Journal of Molecular Neuroscience最新文献

Growth-associated protein 43 (GAP-43), a key regulator of synaptic plasticity, neuronal growth, and memory, has recently been identified as a crucial biomarker for synaptic dysfunction in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. This study aimed to explore the mechanisms underlying GAP-43’s role in cognitive impairment by examining the relationship between CSF GAP-43 levels and amyloid-β (Aβ) accumulation in brain regions like the frontal, temporal, and parietal lobes. This study included 332 participants sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), categorized into three groups: 93 cognitively normal (CN), 218 with MCI, and 21 with AD dementia. Cognitive status was assessed with ADAS-Cog 13, CSF GAP-43 levels via ELISA, and Aβ accumulation using florbetapir PET imaging and Syngo.PET for SUVr values in key brain regions. The results revealed that CSF GAP-43 levels were highest in the AD dementia group, followed by the MCI group, and lowest in the CN group, with a significant difference (p < 0.001), indicating a link between elevated CSF GAP-43 and cognitive impairment. In MCI group, CSF GAP-43 positively correlated with Aβ accumulation in all regions: Globally (β = 0.362, p < 0.001), frontal (β = 0.388, p < 0.001), temporal (β = 0.382, p < 0.001), and parietal lobes (β = 0.344, p < 0.001). In contrast, the AD dementia group exhibited negative correlations between CSF GAP-43 levels and Aβ accumulation, significantly in the frontal (β =  − 0.513, p = 0.035) and parietal lobes (β =  − 0.513, p = 0.035), suggesting a shift in the CSF GAP-43-Aβ relationship in AD dementia. Mediation analysis, adjusted for age, gender, education, and ApoE ɛ4 status, revealed that elevated CSF GAP-43 is linked to increased cognitive impairment via increasing Aβ accumulation solely in MCI, with significant effects in global (β = 0.0894, CI: [0.0427, 0.1457]), frontal (β = 0.0895, CI: [0.0422, 0.1443]), temporal (β = 0.0941, CI: [0.0466, 0.1522]), and parietal (β = 0.0499, CI: [0.0100, 0.0945]) regions. Thus, elevated CSF GAP-43 may contribute to cognitive impairment by promoting Aβ accumulation in individuals with MCI, while in AD dementia, it may be associated with reduced Aβ accumulation, potentially reflecting a compensatory or disease-stage-dependent effect. This dynamic relationship suggests that GAP-43 could play a dual role in neurodegeneration, influencing Aβ pathology differently across disease stages.

Autoimmune encephalitis (AE) is an immune-mediated non-infectious disease, and novel and robust biomarkers are needed to improve the diagnosis and prognostic outcomes of AE. Oxidative stress is a ubiquitous cellular process causing damage to various biological molecules. The aim of our study was to understand the clinical implication and mechanism underlying oxidative stress in AE. Liquid chromatography-mass spectrometry analysis was conducted on the serum of eight patients with AE and seven healthy controls, and oxidative stress was characterized. Experimental autoimmune encephalitis (EAE) models were established in C57BL/6 and SJL mice for investigation of the therapeutic effect and mechanism of anti-oxidative stress N-acetylcysteine (NAC). We provided proteomic landscape in the serum of AE and identified antioxidant ALB, APOE, GPX3, and SOD3 as serum diagnostic markers of AE. The antioxidant markers were lowly expressed both in the serum of AE patients and central nervous system (CNS) of EAE mice. NAC administration improved clinical signs and motor function and alleviated nerve injury of EAE mice as well as lowered oxidative stress (decreased MDA content and ROS accumulation and elevated SOD activity and GSH content). ALB, APOE, GPX3, and SOD3 expressions were elevated by NAC in the CNS of EAE mice. Moreover, NAC reduced tissue-resident CD4⁺ and CD8⁺ T cells and GFAP-marked astrocytes and Iba-1-marked microglia in EAE mice, thus alleviating autoimmunity-mediated damage and neuroinflammation. Our findings facilitate the discovery of novel oxidative stress-related biomarkers for AE and reveal the promise of anti-oxidative stress for AE management.

Low-grade gliomas (LGG) are malignant brain tumors that arise from the brain’s support cells (glial cells). LGG are the most common kind of central nervous system tumors in children and adolescents, accounting for around half of all cases. Tumor Protein p53 (TP53) regulates or promotes DNA damage and repair via a variety of cell cycle, apoptosis, and genomic stability pathways. However, the clinical role of TP53 status in LGG patients is still unknown. Hence, we analyzed clinical data from the Cancer Genomic Atlas (TCGA) of LGG patients to see if TP53 status affects clinical outcomes, molecular signatures of chemokines and microRNAs, and immune cell infiltrations within the tumor’s microenvironment of LGG patients. According to our findings, the most common phenotype in LGG patients is wild-type TP53, which is related to poor clinical outcomes and the expression of Chemokine ligand 14 (CXCL14) in many clinical parameters such as age, gender, stage, race, and purity. Besides, in LGG patients, wild-type TP53 controls prognostic microRNAs such as has-miR-10a-3p and has-miR-155-5p. Furthermore, through activating GATA Binding Protein 3 (GATA3) and decreasing Fatty Acid Synthase (FASN), wild-type TP53 orchestrates M1 macrophage and CD8⁺ T cell infiltration, as well as the formation of brown adipose tissue and decreased white adipose tissue. In this regard, the TP53-CXCL14-GATA3 axis has the potential to predict poor clinical outcomes in patients with wild-type TP53 LGG.

