Convergent and complementary selection shaped gains and losses of eusociality in sweat bees.

Bees Animals Sweat Social Behavior Reproduction Phenotype

Journal

Nature ecology & evolution

ISSN: 2397-334X

Titre abrégé: Nat Ecol Evol

Pays: England

ID NLM: 101698577

Informations de publication

Date de publication:
04 2023

Historique:

received: 09 08 2022

accepted: 18 01 2023

medline: 13 4 2023

pubmed: 22 3 2023

entrez: 21 3 2023

Statut: ppublish

Résumé

Sweat bees have repeatedly gained and lost eusociality, a transition from individual to group reproduction. Here we generate chromosome-length genome assemblies for 17 species and identify genomic signatures of evolutionary trade-offs associated with transitions between social and solitary living. Both young genes and regulatory regions show enrichment for these molecular patterns. We also identify loci that show evidence of complementary signals of positive and relaxed selection linked specifically to the convergent gains and losses of eusociality in sweat bees. This includes two pleiotropic proteins that bind and transport juvenile hormone (JH)-a key regulator of insect development and reproduction. We find that one of these proteins is primarily expressed in subperineurial glial cells that form the insect blood-brain barrier and that brain levels of JH vary by sociality. Our findings are consistent with a role of JH in modulating social behaviour and suggest that eusocial evolution was facilitated by alteration of the proteins that bind and transport JH, revealing how an ancestral developmental hormone may have been co-opted during one of life's major transitions. More broadly, our results highlight how evolutionary trade-offs have structured the molecular basis of eusociality in these bees and demonstrate how both directional selection and release from constraint can shape trait evolution.

Identifiants

DOI: 10.1038/s41559-023-02001-3 PMID: 36941345

pubmed: 36941345

doi: 10.1038/s41559-023-02001-3

pii: 10.1038/s41559-023-02001-3

doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

Pagination

557-569

Subventions

Organisme : NHGRI NIH HHS

ID : UM1 HG009375

Pays : United States

Informations de copyright

Références

Kocher, S. D. & Paxton, R. J. Comparative methods offer powerful insights into social evolution in bees. Apidologie 45, 289–305 (2014).

doi: 10.1007/s13592-014-0268-3

Schwarz, M. P., Richards, M. H. & Danforth, B. N. Changing paradigms in insect social evolution: insights from halictine and allodapine bees. Annu. Rev. Entomol. 52, 127–150 (2007).

pubmed: 16866635 doi: 10.1146/annurev.ento.51.110104.150950

Kocher, S. D. et al. The genetic basis of a social polymorphism in halictid bees. Nat. Commun. 9, 4338 (2018).

pubmed: 30337532 pmcid: 6194137 doi: 10.1038/s41467-018-06824-8

Wcislo, W. T. & Danforth, B. N. Secondarily solitary: the evolutionary loss of social behavior. Trends Ecol. Evol. 12, 468–474 (1997).

pubmed: 21238162 doi: 10.1016/S0169-5347(97)01198-1

Gibbs, J., Brady, S. G., Kanda, K. & Danforth, B. N. Phylogeny of halictine bees supports a shared origin of eusociality for Halictus and Lasioglossum (Apoidea: Anthophila: Halictidae). Mol. Phylogenet. Evol. 65, 926–939 (2012).

pubmed: 22982437 doi: 10.1016/j.ympev.2012.08.013

Danforth, B. N., Conway, L. & Ji, S. Phylogeny of eusocial Lasioglossum reveals multiple losses of eusociality within a primitively eusocial clade of bees (Hymenoptera: Halictidae). Syst. Biol. 52, 23–36 (2003).

pubmed: 12554437 doi: 10.1080/10635150390132687

Batra, S. W. T. Nests and social behavior of halictine bees of India (Hymenoptera: Halictidae). Indian J. Entomol. 28, 375–393 (1966).

Michener, C. D. The Social Behavior of the Bees: A Comparative Study (Belknap Press of Harvard University Press, 1974).

Plateaux-Quénu, C. Un nouveau type de société d’Insectes: Halictus marginatus Brullé (Hym., Apoidea). Ann. Biol. 35, 325–455 (1959).

Michener, C. D. Comparative social behavior of bees. Annu. Rev. Entomol. 14, 299–342 (1969).

doi: 10.1146/annurev.en.14.010169.001503

Kapheim, K. M. et al. Draft genome assembly and population genetics of an agricultural pollinator, the solitary alkali bee (Halictidae: Nomia melanderi). G3 9, 625–634 (2019).

pubmed: 30642875 pmcid: 6404593 doi: 10.1534/g3.118.200865

Kapheim, K. M. et al. Genomic signatures of evolutionary transitions from solitary to group living. Science 348, 1139–1143 (2015).

pubmed: 25977371 pmcid: 5471836 doi: 10.1126/science.aaa4788

Kapheim, K. M. et al. Developmental plasticity shapes social traits and selection in a facultatively eusocial bee. Proc. Natl Acad. Sci. USA 117, 13615–13625 (2020).

pubmed: 32471944 pmcid: 7306772 doi: 10.1073/pnas.2000344117

Johnson, B. R. Taxonomically restricted genes are fundamental to biology and evolution. Front. Genet. 9, 407 (2018).

pubmed: 30294344 pmcid: 6158316 doi: 10.3389/fgene.2018.00407

Jasper, W. C. et al. Large-scale coding sequence change underlies the evolution of postdevelopmental novelty in honey bees. Mol. Biol. Evol. 32, 334–346 (2015).

pubmed: 25351750 doi: 10.1093/molbev/msu292

Johnson, B. R. & Linksvayer, T. A. Deconstructing the superorganism: social physiology, groundplans, and sociogenomics. Q. Rev. Biol. 85, 57–79 (2010).

pubmed: 20337260 doi: 10.1086/650290

Feldmeyer, B., Elsner, D. & Foitzik, S. Gene expression patterns associated with caste and reproductive status in ants: worker-specific genes are more derived than queen-specific ones. Mol. Ecol. 23, 151–161 (2014).

pubmed: 24118315 doi: 10.1111/mec.12490

Ferreira, P. G. et al. Transcriptome analyses of primitively eusocial wasps reveal novel insights into the evolution of sociality and the origin of alternative phenotypes. Genome Biol. 14, R20 (2013).

pubmed: 23442883 pmcid: 4053794 doi: 10.1186/gb-2013-14-2-r20

Johnson, B. R. & Tsutsui, N. D. Taxonomically restricted genes are associated with the evolution of sociality in the honey bee. BMC Genomics 12, 164 (2011).

pubmed: 21447185 pmcid: 3072959 doi: 10.1186/1471-2164-12-164

Simola, D. F. et al. Social insect genomes exhibit dramatic evolution in gene composition and regulation while preserving regulatory features linked to sociality. Genome Res. 23, 1235–1247 (2013).

pubmed: 23636946 pmcid: 3730098 doi: 10.1101/gr.155408.113

Warner, M. R., Qiu, L., Holmes, M. J., Mikheyev, A. S. & Linksvayer, T. A. Convergent eusocial evolution is based on a shared reproductive groundplan plus lineage-specific plastic genes. Nat. Commun. 10, 2651 (2019).

pubmed: 31201311 pmcid: 6570765 doi: 10.1038/s41467-019-10546-w

Molodtsova, D., Harpur, B. A., Kent, C. F., Seevananthan, K. & Zayed, A. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors. Front. Genet. 5, 431 (2014).

pubmed: 25566318 pmcid: 4275039 doi: 10.3389/fgene.2014.00431

Søvik, E., Bloch, G. & Ben-Shahar, Y. Function and evolution of microRNAs in eusocial Hymenoptera. Front. Genet. 6, 193 (2015).

pubmed: 26074950 pmcid: 4444961

Engelmann, F. & Mala, J. The interactions between juvenile hormone (JH), lipophorin, vitellogenin, and JH esterases in two cockroach species. Insect Biochem. Mol. Biol. 30, 793–803 (2000).

pubmed: 10876123 doi: 10.1016/S0965-1748(00)00051-5

de Kort, C. A. D. & Koopmanschap, A. B. Molecular characteristics of lipophorin, the juvenile hormone-binding protein in the hemolymph of the Colorado potato beetle. Arch. Insect Biochem. Physiol. 5, 255–269 (1987).

doi: 10.1002/arch.940050405

Sevala, V. L., Bachmann, J. A. S. & Schal, C. Lipophorin: a hemolymph juvenile hormone binding protein in the german cockroach, Blattella germanica. Insect Biochem. Mol. Biol. 27, 663–670 (1997).

doi: 10.1016/S0965-1748(97)00042-8

Martins, J. R., Nunes, F. M. F., Cristino, A. S., Simões, Z. L. P. & Bitondi, M. M. G. The four hexamerin genes in the honey bee: structure, molecular evolution and function deduced from expression patterns in queens, workers and drones. BMC Mol. Biol. 11, 23 (2010).

