Gut microbiota modulates aversive learning and memory of honeybees (Apis mellifera)

Domain: chemistry
Published: 2025-12-18
Editorial outcome: accept

Abstract
Growing evidence indicates that host behaviors are regulated by the gut-brain interaction. In honeybees (Apis mellifera), a critical defensive behavior is the sting extension reflex. However, it remains unclear how the gut microbiota affects aversive learning and memory of this behavior. Here we demonstrate that the normal gut microbiota contributes to aversive learning and memory of the sting extension reflex in honeybees. Metabolomics analysis reveals that the gut microbiota affects tyrosine metabolism of the gut and hemolymph. Oral reintroduction of Enterococcus faecium or Enterococcus faecalis restores aversive learning and memory of microbiota-free honeybees, with an increase in their brain dopamine levels by supplying exogenous tyrosine. Conversely, applying fluphenazine, which decreases brain dopamine levels, weakens aversive learning and memory of honeybees in normal hives. These findings provide insights into the role of gut bacteria in regulating aversive learning and memory in honeybees, highlighting beneficial effects of the gut microbiota on the cognitive behaviors of social insects.

Introduction
In mammals, including humans, numerous studies have revealed that microbial communities in the gastrointestinal tract can interact with the neural signaling system in the brain, which is well-known as the gut-brain axis1–3. A causal relationship between gut microbiota and neurodegenerative disorders has also been elucidated4. Accordingly, understanding the mechanisms underlying gut and brain interactions is essential for advancing our knowledge of neurological diseases4. In contrast to the complex and diverse gastrointestinal bacteria in mammals, honeybees possess relatively simple and specific gut microbial communities, which are more accessible for isolation, cultivation, and manipulation in vitro5. These characteristics position honeybees as an appropriate model to investigate the gut-brain axis and its influences on the behaviors of social organisms6,7.

In natural environments, the ability of animals to adaptively respond to external stimuli noticeably increases their opportunities for survival and evolutionary fitness8. Varying behavioral paradigms, including aggression, evasion, and defense strategies, illustrate how animals endeavor to tackle intricate ecological challenges for population and habitat viability8,9. In the genus Apis, honeybees (Apis mellifera) cope with external harassment, such as wasps, by engaging in vigilant activities, manifested by flapping wings, releasing pheromones, and extending sting10–12. During the defense process, honeybees associate anger signals with specific odors and differentiate the odors of various predators, both of which are essential for the occurrence of the sting extension reflex (SER)10. Note-worthily, SER, also known as stinging behavior, is the most severe and effective form of defensive behavior in honeybees10. Under laboratory conditions, SER has been widely used in the olfactory associative conditioning paradigm to assess aversive learning and memory in honeybees. This method has also been demonstrated as a canonical behavioral approach for evaluating honeybee defensive behavior10,11,13.

Recent studies have shown that enhanced appetitive reward conditioning in the visual and olfactory modalities of bees can be attributed to an increase in beneficial gut metabolites, such as lysophosphatidic acid14, indole derivatives15, and anandamide16. These promotive effects are further influenced by gut symbiotic bacteria like Lactobacillus apis14,15 or Gilliamella apicola16. Moreover, gut microbiota is shown to regulate honeybee social behavior and their social network structure by influencing amino acid metabolism and chromatin accessibility in the brain7. Further investigation reveals that gut microbiota also modulate brain neurotransmitters, which are central to behavioral regulation3,17–19. For example, dopamine has been shown to affect aversive olfactory learning and memory in honeybees13,20. Increased dopamine levels in the brain are linked to the metabolization of tyrosine and levodopa (L-dopa) into dopamine21–23. Findings in mice have also provided valuable mechanistic insights into how gut microbiota influences dopamine dynamics. Oral supplementation with Enterococcus faecium or Enterococcus faecalis24,25 has been shown to significantly increase brain dopamine levels and improve behavioral symptoms in Parkinson’s disease mice21–23. However, it remains unknown whether the gut-brain axis plays a role in modulating aversive learning and memory of honeybees. Moreover, it is unclear if this modulation is mediated through gut microbiota-driven regulation of metabolism and associated neural pathways.

Herein, we investigate the effects and underlying mechanisms of gut microbiota on aversive learning and memory of honeybees. It is found that gut microbiota modulates aversive learning and memory in honeybees, but does not affect innate stinging behavior to unconditioned noxious stimuli. Metabolomics analysis of gut and hemolymph samples reveals that normal gut microbiota has significant effects on tyrosine metabolism. Specifically, honeybee gut non-core bacteria Enterococcus faecium or Enterococcus faecalis are capable of improving aversive learning and memory of microbiota-free honeybees both in the absence and presence of exogenous tyrosine. Furthermore, applying fluphenazine (a dopamine receptor antagonist) to normal-hive honeybees causes a decrease in aversive learning and memory and brain dopamine levels. Our study provides evidence that gut microbiota modulates aversive learning and memory of honeybees, revealing the potential conservation mechanism of gut microbiota, including non-core bacteria, on host cognition.

