Honey bees are social insects that live in large colonies containing tens of thousands of individuals. The vast majority of bees are sterile females known as worker bees. They perform most of the activities essential for the survival of the colony, including foraging for pollen and nectar and taking care of eggs and larvae.

An individual known as the queen bee is the mother of the colony and is normally the only female who reproduces. She has two massive ovaries and can produce up to two thousand eggs per day. Previous studies indicate that the number and size of the eggs vary according to the conditions inside the colony and in the surrounding environment. Larger eggs contain more nutrients so the resulting embryos may have a better chance of survival. However, producing bigger eggs requires the queen to invest more resources, which is costly to the colony as a whole.

It remains unclear which mechanisms regulate the size of honey bee eggs. To address this question, Han, Wei, Amiri et al. carried out a series of experiments on the Western honey bee, Apis mellifera. The experiments showed that queen bees in small colonies had smaller ovaries and produced bigger eggs than those in large colonies. The difference in egg size appeared to be due to the queen bee’s perception of the size of the colony, rather than its actual size.

An approach called proteomics revealed that 290 ovarian proteins were produced at different levels in big-egg producing ovaries compared to small-egg producing ovaries. Further experiments suggested that a protein known as Rho1 regulates the size of the eggs the queen bees produce.

These findings provide an explanation for how the social environment of the Western honey bee colony may influence the queen bee’s reproductive investment at the molecular level. Further studies to confirm and expand on this work may help to improve honey bee health and also contribute to our general understanding of this life stage in bees and other insects.

Life history trade-offs are a pervasive attribute of life, and how organisms balance growth, reproduction, and survival governs much of their evolution (Stearns, 1989; Flatt, 2020; Bonsall et al., 2004). Simultaneous optimization of all traits is impossible as all individuals are limited by time, resources, or other factors (van Noordwijk and de Jong, 1986). Optimization of offspring provisioning has resulted in a wide variety of reproductive strategies that are characterized by species-specific trade-offs between offspring size and number. Numerous studies have analyzed a possible trade-off between offspring number and size across and within many different species (Smith and Fretwell, 1974; Dani and Kodandaramaiah, 2017; Fox and Czesak, 2000; Berrigan, 1991; Church et al., 2021), while social insects have received little attention despite the exceptional reproductive specialization of their female castes (Church et al., 2021). The topic has gained additional interest due to its implications for inter-generational effects that can profoundly affect organismal phenotypes and evolutionary dynamics (Rasanen and Kruuk, 2007; Sánchez‐Tójar et al., 2020; Bebbington and Groothuis, 2021). Environmental conditions typically lead to plastic responses, most often in the form of variation in offspring number. However, propagule size evolves according to life history and environmental selection (Church et al., 2019) and can also be flexibly adjusted, particularly in plants and insects (Dani and Kodandaramaiah, 2017) and in some bird species (Christians, 2002). While the fitness consequences of egg-size variation have been studied extensively (Fox and Czesak, 2000; Azevedo et al., 1997), little is known about the proximate regulation of egg size, which is equally important for our understanding of this fundamental, complex life history trait (Flatt, 2020; Jha et al., 2015).

Social evolution changes selection pressures and adaptive evolution due to kin selection (Abbot et al., 2011), particularly in eusocial insects with colonies that form a distinct level of selection (Boomsma and Gawne, 2018). In these species, many individuals contribute to a homeostatically regulated colony environment (Oster and Wilson, 1978), pronounced phenotypic plasticity results in individuals specialized for particular functions (Flatt et al., 2013), and resource transfers among kin influence reproductive value (Lee, 2003). Thus, life history evolution in eusocial insects differs fundamentally from that of other species (Shik et al., 2012; Negroni et al., 2016) and has generated some extraordinary trait combinations that defy traditional life history trade-offs (Keller and Genoud, 1997). Specifically, the reproductively specialized queen caste is typically well-provisioned and cared for by nonreproductive workers, which also perform all of the intensive brood care that is characteristic of eusocial insects. As such, social insect queens may not be resource-limited despite their very high reproductive effort (Schrempf et al., 2017). Nevertheless, honey bee (Apis mellifera) queens display plasticity in egg size that is consistent with patterns in solitary species and corresponds to an adaptive investment hypothesis Hall et al., 2020; egg size is relatively small under favorable conditions, such as food abundance and a large colony (>6000 workers), and is relatively large when food availability or colony size declines (Amiri et al., 2020). As in other species (Fox and Czesak, 2000), larger eggs have been associated with a survival advantage for the resulting honey bee worker offspring (Amiri et al., 2020). One honey bee queen typically serves as the sole reproductive in her colony regardless of colony size. The queen is fed and cared for by her workers, although it is unknown how much food she receives and how queen care changes with colony size. Only one, native queen is typically found in a honey bee colony, but queens can be experimentally transferred between colonies, although some may be rejected and killed during this process. Queen condition also affects egg size (Al-Lawati and Bienefeld, 2009), and egg size differs between worker- and queen-destined eggs (Wei et al., 2019) with important consequences for caste determination (Al-Kahtani and Bienefeld, 2021) and queen reproductive potential (Yu et al., 2022). However, none of these phenomena has been explored further to understand the underlying mechanisms that govern variation in egg size.

