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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.

Cognitive impairment (CI) is a disabling concomitant of multiple sclerosis (MS) with a complex and controversial pathogenesis. The cytokine interleukin-17A (IL-17A) is involved in the immune pathogenesis of MS, but its possible effects on synaptic function and cognition are still largely unexplored. In this study, we show that the IL-17A receptor (IL-17RA) is highly expressed by hippocampal neurons in the CA1 area and that exposure to IL-17A dose-dependently disrupts hippocampal long-term potentiation (LTP) through the activation of its receptor and p38 mitogen-activated protein kinase (MAPK). During experimental autoimmune encephalomyelitis (EAE), IL-17A overexpression is paralleled by hippocampal LTP dysfunction. An in vivo behavioral analysis shows that visuo-spatial learning abilities are preserved when EAE is induced in mice lacking IL-17A. Overall, this study suggests a key role for the IL-17 axis in the neuro-immune cross-talk occurring in the hippocampal CA1 area and its potential involvement in synaptic dysfunction and MS-related CI.

The ability to predict invasive fungal infections (IFI) in patients with hematological malignancies is fundamental for successful therapy. Although gut dysbiosis is known to occur in hematological patients, whether airway dysbiosis also contributes to the risk of IFI has not been investigated. Nasal and oropharyngeal swabs were collected for functional microbiota characterization in 173 patients with hematological malignancies recruited in a multicenter, prospective, observational study and stratified according to the risk of developing IFI. A lower microbial richness and evenness were found in the pharyngeal microbiota of high-risk patients that were associated with a distinct taxonomic and metabolic profile. A murine model of IFI provided biologic plausibility for the finding that loss of protective anaerobes, such as Clostridiales and Bacteroidetes, along with an apparent restricted availability of tryptophan, is causally linked to the risk of IFI in hematologic patients and indicates avenues for antimicrobial stewardship and metabolic reequilibrium in IFI.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that is associated with demyelination and neuronal loss. Over recent years, the immunological and neuronal effects of tryptophan (Trp) metabolites have been largely investigated, leading to the hypothesis that these compounds and the related enzymes are possibly involved in the pathophysiology of MS. Specifically, the kynurenine pathway of Trp metabolism is responsible for the synthesis of intermediate products with potential immunological and neuronal effects. More recently, Trp metabolites, originating also from the host microbiome, have been identified in MS, and it has been shown that they are differently regulated in MS patients. Here, we sought to discuss whether, in MS patients, a specific urinary signature of host/microbiome Trp metabolism can be potentially identified so as to select novel biomarkers and guide toward the identification of specific metabolic pathways as drug targets in MS.

T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation. From the network, we identified the Prdm1 gene encoding the B lymphocyte-induced maturation protein 1 as a crucial negative regulator of human TH17 cell differentiation. The results have been validated by perturbing Prdm1 expression on freshly isolated CD4 + naïve T cells: reduction of Prdm1 expression leads to augmentation of IL-17 release. These data unravel a possible novel target to control TH17 polarization in inflammatory disorders. Furthermore, this study represents the first in vitro validation of continuous time Bayesian networks as gene network reconstruction method and as hypothesis generation tool for wet-lab biological experiments.

T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation. From the network, we identified the Prdm1 gene encoding the B lymphocyte-induced maturation protein 1 as a crucial negative regulator of human TH17 cell differentiation. The results have been validated by perturbing Prdm1 expression on freshly isolated CD4 + naïve T cells: reduction of Prdm1 expression leads to augmentation of IL-17 release. These data unravel a possible novel target to control TH17 polarization in inflammatory disorders. Furthermore, this study represents the first in vitro validation of continuous time Bayesian networks as gene network reconstruction method and as hypothesis generation tool for wet-lab biological experiments.

Background: Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models' expressiveness. Results: Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found

The role of IL-17 and Th17 cells in immunity vs. pathology associated with the human commensal Candida albicans remains controversial. Both positive and negative effects on immune resistance have been attributed to IL-17/Th17 in experimental candidiasis. In this study, we provide evidence that IL-22, which is also produced by Th17 cells, has a critical, first-line defense in candidiasis by controlling the growth of infecting yeasts as well as by contributing to the host's epithelial integrity in the absence of acquired Th1-type immunity. The two pathways are reciprocally regulated, and IL-22 is upregulated under Th1 deficiency conditions and vice versa. Whereas both IL-17A and F are dispensable for antifungal resistance, IL-22 mediates protection in IL-17RA-deficient mice, in which IL-17A contributes to disease susceptibility. Thus, our findings suggest that protective immunity to candidiasis is made up of a staged response involving an early, IL-22-dominated response followed by Th1/Treg reactivity that will prevent fungal dissemination and supply memory.