Traces of SARS-CoV-2 RNA in Peripheral Blood Cells of Patients with COVID-19

  1. Ahmed Moustafa
  2. Rana Salah Khalel
  3. Ramy K. Aziz

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Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third virus that caused coronavirus-related outbreaks over the past 20 years. The outbreak was first reported in December 2019 in Wuhan, China, but rapidly progressed into a pandemic of an unprecedented scale since the 1918 flu pandemic. Besides respiratory complications in patients with COVID-19, clinical characterization of severe infection cases showed several other comorbidities, including multiple organ failure, and septic shock. To better understand the systemic pathogenesis of COVID-19, we interrogated the virus's presence in the peripheral blood cells, which might provide a form of trafficking or hiding to the virus. By analyzing >2 billion sequence reads of high-throughput transcriptome sequence data from 180 samples of patients with active SARS-CoV-2 infection or healthy controls collected from 6 studies, we found evidence of traces of SARS-CoV-2 RNA in peripheral blood mononuclear cells in two samples from two independent studies. In contrast, the viral RNA was abundant in bronchoalveolar lavage specimens from the same patients. We also devised a “viral spike-to-actin” RNA normalization as a metric to compare across various samples and minimize errors caused by intersample variability in total human RNA abundance. Our observation suggests immune presentation and discounts the possibility of extensive viral infection of lymphocytes or monocytes.

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  1. Version published to 10.1089/omi.2021.0068
  2. ScreenIT

    SciScore for 10.1101/2020.05.10.20097055: (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
    Comparison of RNA Abundance and Gene Expression Profiles: Filtered FASTQ sequences were searched with blastx (Altschul et al. 1990) against the RefSeq protein database (O’Leary et al. 2016) (release 99) using DIAMOND (Buchfink, Xie, and Huson 2015) with an e-value cutoff < 1e-10.
    Gene Expression Profiles
    suggested: None
    blastx
    suggested: (BLASTX, RRID:SCR_001653)
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    DIAMOND
    suggested: (DIAMOND, RRID:SCR_009457)
    For functional annotation, the matching RefSeq proteins were annotated by Pfam (Finn et al. 2014) using HMMER (Eddy 2011).
    Pfam
    suggested: (Pfam, RRID:SCR_004726)
    HMMER
    suggested: (Hmmer, RRID:SCR_005305)
    Identified SARS-CoV-2 matching sequences were manually inspected and searched against NCBI “nt” by BLAST (blastn) for verification (Altschul et al. 1990).
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.05.10.20097055 on medRxiv