Behavioral responses of animals to external and internal stimuli have evolved to optimize survival and reproduction under average circumstances (Darwin, 1859). However, environmental and interindividual variability commonly causes deviations from the average, resulting in selection for context-specific and condition-dependent behavior (Dall et al., 2004; Bolnick et al., 2003; West-Eberhard, 1989). Such plasticity is limited and evolutionary constraint (Arnold, 1992) of behavior occurs in the form of behavioral syndromes: differences among individuals that manifest across different contexts (Sih et al., 2004). Advantages of behavioral plasticity and specificity have been documented in many systems and some neuroendocrine mechanisms have been identified (O’Connell and Hofmann, 2011; Kim et al., 2017). However, neural mechanisms that allow the sophistication of behavioral repertoires by regulating the context specificity of behavioral responses remain insufficiently understood.

Behavioral regulation is particularly important in social species in which social interactions provide a high diversity of behavioral context (Oliveira, 2009; Gronenberg and Riveros, 2009). Social evolution also allows individuals to restrict their behavioral repertoires through temporal or permanent behavioral specialization (Oster and Wilson, 1978). This specialization and the resulting division of labor are believed to be major contributors to the successful colony life of many social insects despite its potential costs (Jeanson and Weidenmüller, 2014). Advanced social evolution thus allows interindividual plasticity to replace individual behavioral plasticity. Nevertheless, the principal problem of behavioral plasticity across different contexts remains the same, and social insects can be constrained in their behavioral evolution by correlated selection responses across different behaviors or castes (Rueppell et al., 2006; Jandt et al., 2014).

Behavior often occurs in response to a specific stimulus exceeding an individual’s specific response threshold (Tinbergen, 1951; Mayr, 1974). Response thresholds depend on internal physiological states (Ricklefs and Wikelski, 2002) and translate the value of perceived stimuli into probabilities of behavioral responses and vary among individuals (Scheiner et al., 2004). In social insects, individual variation in response thresholds is linked to division of labor (Theraulaz et al., 1998; Beshers et al., 1999; Robinson, 1992; Schulz et al., 2002), and numerous studies have characterized this link across multiple levels of biological organization (Scheiner et al., 2004; Page et al., 2012; Johnson, 2010). The Western honeybee (Apis mellifera) is the most-studied social insect model. Many aspects of its division of labor are driven by a lifelong behavioral ontogeny, leading to age polyethism (Seeley, 1982). Young bees perform numerous inside tasks, most prominently brood care in form of alloparental nursing behavior, before transitioning to a mix of other in-hive tasks (Johnson, 2008). Similar to the highly specialized nursing stage, the final behavioral state of older bees as outside foragers is almost exclusive of other tasks (Seeley, 1982). Moreover, foragers often specialize on collecting only one of the principal food sources, pollen or nectar (Page et al., 2000). Specialized nurse bees (NBs), nectar foragers (NFs), and pollen foragers (PFs) are also found in the closely related Eastern honeybee, Apis cerana (Ji et al., 2020), thus offering a suitable parallel system for our study.

The behavioral specialists of NBs, NFs, and PFs exhibit pronounced differences in their responsiveness to task-related stimuli. Responsiveness to brood pheromones peaks at typical nursing age (Pankiw, 2004). In contrast, foragers have a lower response threshold to sugars and light than nurses (Değirmenci et al., 2018; Ben-Shahar et al., 2003). Among foragers, pollen specialists exhibit higher responsiveness to sucrose and pollen stimuli than NFs (Page et al., 1998; Pankiw and Page, 1999). Response thresholds can be quantified based on the honeybees’ reflexive extension of their proboscis in response to stimuli, such as sucrose (Scheiner et al., 2004). The spontaneous proboscis extension reflex (PER) to sucrose has been expanded to other stimuli that bees spontaneously respond to Nicholls and de Ibarra, 2013; Zhang et al., 2020 and conditioned stimuli to which no spontaneous responses occur (Giurfa and Sandoz, 2012).

Response thresholds can be modified by biogenic amines, and dopamine, 5-hydroxy-tryptamine, octopamine, and tyramine have been implicated in the regulation of different behaviors of worker bees although the mechanisms are not entirely clear (Schulz et al., 2002; Scheiner et al., 2006). Few studies have addressed the role of neuropeptides although they are a diverse group of neurotransmitters that can also act as neurohormones on distal targets to coordinate a wide range of internal states and behavioral processes (Nässel, 2002). Neuropeptides are intimately involved in food perception and social interaction of insects (Schoofs et al., 2017), two processes that are central to division of labor in social insects (Ament et al., 2010). Neuropeptides mediate pheromonal effects on physiology (Shankar et al., 2015; Gendron et al., 2014) and usually exhibit a high degree of specificity (Inagaki et al., 2014; Taghert and Nitabach, 2012). Therefore, neuropeptides are prime candidates for mediating the independent adjustment of socially relevant response thresholds and they have been implicated in honeybee worker specialization and division of labor (Ji et al., 2020; Brockmann et al., 2009).

