Brain single-cell transcriptome-informed Mendelian randomization identifies potential druggable therapeutic targets for Alzheimer's disease

Brain multiomic profiling identifies tau-related transcriptomic dysregulation in Alzheimer’s disease

Identification of gene expression changes in post-mortem brain tissue of Alzheimer’s disease donors compared to controls have implicated numerous biological pathways for Alzheimer’s disease pathophysiology. Nonetheless, there is still limited understanding of how gene expression dysregulation underpins specific proteinopathies core to Alzheimer’s disease. Here we investigate brain transcriptomic changes in a well characterized cohort of Alzheimer’s disease donors to identify genes and networks that associate with Alzheimer’s disease endophenotypes including neuropathology measures (Braak stage, Thal phase and cerebral amyloid angiopathy score) and Alzheimer’s disease-related brain protein levels (Apolipoprotein E, Amyloid-β 40, Amyloid-β 42, tau, and phospho-Tau). Bulk transcriptome measures were collected from the temporal cortex tissue of 477 Alzheimer’s disease donors. Following quality control, transcriptome-wide association studies were performed for each endophenotype. We used weighted gene co-expression network analysis to build co-expression networks and integrated transcriptome with epigenetic and genetic data from the same donors. We detected a total of 5,740 Bonferroni significant temporal cortex gene associations with Alzheimer’s disease endophenotypes, most of which were with brain tau levels. We discovered tau-associated co-expression modules enriched in known and novel Alzheimer’s disease pathways. We found that a beneficial (or neutral) brain biochemical state of higher total tau and lower phospho-Tau are associated with increased levels of synaptic, DNA damage/repair, nucleic acid metabolism and myelin processes. In contrast, in a detrimental state of lower total and higher phospho-Tau, there is upregulation of vascular and immune, and downregulation of mitochondrial and myelin pathways. There are brain gene expression perturbations that are associated with Alzheimer’s disease endophenotypes. While some of these associations are common across multiple endophenotypes, many are distinct for different Alzheimer’s disease-related proteins. Based on these findings, we propose a hypothetical model of dynamic brain gene expression changes that track with progressive Alzheimer’s disease proteostasis. These expression changes hold potential to serve as dynamic, precision biomarkers of brain Alzheimer’s disease progression. This study demonstrates the potential of integrative multiomics and deep Alzheimer’s disease endophenotype analyses in well-characterized brain tissues to uncover with precision the complex biology of Alzheimer’s disease.

Statistical integration of GWAS, eQTL and pQTL data for multi-omics analysis of Alzheimer's Disease

Alzheimer's Disease (AD) is a complex neurodegenerative disorder with a strong genetic architecture. Genome-Wide Association Studies (GWAS) have identified numerous susceptibility loci. However, the majority of associated variants reside in non-coding regions, making it difficult to resolve their functional consequences and identify causal genes. To address this limitation, integration of GWAS with expression Quantitative Trait Loci (eQTL) and protein Quantitative Trait Loci (pQTL) data has emerged as a key strategy for linking genetic variation to downstream molecular phenotypes. This review discusses statistical frameworks for multi-omics integration in AD research, with a focus on approaches that enable causal inference and gene prioritization. Major methods include colocalization analysis for detecting shared causal variants, Mendelian Randomization (MR) for assessing putative causal relationships, and Transcriptome-Wide Association Studies (TWAS) for linking genetically predicted gene expression to disease risk. Applications of these frameworks have facilitated the identification of candidate causal genes and proteins, thereby improving the mechanistic interpretation of AD-associated loci. However, challenges remain, including tissue specificity and cell-type specificity, limited ancestral diversity in available datasets, and constraints in causal inference. Emerging single-cell and spatial multi-omics approaches are expected to provide a more detailed characterization of AD-associated molecular mechanisms while supporting therapeutic target discovery.

Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.

Integrating Multi-Omics and Mendelian Randomization Reveals the Role of Epstein-Barr Virus Infection in Alzheimer's Disease and the Therapeutic Potential of Resveratrol.

