Biology subjects

Gibbons, S.

Publications and source records attributed to Gibbons, S..

3 recordsLinked to original sources

bioRxiv2018

Orthogonal dietary niche enables reversible engraftment of a gut bacterial commensal

Interest in manipulating the gut microbiota to treat disease has led to a need for understanding how organisms can establish themselves when introduced into a host with an intact microbial community. While probiotic or prebiotic approaches typically lead to a transient pulse in an organisms abundance, persistent establishment of an introduced species may require alternative strategies. Here, we introduce the concept of orthogonal niche engineering in the gut, where we include a resource typically absent from the diet, seaweed, to establish a customized niche for an introduced organism. We show that in the short term, co-introduction of this resource at 1% in the diet along with an organism with exclusive access to this resource, B. plebeius DSM 17135, enables it to colonize at a median abundance of 1%, frequently increasing in abundance to 10 or more percent. We construct a mathematical model of the system to infer that B. plebeius competitively acquires endogenous resources. We provide evidence that it competes with native commensals to achieve its observed abundance. We observe a diet-dependent loss in seaweed responsiveness of B. plebeius in the long term and show the potential for IgA-mediated control of putative invaders by the immune system. These results point to the potential for diet-based intervention as a means to introduce target organisms, but also indicate potential modes for failure of this strategy in the long term.

microbiology↗

bioRxiv2017

Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort

Dietary interventions to manipulate the human gut microbiome for improved health have received increasing attention. However, their design has been limited by a lack of understanding of the quantitative impact of diet on a hosts microbiota. We present a highly controlled diet perturbation experiment in a healthy, human cohort in which individual micronutrients are spiked in against a standardized background. We identify strong and predictable responses of specific microbes across participants consuming prebiotic spike-ins, at the level of both strains and functional genes, suggesting fine-scale resource partitioning in the human gut. No predictable responses to non-prebiotic micronutrients were found. Surprisingly, we did not observe decreases in day-to-day variability of the microbiota compared to a complex, varying diet, and instead found evidence of diet-induced stress and an associated loss of biodiversity. Our data offer insights into the effect of a low complexity diet on the gut microbiome, and suggest that effective personalized dietary interventions will rely on functional, strain-level characterization of a patients microbiota.

microbiology↗

bioRxiv2017

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.\n\nAuthor SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fishers method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

bioinformatics↗