More than 100 mature neuropeptides derived from 22 protein precursors have been identified in the Western honeybee, A. mellifera (Han et al., 2015; Boerjan et al., 2010). Several neuropeptides, including allatostatin, leucokinin, and tachykinin-related peptides (TRPs), may be involved in the control of social behavior of A. mellifera and the closely related A. cerana, such as aggression (Pratavieira et al., 2018), foraging (Ji et al., 2020; Brockmann et al., 2009), brood care (Han et al., 2015), and possibly a wide array of other behaviors (Pratavieira et al., 2014). However, most of these results are based on correlations between behavior and neuropeptide expression, and more detailed studies are needed to understand the causal roles of neuropeptides in the behavioral specialization among honeybee workers.

Here, we report the results of a comprehensive study to test the hypothesis that neuropeptides regulate the division of labor in honeybees. We initially compared response thresholds to task-relevant stimuli among behaviorally defined worker groups (NBs, NFs, PFs) of the two closely related honeybee species A. mellifera and A. cerana. These response thresholds were correlated with neuropeptide expression levels, especially TRPs, suggesting a role of TRPs in worker specialization. Based on these results, we demonstrated in a series of TRP injections and RNAi-mediated knockdown of the TRP and its receptor (TRPR) a causal role of this pathway in modulating different response thresholds in a task-specific manner. We characterized the TRP signaling pathway molecularly by studying the TRPR properties and the important downstream events of cAMP and calcium accumulation and extracellular-signal-regulated kinase (ERK) activation in cell culture. Finally, we confirmed this connection in vivo by demonstrating corresponding effects of TRP injections and RNAi-mediated knockdown of TRP and TRPR on ERK phosphorylation.

Behavioral plasticity plays a central role in animal adaptation and modulating behavioral responsiveness to different stimuli and contexts is key to individual fitness. The success of social insects is partly due to their efficient division of labor, a form of behavioral plasticity among instead of within individuals. In this study, we demonstrated that the responsiveness to task-relevant stimuli correlates with behavioral specialization in two different honeybee species. Through parallel characterization of the neuropeptidome, we identified two tachykinin-related peptides (TRP2 and TRP3) as putative mechanism to adjust task-specific response thresholds and thus proximally guide division of labor. Subsequently, we characterized the molecular action of TRP2 and TRP3 in cell culture by verifying their binding to their membrane-bound receptor and demonstrating activation of multiple downstream signaling mechanisms. Finally, we verified causal involvement of TRP signaling in modulating task-specific behavioral response thresholds through complementary outcomes of TRP2 injection and RNAi-mediated knockdown of TRP and TRPR: while injection decreased task-specific responses, downregulation of TRP or TRPR increased the same specific responses. Thus, we present a mechanism that tunes the behavioral responsiveness of animals to specific stimuli compared to others. We use behaviorally specialized honeybee workers as models but hypothesize that this function of TRP signaling could be more widely conserved to adjust the context specificity of behavioral responses in animals.

Among all the signaling molecules in the nervous system, neuropeptides represent the largest and most diverse category and are crucial in orchestrating various biological processes and behavioral actions (Burbach, 2011; Hökfelt et al., 2000). Thus, we quantitatively compared the entire neuropeptidome among three behavioral worker phenotypes of AML and ACC without an a priori assumption. In addition to discovering several new neuropeptides from the ACC and AML brain, we identified TRP2 and TRP3 as candidates. TRPs have been associated with the modulation of appetitive olfactory sensation (Ki et al., 2015; Winther et al., 2006; Gui et al., 2017), foraging (Pratavieira et al., 2018), sex pheromone perception (Shankar et al., 2015), and aggression (Asahina et al., 2014). Particularly in honeybees, TRP is preferentially expressed in the mushroom body and some neurons scattered in the antennal and optic lobes (Takeuchi et al., 2004), and some expression has also been found in antennae (Jain and Brockmann, 2020). These expression patterns are consistent with our hypothesis that TRP signaling may be modulating behavioral responsiveness due to various inputs. Although specific knockdown of TRPs in Drosophila has revealed spatially variable effects (Nässel et al., 2019), the changes in response thresholds that we found are most likely due to systemic manipulation of TRP or TRPR levels. Global TRP expression in the honeybee worker brain also increases during the transition from nursing to foraging, further implicating it in the regulation of honeybee social behavior (Pratavieira et al., 2014; Takeuchi et al., 2003). The spatiotemporal expression of TRPR remains to be explored in A. mellifera and A. cerana, but the specific functions that have been linked to specific neurons in Drosophila (Ki et al., 2015) suggests that this information is critical for more detailed mechanistic models of the behavioral effects of tachykinin in honeybees.

