Large-Scale Multi-omic Analysis of COVID-19 Severity.
Cell Syst
; 12(1): 23-40.e7, 2021 01 20.
Article
in English
| MEDLINE | ID: covidwho-837999
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Severity of Illness Index
/
Sequence Analysis, RNA
/
Machine Learning
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Cell Syst
Year:
2021
Document Type:
Article
Affiliation country:
J.cels.2020.10.003
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