GWAS on family history of Alzheimer's disease

Boosting angiotensin-converting enzyme (ACE) in microglia protects against Alzheimer's disease in 5xFAD mice

Genome-wide association studies 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 innate immune responses and lipid metabolism of microglia (MG). Here we show that boosting the expression of angiotensin-converting enzyme (ACE), a genome-wide association study LOAD risk-associated gene product, specifically in MG, reduces amyloid-β (Aβ) plaque load, preserves vulnerable neurons and excitatory synapses, and significantly reduces learning and memory abnormalities in the 5xFAD amyloid mouse model of AD. ACE-expressing MG surround plaques more frequently and they have increased Aβ phagocytosis, endolysosomal trafficking and spleen tyrosine kinase activation downstream of the major Aβ receptors, triggering receptor expressed on myeloid cells 2 (Trem2) and C-type lectin domain family 7 member A (Clec7a). These findings establish a role for ACE in enhancing microglial immune function and they identify a potential use for ACE-expressing MG as a cell-based therapy to augment endogenous microglial responses to Aβ in AD.

Alzheimer's disease and age-related macular degeneration: Shared and distinct immune mechanisms

Alzheimer's disease (AD) and age-related macular degeneration (AMD) represent the leading causes of dementia and vision impairment in the elderly, respectively. The retina is an extension of the brain, yet these two central nervous system (CNS) compartments are often studied separately. Despite affecting cognition vs. vision, AD and AMD share neuroinflammatory pathways. By comparing these diseases, we can identify converging immune mechanisms and potential cross-applicable therapies. Here, we review immune mechanisms highlighting the shared and distinct aspects of these two age-related neurodegenerative conditions, focusing on responses to hallmark disease manifestations, the opposite role of overlapping immune risk loci, and potential unified therapeutic approaches. We also discuss unique tissue requirements that may dictate different outcomes of conserved immune mechanisms and how we can reciprocally utilize lessons from AD therapeutics to AMD. Looking forward, we suggest promising directions for research, including the exploration of regenerative medicine, gene therapies, and innovative diagnostics.

Modeling Alzheimer's Disease: A Review of Gene-Modified and Induced Animal Models, Complex Cell Culture Models, and Computational Modeling

Alzheimer's disease, a complex neurodegenerative disease, is characterized by the pathological aggregation of insoluble amyloid β and hyperphosphorylated tau. Multiple models of this disease have been employed to investigate the etiology, pathogenesis, and multifactorial aspects of Alzheimer's disease and facilitate therapeutic development. Mammals, especially mice, are the most common models for studying the pathogenesis of this disease in vivo. To date, the scientific literature has documented more than 280 mouse models exhibiting diverse aspects of Alzheimer's disease pathogenesis. Other mammalian species, including rats, pigs, and primates, have also been utilized as models. Selected aspects of Alzheimer's disease have also been modeled in simpler model organisms, such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio. It is possible to model Alzheimer's disease not only by creating genetically modified animal lines but also by inducing symptoms of this neurodegenerative disease. This review discusses the main methods of creating induced models, with a particular focus on modeling Alzheimer's disease on cell cultures. Induced pluripotent stem cell (iPSC) technology has facilitated novel investigations into the mechanistic underpinnings of diverse diseases, including Alzheimer's. Progress in culturing brain tissue allows for more personalized studies on how drugs affect the brain. Recent years have witnessed substantial advancements in intricate cellular system development, including spheroids, three-dimensional scaffolds, and microfluidic cultures. Microfluidic technologies have emerged as cutting-edge tools for studying intercellular interactions, the tissue microenvironment, and the role of the blood-brain barrier (BBB). Modern biology is experiencing a significant paradigm shift towards utilizing big data and omics technologies. Computational modeling represents a powerful methodology for researching a wide array of human diseases, including Alzheimer's. Bioinformatic methodologies facilitate the analysis of extensive datasets generated via high-throughput experimentation. It is imperative to underscore the significance of integrating diverse modeling techniques in elucidating pathogenic mechanisms in their entirety.

Alzheimer disease is (sometimes) highly heritable: Drivers of variation in heritability estimates for binary traits, a systematic review

Estimating heritability has been fundamental in understanding the genetic contributions to complex disorders like late-onset Alzheimer's disease (LOAD), and provides a rationale for identifying genetic factors associated with disease susceptibility. While numerous studies have established substantial genetic contribution for LOAD, the interpretation of heritability estimates remains challenging. These challenges are further complicated by the binary nature of LOAD status, where estimation and interpretation require additional considerations. Through a systematic review, we identified LOAD heritability estimates from 6 twin studies and 17 genome-wide association studies, all conducted in populations of European ancestry. We demonstrate that these heritability estimates for LOAD vary considerably. The variation reflects not only differences in study design and methodological approaches but also the underlying study population characteristics. Our findings indicate that commonly cited heritability estimates, often treated as universal values, should be interpreted within specific population contexts and methodological frameworks.

GWAS by Subtraction to Disentangle RBD Genetic Background from α-Synucleinopathies

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia and abnormal behaviors occurring during REM sleep. Idiopathic RBD (iRBD) is recognized as the strongest prodromal hallmark of α-synucleinopathies, with an established conversion rate to a neurodegenerative condition that reaches up to 96.6% at 15 years of follow-up. Moreover, RBD-converters display a more severe clinical trajectory compared to those that do not present with RBD. However, the extent to which iRBD represents a distinct genetic entity or an early manifestation of neurodegeneration remains unclear. To address this, we applied Genomic Structural Equation Modeling (GenomicSEM) using a GWAS-by-subtraction approach to disentangle the genetic architecture of iRBD from the shared genomic liability across α-synucleinopathies. Our findings highlight the SNCA locus as a key genetic regulator of iRBD susceptibility. While iRBD exhibits a partially distinct genetic signature, residual genomic overlap with neurodegenerative traits suggests that its genetic architecture exists along a continuum of α-synucleinopathy risk. In this scenario, the associations with neuroanatomical correlates may serve as early indicators of a trajectory toward future neurodegeneration. These findings provide a framework for identifying biomarkers that could aid in disease stratification and risk prediction, potentially improving early intervention strategies.

