Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer's disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.
Bibliographical noteFunding Information:
This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/ ) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
The recruitment and clinical characterization of research participants at Washington University were supported by NIH P30AG066444 (JCM), P01AG03991 (JCM), and P01AG026276 (JCM).
This study utilized samples obtained from the Washington University School of Medicine’s COVID-19 biorepository, which is supported by: the Barnes-Jewish Hospital Foundation ; the Siteman Cancer Center grant P30 CA091842 from the National Cancer Institute of the National Institutes of Health ; and the Washington UniversityInstitute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences of the National Institutes of Health . The content is solely the responsibility of the authors and does not necessarily represent the view of the NIH.
This work was supported by grants from the National Institutes of Health ( RF1AG074007 (YJS), R01AG044546 (CC), P01AG003991 (CC, JCM), RF1AG053303 (CC, SC), RF1AG058501 (CC), and U01AG058922 (CC), Alzheimer's Association Research Grant 925002 (SC), the Chan Zuckerberg Initiative (CZI), and the Alzheimer's Association Zenith Fellows Award ( ZEN-22-848604 , awarded to CC).
C.C. has received research support from: Biogen, EISAI, Alector, GSK, an anonymous foundation and Parabon. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. C.C. is a member of the advisory board of Vivid Genomics, and Circular Genomics and own stocks.
© 2023 The Author(s)
- Biological sciences
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