Browsing by Author "Koh, Insong"

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    ADAS-viewer: web-based application for integrative analysis of multi-omics data in Alzheimer’s disease
    (Springer Nature, 2021-03-19) Han, Seonggyun; Shin, Jaehang; Jung, Hyeim; Ryu, Jane; Minassie, Habtamu; Nho, Kwangsik; Koh, Insong; Lee, Younghee; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) is a neurodegenerative disorder and is represented by complicated biological mechanisms and complexity of brain tissue. Our understanding of the complicated molecular architecture that contributes to AD progression benefits from performing comprehensive and systemic investigations with multi-layered molecular and biological data from different brain regions. Since recently different independent studies generated various omics data in different brain regions of AD patients, multi-omics data integration can be a useful resource for better comprehensive understanding of AD. Here we present a web platform, ADAS-viewer, that provides researchers with the ability to comprehensively investigate and visualize multi-omics data from multiple brain regions of AD patients. ADAS-viewer offers means to identify functional changes in transcript and exon expression (i.e., alternative splicing) along with associated genetic or epigenetic regulatory effects. Specifically, it integrates genomic, transcriptomic, methylation, and miRNA data collected from seven different brain regions (cerebellum, temporal cortex, dorsolateral prefrontal cortex, frontal pole, inferior frontal gyrus, parahippocampal gyrus, and superior temporal gyrus) across three independent cohort datasets. ADAS-viewer is particularly useful as a web-based application for analyzing and visualizing multi-omics data across multiple brain regions at both transcript and exon level, allowing the identification of candidate biomarkers of Alzheimer’s disease.
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    Alternative Splicing Regulation of Low-Frequency Genetic Variants in Exon 2 of TREM2 in Alzheimer's Disease by Splicing-Based Aggregation
    (MDPI, 2021-09-13) Han, Seonggyun; Na, Yirang; Koh, Insong; Nho, Kwangsik; Lee, Younghee; Radiology and Imaging Sciences, School of Medicine
    TREM2 is among the most well-known Alzheimer’s disease (AD) risk genes; however, the functional roles of its AD-associated variants remain to be elucidated, and most known risk alleles are low-frequency variants whose investigation is challenging. Here, we utilized a splicing-guided aggregation method in which multiple low-frequency TREM2 variants were bundled together to investigate the functional impact of those variants on alternative splicing in AD. We analyzed whole genome sequencing (WGS) and RNA-seq data generated from cognitively normal elderly controls (CN) and AD patients in two independent cohorts, representing three regions in the frontal lobe of the human brain: the dorsolateral prefrontal cortex (CN = 213 and AD = 376), frontal pole (CN = 72 and AD = 175), and inferior frontal (CN = 63 and AD = 157). We observed an exon skipping event in the second exon of TREM2, with that exon tending to be more frequently skipped (p = 0.0012) in individuals having at least one low-frequency variant that caused loss-of-function for a splicing regulatory element. In addition, genes differentially expressed between AD patients with high vs. low skipping of the second exon (i.e., loss of a TREM2 functional domain) were significantly enriched in immune-related pathways. Our splicing-guided aggregation method thus provides new insight into the regulation of alternative splicing of the second exon of TREM2 by low-frequency variants and could be a useful tool for further exploring the potential molecular mechanisms of multiple, disease-associated, low-frequency variants.
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    Brain Region-Dependent Alternative Splicing of Alzheimer Disease (AD)-Risk Genes Is Associated With Neuropathological Features in AD
    (Korean Continence Society, 2022) Kim, Sara; Han, Seonggyun; Cho, Soo-Ah; Nho, Kwangsik; Koh, Insong; Lee, Younghee; Radiology and Imaging Sciences, School of Medicine
    Purpose: Alzheimer disease (AD) is one of the most complex diseases and is characterized by AD-related neuropathological features, including accumulation of amyloid-β plaques and tau neurofibrillary tangles. Dysregulation of alternative splicing (AS) contributes to these features, and there is heterogeneity in features across brain regions between AD patients, leading to different severity and progression rates; however, brain region-specific AS mechanisms still remain unclear. Therefore, we aimed to systemically investigate AS in multiple brain regions of AD patients and how they affect clinical features. Methods: We analyzed RNA sequencing (RNA-Seq) data obtained from brain regions (frontal and temporal) of AD patients. Reads were mapped to the hg19 reference genome using the STAR aligner, and exon skipping (ES) rates were estimated as percent spliced in (PSI) by rMATs. We focused on AD-risk genes discovered by genome-wide association studies, and accordingly evaluated associations between PSI of skipped exons in AD-risk genes and Braak stage and plaque density mean (PM) for each brain region. We also integrated whole-genome sequencing data of the ascertained samples with RNA-Seq data to identify genetic regulators of feature-associated ES. Results: We identified 26 and 41 ES associated with Braak stage in frontal and temporal regions, respectively, and 10 and 50 ES associated with PM. Among those, 10 were frontal-specific (CLU and NTRK2), 65 temporal-specific (HIF1A and TRPC4AP), and 26 shared ES (APP) that accompanied functional Gene Ontology terms, including axonogenesis in shared-ES genes. We further identified genetic regulators that account for 44 ES (44% of the total). Finally, we present as a case study the systematic regulation of an ES in APP, which is important in AD pathogenesis. Conclusion: This study provides new insights into brain region-dependent AS regulation of the architecture of AD-risk genes that contributes to AD pathologies, ultimately allowing identification of a treatment target and region-specific biomarkers for AD.