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Nature Ecology & Evolution volume 6, pages 1471–1479 (2022)
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The gut microbiota influences animal neurodevelopment and behaviour but has not previously been documented to affect group-level properties of social organisms. Here, we use honeybees to probe the effect of the gut microbiota on host social behaviour. We found that the microbiota increased the rate and specialization of head-to-head interactions between bees. Microbiota colonization was associated with higher abundances of one-third of the metabolites detected in the brain, including amino acids with roles in synaptic transmission and brain energetic function. Some of these metabolites were significant predictors of the number of social interactions. Microbiota colonization also affected brain transcriptional processes related to amino acid metabolism and epigenetic modifications in a brain region involved in sensory perception. These results demonstrate that the gut microbiota modulates the emergent colony social network of honeybees and suggest changes in chromatin accessibility and amino acid biosynthesis as underlying processes.
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Raw RNA-sequencing data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE192784, while raw amplicon-sequence data are available on Sequence Read Archive under accession PRJNA792398.
Raw data tables, metadata and codes are available on GitHub at https://github.com/JoanitoLiberti/The-gut-microbiota-affects-the-social-network-of-honeybees. Additional input files required to reproduce the automated behavioural tracking analyses are available on Zenodo at: https://doi.org/10.5281/zenodo.5797980.
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We thank C. La Mendola and C. Berney for their technical support with RNA extraction and library preparation of honeybee brain samples, T. Steiner for continuous support in the laboratory, M. Rüegg and A. Tuleu for technical assistance with the automated tracking system and J. Wermerssen for the bee drawing in Fig. 1. The order in which the two equally contributing senior authors are listed was determined randomly. This work was funded by the University of Lausanne, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement BRAIN (no. 797113) to J.L., by an ERC Starting Grant (MicroBeeOme, no. 714804), the NCCR microbiomes, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant no. 180575) and a Swiss National Science Foundation project grant (31003 A 160345) to P.E. and by an ERC Advanced Grant (resiliANT, no. 741491) to L. Keller.
These authors contributed equally: Joanito Liberti, Tomas Kay.
These authors jointly supervised this work: Philipp Engel, Laurent Keller.
Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
Joanito Liberti, Tomas Kay, Erik T. Frank, Thomas O. Richardson & Laurent Keller
Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
Joanito Liberti, Andrew Quinn, Lucie Kesner, Amélie Cabirol & Philipp Engel
Department of Animal Ecology and Tropical Biology, University of Würzburg, Würzburg, Germany
Erik T. Frank
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J.L., P.E. and L. Keller conceived and designed the study. J.L., P.E. and L. Keller acquired funding. P.E. and L. Keller supervised the research. J.L. and T.K. performed the automated behavioural tracking experiment. T.K. performed automated behavioural tracking data analyses with assistance from J.L. and T.O.R. J.L. performed statistical analyses. J.L. performed microbiological preparations and gnotobiotic manipulations with assistance from L. Kesner, T.K., A.C. and E.T.F. J.L. extracted DNA and J.L. and L. Kesner performed qPCR analyses. J.L. performed amplicon-sequencing and data analyses. J.L. performed gut and brain dissections and haemolymph collection. A.Q. performed metabolite extractions, GC-MS runs and metabolomics data analyses with assistance from J.L. J.L. extracted RNA and analysed RNA-sequencing data. L. Kesner performed RNA-sequencing library preparations. J.L., T.K. and A.Q. plotted the graphs. J.L., T.K., P.E. and L. Keller drafted the manuscript. All authors contributed to interpreting the data and editing subsequent drafts of the manuscript.
Correspondence to Joanito Liberti, Philipp Engel or Laurent Keller.
The authors declare no competing interests.
Nature Ecology & Evolution thanks Mike O’Donnell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
(a) Principal Coordinate Analysis of Bray–Curtis dissimilarities between gut microbiota profiles. The ordination was performed on Bray–Curtis dissimilarities calculated from a matrix of absolute bacterial abundances of each amplicon-sequence variant (ASV) in each sample. This was obtained by multiplying the relative proportion of each ASV in each sample by the total number of 16 S rRNA gene copies in the sample (normalized by Actin copy numbers). (b) The upper barplots depict the number of 16 S rRNA gene copies measured by qPCR with universal bacterial primers and normalized by Actin copy numbers. Lower stacked bars indicate the relative abundance of community members. Sub-bars of the same colour show distinct ASVs with the same classification. For ease of visualization, the stacked bars show only ASVs that had a minimum of 1% relative abundance in five samples.
