Laboratory for Personalized Medicine and Neurodegenerative Diseases, The Shmunis School of Biomedicine and Cancer Research, The George S. Wise Faculty for Life Sciences, Sagol School of Neurosciences, Tel Aviv University, Ramat Aviv, 69978 Tel Aviv, Israel.
Laboratory for Personalized Medicine and Neurodegenerative Diseases, The Shmunis School of Biomedicine and Cancer Research, The George S. Wise Faculty for Life Sciences, Sagol School of Neurosciences, Tel Aviv University, Ramat Aviv, 69978 Tel Aviv, Israel.
Neurobiol Dis. 2024 Oct 15;201:106667. doi: 10.1016/j.nbd.2024.106667. Epub 2024 Sep 14.
Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.
亨廷顿病(HD)是一种可遗传的神经退行性疾病,由 HTT 基因中 CAG 三核苷酸重复扩增引起,CAG 重复扩增与疾病严重程度直接相关,发病年龄更早。以前我们已经表明,HD 患者的原代皮肤成纤维细胞表现出独特的疾病表型特征,包括明显的核形态和与细胞迁移相关的肌动蛋白帽的扰动,这些特征与 HD 患者的疾病严重程度相关。在这里,我们提供了进一步的证据,表明线粒体裂变融合形态平衡动力学,使用定制的基于图像的高内涵分析(HCA)机器学习工具进行分类,与 HD 严重程度状态的相关性更好。这种线粒体表型得到了 HD 患者成纤维细胞中裂变融合生物标志物(Drp1、MFN1、MFN2、VAT1)水平的适当变化的支持。这些发现共同指向线粒体动力学的失调,与健康对照组相比,HD 细胞中的裂变和融合过程可能都被破坏了。这项研究首次展示了一种能够在患者疾病发作前(前驱期)识别 HD 表型的方法。因此,我们相信该工具具有提高 HD 患者诊断精度的潜力,有可能评估 HD 进展过程中线粒体动力学的变化,为任何疾病阶段的分子机制和药物治疗评估提供有价值的见解。