Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)—belonging in the heme dioxygenase family—degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.

Acerbi, E., Hortova-Kohoutkova, M., Choera, T., Keller, N., Fric, J., Stella, F., et al. (2020). Modeling approaches reveal new regulatory networks in aspergillus fumigatus metabolism. JOURNAL OF FUNGI, 6(3), 1-9 [10.3390/jof6030108].

Modeling approaches reveal new regulatory networks in aspergillus fumigatus metabolism

Acerbi E.;Stella F.;Romani L.;
2020

Abstract

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)—belonging in the heme dioxygenase family—degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.
Articolo in rivista - Articolo scientifico
Aspergillus fumigatus; Bayesian networks; Continuous time Bayesian networks; Gene network inference; Gene network reconstruction; Modeling; Tryptophan metabolism;
English
14-lug-2020
2020
6
3
1
9
108
open
Acerbi, E., Hortova-Kohoutkova, M., Choera, T., Keller, N., Fric, J., Stella, F., et al. (2020). Modeling approaches reveal new regulatory networks in aspergillus fumigatus metabolism. JOURNAL OF FUNGI, 6(3), 1-9 [10.3390/jof6030108].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/302791
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