Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

  1. Stephen W. Bickler
  2. David M. Cauvi
  3. Kathleen M. Fisch
  4. James M. Prieto
  5. Alicia G. Sykes
  6. Hariharan Thangarajah
  7. David A. Lazar
  8. Romeo C. Ignacio
  9. Dale R. Gerstmann
  10. Allen F. Ryan
  11. Philip E. Bickler
  12. Antonio De Maio

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Abstract

Background

Older aged adults and those with pre-existing conditions are at highest risk for severe COVID-19 associated outcomes.

Methods

Using a large dataset of genome-wide RNA-seq profiles derived from human dermal fibroblasts (GSE113957) we investigated whether age affects the expression of pattern recognition receptor (PRR) genes and ACE2, the receptor for SARS-CoV-2.

Results

Extremes of age are associated with increased expression of selected PRR genes, ACE2 and four genes that encode proteins that have been shown to interact with SAR2-CoV-2 proteins.

Conclusions

Assessment of PRR expression might provide a strategy for stratifying the risk of severe COVID-19 disease at both the individual and population levels.

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  1. Version published to 10.1186/s12920-021-00970-7
  2. ScreenIT

    SciScore for 10.1101/2020.06.15.134403: (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
    Enrichment analysis of the differentially expressed genes was performed with ToppGene (34).
    ToppGene
    suggested: ( ToppGene Suite , RRID:SCR_005726)
    Correlation analysis: Pairwise Pearson correlation coefficients were calculated between the normalized gene counts of the 21 PRR genes, ACE2 and age, over all 133 samples using GraphPad Prism version 8.0.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Network visualization was performed using Cytoscape (35) the NDEx v2.4.5 (36).
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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:
    Our study does have some limitations. Foremost, is that health information was not available for the individuals donating skin samples to the dermal fibroblast collection. Although, the skin samples are reported to be from “apparently healthy individuals”, we believe it is unlikely that individuals in the oldest age group were completely free of chronic diseases. Another limitation was that minority groups are inadequately represented in the collection. The dermal fibroblast collection includes samples from one American Indian (<1%), one Hispanic (<1%), two Asians (1.5%), and nine Blacks (6.7%)—way too few to draw any meaningful conclusions on the ethnic groups that have been the hardest hit by the COVID-19 pandemic. Finally, as the scientific community ramps up research in response to the COVID-19 pandemic, the dermal fibroblast model could prove useful for investigating SARS-CoV-2 biology. Fibroblasts have been previously used to investigate host antiviral defenses during Coronavirus infection (29). The potential strength of the dermal fibroblast model is that skin samples can be easily obtained from donors of different ages, sex, and ethnicities, and those with varying comorbidities such a high blood pressure and diabetes; and from smokers and non-smokers. Such a model would also have an advantage over transfection models as these cells would not only have increased expression of ACE2 and TLR4, but also have an aged transcriptome which could be important for the infectivit...

    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

    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 10.1101/2020.06.15.134403v1 on bioRxiv