Integrating protein structure and genomic data to predict antibiotic resistance in Mycobacterium tuberculosis

项目摘要 结核病每年导致100多万人死亡，不断增加的抗生素耐药性正在使 这种疾病更难治疗。结核分枝杆菌耐药基因分型快速诊断， 导致结核病的细菌，需要克服与培养有关的长期治疗延误- 基于方法。以前的工作已经建立了一组对抗生素耐药性更常见的遗传标记 抗生素，但这样的研究需要大量的测序耐药菌株，无法使 对罕见或新观察到的变异的预测。对大量分离株的要求特别高 对五种新引入的抗结核药物来说是有问题的，这些药物的数量很少，但数量在增加 记录在案的耐药菌株。 传统的将基因和表型联系起来的方法假定每个位点都是独立的，并且 因此，需要特定部位的许多突变例子来推断在统计上具有显著意义的 表型变异。生物学知识告诉我们，这一假设不是真的--大多数细菌基因 编码蛋白质，这些蛋白质具有独特的三维形状和功能。导致基因突变的基因突变 蛋白质的相似区域更有可能对表型有相似的影响，潜在地允许共享 可以增加显著性检验的能力的统计信号。 在这个拟议的项目中，我将开发两种使用蛋白质的免费统计方法 三维结构，以增强来自导致M。 肺结核。具体地说，我将首先开发一种非监督统计检验，以确定重复突变 在同一蛋白质内聚集在三维空间中，这表明突变赋予了 健身福利。这种方法将比传统方法具有更高的敏感度，这些方法寻找有意义的 突变的数量，并促进关于突变影响的机械性假说的发展 关于蛋白质的功能。其次，我将使用蛋白质的三维结构作为先验在贝叶斯线性混合 预测抗生素耐药性的模型。这一先例将允许附近的变种相互增强信号，并 建立基因和表型之间的关联，这超出了当前方法的范围。钥匙 这一方法的应用将为新引进的五个品种建立与抗药性有关的基因类型 抗结核药物。这里提出的方法可能会推广到其他细菌病原体和 代表了病原体分子数据在临床应用方面的重要飞跃。

Project Abstract Tuberculosis causes over one million deaths annually, and increasing antibiotic resistance is rendering the disease more difficult to treat. Rapid genotype-based resistance diagnosis of Mycobacterium tuberculosis, the bacterium that causes tuberculosis, is needed to overcome the long treatment delays associated with culture- based methods. Previous work has established sets of genetic markers of antibiotic resistance to more common antibiotics, but such studies require large numbers of sequenced resistant isolates, and are unable to make predictions for rare or newly observed variants. The requirement for large numbers of isolates is especially problematic for five newly introduced antitubercular agents, which have small but increasing numbers of documented resistant isolates. Traditional methods for associating genotype with phenotype assume that every site is independent, and therefore many examples of mutations at a particular site are needed to infer statistically significant effects of variants on phenotype. Biological knowledge tells us that this assumption is not true – most bacterial genes encode proteins, which have distinct three-dimensional shapes and functions. Mutations that causes changes in similar regions of a protein are more likely to have similar effects on phenotype, potentially allowing for sharing of statistical signal that could increase the power of significance testing. In this proposed project, I will develop two complimentary statistical approaches that will use protein three-dimensional structure to boost signal from genetic variants that cause antibiotic resistance in M. tuberculosis. Specifically, I will first develop an unsupervised statistical test to determine if repeated mutations within the same protein are clustered in three-dimensional space, which indicates that the mutations confer a fitness benefit. This approach will have increased sensitivity over traditional methods that look for significant numbers of mutations, and facilitate the development of mechanistic hypotheses about the effects of mutation on protein function. Second, I will use protein three-dimensional structure as a prior in a Bayesian linear mixed model to predict antibiotic resistance. This prior will allow nearby variants to ‘boost’ one another’s signal and establish associations between genotype and phenotype that are beyond the reach of current methods. The key application of this approach will be establishing resistance-conferring genotypes for five newly introduced antitubercular agents. The approach proposed here will likely generalize to other bacterial pathogens and represent an important leap forward in using pathogen molecular data in the clinic.