Parkinson’s disease recognition (PDR) involves identifying Parkinson’s disease using clinical evaluations, imaging studies, and biomarkers, focusing on early symptoms like tremors, rigidity, and bradykinesia to facilitate timely treatment. However, due to noise, variability, and the non-stationary nature of EEG signals, distinguishing PD remains a challenge. Traditional deep learning methods struggle to capture the intricate temporal and spatial dependencies in EEG data, limiting their precision. To address this, a novel fusion framework called graph embedding class-based convolutional recurrent attention network with Brown Bear Optimization Algorithm (GECCR2ANet + BBOA) is introduced for EEG-based PD recognition. Preprocessing is conducted using numerical operations and noise removal with weighted guided image filtering and entropy evaluation weighting (WGIF-EEW). Feature extraction is performed via the improved VGG19 with graph triple attention network (IVGG19-GTAN), which captures spatial and temporal dependencies in EEG data. The extracted features are classified using the graph embedding class-based convolutional recurrent attention network (GECCR2ANet), further optimized through the Brown Bear Optimization Algorithm (BBOA) to enhance classification accuracy. The model achieves 99.9% accuracy, 99.4% sensitivity, and a 99.3% F1-score on the UNM dataset, and 99.8% accuracy, 99.1% sensitivity, and 99.2% F1-score on the UC San Diego dataset, significantly outperforming existing methods. Additionally, it records an error rate of 0.5% and a computing time of 0.25 s. Previous models like 2D-MDAGTS, A-TQWT, and CWCNN achieved below 95% accuracy, while the proposed model’s 99.9% accuracy underscores its superior performance in real-world clinical applications, enhancing early PD detection and improving diagnostic efficiency.

Lysophosphatidylinositol (LPI) is an endogenous signaling molecule for the GPR55 receptor. Previous studies have shown that arachidonoyl-lysophosphatidylinositol (LPI-20:4) produced an increase in the inflammatory mediators NLPR3 (inflammasome-3 marker) and IL-1b in neurons from both rat dorsal root ganglion (DRG) and hippocampal cultures. Because LPI is comprised of a family of lipid structures that vary in fatty acyl composition, the current work examined neuroinflammatory responses to various LPI structures in DRG and hippocampal cultures as assessed by high-content fluorescent imaging. Major endogenous LPI fatty acyl structures consisting of 16:0, 18:0, 18:1, or 20:4 were compared for their effects on IL-1b, NLRP3, and GPR55 immunoreactive areas of neurites and cell bodies after a 6-h treatment. Among these four LPI structures, only LPI-20:4 treatment produced increases in immunoreactive areas for GPR55, NLRP3, and IL-1b in DRG and hippocampal neurites. In contrast, all other LPI structures tested produced a decrease in all of these inflammatory immunoreactive areas in both neurites and cell bodies. Additional studies with LPI-20:4 treatment indicated that IL-6, IL-18, and TNF-α were significantly increased in neurites of DRG and hippocampal cultures. However, oleoyl-lysophosphatidylinositol (LPI-18:1) treatment produced decreases in these three cytokines. Using the viability dye Alamar blue, LPI-20:4 was shown to produce concentration-dependent decreases, whereas all other endogenous LPI structures produced increases with this assay. These studies indicate that fatty acyl structure is the major determinant of LPI for neuroinflammatory responses in DRG and hippocampal cultures, with LPI-20:4 showing pro-inflammatory effects and all other endogenous LPIs tested exhibiting anti-inflammatory responses.

