GWAS on family history of Alzheimer's disease

Gene-gene functional relationships in Alzheimer's disease: CELF1 regulates KLC1 alternative splicing

The causes of Alzheimer's disease (AD) are poorly understood, although many genes are known to be involved in this pathology. To gain insights into the underlying molecular mechanisms, it is essential to identify the relationships between individual AD genes. Previous work has shown that the splice variant E of KLC1 (KLC1_vE) promotes AD, and that the CELF1 gene, which encodes an RNA-binding protein involved in splicing regulation, is at a risk locus for AD. Here, we identified a functional link between CELF1 and KLC1 in AD pathogenesis. Transcriptomic data from human samples from different ethnic groups revealed that CELF1 mRNA levels are low in AD brains, and the splicing pattern of KLC1 is strongly correlated with CELF1 expression levels. Specifically, KLC1_vE is negatively correlated with CELF1. Depletion and overexpression experiments in cultured cells demonstrated that the CELF1 protein down-regulates KLC1_vE. In a cross-linking and immunoprecipitation sequencing (CLIP-seq) database, CELF1 directly binds to KLC1 RNA, following which it likely modulates terminal exon usage, hence KLC1_vE formation. These findings reveal a new pathogenic pathway where a risk allele of CELF1 is associated with reduced CELF1 expression, which up-regulates KLC1_vE to promote AD.

Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease

The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

Comprehensive Overview of Alzheimer's Disease: Etiological Insights and Degradation Strategies

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and affects millions of individuals globally. AD is associated with cognitive decline and memory loss that worsens with aging. A statistical report using U.S. data on AD estimates that approximately 6.9 million individuals suffer from AD, a number projected to surge to 13.8 million by 2060. Thus, there is a critical imperative to pinpoint and address AD and its hallmark tau protein aggregation early to prevent and manage its debilitating effects. Amyloid-β and tau proteins are primarily associated with the formation of plaques and neurofibril tangles in the brain. Current research efforts focus on degrading amyloid-β and tau or inhibiting their synthesis, particularly targeting APP processing and tau hyperphosphorylation, aiming to develop effective clinical interventions. However, navigating this intricate landscape requires ongoing studies and clinical trials to develop treatments that truly make a difference. Genome-wide association studies (GWASs) across various cohorts identified 40 loci and over 300 genes associated with AD. Despite this wealth of genetic data, much remains to be understood about the functions of these genes and their role in the disease process, prompting continued investigation. By delving deeper into these genetic associations, novel targets such as kinases, proteases, cytokines, and degradation pathways, offer new directions for drug discovery and therapeutic intervention in AD. This review delves into the intricate biological pathways disrupted in AD and identifies how genetic variations within these pathways could serve as potential targets for drug discovery and treatment strategies. Through a comprehensive understanding of the molecular underpinnings of AD, researchers aim to pave the way for more effective therapies that can alleviate the burden of this devastating disease.

Sex differences in the associations of socioeconomic factors and cognitive performance with family history of Alzheimer's disease

INTRODUCTION

While higher socioeconomic factors (SEF) and cognitive performance (CP) have been associated with reduced Alzheimer's disease (AD) risk, recent evidence highlighted that these factors may have opposite effects on family history of AD (FHAD).

METHODS

Leveraging data from the UK Biobank (N=448,100) and the All of Us Research Program (N=240,319), we applied generalized linear regression models, polygenic risk scoring (PRS), and one-sample Mendelian randomization (MR) to test the sex-specific SEF and CP associations with AD and FHAD.

RESULTS

Observational and genetically informed analyses highlighted that higher SEF and CP were associated with reduced AD and sibling-FHAD, while these factors were associated with increased parent-FHAD. We also observed that population minorities may present different patterns with respect to sibling-FHAD vs. parent-FHAD. Sex differences in FHAD associations were identified in ancestry-specific and SEF PRS and MR results.

DISCUSSION

This study contributes to understanding the sex-specific relationships linking SEF and CP to FHAD, highlighting the potential role of reporting, recall, and surviving-related dynamics.

The necroptosis cell death pathway drives neurodegeneration in Alzheimer's disease

Although apoptosis, pyroptosis, and ferroptosis have been implicated in AD, none fully explains the extensive neuronal loss observed in AD brains. Recent evidence shows that necroptosis is abundant in AD, that necroptosis is closely linked to the appearance of Tau pathology, and that necroptosis markers accumulate in granulovacuolar neurodegeneration vesicles (GVD). We review here the neuron-specific activation of the granulovacuolar mediated neuronal-necroptosis pathway, the potential AD-relevant triggers upstream of this pathway, and the interaction of the necrosome with the endo-lysosomal pathway, possibly providing links to Tau pathology. In addition, we underscore the therapeutic potential of inhibiting necroptosis in neurodegenerative diseases such as AD, as this presents a novel avenue for drug development targeting neuronal loss to preserve cognitive abilities. Such an approach seems particularly relevant when combined with amyloid-lowering drugs.

The causal effect of Alzheimer's disease and family history of Alzheimer's disease on non-ischemic cardiomyopathy and left ventricular structure and function: a Mendelian randomization study

Background

Previous studies have shown that Alzheimer's disease (AD) can cause myocardial damage. However, whether there is a causal association between AD and non-ischemic cardiomyopathy (NICM) remains unclear. Using a comprehensive two-sample Mendelian randomization (MR) method, we aimed to determine whether AD and family history of AD (FHAD) affect left ventricular (LV) structure and function and lead to NICM, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM).

