Comprehensive Characterization of Missense Mutants in Factor IX

摘要 凝血因子 IX (FIX) 缺乏会导致出血性疾病 B 型血友病，而高水平的 FIX 会导致出血性疾病 B 型血友病 存在血栓形成的风险。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： 降低 FIX 表达或活性，或者可以通过增加 FIX 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，如果改变，其严重程度如何。对于 例如，在国家血友病基因分型项目 MyLifeOurFuture 中，大多数 F9 错义变体 在 B 型血友病患者中发现的（即预计会改变 FIX 氨基酸的 DNA 变化） 没有足够的证据被归类为致病性。功能研究，其中 FIX 变体的稳定性、活性或 其他特性在体外进行评估，可以为 F9 变体的解释提供强有力的证据。 然而，传统的功能研究需要大量时间和资源，因此需要测试数百个 F9 变体 到目前为止我们观察到的做法是不切实际的。我们评估了数千种可能的 FIX 错义变体 随着更多个体被测序，观察到的情况将是不可能的。相反，我们提出了一种新方法 表达并表征 FIX 蛋白中几乎所有可能的错义变体，以增进我们的理解 FIX 生物学的研究，改进对 F9 基因遗传变异的解释，并推进血友病护理和 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用于测量突变的方法。 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 几乎所有可能的 FIX 错义变体都附着在培养的人类细胞的表面。我们建议利用 该 FIX 表面展示库有两个目标：1) 量化几乎所有可能的 F9 错义变体的影响 2) 量化几乎所有可能的 F9 错义变体对 FIX 表达和分泌的影响 特定的 FIX 功能，包括 Gla 结构域 γ-羧化和因子 XIa (FXIa) 激活 FIX。 这些目标将揭示 FIX 中几乎所有可能的错义变异如何影响表达、分泌、γ- 羧化和 FXIa 激活。在目标 3 中，我们将使用这些大规模功能数据来剖析 F9 致病变异破坏 FIX 功能的机制。我们还将使用机器学习，利用 功能数据与其他特征一起预测 FIX 中每个错义变异的致病性。采取 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一起改变 F9 变体的表征。它们还将作为更好地了解 FIX 生物学、改进 FIX 生物学的资源。 F9 遗传信息的临床转化，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.