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  4. Linking COVID-19 and Heme-Driven Pathophysiologies: A Combined Computational-Experimental Approach
 
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2021
Journal Article
Titel

Linking COVID-19 and Heme-Driven Pathophysiologies: A Combined Computational-Experimental Approach

Abstract
The SARS-CoV-2 outbreak was declared a worldwide pandemic in 2020. Infection triggers the respiratory tract disease COVID-19, which is accompanied by serious changes in clinical biomarkers such as hemoglobin and interleukins. The same parameters are altered during hemolysis, which is characterized by an increase in labile heme. We present two computational experimental approaches aimed at analyzing a potential link between heme-related and COVID-19 pathophysiologies. Herein, we performed a detailed analysis of the common pathways induced by heme and SARS-CoV-2 by superimposition of knowledge graphs covering heme biology and COVID-19 pathophysiology. Focus was laid on inflammatory pathways and distinct biomarkers as the linking elements. In a second approach, four COVID-19-related proteins, the host cell proteins ACE2 and TMPRSS2 as well as the viral proteins 7a and S protein were computationally analyzed as potential heme-binding proteins with an experimental validation. The results contribute to the understanding of the progression of COVID-19 infections in patients with different clinical backgrounds and may allow for a more individual diagnosis and therapy in the future.
Author(s)
Hopp, Marie-Thérèse
Universität Bonn
Domingo-Fernández, Daniel
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Gadiya, Yojana
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Detzel, Milena
Universität Bonn
Graf, Regina
Universität Bonn
Schmalohr Benjamin
Universität Bonn
Kodamullil, Alpha Tom
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Imhof, Diana
Universität Bonn
Hofmann-Apitius, Martin
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Zeitschrift
Biomolecules
Project(s)
COPERIMO
Funder
Fraunhofer-Gesellschaft FhG
DOI
10.3390/biom11050644
File(s)
N-634348.pdf (4.39 MB)
Language
English
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Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Tags
  • knowledge graph

  • COVID-19

  • heme

  • bioinformatic

  • machine learning

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