Alzheimer's Disease Genes Research Articles

Unveiling the molecular mechanisms of Danggui-Shaoyao-San against Alzheimer's disease in APP/PS1 mice via integrating proteomic and metabolomic approaches.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder for which no effective therapy is currently available. Given that various attempts to target beta-amyloid (Aβ) have been unsuccessful in clinical trials, other potential pathogenic factors such as brain energy metabolism (EM) have attracted increasing attention. Traditional Chinese medicines, including danggui-shaoyao-san (DSS), play a notable role in AD. However, it remains unclear whether DSS exerts therapeutic effects on AD through EM regulation. In this study, we conducted behavioural tests, Nissl staining, haematoxylin and eosin staining, and thioflavin S staining, in APP/PS1 mice to assess the pharmacodynamic effect of DSS on AD. Subsequently, we integrated the drug target network of herbal ingredients in DSS and evaluated their absorption, distribution, metabolism, excretion, and toxicity properties to identify the core ingredients. We used proteomic and metabolomic approaches to explore the potential mechanisms of action of DSS against AD. Consequently, we verified the mechanism underlying EM using qPCR, western blotting, and ELISA. In vivo experimental results revealed that DSS ameliorated cognitive impairment in APP/PS1 mice, attenuated neuronal apoptosis, and reduced Aβ burden. Furthermore, the drug-target network comprised 6,514 drug-target interactions involving 1,118 herbal ingredients and 218 AD genes, of which 253 were identified as the core ingredients in DSS. The proteomic results implied that DSS could act on EM to alleviate AD, and targeted energy metabolomics suggested that DSS regulated 47 metabolites associated with EM. Mechanistically, we found that DSS could regulate the GSK3β/PGC1α signalling pathway to improve brain glucose uptake and mitigate mitochondrial dysfunction and oxidative stress, ultimately promoting EM to treat AD. Our study is the first to integrate multi-omics approaches to reveal that DSS could regulate the GSK3β/PGC1α signalling pathway to exert therapeutic effects in AD through the promotion of EM, thereby providing new insights into the mechanism of action of DSS against AD.

Synthesizing network pharmacology, bioinformatics, and in vitro experimental verification to screen candidate targets of Salidroside for mitigating Alzheimer's disease.

Alzheimer's disease (AD) is a neurological disorder leading to cognitive deficits. Salidroside (Sal), a primary bioactive ingredient extracted from the roots of Rhodiola rosea L., has potent neuroprotective effects in AD. However, studies on potential targets for Sal-anchored AD are limited. In this study, we combined network pharmacology, bioinformatics, and experimental validation to identify potential targets of Sal treating AD. First, we screened 10 pyroptosis-related genes (PRGs) in Sal and AD using public databases. Then, we used Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis to explore the biological functions of the shared PRGs (Sal and AD). This finding exhibited that pathways linked to inflammation, like the nucleotide oligomerization domain (NOD)-like receptors signaling pathway, are important for Sal to help fight AD. The GeneMANIA functional results subsequently revealed an association between AD and the processes of inflammasome complex and inflammatory response. Additionally, nine hub genes were identified in the protein-protein interaction network of these shared PRGs. Subsequent analysis of the genes and phenotypes confirmed that these nine hub genes were directly correlated with AD. Subsequently, an in vitro AD model was created using rat adrenal pheochromocytoma cell line (PC12) cells induced by amyloid β-peptide (Aβ) 25-35 (20µM). Sal significantly reduced the pyroptosis caused by Aβ 25-35 in PC12 cells and decreased the expression levels of IL-1β, CASP1, IL-18, PYCARD, and NLRP3. Furthermore, molecular docking and molecular dynamics simulations confirmed that Sal could stably bind to NLRP3. Druggability analysis revealed that Sal had excellent druggability. These results demonstrated that Sal could alleviate AD by targeting IL-1β, CASP1, IL-18, PYCARD, and NLRP3 to regulate the NLRP3-mediated pyroptosis signaling pathway.

Efficacy and Mechanism of Schisandra chinensis Fructus Water Extract in Alzheimer's Disease: Insights from Network Pharmacology and Validation in an Amyloid-β Infused Animal Model.

