CD47 as a potential biomarker for the early diagnosis of severe COVID-19

  1. Katie-May McLaughlin
  2. Denisa Bojkova
  3. Marco Bechtel
  4. Joshua D. Kandler
  5. Philipp Reus
  6. Trang Le
  7. Julian U. G. Wagner
  8. Sandra Ciesek
  9. Mark N. Wass
  10. Martin Michaelis
  11. Jindrich Cinatl

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Abstract

The coronavirus SARS-CoV-2 is the cause of the ongoing COVID-19 pandemic. Most SARS-CoV-2 infections are mild or even asymptomatic. However, a small fraction of infected individuals develops severe, life-threatening disease, which is caused by an uncontrolled immune response resulting in hyperinflammation. Antiviral interventions are only effective prior to the onset of hyperinflammation. Hence, biomarkers are needed for the early identification and treatment of high-risk patients. Here, we show in a range of model systems and data from post mortem samples that SARS-CoV-2 infection results in increased levels of CD47, which is known to mediate immune escape in cancer and virus-infected cells. Systematic literature searches also indicated that known risk factors such as older age and diabetes are associated with increased CD47 levels. High CD47 levels contribute to vascular disease, vasoconstriction, and hypertension, conditions which may predispose SARS-CoV-2-infected individuals to COVID-19-related complications such as pulmonary hypertension, lung fibrosis, myocardial injury, stroke, and acute kidney injury. Hence, CD47 is a candidate biomarker for severe COVID-19. Further research will have to show whether CD47 is a reliable diagnostic marker for the early identification of COVID-19 patients requiring antiviral therapy.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.01.433404: (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.
    Cell Line AuthenticationAuthentication: Cells were regularly authenticated by short tandem repeat (STR) analysis and tested for mycoplasma contamination.
    Contamination: Cells were regularly authenticated by short tandem repeat (STR) analysis and tested for mycoplasma contamination.

    Table 2: Resources

    Antibodies
    SentencesResources
    , Sino Biological) was detected with a peroxidase-conjugated anti-rabbit secondary antibody (1:1,000, Dianova), followed by addition of AEC substrate.
    anti-rabbit
    suggested: None
    Detection occurred by using specific antibodies against CD47 (1:100 dilution, CD47 Antibody, anti-human, Biotin, REAfinity™, # 130-101-343, Miltenyi Biotec), SARS-CoV-2 N (1:1000 dilution, SARS-CoV-2 Nucleocapsid Antibody, Rabbit MAb, #40143-R019, Sino Biological), and GAPDH (1:1000 dilution, Anti-G3PDH Human Polyclonal Antibody, #2275-PC-100, Trevigen)
    CD47
    suggested: (Miltenyi Biotec Cat# 130-101-343, RRID:AB_2658412)
    anti-human ,
    suggested: None
    GAPDH
    suggested: (R and D Systems Cat# 2275-PC-100, RRID:AB_2107456)
    Anti-G3PDH
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    2.1 Cell culture: Calu-3 cells (ATCC) were grown at 37°C in minimal essential medium (MEM) supplemented with 10% foetal bovine serum (FBS), 100 IU/mL penicillin, and 100 μg/mL of streptomycin.
    Calu-3
    suggested: KCLB Cat# 30055, RRID:CVCL_0609)
    2.2 Virus infection: SARS-CoV-2/7/Human/2020/Frankfurt (SARS-CoV-2/FFM7) was isolated and cultivated in Caco2 cells (DSMZ) as previously described [Hoehl et al., 2020; Toptan et al., 2020].
    Caco2
    suggested: None
    2.3 Antiviral assay: Confluent layers of CaCo-2 cells in 96-well plates were infected with SARS-CoV–2 FFM7 at a MOI of 0.01.
    CaCo-2
    suggested: None
    Software and Algorithms
    SentencesResources
    P-values were determined by two-sided student’s t-test Raw read counts from post-mortem samples of two COVID-19 patients and two healthy controls, as well as mock infected and SARS-CoV-2-infected Calu-3 cells, were derived from a recent publication [Blanco-Melo et al., 2020] via the gene expression omnibus (GEO) database (accession: GSE147507) and processed using DESeq2.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    P-values were determined by two-sided student’s t-test 2.6 Literature review: Relevant articles were identified by using the search terms ‘CD47 aging’, ‘CD47 hypertension’, ‘CD47 diabetes’, and ‘CD47 obesity’ in PubMed (https://pubmed.ncbi.nlm.nih.gov).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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.
    • 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.

  2. Version published to 10.1101/2021.03.01.433404v1 on bioRxiv