APOE and Alzheimer's disease and related dementias risk among 12,221 Hispanics/Latinos

BACKGROUND

Effect of apolipoprotein E (APOE) on Alzheimer's disease and related dementias (ADRD) risk is heterogeneous across populations, with scarce data on Hispanics/Latinos.

METHODS

APOE genotype was studied in 12,221 Hispanics/Latinos (per cohort and via metanalysis): Caribbean-Hispanics, Mexicans, Mexican-Americans, and Peruvians/Bolivians. A subsample had longitudinal assessment and plasma p-tau. We tested the modifying effects of global and local ancestries. Results were replicated in an independent Peruvian cohort and brain samples.

RESULTS

APOE ε4 effect was strongest in Peruvians/Bolivians (odds ratio [OR] = 6.13, 95% confidence interval [CI] = 2.71-13.83), followed by Mexicans (OR = 4.31, 95% CI = 1.58-11.74), Mexican-Americans (OR = 3.06, 95% CI = 2.04-4.59), and Caribbean-Hispanics (OR = 2.22, 95% CI = 1.99-2.48). Meta-analyses showed OR = 2.32 (95% CI = 2.09-2.57) and OR = 0.81 (95% CI = 0.68-0.97) for the ε4 and ε2 allele, respectively. The APOE ε4 effect was replicated independently in Peruvians (OR = 5.06, 95% CI = 2.48-10.70). ε4 carriers displayed higher ADRD conversions and p-tau levels. Global and local ancestries did not modify ADRD risk, and they were associated with Braak stage.

DISCUSSION

APOE shows a heterogeneous effect on ADRD risk in our Hispanics/Latinos sample, the largest to date.

HIGHLIGHTS

The apolipoprotein E (APOE) ε4 effect is stronger in Peruvians/Bolivians than in other Hispanic/Latino groups. The strong APOE effect size in Peruvians and Bolivians was replicated in a second independent Peruvian cohort. Meta-analysis for ε4 and ε2 confirmed a significant association with Alzheimer's disease and related dementias (ADRD). Global and local ancestry do not modify the association between APOE genotype and ADRD.

Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease

Genome-wide association studies (GWAS) performed on large cohort and biobank datasets have identified many genetic loci associated with Alzheimer's disease (AD). However, the younger demographic of biobank participants relative to the typical age of late-onset AD has resulted in an insufficient number of AD cases, limiting the statistical power of GWAS and any downstream analyses. To mitigate this limitation, several trait imputation methods have been proposed to impute the expected future AD status of individuals who may not have yet developed the disease. This paper explores the use of imputed AD status in nonlinear transcriptome/proteome-wide association studies (TWAS/PWAS) to identify genes and proteins whose genetically regulated expression is associated with AD risk. In particular, we considered the TWAS/PWAS method DeLIVR, which utilizes deep learning to model the nonlinear effects of expression on disease. We trained transcriptome and proteome imputation models for DeLIVR on data from the Genotype-Tissue Expression (GTEx) Project and the UK Biobank (UKB), respectively, with imputed AD status in UKB participants as the outcome. Next, we performed hypothesis testing for the DeLIVR models using clinically diagnosed AD cases from the Alzheimer's Disease Sequencing Project (ADSP). Our results demonstrate that nonlinear TWAS/PWAS trained with imputed AD outcomes successfully identifies known and putative AD risk genes and proteins. Notably, we found that training with imputed outcomes can increase statistical power without inflating false positives, enabling the discovery of molecular exposures with potentially nonlinear effects on neurodegeneration.

Navigating the blurred boundary: Neuropathologic changes versus clinical symptoms in Alzheimer's disease, and its consequences for research in genetics

During decades scientists tried to unveil the genetic architecture of Alzheimer's disease (AD), recurring to increasingly larger sample numbers for genome-wide association studies (GWAS) in hope for higher statistical gains. Here, a retrospective look on the most prominent GWAS was performed, focusing on the quality of the diagnosis associated with the used data and databases. Different methods for AD diagnosis (or absence) carry different levels of accuracy and certainty applied to both subsets of cases and controls. Furthermore, the different phenotypes included in these databases were explored, as several incorporate other ageing comorbidities and might be encompassing many confounding agents as well. Age of the samples' donors and origin populations were also investigated as these could be biasing factors in posterior analyses. A tendency for looser diagnostic methods in more recent GWAS was observed, where greater datasets of individuals are analyzed, which may have been hampering the discovery of associated genetic variants. Specifically for AD, a diagnostic method conveying a clinical outcome may be distinct from the disease neuropathological assessment, since the first has a practical perspective that not necessarily needs a confirmation. Due to its properties and complex diagnosis, this work highlights the importance of the neuropathological confirmation of AD (or its absence) in the subjects considered for research purposes to avoid reaching statistically weak and/or misleading conclusions that may trigger further studies with powerless groundwork.

Development of a k-Nearest Neighbors Model for the Prediction of Late-Onset Alzheimer's Risk by Combining Polygenic Risk Scores and Phenotypic Variables

INTRODUCTION

Alzheimer's disease (AD), and more specifically late-onset Alzheimer's disease (LOAD), represents a considerable challenge in terms of early and timely diagnosis and treatment. Early diagnosis is crucial to improve the efficacy of the therapies and patients' quality of life. The current challenge is to accurately identify at-risk individuals before the manifestations of the first symptoms of AD.

METHODS AND RESULTS

Here, we present an improved model for LOAD risk prediction, which applies the k-nearest neighbors (KNN) algorithm. We have achieved a sensitivity of 0.80 and an area under the curve (AUC) of 0.71, which represents a high performance especially when compared to an AUC of 0.66 reported previously in 2019 using a KNN model.

DISCUSSION

The application of a mathematical model that combines genetic and clinical covariates showed a good prediction of the AD/LOAD risk, with the higher weight being the polygenic genetic risk, APOE haplotype, and age. Compared to previous studies, our model integrates and correlates genetic prediction together with phenotypic information by fine-tuning the parameters of the model in order to achieve the best performance. This algorithm can be used in the general population and does not require the manifestation of any symptoms for its effective application. Thus, we present here an advanced model for risk prediction of LOAD.

