The role of the immune system in Alzheimer disease: Etiology and treatment

Mechanistic Approach to Immunity and Immunotherapy of Alzheimer's Disease: A Review

Alzheimer's disease (AD) is a debilitating neurodegenerative condition characterized by progressive cognitive decline and memory loss, affecting millions of people worldwide. Traditional treatments, such as cholinesterase inhibitors and NMDA receptor antagonists, offer limited symptomatic relief without addressing the underlying disease mechanisms. These limitations have driven the development of more potent and effective therapies. Recent advances in immunotherapy present promising avenues for AD treatment. Immunotherapy strategies, including both active and passive approaches, harness the immune system to target and mitigate AD-related pathology. Active immunotherapy stimulates the patient's immune response to produce antibodies against AD-specific antigens, while passive immunotherapy involves administering preformed antibodies or immune cells that specifically target amyloid-β (Aβ) or tau proteins. Monoclonal antibodies, such as aducanumab and lecanemab, have shown potential in reducing Aβ plaques and slowing cognitive decline in clinical trials, despite challenges related to adverse immune responses and the need for precise targeting. This comprehensive review explores the role of the immune system in AD, evaluates the current successes and limitations of immunotherapeutic approaches, and discusses future directions for enhancing the treatment efficacy.

Association between gynecologic cancer and Alzheimer's disease: a bidirectional mendelian randomization study

BACKGROUND

Alzheimer's disease (AD) manifests with a higher rate of occurrence in women. Previous epidemiological studies have suggested a potential association between AD and gynecological cancers, but the causal relationship between them remains unclear. This study aims to explore the causal link between 12 types of gynecological cancers and AD using a bidirectional Mendelian randomization (MR) approach.

METHODS

We obtained genetic correlation tools for AD using data from the most extensive genome-wide association study. Genetic correlation data for 12 types of gynecological cancers were also sourced from the Finnish Biobank. These cancers include breast cancer (BC), cervical adenocarcinoma (CA), cervical squamous cell carcinoma (CSCC), cervical cancer (CC), endometrial cancer (EC), ovarian endometrioid carcinoma (OEC), ovarian cancer (OC), ovarian serous carcinoma (OSC), breast carcinoma in situ (BCIS), cervical carcinoma in situ (CCIS), endometrial carcinoma in situ (ECIS), and vulvar carcinoma in situ (VCIS). We used the inverse-variance weighted (IVW) model for causal analysis and conducted horizontal pleiotropy tests, heterogeneity tests, MR-PRESSO tests, and leave-one-out analyses to ensure the robustness of our results. We also applied replication analysis and meta-analysis to further validate our experimental results.

RESULTS

The study found that EC (P_IVW =0.037, OR [95% CI] = 1.032 [1.002, 1.064]) and CCIS (P_IVW = 0.046, OR [95% CI] = 1.032 [1.011, 1.064]) increase the risk of AD, whereas OC was negatively correlated with AD (P_IVW = 0.016, OR [95% CI] = 0.974[0.954, 0.995]). In reverse MR analysis, AD increased the risk of CC (P_IVW = 0.039, OR [95% CI] = 1.395 [1.017, 1.914]) and VCIS (P_IVW = 0.041, OR [95% CI] = 1.761 [1.027, 2.021]), but was negatively correlated with OEC (P_IVW = 0.034, OR [95% CI] = 0.634 [0.417, 0.966]). Sensitivity analysis results demonstrated robustness. These findings were further substantiated through replication and meta-analyses.

CONCLUSIONS

Our MR study supports a causal relationship between AD and gynecological cancers. This encourages further research into the incidence of gynecological cancers in female Alzheimer's patients and the active prevention of AD.

Obesity-induced chronic low-grade inflammation in adipose tissue: A pathway to Alzheimer's disease

Alzheimer's disease (AD) is a leading cause of cognitive impairment worldwide. Overweight and obesity are strongly associated with comorbidities, such as hypertension, diabetes, and insulin resistance (IR), which contribute substantially to the development of AD and subsequent morbidity and mortality. Adipose tissue (AT) is a highly dynamic organ composed of a diverse array of cell types, which can be classified based on their anatomic localization or cellular composition. The expansion and remodeling of AT in the context of obesity involves immunometabolic and functional shifts steered by the intertwined actions of multiple immune cells and cytokine signaling within AT, which contribute to the development of metabolic disorders, IR, and systemic markers of chronic low-grade inflammation. Chronic low-grade inflammation, a prolonged, low-dose stimulation by specific immunogens that can progress from localized sites and affect multiple organs throughout the body, leads to neurodystrophy, increased apoptosis, and disruption of homeostasis, manifesting as brain atrophy and AD-related pathology. In this review, we sought to elucidate the mechanisms by which AT contributes to the onset and progression of AD in obesity through the mediation of chronic low-grade inflammation, particularly focusing on the roles of adipokines and AT-resident immune cells.

Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease

Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.

Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease

Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.

The role of m6A modification in the risk prediction and Notch1 pathway of Alzheimer's disease

N6-methyladenosine (m6A) methylation and abnormal immune responses are implicated in neurodegenerative diseases, yet their relationship in Alzheimer's disease (AD) remains unclear. We obtained AD datasets from GEO databases and used AD mouse and cell models, observing abnormal expression of m6A genes in the AD group, alongside disruptions in the immune microenvironment. Key m6A genes (YTHDF2, LRPPRC, and FTO) selected by machine learning were associated with the Notch pathway, with FTO and Notch1 displaying the strongest correlation. Specifically, FTO expression decreased and m6A methylation of Notch1 increased in AD mouse and cell models. We further silenced FTO expression in HT22 cells, resulting in upregulation of the Notch1 signaling pathway. Additionally, increased Notch1 expression in dendritic cells heightened inflammatory cytokine secretion in vitro. These results suggest that reduced FTO expression may contribute to the pathogenesis of AD by activating the Notch1 pathway to interfere with the immune response.

Causal relationships between peripheral immune cells and Alzheimer's disease: a two-sample Mendelian randomization study

OBJECTIVE

Previous research suggests that peripheral immune cells may play a role in the development of Alzheimer's disease (AD). Our study aims to determine if the composition of peripheral immune cells directly contributes to the occurrence of AD.

