Department of Pediatric Neurology, Faculty of Medicine, Comenius University Bratislava and National Institute of Children's Diseases, 83340 Bratislava, Slovakia.
Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 84215 Bratislava, Slovakia.
Genes (Basel). 2023 Dec 3;14(12):2174. doi: 10.3390/genes14122174.
X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from dysfunction of the protein myotubularin encoded by the gene. XLMTM has a high neonatal and infantile mortality rate due to a severe myopathic phenotype and respiratory failure. However, in a minority of XLMTM cases, patients present with milder phenotypes and achieve ambulation and adulthood. Notable facial dysmorphia is also present.
We investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients' cohort (previously published data plus three novel variants, = 414). Based on the facial gestalt difference between XLMTM patients and unaffected controls, we investigated the use of the Face2Gene application.
Significant associations between severe phenotype and truncating variants ( < 0.001), frameshift variants ( < 0.001), nonsense variants ( = 0.006), and in/del variants ( = 0.036) were present. Missense variants were significantly associated with the mild and moderate phenotype ( < 0.001). The Face2Gene application showed a significant difference between XLMTM patients and unaffected controls ( = 0.001).
Using genotype-phenotype correlations could predict the disease course in most XLMTM patients, but still with limitations. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM using the correct algorithm.
X 连锁肌小管肌病(XLMTM)是一种罕见的先天性肌病,由基因编码的肌小管蛋白功能障碍引起。由于严重的肌病表型和呼吸衰竭,XLMTM 的新生儿和婴儿死亡率很高。然而,在少数 XLMTM 病例中,患者表现出较轻的表型,并实现了行走和成年。也存在明显的面部畸形。
我们在一个患者队列中(以前发表的数据加三个新变体,= 414)研究了新诊断的 XLMTM 患者的基因型-表型相关性。基于 XLMTM 患者和未受影响的对照者之间的面部整体差异,我们研究了使用 Face2Gene 应用程序的情况。
严重表型与截断变异体(<0.001)、移码变异体(<0.001)、无意义变异体(= 0.006)和插入/缺失变异体(= 0.036)之间存在显著相关性。错义变异体与轻度和中度表型显著相关(<0.001)。Face2Gene 应用程序在 XLMTM 患者和未受影响的对照者之间显示出显著差异(= 0.001)。
使用基因型-表型相关性可以预测大多数 XLMTM 患者的疾病进程,但仍存在局限性。Face2Gene 应用程序似乎是一种实用的、非侵入性的 XLMTM 诊断方法,使用正确的算法。