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.

Centre for Translational Medicine International Clinical Research Centre St Anne's University Hospital Brno 65691 Brno Czech Republic

Department of Experimental Medicine University of Perugia 06132 Perugia Italy

Department of Informatics Systems and Communication University of Milano Bicocca Viale Sarca 336 Building U14 20126 Milan Italy

Department of Medical Microbiology and Immunology Department of Bacteriology University of Wisconsin Madison WI 53706 USA

Institute of Hematology and Blood Transfusion 12800 Prague Czech Republic

Nlytics Pte Ltd Singapore 637551 Singapore

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Aspergillus fumigatus tryptophan metabolic route differently affects host immunity