Genomics, Development and Disease Section, Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Bioinformatics and Scientific Programming Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Sci Rep. 2022 Feb 9;12(1):2162. doi: 10.1038/s41598-022-06112-y.
Niemann-Pick disease type C1 (NPC1) is a rare, prematurely fatal lysosomal storage disorder which exhibits highly variable severity and disease progression as well as a wide-ranging age of onset, from perinatal stages to adulthood. This heterogeneity has made it difficult to obtain prompt diagnosis and to predict disease course. In addition, small NPC1 patient sample sizes have been a limiting factor in acquiring genome-wide transcriptome data. In this study, primary fibroblasts from an extensive cohort of 41 NPC1 patients were used to validate our previous findings that the lysosomal quantitative probe LysoTracker can be used as a predictor for age of onset and disease severity. We also examined the correlation between these clinical parameters and RNA expression data from primary fibroblasts and identified a set of genes that were significantly associated with lysosomal defects or age of onset, in particular neurological symptom onset. Hierarchical clustering showed that these genes exhibited distinct expression patterns among patient subgroups. This study is the first to collect transcriptomic data on such a large scale in correlation with clinical and cellular phenotypes, providing a rich genomic resource to address NPC1 clinical heterogeneity and discover potential biomarkers, disease modifiers, or therapeutic targets.
尼曼-匹克病 C1 型(NPC1)是一种罕见的、致命性早发的溶酶体贮积症,其严重程度和疾病进展具有高度变异性,发病年龄从围生期到成年期不等。这种异质性使得及时诊断和预测疾病进程变得困难。此外,NPC1 患者的样本量小一直是获得全基因组转录组数据的一个限制因素。在这项研究中,我们使用了一个广泛的 NPC1 患者队列的原代成纤维细胞,验证了我们之前的发现,即溶酶体定量探针 LysoTracker 可作为发病年龄和疾病严重程度的预测指标。我们还研究了这些临床参数与原代成纤维细胞 RNA 表达数据之间的相关性,并确定了一组与溶酶体缺陷或发病年龄,特别是神经症状发病年龄显著相关的基因。层次聚类表明,这些基因在患者亚组中表现出不同的表达模式。这项研究首次在与临床和细胞表型相关的范围内收集了如此大规模的转录组数据,为解决 NPC1 临床异质性和发现潜在的生物标志物、疾病修饰因子或治疗靶点提供了丰富的基因组资源。