Here, we report our findings of an in-depth investigation of how egg-size plasticity in honey bee queens is regulated and contribute knowledge of the molecular regulation of insect egg size in general, which has been difficult to determine because hundreds of genes may be involved (Jha et al., 2015). Our results demonstrate that the previously identified effects of colony size on queen egg size (Amiri et al., 2020) are reversible and not fixed. We further establish that egg size is actively regulated and not simply a passive consequence of egg-laying rate, although egg size and egg-laying rate can be negatively correlated. This correlation may explain why smaller ovaries can produce larger eggs. We further show that the social cue triggering changes in egg size within the queen does not require physical contact. Proteome comparisons between queen ovaries producing small versus large eggs indicate a central role of protein localization and cytoskeleton organization. We additionally demonstrate that the knockdown of the central cytoskeletal regulator Rho1 significantly decreases egg size. Our data thus suggest that social cues can be translated into specific molecular processes to control plastic reproductive provisioning, which presumably evolved as an adaptation to the colonial life cycle of honey bees.

The egg is the major physical connection between generations and thus central to inter-generational epigenetic effects that have major implications for offspring phenotypes (Krist, 2011; Tetreau et al., 2019) and life history evolution (Church et al., 2021; Rasanen and Kruuk, 2007; Church et al., 2019; Plaistow et al., 2006). Despite its importance and its considerable inter- and intra-specific variability, the egg remains a poorly studied life history stage. Here, we provide evidence that egg size – a quantitative measure of maternal provisioning – is actively adjusted by honey bee queens in response to social cues that relate to colony size. We also find that queens in smaller colonies have smaller ovaries, presumably because they produce fewer but larger eggs. We find that protein localization, cytoskeleton organization, and energy generation are key proteomic changes in the ovary that mediate the production of large eggs. Finally, we identify the cytoskeleton organizer Rho1 as a potentially important regulator of the active egg-size adjustment of honey bee queens.

Egg-size variation has been linked to parental or environmental conditions in numerous species (Dani and Kodandaramaiah, 2017; Fox and Czesak, 2000; Sánchez‐Tójar et al., 2020), and we have previously provided evidence that honey bee queens also predictably adjust the size of produced eggs (Amiri et al., 2020). The direction of egg-size adjustments is consistent between solitary species and honey bees; egg size is typically increased under unpredictable or unfavorable conditions (Einum and Fleming, 2004; Rollinson and Hutchings, 2013) and positively correlated to maternal condition (Fox and Czesak, 2000; Yu et al., 2022). The first pattern holds in honey bees because egg size is decreased by the queen upon perception of a large colony. Larger eggs improve survival and offspring quality in honey bees (Amiri et al., 2020; Yu et al., 2022). Small colonies may select for increased individual survival because each individual is proportionally more significant to the colony (Rueppell et al., 2009), but large egg size in small colonies could also be directly related to less consistent brood care and overall colony-level resource availability. Conversely, smaller eggs in large colonies may be beneficial because invariably high levels of brood care do not require well-provisioned eggs. Although we do not know how feeding rates of queens change with colony size, maternal condition may also influence egg size in honey bees. For example, older queens produce smaller eggs than young queens (Al-Lawati and Bienefeld, 2009). However, our finding that queens in small colonies with reduced ovary size produce larger eggs suggests that an inverse relationship between individual condition and egg size can also exist in honey bees. Such a negative relationship between maternal condition and egg size may also exist in other social insects where colony-level instead of individual-level resource availability influences maternal resources and brood care performed by the workers (Amdam and Omholt, 2002).

The conventional trade-off between egg size and number (Smith and Fretwell, 1974; Dani and Kodandaramaiah, 2017) may not apply to honey bees because resources can be distributed from other colony members to the queen (Schrempf et al., 2017; Amdam and Omholt, 2002; Rueppell et al., 2016). Accordingly, little evidence for a trade-off between egg size and number was found in a previous study of honey bee queens (Amiri et al., 2020). In contrast, a negative relation between egg size and number was found here, at least in the majority of comparisons between queens in small and large colonies. Queens in large colonies typically produce more eggs than queens in small colonies. Our finding that queens in large colonies have heavier ovaries indicates a physiological adaptation to satisfy the egg-laying demand in large colonies (Al-Khafaji et al., 2009). However, this result cannot explain why queens in smaller colonies produce larger eggs, why a temporary cessation of egg laying by queens in large colonies does not lead to an increase in egg size, and why queens decrease egg size upon their perception of being in a larger colony. Thus, a simultaneous regulation of egg size and egg number in response to perceived colony size is more likely than a direct trade-off between these two variables. However, further studies of resource flows to queens in colonies of different sizes are necessary to ultimately distinguish regulation from physiological or resource-based trade-offs. Quantitatively restricting egg-laying rates instead of preventing all egg laying is an additional experiment that can elucidate the relation between egg size, egg number, and colony size. The proposed active regulation seems more likely and is also more compatible with short-term egg-size adjustments in other contexts (Wei et al., 2019) and can explain why queens in food-restricted colonies also produce larger eggs than queens with ample food supply (Amiri et al., 2020).

Our study further demonstrates that direct resource availability cannot explain the differences in egg size produced by honey bee queens in small versus large colonies. We find that connecting small colonies to another, larger colony without any physical contact leads to a reduction in egg size that is similar to the effect seen when queens are transferred between these colony types. Thus, our results suggest that the perception of colony size by the queen is sufficient for her to adjust the size of her eggs. We can exclude direct physical contact among individuals, which is used by worker honey bees to assess colony size (Smith et al., 2017), and thus we report a new modality by which honey bees perceive colony size. Multiple cues that travel through a double-screened tunnel could prompt the egg-size adjustment in queens, including sound, temperature, or pheromones and other semiochemicals. Future distinction among these possibilities will permit a subsequent investigation of the mechanisms by which social cues are translated into the physiological adjustments inside the ovary that we document here.

Broad comparisons find pronounced influences of social structure and behavior on egg size because variation in parental care is intricately linked to the initial investment in eggs (Dixit et al., 2017; Summers et al., 2007). Much less is known about social factors that lead to individual egg-size plasticity Maeno et al., 2020, particularly in cases that are as dynamic and reversible as illustrated here. We have not attempted to measure the speed at which queens adjust their egg size, but it could be much faster than the 1–2 weeks provided to queens in our experiments. Adjustments may even be made instantaneously, as queen- and worker-destined eggs differ in size (Wei et al., 2019) even though they are presumably produced at almost the same time. The advantageous caste bias of larger eggs (Al-Kahtani and Bienefeld, 2021) should select for paternal effects to increase egg size, in contrast to other polyandrous species in which males predominantly manipulate female fecundity (Hollis et al., 2019). Egg-size variation due to paternal manipulation remains to be investigated in honey bees in the context of the strong maternal control over egg size that we demonstrate in this study.

Due to the general paucity of a priori information on molecular mechanisms that determine egg size in insects (Jha et al., 2015), we used a naïve, quantitative proteomic comparison to identify the molecular causes of the egg-size plasticity in honey bee queens. The quantity of numerous proteins is associated with the production of either large or small eggs. Plod, which controls egg length in Drosophila (Lerner et al., 2013), is not among these proteins, but collagen IV, which may influence egg size in Drosophila indirectly (Luo et al., 2021), is found more abundantly in large-egg-producing ovaries. The vast majority (almost 95%) of differently abundant proteins are found at higher levels in ovaries that produce large eggs. Thus, the anatomically smaller ovaries are physiologically more active in several key processes than the larger ovaries that produce smaller eggs. The GO enrichment analysis indicates the prominence of two upregulated processes in large-egg-producing ovaries – ‘protein localization’ and ‘cytoskeletal regulation’ – while several energy metabolic processes are highlighted by the KEGG pathway analysis. These functional categories indicate that egg-size variation is not a simple increase of egg volume but reflects real differences in offspring provisioning, although the proteome of small and large eggs remains to be characterized (McDonough-Goldstein et al., 2021). Higher energy generation may be needed to produce more costly large eggs (Wheeler, 1996), and the cytoskeleton and protein localization processes are key to loading the eggs with nutrients in polytrophic ovaries (Shimada et al., 2011; Wilson et al., 2011). Several of the other GO terms, such as ‘multicellular organism development’ and ‘oocyte construction,’ are further plausible candidates to explain some of the observed variation in egg size.

Among all involved processes, we considered ‘cytoskeletal organization’ as the most likely regulatory mechanism, while other processes are more likely involved in downstream effector functions. Thus, we identified Rho1 as a potential candidate because it was centrally located in the PPI network of proteins related to cytoskeletal organization that were abundant in large-egg-producing ovaries. Rho1 is a small, conserved GTPase with a likely role in egg-size regulation (Murphy and Montell, 1996) and thus also a plausible functional candidate. Rho1 has multiple functions but generally plays an important role in cell morphogenesis by regulating the cytoskeleton (Hall, 1998). It primarily has been implicated in actin regulation (Hall, 1998), which is itself important for insect oogenesis (Sokolova et al., 2018) but can also indirectly influence the microtubule network (Pimm and Henty-Ridilla, 2021). The regulation of Rho1 activity is complex (Denk-Lobnig and Martin, 2019), and multiple participating signaling pathways could transduce extracellular signals in the ovary into cytoskeletal reorganization of the eggs.

The observed spatio-temporal expression of Rho1 observed in our RNAscope experiment conforms well with the hypothesis that Rho1 influences egg growth in the vitellarium. Rho1 expression appeared to be focused in the cortex of oocytes close to the follicle cells, which mediate vitellogenin uptake (Fleig, 1995) and can influence egg size (Wu et al., 2020). Vitellogenesis is the period of rapid egg growth, and small differences during this time may cause significant differences in the size of the mature egg. However, the molecular function of Rho1 remains to be elucidated. The role of Rho1 in egg-size determination is further supported by the consistent decrease of egg size when Rho1 is knocked down via siRNA injection. This specific effect occurs robustly in small- and large-egg-producing queens. Resulting eggs only differed in size, suggesting that the knockdown of Rho1 does not cause pathological effects. The practical difficulties of genetic engineering in honey bees prohibited a complementary gain-of-function experiment. The almost perfect correlation between Rho1 expression and egg size of queens across two different colony sizes and RNAi treatment groups strengthens the interpretation that social conditions, inter-individual differences, and RNAi manipulation all act through Rho1 in a comparable manner to influence egg size. The tight correlation between egg size and Rho1 expression was confirmed in a second data set.

Honey bee queens also adjust their egg size depending on whether a worker- or queen-destined egg is laid (Wei et al., 2019), and egg size influences the probability that an egg is raised into a future queen (Al-Kahtani and Bienefeld, 2021) and her reproductive quality (Wei et al., 2019; Yu et al., 2022). Our comparative results of the ovary proteome suggest that larger eggs are indeed of superior quality, but a comparison of the actual egg content remains to be performed to substantiate this argument. Stronger support than what we present here to tie egg-size regulation to Rho1 may be difficult to obtain, but it can be tested whether Rho1 relates to egg-size variation in honey bees in other contexts, such as maternal genotype (Amiri et al., 2020) or age (Al-Lawati and Bienefeld, 2009). If so, Rho1 may become an important indicator of stress (Amiri et al., 2020) and maternal quality (Yu et al., 2022) in the ongoing efforts to sustain honey bee health. Comparative studies in models with a full suite of functional genomic tools, such as Drosophila, may be warranted to test the more general idea of Rho1 as a key egg-size regulator in insects.

