Antibody therapy reverses biological signatures of COVID-19 progression

  1. M. Cyrus Maher
  2. Leah B. Soriaga
  3. Anil Gupta
  4. Yi-Pei Chen
  5. Julia di Iulio
  6. Sarah Ledoux
  7. Megan J. Smithey
  8. Andrea L. Cathcart
  9. Kathleen McKusick
  10. David Sun
  11. Melissa Aldinger
  12. Elizabeth Alexander
  13. Lisa Purcell
  14. Xiao Ding
  15. Amanda Peppercorn
  16. Daren Austin
  17. Erik Mogalian
  18. Wendy W. Yeh
  19. Adrienne E. Shapiro
  20. Davide Corti
  21. Herbert W. Virgin
  22. Phillip S. Pang
  23. Amalio Telenti

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Abstract

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  1. Version published to 10.1016/j.xcrm.2022.100721
  2. ScreenIT

    SciScore for 10.1101/2021.12.21.21268197: (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

    Antibodies
    SentencesResources
    Serum samples were analyzed for anti-SARS-CoV-2 antibody by the Abbott SARS-CoV-2 IgG assay run on the Architect i2000SR immunoassay analyzer (https://www.fda.gov/media/137383/download).
    anti-SARS-CoV-2
    suggested: None
    Due to the potential for cross-reaction of sotrovimab with anti-spike antibody assays, only analysis of anti-nucleocapsid serostatus was conducted.
    anti-spike
    suggested: None
    Software and Algorithms
    SentencesResources
    Serum samples were analyzed for anti-SARS-CoV-2 antibody by the Abbott SARS-CoV-2 IgG assay run on the Architect i2000SR immunoassay analyzer (https://www.fda.gov/media/137383/download).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Reads were cropped to a common read length of 50 bp and low-quality bases and adapters were further trimmed using Trimmomatic (v. 0.39)27.
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Trimmed sequenced reads per library were then aligned to a custom reference genome using STAR (v 2.7.3a)28 and to a custom reference transcriptome using Salmon (v. 1.0.0)29.
    STAR
    suggested: (STAR, RRID:SCR_004463)
    Salmon
    suggested: (Salmon, RRID:SCR_017036)
    The custom reference genome and transcriptome annotation was based on combining the human reference genome and annotation from Gencode (GRCh38, release 30) with the SARS-CoV-2 reference genome and annotation from Ensembl (ASM985889v3 version).
    Gencode
    suggested: (GENCODE, RRID:SCR_014966)
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    Libraries were assessed for total reads (minimum, maximum, and median of 23.4, 94.4, and 33.2 million read pairs), average read length (49 bp), and adapter content, post-trimming with FASTQC (v. 0.11.8).
    FASTQC
    suggested: (FastQC, RRID:SCR_014583)
    Alignment metrics, such as total aligned reads, aligned reads by feature type, gene body coverage and 3’ bias, were assessed using Picard CollectRnaSeqMetrics (v 2.20.2—0).
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Known junction saturation rate as a function of sequencing depth was also profiled for all libraries using RSeQC (v 3.0.1).
    RSeQC
    suggested: (RSeQC, RRID:SCR_005275)
    Data analysis transcriptome: Using the R package DESeq2 (v 1.32.0)31
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    For this baseline analysis, UMAP (from the umap-learn python package; https://umap-learn.readthedocs.io/en/latest/) was run with default parameters.
    python
    suggested: (IPython, RRID:SCR_001658)

    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:
    One caveat of this analysis is that VOCs that have decreased sensitivity to antibodies induced by vaccination (e.g., Delta and Omicron) were not circulating at the time of enrollment in the study. Therefore, additional analysis may be needed to confirm the protective effect of seropositivity in the current phase of the pandemic. Whole blood transcriptome analysis revealed a signature of disease severity that encompassed overexpression of genes involved in interferon response, inflammation and the complement system. We showed that a full transcriptome signature can be captured faithfully with a 10-gene panel. Use of a simple expression signature lowers the bar for an eventual implementation, as recently shown by work to make a three gene tuberculosis signature using point-of-care rapid testing20. An important effort of the present work was to define whether the set of predictive parameters of hospitalization and disease severity was also modified by treatment with sotrovimab; i.e., whether these parameters could serve as surrogate markers of sotrovimab response because they are modified by treatment and strongly associated to the study clinical endpoints of interest. Indeed, sotrovimab accelerated the normalization of NLR and the transcriptome profiles in a statistically significant manner. In particular, hospitalized participants in the placebo group retained the transcriptome profile associated with risk by Day 8 at a time when the majority of study participants normalized t...

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

    IdentifierStatusTitle
    NCT04545060CompletedVIR-7831 for the Early Treatment of COVID-19 in Outpatients

    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 10.1101/2021.12.21.21268197v1 on medRxiv