In our study, only expression of TRP2 and TRP3 varied consistently among behavioral phenotypes of AML and ACC. In both species, TRP2 and TRP3 were most abundant in the brain of NFs, followed by PFs, and finally NBs. This is consistent with the very specific responsiveness of NBs to brood stimuli observed in our PER experiments, while the responsiveness of PFs and NFs was successively less specific: PFs responded specifically to two stimuli, while NFs did not show specifically strong responses to any stimuli. Moreover, the comparison between AML and ACC indicated higher TRP2 and TRP3 abundance in ACC in each behavioral phenotype, commensurate with the less specific PER responsiveness in ACC compared to AML. A few other neuropeptides, such as apidaecins, diuretic hormone, and prohormone-3, showed somewhat similar expression patterns in both species, but none of these was as tightly correlated to behavioral responsiveness and none has previously been connected with behavioral regulation in insects or other animals. Therefore, the TRPs were chosen as candidates of the control of honeybee division of labor for subsequent functional tests and molecular characterization.

The action of most insect neuropeptides is mediated by binding to GPCRs and often involves cAMP and Ca²⁺ as second messengers (Hauser et al., 2006). The TRPR is activated by TRPs triggering intracellular cAMP accumulation and Ca²⁺ mobilization in fruit flies and silkworms (Bombyx mori) (He et al., 2014; Birse et al., 2006), while no cAMP responses were discovered in stable flies (Stomoxys calcitrans) (Poels et al., 2007). The results of our peptide-based binding assays functionally confirmed that the honeybee TRPR is indeed the receptor for TRP2 and TRP3. The subsequent functional assays revealed that TRP signaling results in a dose-dependent increase in both intracellular cAMP and Ca²⁺. Together, these results indicate that TRPs can activate TRPR and trigger second messengers to regulate downstream functions. TRP2 displayed a higher affinity to TRPR and induced higher cAMP and Ca²⁺ signaling than TRP3, leading us to focus on TRP2 in the later in vivo experiments. Moreover, TRP signaling is sensitive to G_αs activation and is significantly blocked by G_αq and PKA inhibitors, suggesting both G_αs and G_αq are involved in TRP signaling in honeybees. Many GPCRs are able to induce mitogen-activated protein kinase cascades via cooperation of G_αs, G_αq, and G_αi signals, leading to the phosphorylation of ERK1/2, which plays critical roles in diverse biological processes (Rozengurt, 2007). Our results indicate that honeybee TRP signaling mediates phosphorylation of ERK1/2 in a dose- and time-dependent manner in both HEK293 and Sf21 cells. In addition, ERK1/2 activation was significantly inhibited by the PKA, PKC, and MEK inhibitors, which is in line with the observation of intracellular cAMP accumulation and Ca²⁺ mobilization. Thus, honeybees seem to be very similar to silkworms with regard to the involvement of the G_αs/cAMP/PKA and G_αq/Ca²⁺/PKC signaling pathways in the regulation of TRP-induced ERK1/2 activation (He et al., 2014). Taken together, our results demonstrate that the honeybee TRPR is specifically activated by TRPs, eliciting intracellular cAMP accumulation, Ca²⁺ mobilization, and ERK phosphorylation by dually coupling G_αs and G_αq signaling pathways.

Our in vitro and in vivo demonstrations that TRP signaling activates the ERK pathway indicated that the basic signaling mechanisms are conforming to the general patterns in insects (Hauser et al., 2006; He et al., 2014; Werry et al., 2005). Thus, our behavioral results may extend to other species. ERK links TRP signaling also to the insulin/insulin-like signaling (IIS) pathway. IIS is controlled by neuropeptides through ERK in Drosophila (Lee et al., 2008), and this connection in honeybees ties TRP back to the age-based division of labor among workers: IIS influences the timing of the behavioral maturation of honeybee workers and brain AmIlp1 is significantly higher expressed in foragers than nurses (Ament et al., 2008), consistent with our finding that TRPs are higher in foragers than nurses. Numerous other physiological changes accompany the transition from in-hive nurses to foragers (Scheiner et al., 2006; Robinson, 1987; Wang et al., 2012; Toth and Robinson, 2005, and our results integrate TRPs as the most important neuropeptides into the regulation of the behavioral ontogeny of honeybee workers and potential feedback loops to the modulation of behavioral response thresholds. The specialization of NFs and PFs has also been linked to IIS (Hunt et al., 2007; Wang et al., 2009) and explained by differences in sucrose response thresholds (Pankiw and Page, 2000). Our findings here may connect the differences in response thresholds and IIS mechanistically through the TRP and ERK signaling pathways.

