Journal of Molecular Neuroscience最新文献

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.

Recent improvements in the accuracy of long-read sequencing (LRS) technologies have expanded the scope for novel transcriptional isoform discovery. Additionally, these advancements have improved the precision of transcript quantification, enabling a more accurate reconstruction of complex splicing patterns and transcriptomes. Thus, this project aims to take advantage of these analytical developments for the discovery and analysis of RNA isoforms in the human brain. A set of novel transcript isoforms was compiled using three bioinformatic tools, quantifying their expression across eight replicates of the cerebellar hemisphere, five replicates of the frontal cortex, and six replicates of the putamen. By taking a subset of the novel isoforms consistent across all discovery methods, a set of 170 highly confident novel RNA isoforms was curated for downstream analysis. This set consisted of 104 messenger RNAs (mRNAs) and 66 long non-coding RNAs (lncRNAs) isoforms. The detailed structure, expression, and potential encoded proteins of novel mRNA isoform BambuTx321 have been further described as an exemplary representative. Additionally, the tissue-specific expression [mean counts per million (CPM) of 5.979] of novel lncRNA, BambuTx1299, in the cerebellar hemisphere was observed. Overall, this project has identified and annotated several novel RNA isoforms across diverse tissues of the human brain, providing insights into their expression patterns and investigating their potential functional roles. Thus, this project has contributed to a more comprehensive understanding of the brain’s transcriptomic landscape for applications in basic research.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, significantly impacting patients’ quality of life. Recent studies have highlighted the roles of sirtuin 1 (SIRT1), a NAD + -dependent deacetylase, in regulating various biological pathways associated with AD pathology, including amyloid-beta metabolism, tau hyperphosphorylation, and neuroinflammation. This review focuses on the therapeutic potential of synthetic and natural compounds that modulate SIRT1 levels, emphasizing their molecular mechanisms of action. We explore a range of SIRT1-modifying agents, including polyphenols such as resveratrol, as well as synthetic analogs and novel pharmaceuticals that aim to enhance SIRT1 activity. Additionally, we discuss emerging innovative therapies, including pharmacological agents that improve SIRT1 signaling through mechanisms like photobiomodulation and nutritional interventions. These compounds not only target SIRT1 but also integrate into broader metabolic and neuroprotective pathways, presenting a promising approach to ameliorating AD symptoms. By elucidating the intricate interactions between SIRT1-modifying compounds and their effects on AD pathology, this review aims to advance the understanding of potential therapeutic strategies that could delay or prevent the progression of AD.

Dystroglycanopathies (DGPs) are a group of autosomal recessive neuromuscular diseases with significant clinical and genetic heterogeneity. They originate due to defects in the O-mannosyl glycosylation of α-dystroglycan (α-DG), a prominent linker between the intracellular cytoskeleton and the extracellular matrix (ECM). Fundamentally, such interactions are crucial for the integrity of muscle fibers and neuromuscular synapses, where their defects are mainly associated with muscle and brain dysfunction. To date, biallelic variants in 18 genes have been associated with DGPs, where the underlying cause is still undefined in a significant proportion of patients. Glycosylation of α-DG generates three core motifs where the core M3 is responsible for interaction with the basement membrane. Consistently, all gene defects that corrupt core M3 maturation have been identified as causes of DGPs. POMGNT1 which stimulates the generation of core M1 is also associated with DGPs, as it plays a central role in core M3 processing. Other genes involved in the glycosylation of α-DG seem unrelated to DPGs. The current review illustrates the O-mannosylation pathway of α-DG highlighting the functional properties of related genes and their contribution to the progression of DPGs. Different classes of DPGs are also elaborated characterizing the clinical features of each distinct type and phenotypes associated with each single gene. Finally, current therapeutic approaches with favorable outcomes are addressed. Potential achievements of preclinical and clinical studies would introduce effective curative therapies for this group of disorders in the near future.

The objective of this study is to explore the association of plasma metal concentrations with impaired cognitive function in different genotypes of ATG7 using multiple models. A cross-sectional survey was conducted in rural China among 994 individuals aged 30 years or older. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Genetic analysis focused on two single-nucleotide polymorphisms (SNPs) in the autophagy-related gene ATG7 (rs2606757 and rs8154). Plasma concentrations of metals were quantified using inductively coupled plasma mass spectrometry. Restricted cubic splines were used to explore the association between serum metal concentration and the occurrence of mild cognitive impairment in individuals with various genotypes. Bayesian Kernel Machine Regression (BKMR) models were used to explore the interactions between individual metals. In a restricted cubic spline model, there is a nonlinear relationship between plasma concentration of Cd and the occurrence of cognitive impairment in individuals carrying the AA (P of Nonlinear = 0.008) and AT (P of Nonlinear = 0.007) genotypes at the rs2606757. However, in people carrying the TT genotype at the rs2606757 locus, the concentration of metals in plasma was not significantly associated with cognitive impairment (P of Nonlinear = 0.534). The results of the BKMR model are consistent with those of the restricted cubic spline model. The TT genotype at rs2606757 in ATG7 appears to confer greater cognitive resilience against Cd-induced cognitive damage. These findings highlight the importance of considering gene-environment interactions in the context of cognitive impairment and suggest potential avenues for preventing cognitive decline in individuals exposed to Cd. Further research is needed to elucidate the precise mechanisms underlying these interactions.