Mutators and Pseudomonas Genome Evolution in the CF-lung Enviornment

描述（由申请方提供）：囊性纤维化（CF）患者对铜绿假单胞菌（一种容易适应CF肺的常见环境微生物）引起的持续性支气管内感染高度敏感。导致慢性感染的进化适应来自基因组结构的大规模变化，如插入、易位、复制和缺失，以及小规模变化，如单碱基对取代。在临床菌株中，小规模和大规模的变化被认为是垂直传播比水平传播更常见。那么，假单胞菌群是如何产生足够的变异，在持续的免疫系统监视和间歇性抗生素治疗的选择压力下迅速进化的呢？“突变体”，即具有高背景突变率的细菌，经常在慢性感染中观察到，但在急性感染中观察不到，这表明DNA修复缺陷的克隆可能在产生遗传变异中起重要作用。我们将通过双管齐下的方法来解决这种可能性。我们将在法国巴黎的内克尔儿童医院对分离自多名CF患者的菌株进行纵向基因组研究。我们将比较基因组进化的克里思和模式在“突变”和“非突变”的血统。与此同时，我们将开发和测试数学模型，预测条件下，突变出现和持续。到目前为止，我们已经产生了每个临床分离株的基因组指纹，每个表型相对于抗生素耐药性，粘液状态，自发利福平耐药突变体的频率。我们将“突变体”定义为产生RifR突变体的克隆，其频率是群体中位数的十倍。我们的初步数据使我们假设：（1）CF-肺中的假单胞菌通过周期性选择适应性偏好的克隆而进化，（2）不同DNA修复中的特定缺陷导致不同的背景突变率，（3）“突变子”增加了我们在非突变子群体中可能预期的遗传变异，（4）含有突变子的谱系比非突变子谱系进化得更快。我们将通过以下方法来检验这些假设：进行基于微阵列的比较基因组杂交（a-CGH）来检测菌株特异性缺失和重复，并将断裂点定位到单基因分辨率;使用多位点序列分型（MLST）来估计小规模基因组变化并推断克隆同源性;筛选DNA修复基因序列以确定每个“突变子”的基础;“以及开发和测试预测突变体出现和持续存在的条件的连续数学模型。具体来说，我们将扩展我们已经开发的连续模型，以包括竞争和异步世代，然后在我们可以控制世代时间，种群密度和抗生素水平的条件下估计突变和回复率。 在这些努力中，我们将得到从事指导性研究的本科生的帮助。本科团队成员将通过奖学金支持的年度周期工作，获得高年级学分，在地方和国家会议上进行正式演讲，并帮助将该项目的关键方面带入课堂。我们的具体目标推进整体NIH区域使命，以支持试点，主要是本科院校的健康相关的研究项目，以及特定的过敏和传染病的国家研究所的目标，以支持在病原体基因组学和进化领域的研究。公共卫生相关性：囊性纤维化（CF）患者对常见环境微生物铜绿假单胞菌引起的慢性呼吸道感染高度敏感。慢性感染大大降低了患者的生活质量，并且呼吸衰竭（通常归因于假单胞菌感染）占CF死亡率的>90%。在生命的早期，CF患者似乎获得了几年后他们屈服的细菌菌株。因为在最初感染和死亡之间经历了数千代细菌，疾病进展是一个进化过程。我们试图更好地了解这些细菌如何在面对持续的免疫系统监测和间歇性抗生素治疗时进化。慢性感染的一个典型特征是出现“突变”菌株，即具有高突变率的细菌，似乎有助于获得多种抗生素耐药性。我们的目标是研究“突变体”如何改变进化的速度和轨迹：我们将对随着时间的推移从多个患者中分离出的菌株进行遗传分析，并为突变体的出现和持续建立模型。我们的目标是帮助临床医生设计抗生素疗法，最大限度地减少慢性感染期间出现多药耐药性的可能性。

DESCRIPTION (provided by applicant): Cystic fibrosis (CF) patients are highly susceptible to persistent endobronchial infection by Pseudomonas aeruginosa, a common environmental microbe that readily adapts to the CF-lung. Evolutionary adaptations that lead to chronic infection arise from large-scale changes in genome architecture such as insertion, translocation, duplication and deletion, as well as small-scale changes such as single-base pair substitutions. Among clinical strains, small- and large- scale changes are thought to be more commonly transmitted vertically than horizontally. How then do Pseudomonas populations generate sufficient variation to evolve rapidly under the selective pressures of constant immune system surveillance and intermittent antibiotic treatment? (PARAGRAPH) "Mutators," bacteria that have high background mutation rates, are frequently observed in chronic, but not acute, infections, suggesting that clones defective in DNA repair may play an important role in producing genetic variation. We will address this possibility via a two-pronged approach. We will conduct a longitudinal genomic study of strains isolated from multiple CF patients at the Necker Children's Hospital in Paris, France. We will compare the tempo and mode of genome evolution in "mutator" and "non-mutator" lineages. Concurrently, we will develop and test mathematical models that predict the conditions under which mutators emerge and persist. To date, we have produced genomic fingerprints for each clinical isolate, phenotyped each with respect to antibiotic resistance, mucoid status, and frequency of spontaneous rifampicin-resistant mutants. We define "mutators" as clones that produce RifR mutants at a