Hasan, A. K. M. M. | bioRxiv Science

bioRxiv2026

Bisphenol S causes deficits in social behaviour by disrupting serotonergic and BDNF-CREB1 signaling pathways

Bisphenol S (BPS), a widely used substitute for bisphenol A, is increasingly detected in aquatic environments; however, its neurodevelopmental effects remain insufficiently understood. This study investigated whether developmental exposure to an environmentally relevant concentration of BPS disrupts social behaviour and underlying neurobiological pathways in zebrafish (Danio rerio). At 21 days post-fertilization, BPS-exposed larvae exhibited a significant reduction in social preference, indicating impaired conspecific interactions. Neurochemical analysis revealed a marked increase in serotonin (5-HT) levels, whereas lipid peroxidation (MDA) remained unchanged, suggesting the absence of overt oxidative damage. Gene expression profiling demonstrated a dysregulated antioxidant response, suppression of apoptotic signaling, and pronounced upregulation of serotonergic receptors and transporters. To resolve system-level mechanisms, protein-protein interaction (PPI) network analysis identified BDNF and CREB1 as dominant regulatory hubs, with the serotonergic synapse pathway as the most significantly enriched term. Molecular docking further demonstrated direct binding of BPS to multiple serotonergic targets, including HTR1A and TPH2, supporting receptor-level interference. Expanded network and pathway analyses revealed coordinated enrichment of monoamine GPCR, oxidative stress, and inflammatory pathways. These findings demonstrate that BPS induces serotonergic dysregulation and network-level reprogramming rather than significant oxidative damage, leading to behavioural impairment. This study provides a multi-scale mechanistic framework linking molecular perturbations to neurobehavioural outcomes, identifying serotonergic signaling and BDNF-CREB1 pathways as central targets of BPS neurotoxicity.

animal behavior and cognition↗

bioRxiv2022

SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction

MotivationProtein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ({phi} and{psi} ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles. ResultsWe present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, CAMEO, and CASP. The experimental results suggest that our proposed self-attention based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementationSAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Contactshams_bayzid@cse.buet.ac.bd Supplementary informationSupplementary material SM.pdf.

bioinformatics↗