Genome-Wide Association and Mechanistic Studies Indicate That Immune Response Contributes to Alzheimer's Disease Development

The association between rs6859 in NECTIN2 gene and Alzheimer's disease is partly mediated by pTau

Introduction

Emerging evidence suggests a connection between vulnerability to infections and Alzheimer's disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infections, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2.

Materials and methods

We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer's disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer's disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor).

Results

The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p < 0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p < 0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect.

Conclusion

This study reported a new potential causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Our finding sheds light on the complex interplay between genetic susceptibility, protein aggregation, and neurodegeneration in AD. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

The association between rs6859 in NECTIN2 gene and Alzheimer's disease is partly mediated by pTau

Introduction

Emerging evidence suggests a connection between vulnerability to infections and Alzheimer's disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infection, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2.

Materials and methods

We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer's disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor).

Results

The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p<0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p<0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect.

Conclusion

This study reported a new causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

Causal relationship between circulating immune cells and the risk of Alzheimer's disease: A Mendelian randomization study

BACKGROUND

Increasing evidence has shown a link between immune cells and Alzheimer's disease (AD). Comprehensive two-sample Mendelian randomization (MR) analysis was performed to determine the causal association between 731 immune cell signatures and AD in this study.

METHODS

We extracted genetic variants of 731 immune cell traits and AD from the publicly available GWAS dataset. The immune features included median fluorescence intensity (MFI), relative cellular (RC), absolute cellular (AC) and morphological parameters (MP). The inverse variance weighted (IVW) method was the main MR analysis method, and sensitivity analyses were used to validate the robustness, heterogeneity and horizontal pleiotropy of the results.

RESULTS

After FDR adjustment, seven immune phenotypes were found to be associated significantly with AD risk: HLA DR on CD33-HLA DR+ (OR = 0.938, PFDR = 0.001), Secreting Treg %CD4 (OR = 0.972, PFDR = 0.021), HLA DR+T cell AC (OR = 0.928, PFDR = 0.041), Activated & resting Treg % CD4 Treg (OR = 1.031, PFDR = 0.002), CD33 on CD33dim HLA DR+CD11b+ (OR = 1.025, PFDR = 0.025), CD33 on CD14+monocyte (OR = 1.026, PFDR = 0.027) and CD33 on CD66b++myeloid cell (OR = 1.027, PFDR = 0.036).

CONCLUSIONS

These findings demonstrated seven immune phenotypes were significantly associated with AD risk. This may provide researchers with a new perspective in exploring the biological mechanisms of AD and may lead to the exploration of earlier treatment.

A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study

Background

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems.

Methods

Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD.

Findings

IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5).

Interpretation

Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials.

Funding

Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.

Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease

The characteristic maximum lifespan varies enormously across animal species from a few hours to hundreds of years. This argues that maximum lifespan, and the ageing process that itself dictates lifespan, are to a large extent genetically determined. Although controversial, this is supported by firm evidence that semelparous species display evolutionarily programmed ageing in response to reproductive and environmental cues. Parabiosis experiments reveal that ageing is orchestrated systemically through the circulation, accompanied by programmed changes in hormone levels across a lifetime. This implies that, like the circadian and circannual clocks, there is a master 'clock of age' (circavital clock) located in the limbic brain of mammals that modulates systemic changes in growth factor and hormone secretion over the lifespan, as well as systemic alterations in gene expression as revealed by genomic methylation analysis. Studies on accelerated ageing in mice, as well as human longevity genes, converge on evolutionarily conserved fibroblast growth factors (FGFs) and their receptors, including KLOTHO, as well as insulin-like growth factors (IGFs) and steroid hormones, as key players mediating the systemic effects of ageing. Age-related changes in these and multiple other factors are inferred to cause a progressive decline in tissue maintenance through failure of stem cell replenishment. This most severely affects the immune system, which requires constant renewal from bone marrow stem cells. Age-related immune decline increases risk of infection whereas lifespan can be extended in germfree animals. This and other evidence suggests that infection is the major cause of death in higher organisms. Immune decline is also associated with age-related diseases. Taking the example of Alzheimer's disease (AD), we assess the evidence that AD is caused by immunosenescence and infection. The signature protein of AD brain, Aβ, is now known to be an antimicrobial peptide, and Aβ deposits in AD brain may be a response to infection rather than a cause of disease. Because some cognitively normal elderly individuals show extensive neuropathology, we argue that the location of the pathology is crucial - specifically, lesions to limbic brain are likely to accentuate immunosenescence, and could thus underlie a vicious cycle of accelerated immune decline and microbial proliferation that culminates in AD. This general model may extend to other age-related diseases, and we propose a general paradigm of organismal senescence in which declining stem cell proliferation leads to programmed immunosenescence and mortality.

