Biology subjects

Borenstein, E.

Publications and source records attributed to Borenstein, E..

4 recordsLinked to original sources

bioRxiv2018

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.\n\nImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in simulated datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies true contributions with very low predictive value, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

microbiology↗

bioRxiv2017

BURRITO: An interactive multi-omic tool for visualizing taxa-function relationships in microbiome data

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

bioinformatics↗

bioRxiv2017

Metabolic Model-Based Analysis of the Emergence of Bacterial Cross-Feeding through Extensive Gene Loss

Metabolic dependencies between microbial species are common and have a significant impact on the assembly and resilience of microbial communities. However, the origins of such metabolic dependencies, the evolutionary forces that drive metabolic cross-feeding, and the impact of metabolic and genomic architecture on their emergence are not clear. To address these questions, we developed a novel simulation-based framework coupling a model of reductive evolution with a multi-species genome-scale model of microbial metabolism. We used this framework to model the evolution of a two-species microbial community, simulating thousands of independent evolutionary trajectories and investigating the link between genome reductive evolution and the emergence of metabolic interactions. Surprisingly, even though our model does not impose explicit selection for cooperation, metabolic dependencies emerged in nearly half of all evolutionary runs. Evolved dependencies involved cross-feeding of a diverse set of metabolites at varying frequencies, reflecting various constraints imposed by the metabolic network architecture. We additionally found metabolic missed opportunities, wherein species failed to capitalize on metabolites made available by their partners. When cross-feeding did evolve, it generally emerged immediately after a metabolite became available, but a complete dependence on such cross-fed metabolites often evolved relatively slowly. Examining the genes deleted and retained in each evolutionary trajectory and the timing of gene deletion events along these trajectories, we were further able to identify both genome-wide properties and specific gene retentions that were associated with metabolic phenotypes. Our findings provide insight into the evolution of cooperative metabolic interaction among microbial species, offering a unique view into the way such relationships could emerge in natural settings.

microbiology↗

bioRxiv2017

The Landscape Of Type VI Secretion Across Human Gut Microbiomes Reveals Its Role In Community Composition

While the composition of the human gut microbiome has been well defined, the forces governing its assembly are poorly understood. Recently, prominent members of this community from the order Bacteroidales were shown to possess the type VI secretion system (T6SS), which mediates contact-dependent antagonism between Gram-negative bacteria. However, the distribution of the T6SS in human gut microbiomes and its role have not yet been characterized. To address this challenge, we construct an extensive catalog of T6SS effector/immunity (E-I) genes from three genetic architectures (GA1-3) found in Bacteroidales genomes. We then use metagenomic analysis to assess the abundances of these genes across a large set of gut microbiome samples. We find that despite E-I diversity across reference strains, each individual microbiome harbors a limited set of E-I genes representing a single E-I genotype. Importantly, for GA1-2, these genotypes are not associated with a specific species, suggesting selection for compatibility. GA3, in contrast, is restricted to B. fragilis, and its low diversity reflects a single B. fragilis strain per sample. We further show that in infant microbiomes GA3 is enriched and B. fragilis strains are replaced over time, suggesting competition for dominance in developing microbiomes. Finally, we find a strong association between the presence of GA3 and increased abundance of Bacteroides, indicating that this system confers a selective advantage in vivo in Bacteroides rich ecosystems. Combined, our findings provide the first comprehensive characterization of the T6SS landscape in the human microbiome, implicating it in both intra- and inter-species interactions.

microbiology↗