Ramp Sequence May Explain Synonymous Variant Association with Alzheimer's Disease in the Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA)

Background: The synonymous variant NC_000007.14:g.100373690T>C (rs2405442:T>C) in the Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA) gene was previously associated with decreased risk for Alzheimer's disease (AD) in genome-wide association studies, but its biological impact is largely unknown. Objective: We hypothesized that rs2405442:T>C decreases mRNA and protein levels by destroying a ramp of slowly translated codons at the 5' end of PILRA. Methods: We assessed rs2405442:T>C predicted effects on PILRA through quantitative polymerase chain reactions (qPCRs) and enzyme-linked immunosorbent assays (ELISAs) using Chinese hamster ovary (CHO) cells. RESULTS: Both mRNA (p = 1.9184 × 10-13) and protein (p = 0.01296) levels significantly decreased in the mutant versus the wildtype in the direction that we predicted based on the destruction of a ramp sequence. Conclusions: We show that rs2405442:T>C alone directly impacts PILRA mRNA and protein expression, and ramp sequences may play a role in regulating AD-associated genes without modifying the protein product.

SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders

Most genetic risk variants for neurological diseases are located in non-coding regulatory regions, where they may often act as expression quantitative trait loci (eQTLs), modulating gene expression and influencing disease susceptibility. However, eQTL studies in bulk brain tissue or specific cell types lack the resolution to capture the brain's cellular diversity. Single-nucleus RNA sequencing (snRNA-seq) offers high-resolution mapping of eQTLs across diverse brain cell types. Here, we performed a meta-analysis, "SingleBrain," integrating publicly available snRNA-seq and genotype data from four cohorts, totaling 5.8 million nuclei from 983 individuals. We mapped cis-eQTLs across major brain cell types and subtypes and employed statistical colocalization and Mendelian randomization to identify genes mediating neurological disease risk. We observed up to a 10-fold increase in cis-eQTLs compared to previous studies and uncovered novel cell type-specific genes linked to Alzheimer's disease, Parkinson's disease, and schizophrenia that were previously undetectable in bulk tissue analyses. Additionally, we prioritized putative causal variants for each locus through fine-mapping and integration with cell type-specific enhancer and promoter regulatory elements. SingleBrain represents a comprehensive single-cell eQTL resource, advancing insights into the genetic regulation of brain disorders.

Neuromorphic deviations associated with transcriptomic expression and specific cell type in Alzheimer's disease

Alzheimer's disease (AD) is known to be associated with cortical anatomical atrophy and neurodegeneration across various brain regions. However, the relationships between brain structural changes in AD and gene expression remain unclear. We perform the morphometric similarity network (MSN) analysis to reveal the consistent cortical structural differences in individuals with AD compared to controls, and investigate the associations between brain-wide gene expression and morphometric changes. Furthermore, we identify abnormally MSN-related genes linked to specific cell types as the major contributors to transcriptomic relationships. MSN-related structural changes are located in the lateral ventral prefrontal cortex, temporal pole and medial prefrontal lobe, which are highly associated with the AD's cognitive decline. Analysis of gene expression shows the spatial correlations between AD-related genes and MSN differences. Examination of cell type-specific signature genes indicates that changes in microglia and neuronal transcriptional profiles largely contribute to AD-specific MSN differences. The study map the disease-specific structural alterations in AD down to the cellular level, offering a novel perspective on the linking surface-level changes to molecular mechanisms.

Interaction of genetic variants and methylation in transcript-level expression regulation in Alzheimer's disease by multi-omics data analysis

BACKGROUND

Alzheimer's disease (AD) presents a significant public health problem and major cause of dementia. Not only genetic but epigenetic factors contribute to complex and heterogeneous molecular mechanisms underlying AD risk; in particular, single nucleotide polymorphisms (SNPs) and DNA methylation can lead to dysregulation of gene expression in the AD brain. Each of these regulators has been independently studied well in AD progression, however, their interactive roles, particularly when they are located differently, still remains unclear. Here, we aimed to explore the interplay between SNPs and DNA methylation in regulating transcript expression levels in the AD brain through an integrative analysis of whole-genome sequencing, RNA-seq, and methylation data measured from the dorsolateral prefrontal cortex.

RESULTS

We identified 179 SNP-methylation combination pairs that showed statistically significant interactions associated with the expression of 67 transcripts (63 unique genes), enriched in functional pathways, including immune-related and post-synaptic assembly pathways. Particularly, a number of HLA family genes (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-K, HLA-DQB1, and HLA-DMA) were observed as having expression changes associated with the interplay.

CONCLUSIONS

Our findings especially implicate immune-related pathways as targets of these regulatory interactions. SNP-methylation interactions may thus contribute to the molecular complexity underlying immune-related pathogenies in AD patients. Our study provides a new molecular knowledge in the context of the interplay between genetic and epigenetic regulations, in that it concerns transcript expression status in AD.

Non-APOE variants predominately expressed in smooth muscle cells contribute to the influence of Alzheimer's disease genetic risk on white matter hyperintensities

INTRODUCTION

White matter hyperintensity volumes (WMHVs) are disproportionally prevalent in individuals with Alzheimer's disease (AD), potentially reflecting neurovascular injury. We quantify the association between AD polygenic risk score (AD-PRS) and WMHV, exploring single-nucleotide polymorphisms (SNPs) that are proximal to genes overexpressed in cerebrovascular cell species.

METHODS

In a UK-Biobank sub-sample (mean age = 64, range = 45-81 years), we associate WMHV with (1) AD-PRS estimated via SNPs across the genome (minus apolipoprotein E [APOE] locus) and (2) AD-PRS estimated with SNPs proximal to specific genes that are overexpressed in cerebrovascular cell species.

RESULTS

We observed a positive association between non-APOE-AD-PRS and WMHVs. We further demonstrate an association between WMHVs and AD-PRS constructed with SNPs that are proximal to genes over-represented in smooth muscles cells (SMCs; β = 0.135, PFWE  < 0.01) and internally replicated (PDISCOVERY+REPLICATION < 0.01).

DISCUSSION

Common AD genetic risk could explain physiological processes underlying vascular pathology in AD. SMC function may offer a treatment target to prevent WMHV-related AD pathophysiology prior to the onset of symptoms.

HIGHLIGHTS

Alzheimer's disease (AD) risk factors such as apolipoprotein E (APOE) ε4, link to increased white matter hyperintensity volume (WMHV). WMHVs indicate vascular risk and neurovascular injury in AD. The broader genetic link between AD risk and WMHV is not fully understood. We quantify AD polygenic risk score (PRS) associations with WMHV, excluding APOE. AD-PRS in smooth muscle cells (SMCs) shows a significant association with increased WMHV.

The Biological Intersection Between Chemotherapy-Related Cognitive Impairment and Alzheimer Disease

Alzheimer disease (AD) is the most common type of dementia and one of the leading causes of death in elderly patients. The number of patients with AD in the United States is projected to double by 2060. Thus, understanding modifiable risk factors for AD is an urgent public health priority. In parallel with the number of patients with AD, the number of cancer survivors is estimated to increase significantly, and up to 80% of cancer patients treated with chemotherapy will develop cognitive deficits, termed chemotherapy-related cognitive impairment. This review discusses biologically plausible pathways underlying both disorders, with the goal of understanding why a proportion of chemotherapy patients may be at higher risk of developing AD. Highlighted are the E4 allele of the apolipoprotein E gene, neuroinflammation, oxidative stress, DNA damage, mitochondrial dysfunction, neuronal and synaptic loss, cellular senescence, brain-derived neurotrophic factor signaling, white matter damage, blood-brain barrier/vascular dysfunction, tau pathology, and transposable element reactivation.

Ramp sequence may explain synonymous variant association with Alzheimer's disease in the Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA)

BACKGROUND

Synonymous variant NC_000007.14:g.100373690T>C (rs2405442:T>C) in the Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA) gene was previously associated with decreased risk for Alzheimer's disease (AD) in genome-wide association studies, but its biological impact is largely unknown.

OBJECTIVE

We hypothesized that rs2405442:T>C decreases mRNA and protein levels by destroying a ramp of slowly translated codons at the 5' end of PILRA.

METHODS

We assessed rs2405442:T>C predicted effects on PILRA through quantitative polymerase chain reactions (qPCR) and enzyme-linked immunosorbent assays (ELISA) using Chinese hamster ovary (CHO) cells.

RESULTS

Both mRNA (P=1.9184 × 10-13) and protein (P=0.01296) levels significantly decreased in the mutant versus the wildtype in the direction that we predicted based on destroying a ramp sequence.

CONCLUSIONS

We show that rs2405442:T>C alone directly impacts PILRA mRNA and protein expression, and ramp sequences may play a role in regulating AD-associated genes without modifying the protein product.

Complex genetic interactions affect susceptibility to Alzheimer's disease risk in the BIN1 and MS4A6A loci

Genetics is the second strongest risk factor for Alzheimer's disease (AD) after age. More than 70 loci have been implicated in AD susceptibility so far, and the genetic architecture of AD entails both additive and nonadditive contributions from these loci. To better understand nonadditive impact of single-nucleotide polymorphisms (SNPs) on AD risk, we examined individual, joint, and interacting (SNPxSNP) effects of 139 and 66 SNPs mapped to the BIN1 and MS4A6A AD-associated loci, respectively. The analyses were conducted by fitting three respective dominant allelic-effect models using data from four independent studies. Joint effects were analyzed by considering pairwise combinations of genotypes of the selected SNPs, i.e., compound genotypes (CompG). The individual SNP analyses showed associations of 18 BIN1 SNPs and 4 MS4A6A SNPs with AD. We identified 589 BIN1 and 217 MS4A6A SNP pairs associated with AD in the CompG analysis, although their individual SNPs were not linked to AD independently. Notably, 34 BIN1 and 10 MS4A6A SNP pairs exhibited both significant SNPxSNP interaction effects and significant CompG effects. The vast majority of nonadditive effects were captured through the CompG analysis. These results expand the current understanding of the contributions of the BIN1 and MS4A6A loci to AD susceptibility. The identified nonadditive effects suggest a significant genetic modulation mechanism underlying the genetic heterogeneity of AD in these loci. Our findings highlight the importance of considering nonadditive genetic impacts on AD risk beyond the traditional SNPxSNP approximation, as they may uncover critical mechanisms not apparent when examining SNPs individually.

regionalpcs improve discovery of DNA methylation associations with complex traits

We have developed the regionalpcs method, an approach for summarizing gene-level methylation. regionalpcs addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease. In contrast to averaging, regionalpcs uses principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrates a 54% improvement in sensitivity over averaging in simulations, providing a robust framework for identifying subtle epigenetic variations. Applying regionalpcs to Alzheimer's disease brain methylation data, combined with cell type deconvolution, we uncover 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci with genome-wide association studies identified 17 genes with potential causal roles in Alzheimer's disease risk, including MS4A4A and PICALM. Available in the Bioconductor package regionalpcs, our approach facilitates a deeper understanding of the epigenetic landscape in Alzheimer's disease and opens avenues for research into complex diseases.

DNA Methylation in Alzheimer's Disease

To date, DNA methylation is the best characterized epigenetic modification in Alzheimer's disease. Involving the addition of a methyl group to the fifth carbon of the cytosine pyrimidine base, DNA methylation is generally thought to be associated with the silencing of gene expression. It has been hypothesized that epigenetics may mediate the interaction between genes and the environment in the manifestation of Alzheimer's disease, and therefore studies investigating DNA methylation could elucidate novel disease mechanisms. This chapter comprehensively reviews epigenomic studies, undertaken in human brain tissue and purified brain cell types, focusing on global methylation levels, candidate genes, epigenome wide approaches, and recent meta-analyses. We discuss key differentially methylated genes and pathways that have been highlighted to date, with a discussion on how new technologies and the integration of multiomic data may further advance the field.

