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Gibson, Travis

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Gibson

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Travis

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Gibson, Travis
Author's Publications: search.filters.isAuthorOfPublication.8c01e230-4e66-47ee-a748-0c97d4de69bb

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    On the Origins and Control of Community Types in the Human Microbiome
    (Public Library of Science, 2016) Gibson, Travis; Bashan, Amir; Cao, Hong-Tai; Weiss, Scott; Liu, Yang-Yu
    Microbiome-based stratification of healthy individuals into compositional categories, referred to as “enterotypes” or “community types”, holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configuration.
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    Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks
    (Nature Publishing Group, 2016) McGeachie, Michael; Sordillo, Joanne; Gibson, Travis; Weinstock, George M.; Liu, Yang-Yu; Gold, Diane; Weiss, Scott; Litonjua, Augusto
    Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p < 0.001), but showed remarkable insensitivity to initial conditions and predicted convergence to a mix of Clostridia, Gammaproteobacteria, and Bacilli. DBNs were able to identify important relationships between microbiome taxa and predict future changes in microbiome composition from measured or synthetic initial conditions. DBNs also provided likelihood estimates for sudden, dramatic shifts in microbiome composition, which may be useful in guiding further analysis of those samples.
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    Universality of human microbial dynamics
    (Springer Nature, 2016) Bashan, Amir; Gibson, Travis; Friedman, Jonathan; Carey, Vincent; Weiss, Scott; Hohmann, Elizabeth; Liu, Yang-Yu
    Human-associated microbial communities have a crucial role in determining our health and well-being and this has led to the continuing development of microbiome-based therapies such as faecal microbiota transplantation. These microbial communities are very complex, dynamic and highly personalized ecosystems exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles9. It is not known whether the underlying ecological dynamics of these communities, which can be parameterized by growth rates, and intra- and inter-species interactions in population dynamics models, are largely host-independent (that is, universal) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology or genetics, then generic microbiome manipulations may have unintended consequences, rendering them ineffective or even detrimental. Alternatively, microbial ecosystems of different subjects may exhibit universal dynamics, with the inter-individual variability mainly originating from differences in the sets of colonizing species Here we develop a new computational method to characterize human microbial dynamics. By applying this method to cross-sectional data from two large-scale metagenomic studies—the Human Microbiome Project and the Student Microbiome Project—we show that gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are probably shaped by differences in the host environment. Notably, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after faecal microbiota transplantation. These results fundamentally improve our understanding of the processes that shape human microbial ecosystems, and pave the way to designing general microbiome-based therapies.