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Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

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 synthesi...

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Autores principales: Acerbi, Enzo, Hortova-Kohoutkova, Marcela, Choera, Tsokyi, Keller, Nancy, Fric, Jan, Stella, Fabio, Romani, Luigina, Zelante, Teresa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557846/
https://www.ncbi.nlm.nih.gov/pubmed/32674323
http://dx.doi.org/10.3390/jof6030108
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author Acerbi, Enzo
Hortova-Kohoutkova, Marcela
Choera, Tsokyi
Keller, Nancy
Fric, Jan
Stella, Fabio
Romani, Luigina
Zelante, Teresa
author_facet Acerbi, Enzo
Hortova-Kohoutkova, Marcela
Choera, Tsokyi
Keller, Nancy
Fric, Jan
Stella, Fabio
Romani, Luigina
Zelante, Teresa
author_sort Acerbi, Enzo
collection PubMed
description 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.
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spelling pubmed-75578462020-10-22 Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism Acerbi, Enzo Hortova-Kohoutkova, Marcela Choera, Tsokyi Keller, Nancy Fric, Jan Stella, Fabio Romani, Luigina Zelante, Teresa J Fungi (Basel) Brief Report 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. MDPI 2020-07-14 /pmc/articles/PMC7557846/ /pubmed/32674323 http://dx.doi.org/10.3390/jof6030108 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Brief Report
Acerbi, Enzo
Hortova-Kohoutkova, Marcela
Choera, Tsokyi
Keller, Nancy
Fric, Jan
Stella, Fabio
Romani, Luigina
Zelante, Teresa
Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title_full Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title_fullStr Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title_full_unstemmed Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title_short Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism
title_sort modeling approaches reveal new regulatory networks in aspergillus fumigatus metabolism
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557846/
https://www.ncbi.nlm.nih.gov/pubmed/32674323
http://dx.doi.org/10.3390/jof6030108
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