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）是一种致命的遗传疾病，其特征是肺部粘液过量生产过多 其次是慢性肺部感染。传统观念是，大多数CF肺部感染涉及 单个主要生物，最常见的是铜绿假单胞菌的致病细菌。进步 与文化无关的技术表明，CF肺部感染很少是单微生物，而是 通常涉及复杂的微生物社区，但是驱动这些社区的种间相互作用 知之甚少。此外，许多研究表明多数菌感染是 比单微生物感染更难用抗生素消除，导致了顽固的概念 社区。人们认为顽固性的机制涉及协同的相互作用 社区成员，但很少有数据可以理解这种现象。与 意识到许多CF患者对可用的抗生素方案的反应不佳，这更详细 了解种间相互作用及其对抗生素顽固的影响以改善治疗 CF感染以及其他多发性疾病。在这里，我们将大数据生物信息学结合在硅中 计算建模和体外培养实验，以了解对代谢相互作用的见解 驱动CF疾病结局和抗生素恢复。该研究将利用可用的数据集 数百种CF患者样本提供了细菌组成数据和临床元数据，包括 肺功能的度量。这些样品将根据其测得的组合物聚类，并且 通过计算代谢建模预测的代谢能力，以检验以下假设 这些许多细菌群落的复杂性可以折叠成少数模型社区 捕获大多数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，这些模型概括了体内最重要的代谢特征 多数群社区（AIM 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 检验社区代谢特征推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社区，以测试 假设社区代谢特征驱动抗生素顽固性并区分社区 根据这些代谢特征对抗生素的反应（AIM 3）。我们的研究将产生小说 洞悉复杂的多生物群落在组成结构上如何形成，代谢相互作用， 促进疾病并对抗生素作出反应。此外，该研究将验证提供的体外模型 开发新型抗菌策略的潜力，以更好地治疗慢性多数感染 CF和其他疾病。我们的跨学科团队提供了生物信息学上必要的专业知识， 计算建模，微生物生理和CF多数感染，以解决这一复杂问题。