Universidad de Guanajuato

Acid sphingomyelinases (aSMases) are enzymes involved in the repair of the plasma membrane in eukaryotic cells. However, neutral sphingomyelinases (nSMases) have also been shown to possess other roles in bacteria and eukaryotic microorganisms, especially as virulence factors. These enzymes exhibit structural conservation but are characterized by elusive homology and the lack of sequence signatures or motifs. In a previous study, we reported the structural features of the complete set of sphingomyelinases (SMases) in Entamoeba histolytica and Trichomonas vaginalis, showing structural homology and functional differences in two aSMases from E. histolytica (EhSMase). However, the approach was limited due to the AlphaFold3 source code not being publicly available at the time. In this report, the structural transitions in the aSMases from T. vaginalis (TvSMase) were measured using open-source AlphaFold3 and collective motions of proteins via Normal Mode Analysis in internal coordinates. They compared them with the models from aSMase4 (EHI_100080) and aSMase6 (EHI_125660) from E. histolytica, containing different combinations of ligands. Using full-length sphingomyelin and the Mg²⁺ and Co²⁺ ions, where Co²⁺ was shown to inhibit the enzymes of both organisms, we demonstrate that the enzymes exhibit limited flexibility and deformability, except for the T. vaginalis TVAG_271580 enzyme, which displays high structural deformability. This contrasts with the inhibitory mechanism elicited by Co²⁺ as shown previously. TVSMase3 (TVAG_222460) could not be modelled with the sphingomyelin in the active site pocket, suggesting a regulatory role rather than a functional active enzyme. Additional physicochemical parameters calculated for T. vaginalis enzymes suggest unstable structures and high internal mobility (estimated using the Internal Coordinate method), which may be associated with the functional role of these enzymes. The results presented here open an avenue for searching for novel inhibitors of aSMases that target their physical properties, which could potentially complement treatment to control the parasite burden. These inhibitors could be valuable for further studying the role of these enzymes in parasite pathobiology and, potentially, as therapeutic targets.

Microplastic (MP) pollution in coastal environments poses a significant threat to biodiversity and ecosystem services, with potential implications for human health, particularly in the Global South, where data on large-scale impacts are scarce. As part of the MICROMar project, we conducted the most extensive standardized survey of MPs in tropical beach sediments, analyzing 4134 samples from 1024 beaches along approximately 7500 km of Brazil's coastline. MPs (from 300 μm to <5 mm) were extracted, quantified, and characterized by morphology, color, type, and chemical composition. MP occurrence varied widely across states (16.5 %-90.6 %), with 69.3 % of beaches contaminated. 24,549 MPs, mostly irregular fragments dominated by white, green, and blue colors, were identified. The most common polymers were polyethylene (PE), expanded polystyrene (EPS), and polypropylene (PP), among 21 types of polymers. Concentrations ranged from 0 to 3483.4 MP/kg, with hotspots in Paraná, Sergipe, São Paulo, and Pernambuco. A national baseline of 27.09 MP/kg was estimated. Pollution, hazard, and ecological risk indices identified priority zones. Predictive models revealed that the state, municipality, proximity to sewage discharge, rivers, urbanization, and the Plastic Pressure and Risk Index (PPRI) significantly influenced the distribution of MP. Our findings provide robust scientific support for monitoring and mitigation efforts, offering one of the most comprehensive coastal MP datasets in the Global South.

Mass Spectrometry Imaging (MSI) generates large datasets that require efficient computational solutions for data handling and visualization. While R and Python are commonly used for MSI analysis, their limited performance can hinder Big Data workflows. Julia is a high-performance programming language widely adopted in computationally demanding fields such as physics and economics. Here, we present JuliaMSI, a graphical user interface (GUI) developed in Julia for reading and analyzing MSI data in open formats (.imzML, .ibd, .mzML). JuliaMSI accelerates data loading, preprocessing, and visualization, with benchmarks showing up to 4.2-fold faster processing on Windows/macOS and 5.2-fold on Linux compared to R-based tools. Beyond speed, JuliaMSI enables interactive analysis through features such as contrast-enhancing filters (TrIQ, median filter), 3D topographic visualizations of ion intensity landscapes, and overlays of ion images with optical reference images. Users can inspect mass spectra linked to ion images, select regions of interest (ROI), and export results in publication-ready formats (.png, .jpg, .bmp). The platform supports seamless integration with downstream workflows via open data standards and provides a computationally efficient, user-friendly environment for large-scale MSI analysis. JuliaMSI is cross-platform (Linux, macOS, Windows) and available under the MIT license at https://codeberg.org/LabABI/JuliaMSI.