pubmed: 20346164 pmcid: 2861669 doi: 10.1186/1471-2199-11-23

Ismail, S. M. & Gillott, C. Identification, characterization, and developmental profile of a high molecular weight, juvenile hormone-binding protein in the hemolymph of the migratory grasshopper, Melanoplus sanguinipes. Arch. Insect Biochem. Physiol. 29, 415–430 (1995).

doi: 10.1002/arch.940290408

Daneman, R. & Barres, B. A. The blood-brain barrier–lessons from moody flies. Cell 123, 9–12 (2005).

pubmed: 16213208 doi: 10.1016/j.cell.2005.09.017

Stork, T. et al. Organization and function of the blood-brain barrier in Drosophila. J. Neurosci. 28, 587–597 (2008).

pubmed: 18199760 pmcid: 6670337 doi: 10.1523/JNEUROSCI.4367-07.2008

Nene, V. et al. Genome sequence of Aedes aegypti, a major arbovirus vector. Science 316, 1718–1723 (2007).

pubmed: 17510324 doi: 10.1126/science.1138878

Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

pubmed: 28336562 pmcid: 5635820 doi: 10.1126/science.aal3327

Ashby, R., Forêt, S., Searle, I. & Maleszka, R. MicroRNAs in honey bee caste determination. Sci. Rep. 6, 18794 (2016).

pubmed: 26739502 pmcid: 4704047 doi: 10.1038/srep18794

Kapheim, K. M. et al. Brain microRNAs among social and solitary bees. R. Soc. Open Sci. 7, 200517 (2020).

pubmed: 32874647 pmcid: 7428247 doi: 10.1098/rsos.200517

Collins, D. H. et al. MicroRNAs associated with caste determination and differentiation in a primitively eusocial insect. Sci. Rep. 7, 45674 (2017).

pubmed: 28361900 pmcid: 5374498 doi: 10.1038/srep45674

Behura, S. K. & Whitfield, C. W. Correlated expression patterns of microRNA genes with age-dependent behavioural changes in honeybee. Insect Mol. Biol. 19, 431–439 (2010).

pubmed: 20491979

Liu, F. et al. Next-generation small RNA sequencing for microRNAs profiling in Apis mellifera: comparison between nurses and foragers. Insect Mol. Biol. 21, 297–303 (2012).

pubmed: 22458842 doi: 10.1111/j.1365-2583.2012.01135.x

Greenberg, J. K. et al. Behavioral plasticity in honey bees is associated with differences in brain microRNA transcriptome. Genes Brain Behav. 11, 660–670 (2012).

pubmed: 22409512 doi: 10.1111/j.1601-183X.2012.00782.x

Nunes, F. M. F., Ihle, K. E., Mutti, N. S., Simões, Z. L. P. & Amdam, G. V. The gene vitellogenin affects microRNA regulation in honey bee (Apis mellifera) fat body and brain. J. Exp. Biol. 216, 3724–3732 (2013).

pubmed: 23788711

Bendena, W. G., Hui, J. H. L., Chin-Sang, I. & Tobe, S. S. Neuropeptide and microRNA regulators of juvenile hormone production. Gen. Comp. Endocrinol. 295, 113507 (2020).

pubmed: 32413346 doi: 10.1016/j.ygcen.2020.113507

Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).

pubmed: 26059717 doi: 10.1093/bioinformatics/btv351

Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature 587, 246–251 (2020).

pubmed: 33177663 pmcid: 7673649 doi: 10.1038/s41586-020-2871-y

Chen, S., Krinsky, B. H. & Long, M. New genes as drivers of phenotypic evolution. Nat. Rev. Genet. 14, 645–660 (2013).

pubmed: 23949544 pmcid: 4236023 doi: 10.1038/nrg3521

Sinha, S., Liang, Y. & Siggia, E. Stubb: a program for discovery and analysis of cis-regulatory modules. Nucleic Acids Res. 34, W555–W559 (2006).

pubmed: 16845069 pmcid: 1538799 doi: 10.1093/nar/gkl224

Lowe, C. B. et al. Three periods of regulatory innovation during vertebrate evolution. Science 333, 1019–1024 (2011).

pubmed: 21852499 pmcid: 3511857 doi: 10.1126/science.1202702

Miura, K., Oda, M., Makita, S. & Chinzei, Y. Characterization of the Drosophila Methoprene-tolerant gene product: juvenile hormone binding and ligand-dependent gene regulation. FEBS J. 272, 1169–1178 (2005).

pubmed: 15720391 doi: 10.1111/j.1742-4658.2005.04552.x

Charles, J.-P. et al. Ligand-binding properties of a juvenile hormone receptor, Methoprene-tolerant. Proc. Natl Acad. Sci. USA 108, 21128–21133 (2011).

