| Summary: | Thesis (Ph.D.)--Boston University === PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. === Understanding microbial metabolisms is fundamental to several aspects of human health, most notably the fight against infectious diseases and the characterization of the beneficial role of microbial communities. I have employed systems-biology approaches to study the role of the human oral flora (the oral microbiome) in inflammatory oral disease, through the analysis of microbial metabolism at various levels of complexity. Individual pathogens, such as Porphyromonas gingivalis , have been traditionally linked with chronic inflammatory diseases, such as periodontitis, as well as with atherosclerosis and cardiovascular disease. However, it is becoming increasingly clear that greater insight into pathogenicity requires approaches that go beyond individual organisms, taking into account the spatio-temporal organization of key species in the biofilm, or even the whole gene content of the microbial community. To better understand the growth and virulence properties of P. gingivalis , I built a genome-scale stoichiometric model of its metabolic network. Based on this model, I used flux balance analysis to generate quantitative predictions of the effects of gene deletions on this organism. An interesting outcome of this analysis was the identification of putative drug targets that mitigate pathogenicity by reducing the production of lipopolysaccharides. P. gingivalis , however, is only one of many organisms that compose the dental biofilm. To address the metabolic role of multiple microbes in determining the spatio-temporal organization of the community, I compared the overlap in metabolic functions (metabolic similarity) between microbes across different layers of the biofilm. I found that the metabolic similarity tends to be maximized in the real biofilm compared to randomized orders of colonization, pointing to a potentially broader principle of microbial ecosystem organization. Finally, in a more comprehensive, top-down approach, I utilized novel metagenomic sequencing data to investigate the community as a whole. Principal component analysis revealed that periodontal disease samples, as compared to healthy controls, occupy a more constrained region within the space of all possible community compositions - consistent with increased parasitic behavior during disease. Future efforts should aim at closing the gaps between different scales, providing a global understanding of the human microbiome and the host-pathogen system. === 2031-01-01
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