Ecology of Cystic Fibrosis: Administrative Supplement

囊性纤维化(CF)的代谢组学几乎是未知的。迫切需要了解CF微生物群落的动态以及哪些微生物在特定疾病状态下是活跃的。代谢组学可以揭示肺组织中活性微生物产生的化合物，为分析这一复杂的微生物群落提供新的途径。然而，CF痰的复杂性使得传统的代谢组学和质谱学(MS)数据分析方法不可行。加州大学圣迭戈分校的多雷斯坦实验室已经开发出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图。分子之间的相似性在MS中的碎裂模式中被揭示出来，这些相似之处可以在称为分子网络的二维网络中可视化。这项研究将把多雷斯坦实验室的分子网络方法扩展到来自人类疾病的复杂的多微生物样本。我们的目标是通过囊性纤维化肺加重(CFPE)的病程监测CF患者，并突出某些疾病状态特有的代谢物和相关的分子簇。此外，我们的目标是确定对CFPE治疗有反应的患者和没有接受CFPE治疗的患者的痰样本代谢组有何不同。这项研究将通过将培养的CF病原体的MS数据播种到CF痰网络中，将一项新的创新应用于分子网络。这将使采样时痰中特定病原体的作用可视化。这一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。S专项目标的实现将为了解CFI感染的动态提供新的信息。识别CFPE发展过程中活跃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跃，将有助于医生对患者采用更有针对性的治疗。S说。这一补充研究项目将从CF痰和细菌病原体中产生代谢物，其中包含数千个分子和分子网络，以可视化这些分子之间的关系。这将为开发信息更丰富和生产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.