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dc.contributor.authorPA-COVID-19 Study group
dc.contributor.authorDemichev, Vadim
dc.contributor.authorTober-Lau, Pinkus
dc.contributor.authorLemke, Oliver
dc.contributor.authorNazarenko, Tatiana
dc.contributor.authorThibeault, Charlotte
dc.contributor.authorWhitwell, Harry
dc.contributor.authorRöhl, Annika
dc.contributor.authorFreiwald, Anja
dc.contributor.authorSzyrwiel, Lukasz
dc.contributor.authorLudwig, Daniela
dc.contributor.authorCorreia-Melo, Clara
dc.contributor.authorAulakh, Simran Kaur
dc.contributor.authorHelbig, Elisa T
dc.contributor.authorStubbemann, Paula
dc.contributor.authorLippert, Lena J
dc.contributor.authorGrüning, Nana-Maria
dc.contributor.authorBlyuss, Oleg
dc.contributor.authorVernardis, Spyros
dc.contributor.authorWhite, Matthew
dc.contributor.authorMessner, Christoph B
dc.contributor.authorJoannidis, Michael
dc.contributor.authorSonnweber, Thomas
dc.contributor.authorKlein, Sebastian J
dc.contributor.authorPizzini, Alex
dc.contributor.authorWohlfarter, Yvonne
dc.contributor.authorSahanic, Sabina
dc.contributor.authorHilbe, Richard
dc.contributor.authorSchaefer, Benedikt
dc.contributor.authorWagner, Sonja
dc.contributor.authorMittermaier, Mirja
dc.contributor.authorMachleidt, Felix
dc.contributor.authorGarcia, Carmen
dc.contributor.authorRuwwe-Glösenkamp, Christoph
dc.contributor.authorLingscheid, Tilman
dc.contributor.authorBosquillon de Jarcy, Laure
dc.contributor.authorStegemann, Miriam S
dc.contributor.authorPfeiffer, Moritz
dc.contributor.authorJürgens, Linda
dc.contributor.authorDenker, Sophy
dc.contributor.authorZickler, Daniel
dc.contributor.authorEnghard, Philipp
dc.contributor.authorZelezniak, Aleksej
dc.contributor.authorCampbell, Archie
dc.contributor.authorHayward, Caroline
dc.contributor.authorPorteous, David J
dc.contributor.authorMarioni, Riccardo E
dc.contributor.authorUhrig, Alexander
dc.contributor.authorMüller-Redetzky, Holger
dc.contributor.authorZoller, Heinz
dc.contributor.authorLöffler-Ragg, Judith
dc.date.accessioned2021-08-05T14:45:01Z
dc.date.available2021-08-05T14:45:01Z
dc.date.issued2021-06-14
dc.identifier.citationPA-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 , White , M , Messner , C B , Joannidis , M , Sonnweber , T , Klein , S J , 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 , M S , Pfeiffer , M , Jürgens , L , Denker , S , Zickler , D , Enghard , P , Zelezniak , A , Campbell , A , Hayward , C , Porteous , D J , Marioni , R E , 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
dc.identifier.issn2405-4712
dc.identifier.otherPubMedCentral: PMC8201874
dc.identifier.otherORCID: /0000-0002-0194-6389/work/98164054
dc.identifier.urihttp://hdl.handle.net/2299/24958
dc.description© 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/
dc.description.abstractCOVID-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.en
dc.format.extent23
dc.format.extent8711848
dc.language.isoeng
dc.relation.ispartofCell systems
dc.subjectCOVID-19
dc.subjectbiomarkers
dc.subjectclinical disease progression
dc.subjectdisease prognosis
dc.subjectlongitudinal profiling
dc.subjectmachine learning
dc.subjectpatient trajectories
dc.subjectphysiological parameters
dc.subjectproteomics
dc.subjectPathology and Forensic Medicine
dc.subjectHistology
dc.subjectCell Biology
dc.titleA time-resolved proteomic and prognostic map of COVID-19en
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85108957689&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1016/j.cels.2021.05.005
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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