Human functional genomics of post-translationally modifying clinical coding variants: FGx-PTMv

翻译后修饰临床编码变体的人类功能基因组学：FGx-PTMv

• 批准号: MR/Y031091/1
• 负责人: Matthew Child
• 金额: $ 536.8万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY031091%2F1
• 关键词: Human; functional; genomics; post; translationally

Rare diseases are debilitating, with one third of children suffering from these diseases dying before their fifth birthday. The 100,000 genomes project delivered by Genomics England and NHS England produced a rich catalogue of genomic variation. By associating genome variants and disease for patients suffering from disease symptoms and syndromes, the project succeeded in determining the genetic basis for many rare diseases. This allowed for clinical diagnoses to be provided for some patients within the rare disease group, but most remain undiagnosed.In general terms, genome variants are separated into two classes: those that sit within the protein-coding regions of genes (exonic variants), and those that sit outside of the protein (intronic variants). Exonic variants affect the amino acid sequence that comprises the protein. In some cases, these variants have a reliably predictable effect, such as the production of non-functional shortened versions of the protein. In other cases, a variant can result in the normal amino acid being replaced by an alternative. These so-called missense variants can subtly affect protein form and function, and it is more challenging to predict the effect a missense variant will have upon a protein. For example, the post-translational modification (PTM) of proteins can regulate their function, and sometimes these missense variants affect these PTMs, changing the amino acid that the modification is normally attached onto. Hypothetically, PTM variants (PTMv) should have a more predictable effect on protein function. To understand how a given missense variant affects protein function it is necessary to experimentally determine the impact of the variant in a laboratory. One result of this is that for most of the missense variants identified in rare disease-associated genes from the 100,000 genomes project, while we can accurately determine correlation, we cannot be certain of causation. Consequently, these missense variants are not used to inform the clinical diagnoses for patients suffering from these rare diseases.We will provide much needed functional information for one thousand missense variants present in rare disease-associated genes from the 100,000 genomes project, and in so doing establish a scalable pipeline for the clinical interpretation of many more. To accomplish this, we have established a cross-disciplinary investigative team that interweaves the disciplines of computational genomics, biomedical informatics, mathematics, digital chemistry, bioinformatics, process automation, functional proteomics, biochemistry, and cell biology. Our modular functional genomics variant interpretation platform is built on well-established and new methods, applied at scale. PTMv will be extracted from the rare disease gene panel of the 100,000 genomes project. They will be computationally modelled and prioritised according to their predicted contribution to protein function using an atomistic bond energy propensity analysis. We will build and deploy a new end-to-end variant engineering bioinformatic toolset alongside high-throughput process automation to test the functional contribution of hundreds of these PTMv in live cells at the same time. This functional information will be stably integrated into the European Bioinformatic Research Institute's Protvar resource for national and international academic research impact, and be fed back into Genomics England's research environment to support clinical diagnoses for rare disease patients and improve clinical practice guidelines.

罕见疾病使人衰弱，三分之一患有这些疾病的儿童在五岁生日前死亡。 Genomics England 和 NHS England 实施的 100,000 个基因组项目产生了丰富的基因组变异目录。通过将基因组变异与患有疾病症状和综合症的患者的疾病联系起来，该项目成功地确定了许多罕见疾病的遗传基础。这使得可以为罕见病组中的一些患者提供临床诊断，但大多数患者仍未确诊。一般来说，基因组变异分为两类：位于基因蛋白质编码区域内的变异（外显子变异），以及位于蛋白质外部的那些（内含子变体）。外显子变体影响构成蛋白质的氨基酸序列。在某些情况下，这些变体具有可靠可预测的效果，例如产生非功能性缩短版本的蛋白质。在其他情况下，变异可能导致正常氨基酸被替代氨基酸取代。这些所谓的错义变体可以微妙地影响蛋白质的形式和功能，并且预测错义变体对蛋白质的影响更具挑战性。例如，蛋白质的翻译后修饰 (PTM) 可以调节其功能，有时这些错义变体会影响这些 PTM，从而改变修饰通常附着的氨基酸。假设，PTM 变体 (PTMv) 对蛋白质功能的影响应该更可预测。为了了解给定的错义变体如何影响蛋白质功能，有必要在实验室中通过实验确定变体的影响。其结果之一是，对于 100,000 个基因组计划中罕见疾病相关基因中发现的大多数错义变异，虽然我们可以准确地确定相关性，但我们无法确定因果关系。因此，这些错义变异不会用于为患有这些罕见疾病的患者提供临床诊断信息。我们将为 100,000 个基因组计划中罕见疾病相关基因中存在的 1000 个错义变异提供急需的功能信息，并以此为基础建立一个可扩展的管道来进行更多的临床解释。为了实现这一目标，我们建立了一个跨学科的研究团队，将计算基因组学、生物医学信息学、数学、数字化学、生物信息学、过程自动化、功能蛋白质组学、生物化学和细胞生物学等学科交织在一起。我们的模块化功能基因组学变异解释平台建立在成熟的新方法之上，并进行了大规模应用。 PTMv将从十万基因组计划的罕见病基因组中提取。将使用原子键能倾向分析根据它们对蛋白质功能的预测贡献对它们进行计算建模和优先级排序。我们将构建和部署一个新的端到端变异工程生物信息学工具集以及高通量过程自动化，以同时测试数百个 PTMv 在活细胞中的功能贡献。这些功能信息将稳定地整合到欧洲生物信息研究所的 Protvar 资源中，以产生国内和国际学术研究影响，并反馈到 Genomics England 的研究环境中，以支持罕见病患者的临床诊断并改进临床实践指南。