Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature

Chromatin accessibility provides a window into the genetic etiology of human brain disease

Neuropsychiatric and neurodegenerative diseases have a significant genetic component. Risk variants often affect the noncoding genome, altering cis-regulatory elements (CREs) and chromatin structure, ultimately impacting gene expression. Chromatin accessibility profiling methods, especially assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), have been used to pinpoint disease-associated SNPs and link them to affected genes and cell types in the brain. The integration of single-cell technologies with genome-wide association studies (GWAS) and transcriptomic data has further advanced our understanding of cell-specific chromatin dynamics. This review discusses recent findings regarding the role played by chromatin accessibility in brain disease, highlighting the need for high-quality data and rigorous computational tools. Future directions include spatial chromatin studies and CRISPR-based functional validation to bridge genetic discovery and clinical applications, paving the way for targeted gene-regulatory therapies.

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

Background

A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

Methods

We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution.

Results

Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

Conclusions

We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

LD-informed deep learning for Alzheimer's gene loci detection using WGS data

INTRODUCTION

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

METHODS

The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD).

RESULTS

30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog.

DISCUSSION

The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies.

Mitochondrial genetic variations in leukemia: a comprehensive overview

Leukemias are a group of heterogeneous hematological malignancies driven by diverse genetic variations, and the advent of genomic sequencing technologies facilitates the investigation of genetic abnormalities in leukemia. However, these sequencing-based studies mainly focus on nuclear DNAs. Increasing evidence indicates that mitochondrial dysfunction is an important mechanism of leukemia pathogenesis, which is closely related to the mitochondrial genome variations. Here, we provide an overview of current research progress concerning mitochondrial genetic variations in leukemia, encompassing gene mutations and copy number variations. We also summarize currently accessible mitochondrial DNA (mtDNA) sequencing methods. Notably, somatic mtDNA mutations may serve as natural genetic barcodes for lineage tracing and longitudinal assessment of clonal dynamics. Collectively, these findings enhance our understanding of leukemia pathogenesis and foster the identification of novel therapeutic targets and interventions.

Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

Alzheimer's disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available at https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Epigenetic control of microglial immune responses

Microglia, the major population of brain-resident macrophages, are now recognized as a heterogeneous population comprising several cell subtypes with different (so far mostly supposed) functions in health and disease. A number of studies have performed molecular characterization of these different microglial activation states over the last years making use of "omics" technologies, that is transcriptomics, proteomics and, less frequently, epigenomics profiling. These approaches offer the possibility to identify disease mechanisms, discover novel diagnostic biomarkers, and develop new therapeutic strategies. Here, we focus on epigenetic profiling as a means to understand microglial immune responses beyond what other omics methods can offer, that is, revealing past and present molecular responses, gene regulatory networks and potential future response trajectories, and defining cell subtype-specific disease relevance through mapping non-coding genetic variants. We review the current knowledge in the field regarding epigenetic regulation of microglial identity and function, provide an exemplary analysis that demonstrates the advantages of performing joint transcriptomic and epigenomic profiling of single microglial cells and discuss how comprehensive epigenetic analyses may enhance our understanding of microglial pathophysiology.

Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response

Some individuals show a discrepancy between cognition and the amount of neuropathological changes characteristic for Alzheimer's disease (AD). This phenomenon has been referred to as 'resilience'. The molecular and cellular underpinnings of resilience remain poorly understood. To obtain an unbiased understanding of the molecular changes underlying resilience, we investigated global changes in gene expression in the superior frontal gyrus of a cohort of cognitively and pathologically well-defined AD patients, resilient individuals and age-matched controls (n = 11-12 per group). 897 genes were significantly altered between AD and control, 1121 between resilient and control and 6 between resilient and AD. Gene set enrichment analysis (GSEA) revealed that the expression of metallothionein (MT) and of genes related to mitochondrial processes was higher in the resilient donors. Weighted gene co-expression network analysis (WGCNA) identified gene modules related to the unfolded protein response, mitochondrial processes and synaptic signaling to be differentially associated with resilience or dementia. As changes in MT, mitochondria, heat shock proteins and the unfolded protein response (UPR) were the most pronounced changes in the GSEA and/or WGCNA, immunohistochemistry was used to further validate these processes. MT was significantly increased in astrocytes in resilient individuals. A higher proportion of the mitochondrial gene MT-CO1 was detected outside the cell body versus inside the cell body in the resilient compared to the control group and there were higher levels of heat shock protein 70 (HSP70) and X-box-binding protein 1 spliced (XBP1s), two proteins related to heat shock proteins and the UPR, in the AD donors. Finally, we show evidence for putative sex-specific alterations in resilience, including gene expression differences related to autophagy in females compared to males. Taken together, these results show possible mechanisms involving MTs, mitochondrial processes and the UPR by which individuals might maintain cognition despite the presence of AD pathology.

Understanding the molecular basis of resilience to Alzheimer's disease

The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer's disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD.