Parkinson's disease (PD) is a common neurodegenerative condition affecting more than 8 million people worldwide. Although, the majority of PD cases are sporadic in nature, there are a growing number of monogenic mutations identified to cause PD in a highly penetrant manner. Many of these familial mutations give rise to a condition that is clinically and neuropathologically similar, if not identical, to sporadic PD. Mutations in genes such as SNCA cause PD in an autosomal dominant manner and patients have motor and non-motor symptoms that are typical for sporadic PD. With the advent of reprogramming technology it is now possible to capture these mutations in induced pluripotent stem cells (iPSCs) to establish models of PD in a dish. There are multiple neuronal subtypes affected in PD including the midbrain dopaminergic (mDA) neurons of the substantia nigra. Robust neuronal differentiation into mDA or other relevant neural cell types are critical to accurately model the disease and ensure the findings are relevant to understanding the disease process. Another challenge for establishing accurate models of PD is being met by the generation of isogenic control iPSC lines with precise correction of mutations using advanced gene editing technology. The contributions of ageing and environmental factors present further challenges to this field, but significant progress is being made in these areas to establish highly relevant and robust models of PD. These human neuronal models, used in conjunction with other model systems, will vastly improve our understanding of the early stages of the PD, which will be key to identifying disease-modifying and preventative treatments.

译文

帕金森氏病 (PD) 是一种常见的神经退行性疾病,影响着全世界800万多人。尽管大多数PD病例本质上是零星的,但越来越多的单基因突变被确定以高度渗透的方式引起PD。这些家族突变中的许多引起的疾病在临床和神经病理学上与散发性PD相似,即使不相同。诸如SNCA之类的基因突变以常染色体显性遗传方式引起PD,并且患者具有散发性PD典型的运动和非运动症状。随着重编程技术的出现,现在可以在诱导多能干细胞 (ipsc) 中捕获这些突变,以在培养皿中建立PD模型。PD中有多种神经元亚型,包括黑质的中脑多巴胺能 (mDA) 神经元。强大的神经元分化为mDA或其他相关的神经细胞类型对于准确建模疾病并确保发现与理解疾病过程相关至关重要。建立PD准确模型的另一个挑战是通过使用先进的基因编辑技术对突变进行精确校正的等基因对照iPSC系的产生来满足。老龄化和环境因素的贡献给该领域带来了进一步的挑战,但是在建立高度相关和可靠的PD模型方面,这些领域正在取得重大进展。这些人类神经元模型与其他模型系统结合使用,将极大地提高我们对PD早期阶段的理解,这将是识别疾病缓解和预防治疗的关键。

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