Alzheimer's Disease Samples Research Articles

HSPA1A, HSPA2, and HSPA8 Are Potential Molecular Biomarkers for Prognosis among HSP70 Family in Alzheimer's Disease.

Alzheimer's disease (AD) is a chronic neurodegenerative disease, which leads to impairment of cognition and memory. The heat shock protein 70 (HSP70) family plays an important role in the pathogenesis of AD. It is known to regulate protein misfolding in a variety of diseases, including inhibition of Aβ aggregation and NFT formation in AD. As yet, the diagnostic molecular markers of AD remain unclear. Herein, we sought to investigate molecular markers of HSP70 family that can affect diagnosis and treatment in AD through computational analysis. In this study, the intersection between HSP70 family members and immune molecules was taken to screen immune-related HSP70 family genes. Based on the datasets from the NCBI-Gene Expression Omnibus (GEO) database, we found that the expression levels of HSPA1A and HSPA2 were significantly increased in AD samples, while HSPA8 significantly decreased. Surprisingly, the combination of the 3 hub genes had a good diagnosis of AD via receiver operating characteristic curve (ROC). Moreover, the clinical value of the 3 hub genes was further assessed by the Spearman correlation analysis with AD-related genes, β-secretase activity, and γ-secretase activity. In terms of immune cell infiltration, we showed that the distribution of seven immune cell types (macrophages M2, neutrophils, T cells CD4 memory activated, macrophages M0, NK cells activated, plasma cells, and T cells follicular helper) was associated with the occurrence of AD by CIBERSORT. Furthermore, our data suggested that EP300, MYC, TP53, JUN, CREBBP, and ESR1 might be key transcription factors (TFs) for the 3 hub genes. In general, these findings suggest that HSPA1A, HSPA2, and HSPA8 are potential molecular biomarkers for prognosis among HSP70 family in AD, and it provides a new perspective on diagnostic and therapeutic targets for AD.

Weakly activated core neuroinflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease.

ObjectivesNeuroinflammation signaling has been identified as an important hallmark of Alzheimer’s disease (AD) in addition to amyloid β plaques (Aβ) and neurofibrillary tangles (NFTs). However, the molecular mechanisms and biological processes of neuroinflammation remain unclear and have not well delineated using transcriptomics data available. Our objectives are to uncover the core neuroinflammation signaling pathways in AD using integrative network analysis on the transcriptomics data.Materials and methodsFrom a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), we developed integrative and systems biology network analysis to uncover potential core neuroinflammation signaling targets and pathways in AD using the two large-scale transcriptomics datasets, i.e., Mayo Clinic (77 controls and 81 AD samples) and ROSMAP (97 controls and 260 AD samples).ResultsOur analysis identified interesting core neuroinflammation signaling pathways, which are not systematically reported in the previous studies of AD. Specifically, we identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of the genes in the virus infection, immune response, and x-core signaling pathways are associated with inflammation molecular functions. The x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo, and TNF, which indicated the core neuroinflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia and leading to the chronic neurodegeneration. It is interesting to investigate the detailed signaling cascades of these weakly activated neuroinflammation signaling pathways causing neurodegeneration in a chronic process, and consequently uncover novel therapeutic targets for effective AD treatment and prevention.ConclusionsThe potential core neuroinflammation and associated signaling targets and pathways were identified using integrative network analysis on two large-scale transcriptomics datasets of AD.