The Contribution of the Renin-Angiotensin System to Alzheimer's Disease

The renin-angiotensin system (RAS) is becoming increasingly recognised as a biochemical pathway relevant to the development and progression of Alzheimer's disease (AD). RAS involvement in AD was initially linked to AD via numerous genetic association studies and more recent Genome-Wide Association Studies (GWAS), and in some cases in relation to classical hallmarks of AD pathology. Since these initial findings, which will be summarised here, several complementary areas of research are converging in support of what has been proposed as the Angiotensin Hypothesis for Alzheimer's disease. This hypothesis proposes how the RAS and disease-associated changes to the normal balance between opposing regulatory pathways within RAS warrant careful consideration in the pathogenesis of AD and its pathology. We discuss some of these in relation to RAS-targeting therapeutics, originally developed for the treatment of cardiovascular conditions, and how they might be repurposed as interventions for AD.

Cardiovascular health, genetic predisposition, and dementia risk among atherosclerotic cardiovascular disease patients

BACKGROUND

While optimal cardiovascular health (CVH) has been linked to a lower risk of dementia, few studies considered individuals' genetic background. We aimed to examine the interaction between CVH and genetic predisposition on dementia risk among individuals with atherosclerotic cardiovascular disease (ASCVD).

METHODS

We included 30,818 ASCVD patients from the UK Biobank. CVH was assessed using Life's Essential 8, and genetic predisposition determined by a genetic risk score (GRS) incorporating 85 genetic variants. Cox proportional hazard models were used to estimate hazard ratios (HRs) for all-cause dementia, Alzheimer's disease (AD), and vascular dementia (VaD).

RESULTS

Over a median follow-up of 13.5 years, 1,360 cases of all-cause dementia were identified, including 489 AD and 440 VaD cases. Higher CVH levels were associated with a reduced risk of all-cause dementia (HR for high vs. low CVH: 0.60; 95 % CI: 0.47-0.77) and VaD (HR for high vs. low CVH: 0.32; 95 % CI: 0.19-0.54), with a stronger association in individuals with lower GRS. Although the overall CVH score was not associated with the risk of dementia in individuals with high GRS, higher levels of sleep and glucose control were associated with a lower risk of VaD. CVH levels showed no association with the risk of AD.

CONCLUSION

Higher CVH levels were associated with a lower risk of VaD, not AD, with a stronger association in individuals with low GRS. Improvements in specific LE8 components, particularly sleep health and blood glucose management, were associated with reduced VaD risk across various genetic risk strata.

Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease

Background

Late-onset Alzheimer's disease (LOAD) has been associated with alterations in the morphology of multiple brain structures, and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.

Objective

To determine causal pathways from genetic risk variants of LOAD via brain morphology to LOAD.

Methods

Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer's Disease Neuroimaging Initiative.

Results

Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala and inferior lateral ventricle mediated the effect of APOE ε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the inferior lateral ventricles, increased the risk of LOAD as well as vice versa.

Conclusions

Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.

Heritable polygenic editing: the next frontier in genomic medicine?

Polygenic genome editing in human embryos and germ cells is predicted to become feasible in the next three decades. Several recent books and academic papers have outlined the ethical concerns raised by germline genome editing and the opportunities that it may present1-3. To date, no attempts have been made to predict the consequences of altering specific variants associated with polygenic diseases. In this Analysis, we show that polygenic genome editing could theoretically yield extreme reductions in disease susceptibility. For example, editing a relatively small number of genomic variants could make a substantial difference to an individual's risk of developing coronary artery disease, Alzheimer's disease, major depressive disorder, diabetes and schizophrenia. Similarly, large changes in risk factors, such as low-density lipoprotein cholesterol and blood pressure, could, in theory, be achieved by polygenic editing. Although heritable polygenic editing (HPE) is still speculative, we completed calculations to discuss the underlying ethical issues. Our modelling demonstrates how the putatively positive consequences of gene editing at an individual level may deepen health inequalities. Further, as single or multiple gene variants can increase the risk of some diseases while decreasing that of others, HPE raises ethical challenges related to pleiotropy and genetic diversity. We conclude by arguing for a collectivist perspective on the ethical issues raised by HPE, which accounts for its effects on individuals, their families, communities and society4.

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations

Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.

Expansion of peripheral cytotoxic CD4+ T cells in Alzheimer's disease: New insights from multi-omics evidence

The significance of the adaptive immune response in Alzheimer's disease (AD) is increasingly recognized. We analyzed scRNA-Seq data from AD patients, revealing a notable rise in CD4 cytotoxic T cells (CD4-CTLs) in peripheral blood mononuclear cells (PBMCs), validated in vivo and in vitro. This rise correlates with cognitive decline in AD patients. We also identified transcription factors TBX21 and MYBL1 as key drivers of CD4-CTL expansion. Further analyses indicate these cells are terminally differentiated, showing clonal expansion, metabolic changes, and unique communication patterns. Mendelian randomization identified risk genes SRGN and ITGB1, suggesting their genetic regulation in CD4-CTLs may contribute to AD. To summarize, our findings characterize the expansion of CD4-CTLs in the PBMCs of AD patients, providing valuable understanding into the possible mechanisms involved in the expansion of CD4-CTLs in AD.

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors

BACKGROUND

Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. However, there is a lack of deep learning-based survival analysis models that integrate both genetic and clinical factors to develop and validate individualized dynamic dementia risk prediction models.

METHODS AND RESULTS

This study is based on a large prospective cohort from the UK Biobank, which includes a total of 41,484 participants with an average follow-up period of 12.6 years. Initially, 364 candidate features (predictor variables) were screened. The top 30 key features were then identified by ranking the importance of each predictor variable using the Gradient Boosting Machine (GBM) model. A multi-model comparison strategy was employed to evaluate the predictive performance of four survival analysis models: DeepSurv, DeepHit, Kaplan-Meier estimation, and the Cox proportional hazards model (CoxPH). The results showed that the average Harrell's C-index for the DeepSurv model was 0.743, for the DeepHit model it was 0.633, for the CoxPH model it was 0.749, and for the Kaplan-Meier estimator model it was 0.500. In addition, the average D-Calibration Survival Measure was 6.014, 4408.086, 32274.743, and 1.508, respectively. The Brier score (BS) was used to assess the importance of features for the DeepSurv dementia prediction model, and the relationship between features and dementia was visualized using a partial dependence plot (PDP). To facilitate further research, the team deployed the DeepSurv dementia prediction model on AliCloud servers and designated it as the UKB-DementiaPre Tool.

