Investigating treatment resistance mechanisms in chronic bacterial infections

研究慢性细菌感染的治疗耐药机制

• 批准号: 8685119
• 负责人: James Edward Bruce
• 金额: $ 59.49万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8685119
• 关键词: Acute; Affect; Animals; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Bacteria; Bacterial Genes; Bacterial Infections; Bacterial Physiology; Bacterial Proteins; Cells; Chronic; Clinical; Clinical Microbiology; Clinical Trials; Cystic Fibrosis; Engineering; Exhibits; Figs - dietary; Functional disorder; Gene Expression; Genetic Variation; Hereditary Disease; Human; Infection; Knowledge; Link; Lung; Mass Spectrum Analysis; Mediating; Microbial Biofilms; Minimum Inhibitory Concentration measurement; Modeling; Organism; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Population; Proteins; Proteomics; Pseudomonas aeruginosa; Regulatory Pathway; Relative (related person); Research Infrastructure; Resistance; Site; Sputum; Stress; Testing; Variant; Work; cystic fibrosis patients; in vivo; link protein; multiple reaction monitoring; novel; novel therapeutic intervention; novel therapeutics; protein expression; resistance mechanism; tool; trait; treatment response

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.

描述（由申请人提供）：慢性细菌感染本质上对治疗有抵抗力。即使生物体对抗生素敏感并且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）的感染就是一个典型的例子。感染一旦形成就无法根除，慢性感染引起的肺功能障碍夺去了绝大多数患者的生命。重要的是，人们对CF和其他慢性感染产生治疗耐药性的机制知之甚少。在这里，我们利用正在进行的临床试验的基础设施以及有关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菌株进化产生遗传多样性（但克隆相关）的细菌群体。我们发现，随着患者接受抗生素治疗，亚群的相对丰度发生变化。这很重要，因为在治疗期间丰度增加的亚群具有使它们能够抵抗体内治疗的表型，而丰度减少的亚群则缺乏抵抗功能。研究这些亚群可以确定细菌在人类慢性感染中抵抗抗生素的机制。目标 1. 鉴定铜绿假单胞菌亚群和与体内耐药性相关的耐受表型。我们将鉴定并储存对体内抗生素治疗表现出敏感性和耐药性的变异亚群。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。我们还将使用公正的方法来识别敏感和耐药亚群的表型差异。目标 2. 敏感亚群和耐药亚群的蛋白质表达有何不同？为了确定可能产生体内耐药性的候选细菌功能，我们将比较敏感亚群和耐药亚群的蛋白质组谱。这项工作将确定与多个患者的治疗耐药性相关的蛋白质和调节途径，这是寻找介导慢性感染耐药性的细菌功能的关键的第一步。目标 3. 哪些铜绿假单胞菌蛋白可能在体内产生耐药性？我们将使用多种分析来确定哪些差异表达的铜绿假单胞菌蛋白最有可能导致体内治疗耐药性。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，我们将使用工程铜绿假单胞菌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 (MRM) 来确定哪些细菌蛋白在 CF 患者的痰液中表现出一致的表达变化。