Schisandra chinensis Fructus (SCF) is a traditional medicinal herb containing lignans that improves glucose metabolism by mitigating insulin resistance. We aimed to investigate the therapeutic potential and action mechanism of SCF for Alzheimer's disease (AD) using a network pharmacology analysis, followed by experimental validation in an AD rat model. The biological activities of SCF's bioactive compounds were assessed through a network pharmacology analysis. An AD rat model was generated by infusing amyloid-β peptide (Aβ) (25-35) into the hippocampus to induce Aβ accumulation. The AD rats were fed either 0.5% dextrin (AD-Con) or 0.5% SCF (AD-SCF group) in a high-fat diet for seven weeks. The rats in the normal/control group received an Aβ (35-25) infusion (no Aβ deposition) and were fed a control diet (Normal-C). Aβ deposition, memory function, inflammation, and glucose/lipid metabolism were evaluated. The network analysis revealed significant intersections between AD-related targets and bioactive SCF compounds, like gomisin A, schisandrin, and longikaurin A. Key AD genes prostaglandin-endoperoxide synthase-2 (PTGS2, cyclooxygenase-2) and acetylcholinesterase (AChE) were linked to SCF compounds. In the rats with AD induced by bilaterally infusing amyloid-β (25-35) into the hippocampus, the 0.5% SCF intake mitigated hippocampal amyloid-β deposition, neuroinflammation, memory deficits, and dysregulated glucose and lipid metabolism versus the AD controls. SCF reduced hippocampal AChE activity, inflammatory cytokine expression related to PTGS2, and malondialdehyde contents and preserved neuronal cell survival-related factors such as brain-derived neurotrophic factor and ciliary neurotrophic factor similar to normal rats. The neuroprotective effects validated the network analysis findings. SCF could be a potential AD therapeutic agent by activating the parasympathetic nervous system to reduce hippocampal oxidative stress and inflammation, warranting further clinical investigations of its efficacy.

IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA (Information Diffusion model for integrating gene Expression and EEG data in identifying Alzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and EEG data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that
IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10-13 Hz) frequency band yielded 8 AD associated genes out of the top 100 most frequently selected genes by our algorithm, with a p-value of
0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Hippocampal tau-induced GRIN3A deficiency in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by significant alterations in hippocampal function and structure, but the molecular mechanisms underlying the hippocampal region remain elusive. We integrated multiple transcriptome datasets including human or rat hippocampus (GSE173955, GSE129051, GSE84422) to identify candidate genes. Subsequent analyses including gene ontology analysis and protein-protein interaction mapping were performed to identify key genes and pathways. We found that glutamate ionotropic receptor NMDA-type subunit 3A (GRIN3A) and glutamate metabotropic receptor 8 (GRM8), which are related to the glutamatergic system, were the top two annotated genes and directly related to MAPT, which encodes a tau protein. Since there is no direct evidence of interaction between tauopathy and these genes in AD, further transcriptomic data(GSE125957, GSE56772) from tau transgenic mice and experimental validations through primary rat hippocampal neurons and induced pluripotent stem cell (iPSC)-derived brain organoids were performed. Interestingly, we identified that decreased NR3A (encoded by GRIN3A) and mGluR8 (encoded by GRM8) are correlated with tauopathy and loss of postsynaptic function in AD. Taken together, our results identified a novel tauopathy biomarker GRIN3A in AD. Furthermore, our findings suggest that an integrated approach combining public databases and diverse experimental validations can contribute to the advancement of precision medicine therapies.

The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer’s disease patients

Alzheimer’s Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.

A Drug Repositioning Approach Reveals Ergotamine May Be a Potential Drug for the Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that is the most common form of dementia in the elderly. The drugs currently used to treat AD only have limited effects and are not able to cure the disease. Drug repositioning has increasingly become a promising approach to find potential drugs for diseases like AD. To screen potential drug candidates for AD based on the relationship between risk genes of AD and drugs. We collected the risk genes of AD and retrieved the information of known drugs from DrugBank. Then, the AD-related genes and the targets of each drug were mapped to the human protein-protein interaction network (PPIN) to represent AD and the drugs on the network. The network distances between each drug and AD were calculated to screen the drugs proximal to AD-related genes on PPIN, and the screened drug candidates were further analyzed by molecular docking and molecular dynamics simulations. We compiled a list of 714 genes associated with AD. From 5,833 drugs used for human diseases, we identified 1,044 drugs that could be potentially used to treat AD. Then, amyloid-β (Aβ) protein, the key molecule involved in the pathogenesis of AD was selected as the target to further screen drugs that may inhibit Aβ aggregation by molecular docking. We found that ergotamine and RAF-265 could bind stably with Aβ. In further analysis by molecular dynamics simulations, both drugs exhibited reasonable stability. Our work indicated that ergotamine and RAF-265 may be potential candidates for treating AD.