METHODS

We utilized a two-sample Mendelian randomization (MR) approach to examine the association between peripheral immune cells and AD.The primary analysis method used was the inverse variance weighted (IVW) method, and we also conducted analyses using MR Egger, weighted median, simple mode, and weighted mode methods to ensure the accuracy of the results.Heterogeneity and horizontal pleiotropy were evaluated using Cochran's Q statistics and the MR Egger intercept, respectively.

RESULTS

The study found a significant correlation between increased IgD + CD24- AC cells (Odds Ratio [OR] = 1.03, 95% Confidence Interval [CI] = 1.01-1.06, P = 0.0172), increased CD4 + %leukocyte (OR = 1.08, 95% CI = 1.02-1.14, P = 0.0086), and increased CD4 + CD8dim AC cells (OR = 1.06, 95% CI = 1.01-1.11, P = 0.0218), with an increased susceptibility to AD. Conversely, an increase in EM DN (CD4-CD8-) %T cells (OR = 0.95, 95% CI = 0.92-0.99, P = 0.0164) and an increase in DN (CD4-CD8-) AC cells (OR = 0.93, 95% CI = 0.88-0.99, P = 0.0145) were associated with a protective effect against AD.

CONCLUSION

Our findings establish a causal link between peripheral immune cells and AD. This study is the first to examine the relationship between peripheral immune cells and AD using MR, offering valuable insights for early diagnosis and treatment decisions.

SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease

The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression

Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.

The ABC's of Alzheimer risk gene ABCA7

Alzheimer's disease (AD) is a growing problem worldwide. Since ABCA7's identification as a risk gene, it has been extensively researched for its role in the disease. We review its recently characterized structure and what the mechanistic insights teach us about its function. We furthermore provide an overview of identified ABCA7 mutations, their presence in different ancestries and protein domains and how they might cause AD. For ABCA7 PTC variants and a VNTR expansion, haploinsufficiency is proposed as the most likely mode-of-action, although splice events could further influence disease risk. Overall, the need to better understand expression of canonical ABCA7 and its isoforms in disease is indicated. Finally, ABCA7's potential functions in lipid metabolism, phagocytosis, amyloid deposition, and the interplay between these three, is described. To conclude, in this review, we provide a comprehensive overview and discussion about the current knowledge on ABCA7 in AD, and what research questions remain. HIGHLIGHTS: Alzheimer's risk-increasing variants in ABCA7 can be found in up to 7% of AD patients. We review the recently characterized protein structure of ABCA7. We present latest insights in genetics, expression patterns, and functions of ABCA7.

Genetic markers associated with ferroptosis in Alzheimer's disease

Objective

Ferroptosis is implicated in the pathogenesis of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and vascular dementia, implying that it may have a regulatory effect on the progression of these diseases. However, the specific role of ferroptosis-related genes (FRGs) in Alzheimer's disease (AD) is not yet fully understood. The aim of the study was to detect ferroptosis related genes with regulatory functions in the disease and explore potential mechanisms in AD.

Methods

Hub FRGs were obtained through multiple algorithms based on the GSE5281 dataset. The screening process was implemented by R packages including limma, WGCNA, glm and SVM-RFE. Gene Ontology classification and pathway enrichment analysis were performed based on FRGs. Biological processes involved with hub FRGs were investigated through GSVA and GSEA methods. Immune infiltration analysis was performed by the R package CIBERSORT. Receiver operating characteristic curve (ROC) was utilized to validate the accuracy of hub FRGs. The CeRNA network attempted to find non-coding RNA transcripts which may play a role in disease progression.

Results

DDIT4, MUC1, KLHL24, CD44, and RB1 were identified as hub FRGs. As later revealed by enrichment analysis, the hub FRGs had important effects on AD through involvement in diverse AD pathogenesis-related pathways such as autophagy and glutathione metabolism. The immune microenvironment in AD shows increased numbers of resting NK cells, macrophages, and mast cells, with decreased levels of CD8 T cells when compared to healthy samples. Regulatory T cells were positively correlated with MUC1, KLHL24, and DDIT4 expression, while RB1 showed negative correlations with eosinophils and CD8 T cells, suggesting potential roles in modulating the immune environment in AD.

Conclusion

Our research has identified five hub FRGs in AD. We concluded that ferroptosis may be involved in the disease.

Dynamic changes in lysosome-related pathways in APP/PS1 mice with aging

Senile plaque, composed of amyloid β protein (Aβ) aggregates, is a critical pathological feature in Alzheimer's disease (AD), leading to cognitive dysfunction. However, how Aβ aggregates exert age-dependent toxicity and temporal cognitive dysfunction in APP/PS1 mice remains incompletely understood. In this study, we investigated AD pathogenesis and dynamic alterations in lysosomal pathways within the hippocampus of age-gradient male mice using transcriptome sequencing, molecular biology assays, and histopathological analyses. We observed high levels of β-amyloid precursor protein (APP) protein expression in the hippocampus at an early stage and age-dependent Aβ deposition. Transcriptome sequencing revealed the enrichment of differential genes related to the lysosome pathway. Furthermore, the protein expression of ATP6V0d2 and CTSD associated with lysosomal functions exhibited dynamic changes with age, increasing in the early stage and decreasing later. Similar age-dependent patterns were observed for the endosome function, autophagy pathway, and SGK1/FOXO3a pathway. Nissl and Golgi staining in the hippocampal region showed age-dependent neuronal loss and synaptic damage, respectively. These findings clearly define the age-gradient changes in the autophagy-lysosome system, the endosome/lysosome system, and the SGK1/FOXO3a pathway in the hippocampus of APP/PS1 mice, providing new perspectives and clues for understanding the possible mechanisms of AD, especially the transition from compensatory to decompensated state.

Investigating the causal relationship between immune cell and Alzheimer's disease: a mendelian randomization analysis

BACKGROUND

Complex interactions between the immune system and the brain may affect neural development, survival, and function, with etiological and therapeutic implications for neurodegenerative diseases. However, previous studies investigating the association between immune inflammation and Alzheimer's disease (AD) have yielded inconsistent results.

METHODS

We applied Mendelian randomization (MR) to examine the causal relationship between immune cell traits and AD risk using genetic variants as instrumental variables. MR is an epidemiological study design based on genetic information that reduces the effects of confounding and reverse causation. We analyzed the causal associations between 731 immune cell traits and AD risk based on publicly available genetic data.