Exome-wide analysis reveals role of LRP1 and additional novel loci in cognition

Cognitive functioning is heritable, with metabolic risk factors known to accelerate age-associated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial. Here, we undertake single-variant and gene-based association analyses upon 6 neurocognitive phenotypes across 6 cognition domains in whole-exome sequencing data from 157,160 individuals of the UK Biobank cohort to expound the genetic architecture of human cognition. We report 20 independent loci associated with 5 cognitive domains while controlling for APOE isoform-carrier status and metabolic risk factors; 18 of which were not previously reported, and implicated genes relating to oxidative stress, synaptic plasticity and connectivity, and neuroinflammation. A subset of significant hits for cognition indicates mediating effects via metabolic traits. Some of these variants also exhibit pleiotropic effects on metabolic traits. We further identify previously unknown interactions of APOE variants with LRP1 (rs34949484 and others, suggestively significant), AMIGO1 (rs146766120; pAla25Thr, significant), and ITPR3 (rs111522866, significant), controlling for lipid and glycemic risks. Our gene-based analysis also suggests that APOC1 and LRP1 have plausible roles along shared pathways of amyloid beta (Aβ) and lipid and/or glucose metabolism in affecting complex processing speed and visual attention. In addition, we report pairwise suggestive interactions of variants harbored in these genes with APOE affecting visual attention. Our report based on this large-scale exome-wide study highlights the effects of neuronal genes, such as LRP1, AMIGO1, and other genomic loci, thus providing further evidence of the genetic underpinnings for cognition during aging.

Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins

BACKGROUND

Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis.

METHODS

Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables.

RESULTS

We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways.

CONCLUSIONS

Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

Sex and APOE Genotype Alter the Basal and Induced Inflammatory States of Primary Microglia from APOE Targeted Replacement Mice

The sex and APOE4 genotype are significant risk factors for Alzheimer’s disease (AD); however, the mechanism(s) responsible for this interaction are still a matter of debate. Here, we assess the responses of mixed-sex and sex-specific APOE3 and APOE4 primary microglia (PMG) to lipopolysaccharide and interferon-gamma. In our investigation, inflammatory cytokine profiles were assessed by qPCR and multiplex ELISA assays. Mixed-sex APOE4 PMG exhibited higher basal mRNA expression and secreted levels of TNFa and IL1b. In sex-specific cultures, basal expression and secreted levels of IL1b, TNFa, IL6, and NOS2 were 2−3 fold higher in APOE4 female PMG compared to APOE4 males, with both higher than APOE3 cells. Following an inflammatory stimulus, the expression of pro-inflammatory cytokines and the secreted cytokine level were upregulated in the order E4 female > E4 male > E3 female > E3 male in sex-specific cultures. These data indicate that the APOE4 genotype and female sex together contribute to a greater inflammatory response in PMG isolated from targeted replacement humanized APOE mice. These data are consistent with clinical data and indicate that sex-specific PMG may provide a platform for exploring mechanisms of genotype and sex differences in AD related to neuroinflammation and neurodegeneration.

A large-scale genome-wide cross-trait analysis reveals shared genetic architecture between Alzheimer's disease and gastrointestinal tract disorders

Consistent with the concept of the gut-brain phenomenon, observational studies suggest a relationship between Alzheimer's disease (AD) and gastrointestinal tract (GIT) disorders; however, their underlying mechanisms remain unclear. Here, we analyse several genome-wide association studies (GWAS) summary statistics (N = 34,652-456,327), to assess the relationship of AD with GIT disorders. Findings reveal a positive significant genetic overlap and correlation between AD and gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), gastritis-duodenitis, irritable bowel syndrome and diverticulosis, but not inflammatory bowel disease. Cross-trait meta-analysis identifies several loci (P_{meta-analysis} < 5 × 10^-8) shared by AD and GIT disorders (GERD and PUD) including PDE4B, BRINP3, ATG16L1, SEMA3F, HLA-DRA, SCARA3, MTSS2, PHB, and TOMM40. Colocalization and gene-based analyses reinforce these loci. Pathway-based analyses demonstrate significant enrichment of lipid metabolism, autoimmunity, lipase inhibitors, PD-1 signalling, and statin mechanisms, among others, for AD and GIT traits. Our findings provide genetic insights into the gut-brain relationship, implicating shared but non-causal genetic susceptibility of GIT disorders with AD's risk. Genes and biological pathways identified are potential targets for further investigation in AD, GIT disorders, and their comorbidity.

