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

TLDR

The study introduces covid‑omics.app, a web platform that lets users explore a multi‑omic COVID‑19 dataset and demonstrates its use for predicting disease severity with machine learning. Researchers performed RNA‑seq and high‑resolution mass spectrometry on 128 blood samples, quantified transcripts, proteins, metabolites, and lipids, linked them to clinical outcomes in a relational database, and built the web tool to enable systems analysis and cross‑ome correlations. They mapped 219 molecular features significantly associated with COVID‑19 status and severity, uncovering complement activation, lipid transport dysregulation, neutrophil activation, platelet dysfunction, coagulation, acute‑phase response, and endotheliopathy, and showed that covarying molecules offer therapeutic insights and that the web tool can predict severity.

Abstract

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were 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 of which were 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 machine learning approach for prediction of COVID-19 severity.