Dynamics of airway microbiota at pulmonary exacerbation in cystic fibrosis

项目摘要 越来越多的人认识到囊性纤维化（CF）患者的气道通常含有复杂的 包括许多细菌物种的多微生物群落。然而，目前尚不清楚， 这些群落与肺部疾病的进展有关。更具体地说， 和/或气道细菌群落的活性与间歇性肺恶化相关 表征CF的聚合物弹性体（PEx）尚未进行系统研究。缺乏呼吸道标本， 在此领域中，来自PEX之前的时间段的颗粒状临床数据一直是进展的关键障碍。 该项目的科学前提是，更好地了解气道微生物群落动态 将为CF患者提供更好的护理提供新的机会。总体 假设是这些群落的结构和/或活性的变化驱动了 Pexs在短期内，这项研究将确定直接转化临床利益的目标。从长远 本项目将阐明微生物群落的调控机制以及与之相关的寄主变化 关于PEX该项目的具体目标是（i）表征微生物群落和宿主炎症 与PEX发生的变化，（ii）开发PEX分辨率的预测模型，以及（iii）开发动态 预测PEx期间微生物-微生物和宿主-微生物相互作用的模型。该项目的可行性是 在一个试点项目的支持下，我们前瞻性地收集了CF队列的每日痰液样本， 患者在长达两年的时间里。每日样本和临床元数据的前瞻性收集 目前项目中提出的方法包括几项改进，将产生更有力的结果。下一个- 代DNA测序和细菌代谢产物的分析将用于表征结构和 在肺部恶化前后获得的样品中细菌群落的活性。 将确定与急性加重发作、严重程度和恢复相关的微生物群落变化。 这些变化的建模将描述与以下相关的微生物-微生物和宿主-微生物相互作用： 在整个PEx周期中临床状态的转变。这些信息将构成后续研究的基础， 更好地了解PEX在CF中的病理生理机制。

Project Summary There is an increasing recognition that the airways of persons with cystic fibrosis (CF) typically harbor complex polymicrobial communities that include numerous bacterial species. It is unknown, however, how changes in these communities relate to the progression of lung disease. More specifically, how changes in the structure and/or activity of airway bacterial communities are associated with the intermittent pulmonary exacerbations (PExs) that characterize CF has not been systematically investigated. The lack of respiratory specimens and granular clinical data from periods of time preceding PExs has been a critical barrier to progress in this area. The scientific premise for this project is that a better understanding of airway microbial community dynamics with respect to PExs will provide new opportunities to better care to persons with CF. The overarching hypothesis is that changes in the structure and/or activity of these communities drive the pathophysiology of PExs. In the short term, this study will identify targets of immediate translational clinical interest. In the longer term, this project will elucidate the mechanisms governing microbial community and host changes associated with PExs. The specific aims of this project are to (i) characterize microbial community and host inflammatory changes that occur with PEx, (ii) develop predictive models of PEX resolution, and (iii) develop dynamic models that predict microbe-microbe and host-microbe interactions during PEx. The feasibility of this project is supported by a pilot project in which we prospectively collected daily sputum samples from a cohort of CF patients during the course of up to two years. The prospective collection of daily samples and clinical metadata proposed in the current project incorporates several improvements that will yield more robust results. Next- generation DNA sequencing and analysis of bacterial metabolites will be used to characterize the structure and activity of the bacterial communities in samples obtained around the time of pulmonary exacerbations. Microbial community changes that correlate with exacerbation onset, severity and recovery will be identified. Modeling of these changes will describe the microbial-microbial and host-microbial interactions associated with transitions in clinical state throughout the PEx cycle. This information will form the basis of follow on studies to better understand the pathophysiologic mechanisms of PEx in CF.