Results
Normal gut microbial communities contribute to aversive learning and memory of honeybees
To determine whether gut microbiota modulates aversive learning and memory of honeybees, we first artificially established microbiota-free honeybees (MFB) as illustrated in Supplementary Fig. 1. The success of generating MFB was confirmed by real-time quantitative polymerase chain reaction (RT-qPCR), where the total 16S rRNA gene copies in MFB were less than 104 (Supplementary Fig. 2a), and corroborated by culture assays and 16S rRNA gene sequencing (Supplementary Fig. 2b), indicating successful maintenance of microbiota-free conditions. Whereas gut bacterial abundance of normal-hive honeybees (NHB) was significantly higher than that of MFB (Supplementary Fig. 2a, p<0.0001), and gut microbial composition in NHB was more diverse than in MFB (Supplementary Fig. 2b). With these models successfully established, we subsequently investigated whether gut microbiota influences their aversive learning and memory by comparing SER of MFB and NHB (Fig. 1a). In the unconditioned stinging behavior assay, MFB showed a slightly lower tendency exhibiting innate SER in response to unconditioned isopentyl acetate (IPA) compared to NHB, but this difference did not reach statistical significance (Supplementary Fig. 3a, p=0.1250). Next, to assess aversive learning and memory, the SER of honeybees trained with an electric shock-associated hexanol odor was examined, with high aversive learning and memory ability indicated if honeybees exhibited SER to conditioned hexanol. The results showed that SER percentage during aversive learning acquisition and acquisition index of NHB were significantly higher than those of MFB (Fig. 1b, p=4.92×10−6; Fig. 1c, p=0.0024). Moreover, NHB required fewer acquisition trials to acquire aversive learning compared to MFB (Fig. 1d, p=0.0008). In the memory retrieval assay, the percentage exhibiting successful aversive memory in NHB was significantly higher than in MFB (Fig. 1e, p<0.0001). These phenomena suggest that unconditioned stinging behavior per se is not affected by gut microbiota, whereas depletion of gut microbiota impairs aversive learning and memory in honeybees, as revealed by deficits in conditioned SER performance.

To further examine whether reintroduction of gut microbiota to MFB could restore their aversive learning and memory, fecal microbiota transplantation, derived from hindgut substance of guard or nurse honeybees, was used to recover gut microbial communities (i.e., guard hindgut-colonized honeybees, HCB_Guard and nurse hindgut-colonized honeybees, HCB_Nurse)15 (Supplementary Fig. 1). And bacterial recolonization of HCB_Guard and HCB_Nurse was confirmed by RT-qPCR, showing the total 16S rRNA gene copies in HCB_Guard and HCB_Nurse were significantly higher than in MFB (Supplementary Fig. 2a, HCB_Guard vs. MFB, p<0.0001; HCB_Nurse vs. MFB, p=0.0002). In addition, a more abundant composition of gut microbiota in HCB_Guard and HCB_Nurse, similar to NHB, was observed as compared with MFB (Supplementary Fig. 2b), demonstrating successful reintroduction and colonization of gut microbiota. Aversive learning and memory in recolonized HCB_Guard and HCB_Nurse were tested further. In the unconditioned stinging behavior assay, there was no significant difference in the percentage of honeybees exhibiting SER to IPA among MFB, HCB_Guard, and HCB_Nurse (Supplementary Fig. 3a, HCB_Guard vs. MFB, p>0.05; HCB_Nurse vs. MFB, p=0.1906), indicating that gut microbiota does not affect the innate stinging behavior of honeybees to unconditioned odor stimulus. However, in the aversive learning and memory assays, SER percentage during aversive learning acquisition and acquisition index in HCB_Guard were significantly higher than in MFB (Fig. 1b, p=0.0003; Fig. 1c, p=0.0148). In addition, acquisition trials of HCB_Guard were significantly fewer than those of MFB (Fig. 1d, p=0.0336). A significant increase in the percentage of honeybees exhibiting successful aversive memory in the memory retrieval assay was observed in HCB_Guard compared to MFB (Fig. 1e, p=0.0009). However, there was no significant difference in aversive learning between MFB and HCB_Nurse (Fig. 1b, p=0.1668; Fig. 1c, p=0.2132; Fig. 1d, p=0.2630). The percentage of successful HCB_Nurse in the memory retrieval assay was significantly higher than that of MFB (Fig. 1e, p=0.0113). Hence, these results indicate that fecal microbiota transplantation, particularly from guard honeybees, can effectively restore aversive learning and memory of MFB.

In order to explore whether differences in gut microbiota could influence the physiological status of honeybees and subsequently affect their aversive learning and memory, the survival rates of MFB, HCB_Guard, and HCB_Nurse were measured. No significant difference in the survival rate among these groups was found (Supplementary Fig. 4, HCB_Guard vs. MFB, p=0.1754; HCB_Nurse vs. MFB, p=0.2609). To further examine whether differences in aversive learning and memory were related to the voltage sensitivity of MFB, NHB, HCB_Guard, and HCB_Nurse, SER were tested at different voltage intensities. Here too, no significant difference in the SER percentage to 7.5V voltage stimulation was found among these honeybees (Supplementary Fig. 5a, b, NHB vs. MFB, p=0.1029; HCB_Guard vs. MFB, p=0.0684; HCB_Nurse vs. MFB, p=0.2167), showing that gut microbiota does not affect the innate stinging behavior of honeybees to unconditioned electric shock. These results indicate that variations in aversive learning and memory of honeybees are not induced by physiological status or external voltage stimuli. Consequently, our findings suggest that normal gut microbial communities are indeed contributive to establishing aversive learning and memory in honeybees.

