Determining How a Dynamic Microbiome Contributes to Cystic Fibrosis Lung Disease

确定动态微生物组如何导致囊性纤维化肺病

• 批准号: 9979750
• 负责人: Robert Andrew Quinn
• 金额: $ 75.3万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-17 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979750
• 关键词: Acute; Affect; Anaerobic Bacteria; Antibiotic Therapy; Antibiotics; Automobile Driving; Bacteria; Behavior; Bioinformatics; Biological; Bronchioles; Carbon; Cessation of life; Characteristics; Chemicals; Chemistry; Chronic; Chronic Phase; Chronic lung disease; Clinical; Clinical Research; Communities; Complex; Complication; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Data; Data Analyses; Development; Disease; Drops; Ecosystem; Energy-Generating Resources; Environment; Enzymes; Epithelial; Epithelial Cells; Epithelium; Event; Fermentation; Growth; Height; Immune response; Immune system; Individual; Infection; Intravenous; Ion Transport; Laboratories; Lead; Literature; Lung; Lung diseases; Lung infections; Measures; Metabolic; Metabolism; Methods; Microbial Physiology; Microbial Taxonomy; Modeling; Mucous body substance; Multiomic Data; Mutation; Oral; Outcome; Oxygen; Pathology; Patients; Physiological; Physiology; Play; Prediction of Response to Therapy; Production; Pseudomonas aeruginosa; Pulmonary Cystic Fibrosis; Pulmonary Fibrosis; Quality of life; Recovery; Regulator Genes; Research Personnel; Resistance; Respiratory physiology; Role; Sampling; Severities; Shapes; Source; Sputum; Structure; Symptoms; System; Testing; Treatment outcome; Validation; Virulence; antimicrobial; bacterial community; bronchial epithelium; clinically relevant; cystic fibrosis mucus; cystic fibrosis patients; dysbiosis; effective therapy; experience; improved; in vivo; laboratory experiment; lung microbiome; mathematical model; member; metabolome; metabolomics; microbial; microbial community; microbial host; microbiome; microbiome sequencing; microorganism interaction; multiple omics; neutrophil; novel; pathogen; patient population; pressure; programs; response; targeted treatment; therapy outcome; treatment strategy

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.

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，并需要积极的抗生素 治疗。而慢性感染肺部的细菌群落被认为与 随着病情的恶化，人们对这种情况是如何发生的或为什么发生的了解很少。我们的初步证据 表明在慢性肺衰竭(CFPE)期间发生了一种反生菌转移到 厌氧细菌的优势。这一提议试图进一步检验这一假设。 并揭示了在CFPE过程中驱动微生物组动态的机制。 囊性纤维化是一种慢性肺部疾病，由囊性纤维化基因突变引起。 纤维化跨膜电导调节基因(CFTR)。这些突变会导致 肺内粘液是由于上皮细胞离子转运中断所致。这种丰富的粘液堵塞了 导气管，由于细菌的存在，创造了厌氧环境。虽然 厌氧细菌作为慢性肺病的病原体没有得到很好的描述，它在肺部普遍存在，据信 在疾病中起到一定的作用。我们的初步数据表明，从病原体到 在CFPE发育过程中对厌氧菌的优势。抗生素治疗可杀死这些厌氧菌 使铜绿假单胞菌等CF的经典病原体再次侵占肺部。 我们能够在实验室中重现这些动力学，使我们能够直接研究它们的原因。 该项目将使用患者的临床样本、实验室来研究CFPE微生物动力学 实验和数学建模。 我们将通过恶化事件收集患者的纵向痰样本，并 分析微生物分类组成、寄主反应和代谢物产生。此外, 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)来 在体内测试我们的假设。可以操纵WinCF来测试化学和生物的效果 对Cf微生物群落结构、代谢和毒力的扰动。我们将测试 关于CF微生物群落结构和功能的多个变量，试图 了解在病情恶化期间观察到的动态变化的驱动因素。这些研究的结果将是 使用最近开发的在粘液中生长的CF微生物群的模型进行数学建模- 细支气管塞。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。我们的 科学原理是，更好地理解是什么导致了CFPE过程中微生物的变化 导致针对病原体的更有效和更有针对性的治疗。