The LC-MS/MS data and search results were deposited in ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) under accession number PXD029859. All other data are provided as supplementary files.

1. 1. Han B
   2. Xu S

   (2022) ProteomeXchange

   ID PXD029859. Honeybee queen ovary proteomics.

   http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD029859

1. 1. Abbot P
   2. Abe J
   3. Alcock J
   4. Alizon S
   5. Alpedrinha JAC
   6. Andersson M
   7. Andre JB
   8. van Baalen M
   9. Balloux F
   10. Balshine S
   11. Barton N
   12. Beukeboom LW
   13. Biernaskie JM
   14. Bilde T
   15. Borgia G
   16. Breed M
   17. Brown S
   18. Bshary R
   19. Buckling A
   20. Burley NT
   21. Burton-Chellew MN
   22. Cant MA
   23. Chapuisat M
   24. Charnov EL
   25. Clutton-Brock T
   26. Cockburn A
   27. Cole BJ
   28. Colegrave N
   29. Cosmides L
   30. Couzin ID
   31. Coyne JA
   32. Creel S
   33. Crespi B
   34. Curry RL
   35. Dall SRX
   36. Day T
   37. Dickinson JL
   38. Dugatkin LA
   39. El Mouden C
   40. Emlen ST
   41. Evans J
   42. Ferriere R
   43. Field J
   44. Foitzik S
   45. Foster K
   46. Foster WA
   47. Fox CW
   48. Gadau J
   49. Gandon S
   50. Gardner A
   51. Gardner MG
   52. Getty T
   53. Goodisman MAD
   54. Grafen A
   55. Grosberg R
   56. Grozinger CM
   57. Gouyon PH
   58. Gwynne D
   59. Harvey PH
   60. Hatchwell BJ
   61. Heinze J
   62. Helantera H
   63. Helms KR
   64. Hill K
   65. Jiricny N
   66. Johnstone RA
   67. Kacelnik A
   68. Kiers ET
   69. Kokko H
   70. Komdeur J
   71. Korb J
   72. Kronauer D
   73. Kümmerli R
   74. Lehmann L
   75. Linksvayer TA
   76. Lion S
   77. Lyon B
   78. Marshall JAR
   79. McElreath R
   80. Michalakis Y
   81. Michod RE
   82. Mock D
   83. Monnin T
   84. Montgomerie R
   85. Moore AJ
   86. Mueller UG
   87. Noë R
   88. Okasha S
   89. Pamilo P
   90. Parker GA
   91. Pedersen JS
   92. Pen I
   93. Pfennig D
   94. Queller DC
   95. Rankin DJ
   96. Reece SE
   97. Reeve HK
   98. Reuter M
   99. Roberts G
   100. Robson SKA
   101. Roze D
   102. Rousset F
   103. Rueppell O
   104. Sachs JL
   105. Santorelli L
   106. Schmid-Hempel P
   107. Schwarz MP
   108. Scott-Phillips T
   109. Shellmann-Sherman J
   110. Sherman PW
   111. Shuker DM
   112. Smith J
   113. Spagna JC
   114. Strassmann B
   115. Suarez AV
   116. Sundström L
   117. Taborsky M
   118. Taylor P
   119. Thompson G
   120. Tooby J
   121. Tsutsui ND
   122. Tsuji K
   123. Turillazzi S
   124. Ubeda F
   125. Vargo EL
   126. Voelkl B
   127. Wenseleers T
   128. West SA
   129. West-Eberhard MJ
   130. Westneat DF
   131. Wiernasz DC
   132. Wild G
   133. Wrangham R
   134. Young AJ
   135. Zeh DW
   136. Zeh JA
   137. Zink A

   (2011) Inclusive fitness theory and eusociality

   Nature 471:E1–E4.

   https://doi.org/10.1038/nature09831

   • PubMed
   • Google Scholar
2. 1. Al-Kahtani SN
   2. Bienefeld K

   (2021) Strength surpasses relatedness-queen larva selection in honeybees

   PLOS ONE 16:e0255151.

   https://doi.org/10.1371/journal.pone.0255151

   • PubMed
   • Google Scholar
3. 1. Al-Khafaji K
   2. Tuljapurkar S
   3. Carey JR
   4. Page RE

   (2009) Life in the colonies: hierarchical demography of a social organism

   Ecology 90:556–566.

   https://doi.org/10.1890/08-0402.1

   • Google Scholar
4. 1. Al-Lawati H
   2. Bienefeld K

   (2009) Maternal age effects on embryo mortality and juvenile development of offspring in the honey bee (hymenoptera: apidae)

   Annals of the Entomological Society of America 102:881–888.

   https://doi.org/10.1603/008.102.0514

   • Google Scholar
5. 1. Amdam GV
   2. Omholt SW

   (2002) The regulatory anatomy of honeybee lifespan

   Journal of Theoretical Biology 216:209–228.

   https://doi.org/10.1006/jtbi.2002.2545

   • PubMed
   • Google Scholar
6. 1. Amiri E
   2. Le K
   3. Melendez CV
   4. Strand MK
   5. Tarpy DR
   6. Rueppell O

   (2020) Egg-size plasticity in apis mellifera: honey bee queens alter egg size in response to both genetic and environmental factors

   Journal of Evolutionary Biology 33:534–543.

