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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.

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.

References

  1. Avalos, A. et al. Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies. Proc. Natl Acad. Sci. USA 117, 17135–17141 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sokolowski, M. B. Honey bee colony aggression and indirect genetic effects. Proc. Natl Acad. Sci. USA 117, 18148–18150 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Avalos, A. et al. A soft selective sweep during rapid evolution of gentle behaviour in an Africanized honeybee. Nat. Commun. 8, 1550 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Walsh, J. T., Garonski, A., Jackan, C. & Linksvayer, T. A. The collective behavior of ant groups depends on group genotypic composition. J. Hered. 113, 102–108 (2022).

    Article  PubMed  Google Scholar 

  5. Breed, M. D., Guzmán-Novoa, E. & Hunt, G. J. Defensive behavior of honey bees: organization, genetics, and comparisons with other bees. Annu. Rev. Entomol. 49, 271–298 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Giray, T. et al. Genetic variation in worker temporal polyethism and colony defensiveness in the honey bee, Apis mellifera. Behav. Ecol. 11, 44–55 (2000).

    Article  Google Scholar 

  7. Avalos, A., Rodríguez-Cruz, Y. & Giray, T. Individual responsiveness to shock and colony-level aggression in honey bees: evidence for a genetic component. Behav. Ecol. Sociobiol. 68, 761–771 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Schneider, S. S. & McNally, L. C. Colony defense in the African honey bee in Africa (Hymenoptera: Apidae). Environ. Entomol. 21, 1362–1370 (1992).

    Article  Google Scholar 

  9. Hunt, G. J. et al. Behavioral genomics of honeybee foraging and nest defense. Naturwissenschaften 94, 247–267 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Breed, M. D., Robinson, G. E. & Page, R. E. Division of labor during honey bee colony defense. Behav. Ecol. Sociobiol. 27, 395–401 (1990).

    Article  Google Scholar 

  11. Amdam, G. V., Norberg, K., Fondrk, M. K. & Page, R. E. Reproductive ground plan may mediate colony-level selection effects on individual foraging behavior in honey bees. Proc. Natl Acad. Sci. USA 101, 11350–11355 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Darwin, C. On the Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).

  13. Wilson, E. O. & Hölldobbler, B. The Superorganism: the Beauty, Elegance, and Strangeness of Insect Societies (WW Norton & Company, 2009).

  14. Robinson, G. E. Regulation of division of labor in insect societies. Annu. Rev. Entomol. 37, 637–665 (1992).

    Article  CAS  PubMed  Google Scholar 

  15. Winston, M. L. The Biology Of The Honey Bee (Harvard Univ. Press, 1991).

  16. Huang, Z.-Y., Robinson, G. E. & Borst, D. W. Physiological correlates of division of labor among similarly aged honey bees. J. Comp. Physiol. A 174, 731–739 (1994).

    Article  CAS  PubMed  Google Scholar 

  17. Wagener-Hulme, C., Kuehn, J. C., Schulz, D. J. & Robinson, G. E. Biogenic amines and division of labor in honey bee colonies. J. Comp. Physiol. A 184, 471–479 (1999).

    Article  CAS  PubMed  Google Scholar 

  18. Fagny, M. & Austerlitz, F. Polygenic adaptation: integrating population genetics and gene regulatory networks. Trends Genet. 37, 631–638 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Sinha, S. et al. Behavior-related gene regulatory networks: a new level of organization in the brain. Proc. Natl Acad. Sci. USA 117, 23270–23279 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998.e20 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Croset, V., Treiber, C. D. & Waddell, S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife 7, e34550 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Konstantinides, N. et al. Phenotypic convergence: distinct transcription factors regulate common terminal features. Cell 174, 622–635.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sheng, L. et al. Social reprogramming in ants induces longevity-associated glia remodeling. Sci. Adv. 6, eaba9869 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220.e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Traniello, I. M. et al. Meta-analysis of honey bee neurogenomic response links Deformed wing virus type A to precocious behavioral maturation. Sci. Rep. 10, 3101 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Suenami, S., Oya, S., Kohno, H. & Kubo, T. Kenyon cell subtypes/populations in the honeybee mushroom bodies: possible function based on their gene expression profiles, differentiation, possible evolution, and application of genome editing. Front. Psychol. 9, 1717 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hill, M. S., Vande Zande, P. & Wittkopp, P. J. Molecular and evolutionary processes generating variation in gene expression. Nat. Rev. Genet. 22, 203–215 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Erwin, D. H. & Davidson, E. H. The evolution of hierarchical gene regulatory networks. Nat. Rev. Genet. 10, 141–148 (2009).

    Article  CAS  PubMed  Google Scholar 

  30. Gao, L. et al. Identifying noncoding risk variants using disease-relevant gene regulatory networks. Nat. Commun. 9, 702 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gustafsson, M. et al. A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases. Sci. Transl. Med. 7, 313ra178 (2015).

    Article  PubMed  Google Scholar 

  32. Peng, J. et al. SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes. Commun. Biol. 5, 351 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Farine, D. R., Montiglio, P. O. & Spiegel, O. From individuals to groups and back: the evolutionary implications of group phenotypic composition. Trends Ecol. Evol. 30, 609–621 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Traniello, I. M., Chen, Z., Bagchi, V. A. & Robinson, G. E. Valence of social information is encoded in different subpopulations of mushroom body Kenyon cells in the honeybee brain. Proc. R. Soc. B 286, 20190901 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Alaux, C. et al. Honey bee aggression supports a link between gene regulation and behavioral evolution. Proc. Natl Acad. Sci. USA 106, 15400–15405 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chandrasekaran, S. et al. Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states. Proc. Natl Acad. Sci. USA 108, 18020–18025 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Grubman, A. et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat. Neurosci. 22, 2087–2097 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. López-Gutiérrez, M. F. et al. Brain functional networks associated with social bonding in monogamous voles. eLife 10, e55081 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kabelik, D. et al. Neural activity in the social decision-making network of the brown anole during reproductive and agonistic encounters. Horm. Behav. 106, 178–188 (2018).

