Overview and findings from the religious orders study

Multi Layered Omics Approaches Reveal Glia Specific Alterations in Alzheimer's Disease: A Systematic Review and Future Prospects

Alzheimer's disease (AD) is the most common neurodegenerative dementia with multi-layered complexity in its molecular etiology. Multiple omics-based approaches, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics are enabling researchers to dissect this molecular complexity, and to uncover a plethora of alterations yielding insights into the pathophysiology of this disease. These approaches reveal multi-omics alterations essentially in all cell types of the brain, including glia. In this systematic review, we screen the literature for human studies implementing any omics approach within the last 10 years, to discover AD-associated molecular perturbations in brain glial cells. The findings from over 200 AD-related studies are reviewed under four different glial cell categories: microglia, oligodendrocytes, astrocytes and brain vascular cells. Under each category, we summarize the shared and unique molecular alterations identified in glial cells through complementary omics approaches. We discuss the implications of these findings for the development, progression and ultimately treatment of this complex disease as well as directions for future omics studies in glia cells.

Association of late-life variability in hemoglobin A1C with postmortem neuropathologies

INTRODUCTION

To study the relationship of late-life hemoglobin A1C (A1C) with postmortem neuropathology in older adults with and without diabetes mellitus (DM).

METHODS

A total of 990 participants from five cohort studies of aging and dementia with at least two annually-collected A1C measures, who had autopsy. Neuropathologic evaluations documented cerebrovascular disease, Alzheimer's disease (AD), and other pathologies. To evaluate the association of A1C mean and variability (standard deviation [SD]) with neuropathology, we used a series of adjusted regression models.

RESULTS

Participants (mean age at death = 90.8 years; education = 15.8 years; 76% women) had six A1C measurements on average. Mean A1C was associated with greater odds of macroinfarcts (estimate = 0.14; p = 0.04) and subcortical infarcts (estimate = 0.16; p = 0.02). A1C variability was not associated with cerebrovascular pathology. A1C mean and variability were inversely associated with AD pathology.

DISCUSSION

The A1C average over time was associated with infarcts, and the A1C average and variability were inversely associated with AD pathology. Future studies should explore the underlying mechanisms linking A1C to dementia-related neuropathologies.

HIGHLIGHTS

Hemoglobin A1C (A1C), a measure of peripheral insulin resistance, is used to assess glycemic control. Higher A1C mean was associated with greater odds of macroscopic subcortical infarcts. A1C variability was not associated with cerebrovascular pathology. Both A1C mean and variability had inverse associations with AD pathology. None of the associations varied by diabetes mellitus status.

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes

Background & Objective

Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we leveraged high-throughput transcriptomic data from sporadic AD cases at different stages of cognitive impairment across multiple independent cohorts and brain regions to examine the role of epilepsy-causing genes.

Methods

Epilepsy-causing genes were manually curated, and their expression levels were analyzed across bulk transcriptomic data from three AD cohorts and three brain regions. RNA-seq data from sporadic AD and control cases from the Knight ADRC, MSBB, and ROSMAP cohorts were processed and analyzed under the same analytical pipeline. An integrative clustering approach employing machine learning and multi-omics data was employed to identify molecularly defined profiles with different cognitive scores.

Results

We found several epilepsy-associated genes/pathways significantly dysregulated in a group of AD patients with more severe cognitive impairment. We observed 15 genes consistently downregulated across the three cohorts, including sodium and potassium channels, suggesting that these genes play fundamental roles in cognitive function or AD progression. Notably, we found 25 of these genes dysregulated in earlier stages of AD and become worse with AD progression.

Conclusion

Our findings showed that epilepsy-causing genes showed changes in the early and late stages of AD progression, suggesting that they might be playing a role in AD progression. We can not establish directionality or cause-effect with our findings. However, changes in the epilepsy-causing genes might underlie the presence of seizures in AD patients, which might be present before or concurrently with the initial stages of AD.

Brain multi-omic Mendelian randomisation to identify novel drug targets for gliomagenesis

BACKGROUND

Genetic variants associated with molecular traits that are also associated with liability to glioma can provide causal evidence for the identification and prioritisation of drug targets.

METHODS

We performed comprehensive two-sample Mendelian randomisation (Wald ratio and/or IVW) and colocalisation analyses of molecular traits on glioma. Instrumentable traits (QTLs P < 5 × 10-8) were identified amongst 11 985 gene expression measures, 13 285 splicing isoforms and 10 198 protein abundance measures, derived from 15 brain regions. Glioma summary-level data was extracted from a genome-wide association meta-analysis of 12 496 cases and 18 190 controls.

RESULTS

We found evidence for causal effect of 22 molecular traits (across 18 genes/proteins) on glioma risk. Thirteen molecular traits have been previously linked with glioma risk and five were novel; HBEGF (5q31.3) expression and all glioma [OR 1.36 (95%CI 1.19-1.55); P = 4.41 × 10-6]; a CEP192 (18p11.21) splice isoform and glioblastoma [OR 4.40 (95%CI 2.28-8.48); P = 9.78 × 10-4]; a FAIM (3q22.3) splice isoform and all glioma [OR 2.72-3.43; P = 1.03 × 10-5 to 1.09 × 10-5]; a SLC8A1 (2p22.1) splice isoform and all glioma [OR 0.37 (95%CI 0.24-0.56; P = 5.72 × 10-6]; D2HGDH (2q37.3) protein and all glioma [OR 0.86 (95%CI 0.80-0.92); P = 5.94 × 10-6)].

CONCLUSIONS

We provide robust causal evidence for prioritising genes and their protein products in glioma research. Our results highlight the importance of alternative splicing as a mechanism in gliomagenesis and as an avenue for exploration of drug targets.

Sex differences in the association between age-related decline in blood pressure and decline in cognition: A prospective cohort study

Background

Both high and declining blood pressure (BP) are associated with cognitive decline risk in older adults. In late-life, women have higher rates of hypertension, experience faster cognitive decline, and represent two-thirds of individuals with Alzheimer's disease dementia. However, sex differences in the association between BP decline and cognitive decline are unknown.

Methods

Data were analyzed from 4719 older adults without known baseline dementia (mean age = 76.7 [SD = 7.7] years; 74% women) enrolled in one of five US-based prospective community-based cohort studies, followed annually for up to 31 years (mean = 8.7 [SD = 5.7] years). A 19-test cognitive battery, yielding composite global and five domain-specific scores, and BP were assessed annually. Bivariate mixed-effects models simultaneously estimated change in BP and cognition, for the total group and by sex.

