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

Author:

Völlmy Franziska12,van den Toorn Henk12,Zenezini Chiozzi Riccardo12,Zucchetti Ottavio3,Papi Alberto4,Volta Carlo Alberto5,Marracino Luisa6,Vieceli Dalla Sega Francesco7,Fortini Francesca7,Demichev Vadim8910,Tober-Lau Pinkus11ORCID,Campo Gianluca37,Contoli Marco4,Ralser Markus89,Kurth Florian1112,Spadaro Savino5ORCID,Rizzo Paola67,Heck Albert JR12ORCID

Affiliation:

1. Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands

2. Netherlands Proteomics Center, Utrecht, The Netherlands

3. Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, University of Ferrara, Ferrara, Italy

4. Respiratory Section, Department of Translational Medicine, University of Ferrara, Ferrara, Italy and Respiratory Disease Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy

5. Department of Translational Medicine University of Ferrara, Ferrara, Italy and Intensive Care Unit, Azienda Ospedaliero-Universitaria di Ferrara, Italy

6. Department of Translational Medicine and Laboratory for Technology of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy

7. Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy

8. Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany

9. The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK

10. The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge, UK

11. Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany

12. National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Abstract

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.

Funder

The Netherlands Proteomics Centre through the X-omics Road Map program

EU Horizon 2020 program INFRAIA project Epic-XS

Publisher

Life Science Alliance, LLC

Subject

Health, Toxicology and Mutagenesis,Plant Science,Biochemistry, Genetics and Molecular Biology (miscellaneous),Ecology

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