CONCLUSION

This study successfully developed and validated the DeepSurv dementia prediction model for individuals aged 60 years and above, integrating both genetic and clinical data. The model was then deployed on AliCloud servers to promote its clinical translation. It is anticipated that this prediction model will provide more accurate decision support for clinical treatment and will serve as a valuable tool for the primary prevention of dementia.

Associations of Coffee and Tea Consumption on Neural Network Connectivity: Unveiling the Role of Genetic Factors in Alzheimer's Disease Risk

BACKGROUND

Coffee and tea are widely consumed beverages, but their long-term effects on cognitive function and aging remain largely unexplored. Lifestyle interventions, particularly dietary habits, offer promising strategies for enhancing cognitive performance and preventing cognitive decline.

METHODS

This study utilized data from the UK Biobank cohort (n = 12,025) to examine the associations between filtered coffee, green tea, and standard tea consumption and neural network functional connectivity across seven resting-state networks. We focused on networks spanning prefrontal and occipital areas that are linked to complex cognitive and behavioral functions. Linear mixed models were used to assess the main effects of coffee and tea consumption, as well as their interactions with Apolipoprotein E (APOE) genetic risk-the strongest genetic risk factor for Alzheimer's disease (AD).

RESULTS

Higher filtered coffee consumption was associated with increased functional connectivity in several networks, including Motor Execution, Sensorimotor, Fronto-Cingular, and a Prefrontal + 'What' Pathway Network. Similarly, greater green tea intake was associated with enhanced connectivity in the Extrastriate Visual and Primary Visual Networks. In contrast, higher standard tea consumption was linked to reduced connectivity in networks such as Memory Consolidation, Motor Execution, Fronto-Cingular, and the "What" Pathway + Prefrontal Network. The APOE4 genotype and family history of AD influenced the relationship between coffee intake and connectivity in the Memory Consolidation Network. Additionally, the APOE4 genotype modified the association between standard tea consumption and connectivity in the Sensorimotor Network.

CONCLUSIONS

The distinct patterns of association between coffee, green tea, and standard tea consumption and resting-state brain activity may provide insights into AD-related brain changes. The APOE4 genotype, in particular, appears to play a significant role in modulating these relationships. These findings enhance our knowledge of how commonly consumed beverages may influence cognitive function and potentially AD risk among older adults.

NIAGADS: A Comprehensive National Data Repository for Alzheimer's Disease and Related Dementia Genetics and Genomics Research

NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimer's Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud-based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimer's disease.

Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer's disease

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and nonrandom participation in parental illness surveys, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that the current AD GWAX provide highly misleading genetic correlations between AD risk and higher education, which subsequently affects a variety of genetic epidemiological applications involving AD and cognition. Our study sheds light on potential issues in the design and analysis of middle-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease

Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts. We introduce "genome-wide association neural networks (GWANN)" a novel approach that uses neural networks (NNs) to perform a gene-level association study with family history of Alzheimer's disease (AD). In UK Biobank, we defined cases (n = 42 110) as those with AD or family history of AD and sampled an equal number of controls. The data was split into an 80:20 ratio of training and testing samples, and GWANN was trained on the former followed by identifying associated genes using its performance on the latter. Our method identified 18 genes to be associated with family history of AD. APOE, BIN1, SORL1, ADAM10, APH1B, and SPI1 have been identified by previous AD GWAS. Among the 12 new genes, PCDH9, NRG3, ROR1, LINGO2, SMYD3, and LRRC7 have been associated with neurofibrillary tangles or phosphorylated tau in previous studies. Furthermore, there is evidence for differential transcriptomic or proteomic expression between AD and healthy brains for 10 of the 12 new genes. A series of post hoc analyses resulted in a significantly enriched protein-protein interaction network (P-value < 1 × 10-16), and enrichment of relevant disease and biological pathways such as focal adhesion (P-value = 1 × 10-4), extracellular matrix organization (P-value = 1 × 10-4), Hippo signaling (P-value = 7 × 10-4), Alzheimer's disease (P-value = 3 × 10-4), and impaired cognition (P-value = 4 × 10-3). Applying NNs for GWAS illustrates their potential to complement existing algorithms and methods and enable the discovery of new associations without the need to expand existing cohorts.

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 set of SNPs significantly 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 around APOE region 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.

Neuronal ACE1 knockout disrupts the hippocampal renin angiotensin system leading to memory impairment and vascular loss in normal aging

Angiotensin I converting enzyme (ACE1) maintains blood pressure homeostasis by converting angiotensin I into angiotensin II in the renin-angiotensin system (RAS). ACE1 is expressed in the brain, where an intrinsic RAS regulates complex cognitive functions including learning and memory. ACE1 has been implicated in neurodegenerative disorders including Alzheimer's disease and Parkinson's disease, but the mechanisms remain incompletely understood. Here, we performed single-nucleus RNA sequencing to characterize the expression of RAS genes in the hippocampus and discovered that Ace is mostly expressed in CA1 region excitatory neurons. To gain a deeper understanding of the function of neuronal ACE1, we generated ACE1 conditional knockout (cKO) mice lacking ACE1 expression specifically in hippocampal and cortical excitatory neurons. ACE1 cKO mice exhibited hippocampus-dependent memory impairment in the Morris water maze, y-maze, and fear conditioning tests. Total ACE1 level was significantly reduced in the cortex and hippocampus of ACE1 cKO mice showing that excitatory neurons are the predominant cell type expressing ACE1 in the forebrain. Despite similar reductions in total ACE1 level in both the hippocampus and cortex, the RAS pathway was dysregulated in the hippocampus only. Importantly, ACE1 cKO mice exhibited age-related capillary loss selectively in the hippocampus. Here, we show selective vulnerability of the hippocampal microvasculature and RAS pathway to neuronal ACE1 knockout. Our results provide important insights into the function of ACE1 in the brain and demonstrate a connection between neuronal ACE1 and cerebrovascular function in the hippocampus.