Findings

Systolic BP, diastolic BP, and cognition all declined over time (ps <0.01). Bivariate mixed-effect models revealed a sex difference in the correlation of decline in systolic BP and decline in global cognition (women: r = 0.26, 95%CI: 0.17 - 0.37; men: r = 0.01, 95%CI: -0.13 - 0.11), such that women exhibited a stronger correlation than men. Decline in systolic BP was related to decline in global and all five cognitive domains in women but none in men, with another sex difference identified in the working memory domain. An increase of diastolic BP was related to decline in working memory in men, and no other associations with diastolic BP were significant for either sex.

Interpretation

Systolic BP decline in late-life is related to decline in global and domain-specific cognition in women but not men, with sex differences in global cognition and the working memory domain. These findings suggest that in older women, declining systolic BP - a routinely-used clinical measure - may be an important marker of concurrent cognitive decline.

Distinct transcriptional alterations distinguish Lewy body disease from Alzheimer's disease

Lewy body dementia and Alzheimer's disease (AD) are leading causes of cognitive impairment, characterized by distinct but overlapping neuropathological hallmarks. Lewy body disease (LBD) is characterized by α-synuclein aggregates in the form of Lewy bodies as well as the deposition of extracellular amyloid plaques, with many cases also exhibiting neurofibrillary tangle (NFT) pathology. In contrast, AD is characterized by amyloid plaques and neurofibrillary tangles. Both conditions often co-occur with additional neuropathological changes, such as vascular disease and TDP-43 pathology. To elucidate shared and distinct molecular signatures underlying these mixed neuropathologies, we extensively analysed transcriptional changes in the anterior cingulate cortex, a brain region critically involved in cognitive processes. We performed bulk tissue RNA sequencing from the anterior cingulate cortex and determined differentially expressed genes (q-value <0.05) in control (n = 81), LBD (n = 436), AD (n = 53) and pathological amyloid cases consisting of amyloid pathology with minimal or no tau pathology (n = 39). We used gene set enrichment and weighted gene correlation network analysis to understand the pathways associated with each neuropathologically defined group. LBD cases had strong upregulation of inflammatory pathways and downregulation of metabolic pathways. The LBD cases were further subdivided into either high Thal amyloid, Braak NFT, or low pathological burden cohorts. Compared to the control cases, the LBD cohorts consistently showed upregulation for genes involved in protein folding and cytokine immune response, as well as downregulation of fatty acid metabolism. Surprisingly, concomitant tau pathology within the LBD cases resulted in no additional changes. Some core inflammatory pathways were shared between AD and LBD but with numerous disease-specific changes. Direct comparison of LBD cohorts versus AD cases revealed strong enrichment of synaptic signalling, behaviour and neuronal system pathways. Females had a stronger response overall in both LBD and AD, with several sex-specific changes. Overall, the results identify genes commonly and uniquely dysregulated in neuropathologically defined LBD and AD cases, shedding light on shared and distinct molecular pathways. Additionally, the study underscores the importance of considering sex-specific changes in understanding the complex transcriptional landscape of these neurodegenerative diseases.

Review of Associations of Diabetes and Insulin Resistance With Brain Health in Three Harmonised Cohort Studies of Ageing and Dementia

Diabetes increases the risk of dementia, and insulin resistance (IR) has emerged as a potential unifying feature. Here, we review published findings over the past 2 decades on the relation of diabetes and IR to brain health, including those related to cognition and neuropathology, in the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study (ROS/MAP/MARS), three harmonised cohort studies of ageing and dementia at the Rush Alzheimer's Disease Center (RADC). A wide range of participant data, including information on medical conditions such as diabetes and neuropsychological tests, as well as other clinical and laboratory-based data collected annually. Neuropathology data are collected in participants who agree to autopsy at death. Recent studies have measured additional peripheral and brain IR data, including multi-omics. This review summarises findings from the RADC cohort studies that investigate the relation of diabetes and IR in older adults to cognition, neuropathology, omics in dementia, and other brain health measures. Examining the risk of clinically diagnosed dementia in older adults, our study found a 65% increased risk of Alzheimer's disease (AD) dementia in individuals with diabetes compared with those without. Regarding cognitive function, we have consistently observed associations of diabetes, as well as both peripheral and brain IR, with worse and declining performance in global cognition and specific cognitive domains, particularly semantic memory and perceptual speed. Studies utilising neuropathological data showed associations of diabetes and peripheral IR with brain infarcts, while brain IR measures, notably alpha serine/threonine-protein kinase1 (AKT1), were associated with both brain infarcts and AD pathology. Multi-omics studies suggested shared causal genes and pathways between diabetes and dementia. Recent epigenetic studies have revealed associations between IR and AD risk, along with distinct 5-hydroxymethylcytosine signatures in diabetes-associated AD. Furthermore, our studies have utilised other available data to investigate the impact of diabetes on neurological outcomes other than cognition and reported worsening of parkinsonian-like signs in diabetes. Recent studies have also explored risk factors for diabetes and have reported associations between lower literacy and decision-making abilities with elevated haemoglobin A1C levels, a peripheral IR measure. Overall, our findings, as summarised in this review, illustrate a range of mechanistic and other insights into the complex relationship of diabetes and IR with brain health. These findings may have important implications for future research on the ageing brain, including the prevention of cognitive decline and dementia in persons at risk for or with diabetes.

Therapeutic agents for Alzheimer's disease: a critical appraisal

Alzheimer's disease (AD) is the most common form of dementia. Mutations in genes and precursors of β amyloid (Aβ) are found in the familial form of the disease. This led to the evaluation of seven monoclonal antibodies against Aβ in subjects with AD, two of which were approved for use by the FDA. They caused only a small improvement in cognitive function, probably because they were given to those with much more prevalent sporadic forms of dementia. They also have potentially serious adverse effects. Oxidative stress and elevated pro-inflammatory cytokines are present in all subjects with AD and are well correlated with the degree of memory impairment. Drugs that affect these processes include TNFα blocking antibodies and MAPK p38 inhibitors that reduce cognitive impairment when given for other inflammatory conditions. However, their adverse effects and inability to penetrate the brain preclude their use for dementia. Rosiglitazone is used to treat diabetes, a risk factor for AD, but failed in a clinical trial because it was given to subjects that already had dementia. Ladostigil reduces oxidative stress and suppresses the release of pro-inflammatory cytokines from activated microglia without blocking their effects. Chronic oral administration to aging rats prevented the decline in memory and suppressed overexpression of genes adversely affecting synaptic function in relevant brain regions. In a phase 2 trial, ladostigil reduced the decline in short-term memory and in whole brain and hippocampal volumes in human subjects with mild cognitive impairment and had no more adverse effects than placebo.

Association of ten VEGF family genes with Alzheimer's disease endophenotypes at single cell resolution

INTRODUCTION

Using a single-nucleus transcriptome derived from the dorsolateral prefrontal cortex of 424 Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP) participants, we investigated the cell type-specific effect of ten vascular endothelial growth factor (VEGF) genes on Alzheimer's disease (AD) endophenotypes.

