Large-Scale Multi-omic Analysis of COVID-19 Severity

  1. Katherine A. Overmyer
  2. Evgenia Shishkova
  3. Ian J. Miller
  4. Joseph Balnis
  5. Matthew N. Bernstein
  6. Trenton M. Peters-Clarke
  7. Jesse G. Meyer
  8. Qiuwen Quan
  9. Laura K. Muehlbauer
  10. Edna A. Trujillo
  11. Yuchen He
  12. Amit Chopra
  13. Hau C. Chieng
  14. Anupama Tiwari
  15. Marc A. Judson
  16. Brett Paulson
  17. Dain R. Brademan
  18. Yunyun Zhu
  19. Lia R. Serrano
  20. Vanessa Linke
  21. Lisa A. Drake
  22. Alejandro P. Adam
  23. Bradford S. Schwartz
  24. Harold A. Singer
  25. Scott Swanson
  26. Deane F. Mosher
  27. Ron Stewart
  28. Joshua J. Coon
  29. Ariel Jaitovich

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Abstract

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  1. Version published to 10.1016/j.cels.2020.10.003
  2. ScreenIT

    SciScore for 10.1101/2020.07.17.20156513: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Raw files were searched using MaxQuant quantitative software package (Cox et al., 2014)
    MaxQuant
    suggested: (MaxQuant, RRID:SCR_014485)
    Identification and quantification of targeted peptides were performed using Skyline open access software package (version 20.1).
    Skyline
    suggested: (Skyline, RRID:SCR_014080)
    For the Englert results, fastq files were downloaded from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/), accession GSE84439, samples/ runs SRR3923733-SRR3923739 (omitting SRR3923734) and SRR3923747-SRR3923753.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Only mRNA (accessions NM_xxxx and XM_xxxx) and rRNA (excluding 5.8S) was then extracted, and immunoglobulin transcripts were downloaded from ENSEMBL (IG_C, IG_D, IG_J and IG_V).
    ENSEMBL
    suggested: (Ensembl, RRID:SCR_002344)
    RNA-Seq expression estimation was performed by RSEM v 1.3.0 (parameters: seed-length=20, no-qualities, bowtie2-k=200, bowtie2-sensitivity-level=sensitive) (Li and Dewey, 2011), with bowtie-2 (v 2.3.4.1) for the alignment step (Langmead and Salzberg, 2012), using a custom hg38 reference.
    RSEM
    suggested: (RSEM, RRID:SCR_013027)
    We examined all differentially expressed genes associated with an FDR value less than 0.01 and fold-change greater than two as determined by EBSeq (Englert et al., 2019b; Leng et al., 2013).
    EBSeq
    suggested: (EBSeq, RRID:SCR_003526)
    We considered all gene sets associated with Gene Ontology terms (Ashburner et al., 2000), gene sets in the Hallmark collection (Liberzon et al., 2015), and gene sets in the Canonical Pathway collection in the MSigDB repository (Liberzon et al., 2011), which includes pathways from KEGG (Kanehisa and Goto, 2000) and Reactome (Joshi-Tope et al., 2005).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Creation of covid-omics.app: The webtool was developed in Python (3.7.4) using the Plotly Dash package (1.12.0) and the source code is accessible via GitHub (https://github.com/ijmiller2/COVID-19_Multi-Omics/), under the src/dash directory.
    Plotly
    suggested: (Plotly, RRID:SCR_013991)
    The R2 and p-value metrics displayed on the linear regression page are calculated using the statsmodels (0.11.1) library in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)
    ExtraTrees classification of severe and non-severe cases: Molecular measurements of metabolites, lipids, proteins, and transcripts were read into Python from the SQLite database.
    SQLite
    suggested: (SQLite, RRID:SCR_017672)
    All metrics were computed with the standard functions in scikit-learn, and the reported feature importances are the Gini type.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    RSEM expression estimates for Englert samples can be downloaded from the MassIVE database.
    MassIVE
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    All large-scale omics studies have limitations but, ideally, still stimulate the generation of numerous testable hypotheses. This work is no exception; and, in a very real sense, it represents a starting point in the response to the urgent need to wholly define this devastating disease. Future work should include a larger and broader patient population. The samples derived here came from a single center, and although our population is racially diverse, it does not necessarily replicate factors related to geography or population socioeconomic status, among others. Another limitation was that the control group was generated by blindly enrolling patients presenting with COVID-19-compatible symptoms. While this approach provided an important reference, the non-COVID-19 patients admitted to the ICU, not all of these patients presented with the same criteria of ARDS. We recognize this approach is not ideal and expect that future studies with prospective enrollment of patients fully matching the present COVID-19 clinical features will provide a better reference to the current cohort. The practicalities associated with study design and implementation during a pandemic presented another possible limitation. For example, while we enrolled the patients at the time of hospital admission, we could not control the period that elapsed between the disease development and the blood sampling. Nevertheless, previous research on the genomic landscape of patients with sepsis indicates the timing ...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT03466073CompletedA Phase 1b/2a Study of the Safety and Pharmacokinetics of Rh…
    NCT04358406RecruitingRhu-pGSN for Severe Covid-19 Pneumonia
    NCT04324021TerminatedEfficacy and Safety of Emapalumab and Anakinra in Reducing H…
    NCT04326790RecruitingThe GReek Study in the Effects of Colchicine in Covid-19 cOm…

    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

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  3. Version published to 10.1101/2020.07.17.20156513 on medRxiv