Journal of Molecular Neuroscience最新文献

Parkinson's disease (PD) is a progressive and multifactorial neurodegenerative disorder primarily caused by the loss of dopaminergic neurons in the substantia nigra. This neuronal loss leads to characteristic motor symptoms such as tremors, rigidity, and slowness of movement. Although PD has long been regarded as a disorder originating in the brain, recent findings suggest that the gut-brain axis, the intricate communication network between the gastrointestinal tract and the central nervous system also plays an important role in the development and progression of PD. Interestingly, early non-motor symptoms such as constipation and other bowel irregularities often appear several years before the onset of motor symptoms, indicating that changes in gut function may precede and even contribute to neurodegeneration. The gut microbiota influences neuronal signaling, immune activity, and metabolic balance through neuroactive molecules such as neurotransmitters, short-chain fatty acids (SCFAs), and cytokines. In PD, microbial imbalance, intestinal barrier dysfunction, and chronic inflammation are closely linked to the misfolding and accumulation of α-synuclein (α-syn), which can spread from the gut to the brain through the vagus nerve in a prion-like manner. Current therapeutic approaches are increasingly exploring ways to restore gut microbial balance using probiotics, prebiotics, dietary interventions, fecal microbiota transplantation (FMT), and SCFA supplementation. These strategies not only aim to relieve symptoms but may also have the potential to slow disease progression. This review discusses the mechanisms through which the gut-brain axis contributes to PD, summarizes key clinical and preclinical findings, and highlights emerging gut-targeted therapeutic approaches.

Ferroptosis, an iron-dependent regulated cell death form, is a key pathogenic mechanism in Alzheimer's disease (AD), especially in the entorhinal cortex, a brain region selectively vulnerable to early AD neuropathology. This study aimed to identify peroxiredoxin 6 (PRDX6) as a novel ferroptosis-related hub gene in the entorhinal cortex and validate its diagnostic and therapeutic potential in AD. Gene expression datasets (GSE138852, GSE5281, GSE48350, GSE118553) from the Gene Expression Omnibus (GEO) and ferroptosis-related genes (FRGs) from FerrDb were analyzed. Differential expressed genes (DEGs) were identified using Limma (|log2FC| > 1, P < 0.05), followed by Weighted Gene Coexpression Network Analysis (WGCNA) to delineate AD-associated modules. Machine learning approaches (LASSO and random forest) were employed to screen candidate hub genes, and CIBERSORT was utilized to assess correlations with immune cell infiltration. Single-cell RNA sequencing (scRNA-seq) data from GSE138852 mapped gene distribution across entorhinal cortex cell populations. Validation included analyses in the Alzdata database, receiver operating characteristic (ROC) curves for diagnostic accuracy, and Western blot assays in Aβ_1-42-induced U251 astrocyte models. Functional enrichment analyses of WGCNA key module genes revealed involvement in anti-apoptosis regulation, cytosolic processes, enzyme binding, and the ferroptosis pathway. Machine learning identified six candidate genes, among which PRDX6 showed significant upregulation in the AD entorhinal cortex (Alzdata), correlation with both Aβ and tau pathologies, and a negative association with neutrophils. Single-cell profiling localized PRDX6 predominantly to astrocytes. ROC curves confirmed PRDX6 as the optimal hub gene, and Western blot validation demonstrated significantly elevated PRDX6 protein expression in Aβ_1-42-induced U251 cells, consistent with bioinformatics findings. These findings establish PRDX6 as a pivotal mediator linking ferroptosis, immune cell dynamics, and AD neuropathology. Targeting PRDX6-mediated antioxidant pathways holds promise for intervening in ferroptosis-driven neurodegeneration and provides a novel avenue for AD diagnosis and therapeutic development.

Hypokalemic periodic paralysis (HypoPP) is a muscle disease caused by abnormal ion channels and is characterized by recurrent skeletal muscle relaxation paralysis and hypokalemia. There are obvious triggers before disease onset, such as cold, excessive exercise, excessive consumption of sugary and high-energy foods, and overeating. The aim of this study was to elucidate the pathogenic mechanism of novel mutations in the voltage-dependent L-type calcium channel subunit alpha-1 S (CACNA1S) gene associated with HypoPP. Method: Whole-exome sequencing and American College of Medical Genetics and Genomics (ACMG) compliance analysis were performed, supplemented by serum potassium and blood biochemistry tests for bioinformatics analysis. We report a 13-year-old adolescent male patient with hypokalemic periodic paralysis, who complained of limb muscle weakness accompanied by pain for 10 h. Whole-exome sequencing revealed a mutation in the CACNA1S gene (NM_000069.3: exon27: c.3491 A>C [p. Glu1164Ala]), which was classified as an uncertain mutation. The clinical presentation and protein structure prediction of the gene mutation confirmed its pathogenic role and mechanism. The mutation caused a conformational change in the calcium ion channel. This study revealed a new mutation site in the HypoPP gene and proposed the possibility of a new pathogenesis. Moreover, obesity and low magnesium are two factors that induce HypoPP, which may increase the risk of disease.