   https://doi.org/10.1111/jeb.13589

   • PubMed
   • Google Scholar
7. 1. Azevedo RBR
   2. French V
   3. Partridge L

   (1997) life‐history consequences of egg size in Drosophila melanogaster

   The American Naturalist 150:250–282.

   https://doi.org/10.1086/286065

   • PubMed
   • Google Scholar
8. 1. Bebbington K
   2. Groothuis TGG

   (2021) Who listens to mother? A whole-family perspective on the evolution of maternal hormone allocation

   Biological Reviews of the Cambridge Philosophical Society 96:1951–1968.

   https://doi.org/10.1111/brv.12733

   • PubMed
   • Google Scholar
9. 1. Berrigan D

   (1991) The allometry of egg size and number in insects

   Oikos 60:313.

   https://doi.org/10.2307/3545073

   • Google Scholar
10. 1. Bindea G
    2. Mlecnik B
    3. Hackl H
    4. Charoentong P
    5. Tosolini M
    6. Kirilovsky A
    7. Fridman WH
    8. Pagès F
    9. Trajanoski Z
    10. Galon J

    (2009) ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

    Bioinformatics 25:1091–1093.

    https://doi.org/10.1093/bioinformatics/btp101

    • PubMed
    • Google Scholar
11. 1. Bonsall MB
    2. Jansen VAA
    3. Hassell MP

    (2004) Life history trade-offs assemble ecological guilds

    Science 306:111–114.

    https://doi.org/10.1126/science.1100680

    • PubMed
    • Google Scholar
12. 1. Boomsma JJ
    2. Gawne R

    (2018) Superorganismality and caste differentiation as points of NO return: how the major evolutionary transitions were lost in translation

    Biological Reviews of the Cambridge Philosophical Society 93:28–54.

    https://doi.org/10.1111/brv.12330

    • PubMed
    • Google Scholar
13. 1. Chen C
    2. Chen H
    3. Zhang Y
    4. Thomas HR
    5. Frank MH
    6. He Y
    7. Xia R

    (2020) TBtools: an integrative toolkit developed for interactive analyses of big biological data

    Molecular Plant 13:1194–1202.

    https://doi.org/10.1016/j.molp.2020.06.009

    • PubMed
    • Google Scholar
14. 1. Christians JK

    (2002) Avian egg size: variation within species and inflexibility within individuals

    Biological Reviews of the Cambridge Philosophical Society 77:1–26.

    https://doi.org/10.1017/s1464793101005784

    • PubMed
    • Google Scholar
15. 1. Church SH
    2. Donoughe S
    3. de Medeiros BAS
    4. Extavour CG

    (2019) Insect egg size and shape evolve with ecology but not developmental rate

    Nature 571:58–62.

    https://doi.org/10.1038/s41586-019-1302-4

    • Google Scholar
16. 1. Church SH
    2. de Medeiros BAS
    3. Donoughe S
    4. Márquez Reyes NL
    5. Extavour CG

    (2021) Repeated loss of variation in insect ovary morphology highlights the role of development in life-history evolution

    Proceedings. Biological Sciences 288:20210150.

    https://doi.org/10.1098/rspb.2021.0150

    • PubMed
    • Google Scholar
17. 1. Dani KGS
    2. Kodandaramaiah U

    (2017) Plant and animal reproductive strategies: lessons from offspring size and number tradeoffs

    Frontiers in Ecology and Evolution 5:38.

    https://doi.org/10.3389/fevo.2017.00038

    • Google Scholar
18. 1. Denk-Lobnig M
    2. Martin AC

    (2019) Modular regulation of Rho family GTPases in development

    Small GTPases 10:122–129.

    https://doi.org/10.1080/21541248.2017.1294234

    • PubMed
    • Google Scholar
19. 1. Dixit T
    2. English S
    3. Lukas D

    (2017) The relationship between egg size and helper number in cooperative breeders: a meta-analysis across species

    PeerJ 5:e4028.

    https://doi.org/10.7717/peerj.4028

    • PubMed
    • Google Scholar
20. 1. Einum S
    2. Fleming IA

    (2004)

    Environmental unpredictability and offspring size: conservative versus diversified bet-hedging

    Evolutionary Ecology Research 6:443–455.

    • Google Scholar
21. 1. Fang Y
    2. Feng M
    3. Han B
    4. Lu X
    5. Ramadan H
    6. Li J

    (2014) In-Depth proteomics characterization of embryogenesis of the honey bee worker (Apis mellifera ligustica)

    Molecular & Cellular Proteomics 13:2306–2320.

    https://doi.org/10.1074/mcp.M114.037846

    • PubMed
    • Google Scholar
22. 1. Flatt T
    2. Amdam GV
    3. Kirkwood TBL
    4. Omholt SW

    (2013) Life-History evolution and the polyphenic regulation of somatic maintenance and survival

    The Quarterly Review of Biology 88:185–218.

    https://doi.org/10.1086/671484

    • PubMed
    • Google Scholar
23. 1. Flatt T

    (2020) Life-History evolution and the genetics of fitness components in Drosophila melanogaster

    Genetics 214:3–48.

    https://doi.org/10.1534/genetics.119.300160

    • PubMed
    • Google Scholar
24. 1. Fleig R

    (1995) Role of the follicle cells for yolk uptake in ovarian follicles of the honey bee Apis mellifera L. (Hymenoptera: Apidae)

    International Journal of Insect Morphology and Embryology 24:427–433.

    https://doi.org/10.1016/0020-7322(95)98841-Z

    • Google Scholar
25. 1. Fox CW
    2. Czesak ME

    (2000) Evolutionary ecology of progeny size in arthropods

    Annual Review of Entomology 45:341–369.

