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.
Currently, we are limited in our capabilities to analyze the metabolomics of complex systems such as human infections. This project will develop methods for the visualization of metabolomes in a human polymicrobial infection and highlight how those metabolomes change during different stages of disease.
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