Methods

The summary statistics for exposures [AD, paternal history of AD (PH-AD), and maternal history of AD (MH-AD)] and outcomes (NICM, HCM, DCM, and LV traits) were obtained from the large European genome-wide association studies. The causal effects were estimated using inverse variance weighted, MR-Egger, and weighted median methods. Sensitivity analyses were conducted, including Cochran's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outlier, MR Steiger test, leave-one-out analysis, and the funnel plot.

Results

Genetically predicted AD was associated with a lower risk of NICM [odds ratio (OR) 0.9306, 95% confidence interval (CI) 0.8825-0.9813, p = 0.0078], DCM (OR 0.8666, 95% CI 0.7752-0.9689, p = 0.0119), and LV remodeling index (OR 0.9969, 95% CI 0.9940-0.9998, p = 0.0337). Moreover, genetically predicted PH-AD was associated with a decreased risk of NICM (OR 0.8924, 95% CI 0.8332-0.9557, p = 0.0011). MH-AD was also strongly associated with a decreased risk of NICM (OR 0.8958, 95% CI 0.8449-0.9498, p = 0.0002). Different methods of sensitivity analysis demonstrated the robustness of the results.

Conclusion

Our study revealed that AD and FHAD were associated with a decreased risk of NICM, providing a new genetic perspective on the pathogenesis of NICM.

Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank

BACKGROUND

In 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes.

METHODS

We evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors).

RESULTS

SCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes.

CONCLUSIONS

We observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.

Progress and trends in neurological disorders research based on deep learning

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Angiotensin Converting Enzyme (ACE) expression in microglia reduces amyloid β deposition and neurodegeneration by increasing SYK signaling and endolysosomal trafficking

Genome-wide association studies (GWAS) have identified many gene polymorphisms associated with an increased risk of developing Late Onset Alzheimer's Disease (LOAD). Many of these LOAD risk-associated alleles alter disease pathogenesis by influencing microglia innate immune responses and lipid metabolism. Angiotensin Converting Enzyme (ACE), a GWAS LOAD risk-associated gene best known for its role in regulating systemic blood pressure, also enhances innate immunity and lipid processing in peripheral myeloid cells, but a role for ACE in modulating the function of myeloid-derived microglia remains unexplored. Using novel mice engineered to express ACE in microglia and CNS associated macrophages (CAMs), we find that ACE expression in microglia reduces Aβ plaque load, preserves vulnerable neurons and excitatory synapses, and greatly reduces learning and memory abnormalities in the 5xFAD amyloid mouse model of Alzheimer's Disease (AD). ACE-expressing microglia show enhanced Aβ phagocytosis and endolysosomal trafficking, increased clustering around amyloid plaques, and increased SYK tyrosine kinase activation downstream of the major Aβ receptors, TREM2 and CLEC7A. Single microglia sequencing and digital spatial profiling identifies downstream SYK signaling modules that are expressed by ACE expression in microglia that mediate endolysosomal biogenesis and trafficking, mTOR and PI3K/AKT signaling, and increased oxidative phosphorylation, while gene silencing or pharmacologic inhibition of SYK activity in ACE-expressing microglia abrogates the potentiated Aβ engulfment and endolysosomal trafficking. These findings establish a role for ACE in enhancing microglial immune function and they identify a potential use for ACE-expressing microglia as a cell-based therapy to augment endogenous microglial responses to Aβ in AD.

The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people

BACKGROUND

An elevated neutrophil-lymphocyte ratio (NLR) in blood has been associated with Alzheimer's disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-β42 (Aβ42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally.

RESULTS

A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aβ-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aβ42 (β = -12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (β = 26.812, p = 0.019) and p-tau (β = 3.441, p = 0.015), but not Aβ42. In the NYU cohort alone, subjects classified as Aβ + (n = 38) displayed a stronger association between the NLR and t-tau (β = 100.476, p = 0.037) compared to Aβ- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data.

CONCLUSIONS

We report associations between the NLR and Aβ42 in the older ADNI cohort, and between the NLR and t-tau and p-tau in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

The broken Alzheimer's disease genome

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

A comparative study of structural variant calling in WGS from Alzheimer's disease families

Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer's Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins. We developed a novel metric, D-score, to evaluate caller specificity for deletions. The accuracy of deletions was evaluated by Sanger sequencing. We generated a high-quality call set of 152,301 deletions of diverse sizes. Sanger sequencing validated 114 of 146 detected deletions (78.1%). Scalpel excelled in accuracy for deletions ≤100 bp, whereas Parliament was optimal for deletions >900 bp. Overall, 83.0% and 72.5% of calls by Scalpel and Parliament were validated, respectively, including all 11 deletions called by both Parliament and Scalpel between 101 and 900 bp. Our flexible protocol successfully generated a high-quality deletion call set and a truth set of Sanger sequencing-validated deletions with precise breakpoints spanning 1-17,000 bp.

Genetic Complexities of Cerebral Small Vessel Disease, Blood Pressure, and Dementia

Importance

Vascular disease is a treatable contributor to dementia risk, but the role of specific markers remains unclear, making prevention strategies uncertain.

Objective

To investigate the causal association between white matter hyperintensity (WMH) burden, clinical stroke, blood pressure (BP), and dementia risk, while accounting for potential epidemiologic biases.

