Alzheimer's Disease Genes Research Articles

Identification of disulfidptosis-related genes and subgroups in Alzheimer's disease.

Alzheimer's disease (AD), a common neurological disorder, has no effective treatment due to its complex pathogenesis. Disulfidptosis, a newly discovered type of cell death, seems to be closely related to the occurrence of various diseases. In this study, through bioinformatics analysis, the expression and function of disulfidptosis-related genes (DRGs) in Alzheimer's disease were explored. Differential analysis was performed on the gene expression matrix of AD, and the intersection of differentially expressed genes and disulfidptosis-related genes in AD was obtained. Hub genes were further screened using multiple machine learning methods, and a predictive model was constructed. Finally, 97 AD samples were divided into two subgroups based on hub genes. In this study, a total of 22 overlapping genes were identified, and 7 hub genes were further obtained through machine learning, including MYH9, IQGAP1, ACTN4, DSTN, ACTB, MYL6, and GYS1. Furthermore, the diagnostic capability was validated using external datasets and clinical samples. Based on these genes, a predictive model was constructed, with a large area under the curve (AUC = 0.8847), and the AUCs of the two external validation datasets were also higher than 0.7, indicating the high accuracy of the predictive model. Using unsupervised clustering based on hub genes, 97 AD samples were divided into Cluster1 (n = 24) and Cluster2 (n = 73), with most hub genes expressed at higher levels in Cluster2. Immune infiltration analysis revealed that Cluster2 had a higher level of immune infiltration and immune scores. A close association between disulfidptosis and Alzheimer's disease was discovered in this study, and a predictive model was established to assess the risk of disulfidptosis subtype in AD patients. This study provides new perspectives for exploring biomarkers and potential therapeutic targets for Alzheimer's disease.

Klotho Gene Expression Is Decreased in Peripheral Blood Mononuclear Cells in Patients with Alzheimer's Disease and Frontotemporal Dementia.

The longevity gene Klotho (KL) was recently associated with neurodegenerative diseases including Alzheimer's disease (AD). Its role in the brain has not been completely elucidated, although evidence suggests that KL-VS heterozygosity is associated with a reduced risk of AD in Apolipoprotein E ɛ4 carriers. Conversely, no data about genetic association with frontotemporal dementia (FTD) are available so far. To investigate the involvement of KL in AD and FTD by the determination of the genetic frequency of KL-VS variant and the expression analysis of KL gene. A population consisting of 438 patients and 240 age-matched controls was enrolled for the study. KL-VS and APOE genotypes were assessed by allelic discrimination through a QuantStudio 12K system. KL gene expression analysis was performed in a restricted cohort of patients consisting of 43 AD patients, 41 FTD patients and 19 controls. KL gene expression was assessed in peripheral blood mononuclear cells with specific TaqMan assay. Statistical analysis was performed using GraphPad 9 Prims software. KL-VS frequency was comparable to the ones found in literature and no differences were found in both allelic and genotypic frequencies between patients and controls were found. Conversely, KL expression levels were significantly lower in AD and FTD patients compared with controls (mean fold regulation - 4.286 and - 6.561 versus controls in AD and FTD, respectively, p = 0.0037). This is the first study investigating KL in FTD. We showed a decreased expression of the gene in AD and FTD, independent of the genotype, suggesting a role of Klotho in common steps during neurodegeneration.

Bioinformatics-Based Analysis of Circadian Rhythm Regulation Mechanisms in Alzheimer's Disease.