RESULTS

We observed that 5 immune cell traits conferred protection against AD, while 7 immune cell traits increased the risk of AD. These immune cell traits mainly involved T cell regulation, monocyte activation and B cell differentiation. Our findings suggest that immune regulation may influence the development of AD and provide new insights into potential targets for AD prevention and treatment. We also conducted various sensitivity analyses to test the validity and robustness of our results, which revealed no evidence of pleiotropy or heterogeneity.

CONCLUSION

Our research shows that immune regulation is important for AD and provides new information on potential targets for AD prevention and treatment. However, this study has limitations, including the possibility of reverse causality, lack of validation in independent cohorts, and potential confounding by population stratification. Further research is needed to validate and amplify these results and to elucidate the potential mechanisms of the immune cell-AD association.

An analysis of omega-3 clinical trials and a call for personalized supplementation for dementia prevention

INTRODUCTION

Targeted interventions are needed to delay or prevent the onset of neurodegenerative diseases. Poor dietary habits are associated with cognitive decline, highlighting the benefits of a healthy diet with fish and polyunsaturated fatty acids (PUFAs). Intake of omega-3 PUFAs docosahexaenoic acid (DHA), α-linolenic acid (ALA) and eicosapentaenoic acid (EPA) is linked with healthy aging, cardiovascular benefits, and reduced risk of Alzheimer's disease. Although omega-3 has health benefits, its intake is often inadequate and insufficient in modern diets. Although fish oil supplements offer an alternative source, inconsistent results from clinical trials raise questions about the factors determining their success.

AREAS COVERED

In this this review, the authors discuss the aforementioned determining factors and highlight strategies that could enhance the effectiveness of omega-3 PUFAs interventions for dementia and cognitive decline. Moreover, the authors provide suggestions for potential future research.

EXPERT OPINION

Factors such as diet, lifestyle, and genetic predisposition can all influence the effectiveness of omega-3 supplementation. When implementing clinical trials, it is crucial to consider these factors and recognize their potential impact on the interpretation of results. It is important to study each variable independently and the interactions between them.

Neuroimmune interactions and their roles in neurodegenerative diseases

The nervous system possesses bidirectional, sophisticated and delicate communications with the immune system. These neuroimmune interactions play a vitally important role in the initiation and development of many disorders, especially neurodegenerative diseases. Although scientific advancements have made tremendous progress in this field during the last few years, neuroimmune communications are still far from being elucidated. By organizing recent research, in this review, we discuss the local and intersystem neuroimmune interactions and their roles in Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. Unveiling these will help us gain a better understanding of the process of interplay inside the body and how the organism maintains homeostasis. It will also facilitate a view of the diseases from a holistic, pluralistic and interconnected perspective, thus providing a basis of developing novel and effective methods to diagnose, intervene and treat diseases.

Cognitive impairment in Alzheimer's disease FAD4T mouse model: Synaptic loss facilitated by activated microglia via C1qA

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder characterized by cognitive dysfunction. The connection between neuroinflammation and abnormal synaptic function in AD is recognized, but the underlying mechanisms remain unclear. In this study, we utilized a mouse model of AD, FAD4T mice aged 6-7 months, to investigate the molecular changes affecting cognitive impairment. Behavior tests showed that FAD4T mice exhibited impaired spatial memory compared with their wild-type littermates. Immunofluorescence staining revealed the presence of Aβ plaques and abnormal glial cell activation as well as changes in microglial morphology in the cortex and hippocampus of FAD4T mice. Synaptic function was impaired in FAD4T mice. Patch clamp recordings of hippocampal neurons revealed reduced amplitude of miniature excitatory postsynaptic currents. Additionally, Golgi staining showed decreased dendritic spine density in the cortex and hippocampus of FAD4T mice, indicating aberrant synapse morphology. Moreover, hippocampal PSD-95 and NMDAR1 protein levels decreased in FAD4T mice. RNA-seq analysis revealed elevated expression of immune system and proinflammatory genes, including increased C1qA protein and mRNA levels, as well as higher expression of TNF-α and IL-18. Taken together, our findings suggest that excessive microglia activation mediated by complement factor C1qA may contribute to aberrant synaptic pruning, resulting in synapse loss and disrupted synaptic transmission, ultimately leading to AD pathogenesis and behavioral impairments in the FAD4T mouse model. Our study provides valuable insights into the underlying mechanisms of cognitive impairments and preliminarily explores a potentially effective treatment approach targeting on C1qA for AD.

A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction

BACKGROUND

Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD.

METHODS

In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI).

RESULTS

Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05).

CONCLUSIONS

Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.

Microglia as a Possible Alternative Therapeutic for Dementia

Dementia is a syndrome in which there is deterioration in memory, behavior, and the ability to perform everyday activities. Alzheimer's disease and vascular dementia are the most common forms of dementia. There is evidence supporting the hypothesis that inflammatory and immune mechanisms are involved in dementia. Microglia, the resident macrophage tissues in the central nervous system, play a significant role in neuroinflammation and play an important role in amyloid-β clearance in the brain, and impaired microglial clearance of amyloid-β has also been shown to be involved in the pathogenesis of Alzheimer's disease. However, there is also abundant evidence that microglia have harmful actions in dementia. Once activated, they can mediate uptake at neuronal synapses. They can also exacerbate tau pathology and secrete deleterious inflammatory factors that can directly or indirectly damage neurons. Thus, depending on the stage of the disease, microglia can act both protectively and detrimentally. Therefore, it is still necessary to continue with studies to better understand the role of microglia in the pathology of dementia. Currently available drugs can only improve cognitive symptoms, have no impact on progression and are not curative, so identifying and studying new therapeutic approaches is important. Considering the role played by microglia in this pathology, it has been pointed out as a possible therapeutic approach. This manuscript aims to address the relationship between microglia and dementia and how this relationship could be used for therapeutic purposes.

Characteristics of T Cells in Single-Cell Datasets of Peripheral Blood and Cerebrospinal Fluid in Alzheimer's Disease Patients

Background

Alzheimer's disease (AD) is the most common type of dementia, causing a huge socioeconomic burden. In parallel with the widespread uptake of single-cell RNA sequencing (scRNA-seq) technology, there has been a rapid accumulation of data produced by researching AD at single-cell resolution, which is more conductive to explore the neuroimmune-related mechanism of AD.

Objective

To explore the potential features of T cells in the peripheral blood and cerebrospinal fluid of AD patients.

Methods

Two datasets, GSE181279 and GSE134578, were integrated from GEO database. Seurat, Monocle, CellChat, scRepertoire, and singleR packages were mainly employed for data analysis.