Gut microbiota modulates tyrosine metabolism in the hindgut and hemolymph of honeybees
Gut microbiota can interact with the host nervous system via their metabolites, thereby influencing host behavior3,16. Given the great difference in aversive learning and memory between MFB and NHB, we hypothesized that this disparity could be attributed to the modulation of specific metabolic pathways by gut microbiota. Therefore, the hindgut metabolomics of MFB and NHB were compared to identify the metabolic mechanisms through which gut microbiota modulate aversive learning and memory of honeybees. The PCA result of hindgut metabolites showed a clear distinction in metabolic profiles between MFB and NHB (Fig. 2a). A total of 2370 metabolites were significantly altered, with 1267 up-regulated and 1103 down-regulated metabolites in NHB relative to MFB. KEGG pathway enrichment analysis of these differential metabolites showed the top 10 KEGG enriched pathways, which were mainly associated with amino acid metabolism, including cysteine and methionine metabolism, D-amino acid metabolism, tyrosine metabolism, glycine, serine, and threonine metabolism, arginine and proline metabolism, and tryptophan metabolism (Fig. 2b). We focused on tyrosine metabolism due to its role in dopamine synthesis, which is important for aversive learning and memory in honeybees13,20, and may explain the observed differences. The relative abundance of main metabolites in the tyrosine metabolic pathway was further examined (Fig. 2c). A significant reduction in the relative abundance of tyrosine, L-dopa, and tyramine was found in NHB compared with MFB (Fig. 2d, p<0.0001; Fig. 2e, p<0.0001; Fig. 2g, p=0.0168). Prior studies have indicated that tyrosine metabolism is dependent on the intestinal bacteria21–23. In the absence of gut microbiota in MFB, precursor metabolites accumulated, possibly due to less efficient conversion of tyrosine and L-dopa to downstream metabolites. Conversely, the relative abundance of dopamine, 4-hydroxyphenylpyruvic acid, and 4-hydroxyphenylacetic acid in NHB was significantly higher than in MFB (Fig. 2f, p<0.0001; Fig. 2h, p<0.0001; Fig. 2i, p=0.0446), suggesting that gut microbiota enhances tyrosine metabolism and boosts dopamine synthesis in the gut.

Metabolites from the intestinal tract can interact with the neural system via blood circulation through the gut-brain axis, thereby regulating host behaviors2,16. Accordingly, we hypothesized that gut microbiota affects hemolymph metabolite levels to modulate aversive learning and memory of honeybees. To further test this hypothesis, we analyzed the hemolymph metabolomics of MFB and NHB. Consistent with hindgut findings, the PCA result showed a clear distinction in metabolic profiles of hemolymph between MFB and NHB (Fig. 3a). There were 1260 significant differential metabolites, with 624 up-regulated and 636 down-regulated metabolites in NHB compared to MFB. KEGG pathway enrichment analysis of these differential metabolites showed that the top 10 enriched pathways included several related to amino acid metabolism, such as beta-alanine metabolism, lysine degradation, phenylalanine, tyrosine, and tryptophan biosynthesis, arginine and proline metabolism, and tyrosine metabolism (Fig. 3b). Given dopamine involved in aversive learning and memory of honeybees, further investigation was performed into principal metabolites within the tyrosine metabolic pathway (Fig. 3c). There was a significant elevation in the relative abundance of tyrosine, L-dopa, dopamine, and 4-hydroxyphenylpyruvic acid in NHB compared to MFB (Fig. 3d, p=0.0017; Fig. 3e, p=0.0104; Fig. 3f, p<0.0001; Fig. 3h, p<0.0001), which was possibly due to metabolites in the gut entering the hemolymph. In contrast, the abundance of tyramine in hemolymph was slightly lower in NHB relative to MFB (Fig. 3g, p=0.0830), which aligned with the reduced levels of tyramine in the hindgut of NHB (Fig. 2g, p=0.0168), indicating the tyramine synthesis pathway was inhibited in NHB. In addition, 4-hydroxyphenylacetic acid was not detected in the hemolymph of either group.

In summary, metabolomics analysis revealed significantly higher dopamine levels in the hindgut and hemolymph of NHB compared to MFB (Fig. 2f, p<0.0001; Fig. 3f, p<0.0001), indicating the importance of normal gut microbial communities for dopamine biosynthesis. Moreover, it was found that no significant difference in the relative abundance of serotonin in the hindgut and hemolymph of NHB and MFB (Supplementary Fig. 6a, p=0.1936; Supplementary Fig. 6b, p=0.5558). The octopamine was not detected in the hindgut and hemolymph of NHB and MFB. Therefore, these findings demonstrate that gut microbiota positively influences the tyrosine metabolic pathway, particularly dopamine signaling, in the gut and hemolymph of honeybees.

Enterococcus monocolonization enhances aversive learning and memory of honeybees
Studies have suggested that Enterococcus faecium and Enterococcus faecalis can effectively convert tyrosine and L-dopa into dopamine through the catalysis of tyrosine decarboxylase21–23. As illustrated by the behavioral and metabolomics results above, we hypothesized that gut microbiota may modulate aversive learning and memory of honeybees by regulating tyrosine metabolism, especially influencing dopamine signaling. We further examined whether Enterococcus faecium and Enterococcus faecalis, isolated from honeybee guts, could restore impaired aversive learning and memory of MFB. These two bacterial strains were found to contain the gene encoding tyrosine decarboxylase, as detected through PCR and agarose gel electrophoresis imaging in Supplementary Fig. 7. Oral administration of Enterococcus faecium and Enterococcus faecalis to MFB was used to generate Enterococcus faecium–colonized honeybees (EfmB) and Enterococcus faecalis–colonized honeybees (EfsB). The gut bacterial load in EfmB and EfsB was higher than in MFB (Supplementary Fig. 2a, EfmB vs. MFB, p=0.3394; EfsB vs. MFB, p=0.3382). In addition, 16S rRNA gene sequencing also confirmed successful colonization of Enterococcus faecium and Enterococcus faecalis in honeybee hindguts (Supplementary Fig. 2b). The relative abundance of Enterococcus in EfmB and EfsB was higher than in MFB (Supplementary Fig. 2c, EfmB vs. MFB, p=0.0182; EfsB vs. MFB, p=0.2017).

