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: 10624262
• 负责人: George A. O'Toole
• 金额: $ 45.05万
• 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/10624262
• 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; Output; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Production; Prognosis; Pseudomonas aeruginosa; Pseudomonas aeruginosa infection; Pulmonary Cystic Fibrosis; 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; 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 community; mortality; novel; novel therapeutics; pathogenic bacteria; polymicrobial biofilm; polymicrobial disease; poor communities; 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，大多数慢性肺泡炎肺部感染都与 单一优势生物体，最常见的致病菌为铜绿假单胞菌。最新进展 与培养无关的技术表明，CF型肺部感染很少是单一微生物的，而是 通常涉及复杂的微生物群落，但推动这些群落的物种间相互作用 人们对此了解甚少。此外，许多研究表明，多菌体感染是 比单一微生物感染更难用抗生素根除，导致了顽固不化的概念 社区。顽固性的潜在机制被认为涉及相互之间的协同作用 社区成员，但了解这一现象的数据很少。结合了 意识到许多CF患者对可用的抗生素方案反应不佳，迫使他们进行更详细的 了解种间相互作用及其对抗生素耐药的影响，以改进治疗 其他多发性微生物疾病也是如此。在这里，我们将大数据生物信息学结合在一起 计算模型和体外培养实验，以深入了解代谢相互作用 导致慢性萎缩性胃炎的疾病转归和抗菌素抵抗。这项研究将利用现有的数据集 数百个提供细菌组成数据和临床元数据的CF患者样本，包括 肺功能的测量。这些样本将根据其测量的组成和 通过计算代谢模型来预测代谢能力，以检验以下假设 这些细菌群落的复杂性可以分解成少量的模型群落 它们捕捉到了大部分观察到的新陈代谢变化。这些计算预测将得到以下测试 发展体外细胞培养模型，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 测试社区新陈代谢特征驱动疾病结果的假设以及 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 群落代谢特征驱动抗生素抵抗和区分群落的假说 根据这些代谢特征对抗生素的反应性(目标3)。我们的研究将产生新奇的东西 洞察复杂的多菌群落是如何组成结构、代谢相互作用、 会导致疾病，并对抗生素产生反应。此外，这项研究将验证体外模型提供的 开发新的抗菌策略以更好地治疗慢性、多菌感染的潜力 Cf和其他疾病。我们的跨学科团队在生物信息学方面提供必要的专业知识， 计算建模、微生物生理学和CF多菌素感染来解决这一复杂问题。

项目成果

The History of Microbial Model Systems.

微生物模型系统的历史。

• DOI: 10.1128/jb.00030-23
• 发表时间: 2023
• 期刊: Journal of bacteriology
• 影响因子: 3.2
• 作者: O'Toole,GeorgeA