Design, Setting, and Participants

This study first examined the association of genetically determined WMH burden, stroke, and BP levels with Alzheimer disease (AD) in a 2-sample mendelian randomization (2SMR) framework. Second, using population-based studies (1979-2018) with prospective dementia surveillance, the genetic association of WMH, stroke, and BP with incident all-cause dementia was examined. Data analysis was performed from July 26, 2020, through July 24, 2022.

Exposures

Genetically determined WMH burden and BP levels, as well as genetic liability to stroke derived from genome-wide association studies (GWASs) in European ancestry populations.

Main Outcomes and Measures

The association of genetic instruments for WMH, stroke, and BP with dementia was studied using GWASs of AD (defined clinically and additionally meta-analyzed including both clinically diagnosed AD and AD defined based on parental history [AD-meta]) for 2SMR and incident all-cause dementia for longitudinal analyses.

Results

In 2SMR (summary statistics-based) analyses using AD GWASs with up to 75 024 AD cases (mean [SD] age at AD onset, 75.5 [4.4] years; 56.9% women), larger WMH burden showed evidence for a causal association with increased risk of AD (odds ratio [OR], 1.43; 95% CI, 1.10-1.86; P = .007, per unit increase in WMH risk alleles) and AD-meta (OR, 1.19; 95% CI, 1.06-1.34; P = .008), after accounting for pulse pressure for the former. Blood pressure traits showed evidence for a protective association with AD, with evidence for confounding by shared genetic instruments. In the longitudinal (individual-level data) analyses involving 10 699 incident all-cause dementia cases (mean [SD] age at dementia diagnosis, 74.4 [9.1] years; 55.4% women), no significant association was observed between larger WMH burden and incident all-cause dementia (hazard ratio [HR], 1.02; 95% CI, 1.00-1.04; P = .07). Although all exposures were associated with mortality, with the strongest association observed for systolic BP (HR, 1.04; 95% CI, 1.03-1.06; P = 1.9 × 10-14), there was no evidence for selective survival bias during follow-up using illness-death models. In secondary analyses using polygenic scores, the association of genetic liability to stroke, but not genetically determined WMH, with dementia outcomes was attenuated after adjusting for interim stroke.

Conclusions

These findings suggest that WMH is a primary vascular factor associated with dementia risk, emphasizing its significance in preventive strategies for dementia. Future studies are warranted to examine whether this finding can be generalized to non-European populations.

regionalpcs: improved discovery of DNA methylation associations with complex traits

We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.

Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies

Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied. Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels. Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1, FERMT2, GRK4, LINC01929, LRFN2, MADD, RP11-368P15.1, RP11-166N6.2, RNA5SP459, and ZNF652 between AD and FI, and 8 shared loci between AD and FFS implicated 11 genes: AFF3, C1QTNF4, CLEC16A, FAM180B, FBXL19, GRK4, LINC01104, MAD1L1, RGS12, ZDHHC5, and ZNF521. The loci 4p16.3 (GRK4) was identified in both meta-analyses. The colocalization analysis supported the results of our meta-analysis in these loci. The gene-based analysis revealed 80 genes between AD and frailty, and 4 genes were initially identified in our meta-analyses: C1QTNF4, CRHR1, MAD1L1, and RGS12. The pathway analysis showed enrichment for lipoprotein particle plasma, amyloid fibril formation, protein kinase regulator, and tau protein binding. Conclusion: Overall, our results provide new insights into the genetics of AD and frailty, suggesting the existence of non-causal shared genetic mechanisms between these conditions.

Associations of Folate/Folic Acid Supplementation Alone and in Combination With Other B Vitamins on Dementia Risk and Brain Structure: Evidence From 466 224 UK Biobank Participants

Previous researchers have tried to explore the association between folate/folic acid intake and dementia incidence, but the results remain controversial. We evaluated the associations of folate/folic acid supplementation alone and in combination with other B vitamins on dementia risk and brain structure. A total of 466 224 UK Biobank participants were investigated. Cox proportional hazards models were used to assess the associations between folate/folic acid supplementation status and the risk of Alzheimer's disease (AD) and vascular dementia (VD). Multivariable linear regression models were employed to evaluate the association between folate/folic acid supplementation status and brain structure. In the final model, folate/folic acid supplementation alone was significantly associated with a higher risk of AD (hazard ratio [HR] = 1.34, 95% confidence interval [CI] = 1.06-1.69, p = .015) and VD (HR = 1.61, 95% CI = 1.21-2.13, p = .001). Folate/folic acid supplementation alone was associated with a reduction in the hippocampus (β = -95.25 mm3, 95% CI = -165.31 to -25.19 mm3, p = .014) and amygdala (β = -51.85 mm3, 95% CI = -88.02 to -15.68 mm3, p = .012). The risk of AD and VD, as well as brain structure, in the group with combined folate/folic acid supplementation and other B vitamins did not show a statistically significant difference compared to the reference group (all p > .05). Folate/folic acid supplementation alone is significantly associated with a higher risk of AD and VD, as well as adverse alterations in brain structure. However, when combined with other B vitamins, these detrimental effects can be counteracted.

