Comprehensive Characterization of Missense Mutants in Factor IX

总结 凝血因子IX（FIX）缺陷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基因的遗传变异可通过以下方式影响出血： 降低FIX表达或活性，或可通过增加FIX表达或活性影响血栓形成。 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， 可以确信地确定该变体是否改变了编码的FIX蛋白的功能，如果是，改变的程度如何。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 (i.e.在血友病B患者中发现的DNA变化（预计会改变FIX氨基酸）， 没有足够的证据将其归类为致病性。功能研究，其中FIX变体的稳定性、活性或 在体外评估其他特性，可以提供强有力的证据来解释F9变体。 然而，传统的功能研究是时间和资源密集型的，因此测试数百种F9变体 是不切实际的通过评估数千种可能的FIX错义变体， 观察更多的个体是不可能的。相反，我们提出了一种新的方法， 表达和表征FIX蛋白中几乎所有可能的错义变体，以促进我们的理解 FIX生物学，改善F9基因遗传变异的解释，并推进血友病护理， 治疗。 为了实现这一目标，我们将采用深度突变扫描，这是我们开发的一种测量 大量错义变体的影响。在这里，我们将展示一个 几乎所有可能的FIX错义变体都与培养的人细胞表面相连。我们打算利用 该FIX表面展示文库有两个目的：1）定量几乎每种可能的F9错义变体的作用 2）定量几乎每种可能的F9错义变体对FIX表达和分泌的影响， 特异性FIX功能，包括Gla结构域γ-羧化和因子XIa（FXIa）对FIX的激活。 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、γ- 羧化和FXIa活化。在aim 3中，我们将使用这些大规模的函数数据来剖析 F9致病性变体破坏FIX功能的机制。我们还将使用机器学习， 功能数据沿着其他特征，以预测FIX中每种错义变体的致病性。采取 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改变 F9变体的表征。它们还将作为一种资源，以更好地了解FIX生物学，改善 F9遗传信息的临床翻译，并为新的治疗提供信息。

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.