Results

Our analysis demonstrated that in peripheral blood, T cells were significantly expanded, and these expanded T cells were possessed effector function, such as CD8+TEMRA, CD4+TEMRA, and CD8+TEM. Interestingly, CD8+TEMRA and CD4+TEMRA cells positioned adjacently after dimensions reduction and clustering. Notably, we identified that the expanded T cells were developed from Naïve T cells and TCM cells, and TEM cells was in the intermediate state of this developing process. Additionally, in cerebrospinal fluid of AD patients, the amplified T cells were mainly CD8+TEMRA cells, and the number and strength of communication between CD4+TEM, CD8+TEM, and CD8+TEMRA were decreased in AD patients.

Conclusions

Our comprehensive analyses identified the cells in cerebrospinal fluid from AD patients are expanded TEMRA or TEM cells and the TEMRA cells communicating with other immune cells is weakened, which may be an important immune feature that leads to AD.

CSF biomarker analysis of ABCA7 mutation carriers suggests altered APP processing and reduced inflammatory response

BACKGROUND

The Alzheimer's disease (AD) risk gene ABCA7 has suggested functions in lipid metabolism and the immune system. Rare premature termination codon (PTC) mutations and an expansion of a variable number of tandem repeats (VNTR) polymorphism in the gene, both likely cause a lower ABCA7 expression and hereby increased risk for AD. However, the exact mechanism of action remains unclear. By studying CSF biomarkers reflecting different types of AD-related pathological processes, we aim to get a better insight in those processes and establish a biomarker profile of mutation carriers.

METHODS

The study population consisted of 229 AD patients for whom CSF was available and ABCA7 sequencing and VNTR genotyping had been performed. This included 28 PTC mutation and 16 pathogenic expansion carriers. CSF levels of Aβ1-42, Aβ1-40, P-tau181, T-tau, sAPPα, sAPPβ, YKL-40, and hFABP were determined using ELISA and Meso Scale Discovery assays. We compared differences in levels of these biomarkers and the Aβ ratio between AD patients with or without an ABCA7 PTC mutation or expansion using linear regression on INT-transformed data with APOE-status, age and sex as covariates.

RESULTS

Carriers of ABCA7 expansion mutations had significantly lower Aβ1-42 levels (P = 0.022) compared with non-carrier patients. The effect of the presence of ABCA7 mutations on CSF levels was especially pronounced in APOE ε4-negative carriers. In addition, VNTR expansion carriers had reduced Aβ1-40 (P = 0.023), sAPPα (P = 0.047), sAPPβ (P = 0.016), and YKL-40 (P = 0.0036) levels.

CONCLUSIONS

Our results are suggestive for an effect on APP processing by repeat expansions given the changes in the amyloid-related CSF biomarkers that were found in carriers. The decrease in YKL-40 levels in expansion carriers moreover suggests that these patients potentially have a reduced inflammatory response to AD damage. Moreover, our findings suggest the existence of a mechanism, independent of lowered expression, affecting neuropathology in expansion carriers.

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.

Proteomic Analysis Reveals Potential Exosomal Biomarkers in Patients With Sporadic Alzheimer Disease

BACKGROUND

Despite substantial progress made in the past decades, the pathogenesis of sporadic Alzheimer disease (sAD) and related biological markers of the disease are still controversially discussed. Cerebrospinal fluid and functional brain imaging markers have been established to support the clinical diagnosis of sAD. Yet, due to the invasiveness of such diagnostics, less burdensome markers have been increasingly investigated in the past years. Among such markers, extracellular vesicles may yield promise in (early) diagnostics and treatment monitoring in sAD.

MATERIALS AND METHODS

In this pilot study, we collected the blood plasma of 18 patients with sAD and compared the proteome of extracted extracellular vesicles with the proteome of 11 age-matched healthy controls. The resulting proteomes were characterized by Gene Ontology terms and between-group statistics.

RESULTS

Ten distinct proteins were found to significantly differ between sAD patients and controls (P<0.05, False Discovery Rate, corrected). These proteins included distinct immunoglobulins, fibronectin, and apolipoproteins.

CONCLUSIONS

These findings lend further support for exosomal changes in neurodegenerative disorders, and particularly in sAD. Further proteomic research could decisively advance our knowledge of sAD pathophysiology as much as it could foster the development of clinically meaningful biomarkers.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes

BACKGROUND

Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown.

METHODS

Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets.

RESULTS

Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD.

CONCLUSION

We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

The association of microbial infection and adaptive immune cell activation in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common form of dementia. Early symptoms include the loss of memory and mild cognitive ability; however, as the disease progresses, these symptoms can present with increased severity manifesting as mood and behaviour changes, disorientation, and a loss of motor/body control. AD is one of the leading causes of death in the UK, and with an ever-increasing ageing society, patient numbers are predicted to rise posing a significant global health emergency. AD is a complex neurophysiological disorder where pathology is characterized by the deposition and aggregation of misfolded amyloid-beta (Aβ)-protein that in-turn promotes excessive tau-protein production which together drives neuronal cell dysfunction, neuroinflammation, and neurodegeneration. It is widely accepted that AD is driven by a combination of both genetic and immunological processes with recent data suggesting that adaptive immune cell activity within the parenchyma occurs throughout disease. The mechanisms behind these observations remain unclear but suggest that manipulating the adaptive immune response during AD may be an effective therapeutic strategy. Using immunotherapy for AD treatment is not a new concept as the only two approved treatments for AD use antibody-based approaches to target Aβ. However, these have been shown to only temporarily ease symptoms or slow progression highlighting the urgent need for newer treatments. This review discusses the role of the adaptive immune system during AD, how microbial infections may be contributing to inflammatory immune activity and suggests how adaptive immune processes can pose as therapeutic targets for this devastating disease.

Identification of mitochondrial related signature associated with immune microenvironment in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is the most common neurodegenerative disease. Mitochondrial dysfunction and immune responses are important factors in the pathogenesis of AD, but their crosstalk in AD has not been studied. In this study, the independent role and interaction of mitochondria-related genes and immune cell infiltration in AD were investigated using bioinformatics methods.

