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

: Hopp, Marie-Thérèse; Domingo-Fernández, Daniel; Gadiya, Yojana; Detzel, Milena; Graf, Regina; Schmalohr Benjamin; Kodamullil, Alpha Tom; Imhof, Diana; Hofmann-Apitius, Martin

Volltext urn:nbn:de:0011-n-6343482 (4.3 MByte PDF)
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Erstellt am: 29.4.2021

Biomolecules 11 (2021), Nr.5, Art. 644, 18 S.
ISSN: 2218-273X
Fraunhofer-Gesellschaft FhG
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
knowledge graph; COVID-19; heme; bioinformatic; machine learning

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.