    Article  PubMed  Google Scholar 

  40. Fagny, M. et al. Exploring regulation in tissues with eQTL networks. Proc. Natl Acad. Sci. USA 114, E7841–E7850 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang, X. F. et al. Apontic directly activates hedgehog and cyclin E for proper organ growth and patterning. Sci. Rep. 7, 12470 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Boccuto, L. et al. Decreased tryptophan metabolism in patients with autism spectrum disorders. Mol. Autism 4, 16 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. de Boer, S. F., Olivier, B., Veening, J. & Koolhaas, J. M. The neurobiology of offensive aggression: revealing a modular view. Physiol. Behav. 146, 111–127 (2015).

    Article  PubMed  Google Scholar 

  44. Hunt, G. J. Flight and fight: a comparative view of the neurophysiology and genetics of honey bee defensive behavior. J. Insect Physiol. 53, 399–410 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Clandinin, T. R. et al. Drosophila LAR regulates R1-R6 and R7 target specificity in the visual system. Neuron 32, 237–248 (2001).

    Article  CAS  PubMed  Google Scholar 

  46. Nouvian, M. et al. Cooperative defence operates by social modulation of biogenic amine levels in the honey bee brain. Proc. R. Soc. B 285, 20172653 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rittschof, C. C., Coombs, C. B., Frazier, M., Grozinger, C. M. & Robinson, G. E. Early-life experience affects honey bee aggression and resilience to immune challenge. Sci. Rep. 5, 15572 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Romero, I. G., Ruvinsky, I. & Gilad, Y. Comparative studies of gene expression and the evolution of gene regulation. Nat. Rev. Genet. 13, 505–516 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).

  52. Rivera-Marchand, B., Giray, T. & Guzmán-Novoa, E. The cost of defense in social insects: insights from the honey bee. Entomol. Exp. Appl. https://doi.org/10.1111/j.1570-7458.2008.00747.x (2008).

  53. Grume, G. J., Biedenbender, S. P. & Rittschof, C. C. Honey robbing causes coordinated changes in foraging and nest defence in the honey bee, Apis mellifera. Anim. Behav. 173, 53–65 (2021).

    Article  Google Scholar 

  54. Gauthier, M. & Grünewald, B. in Honeybee Neurobiology and Behavior (eds Giovanni Galizia, C. et al.) 155–169 (Springer, 2012). https://doi.org/10.1007/978-94-007-2099-2_13

  55. Ismail, N., Christine, S., Robinson, G. E. & Fahrbach, S. E. Pilocarpine improves recognition of nestmates in young honey bees. Neurosci. Lett. 439, 178–181 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Nuzhdin, S. V., Rychkova, A. & Hahn, M. W. The strength of transcription-factor binding modulates co-variation in transcriptional networks. Trends Genet. 26, 51–53 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. MacArthur, S. et al. Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions. Genome Biol. 10, R80 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Arbiza, L. et al. Genome-wide inference of natural selection on human transcription factor binding sites. Nat. Genet. 45, 723–729 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rittschof, C. C. & Robinson, G. E. Manipulation of colony environment modulates honey bee aggression and brain gene expression. Genes Brain Behav. 12, 802–811 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Hunt, G. J., Guzmán-Novoa, E., Fondrk, M. K. & Page, R. E. Quantitative trait loci for honey bee stinging behavior and body size. Genetics 148, 1203–1213 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Guzmán-Novoa, E. et al. Relative reliability of four field assays to test defensive behaviour of honey bees (Apis mellifera). J. Apicult. Res. 42, 42–46 (2015).

    Article  Google Scholar 

  62. Shpigler, H. Y. et al. Behavioral, transcriptomic and epigenetic responses to social challenge in honey bees. Genes Brain Behav. 16, 579–591 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 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).

    Article  CAS  PubMed  Google Scholar 

  65. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  66. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

    Article  Google Scholar 

  67. Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 10, 48 (2009).

    Article  Google Scholar 

  68. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shen, L. GeneOverlap: an R package to test and visualize gene overlaps. GitHub http://shenlab-sinai.github.io/shenlab-sinai/ (2014).

  70. Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Galluzzi, L., Kepp, O. & Kroemer, G. Mitochondria: master regulators of danger signalling. Nat. Rev. Mol. Cell Biol. 13, 780–788 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Detmer, S. A. & Chan, D. C. Functions and dysfunctions of mitochondrial dynamics. Nat. Rev. Mol. Cell Biol. 8, 870–879 (2007).

    Article  CAS  PubMed  Google Scholar 

  74. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zhang, W. et al. Single-cell transcriptomic analysis of honeybee brains identifies vitellogenin as caste differentiation-related factor. iScience 25, 104643 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Fahrbach, S. E., Strande, J. L. & Robinson, G. E. Neurogenesis is absent in the brains of adult honey bees and does not explain behavioral neuroplasticity. Neurosci. Lett. 197, 145–148 (1995).

    Article  CAS  PubMed  Google Scholar 

  77. Rittschof, C. C. & Schirmeier, S. Insect models of central nervous system energy metabolism and its links to behavior. Glia 66, 1160–1175 (2018).

    Article  PubMed  Google Scholar 

  78. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Shpigler, H. Y. et al. Honey bee neurogenomic responses to affiliative and agonistic social interactions. Genes Brain Behav. 18, e12509 (2018).

    Article  PubMed  Google Scholar 

  80. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, e12776 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  81. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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).

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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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