Use this URL to cite or link to this record in EThOS:
Title: Novel approaches to analysing 16S ribosomal RNA gene sequencing profiles of the gut microbiota in human health research
Author: Jackson, Matthew Albert
ISNI:       0000 0004 7232 0898
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
There is an increasing body of research associating the gut microbiome with human health. This has been fuelled by the development of untargeted techniques to profile whole microbial communities. A common approach is to sequence the 16S rRNA gene as a marker for taxonomic quantification. However, the results of such analyses are of limited use. The high-dimensional sequencing data are typically reduced to operational taxonomic units (OTUs), which are purely analytical units with no direct biological interpretation. Furthermore, experimental and analytical variation can lead to poor reproducibility of results between such studies. Here, I present novel methods to address these limitations and improve the application of 16S rRNA gene sequencing to profiling of the gut microbiota in human health research. I first explore existing and simpler approaches to the analysis of 16S rRNA gene sequencing data by investigating gut microbiota associations with two phenotypes, host frailty and proton pump inhibitor use, identifying several novel associations with both. I then tackle the limited biological representation of OTUs by presenting a comprehensive comparison of OTU clustering algorithms, using heritability as a novel and biologically motivated quality measure. The results of this comparison provide guidance for the approaches used in later analyses. Finally, I present two novel, more complex, methods to summarise 16S rRNA gene sequencing data in human health studies; both of which address the high dimensionality and limited reproducibility and biological relevance of these datasets. Firstly, I identify gut microbiota associations with multiple diseases within a single study. This generates comparable results that enable the identification of key marker taxa consistently associated with health or disease. From this, I generate an index that can represent widescale gut microbiota composition within a single score that is also associated with host health. Secondly, I present a method to identify communities of interacting microbes within the gut microbiota and demonstrate that these reliably associate with host phenotypes across geographically diverse populations. In summary, this thesis addresses several issues currently associated with the analysis of 16S rRNA gene sequencing-based gut microbiota profiles and in turn identifies several novel biological phenomena. The techniques described herein should improve the utility of microbiota profiles derived from 16S rRNA gene sequencing and advance the field of gut microbiome research in human health.
Supervisor: Bell, Jordana Tzenova ; Spector, Timothy David ; Steves, Claire Joanne Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available