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Environmental spread of microbes impacts the development of metabolic phenotypes in mice transplanted with microbial communities from humans.

ABSTRACT: Microbiota transplantation to germ-free animals is a powerful method to study involvement of gut microbes in the aetiology of metabolic syndrome. Owing to large interpersonal variability in gut microbiota, studies with broad coverage of donors are needed to elucidate the establishment of human-derived microbiotas in mice, factors affecting this process and resulting impact on metabolic health. We thus transplanted faecal microbiotas from humans (16 obese and 16 controls) separately into 64 germ-free Swiss Webster mice caged in pairs within four isolators, with two isolators assigned to each phenotype, thereby allowing us to explore the extent of microbial spread between cages in a well-controlled environment. Despite high group-wise similarity between obese and control human microbiotas, transplanted mice in the four isolators developed distinct gut bacterial composition and activity, body mass gain, and insulin resistance. Spread of microbes between cages within isolators interacted with establishment of the transplanted microbiotas in mice, and contributed to the transmission of metabolic phenotypes. Our findings highlight the impact of donor variability and reveal that inter-individual spread of microbes contributes to the development of metabolic traits. This is of major importance for design of animal studies, and indicates that environmental transfer of microbes between individuals may affect host metabolic traits.

SUBMITTER: Zhang L

PROVIDER: S-EPMC5322303 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Environmental spread of microbes impacts the development of metabolic phenotypes in mice transplanted with microbial communities from humans.

Zhang Li L Bahl Martin Iain MI Roager Henrik Munch HM Fonvig Cilius Esmann CE Hellgren Lars I LI Frandsen Henrik Lauritz HL Pedersen Oluf O Holm Jens-Christian JC Hansen Torben T Licht Tine Rask TR

The ISME journal 20161118 3

Microbiota transplantation to germ-free animals is a powerful method to study involvement of gut microbes in the aetiology of metabolic syndrome. Owing to large interpersonal variability in gut microbiota, studies with broad coverage of donors are needed to elucidate the establishment of human-derived microbiotas in mice, factors affecting this process and resulting impact on metabolic health. We thus transplanted faecal microbiotas from humans (16 obese and 16 controls) separately into 64 germ- ...[more]

PMID: 27858930

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Project description:The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence ("1") or absence ("0") of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn's disease cohorts.

Project description:BackgroundCharacterization of microbial activity is essential to the understanding of the basic biology of microbial communities, as the function of a microbiome is defined by its biochemically active ("viable") community members. Current sequence-based technologies can rarely differentiate microbial activity, due to their inability to distinguish live and dead sourced DNA. As a result, our understanding of microbial community structures and the potential mechanisms of transmission between humans and our surrounding environments remains incomplete. As a potential solution, 16S rRNA transcript-based amplicon sequencing (16S-RNA-seq) has been proposed as a reliable methodology to characterize the active components of a microbiome, but its efficacy has not been evaluated systematically. Here, we present our work to benchmark RNA-based amplicon sequencing for activity assessment in synthetic and environmentally sourced microbial communities.ResultsIn synthetic mixtures of living and heat-killed Escherichia coli and Streptococcus sanguinis, 16S-RNA-seq successfully reconstructed the active compositions of the communities. However, in the realistic environmental samples, no significant compositional differences were observed in RNA ("actively transcribed - active") vs. DNA ("whole" communities) spiked with E. coli controls, suggesting that this methodology is not appropriate for activity assessment in complex communities. The results were slightly different when validated in environmental samples of similar origins (i.e., from Boston subway systems), where samples were differentiated both by environment type as well as by library type, though compositional dissimilarities between DNA and RNA samples remained low (Bray-Curtis distance median: 0.34-0.49). To improve the interpretation of 16S-RNA-seq results, we compared our results with previous studies and found that 16S-RNA-seq suggests taxon-wise viability trends (i.e., specific taxa are universally more or less likely to be viable compared to others) in samples of similar origins.ConclusionsThis study provides a comprehensive evaluation of 16S-RNA-seq for viability assessment in synthetic and complex microbial communities. The results found that while 16S-RNA-seq was able to semi-quantify microbial viability in relatively simple communities, it only suggests a taxon-dependent "relative" viability in realistic communities. Video Abstract.

Dataset Information

Environmental spread of microbes impacts the development of metabolic phenotypes in mice transplanted with microbial communities from humans.

Publications

Environmental spread of microbes impacts the development of metabolic phenotypes in mice transplanted with microbial communities from humans.

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