Involvement of transposable elements in Alzheimer's disease pathogenesis

Alzheimer's disease affects an average of 5 % of the population with a significant increase in prevalence with age, suggesting that the same mechanisms that underlie aging may influence this pathology. Investigation of these mechanisms is promising for effective methods of treatment and prevention of the disease. Possible participants in these mechanisms are transposons, which serve as drivers of epigenetic regulation, since they form species-specific distributions of non-coding RNA genes in genomes in evolution. Study of miRNA involvement in Alzheimer's disease pathogenesis is relevant, since the associations of protein-coding genes (APOE4, ABCA7, BIN1, CLU, CR1, PICALM, TREM2) with the disease revealed as a result of GWAS make it difficult to explain its complex pathogenesis. Specific expression changes of many genes were found in different brain parts of Alzheimer's patients, which may be due to global regulatory changes under the influence of transposons. Experimental and clinical studies have shown pathological activation of retroelements in Alzheimer's disease. Our analysis of scientific literature in accordance with MDTE DB revealed 28 miRNAs derived from transposons (17 from LINE, 5 from SINE, 4 from HERV, 2 from DNA transposons), the expression of which specifically changes in this disease (decreases in 17 and increases in 11 microRNA). Expression of 13 out of 28 miRNAs (miR-151a, -192, -211, -28, -31, -320c, -335, -340, -378a, -511, -576, -708, -885) also changes with aging and cancer development, which indicates the presence of possible common pathogenetic mechanisms. Most of these miRNAs originated from LINE retroelements, the pathological activation of which is associated with aging, carcinogenesis, and Alzheimer's disease, which supports the hypothesis that these three processes are based on the primary dysregulation of transposons that serve as drivers of epigenetic regulation of gene expression in ontogeny.

Whole genome-wide sequence analysis of long-lived families (Long-Life Family Study) identifies MTUS2 gene associated with late-onset Alzheimer's disease

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) has a strong genetic component. Participants in Long-Life Family Study (LLFS) exhibit delayed onset of dementia, offering a unique opportunity to investigate LOAD genetics.

METHODS

We conducted a whole genome sequence analysis of 3475 LLFS members. Genetic associations were examined in six independent studies (N = 14,260) with a wide range of LOAD risk. Association analysis in a sub-sample of the LLFS cohort (N = 1739) evaluated the association of LOAD variants with beta amyloid (Aβ) levels.

RESULTS

We identified several single nucleotide polymorphisms (SNPs) in tight linkage disequilibrium within the MTUS2 gene associated with LOAD (rs73154407, p = 7.6 × 10-9). Association of MTUS2 variants with LOAD was observed in the five independent studies and was significantly stronger within high levels of Aβ42/40 ratio compared to lower amyloid.

DISCUSSION

MTUS2 encodes a microtubule associated protein implicated in the development and function of the nervous system, making it a plausible candidate to investigate LOAD biology.

HIGHLIGHTS

Long-Life Family Study (LLFS) families may harbor late onset Alzheimer's dementia (LOAD) variants. LLFS whole genome sequence analysis identified MTUS2 gene variants associated with LOAD. The observed LLFS variants generalized to cohorts with wide range of LOAD risk. The association of MTUS2 with LOAD was stronger within high levels of beta amyloid. Our results provide evidence for MTUS2 gene as a novel LOAD candidate locus.

Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease

Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.

AIBP controls TLR4 inflammarafts and mitochondrial dysfunction in a mouse model of Alzheimer's disease

Microglia-driven neuroinflammation plays an important role in the development of Alzheimer's disease (AD). Microglia activation is accompanied by the formation and chronic maintenance of TLR4 inflammarafts, defined as enlarged and cholesterol-rich lipid rafts serving as an assembly platform for TLR4 dimers and complexes of other inflammatory receptors. The secreted apoA-I binding protein (APOA1BP or AIBP) binds TLR4 and selectively targets cholesterol depletion machinery to TLR4 inflammaraft expressing inflammatory, but not homeostatic microglia. Here we demonstrated that amyloid-beta (Aβ) induced formation of TLR4 inflammarafts in microglia in vitro and in the brain of APP/PS1 mice. Mitochondria in Apoa1bp-/- APP/PS1 microglia were hyperbranched and cupped, which was accompanied by increased ROS and the dilated ER. The size and number of Aβ plaques and neuronal cell death were significantly increased, and the animal survival was decreased in Apoa1bp-/- APP/PS1 compared to APP/PS1 female mice. These results suggest that AIBP exerts control of TLR4 inflammarafts and mitochondrial dynamics in microglia and plays a protective role in AD associated oxidative stress and neurodegeneration.

Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

Integrating multi-omics data to reveal the effect of genetic variant rs6430538 on Alzheimer's disease risk

Introduction

Growing evidence highlights a potential genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD); however, the role of the PD risk variant rs6430538 in AD remains unclear.

Methods

In Stage 1, we investigated the risk associated with the rs6430538 C allele in seven large-scale AD genome-wide association study (GWAS) cohorts. In Stage 2, we performed expression quantitative trait loci (eQTL) analysis to calculate the cis-regulated effect of rs6430538 on TMEM163 in both AD and neuropathologically normal samples. Stage 3 involved evaluating the differential expression of TMEM163 in 4 brain tissues from AD cases and controls. Finally, in Stage 4, we conducted a transcriptome-wide association study (TWAS) to identify any association between TMEM163 expression and AD.

Results

The results showed that genetic variant rs6430538 C allele might increase the risk of AD. eQTL analysis revealed that rs6430538 up-regulated TMEM163 expression in AD brain tissue, but down-regulated its expression in normal samples. Interestingly, TMEM163 showed differential expression in entorhinal cortex (EC) and temporal cortex (TCX). Furthermore, the TWAS analysis indicated strong associations between TMEM163 and AD in various tissues.

Discussion

In summary, our findings suggest that rs6430538 may influence AD by regulating TMEM163 expression. These discoveries may open up new opportunities for therapeutic strategies targeting AD.

