Browsing by Author "Liu, Shiwei"

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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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    Pathway enrichment in genome‐wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes
    (Wiley, 2024) Rosewood, Thea J.; Nho, Kwangsik; Risacher, Shannon L.; Liu, Shiwei; Gao, Sujuan; Shen, Li; Foroud, Tatiana; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Medical and Molecular Genetics, School of Medicine
    Introduction: The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets. Methods: Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs. Results: A total of 33 and 19 statistically significant pathways were identified associating with the intercept and longitudinal trajectory, respectively. The longitudinal intercept pathways represent eight groups: immune, metabolic, cell growth and survival, DNA maintenance, neuronal signaling, RAS/MAPK/ERK signaling pathways, vesicle and lysosomal transport, and transcription modification. Longitudinal trajectory pathways represented six groups: Immune, metabolic, cell signaling, cytoskeleton, and glycosylation. Discussion: Longitudinal enrichment identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition, providing new insight into AD course-related mechanisms and potential new therapeutic targets. Highlights: A systematic genome-wide analysis with longitudinal AD biomarker endophenotypes was performed. Enriched pathways were identified with functionally derived SNP to gene analysis. Fifty-two pathways were associated with longitudinal trajectory and intercept. Many of the identified pathways are specific steps in larger pathways implicated in AD. The identified pathways may provide therapeutic targets and areas for further study.
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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.