20.500.12556/RUL-136722 Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs. COVID-19 genome-scale metabolic models model extraction methods context-specific models metabolic enrichment analysis COVID-19 metabolni modeli na nivoju genoma metode ekstrakcije modelov kontekstno specifični modeli metabolna analiza obogatenosti true false true Angleški jezik Slovenski jezik Članek v reviji 2022-05-18 07:19:23 2022-05-18 07:19:24 2022-09-19 12:29:21 0000-00-00 00:00:00 2022 0 0 10 str. art. 105428 Vol. 145 Jun. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004:578.834 0010-4825 10.1016/j.compbiomed.2022.105428 102526467 1-s2.0-S0010482522002207-main.pdf 1-s2.0-S0010482522002207-main.pdf 1 2DC31B48099A0C68DF2860CCB3C8CC90 545ce2bed0edee23f62a7399fb215e8d166b88382c074215cb09269e32357f5b 4067624a-d66f-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156478 https://www.sciencedirect.com/science/article/pii/S0010482522002207 0 f6a97fdb-d669-11ec-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=156477 Medicinska fakulteta Fakulteta za računalništvo in informatiko 0 0 0