Use of integrated genomic and functional genomic approaches to the diagnosis of rare inherited disease linked to nonsense mutations

使用整合基因组和功能基因组方法诊断与无义突变相关的罕见遗传病

• 批准号: 1926882
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1926882
• 关键词: Use; integrated; genomic; functional; approaches

Since the advent of Next Generation Sequencing (NGS), the rate of gene discovery in human disease has increased exponentially. This has supported a positive impact for NHS patients in whom diagnoses can now be made, guiding patient care and allowing more accurate genetic counselling. However, even then, for a significant cohort of patients a diagnosis is still not possible. Indeed despite the observation of apparently nonsense or potentially pathogenic splice site variants in a number of cases, NHS laboratories are unable to assign pathogenicity, as they cannot determine whether the variant has functional consequence. To overcome this lack of functional information, and to maximise findings from NGS, there is an absolute need for a novel integrated diagnostic approach. Specifically, combined interrogation of the clinical phenotype, NGS data, integrated bioinformatics (including in silico modelling of variant consequences), and transcriptomic and proteomic analysis will underpin a holistic approach to variant analysis and enhance diagnosis. Furthermore, if the genetic basis of a disorder is determined and its pathogenic impact understood at the molecular level via RNA and protein studies, this knowledge can be utilised to drive the development of personalised medicine.Extensive NGS data, representing a diverse array of human phenotypes has already been derived at The Manchester Centre for Genomic Medicine, CMFT. This project will initially exploit those existing datasets from a bioinformatic and RNA perspective, specifically targeting a subset with a nonsense mutation variant. NGS data will be analysed using splicing and prediction variant tools and linked to other data such as those from protein-protein interaction databases, pathway and Gene Ontology enrichments, and information on post-translational modifications. For a cohort of selected patients, analysis of nonsense mediated decay, in vitro and cell based splicing assays and RNA sequencing of affected tissues/cells will allow predicted consequences to be assessed functionally at an RNA level. Mass spectrometry will support discovery and targeted proteomic strategies to extend the RNA analysis and to validate and characterise selected proteins from patient samples. Integrated bioinformatics will support mechanistic understanding of the consequences of the variants, both cis and trans acting.This project operates at the boundaries of medical, computational and biological science. The student will develop unique interdisciplinary skills and work towards the ultimate aim of developing an informatics and 'omics pipeline. This pipeline will enhance the analysis of molecular genetic variants and thus improve clinical diagnosis rates, facilitate disease sub-type stratification and guide precision therapy.

自从下一代测序（NGS）出现以来，人类疾病中的基因发现率呈指数级增长。这对现在可以进行诊断的NHS患者产生了积极影响，指导了患者护理并允许更准确的遗传咨询。然而，即使这样，对于一个重要的患者队列的诊断仍然是不可能的。事实上，尽管在许多情况下观察到明显无义或潜在致病性剪接位点变体，但NHS实验室无法确定致病性，因为他们无法确定变体是否具有功能性后果。为了克服这种功能信息的缺乏，并最大限度地提高NGS的发现，绝对需要一种新的综合诊断方法。具体来说，对临床表型、NGS数据、集成生物信息学（包括变异后果的计算机模拟）以及转录组学和蛋白质组学分析的联合询问将支持变异分析的整体方法并增强诊断。此外，如果通过RNA和蛋白质研究确定了疾病的遗传基础，并在分子水平上了解了其致病影响，则可以利用这些知识来推动个性化医疗的发展。曼彻斯特基因组医学中心（CMFT）已经获得了广泛的NGS数据，代表了各种人类表型。该项目最初将从生物信息学和RNA的角度利用这些现有的数据集，专门针对具有无义突变变体的子集。NGS数据将使用剪接和预测变体工具进行分析，并与其他数据相关联，例如来自蛋白质-蛋白质相互作用数据库的数据，途径和基因本体富集以及翻译后修饰的信息。对于一组选定的患者，无义介导的衰变分析、体外和基于细胞的剪接测定以及受影响组织/细胞的RNA测序将允许在RNA水平上对预测的后果进行功能性评估。质谱将支持发现和靶向蛋白质组学策略，以扩展RNA分析，并验证和鉴定来自患者样本的选定蛋白质。综合生物信息学将支持对顺式和反式变体后果的机械理解。该项目在医学、计算和生物科学的边界上运作。学生将发展独特的跨学科技能，并朝着发展信息学和“组学管道”的最终目标努力。该管道将加强对分子遗传变异的分析，从而提高临床诊断率，促进疾病亚型分层并指导精确治疗。

项目成果

Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

• DOI: 10.20944/preprints201911.0085.v1
• 发表时间: 2019-11
• 期刊: Cells
• 影响因子: 6
• 作者: Charlie F. Rowlands;D. Baralle;J. Ellingford

MRSD: A quantitative approach for assessing suitability of RNA-seq in the investigation of mis-splicing in Mendelian disease.

• DOI: 10.1016/j.ajhg.2021.12.014
• 发表时间: 2022-02-03
• 期刊: American journal of human genetics
• 影响因子: 9.8
• 作者: Rowlands CF;Taylor A;Rice G;Whiffin N;Hall HN;Newman WG;Black GCM;kConFab Investigators;O'Keefe RT;Hubbard S;Douglas AGL;Baralle D;Briggs TA;Ellingford JM
• 通讯作者: Ellingford JM