The human microbiome, the complex and dynamic ecosystem that populates our body, performs essential functions such as aiding digestion and protecting us from harmful pathogens. An increasing number of diseases are found to be associated with a shift of balance - or dysbiosis - of the microbiome. However, we still know little about this delicate balance and how it depends on different microbial functions.
In this thesis project, I used metagenomic sequencing data to study the variability of microbes and their functions in different areas of the human body. First, in an attempt to characterize the dysbiosis associated with periodontitis, I examined the microbial community of the oral cavity in presence and absence of this chronic inflammatory disease. Specifically, I catalogued the phylogenetic signatures composed of tetramer nucleotide frequencies and observed that the disease state occupies a much narrower region than the healthy one. This result suggests that upon onset of the disease, through host cell invasion, pathogenic bacteria may find a more consistent environment for their parasitic lifestyle.
Motivated by these findings, I sought further evidence of an environment-specific use of metabolic functions in the oral and gut communities. Rather than focusing on the abundance of individual metabolic functions, I evaluated their diversity, i.e., the extent to which these functions are performed by different classes of organisms. My hypothesis was that such diversity may confer increased robustness to taxonomic variability. Using metagenomic sequencing data and NCBI's Protein Clusters database, I characterized the multiplicity of gene families associated with a given metabolic function. I found that different human body sites display different degrees of metabolic functional diversity, as assessed by Shannon entropy. For some well-studied gene functions, such as those involved in glycolytic pathways, I found entropy signatures consistent with the known degree of oxygen availability of different environmental niches. Conversely, in an unsupervised analysis, I identified functions with nontrivial entropy signatures.
These results pave the way for a new way to inspect human microbiome activity, and could help understand its functional resilience and suggest ways to shift its balance towards healthy configurations.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/14310 |
| Date | 22 January 2016 |
| Creators | Faller, Lina Luise |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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