METHODS

The datasets of AD were obtained from NCBI Gene Expression Omnibus (GEO), and the data of mitochondrial genes was from MitoCarta3.0 database. Subsequently, differential expression genes (DEGs) screening and GSEA functional enrichment analysis were performed. The intersection of DEGs and mitochondrial related genes was used to obtain MitoDEGs. The MitoDEGs most relevant to AD were determined by Least absolute shrinkage and selection operator and multiple support vector machine recursive feature elimination, as well as protein-protein interactions (PPI) network and random forest. The infiltration of 28 kinds of immune cells in AD was analyzed by ssGSEA, and the relationship between hub MitoDEGs and the proportion of immune infiltration was studied. The expression levels of hub MitoDEGs were verified in cell models and AD mice, and the role of OPA1 in mitochondrial damage and neuronal apoptosis was investigated.

RESULTS

The functions and pathways of DEGs were significantly enriched in AD, including immune response activation, IL1R pathway, mitochondrial metabolism, oxidative damage response and electron transport chain-oxphos system in mitochondria. Hub MitoDEGs closely related to AD were obtained based on PPI network, random forest and two machine learning algorithms. Five hub MitoDEGs associated with neurological disorders were identified by biological function examination. The hub MitoDEGs were found to be correlated with memory B cell, effector memory CD8 T cell, activated dendritic cell, natural killer T cell, type 17 T helper cell, Neutrophil, MDSC, plasmacytoid dendritic cell. These genes can also be used to predict the risk of AD and have good diagnostic efficacy. In addition, the mRNA expression levels of BDH1, TRAP1, OPA1, DLD in cell models and AD mice were consistent with the results of bioinformatics analysis, and expression levels of SPG7 showed a downward trend. Meanwhile, OPA1 overexpression alleviated mitochondrial damage and neuronal apoptosis induced by Aβ1-42.

CONCLUSIONS

Five potential hub MitoDEGs most associated with AD were identified. Their interaction with immune microenvironment may play a crucial role in the occurrence and prognosis of AD, which provides a new insight for studying the potential pathogenesis of AD and exploring new targets.

Amyloid β-based therapy for Alzheimer's disease: challenges, successes and future

Amyloid β protein (Aβ) is the main component of neuritic plaques in Alzheimer's disease (AD), and its accumulation has been considered as the molecular driver of Alzheimer's pathogenesis and progression. Aβ has been the prime target for the development of AD therapy. However, the repeated failures of Aβ-targeted clinical trials have cast considerable doubt on the amyloid cascade hypothesis and whether the development of Alzheimer's drug has followed the correct course. However, the recent successes of Aβ targeted trials have assuaged those doubts. In this review, we discussed the evolution of the amyloid cascade hypothesis over the last 30 years and summarized its application in Alzheimer's diagnosis and modification. In particular, we extensively discussed the pitfalls, promises and important unanswered questions regarding the current anti-Aβ therapy, as well as strategies for further study and development of more feasible Aβ-targeted approaches in the optimization of AD prevention and treatment.

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models

Introduction

Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder that primarily affects older individuals. N7-methylguanosine (m7G) is a common RNA chemical modification that impacts the development of numerous diseases. Thus, our work investigated m7G-related AD subtypes and established a predictive model.

Methods

The datasets for AD patients, including GSE33000 and GSE44770, were obtained from the Gene Expression Omnibus (GEO) database, which were derived from the prefrontal cortex of the brain. We performed differential analysis of m7G regulators and examined the immune signatures differences between AD and matched-normal samples. Consensus clustering was employed to identify AD subtypes based on m7G-related differentially expressed genes (DEGs), and immune signatures were explored among different clusters. Furthermore, we developed four machine learning models based on the expression profiles of m7G-related DEGs and identified five important genes from the optimal model. We evaluated the predictive power of the 5-gene-based model using an external AD dataset (GSE44770).

Results

A total of 15 genes related to m7G were found to be dysregulated in patients with AD compared to non-AD patients. This finding suggests that there are differences in immune characteristics between these two groups. Based on the differentially expressed m7G regulators, we categorized AD patients into two clusters and calculated the ESTIMATE score for each cluster. Cluster 2 exhibited a higher ImmuneScore than Cluster 1. We performed the receiver operating characteristic (ROC) analysis to compare the performance of four models, and we found that the Random Forest (RF) model had the highest AUC value of 1.000. Furthermore, we tested the predictive efficacy of a 5-gene-based RF model on an external AD dataset and obtained an AUC value of 0.968. The nomogram, calibration curve, and decision curve analysis (DCA) confirmed the accuracy of our model in predicting AD subtypes.

Conclusion

The present study systematically examines the biological significance of m7G methylation modification in AD and investigates its association with immune infiltration characteristics. Furthermore, the study develops potential predictive models to assess the risk of m7G subtypes and the pathological outcomes of patients with AD, which can facilitate risk classification and clinical management of AD patients.

Study on the Role of an Erythrocyte Membrane-Coated Nanotheranostic System in Targeted Immune Regulation of Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the elderly population. Despite significant advances in studies of the pathobiology on AD, there is still no effective treatment. Here, an erythrocyte membrane-camouflaged nanodrug delivery system (TR-ZRA) modified with transferrin receptor aptamers that can be targeted across the blood-brain barrier to ameliorate AD immune environment is established. Based on metal-organic framework (Zn-CA), TR-ZRA is loaded with CD22shRNA plasmid to silence the abnormally high expression molecule CD22 in aging microglia. Most importantly, TR-ZRA can enhance the ability of microglia to phagocytose Aβ and alleviate complement activation, which can promote neuronal activity and decrease inflammation level in the AD brain. Moreover, TR-ZRA is also loaded with Aβ aptamers, which allow rapid and low-cost monitoring of Aβ plaques in vitro. After treatment with TR-ZRA, learning, and memory abilities are enhanced in AD mice. In conclusion, the biomimetic delivery nanosystem TR-ZRA in this study provides a promising strategy and novel immune targets for AD therapy.

Identification of biomarkers differentiating Alzheimer's disease from other neurodegenerative diseases by integrated bioinformatic analysis and machine-learning strategies

Background

Alzheimer's disease (AD) is the most common neurodegenerative disease, imposing huge mental and economic burdens on patients and society. The specific molecular pathway(s) and biomarker(s) that distinguish AD from other neurodegenerative diseases and reflect the disease progression are still not well studied.

Methods

Four frontal cortical datasets of AD were integrated to conduct differentially expressed genes (DEGs) and functional gene enrichment analyses. The transcriptional changes after the integrated frontal cortical datasets subtracting the cerebellar dataset of AD were further compared with frontal cortical datasets of frontotemporal dementia and Huntingdon's disease to identify AD-frontal-associated gene expression. Integrated bioinformatic analysis and machine-learning strategies were applied for screening and determining diagnostic biomarkers, which were further validated in another two frontal cortical datasets of AD by receiver operating characteristic (ROC) curves.

