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

PA-COVID-19 Study group; Vadim Demichev; Pinkus Tober-Lau; Oliver Lemke; Tatiana Nazarenko; Charlotte Thibeault; Harry Whitwell; Annika Röhl; Anja Freiwald; Lukasz Szyrwiel; Daniela Ludwig; Clara Correia-Melo; Simran Kaur Aulakh; Elisa T Helbig; Paula Stubbemann; Lena J Lippert; Nana-Maria Grüning; Oleg Blyuss; Spyros Vernardis; Matthew White; Christoph B Messner; Michael Joannidis; Thomas Sonnweber; Sebastian J Klein; Alex Pizzini; Yvonne Wohlfarter; Sabina Sahanic; Richard Hilbe; Benedikt Schaefer; Sonja Wagner; Mirja Mittermaier; Felix Machleidt; Carmen Garcia; Christoph Ruwwe-Glösenkamp; Tilman Lingscheid; Laure Bosquillon de Jarcy; Miriam S Stegemann; Moritz Pfeiffer; Linda Jürgens; Sophy Denker; Daniel Zickler; Philipp Enghard; Aleksej Zelezniak; Archie Campbell; Caroline Hayward; David J Porteous; Riccardo E Marioni; Alexander Uhrig; Holger Müller-Redetzky; Heinz Zoller; Judith Löffler-Ragg

doi:10.1016/j.cels.2021.05.005

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

PA-COVID-19 Study group, Vadim Demichev, Pinkus Tober-Lau, Oliver Lemke, Tatiana Nazarenko, Charlotte Thibeault, Harry Whitwell, Annika Röhl, Anja Freiwald, Lukasz Szyrwiel, Daniela Ludwig, Clara Correia-Melo, Simran Kaur Aulakh, Elisa T Helbig, Paula Stubbemann, Lena J Lippert, Nana-Maria Grüning, Oleg Blyuss, Spyros Vernardis, Matthew WhiteChristoph B Messner, Michael Joannidis, Thomas Sonnweber, Sebastian J Klein, Alex Pizzini, Yvonne Wohlfarter, Sabina Sahanic, Richard Hilbe, Benedikt Schaefer, Sonja Wagner, Mirja Mittermaier, Felix Machleidt, Carmen Garcia, Christoph Ruwwe-Glösenkamp, Tilman Lingscheid, Laure Bosquillon de Jarcy, Miriam S Stegemann, Moritz Pfeiffer, Linda Jürgens, Sophy Denker, Daniel Zickler, Philipp Enghard, Aleksej Zelezniak, Archie Campbell, Caroline Hayward, David J Porteous, Riccardo E Marioni, Alexander Uhrig, Holger Müller-Redetzky, Heinz Zoller, Judith Löffler-Ragg

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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.

Original language	English
Number of pages	23
Journal	Cell systems
Early online date	14 Jun 2021
DOIs	https://doi.org/10.1016/j.cels.2021.05.005
Publication status	E-pub ahead of print - 14 Jun 2021

Keywords

COVID-19
biomarkers
clinical disease progression
disease prognosis
longitudinal profiling
machine learning
patient trajectories
physiological parameters
proteomics

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10.1016/j.cels.2021.05.005Licence: CC BY

final-version
© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license. https://creativecommons.org/licenses/by/4.0/
Final published version, 8.31 MBLicence: CC BY

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PA-COVID-19 Study group, 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, S. K., Helbig, E. T., Stubbemann, P., Lippert, L. J., Grüning, N.-M., Blyuss, O., Vernardis, S., ... Löffler-Ragg, J. (2021). A time-resolved proteomic and prognostic map of COVID-19. Cell systems. Advance online publication. https://doi.org/10.1016/j.cels.2021.05.005

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title = "A time-resolved proteomic and prognostic map of COVID-19",

