Polymicrobial disease and inflammation in cystic fibrosis

描述（由申请人提供）：本项目旨在了解囊性纤维化（CF）患者气道中竞争细菌种类与人体免疫系统之间的复杂相互作用。我们的总体假设是CF患者气道中细菌种类之间的复杂相互作用决定了宿主防御反应并控制了气道疾病的进展。我们的总体目标是了解CF中最常见的肺部感染细菌金黄色葡萄球菌和铜绿假单胞菌之间的相互作用以及它们与气道的相互作用。这些相互作用似乎改变了死亡风险，并改变了导致气道损伤和肺功能恶化的严重炎症的进展。具体来说，我们将使用流行病学和数学模型对细菌和气道反应之间的相互作用做出精确的可测试的预测。为了提供数据来修改和塑造我们的模型，我们将使用我们新开发的实时定量PCR方法来测量气道中每种细菌的数量。我们将在许多不同的临床情况下进行测量，以了解这些细菌的负担如何改变疾病的进程。使用完善的方法，我们将像其他人之前在CF中所做的那样测量炎症，但是，与过去的努力相反，我们的重点将是研究炎症强度如何随着细菌负担和临床情况的变化而变化。我们的项目将提高我们对CF气道中两种主要细菌如何改变疾病进程的理解。我们新的实时定量PCR方法可以快速、精确和准确地测量细菌，将为评估感染程度提供新的临床工具。这些新工具将提高我们对CF患者和其他金黄色葡萄球菌和铜绿假单胞菌感染患者的护理能力，例如，通过提高我们在最有利的时间使用抗生素以最少的努力和成本控制疾病发作的能力。炎症强度与感染程度的数学关联将为理解感染的后果和治疗的影响提供新的机会，并可能为治疗CF提供新的潜在疗法和策略。

DESCRIPTION (provided by applicant): This project seeks to understand the complex interactions between competing species of bacteria and the human immune system in the airways of patients with cystic fibrosis (CF). Our overall hypothesis is that complex interactions between bacterial species in the airways of patients with CF determine host defense responses and control the progression of airway disease. Our overall aim is to understand the interactions between Staphylococcus aureus and Pseudomonas aeruginosa, the most common bacteria infecting the lung in CF, and their interactions with the airway. These interactions appear to change the risk of death and vary the progression of the severe inflammation that causes airway damage and worsening lung function. Specifically, we will use epidemiologic and mathematical models to make precise testable predictions about the interactions between bacteria and the response of the airway. To provide data to modify and shape our models, we will use real-time quantitative PCR methods that we have newly developed to measure the numbers of each type of bacteria in the airway. We will perform measurements in a number of different clinical situations in order to understand how the burdens of these bacteria alter the course of disease. Using well established methods, we will measure inflammation as others have done before in CF, but, in contrast to past efforts, our focus will be to study how the intensity of inflammation varies as the burden of bacteria and the clinical circumstances vary. Our project will improve our understanding of how the two main species of bacteria in the CF airway alter disease course. Our new real-time quantitative PCR methods for rapid, precise and accurate measurement of bacteria will provide new clinical tools for assessing the extent of infection. These new tools will improve our ability to care for CF patients and other patients with infections by S. aureus and P. aeruginosa, for example, by improving our ability to apply antibiotics at the most favorable time to control flares of disease at the least effort and cost. Mathematically relating intensity of inflammation to the extent of infection will provide new opportunities for understanding the consequences of infection and the impact of treatments and may suggest new potential therapies and strategies for treating CF.