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

Identification of neutrophil extracellular trap-related genes in Alzheimer's disease based on comprehensive bioinformatics analysis.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.

Developing diagnostic biomarkers for Alzheimer's disease based on histone lactylation-related gene

BackgroundResearch underscores the significant influence of histone lactylation pathways in the progression of Alzheimer's disease (AD), though the molecular mechanisms associated with histone lactylation-related genes (HLRGs) in AD are still insufficiently investigated. MethodsThis study employed datasets GSE85426 and GSE97760 to identify candidate genes by intersecting weighted gene co-expression network analysis (WGCNA) module genes with AD-control differentially expressed genes (DEGs). Subsequently, machine learning refined key genes, validated by receiver operating characteristic (ROC) curve performance. Gene-set enrichment analysis (GSEA) explored the molecular mechanisms of these diagnostic markers. Concurrently, the association between the diagnostic genes and both differential immune cells and immune responses was examined. Furthermore, a ceRNA and gene-drug network was developed. Finally, the expression of the selected genes was validated using brain tissues from AD model mice. ResultsThis study identified five genes (ARID5B, NSMCE4A, SESN1, THADA, and XPA) with significant diagnostic utility, primarily enriched in olfactory transduction and N-glycan biosynthesis pathways. Correlation analysis demonstrated a strong positive association between all diagnostic genes and naive B cells. The ceRNA regulatory network comprised 7 miRNAs, 2 mRNAs, and 25 lncRNAs. Additionally, 33 drugs targeting the diagnostic genes were predicted. Following expression validation through training and validation sets, three genes (ARID5B, SESN1, XPA) were ultimately confirmed as biomarkers for this study. RT-qPCR and Western blot analyses revealed upregulated expression of ARID5B, SESN1, and XPA in the cerebral tissue of AD model mice. ConclusionThree histone lactylation-linked genes (ARID5B, SESN1, XPA) were identified as potential AD biomarkers, indicating a strong association with disease progression.

Identification of cross-talk pathways and PANoptosis-related genes in periodontitis and Alzheimer's disease by bioinformatics analysis and machine learning.

Periodontitis (PD), a chronic inflammatory disease, is a serious threat to oral health and is one of the risk factors for Alzheimer's disease (AD). A growing body of evidence suggests that the two diseases are closely related. However, current studies have not provided a comprehensive understanding of the common genes and common mechanisms between PD and AD. This study aimed to screen the crosstalk genes of PD and AD and the potential relationship between cross-talk and PANoptosis-related genes. The relationship between core genes and immune cells will be analyzed to provide new targets for clinical treatment. The PD and AD datasets were downloaded from the GEO database and differential expression analysis was performed to obtain DEGs. Overlapping DEGs had cross-talk genes linking PD and OP, and PANoptosis-related genes were obtained from a literature review. Pearson coefficients were used to compute cross-talk and PANoptosis-related gene correlations in the PD and AD datasets. Cross-talk genes were obtained from the intersection of PD and AD-related genes, protein-protein interaction(PPI) networks were constructed and cross-talk genes were identified using the STRING database. The intersection of cross-talk and PANoptosis-related genes was defined as cross-talk-PANoptosis genes. Core genes were screened using ROC analysis and XGBoost. PPI subnetwork, gene-biological process, and gene-pathway networks were constructed based on the core genes. In addition, immune infiltration on the PD and AD datasets was analyzed using the CIBERSORT algorithm. 366 cross-talk genes were overlapping between PD DEGs and AD DEGs. The intersection of cross-talk genes with 109 PANoptosis-related genes was defined as cross-talk-PANoptosis genes. ROC and XGBoost showed that MLKL, DCN, IL1B, and IL18 were more accurate than the other cross-talk-PANoptosis genes in predicting the disease, as well as better in overall characterization. GO and KEGG analyses showed that the four core genes were involved in immunity and inflammation in the organism. Immune infiltration analysis showed that B cells naive, Plasma cells, and T cells gamma delta were significantly differentially expressed in patients with PD and AD compared with the normal group. Finally, 10 drugs associated with core genes were retrieved from the DGIDB database. This study reveals the joint mechanism between PD and AD associated with PANoptosis. Analyzing the four core genes and immune cells may provide new therapeutic directions for the pathogenesis of PD combined with AD.

Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts.

Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer's disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient. This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD. AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology. We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction. We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.

Bulk-RNA and single-nuclei RNA seq analyses reveal the role of lactate metabolism-related genes in Alzheimer's disease.

Dysfunctional lactate metabolism in the brain has been implicated in neuroinflammation, Aβ deposition, and cell disturbance, all of which play a significant role in the pathogenesis of Alzheimer's disease (AD). In this study, we aimed to investigate the lactate metabolism-related genes (LMRGs) in AD via an integrated bulk RNA and single-nuclei RNA sequencing (snRNA-seq) analysis, with a specific focus on microglia. We obtained 26 HC and 24 AD snRNA-seq samples originated from human prefrontal cortex in Gene Expression Omnibus (GEO) database and collected 873 LMRGs from three databases, namely MSigDB, The Human Protein Atlas and GeneCards. Bulk RNA was analyzed with LMRG characteristics in AD by using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the protein-protein interaction (PPI), CytoHubba-MCC, Support Vector Machine (SVM) algorithms analyses. Then we conducted the Receiver Operating Characteristic (ROC) curve, correlation, and connection network analyses for biomarkers. Their differential expression validation was performed using AlzData database. The single-nuclei RNA analysis of microglia was applied to identify hub genes and pathways using cell-cell communication analysis and high dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Support Vector Machine (SVM) algorithm showed an AUC of 0.967, a sensitivity of 93.30% and a specificity of 100.00%. Our analysis identified biomarkers with LMRG characteristics, namely INSR, CDKL1, and PNISR. ROC analysis revealed that each of these biomarkers exhibited excellent diagnostic potential, as evidenced by their respective area under the curve (AUC) values: INSR (AUC: 0.679), CDKL1 (AUC: 0.788), and PNISR (AUC: 0.724). Correlation analysis showed that biomarkers exhibited a positive correlation with each other. Connection network illustrated their shared biological processes: aging, phosphorylation, metabolic process, and apoptosis. Cell-cell communication analysis revealed that GALECTIN signaling pathway was exclusively expressed in AD microglia, and only LGALS9 exhibited significant overexpression. HdWGCNA identified FTH1 as a hub gene enriched in ferroptosis and mineral absorption pathways within microglia. The roles of INSR, CDKL1, PNISR, LGALS9, and FTH1 should be taken into account to enhance our understanding of lactate metabolism in the context of AD.

In Silico Analysis of Alzheimer's Disease Mechanism Through DNA Methylation and Gene Expression Data

Alzheimer's Disease (AD) is a debilitating disease impairing memory and thought process with currently no cure. In the current study, GEO2R, an integrated tool in the GEO database, was used to analyze DNA methylation and gene expression datasets associated with AD. Data from the BioGRID Database were used to create a PPI network of differentially methylation and expressed AD genes (DEMEGs). Cytoscape was used to image and analyze the PPI network topologically. The DAVID bioinformatics program was utilized to do enrichment analysis in order to uncover disease associations and signaling pathways. Furthermore, small molecules were predicted using Connectivity Map 2 (Cmap 2) as potential therapeutic agents that might be exploited as pharmacological targets for the study's DEMEGs. As a result, 502 mutual DEMEGs and several hubs that may be researched further as new biomarker candidates for AD such as SMURF1 and UBE2D2 were identified. The link between AD and the MAPK signaling pathway, as well as addiction and brain diseases such as ADHD and epilepsy has been established. Additionally, candidate small molecules that can be used as therapeutics such as flucloxacillin, butamben, and acetohexamide were proposed. This study integrated DNA methylation and gene expression data to further our knowledge of the AD disease mechanism.

Integrative transcriptome-proteome approach reveals key hypoxia-related features involved in the neuroprotective effects of Yang Xue oral liquid on Alzheimer's and Parkinson's disease.