Astrocyte transcriptomic analysis identifies glypican 5 downregulation as a contributor to synaptic dysfunction in Alzheimer's disease models

Synaptic dysfunction is an early feature in Alzheimer's disease (AD) and correlates with cognitive decline. Astrocytes are essential regulators of synapses, impacting synapse formation, maturation, elimination and function. To understand if synapse-supportive functions of astrocytes are altered in AD, we used astrocyte BacTRAP mice to generate a comprehensive dataset of hippocampal astrocyte transcriptional alterations in two mouse models of Alzheimer's pathology (APPswe/PS1dE9 and Tau P301S), characterizing sex and age-dependent changes. We found that astrocytes from both models downregulate genes important for synapse regulation and function such as the synapse-maturation factor Glypican 5. This transcriptional signature is shared with human post-mortem AD patients. Manipulating a key component of this signature by in vivo overexpression of Glypican 5 in astrocytes is sufficient to prevent early synaptic dysfunction and improve spatial learning in APPswe/PS1dE9 mice. These findings open new avenues to target astrocytic factors to mitigate AD synaptic dysfunction.

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

Blood-based multivariate methylation risk score for cognitive impairment and dementia

INTRODUCTION

The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection.

METHODS

In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts.

RESULTS

We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10-3). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia = 2.59).

DISCUSSION

Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk.

HIGHLIGHTS

We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level.

Association between probable sarcopenia and dementia risk: a prospective cohort study with mediation analysis

The role of alcohol consumption as a mediator in the risk between sarcopenia and dementia remains inadequately studied. There is currently limited research on whether the association between sarcopenia and the risk of Alzheimer's disease (AD) is influenced by genetic susceptibility. Our study incorporated a total of 483,637 baseline non-dementia participants, who were classified into groups of individuals with no sarcopenia and those with probable sarcopenia based on the definition. We employed Cox proportional hazards models to evaluate the association between probable sarcopenia and the risk of all cause dementia (ACD), AD, and vascular dementia (VD). We conducted mediation analysis to explore the role of alcohol consumption in the association between probable sarcopenia and the risk of ACD, AD, and VD. During the median follow-up period of 13.6 years, we documented 9000 new cases of ACD (including 4061 AD and 2025 VD). Fully adjusted multivariate model revealed a significant correlation between probable sarcopenia and elevated risk for ACD (HR = 1.54, 95% CI: 1.46-1.62, p < 0.001), AD (HR = 1.32, 95% CI: 1.21-1.43, p < 0.001), and VD (HR = 1.69, 95% CI: 1.52-1.89, p < 0.001). Mediation analysis elucidates that alcohol consumption explained 12.8%, 15.2%, and 11.1% of the associations of probable sarcopenia with the risk of ACD, AD, and VD, respectively. An interactive relationship prevails between probable sarcopenia and genetic factors (p for interaction <0.001), and regardless of the degree of genetic risk, probable sarcopenia correlates with an elevated AD risk. Our study reveals a significant association between probable sarcopenia and an increased risk of dementia, with alcohol consumption playing a mediating role in this association. There is an interaction between probable sarcopenia and genetic susceptibility related to the risk of AD.

Microglia signaling in health and disease - Implications in sex-specific brain development and plasticity

Microglia, the intrinsic neuroimmune cells residing in the central nervous system (CNS), exert a pivotal influence on brain development, homeostasis, and functionality, encompassing critical roles during both aging and pathological states. Recent advancements in comprehending brain plasticity and functions have spotlighted conspicuous variances between male and female brains, notably in neurogenesis, neuronal myelination, axon fasciculation, and synaptogenesis. Nevertheless, the precise impact of microglia on sex-specific brain cell plasticity, sculpting diverse neural network architectures and circuits, remains largely unexplored. This article seeks to unravel the present understanding of microglial involvement in brain development, plasticity, and function, with a specific emphasis on microglial signaling in brain sex polymorphism. Commencing with an overview of microglia in the CNS and their associated signaling cascades, we subsequently probe recent revelations regarding molecular signaling by microglia in sex-dependent brain developmental plasticity, functions, and diseases. Notably, C-X3-C motif chemokine receptor 1 (CX3CR1), triggering receptors expressed on myeloid cells 2 (TREM2), calcium (Ca2+), and apolipoprotein E (APOE) emerge as molecular candidates significantly contributing to sex-dependent brain development and plasticity. In conclusion, we address burgeoning inquiries surrounding microglia's pivotal role in the functional diversity of developing and aging brains, contemplating their potential implications for gender-tailored therapeutic strategies in neurodegenerative 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. Microglia activation is accompanied by the formation and chronic expression 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 reactive oxygen species and the dilated endoplasmic reticulum. 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 Alzheimer's disease associated oxidative stress and neurodegeneration.

Genomic study of taste perception genes in African Americans reveals SNPs linked to Alzheimer's disease

While previous research has shown the potential links between taste perception pathways and brain-related conditions, the area involving Alzheimer's disease remains incompletely understood. Taste perception involves neurotransmitter signaling, including serotonin, glutamate, and dopamine. Disruptions in these pathways are implicated in neurodegenerative diseases. The integration of olfactory and taste signals in flavor perception may impact brain health, evident in olfactory dysfunction as an early symptom in neurodegenerative conditions. Shared immune response and inflammatory pathways may contribute to the association between altered taste perception and conditions like neurodegeneration, present in Alzheimer's disease. This study consists of an exploration of expression-quantitative trait loci (eQTL), utilizing whole-blood transcriptome profiles, of 28 taste perception genes, from a combined cohort of 475 African American subjects. This comprehensive dataset was subsequently intersected with single-nucleotide polymorphisms (SNPs) identified in Genome-Wide Association Studies (GWAS) of Alzheimer's Disease (AD). Finally, the investigation delved into assessing the association between eQTLs reported in GWAS of AD and the profiles of 741 proteins from the Olink Neurological Panel. The eQTL analysis unveiled 3,547 statistically significant SNP-Gene associations, involving 412 distinct SNPs that spanned all 28 taste genes. In 17 GWAS studies encompassing various traits, a total of 14 SNPs associated with 12 genes were identified, with three SNPs consistently linked to Alzheimer's disease across four GWAS studies. All three SNPs demonstrated significant associations with the down-regulation of TAS2R41, and two of them were additionally associated with the down-regulation of TAS2R60. In the subsequent pQTL analysis, two of the SNPs linked to TAS2R41 and TAS2R60 genes (rs117771145 and rs10228407) were correlated with the upregulation of two proteins, namely EPHB6 and ADGRB3. Our investigation introduces a new perspective to the understanding of Alzheimer's disease, emphasizing the significance of bitter taste receptor genes in its pathogenesis. These discoveries set the stage for subsequent research to delve into these receptors as promising avenues for both intervention and diagnosis. Nevertheless, the translation of these genetic insights into clinical practice requires a more profound understanding of the implicated pathways and their pertinence to the disease's progression across diverse populations.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer's disease dementia

Background

Proteome-wide association study (PWAS) integrating proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer's disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia.

