A serum proteome signature to predict mortality in severe COVID-19 patients

  • Franziska Völlmy
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
  • Henk van den Toorn
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
  • Riccardo Zenezini Chiozzi
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
  • Ottavio Zucchetti
    Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, University of Ferrara, Ferrara, Italy
  • Alberto Papi
    Respiratory Section, Department of Translational Medicine, University of Ferrara, Ferrara, Italy and Respiratory Disease Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy
  • Carlo Alberto Volta
    Department of Translational Medicine University of Ferrara, Ferrara, Italy and Intensive Care Unit, Azienda Ospedaliero-Universitaria di Ferrara, Italy
  • Luisa Marracino
    Department of Translational Medicine and Laboratory for Technology of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
  • Francesco Vieceli Dalla Sega
    Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy
  • Francesca Fortini
    Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy
  • Vadim Demichev
    Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
  • Pinkus Tober-Lau
    Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
  • Gianluca Campo
    Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, University of Ferrara, Ferrara, Italy
  • Marco Contoli
    Respiratory Section, Department of Translational Medicine, University of Ferrara, Ferrara, Italy and Respiratory Disease Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy
  • Markus Ralser
    Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
  • Florian Kurth
    Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
  • Savino Spadaro
    Department of Translational Medicine University of Ferrara, Ferrara, Italy and Intensive Care Unit, Azienda Ospedaliero-Universitaria di Ferrara, Italy
  • Paola Rizzo
    Department of Translational Medicine and Laboratory for Technology of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
  • Albert JR Heck
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands

書誌事項

公開日
2021-07-05
権利情報
  • https://creativecommons.org/licenses/by/4.0/
DOI
  • 10.26508/lsa.202101099
公開者
Life Science Alliance, LLC

説明

<jats:p>Here, we recorded serum proteome profiles of 33 severe COVID-19 patients admitted to respiratory and intensive care units because of respiratory failure. We received, for most patients, blood samples just after admission and at two more later time points. With the aim to predict treatment outcome, we focused on serum proteins different in abundance between the group of survivors and non-survivors. We observed that a small panel of about a dozen proteins were significantly different in abundance between these two groups. The four structurally and functionally related type-3 cystatins AHSG, FETUB, histidine-rich glycoprotein, and KNG1 were all more abundant in the survivors. The family of inter-α-trypsin inhibitors, ITIH1, ITIH2, ITIH3, and ITIH4, were all found to be differentially abundant in between survivors and non-survivors, whereby ITIH1 and ITIH2 were more abundant in the survivor group and ITIH3 and ITIH4 more abundant in the non-survivors. ITIH1/ITIH2 and ITIH3/ITIH4 also showed opposite trends in protein abundance during disease progression. We defined an optimal panel of nine proteins for mortality risk assessment. The prediction power of this mortality risk panel was evaluated against two recent COVID-19 serum proteomics studies on independent cohorts measured in other laboratories in different countries and observed to perform very well in predicting mortality also in these cohorts. This panel may not be unique for COVID-19 as some of the proteins in the panel have previously been annotated as mortality markers in aging and in other diseases caused by different pathogens, including bacteria.</jats:p>

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