Repository logo
 

A time-resolved proteomic and prognostic map of COVID-19.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Demichev, Vadim 
Tober-Lau, Pinkus 
Lemke, Oliver 
Nazarenko, Tatiana 
Thibeault, Charlotte 

Abstract

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Description

Keywords

COVID-19, biomarkers, clinical disease progression, disease prognosis, longitudinal profiling, machine learning, patient trajectories, physiological parameters, proteomics, Age Factors, Biomarkers, Blood Cell Count, Blood Gas Analysis, COVID-19, Disease Progression, Enzyme Activation, Humans, Inflammation, Machine Learning, Prognosis, Proteome, Proteomics, SARS-CoV-2

Journal Title

Cell Syst

Conference Name

Journal ISSN

2405-4712
2405-4720

Volume Title

12

Publisher

Elsevier BV
Sponsorship
Biotechnology and Biological Sciences Research Council (BB/N015282/1)