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Single-cell dissection of aggression in honeybee colonies

Nature Ecology & Evolution volume 7, pages 1232–1244 (2023)Cite this article

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Abstract

Understanding how genotypic variation results in phenotypic variation is especially difficult for collective behaviour because group phenotypes arise from complex interactions among group members. A genome-wide association study identified hundreds of genes associated with colony-level variation in honeybee aggression, many of which also showed strong signals of positive selection, but the influence of these ‘colony aggression genes’ on brain function was unknown. Here we use single-cell (sc) transcriptomics and gene regulatory network (GRN) analyses to test the hypothesis that genetic variation for colony aggression influences individual differences in brain gene expression and/or gene regulation. We compared soldiers, which respond to territorial intrusion with stinging attacks, and foragers, which do not. Colony environment showed stronger influences on soldier-forager differences in brain gene regulation compared with brain gene expression. GRN plasticity was strongly associated with colony aggression, with larger differences in GRN dynamics detected between soldiers and foragers from more aggressive relative to less aggressive colonies. The regulatory dynamics of subnetworks composed of genes associated with colony aggression genes were more strongly correlated with each other across different cell types and brain regions relative to other genes, especially in brain regions involved with olfaction and vision and multimodal sensory integration, which are known to mediate bee aggression. These results show how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

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Fig. 1: Dissimilarity of brain GRN architecture between soldiers and foragers is greater in high-aggression honeybee colonies.
Fig. 2: Evolutionary changes in honeybee colony aggression are associated with brain cell GRN architecture between soldiers and foragers.
Fig. 3: Serotonin metabolism as a representative example of the relationship between colony genetics, regulatory plasticity and individual behavioural variation.

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Data availability

Whole-tissue and single-cell RNA-sequencing data can be accessed via GEO accession numbers GSE146085 and GSE142044, respectively.

Code availability

The data that support the findings presented in this paper can be found in Supplementary Tables and in the figshare repository at https://doi.org/10.6084/m9.figshare.16832518.

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Acknowledgements

We thank A. Hernandez and C. Wright at the Roy J. Carver Biotechnology Center (UIUC) for sequencing services; C. Desplan for useful discussion; and M.B. Sokolowski, members of the Gene Networks in Neural and Developmental Plasticity theme (IGB), and the Robinson lab for valuable comments and feedback on the manuscript. This work was funded by the Illinois Sociogenomics Initiative (GER). Genome sequencing from ref. 1 was funded by a Lundbeckfonden grant (G. Zhang).

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  1. Ian M. Traniello

    Present address: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

Authors and Affiliations

  1. Neuroscience Program, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA

    Ian M. Traniello & Gene E. Robinson

  2. Carl R Woese Institute for Genomic Biology, UIUC, Urbana, IL, USA

    Ian M. Traniello, Syed Abbas Bukhari, Amy Cash Ahmed, Alison L. Sankey, Saurabh Sinha, Sihai Dave Zhao, Julian Catchen & Gene E. Robinson

  3. Department of Computer Science, UIUC, Urbana, IL, USA

    Payam Dibaeinia & Saurabh Sinha

  4. Computational Biology Program, CIMA University of Navarra, Pamplona, Spain

    Guillermo Serrano & Mikel Hernaez

  5. Honey Bee Breeding, Genetics and Physiology Research Laboratory, Agricultural Research Services, United States Department of Agriculture, Baton Rouge, LA, USA

    Arian Avalos

  6. Department of Statistics, UIUC, Urbana, IL, USA

    Sihai Dave Zhao

  7. Department of Evolution, Ecology and Behavior, UIUC, Urbana, IL, USA

    Julian Catchen

  8. Department of Entomology, UIUC, Urbana, IL, USA

    Gene E. Robinson

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Contributions

I.M.T., A.A. and G.E.R. conceptualized the project. I.M.T., S.A.B., P.D., G.S. and A.C.A. developed the methodology. I.M.T., A.C.A. and A.L.S. conducted the investigation. I.M.T., P.D. and G.S. performed visualization. G.E.R. acquired funding and administered the project. M.H., S.D.Z., S.S., J.C. and G.E.R. supervised the project. I.M.T. and G.E.R. wrote the original draft. I.M.T., S.A.B., P.D., G.S., A.A., A.C.A., M.H., S.S., S.D.Z., J.C. and G.E.R. reviewed and edited the manuscript.

Corresponding authors

Correspondence to Ian M. Traniello or Gene E. Robinson.

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Nature Ecology & Evolution thanks Weiwei Liu, Robert Anholt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Whole-brain transcriptomic profiles of soldiers and foragers.