Introduction: This study investigates the role of hypoxia-related genes in the neuroprotective efficacy of Yang Xue oral liquid (YXKFY) in Alzheimer's disease (AD) and Parkinson's disease (PD). Methods and results: Using differential expression and weighted gene co-expression network analysis (WGCNA), we identified 106 and 9 hypoxia-associated genes in AD and PD, respectively, that are implicated in the transcriptomic and proteomic profiles. An artificial intelligence-driven hypoxia signature (AIDHS), comprising 17 and 3 genes for AD and PD, was developed and validated across nine independent cohorts (n = 1713), integrating 10 machine learning algorithms and 113 algorithmic combinations. Significant associations were observed between AIDHS markers and immune cells in AD and PD, including naive CD4+ T cells, macrophages, and neutrophils. Interactions with miRNAs (hsa-miR-1, hsa-miR-124) and transcription factors (USF1) were also identified. Single-cell RNA sequencing (scRNA-seq) data highlighted distinct expression patterns of AIDHS genes in various cell types, such as high expression of TGM2 in endothelial cells, PDGFRB in endothelial and mesenchymal cells, and SYK in microglia. YXKFY treatment was shown to repair cellular damage and decrease reactive oxygen species (ROS) levels. Notably, genes with previously dysfunctional expression, including FKBPL, TGM2, PPIL1, BLVRB, and PDGFRB, exhibited significant recovery after YXKFY treatment, associated with riboflavin and lysicamine. Conclusion: The above genes are suggested to be central to hypoxia and neuroinflammation responses in AD and PD, and are potential key mediators of YXKFY's neuroprotective action.

Identification and experimental validation of m7G-related molecular subtypes, immune signature, and feature genes in Alzheimer's disease

BackgroundStudies has shown that N7-methylguanosine (m7G) modification plays a critical role in neurological diseases. However, the exact role and association of m7G with the immune microenvironment in Alzheimer's disease (AD) remain largely unknown and unexplored. MethodsThe study datasets comprised 667 AD samples and 503 control samples selected from eight datasets in the Gene Expression Omnibus database; m7G regulator genes were obtained from previous literature. The AD subtypes were identified by consensus clustering analysis according to m7G regulator genes. The clinical characteristics, immune infiltration, and biological functions of the AD subgroups were evaluated. A combination of different types of machine-learning algorithms were used for the identification of AD genes. We also assessed and validated the diagnostic performance of the identified genes via qRT-PCR, immunofluorescence, and immunohistochemical analyses. ResultsTwo AD distinct subgroups, namely cluster A and cluster B, were identified. Cluster A had poor pathological progression and immune infiltration, representing a high-risk subgroup for AD. The differentially expressed genes of cluster A were enriched in immune and synapse-related pathways, suggesting that these genes probably contribute to AD progression by regulating immune-related pathways. Additionally, five feature genes (AEBP1, CARTPT, AK5, NPTX2, and COPG2IT1) were identified, which were used to construct a nomogram model with good ability to predict AD. The animal experiment analyses further confirmed that these feature genes were associated with AD development. ConclusionTo the best of our knowledge, this is the first study to reveal close correlations among m7G RNA modification, the immune microenvironment, and the pathogenesis of AD. We also identified five feature genes associated with AD, further contributing to our understanding of the underlying mechanisms and potential therapeutic targets for AD.

Single-cell RNA sequencing and multiple bioinformatics methods to identify the biomarkers of ischemic stroke to alzheimer’s disease

Ranked as the world’s second leading cause of mortality, ischemic stroke (IS) imposes a significant global burden, marked by its substantial incidence and economic impact across nations and regions, chiefly attributed to its elevated disability rates. Alzheimer’s disease (AD) is linked to a heightened susceptibility to IS. However, few studies have been reported to explain the potential correlation between IS and AD. We investigated the relationship and relevant mechanisms between IS and AD using single-cell RNA sequencing and multiple bioinformatics approaches. By intersecting differentially expressed genes and WGCNA critical module genes, we obtained 15 IS related AD genes. According to the KEGG pathway analysis, the IS related AD disease genes were significantly enriched in the parkinson disease pathway, oxidative phosphorylation pathway, thermogenesis pathway, alzheimer disease pathway and neurodegeneration pathway. Finally, five genes associated with immunity (TXN, HINT1, TOMM7, COX7C, RPL17) were identified in IS related AD. Significantly, it was found that all hub genes were mainly highly expressed in epithelial cells, endothelial cells, monocytes and oligodendrocytes by single cell RNA sequencing. We believe these hub genes may regulate the immune pathway which may contribute to the development of IS related AD. In addition, these genes may be potential targets for the treatment of IS related AD.

