Browsing by Author "Park, Tamina"

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    Association of amyloid and cardiovascular risk with cognition: Findings from KBASE
    (Wiley, 2024) Chaudhuri, Soumilee; Dempsey, Desarae A.; Huang, Yen-Ning; Park, Tamina; Cao, Sha; Chumin, Evgeny J.; Craft, Hannah; Crane, Paul K.; Mukherjee, Shubhabrata; Choi, Seo-Eun; Scollard, Phoebe; Lee, Michael; Nakano, Connie; Mez, Jesse; Trittschuh, Emily H.; Klinedinst, Brandon S.; Hohman, Timothy J.; Lee, Jun-Young; Kang, Koung Mi; Sohn, Chul-Ho; Kim, Yu Kyeong; Yi, Dahyun; Byun, Min Soo; Risacher, Shannon L.; Nho, Kwangsik; Saykin, Andrew J.; Lee, Dong Young; KBASE Research Group; Radiology and Imaging Sciences, School of Medicine
    Background: Limited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. Methods: Vascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. Results: Baseline analyses revealed that amyloid-negative (Aβ-) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ- CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. Conclusion: Our findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ- individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid-positive individuals had worse cognitive performance than Aβ- individuals. Highlights: Vascular risk significantly affects cognition in amyloid-negative older Koreans. Amyloid-negative CN older adults with high vascular risk had lower baseline cognition. Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk. The study underscores the impact of vascular health on the AD disease spectrum.
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    Functional insight into East Asian-specific genetic risk loci for Alzheimer's disease
    (Wiley, 2025) Cho, Minyoung; Chaudhuri, Soumilee; Liu, Shiwei; Park, Tamina; Huang, Yen-Ning; Rosewood, Thea; Bice, Paula J.; Saykin, Andrew J.; Won, Hong-Hee; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Introduction: The functional study of genetic risk factors for Alzheimer's disease (AD) provides insights into the underlying mechanisms and identification of potential therapeutic targets. Investigating AD-associated genetic loci identified in East Asian populations using single-nucleus RNA-sequencing data may identify novel functional genetic contributors. Methods: Cell type-specific expression quantitative trait loci (eQTL) and peak-to-gene links were used to identify functional genes associated with 26 genetic loci from seven genome-wide association studies (GWAS) for AD in East Asians. Results: KCNJ6 and MAPK1IP1L were identified as significant eQTLs with AD risk loci. AD risk loci were in peaks related to four genes, with CLIC4 being connected across different cell types. Genes identified in European and East Asian GWAS interacted within networks and were enriched in AD pathology pathways in astrocytes. Discussion: Our findings suggest KCNJ6 and CLIC4 as novel AD-associated functional genes, providing insight into the genetic architecture of AD in East Asians. Highlights: Integrated functional analysis of Alzheimer's disease (AD) loci in seven East Asian genome-wide association studies (GWAS) was performed. Cell type-specific expression quantitative trait loci (eQTLs) and assay for transposase-accessible chromatin peaks were used to identify AD functional genes. An AD risk variant was linked to KCNJ6 through an oligodendrocyte progenitor cell-specific eQTL. An AD risk variant maps to open chromatin, linked to CLIC4 across six cell types. Astrocyte differentially expressed genes by AD pathology are enriched in East Asian and European GWAS genes.
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    Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort
    (Wiley, 2024) Park, Tamina; Hwang, Jiyun; Liu, Shiwei; Chaudhuri, Soumilee; Han, Sang Won; Yi, Dahyun; Byun, Min Soo; Huang, Yen-Ning; Rosewood, Thea; Jung, Gijung; Kim, Min Jeong; Ahn, Hyejin; Lee, Jun-Young; Kim, Yu Kyeong; Cho, MinYoung; Bice, Paula J.; Craft, Hannah; Risacher, Shannon L.; Gao, Hongyu; Liu, Yunlong; Kim, SangYun; Park, Young Ho; Lee, Dong Young; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Introduction: Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations. Methods: RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography. Results: DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data. Discussion: Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies. Highlights: Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network modules were associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβ deposition. Dysregulated genes were related to a NK cell-mediated immunity.
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    Integration of GWAS summary statistics with cell type‐specific eQTLs prioritizes potential causal genes for Alzheimer’s disease
    (Wiley, 2025-01-09) Liu, Shiwei; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Cho, Min Young; Jacobson Rosewood, Thea; Bennett, David A.; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    Background: Analyzing disease‐linked genetic variants via expression quantitative trait loci (eQTLs) is crucial for identifying disease‐causing genes. Previous research prioritized genes by integrating Genome‐Wide Association Study (GWAS) results with tissue‐level eQTLs. Recent studies explored brain cell type‐specific eQTLs, but they lack a systematic analysis across various AD GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type‐specific eQTL datasets. Here, we integrated brain cell type‐specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level. Method: To prioritize disease‐causing genes, we used summary data‐based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the AD GWAS summary statistics with cell type‐specific eQTLs in human brain. We utilized five latest AD GWAS datasets and a cell type‐specific eQTL dataset comprising 424 participants of the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) cohort. We replicated our analysis using a cell type‐specific eQTL dataset of 192 participants from Bryois et al., 2021. For comparison, we utilized a previous tissue‐level metabrain eQTL dataset from a meta‐analysis of 14 datasets. Furthermore, we visualized the colocalization of novel candidate causal genes using eQTpLot. Result: We identified 17 cell type‐specific candidate causal genes using the ROSMAP eQTL dataset. Our results showed that the largest number of candidate causal genes are identified in microglia, followed by astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and oligodendrocyte progenitor cells (OPCs). Four candidate causal genes were common across different cell types. Interestingly, JAZF1, detected as a candidate causal gene affected by the same leading variant in both microglia and OPCs, showed a congruous (same direction) colocalized SNP effect on the gene expression level and AD in OPCs, but an incongruous (opposite direction) colocalized SNP effect in microglia. After comparing our results with previously known prioritized causal genes, we identified PABPC1 in astrocyte as a novel potential causal gene. Conclusion: 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.