After successfully rearing Enterococcus monocolonized honeybees for 14 days, aversive learning and memory of EfmB and EfsB were compared with those of MFB (Fig. 4). For unconditioned stinging behavior, no significant difference was observed in the percentage of honeybees that exhibited SER to IPA among MFB, EfmB, and EfsB (Supplementary Fig. 3b, EfmB vs. MFB, p=0.0800; EfsB vs. MFB, p>0.05), showing that Enterococcus monocolonization does not affect the innate stinging behavior of honeybees to unconditioned odor stimulus. In the aversive learning and memory assays, compared to MFB, both EfmB and EfsB showed significantly higher SER percentage during aversive learning acquisition (Fig. 4a, EfmB vs. MFB, p=3.77×10−5; EfsB vs. MFB, p=2.60×10−5), greater acquisition index (Fig. 4c, EfmB vs. MFB, p=0.0022; EfsB vs. MFB, p=0.0038), and required fewer acquisition trials to acquire aversive learning (Fig. 4d, EfmB vs. MFB, p=0.0030; EfsB vs. MFB, p=0.0229). Aversive memory retrieval was markedly improved in EfmB and EfsB compared to MFB (Fig. 4e, EfmB vs. MFB, p=0.0107; EfsB vs. MFB, p=0.0064). Notably, the survival rates and SER percentage to electric shock (7.5V) in EfmB and EfsB were comparable to those of MFB (Supplementary Fig. 4, EfmB vs. MFB, p=0.0602; EfsB vs. MFB, p=0.9902; Supplementary Fig. 5, EfmB vs. MFB, p=0.4080; EfsB vs. MFB, p=0.4080), again suggesting that the observed differences in aversive learning and memory were not elicited by physiological factors or electric stimuli. Therefore, these results demonstrate that Enterococcus faecium and Enterococcus faecalis can indeed restore aversive learning and memory of MFB.

Subsequently, to further investigate whether Enterococcus faecium and Enterococcus faecalis could metabolize tyrosine to enhance aversive learning and memory of honeybees, we supplemented microbiota-free and Enterococcus monocolonized honeybees with 2 mg/mL L-tyrosine to generate MFB & Tyr, EfmB & Tyr, and EfsB & Tyr. The unconditioned stinging behavior results indicated no significant variation in SER to IPA among MFB, MFB & Tyr, EfmB & Tyr, and EfsB & Tyr (Supplementary Fig. 3b, MFB & Tyr vs. MFB, p=0.4076; EfmB & Tyr vs. MFB, p=0.0800; EfsB & Tyr vs. MFB, p>0.05; EfmB & Tyr vs. MFB & Tyr, p=0.3453; EfsB & Tyr vs. MFB & Tyr, p=0.2462). This finding again suggests the stinging behavior to unconditioned IPA is not affected per se. For the aversive learning and memory assays, oral administration of tyrosine to MFB did not significantly improve their aversive learning and memory (Fig. 4b, MFB & Tyr vs. MFB, p=0.9950; Fig. 4c, MFB & Tyr vs. MFB, p>0.05; Fig. 4d, MFB & Tyr vs. MFB, p>0.05; Fig. 4e, MFB & Tyr vs. MFB, p=0.5441). In contrast to MFB and MFB & Tyr, Enterococcus monocolonized honeybees supplemented with tyrosine (EfmB & Tyr and EfsB & Tyr) exhibited significantly higher SER percentage during aversive learning acquisition (Fig. 4b, EfmB & Tyr vs. MFB, p=2.13×10−5; EfsB & Tyr vs. MFB, p=9.95×10−5; EfmB & Tyr vs. MFB & Tyr, p=1.87×10−5; EfsB & Tyr vs. MFB & Tyr, p=1.09×10−4), greater acquisition index (Fig. 4c, EfmB & Tyr vs. MFB, p=0.0001; EfsB & Tyr vs. MFB, p=0.0008; EfmB & Tyr vs. MFB & Tyr, p=0.0002; EfsB & Tyr vs. MFB & Tyr, p=0.0012), and required fewer acquisition trials (Fig. 4d, EfmB & Tyr vs. MFB, p=0.0005; EfsB & Tyr vs. MFB, p=0.0010; EfmB & Tyr vs. MFB & Tyr, p=0.0006; EfsB & Tyr vs. MFB & Tyr, p=0.0013). Moreover, memory retrieval results indicated that the percentage of honeybees that succeeded in aversive memory was markedly increased in EfmB & Tyr and EfsB & Tyr compared to MFB and MFB & Tyr (Fig. 4e, EfmB & Tyr vs. MFB, p<0.0001; EfsB & Tyr vs. MFB, p<0.0001; EfmB & Tyr vs. MFB & Tyr, p=0.0003; EfsB & Tyr vs. MFB & Tyr, p=0.0007). However, there was no significant difference in unconditioned stinging behavior and aversive learning and memory between Enterococcus-colonized honeybees (EfmB and EfsB) and Enterococcus-colonized honeybees supplied with tyrosine (EfmB & Tyr and EfsB & Tyr) (Supplementary Fig. 3b, Fig. 4a–e, p>0.05), indicating that exogenous tyrosine supplementation has no significant enhanced effects on aversive learning and memory of Enterococcus-colonized honeybees (Fig. 4a–e, p>0.05). To summarize, these results suggest that monocolonization of Enterococcus faecium or Enterococcus faecalis is sufficient to enhance aversive learning and memory of MFB, and thus, additional supplementation of tyrosine does not lead to further significant improvement at the behavioral level.