pubmed: 22167806 pmcid: 3248530 doi: 10.1073/pnas.1116123109

Li, M., Mead, E. A. & Zhu, J. Heterodimer of two bHLH-PAS proteins mediates juvenile hormone-induced gene expression. Proc. Natl Acad. Sci. USA 108, 638–643 (2011).

pubmed: 21187375 doi: 10.1073/pnas.1013914108

Zhang, Z., Xu, J., Sheng, Z., Sui, Y. & Palli, S. R. Steroid receptor co-activator is required for juvenile hormone signal transduction through a bHLH-PAS transcription factor, Methoprene tolerant. J. Biol. Chem. 286, 8437–8447 (2011).

pubmed: 21190938 doi: 10.1074/jbc.M110.191684

Otto, N. et al. The sulfite oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia. Nat. Commun. 9, 3514 (2018).

pubmed: 30158546 pmcid: 6115356 doi: 10.1038/s41467-018-05645-z

Quinn, P. M. J., Moreira, P. I., Ambrósio, A. F. & Alves, C. H. PINK1/PARKIN signalling in neurodegeneration and neuroinflammation. Acta Neuropathol. Commun. 8, 189 (2020).

pubmed: 33168089 pmcid: 7654589 doi: 10.1186/s40478-020-01062-w

Wittwer, B. et al. Solitary bees reduce investment in communication compared with their social relatives. Proc. Natl Acad. Sci. USA 114, 6569–6574 (2017).

pubmed: 28533385 pmcid: 5488929 doi: 10.1073/pnas.1620780114

Lahti, D. C. et al. Relaxed selection in the wild. Trends Ecol. Evol. 24, 487–496 (2009).

pubmed: 19500875 doi: 10.1016/j.tree.2009.03.010

Wertheim, J. O., Murrell, B., Smith, M. D., Kosakovsky Pond, S. L. & Scheffler, K. RELAX: detecting relaxed selection in a phylogenetic framework. Mol. Biol. Evol. 32, 820–832 (2015).

pubmed: 25540451 doi: 10.1093/molbev/msu400

Martin, R. M. & Cardoso, M. C. Chromatin condensation modulates access and binding of nuclear proteins. FASEB J. 24, 1066–1072 (2010).

pubmed: 19897663 pmcid: 2845425 doi: 10.1096/fj.08-128959

Wyatt, C. D. R. et al. Genetic toolkit for sociality predicts castes across the spectrum of social complexity in wasps. Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.407056 (2020).

Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).

pubmed: 26603754 pmcid: 4658477 doi: 10.1038/srep16923

Loiseau, P., Davies, T., Williams, L. S., Mishima, M. & Palacios, I. M. Drosophila PAT1 is required for Kinesin-1 to transport cargo and to maximize its motility. Development 137, 2763–2772 (2010).

pubmed: 20630947 pmcid: 2910386 doi: 10.1242/dev.048108

Hidayat, P. & Goodman, W. G. Juvenile hormone and hemolymph juvenile hormone binding protein titers and their interaction in the hemolymph of fourth stadium Manduca sexta. Insect Biochem. Mol. Biol. 24, 709–715 (1994).

doi: 10.1016/0965-1748(94)90058-2

Hartfelder, K. & Emlen, D. J. in Insect Endocrinology (ed. Gilbert, L. I.) 464–522 (Academic Press, 2012).

Burmester, T. Evolution and function of the insect hexamerins. Eur. J. Entomol. 96, 213–226 (1999).

Smolenaars, M. M. W., Madsen, O., Rodenburg, K. W. & Van der Horst, D. J. Molecular diversity and evolution of the large lipid transfer protein superfamily. J. Lipid Res. 48, 489–502 (2007).

pubmed: 17148551 doi: 10.1194/jlr.R600028-JLR200

Telfer, W. H. & Kunkel, J. G. The function and evolution of insect storage hexamers. Annu. Rev. Entomol. 36, 205–228 (1991).

pubmed: 2006868 doi: 10.1146/annurev.en.36.010191.001225

Fan, Y., Schal, C., Vargo, E. L. & Bagnères, A.-G. Characterization of termite lipophorin and its involvement in hydrocarbon transport. J. Insect Physiol. 50, 609–620 (2004).

pubmed: 15234621 doi: 10.1016/j.jinsphys.2004.04.007

Gu, X., Quilici, D., Juarez, P., Blomquist, G. J. & Schal, C. Biosynthesis of hydrocarbons and contact sex pheromone and their transport by lipophorin in females of the German cockroach (Blattella germanica). J. Insect Physiol. 41, 257–267 (1995).

