Banner & Appendix

Knowledge DB

Genomics DB

Neurotools

Neuroanatomy

Grant Sponsors and Gov. Organization

News

Publication

Department of Chemitry

International Journal

Aging Cell
년도 2024
학술지명 Aging Cell
논문명 Multi-proteomic analysis of 5xFAD mice reveals new molecular signatures for early stage of Alzheimer’s disease
게재권/집
수록페이지
저자명 Seulah Lee, Kuk-In Jang, Hagyeong Lee, Yeon Suk Jo, Dayoung Kwon, Geuna Park, Sungwon Bae, Yang Woo Kwon, Jin-Hyeok Jang, Yong-Seok Oh, Chany Lee, Jong Hyuk Yoon
Link 관련링크 https://onlinelibrary.wiley.com/doi/full/10.1111/acel.14137 729회 연결
Abstract

An early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis.