Browsing by Author "Mota, C."
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- Human regulatory T-cell development is dictated by Interleukin-2 and -15 expressed in a non-overlapping pattern in the thymusPublication . Caramalho, I.; Nunes-Silva, V.; Pires, A. R.; Mota, C.; Pinto, A. I.; Nunes-Cabaço, H.; Foxall, R. B.; Sousa, A. E.Thymus-derived FOXP3-expressing regulatory T-cells (tTregs) are master orchestrators of physiological and pathological immune responses, thus constituting ideal targets for the treatment of autoimmunity. Despite their clinical importance, the developmental program governing their differentiation in the human thymus remains poorly understood. Here, we investigated the role of common gamma-chain cytokines in human tTreg differentiation, by performing gain- and loss-of-function experiments in 3D and 2D postnatal thymic cultures. We identified IL-2 and IL-15 as key molecular determinants in this process and excluded a major function for IL-4, IL-7 and IL-21. Mechanistically, IL-2 and IL-15 were equally able to drive tTreg precursor differentiation into FOXP3(+) cells, and promote tTreg proliferation and survival. Both cytokines also increased the expression levels of molecules associated with effector function within FOXP3(+) subsets, supporting their involvement in tTreg functional maturation. Furthermore, we revealed that IL-2 and IL-15 are expressed in a non-overlapping pattern in the human thymus, with the former produced mainly by mature αβ and γδ thymocytes and the latter by monocyte/macrophages and B lymphocytes. Our results identify core mechanisms dictating human tTreg development, with IL-2 and IL-15 defining specific niches required for tTreg lineage stabilization and differentiation, with implications for their therapeutic targeting in autoimmune conditions.
- A method to produce a flexible and customized fuel models datasetPublication . Sá, A.C.L.; Benali, A.; Aparicio, B.A.; Bruni, C.; Mota, C.; Pereira, J.M.C.; Fernandes, P.M.Simulation of vegetation fires very often resorts to fire-behavior models that need fuel models as input. The lack of fuel models is a common problem for researchers and fire managers because its quality depends on the quality/availability of data. In this study we present a method that combines expert- and research-based knowledge with several sources of data (e.g. satellite and fieldwork) to produce customized fuel models maps. Fuel model classes are assigned to land cover types to produce a basemap, which is then updated using empirical and user-defined rules. This method produces a map of surface fuel models as detailed as possible. It is reproducible, and its flexibility relies on juxtaposing independent spatial datasets, depending on their quality or availability. This method is developed in a ModelBuilder/ArcGis toolbox named FUMOD that integrates ten sub-models. FUMOD has been used to map the Portuguese annual fuel models grids since 2019, supporting regional fire risk assessments and suppression decisions. Datasets, models and supplementary files are available in a repository (https://github.com/anasa30/PT_ FuelModels).