    https://doi.org/10.1146/annurev.ento.45.1.341

    • PubMed
    • Google Scholar
26. 1. Hall A

    (1998) Rho GTPases and the actin cytoskeleton

    Science 279:509–514.

    https://doi.org/10.1126/science.279.5350.509

    • PubMed
    • Google Scholar
27. 1. Hall JM
    2. Mitchell TS
    3. Thawley CJ
    4. Stroud JT
    5. Warner DA

    (2020) Adaptive seasonal shift towards investment in fewer, larger offspring: evidence from field and laboratory studies

    The Journal of Animal Ecology 89:1242–1253.

    https://doi.org/10.1111/1365-2656.13182

    • PubMed
    • Google Scholar
28. 1. Han B
    2. Wei Q
    3. Wu F
    4. Hu H
    5. Ma C
    6. Meng L
    7. Zhang X
    8. Feng M
    9. Fang Y
    10. Rueppell O
    11. Li J

    (2021) Tachykinin signaling inhibits task-specific behavioral responsiveness in honeybee workers

    eLife 10:e64830.

    https://doi.org/10.7554/eLife.64830

    • PubMed
    • Google Scholar
29. 1. Hollis B
    2. Koppik M
    3. Wensing KU
    4. Ruhmann H
    5. Genzoni E
    6. Erkosar B
    7. Kawecki TJ
    8. Fricke C
    9. Keller L

    (2019) Sexual conflict drives male manipulation of female postmating responses in Drosophila melanogaster

    PNAS 116:8437–8444.

    https://doi.org/10.1073/pnas.1821386116

    • Google Scholar
30. 1. Jha AR
    2. Miles CM
    3. Lippert NR
    4. Brown CD
    5. White KP
    6. Kreitman M

    (2015) Whole-Genome resequencing of experimental populations reveals polygenic basis of egg-size variation in Drosophila melanogaster

    Molecular Biology and Evolution 32:2616–2632.

    https://doi.org/10.1093/molbev/msv136

    • PubMed
    • Google Scholar
31. 1. Keller L
    2. Genoud M

    (1997) Extraordinary lifespans in ants: a test of evolutionary theories of ageing

    Nature 389:958–960.

    https://doi.org/10.1038/40130

    • Google Scholar
32. 1. Kim JG
    2. Islam R
    3. Cho JY
    4. Jeong H
    5. Cap KC
    6. Park Y
    7. Hossain AJ
    8. Park JB

    (2018) Regulation of rhoa gtpase and various transcription factors in the rhoa pathway

    Journal of Cellular Physiology 233:6381–6392.

    https://doi.org/10.1002/jcp.26487

    • PubMed
    • Google Scholar
33. 1. Krist M

    (2011) Egg size and offspring quality: a meta-analysis in birds

    Biological Reviews 86:692–716.

    https://doi.org/10.1111/j.1469-185X.2010.00166.x

    • PubMed
    • Google Scholar
34. Book

    1. Laidlaw HH
    2. Page RE

    (1997)

    Queen Rearing and Bee Breeding

    Wicwas Press.

    • Google Scholar
35. 1. Lee RD

    (2003) Rethinking the evolutionary theory of aging: transfers, not births, shape senescence in social species

    PNAS 100:9637–9642.

    https://doi.org/10.1073/pnas.1530303100

    • PubMed
    • Google Scholar
36. 1. Lerner DW
    2. McCoy D
    3. Isabella AJ
    4. Mahowald AP
    5. Gerlach GF
    6. Chaudhry TA
    7. Horne-Badovinac S

    (2013) A rab10-dependent mechanism for polarized basement membrane secretion during organ morphogenesis

    Developmental Cell 24:159–168.

    https://doi.org/10.1016/j.devcel.2012.12.005

    • PubMed
    • Google Scholar
37. 1. Livak KJ
    2. Schmittgen TD

    (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2 (-delta delta C (T)) method

    Methods 25:402–408.

    https://doi.org/10.1006/meth.2001.1262

    • PubMed
    • Google Scholar
38. 1. Luo W
    2. Liu S
    3. Zhang W
    4. Yang L
    5. Huang J
    6. Zhou S
    7. Feng Q
    8. Palli SR
    9. Wang J
    10. Roth S
    11. Li S

    (2021) Juvenile hormone signaling promotes ovulation and maintains egg shape by inducing expression of extracellular matrix genes

    PNAS 118:e2104461118.

    https://doi.org/10.1073/pnas.2104461118

    • PubMed
    • Google Scholar
39. 1. Maeno KO
    2. Piou C
    3. Ghaout S

    (2020) The desert locust, schistocerca gregaria, plastically manipulates egg size by regulating both egg numbers and production rate according to population density

    Journal of Insect Physiology 122:104020.

    https://doi.org/10.1016/j.jinsphys.2020.104020

    • Google Scholar
40. 1. McDonough-Goldstein CE
    2. Pitnick S
    3. Dorus S

    (2021) Drosophila oocyte proteome composition covaries with female mating status

    Scientific Reports 11:1–12.

    https://doi.org/10.1038/s41598-021-82801-4

    • PubMed
    • Google Scholar
41. 1. Murphy AM
    2. Montell DJ

    (1996) Cell type-specific roles for Cdc42, Rac, and rhol in Drosophila oogenesis

    The Journal of Cell Biology 133:617–630.

    https://doi.org/10.1083/jcb.133.3.617

    • PubMed
    • Google Scholar
42. 1. Negroni MA
    2. Jongepier E
    3. Feldmeyer B
    4. Kramer BH
    5. Foitzik S

    (2016) Life history evolution in social insects: a female perspective

    Current Opinion in Insect Science 16:51–57.

    https://doi.org/10.1016/j.cois.2016.05.008

    • PubMed
    • Google Scholar
43. Book

    1. Oster GF
    2. Wilson EO

    (1978)

    Caste and Ecology in the Social Insects

    Princeton University Press.