There is a close association between Alzheimer's disease (AD) and circadian rhythms, and neuroinflammatory-related pathways are associated with both interactions. To reveal the relationship between circadian rhythm (CR) and AD at the level of genes, pathways, and molecular functions through bioinformatics. We analyzed the differential genes between AD and control groups in GSE122063 and found the important gene modules; obtained CR-related genes from GeenCard database; used Venn 2.1 database to obtain the intersection of genes of AD important modules with CR-related genes; and used STRING database and Cytoscape 3.7.1 to construct the gene protein-protein interaction network. The MCODE plugin was used to screen pivotal genes and analyze their differential expression. We trranslated with www.DeepL.com/Translator (free version) to obtain transcriptional regulatory relationships from the TRRUST database and construct a hub gene-transcription factor relationship network. A total of 42 common genes were screened from AD and CR genes, mainly involving signaling pathways such as neuroactive ligand-receptor interactions. A total of 10 pivotal genes were screened from the common genes of CR and AD, which were statistically significant in the comparison of AD and control groups (p < 0.001), and ROC analysis showed that all these pivotal genes had good diagnostic significance. A total of 36 TFs of pivotal genes were obtained. We identified AD- and CR-related signaling pathways and 10 hub genes and found strong associations between these related genes and biological processes such as inflammation.

Acupuncture Therapy on Dementia: Explained with an Integrated Analysis on Therapeutic Targets and Associated Mechanisms.

Dementia, mainly Alzheimer's disease (AD) and vascular dementia (VaD), remains a global health challenge. Previous studies have demonstrated the benefits of acupuncture therapy (AT) in improving dementia. Nevertheless, the therapeutic targets and integrated biological mechanisms involved remain ambiguous. To identify therapeutic targets and biological mechanisms of AT in treating dementia by integrated analysis strategy. By the identification of differentially expressed genes (DEGs) of AD, VaD, and molecular targets of AT active components, the acupuncture therapeutic targets associated with the biological response to AD and VaD were extracted. Therapeutic targets-based functional enrichment analysis was conducted, and multiple networks were constructed. AT-therapeutic crucial targets were captured by weighted gene co-expression network analysis (WGCNA). The interactions between crucial targets with AT active components were verified by molecular docking. Our results demonstrated that 132 and 76 acupuncture therapeutic targets were associated with AD and VaD. AT-therapeutic crucial targets including 58 for AD and 24 for VaD were captured by WGCNA, with 11 in shared, including NMU, GRP, TAC1, ADRA1D, and SST. In addition, 35 and 14 signaling pathways were significantly enriched by functional enrichment analysis, with 6 mutual pathways including neuroactive ligand-receptor interaction, GABAergic synapse, calcium signaling pathway, cAMP signaling pathway, chemokine signaling pathway, and inflammatory mediator regulation of TRP channels. The improvement of AD and VaD by AT was associated with modulation of synaptic function, immunity, inflammation, and apoptosis. Our study clarified the therapeutic targets of AT on dementia, providing valuable clues for complementing and combining pharmacotherapy.

Identification of common core ion channel genes in epilepsy and Alzheimer's disease.

Although available literature indicates that the incidence of dementia in the epilepsy population and the risk of seizures in the Alzheimer's disease (AD) population are high, the specific genetic risk factors and the interaction mechanism are unclear, rendering rational genetic interpretation rather challenging. Our work aims to identify the common core ion channel genes in epilepsy and AD. In this study, we first integrated gene expression omnibus datasets (GSE48350 and GSE6834) on AD and epilepsy to identify differentially expressed genes (DEGs), performing Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. The related protein-protein interaction (PPI) network was constructed for DEGs, and the hub gene was evaluated. A total of 2800 and 35 genes were identified in GSE48350 and GSE6834, and 12 DEGs were significantly differentially expressed between the datasets. KEGG pathway analysis showed that DEGs were primarily enriched in glutamatergic synapse and dopaminergic synapse pathways. SCN2A, GRIA1, and KCNJ9 were the hub genes with high connectivity. The findings suggest that the three genes, SCN2A, GRIA1, and KCNJ9, may serve as potential targets for treating AD comorbid with epilepsy.

Alteration of Cognitive Function in Aging and Alzheimer's Disease Mice Is Related to Dysfunction of the Neuroimmune System.

