The learning and memory network is highly complex and remains unclear. The hippocampus is the location of learning and memory function. Impairment of synaptic morphology and synaptic plasticity (i.e., long-term potentiation) appears to cause learning and memory deficits. Several studies have indicated the role of NRXN1 in regulating the synaptic function, but little is known on its role in learning and memory dysfunction associated with attention deficit and hyperactivity disorder (ADHD). Our results showed that overexpression and interference of NRXN1 in vivo, respectively, affected learning and memory, as was assessed by Morris water maze tests, in spontaneously hypertensive rats (SHRs) and Sprague Dawley (SD) rats. We found that SD rats performed better after methylphenidate (MPH) treatment in salvage trials. Accordingly, the change of NRXN1 led to altered synapse-related gene (PSD95, SYN1, GAP43, NLGN1) expression, further providing evidence of its role in the maintenance of synaptic plasticity. We also verified that the expression of synapse-related genes synchronously changed with NRXN1expression in the behavioral assessment. The expression of NRXN1 was confirmed to affect the expression of synapse-related genes after its interference and overexpression in the primary hippocampal neurons in vitro. These results confirmed our hypothesis that NRXN1 might nucleate an overall trans-synaptic signaling network that controls synaptic plasticity and is responsible for impairments in learning and memory in ADHD. These findings suggest a possible protective role of NRXN1 in learning and memory in ADHD. Further RNA-seq sequencing revealed significant differences in the expression of 5-hydroxytryptamine receptor (5-HTR), which was further verified at the cellular level, and the mechanism of NRXN1 affecting synaptic plasticity was preliminarily discussed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.expneurol.2021.113806 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Varenicline Attenuates Memory Impairment in Amyloid-Beta-Induced Rat Model of Alzheimer's Disease.
Neurochem Res
January 2025
Neurosciences Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder characterized by cognitive decline. Despite extensive research, therapeutic options remain limited. Varenicline, an αβ nicotinic acetylcholine receptor agonist, shows promise in enhancing cognitive function.
View Article and Find Full Text PDFEvaluation of developmental toxicity of chlorpyrifos through new approach methodologies: a systematic review.
Arch Toxicol
January 2025
Applied Biology Department, Miguel Hernández de Elche University, Elche, Spain.
Chlorpyrifos (CPF) is an organophosphorus pesticide of concern because many in vivo animal studies have demonstrated developmental toxicity exerted by this substance; however, despite its widespread use, evidence from epidemiological studies is still limited. In this study, we have collected all the information generated in the twenty-first century on the developmental toxicity of CPF using new approach methodologies. We have critically evaluated and integrated information coming from 70 papers considering human, rodent, avian and fish models.
View Article and Find Full Text PDFDeep learning sequence models trained on personalized genomics can improve variant effect prediction, however, applications of these models are limited by computational requirements for storing and reading large datasets. We address this with GenVarLoader, which stores personalized genomic data in new memory-mapped formats with optimal data locality to achieve ∼1,000x faster throughput and ∼2,000x better compression compared to existing alternatives.
View Article and Find Full Text PDFEffectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment.
Water Res X
May 2025
Institute for Artificial Intelligence R&D of Serbia, Fruškogorska 1, Novi Sad 21000, Serbia.
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.
View Article and Find Full Text PDFAntin-diabetic cognitive dysfunction effects and underpinning mechanisms of phytogenic bioactive peptides: a review.
Front Nutr
January 2025
College of Food Science and Technology, Yunan Agricultural University, Kunming, China.
Diabetic cognitive dysfunction is one of the important comorbidities and complications of diabetes, which is mainly manifested by loss of learning ability and memory, behavioural disorders, and may even develop into dementia. While traditional anti-diabetic medications are effective in improving cognition and memory, long-term use of these medications can be accompanied by undesirable side effects. Therefore, there is an urgent need to find safe and effective alternative therapies.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!