COVID-19 Proteome Diagnostics: In-depth characterization of the plasma and urine proteome of COVID-19 patients for disease course prediction (COVID-19 ProDiag)

  • Funded by Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Total publications:0 publications

Grant number: 01KI20377A 01KI20377B

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $1,264,175.76
  • Funder

    Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Principle Investigator

    Pending
  • Research Location

    Germany, Europe
  • Lead Research Institution

    Klinikum der Universität München (LMU), OmicEra
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Prognostic factors for disease severity

  • Special Interest Tags

    Gender

  • Study Subject

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

The Corona Virus Disease 2019 (COVID-19) outbreak has rapidly spread around the world and infects millions of people. The high number of patients with severe COVID-19 response has led to unmanageable situations for healthcare systems, already resulting in thousands of deaths. To this end, we will use well-documented longitudinal COVID-19 cohorts, along with matching non-COVID-19 controls, comprising of plasma and urine samples collected in a highly standardized fashion. Each cohort will consist of a subgroup of patients showing mild symptoms and patients whose condition has deteriorated to a critical status. We will use a standardized sample collection pipeline including laboratory medicine analysis for consecutive sampling of a discovery and a validation cohort. Collection is ongoing with >400 samples per day (sample count: 7685 as of April 21, 2020). The samples will be selected based on clinical data out of our local COVID-19 patient registry CORKUM at LMU Hospital and analyzed using our cutting-edge mass spectrometry (MS)-based proteomics pipeline. The results will mirror the patients' disease state as reflected by concentrations of proteins in blood plasma and urine. We will apply our bioinformatics pipeline to uncover biomarkers and combine them in different risk assessment models (1) to differentiate between COVID-19 and non-COVID-19 patients, (2) to predict the disease trajectory in mild or severe disease courses, and (3) to predict the clinical outcome. This will ultimately allow personalized interventions and guiding the allocation of medical resources.