Fluphenazine administration impairs aversive learning and memory of honeybees
Dopamine, a main metabolite in the tyrosine metabolic pathway, serves as a brain neurotransmitter involved in aversive learning and memory of honeybees13,20. To further reveal potential implications of dopamine signaling, we administered a dopamine receptor agonist (6,7-ADTN) and antagonist (fluphenazine) to normal-hive honeybees, and then assessed their aversive learning and memory. The results of unconditioned stinging behavior showed that the percentage of honeybees exhibiting SER to IPA in 6,7-ADTN- and fluphenazine-treated honeybees was comparable to NHB (Supplementary Fig. 3c, 6,7-ADTN vs. NHB, p=0.5119; Fluphenazine vs. NHB, p=0.2112), indicating that neither drugs related to dopamine signaling affected the innate stinging behavior of honeybees to unconditioned odor stimulus. For the aversive learning and memory assays, compared to untreated NHB, 6,7-ADTN did not result in significant changes in aversive learning and memory (Fig. 5a–d, p>0.05). However, fluphenazine significantly decreased SER percentage during aversive learning acquisition and acquisition index (Fig. 5a, p=3.15×10−5; Fig. 5b, p=0.0009), increased acquisition trials (Fig. 5c, p=0.0020), and decreased the percentage of successful honeybees in the memory retrieval assay (Fig. 5d, p=0.0059). These findings suggest that oral administration of dopamine receptor agonist 6,7-ADTN does not enhance aversive learning and memory, whereas the antagonist fluphenazine significantly weakens aversive learning and memory in normal-hive honeybees.

Gut microbiota influences dopamine levels in the honeybee brain
Based on the findings that gut microbiota modulate dopamine synthesis in the hindgut and hemolymph via tyrosine metabolism, we wanted to assess whether dopamine levels in honeybee brains were influenced by gut microbial communities, thereby impacting aversive learning and memory, given the crucial role of the nervous system in governing behavior. To investigate this hypothesis, we first measured brain dopamine levels in NHB and MFB, showing that brain dopamine levels in NHB were significantly higher than in MFB (Fig. 6a, p<0.0001). Meanwhile, we also detected the levels of octopamine and serotonin in the brains of NHB and MFB, with no significant difference (Supplementary Fig. 8a, p=0.8068; Supplementary Fig. 8b, p=0.3102). These results indicate that normal gut microbiota is associated with higher dopamine levels in the honeybee brain.

We then recolonized MFB with Enterococcus faecium and Enterococcus faecalis, the bacteria known to metabolize tyrosine, to assess whether the restoration of impaired aversive learning and memory in MFB was linked to changes in brain dopamine levels induced by recolonization. Enterococcus monocolonization did not cause a significant variation in the brain dopamine levels of EfmB and EfsB as compared to MFB (Fig. 6b, EfmB vs. MFB, p>0.05; EfsB vs. MFB, p=0.0751), despite its ability to restore aversive learning and memory in MFB, suggesting that behavioral restoration by Enterococcus can occur even in the absence of detectable increases in brain dopamine levels. Since there exists the gene for encoding tyrosine decarboxylase (TyrDC), which can decarboxylate tyrosine and L-dopa to dopamine in Enterococcus faecium and Enterococcus faecalis (Supplementary Fig. 7), we further investigated whether Enterococcus faecium and Enterococcus faecalis could metabolize exogenously supplied tyrosine to synthesize more dopamine, thereby affecting brain dopamine levels in Enterococcus monocolonized honeybees. Supplementing MFB with tyrosine (MFB & Tyr) did not lead to significant changes in brain dopamine levels compared to MFB (Fig. 6b, p>0.05), whereas an elevation in brain dopamine levels was observed in Enterococcus monocolonized honeybees supplied with tyrosine (Fig. 6b, EfmB & Tyr vs. MFB, p<0.0001; EfsB & Tyr vs. MFB, p=0.9777). Moreover, the level of brain dopamine in EfmB & Tyr and EfsB & Tyr was significantly higher than that of EfmB and EfsB (Fig. 6b, EfmB & Tyr vs. EfmB, p<0.0001; EfsB & Tyr vs. EfsB, p<0.0007). These results indicate that Enterococcus faecium and Enterococcus faecalis can metabolize exogenous tyrosine to increase brain dopamine levels in honeybees.

In addition, to further investigate whether brain dopamine levels are directly influenced by exogenous pharmacological treatments related to dopamine signaling, we measured brain dopamine levels of honeybees treated with the dopamine receptor agonist, 6,7-ADTN, or the antagonist, fluphenazine. There was no significant difference in brain dopamine levels between NHB and the 6,7-ADTN-treated honeybees (Fig. 6c, p=0.4594); whereas fluphenazine treatment induced a significant reduction in brain dopamine levels (Fig. 6c, p<0.0001), highlighting the pharmacological inhibitory effects of oral supplementation of fluphenazine. Taken together, these findings indicate that gut microbial communities are implicated in regulating brain dopamine levels in honeybees.