    • Google Scholar
44. 1. Pimm ML
    2. Henty-Ridilla JL

    (2021) New twists in actin-microtubule interactions

    Molecular Biology of the Cell 32:211–217.

    https://doi.org/10.1091/mbc.E19-09-0491

    • PubMed
    • Google Scholar
45. 1. Plaistow SJ
    2. Lapsley CT
    3. Benton TG

    (2006) Context-dependent intergenerational effects: the interaction between past and present environments and its effect on population dynamics

    The American Naturalist 167:206–215.

    https://doi.org/10.1086/499380

    • PubMed
    • Google Scholar
46. 1. Rasanen K
    2. Kruuk LEB

    (2007) Maternal effects and evolution at ecological time-scales

    Functional Ecology 21:408–421.

    https://doi.org/10.1111/j.1365-2435.2007.01246.x

    • Google Scholar
47. 1. Rollinson N
    2. Hutchings JA

    (2013) Environmental quality predicts optimal egg size in the wild

    The American Naturalist 182:76–90.

    https://doi.org/10.1086/670648

    • PubMed
    • Google Scholar
48. 1. Rueppell O
    2. Kaftanouglu O
    3. Page RE

    (2009) Honey bee (apis mellifera) workers live longer in small than in large colonies

    Experimental Gerontology 44:447–452.

    https://doi.org/10.1016/j.exger.2009.04.003

    • PubMed
    • Google Scholar
49. 1. Rueppell O
    2. Aumer D
    3. Moritz RF

    (2016) Ties between ageing plasticity and reproductive physiology in honey bees (Apis mellifera) reveal a positive relation between fecundity and longevity as consequence of advanced social evolution

    Current Opinion in Insect Science 16:64–68.

    https://doi.org/10.1016/j.cois.2016.05.009

    • PubMed
    • Google Scholar
50. 1. Sánchez‐Tójar A
    2. Lagisz M
    3. Moran NP
    4. Nakagawa S
    5. Noble DWA
    6. Reinhold K
    7. Munch S

    (2020) The jury is still out regarding the generality of adaptive’‘ransgenerational’’effects

    Ecology Letters 23:1715–1718.

    https://doi.org/10.1111/ele.13479

    • PubMed
    • Google Scholar
51. 1. Schrempf A
    2. Giehr J
    3. Röhrl R
    4. Steigleder S
    5. Heinze J

    (2017) Royal Darwinian demons: enforced changes in reproductive efforts do not affect the life expectancy of ant queens

    The American Naturalist 189:436–442.

    https://doi.org/10.1086/691000

    • PubMed
    • Google Scholar
52. 1. Shik JZ
    2. Hou C
    3. Kay A
    4. Kaspari M
    5. Gillooly JF

    (2012) Towards a general life-history model of the superorganism: predicting the survival, growth and reproduction of ant societies

    Biology Letters 8:1059–1062.

    https://doi.org/10.1098/rsbl.2012.0463

    • PubMed
    • Google Scholar
53. 1. Shimada Y
    2. Burn KM
    3. Niwa R
    4. Cooley L

    (2011) Reversible response of protein localization and microtubule organization to nutrient stress during Drosophila early oogenesis

    Developmental Biology 355:250–262.

    https://doi.org/10.1016/j.ydbio.2011.04.022

    • PubMed
    • Google Scholar
54. 1. Smith CC
    2. Fretwell SD

    (1974) The optimal balance between size and number of offspring

    The American Naturalist 108:499–506.

    https://doi.org/10.1086/282929

    • Google Scholar
55. 1. Smith ML
    2. Koenig PA
    3. Peters JM

    (2017) The cues of colony size: how honey bees sense that their colony is large enough to begin to invest in reproduction

    The Journal of Experimental Biology 220:1597–1605.

    https://doi.org/10.1242/jeb.150342

    • PubMed
    • Google Scholar
56. 1. Sokolova M
    2. Moore HM
    3. Prajapati B
    4. Dopie J
    5. Meriläinen L
    6. Honkanen M
    7. Matos RC
    8. Poukkula M
    9. Hietakangas V
    10. Vartiainen MK

    (2018) Nuclear actin is required for transcription during Drosophila oogenesis

    IScience 9:63–70.

    https://doi.org/10.1016/j.isci.2018.10.010

    • PubMed
    • Google Scholar
57. 1. Stearns SC

    (1989) Trade-offs in life-history evolution

    Functional Ecology 3:259.

    https://doi.org/10.2307/2389364

    • Google Scholar
58. 1. Summers K
    2. McKeon CS
    3. Heying H
    4. Hall J
    5. Patrick W

    (2007) Social and environmental influences on egg size evolution in frogs

    Journal of Zoology 271:225–232.

    https://doi.org/10.1111/j.1469-7998.2006.00213.x

    • Google Scholar
59. 1. Szklarczyk D
    2. Franceschini A
    3. Wyder S
    4. Forslund K
    5. Heller D
    6. Huerta-Cepas J
    7. Simonovic M
    8. Roth A
    9. Santos A
    10. Tsafou KP
    11. Kuhn M
    12. Bork P
    13. Jensen LJ
    14. von Mering C