Identification of disease-specific bio-markers through network-based analysis of gene co-expression: A case study on Alzheimer's disease

Finding biomarker genes for complex diseases attracts persistent attention due to its application in clinics. In this paper, we propose a network-based method to obtain a set of biomarker genes. The key idea is to construct a gene co-expression network among sensitive genes and cluster the genes into different modules. For each module, we can identify its representative, i.e., the gene with the largest connectivity and the smallest average shortest path length to other genes within the module. We believe these representative genes could serve as a new set of potential biomarkers for diseases. As a typical example, we investigated Alzheimer's disease, obtaining a total of 16 potential representative genes, three of which belong to the non-transcriptome. A total of 11 out of these genes are found in literature from different perspectives and methods. The incipient groups were classified into two different subtypes using machine learning algorithms. We subjected the two subtypes to Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis with healthy groups and moderate groups, respectively. The two sub-type groups were involved in two different biological processes, demonstrating the validity of this approach. This method is disease-specific and independent; hence, it can be extended to classify other kinds of complex diseases.

Resting heart rate and risk of dementia: a Mendelian randomization study in the international genomics of Alzheimer's Project and UK Biobank

Background

Observational studies have demonstrated that a higher resting heart rate (RHR) is associated with an increased risk of dementia. However, it is not clear whether the association is causal. This study aimed to determine the causal effects of higher genetically predicted RHR on the risk of dementia.

Methods

We performed a two-sample Mendelian randomization analysis to investigate the causal effect of higher genetically predicted RHR on Alzheimer's disease (AD) using summary statistics from genome-wide association studies. The generalized summary Mendelian randomization (GSMR) analysis was used to analyze the corresponding effects of RHR on following different outcomes: 1) diagnosis of AD (International Genomics of Alzheimer's Project), 2) family history (maternal and paternal) of AD from UK Biobank, 3) combined meta-analysis including these three GWAS results. Further analyses were conducted to determine the possibility of reverse causal association by adjusting for RHR modifying medication.

Results

The results of GSMR showed no significant causal effect of higher genetically predicted RHR on the risk of AD (βGSMR = 0.12, P = 0.30). GSMR applied to the maternal family history of AD (βGSMR = -0.18, P = 0.13) and to the paternal family history of AD (βGSMR = -0.14, P = 0.39) showed the same results. Furthermore, the results were robust after adjusting for RHR modifying drugs (βGSMR = -0.03, P = 0.72).

Conclusion

Our study did not find any evidence that supports a causal effect of RHR on dementia. Previous observational associations between RHR and dementia are likely attributed to the correlation between RHR and other cardiovascular diseases.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people

Background

An elevated neutrophil-lymphocyte ratio (NLR) in blood has been associated with Alzheimer's disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-β42 (Aβ42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally.

Results

A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aβ-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aβ42 (β=-12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (β = 26.812, p = 0.019) and p-tau (β = 3.441, p = 0.015), but not Aβ42. In the NYU cohort alone, subjects classified as Aβ+ (n = 38) displayed a stronger association between the NLR and t-tau (β = 100.476, p = 0.037) compared to Aβ- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data.

Conclusions

We report associations between the NLR and Aβ42 in the older ADNI cohort, and between the NLR and t-tau and p-tau181 in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

A multi-looping chromatin signature predicts dysregulated gene expression in neurons with familial Alzheimer's disease mutations

Mammalian genomes fold into tens of thousands of long-range loops, but their functional role and physiologic relevance remain poorly understood. Here, using human post-mitotic neurons with rare familial Alzheimer's disease (FAD) mutations, we identify hundreds of reproducibly dysregulated genes and thousands of miswired loops prior to amyloid accumulation and tau phosphorylation. Single loops do not predict expression changes; however, the severity and direction of change in mRNA levels and single-cell burst frequency strongly correlate with the number of FAD-gained or -lost promoter-enhancer loops. Classic architectural proteins CTCF and cohesin do not change occupancy in FAD-mutant neurons. Instead, we unexpectedly find TAATTA motifs amenable to binding by DLX homeodomain transcription factors and changing noncoding RNAPolII signal at FAD-dynamic promoter-enhancer loops. DLX1/5/6 mRNA levels are strongly upregulated in FAD-mutant neurons coincident with a shift in excitatory-to-inhibitory gene expression and miswiring of multi-loops connecting enhancers to neural subtype genes. DLX1 overexpression is sufficient for loop miswiring in wildtype neurons, including lost and gained loops at enhancers with tandem TAATTA arrays and singular TAATTA motifs, respectively. Our data uncover a genome structure-function relationship between multi-loop miswiring and dysregulated excitatory and inhibitory transcriptional programs during lineage commitment of human neurons homozygously-engineered with rare FAD mutations.

Joint Exposure to Multiple Air Pollutants, Genetic Susceptibility, and Incident Dementia: A Prospective Analysis in the UK Biobank Cohort

Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM2.5, PM10, NO2, and NOx with dementia and examined the modifying effects of genetic susceptibility. Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype. Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (P trend = 2.17 × 10-5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility. Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.

Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches

Genetic abnormalities play a crucial role in the development of neurodegenerative disorders (NDDs). Genetic exploration has indeed contributed to unraveling the molecular complexities responsible for the etiology and progression of various NDDs. The intricate nature of rare and common variants in NDDs contributes to a limited understanding of the genetic risk factors associated with them. Advancements in next-generation sequencing have made whole-genome sequencing and whole-exome sequencing possible, allowing the identification of rare variants with substantial effects, and improving the understanding of both Mendelian and complex neurological conditions. The resurgence of gene therapy holds the promise of targeting the etiology of diseases and ensuring a sustained correction. This approach is particularly enticing for neurodegenerative diseases, where traditional pharmacological methods have fallen short. In the context of our exploration of the genetic epidemiology of the three most prevalent NDDs-amyotrophic lateral sclerosis, Alzheimer's disease, and Parkinson's disease, our primary goal is to underscore the progress made in the development of next-generation sequencing. This progress aims to enhance our understanding of the disease mechanisms and explore gene-based therapies for NDDs. Throughout this review, we focus on genetic variations, methodologies for their identification, the associated pathophysiology, and the promising potential of gene therapy. Ultimately, our objective is to provide a comprehensive and forward-looking perspective on the emerging research arena of NDDs.

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

Treatment with glatiramer acetate in APPswe/PS1dE9 mice at an early stage of Alzheimer's disease prior to amyloid-beta deposition delays the disease's pathological development and ameliorates cognitive decline

Background

Alzheimer's disease (AD) is characterized by neuroinflammation, which is frequently accompanied by immune system dysfunction. Although the mechanism of neurodegenerative lesions is unclear, various clinical trials have highlighted that early intervention in AD is crucial to the success of treatment. In order to explore the potential of immunotherapy in the early period of AD, the present study evaluated whether application of glatiramer acetate (GA), an immunomodulatory agent approved for remitting-relapsing multiple sclerosis (RRMS), in the early stages of AD prior to amyloid beta (Aβ) deposition altered the Aβ pathology and cognitive impairments in APPswe/PSEN1dE9 (APP/PS1) transgenic mice.

Methods

We treated two cohorts of pre-depositing and amyloid-depositing (2- and 6-month-old) APP/PS1 mice with weekly-GA subcutaneous injection over a 12-week period. We then tested spatial learning and memory using the Morris water maze (MWM) and the Y maze. Immunohistochemistry staining was utilized to analyze Aβ burden in the brain as well as activated microglia. Furthermore, the inflammatory cytokine milieu within brains was estimated by quantitative real-time polymerase chain reaction, and the peripheral CD4+CD25+Foxp3+ regulatory T cells (Tregs) in the spleen were measured by flow cytometry.

Results

We found that early GA administration reduced Aβ burden and ameliorated cognitive decline. Meanwhile, the immune microenvironment had changed in the brain, with an increase in the production of anti-inflammatory cytokines and a decrease in microglial activation. Interestingly, early GA administration also modulated the peripheral immune system through the amplification of Tregs in the spleen.

Conclusion

Overall, our findings revealed that GA treatment might enhance the central and peripheral immune systems' protective capabilities in the early stages of AD, eventually improving cognitive deficits. Our research supports the advantages of immunomodulatory treatments for AD at an early stage.

Knowledge domains and emerging trends of Genome-wide association studies in Alzheimer's disease: A bibliometric analysis and visualization study from 2002 to 2022

OBJECTIVES

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive and behavioral function. Studies have shown that genetic factors are one of the main causes of AD risk. genome-wide association study (GWAS), as a novel and effective tool for studying the genetic risk of diseases, has attracted attention from researchers in recent years and a large number of studies have been conducted. This study aims to summarize the literature on GWAS in AD by bibliometric methods, analyze the current status, research hotspots and future trends in this field.

METHODS

We retrieved articles on GWAS in AD published between 2002 and 2022 from Web of Science. CiteSpace and VOSviewer software were applied to analyze the articles for the number of articles published, countries/regions and institutions of publication, authors and cited authors, highly cited literature, and research hotspots.

RESULTS

We retrieved a total of 2,751 articles. The United States had the highest number of publications in this field, and Columbia University was the institution with the most published articles. The identification of AD-related susceptibility genes and their effects on AD is one of the current research hotspots. Numerous risk genes have been identified, among which APOE, CLU, CD2AP, CD33, EPHA1, PICALM, CR1, ABCA7 and TREM2 are the current genes of interest. In addition, risk prediction for AD and research on other related diseases are also popular research directions in this field.

CONCLUSION

This study conducted a comprehensive analysis of GWAS in AD and identified the current research hotspots and research trends. In addition, we also pointed out the shortcomings of current research and suggested future research directions. This study can provide researchers with information about the knowledge structure and emerging trends in the field of GWAS in AD and provide guidance for future research.

Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

The role of SPP/SPPL intramembrane proteases in membrane protein homeostasis

Signal peptide peptidase (SPP) and the four SPP-like proteases SPPL2a, SPPL2b, SPPL2c and SPPL3 constitute a family of aspartyl intramembrane proteases with homology to presenilins. The different members reside in distinct cellular localisations within the secretory pathway and the endo-lysosomal system. Despite individual cleavage characteristics, they all cleave single-span transmembrane proteins with a type II orientation exhibiting a cytosolic N-terminus. Though the identification of substrates is not complete, SPP/SPPL-mediated proteolysis appears to be rather selective. Therefore, according to our current understanding cleavage by SPP/SPPL proteases rather seems to serve a regulatory function than being a bulk proteolytic pathway. In the present review, we will summarise our state of knowledge on SPP/SPPL proteases and in particular highlight recently identified substrates and the functional and/or (patho)-physiological implications of these cleavage events. Based on this, we aim to provide an overview of the current open questions in the field. These are connected to the regulation of these proteases at the cellular level but also in context of disease and patho-physiological processes. Furthermore, the interplay with other proteostatic systems capable of degrading membrane proteins is beginning to emerge.

