Paliperidone alleviates MK-801-induced damage to prefrontal cortical neurons via the PP2A/PTEN pathway

Evaluation of the potential role of glutamatergic, cholinergic, and nitrergic systems in the dopamine release induced by the pesticide glyphosate in rat striatum

Glyphosate (GLY) is a pesticide that severely alters nigrostriatal dopaminergic neurotransmission, inducing great increases in dopamine release from rat dorsal striatum. This GLY-induced striatal dopamine overflow occurs through mechanisms not yet fully understood, hence the interest in evaluating the role of other neurotransmitter systems in such effects. So, the main objective of this mechanistic study was to evaluate the possible mediation of the glutamatergic, cholinergic, and nitrergic systems in the GLY-induced in vivo dopamine release from rat dorsal striatum. The extracellular dopamine levels were measured by cerebral microdialysis and HPLC with electrochemical detection. Intrastriatal administration of GLY (5 mmol/L) significantly increased the dopamine release (1102%). Pretreatment with MK-801 (50 or 400 μmol/L), a non-competitive antagonist of NMDA receptors, significantly decreased the effect of GLY (by 70% and 74%, respectively), whereas AP-5 (400 μmol/L), a competitive antagonist of NMDA receptors, or CNQX (500 μmol/L), an AMPA/kainate receptor antagonist, had no significant effect. Administration of the nitric oxide synthase inhibitors, L-nitroarginine (L-NAME, 100 μmol/L) or 7-nitroindazole (7-NI, 100 μmol/L), also did not alter the effect of GLY on dopamine release. Finally, pretreatment of the animals with mecamylamine, an antagonist of nicotinic receptors, decreased the effect of GLY on dopamine release by 49%, whereas atropine, a muscarinic antagonist, had no significant effect. These results indicate that GLY-induced dopamine release largely depends on the activation of NMDA and nicotinic receptors in rat dorsal striatum. Future research is needed to determine the effects of this pesticide at environmentally relevant concentrations.

Progress in The Research of Lactate Metabolism Disruption And Astrocyte-Neuron Lactate Shuttle Impairment in Schizophrenia: A Comprehensive Review

Schizophrenia (SCZ) is a complex neuropsychiatric disorder widely recognized for its impaired bioenergy utilization. The astrocyte-neuron lactate shuttle (ANLS) plays a critical role in brain energy supply. Recent studies have revealed abnormal lactate metabolism in SCZ, which is associated with mitochondrial dysfunction, tissue hypoxia, gastric acid retention, oxidative stress, neuroinflammation, abnormal brain iron metabolism, cerebral white matter hypermetabolic activity, and genetic susceptibility. Furthermore, astrocytes, neurons, and glutamate abnormalities are prevalent in SCZ with abnormal lactate metabolism, which are essential components for maintaining ANLS in the brain. Therefore, an in-depth study of the pathophysiological mechanisms of ANLS in SCZ with abnormal lactate metabolism will contribute to a better understanding of the pathogenesis of SCZ and provide new ideas and approaches for the diagnosis and treatment of SCZ.

Autophagy-related gene model as a novel risk factor for schizophrenia

Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, we cataloged 232 ARGs and pinpointed 80 autophagy-related DEGs (AR-DEGs) after intersecting them with DEGs. Subsequent analyses, including metascape gene annotation, pathway and process enrichment, and protein-protein interaction enrichment, were performed on the 80 AR-DEGs to delve deeper into their biological roles and associated molecular pathways. From this, we identified 34 candidate risk AR-DEGs (RAR-DEGs) and honed this list to final RAR-DEGs via a constructed and optimized logistic regression model. These genes include VAMP7, PTEN, WIPI2, PARP1, DNAJB9, SH3GLB1, ATF4, EIF4G1, EGFR, CDKN1A, CFLAR, FAS, BCL2L1 and BNIP3. Using these findings, we crafted a nomogram to predict SCZ risk for individual samples. In summary, our study offers deeper insights into SCZ's molecular pathogenesis and paves the way for innovative approaches in risk prediction, gene-targeted diagnosis, and community-based SCZ treatments.