Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
Institute of Molecular Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
PLoS One. 2020 Apr 14;15(4):e0231000. doi: 10.1371/journal.pone.0231000. eCollection 2020.
Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3'-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.
肌强直性营养不良 1 型（DM1）是一种罕见的遗传性疾病，其特征为肌肉萎缩、肌强直和其他症状。DM1 是由 DMPK 3'非翻译区 CTG 重复扩展引起的。更长的 CTG 扩展与更严重的症状和更早的发病年龄相关。发病机制的主要机制被认为是由含 CUG 的 RNA 的功能获得介导的，这导致许多其他基因的 RNA 代谢的反式失调。具体而言，许多基因的可变剪接（AS）和可变多聚腺苷酸化（APA）已知被破坏。在新兴 DM1 治疗的临床试验背景下，能够在分子生物标志物水平上客观地量化治疗效果非常重要。我们展示了如何使用现代高维统计学（机器学习）和血液和肌肉 mRNA 微阵列数据集，使用以前描述的候选 mRNA 生物标志物来模拟 CTG 长度的有效减少。我们展示了如何在临床试验背景下使用该模型检测治疗效果。
