Parallel phenotyping to dissect genetic determinants of bacterial strain diversity

项目摘要 所有病原体都具有遗传多样性，可以影响临床相关的表型，如毒力， 对药物的敏感性和疫苗的效力。在后基因组时代，我们对微生物基因型进行分类的能力 已经远远超出了我们对微生物表型的分析能力。这限制了我们建立基因型的能力- 表型图用于感兴趣的性状并阻碍了广泛有效的新抗微生物剂的开发， 疫苗和公共卫生干预。为了应对这一挑战，我开发了一种分子条形码， 该方法允许在单个体外或体外试验中对数百种细菌临床分离株进行平行适应性表型分析， 体内实验。我在病原体结核分枝杆菌中开发并验证了这种新方法 并揭示了感染期间和接种疫苗后细菌适应性的菌株特异性差异， 小鼠模型在这里，我建议使用这种新的工具来询问表型的遗传基础， 相关分枝杆菌病原体鸟分枝杆菌（MAC）的异质性。MAC是一种环境 微生物，可导致慢性和治疗无效的感染，发病率正在上升。一个主要 MAC疾病管理的挑战是病程和治疗结果的可变性， 这种变异性的细菌决定因素是未知的。在这里，我将利用我的菌株条形码方法 和这种微生物的自然生物多样性，以阐明遗传决定因素和分子机制， MAC致病性和抗生素反应。这些努力将有助于改进诊断方法 以及治疗这种和其他慢性细菌感染的药物。更广泛地说，这项工作将提供一个 知识框架和实验工具包，以揭示传染病异质性的生物学基础， 疾病表型

PROJECT SUMMARY All pathogens possess genetic diversity that can impact clinically relevant phenotypes such as virulence, susceptibility to drugs, and vaccine efficacy. In the post-genomic era, our ability to catalog microbial genotypes has far outstripped our capacity to profile microbial phenotypes. This limits our ability to build genotype- phenotype maps for traits of interest and hinders the development of broadly effective new antimicrobials, vaccines, and public health interventions. To address this challenge, I developed a molecular barcoding approach that permits parallel fitness phenotyping of hundreds of bacterial clinical isolates in a single in vitro or in vivo experiment. I developed and validated this novel approach in the pathogen Mycobacterium tuberculosis and uncovered strain-specific differences in bacterial fitness during infection and following vaccination in the mouse model. Here, I propose to use this novel tool to interrogate the genetic basis of phenotypic heterogeneity in a related mycobacterial pathogen, Mycobacterium avium (MAC). MAC is an environmental microbe that can cause chronic and treatment-recalcitrant infections and is increasing in incidence. A major challenge in the management of MAC disease is the variability in disease course and treatment outcome, and the bacterial determinants of this variability are unknown. Here, I will leverage my strain barcoding approach and the natural biodiversity of this microbe to elucidate genetic determinants and molecular mechanisms of MAC pathogenicity and antibiotic response. These efforts will inform the development of improved diagnostics and therapeutics for this, and other, chronic bacterial infections. More broadly, this work will provide an intellectual framework and experimental toolkit to uncover the biological basis of heterogeneity in infectious disease phenotypes.