Robert Quinn (Presenter)
University of California at San Diego
Bio: I am an Associate Project Scientist in Pieter Dorrestein's lab at UCSD. I study the human microbiome with integrated DNA sequencing and mass spectrometry methods.
Authorship: Robert A. Quinn, Doug Conrad, Greg Humphrey, Lindsay DeRight-Goldasich, Gabe Frische, Rob Knight and Pieter Dorrestein
University of California at San Diego, San Diego, California, 92093
This study describes the largest longitudinal sampling of microbiome and metabolome data ever collected on a chronic disease. We integrate microbial and metabolite information from patient samples collected on a daily basis, showing connections between metabolite production, microbial abundances and clinical disease state.
As microbiome and metabolome data begins to be explored as a tool for precision medicine, the power of longitudinal data has become increasingly evident. Observations of differences between acute and chronic states of disease are confounded by a general lack of knowledge of the temporal dynamics of these complex microbial communities and their chemical environment.
To address this issue we collected samples from six patients with the chronic lung disease cystic fibrosis on a daily basis for one year to assess the microbial and metabolite dynamics (n = 590 total samples). LC-MS/MS and GC-MS metabolomics data was generated and analyzed using the novel mass spectrometry database GNPS (gnps.ucsd.edu). Bacterial 16S rDNA sequence amplicon data was quantified and analyzed using the Qiita software (qiita.ucsd.edu)
The personalized nature of the metabolome was strongly evident, where each patient clustered separately from one another using multivariate statistical approaches. Flares of CF disease were captured in these collections including an event that led to the death of one patient, which is reflected in changes in the metabolomic data. Virulence factor metabolites from the bacterial pathogen Pseudomonas aeruginosa were highly dynamic through the daily samples, being detected for periods of time in a particular patient then disappearing. Drugs and their metabolism were also evident, with particular antibiotics persisting long after administration and others detected only during known treatment events.
Conclusions & Discussion
This study shows that the microbial metabolome is highly dynamic through time in chronic infections, while the general trends of the metabolome and the microbiome maintain their personalized signatures. Thus, untargeted and targeted metabolomics may become a particularly useful for the identification of active infections from pathogenic bacteria.
References & Acknowledgements:
IP Royalty: no
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