German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany; Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany.
German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany; Center of Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany.
Neurobiol Dis. 2019 Jul;127:1-12. doi: 10.1016/j.nbd.2019.01.023. Epub 2019 Jan 31.
While the link between GBA and Parkinson's disease (PD) was initially unexpected, it is now well established that GBA mutations are the most frequent genetic risk for PD. GBA has also been linked to sporadic PD, dementia with Lewy bodies, and ageing. Thus, GBA represents a promising target to counteract brain disease and the age-related decline of lysosomal function. The exact mechanisms involved in the risk of developing PD in GBA mutation carriers are still unclear and research in this field has faced the major challenge of a lack of proper modeling systems. Induced pluripotent stem cells (iPSCs) as well as advances in disease modeling and genome editing have facilitated studies of human brain disease. With regard to GBA-PD, iPSCs offer several advantages including the possibility of investigating sphingolipid (SPL) biology in relevant cells, the role of dopamine metabolism as well as non-cell autonomous mechanisms that are likely involved in the disease process. This review will summarize findings that emerged from iPSC-based studies in the context of GBA-PD pathology and therapy. We also highlight current advantages and challenges of stem cell models for neurological disease modeling and drug discovery.
虽然 GBA 与帕金森病 (PD) 之间的联系最初出人意料,但现在已经确定 GBA 突变是 PD 的最常见遗传风险因素。GBA 也与散发性 PD、路易体痴呆和衰老有关。因此,GBA 代表了对抗大脑疾病和与年龄相关的溶酶体功能下降的有希望的靶点。在 GBA 突变携带者中发展为 PD 的风险所涉及的确切机制仍不清楚,该领域的研究面临着缺乏适当建模系统的主要挑战。诱导多能干细胞 (iPSC) 以及疾病建模和基因组编辑方面的进步促进了人类大脑疾病的研究。就 GBA-PD 而言,iPSC 具有几个优势,包括研究相关细胞中鞘脂 (SPL) 生物学的可能性、多巴胺代谢的作用以及可能涉及疾病过程的非细胞自主机制。这篇综述将总结基于 iPSC 的研究在 GBA-PD 病理学和治疗方面的发现。我们还强调了干细胞模型在神经疾病建模和药物发现方面的当前优势和挑战。