METHODS

Negative binomial mixed models were used for differential gene expression and association analysis with AD endophenotypes. VEGF-associated intercellular communication was also profiled.

RESULTS

Higher microglia FLT1, endothelial FLT4, and oligodendrocyte VEGFB are associated with greater amyloid beta (Aβ) load, whereas higher VEGFB expression in inhibitory neurons is associated with lower Aβ load. Higher astrocyte NRP1 is associated with lower tau density. Higher microglia and endothelial FLT1 are associated with worse cognition performance. Endothelial and microglial FLT1 expression was upregulated in clinical AD patients compared to cognitively normal controls. Finally, AD cells showed a significant reduction in VEGF signaling compared to controls.

DISCUSSION

Our results highlight key changes in VEGF receptor expression in endothelial and microglial cells during AD, and the potential protective role of VEGFB in neurons.

HIGHLIGHTS

The prefrontal cortical expression of FLT1 and FLT4 was associated with worse cross-sectional global cognitive function, longitudinal cognitive trajectories, and more Alzheimer's disease (AD) neuropathology. The associations between FLT1 or FLT4 and AD endophenotypes appear to be driven by endothelial and microglial cells. VEGFB expression seems to have opposing effects on the Aβ burden in AD depending on cell types, highlighting its potential protective role in neurons.

Biological validation of peak-width of skeletonized mean diffusivity as a VCID biomarker: The MarkVCID Consortium

BACKGROUND

Peak-width of skeletonized mean diffusivity (PSMD), a neuroimaging marker of cerebral small vessel disease (SVD), has shown excellent instrumental properties. Here, we extend our work to perform a biological validation of PSMD.

METHODS

We included 396 participants from the Biomarkers for Vascular Contributions to Cognitive Impairment and Dementia (MarkVCID-1) Consortium and three replication samples (Cohorts for Heart and Aging Research in Genomic Epidemiology = 6172, Rush University Medical Center = 287, University of California Davis Alzheimer's Disease Research Center = 567). PSMD was derived from diffusion tensor imaging using an automated algorithm. We related PSMD to a composite measure of general cognitive function using linear regression models adjusting for confounders.

RESULTS

Higher PSMD was associated with lower general cognition in MarkVCID-1 independent of age, sex, education, and intracranial volume (Beta [95% confidence interval], -0.8 [-1.2, -0.4], P < 0.001). These findings were replicated in independent samples. Furthermore, PSMD explained cognitive status above and beyond white matter hyperintensities.

DISCUSSION

Our biological validation work supports the pursuit of larger clinical validation studies evaluating PSMD as a susceptibility/risk biomarker of small vessel disease contributing to cognitive impairment and dementia.

HIGHLIGHTS

Peak-width of skeletonized mean diffusivity (PSMD) is a novel small vessel disease neuroimaging biomarker. A prior instrumental validation study demonstrated that PSMD is a robust biomarker. This biological validation study shows that high PSMD relates to worse cognition. PSMD explains cognitive function above and beyond white matter hyperintensities. Future clinical validation will assess PSMD as a vascular contribution to cognitive impairment and dementia biomarker in clinical trials.

A cross-disease resource of living human microglia identifies disease-enriched subsets and tool compounds recapitulating microglial states

Human microglia play a pivotal role in neurological diseases, but we still have an incomplete understanding of microglial heterogeneity, which limits the development of targeted therapies directly modulating their state or function. Here, we use single-cell RNA sequencing to profile 215,680 live human microglia from 74 donors across diverse neurological diseases and CNS regions. We observe a central divide between oxidative and heterocyclic metabolism and identify microglial subsets associated with antigen presentation, motility and proliferation. Specific subsets are enriched in susceptibility genes for neurodegenerative diseases or the disease-associated microglial signature. We validate subtypes in situ with an RNAscope-immunofluorescence pipeline and high-dimensional MERFISH. We also leverage our dataset as a classification resource, finding that induced pluripotent stem cell model systems capture substantial in vivo heterogeneity. Finally, we identify and validate compounds that recapitulate certain subtypes in vitro, including camptothecin, which downregulates the signature of disease-enriched subtypes and upregulates a signature previously associated with Alzheimer's disease.

Elevated expression of the retrotransposon LINE-1 drives Alzheimer's disease-associated microglial dysfunction

Aberrant activity of the retrotransposable element long interspersed nuclear element-1 (LINE-1) has been hypothesized to contribute to cellular dysfunction in age-related disorders, including late-onset Alzheimer's disease (LOAD). However, whether LINE-1 is differentially expressed in cell types of the LOAD brain, and whether these changes contribute to disease pathology is largely unknown. Here, we examined patterns of LINE-1 expression across neurons, astrocytes, oligodendrocytes, and microglia in human postmortem prefrontal cortex tissue from LOAD patients and cognitively normal, age-matched controls. We report elevated immunoreactivity of the open reading frame 1 protein (ORF1p) encoded by LINE-1 in microglia from LOAD patients and find that this immunoreactivity correlates positively with disease-associated microglial morphology. In human iPSC-derived microglia (iMG), we found that CRISPR-mediated transcriptional activation of LINE-1 drives changes in microglial morphology and cytokine secretion and impairs the phagocytosis of amyloid beta (Aβ). We also find LINE-1 upregulation in iMG induces transcriptomic changes genes associated with antigen presentation and lipid metabolism as well as impacting the expression of many AD-relevant genes. Our data posit that heightened LINE-1 expression may trigger microglial dysregulation in LOAD and that these changes may contribute to disease pathogenesis, suggesting a central role for LINE-1 activity in human LOAD.

Multifaceted Role of Specialized Neuropeptide-Intensive Neurons on the Selective Vulnerability to Alzheimer's Disease in the Human Brain

Regarding Alzheimer's disease (AD), specific neuronal populations and brain regions exhibit selective vulnerability. Understanding the basis of this selective neuronal and regional vulnerability is essential to elucidate the molecular mechanisms underlying AD pathology. However, progress in this area is currently hindered by the incomplete understanding of the intricate functional and spatial diversity of neuronal subtypes in the human brain. Previous studies have demonstrated that neuronal subpopulations with high neuropeptide (NP) co-expression are disproportionately absent in the entorhinal cortex of AD brains at the single-cell level, and there is a significant decline in hippocampal NP expression in naturally aging human brains. Given the role of NPs in neuroprotection and the maintenance of microenvironments, we hypothesize that neurons expressing higher levels of NPs (HNP neurons) possess unique functional characteristics that predispose them to cellular abnormalities, which can manifest as degeneration in AD with aging. To test this hypothesis, multiscale and spatiotemporal transcriptome data from ~1900 human brain samples were analyzed using publicly available datasets. The results indicate that HNP neurons experienced greater metabolic burden and were more prone to protein misfolding. The observed decrease in neuronal abundance during stages associated with a higher risk of AD, coupled with the age-related decline in the expression of AD-associated neuropeptides (ADNPs), provides temporal evidence supporting the role of NPs in the progression of AD. Additionally, the localization of ADNP-producing HNP neurons in AD-associated brain regions provides neuroanatomical support for the concept that cellular/neuronal composition is a key factor in regional AD vulnerability. This study offers novel insights into the molecular and cellular basis of selective neuronal and regional vulnerability to AD in human brains.

