MassIVE MSV000085703

Partial Public

Large-scale Multi-omic Analysis of COVID-19 Severity

Description

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. [doi:10.25345/C5F74G] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: SARS-CoV-2 ; coronavirus ; COVID-19 ; CoronaMassKB ; multi-omics

Contact

Principal Investigators:
(in alphabetical order)
Ariel Jaitovich, Albany Medical College, USA
Joshua Coon, University of Wisconsin - Madison, United States
Submitting User: lmuehlbauer

Publications

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.
Large-Scale Multi-omic Analysis of COVID-19 Severity.
Cell Syst. 2021 01 20;12(1):23-40.e7. Epub 2020 10 08.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.