The generation of engineered neurons via Neurogenin-2 (NGN2) overexpression, starting from human induced pluripotent stem cells (hiPSCs), is a powerful tool for modeling neurological diseases. However, using stabilized hiPSCs as a starting point significantly increases the time required to obtain a valuable human neuronal model in vitro. Here, we demonstrated that as little as 3 days of transient expression of reprogramming factors in human fibroblasts can unlock the ability of these cells to transdifferentiate into neurons upon overexpression of NGN2. We used single-cell transcriptomic data to dissect the distinct cell identities that emerge during reprogramming. We identified three distinct reprogramming intermediate populations responsive to NGN2-mediated neuronal induction and found that partial reprogramming for only 3 days is sufficient to mediate NGN2 neuronal conversion of human fibroblasts. Moreover, we found that the efficiency in neuronal fate acquisition mediated by NGN2 overexpression is strictly correlated with the stage of reprogramming used as a starting point.

This study investigates the relationship between stress coping ability, salivary antioxidant capacity (AOC), and trace element concentrations, focusing on zinc (Zn) and potassium (K). A cohort of 73 participants, divided into groups based on stress coping ability (SCA) ("adaptive", "intermediate", and "maladaptive"), underwent cognitive tasks while physiological and behavioral data were collected. Saliva samples were analyzed for AOC and trace elements, including Zn, K, total phosphorus (P), and total sulfur (S). Results revealed that individuals with effective stress coping strategies (the "adaptive" group) exhibited significantly higher AOC and Zn levels, along with lower K levels, compared to those with maladaptive coping abilities. Positive correlations were observed between Zn and AOC, while K showed a negative correlation with AOC. Behavioral data indicated that the "maladaptive" group demonstrated a pronounced decline in self-assessment as task difficulty increased, despite similar task performance across groups. These findings suggest that stress coping ability is a stable trait influencing physiological homeostasis, with effective coping associated with enhanced antioxidant defenses and balanced trace element regulation. The study highlights the importance of stress management in maintaining oxidative balance and emotional resilience, offering potential pathways for interventions targeting stress-related physiological and cognitive dysregulation.

This study aimed to evaluate the diagnostic accuracy of cerebrospinal fluid presepsin and procalcitonin in patients who had undergone neurosurgery between November 2023 and December 2024 enrolled on the basis of specific guidelines. Cerebrospinal fluid presepsin and procalcitonin levels were evaluated via ELISA. Machine learning models were implemented to assess the diagnostic accuracy. A total of 120 patients were included in the study and categorized into three different groups. Machine learning model: random forest model was implemented for ROC curve analysis and the model had an accuracy of 94.5%. The optimal presepsin cut-off value for discriminating between the infectious and non-infectious group was 1729 pg/ml. The specificity and sensitivity for presepsin was 0.875 and 0.632, respectively, and the AUCs for all groups were greater than 0.800 in the random forest model. The specificity and sensitivity for PCT were 0.458 and 0.789, respectively, and the AUCs for the confirmed and probable groups were 0.810 and 0.800 respectively. The variable importance plot revealed presepsin to be the second most useful parameter in model prediction. The random forest model has good performance in predicting infections among neurosurgical patients. CSF presepsin clearly distinguished the three groups, and the median PCT levels were similar across the three groups. The optimal cut-off for PCT is not suggestive compared with presepsin. CSF presepsin is a better biomarker than CSF PCT in diagnosing post-neurosurgery patients and can be implemented in routine diagnostic procedures.

Brain tumors (BT) are actually an uncontrolled growth of cancer cells inside the body that can be classified into several classes according to their characteristics and accessible therapies. Brain tumors require a thorough examination by medical professionals due to their seriousness and risk for death. One of the advanced digital image processing techniques utilized for categorized tumors is magnetic resonance imaging (MRI). Recently, a number of Deep Learning (DL) models have been created to help diagnose BT. Many of these kinds of models have poor accuracy, which could result in incorrect diagnosis. The Robust Peak Guided Filter R2U++ Multilayer Attention SO(3) Equivariant Graph Neural Network with Snow Geese Algorithm (RPGFR2U++MASO(3)EGNN-SGA) is a proposed methodology that uses data from the Contrast-Enhanced MRI (CE-MRI) and BRATS 2018 datasets to improve brain tumor classification. It employs the Iterative Robust Peak-Aware Guided Filter (RPAGF) to reduce noise and preserve critical features. The Multilayer Edge Attention (MEA-Net) enhances feature extraction and refinement, while SO(3)-equivariant Graph Neural Networks ensure precise graph-based feature analysis. The results express how well the proposed method performs, demonstrating its positive potential for cancer diagnosis. The suggested technique, RPGFR2U++MASO(3)EGNN-SGA, demonstrated its efficacy across a range of datasets with impressive accuracy of 99.6% for the BRATS 2018 dataset and 99.7% for the CE-MRI dataset. These results reveal that the suggested methodology outperforms existing methods, demonstrating its capabilities and potential for future breakthroughs in BT identification and classification.