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通过直接神经元重编程来模拟迟发性阿尔茨海默病的神经病理学。

Modeling late-onset Alzheimer's disease neuropathology via direct neuronal reprogramming.

机构信息

Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.

Center for Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA.

出版信息

Science. 2024 Aug 2;385(6708):adl2992. doi: 10.1126/science.adl2992.

Abstract

Late-onset Alzheimer's disease (LOAD) is the most common form of Alzheimer's disease (AD). However, modeling sporadic LOAD that endogenously captures hallmark neuronal pathologies such as amyloid-β (Aβ) deposition, tau tangles, and neuronal loss remains an unmet need. We demonstrate that neurons generated by microRNA (miRNA)-based direct reprogramming of fibroblasts from individuals affected by autosomal dominant AD (ADAD) and LOAD in a three-dimensional environment effectively recapitulate key neuropathological features of AD. Reprogrammed LOAD neurons exhibit Aβ-dependent neurodegeneration, and treatment with β- or γ-secretase inhibitors before (but not subsequent to) Aβ deposit formation mitigated neuronal death. Moreover inhibiting age-associated retrotransposable elements in LOAD neurons reduced both Aβ deposition and neurodegeneration. Our study underscores the efficacy of modeling late-onset neuropathology of LOAD through high-efficiency miRNA-based neuronal reprogramming.

摘要

晚发性阿尔茨海默病(LOAD)是最常见的阿尔茨海默病(AD)形式。然而,模拟散发性 LOAD,其内源性地捕获标志性神经元病理学,如淀粉样蛋白-β(Aβ)沉积、tau 缠结和神经元丢失,仍然是一个未满足的需求。我们证明了通过源自常染色体显性 AD(ADAD)和 LOAD 个体的成纤维细胞的 miRNA 为基础的直接重编程,在三维环境中产生的神经元有效地再现了 AD 的关键神经病理学特征。重编程的 LOAD 神经元表现出 Aβ依赖性神经退行性变,并且在用β-或γ-分泌酶抑制剂处理(但不在 Aβ沉积形成之后)之前减轻了神经元死亡。此外,抑制 LOAD 神经元中的年龄相关逆转录元件减少了 Aβ沉积和神经退行性变。我们的研究强调了通过高效的 miRNA 为基础的神经元重编程来模拟 LOAD 的晚期神经病理学的功效。

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