Both Alzheimer's disease (AD) and aging have aging-related cognitive dysfunction with a high incidence. These neurological diseases cause serious cognitive problems in patients' daily life. But the cognitive dysfunction mechanism in-depth of aging is far less known than that of AD. To reveal the different mechanisms of AD and aging-related cognitive dysfunction, we compared the mechanisms of aging and AD through analysis of differentially expressed genes. Mice were divided into four groups (3-month C57BL, 16-month C57BL, 3-month 3xTg AD mice, and 16-month 3xTg AD mice) according to genotype and age. The Morris water maze was employed to investigate the spatial cognition of mice. Differential expressions of genes of AD and aging were analyzed through RNA sequencing and GO, KEGG, Reactome analysis, and the dynamic change trend analysis. Microglia was stained with immunofluorescence and its numbers were counted for analysis. The cognitive function of elderly mice were worse through testing with the Morris water maze. The cognitive function of 16-month 3xTg AD mice were worse than 16-month C57BL mice. The alteration tendencies of DE genes were uncovered, and microglia numbers increased during aging and AD progression through immunofluorescence. These results suggest that immune-related pathways might play a critical role in aging and AD-related cognitive dysfunction. Our research will help to provide some new potential targets for treating cognitive dysfunction in aging and AD.

MicroRNA networks linked with BRCA1/2, PTEN, and common genes for Alzheimer's disease and breast cancer share highly enriched pathways that may unravel targets for the AD/BC comorbidity treatment

MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer's disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir-6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes pathology.

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics.

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.

ABCA7-Associated Clinical Features and Molecular Mechanisms in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common type of neurodegenerative disease and its pathogenesis is still unclear. Genetic factors are thought to account for a large proportion of the overall AD phenotypes. ATP-binding cassette transporter A7 (ABCA7) is one of the most important risk gene for AD. Multiple forms of ABCA7 variants significantly increase the risk of AD, such as single-nucleotide polymorphisms, premature termination codon variants, missense variants, variable number tandem repeat, mutations, and alternative splicing. AD patients with ABCA7 variants usually exhibit typical clinical and pathological features of traditional AD with a wide age of onset range. ABCA7 variants can alter ABCA7 protein expression levels and protein structure to affect protein functions such as abnormal lipid metabolism, amyloid precursor protein (APP) processing, and immune cell function. Specifically, ABCA7 deficiency can cause neuronal apoptosis by inducing endoplasmic reticulum stress through the PERK/eIF2α pathway. Second, ABCA7 deficiency can increase Aβ production by upregulating the SREBP2/BACE1 pathway and promoting APP endocytosis. In addition, the ability of microglia to phagocytose and degrade Aβ is destroyed by ABCA7 deficiency, leading to reduced clearance of Aβ. Finally, disturbance of lipid metabolism may also be an important method by which ABCA7 variants influence the incidence rate of AD. In the future, more attention should be given to different ABCA7 variants and ABCA7 targeted therapies for AD.

Genetic insights into immune mechanisms of Alzheimer's and Parkinson's disease.

Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer's disease (AD) and Parkinson's disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders.

Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer's disease.

Cerebrovascular dysregulation is a hallmark of Alzheimer's disease (AD), but the changes that occur in specific cell types have not been fully characterized. Here, we profile single-nucleus transcriptomes in the human cerebrovasculature in six brain regions from 220 individuals with AD and 208 age-matched controls. We annotate 22,514 cerebrovascular cells, including 11 subtypes of endothelial, pericyte, smooth muscle, perivascular fibroblast and ependymal cells. We identify 2,676 differentially expressed genes in AD, including downregulation of PDGFRB in pericytes, and of ABCB1 and ATP10A in endothelial cells, and validate the downregulation of SLC6A1 and upregulation of APOD, INSR and COL4A1 in postmortem AD brain tissues. We detect vasculature, glial and neuronal coexpressed gene modules, suggesting coordinated neurovascular unit dysregulation in AD. Integration with AD genetics reveals 125 AD differentially expressed genes directly linked to AD-associated genetic variants. Lastly, we show that APOE4 genotype-associated differences are significantly enriched among AD-associated genes in capillary and venule endothelial cells, as well as subsets of pericytes and fibroblasts.