Associations between the use of common nonsteroidal anti-inflammatory drugs, genetic susceptibility and dementia in participants with chronic pain: A prospective study based on 194,758 participants from the UK Biobank

OBJECTIVE

To investigate the potential relationship between common nonsteroidal anti-inflammatory drugs (NSAIDs), genetic susceptibility and all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD) among individuals experiencing chronic pain.

METHODS

This study was based on 194,758 chronic pain participants form UK biobank with a median follow-up of 13.7 years. Participants were categorized into different NSAIDs painkiller regimen groups: No NSAIDs group, Aspirin group, Ibuprofen group, Paracetamol group, and 2-3 NSAIDs group. Cox proportional risk models were used to examine the correlation between regular NSAIDs usage and the risk of ACD, AD, and VD. In addition, we further performed subgroup analyses and sensitivity analyses.

RESULTS

1) Compared to the No NSAIDs group, the aspirin group (HR = 1.12, 95% CI:1.01-1.24, P < 0.05), the paracetamol group (HR = 1.15, 95% CI:1.05-1.27, P < 0.01), and the 2-3 NSAIDs group (HR = 1.2, 95% CI:1.08-1.33, P < 0.05) showed a higher risk of ACD. Furthermore, the 2-3 NSAIDs group was also associated with a higher risk of VD (HR = 1.39, 95% CI: 1.08-1.33, P < 0.05). 2) At high dementia GRS participants with chronic pain, the paracetamol group (HR = 1.2, 95% CI: 1.03-1.43, P < 0.05) and the NSAIDs group (HR = 1.3, 95% CI: 1.07-1.59, P < 0.05) were associated with a higher risk of ACD compared to the no painkiller group. 3) There was no significant association between ibuprofen use and higher risk of dementia.

CONCLUSION

In individuals with chronic pain, the use of aspirin and paracetamol was associated with a higher risk of ACD, whereas the use of ibuprofen was not significantly associated with a higher risk of ACD.

The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders

Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.

Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain

Phospholipase C gamma-2 (PLCγ2) catalyzes the hydrolysis of the membrane phosphatidylinositol-4,5-bisphosphate (PIP2) to form diacylglycerol (DAG) and inositol trisphosphate (IP3), which subsequently feed into numerous downstream signaling pathways. PLCG2 polymorphisms are associated with both reduced and increased risk of Alzheimer's disease (AD) and with longevity. In the brain, PLCG2 is highly expressed in microglia, where it is proposed to regulate phagocytosis, secretion of cytokines/chemokines, cell survival and proliferation. We analyzed the brains of three-month-old PLCγ2 knockout (KO), heterozygous (HET), and wild-type (WT) mice using multiomics approaches, including shotgun lipidomics, proteomics, and gene expression profiling, and immunofluorescence. Lipidomic analyses revealed sex-specific losses of total cerebrum PIP2 and decreasing trends of DAG content in KOs. In addition, PLCγ2 depletion led to significant losses of myelin-specific lipids and decreasing trends of myelin-enriched lipids. Consistent with our lipidomics results, RNA profiling revealed sex-specific changes in the expression levels of several myelin-related genes. Further, consistent with the available literature, gene expression profiling revealed subtle changes on microglia phenotype in mature adult KOs under baseline conditions, suggestive of reduced microglia reactivity. Immunohistochemistry confirmed subtle differences in density of microglia and oligodendrocytes in KOs. Exploratory proteomic pathway analyses revealed changes in KO and HET females compared to WTs, with over-abundant proteins pointing to mTOR signaling, and under-abundant proteins to oligodendrocytes. Overall, our data indicate that loss of PLCγ2 has subtle effects on brain homeostasis that may underlie enhanced vulnerability to AD pathology and aging via novel mechanisms in addition to regulation of microglia function.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

Sequence Variant Analysis of the APOCII Locus among an Arab Cohort

Apolipoprotein CII (ApocII) plays a key role in regulating lipoprotein lipase (LPL) in lipid metabolism and transport. Numerous polymorphisms within APOCII are reportedly associated with type 2 diabetes mellitus (T2DM), dyslipidemia, and aberrant plasma lipid levels. Few studies have investigated sequence variants at APOCII loci and their association with metabolic disorders. This study aimed to identify and characterize genetic variants by sequencing the full APOCII locus and its flanking sequences in a sample of the Kuwaiti Arab population, including patients with T2DM, hypertriglyceridemia, non-Arab patients with T2DM, and healthy Arab controls. A total of 52 variants were identified in the noncoding sequences: 45 single nucleotide polymorphisms, wherein five were novel, and seven insertion deletions. The minor allele frequency (MAF) of the 47 previously reported variants was similar to the global MAF and to that reported in major populations. Sequence variant analysis predicted a conserved role for APOCII with a potential role for rs5120 in T2DM and rs7133873 as an informative ethnicity marker. This study adds to the ongoing research that attempts to identify ethnicity-specific variants in the apolipoprotein gene loci and associated LPL genes to elucidate the molecular mechanisms of metabolic disorders.

Associations of screen-based sedentary activities with all cause dementia, Alzheimer's disease, vascular dementia: a longitudinal study based on 462,524 participants from the UK Biobank

BACKGROUND

Current drug treatments for dementia aren't effective. Studying gene-environment interactions can help develop personalized early intervention strategies for Alzheimer's disease (AD). However, no studies have examined the relationship between screen-based sedentary activities and genetic susceptibility to AD risk, and further understanding of the causal relationship is also crucial.