Construction of a Dataset for All Expressed Transcripts for Alzheimer's Disease Research

Accurate identification and functional annotation of splicing isoforms and non-coding RNAs (lncRNAs), alongside full-length protein-encoding transcripts, are critical for understanding gene (mis)regulation and metabolic reprogramming in Alzheimer's disease (AD). This study aims to provide a comprehensive and accurate transcriptome resource to improve existing AD transcript databases. Background/Objectives: Gene mis-regulation and metabolic reprogramming play a key role in AD, yet existing transcript databases lack accurate and comprehensive identification of splicing isoforms and lncRNAs. This study aims to generate a refined transcriptome dataset, expanding the understanding of AD onset and progression. Methods: Publicly available RNA-seq data from pre-AD and AD tissues were utilized. Advanced bioinformatics tools were applied to assemble and annotate full-length transcripts, including splicing isoforms and lncRNAs, with an emphasis on correcting errors and enhancing annotation accuracy. Results: A significantly improved transcriptome dataset was generated, which includes detailed annotations of splicing isoforms and lncRNAs. This dataset expands the scope of existing AD transcript databases and provides new insights into the molecular mechanisms underlying AD. The findings demonstrate that the refined dataset captures more relevant details about AD progression compared to publicly available data. Conclusions: The newly developed transcriptome resource and the associated analysis tools offer a valuable contribution to AD research, providing deeper insights into the disease's molecular mechanisms. This work supports future research into gene regulation and metabolic reprogramming in AD and serves as a foundation for exploring novel therapeutic targets.

Is the Relationship Between Cardiovascular Disease and Alzheimer's Disease Genetic? A Scoping Review

BACKGROUND/OBJECTIVES

Cardiovascular disease (CVD) and Alzheimer's disease (AD) are two diseases highly prevalent in the aging population and often co-occur. The exact relationship between the two diseases is uncertain, though epidemiological studies have demonstrated that CVDs appear to increase the risk of AD and vice versa. This scoping review aims to examine the current identified overlapping genetics between CVDs and AD at the individual gene level and at the shared pathway level.

METHODS

Following PRISMA-ScR guidelines for a scoping review, we searched the PubMed and Scopus databases from 1990 to October 2024 for articles that involved (1) CVDs, (2) AD, and (3) used statistical methods to parse genetic relationships.

RESULTS

Our search yielded 2918 articles, of which 274 articles passed screening and were organized into two main sections: (1) evidence of shared genetic risk; and (2) shared mechanisms. The genes APOE, PSEN1, and PSEN2 reportedly have wide effects across the AD and CVD spectrum, affecting both cardiac and brain tissues. Mechanistically, changes in three main pathways (lipid metabolism, blood pressure regulation, and the breakdown of the blood-brain barrier (BBB)) contribute to subclinical and etiological changes that promote both AD and CVD progression. However, genetic studies continue to be limited by the availability of longitudinal data and lack of cohorts that are representative of diverse populations.

CONCLUSIONS

Highly penetrant familial genes simultaneously increase the risk of CVDs and AD. However, in most cases, sets of dysregulated genes within larger-scale mechanisms, like changes in lipid metabolism, blood pressure regulation, and BBB breakdown, increase the risk of both AD and CVDs and contribute to disease progression.

Pure LATE-NC: Frequency, clinical impact, and the importance of considering APOE genotype when assessing this and other subtypes of non-Alzheimer's pathologies

Pure limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (pure LATE-NC) is a term used to describe brains with LATE-NC but lacking intermediate or severe levels of Alzheimer's disease neuropathologic changes (ADNC). Focusing on pure LATE-NC, we analyzed data from the National Alzheimer's Coordinating Center (NACC) Neuropathology Data Set, comprising clinical and pathological information aggregated from 32 NIH-funded Alzheimer's Disease Research Centers (ADRCs). After excluding subjects dying with unusual conditions, n = 1,926 autopsied subjects were included in the analyses. For > 90% of these participants, apolipoprotein E (APOE) allele status was known; 46.5% had at least one APOE 4 allele. In most human populations, only 15-25% of people are APOE ε4 carriers. ADRCs with higher documented AD risk allele (APOE or BIN1) rates had fewer participants lacking ADNC, and correspondingly low rates of pure LATE-NC. Among APOE ε4 non-carries, 5.3% had pure LATE-NC, 37.0% had pure ADNC, and 3.6% had pure neocortical Lewy body pathology. In terms of clinical impact, participants with pure LATE-NC tended to die after having received a diagnosis of dementia: 56% died with dementia among APOE ε4 non-carrier participants, comparable to 61% with pure ADNC. LATE-NC was associated with increased Clinical Dementia Rating Sum of Boxes (CDR-SOB) scores, i.e. worsened global cognitive impairments, in participants with no/low ADNC and no neocortical Lewy body pathology (p = 0.0023). Among pure LATE-NC cases, there was a trend for higher LATE-NC stages to be associated with worse CDR-SOB scores (p = 0.026 for linear trend of LATE-NC stages). Pure LATE-NC was not associated with clinical features of disinhibition or primary progressive aphasia. In summary, LATE-NC with no or low levels of ADNC was less frequent than pure ADNC but was not rare, particularly among individuals who lacked the APOE 4 allele, and in study cohorts with APOE 4 frequencies similar to those in most human populations.

Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data

Introduction

The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.

Methods

In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.

Results

We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.

Discussion

In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.

SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ). SGUQ begins with mRNA and progressively incorporates DNA methylation and miRNA data only when necessary, reducing overall costs and exposure to harmful tests. Experimental results indicate that 46.23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16.04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved an accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods significantly. The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. Our implementation is publicly available at https://github.com/chenzhao2023/multiomicsuncertainty.

Racial and ethnic minority representation in dementia risk factor research: a scoping review of cohort studies

BACKGROUND

Despite a potentially greater burden of dementia, racial and ethnic minority populations around the world may be more likely to be excluded from research examining risk factors for incident dementia. We aimed to systematically investigate and quantify racial and ethnic minority representation in dementia risk factor research.