Genome‐Wide Association Study of Dementia in a Community‐Based Sample of Older Subjects

AbstractBackgroundDementia broadly describes conditions that affect a person’s memory, cognition, and behavior with Alzheimer’s disease (AD) being the most common cause. AD affects millions of people in the U.S. and around the world causing a significant financial burden and placing significant stress on caretakers. AD is a complex disease influenced by both the environment and genetics, however, much of the genetic component remains unaccounted for. The purpose of this work was to use genome‐wide association analyses to detect genetic associations with incident AD in a sample of older adults age 75 and above.MethodWe performed a genome‐wide association study (GWAS) on the genome‐wide genotyped and imputed data (14,113,004 variants) on the Gingko Evaluation of Memory (GEM) study sample consisting of 424 incident dementia cases (mean age ± sd = 84.64±3.95) and 2,206 non‐demented subjects (mean age ± sd = 84.55±3.23).ResultThe established association of APOE*4 with AD was confirmed in this community‐based sample of older subjects (OR = 2.22; P = 9.36E‐14). In addition, a genome‐wide significant novel locus on chromosome 12 was observed near FAR2 and CCDC91 genes (OR = 3.31; P = 1.66E‐08). Sex‐stratified analyses showed a stronger association of APOE*4 with AD in females (P = 1.74E‐10) than in males (P = 2.43E‐05). In males, a novel SNP on chromosome 1 near LOC729987 and SNX7 genes reached genome‐wide significance (OR = 4.51; P = 3.72E‐08). Of the known AD genes, SNPs near or at TREM2, NME8/EPDR1, MS4A6A/MS4A4E, PICALM, APH1B, and PLCG2 showed nominal significance.ConclusionThe use of community‐based samples of older individuals and incident dementia as a phenotype may be a helpful approach for the identification of novel genes for AD, which may not be detected in standard case‐control studies. Replication of these signals and further study of these regions and genes will help to provide a clearer picture for their role in AD.

Network pharmacology-based prediction of efficacy and mechanism of Chongmyunggongjin-dan acting on Alzheimer’s disease

Objectives: Network pharmacology is a method of constructing and analyzing a drug-compound-target network to predict potential efficacy and mechanisms related to drug targets. In that large-scale analysis can be performed in a short time, it is considered a suitable tool to explore the function and role of herbal medicine. Thus, we investigated the potential functions and pathways of Chongmyunggongjin-dan (CMGJD) on Alzheimer’s disease (AD) via network pharmacology analysis.Methods: Using public databases and PubChem database, compounds of CMGJD and their target genes were collected. The putative target genes of CMGJD and known target genes of AD were compared and found the correlation. Then, the network was constructed using Cytoscape 3.9.1. and functional enrichment analysis was conducted based on the Gene Ontology (GO) Biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways to predict the mechanisms.Results: The result showed that total 104 compounds and 1157 related genes were gathered from CMGJD. The network consisted of 1157nodes and 10034 edges. 859 genes were interacted with AD gene set, suggesting that the effects of CMGJD are closely related to AD. Target genes of CMGJD are considerably associated with various pathways including ‘Positive regulation of chemokine production’, ‘Cellular response to toxic substance’, ‘Arachidonic acid metabolic process’, ‘PI3K-Akt signaling pathway’, ‘Metabolic pathways’, ‘IL-17 signaling pathway’ and ‘Neuroactive ligand-receptor interaction’.Conclusion: Through a network pharmacological method, CMGJD was predicted to have high relevance with AD by regulating inflammation. This study could be used as a basis for effects of CMGJD on AD.

Matrix disequilibrium in Alzheimer's disease and conditions that increase Alzheimer's disease risk.