INTRODUCTION The pathogenesis of Alzheimer's disease (AD) is complex, with immune system dysregulation playing a critical role. However, the specific molecular mechanisms linking peripheral immune responses to central pathologies in AD remain unclear. This study aims to systematically screen for reliable plasma biomarkers of AD by integrating transcriptomics, Mendelian randomization (MR) of plasma proteomics, and bioinformatics analyses, and to explore their potential pathogenic mechanisms and therapeutic drugs. MATERIALS AND METHODS Transcriptomic sequencing of plasma samples from three AD patients and three healthy controls was first performed to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. The aim of this study is to provide a preliminary indication of gene expression changes based on real patient samples for subsequent MR and bioinformatics analyses, rather than serving as confirmatory evidence. Following this, two-sample MR was performed to explore the potential causal relationship between plasma proteins and AD in genetic prediction, and MR-positive results were intersected with transcriptome DEGs to identify highconfidence targets. After that, protein-protein interaction (PPI) analysis, functional enrichment (GO/KEGG), and transcription factor (TF) target network analysis were conducted. Based on the KEGG pathway analysis, the causal association between antibodies related to Epstein-Barr virus and AD in genetic prediction was further evaluated. In the end, the diagnostic power of core biomarkers was validated in the GEO dataset. Potential therapeutic drugs were screened in the CTD database, followed by verification through molecular docking and molecular dynamics simulation. RESULT Our transcriptomic enrichment analysis of DEGs indicates that AD is significantly correlated with viral infection and immune and inflammatory pathways. According to the results of MR analyses, 36 plasma proteins have a causal effect on AD in genetic prediction. Among these 36 targets, two pathways are identified as enriched: "EBV Infection" and "Efferocytosis". Seven core targets are CR2, ICAM1, TAPBP, TNFAIP3, THBS1, SCARF1, and SIRPG. Also, the concentration of antibodies against EBV EBNA-1 and VCA p18 was confirmed by MR analyses to be risk factors for AD. According to drug predictions, molecular docking, and molecular dynamics simulations, resveratrol can stabilize CR2. DISCUSSION This study systematically identifies major plasma immune biomarkers associated with AD and proposes a mechanism by which EBV infection regulates plasma proteins CR2, TNFAIP3, and THBS1, which may affect AD risk. Resveratrol is thought to have preventive and protective effects, as predicted computationally. CONCLUSION This study systematically identified key plasma markers associated with AD. Resveratrol is likely to become a potentially effective preventive and protective drug in the prevention and treatment of AD, providing new ideas and targets for immune intervention of AD.

Proteome-wide Mendelian randomization identifies causal plasma proteins in frontotemporal dementia

Integrative Transcriptomics and Mendelian Randomization Identify RGS1 as a Causal Immune Regulator in Alzheimer’s Disease

Alzheimer’s disease (AD) has a complex pathogenesis, involving molecular and neuroimmune dysregulation, but the causal drivers linking transcriptomic changes to immune remodeling are not yet clear. In a discovery cohort, differential expression analysis was performed, and it was independently validated in two external cohorts. Key genes were prioritized via LASSO/logistic regression, functionally annotated, and causally linked to AD using two-sample MR. The neuroimmune landscape was mapped by ssGSEA, and top candidates were validated in vitro. RGS1 is a key node in neuroinflammation and cytoskeletal dynamics, which is prioritized by the algorithmic intersection. MR analysis suggested a potential causal association between genetically predicted RGS1 expression and AD risk. RGS1 was consistently upregulated in both discovery and validation cohorts (AUC: 0.61–0.67) and was confirmed in vitro. Immune deconvolution showed that AD-specific enrichment profiles occur, and RGS1 is strongly correlated with activated CD4+ T cells and pro-inflammatory chemokines. RGS1 is identified as a robust key gene that may contribute to immune microenvironment dysregulation in AD, and combining discovery-validation transcriptomics, causal inference, and experimental validation, we find that RGS1 is a potential immunomodulatory target.