Ecology of Cystic Fibrosis: Administrative Supplement

囊性纤维化（CF）的代谢组学几乎未被探索。迫切需要了解CF微生物群落的动态以及在特定疾病状态下哪些微生物是活跃的。代谢组学可以揭示CF肺中活性微生物产生的化学物质，为分析这一复杂的微生物群落提供了新的途径。然而，CF痰液的复杂性使得传统的代谢组学和质谱（MS）数据分析方法不可行。加州大学圣地亚哥分校的Dorrestein实验室开发了一种计算方法来比较微生物培养产生的母体代谢物碎片模式的质谱。分子之间的相似性在质谱中的碎片模式中被揭示出来，这些相似性可以在称为“分子网络”的二维网络中可视化。这项研究将把Dorrestein实验室的分子网络方法扩展到来自人类疾病的复杂多微生物样本。我们的目标是通过囊性纤维化肺恶化（CFPE）的过程监测CF患者，并突出特定疾病状态的代谢物和相关分子簇。此外，我们的目标是确定对CFPE治疗有反应的患者和对CFPE治疗没有反应的患者之间痰样本代谢组的差异。本研究将采用一种新颖的创新方法，将培养的CF病原体的MS数据播种到CF痰网络中。这将使在取样时痰中特定病原体的作用可视化。这种方法将彻底改变代谢组学领域，并可应用于含有复杂微生物群落的人类和环境样品。该项目的具体目标的实现将提供有关CF感染动力学的新信息。鉴定CFPE发展过程中活性微生物产生的代谢物，以及哪些微生物在无效治疗期间保持活性，将有助于医生对CFPE患者采用更有针对性的治疗方法。这个补充研究项目将从CF痰液和细菌病原体中产生代谢组，其中包含数千个分子和分子网络，可以可视化这些分子之间的关系。这将为开发信息更丰富、效率更高的代谢组学数据库奠定基础，这些数据库已经落后于类似的序列数据库。

The metabolomics of cystic fibrosis (CF) is virtually unexplored. There is an urgent need to understand the dynamics of the CF microbial community and which microbes are active during particular disease states. Metabolomics can reveal the chemical compounds produced by active microbes in the CF lung and provide a new approach for analysis of this complex microbial community. However, the complexity of CF sputum makes traditional metabolomics and mass spectrometry (MS) data analysis methods unfeasible. The Dorrestein lab at UCSD has developed computational methods to compare mass spectra of parent metabolite fragmentation patterns produced by microbial cultures. The similarity between molecules is revealed in their fragmentation patterns in MS and these similarities can be visualized in a 2-dimensional network called a ¿molecular network¿. This study will expand the molecular networking methods of the Dorrestein lab to a complex polymicrobial sample from a human disease. We aim to monitor CF patients through the course of a cystic fibrosis pulmonary exacerbation (CFPE) and highlight metabolites and related clusters of molecules specific to certain states of disease. In addition, we aim to determine how the metabolome of a sputum sample is different between patients that respond to CFPE treatment and those who don¿t. This study will apply a novel innovation to molecular networking by seeding CF sputum networks with MS data from cultured CF pathogens. This will allow visualization of the action of particular pathogens in sputum at time of sampling. This methodology will revolutionize the field of metabolomics and can be applied to human and environmental samples containing complex microbial communities. Fulfillment of this project¿s specific aims will provide new information about the dynamics of CF infections. Identification of metabolites produced by active microbes during CFPE development and which microbes remain active during an ineffective treatment will aid doctors in employing more targeted therapies to a patient¿s CFPE. This supplemental research project will produce metabolomes from CF sputum and bacterial pathogens containing thousands of molecules and molecular networks that visualizes the relationships of these molecules. This will allow a basis for developing more informative and productive databases for metabolomics that have lagged behind analogous sequence databases.