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.
Wilson, E. O. Sociobiology: The New Synthesis (Harvard Univ. Press, 1975).
Diamond, J. M. & Ordunio, D. Guns, Germs, and Steel (Books on Tape, 1999).
Couzin, I. D. et al. Self-organization and collective behavior in vertebrates. Adv. Study Behav. 32, 1–75 (2003).
Keller, L. Adaptation and the genetics of social behaviour. Philos. Trans. R. Soc. Lond. B 364, 3209–3216 (2009).
Kay, T., Keller, L. & Lehmann, L. The evolution of altruism and the serial rediscovery of the role of relatedness. Proc. Natl Acad. Sci. USA 117, 28894–28898 (2020).
Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).
Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).
Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).
Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).
Sharon, G. et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618 (2019).
Zhang, M. et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci. Adv. 6, eaba3760 (2020).
Wu, W.-L. et al. Microbiota regulate social behaviour via stress response neurons in the brain. Nature 595, 409–414 (2021).
Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).
Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).
Schretter, C. E. Links between the gut microbiota, metabolism, and host behavior. Gut Microbes 11, 245–248 (2020).
Liberti, J. & Engel, P. The gut microbiota–brain axis of insects. Curr. Opin. Insect Sci. 39, 6–13 (2020).
O’Donnell, M. P., Fox, B. W., Chao, P.-H., Schroeder, F. C. & Sengupta, P. A neurotransmitter produced by gut bacteria modulates host sensory behaviour. Nature 583, 415–420 (2020).
Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).
Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, 1990).
Teseo, S. et al. The scent of symbiosis: gut bacteria may affect social interactions in leaf-cutting ants. Anim. Behav. 150, 239–254 (2019).
Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, eabd3431 (2020).
Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).
Choi, S. H. et al. Individual variations lead to universal and cross-species patterns of social behavior. Proc. Natl Acad. Sci. USA 117, 31754–31759 (2020).
Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl Acad. Sci. USA 117, 10406–10413 (2020).
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).
Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).
Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).
Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl Acad. Sci. USA 114, 4775–4780 (2017).
Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, e2003467 (2017).
Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).
Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).
Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).
Kao, A. B. & Couzin, I. D. Modular structure within groups causes information loss but can improve decision accuracy. Philos. Trans. R. Soc. Lond. B 374, 20180378 (2019).
de Groot, A. P. Protein and amino acid requirements of the honeybee (Apis mellifica L.). Physiol. Comp. Oecol. 3, 197–285 (1953).
Billard, J.-M. d-Amino acids in brain neurotransmission and synaptic plasticity. Amino Acids 43, 1851–1860 (2012).
Marcaggi, P. & Attwell, D. Role of glial amino acid transporters in synaptic transmission and brain energetics. Glia 47, 217–225 (2004).
Gage, S. L., Calle, S., Jacobson, N., Carroll, M. & DeGrandi-Hoffman, G. Pollen alters amino acid levels in the honey bee brain and this relationship changes with age and parasitic stress. Front. Neurosci. 14, 231 (2020).
Kawase, T. et al. Gut microbiota of mice putatively modifies amino acid metabolism in the host brain. Br. J. Nutr. 117, 775–783 (2017).
Socha, E., Koba, M. & Koslinski, P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids 51, 367–371 (2019).
Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).
Maynard, T. M. & Manzini, M. C. Balancing act: maintaining amino acid levels in the autistic brain. Neuron 93, 476–479 (2017).
Kurochkin, I. et al. Metabolome signature of autism in the human prefrontal cortex. Commun. Biol. 2, 234 (2019).
van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93 (2019).
Aldana, B. I. et al. Glutamate–glutamine homeostasis is perturbed in neurons and astrocytes derived from patient iPSC models of frontotemporal dementia. Mol. Brain 13, 125 (2020).
Galizia, C. G., Eisenhardt, D. & Giurfa M. (eds) Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel (Springer Science & Business Media, 2011).
Menzel, R. The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768 (2012).
Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).
Bruno, F., Angilica, A., Cosco, F., Luchi, M. L. & Muzzupappa, M. Mixed prototyping environment with different video tracking techniques. In IMProVe 2011 International Conference on Innovative Methods in Product Design (eds Concheri, G. et al.) 105–113 (Libreria Internazionale Cortina Padova, 2011).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Anderson, K. E., Rodrigues, P. A. P., Mott, B. M., Maes, P. & Corby-Harris, V. Ecological succession in the honey bee gut: shift in Lactobacillus strain dominance during early adult development. Microb. Ecol. 71, 1008–1019 (2016).
Almasri, H., Liberti, J., Brunet, J. L., Engel, P. & Belzunces, L. P. Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota. Sci. Rep. 12, 4281 (2022).
Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).
Gallup, J. M. in PCR Troubleshooting and Optimization: The Essential Guide (eds Kennedy, S. & Oswald, N.) 23–65 (Caister Academic Press, 2011).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).
Patassini, S. et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem. Biophys. Res. Commun. 468, 161–166 (2015).
Gonzalez-Riano, C., Garcia, A. & Barbas, C. Metabolomics studies in brain tissue: a review. J. Pharm. Biomed. Anal. 130, 141–168 (2016).
Belle, J. E. L., Harris, N. G., Williams, S. R. & Bhakoo, K. K. A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed. 15, 37–44 (2002).
Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics 20, 275 (2019).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).
Reijnders, M. J. & Waterhouse, R. M. Summary visualisations of gene ontology terms with GO-Figure! Front. Bioinform. 1, 638255 (2021).
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.
The authors declare no competing interests.
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Extended Data Fig. 1 Bacterial loads and microbiota composition in the guts of bees of the automated behavioural tracking experiment.
(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.
Extended Data Fig. 2 Social interactions per bee in each of the experimental replicates of the automated behavioural tracking experiment.
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.
Extended Data Fig. 3 Average standard deviation of speed (pixels/s) (A) and mortality of tracked bees (B) per subcolony during the 152 h of automated behavioural tracking.
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.
Extended Data Fig. 4 Bacterial loads and microbiota composition in the guts of bees of the RNA-sequencing experiment.
(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.
Extended Data Fig. 5 Differential gene expression in the gut of gnotobiotic honeybees.
(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.
Extended Data Fig. 6 Differential gene expression in the brain of gnotobiotic honeybees.
(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.
Extended Data Fig. 7 Example of the post-processing procedure to determine the orientation of a tracked bee.
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.
Extended Data Fig. 8 Social interactions in a subset of tracked bees by gut microbiota treatment group and experimental replicate.
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.
Extended Data Fig. 9 Principal Component Analysis of overall gene expression in brain samples.
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.
Supplementary Video 1
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