METHODS

This study included 462,524 participants from the UK Biobank with a follow-up of 13.6 years. Participants' screen-based sedentary activities time was categorized into three groups based on recorded time: ≥ 4 h/day, 2-3 h/day, and ≤ 1 h/day. Cox proportional risk models were used to analyze the association between computer use/TV viewing groups and the risk of all-cause dementia, AD and vascular dementia (VD). Generalized linear model (GLM) were used to examine the relationship between screen-based sedentary activities and brain structure. Bidirectional Mendelian randomization (MR) was used to validate the causal relationship between TV viewing and AD.

RESULTS

Compared to TV viewing ≤ 1 h/day, 1)TV viewing 2-3 h/day was correlated with a higher risk of all-cause dementia (HR = 1.09, 95% CI:1.01-1.18, P < 0.05), and TV viewing ≥ 4 h/day was associated with a higher risk of all-cause dementia (HR = 1.29, 95% CI: 1.19-1.40, P < 0.001), AD (HR = 1.25, 95% CI:1.1-1.42, P < 0.001), and VD (HR = 1.24, 95% CI: 1.04-1.49, P < 0.05); 2) TV viewing ≥ 4 h/day was correlated with a higher AD risk at intermediate (HR = 1.34, 95% CI: 1.03-1.75, P < 0.001) and high AD genetic risk score (AD-GRS) (HR = 2.18, 95% CI: 1.65-2.87, P < 0.001);3) TV viewing 2-3 h/day [β = (-94.8), 95% CI: (-37.9) -(-151.7), P < 0.01] and TV viewing ≥ 4 h/day [β = (-92.94), 95% CI: (-17.42) -(-168.46), P < 0.05] were correlated with a less hippocampus volume. In addition, a causal effect of TV viewing times was observed on AD analyzed using MR Egger (OR = 5.618, 95%CI:1.502-21.013, P < 0.05).

CONCLUSIONS

There was a causal effect between TV viewing time and AD analyzed using bidirectional MR, and more TV viewing time exposure was correlated with a higher AD risk. Therefore, it is recommended that people with intermediate and high AD-GRS should control their TV viewing time to be less than 4 h/ day or even less than 1 h/day.

Effects of copy number variations on longevity in late-onset Alzheimer's disease patients: insights from a causality network analysis

Alzheimer's disease (AD), particularly late-onset Alzheimer's disease (LOAD), is a prevalent form of dementia that significantly affects patients' cognitive and behavioral capacities and longevity. Although approximately 70 genetic risk factors linked with AD have been identified, their influence on patient longevity remains unclear. Further, recent studies have associated copy number variations (CNVs) with the longevity of healthy individuals and immune-related pathways in AD patients. This study aims to investigate the role of CNVs on the longevity of AD patients by integrating the Whole Genome Sequencing (WGS) and transcriptomics data from the Religious Orders Study/Memory and Aging Project (ROSMAP) cohort through causality network inference. Our comprehensive analysis led to the construction of a CNV-Gene-Age of Death (AOD) causality network. We successfully identified three key CNVs (DEL5006, mCNV14192, and DUP42180) and seven AD-longevity causal genes (PLGRKT, TLR1, PLAU, CALB2, SYTL2, OTOF, and NT5DC1) impacting AD patient longevity, independent of disease severity. This outcome emphasizes the potential role of plasminogen activation and chemotaxis in longevity. We propose several hypotheses regarding the role of identified CNVs and the plasminogen system on patient longevity. However, experimental validation is required to further corroborate these findings and uncover precise mechanisms. Despite these limitations, our study offers promising insights into the genetic influence on AD patient longevity and contributes to paving the way for potential therapeutic interventions.

Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Increased Maternal Compared to Paternal Transmission of Alzheimer's Disease May Be Due to Increased Incidence of Depression in Women

BACKGROUND

Mothers transmit Alzheimer's disease (AD) more frequently than fathers. Factors other than female longevity may be at work to promote maternal transmission of AD. Among these are the X chromosome, mitochondrial DNA, and AD comorbidities, especially depression. A recent study associated mitochondrial SNP rs2853499 with AD.

MATERIALS AND METHODS

We used UK Biobank (UKBB) data to investigate the relation of mitochondrial SNP rs2853499, with AD. To identify cases of AD we used ICD10 code G30.9. Data processing was performed on Minerva, a Linux mainframe with Centos 7.6, at the Icahn School of Medicine at Mount Sinai. We used PLINK, a whole-genome association analysis toolset, to analyze the UKB22418 mitochondrial hard-called chromosome file.

RESULTS

Of 953 AD cases, 493 were male (51.7%) and 460 were female (48.3%). Mothers were twice as likely to transmit AD compared to fathers. We found that in individuals with AD, 22.3% (n=201) carried the A allele of SNP rs2853499, 77.7% (n=700) carried the G allele. In individuals without AD, 22.2% (n=10,7726) carried the A allele of SNP rs2853499, 77.8% (n=378,535) carried the G allele. This difference was not significant (p=0.91, two-tailed Fisher exact test). Therefore, factors other than mitochondrial SNP rs2853499 may be at work to promote maternal transmission of AD.

CONCLUSION

We conclude that depression, a multigenic illness, in the mother is most likely the basis for the fact that mothers transmit AD twice as often as fathers.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

BACKGROUND

Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

METHOD

To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.

RESULTS

We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease).

CONCLUSION

We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.