAVID bioinformatics database v6.8 [31]
    DAVID
    suggested: (DAVID, RRID:SCR_001881)
    PCoA plot and heatmap were drawn using Vegan, ggplot2, and pheatmap packages in R 4.0.0.
    Vegan
    suggested: (vegan, RRID:SCR_011950)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Data Availability: All Illumina sequencing data used in this study could be found from BioProject No. PRJNA706783 and assembled genomes of P. aeruginosa LYSZa2 and P. aeruginosa LYSZa5 could be found from BioProject No. PRJNA712958 and PRJNA712961 on NCBI.
    BioProject
    suggested: (NCBI BioProject, RRID:SCR_004801)

    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.