Methods

We conducted PWAS of AD dementia by integrating publicly available summary pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, to test whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian randomization analyses.

Results

We identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 9 for CSF, and 16 for plasma tissues. Four of these were shared by at least two tissues, and gene MAPK3 was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD or AD pathological hall marks as shown in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS); and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk gene APOE. Especially, these PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%).

Conclusions

Our findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma tissues, and targets for developing therapeutic interventions. We also demonstrated the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.

The Mega Vascular Cognitive Impairment and Dementia (MEGAVCID) consortium. A genome-wide association meta-analysis of all-cause and vascular dementia

INTRODUCTION

Dementia is a multifactorial disease with Alzheimer's disease (AD) and vascular dementia (VaD) pathologies making the largest contributions. Yet, most genome-wide association studies (GWAS) focus on AD.

METHODS

We conducted a GWAS of all-cause dementia (ACD) and examined the genetic overlap with VaD. Our dataset includes 800,597 individuals, with 46,902 and 8702 cases of ACD and VaD, respectively. Known AD loci for ACD and VaD were replicated. Bioinformatic analyses prioritized genes that are likely functionally relevant and shared with closely related traits and risk factors.

RESULTS

For ACD, novel loci identified were associated with energy transport (SEMA4D), neuronal excitability (ANO3), amyloid deposition in the brain (RBFOX1), and magnetic resonance imaging markers of small vessel disease (SVD; HBEGF). Novel VaD loci were associated with hypertension, diabetes, and neuron maintenance (SPRY2, FOXA2, AJAP1, and PSMA3).

DISCUSSION

Our study identified genetic risks underlying ACD, demonstrating overlap with neurodegenerative processes, vascular risk factors, and cerebral SVD.

HIGHLIGHTS

We conducted the largest genome-wide association study of all-cause dementia (ACD) and vascular dementia (VaD). Known genetic variants associated with AD were replicated for ACD and VaD. Functional analyses identified novel loci for ACD and VaD. Genetic risks of ACD overlapped with neurodegeneration, vascular risk factors, and cerebral small vessel disease.

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.

Multimodal analysis of disease onset in Alzheimer's disease using Connectome, Molecular, and genetics data

Alzheimer's disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer's disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.

Dynamic convergence of autism disorder risk genes across neurodevelopment

Over a hundred risk genes underlie risk for autism spectrum disorder (ASD) but the extent to which they converge on shared downstream targets to increase ASD risk is unknown. To test the hypothesis that cellular context impacts the nature of convergence, here we apply a pooled CRISPR approach to target 29 ASD loss-of-function genes in human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells, glutamatergic neurons, and GABAergic neurons. Two distinct approaches (gene-level and network-level analyses) demonstrate that convergence is greatest in mature glutamatergic neurons. Convergent effects are dynamic, varying in strength, composition, and biological role between cell types, increasing with functional similarity of the ASD genes examined, and driven by cell-type-specific gene co-expression patterns. Stratification of ASD genes yield targeted drug predictions capable of reversing gene-specific convergent signatures in human cells and ASD-related behaviors in zebrafish. Altogether, convergent networks downstream of ASD risk genes represent novel points of individualized therapeutic intervention.

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

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

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.

Genomic Study of Taste Perception Genes in African Americans Reveals SNPs Linked to Alzheimer's Disease

Background

While previous research has shown the potential links between taste perception pathways and brain-related conditions, the area involving Alzheimer's disease remains incompletely understood. Taste perception involves neurotransmitter signaling, including serotonin, glutamate, and dopamine. Disruptions in these pathways are implicated in neurodegenerative diseases. The integration of olfactory and taste signals in flavor perception may impact brain health, evident in olfactory dysfunction as an early symptom in neurodegenerative conditions. Shared immune response and inflammatory pathways may contribute to the association between altered taste perception and conditions like neurodegeneration, present in Alzheimer's disease.

Methods

This study consists of an exploration of expression-quantitative trait loci (eQTL), utilizing whole-blood transcriptome profiles, of 28 taste perception genes, from a combined cohort of 475 African American subjects. This comprehensive dataset was subsequently intersected with single-nucleotide polymorphisms (SNPs) identified in Genome-Wide Association Studies (GWAS) of Alzheimer's Disease (AD). Finally, the investigation delved into assessing the association between eQTLs reported in GWAS of AD and the profiles of 741 proteins from the Olink Neurological Panel.

Results

The eQTL analysis unveiled 3,547 statistically significant SNP-Gene associations, involving 412 distinct SNPs that spanned all 28 taste genes. In 17 GWAS studies encompassing various traits, a total of 14 SNPs associated with 12 genes were identified, with three SNPs consistently linked to Alzheimer's disease across four GWAS studies. All three SNPs demonstrated significant associations with the down-regulation of TAS2R41, and two of them were additionally associated with the down-regulation of TAS2R60. In the subsequent pQTL analysis, two of the SNPs linked to TAS2R41 and TAS2R60 genes (rs117771145 and rs10228407) were correlated with the upregulation of two proteins, namely EPHB6 and ADGRB3.

Conclusions

Our investigation introduces a new perspective to the understanding of Alzheimer's disease, emphasizing the significance of bitter taste receptor genes in its pathogenesis. These discoveries set the stage for subsequent research to delve into these receptors as promising avenues for both intervention and diagnosis. Nevertheless, the translation of these genetic insights into clinical practice requires a more profound understanding of the implicated pathways and their pertinence to the disease's progression across diverse populations.