Crossing borders in Alzheimer's disease: A T cell's perspective

Alzheimer's disease (AD) is the most common form of dementia affecting millions of people worldwide. While different immunotherapies are imminent, currently only disease-modifying medications are available and a cure is lacking. Over the past decade, immunological interfaces of the central nervous system (CNS) and their role in neurodegenerative diseases received increasing attention. Specifically, emerging evidence shows that subsets of circulating CD8⁺ T cells cross the brain barriers and associate with AD pathology. To gain more insight into how the adaptive immune system is involved in disease pathogenesis, we here provide a comprehensive overview of the contribution of T cells to AD pathology, incorporating changes at the brain barriers. In addition, we review studies that provide translation of these findings by targeting T cells to combat AD pathology and cognitive decline. Importantly, these data show that immunological changes in AD are not confined to the CNS and that AD-associated systemic immune changes appear to affect brain homeostasis.

A survey on protein–DNA-binding sites in computational biology

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.

Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method

Gene regulatory network (GRN) inference with biology data is a difficult and serious issue in the field of system biology. In order to detect the direct associations of GRN more accurately, a novel two-step GRN inference technique based on the time-delayed correlation coefficient (TDCC) and time-delayed complex-valued S-system model (TDCVSS) is proposed. First, a TDCC algorithm is utilized to construct an initial network. Second, a TDCVSS model is utilized to prune the network topology in order to delete false-positive regulatory relationships for each target gene. The complex-valued restricted additive tree and complex-valued differential evolution are proposed to approximate the optimal TDCVSS model. Finally, the overall network could be inferred by integrating the regulations of all target genes. Two real gene expression datasets from E. coli and S. cerevisiae gene networks are utilized to evaluate the performances of our proposed two-step GRN inference algorithm. The results demonstrated that the proposed algorithm could infer GRN more correct than classical methods and time-delayed methods.

Synthetic amyloid beta does not induce a robust transcriptional response in innate immune cell culture systems

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease that impacts nearly 400 million people worldwide. The accumulation of amyloid beta (Aβ) in the brain has historically been associated with AD, and recent evidence suggests that neuroinflammation plays a central role in its origin and progression. These observations have given rise to the theory that Aβ is the primary trigger of AD, and induces proinflammatory activation of immune brain cells (i.e., microglia), which culminates in neuronal damage and cognitive decline. To test this hypothesis, many in vitro systems have been established to study Aβ-mediated activation of innate immune cells. Nevertheless, the transcriptional resemblance of these models to the microglia in the AD brain has never been comprehensively studied on a genome-wide scale.

METHODS

We used bulk RNA-seq to assess the transcriptional differences between in vitro cell types used to model neuroinflammation in AD, including several established, primary and iPSC-derived immune cell lines (macrophages, microglia and astrocytes) and their similarities to primary cells in the AD brain. We then analyzed the transcriptional response of these innate immune cells to synthetic Aβ or LPS and INFγ.

RESULTS

We found that human induced pluripotent stem cell (hIPSC)-derived microglia (IMGL) are the in vitro cell model that best resembles primary microglia. Surprisingly, synthetic Aβ does not trigger a robust transcriptional response in any of the cellular models analyzed, despite testing a wide variety of Aβ formulations, concentrations, and treatment conditions. Finally, we found that bacterial LPS and INFγ activate microglia and induce transcriptional changes that resemble many, but not all, aspects of the transcriptomic profiles of disease associated microglia (DAM) present in the AD brain.

CONCLUSIONS

These results suggest that synthetic Aβ treatment of innate immune cell cultures does not recapitulate transcriptional profiles observed in microglia from AD brains. In contrast, treating IMGL with LPS and INFγ induces transcriptional changes similar to those observed in microglia detected in AD brains.

Common variants contribute to intrinsic human brain functional networks

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P < 2.8 × 10^-11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOE ε4 locus with Alzheimer's disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.

Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature

Background

Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms.

Results

The ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy.

Conclusions

The sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the comparison with the traditional method, and improved the intelligence of the classification of discharge types. It could further help us to recognize and identify the nerve discharge activities in a new way.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04592-3.