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.",

keywords = "COVID-19, biomarkers, clinical disease progression, disease prognosis, longitudinal profiling, machine learning, patient trajectories, physiological parameters, proteomics",

author = "{PA-COVID-19 Study group} and Vadim Demichev and Pinkus Tober-Lau and Oliver Lemke and Tatiana Nazarenko and Charlotte Thibeault and Harry Whitwell and Annika R{\"o}hl and Anja Freiwald and Lukasz Szyrwiel and Daniela Ludwig and Clara Correia-Melo and Aulakh, {Simran Kaur} and Helbig, {Elisa T} and Paula Stubbemann and Lippert, {Lena J} and Nana-Maria Gr{\"u}ning and Oleg Blyuss and Spyros Vernardis and Matthew White and Messner, {Christoph B} and Michael Joannidis and Thomas Sonnweber and Klein, {Sebastian J} and Alex Pizzini and Yvonne Wohlfarter and Sabina Sahanic and Richard Hilbe and Benedikt Schaefer and Sonja Wagner and Mirja Mittermaier and Felix Machleidt and Carmen Garcia and Christoph Ruwwe-Gl{\"o}senkamp and Tilman Lingscheid and {Bosquillon de Jarcy}, Laure and Stegemann, {Miriam S} and Moritz Pfeiffer and Linda J{\"u}rgens and Sophy Denker and Daniel Zickler and Philipp Enghard and Aleksej Zelezniak and Archie Campbell and Caroline Hayward and Porteous, {David J} and Marioni, {Riccardo E} and Alexander Uhrig and Holger M{\"u}ller-Redetzky and Heinz Zoller and Judith L{\"o}ffler-Ragg",

note = "{\textcopyright} 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license. https://creativecommons.org/licenses/by/4.0/",

year = "2021",

month = jun,

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doi = "10.1016/j.cels.2021.05.005",

language = "English",

journal = "Cell systems",

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PA-COVID-19 Study group, 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 2021, 'A time-resolved proteomic and prognostic map of COVID-19', Cell systems. https://doi.org/10.1016/j.cels.2021.05.005

TY - JOUR

T1 - A time-resolved proteomic and prognostic map of COVID-19

AU - PA-COVID-19 Study group

AU - Demichev, Vadim

AU - Tober-Lau, Pinkus

AU - Lemke, Oliver

AU - Nazarenko, Tatiana

AU - Thibeault, Charlotte

AU - Whitwell, Harry

AU - Röhl, Annika

AU - Freiwald, Anja

AU - Szyrwiel, Lukasz

AU - Ludwig, Daniela

AU - Correia-Melo, Clara

AU - Aulakh, Simran Kaur

AU - Helbig, Elisa T

AU - Stubbemann, Paula

AU - Lippert, Lena J

AU - Grüning, Nana-Maria

AU - Blyuss, Oleg

AU - Vernardis, Spyros

AU - White, Matthew

AU - Messner, Christoph B

AU - Joannidis, Michael

AU - Sonnweber, Thomas

AU - Klein, Sebastian J

AU - Pizzini, Alex

AU - Wohlfarter, Yvonne

AU - Sahanic, Sabina

AU - Hilbe, Richard

AU - Schaefer, Benedikt

AU - Wagner, Sonja

AU - Mittermaier, Mirja

AU - Machleidt, Felix

AU - Garcia, Carmen

AU - Ruwwe-Glösenkamp, Christoph

AU - Lingscheid, Tilman

AU - Bosquillon de Jarcy, Laure

AU - Stegemann, Miriam S

AU - Pfeiffer, Moritz

AU - Jürgens, Linda

AU - Denker, Sophy

AU - Zickler, Daniel

AU - Enghard, Philipp

AU - Zelezniak, Aleksej

AU - Campbell, Archie

AU - Hayward, Caroline

AU - Porteous, David J

AU - Marioni, Riccardo E

AU - Uhrig, Alexander

AU - Müller-Redetzky, Holger

AU - Zoller, Heinz

AU - Löffler-Ragg, Judith

PY - 2021/6/14

Y1 - 2021/6/14

N2 - 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.

AB - 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.

KW - COVID-19

KW - biomarkers

KW - clinical disease progression

KW - disease prognosis

KW - longitudinal profiling

KW - machine learning

KW - patient trajectories

KW - physiological parameters

KW - proteomics

UR - http://www.scopus.com/inward/record.url?scp=85108957689&partnerID=8YFLogxK

U2 - 10.1016/j.cels.2021.05.005

DO - 10.1016/j.cels.2021.05.005

M3 - Article

C2 - 34139154

SN - 2405-4712

JO - Cell systems

JF - Cell systems

ER -

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

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Keywords

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