Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease

Jul 13, 2025Journal of translational medicine

Using multiple biological analyses to find drug targets in specific brain cells and overall tissue in Alzheimer's disease

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Abstract

A total of 28 candidate causal genes for Alzheimer's disease were identified, with 12 detected uniquely at the cell-type level.

Candidate causal genes were identified from an integration of five Alzheimer's disease datasets and multiple datasets.
Microglia contributed the highest number of candidate causal genes, followed by excitatory neurons and other brain cell types.
PABPC1 was identified as a novel candidate causal gene specifically in astrocytes.
Pathways related to membrane organization, cell migration, and specific signaling pathways were enriched among the candidate genes.
Imatinib mesylate was highlighted as a potential drug candidate based on the identified drug-target interactions.

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BACKGROUND: Analyzing disease-linked genetic variants via expression quantitative trait loci () helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study () results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but a systematic analysis across multiple Alzheimer's disease (AD) genome-wide association study (GWAS) datasets or comparisons between tissue-level and cell type-specific effects remain limited. Here, we integrated brain cell type-level and bulk-level eQTL datasets with AD GWAS datasets to identify potential causal genes.

METHODS: We used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with eQTLs datasets. Combining data from five AD GWAS, two single-cell eQTL datasets, and one bulk eQTL dataset, we identified novel candidate causal genes and further confirmed known ones. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction (PPI) and pathway enrichment, and conducted a drug/compound enrichment analysis with Drug Signatures Database (DSigDB) to support drug repurposing for AD.

RESULTS: We identified 28 candidate causal genes for AD, of which 12 were uniquely detected at the cell-type level, 9 were exclusive to the bulk level and 7 detected in both. Among the 19 cell-type level candidate causal genes, microglia contributed the highest number of candidate genes, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel candidate causal gene in astrocytes. We generated PPI networks for the candidate causal genes and found that pathways such as membrane organization, cell migration, and ERK1/2 and PI3K/AKT signaling were enriched. The AD-risk variant associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the 28 genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

CONCLUSIONS: We systematically prioritized AD candidate causal genes based on cell type-level and bulk level molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates interpretation of AD GWAS results.

Key numbers

Identified

Total identified from AD and datasets.

12 of 28

Cell-Type Specific Genes

uniquely detected at the cell-type level.

Druge Candidate

Key candidate drug identified for AD treatment.

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Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease

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