(a) Whole-brain (‘bulk’) RNA-Sequencing of the same soldier and forager bees used for genome sequencing and GWAS1 revealed thousands of differentially expressed genes (DEGs) associated with behavioural state, termed ‘soldier-’ or ‘forager-biased’ based on fold-change following a pairwise comparison between groups. Red or yellow dots represent genes upregulated in soldiers or foragers, respectively, following likelihood ratio test with Benjamini-Hochberg correction, that passed a false discovery rate (FDR)-corrected P value threshold of 0.05; grey dots represent tested genes that did not pass this threshold. A total of 4278 DEGs were identified, with 2242 and 2036 that were soldier- or forager-biased, respectively (Data S1). (b) REVIGO plots show Biological Process terms identified by a Gene Ontology (GO) enrichment analysis associated with soldier- or forager-biased gene lists. Circles are organized in semantic space such that more similar terms in the GO hierarchy are positioned more closely together. Circle diameter is inversely correlated with the specificity of each GO term, such that smaller circles represent more specific terms. All Biological Process, Molecular Function, and Cellular Component GO terms can be found in Supplementary Table 2.

Extended Data Fig. 2 Clustering and quality control of single-cell brain transcriptomic profiles of honeybee soldiers and foragers.

(a) Clustering at resolution of 0.8 was used to perform differential gene expression and regulatory dissimilarity analysis, whereas a resolution of 0.2 was used to obtain enough cells per cluster to successfully perform a deconvolution analysis using the bulk data. Cell type annotations were consistent across clustering strategies, as shown in Supplementary Table 4. (b) Whole-brain ‘bulk’ transcriptomic data from gAHB samples was strongly correlated with whole-brain sc ‘pseudobulk’ data from EHB samples (Spearman’s rank-order correlation). (c) Similar proportions of annotated cell populations were identified in both soldier and forager sc replicates. N = four biologically independent soldier or forager samples (5 brains pooled per sample) collected from four colonies. Mean and standard error are shown in figure. (d) overall UMAP embedding was highly consistent across both behavioural group and colony replicates.

Extended Data Fig. 3 Single-cell RNA-Sequencing reveals that gene regulatory dissimilarity, but not differential expression, changes across soldiers and foragers as a function of colony aggression.

(a) Heatmap showing cell type-specific Kolmogorov-Smirnov (KS) distances distinguishing gene regulatory activity in soldiers compared to foragers. Specific TFs (red box) consistently differed in regulatory activity between soldiers and foragers across all cell types. (b) Following field-based colony aggression assays, we compared colony aggression scores (see Methods) from our EHB dataset to the nine colonies sampled in ref. 1. We considered EHB colonies that were in the top 15% of the Puerto Rico colony aggression distribution1 to be ‘high-aggression’ (orange vertical lines) and colonies that were in the bottom 25% to be ‘low-aggression’ (green vertical lines). X-axis refers to number of stings delivered by resident aggressors to small leather patches placed in front of the entrance during colony disturbance (Methods). (c) The rank-order of cell type-specific regulatory dissimilarity (RD) scores, as described in Fig. 1h, was preserved across high- and low-aggression colonies (Pearson’s product-moment correlation).

Extended Data Fig. 4 Random permutation subnetworks are less connected than eGene subnetwork.

(a). Heatmaps as described in Fig. 2 for each of the 15 random permutation subnetworks. (b). Analysis described in Fig. 2b, performed for 100 additional permutations; each random permutation consistently falls below the minimum connectivity of the eGene subnetwork blocks.

Extended Data Fig. 5 Transcription factor (TF) – target gene (TG) interactions derived from eGene subnetwork.

Cytoscape network graphs show TF-TG interactions that are more strongly correlated in soldiers (left) or in foragers (right). Each line linking a TF and TG represents a significant interaction in one cell cluster (see Methods), with multiple connecting lines signifying a TF-TG relationship occurring in multiple clusters. Gene names are presented as one-to-one orthologs in D. melanogaster; if no such orthology was identified, honeybee ‘LOC’ identifier is listed. Raw eGene subnetwork data are presented in Supplementary Table 7.

Extended Data Fig. 6 Molecular correlates of colony aggression identified in soldier and forager whole- brain transcriptomics.

(a) Brain gene expression was regressed on colony-level aggression for each individual soldier and forager in each colony, identifying 1034 genes correlated with colony aggression. (b) None of these genes were found to significantly overlap with the colony aggression genes1 or eGenes identified in this study. (c) Going deeper, we performed the same analysis independently in soldiers and foragers (fold-change on volcano plots as described in [A]) and (d) and identified a significantly overlapping set of genes that were (e) highly concordant in terms of differential expression associated with colony aggression (Spearman’s rank-order correlation). All volcano plots represent likelihood ratio test with Benjamini-Hochberg correction to control for false positives, and a false discovery rate of 0.05 was used. (f) Performing an overlap analysis now resolved to the level of soldiers and foragers identified a significant overlap between genes correlated with colony aggression in foragers and eGenes; no other comparison was significant (hypergeometric test, P > 0.1).

Supplementary information

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Supplementary Tables

Supplementary Tables 1–10 with column descriptions.

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Traniello, I.M., Bukhari, S.A., Dibaeinia, P. et al. Single-cell dissection of aggression in honeybee colonies. Nat Ecol Evol 7, 1232–1244 (2023). https://doi.org/10.1038/s41559-023-02090-0

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