Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease

Overview

Journal medRxiv

Date 2025 Jan 20

PMID 39830273

Authors

Shiwei Liu

Min Young Cho

Yen-Ning Huang

Tamina Park

Soumilee Chaudhuri

Thea Jacobson Rosewood

Paula J Bice

Dongjun Chung

David A Bennett

Nilufer Ertekin-Taner

Andrew J Saykin

Kwangsik Nho

Affiliations

Soon will be listed here.

Abstract

Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying 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 they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level.

Methods: To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD.

Results: We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene is located near or within enhancers only active in astrocytes. We classified the 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-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.

References

Fujita M, Gao Z, Zeng L, McCabe C, White C, Ng B . Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain. Nat Genet. 2024; 56(4):605-614. DOI: 10.1038/s41588-024-01685-y. View

Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F . The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2013; 42(Database issue):D358-63. PMC: 3965093. DOI: 10.1093/nar/gkt1115. View

Maziuk B, Apicco D, Cruz A, Jiang L, Ash P, Lummertz da Rocha E . RNA binding proteins co-localize with small tau inclusions in tauopathy. Acta Neuropathol Commun. 2018; 6(1):71. PMC: 6069705. DOI: 10.1186/s40478-018-0574-5. View

Giambartolomei C, Vukcevic D, Schadt E, Franke L, Hingorani A, Wallace C . Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014; 10(5):e1004383. PMC: 4022491. DOI: 10.1371/journal.pgen.1004383. View

Alvarado C, Makarious M, Weller C, Vitale D, Koretsky M, Bandres-Ciga S . omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases. Am J Hum Genet. 2024; 111(1):150-164. PMC: 10806756. DOI: 10.1016/j.ajhg.2023.12.006. View

Schwartzentruber J, Cooper S, Liu J, Barrio-Hernandez I, Bello E, Kumasaka N . Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer's disease risk genes. Nat Genet. 2021; 53(3):392-402. PMC: 7610386. DOI: 10.1038/s41588-020-00776-w. View

Bihorel S, Camenisch G, Lemaire M, Scherrmann J . Influence of breast cancer resistance protein (Abcg2) and p-glycoprotein (Abcb1a) on the transport of imatinib mesylate (Gleevec) across the mouse blood-brain barrier. J Neurochem. 2007; 102(6):1749-1757. DOI: 10.1111/j.1471-4159.2007.04808.x. View

Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi A, Tanaseichuk O . Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019; 10(1):1523. PMC: 6447622. DOI: 10.1038/s41467-019-09234-6. View

Maziuk B, Ballance H, Wolozin B . Dysregulation of RNA Binding Protein Aggregation in Neurodegenerative Disorders. Front Mol Neurosci. 2017; 10:89. PMC: 5378767. DOI: 10.3389/fnmol.2017.00089. View

10.

Strober B, Elorbany R, Rhodes K, Krishnan N, Tayeb K, Battle A . Dynamic genetic regulation of gene expression during cellular differentiation. Science. 2019; 364(6447):1287-1290. PMC: 6623972. DOI: 10.1126/science.aaw0040. View

11.

Majewski J, Pastinen T . The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet. 2010; 27(2):72-9. DOI: 10.1016/j.tig.2010.10.006. View

12.

Kumar M, Kulshrestha R, Singh N, Jaggi A . Expanding spectrum of anticancer drug, imatinib, in the disorders affecting brain and spinal cord. Pharmacol Res. 2019; 143:86-96. DOI: 10.1016/j.phrs.2019.03.014. View

13.

Bellenguez C, Kucukali F, Jansen I, Kleineidam L, Moreno-Grau S, Amin N . New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet. 2022; 54(4):412-436. PMC: 9005347. DOI: 10.1038/s41588-022-01024-z. View

14.

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson M, Powell J . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5):481-7. DOI: 10.1038/ng.3538. View

15.

Guan Z, Yu W, Nordberg A . Dual effects of nicotine on oxidative stress and neuroprotection in PC12 cells. Neurochem Int. 2003; 43(3):243-9. DOI: 10.1016/s0197-0186(03)00009-3. View

16.

Siletti K, Hodge R, Mossi Albiach A, Lee K, Ding S, Hu L . Transcriptomic diversity of cell types across the adult human brain. Science. 2023; 382(6667):eadd7046. DOI: 10.1126/science.add7046. View

17.

Lambert J, Ibrahim-Verbaas C, Harold D, Naj A, Sims R, Bellenguez C . Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013; 45(12):1452-8. PMC: 3896259. DOI: 10.1038/ng.2802. View

18.

Iqbal K, Grundke-Iqbal I . Alzheimer's disease, a multifactorial disorder seeking multitherapies. Alzheimers Dement. 2010; 6(5):420-4. PMC: 2946155. DOI: 10.1016/j.jalz.2010.04.006. View

19.

Yoo M, Shin J, Kim J, Ryall K, Lee K, Lee S . DSigDB: drug signatures database for gene set analysis. Bioinformatics. 2015; 31(18):3069-71. PMC: 4668778. DOI: 10.1093/bioinformatics/btv313. View

20.

Drivas T, Lucas A, Ritchie M . eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals. BioData Min. 2021; 14(1):32. PMC: 8285863. DOI: 10.1186/s13040-021-00267-6. View