doi: 10.1016/0022-1910(94)00100-U

Fan, Y., Chase, J., Sevala, V. L. & Schal, C. Lipophorin-facilitated hydrocarbon uptake by oocytes in the German cockroach Blattella germanica (L.). J. Exp. Biol. 205, 781–790 (2002).

pubmed: 11914386 doi: 10.1242/jeb.205.6.781

Schal, C. et al. Tissue distribution and lipophorin transport of hydrocarbons and sex pheromones in the house fly, Musca domestica. J. Insect Sci. 1, 12 (2001).

pubmed: 15455072 pmcid: 355896 doi: 10.1673/031.001.1201

Zhou, X., Oi, F. M. & Scharf, M. E. Social exploitation of hexamerin: RNAi reveals a major caste-regulatory factor in termites. Proc. Natl Acad. Sci. USA 103, 4499–4504 (2006).

pubmed: 16537425 pmcid: 1450200 doi: 10.1073/pnas.0508866103

Scharf, M. E., Buckspan, C. E., Grzymala, T. L. & Zhou, X. Regulation of polyphenic caste differentiation in the termite Reticulitermes flavipes by interaction of intrinsic and extrinsic factors. J. Exp. Biol. 210, 4390–4398 (2007).

pubmed: 18232118 doi: 10.1242/jeb.010876

Hunt, J. H. et al. A diapause pathway underlies the gyne phenotype in Polistes wasps, revealing an evolutionary route to caste-containing insect societies. Proc. Natl Acad. Sci. USA 104, 14020–14025 (2007).

pubmed: 17704258 pmcid: 1955821 doi: 10.1073/pnas.0705660104

Hawkings, C., Calkins, T. L., Pietrantonio, P. V. & Tamborindeguy, C. Caste-based differential transcriptional expression of hexamerins in response to a juvenile hormone analog in the red imported fire ant (Solenopsis invicta). PLoS ONE 14, e0216800 (2019).

pubmed: 31107891 pmcid: 6527210 doi: 10.1371/journal.pone.0216800

Hunt, J. H. et al. Differential gene expression and protein abundance evince ontogenetic bias toward castes in a primitively eusocial wasp. PLoS ONE 5, e10674 (2010).

pubmed: 20498859 pmcid: 2871793 doi: 10.1371/journal.pone.0010674

Zhou, X., Tarver, M. R. & Scharf, M. E. Hexamerin-based regulation of juvenile hormone-dependent gene expression underlies phenotypic plasticity in a social insect. Development 134, 601–610 (2007).

pubmed: 17215309 doi: 10.1242/dev.02755

Murrell, B. et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012).

pubmed: 22807683 pmcid: 3395634 doi: 10.1371/journal.pgen.1002764

Hartfelder, K. Insect juvenile hormone: from “status quo” to high society. Braz. J. Med. Biol. Res. 33, 157–177 (2000).

pubmed: 10657056 doi: 10.1590/S0100-879X2000000200003

Smith, A. R., Kapheim, K. M., Pérez-Ortega, B., Brent, C. S. & Wcislo, W. T. Juvenile hormone levels reflect social opportunities in the facultatively eusocial sweat bee Megalopta genalis (Hymenoptera: Halictidae). Horm. Behav. 63, 1–4 (2013).

pubmed: 22986338 doi: 10.1016/j.yhbeh.2012.08.012

Tibbetts, E. A. & Izzo, A. S. Endocrine mediated phenotypic plasticity: condition-dependent effects of juvenile hormone on dominance and fertility of wasp queens. Horm. Behav. 56, 527–531 (2009).

pubmed: 19751736 doi: 10.1016/j.yhbeh.2009.09.003

Frederik Nijhout, H. Insect Hormones (Princeton Univ. Press, 1998).

Bloch, G., Wheeler, D. E. & Robinson, G. E. in Hormones, Brain and Behavior (eds Pfaff, D. W. et al.) 195–235 (Academic Press, 2002).

West-Eberhard, M. J. in Natural History and Evolution of Paper Wasps (eds Mary Jane West-Eberhard, M. J. & Turillazzi, S.) 290–317 (Oxford Univ. Press, 1996).

Brankatschk, M. & Eaton, S. Lipoprotein particles cross the blood-brain barrier in Drosophila. J. Neurosci. 30, 10441–10447 (2010).

pubmed: 20685986 pmcid: 6634680 doi: 10.1523/JNEUROSCI.5943-09.2010

Pandey, A. & Bloch, G. Juvenile hormone and ecdysteroids as major regulators of brain and behavior in bees. Curr. Opin. Insect Sci. 12, 26–37 (2015).

doi: 10.1016/j.cois.2015.09.006

Glastad, K. M. et al. Epigenetic regulator CoREST controls social behavior in ants. Mol. Cell 77, 338–351.e6 (2020).

pubmed: 31732456 doi: 10.1016/j.molcel.2019.10.012

Rubin, B. E. R., Jones, B. M., Hunt, B. G. & Kocher, S. D. Rate variation in conserved noncoding DNA reveals regulatory pathways associated with social evolution. Phil. Trans. R. Soc. B 374, 20180247 (2019).

pubmed: 31154980 pmcid: 6560270 doi: 10.1098/rstb.2018.0247

Tong, C., Avilés, L., Rayor, L.S., Mikheyev, A. S. & Linksvayer, T. A. Genomic signatures of recent convergent transitions to social life in spiders. Nat. Commun. 13, 6967 (2022).

pubmed: 36414623 pmcid: 9681848 doi: 10.1038/s41467-022-34446-8

Nijhout, H. F. & Wheeler, D. E. Juvenile hormone and the physiological basis of insect polymorphisms. Q. Rev. Biol. 57, 109–133 (1982).

doi: 10.1086/412671

Nijhout, H. F. & Reed, M. C. A mathematical model for the regulation of juvenile hormone titers. J. Insect Physiol. 54, 255–264 (2008).

pubmed: 18022634 doi: 10.1016/j.jinsphys.2007.09.008

West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).

doi: 10.1146/annurev.es.20.110189.001341

Ju, L. et al. Hormonal gatekeeping via the blood brain barrier governs behavior. Preprint at bioRxiv https://doi.org/10.1101/2022.12.01.518733 (2022).

Corbitt, T. S. & Hardie, J. Juvenile hormone effects on polymorphism in the pea aphid, Acyrthosiphon pisum. Entomol. Exp. Appl. 38, 131–135 (1985).

doi: 10.1111/j.1570-7458.1985.tb03509.x

Zera, A. J. & Denno, R. F. Physiology and ecology of dispersal polymorphism in insects. Annu. Rev. Entomol. 42, 207–230 (1997).

pubmed: 15012313 doi: 10.1146/annurev.ento.42.1.207

Bell, W. J. Factors controlling initiation of vitellogenesis in a primitively social bee, Lasioglossum zephyrum (Hymenoptera: Halictidae). Insectes Soc. 20, 253–260 (1973).

doi: 10.1007/BF02223194

Shpigler, H. Y. et al. Juvenile hormone regulates brain-reproduction tradeoff in bumble bees but not in honey bees. Horm. Behav. 126, 104844 (2020).

pubmed: 32860832 doi: 10.1016/j.yhbeh.2020.104844

Pandey, A., Motro, U. & Bloch, G. Juvenile hormone interacts with multiple factors to modulate aggression and dominance in groups of orphan bumble bee (Bombus terrestris) workers. Horm. Behav. 117, 104602 (2020).

pubmed: 31647921 doi: 10.1016/j.yhbeh.2019.104602

Amdam, G. V., Csondes, A., Fondrk, M. K. & Page, R. E. Jr. Complex social behaviour derived from maternal reproductive traits. Nature 439, 76–78 (2006).

pubmed: 16397498 pmcid: 2665028 doi: 10.1038/nature04340

Amdam, G. V., Norberg, K., Fondrk, M. K. & Page, R. E. Jr. 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).

pubmed: 15277665 pmcid: 509206 doi: 10.1073/pnas.0403073101

Page, R. E., Scheiner, R., Erber, J. & Amdam, G. V. The development and evolution of division of labor and foraging specialization in a social insect (Apis mellifera L.). Curr. Top. Dev. Biol. 74, 253–286 (2006).

Turillazzi, S. & West-Eberhard, M. J. Natural History and Evolution of Paper Wasps (Oxford Univ. Press, 1996).

West-Eberhard, M. J. Flexible strategy and social evolution. In Animal Societies. Theories and Facts (eds Ito, Y., Brown, J. L. and Kikkawa, J.) 35–51 (Japan Scientific Societies Press, Ltd., Tokyo, 1987).

West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford Univ. Press, 2003).