The PER paradigm is well suited to test behavioral response thresholds and has been used for over 50 years in honeybees (Giurfa and Sandoz, 2012). Consistent with previous studies, we found PFs to be more responsive to sucrose than NFs and NBs in A. mellifera (Page et al., 1998). Moreover, we found corresponding differences between these behavioral groups in the closely related A. cerana. The PF’s responsiveness to low sucrose concentrations might also make them more responsive to pollen, but the causation of the PER to pollen is unclear (Grüter et al., 2008) and other components of pollen may functionally distinguish pollen from sucrose responsiveness (Nicholls and de Ibarra, 2013). Our results support the view that pollen and sucrose are distinct stimuli: While our experimental manipulations of TRP signaling altered the responsiveness of PFs to pollen and sucrose, only responsiveness to sucrose was affected in NFs and only responsiveness to larvae was affected in NBs. The functional significance of the PER in response to larvae is currently unclear, but we show that it is specific to nurses and it has previously been linked to brood provisioning (Zhang et al., 2020). Thus, our diverse PER results in two species comprehensively support the hypothesis that task-specific response thresholds guide behavioral specialization, leading to division of labor among honeybee workers (Theraulaz et al., 1998; Beshers et al., 1999; Robinson, 1992).

TRPs may adjust specific sensory neural circuits, potentially acting in concert with other neuromodulators (Kahsai et al., 2010; Jung et al., 2013). However, we have currently no evidence to support the hypothesis of different molecular TRP actions in different stimulus-response pathways (cf Ki et al., 2015). Thus, we have to restrict our conclusion to the simple hypothesis that TRP signaling decreases task-specific response thresholds of behavioral specialists without affecting non-task-related thresholds: It decreases pollen and sucrose responsiveness in PFs, sucrose responsiveness in NFs, and responsiveness to larvae in NBs. TRP signaling in honeybees may thus be a general regulator of how task-specific stimuli are weighted relative to others and consequently how specialized behavioral specialists are (Figure 7). This effect translates to different degrees of division of labor in social insect colonies and may control the context specificity of behavioral responses in animals more generally (Sih et al., 2004).

Figure 7

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Although AML and ACC are close relatives with similar basic biology, some behavioral differences have evolved since their speciation (Oldroyd and Wongsiri, 2009). AML and ACC share the age-based division of labor, with younger bees specializing on nursing before maturing to foraging activities (Hepburn and Radloff, 2011) and ACC foragers also specialize in nectar or pollen collection (Ji et al., 2020; Rueppell et al., 2008) similar to AML (Page et al., 2000). Accordingly, we found the main differences of stimulus responsiveness and TRPs' expression among worker phenotypes conserved. However, ACC exhibited less responses to the task-specific stimuli than AML. Consistent PER differences in AML and ACC between NFs and PFs and a generally lower responsiveness of ACC have been identified before (Yang et al., 2013), but the biological interpretation has remained unclear. It is possible that the species differences arise due to methodological bias, favoring AML performance in PER assays. However, our study offers the alternative explanation that ACC workers are less specialized than AML workers due to higher TRP signaling. Lower innate specialization may accompany better learning of ACC (Qin et al., 2012), facilitating its more opportunistic worker task allocation and resource exploitation than AML (Tan et al., 2008). These alternative life history strategies are plausible, given the typical differences in colony size and habitat (Jung et al., 2013; Sih et al., 2004; Ruttner, 1988). All three worker phenotypes of ACC exhibited higher levels of TRPs than their AML counterparts, but functional verification at the level of colony phenotypes will be required to unambiguously link TRP signaling to such interspecific differences in life history.

Original data have been deposited to ProteomeXchange Consortium with the dataset identifier PXD018713 under http://proteomecentral.proteomexchange.org or are provided as supplementary data files.

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Author details

1. Bin Han

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

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Writing - original draft

   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, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Fan Wu

   1. Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China
   2. Biometrology and Inspection & Quarantine, College of Life Science, China Jiliang University, Hangzhou, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7923-3808

4. Han Hu

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Chuan Ma

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

6. Lifeng Meng

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

7. Xufeng Zhang

   1. Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China
   2. Institute of Horticultural Research, Shanxi Academy of Agricultural Sciences, Shanxi Agricultural University, Taiyuan, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

8. Mao Feng

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

9. Yu Fang

   Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

10. Olav Rueppell

    1. Department of Biology, University of North Carolina Greensboro, Greensboro, United States
    2. Department of Biological Sciences, University of Alberta, Edmonton, Canada

    Contribution

    Formal analysis, Supervision, 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

11. Jianke Li

    Institute of Apicultural Research/Key Laboratory of Pollinating Insect Biology, Ministry of Agriculture, Chinese Academy of Agricultural Science, Beijing, China

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration

    For correspondence

    apislijk@126.com

    Competing interests

    No competing interests declared

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