METHODS

We performed a two-stage systematic search of databases-MEDLINE (Ovid SP), Embase (Ovid SP) and Scopus-from inception to March 2021 to identify population-based cohort studies looking at risk factors for dementia incidence. We included cohort studies which were population-based and incorporated a clinical dementia diagnosis.

RESULTS

Out of the 97 identified cohort studies, fewer than half (40 studies; 41%) reported the race or ethnicity of participants and just under one-third (29 studies; 30%) reported the inclusion of racial and ethnic minority groups. We found that inadequate reporting frequently prevented assessment of selection bias and only six studies that included racial and ethnic minority participants were at low risk for measurement bias in dementia diagnosis. In cohort studies including a multiethnic cohort, only 182 out of 337 publications incorporated race or ethnicity in data analysis-predominantly (90%) through adjustment for race or ethnicity as a confounder. Only 14 publications (4.2% of all publications reviewed) provided evidence about drivers of any observed inequalities.

CONCLUSIONS

Racial and ethnic minority representation in dementia risk factor research is inadequate. Comparisons of dementia risk between different racial and ethnic groups are likely hampered by significant selection and measurement bias. Moreover, the focus on 'adjusting out' the effect of race and ethnicity as a confounder prevents understanding of underlying drivers of observed inequalities. There is a pressing need to fundamentally change the way race, ethnicity and the inclusion of racial and ethnic minorities are considered in research if health inequalities are to be adequately addressed.

Loss of the APP regulator RHBDL4 preserves memory in an Alzheimer's disease mouse model

Characteristic cerebral pathological changes of Alzheimer's disease (AD) such as glucose hypometabolism or the accumulation of cleavage products of the amyloid precursor protein (APP), known as Aβ peptides, lead to sustained endoplasmic reticulum (ER) stress and neurodegeneration. To preserve ER homeostasis, cells activate their unfolded protein response (UPR). The rhomboid-like-protease 4 (RHBDL4) is an enzyme that participates in the UPR by targeting proteins for proteasomal degradation. We demonstrated previously that RHBLD4 cleaves APP in HEK293T cells, leading to decreased total APP and Aβ. More recently, we showed that RHBDL4 processes APP in mouse primary mixed cortical cultures as well. Here, we aim to examine the physiological relevance of RHBDL4 in the brain. We first found that brain samples from AD patients and an AD mouse model (APPtg) showed increased RHBDL4 mRNA and protein expression. To determine the effects of RHBDL4's absence on APP physiology in vivo, we crossed APPtg mice to a RHBDL4 knockout (R4-/-) model. RHBDL4 deficiency in APPtg mice led to increased total cerebral APP and amyloidogenic processing when compared to APPtg controls. Contrary to expectations, as assessed by cognitive tests, RHBDL4 absence rescued cognition in 5-month-old female APPtg mice. Informed by unbiased RNAseq data, we demonstrated in vitro and in vivo that RHBDL4 absence leads to greater levels of active β-catenin due to decreased proteasomal clearance. Decreased β-catenin activity is known to underlie cognitive defects in APPtg mice and AD. Our work suggests that RHBDL4's increased expression in AD, in addition to regulating APP levels, leads to aberrant degradation of β-catenin, contributing to cognitive impairment.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

Chromosome X-wide association study in case control studies of pathologically confirmed Alzheimer's disease in a European population

Although there are several genome-wide association studies available which highlight genetic variants associated with Alzheimer's disease (AD), often the X chromosome is excluded from the analysis. We conducted an X-chromosome-wide association study (XWAS) in three independent studies with a pathologically confirmed phenotype (total 1970 cases and 1113 controls). The XWAS was performed in males and females separately, and these results were then meta-analysed. Four suggestively associated genes were identified which may be of potential interest for further study in AD, these are DDX53 (rs12006935, OR = 0.52, p = 6.9e-05), IL1RAPL1 (rs6628450, OR = 0.36, p = 4.2e-05; rs137983810, OR = 0.52, p = 0.0003), TBX22 (rs5913102, OR = 0.74, p = 0.0003) and SH3BGRL (rs186553004, OR = 0.35, p = 0.0005; rs113157993, OR = 0.52, p = 0.0003), which replicate across at least two studies. The SNP rs5913102 in TBX22 achieves chromosome-wide significance in meta-analysed data. DDX53 shows highest expression in astrocytes, IL1RAPL1 is most highly expressed in oligodendrocytes and neurons and SH3BGRL is most highly expressed in microglia. We have also identified SNPs in the NXF5 gene at chromosome-wide significance in females (rs5944989, OR = 0.62, p = 1.1e-05) but not in males (p = 0.83). The discovery of relevant AD associated genes on the X chromosome may identify AD risk differences and similarities based on sex and lead to the development of sex-stratified therapeutics.

Rapid iPSC inclusionopathy models shed light on formation, consequence, and molecular subtype of α-synuclein inclusions

The heterogeneity of protein-rich inclusions and its significance in neurodegeneration is poorly understood. Standard patient-derived iPSC models develop inclusions neither reproducibly nor in a reasonable time frame. Here, we developed screenable iPSC "inclusionopathy" models utilizing piggyBac or targeted transgenes to rapidly induce CNS cells that express aggregation-prone proteins at brain-like levels. Inclusions and their effects on cell survival were trackable at single-inclusion resolution. Exemplar cortical neuron α-synuclein inclusionopathy models were engineered through transgenic expression of α-synuclein mutant forms or exogenous seeding with fibrils. We identified multiple inclusion classes, including neuroprotective p62-positive inclusions versus dynamic and neurotoxic lipid-rich inclusions, both identified in patient brains. Fusion events between these inclusion subtypes altered neuronal survival. Proteome-scale α-synuclein genetic- and physical-interaction screens pinpointed candidate RNA-processing and actin-cytoskeleton-modulator proteins like RhoA whose sequestration into inclusions could enhance toxicity. These tractable CNS models should prove useful in functional genomic analysis and drug development for proteinopathies.

Cellular communities reveal trajectories of brain ageing and Alzheimer's disease

Alzheimer's disease (AD) has recently been associated with diverse cell states1-11, yet when and how these states affect the onset of AD remains unclear. Here we used a data-driven approach to reconstruct the dynamics of the brain's cellular environment and identified a trajectory leading to AD that is distinct from other ageing-related effects. First, we built a comprehensive cell atlas of the aged prefrontal cortex from 1.65 million single-nucleus RNA-sequencing profiles sampled from 437 older individuals, and identified specific glial and neuronal subpopulations associated with AD-related traits. Causal modelling then prioritized two distinct lipid-associated microglial subpopulations-one drives amyloid-β proteinopathy while the other mediates the effect of amyloid-β on tau proteinopathy-as well as an astrocyte subpopulation that mediates the effect of tau on cognitive decline. To model the dynamics of cellular environments, we devised the BEYOND methodology, which identified two distinct trajectories of brain ageing, each defined by coordinated progressive changes in certain cellular communities that lead to (1) AD dementia or (2) alternative brain ageing. Thus, we provide a cellular foundation for a new perspective on AD pathophysiology that informs personalized therapeutic development, targeting different cellular communities for individuals on the path to AD or to alternative brain ageing.