Results

Six hundred and twenty-six DEGs were identified as AD frontal associated, including 580 downregulated genes and 46 upregulated genes. The functional enrichment analysis revealed that immune response and oxidative stress were enriched in AD patients. Decorin (DCN) and regulator of G protein signaling 1 (RGS1) were screened as diagnostic biomarkers in distinguishing AD from frontotemporal dementia and Huntingdon's disease of AD. The diagnostic effects of DCN and RGS1 for AD were further validated in another two datasets of AD: the areas under the curve (AUCs) reached 0.8148 and 0.8262 in GSE33000, and 0.8595 and 0.8675 in GSE44770. There was a better value for AD diagnosis when combining performances of DCN and RGS1 with the AUCs of 0.863 and 0.869. Further, DCN mRNA level was correlated to CDR (Clinical Dementia Rating scale) score (r = 0.5066, p = 0.0058) and Braak staging (r = 0.3348, p = 0.0549).

Conclusion

DCN and RGS1 associated with the immune response may be useful biomarkers for diagnosing AD and distinguishing the disease from frontotemporal dementia and Huntingdon's disease. DCN mRNA level reflects the development of the disease.

Advances in the Study of APOE and Innate Immunity in Alzheimer's Disease

Alzheimer's disease (AD) is a progressive degenerative disease of the nervous system (CNS) with an insidious onset. Clinically, it is characterized by a full range of dementia manifestations including memory impairment, aphasia, loss of speech, loss of use, loss of recognition, impairment of visuospatial skills, and impairment of executive function, as well as changes in personality and behavior. The exact cause of AD has not yet been identified. Nevertheless, modern research indicates that genetic factors contribute to 70% of human's risk of AD. Apolipoprotein (APOE) accounts for up to 90% of the genetic predisposition. APOE is a crucial gene that cannot be overstated. In addition, innate immunity plays a significant role in the etiology and treatment of AD. Understanding the different subtypes of APOE and their interconnections is of paramount importance. APOE and innate immunity, along with their relationship to AD, are primary research motivators for in-depth research and clinical trials. The exploration of novel technologies has led to an increasing trend in the study of AD at the cellular and molecular levels and continues to make more breakthroughs and progress. As of today, there is no effective treatment available for AD around the world. This paper aims to summarize and analyze the role of APOE and innate immunity, as well as development trends in recent years. It is anticipated that APOE and innate immunity will provide a breakthrough for humans to hinder AD progression in the near future.

Probiotics as functional foods: How probiotics can alleviate the symptoms of neurological disabilities

Neurological disorders are diseases of the central nervous system with progressive loss of nervous tissue. One of the most difficult problems associated with neurological disorders is that there is no clear treatment for these diseases. In this review, the physiopathology of some neurodegenerative diseases, etiological causes, drugs used and their side effects, and finally the role of probiotics in controlling the symptoms of these neurodegenerative diseases are presented. Recently, researchers have focused more on the microbiome and the gut-brain axis, which may play a critical role in maintaining brain health. Probiotics are among the most important bacteria that have positive effects on the balance of homeostasis via influencing the microbiome. Other important functions of probiotics in alleviating symptoms of neurological disorders include anti-inflammatory properties, short-chain fatty acid production, and the production of various neurotransmitters. The effects of probiotics on the control of abnormalities seen in neurological disorders led to probiotics being referred to as "psychobiotic. Given the important role of the gut-brain axis and the imbalance of the gut microbiome in the etiology and symptoms of neurological disorders, probiotics could be considered safe agents that positively affect the balance of the microbiome as complementary treatment options for neurological disorders.

Newer modalities in the management of Alzheimer's dementia along with the role of aducanumab and lecanemab in the treatment of its refractory cases

Alzheimer's disease (AD) is a common neurological condition characterized by a gradual and progressive decline in memory, language, emotion, and cognition. It mainly affects elderly people. Due to the effects of AD, pharmaceutical medications and anticholinesterases have been vigorously promoted and approved by the FDA as a form of AD therapy. However, it was progressively found that these drugs did not address the underlying causes of AD pathogenesis; rather, they focused on the symptoms in order to enhance patients' cognitive outcomes. Consequently, a hunt for superior disease-modifying options is launched. Designing new therapeutic agents requires a thorough understanding of the neuroprotective processes and varied functions carried out by certain genes, and antibodies. In this comprehensive review article, we give an overview of the history of Alzheimer's disease, the significance of the blood-brain barrier in determining the scope of treatment options, as well as the advantages and disadvantages of the current therapeutic treatment options for stem cell therapy, immunotherapy, regenerative therapy, and improved Alzheimer's disease care and diagnosis. We have also included a discussion on the potential role of aducanumab and Lecanemab as a cutting-edge therapy in refractory Alzheimer's disease patients. Lecanemab has been recently approved by the FDA for the treatment of Alzheimer's disease.

Immunological evaluation of patients with Alzheimer's disease based on mitogen-stimulated cytokine productions and mitochondrial DNA indicators

BACKGROUND

Based on its objective characteristics, laboratory markers have always been the research direction of clinical diagnosis and assessment of mental disorders including Alzheimer's disease.

METHODS

MTT Colorimetric Assay, ELISA, and quantitative PCR were used to investigate the responsiveness of peripheral blood mononuclear cells (PBMCs) to mitogen Lipopolysaccharides (LPS) and Phytohemagglutinin (PHA), PBMCs genomic methylation and hydroxymethylation levels, nuclear DNA and mitochondrial DNA damage, respiratory chain enzyme activities, and circulating cell-free mitochondrial DNA levels were detected in 90 patients with Alzheimer's disease.

RESULTS

In the Alzheimer's disease group, LPS stimulated PBMCs viability, TNF-α secretion, PHA stimulated IL-10 secretion, genomic DNA methylation levels, circulating cell-free mitochondrial DNA copies, citrate synthase activity were reduced compared to the control; while the LPS stimulated PBMCs IL-1α secretion, PHA stimulated IL-1α and IFN-γ secretion, plasma IL-6 and TNF-α, mitochondrial DNA damages were increased compared to the control.

CONCLUSIONS

The reactivity of peripheral blood mononuclear cells to mitogens, mitochondrial DNA integrity characteristics, and cell-free mitochondrial DNA copies may be used as candidate laboratory biomarkers to help clinical management of Alzheimer's disease.

An insight into the TAM system in Alzheimer's disease

The TAM receptors may help delay the progression of Alzheimer's disease (AD). AD is the most common neurodegenerative disease associated with human aging. The TAM receptors, derived from the first letter of its three constituents -Tyro3, Axl, and Mertk, are associated with immune responses, cellular differentiation and migration, and clearance of apoptotic cells and debris, with the two canonical ligands, Growth Arrest Specific 6 (Gas6) and ProS1. Several kinds of research have indicated the participation of the TAM system in AD pathology. Also, the TAMs regulate multiple features of microglia, the significant sensors of disorder in the central nervous system (CNS). In this review, we describe the biology of the TAM receptors and ligands in the CNS. Then, we discuss the relationship between the TAM system and AD, specially focusing on its functional expression in the microglia. Finally, we also summarize some agents that could interfere with the TAM signaling pathways and discuss potential difficulties and strategies for drug development.

Clinical and biological significance of RNA N6-methyladenosine regulators in Alzheimer disease

RNA N6-methyladenosine (m6A) regulators are essential for a variety of biological functions, such as early development, viral infections, and cancer. However, their roles in Alzheimer disease (AD) are still not very clear. Here, 16 significant m6A regulators were identified using difference analysis between AD patients and non-demented controls based on the GSE132903 dataset from the Gene Expression Omnibus database. Using these 16 m6A regulators, a nomogram model was established to predict the prevalence of AD. We found that patients could obtain a good clinical benefit based on this model. In addition, we revealed 2 distinct m6A patterns and 2 distinct m6A gene patterns in AD and demonstrated their prognostic and risk assessment significance. This present work comprehensively evaluated the functions of m6A regulators in the diagnosis and subtype classification of AD. These results suggested they have potential prognostic and risk assessment significance in AD.

Elevated Levels of Naturally-Occurring Autoantibodies Against the Extracellular Domain of p75NTR Aggravate the Pathology of Alzheimer's Disease

The extracellular domain (p75ECD) of p75 neurotrophin receptor (p75NTR) antagonizes Aβ neurotoxicity and promotes Aβ clearance in Alzheimer's disease (AD). The impaired shedding of p75ECD is a key pathological process in AD, but its regulatory mechanism is largely unknown. This study was designed to investigate the presence and alterations of naturally-occurring autoantibodies against p75ECD (p75ECD-NAbs) in AD patients and their effects on AD pathology. We found that the cerebrospinal fluid (CSF) level of p75ECD-NAbs was increased in AD, and negatively associated with the CSF levels of p75ECD. Transgenic AD mice actively immunized with p75ECD showed a lower level of p75ECD and more severe AD pathology in the brain, as well as worse cognitive functions than the control groups, which were immunized with Re-p75ECD (the reverse sequence of p75ECD) and phosphate-buffered saline, respectively. These findings demonstrate the impact of p75ECD-NAbs on p75NTR/p75ECD imbalance, providing a novel insight into the role of autoimmunity and p75NTR in AD.

Immunosenescence of brain accelerates Alzheimer's disease progression

Most of Alzheimer's disease (AD) cases are sporadic and occur after age 65. With prolonged life expectancy and general population aging, AD is becoming a significant public health concern. The immune system supports brain development, plasticity, and homeostasis, yet it is particularly vulnerable to aging-related changes. Aging of the immune system, called immunosenescence, is the multifaceted remodeling of the immune system during aging. Immunosenescence is a contributing factor to various age-related diseases, including AD. Age-related changes in brain immune cell phenotype and function, crosstalk between immune cells and neural cells, and neuroinflammation work together to promote neurodegeneration and age-related cognitive impairment. Although numerous studies have confirmed the correlation between systemic immune changes and AD, few studies focus on the immune state of brain microenvironment in aging and AD. This review mainly addresses the changes of brain immune microenvironment in aging and AD. Specifically, we delineate how various aspects of the brain immune microenvironment, including immune gateways, immune cells, and molecules, and the interplay between immune cells and neural cells, accelerate AD pathogenesis during aging. We also propose a theoretical framework of therapeutic strategies selectively targeting the different mechanisms to restore brain immune homeostasis.

Disease-gene prediction based on preserving structure network embedding

Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the disease-gene relationship network to predict disease genes better is still an open problem. In this paper, a disease-gene-prediction method based on preserving structure network embedding (PSNE) is introduced. In order to predict pathogenic genes more effectively, a heterogeneous network with multiple types of bio-entities was constructed by integrating disease-gene associations, human protein network, and disease-disease associations. Furthermore, the low-dimension features of nodes extracted from the network were used to reconstruct a new disease-gene heterogeneous network. Compared with other advanced methods, the performance of PSNE has been confirmed more effective in disease-gene prediction. Finally, we applied the PSNE method to predict potential pathogenic genes for age-associated diseases such as AD and PD. We verified the effectiveness of these predicted potential genes by literature verification. Overall, this work provides an effective method for disease-gene prediction, and a series of high-confidence potential pathogenic genes of AD and PD which may be helpful for the experimental discovery of disease genes.

Multiomics and artificial intelligence enabled peripheral blood-based prediction of amnestic mild cognitive impairment

BACKGROUND

Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed.

METHODS

Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most important features for predicting aMCI.

FINDINGS

We found that hsa-miR-4455 is the best biomarker in all omics analyses. The diagnostic index taking a ratio of hsa-miR-4455 to hsa-let-7b-3p predicted aMCI patients against healthy subjects with 97% overall accuracy. An integrated review of multiomics data suggested that a subset of T cells and the GCN (general control nonderepressible) pathway are associated with aMCI.

INTERPRETATION

The multiomics approach has enabled aMCI biomarkers with high specificity and illuminated the accompanying changes in peripheral blood. Future large-scale studies are necessary to validate candidate biomarkers for clinical use.

