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

Demichev V, Tober-Lau P, Lemke O, Nazarenko T, Thibeault C, Whitwell H, Röhl A, Freiwald A, Szyrwiel L, Ludwig D, Correia-Melo C, Aulakh SK, Helbig ET, Stubbemann P, Lippert LJ, Grüning N, Blyuss O, Vernardis S, White M, Messner CB, Joannidis M, Sonnweber T, Klein SJ, Pizzini A, Wohlfarter Y, Sahanic S, Hilbe R, Schaefer B, Wagner S, Mittermaier M, Machleidt F, Garcia C, Ruwwe-Glösenkamp C, Lingscheid T, Bosquillon de Jarcy L, Stegemann MS, Pfeiffer M, Jürgens L, Denker S, Zickler D, Enghard P, Zelezniak A, Campbell A, Hayward C, Porteous DJ, Marioni RE, Uhrig A, Müller-Redetzky H, Zoller H, Löffler-Ragg J, Keller MA, Tancevski I, Timms JF, Zaikin A, Hippenstiel S, Ramharter M, Witzenrath M, Suttorp N, Lilley K, Mülleder M, Sander LE, PA-COVID-19 Study group , Ralser M, Kurth F

Cell Syst - (-) - [2021-06-14; online 2021-06-14]

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.

Category: Biochemistry

Type: Journal article

PubMed 34139154

DOI 10.1016/j.cels.2021.05.005

Crossref 10.1016/j.cels.2021.05.005

pii: S2405-4712(21)00160-5
pmc: PMC8201874


Publications 7.1.2