Untangling huge literature to disinter genetic underpinnings of Alzheimer's Disease: A systematic review and meta-analysis

Drug discovery for Alzheimer's Disease (AD) is channeled towards unravelling key disease specific drug targets/genes to predict promising therapeutic candidates. Though enormous literature on AD genetics is available, there exists dearth in data pertinent to drug targets and crucial pathological pathways intertwined in disease progression. Further, the research findings revealing genetic associations failed to demonstrate consistency across different studies. This scenario prompted us to initiate a systematic review and meta-analysis with an aim of unearthing significant genetic hallmarks of AD. Initially, a Boolean search strategy was developed to retrieve case-control studies from PubMed, Cochrane, ProQuest, Europe PMC, grey literature and HuGE navigator. Subsequently, certain inclusion and exclusion criteria were framed to shortlist the relevant studies. These studies were later critically appraised using New Castle Ottawa Scale and Q-Genie followed by data extraction. Later, meta-analysis was performed only for those Single Nucleotide Polymorphisms (SNPs) which were evaluated in at least two different ethnicities from two different reports. Among, 204,351 studies retrieved, 820 met our eligibility criteria and 117 were processed for systematic review after critical appraisal. Ultimately, meta-analysis was performed for 23 SNPs associated with 15 genes which revealed significant associations of rs3865444 (CD33), rs7561528 (BIN1) and rs1801133 (MTHFR) with AD risk.

Privacy protection of medical data in social network

BACKGROUND

Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data.

METHODS

Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset.

RESULTS

The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. [Formula: see text] = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3.

CONCLUSION

We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.

Identifying Imaging Genetics Biomarkers of Alzheimer's Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression

Imaging genetics combines neuroimaging and genetics to assess the relationships between genetic variants and changes in brain structure and metabolism. Sparse canonical correlation analysis (SCCA) models are well-known tools for identifying meaningful biomarkers in imaging genetics. However, most SCCA models incorporate only diagnostic status information, which poses challenges for finding disease-specific biomarkers. In this study, we proposed a multi-task sparse canonical correlation analysis and regression (MT-SCCAR) model to reveal disease-specific associations between single nucleotide polymorphisms and quantitative traits derived from multi-modal neuroimaging data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. MT-SCCAR uses complementary information carried by multiple-perspective cognitive scores and encourages group sparsity on genetic variants. In contrast with two other multi-modal SCCA models, MT-SCCAR embedded more accurate neuropsychological assessment information through linear regression and enhanced the correlation coefficients, leading to increased identification of high-risk brain regions. Furthermore, MT-SCCAR identified primary genetic risk factors for Alzheimer’s disease (AD), including rs429358, and found some association patterns between genetic variants and brain regions. Thus, MT-SCCAR contributes to deciphering genetic risk factors of brain structural and metabolic changes by identifying potential risk biomarkers.

Early Prediction of Organ Failures in Patients with Acute Pancreatitis Using Text Mining

It is of great significance to establish an assessment model for organ failures in the early stage of admission in acute pancreatitis (AP). And the clinical notes are underutilized. To predict organ failures for AP patients using early clinical notes in hospital, early text features obtained from the pretrained Chinese Bidirectional Encoder Representations from Transformers model and attention-based LSTM were combined with early structured features (laboratory tests, vital signs, and demographic characteristics) to predict organ failures (respiratory, cardiovascular, and renal) in 12,748 AP inpatients in West China Hospital, Sichuan University, from 2008 to 2018. The text plus structured features fusion model was used to predict organ failures, compared to the baseline model with only structured features. The performance of the model with text features added is superior to the model that only includes structured features.

