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. 2020 Jul 14;6(3):108.
doi: 10.3390/jof6030108.

Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

Enzo Acerbi  1 , Marcela Hortova-Kohoutkova  2 , Tsokyi Choera  3 , Nancy Keller  3 , Jan Fric  2   4 , Fabio Stella  5 , Luigina Romani  6 , Teresa Zelante  6
Affiliations

Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

Enzo Acerbi et al. J Fungi (Basel). .

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.

Keywords: Aspergillus fumigatus; Bayesian networks; continuous time Bayesian networks; gene network inference; gene network reconstruction; modeling; tryptophan metabolism.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Known literature-based interactions between catabolic axis and the amino acid Trp in A. fumigatus.
Figure 2
Figure 2
Experimental setting for model construction. A, wild type fungal strain was exposed to different concentrations of Trp (Low or High). B, ∆aroH fungal strain exposed to different concentration of Trp (Low or High). X, Y, Z represent generical networks that will be eventually generated in the A and B conditions.
Figure 3
Figure 3
Networks of predicted regulatory interactions learned from RT-PCR time-course data for wild-type strain (Panel A), and aroH deletion (Panel B) experimental conditions. In both cases, separate networks were inferred by using data from low-Trp (off) and high-Trp (on) experiments, for a total of four separate networks.
See this image and copyright information in PMC

References

    1. Cantone I., Marucci L., Iorio F., Ricci M.A., Belcastro V., Bansal M., Santini S., di Bernardo M., di Bernardo D., Cosma M.P. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell. 2009;137:172–181. doi: 10.1016/j.cell.2009.01.055. - DOI - PubMed
    1. Choera T., Zelante T., Romani L., Keller N.P. A Multifaceted Role of Tryptophan Metabolism and Indoleamine 2,3-Dioxygenase Activity in Aspergillus fumigatus-Host Interactions. Front. Immunol. 2017;8:1996. doi: 10.3389/fimmu.2017.01996. - DOI - PMC - PubMed
    1. Keller N.P. Fungal secondary metabolism: Regulation, function and drug discovery. Nat. Rev. Microbiol. 2019;17:167–180. doi: 10.1038/s41579-018-0121-1. - DOI - PMC - PubMed
    1. Wang P.M., Choera T., Wiemann P., Pisithkul T., Amador-Noguez D., Keller N.P. TrpE feedback mutants reveal roadblocks and conduits toward increasing secondary metabolism in Aspergillus fumigatus. Fungal Genet. Biol. 2016;89:102–113. doi: 10.1016/j.fgb.2015.12.002. - DOI - PMC - PubMed
    1. Arenas-Huertero F., Zaragoza-Ojeda M., Sanchez-Alarcon J., Milic M., Segvic Klaric M., Montiel-Gonzalez J.M., Valencia-Quintana R. Involvement of Ahr Pathway in Toxicity of Aflatoxins and Other Mycotoxins. Front. Microbiol. 2019;10:2347. doi: 10.3389/fmicb.2019.02347. - DOI - PMC - PubMed