Gene Expression Risk Scores for COVID-19 Illness Severity

  1. Derick R Peterson
  2. Andrea M Baran
  3. Soumyaroop Bhattacharya
  4. Angela R Branche
  5. Daniel P Croft
  6. Anthony M Corbett
  7. Edward E Walsh
  8. Ann R Falsey
  9. Thomas J Mariani

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Abstract

Background

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

Methods

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19.

Results

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

Conclusions

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

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  1. Version published to 10.1093/infdis/jiab568
  2. ScreenIT

    SciScore for 10.1101/2021.08.24.457521: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: Sample Collection and Processing: Approximately 3 ml of whole blood was collected in a Tempus™ Blood RNA Tube at the time of enrollment and stored at -80C until the time of processing.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequences were aligned against the human genome version hg38 using the Splice Transcript Alignment to a Reference (STAR) algorithm [11], and counts were generated using HTSeq [12].
    STAR
    suggested: (STAR, RRID:SCR_004463)
    HTSeq
    suggested: (HTSeq, RRID:SCR_005514)
    Pathway analysis of significantly differentially expressed genes was performed using ENRICHR [13].
    ENRICHR
    suggested: (Enrichr, RRID:SCR_001575)

    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 current study has several limitations which are worth noting, including its relatively small sample size, the non-standardized interval between symptom onset and sample collection, and blood collection at one time point. The complexity of the clinical data among hospitalized participants (i.e. admissions only for isolation, persons with chronic oxygen requirements, COVID testing for procedures) made objective criteria to distinguish mild from moderate disease difficult, necessitating the need for clinical adjudication. Lastly, certain laboratory studies were not available for all subjects.

    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.

    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.

  3. Version published to 10.1101/2021.08.24.457521v1 on bioRxiv