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    Th17 cells require the DNA repair sensor xeroderma pigmentosum complementation Group C to control oxidative DNA damage in a murine model
    (2026) Leite, Jefferson Antonio; Bos, Natalia Notarberardino; Silva, Luísa Menezes; Silva, Eloisa Martins da; Leandro, Giovana da Silva; Góes, Guilherme Comparatto Moreira de; Silva, Patrick da; Oliveira, Samuel; Santos, Éder Soares de Almeida; Silva, Hedden Ranfley Magalhães; Palagi, Ilaria; Neves, Bruno Junior
    T helper 17 cells play essential roles in mucosal immunity and autoimmunity, yet the mechanisms that protect these cells from oxidative DNA damage remainpoorly defined. Here we show, in a murine model, that the nucleotide excisionrepair sensor Xeroderma Pigmentosum Complementation Group C preservesgenomic stability and metabolic fitness during T helper 17 cell differentiation.Loss of this factor reduces interleukin 17 production and increases mitochon-drial reactive oxygen species and oxidative DNA damage, resulting in alteredmetabolic programs. Mechanistically, Xeroderma Pigmentosum Com-plementation Group C interacts with the base excision repair enzyme 8-oxoguanine DNA glycosylase, and its absence enhances oxidative lesion inci-sion activity, indicating defective coordination between DNA repair pathways. Restoring antioxidant capacity rescues cytokine production and limits DNA damage in deficient cells. Together, these findings identify Xeroderma Pig-mentosum Complementation Group C as a key coordinator of DNA repair and redox control required for T helper 17 cell function in inflammatory settings.
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    Deep learning-enhanced QSAR modeling for predicting developmental neurotoxicity based on molecular initiating events from adverse outcome pathways
    (2026) Pereira, Eufrasia de Sousa; Costa, Vinícius Alexandre Fiaia; Santos, Eder Soares de Almeida; Neves, Bruno Junior
    Developmental neurotoxicity (DNT) is linked to chemical exposure that disrupts the nervous system in humans or animals. Traditional methods for assessing chemical toxicity are valuable but often time-consuming, costly, and involve significant animal use, making it impractical to meet growing demands. To address this, we developed a deep learning-enhanced QSAR modeling framework aimed at predicting binding affinities towards molecular initiating events (MIEs) and key events (KEs) within the Adverse Outcome Pathway (AOP) relevant to exposure to pesticide-contaminated cannabis. Our model was trained on data from 24,476 compounds, sourced from the ChEMBL database, and tested against 4 MIE and 6 KE tasks. The DNNs showed superior performance, with an average correlation coefficient of 0.82±0.05 and a root mean square error of 0.72±0.08 for the test set. To enhance interpretability, we used SHAP values to explain the model’s predictions clearly. Furthermore, ECFP4 feature contributions were mapped onto known neurotoxic compounds to high- light regions likely responsible for MIEs visually. Our results confirm that developed models accurately predict DNT and effectively identify the correct MIEs and KEs for several neurotoxicants.
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    Activation of the complement system: morphological differences in the surface of PIBCA nanoparticles coated with polysaccharides
    (2026) Cavalcanti, Iago Dillion Lima; Costa, Vinícius Alexandre Fiaia; Seixas, Danilo Rosa; Neves, Bruno Junior; Xavier Junior, Francisco Humberto; Nogueira, Mariane Cajuba de Britto Lira; Magalhães, Nereide Stela Santos; Ponchel, Eloisa Berbel Manaia Gilles
    Drug-loaded nanosystems are often associated with the advantages of controlled drug release and reduced toxicity. Under-standing the activation of the complement system by polysaccharide- coated nanosystems becomes a crucial step in the development of new nanomedicines since the nonactivation of the complement system allows the nanosystems to circulate blood for longer. The aim of this study was to evaluate the activation of the complement system via the C3 protein pathway by polyisobutylcyanoacrylate (PIBCA) nanoparticles coated with polysaccharides (fucoidan, chitosan, or levan) with different surface morphologies. To this end, the nanoparticles were developed using anionic emulsion polymerization (AEP) and redox emulsion polymerization (RREP) techniques were applied to obtain nanoparticles with the same chemical composition but different polysaccharide architectures. The complement system activation studies were carried out using the 2D immunoelectrophoresis technique. Polysaccharide-coated nanoparticles were obtained with sizes ranging from 99.0 ± 0.5 to 659.9 ± 39.0 nm. As expected, the surface charge of the nanoparticles varied as a function of the polysaccharide coating: positive charges for chitosan-NPs, negative charges for fucoidan-NPs, and neutral charges for levan-NPs. According to the results, Chi-NPs did not activate the complement system, while Fuc-NPs and Lev-NPs did, depending on the surface morphology of the polysaccharides. The nanoparticles (Fuc-NPs and Lev-NPs) obtained by the AEP technique were strong complement activators, and those obtained by the RREP technique seemed to induce the formation of aggregates with the C3b protein. The molecular docking results reinforce these findings, highlighting the regions of the polysaccharide interaction with the C3 and C3b proteins. The presence of some chemical groups, such as the sulfate groups present in fucoidan, on the surface of the nanoparticles may contribute to the activation of the complement system.