Alzheimer's Disease (AD) and related dementias are a leading cause of death globally and are predicted to increase in prevalence. Despite this expected increase in the prevalence of AD, we have yet to elucidate the causality of the neurodegeneration observed in AD and we lack effective therapeutics to combat the progressive neuronal loss. Throughout the past 30 years, several non-mutually exclusive hypotheses have arisen to explain the causative pathologies in AD: amyloid cascade, hyper-phosphorylated tau accumulation, cholinergic loss, chronic neuroinflammation, oxidative stress, and mitochondrial and cerebrovascular dysfunction. Published studies in this field have also focused on changes in neuronal extracellular matrix (ECM), which is critical to synaptic formation, function, and stability. Two of the greatest non-modifiable risk factors for development of AD (aside from autosomal dominant familial AD gene mutations) are aging and APOE status, and two of the greatest modifiable risk factors for AD and related dementias are untreated major depressive disorder (MDD) and obesity. Indeed, the risk of developing AD doubles for every 5 years after ≥ 65, and the APOE4 allele increases AD risk with the greatest risk in homozygous APOE4 carriers. In this review, we will describe mechanisms by which excess ECM accumulation may contribute to AD pathology and discuss pathological ECM alterations that occur in AD as well as conditions that increase the AD risk. We will discuss the relationship of AD risk factors to chronic central nervous system and peripheral inflammation and detail ECM changes that may follow. In addition, we will discuss recent data our lab has obtained on ECM components and effectors in APOE4/4 and APOE3/3 expressing murine brain lysates, as well as human cerebrospinal fluid (CSF) samples from APOE3 and APOE4 expressing AD individuals. We will describe the principal molecules that function in ECM turnover as well as abnormalities in these molecular systems that have been observed in AD. Finally, we will communicate therapeutic interventions that have the potential to modulate ECM deposition and turnover in vivo.

Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning.

Alzheimer's disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer’s disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10–311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10–09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10–10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10–09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10–08, MAF = 0.006, sex-interaction P = 9.8 × 10–07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10–08, MAF = 0.004, sex-interaction P = 1.3 × 10–03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

APP in the Neuromuscular Junction for the Development of Sarcopenia and Alzheimer's Disease.

Sarcopenia, an illness condition usually characterized by a loss of skeletal muscle mass and muscle strength or function, is often associated with neurodegenerative diseases, such as Alzheimer's disease (AD), a common type of dementia, leading to memory loss and other cognitive impairment. However, the underlying mechanisms for their associations and relationships are less well understood. The App, a Mendelian gene for early-onset AD, encodes amyloid precursor protein (APP), a transmembrane protein enriched at both the neuromuscular junction (NMJ) and synapses in the central nervous system (CNS). Here, in this review, we highlight APP and its family members' physiological functions and Swedish mutant APP (APPswe)'s pathological roles in muscles and NMJ. Understanding APP's pathophysiological functions in muscles and NMJ is likely to uncover insights not only into neuromuscular diseases but also AD. We summarize key findings from the burgeoning literature, which may open new avenues to investigate the link between muscle cells and brain cells in the development and progression of AD and sarcopenia.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.

Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex. In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced. The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019). The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

Integrative Genomic Analysis of m6a-SNPs Identifies Potential Functional Variants Associated with Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects 35 million people worldwide. However, no potential therapeutics currently are available for AD because of the multiple factors involved in it, such as regulatory factors with their candidate genes, factors associated with the expression levels of its corresponding genes, and many others. To date, 29 novel loci from GWAS have been reported for AD by the Psychiatric Genomics Consortium (PGC2). Nevertheless, the main challenge of the post-GWAS era, namely to detect significant variants of the target disease, has not been conducted for AD. N6-methyladenosine (m6a) is reported as the most prevalent mRNA modification that exists in eukaryotes and that influences mRNA nuclear export, translation, splicing, and the stability of mRNA. Furthermore, studies have also reported m6a's association with neurogenesis and brain development. We carried out an integrative genomic analysis of AD variants from GWAS and m6a-SNPs from m6AVAR to identify the effects of m6a-SNPs on AD and identified the significant variants using the statistically significance value (p-value <0.05). The cis-regularity variants with their corresponding genes and their influence on gene expression in the gene expression profiles of AD patients were determined, and showed 1458 potential m6a-SNPs (based on p-value <0.05) associated with AD. eQTL analysis showed that 258 m6a-SNPs had cis-eQTL signals that overlapped with six significant differentially expressed genes based on p-value <0.05 in two datasets of AD gene expression profiles. A follow-up study to elucidate the impact of our identified m6a-SNPs in the experimental study would validate our findings for AD, which would contribute to the etiology of AD.

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.