Distinct sex-specific DNA methylation differences in Alzheimer’s disease

BackgroundSex is increasingly recognized as a significant factor contributing to the biological and clinical heterogeneity in AD. There is also growing evidence for the prominent role of DNA methylation (DNAm) in Alzheimer’s disease (AD).MethodsWe studied sex-specific DNA methylation differences in the blood samples of AD subjects compared to cognitively normal subjects, by performing sex-specific meta-analyses of two large blood-based epigenome-wide association studies (ADNI and AIBL), which included DNA methylation data for a total of 1284 whole blood samples (632 females and 652 males). Within each dataset, we used two complementary analytical strategies, a sex-stratified analysis that examined methylation to AD associations in male and female samples separately, and a methylation-by-sex interaction analysis that compared the magnitude of these associations between different sexes. After adjusting for age, estimated immune cell type proportions, batch effects, and correcting for inflation, the inverse-variance fixed-effects meta-analysis model was used to identify the most consistent DNAm differences across datasets. In addition, we also evaluated the performance of the sex-specific methylation-based risk prediction models for AD diagnosis using an independent external dataset.ResultsIn the sex-stratified analysis, we identified 2 CpGs, mapped to the PRRC2A and RPS8 genes, significantly associated with AD in females at a 5% false discovery rate, and an additional 25 significant CpGs (21 in females, 4 in males) at P-value < 1×10−5. In methylation-by-sex interaction analysis, we identified 5 significant CpGs at P-value < 10−5. Out-of-sample validations using the AddNeuroMed dataset showed in females, the best logistic prediction model included age, estimated immune cell-type proportions, and methylation risk scores (MRS) computed from 9 of the 23 CpGs identified in AD vs. CN analysis that are also available in AddNeuroMed dataset (AUC = 0.74, 95% CI: 0.65–0.83). In males, the best logistic prediction model included only age and MRS computed from 2 of the 5 CpGs identified in methylation-by-sex interaction analysis that are also available in the AddNeuroMed dataset (AUC = 0.70, 95% CI: 0.56–0.82).ConclusionsOverall, our results show that the DNA methylation differences in AD are largely distinct between males and females. Our best-performing sex-specific methylation-based prediction model in females performed better than that for males and additionally included estimated cell-type proportions. The significant discriminatory classification of AD samples with our methylation-based prediction models demonstrates that sex-specific DNA methylation could be a predictive biomarker for AD. As sex is a strong factor underlying phenotypic variability in AD, the results of our study are particularly relevant for a better understanding of the epigenetic architecture that underlie AD and for promoting precision medicine in AD.

Association of B cell profile and receptor repertoire with the progression of Alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent type of dementia. Reports have revealed that the peripheral immune system is linked to neuropathology; however, little is known about the contribution of B lymphocytes in AD. For this longitudinal study, 133 participants are included at baseline and second-year follow-up. Also, we analyze B cell receptor (BCR) repertoire data generated from a public dataset of three normal and 10 AD samples and perform BCR repertoire profiling and pairwise sharing analysis. As a result, longitudinal increase in B lymphocytes is associated with increased cerebral amyloid deposition and hyperactivates induced pluripotent stem cell-derived microglia with loss-of-function for beta-amyloid clearance. Patients with AD share similar class-switched BCR sequences with identical isotypes, despite the high somatic hypermutation rate. Thus, BCR repertoire profiling can lead to the development of individualized immune-based therapeutics and treatment. We provide evidence of both quantitative and qualitative changes in B lymphocytes during AD pathogenesis.

Identification of 5 Potential Predictive Biomarkers for Alzheimer's Disease by Integrating the Unified Test for Molecular Signatures and Weighted Gene Coexpression Network Analysis.

Previous transcriptome-wide association study (TWAS) has documented 21 genes associated with Alzheimer's disease (AD) risk, but the predictive biomarkers remain unexplored. TWAS leveraging the unified test for molecular signatures (UTMOST) was performed in 75,000 cases and 420,000 controls with 10 brain tissue gene expression references. Weighted gene coexpression network analysis (WGCNA) was conducted in GSE5281 and GSE48350 data sets containing 167 AD samples and 247 controls. Random forest (RF) analysis was applied to screen the potential predictive biomarkers based on overlapping genes identified by TWAS and WGCNA, followed by comprehensive bioinformatic analyses with differential gene expression, functional enrichment, and correlation with immune cells. A nomogram was established to verify the predictive power of the identified biomarkers. TWAS revealed 78 candidate genes (p < 2.89 × 10-6). In WGCNA turquoise module, 3 718 AD-related genes were screened. RF identified 5 predictive biomarkers (FAM71E1, DDB2, AP4M1, GPR4, DOC2A), which are enriched in the global genome nucleotide excision repair pathway and associated with immune cell designations "Natural.killer.T.cell," "Memory.B.cell," "T.follicular.helper.cell," "Neutrophil," and "MDSC." The nomogram based on the 5 markers showed a high predictive power. Five potential predictive biomarkers for AD were identified, providing new insights into the pathogenesis and etiology of AD.

Genetic variant rs11136000 upregulates clusterin expression and reduces Alzheimer's disease risk.