Caloric restriction (CR) is a dietary intervention that reduces calorie intake without inducing malnutrition, demonstrating lifespan-extending effects in preclinical studies and some human trials, along with potential benefits in ameliorating age-related ailments. Caloric restriction mimetics (CRMs) are compounds mimicking CR effects, offering a potential therapeutic avenue for age-related diseases. This study explores the potential protective effects of CR on the brain neocortex (GSE11291) and the identification of CRMs using integrative bioinformatics and systems biology approaches. Our findings indicate that long-term CR activates cellular pathways improving mitochondrial function, enhancing antioxidant capacity, and reducing inflammation, potentially providing neuroprotection. The key signaling pathways enriched in our study include PPAR, mTOR, FoxO, AMPK, and Notch signaling pathways, which are crucial regulators of metabolism, cellular stress response, neuroprotection, and longevity. We identify key signaling molecules and molecular mechanisms associated with CR, including transcription factors, kinase regulators, and microRNAs linked to differentially expressed genes. Furthermore, potential CRMs such as rapamycin, replicating CR-related health benefits, are identified. Additionally, machine learning models were developed to classify small molecules based on their CNS activity and anti-inflammatory properties. As a proof of concept, we have demonstrated the ischemic neuroprotective effects of two top-ranked candidate reference molecules (CRMs) using the oxygen–glucose deprivation (OGD) model, an established in vitro stroke model. However, further investigations are essential to fully elucidate the therapeutic potential of these CRMs. In summary, our study suggests that long-term CR entails protective mechanisms preserving and safeguarding neuronal function, potentially impacting the treatment of age-related neurological diseases. Moreover, our findings contribute to the identification of potential genes and regulatory molecules involved in CR, along with potential CRMs, providing a promising foundation for future research in the field of neurological disorder treatment.

Graphical Abstract

Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), are characterized by the progressive and gradual degeneration of neurons. The prevalence and rates of these disorders rise significantly with age. As life spans continue to increase in many countries, the number of cases is expected to grow in the foreseeable future. Early and precise diagnosis, along with appropriate surveillance, continues to pose a challenge. The high heterogeneity of neurodegenerative diseases calls for more accurate and definitive biomarkers to improve clinical therapy. Cell-free DNA (cfDNA), including fragmented DNA released into bodily fluids via apoptosis, necrosis, or active secretion, has emerged as a promising non-invasive diagnostic tool for various disorders including neurodegenerative diseases. cfDNA can serve as an indicator of ongoing cellular damage and mortality, including neuronal loss, and may provide valuable insights into disease processes, progression, and therapeutic responses. This review will first cover the key aspects of cfDNA and then examine recent advances in its potential use as a biomarker for neurodegenerative disorders.

As the neuroimaging technology improves, the detection rate of unruptured intracranial aneurysms (UIA) is gradually increasing. However, there is currently no effective means to evaluate and predict the risk of rupture for small intracranial aneurysm (sIA, diameter < 7 mm). We previously identified extracellular matrix protein 2 (ECM2) as a potential candidate biomarker for predicting intracranial aneurysm (IA) rupture through iTRAQ combined with LC–MS/MS protein quantification technology, so this study aimed to further validate the ability of plasma ECM2 expression levels to predict IA rupture. This prospective, observational, single-center cohort study enrolled 322 individuals with ruptured intracranial aneurysm (RIA, N = 123), UIA (N = 89), traumatic subarachnoid hemorrhage (tSAH, N = 55), or healthy controls (HC, N = 55). ECM2 plasma levels were quantified using enzyme-linked immunosorbent assay (ELISA). The Spearman rank correlation analysis was employed to examine the relationship between variables. Independent risk factors of sIA rupture were identified using logistic regression analysis. The ROC curve assessed the predictive capability for sIA rupture. Plasma ECM2 was notably higher in RIA patients than in UIA, tSAH, and HC groups. Plasma ECM2 levels showed no significant difference among the asymptomatic UIA, HC, and tSAH groups. There was also no significant difference in plasma ECM2 levels between symptomatic UIA patients and RIA patients. Furthermore, the plasma ECM2 level was closely related to hypertension history in sIA patients. ECM2 plasma level was an independent risk factor for sIA rupture. The plasma ECM2 cutoff level for predicting IA rupture was determined to be 1540.67 pg/ml. The combination of ECM2 levels and aneurysm location increased predictive accuracy (AUC = 0.828, sensitivity 87.0%, specificity 68.8%, accuracy 83.2%), surpassing the performance of PHASES and ELPASS scores. ECM2 could potentially act as an early warning biomarker for predicting the rupture of sIAs.

Diabetes is a key risk factor for ischemic stroke and negatively impacts long-term outcomes post-stroke. However, genomic studies on diabetic stroke remain insufficient. This study aims to investigate the interaction between diabetes and stroke from the acute phase to the early recovery phase by establishing a diabetic stroke animal model and comparing transcriptome sequencing results with those of non-diabetic stroke models. The study identified a greater number of downregulated genes in the diabetic stroke group compared to the non-diabetic group at different stages post-stroke. Functional enrichment analysis revealed an enhanced immune response and a relatively lower neurodegeneration potential in the diabetic group. Post-stroke, a higher presence of CD4 + T cells, eosinophils, and M1 macrophages was observed in the diabetic group. Additionally, time-series analysis identified a set of genes with time-specific expression patterns following diabetic stroke. This study underscores the role of inflammation and immune responses as potential factors exacerbating ischemic stroke in diabetes while also identifying gene regulatory networks at different stages post-stroke. These findings provide new insights into the role of diabetes in stroke and suggest potential therapeutic targets for improving outcomes in diabetic patients.