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Predicting Colonization of Microbes in the Human Gut

dc.contributor.advisor David, Lawrence
dc.contributor.author Midani, Firas Said
dc.date.accessioned 2018-09-21T16:07:54Z
dc.date.available 2020-08-30T08:17:07Z
dc.date.issued 2018
dc.identifier.uri https://hdl.handle.net/10161/17463
dc.description.abstract <p>The human gut is home to trillions of microbes that interact intimately with the host and its diet. An important emergent phenotype by these microbes is colonization resistance, the process by which a microbial community resists colonization of an exogenous microbe. This resistance barrier is critical for protecting humans from infectious enteric pathogens. However, it is detrimental to the deliberate engraftment of probiotics which are live microbes beneficial to the host. The mechanisms behind these barriers have been studied extensively, but the microbiome is a network of many biological nodes and ecological edges that can interact with an invader in numerous ways. Therefore, defining the precise mechanisms for resistance of a specific pathogen or probiotic is challenging, due in large to this combinatorial challenge. In the second chapter of this dissertation, I demonstrate a novel approach that can suggest key taxa or host factors associated with clinical outcomes of interest including colonization resistance. In particular, I leveraged a rare prospective cohort study with machine learning methods for identifying gut bacterial signatures associated with susceptibility to cholera. I demonstrated that the human gut microbiota can predict the susceptibility of its host to the diarrheal disease. One of the predictive gut microbes identified by my model, Paracoccus aminovorans, facilitated the growth of Vibrio cholera, the etiologic agent of cholera, in vitro. My model also linked gut microbiota structure, clinical outcomes, and age. This integrative approach suggested that gastrointestinal immaturity of the host and its gut microbiome may be crucial for resisting colonization of enteric pathogens. The predictive model was also over-represented with members of the Bacteroidetes phylum, including several Bacteroides species. These taxa belong to a genus that is dominant in the gut of human on a western diet. Genomic, biochemical, and metabolic studies have vastly studied the traits of these Bacteroides species and how they interact with the host. As obligate anaerobes that are stably colonized in the human gut, the Bacteroides is an ideal model genus for studying ecological mechanisms of colonization resistance. In the third chapter of this dissertation, I developed a high-throughput rapid approach for inferring the relative abundance of several Bacteroides species in a mixed community grown on single carbon substrates. I validated the utility of this method by investigating whether Bacteroides species cooperate or compete when carbon resources are limited. By profiling the growth of mixed cultures on single carbohydrates, I show that Bacteroides exhibit both patterns of resource cooperation and competition. Together, these chapters show that development and application of novel computational and experimental tools can shed light on the intimate interactions between diet, microbiome, and the host in the context of colonization resistance.</p>
dc.subject Microbiology
dc.subject Ecology
dc.subject Biostatistics
dc.subject Bacteroides
dc.subject Colonization resistance
dc.subject Machine learning
dc.subject Microbial ecology
dc.subject Vibrio cholerae
dc.title Predicting Colonization of Microbes in the Human Gut
dc.type Dissertation
dc.department Computational Biology and Bioinformatics
duke.embargo.months 23


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