Genetic predisposition, Aβ misfolding in blood plasma, and Alzheimer's disease

Alzheimer's disease is highly heritable and characterized by amyloid plaques and tau tangles in the brain. The aim of this study was to investigate the association between genetic predisposition, Aβ misfolding in blood plasma, a unique marker of Alzheimer associated neuropathological changes, and Alzheimer's disease occurrence within 14 years. Within a German community-based cohort, two polygenic risk scores (clinical Alzheimer's disease and Aβ42 based) were calculated, APOE genotype was determined, and Aβ misfolding in blood plasma was measured by immuno-infrared sensor in 59 participants diagnosed with Alzheimer's disease during 14 years of follow-up and 581 participants without dementia diagnosis. Associations between each genetic marker and Aβ misfolding were assessed through logistic regression and the ability of each genetic marker and Aβ misfolding to predict Alzheimer's disease was determined. The Alzheimer's disease polygenic risk score and APOE ε4 presence were associated to Aβ misfolding (odds ratio, 95% confidence interval: per standard deviation increase of score: 1.25, 1.03-1.51; APOE ε4 presence: 1.61, 1.04-2.49). No association was evident for the Aβ polygenic risk score. All genetic markers were predictive of Alzheimer's disease diagnosis albeit much less so than Aβ misfolding (areas under the curve: Aβ polygenic risk score: 0.55; AD polygenic risk score: 0.59; APOE ε4: 0.63; Aβ misfolding: 0.84). Clinical Alzheimer's genetic risk was associated to early pathological changes (Aβ misfolding) measured in blood, however, predicted Alzheimer's disease less accurately than Aβ misfolding itself. Genetic predisposition may provide information regarding disease initiation, while Aβ misfolding could be important in clinical risk prediction.

Genome-wide association studies of brain imaging data via weighted distance correlation

MOTIVATION

Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships.

RESULTS

To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI).

AVAILABILITY AND IMPLEMENTATION

Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An epoxide hydrolase inhibitor reduces neuroinflammation in a mouse model of Alzheimer's disease

Neuroinflammation has been increasingly recognized to play a critical role in Alzheimer's disease (AD). The epoxy fatty acids (EpFAs) are derivatives of the arachidonic acid metabolism pathway and have anti-inflammatory activities. However, their efficacy is limited because of their rapid hydrolysis by the soluble epoxide hydrolase (sEH). We report that sEH is predominantly expressed in astrocytes and is elevated in postmortem brain tissue from patients with AD and in the 5xFAD β amyloid mouse model of AD. The amount of sEH expressed in AD mouse brains correlated with a reduction in brain EpFA concentrations. Using a specific small-molecule sEH inhibitor, 1-trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl) urea (TPPU), we report that TPPU treatment protected wild-type mice against LPS-induced inflammation in vivo. Long-term administration of TPPU to the 5xFAD mouse model via drinking water reversed microglia and astrocyte reactivity and immune pathway dysregulation. This was associated with reduced β amyloid pathology and improved synaptic integrity and cognitive function on two behavioral tests. TPPU treatment correlated with an increase in EpFA concentrations in the brains of 5xFAD mice, demonstrating brain penetration and target engagement of this small molecule. These findings support further investigation of TPPU as a potential therapeutic agent for the treatment of AD.

A Comprehensive Analysis of Unique and Recurrent Copy Number Variations in Alzheimer's Disease and its Related Disorders

BACKGROUND

Copy number variations (CNVs) play an important role in the genetic etiology of various neurological disorders, including Alzheimer's disease (AD). Type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD) were shown to have share mechanisms and signaling pathways with AD.

OBJECTIVE

We aimed to assess CNVs regions that may harbor genes contributing to AD, T2DM, and MDD in 67 Saudi familial and sporadic AD patients, with no alterations in the known genes of AD and genotyped previously for APOE.

METHODS

DNA was analyzed using the CytoScan-HD array. Two layers of filtering criteria were applied. All the identified CNVs were checked in the Database of Genomic Variants (DGV).

RESULTS

A total of 1086 CNVs (565 gains and 521 losses) were identified in our study. We found 73 CNVs harboring genes that may be associated with AD, T2DM or MDD. Nineteen CNVs were novel. Most importantly, 42 CNVs were unique in our studied cohort existing only in one patient. Two large gains on chromosomes 1 and 13 harbored genes implicated in the studied disorders. We identified CNVs in genes that encode proteins involved in the metabolism of amyloid-β peptide (AGRN, APBA2, CR1, CR2, IGF2R, KIAA0125, MBP, RER1, RTN4R, VDR and WISPI) or Tau proteins (CACNAIC, CELF2, DUSP22, HTRA1 and SLC2A14).

CONCLUSION

The present work provided information on the presence of CNVs related to AD, T2DM, and MDD in Saudi Alzheimer's patients.

