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Title: The gut microbiota in homeostasis and inflammation
Author: Singh, Gurdeep
ISNI:       0000 0004 8504 7405
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2019
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The gut has a substantial resident microbiota localised in two major niches: the lumen and the intestinal mucus layer. The microbiota is vital for host health, yet the gut must also prevent uncontrolled incursion by pathogens or commensal microbes. Integral to host-microbe interaction is a gut barrier; comprising the intestinal epithelial cells, mucus and antimicrobials that regulate microbial entry, composition and the transit of metabolites and other molecules. Disruption of the gut barrier and the microbiota are associated with inflammation such as the autoimmune disorder, inflammatory bowel disease. I hypothesised that host-health is associated with characteristic changes in the gut microbiota, with the mucus-resident bacteria having the most profound impact on the host, due to their closer proximity to host cells. Common techniques to probe microbiome datasets are based purely on subjective analysis and biases. In this thesis, I developed a novel method for exploring microbiome data. By constructing a phylogenetic tree of 16S rRNA sequence data derived from the stools and mucus of wildtype mice, and those that develop spontaneous colitis (mdr1a-/- mice), I used a random forest model on phylogenetically-defined clades. I found that the gut microbiota could be used to distinguish our treatment groups, such as mouse age, and identified the microbial characteristics that facilitated these associations. Hence, this method can be used to provide informative information about the microbiota and its associations with other conditions of interest. To further explore the importance of the mucus microbiome, I then sought to define the contribution of mucus-resident metabolites to host function. I explored the mucus metabolite profile in the mdr1a-/- mouse model. I show that although there were no overall differences in the metabolite profile, there was variability in individual metabolites between wildtype and mdr1a-/- mice. These differences were also concordant with significant intestinal transcriptional changes. These data would suggest that changes to the mucus microbiota coincide with metabolomic and transcriptional differences in mdr1a-/- mice that predispose them to colitis. My data highlighted the importance of microbial niche in colitis. To further explore the mucus microbiota and its interaction with the host, I investigated the microbiome in eosinophil-deficient (ΔdblGATA-1-/-) mice that had altered immune and barrier function. My data showed that there was a significant reduction in bacterial diversity in the mucus that was not seen in stool samples in ΔdblGATA-1-/- mice. Although I saw overall differences in the microbiota of mice that lack eosinophils, a focused qPCR panel revealed that the biggest differences in the microbiota lay between different microbial niches, i.e. stool, colonic and small intestinal mucus. Collectively, my studies confirm that a focus on the stool microbiome alone is insufficient to capture the diversity of the gut microbiome. As a result, it is vital to explore all niches within the gut, wherever possible, in order to gain a comprehensive insight into the role of the gut microbiome in host health.
Supervisor: Brass, Andrew ; Knight, Christopher ; Cruickshank, Sheena Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Metabolite ; mdr1a ; GATA-1 ; Cage Effect ; NGS ; Littermate Control ; V3-V4 ; 16S ; Eosinophil ; Microbiota ; Phylogenetic ; IgA ; Mucus ; Stool ; Microbiome ; Machine Learning ; Inflammation ; Colitis ; Gut