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

整合蛋白质结构和基因组数据来预测结核分枝杆菌的抗生素耐药性

• 批准号: 10312207
• 负责人: Anna Gustafson Green
• 金额: $ 6.64万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2023-10-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10312207
• 关键词: 3-Dimensional; Active Sites; Address; Affect; Antibiotic Resistance; Antibiotics; Antitubercular Agents; Antitubercular Antibiotics; Bacteria; Bacterial Genes; Benchmarking; Biological; Cessation of life; Clinic; Clinical; Computer Analysis; Consumption; Crystallization; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Evolution; Frequencies; Genes; Genetic Markers; Genome; Genomics; Genotype; Knowledge; Lead; Ligand Binding; Location; M. tuberculosis genome; Maps; Measures; Methods; Microbe; Modeling; Molecular; Mutation; Mycobacterium tuberculosis; Pharmaceutical Preparations; Phenotype; Prevalence; Proteins; Proteome; Resistance; Shapes; Signal Transduction; Site; Source; Statistical Methods; Structure; Sum; Testing; Time; Tuberculosis; Variant; Work; base; fitness; genetic association; genetic variant; genomic data; human pathogen; microbial genome; pathogen; pathogenic bacteria; phenotypic data; protein function; protein structure; protein structure prediction; resistant strain; three dimensional structure; tool

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.

项目摘要 结核病每年造成100多万人死亡，而抗生素耐药性的增加正在使 结核病更难治疗。基于基因型的结核分枝杆菌耐药性快速诊断， 这种导致结核病的细菌，需要克服与培养相关的长期治疗延迟- 基于方法。以前的工作已经建立了一套遗传标记的抗生素耐药性更常见的 抗生素，但这样的研究需要大量的测序耐药菌株， 预测罕见或新观察到的变异。特别需要大量分离株， 对于五种新引入的抗结核药物来说，这是一个问题，这些药物的数量很少，但数量越来越多。 记录的耐药分离株。 将基因型与表型相关联的传统方法假定每个位点都是独立的， 因此，需要在特定位点处的突变的许多实例来推断以下的统计学显著影响： 表型上的变体。生物学知识告诉我们，这种假设是不正确的-大多数细菌基因 编码蛋白质，具有独特的三维形状和功能。突变会导致 蛋白质的相似区域更可能对表型有相似的影响，这可能允许共享 可以增加显著性检验能力的统计信号。 在这个提议的项目中，我将开发两种互补的统计方法， 三维结构，以增强导致M抗生素耐药性的遗传变异的信号。 结核具体来说，我将首先开发一个无监督的统计测试，以确定是否重复突变 在同一蛋白质内的突变在三维空间中聚集，这表明突变赋予了一个 健身福利这种方法将比传统方法具有更高的灵敏度， 突变的数量，并促进发展的机制假说的影响，突变 蛋白质的功能。其次，将蛋白质三维结构作为贝叶斯线性混合先验 预测抗生素耐药性的模型。这种先验将允许附近的变体“增强”彼此的信号， 建立基因型和表型之间的关联，这超出了现有方法的范围。关键 该方法的应用将是为五个新引进的 抗结核药。这里提出的方法可能会推广到其他细菌病原体， 代表了在临床中使用病原体分子数据的重要飞跃。