Toth, A. L. & Robinson, G. E. Evo-devo and the evolution of social behavior. Trends Genet. 23, 334–341 (2007).

pubmed: 17509723 doi: 10.1016/j.tig.2007.05.001

Toth, A. L. et al. Brain transcriptomic analysis in paper wasps identifies genes associated with behaviour across social insect lineages. Proc. Biol. Sci. 277, 2139–2148 (2010).

pubmed: 20236980 pmcid: 2880150

Berens, A. J., Hunt, J. H. & Toth, A. L. Comparative transcriptomics of convergent evolution: different genes but conserved pathways underlie caste phenotypes across lineages of eusocial insects. Mol. Biol. Evol. 32, 690–703 (2015).

pubmed: 25492498 doi: 10.1093/molbev/msu330

Rittschof, C. C. & Robinson, G. E. Behavioral genetic toolkits: toward the evolutionary origins of complex phenotypes. Curr. Top. Dev. Biol. 119, 157–204 (2016).

pubmed: 27282026 doi: 10.1016/bs.ctdb.2016.04.001

Qiu, B. et al. Towards reconstructing the ancestral brain gene-network regulating caste differentiation in ants. Nat. Ecol. Evol. 2, 1782–1791 (2018).

pubmed: 30349091 pmcid: 6217981 doi: 10.1038/s41559-018-0689-x

Feldmeyer, B. et al. Evidence for a conserved queen-worker genetic toolkit across slave-making ants and their ant hosts. Mol. Ecol. 31, 4991–5004 (2022).

pubmed: 35920076 doi: 10.1111/mec.16639

Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).

pubmed: 28381613 pmcid: 5411770 doi: 10.1101/gr.214874.116

Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).

pubmed: 29220515 doi: 10.1093/molbev/msx319

Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).

pubmed: 31020564 doi: 10.1007/978-1-4939-9173-0_14

Jackman, S. D. et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 27, 768–777 (2017).

pubmed: 28232478 pmcid: 5411771 doi: 10.1101/gr.214346.116

Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).

pubmed: 24185095 pmcid: 4117202 doi: 10.1038/nbt.2727

Dudchenko, O. et al. The Juicebox Assembly Tools module facilitates de novo assembly of mammalian genomes with chromosome-length scaffolds for under $1000. Preprint at https://www.biorxiv.org/content/10.1101/254797v1 (2018).

Hoff, K. J., Lange, S., Lomsadze, A., Borodovsky, M. & Stanke, M. BRAKER1: unsupervised RNA-seq-based genome annotation with GeneMark-ET and AUGUSTUS. Bioinformatics 32, 767–769 (2016).

pubmed: 26559507 doi: 10.1093/bioinformatics/btv661

Hoff, K. J., Lomsadze, A., Borodovsky, M. & Stanke, M. in Gene Prediction: Methods and Protocols (ed. Kollmar, M.) 65–95 (Springer, 2019).

Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

pubmed: 31375807 pmcid: 7605509 doi: 10.1038/s41587-019-0201-4

Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics 12, 491 (2011).

pubmed: 22192575 pmcid: 3280279 doi: 10.1186/1471-2105-12-491

Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008).

pubmed: 18025269 pmcid: 2134774 doi: 10.1101/gr.6743907

Haas, B. J. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 31, 5654–5666 (2003).

pubmed: 14500829 pmcid: 206470 doi: 10.1093/nar/gkg770

Haas, B. J., Zeng, Q., Pearson, M. D., Cuomo, C. A. & Wortman, J. R. Approaches to fungal genome annotation. Mycology 2, 118–141 (2011).

pubmed: 22059117

Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

pubmed: 23845962 doi: 10.1038/nprot.2013.084

Zdobnov, E. M. et al. OrthoDB v9.1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs. Nucleic Acids Res. 45, D744–D749 (2017).

pubmed: 27899580 doi: 10.1093/nar/gkw1119

Emms, D. M. & Kelly, S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 16, 157 (2015).

pubmed: 26243257 pmcid: 4531804 doi: 10.1186/s13059-015-0721-2

Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

pubmed: 31727128 pmcid: 6857279 doi: 10.1186/s13059-019-1832-y

Bryant, D. M. et al. A tissue-mapped axolotl de novo transcriptome enables identification of limb regeneration factors. Cell Rep. 18, 762–776 (2017).

pubmed: 28099853 pmcid: 5419050 doi: 10.1016/j.celrep.2016.12.063

Klopfenstein, D. V. et al. GOATOOLS: a Python library for gene ontology analyses. Sci. Rep. 8, 10872 (2018).

pubmed: 30022098 pmcid: 6052049 doi: 10.1038/s41598-018-28948-z

Wang, K. & Nishida, H. REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies. BMC Bioinformatics 16, 114 (2015).

pubmed: 25880930 pmcid: 4411712 doi: 10.1186/s12859-015-0552-x

Domazet-Lošo, T., Brajković, J. & Tautz, D. A phylostratigraphy approach to uncover the genomic history of major adaptations in metazoan lineages. Trends Genet. 23, 533–539 (2007).