Biomarkers: Our Path Towards a Cure for Alzheimer Disease

Over the last decade, biomarkers have significantly improved our understanding of the pathophysiology of Alzheimer disease (AD) and provided valuable tools to examine different disease mechanisms and their progression over time. While several markers of amyloid, tau, neuronal, synaptic, and axonal injury, inflammation, and immune dysregulation in AD have been identified, there is a relative paucity of biomarkers which reflect other disease mechanisms such as oxidative stress, mitochondrial injury, vascular or endothelial injury, and calcium-mediated excitotoxicity. Importantly, there is an urgent need to standardize methods for biomarker assessments across different centers, and to identify dynamic biomarkers which can monitor disease progression over time and/or response to potential disease-modifying treatments. The updated research framework for AD, proposed by the National Institute of Aging- Alzheimer’s Association (NIA-AA) Work Group, emphasizes the importance of incorporating biomarkers in AD research and defines AD as a biological construct consisting of amyloid, tau, and neurodegeneration which spans pre-symptomatic and symptomatic stages. As results of clinical trials of AD therapeutics have been disappointing, it has become increasingly clear that the success of future AD trials will require the incorporation of biomarkers in participant selection, prognostication, monitoring disease progression, and assessing response to treatments. We here review the current state of fluid AD biomarkers, and discuss the advantages and limitations of the updated NIA-AA research framework. Importantly, the integration of biomarker data with clinical, cognitive, and imaging domains through a systems biology approach will be essential to adequately capture the molecular, genetic, and pathological heterogeneity of AD and its spatiotemporal evolution over time.

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features

Arginine methylation is one of the most essential protein post-translational modifications. Identifying the site of arginine methylation is a critical problem in biology research. Unfortunately, biological experiments such as mass spectrometry are expensive and time-consuming. Hence, predicting arginine methylation by machine learning is an alternative fast and efficient way. In this paper, we focus on the systematic characterization of arginine methylation with composition-transition-distribution (CTD) features. The presented framework consists of three stages. In the first stage, we extract CTD features from 1750 samples and exploit decision tree to generate accurate prediction. The accuracy of prediction can reach 96%. In the second stage, the support vector machine can predict the number of arginine methylation sites with 0.36 R-squared. In the third stage, experiments carried out with the updated arginine methylation site data set show that utilizing CTD features and adopting random forest as the classifier outperform previous methods. The accuracy of identification can reach 82.1 and 82.5% in single methylarginine and double methylarginine data sets, respectively. The discovery presented in this paper can be helpful for future research on arginine methylation.

Association of Uncommon, Noncoding Variants in the APOE Region With Risk of Alzheimer Disease in Adults of European Ancestry

IMPORTANCE

The ε2 and ε4 alleles of the apolipoprotein E (APOE) gene are associated with Alzheimer disease (AD) risk. Although nearby genetic variants have also been shown to be associated with AD, including rs2075650 in the TOMM40 gene and rs4420638 near the APOC1 gene, it is unknown whether these associations are independent of the ε2 and ε4 alleles.

OBJECTIVE

To assess whether variants near APOE are associated with AD independently of the ε2/ε3/ε4 genotype.

DESIGN, SETTING, AND PARTICIPANTS

In this genetic association study of the Alzheimer's Disease Genetics Consortium imputed genotype at data, 14 415 variants near APOE (±500 kilobase) for 18 795 individuals with European ancestry were tested for association with AD using 4 logistic mixed models adjusting for sex, cohort, population structure, and relatedness. Model 1 had no APOE adjustment, and model 2 adjusted for the count of ε2 and ε4 alleles. Model 3 was restricted to ε3 homozygotes, and model 4 was restricted to ε4 homozygotes. Data were downloaded from May 31, 2018, to June 3, 2018, and analyzed from November 1, 2018, to June 24, 2020.

MAIN OUTCOMES AND MEASURES

Alzheimer disease affectation status was defined by clinicians using standard National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer Disease and Related Disorders Association criteria. Association was evaluated using Score tests; results with P < .05 divided by the number of independent tests per model were considered statistically significant.

RESULTS

Among the 18 795 individuals in the study, 9704 were affected by AD and 9066 were control individuals; the median age at onset/evaluation was 76 (interquartile range, 70-82) years; and 11 167 were female (59.4%). Associations with AD were found for rs2075650 (odds ratio [OR], 2.59; 95% CI, 2.45-2.75; P = 3.19 × 10-228) and rs4420638 (OR, 2.77; 95% CI, 2.62-2.94; P = 2.99 × 10-254) without APOE adjustment. Although rs2075650 was nominally associated with AD among the ε4 homozygotes (OR, 1.33; 95% CI, 1.00-1.77; P = .047), the association between rs4420638 and AD was eliminated by APOE adjustment (model 2 OR, 1.06 [95% CI, 0.96-1.18; P = .24]; model 3 OR, 1.13 [95% CI, 0.95-1.34; P = .18]; model 4 OR, 0.90 [95% CI, 0.56-1.45; P = .66]). There was a significant association between rs192879175 and AD among ε3 homozygotes (OR, 0.50; 95% CI, 0.37-0.68; P = 8.30 × 10-6).