Clusterin (CLU) is an extracellular chaperone involved in reducing amyloid beta (Aβ) toxicity and aggregation. Although previous genome-wide association studies (GWAS) have reported a potential protective effect of CLU on Alzheimer’s disease (AD) patients, how intron-located rs11136000 (CLU) affects AD risk by regulating CLU expression remains unknown. In this study, we integrated multiple omics data to construct the regulated pathway of rs11136000-CLU-AD. In step 1, we investigated the effects of variant rs11136000 on AD risk with different genders and diagnostic methods using GWAS summary statistics for AD from International Genomics of Alzheimer’s Project (IGAP) and UK Biobank. In step 2, we assessed the regulation of rs11136000 on CLU expression in AD brain samples from Mayo clinic and controls from Genotype-Tissue Expression (GTEx). In step 3, we investigated the differential gene/protein expression of CLU in AD and controls from four large cohorts. The results showed that rs11136000 T allele reduced AD risk in either clinically diagnosed or proxy AD patients. By using expression quantitative trait loci (eQTL) analysis, rs11136000 variant downregulated CLU expression in 13 normal brain tissues, but upregulated CLU expression in cerebellum and temporal cortex of AD samples. Importantly, CLU was significantly differentially expressed in temporal cortex, dorsolateral prefrontal cortex and anterior prefrontal cortex of AD patients compared with normal controls. Together, rs11136000 may reduce AD risk by regulating CLU expression, which may provide important information about the biological mechanism of rs9848497 in AD progress.

Induction of A Disintegrin and Metalloproteinase with Thrombospondin motifs 1 by a rare variant or cognitive activities reduces hippocampal amyloid-β and consequent Alzheimer’s disease risk

Amyloid-β (Aβ) derived from amyloid precursor protein (APP) hydrolysis is acknowledged as the predominant hallmark of Alzheimer’s disease (AD) that especially correlates to genetics and daily activities. In 2019, meta-analysis of AD has discovered five new risk loci among which A Disintegrin and Metalloproteinase with Thrombospondin motifs 1 (ADAMTS1) has been further suggested in 2021 and 2022. To verify the association, we re-sequenced ADAMTS1 of clinical AD samples and subsequently identified a novel rare variant c.–2067A > C with watchable relevance (whereas the P-value was not significant after adjustment). Dual-luciferase assay showed that the variant sharply stimulated ADAMTS1 expression. In addition, ADAMTS1 was also clearly induced by pentylenetetrazol-ignited neuronal activity and enriched environment (EE). Inspired by the above findings, we investigated ADAMTS1’s role in APP metabolism in vitro and in vivo. Results showed that ADAMTS1 participated in APP hydrolysis and consequently decreased Aβ generation through inhibiting β-secretase-mediated cleavage. In addition, we also verified that the hippocampal amyloid load of AD mouse model was alleviated by the introduction of ADAMTS1, and thus spatial cognition was restored as well. This study revealed the contribution of ADAMTS1 to the connection of genetic and acquired factors with APP metabolism, and its potential in reducing hippocampal amyloid and consequent risk of AD.

Synaptosome microRNAs regulate synapse functions in Alzheimer’s disease

MicroRNAs (miRNAs) are found in nerve terminals, synaptic vesicles, and synaptosomes, but it is unclear whether synaptic and cytosolic miRNA populations differ in Alzheimer’s disease (AD) or if synaptosomal miRNAs affect AD synapse activity. To address these questions, we generated synaptosomes and cytosolic fractions from postmortem brains of AD and unaffected control (UC) samples and analyzed them using a global Affymetrix miRNAs microarray platform. A group of miRNAs significantly differed (P < 0.0001) with high fold changes variance (+/− >200-fold) in their expressions in different comparisons: (1) UC synaptosome vs UC cytosol, (2) AD synaptosomes vs AD cytosol, (3) AD cytosol vs UC cytosol, and (4) AD synaptosomes vs UC synaptosomes. MiRNAs data analysis revealed that some potential miRNAs were consistently different across sample groups. These differentially expressed miRNAs were further validated using AD postmortem brains, brains of APP transgenic (Tg2576), Tau transgenic (P301L), and wild-type mice. The miR-501-3p, miR-502-3p, and miR-877-5p were identified as potential synaptosomal miRNAs upregulated with disease progression based on AD Braak stages. Gene Ontology Enrichment and Ingenuity Pathway Analysis of synaptosomal miRNAs showed the involvement of miRNAs in nervous system development, cell junction organization, synapse assembly formation, and function of GABAergic synapse. This is the first description of synaptic versus cytosolic miRNAs in AD and their significance in synapse function.

Identification of diagnostic signatures associated with immune infiltration in Alzheimer's disease by integrating bioinformatic analysis and machine-learning strategies.

