Development and analysis of individual-based gut microbiome metabolic models

Doctoral thesis (2017)

The human gut microbiota plays a large role in the metabolism of our diet. These microorganisms can break down indigestible materials such as polysaccharides and convert them into metabolites that the ... [more ▼]

The human gut microbiota plays a large role in the metabolism of our diet. These microorganisms can break down indigestible materials such as polysaccharides and convert them into metabolites that the human body can take up and utilize (e.g., vitamins, essential amino acids, and short-chain fatty acids). Disbalances in the gut microbiome have been associated with several diseases, including diabetes and obesity. However, little is known about the detailed metabolic crosstalk that occurs between individual organisms within the microbiome and between the microbiome and the human intestinal cells. Because of the complexity of the intestinal ecosystem, these interactions are difficult to determine using existing experimental methods. Constraint-based reconstruction and analysis (COBRA) can help identify the possible metabolic mechanisms at play in the human gut. By combining mathematical, computational, and experimental methods, we can generate hypotheses and design targeted experiments to elucidate the metabolic mechanisms in the gut microbiome. In this thesis, I first applied comparative genomics to analyze the biosynthesis pathways of eight B-vitamins in hundreds of human gut microbial species. The results suggested that many gut microbes do not synthesize any B-vitamins, that is, they depend on the host’s diet and neighboring bacteria for these essential nutrients. Second, I developed a semi-automatic reconstruction refinement pipeline that quickly generates biologically relevant genome-scale metabolic reconstructions (GENREs) of human gut microbes based on automatically generated metabolic reconstructions, comparative genomics data, and data extracted from biochemical experiments on the relevant organisms. The pipeline generated metabolically diverse reconstructions that maintain high accuracy with known biochemical data. Finally, the refined GENREs were combined with metagenomic data from individual stool samples to build personalized human gut microbiome metabolic reconstructions. The resulting large-scale microbiome models were both taxonomically and functionally diverse. The work presented in this thesis has enabled the generation of biologically relevant human gut microbiome metabolic reconstructions. Metabolic models resulting from such reconstructions can be applied to study metabolism within the human gut microbiome and between the gut microbiome and the human host. Additionally, they can be used to study the effects of different dietary components on the metabolic exchanges in the gut microbiome and the metabolic differences between healthy and diseased microbiomes. [less ▲]