Large-Scale Multi-omic Analysis of COVID-19 Severity.
Cell Syst
; 12(1): 23-40.e7, 2021 01 20.
Article
em En
| MEDLINE
| ID: mdl-33096026
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Índice de Gravidade de Doença
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Análise de Sequência de RNA
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Aprendizado de Máquina
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COVID-19
Tipo de estudo:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article