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.

中文翻译：

---

通过直接神经元重编程模拟迟发性阿尔茨海默病神经病理学


晚发性阿尔茨海默病 (LOAD) 是阿尔茨海默病 (AD) 最常见的形式。然而，对内源性捕获标志性神经元病理学（例如淀粉样蛋白-β (Aβ) 沉积、tau 缠结和神经元丢失）的零星负载进行建模仍然是一个未满足的需求。我们证明，在三维环境中，受常染色体显性 AD (ADAD) 和 LOAD 影响的个体的成纤维细胞基于 microRNA (miRNA) 直接重编程生成的神经元有效地概括了 AD 的关键神经病理学特征。重编程的 LOAD 神经元表现出 Aβ 依赖性神经变性，在 Aβ 沉积物形成之前（但不是之后）使用 β-或 γ-分泌酶抑制剂治疗可减轻神经元死亡。此外，抑制 LOAD 神经元中与年龄相关的逆转录转座元件可减少 Aβ 沉积和神经变性。我们的研究强调了通过基于 miRNA 的高效神经元重编程来模拟 LOAD 迟发性神经病理学的有效性。