ObjectiveAs a chronic neurodegenerative disorder, Alzheimer’s disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology.MethodsThe expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD.ResultsA total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4+ T memory cells, resting mast cells, memory B cells, and resting/activated dendritic cells may participate in the development of AD. Additionally, all diagnostic signatures presented different degrees of correlation with different infiltrating immune cells.ConclusionMAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.

Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease.

Alzheimer's disease (AD), the most common neurodegenerative disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. Meanwhile, abnormalities in iron metabolism have been demonstrated in patients and mouse models with AD. Therefore, this study sought to find hub genes based on iron metabolism that can influence the diagnosis and treatment of AD. First, gene expression profiles were downloaded from the GEO database, including non-demented (ND) controls and AD samples. Fourteen iron metabolism-related gene sets were downloaded from the MSigDB database, yielding 520 iron metabolism-related genes. The final nine hub genes associated with iron metabolism and AD were obtained by differential analysis and WGCNA in brain tissue samples from GSE132903. GO analysis revealed that these genes were mainly involved in two major biological processes, autophagy and iron metabolism. Through stepwise regression and logistic regression analyses, we selected four of these genes to construct a diagnostic model of AD. The model was validated in blood samples from GSE63061 and GSE85426, and the AUC values showed that the model had a relatively good diagnostic performance. In addition, the immune cell infiltration of the samples and the correlation of different immune factors with these hub genes were further explored. The results suggested that these genes may also play an important role in immunity to AD. Finally, eight drugs targeting these nine hub genes were retrieved from the DrugBank database, some of which were shown to be useful for the treatment of AD or other concomitant conditions, such as insomnia and agitation. In conclusion, this model is expected to guide the diagnosis of patients with AD by detecting the expression of several genes in the blood. These hub genes may also assist in understanding the development and drug treatment of AD.

Altered Balance of Reelin Proteolytic Fragments in the Cerebrospinal Fluid of Alzheimer's Disease Patients.

Reelin binds to the apolipoprotein E receptor apoER2 to activate an intracellular signaling cascade. The proteolytic cleavage of reelin follows receptor binding but can also occur independently of its binding to receptors. This study assesses whether reelin proteolytic fragments are differentially affected in the cerebrospinal fluid (CSF) of Alzheimer’s disease (AD) subjects. CSF reelin species were analyzed by Western blotting, employing antibodies against the N- and C-terminal domains. In AD patients, we found a decrease in the 420 kDa full-length reelin compared with controls. In these patients, we also found an increase in the N-terminal 310 kDa fragment resulting from the cleavage at the so-called C-t site, whereas the 180 kDa fragment originated from the N-t site remained unchanged. Regarding the C-terminal proteolytic fragments, the 100 kDa fragment resulting from the cleavage at the C-t site also displayed increased levels, whilst the one resulting from the N-t site, the 250 kDa fragment, decreased. We also detected the presence of an aberrant reelin species with a molecular mass of around 500 kDa present in AD samples (34 of 43 cases), while it was absent in the 14 control cases analyzed. These 500 kDa species were only immunoreactive to N-terminal antibodies. We validated the occurrence of these aberrant reelin species in an Aβ42-treated reelin-overexpressing cell model. When we compared the AD samples from APOE genotype subgroups, we only found minor differences in the levels of reelin fragments associated to the APOE genotype, but interestingly, the levels of fragments of apoER2 were lower in APOE ε4 carriers with regards to APOE ε3/ε3. The altered proportion of reelin/apoER2 fragments and the occurrence of reelin aberrant species suggest a complex regulation of the reelin signaling pathway, which results impaired in AD subjects.

Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.

Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.

Highly Specific and Sensitive Target Binding by the Humanized pS396-Tau Antibody hC10.2 Across a Wide Spectrum of Alzheimer’s Disease and Primary Tauopathy Postmortem Brains

Background: Deposits of hyperphosphorylated tau fibrils are hallmarks of a broad spectrum of tauopathies, including Alzheimer’s disease (AD). Objective: To investigate heterogeneity of tau pathology across brain extracts from a broad selection of different tauopathies and examine the binding properties of the humanized pS396-tau antibody hC10.2 and six other anti-tau antibodies. Methods: 76 individual tauopathy tissue samples were analyzed in a battery of assays: immunohistochemistry, ELISA, tau aggregation assay, western blot, [3H]PI-2620 and [3H]MK-6240 tau tracer binding, and aggregated seeding activity in RD_P301S HEK293T Biosensor cells. The efficiency of seven anti-tau antibodies to engage with pathological tau species was directly compared. Results: Our data indicate that a strong correlation existed between the tau tracer binding, amount of tau aggregates, pS396-tau phosphorylation, and seeding activity. The hC10.2 antibody, which has entered clinical development, effectively engaged with its epitope across all individual cases of mid-stage and late AD, and primary tauopathies. hC10.2 was superior compared to other phospho- and total tau antibodies to prevent seeded tau aggregation in the biosensor cells. hC10.2 effectively depleted hyperphosphorylated and aggregated tau species across all tauopathy samples proportionally to the amount of tau aggregates. In AD samples, hC10.2 bound to ghost tangles which represent extracellular pathological tau species. Conclusion: S396 hyperphosphorylation is a feature of the formation of seeding-competent tau across different tauopathies and it is present both in intra- and extracellular pathological tau. hC10.2 represents an excellent candidate for a hyperphosphorylation-selective therapeutic tau antibody for the treatment of AD and primary tauopathies.

An Alternatively Spliced TREM2 Isoform Lacking the Ligand Binding Domain is Expressed in Human Brain.

Genetic variants in TREM2 are strongly associated with Alzheimer's disease (AD) risk but alternative splicing in TREM2 transcripts has not been comprehensively described. Recognizing that alternative splice variants can result in reduced gene expression and/or altered function, we sought to fully characterize splice variation in TREM2. Human anterior cingulate autopsy tissue from 61 donors was used for end-point and quantitative PCR and western blotting to identify and quantify novel TREM2 isoforms. In addition to previously described transcripts lacking exon 3 or exon 4, or retaining part of intron 3, we identified novel isoforms lacking exon 2, along with isoforms lacking multiple exons. Isoforms lacking exon 2 were predominant at approximately 10% of TREM2 mRNA in the brain. Expression of TREM2 and frequency of exon 2 skipping did not differ between AD samples and non-AD controls (p = 0.1268 and p = 0.4909, respectively). Further, these novel splice isoforms were also observed across multiple tissues with similar frequency (range 5.3 -13.0%). We found that the exon 2 skipped isoform D2-TREM2 is translated to protein and localizes similarly to full-length TREM2 protein, that both proteins are primarily retained in the Golgi complex, and that D2-TREM2 is expressed in AD and non-AD brain. Since the TREM2 ligand binding domain is encoded by exon 2, and skipping this exon retains reading frame while conserving localization, we hypothesize that D2-TREM2 acts as an inhibitor of TREM2 and targeting TREM2 splicing may be a novel therapeutic pathway for AD.

Exploring the zinc-related transcriptional landscape in Alzheimer's disease.

Alzheimer’s disease (AD) is a progressive neurological disorder, and increasing evidence suggests AD pathology is driven by metabolic dysfunction in the brain. Zinc is the second most abundant trace element found in the human body and is required by all living organisms. Zinc is used extensively in many biological processes, and alterations in zinc levels are implicated in the pathogenesis of numerous diseases, including AD. Since small fluctuations in brain zinc levels appear to effect AD progression, we investigated the zinc-related transcriptional responses in an AD versus non-AD state using microarray and RNA-sequencing (RNA-seq) datasets from cultured cells, mice, and humans. We identified 582 zinc-related differentially expressed genes (DEG) in human dorsolateral prefrontal cortex samples of late-onset AD (LOAD) versus non-AD controls, 146 zinc-related DEG in 5XFAD versus wild-type mice, and 95 zinc-related DEG in lipopolysaccharide (LPS)-stimulated N9 microglia versus unstimulated control cells, with 19 zinc-related DEG common to all three datasets. Of the 19 common DEG, functional enrichment and network analyses identified several biological processes and molecular functions, such as mRNA destabilization and nucleic acid binding, which may be important in neuroinflammation and AD development. Furthermore, therapeutic drugs targeting zinc-related DEG in the human dataset were identified. Taken together, these data provide insights into zinc utilization for gene transcription during AD progression which may further our understanding of AD pathogenesis and could identify new targets for therapeutic strategies targeted towards AD.

Localization of Thioredoxin-Interacting Protein in Aging and Alzheimer's Disease Brains.

