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-19Start & end year
20202021Known Financial Commitments (USD)
$1,264,175.76Funder
Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)Principal Investigator
PendingResearch Location
GermanyLead Research Institution
Klinikum der Universität München (LMU), OmicEraResearch Priority Alignment
N/A
Research Category
Clinical characterisation and management
Research Subcategory
Prognostic factors for disease severity
Special Interest Tags
N/A
Study Type
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.