Methods
The establishment of the honeybee model
Honeybees (Apis mellifera) in this study were supplied by the experimental apiary of the Institute of Apicultural Research, Chinese Academy of Agricultural Sciences. Three capped brood frames from three different healthy hives were placed in an incubator (35°C, relative humidity 60%±5%) before manual pupae extraction to avoid the variation of environmental conditions and genetic interference. The pupae with darkened eyes (>9 days of pupae) were manually extracted from the capped honeycombs over 3 consecutive days to ensure sufficient honeybees, and then cultivated under a germ-free incubator (35°C, relative humidity 60%±5%) that was beforehand sterilized by ultraviolet light for 2 h. After eclosion (within 24 h), newly emerged honeybees were randomly allocated and maintained in specialized axenic cages (6 cages in each group, 40 honeybees in each cage) with sterile sucrose solution (50%, w/v) for one day. Subsequently, these honeybees were treated with different diets. Microbiota-free honeybees (MFB) were generated by the prior study with some modifications15. Honeybees were fed with sterile sucrose solution (50%, w/v) mixed with sterile 1× phosphate buffer saline (PBS) (Solarbio, Beijing, China) at a 1:1 ratio for 2 days, and then were raised with sterile sucrose solution (50%, w/v) for 11 days. As SER is closely associated with defensive behavior, guard hindgut-colonized honeybees (HCB_Guard) were established using hindgut microbiota from guard honeybees responsible for colony defense. Ten hindguts of guard honeybees captured from the entrance of the native hive were homogenized in 5 mL sterile 1× PBS, combined with 5 mL sterile sucrose solution (50%, w/v). The hindgut mixture was fed to 1-day-old MFB for 2 days. Next, these honeybees were provided with a sterile sucrose solution (50%, w/v) for 11 days. As a comparison of different worker honeybee types, nurse hindgut-colonized honeybees (HCB_Nurse) were additionally reared in accordance with the establishment method of HCB_Guard, but fed on the hindgut homogenate of nurse honeybees for 2 days. For control comparison, normal-hive honeybees (NHB) were included. These newly emerged honeybees were collected, abdominally marked with a non-toxic red dye (POSCA, Tokyo, Uni Mitsubishi Pencil), and then reintroduced into a native hive for 14 days.

To determine the role of specific gut microbiota in modulating aversive learning and memory, monocolonized honeybees were generated. Enterococcus faecium and Enterococcus faecalis were isolated from honeybee guts and verified by 16S rRNA gene sequencing61. After cultivating in the de Man, Rogosa, and Sharpe (MRS) medium at 37°C for 24 h, Enterococcus faecium or Enterococcus faecalis was resuspended with sterile 1× PBS to OD600nm = 1, and then mixed with sterile sucrose solution (50%, w/v) at a 1:1 ratio. The fresh mixture was daily supplied to 1-day-old MFB for 2 days to generate Enterococcus faecium–colonized honeybees (EfmB) or Enterococcus faecalis–colonized honeybees (EfsB). Subsequently, EfmB and EfsB were fed with a sterile sucrose solution (50%, w/v) for 11 days.

To evaluate the effect of tyrosine metabolism on aversive learning and memory and the metabolic capabilities of Enterococcus faecium and Enterococcus faecalis to tyrosine, microbiota-free and monocolonized honeybees were reared according to the above methods over the first three days, and then were orally treated with 2 mg/mL L-tyrosine (Sigma-Aldrich, Darmstadt, Germany) dissolved in sterile sucrose solution (50%, w/v) for 11 days62,63. These honeybees were correspondingly defined as (1) MFB & Tyr, (2) EfmB & Tyr, and (3) EfsB & Tyr.

The survival rates of MFB, HCB_Guard, HCB_Nurse, and monocolonized honeybees (EfmB and EfsB) were recorded for 14 consecutive days, where an additional 3 cages of honeybees in each group were reared for survival assay (n=100 in total; 33−34 honeybees in each cage).

The detection of tyrosine decarboxylase in Enterococcus
Enterococcus faecium and Enterococcus faecalis were cultivated in 5 mL of MRS medium at 37°C for 24 h. First, 1 mL of bacterial culture medium was collected, and then centrifuged at 10000 x g at 4°C for 5 min. The total bacterial DNA was extracted using FastPure Blood/Cell/Tissue/Bacteria DNA Isolation Mini Kit (Vazyme, Nanjing, China) in accordance with the established manual. Subsequently, the bacterial DNA was amplified by the polymerase chain reaction (PCR) using the specific primers of tyrosine decarboxylase (tyrdc) (forward primer: TGGTGCCGCGCGGCAGCCATATGAAAAACGAAAAATTAGCAAAAG; reverse primer: TGGTGGTGGTGGTGCTCGAGTTATTTTACGTCGTAAATTTGTTC)21. Finally, the amplification production was used for the imaging of agarose gel electrophoresis.

Absolute quantification of honeybee gut bacterial load
To quantify the bacterial number in each type of honeybee, MFB (n=10), NHB (n=10), HCB_Guard (n=6), HCB_Nurse (n=6), and Enterococcus monocolonized honeybees (EfmB, n=8; EfsB, n=6) were randomly sampled from different cages. The bacterial load analysis of the individual honeybee gut was performed using RT-qPCR37,64. Absolute bacterial quantification was based on the standard curve constructed by a 10-fold serial dilution of eligible plasmids, ranging from 102 to 108 copies/μL37,64, which were produced by the 16S rRNA gene and pMD18−T (TaKaRa, Kusatsu, Japan). The bacterial DNA of individual honeybee gut was extracted by the CTAB method (CTAB lysis buffer, cetyltrimethylammonium bromide: β-mercaptoethanol, 500:1, 65°C)65. The final concentration of DNA in the individual gut sample was approximately 100 ng/μL. Universal 16S rRNA gene primers (27F: 5’-AGAGTTTGATCCTGGCTCAG-3’; 355R: 5’-CTGCTGCCTCCCGTAGGAGT-3’) were utilized to amplify the 16S rRNA gene in each sample64. RT-qPCR was conducted on a LineGene 9600 instrument (BIOER, Hangzhou, China), employing a total reaction volume of 20 μL: 10 μL of TB Green Premix Ex Taq Ⅱ (TaKaRa, Kusatsu, Japan), 1 μL of DNA, 0.8 μL of forward primer, 0.8 μL of reverse primer, and 7.4 μL of nuclease-free water. The reaction conditions of RT-qPCR were as follows: initial denaturation at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 5 s and annealing at 60°C for 30 s, dissociation (95°C for 15 s, 60°C for 30 s, 95°C for 15 s). Each sample in each reaction was performed technically in triplicate.