Long-read RNA-seq demarcates cis- and trans-directed alternative RNA splicing.

Genetic regulation of alternative splicing constitutes an important link between genetic variation and disease. Nonetheless, RNA splicing is regulated by both cis-acting elements and trans-acting splicing factors. Determining splicing events that are directed primarily by the cis- or trans-acting mechanisms will greatly inform our understanding of the genetic basis of disease. Here, we show that long-read RNA-seq, combined with our new method isoLASER, enables a clear segregation of cis- and trans-directed splicing events for individual samples. The genetic linkage of splicing is largely individual-specific, in stark contrast to the tissue-specific pattern of splicing profiles. Analysis of long-read RNA-seq data from human and mouse revealed thousands of cis-directed splicing events susceptible to genetic regulation. We highlight such events in the HLA genes whose analysis was challenging with short-read data. We also highlight novel cis-directed splicing events in Alzheimer's disease-relevant genes such as MAPT and BIN1. Together, the clear demarcation of cis- and trans-directed splicing paves ways for future studies of the genetic basis of disease.

Synthesis of novel hybrids of 1,2,3-triazoles-hydrazone: targeting cholinesterases and Alzheimer's related genes.

Aim: A new series of 1,2,3-triazole-hydrazone derivatives were developed to evaluate their anti-Alzheimer's activity. Materials & methods: All compounds were screened toward cholinesterases via the modified Ellman's method. The toxicity assay on SH-SY5Y cells was performed using the MTT assay, and the expression levels of GSK-3α, GSK-3β, DYRK1 and CDK5 were assessed in the presence of compounds 6m and 6p.Results:6m and 6p; acting as mixed-type inhibitors, exhibited promising acetylcholinesterase and butyrylcholinesterase inhibitory activity, respectively. 6m demonstrated no toxicity under tested concentrations on the SH-SY5Y cells and positively impacted neurodegenerative pathways. Notably, 6m displayed a significant downregulation in mRNA levels of GSK-3α, GSK-3β and CDK5.Conclusion: The target compounds could be considered in developing anti-Alzheimer's diseaseagents.

Bioinformatics analysis of the potential receptor and therapeutic drugs for Alzheimer's disease with comorbid Parkinson's disease.

Now, there are no sensitive biomarkers for improving Alzheimer's disease (AD) and comorbid Parkinson's disease (PD). The aim of the present study was to analyze differentially expressed genes (DEGs) in brain tissue from AD and PD patients via bioinformatics analysis, as well as to explore precise diagnostic and therapeutic targets for AD and comorbid PD. GFE122063 and GSE7621 data sets from GEO in NCBI, were used to screen differentially expressed genes (DEGs) for AD and PD, and identify the intersected genes, respectively. Intersected genes were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, STRING site and Cytoscape were used to construct a protein-protein interaction (PPI) network, CytoNCA algorithm to analyze and evaluate centrality, Mcode plug-in to analyze module, and Cytohubba to screen key genes. Combined GO-KEGG enrichment analysis with Cytoscape algorithm to screen the key gene in AD complicated with PD. Then, the DEGs for AD and PD were imported into the Association Map (CMap) online platform to screen out the top 10 small molecule drugs, and using molecular docking techniques to evaluate the interactions between small molecule drugs and key genes receptors. In total, 231 upregulated genes and 300 downregulated genes were identified. GO analysis revealed that the DEGs were highly enriched in signal transduction, and KEGG analysis revealed that the DEGs were associated with the MAPK and PI3K-Akt signaling pathways. Epidermal growth factor receptor (EGFR) was identified as a potential receptor gene in AD and comorbid PD. EGFR was upregulated in both AD and PD, and the proteins that interact with EGFR were enriched in the Ras/Raf/MAPK and PI3K/Akt signaling pathways. Semagacestat was identified as a drug with therapeutic potential for treating AD complicated with PD. There was a high binding affinity between semagacestat and EGFRNTD, with seven hydrogen bonds and one hydrophobic bond. Semagacestat may improve the health of patients with AD complicated with PD through the regulation of the Ras/Raf/MAPK and PI3K/Akt signaling pathways by EGFR, providing evidence supporting the structural modification of semagacestat to develop a more effective drug for treating AD complicated with PD.