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Cellular models of Batten disease.
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease ( IF 4.2 ) Pub Date : 2019-10-23 , DOI: 10.1016/j.bbadis.2019.165559
Christopher J Minnis 1 , Christopher D Thornton 2 , Lorna M FitzPatrick 2 , Tristan R McKay 2
Affiliation  

The Neuronal Ceroid Lipofuscinoses (NCL), otherwise known as Batten disease, are a group of neurodegenerative diseases caused by mutations in 13 known genes. All except one NCL is autosomal recessive in inheritance, with similar aetiology and characterised by the accumulation of autofluorescent storage material in the lysosomes of cells. Age of onset and the rate of progression vary between the NCLs. They are collectively one of the most common lysosomal storage diseases, but the enigma remains of how genetically distinct diseases result in such remarkably similar pathogenesis. Much has been learnt from cellular studies about the function of the proteins encoded by the affected genes. Such research has utilised primitive unicellular models such as yeast and amoeba containing gene orthologues, cells derived from naturally occurring (sheep) and genetically engineered (mouse) animal models or patient-derived cells. Most recently, patient-derived induced pluripotent stem cell (iPSC) lines have been differentiated into neural cell-types to study molecular pathogenesis in the cells most profoundly affected by disease. Here, we review how cell models have informed much of the biochemical understanding of the NCLs and how more complex models are being used to further this understanding and potentially act as platforms for therapeutic efficacy studies in the future.



中文翻译:

Batten病的细胞模型。

神经元蜡质脂褐质沉积症 (NCL),也称为巴顿病,是一组由 13 个已知基因突变引起的神经退行性疾病。除了一种 NCL 外,所有的 NCL 都是常染色体隐性遗传,具有相似的病因,其特征是细胞溶酶体中自发荧光储存材料的积累。NCL 的发病年龄和进展速度各不相同。它们是最常见的溶酶体贮积病之一,但基因上不同的疾病如何导致如此非常相似的发病机制仍然是个谜。从有关受影响基因编码的蛋白质的功能的细胞研究中已经了解到很多。此类研究利用了原始单细胞模型,例如含有基因直向同源物的酵母和变形虫,源自天然(绵羊)和基因工程(小鼠)动物模型或源自患者的细胞的细胞。最近,源自患者的诱导多能干细胞 (iPSC) 系已分化为神经细胞类型,以研究受疾病影响最深的细胞的分子发病机制。在这里,我们回顾了细胞模型如何为 NCL 的大部分生化理解提供信息,以及如何使用更复杂的模型来进一步理解这种理解,并有可能在未来充当治疗功效研究的平台。

更新日期:2020-04-20
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