Doctor of Philosophy
Microbiology and Immunology
The metabolism of microbial communities is extremely complex, having contributions from multiple species as well as the host. The metabolome (the complete set of detectable small molecules in a given environment) offers a window into the culmination of these events. The goal of this thesis was to apply metabolomics to improve our understanding of the metabolism of microbial communities, with specific focus on the vaginal microbiota.
A combination of analytical chemistry techniques were employed to profile the vaginal metabolome of women with a dysbiotic vaginal microbiota, termed Bacterial Vaginosis (BV). The vaginal metabolome was closely associated with bacterial diversity and women with BV had a distinct metabolic profile compared to healthy women (N= 131). A number of novel biomarkers were identified, the most sensitive and specific being gamma-hydroxybutyrate (GHB) and 2-hydroxyisovalerate (2HV). These biomarkers were validated in three independent cohorts of diverse geographical locations and ethnicities. Correlations between the microbiota and metabolome identified putative microbe-product relationships, including production of GHB by Gardnerella vaginalis which was confirmed in vitro. Combining these data with meta-transcriptome information, metabolites could be linked to specific transcripts and microbes with increased confidence. The fibronectin binding capabilities of Lactobacillus iners, the most prevalent species in the vagina, was also investigated and confirmed.
To extend the tools developed during investigations of the vaginal microbiota to other systems, a study of stool and plasma samples from children with severe acute malnutrition (SAM) was conducted. Although the stool microbiota and metabolome did not discriminate children with SAM from controls, a number of metabolites differed significantly in plasma. Most of these metabolites had not been associated with SAM previously, including oxylipins, 2C6-disaccharides, truncated fibrinopepetides, and heme. These metabolic perturbations provide novel insight into the pathogenesis of SAM, and could serve as predictors of mortality/recovery and enteropathy. This study also led to the development of a novel method to filter out salt cluster artefacts in LC-MS metabolomics data using mass defect filtering.
Collectively, these studies have demonstrated how analytical chemistry, computational biology and microbiology can be integrated to advance our understanding of the metabolism of the microbiome and identify novel biomarkers of disease.
McMillan, Amy, "Utilizing untargeted metabolomics to characterize microbial communities and identify biomarkers of an unhealthy state" (2016). Electronic Thesis and Dissertation Repository. 3908.
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