Resolving Uncertainty in Alagille Syndrome Diagnostics

项目总结/摘要 基因组诊断的不确定性对实现基因组医学的全部潜力造成了障碍。 不确定性是显而易见的：1）我们无法确定一些DNA变异是致病的还是良性的，2）我们的 无法预测在何种程度上一个人与致病变异将受到影响，由于变量 表现力本提案将研究常染色体显性遗传病Alagille综合征的两种现象， 由Notch信号通路中的两个基因之一突变引起，即配体JAGGED 1（JAG 1）或 受体，NOTCH 2。Alagille综合征的特征在于儿科肝脏、心脏、脊椎、肾脏、眼部和 表情多变的面部异常JAG 1相关Alagille的发病机制 综合征是单倍功能不全，而NOTCH 2相关的Alagille综合征的机制尚不清楚， 报告的变异较少，功能特征较少，错义变异的发生率较高 （>50%）。错义变体很难分类，通常需要功能验证来支持或拒绝 致病性在Alagille综合征中，仅对19/125例报告的患者进行了功能表征 因此，尽管检测率高，但由于这种不确定性，诊断率较低。 我们建议使用设计的测定来解决Alagille综合征诊断中的不确定性， 表征JAG 1和NOTCH 2错义变体的致病性并分析基因表达数据 以鉴定可用于基因型-表型分析的基因表达特征 评价。在目标1中，我们将设计所有可能的核苷酸排列的位点饱和变体文库， 在JAG 1 C-末端具有高错义变体不确定性的区域上的每个核苷酸位置， 通过开发一种用于变异效应的多重测定（MAVE）来测试该文库， 定位JAG 1作为蛋白质功能的读数。在目标2中，我们将使用FFPE肝组织样本， 分析Alagille综合征患者和对照之间的基因表达差异，以及 轻度与重度肝病的Alagille综合征患者。在目标3中，我们将研究 通过使用突变细胞系的功能、表达和酶促测定的NOTCH 2变体。我们 假设这些提出的测定法将鉴定出高通量方法来测试错义致病性 (Aim 1）确定Alagille综合征患者和对照之间的基因表达差异以及基因表达差异。 Alagille综合征轻度与重度肝病患者之间的不同表达特征 疾病（目的2），并确定NOTCH 2变异体通过以下途径引起Alagille综合征的机制： 功能分析（目标3）。最终，这些数据将改善Alagille综合征的变异分析， 提高我们对Alagille综合征肝病分子基础的理解，并建立 错义变体的可扩展分类框架，提供诊断信息， 直接帮助临床医生。

PROJECT SUMMARY/ABSTRACT Uncertainty in genomic diagnostics creates a barrier to realizing the full potential of genomic medicine. Uncertainty is evident in: 1) our inability to determine if some DNA variants are pathogenic or benign and 2) our inability to predict to what extent a person with a disease-causing variant will be affected, due to variable expressivity. This proposal will study both phenomena for the autosomal dominant disorder Alagille syndrome, caused by mutations in one of two genes in the Notch signaling pathway, the ligand JAGGED1 (JAG1) or the receptor, NOTCH2. Alagille syndrome is characterized by pediatric liver, heart, vertebral, renal, ocular, and facial anomalies with highly variable expressivity. The mechanism of disease for JAG1-related Alagille syndrome is haploinsufficiency whereas the mechanism for NOTCH2-related Alagille syndrome is less clear, with fewer reported variants, less functional characterization, and a higher prevalence of missense variants (>50%). Missense variants are difficult to classify, often requiring functional validation to support or reject pathogenicity. In Alagille syndrome, functional characterization has been carried out for only 19/125 reported missense mutations, thus, despite a high detection rate, the diagnostic rate is lower due to this uncertainty. We propose to resolve uncertainty in the diagnostics of Alagille syndrome using assays designed to characterize the pathogenicity of JAG1 and NOTCH2 missense variants and analysis of gene expression data from patient liver samples to identify gene expression signatures that can be used for genotype-phenotype evaluations. In Aim 1, we will design a Site Saturation Variant Library of all possible nucleotide permutations at each nucleotide position across a region with high missense variant uncertainty in the JAG1 C-terminus and test this library by developing a Multiplexed Assay for Variant Effects (MAVEs) that will measure cellular localization of JAG1 as a readout of protein function. In Aim 2, we will use FFPE liver tissue samples to analyze gene expression differences between Alagille syndrome patients and controls, as well as between Alagille syndrome patients with mild versus severe liver disease. In Aim 3, we will study the molecular basis of NOTCH2 variants through functional, expression, and enzymatic assays using mutant cell lines. We hypothesize that these proposed assays will identify a high-throughput method to test missense pathogenicity (Aim 1), identify gene expression differences between Alagille syndrome patients and controls as well as gene expression signatures that are different between Alagille syndrome patients with mild versus severe liver disease (Aim 2), and determine the mechanism by which NOTCH2 variants cause Alagille syndrome through functional analysis (Aim 3). Ultimately, these data will improve variant analysis for Alagille syndrome, improve our understanding of the molecular basis of liver disease in Alagille syndrome, and establish a framework for scalable classification of missense variants, delivering diagnostic information that can directly help clinicians.