Sulfotransferase 1A1 (SULT1A1) is responsible for catalyzing various types of endogenous and exogenous compounds. Accumulating data indicates that the polymorphism rs9282861 (R213H) is responsible for inefficient enzymatic activity and associated with cancer progression. To characterize the detailed functional consequences of this mutation behind the loss-of-function of SULT1A1, the present study deployed molecular dynamics simulation to get insights into changes in the conformation and binding energy. The dynamics scenario of SULT1A1 in both wild and mutated types as well as with and without ligand showed that R213H induced local conformational changes, especially in the substrate-binding loop rather than impairing overall stability of the protein structure. The higher conformational changes were observed in the loop3 (residues, 235–263), turning loop conformation to A-helix and B-bridge, which ultimately disrupted the plasticity of the active site. This alteration reduced the binding site volume and hydrophobicity to decrease the binding affinity of the enzyme to substrates, which was highlighted by the MM-PBSA binding energy analysis. These findings highlight the key insights of structural consequences caused by R213H mutation, which would enrich the understanding regarding the role of SULT1A1 mutation in cancer development and also xenobiotics management to individuals in the different treatment stages.

Huntington’s disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on external datasets to validate the discriminatory power of the identified genes. Based on the results of an inverse variance weighted meta-analysis of the AUCs across both human and mouse cohorts, GABRD and PHACTR1 were identified as the most robust candidates and were selected as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research.

Bacopa monnieri (L.) Wettst. (Family: Scrophulariaceae) is a well-known edible plant used in ethnic and Ayurveda medicine for centuries to improve memory deficit, enhance cognitive function, and treat nervous system disorders. Despite accumulating in vivo evidence for its cognitive benefits, the detailed mechanisms by which its bioactive compounds act on primary neurons remain elusive. In the present study, we dissect the mechanism by which Bacopa monnieri promotes neuronal development by treating primary hippocampal neuronal cultures with its ethanolic extract (BMEE) and integrating insights from in silico network pharmacology. We identified that BMEE at different concentrations promotes neuritogenesis and has a remarkable impact on early neuronal maturation, and axonal and dendritic outgrowth. Also, BMEE regulated synaptic plasticity by increasing the expression of NMDA receptors. Metabolites of BMEE were identified by gas chromatography–mass spectrometry (GC-MS) analysis, from which a network pharmacology model was constructed, in which BMEE metabolites were projected to regulate the neurotrophin signaling pathway. Indeed, the BMEE-mediated neuritogenic effect was abolished by the presence of a TrkA receptor-specific inhibitor, suggesting that the neuritogenic effect of BMEE is TrkA-dependent. Also, molecular docking following MD simulations supported the idea that BMEE metabolites, particularly δ-Tocopherol and O-methyl-, bind with high affinity to the TrkA receptor (NGF-binding domain). This study collectively illuminates the TrkA-mediated pathway through which Bacopa monnieri promotes neuronal development and suggests that bioactive metabolites from BMEE might hold potential as a source for designing therapeutic agents for various cognitive disorders.

Huntington's disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on an external dataset to validate the discriminatory power of the identified genes, which led to the selection of GABRD and PHACTR1 as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research.