pubmed: 18029048 doi: 10.1016/j.tig.2007.08.014

Drost, H.-G., Gabel, A., Grosse, I. & Quint, M. Evidence for active maintenance of phylotranscriptomic hourglass patterns in animal and plant embryogenesis. Mol. Biol. Evol. 32, 1221–1231 (2015).

pubmed: 25631928 pmcid: 4408408 doi: 10.1093/molbev/msv012

Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767 (2014).

pubmed: 25378627 doi: 10.1126/science.1257570

Branstetter, M. G. et al. Phylogenomic insights into the evolution of stinging wasps and the origins of ants and bees. Curr. Biol. 27, 1019–1025 (2017).

pubmed: 28376325 doi: 10.1016/j.cub.2017.03.027

Dohrmann, M. & Wörheide, G. Dating early animal evolution using phylogenomic data. Sci. Rep. 7, 3599 (2017).

pubmed: 28620233 pmcid: 5472626 doi: 10.1038/s41598-017-03791-w

Bradley, R. K. et al. Fast statistical alignment. PLoS Comput. Biol. 5, e1000392 (2009).

pubmed: 19478997 pmcid: 2684580 doi: 10.1371/journal.pcbi.1000392

Sackton, T. B. et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science 364, 74–78 (2019).

pubmed: 30948549 doi: 10.1126/science.aat7244

Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).

pubmed: 25697341 pmcid: 4408413 doi: 10.1093/molbev/msv022

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

pubmed: 28263959 pmcid: 5600148 doi: 10.1038/nmeth.4197

Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

pubmed: 15388519 doi: 10.1093/bioinformatics/bti042

Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30, 2216–2218 (2014).

pubmed: 24728855 doi: 10.1093/bioinformatics/btu181

Friedländer, M. R., Mackowiak, S. D., Li, N., Chen, W. & Rajewsky, N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 40, 37–52 (2012).

pubmed: 21911355 doi: 10.1093/nar/gkr688

Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

pubmed: 24275495 doi: 10.1093/nar/gkt1181

Enright, A. J. et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1 (2003).

pubmed: 14709173 pmcid: 395733 doi: 10.1186/gb-2003-5-1-r1

Krüger, J. & Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 34, W451–W454 (2006).

pubmed: 16845047 pmcid: 1538877 doi: 10.1093/nar/gkl243

Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

pubmed: 34062119 pmcid: 8238499 doi: 10.1016/j.cell.2021.04.048

Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

pubmed: 31870423 pmcid: 6927181 doi: 10.1186/s13059-019-1874-1

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).

pubmed: 26653891 pmcid: 4676162 doi: 10.1186/s13059-015-0844-5

Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011).

pubmed: 21665927 pmcid: 3166836 doi: 10.1101/gr.123356.111

Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011).

pubmed: 21278375 doi: 10.1093/bib/bbq072

Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

pubmed: 16024819 pmcid: 1182216 doi: 10.1101/gr.3715005

Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

pubmed: 19505945 pmcid: 2712344 doi: 10.1093/bioinformatics/btp348

Yang, Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13, 555–556 (1997).

pubmed: 9367129

Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

pubmed: 17483113 doi: 10.1093/molbev/msm088

Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).

pubmed: 16928733 doi: 10.1093/bioinformatics/btl446

Wang, L. et al. Peak annotation and verification engine (PAVE) for untargeted LC-MS metabolomics. Anal. Chem. 91, 1838–1846 (2019).

pubmed: 30586294 pmcid: 6501219 doi: 10.1021/acs.analchem.8b03132

Pahlke, S., Seid, M. A., Jaumann, S. & Smith, A. The loss of sociality is accompanied by reduced neural investment in mushroom body volume in the sweat bee Augochlora pura (Hymenoptera: Halictidae). Ann. Entomol. Soc. Am. 114, 637–642 (2021).

pubmed: 34512860 doi: 10.1093/aesa/saaa019

Le Guilloux, V., Schmidtke, P. & Tuffery, P. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10, 168 (2009).

pubmed: 19486540 pmcid: 2700099 doi: 10.1186/1471-2105-10-168

Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

pubmed: 25950237 pmcid: 5298202 doi: 10.1038/nprot.2015.053

Kelley, L. A. & Sternberg, M. J. E. Protein structure prediction on the Web: a case study using the Phyre server. Nat. Protoc. 4, 363–371 (2009).

pubmed: 19247286 doi: 10.1038/nprot.2009.2

Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004).

pubmed: 15318951 pmcid: 517706 doi: 10.1186/1471-2105-5-113