Large-scale Multi-omic Analysis of COVID-19 Severity #MMPMID32743614
Overmyer KA; Shishkova E; Miller IJ; Balnis J; Bernstein MN; Peters-Clarke TM; Meyer JG; Quan Q; Muehlbauer LK; Trujillo EA; He Y; Chopra A; Chieng HC; Tiwari A; Judson MA; Paulson B; Brademan DR; Zhu Y; Serrano LR; Linke V; Drake LA; Adam AP; Schwartz BS; Singer HA; Swanson S; Mosher DF; Stewart R; Coon JJ; Jaitovich A
medRxiv 2020[Jul]; ä (ä): ä PMID32743614show ga
We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.
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medRxiv 2020 ; ä (ä): ä