Reduced PIN1 expression in neocortical and limbic brain regions in female Alzheimer's patients correlates with cognitive and neuropathological phenotypes

Women have a higher incidence of Alzheimer's disease (AD), even after adjusting for increased longevity. Thus, there is an urgent need to identify genes that underpin sex-associated risk of AD. PIN1 is a key regulator of the tau phosphorylation signaling pathway; however, potential differences in PIN1 expression, in males and females, are still unknown. We analyzed brain transcriptomic datasets focusing on sex differences in PIN1 mRNA levels in an aging and AD cohort, which revealed reduced PIN1 levels primarily within females. We validated this observation in an independent dataset (ROS/MAP), which also revealed that PIN1 is negatively correlated with multiregional neurofibrillary tangle density and global cognitive function in females only. Additional analysis revealed a decrease in PIN1 in subjects with mild cognitive impairment (MCI) compared with aged individuals, again driven predominantly by female subjects. Histochemical analysis of PIN1 in AD and control male and female neocortex revealed an overall decrease in axonal PIN1 protein levels in females. These findings emphasize the importance of considering sex differences in AD research.

Elucidating the Functional Roles of Long Non-Coding RNAs in Alzheimer's Disease

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on the elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of the dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network was constructed. Maximal subnetworks with dense connections were identified as functional clusters. Pathway enrichment analyses were conducted over mRNAs and lncRNAs in each cluster, which served as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously built based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles of lncRNAs in activities related to stress response, reprogrammed metabolism, cell polarity, and development. Our analyses also revealed that lncRNAs have the discerning power to distinguish between AD samples of each stage and healthy controls. This study represents the first of its kind.

Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study

BACKGROUND

The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment.

METHODS

We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies.

RESULTS

We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes.

LIMITATIONS

The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects.

CONCLUSION

The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.

Elevated protease-activated receptor 4 (PAR4) gene expression in Alzheimer's disease predicts cognitive decline

Platelet activation of protease-activated receptor 4 (PAR4) and thrombin are at the top of a chain of events leading to fibrin deposition, microinfarcts, blood-brain barrier disruption, and inflammation. We evaluated mRNA expression of the PAR4 gene F2RL3 in human brain and global cognitive performance in participants with and without cognitive impairment or dementia. Data were acquired from the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP). F2RL3 mRNA was elevated in AD cases and was associated with worse retrospective longitudinal cognitive performance. Moreover, F2RL3 expression interacted with clinical AD diagnosis on longitudinal cognition whereas this relationship was attenuated in individuals without cognitive impairment. Additionally, when adjusting for the effects of AD neuropathology, F2RL3 expression remained a significant predictor of cognitive decline. F2RL3 expression correlated positively with transcript levels of proinflammatory markers including TNFα, IL-1β, NFκB, and fibrinogen α/β/γ. Together, these results reveal that F2RL3 mRNA expression is associated with multiple AD-relevant outcomes and its encoded product, PAR4, may play a role in disease pathogenesis.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer's disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

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.

Psychosocial experiences are associated with human brain mitochondrial biology

Psychosocial experiences affect brain health and aging trajectories, but the molecular pathways underlying these associations remain unclear. Normal brain function relies on energy transformation by mitochondria oxidative phosphorylation (OxPhos). Two main lines of evidence position mitochondria both as targets and drivers of psychosocial experiences. On the one hand, chronic stress exposure and mood states may alter multiple aspects of mitochondrial biology; on the other hand, functional variations in mitochondrial OxPhos capacity may alter social behavior, stress reactivity, and mood. But are psychosocial exposures and subjective experiences linked to mitochondrial biology in the human brain? By combining longitudinal antemortem assessments of psychosocial factors with postmortem brain (dorsolateral prefrontal cortex) proteomics in older adults, we find that higher well-being is linked to greater abundance of the mitochondrial OxPhos machinery, whereas higher negative mood is linked to lower OxPhos protein content. Combined, positive and negative psychosocial factors explained 18 to 25% of the variance in the abundance of OxPhos complex I, the primary biochemical entry point that energizes brain mitochondria. Moreover, interrogating mitochondrial psychobiological associations in specific neuronal and nonneuronal brain cells with single-nucleus RNA sequencing (RNA-seq) revealed strong cell-type-specific associations for positive psychosocial experiences and mitochondria in glia but opposite associations in neurons. As a result, these "mind-mitochondria" associations were masked in bulk RNA-seq, highlighting the likely underestimation of true psychobiological effect sizes in bulk brain tissues. Thus, self-reported psychosocial experiences are linked to human brain mitochondrial phenotypes.

In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus

INTRODUCTION

TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification.

METHODS

Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics.

RESULTS

Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition.

DISCUSSION

Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory.

HIGHLIGHTS

Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.

TRABD modulates mitochondrial homeostasis and tissue integrity

High TRABD expression is associated with tau pathology in patients with Alzheimer's disease; however, the function of TRABD is unknown. Human TRABD encodes a mitochondrial outer-membrane protein. The loss of TRABD resulted in mitochondrial fragmentation, and TRABD overexpression led to mitochondrial clustering and fusion. The C-terminal tail of the TRABD anchored to the mitochondrial outer membrane and the TraB domain could form homocomplexes. Additionally, TRABD forms complexes with MFN2, MIGA2, and PLD6 to facilitate mitochondrial fusion. Flies lacking dTRABD are viable and have normal lifespans. However, aging flies exhibit reduced climbing ability and abnormal mitochondrial morphology in their muscles. The expression of dTRABD is increased in aged flies. dTRABD overexpression leads to neurodegeneration and enhances tau toxicity in fly eyes. The overexpression of dTRABD also increased reactive oxygen species (ROS), ATP production, and protein turnover in the mitochondria. This study suggested that TRABD-induced mitochondrial malfunctions contribute to age-related neurodegeneration.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease

Background

Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD.

Method

The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants.

Results

Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]).

Conclusion

The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Brain and serum lipidomic profiles implicate Lands cycle acyl chain remodeling association with APOEε4 and mild cognitive impairment

Introduction

At least one-third of the identified risk alleles from Genome-Wide Association Studies (GWAS) of Alzheimer's disease (AD) are involved in lipid metabolism, lipid transport, or direct lipid binding. In fact, a common genetic variant (ε4) in a cholesterol and phospholipid transporter, Apolipoprotein E (APOEε4), is the primary genetic risk factor for late-onset AD. In addition to genetic variants, lipidomic studies have reported severe metabolic dysregulation in human autopsy brain tissue, cerebrospinal fluid, blood, and multiple mouse models of AD.

