Investigating treatment resistance mechanisms in chronic bacterial infections

描述(申请人提供)：慢性细菌感染天生对治疗有抵抗力。即使生物体对抗生素敏感，高浓度的药物到达感染部位，情况也是如此。患有遗传性疾病囊性纤维化(CF)患者的感染就是一个很好的例子。感染一旦确定，就无法根除，而慢性感染造成的肺功能障碍夺去了绝大多数患者的生命。重要的是，在CF和其他慢性感染中产生治疗耐药的机制还知之甚少。在这里，我们利用正在进行的临床试验的基础设施，以及关于感染铜绿假单胞菌种群内遗传多样性的新发现，作为研究治疗耐药性的工具。在初步研究中，我们发现感染铜绿假单胞菌的菌株进化为产生遗传多样性(但克隆相关)的细菌种群。我们发现，随着患者接受抗生素治疗，亚群的相对丰度会发生变化。这一点很重要，因为在治疗期间增加的亚群具有使他们能够在体内抵抗治疗的表型，而减少的亚群缺乏抗性功能。研究这些亚群可以确定细菌在慢性人类感染中抵抗抗生素的机制。目的1.鉴定与体内耐药相关的铜绿假单胞菌亚群和耐药表型。我们将在体内识别和储存对抗生素治疗敏感和耐药的变异亚群。我们将使用敏感和耐药亚群来测试将治疗耐药性与抗生素耐受机制联系起来的假设。我们还将使用一种公正的方法来识别敏感和抗性亚群的表型差异。目的2.敏感和抗性亚群在蛋白质表达上有何不同？为了确定可能在体内产生耐药性的候选细菌功能，我们将比较敏感和耐药亚群的蛋白质组图谱。这项工作将确定与多个患者的治疗耐药相关的蛋白质和调控途径，这是发现介导慢性感染耐药的细菌功能的关键第一步。目的3.体内哪些铜绿假单胞菌蛋白可能耐药？我们将使用一些分析来确定哪些差异表达的铜绿假单胞菌蛋白最有可能在体内造成治疗耐药性。我们将优先考虑在多个患者中表现出平行变化的蛋白质，以及那些表现出与耐受表型一致的变化的蛋白质。然后，我们将使用铜绿假单胞菌工程菌株将这些蛋白质与耐受机制联系起来。最后，我们将使用多反应监测质谱仪(MRM)来确定哪些细菌蛋白在CF患者的痰中表现出一致的表达变化。

DESCRIPTION (provided by applicant): Chronic bacterial infections are inherently resistant to treatment. This is true even if organisms are antibiotic-sensitive, and high concentrations of drugs reach infection sites. The infections that afflict patients with the genetic disease cystic fibrosis (CF) are a prime example. Once infection is established it cannot be eradicated, and lung dysfunction caused by chronic infections claim the lives of the vast majority patients. Importantly, the mechanisms producing treatment resistance in CF and other chronic infections are poorly understood. Here we exploit the infrastructure of an ongoing clinical trial, and new findings about genetic diversity within infecting P. aeruginosa populations as tools to study treatment resistance. In preliminary studies, we found that infecting P. aeruginosa strains evolve to produce genetically diverse (but clonally-related) bacterial populations. We found that the relative abundance of subpopulations change as patients are treated with antibiotics. This is important because subpopulations that increase in abundance during treatment possess phenotypes that enable them to resist treatment in vivo, while subpopulations that decrease lack resistance functions. Studying these subpopulations could identify the mechanisms used by bacteria to withstand antibiotics in chronic human infections. Aim 1. Identify P. aeruginosa subpopulations and tolerance phenotypes linked to in vivo resistance. We will identify and bank variant subpopulations that show sensitivity and resistance to antibiotic treatment in vivo. We will use the sensitive and resistant subpopulations to test hypotheses that link treatment resistance to antibiotic tolerance mechanisms. We will also use an unbiased approach to identify phenotypic differences in sensitive and resistant subpopulations. Aim 2. How do sensitive and resistant subpopulations differ in protein expression? To identify candidate bacterial functions that may produce in vivo resistance, we will compare the proteomic profiles of sensitive and resistant subpopulations. This work will identify proteins and regulatory pathways associated with treatment resistance across multiple patients, a key first step in finding bacterial functions that mediate resistance in chronic infections. Aim 3. Which P. aeruginosa proteins likely treatment resistance in vivo? We will use several analyses to determine which differentially expressed P. aeruginosa proteins are most likely to contribute to treatment resistance in vivo. We will prioritize proteins exhibiting parallel changes in multiple patients, and those showing changes consistent with the tolerance phenotypes. We will then use engineered P. aeruginosa strains to link these proteins to tolerance mechanisms. Finally, we will use multiple reaction monitoring mass spectrometry (MRM) to determine which bacterial proteins show consistent expression changes in sputum from CF patients.