Predicting dominant mutations in genetic disorders associated with the misassembly of cytoskeletal proteins

预测与细胞骨架蛋白错误组装相关的遗传性疾病中的显性突变

• 批准号: 2096466
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2096466
• 关键词: Predicting; dominant; mutations; genetic; disorders

Tubulin is the building block of microtubules, which carry out a diverse set of functions including chromosomal alignment during mitosis and molecular trafficking. Therefore tubulin mutations are linked to a number of phenotypes such as neuronal disorders and resistance to chemotherapy. The first part of the project involves defining the properties of different tubulin isotypes at the cellular level spatially and quantitatively. Each isotype can be visualised using fluorescence microscopy, giving information about its particular localisation as well as the duration and intensity during specific phases of the cell cycle. Alongside this, techniques will be developed to use neuronal cells as a system to study tubulin isotypes further. To define the mechanism of tubulin mutations that results in pathogenicity, I will focus on tubulin beta III and beta IV.The project will then move on to reconstituting and purifying single tubulin isoforms in vitro, where different isoforms can be substituted allowing differences in microtubule function to be analysed. To achieve this, tubulin will be recombinantly expressed in eukaryotic Sf9 cells and purified using affinity purification. This tubulin will then be used in reconstitution assays to be performed in vitro on individual microtubules using TIRF microscopy to define whether different isotypes influence microtubule dynamics. Tubulin mutants will also be expressed and purified alongside. To test whether tubulin isotype also regulates microtubule motor motility and residency time on microtubule, quantitative analysis will be performed to measure molecular motor transport using available motors in the lab and biochemical assays to detect interactions with other non-motor microtubule-associated proteins (MAPs). From this work, I anticipate to define the molecular mechanism underlying the pathogenicity associated with tubulin beta III and beta IV mutations.The next phase involves the use of bioinformatics approaches to computationally analyse mutations in relevant isoforms highlighted by the previous experimental work. A wide array of human variants mapped onto the recently available microtubule structures will be used for this, prioritising pathogenic mutations that have not been predicted by such methods previously. Tools such as patient genome data, multiple sequence alignments and molecular modelling techniques can also be incorporated for this purpose, where mutations indicated to be the most pathogenic by these methods will be taken into experimental work. This comprises directly comparing neuronal cells containing the prioritised pathogenic mutations to wild type where microtubule function is examined as previously described. Applying the mutations highlighted by the bioinformatics approaches into this experimental work will allow the most pathogenic mutations to be determined as they would also be validated within a biological model. Finally, data gathered throughout the project using both bioinformatics and experimental approaches can be integrated into machine learning methods. This will lead to the design of an algorithm that is able to predict which tubulin mutations are most likely to cause neuronal disorders or confer resistance to taxol.

微管蛋白是微管的构建块，其执行多种功能，包括有丝分裂期间的染色体排列和分子运输。因此，微管蛋白突变与许多表型相关，如神经元疾病和化疗耐药性。该项目的第一部分涉及在细胞水平上空间和定量地定义不同微管蛋白同种型的性质。每种同种型都可以使用荧光显微镜观察，提供有关其特定定位以及细胞周期特定阶段的持续时间和强度的信息。除此之外，还将开发使用神经元细胞作为系统来进一步研究微管蛋白同种型的技术。为了定义导致致病性的微管蛋白突变的机制，我将专注于微管蛋白β III和β IV。该项目将继续在体外重建和纯化单个微管蛋白亚型，其中不同的亚型可以被替换，从而分析微管功能的差异。为了实现这一点，微管蛋白将在真核Sf9细胞中重组表达并使用亲和纯化进行纯化。然后将该微管蛋白用于使用TIRF显微镜在体外对单个微管进行的重建测定中，以确定不同的同种型是否影响微管动力学。微管蛋白突变体也将被表达和纯化。为了测试微管蛋白同种型是否也调节微管运动和微管上的停留时间，将进行定量分析以使用实验室中可用的马达测量分子马达转运，并进行生化测定以检测与其他非马达微管相关蛋白（MAP）的相互作用。从这项工作中，我预计将定义与微管蛋白β III和β IV突变相关的致病性的分子机制。下一阶段涉及使用生物信息学方法来计算分析突变的相关亚型突出了以前的实验工作。一系列映射到最近可用的微管结构上的人类变体将用于此，优先考虑以前未通过此类方法预测的致病性突变。为此目的，还可以采用患者基因组数据、多重序列比对和分子建模技术等工具，将这些方法表明致病性最强的突变纳入实验工作。这包括直接比较含有优先致病突变的神经元细胞与野生型，其中如前所述检查微管功能。将生物信息学方法所强调的突变应用到这项实验工作中将允许确定最致病的突变，因为它们也将在生物模型中得到验证。最后，整个项目使用生物信息学和实验方法收集的数据可以集成到机器学习方法中。这将导致设计一种算法，能够预测哪些微管蛋白突变最有可能导致神经元疾病或赋予紫杉醇耐药性。