Tadeja Režen (Avtor), Alexandre Martins (Avtor), Miha Mraz (Avtor), Nikolaj Zimic (Avtor), Damjana Rozman (Avtor), Miha Moškon (Avtor)

Povzetek

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.

Ključne besede

COVID-19;metabolni modeli na nivoju genoma;metode ekstrakcije modelov;kontekstno specifični modeli;metabolna analiza obogatenosti;genome-scale metabolic models;model extraction methods;context-specific models;metabolic enrichment analysis;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FRI - Fakulteta za računalništvo in informatiko
UDK: 004:578.834
COBISS: 102526467 Povezava se bo odprla v novem oknu
ISSN: 0010-4825
Št. ogledov: 121
Št. prenosov: 66
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: COVID-19;metabolni modeli na nivoju genoma;metode ekstrakcije modelov;kontekstno specifični modeli;metabolna analiza obogatenosti;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-10
Zvezek: ǂVol. ǂ145
Čas izdaje: Jun. 2022
DOI: 10.1016/j.compbiomed.2022.105428
ID: 15347662