CONCLUSIONS AND RELEVANCE

The results of this genetic association study suggest that ε2/ε3/ε4 alleles are not the only variants in the APOE region that are associated with AD risk. Additional work with independent data is needed to replicate these results.

Human Herpesvirus 6 Detection in Alzheimer's Disease Cases and Controls across Multiple Cohorts

The interplay between viral infection and Alzheimer's disease (AD) has long been an area of interest, but proving causality has been elusive. Several recent studies have renewed the debate concerning the role of herpesviruses, and human herpesvirus 6 (HHV-6) in particular, in AD. We screened for HHV-6 detection across three independent AD brain repositories using (1) RNA sequencing (RNA-seq) datasets and (2) DNA samples extracted from AD and non-AD control brains. The RNA-seq data were screened for pathogens against taxon references from over 25,000 microbes, including 118 human viruses, whereas DNA samples were probed for PCR reactivity to HHV-6A and HHV-6B. HHV-6 demonstrated little specificity to AD brains over controls by either method, whereas other viruses, such as Epstein-Barr virus (EBV) and cytomegalovirus (CMV), were detected at comparable levels. These direct methods of viral detection do not suggest an association between HHV-6 and AD.

Two-stage Bayesian GWAS of 9576 individuals identifies SNP regions that are targeted by miRNAs inversely expressed in Alzheimer's and cancer

INTRODUCTION

We compared genetic variants between Alzheimer's disease (AD) and two age-related cancers-breast and prostate -to identify single-nucleotide polymorphisms (SNPs) that are associated with inverse comorbidity of AD and cancer.

METHODS

Bayesian multinomial regression was used to compare sex-stratified cases (AD and cancer) against controls in a two-stage study. A ±500 KB region around each replicated hit was imputed and analyzed after merging individuals from the two stages. The microRNAs (miRNAs) that target the genes involving these SNPs were analyzed for miRNA family enrichment.

RESULTS

We identified 137 variants with inverse odds ratios for AD and cancer located on chromosomes 19, 4, and 5. The mapped miRNAs within the network were enriched for miR-17 and miR-515 families.

DISCUSSION

The identified SNPs were rs4298154 (intergenic), within TOMM40/APOE/APOC1, MARK4, CLPTM1, and near the VDAC1/FSTL4 locus. The miRNAs identified in our network have been previously reported to have inverse expression in AD and cancer.

TOMM40 polymorphism is associated with resting-state functional MRI results in patients with Alzheimer's disease

OBJECTIVE

Translocase of outer mitochondrial membrane 40 (TOMM40) encodes translocase of the outer mitochondrial membrane (TOM), which is associated with mitochondrial dysfunction in Alzheimer's disease (AD). TOMM40 rs157581-G has been reported to increase susceptibility to AD. However, the effect of TOMM40 rs157581-G in resting-state functional MRI (rs-fMRI) on AD has not been studied. Therefore, we aimed to investigate the role of TOMM40 rs157581-G on rs-fMRI results in AD patients.

METHODS

Twenty-four AD patients were divided into two groups based on TOMM40 rs157581-G status, and clinical and imaging data were compared between the groups.

RESULTS

TOMM40 rs157581-G carriers of AD showed decreased regional homogeneity in the left precuneus and decreased amplitude of low-frequency fluctuations in the bilateral temporal poles compared with noncarriers of AD. TOMM40 rs157581-G carriers of AD also showed increased functional connectivity between the right middle occipital gyrus and the left supramarginal gyrus and decreased connectivity between the left superior occipital gyrus and the right transverse temporal gyrus in comparison with TOMM40 rs157581-G noncarriers.

CONCLUSION

We analyzed rs-fMRI characteristics of TOMM40 rs157581-G carriers of AD for the first time, which suggest that TOMM40 rs157581-G plays a harmful role in AD patients.