    (2015) String v10: protein-protein interaction networks, integrated over the tree of life

    Nucleic Acids Research 43:D447–D452.

    https://doi.org/10.1093/nar/gku1003

    • PubMed
    • Google Scholar
60. 1. Tetreau G
    2. Dhinaut J
    3. Gourbal B
    4. Moret Y

    (2019) Trans-generational immune priming in invertebrates: current knowledge and future prospects

    Frontiers in Immunology 10:1938.

    https://doi.org/10.3389/fimmu.2019.01938

    • Google Scholar
61. 1. van Noordwijk AJ
    2. de Jong G

    (1986) Acquisition and allocation of resources: their influence on variation in life history tactics

    The American Naturalist 128:137–142.

    https://doi.org/10.1086/284547

    • Google Scholar
62. 1. Vandesompele J
    2. De Preter K
    3. Pattyn F
    4. Poppe B
    5. Van Roy N
    6. De Paepe A
    7. Speleman F

    (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

    Genome Biology 3:1–12.

    https://doi.org/10.1186/gb-2002-3-7-research0034

    • PubMed
    • Google Scholar
63. 1. Waddington KD

    (1989) Implications of variation in worker body size for the honey bee recruitment system

    Journal of Insect Behavior 2:91–103.

    https://doi.org/10.1007/BF01053620

    • Google Scholar
64. 1. Wagner CR
    2. Mahowald AP
    3. Miller KG

    (2002) One of the two cytoplasmic actin isoforms in Drosophila is essential

    PNAS 99:8037–8042.

    https://doi.org/10.1073/pnas.082235499

    • PubMed
    • Google Scholar
65. 1. Wang F
    2. Flanagan J
    3. Su N
    4. Wang LC
    5. Bui S
    6. Nielson A
    7. Wu X
    8. Vo HT
    9. Ma XJ
    10. Luo Y

    (2012) RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues

    The Journal of Molecular Diagnostics 14:22–29.

    https://doi.org/10.1016/j.jmoldx.2011.08.002

    • PubMed
    • Google Scholar
66. 1. Wei H
    2. He XJ
    3. Liao CH
    4. Wu XB
    5. Jiang WJ
    6. Zhang B
    7. Zhou LB
    8. Zhang LZ
    9. Barron AB
    10. Zeng ZJ

    (2019) A maternal effect on queen production in honeybees

    Current Biology 29:2208–2213.

    https://doi.org/10.1016/j.cub.2019.05.059

    • PubMed
    • Google Scholar
67. 1. Wheeler D

    (1996) The role of nourishment in oogenesis

    Annual Review of Entomology 41:407–431.

    https://doi.org/10.1146/annurev.en.41.010196.002203

    • PubMed
    • Google Scholar
68. 1. Wilson MJ
    2. Abbott H
    3. Dearden PK

    (2011) The evolution of oocyte patterning in insects: multiple cell-signaling pathways are active during honeybee oogenesis and are likely to play a role in axis patterning

    Evolution & Development 13:127–137.

    https://doi.org/10.1111/j.1525-142X.2011.00463.x

    • PubMed
    • Google Scholar
69. 1. Wu Z
    2. Yang L
    3. He Q
    4. Zhou S

    (2020) Regulatory mechanisms of vitellogenesis in insects

    Frontiers in Cell and Developmental Biology 8:593613.

    https://doi.org/10.3389/fcell.2020.593613

    • PubMed
    • Google Scholar
70. 1. Xie C
    2. Mao X
    3. Huang J
    4. Ding Y
    5. Wu J
    6. Dong S
    7. Kong L
    8. Gao G
    9. Li CY
    10. Wei L

    (2011) KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

    Nucleic Acids Research 39:W316–W322.

    https://doi.org/10.1093/nar/gkr483

    • PubMed
    • Google Scholar
71. 1. Yu L
    2. Shi X
    3. He X
    4. Zeng Z
    5. Yan W
    6. Wu X

    (2022) High-quality queens produce high-quality offspring queens

    Insects 13:486.

    https://doi.org/10.3390/insects13050486

    • PubMed
    • Google Scholar

Author details

1. Bin Han

   1. Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China
   2. Department of Biology, University of North Carolina Greensboro, Greensboro, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - original draft, Writing - review and editing

   Contributed equally with

   Qiaohong Wei and Esmaeil Amiri

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6974-8699

2. Qiaohong Wei

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Investigation

   Contributed equally with

   Bin Han and Esmaeil Amiri

   Competing interests

   No competing interests declared

3. Esmaeil Amiri

   1. Department of Biology, University of North Carolina Greensboro, Greensboro, United States
   2. Delta Research and Extension Center, Mississippi State University, Stoneville, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing - original draft, Writing - review and editing

   Contributed equally with

   Bin Han and Qiaohong Wei

   Competing interests

   No competing interests declared

4. Han Hu

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Lifeng Meng

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Micheline K Strand

   Biological and Biotechnology Sciences Branch, U.S. Army Research Office, DEVCOM-ARL, Baltimore, United States

   Contribution

   Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

7. David R Tarpy

   Department of Applied Ecology, North Carolina State University, Raleigh, Canada

   Contribution

   Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8601-6094

8. Shufa Xu

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

9. Jianke Li

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Resources, Funding acquisition, Writing - review and editing

   For correspondence

   apislijk@126.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9344-0886

10. Olav Rueppell

    Department of Biological Sciences, University of Alberta, Edmonton, Canada

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing

    For correspondence

    olav@ualberta.ca

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5370-4229

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