16S rRNA gene sequencing of the honeybee hindgut
To detect the composition and abundance of gut microbial communities, 16S rRNA gene sequencing was performed. The microbial DNA from individual hindgut was extracted using the CTAB method65, with a total of 6 hindguts from a randomly chosen honeybee in each of the 6 cages of MFB, NHB, HCB_Guard, HCB_Nurse, and Enterococcus monocolonized honeybees (EfmB and EfsB). The primer set 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) was used for the V3-V4 region amplification of bacterial 16S rRNA genes66. The sequencing was conducted on an Illumina PE300 platform (Illumina, San Diego, USA). The raw sequencing data have been uploaded into the NCBI sequence read archive (SRA) database (Accession Number: PRJNA1189875).

Raw FASTQ files were filtered for quality with fastp (version 0.19.6) and merged by FLASH (version 1.2.7). The high-quality sequences were denoised using the DADA2 plugin (annotation method: Bayes, confidence level: 0.7) in the QIIME2 (version 2020.2), subsequently obtaining amplicon sequence variants (ASVs). To obtain species classification information corresponding to each ASV, the Ribosomal Database Project (RDP) classifier Bayesian algorithm was used for taxonomic analysis, and the selected database was Silva v138 (http://www.arb-silva.de). Based on the ASV results, bioinformatic analysis, including alpha and beta diversity, was performed by the Majorbio Cloud platform (https://cloud.majorbio.com)67.

Voltage sensitivity test of honeybees
To evaluate the sensitivity of honeybees to different voltage intensities68, 10 honeybees were randomly selected from each of 6 cages per group, with a total of 60 honeybees per group. Honeybees were mounted on a custom-made copper device, on which their stinger will extend when electrified. To ensure a normal physiological status, honeybees were fed with 10 µL sterile sucrose solution (50%, w/v) and were placed in a dark incubator for a 1 h interval. The voltage range tested included 2.5V, 5V, 7.5V, 10V, and 12.5V. Each electric shock lasted for 3 s with a 5 min interval between successive stimulations to prevent adaptation. The SER occurrence of honeybees was recorded.

Unconditioned stinging behavior assay of honeybees
Defensive behavior in honeybees is often triggered by alarm pheromones or direct threats, with SER representing one such behavior10,11,13. The unconditioned stinging behavior was used to assess the innate stinging behavior of honeybees to unconditioned odor stimuli such as alarm pheromone. The 14-day-old honeybees were mounted on a customized copper device and subsequently stimulated with isopentyl acetate (IPA) (J&K Scientific, Beijing, China), which is the main component of the alarm pheromone. A 10 mL syringe containing filter paper (1 cm × 1 cm) immersed with 10 μL of IPA solution was used to stimulate honeybee antennae. Meanwhile, an air extractor was placed 20 cm away from the honeybee to eliminate residual odor. The duration of each odor was 6 s. The unconditioned stinging response of each honeybee to IPA was recorded during the odor representation. Ten honeybees were randomly selected from each of 6 cages per group, resulting in a total of 60 honeybees per group.

Aversive learning and memory assays in honeybees
The olfactory associative conditioning assay of SER was conducted to examine aversive learning and memory of honeybees, which is a widely used behavioral paradigm in controlled laboratory conditions11,13,20. According to a well-established method with some modifications13, the 14-day-old honeybees were mounted on our electrical conducting copper device. Based on prior research and our results of the voltage sensitivity test, a mild electric shock of 7.5V was selected for the assay11,13. When shocked, honeybees exhibited a sting extension response (Fig. 1a). Given the role of odors as an alarm for triggering stinging behavior, hexanol (Sigma-Aldrich, Darmstadt, Germany) and linalool (Tokyo Chemical Industry, Tokyo, Japan) were used as conditioned stimuli (CS)13. Filter paper (1 cm × 1 cm) containing 10 μL of either hexanol or linalool was placed in a 10 mL syringe. To minimize odor interference, an air extractor was placed 20 cm from the honeybee. Aversive learning consisted of five trials, each comprising two sessions presented in a pseudorandom sequence: (1) exposure to hexanol (CS+) for 6 s, with a 7.5V electric shock during the last 2 s; and (2) exposure to linalool (CS-) for 6 s without an electric shock. The interval between stimuli was 10 minutes. Ten honeybees were randomly selected from each of 6 cages per group, resulting in a total of 60 honeybees per group. Honeybees that exhibited SER to hexanol at the first trial were discarded. Three parameters were used to assess aversive learning: (1) Aversive learning acquisition, the percentage of SER in each group, with higher values reflecting a stronger aversive learning ability to recognize and respond defensively to perceived threats; (2) Acquisition index, the number of trials in which honeybees exhibited SER during aversive learning, ranging from 0 to 4, with higher values indicating stronger aversive learning capability; and (3) Acquisition trial, the trial number at which honeybees first successfully associated hexanol with SER, indicating the speed of aversive learning in the SER. Honeybees that did not respond to hexanol or linalool during the five learning trials were assigned a hypothetical trial number of 6. These parameters were recorded in the first 4 s immediately following hexanol or linalool presentation. Successful aversive learning was defined by exhibiting SER to hexanol (CS+) but not to linalool (CS-), ensuring specificity in the association of hexanol with electric shock. Responses were recorded as either 1 for a sting extension or 0 for no response. After aversive learning trials, memory retrieval was assessed 1 h later by re-exposing all honeybees to the conditioned stimuli (hexanol and linalool odor) for 6 s to evaluate their aversive memory. The number of honeybees exhibiting SER was recorded. All aversive learning and memory assays were conducted by at least 3 expert researchers within 3 days, following standardized protocols.

