Metabolic Basis of Bacterial Community Function in the Cystic Fibrosis Airway

囊性纤维化气道细菌群落功能的代谢基础

• 批准号: 10293007
• 负责人: George A. O'Toole
• 金额: $ 46.65万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-02 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10293007
• 关键词: Acute; Acute Disease; Antibiotics; Antimicrobial Resistance; Bacteria; Big Data; Bioinformatics; Cell Culture Techniques; Chronic; Chronic Disease; Clinical; Clinical Data; Communities; Complex; Computer Models; Consumption; Culture-independent methods; Cystic Fibrosis; Data; Data Set; Development; Disease; Disease Outcome; Dose; Energy-Generating Resources; Event; Exhibits; Experimental Models; Exposure to; Feeding Patterns; Genetic Diseases; Genetic study; In Vitro; Individual; Infection; Lung; Lung infections; Machine Learning; Measures; Metabolic; Metadata; Microbe; Microbial Physiology; Minimum Inhibitory Concentration measurement; Modeling; Morbidity - disease rate; Mucous body substance; Mutation; Nature; Organism; Outcome; Outcome Measure; Output; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Pseudomonas aeruginosa; Pseudomonas aeruginosa infection; Pulmonary Function Test/Forced Expiratory Volume 1; Regimen; Research; Resistance; Role; Sampling; Sputum; Staphylococcus aureus; Streptococcus; Structure; Testing; Virulence; Work; antibiotic tolerance; antimicrobial; antimicrobial drug; bacterial community; base; bioinformatics tool; cystic fibrosis airway; cystic fibrosis infection; cystic fibrosis patients; experimental study; feeding; improved; in silico; in vitro Model; in vivo; insight; member; metabolomics; microbial; microbial community; mortality; novel; novel therapeutics; pathogenic bacteria; polymicrobial biofilm; polymicrobial disease; pulmonary function; pulmonary function decline; success; tool; translational impact

Abstract. Cystic fibrosis (CF) is a fatal genetic disease characterized by overproduction of mucus in the lungs followed by chronic lung infections. Conventional wisdom has been that most CF lung infections involve a single dominant organism, most commonly the pathogenic bacterium Pseudomonas aeruginosa. Advances in culture-independent techniques have revealed that CF lung infections are rarely mono-microbial and instead usually involve complex microbial communities, yet the interspecies interactions that drive these communities are poorly understood. Furthermore, numerous studies have demonstrated that polymicrobial infections are more difficult than mono-microbial infections to eradicate with antibiotics, leading to the concept of recalcitrant communities. The mechanisms underlying recalcitrance are thought to involve synergistic interactions between community members, but very little data are available to understand this phenomenon. Combined with the realization that many CF patients respond poorly to available antibiotic regimens compels a more detailed understanding of interspecies interactions and their impacts on antibiotic recalcitrance to improve the treatment of CF infections, as well as other polymicrobial diseases. Here, we combine big-data bioinformatics, in silico computational modeling and in vitro culture experiments to gain insights into the metabolic interactions that drive CF disease outcomes and antibiotic recalcitrance. The research will leverage an available data set of hundreds of CF patient samples that provide both bacterial composition data and clinical metadata, including measures of lung function. These samples will be clustered according to their measured compositions and metabolic capabilities predicted through computational metabolic modeling to test the hypothesis that the vast complexity of these many bacterial communities can be collapsed into a small number of model communities that capture most of the observed metabolic variability. These computational predictions will be tested by developing in vitro cell culture models that recapitulate the most important metabolic features of the in vivo polymicrobial communities (Aim 1). By applying bioinformatics and modeling to the same clinical data, we will test the hypothesis that community metabolic features drive disease outcomes and the virulence potential of these communities (Aim 2). Finally, we will interrogate the clinical data and in vitro communities to test the hypothesis that community metabolic features drive antibiotic recalcitrance and differentiate community responsiveness to antibiotics according to these metabolic features (Aim 3). Our research will yield novel insights into how complex polymicrobial communities are compositionally structured, interact metabolically, contribute to disease and respond to antibiotics. Moreover, the research will validate in vitro models that offer the potential for development of novel antimicrobial strategies to better treat chronic, polymicrobial infections in CF and other diseases. Our transdisciplinary team offers the necessary expertise in bioinformatics, computational modeling, microbial physiology and CF polymicrobial infections to tackle this complex problem.

抽象的。囊性纤维化（CF）是一种致命的遗传性疾病，其特征是肺部粘液过度产生 其次是慢性肺部感染。传统观点认为，大多数 CF 肺部感染涉及 单一优势生物体，最常见的是致病菌铜绿假单胞菌。进展 独立于培养的技术表明，CF 肺部感染很少是单一微生物感染，而是 通常涉及复杂的微生物群落，但驱动这些群落的种间相互作用 人们了解甚少。此外，大量研究表明，多种微生物感染是 比单一微生物感染更难以用抗生素根除，从而产生了顽固性的概念 社区。顽抗背后的机制被认为涉及之间的协同相互作用 社区成员，但很少有数据可以理解这一现象。结合 认识到许多 CF 患者对现有抗生素治疗方案反应不佳，迫使我们进行更详细的研究 了解种间相互作用及其对抗生素耐药性的影响，以改善治疗 CF 感染以及其他多种微生物疾病。在这里，我们结合了大数据生物信息学，在计算机上 计算模型和体外培养实验，以深入了解代谢相互作用 驱动 CF 疾病结果和抗生素耐药性。该研究将利用可用的数据集 数百个 CF 患者样本，提供细菌成分数据和临床元数据，包括 肺功能的测量。这些样本将根据其测量的成分进行聚类， 通过计算代谢模型预测代谢能力，以检验巨大的假设 这些许多细菌群落的复杂性可以分解为少数模型群落 捕获大部分观察到的代谢变异性。这些计算预测将通过 开发体外细胞培养模型，概括体内最重要的代谢特征 多种微生物群落（目标 1）。通过将生物信息学和建模应用于相同的临床数据，我们将 检验群落代谢特征驱动疾病结果和毒力潜力的假设 这些社区（目标 2）。最后，我们将询问临床数据和体外群体来测试 假设群落代谢特征会导致抗生素耐药性并区分群落 根据这些代谢特征对抗生素的反应（目标 3）。我们的研究将产生新颖的 深入了解复杂的多微生物群落的组成结构、代谢相互作用， 导致疾病并对抗生素产生反应。此外，该研究将验证体外模型 开发新型抗菌策略以更好地治疗慢性多种微生物感染的潜力 CF等疾病。我们的跨学科团队提供生物信息学方面必要的专业知识， 计算模型、微生物生理学和 CF 多种微生物感染来解决这个复杂的问题。