COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

Eva Kočar; Sonja Katz; Žiga Pušnik; Petra Bogovič; Gabriele Turel; Cene Skubic; Tadeja Režen; Franc Strle; Vitor A P Martins Dos Santos; Miha Mraz; Miha Moškon; Damjana Rozman

doi:10.1016/j.isci.2023.107799

COVID-19 and cholesterol biosynthesis: Towards innovative decision support systems

iScience. 2023 Aug 31;26(10):107799. doi: 10.1016/j.isci.2023.107799. eCollection 2023 Oct 20.

Authors

Eva Kočar¹, Sonja Katz^{2

3}, Žiga Pušnik⁴, Petra Bogovič⁵, Gabriele Turel⁵, Cene Skubic¹, Tadeja Režen¹, Franc Strle⁵, Vitor A P Martins Dos Santos^{2

3}, Miha Mraz⁴, Miha Moškon⁴, Damjana Rozman¹

Affiliations

¹ Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, SI-1000 Ljubljana, Slovenia.
² LifeGlimmer GmbH, Markelstraße 38, 12163 Berlin, Germany.
³ Biomanufacturing and Digital Twins Group, Bioprocess Engineering Laboratory, Wageningen University and Research, Droevendaalsesteeg 1, 6708PB Wageningen, the Netherlands.
⁴ Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia.
⁵ Department of Infectious Diseases, University Medical Centre Ljubljana, Japljeva ulica 2, SI-1000 Ljubljana, Slovenia.

Abstract

With COVID-19 becoming endemic, there is a continuing need to find biomarkers characterizing the disease and aiding in patient stratification. We studied the relation between COVID-19 and cholesterol biosynthesis by comparing 10 intermediates of cholesterol biosynthesis during the hospitalization of 164 patients (admission, disease deterioration, discharge) admitted to the University Medical Center of Ljubljana. The concentrations of zymosterol, 24-dehydrolathosterol, desmosterol, and zymostenol were significantly altered in COVID-19 patients. We further developed a predictive model for disease severity based on clinical parameters alone and their combination with a subset of sterols. Our machine learning models applying 8 clinical parameters predicted disease severity with excellent accuracy (AUC = 0.96), showing substantial improvement over current clinical risk scores. After including sterols, model performance remained better than COVID-GRAM. This is the first study to examine cholesterol biosynthesis during COVID-19 and shows that a subset of cholesterol-related sterols is associated with the severity of COVID-19.

Keywords: Assessment in health technology; Bioinformatics; Lipid.