Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease
Abstract Background Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have ex...
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BMC
2025-07-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06739-1 |
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| author | Shiwei Liu Minyoung Cho Yen-Ning Huang Tamina Park Soumilee Chaudhuri Thea J. Rosewood Paula J. Bice Dongjun Chung David A. Bennett Nilüfer Ertekin-Taner Andrew J. Saykin Kwangsik Nho |
| author_facet | Shiwei Liu Minyoung Cho Yen-Ning Huang Tamina Park Soumilee Chaudhuri Thea J. Rosewood Paula J. Bice Dongjun Chung David A. Bennett Nilüfer Ertekin-Taner Andrew J. Saykin Kwangsik Nho |
| author_sort | Shiwei Liu |
| collection | DOAJ |
| description | Abstract Background Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) 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. |
| format | Article |
| id | doaj-art-f15be6ed47f4402798c18c3e72f92fef |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | Journal of Translational Medicine |
| spelling | doaj-art-f15be6ed47f4402798c18c3e72f92fef2025-08-20T04:03:07ZengBMCJournal of Translational Medicine1479-58762025-07-0123111810.1186/s12967-025-06739-1Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s diseaseShiwei Liu0Minyoung Cho1Yen-Ning Huang2Tamina Park3Soumilee Chaudhuri4Thea J. Rosewood5Paula J. Bice6Dongjun Chung7David A. Bennett8Nilüfer Ertekin-Taner9Andrew J. Saykin10Kwangsik Nho11Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Neurological Science, Rush Alzheimer’s Disease Center, Rush University Medical CenterDepartment of Neuroscience, Mayo ClinicCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of MedicineAbstract Background Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) 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.https://doi.org/10.1186/s12967-025-06739-1Causal geneseQTLAlzheimer’s diseaseGWASSNPGenetic variant |
| spellingShingle | Shiwei Liu Minyoung Cho Yen-Ning Huang Tamina Park Soumilee Chaudhuri Thea J. Rosewood Paula J. Bice Dongjun Chung David A. Bennett Nilüfer Ertekin-Taner Andrew J. Saykin Kwangsik Nho Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease Journal of Translational Medicine Causal genes eQTL Alzheimer’s disease GWAS SNP Genetic variant |
| title | Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease |
| title_full | Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease |
| title_fullStr | Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease |
| title_full_unstemmed | Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease |
| title_short | Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer’s disease |
| title_sort | multi omics analysis for identifying cell type specific and bulk level druggable targets in alzheimer s disease |
| topic | Causal genes eQTL Alzheimer’s disease GWAS SNP Genetic variant |
| url | https://doi.org/10.1186/s12967-025-06739-1 |
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