Genome-Wide Association Studies for Cerebrospinal Fluid Soluble TREM2 in Alzheimer's Disease

Alzheimer’s disease (AD) is the most common form of dementia. Rare variants in triggering receptor expressed on myeloid cells 2 (TREM2) have been identified as risk factors for AD. Soluble TREM2 (sTREM2) in the cerebrospinal fluid (CSF) is a potential and novel biomarker of neuroinflammation implicated in the onset and progression of AD. To explore the roles of CSF sTREM2 on the pathogenesis of AD, we performed genome-wide association studies (GWAS) by using the data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). We found CSF sTREM2 levels were elevated with the disease stages, but there was no significant difference between that of AD patients and normal participants. CSF sTREM2 was positively correlated with CSF total tau and phosphorylated-tau levels (ρ > 0.35, p < 1e-06; ρ > 0.32, p < 1e-05, respectively) for all disease states. We identified the most significant CSF sTREM2 related locus was rs7232 (FDR = 3.01e-08), a missense variant in MS4A6A gene of chromosome 11. Moreover, we also detected rs7232 was highly associated with MS4A6A gene expression (FDR = 1.37e-18). In addition, our pathway analysis for our significant GWAS results showed that biological processes for regulation of viruses and immune response were highly overrepresented or enriched. Our study suggests that CSF sTREM2 plays an informative role in AD progression. Moreover, CSF sTREM2 and AD is highly related to viral infections and immune response.

Nuclear Tau, p53 and Pin1 Regulate PARN-Mediated Deadenylation and Gene Expression

While nuclear tau plays a role in DNA damage response (DDR) and chromosome relaxation, the mechanisms behind these functions are not fully understood. Here, we show that tau forms complex(es) with factors involved in nuclear mRNA processing such as tumor suppressor p53 and poly(A)-specific ribonuclease (PARN) deadenylase. Tau induces PARN activity in different cellular models during DDR, and this activation is further increased by p53 and inhibited by tau phosphorylation at residues implicated in neurological disorders. Tau's binding factor Pin1, a mitotic regulator overexpressed in cancer and depleted in Alzheimer's disease (AD), also plays a role in the activation of nuclear deadenylation. Tau, Pin1 and PARN target the expression of mRNAs deregulated in AD and/or cancer. Our findings identify novel biological roles of tau and toxic effects of hyperphosphorylated-tau. We propose a model in which factors involved in cancer and AD regulate gene expression by interactions with the mRNA processing machinery, affecting the transcriptome and suggesting insights into alternative mechanisms for the initiation and/or developments of these diseases.

Roles for the adaptive immune system in Parkinson's and Alzheimer's diseases

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, affect millions of people and pose major personal and socioeconomic burdens. The causes of neurodegeneration are mostly unknown, although current efforts have described an autoimmune aspect to these diseases. Here we discuss recent findings that shed light on the involvement of the adaptive immune system in Parkinson's and Alzheimer's diseases, and provide a model and outlook for further investigation of T cell responses in neurodegenerative disease. We focus on the identification of T cell epitopes from proteins involved in disease pathogenesis and describe the identification of α-synuclein-specific epitopes in Parkinson's disease which provided a crucial link between disease susceptibility and T cell recognition.

Herpesviral infections and antimicrobial protection for Alzheimer's disease: Implications for prevention and treatment

Accumulating evidence suggests that infections by herpesviruses might be closely linked to Alzheimer's disease (AD). Pathological hallmarks of AD brains include senile plaques induced by amyloid β peptide (Aβ) in the extracellular space and intracellular neurofibrillary tangles (NFTs) consisting of phosphorylated tau protein. The prevailing hypothesis for the mechanism of AD is amyloid cascade reaction. Recent studies revealed that infections by herpesviruses induce the similar pathological hallmarks of AD, including Aβ production, phosphorylation of tau (P-tau), oxidative stress, neuroinflammation, etc. Aβ peptide is regarded as one of the antimicrobial peptides, which inhibits HSV-1 replication. In the elderly, reactivation of herpesviruses might act as an initiator for amyloid cascade reaction in vulnerable individuals, triggering the neurofibrillary formation of phosphorylated tau and inducing oxidative stress and neuroinflammation, which can further contribute to the accumulation of Aβ and P-tau by impairing mitochondria and autophagosome. Epidemiological studies have shown AD susceptibility genes, such as APOE-ε4 allele, are highly linked to infections by herpesviruses. Interestingly, anti-herpesviral therapy significantly reduced the risk of AD in a large population study. Given that herpesviruses are arguably the most prevalent opportunistic pathogens and often reactivate in the elderly, it is reasonable to argue reactivation of herpesviruses might be major culprits for initiating AD in individuals carrying AD susceptibility genes. In this review, we summarize epidemiological and molecular evidence that support for a hypothesis of herpesviral infections and antimicrobial protection in the development of AD, and discuss the implications for future prevention and treatment of the disease.