The human gut microbiome exists as a community of microorganisms in symbiosis with its host. Prebiotics are functional compounds that modulate this microbial community, promoting the growth and activity of bacteria that are beneficial to human health. Resistant starches (RS), a subclass of prebiotics, are compounds linked to a number of host-beneficial effects when included in human diets. Understanding how RS shapes gut flora composition and function is crucial to understanding these effects; however, these effects are clouded by the complexity of the microbiome’s interactions. Comprehensively characterizing microbiome shifts as the result of prebiotics is an intriguing bioanalytical problem. In the thesis project, I hypothesize that: RS changes microbiome biochemical pathway expression community-wide and at different taxonomic levels; that RS forms will affect microbiome bacterial taxonomic distribution; and that a linear programming optimization approach can parsimoniously distribute ambiguous peptide abundances amongst their constituent species, leading to different interpretations of functional and structural characteristics in microbiome metaproteomics data. To address these hypotheses, the thesis project utilizes a combined metaproteomics and bioinformatics approach. The Figeys lab-developed RapidAIM bioanalytical assay is deployed to generate label-free mass spectrometry metaproteomics data, testing for these effects experimentally. Further, Cerberus, a bioinformatics platform for microbiome metaproteomics analyses, was developed to integrate workflows from different software sources into a unified pipeline. Cerberus also implements a novel linear optimization approach addressing the shared-peptide problem. Through experimental data analyses using Cerberus, microbiomes encountering RS showed concerted taxonomic shifts, general and specific functional modulations linked to these taxonomic changes, and a significantly variable pathway expression profile for host-beneficial microbiome processes. The peptide-species linear optimization procedure demonstrates how naïve approaches to the shared-peptide problem greatly skew downstream taxonomic and functional analyses in metaproteomics experiments, marking an important consideration for microbiome studies seeking to resolve taxon-specific alterations.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/39717 |
| Date | 15 October 2019 |
| Creators | Ryan, James |
| Contributors | Figeys, Daniel, Lavallée-Adam, Mathieu |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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