frequency ten-fold greater than the population median. Our preliminary data have led us to hypothesize that: (1) Pseudomonas in the CF-lung evolves by periodic selection of adaptively favored clones, (2) specific defects in different DNA repair result in different background mutation rates, (3) "mutators" increase genetic variation over what we might expect in non-mutator populations, (4) mutator-containing lineages evolve more rapidly than non-mutator lineages. We will test these hypotheses by: performing microarray-based comparative genomic hybridization (a-CGH) to detect strain-specific deletions and duplications and localize breakpoints to single-gene resolution; using multilocus sequence typing (MLST) to estimate small-scale genomic change and infer clonal phylogeny; screening DNA repair gene sequences to determine the basis for each "mutator;" and developing and testing continuous mathematical models that predict conditions for emergence and persistence of mutators. Specifically, we will extend continuous models we have developed to include competition and asynchronous generations, then estimate mutation and reversion rates under conditions where we can control for generation time, population density, and levels of antibiotic. (PARAGRAPH) We will be assisted in these efforts by undergraduates engaged in mentored research. Undergraduate team members will work through an annual cycle supported by fellowships, receive upper-division credit, give formal presentations at local and national conferences, and help bring key aspects of this project into the classroom. Our Specific Aims advance the overall NIH-AREA mission to support pilot, health-related research projects at predominantly undergraduate institutions, as well as specific National Institute of Allergy and Infectious Diseases objectives to support meritorious research in the areas of pathogen genomics and evolution. PUBLIC HEALTH RELEVANCE: Cystic fibrosis (CF) patients are highly susceptible to chronic respiratory tract infection by the common environmental microbe, Pseudomonas aeruginosa. Chronic infections greatly diminish patient quality of life, and respiratory failure, often attributable to Pseudomonas infection, accounts for >90% CF mortality. Early in life, CF patients seem to acquire the bacterial strain to which they succumb years later. Because thousands of bacterial generations elapse between initial infection and death, disease progression is an evolutionary process. We seek to better understand how these bacteria evolve in the face of constant immune system surveillance and intermittent antibiotic treatment. A characteristic feature of chronic infections is the emergence of "mutator" strains, bacteria that have high mutation rates and seemingly facilitate acquisition of multiple antibiotic resistance. We aim to investigate how "mutators" alter the pace and trajectory of evolution: we will genetically analyze strains isolated from multiple patients over time, and model conditions for mutators to arise and persist. Our goal is to help clinicians devise antibiotic therapies that minimize the likelihood that multi-drug resistance emerges during chronic infections.