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. 2014 Sep 30;5(5):e01631-14.
doi: 10.1128/mBio.01631-14.

Metagenome-wide association of microbial determinants of host phenotype in Drosophila melanogaster

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Metagenome-wide association of microbial determinants of host phenotype in Drosophila melanogaster (V体育官网)

John M Chaston et al. mBio. .

Abstract

Animal-associated bacteria (microbiota) affect host behaviors and physiological traits. To identify bacterial genetic determinants of microbiota-responsive host traits, we employed a metagenome-wide association (MGWA) approach in two steps. First, we measured two microbiota-responsive host traits, development time and triglyceride (TAG) content, in Drosophila melanogaster flies monoassociated with each of 41 bacterial strains. The effects of monoassociation on host traits were not confined to particular taxonomic groups. Second, we clustered protein-coding sequences of the bacteria by sequence similarity de novo and statistically associated the magnitude of the host trait with the bacterial gene contents. The animals had been monoassociated with genome-sequenced bacteria, so the metagenome content was unambiguous. This analysis showed significant effects of pyrroloquinoline quinone biosynthesis genes on development time, confirming the results of a published transposon mutagenesis screen, thereby validating the MGWA; it also identified multiple genes predicted to affect host TAG content, including extracellular glucose oxidation pathway components VSports手机版. To test the validity of the statistical associations, we expressed candidate genes in a strain that lacks them. Monoassociation with bacteria that ectopically expressed a predicted oxidoreductase or gluconate dehydrogenase conferred reduced Drosophila TAG contents relative to the TAG contents in empty vector controls. Consistent with the prediction that glucose oxidation pathway gene expression increased bacterial glucose utilization, the glucose content of the host diet was reduced when flies were exposed to these strains. Our findings indicate that microbiota affect host nutritional status through modulation of nutrient acquisition. Together, these findings demonstrate the utility of MGWA for identifying bacterial determinants of host traits and provide mechanistic insight into how gut microbiota modulate the nutritional status of a model host. .

Importance: To understand how certain gut bacteria promote the health of their animal hosts, we need to identify the bacterial genes that drive these beneficial relationships. This task is challenging because the bacterial communities can vary widely among different host individuals. To overcome this difficulty, we quantified how well each of 41 bacterial species protected Drosophila fruit flies from high fat content V体育安卓版. The genomes of the chosen bacterial strains were previously sequenced, so we could statistically associate specific bacterial genes with bacterially mediated reduction in host fat content. Bacterial genes that promote glucose utilization were strongly represented in the association, and introducing these genes into the gut bacteria was sufficient to lower the animal's fat content. Our method is applicable to the study of many other host-microbe interactions as a way to uncover microbial genes important for host health. .

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Figures

FIG 1
FIG 1
Larval development time and TAG content in monoassociated D. melanogaster flies. Traits for D. melanogaster that were monoassociated with each of 41 bacterial strains were measured. Phylogenetic trees were calculated using 16S sequences with unweighted branch lengths. Taxon abbreviations are defined in Table 1. Significant differences between treatments after Bonferroni correction (P < 0.05) are indicated by different letters next to bars. (A) Differences in bacterial effects on larval time to pupariation (development time) were observed between strains. Survival analysis using a Cox mixed model was used to identify significant differences between treatments, with experimental replicate and vial as random effects. To facilitate visualization, data are presented as the mean times to development ± standard errors of the means (SEM). (B) Differences in bacterial effects on TAG content were observed between strains. A linear mixed model was used to identify significant differences between treatments, with experimental replicate as a random effect. Data are presented as mean TAG content ± SEM (mean of experimental means). Red, Proteobacteria; blue, Firmicutes; gray, Bacteroidetes.
FIG 2
FIG 2
Experimental validation of candidate microbiota genes associated with host TAG level. (A) TAG content is shown for gnotobiotic flies monoassociated with either A. pasteurianus 3P3 or A. tropicalis DmCS_006 bearing the plasmids indicated. (B) Glucose content of fly diet after gnotobiotic rearing from egg to adulthood with recombinant strains bearing the indicated plasmids. Values are means ± standard errors for 3 experiments with 7 to 9 technical replicates each. Significant difference from the results for the control were determined by Dunnet’s test (*, P < 0.05; **, P < 0.01; ***, P < 0.001).
FIG 3
FIG 3
Microbiota effects on diet. (A) The glucose contents of fly diet after gnotobiotic rearing from egg to adulthood with a subset of strains from the 41-strain panel are shown. Microbiota treatments are indicated along the x axes. Values are means ± standard errors for 3 experiments with 7 to 9 technical replicates each. Significant differences from the results for the control were determined by Dunnet’s test (*, P < 0.05; **, P < 0.01; ***, P < 0.001). (B) Correlation between TAG contents of gnotobiotic flies and glucose contents of food remaining after rearing. Statistics are from Spearman’s rank order test.

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