Methods

We aimed to identify an overarching metabolic pathway in lipid metabolism by integrating analyses of lipidomics and transcriptomics from the Religious Order Study and Rush Memory Aging Project (ROSMAP) using differential analysis and network correlation analysis.

Results

Coordinated differences in lipids were found to be dysregulated in association with both mild cognitive impairment (MCI) and APOEε4 carriers. Interestingly, these correlations were weakened when adjusting for education. Indeed, the cognitively non-impaired APOEε4 carriers have higher education levels in the ROSMAP cohort, suggesting that this lipid signature may be associated with a resilience phenotype. Network correlation analysis identified multiple differential lipids within a single module that are substrates and products in the Lands Cycle for acyl chain remodeling. In addition, our analyses identified multiple genes in the Lands Cycle acyl chain remodeling pathway, which were associated with cognitive decline independent of amyloid-β (Aβ) load and tau tangle pathologies.

Discussion

Our studies highlight the critical differences in acyl chain remodeling in brain tissue from APOEε4 carriers and individual non-carriers with MCI. A coordinated lipid profile shift in dorsolateral prefrontal cortex from both APOEε4 carriers and MCI suggests differences in lipid metabolism occur early in disease stage and highlights lipid homeostasis as a tractable target for early disease modifying intervention.

Limbic-predominant age-related TDP-43 encephalopathy (LATE-NC): Co-pathologies and genetic risk factors provide clues about pathogenesis

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is detectable at autopsy in more than one-third of people beyond age 85 years and is robustly associated with dementia independent of other pathologies. Although LATE-NC has a large impact on public health, there remain uncertainties about the underlying biologic mechanisms. Here, we review the literature from human studies that may shed light on pathogenetic mechanisms. It is increasingly clear that certain combinations of pathologic changes tend to coexist in aging brains. Although "pure" LATE-NC is not rare, LATE-NC often coexists in the same brains with Alzheimer disease neuropathologic change, brain arteriolosclerosis, hippocampal sclerosis of aging, and/or age-related tau astrogliopathy (ARTAG). The patterns of pathologic comorbidities provide circumstantial evidence of mechanistic interactions ("synergies") between the pathologies, and also suggest common upstream influences. As to primary mediators of vulnerability to neuropathologic changes, genetics may play key roles. Genes associated with LATE-NC include TMEM106B, GRN, APOE, SORL1, ABCC9, and others. Although the anatomic distribution of TDP-43 pathology defines the condition, important cofactors for LATE-NC may include Tau pathology, endolysosomal pathways, and blood-brain barrier dysfunction. A review of the human phenomenology offers insights into disease-driving mechanisms, and may provide clues for diagnostic and therapeutic targets.

Association of 10 VEGF Family Genes with Alzheimer's Disease Endophenotypes at Single Cell Resolution

The cell-type specific role of the vascular endothelial growth factors (VEGFs) in the pathogenesis of Alzheimer's disease (AD) is not well characterized. In this study, we utilized a single-nucleus RNA sequencing dataset from Dorsolateral Prefrontal Cortex (DLFPC) of 424 donors from the Religious Orders Study and Memory and Aging Project (ROS/MAP) to investigate the effect of 10 VEGF genes ( VEGFA, VEGFB, VEGFC, VEGFD, PGF, FLT1, FLT4, KDR, NRP1 , and NRP2 ) on AD endophenotypes. Mean age of death was 89 years, among which 68% were females, and 52% has AD dementia. Negative binomial mixed models were used for differential expression analysis and for association analysis with β-amyloid load, PHF tau tangle density, and both cross-sectional and longitudinal global cognitive function. Intercellular VEGF-associated signaling was profiled using CellChat. We discovered prefrontal cortical FLT1 expression was upregulated in AD brains in both endothelial and microglial cells. Higher FLT1 expression was also associated with worse cross-sectional global cognitive function, longitudinal cognitive trajectories, and β-amyloid load. Similarly, higher endothelial FLT4 expression was associated with more β-amyloid load. In contrast to the receptors, VEGFB showed opposing effects on β-amyloid load whereby higher levels in oligodendrocytes was associated with high amyloid burden, while higher levels in inhibitory neurons was associated with lower amyloid burden. Finally, AD cells showed significant reduction in overall VEGF signaling comparing to those from cognitive normal participants. Our results highlight key changes in VEGF receptor expression in endothelial and microglial cells during AD, and the potential protective role of VEGFB in neurons.

Multifaceted impact of specialized neuropeptide-intensive neurons on the selective vulnerability in Alzheimer's disease

INTRODUCTION

Widespread disruption of neuropeptide (NP) networks in Alzheimer's disease (AD) and disproportionate absence of neurons expressing high NP-producing, coined as HNP neurons, have been reported for the entorhinal cortex (EC) of AD brains. Hypothesizing that functional features of HNP neurons are involved in the early pathogenesis of AD, we aim to understand the molecular mechanisms underlying these observations.

METHODS

Multiscale and spatiotemporal transcriptomic analysis was used to investigate AD-afflicted and healthy brains. Our focus encompassed NP expression dynamics in AD, AD-associated NPs (ADNPs) trajectories with aging, and the neuroanatomical distribution of HNP neuron.

RESULTS

Findings include that 1) HNP neurons exhibited heightened metabolic needs and an upregulation of gene expressions linked to protein misfolding; 2) dysfunctions of ADNP production occurred in aging and mild cognitive decline; 3) HNP neurons co-expressing ADNPs were preferentially distributed in brain regions susceptible to AD.

DISCUSSION

We identified potential mechanisms that contribute to the selective vulnerability of HNP neurons to AD. Our results indicate that the functions of HNP neurons predispose them to oxidative stress and protein misfolding, potentially serving as inception sites for misfolded proteins in AD.

An integrated toolkit for human microglia functional genomics

BACKGROUND

Microglia, the brain's resident immune cells, play vital roles in brain development, and disorders like Alzheimer's disease (AD). Human iPSC-derived microglia (iMG) provide a promising model to study these processes. However, existing iMG generation protocols face challenges, such as prolonged differentiation time, lack of detailed characterization, and limited gene function investigation via CRISPR-Cas9.

METHODS

Our integrated toolkit for in-vitro microglia functional genomics optimizes iPSC differentiation into iMG through a streamlined two-step, 20-day process, producing iMG with a normal karyotype. We confirmed the iMG's authenticity and quality through single-cell RNA sequencing, chromatin accessibility profiles (ATAC-Seq), proteomics and functional tests. The toolkit also incorporates a drug-dependent CRISPR-ON/OFF system for temporally controlled gene expression. Further, we facilitate the use of multi-omic data by providing online searchable platform that compares new iMG profiles to human primary microglia: https://sherlab.shinyapps.io/IPSC-derived-Microglia/ .