Reconsidering the role of blood-brain barrier in Alzheimer's disease: From delivery to target

The existence of a selective blood-brain barrier (BBB) and neurovascular coupling are two unique central nervous system vasculature features that result in an intimate relationship between neurons, glia, and blood vessels. This leads to a significant pathophysiological overlap between neurodegenerative and cerebrovascular diseases. Alzheimer's disease (AD) is the most prevalent neurodegenerative disease whose pathogenesis is still to be unveiled but has mostly been explored under the light of the amyloid-cascade hypothesis. Either as a trigger, bystander, or consequence of neurodegeneration, vascular dysfunction is an early component of the pathological conundrum of AD. The anatomical and functional substrate of this neurovascular degeneration is the BBB, a dynamic and semi-permeable interface between blood and the central nervous system that has consistently been shown to be defective. Several molecular and genetic changes have been demonstrated to mediate vascular dysfunction and BBB disruption in AD. The isoform ε4 of Apolipoprotein E is at the same time the strongest genetic risk factor for AD and a known promoter of BBB dysfunction. Low-density lipoprotein receptor-related protein 1 (LRP-1), P-glycoprotein, and receptor for advanced glycation end products (RAGE) are examples of BBB transporters implicated in its pathogenesis due to their role in the trafficking of amyloid-β. This disease is currently devoid of strategies that change the natural course of this burdening illness. This unsuccess may partly be explained by our misunderstanding of the disease pathogenesis and our inability to develop drugs that are effectively delivered to the brain. BBB may represent a therapeutic opportunity as a target itself or as a therapeutic vehicle. In this review, we aim to explore the role of BBB in the pathogenesis of AD including the genetic background and detail how it can be targeted in future therapeutic research.

Integration of bulk RNA sequencing and single-cell analysis reveals a global landscape of DNA damage response in the immune environment of Alzheimer's disease

Background

We developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer's disease (AD).

Methods

We thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.

Results

The progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively.

Conclusions

In conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.

Herbal Therapeutics for Alzheimer's Disease: Ancient Indian Medicine System from the Modern Viewpoint

Alzheimer's is a chronic neurodegenerative disease where amyloid-beta (Aβ) plaques and neurofibrillary tangles are formed inside the brain. It is also characterized by progressive memory loss, depression, neuroinflammation, and derangement of other neurotransmitters. Due to its complex etiopathology, current drugs have failed to completely cure the disease. Natural compounds have been investigated as an alternative therapy for their ability to treat Alzheimer's disease (AD). Traditional herbs and formulations which are used in the Indian ayurvedic system are rich sources of antioxidant, anti-amyloidogenic, neuroprotective, and anti-inflammatory compounds. They promote quality of life by improving cognitive memory and rejuvenating brain functioning through neurogenesis. A rich knowledge base of traditional herbal plants (Turmeric, Gingko, Ashwagandha, Shankhpushpi, Giloy, Gotu kola, Garlic, Tulsi, Ginger, and Cinnamon) combined with modern science could suggest new functional leads for Alzheimer's drug discovery. In this article Ayurveda, the ancient Indian herbal medicine system based on multiple clinical and experimental, evidence have been reviewed for treating AD and improving brain functioning. This article presents a modern perspective on the herbs available in the ancient Indian medicine system as well as their possible mechanisms of action for AD treatment. The main objective of this research is to provide a systematic review of herbal drugs that are easily accessible and effective for the treatment of AD.

Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning

Background

Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.

Methods

ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.

Results

It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.

Conclusion

The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

NetTDP: permutation-based true discovery proportions for differential co-expression network analysis

Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.

Immunosenescence and Aging: Neuroinflammation Is a Prominent Feature of Alzheimer's Disease and Is a Likely Contributor to Neurodegenerative Disease Pathogenesis

Alzheimer's disease (AD) is a chronic multifactorial and complex neuro-degenerative disorder characterized by memory impairment and the loss of cognitive ability, which is a problem affecting the elderly. The pathological intracellular accumulation of abnormally phosphorylated Tau proteins, forming neurofibrillary tangles, and extracellular amyloid-beta (Aβ) deposition, forming senile plaques, as well as neural disconnection, neural death and synaptic dysfunction in the brain, are hallmark pathologies that characterize AD. The prevalence of the disease continues to increase globally due to the increase in longevity, quality of life, and medical treatment for chronic diseases that decreases the mortality and enhance the survival of elderly. Medical awareness and the accurate diagnosis of the disease also contribute to the high prevalence observed globally. Unfortunately, no definitive treatment exists that can be used to modify the course of AD, and no available treatment is capable of mitigating the cognitive decline or reversing the pathology of the disease as of yet. A plethora of hypotheses, ranging from the cholinergic theory and dominant Aβ cascade hypothesis to the abnormally excessive phosphorylated Tau protein hypothesis, have been reported. Various explanations for the pathogenesis of AD, such as the abnormal excitation of the glutamate system and mitochondrial dysfunction, have also been suggested. Despite the continuous efforts to deliver significant benefits and an effective treatment for this distressing, globally attested aging illness, multipronged approaches and strategies for ameliorating the disease course based on knowledge of the underpinnings of the pathogenesis of AD are urgently needed. Immunosenescence is an immune deficit process that appears with age (inflammaging process) and encompasses the remodeling of the lymphoid organs, leading to alterations in the immune function and neuroinflammation during advanced aging, which is closely linked to the outgrowth of infections, autoimmune diseases, and malignant cancers. It is well known that long-standing inflammation negatively influences the brain over the course of a lifetime due to the senescence of the immune system. Herein, we aim to trace the role of the immune system in the pathogenesis of AD. Thus, we explore alternative avenues, such as neuroimmune involvement in the pathogenesis of AD. We determine the initial triggers of neuroinflammation, which is an early episode in the pre-symptomatic stages of AD and contributes to the advancement of the disease, and the underlying key mechanisms of brain damage that might aid in the development of therapeutic strategies that can be used to combat this devastating disease. In addition, we aim to outline the ways in which different aspects of the immune system, both in the brain and peripherally, behave and thus to contribute to AD.

Association between genetic variants and the risk of nivolumab-induced immune-related adverse events

Aim: We sought to identify the variants that could predict the risk of nivolumab-induced immune-related adverse events (irAEs) in patients with cancer. Patients & methods: We enrolled 622 Japanese patients and carried out a genome-wide association study. The associations for 507 single nucleotide polymorphisms (SNPs) showing p < 0.001 were further investigated using an independent cohort. Results: In the combined analysis, possible associations were found for a total of 90 SNPs. Although no SNPs were identified to be significantly associated with nivolumab-induced irAEs, the SNP most strongly associated with nivolumab-induced irAEs was rs469490. Conclusion: This study is an important hypothesis-generating study to guide future studies in larger and/or other ethnic cohorts.

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.