Comprehensive Characterization of Missense Mutants in Factor IX

摘要 凝血因子IX缺乏(FIX)导致出血性疾病血友病B，而高水平的FIX 有血栓形成的风险。因此，编码FIX的F9基因的遗传变异可以通过以下方式影响出血 减少FIX的表达或活性，或者可以通过增加FIX的表达或活性来影响血栓形成。 然而，当在F9基因中发现新的DNA变体时，我们通常缺乏所需的证据 自信地确定该变体是否改变了编码的FIX蛋白的功能，如果是，改变的严重程度。为 例如，在国家血友病基因分型项目MyLifeOurFuture中，大多数F9错义变体 (即预测会改变固定氨基酸的DNA变化)在血友病B患者中发现 证据不足，不能归类为致病因素。功能研究，其中修复变异体的稳定性、活性或 其他特性在体外进行评估，可以为解释F9变异体提供强有力的证据。 然而，传统的功能研究是时间和资源密集型的，所以测试数百个F9变种 我们到目前为止所观察到的是不切实际的。评估我们可能修复的数千个错义变体 随着更多的个体被测序而观察到的是不可能的。相反，我们提出了一种新的方法来 表达和鉴定FIX蛋白中几乎每一个可能的错义变体，以促进我们的理解 FIX生物学，改进对F9基因遗传变异的解释，并促进血友病治疗和 治疗。 为了实现这一目标，我们将使用深度突变扫描，这是我们开发的一种测量 一种蛋白质的大量错义变体同时产生的影响。在这里，我们将展示一个库 几乎所有可能修复的错义变体都系在培养的人类细胞表面。我们打算利用 这个固定表面展示文库有两个目的：1)量化几乎所有可能的F9错义变体的影响 关于FIX的表达和分泌，以及2)量化几乎所有可能的F9错义变体对 特异的FIX功能包括GLA结构域的伽马羧化和激活因子Xia(Fxia)的FIX。 这些目标将揭示FIX中几乎所有可能的错义变体如何影响表达、分泌、伽马- 羧化，并被FXIa激活。在目标3中，我们将使用这些大规模的函数数据来剖析 F9致病变异体破坏FIX功能的机制。我们还将使用机器学习，利用 功能数据和其他特征一起预测FIX中每个错义变体的致病性。已被占用 总之，我们生成的功能数据、我们提出的分析以及我们构建的工具将转变 F9变异体的特征。它们还将成为更好地了解修复生物学、改进 临床翻译F9基因信息，并告知新的治疗方法。

SUMMARY Deficiencies in coagulation factor IX (FIX) cause the bleeding disorder hemophilia B, while high levels of FIX pose a risk for thrombosis. Thus, genetic variation in the F9 gene encoding FIX can impact bleeding by decreasing FIX expression or activity, or can impact thrombosis by increasing FIX expression or activity. However, when a new DNA variant is discovered in the F9 gene, we typically lack the evidence needed to confidently determine if the variant alters the function of the encoded FIX protein and, if so, how severely. For example, in the national hemophilia genotyping project MyLifeOurFuture, the majority of F9 missense variants (i.e. DNA changes predicted to change a FIX amino acid) discovered in patients with hemophilia B had insufficient evidence to be classified as pathogenic. Functional studies, where a FIX variant’s stability, activity or other properties are evaluated in vitro, can provide strong evidence to inform interpretation of F9 variants. However, traditional functional studies are time- and resource-intensive, so testing the hundreds of F9 variant we have observed so far would be impractical. Evaluating the thousands of possible FIX missense variants we could observe as more individuals are sequenced would be impossible. Instead, we propose a new approach to express and characterize nearly every possible missense variant in the FIX protein to advance our understanding of FIX biology, improve the interpretation of genetic variation in the F9 gene, and advance hemophilia care and treatments. To accomplish this goal, we will employ deep mutational scanning, a method we developed for measuring the effects of massive numbers of missense variants of a protein simultaneously. Here, we will display a library of nearly all possible FIX missense variants tethered to the surface of cultured human cells. We propose to exploit this FIX surface display library in two aims: 1) Quantifying the effect of nearly every possible F9 missense variant on FIX expression and secretion, and 2) Quantifying the effect of nearly every possible F9 missense variant on specific FIX functions including Gla-domain gamma-carboxylation and activation of FIX by factor XIa (FXIa). These aims will reveal how nearly all possible missense variants in FIX impact expression, secretion, gamma- carboxylation, and activation by FXIa. In aim 3, we will use these large-scale functional data to dissect the mechanism by which F9 pathogenic variants disrupt FIX function. We will also use machine learning, leveraging the functional data along with other features to predict pathogenicity for each missense variant in FIX. Taken together, the functional data we generate, the analyses we propose, and tools we build will transform the characterization of F9 variants. They will also serve as a resource to better understand FIX biology, improve the clinical translation of F9 genetic information, and inform new treatments.