Intestinal IL-33 promotes microbiota-derived trimethylamine N -oxide synthesis and drives metabolic dysfunction-associated steatotic liver disease progression by exerting dual regulation on HIF-1α.

BACKGROUND AND AIMS: Gut microbiota plays a prominent role in the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). IL-33 is highly expressed at mucosal barrier sites and regulates intestinal homeostasis. Herein, we aimed to investigate the role and mechanism of intestinal IL-33 in MASLD. APPROACH AND RESULTS: In both humans and mice with MASLD, hepatic expression of IL-33 and its receptor suppression of tumorigenicity 2 (ST2) showed no significant change compared to controls, while serum soluble ST2 levels in humans, as well as intestinal IL-33 and ST2 expression in mice were significantly increased in MASLD. Deletion of global or intestinal IL-33 in mice alleviated metabolic disorders, inflammation, and fibrosis associated with MASLD by reducing intestinal barrier permeability and rectifying gut microbiota dysbiosis. Transplantation of gut microbiota from IL-33 deficiency mice prevented MASLD progression in wild-type mice. Moreover, IL-33 deficiency resulted in a decrease in the abundance of trimethylamine N -oxide-producing bacteria. Inhibition of trimethylamine N -oxide synthesis by 3,3-dimethyl-1-butanol mitigated hepatic oxidative stress in mice with MASLD. Nuclear IL-33 bound to hypoxia-inducible factor-1α and suppressed its activation, directly damaging the integrity of the intestinal barrier. Extracellular IL-33 destroyed the balance of intestinal Th1/Th17 and facilitated Th1 differentiation through the ST2- Hif1a - Tbx21 axis. Knockout of ST2 resulted in a diminished MASLD phenotype resembling that observed in IL-33 deficiency mice. CONCLUSIONS: Intestinal IL-33 enhanced gut microbiota-derived trimethylamine N -oxide synthesis and aggravated MASLD progression through dual regulation on hypoxia-inducible factor-1α. Targeting IL-33 and its associated microbiota may provide a potential therapeutic strategy for managing MASLD.

Identification of novel NLRP3 inhibitors as therapeutic options for epilepsy by machine learning-based virtual screening, molecular docking and biomolecular simulation studies.

The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic target for the treatment of epilepsy and has been reported to regulate inflammation in several neurological diseases. In this study, a machine learning-based virtual screening strategy has investigated candidate active compounds that inhibit the NLRP3 inflammasome. As machine learning-based virtual screening has the potential to accurately predict protein-ligand binding and reduce false positives outcomes compared to traditional virtual screening. Briefly, classification models were created using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) machine learning methods. To determine the most crucial features of a molecule's activity, feature selection was carried out. By utilizing 10-fold cross-validation, the created models were analyzed. Among the generated models, the RF model obtained the best results as compared to others. Therefore, the RF model was used as a screening tool against the large chemical databases. Molecular operating environment (MOE) and PyRx software's were applied for molecular docking. Also, using the Amber Tools program, molecular dynamics (MD) simulation of potent inhibitors was carried out. The results showed that the KNN, SVM, and RF accuracy was 0.911 %, 0.906 %, and 0.946 %, respectively. Moreover, the model has shown sensitivity of 0.82 %, 0.78 %, and 0.86 % and specificity of 0.95 %, 0.96 %, and 0.98 % respectively. By applying the model to the ZINC and South African databases, we identified 98 and 39 compounds, respectively, potentially possessing anti-NLRP3 activity. Also, a molecular docking analysis produced ten ZINC and seven South African compounds that has comparable binding affinities to the reference drug. Moreover, MD analysis of the two complexes revealed that the two compounds (ZINC000009601348 and SANC00225) form stable complexes with varying amounts of binding energy. The in-silico studies indicate that both compounds most likely display their inhibitory effect by inhibiting the NLRP3 protein.

Loss-of-function variant in the LRR domain of SLITRK2 implicated in a neurodevelopmental disorder.

Background: Neurodevelopmental disorders are characterized by different combinations of intellectual disability (ID), communication and social skills deficits, and delays in achieving motor or language milestones. SLITRK2 is a postsynaptic cell-adhesion molecule that promotes neurite outgrowth and excitatory synapse development. Methods and Results: In the present study, we investigated a single patient segregating Neurodevelopmental disorder. SLITRK2 associated significant neuropsychological issues inherited in a rare X-linked fashion have recently been reported. Whole-exome sequencing and data analysis revealed a novel nonsense variant [c.789T>A; p.(Cys263*); NM_032539.5; NP_115928.1] in exon 5 of the SLITRK2 gene (MIM# 300561). Three-dimensional protein modeling revealed substantial changes in the mutated SLITRK2 protein, which might lead to nonsense-medicated decay. Conclusion: This study confirms the role of SLITRK2 in neuronal development and highlights the importance of including the SLITRK2 gene in the screening of individuals presenting neurodevelopmental disorders.