Proteome reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients

  1. Alina Bauer
  2. Elisabeth Pachl
  3. Johannes C. Hellmuth
  4. Nikolaus Kneidinger
  5. Marion Frankenberger
  6. Hans C. Stubbe
  7. Bernhard Ryffel
  8. Agnese Petrera
  9. Stefanie M. Hauck
  10. Jürgen Behr
  11. Rainer Kaiser
  12. Clemens Scherer
  13. Li Deng
  14. Daniel Teupser
  15. Narges Ahmidi
  16. Maximilian Muenchhoff
  17. Benjamin Schubert
  18. Anne Hilgendorff

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Abstract

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Many studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited.

To this end, we biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of this specific disease phase assignment, proteome analysis revealed a severity dependent general type-2 centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response together with the regulation of proteins related to SARS-CoV-2-specific symptoms by unbiased proteome screening both confirms results from targeted approaches and provides novel information for biomarker and therapy development.

Graphical Abstract

Sars-CoV-2 remains a challenging threat to our health care system with many pathophysiological mechanisms not fully understood, especially in high-risk patients. Therefore, we characterized a cohort of hospitalized COVID-19 patients with multiple comorbidities by quantitative plasma proteomics and deep clinical phenotyping. The individual patient’s disease progression was determined and the subsequently assigned proteome profiles compared with a healthy and a chronically inflamed control cohort. The identified disease phase and severity specific protein profiles revealed an antiviral immune response together with coagulation activation indicating the formation of NETosis side-by-side with tissue remodeling related to the inflammatory signature.

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  1. ScreenIT

    SciScore for 10.1101/2022.03.02.22271106: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the ethics committee of the Ludwig-Maximilians-Universität (LMU), Munich, Germany (Study title: “COVID-19 Register des LMU Klinikums (CORKUM)”; Project No: 20-245 (initial approval date:
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We used Python’s SciPy package (Inglett et al, 2015) to perform the statistical analysis.
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    For the proteomics analysis, we used the R package limma (“Linear models for microarray data”) (Ritchie et al, 2015) adjusted for the following confounders: age, gender, cardiovascular diseases, diabetes, high cholesterol, lung disease, kidney disease, immuno-compromised status, superinfection during proteomics sampling, steroid treatment during hospital stay during or before proteomics sampling (Table S11).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    We conducted overrepresentation tests (based on hypergeometric models with a minimum count of three proteins) for biological processes and pathways using ClusterProfiler (Yu et al, 2012) and ReactomePA (Yu & He, 2016), while the Enrichplot (Yu, 2018) package was used for visualization of the overrepresentation results.
    ClusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)

    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:
    Limitations of the present study include its observational design and the retrospective analysis resulting in missing data in a small number of patients. Partially counteracting these imitations, the study benefits from homogenous and comprehensive clinical monitoring in a high-risk patient collective that continuously dominates patient admission in university hospitals during the COVID pandemic. While providing a very good basis for biomarker identification in different disease phases and severity grades, results have to be confirmed in targeted approaches in different clinical centers. These prospective studies need to include - amongst others - environmental or social factors not investigated in the current study while considering the impact of emerging SARS-CoV-2 variants and the effect of the potentially gender-dependent vaccination status, not present in the first pandemic wave addressed in our approach (Ovies et al, 2021). In summary, we identified a COVID-related protein signature that indicates an antiviral response together with NET / inflammasome activation predominantly driven by their regulation in severely affected patients. In contrast, regulation in less severely diseased patients was found to be characterized by a type-2 centered immune response. The findings were enabled by the newly identified disease trajectories based on the individual course of important routine laboratory variables. This approach both confirms findings from previous studies and also fac...

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2022.03.02.22271106v1 on medRxiv