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

Title: A time-resolved proteomic and prognostic map of COVID-19
Authors: 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-M
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
Ralser, M
Kurth, F
Kleinschmidt, M
Heim, KM
Millet, B
Meyer-Arndt, L
Hübner, RH
Andermann, T
Doehn, JM
Opitz, B
Sawitzki, B
Grund, D
Radünzel, P
Schürmann, M
Zoller, T
Alius, F
Knape, P
Breitbart, A
Li, Y
Bremer, F
Pergantis, P
Schürmann, D
Temmesfeld-Wollbrück, B
Wendisch, D
Brumhard, S
Haenel, SS
Conrad, C
Georg, P
Eckardt, K-U
Lehner, L
Kruse, JM
Ferse, C
Körner, R
Spies, C
Edel, A
Weber-Carstens, S
Krannich, A
Zvorc, S
Li, L
Behrens, U
Schmidt, S
Rönnefarth, M
Dang-Heine, C
Röhle, R
Lieker, E
Kretzler, L
Wirsching, I
Wollboldt, C
Wu, Y
Schwanitz, G
Hillus, D
Kasper, S
Olk, N
Horn, A
Briesemeister, D
Treue, D
Hummel, M
Corman, VM
Drosten, C
Von Kalle, C
Item Type: Journal Article
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.
Issue Date: Aug-2021
Date of Acceptance: 7-May-2021
URI: http://hdl.handle.net/10044/1/91207
DOI: 10.1016/j.cels.2021.05.005
ISSN: 2405-4712
Publisher: Elsevier BV
Start Page: 780
End Page: 794.e7
Journal / Book Title: Cell Systems
Volume: 12
Issue: 8
Copyright Statement: © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: 0601 Biochemistry and Cell Biology
Publication Status: Published
Online Publication Date: 2021-06-14
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Imperial College London COVID-19



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