Thioredoxin-Interacting Protein (TXNIP) has been shown to have significant pathogenic roles in many human diseases, particularly those associated with diabetes and hyperglycemia. Its main mode of action is to sequester thioredoxins, resulting in enhanced oxidative stress. The aim of this study was to identify if cellular expression of TXNIP in human aged and Alzheimer's disease (AD) brains correlated with pathological structures. This study employed fixed tissue sections and protein extracts of temporal cortex from AD and aged control brains. Studies employed light and fluorescent immunohistochemical techniques using the monoclonal antibody JY2 to TXNIP to identify cellular structures. Immunoblots were used to quantify relative amounts of TXNIP in brain protein extracts. The major finding was the identification of TXNIP immunoreactivity in selective neuronal populations and structures, particularly in non-AD brains. In AD brains, less neuronal TXNIP but increased numbers of TXNIP-positive plaque-associated microglia were observed. Immunoblot analyses showed no significant increase in levels of TXNIP protein in the AD samples tested. In conclusion, this study identified altered patterns of expression of TXNIP in human brains with progression of AD pathology.

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease.

Alzheimer’s disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.

Gene‐to‐gene damaging variant rate analysis uncovers novel Alzheimer’s disease‐associated genes

Alzheimer’s disease (AD) is a progressively debilitating neurodegenerative disease that exacts a heavy toll on our social and health care systems. AD is presumably caused by pathogenic variants in a large number of genes working in concert with environmental factors and personal behaviors. However, despite years of worldwide efforts, only a few dozen AD‐associated genes have been identified to date. Employing the gene‐to‐gene damaging variant rate (g2gDVR) analysis of published datasets containing 181,388 subjects we discovered 2114 potentially AD‐associated genes, of which 711 were linked to the higher AD prevalence among African‐Americans. The DVRs of these genes in AD population (55,460 subjects found in NIAGADS) were at least two‐fold higher than those in the general population (125,748 subjects in gnomAD) with false discovery rate less than 1x10‐12. The advantage of the g2gDVR method for studies of disease is that it focuses on variants expected to impact gene function, in contrast to methods based on SNP analysis, such as GWAS, that are gene‐agnostic. In combination with transcriptome profiling of 21 brain regions from 2728 human AD and control samples (collected from various GEO datasets) we identified three of the 711 genes (GNB5, SNAP91 and AKAP11) that were candidate causative genes based on their reduced expression in both hippocampus and entorhinal cortex regions of AD patient brains. In the context of the APP/PSEN1 mouse model of AD, we further confirmed that Gnb5 heterozygosity exacerbates the formation of both amyloid plaques and neurofibrillary tangles, the hallmarks of AD brain neuropathology. These results suggested that the g2gDVR analysis method is highly effective in identification of AD‐associated genes and is expected to be broadly applicable to other polygenic diseases.

Serpin Signatures in Prion and Alzheimer’s Diseases

Serpins represent the most broadly distributed superfamily of proteases inhibitors. They contribute to a variety of physiological functions and any alteration of the serpin-protease equilibrium can lead to severe consequences. SERPINA3 dysregulation has been associated with Alzheimer’s disease (AD) and prion diseases. In this study, we investigated the differential expression of serpin superfamily members in neurodegenerative diseases. SERPIN expression was analyzed in human frontal cortex samples from cases of sporadic Creutzfeldt-Jakob disease (sCJD), patients at early stages of AD–related pathology, and age-matched controls not affected by neurodegenerative disorders. In addition, we studied whether Serpin expression was dysregulated in two animal models of prion disease and AD.Our analysis revealed that, besides the already observed upregulation of SERPINA3 in patients with prion disease and AD, SERPINB1, SERPINB6, SERPING1, SERPINH1, and SERPINI1 were dysregulated in sCJD individuals compared to controls, while only SERPINB1 was upregulated in AD patients. Furthermore, we analyzed whether other serpin members were differentially expressed in prion-infected mice compared to controls and, together with SerpinA3n, SerpinF2 increased levels were observed. Interestingly, SerpinA3n transcript and protein were upregulated in a mouse model of AD. The SERPINA3/SerpinA3nincreased anti-protease activity found in post-mortem brain tissue of AD and prion disease samples suggest its involvement in the neurodegenerative processes. A SERPINA3/SerpinA3n role in neurodegenerative disease-related protein aggregation was further corroborated by in vitro SerpinA3n-dependent prion accumulation changes. Our results indicate SERPINA3/SerpinA3n is a potential therapeutic target for the treatment of prion and prion-like neurodegenerative diseases.

Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of GJA1 is the highest among all potential driver genes of AD. Moreover, we found GJA1 interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene GJA1 is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.