Line plots show the number of head-to-head interactions per bee (HH per bee) per hour. Columns correspond to experimental replicates. Top row = nest arena; bottom row = foraging arena. Cyan lines = CL subcolonies; purple lines = MD subcolonies. Background bars show night (grey) and day (white). The expected circadian pattern of interaction frequency is apparent.
Lines connect paired colonies in each experimental replicate. Boxplots show the median and first and third quartiles, while upper and lower whiskers report largest and lowest values within 1.5 times the interquartile ranges above and below the 75th and 25th percentiles, respectively. NS = not significant. n = 18 subcolonies examined over nine independent experiments.
(a) Principal Coordinate Analysis of Bray–Curtis dissimilarities between gut microbiota profiles. Bray–Curtis dissimilarities were calculated from a matrix of absolute bacterial abundances of each amplicon-sequence variant (ASV) in each sample. Absolute abundances were obtained by multiplying the relative proportion of each ASV in each sample by the total number of 16 S rRNA gene copies in the sample (normalized by Actin copy numbers). (b) The upper barplots depict the number of 16 S rRNA gene copies measured by qPCR with universal bacterial primers and normalized by Actin copy numbers. Lower stacked bars indicate the relative abundance of community members. Sub-bars of the same colour show distinct ASVs with the same classification. For ease of visualization, the stacked bars show only ASVs that had a minimum of 1% relative abundance in two samples.
(a) Principal Component Analysis of differentially expressed genes in honeybee gut samples. The ordination clusters the samples based on the expression (trimmed mean of M values (TMM) normalized counts) of 4,988 DEGs identified in contrasts of colonized treatments and microbiota-depleted controls. Samples are colour-coded by gut microbiota treatment group. (b) Venn diagram reporting overlap in differentially expressed genes between contrasts of colonized treatments and microbiota-depleted controls in the gut. Note that additional comparisons between MD vs. both CL_13 and CL and between MD vs. all colonization treatments combined (CL, CL_13, and CL_Bifi) have been omitted here for ease of visualization. See Supplementary Table 5 for complete DEG lists.
(a) Principal Component Analyses of brain region-specific expression of genes altered by the honeybee gut microbiota. The ordinations cluster samples based on the expression (TMM-normalized counts) of the 91 differentially expressed genes identified across whole-brain and region-specific contrasts of all colonized treatments against microbiota-depleted controls. Samples are colour-coded by gut microbiota treatment group. AL = antennal lobes and suboesophageal ganglion, MB = mushroom bodies and central complex, OL = optic lobes. (b) Venn diagram reporting overlap in differentially expressed genes between contrasts of colonized treatments and microbiota-depleted controls in the brain, combining results of whole-brain and region-specific analyses. Note that additional comparisons between MD vs. both CL_13 and CL and between MD vs. all colonization treatments combined (CL, CL_13 and CL_Bifi) have been omitted here for ease of visualization. The three DEGs shared between the three pair-wise comparisons are: ‘uncharacterized LOC102654070’, ‘DNA helicase MCM8’ (LOC412034), and ‘glutamyl aminopeptidase’ (LOC551518). See Supplementary Table 6 for complete DEG lists.
In FortStudio, a line was drawn from the tip of the abdomen to the front edge of the clypeus to derive the orientation of the tag relative to the body of the bee.
The plot shows the number of head-to-head interactions of the tracked bees for which we also obtained gut microbiota and metabolome data, normalized by group size. For six of these 180 bees the number of head-to-head interactions could not be retrieved due to deterioration of the tags at the end of the week of tracking. Boxplots show the median and first and third quartiles, while upper and lower whiskers report largest and lowest values within 1.5 times the interquartile ranges above and below the 75th and 25th percentiles, respectively.
The ordination clusters samples based on the expression (TMM-normalized counts) of 10,493 genes retained after filtering out those with low expression and removing the experimental batch effect. Colour indicates gut microbiota treatment group and shape indicates the different brain regions. AL = antennal lobes and suboesophageal ganglion, MB = mushroom bodies and central complex, OL = optic lobes.
Supplementary Tables 1–7.
Monitoring of social interactions under an automated behavioural tracking system. The video shows the nest box of one subcolony. In this video orange lines connect bees whenever any kind of interaction occurs: body-to-body, head-to-head or head-to-body. Playback speed is 4× the actual speed.
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Liberti, J., Kay, T., Quinn, A. et al. The gut microbiota affects the social network of honeybees. Nat Ecol Evol 6, 1471–1479 (2022). https://doi.org/10.1038/s41559-022-01840-w
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