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The Bacterial Communities Associated with Honey Bee (Apis mellifera) Foragers

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TLDR
The crop microbial environment is influenced by worker task, and may function in both decontamination and inoculation, concluding that the crop taxa at low abundance include core hindgut bacteria in transit to their primary niche, and potential pathogens or food spoilage organisms seemingly vectored from the pollination environment.
Abstract
The honey bee is a key pollinator species in decline worldwide. As part of a commercial operation, bee colonies are exposed to a variety of agricultural ecosystems throughout the year and a multitude of environmental variables that may affect the microbial balance of individuals and the hive. While many recent studies support the idea of a core microbiota in guts of younger in-hive bees, it is unknown whether this core is present in forager bees or the pollen they carry back to the hive. Additionally, several studies hypothesize that the foregut (crop), a key interface between the pollination environment and hive food stores, contains a set of 13 lactic acid bacteria (LAB) that inoculate collected pollen and act in synergy to preserve pollen stores. Here, we used a combination of 454 based 16S rRNA gene sequencing of the microbial communities of forager guts, crops, and corbicular pollen and crop plate counts to show that (1) despite a very different diet, forager guts contain a core microbiota similar to that found in younger bees, (2) corbicular pollen contains a diverse community dominated by hive-specific, environmental or phyllosphere bacteria that are not prevalent in the gut or crop, and (3) the 13 LAB found in culture-based studies are not specific to the crop but are a small subset of midgut or hindgut specific bacteria identified in many recent 454 amplicon-based studies. The crop is dominated by Lactobacillus kunkeei, and Alpha 2.2 (Acetobacteraceae), highly osmotolerant and acid resistant bacteria found in stored pollen and honey. Crop taxa at low abundance include core hindgut bacteria in transit to their primary niche, and potential pathogens or food spoilage organisms seemingly vectored from the pollination environment. We conclude that the crop microbial environment is influenced by worker task, and may function in both decontamination and inoculation.

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Gut microbial communities of social bees

TL;DR: The role of the microbiota in bee health and its potential as a model for studying the ecology and evolution of gut symbionts are drawn attention.
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Lifestyles in transition: evolution and natural history of the genus Lactobacillus.

TL;DR: This model can provide a framework for the elucidation of the natural and evolutionary history of Lactobacillus species and valuable information to improve the use of this important genus in industrial and therapeutic applications.
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Dynamic microbiome evolution in social bees

TL;DR: The emergence of the eusocial corbiculate bees from solitary ancestors appears to coincide with the acquisition of five core gut bacterial lineages, supporting the hypothesis that host sociality facilitates the development and maintenance of specialized microbiomes.
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The role of the gut microbiome in health and disease of adult honey bee workers.

TL;DR: Evidence suggests that the gut microbiome plays an important role in bee health and disease.
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Disentangling metabolic functions of bacteria in the honey bee gut.

TL;DR: The honey bee gut microbiota utilizes a wide range of pollen-derived substrates, including flavonoids and outer pollen wall components, suggesting a key role for degradation of recalcitrant secondary plant metabolites and pollen digestion.
References
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Journal ArticleDOI

MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

TL;DR: The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models, inferring ancestral states and sequences, and estimating evolutionary rates site-by-site.
Journal ArticleDOI

MUSCLE: multiple sequence alignment with high accuracy and high throughput

TL;DR: MUSCLE is a new computer program for creating multiple alignments of protein sequences that includes fast distance estimation using kmer counting, progressive alignment using a new profile function the authors call the log-expectation score, and refinement using tree-dependent restricted partitioning.
Journal ArticleDOI

Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

TL;DR: The RDP Classifier can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes, and the majority of the classification errors appear to be due to anomalies in the current taxonomies.
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