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  1. Review 3: "The glycosylated extracellular domain of MUC1 protects against SARS-CoV-2 infection at the respiratory surface"

    Chatterjee et al examine the role of host mucins in SARS-CoV-2 infection and describe a role for glycosylated mucin MUC1 in restricting viral access to ACE2. The reviewers found the main claims reliable and potentially informative.

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  2. Ieva Bagdonaite

    Review 2: "The glycosylated extracellular domain of MUC1 protects against SARS-CoV-2 infection at the respiratory surface"

    Chatterjee et al examine the role of host mucins in SARS-CoV-2 infection and describe a role for glycosylated mucin MUC1 in restricting viral access to ACE2. The reviewers found the main claims reliable and potentially informative.

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  3. Review 1: "The glycosylated extracellular domain of MUC1 protects against SARS-CoV-2 infection at the respiratory surface"

    Chatterjee et al examine the role of host mucins in SARS-CoV-2 infection and describe a role for glycosylated mucin MUC1 in restricting viral access to ACE2. The reviewers found the main claims reliable and potentially informative.

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  4. Strength of evidence

    Reviewers: Annemieke Smet (University of Antwerp) | 📗📗📗📗◻️
    Ieva Bagdonaite (University of Copenhagen) | 📒📒📒◻️◻️
    Thaher Pelaseyed (University of Gothenburg) | 📒📒📒◻️◻️

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  5. SciScore for 10.1101/2021.10.29.466408: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For detection of the MUC1 ED, 5% mucin gels and a boric acid-Tris system were used as described previously45. α-MUC1-ED antibody 214D4 was used to detect MUC1 at a dilution of 1:1,000 in TSMT buffer.
    α-MUC1-ED
    suggested: None
    For detection of the CT of MUC1, 12% SDS-PAGE gel and α-MUC1-CT antibody CT2 was used.
    MUC1
    suggested: None
    α-MUC1-CT
    suggested: None
    For ACE2 detection, 10% SDS-PAGE gel and anti-ACE2 antibody (1:1,000, HPA000288, Sigma-Aldrich) was used.
    anti-ACE2
    suggested: (Sigma-Aldrich Cat# HPA000288, RRID:AB_1078160)
    Actin was detected using α-actin antibody (1:5,000; bs-0061R, Bioss).
    α-actin
    suggested: None
    Secondary antibodies used were α-mouse IgG secondary antibody (1:10,000; A2304, Sigma), α-Armenian hamster IgG (1:10,000; GTX25745, Genetex) and α-rabbit IgG (1:10,000; A4914, Sigma).
    IgG
    suggested: (Sigma-Aldrich Cat# A2304, RRID:AB_257993)
    α-Armenian hamster IgG
    suggested: None
    α-rabbit IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Cell culture: Calu-3 cells (ATCC Catalog # HTB-55), HEK-293T (ATCC Catalog # CRL-3216) and BHK-21 cells (ATCC Catalog # CCL-10) cells were routinely grown in 25 cm2 flasks in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal calf serum (FCS) at 37°C in 5% CO2.
    HEK-293T
    suggested: None
    BHK-21
    suggested: ATCC Cat# CCL-10, RRID:CVCL_1915)
    For infection experiments with the authentic SARS-CoV-2 virus, Calu-3 cells were prepared as described above and inoculated with approximately 200 pfu of SARS-CoV-2.
    Calu-3
    suggested: None
    Software and Algorithms
    SentencesResources
    Dimensionality reduction was done using the Seurat Package42 in Rstudio (version 1.2.5019), starting with a principle component analysis.
    Rstudio
    suggested: (RStudio, RRID:SCR_000432)
    Immunofluorescent stainings were performed as described for SARS-CoV-2 stock production and scanned plates were analyzed using ImageQuant TL software.
    ImageQuant
    suggested: (ImageQuant, RRID:SCR_014246)
    The GraphPad Prism 9 software package was used for all statistical analyses.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: 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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 17, 19 and 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.


    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.

    Results from scite Reference Check: We found no unreliable references.


    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.

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