Determining How a Dynamic Microbiome Contributes to Cystic Fibrosis Lung Disease

项目摘要 肺部恶化是慢性肺部感染的一种普遍并发症。这些事件 导致症状严重程度增加，肺功能丧失，需要积极的抗生素 治疗虽然慢性感染肺部的细菌群落被认为与 对于急性加重，人们对这种情况如何发生或为什么发生的了解甚少。我们的初步证据 表明CF肺加重（CFPE）期间发生了生态失调转变， 厌氧菌的优势。本提案试图进一步检验这一假设 并揭示CFPE期间驱动微生物组动态的机制。 囊性纤维化（CF）是一种由囊性纤维化基因突变引起的慢性肺部疾病 纤维化跨膜传导调节基因（CFTR）。这些突变会导致 由于上皮细胞离子转运被破坏而导致肺中的粘液。这种丰富的粘液堵塞了 由于细菌的存在，导致了厌氧环境。虽然 厌氧细菌作为CF病原体的特征不明确，但在CF肺中普遍存在， 在疾病中发挥作用。我们的初步数据表明，有一个从病原体的生态失调转变， 在CFPE发育过程中以厌氧菌为主。抗生素治疗杀死这些厌氧菌 使得CF的典型病原体如铜绿假单胞菌再次占据肺部。 我们能够在实验室中重现这些动态，使我们能够直接研究其原因。 本项目将使用来自患者的临床样本、实验室样本和来自临床样本的样本， 实验和数学建模。 我们将收集患者在急性加重事件期间的纵向痰液样本， 分析微生物的分类组成、宿主反应和代谢产物的产生。此外，本发明还提供了一种方法， 我们将使用一种模拟CF肺环境的新型培养模型（称为WinCF模型）， 在体内验证我们的假设WinCF可以被操纵来测试化学和生物的影响 对CF微生物群落结构、代谢和毒力的扰动。我们将测试 CF微生物群落结构和功能的多个变量，试图 了解急性加重期间观察到的动力学驱动因素。这些研究的结果将是 使用最近开发的在粘液中生长的CF微生物组的模型进行数学建模， 堵塞的细支气管整个项目将利用尖端的多组学方法，包括 微生物组测序、代谢组学和新型生物信息学数据分析平台。我们 科学原理是，更好地了解CFPE期间导致微生物变化的原因， 导致针对病原体更有效和更有针对性的治疗。

Project Summary Pulmonary exacerbations are a pervasive complication of chronic lung infection. These events cause an increase in symptom severity, loss of lung function and require aggressive antibiotic treatment. While the bacterial communities in chronically infected lungs are believed to be involved in exacerbations, there is a poor understanding of how or why this happens. Our preliminary evidence indicates there is a dysbiotic shift that occurs during CF pulmonary exacerbation (CFPE) to the dominance of anaerobic bacteria. This proposal attempts to further test this hypothesis and reveal mechanisms driving microbiome dynamics during CFPEs. Cystic Fibrosis (CF) is a chronic lung disease that is caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator gene (CFTR). These mutations cause a buildup of mucus in the lungs due to disrupted epithelial ion transport. This abundant mucus plugs the conducting airways, and due to the presence of bacteria, creates anaerobic environments. Although poorly characterized as CF pathogens, anaerobic bacteria are ubiquitous in CF lungs and believed to play a role in the disease. Our preliminary data indicates that there is a dysbiotic shift from pathogen dominance to anaerobes during CFPE development. Antibiotic treatment kills these anaerobes allowing the classic pathogens of CF, such as Pseudomonas aeruginosa, to again take over the lung. We are able to reproduce these dynamics in the laboratory allowing us to directly study their cause. This project will investigate CFPE microbial dynamics using clinical samples from patients, laboratory experiments and mathematical modeling. We will collect longitudinal sputum samples from patients through exacerbation events and analyze the microbial taxonomic composition, host response and metabolite production. In addition, we will use a novel culture model that mimics the CF lung environment (called the WinCF model) to test our hypothesis in vivo. WinCF can be manipulated to test the effect of chemical and biological perturbations on the CF microbial community structure, metabolism and virulence. We will test multiple variables on the structure and function of the CF microbial community in an attempt to understand the drivers of dynamics observed during exacerbations. Results from these studies will be mathematically modeled using a recently developed model of the CF microbiome growing in mucus- plugged bronchioles. The entire project will utilize cutting edge multi-omics methods including microbiome sequencing, metabolomics and novel bioinformatics data analysis platforms. Our scientific rationale is that a better understanding of what causes microbial changes during CFPEs will lead to more efficacious and targeted therapy against pathogens.