RESULTS

Our method generates iMG that closely align with human primary microglia in terms of transcriptomic, proteomic, and chromatin accessibility profiles. Functionally, these iMG exhibit Ca2 + transients, cytokine driven migration, immune responses to inflammatory signals, and active phagocytosis of CNS related substrates including synaptosomes, amyloid beta and myelin. Significantly, the toolkit facilitates repeated iMG harvesting, essential for large-scale experiments like CRISPR-Cas9 screens. The standalone ATAC-Seq profiles of our iMG closely resemble primary microglia, positioning them as ideal tools to study AD-associated single nucleotide variants (SNV) especially in the genome regulatory regions.

CONCLUSIONS

Our advanced two-step protocol rapidly and efficiently produces authentic iMG. With features like the CRISPR-ON/OFF system and a comprehensive multi-omic data platform, our toolkit equips researchers for robust microglial functional genomic studies. By facilitating detailed SNV investigation and offering a sustainable cell harvest mechanism, the toolkit heralds significant progress in neurodegenerative disease drug research and therapeutic advancement.

Clinical Characterization of Veterans With Alzheimer Disease by Disease Severity in the United States

PURPOSE

We aimed to examine the clinical characteristics of US veterans who underwent neurocognitive test score-based assessments of Alzheimer disease (AD) stage in the Veterans Affairs Healthcare System (VAHS).

METHODS

Test dates for specific stages of AD were referenced as index dates to study behavioral and psychological symptoms of dementia (BPSD) and other patient characteristics related to utilization/work-up and time to death.

PATIENTS

We identified veterans with AD and neurocognitive evaluations using the VAHS Electronic Health Record (EHR).

RESULTS

Anxiety and sleep disorders/disturbances were the most documented BPSDs across all AD severity stages. Magnetic resonance imaging, neurology and psychiatry consultations, and neuropsychiatric evaluations were slightly higher in veterans with mild AD than in those at later stages. The overall average time to death from the first AD severity record was 5 years for mild and 4 years for moderate/severe AD.

CONCLUSION

We found differences in clinical symptoms, healthcare utilization, and survival among the mild, moderate, and severe stages of AD. These differences are limited by the low documentation of BPSDs among veterans with test score-based AD stages. These data support the hypothesis that our cohorts represent coherent subgroups of patients with AD based on disease severity.

Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.

Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

Association of TMEM106B with Cortical APOE Gene Expression in Neurodegenerative Conditions

The e4 allele of the apolipoprotein E gene is the strongest genetic risk factor for sporadic Alzheimer's disease. Nevertheless, how APOE is regulated is still elusive. In a trans-eQTL analysis, we found a genome-wide significant association between transmembrane protein 106B (TMEM106B) genetic variants and cortical APOE mRNA levels in human brains. The goal of this study is to determine whether TMEM106B is mis-regulated in Alzheimer's disease or in other neurodegenerative conditions. Available genomic, transcriptomic and proteomic data from human brains were downloaded from the Mayo Clinic Brain Bank and the Religious Orders Study and Memory and Aging Project. An in-house mouse model of the hippocampal deafferentation/reinnervation was achieved via a stereotaxic lesioning surgery to the entorhinal cortex, and mRNA levels were measured using RNAseq technology. In human temporal cortices, the mean TMEM106B expression was significantly higher in Alzheimer's disease compared to cognitively unimpaired individuals. In the mouse model, hippocampal Tmem106b reached maximum levels during the early phase of reinnervation. These results suggest an active response to tissue damage that is consistent with compensatory synaptic and terminal remodeling.

TMEM106B coding variant is protective and deletion detrimental in a mouse model of tauopathy

TMEM106B is a risk modifier of multiple neurological conditions, where a single coding variant and multiple non-coding SNPs influence the balance between susceptibility and resilience. Two key questions that emerge from past work are whether the lone T185S coding variant contributes to protection, and if the presence of TMEM106B is helpful or harmful in the context of disease. Here, we address both questions while expanding the scope of TMEM106B study from TDP-43 to models of tauopathy. We generated knockout mice with constitutive deletion of TMEM106B, alongside knock-in mice encoding the T186S knock-in mutation (equivalent to the human T185S variant), and crossed both with a P301S transgenic tau model to study how these manipulations impacted disease phenotypes. We found that TMEM106B deletion accelerated cognitive decline, hind limb paralysis, tau pathology, and neurodegeneration. TMEM106B deletion also increased transcriptional correlation with human AD and the functional pathways enriched in KO:tau mice aligned with those of AD. In contrast, the coding variant protected against tau-associated cognitive decline, synaptic impairment, neurodegeneration, and paralysis without affecting tau pathology. Our findings reveal that TMEM106B is a critical safeguard against tau aggregation, and that loss of this protein has a profound effect on sequelae of tauopathy. Our study further demonstrates that the coding variant is functionally relevant and contributes to neuroprotection downstream of tau pathology to preserve cognitive function.

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.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.

A single nuclear transcriptomic characterisation of mechanisms responsible for impaired angiogenesis and blood-brain barrier function in Alzheimer's disease

Brain perfusion and blood-brain barrier (BBB) integrity are reduced early in Alzheimer's disease (AD). We performed single nucleus RNA sequencing of vascular cells isolated from AD and non-diseased control brains to characterise pathological transcriptional signatures responsible for this. We show that endothelial cells (EC) are enriched for expression of genes associated with susceptibility to AD. Increased β-amyloid is associated with BBB impairment and a dysfunctional angiogenic response related to a failure of increased pro-angiogenic HIF1A to increased VEGFA signalling to EC. This is associated with vascular inflammatory activation, EC senescence and apoptosis. Our genomic dissection of vascular cell risk gene enrichment provides evidence for a role of EC pathology in AD and suggests that reducing vascular inflammatory activation and restoring effective angiogenesis could reduce vascular dysfunction contributing to the genesis or progression of early AD.

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.

Cognition-associated long noncoding RNAs are dysregulated upon severe COVID-19

Severe COVID-19 leads to widespread transcriptomic changes in the human brain, mimicking diminished cognitive performance. As long noncoding RNAs (lncRNAs) play crucial roles in the regulation of gene expression, identification of the lncRNAs differentially expressed upon COVID-19 may nominate key regulatory nodes underpinning cognitive changes. Here we identify hundreds of lncRNAs differentially expressed in the brains of COVID-19 patients relative to uninfected age/sex-matched controls, many of which are associated with decreased cognitive performance and inflammatory cytokine response. Our analyses reveal pervasive transcriptomic changes in lncRNA expression upon severe COVID-19, which may serve as key regulators of neurocognitive changes in the brain.