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    Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer’s Disease
    (medRxiv, 2025-01-09) Liu, Shiwei; Cho, Min Young; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Jacobson Rosewood, Thea; Bice, Paula J.; Chung, Dongjun; Bennett, David A.; Ertekin-Taner, Nilüfer; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
    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). PABPC1 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 PABPC1 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.
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    Plasma miRNAs across the Alzheimer's disease continuum: Relationship to central biomarkers
    (Wiley, 2024) Liu, Shiwei; Park, Tamina; Krüger, Dennis M.; Pena-Centeno, Tonatiuh; Burkhardt, Susanne; Schutz, Anna-Lena; Huang, Yen-Ning; Rosewood, Thea; Chaudhuri, Soumilee; Cho, MinYoung; Risacher, Shannon L.; Wan, Yang; Shaw, Leslie M.; Sananbenesi, Farahnaz; Brodsky, Alexander S.; Lin, Honghuang; Krunic, Andre; Krzysztof Blusztajn, Jan; Saykin, Andrew J.; Delalle, Ivana; Fischer, Andre; Nho, Kwangsik; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis. Methods: We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis. Machine learning approaches were applied to investigate the role of miRNAs as blood biomarkers. Results: We identified nine, two, and eight miRNAs significantly associated with A/T/N positivity, respectively. We identified 271 genes targeted by amyloid-related miRNAs with estrogen signaling receptor-mediated signaling among the enriched pathways. Additionally, 220 genes targeted by neurodegeneration-related miRNAs showed enrichment in pathways including the insulin growth factor 1 pathway. The classification performance of demographic information for A/T/N positivity was increased up to 9% with the inclusion of miRNAs. Discussion: Plasma miRNAs were associated with central A/T/N biomarkers, highlighting their potential as blood biomarkers. Highlights: We performed association analysis of microRNAs (miRNAs) with amyloid/tau/neurodegeneration (A/T/N) biomarker positivity. We identified dysregulated miRNAs for A/T/N biomarker positivity. We identified Alzheimer's disease biomarker-specific/common pathways related to miRNAs. miRNAs improved the classification for A/T/N positivity by up to 9%. Our study highlights the potential of miRNAs as blood biomarkers.
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    The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals
    (Wiley, 2024) Krüger, Dennis M.; Pena-Centeno, Tonatiuh; Liu, Shiwei; Park, Tamina; Kaurani, Lalit; Pradhan, Ranjit; Huang, Yen-Ning; Risacher, Shannon L.; Burkhardt, Susanne; Schütz, Anna-Lena; Wan, Yang; Shaw, Leslie M.; Brodsky, Alexander S.; DeStefano, Anita L.; Lin, Honghuang; Schroeder, Robert; Krunic, Andre; Hempel, Nina; Sananbenesi, Farahnaz; Krzysztof Blusztajn, Jan; Saykin, Andrew J.; Delalle, Ivana; Nho, Kwangsik; Fischer, Andre; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Introduction: MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). Methods: We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Results: We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Discussion: Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. Highlights: We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.
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    Transcriptome analysis of early‐ and late‐onset Alzheimer's disease in Korean cohorts
    (Wiley, 2025) Han, Sang-Won; Hwang, Jiyun; Park, Tamina; Pyun, Jung-Min; Lee, Joo-Yeon; Park, Jeong Su; Bice, Paula J.; Liu, Shiwei; Yun, Sunmin; Jeong, Jibin; Risacher, Shannon L.; Saykin, Andrew J.; Byun, Min Soo; Yi, Dahyun; Sung, Joohon; Lee, Dong Young; Kim, Sang Yun; Nho, Kwangsik; Park, Young Ho; Radiology and Imaging Sciences, School of Medicine
    Introduction: The molecular mechanisms underlying early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) remain incompletely understood, particularly in Asian populations. Methods: RNA-sequencing was carried out on blood samples from 248 participants in the Seoul National University Bundang Hospital cohort to perform differential gene expression (DGE) and weighted gene co-expression network analysis. Findings were replicated in an independent Korean cohort (N = 275). Results: DGE analysis identified 18 and 88 dysregulated genes in EOAD and LOAD, respectively. Network analysis identified a LOAD-associated module showing a significant enrichment in pathways related to mitophagy, 5' adenosine monophosphate-activated protein kinase signaling, and ubiquitin-mediated proteolysis. In the replication cohort, downregulation of SMOX and PLVAP in LOAD was replicated, and the LOAD-associated module was highly preserved. In addition, SMOX and PLVAP were associated with brain amyloid beta deposition. Discussion: Our findings suggest distinct molecular signatures for EOAD and LOAD in a Korean population, providing deeper understanding of their diagnostic potential and molecular mechanisms. Highlights: Analysis identified 18 and 88 dysregulated genes in early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD), respectively. Expression levels of SMOX and PLVAP were downregulated in LOAD. Expression levels of SMOX and PLVAP were associated with brain amyloid beta deposition. Pathways including mitophagy and 5' adenosine monophosphate-activated protein kinase signaling were enriched in a LOAD module. A LOAD module was highly preserved across two independent cohorts.