Determinants of expression of SARS‐CoV‐2 entry‐related genes in upper and lower airways

  1. Hananeh Aliee
  2. Florian Massip
  3. Cancan Qi
  4. Maria Stella de Biase
  5. Jos van Nijnatten
  6. Elin T. G. Kersten
  7. Nazanin Z. Kermani
  8. Basil Khuder
  9. Judith M. Vonk
  10. Roel C. H. Vermeulen
  11. U‐BIOPRED study group
  12. Cambridge Lung Cancer Early Detection Programme
  13. INER‐Ciencias Mexican Lung Program
  14. Margaret Neighbors
  15. Gaik W. Tew
  16. Michele A. Grimbaldeston
  17. Nick H. T. ten Hacken
  18. Sile Hu
  19. Yike Guo
  20. Xiaoyu Zhang
  21. Kai Sun
  22. Pieter S. Hiemstra
  23. Bruce A. Ponder
  24. Mika J. Mäkelä
  25. Kristiina Malmström
  26. Robert C Rintoul
  27. Paul A. Reyfman
  28. Fabian J. Theis
  29. Corry‐Anke Brandsma
  30. Ian M. Adcock
  31. Wim Timens
  32. Cheng‐Jian Xu
  33. Maarten van den Berge
  34. Roland F. Schwarz
  35. Gerard H. Koppelman
  36. M.C. Nawijn
  37. Alen Faiz

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Abstract

No abstract available

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  1. Version published to 10.1111/all.15152
  2. ScreenIT

    SciScore for 10.1101/2020.08.31.20169946: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Gene expression analysis: Differential gene expression analysis was conducted across seven different cohort studies (Table 1) using the R packages EdgeR and Limma for RNA-Seq and microarray studies, respectively.
    EdgeR
    suggested: (edgeR, RRID:SCR_012802)
    Limma
    suggested: (LIMMA, RRID:SCR_010943)

    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:
    There were a number of limitations associated with the current study. In the current study we found a number of associations of COVID related genes with epidemiological factors and current treatment at a gene expression level. However, RNA does not always equal protein levels and extensive immunohistochemical stainings for these genes needs to be done to confirm a number of these findings. Furthermore, the current data provides only a snapshot pre-infection, and many of these genes will change rapidly following a COVID-19 infection. The current study provides the first longitudinal evaluation of ACE2 expression in bronchial biopsies on a limited number of samples which deserves expansion in subsequent studies. In conclusion we find that genes required for COVID-19 entry into cells are highly expressed in the upper airways compared to the lower airways by analysis of matched samples. Both first and second hand smoke exposure were found to be associated with higher expression of genes required for SARS-CoV-2 entry into cells. Methylation was found to influence the expression of a number of these genes in the bronchus and nose, which appeared to be regulated by age, gender and smoking status. Finally ICS were found to decrease the expression of ACE2 expression in bronchial biopsies.

    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.

  3. Version published to 10.1101/2020.08.31.20169946v1 on medRxiv