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    In silico analysis of the multitarget potential of GlyT1 inhibitors in SLC6 transporters
    (2026) Nascimento, Lucas Rodrigues Couto; Tambwe, Paul Magogo; Carvalho, Gustavo Almeida de; Zanchi, Fernando Berton; Neves, Bruno Junior; Ulrich, Alexander Henning; Pinto, Mauro Cunha Xavier
    The solute carrier family 6 (SLC6) transporters are essential for regulating neurotransmitter homeostasis through the reuptake of amino acids and monoamines. Among them, the glycine transporter type 1 (GlyT1, SLC6A9) plays a central role in modulating NMDA receptor function and glutamatergic signaling. Despite their therapeutic relevance, the selectivity profiles of GlyT1 inhibitors remain poorly defined, raising concerns about off-target effects. In this study, we employed an integrative in silico approach combining homology modeling, molecular docking, consensus scoring, and molecular dynamics simulations to characterize the multitarget potential of GlyT1 inhibitors toward related SLC6 transporters—GlyT2, PROT, SERT, NET, and DAT. High-quality three-dimensional models were generated and validated through structural refinement and quality metrics. Consensus docking with DockThor, GOLD, and AutoDock Vina followed by Exponential Consensus Ranking (ECR) identified NFPS_2 as the most potent GlyT1 ligand (ECR = 1.896), forming π–π interactions with TYR99 and TRP279, while Org 24598_2 preferentially bound GlyT2, and Bitopertin showed high affinity for DAT. Molecular dynamics simulations (300 ns) confirmed the structural stability of all complexes (RMSD < 0.25 nm), with low residue fluctuations within the binding pockets and stable solvent exposure profiles. MMPBSA energy analyses revealed favorable binding free energies for GlyT1, NET, and DAT (ΔG ≈ −25 to −30 kcal/mol). These results demonstrate the intrinsic multitarget behavior of GlyT1 inhibitors, highlighting conserved interaction motifs within the SLC6 family. Collectively, our findings emphasize the importance of structure-guided optimization to improve selectivity and reduce potential off-target effects while maintaining therapeutic efficacy.
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    Multimodal cross-attentive graph-based framework for predicting in vivo endocrine disruptors
    (2026) Santos, Eder Soares de Almeida; Sandes, Gustavo Felizardo Santos; Silva, Artur Christian Garcia da; Martin, Holli-Joi; Muratov, Eugene N.; Braga, Rodolpho de Campos; Neves, Bruno Junior
    Endocrine hazard assessment needs models that are accurate and mechanistically transparent. We present a multimodal cross- attentive graph framework that fuses molecular graphs with adverse-outcome-pathway (AOP)–anchored assay signals to predict organism-level outcomes in the organisation for economic co-operation and development (OECD) Hershberger and uterotrophic assays. In Tier-1, multitask graph neural networks (GNNs) learn estrogen and androgen receptor molecular-initiating and key events across 46 in vitro ToxCast/Tox21 assays. In Tier-2, a cross-attentive multimodal GNN integrates Tier-1 pathway signals with molecular graphs, yielding high predictive performance for both the in vivo Hershberger(AUROC = 0.97 ± 0.014) and uterotrophic (AUROC = 0.97 ± 0.008) assays. Retrospective analysis of literature compounds showed 88% concordance (Hershberger 15/18; uterotrophic 23/26). Bidirectional cross-attention highlights associations between molecular substructures and pathway-level assay nodes, while counterfactual perturbations rank assays and structural motifs most influential for each decision. The framework couple’s high accuracy with assay-traceable explanations,supporting targeted testing within the integrated approaches.