iScience
Volume 26, Issue 4, 21 April 2023, 106408
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Article
Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer’s and coronary disease pathways

https://doi.org/10.1016/j.isci.2023.106408Get rights and content
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open access

Highlights

  • 1,449 plasma proteins are altered by COVID-19 infection, ventilation, or death

  • Distinct prognostic models for ventilation and death after COVID-19 infection

  • Altered proteins link to Alzheimer’s and coronary artery disease pathways

  • Eight proteins are potentially causal for COVID-19 infection

Summary

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.

Subject areas

Biochemistry
Biological sciences
Disease
Protein

Data and code availability

Proteomic data from the Knight ADRC participants are available at the NIAGADS and can be accessed at https://www.niagads.org/Knight ADRC-collection (NIAGADS dataset#: ng00128);

The summary results using these data are also available to the scientific community through a public web browser: https://covid.proteomics.wustl.edu/. All our analyses used open-source software that have been included in the key resources table.

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