Untargeted metabolomics analysis of honeybee hindgut and hemolymph
The hindgut and hemolymph samples from MFB and NHB were analyzed using untargeted metabolomics with ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) (Thermo Fisher Scientific, Waltham, USA), with eight replicates in each group. Each replicate consisted of 50 mg of hindgut or 100 μL of hemolymph, collected from multiple honeybees randomly selected from the 6 cages per group. Specifically, 50 mg of mixed hindgut sample was treated with 400 μL of methanol: water (4:1, v/v) solution. 100 μL of mixed hemolymph sample was treated using 400 μL of methanol: acetonitrile (1:1, v/v) solution. The mixtures were then sonicated at 40 kHz for 30 min at 5°C, followed by centrifugation at 10000 x g at 4°C for 15 min. Supernatants were transferred to sample vials for UHPLC-MS/MS analysis.

Each sample was injected at a volume of 2 μL into the HSS T3 column (100 mm × 2.1 mm; i.d., 1.8 μm) maintained at 40°C. Eluent A consisted of 0.1% formic acid in water: acetonitrile (95:5, v/v). Eluent B consisted of 0.1% formic acid in acetonitrile: isopropanol: water (47.5:47.5:5, v/v). The eluent gradient changed according to the following conditions: 0~3.5 min, 0% ~24.5% B (flow rate: 0.4 mL/min); 3.5~5 min, 24.5% ~65% B (flow rate: 0.4 mL/min); 5~5.5 min, 65% ~100% B (flow rate: 0.4 mL/min); 5.5~7.4 min, 100% B (flow rate: 0.4 mL/min to 0.6 mL/min); 7.4~7.6 min, 100% ~51.5% B (flow rate: 0.6 mL/min); 7.6~7.8 min, 51.5% ~0% B (flow rate: 0.6 mL/min to 0.5 mL/min); 7.8~9 min, 0% B (flow rate: 0.5 mL/min to 0.4 mL/min); 9~10 min, 0% B (flow rate: 0.4 mL/min). The mass spectrometric data were collected using a Thermo UHPLC-Q Exactive HF-X Mass Spectrometer, equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. Optimized MS parameters were as follows: heater temperature, 425°C; capillary temperature, 325°C; sheath gas flow rate, 50 arb; aux gas flow rate, 13 arb; ion-spray voltage floating (ISVF), −3500 V in negative mode and 3500 V in positive mode, respectively; normalized collision energy, 20-40-60 V rolling for MS/MS. Full MS resolution was 60000, and MS/MS resolution was 7500. Data acquisition was performed in Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70–1050 m/z.

Raw UHPLC-MS/MS data were processed using Progenesis QI software (Waters Corporation, Milford, USA). Metabolites were searched and identified from the main database (HMDB (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/), and Majorbio Database), with metabolites detected in at least 80% of samples retained. Variables with a relative standard deviation (RSD) > 30% of quality control (QC) samples were removed. The principal component analysis (PCA) based on metabolite features was performed, and significantly differential metabolites were selected using variable importance in the projection (VIP) and p-value. Metabolites with VIP > 1 and p < 0.05 were defined as significantly differential metabolites. Differential metabolites were mapped onto the biochemical pathways using metabolic pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/). Statistically significant pathways were identified using Fisher’s exact test. All data analyses were completed using the Majorbio cloud platform (https://cloud.majorbio.com)67.

Pharmacological administration for honeybees
To further examine the role of dopamine in aversive learning and memory of honeybees, 14-day-old NHB marked with dye were captured from their native hive. One hour before the aversive learning and memory assays, honeybees were orally administered 30 μL of 2 mg/mL of either dopamine receptor agonist 2-amino-6,7-dihydroxy-1,2,3,4-tetrahydronaphthalene hydrobromide (6,7-ADTN, Abcam, Cambridge, UK) or dopamine receptor antagonist fluphenazine dihydrochloride (Fluphenazine, MedChemExpress, Monmouth Junction, USA)54, and then used for the aversive learning and memory assays. Ten honeybees were randomly selected from each of 6 cages per group, resulting in a total of 60 honeybees per group.

Detection of biogenic amine in single honeybee brain
Following the aversive learning and memory assays, the level of biogenic amines, including dopamine (DA), octopamine (OA), and serotonin (5-HT) in the individual honeybee brain was analyzed using the Agilent 6495 Triple Quadrupole LC/MS system (Agilent, Santa Clara, USA). The dissected brain was treated with 200 μL of formic acid: acetonitrile (1:50, v/v) and homogenized by a tissue homogenizer (Cgrinder, Beijing, China) at 70 Hz for 60 s. The homogenate was sonicated at 40 kHz for 30 min at 4°C, and then centrifuged at 10000 x g for 10 min at 4°C. The supernatant was subsequently transferred to sample vials for LC/MS analysis. The chromatographic column was ZORBAX Eclipse Plus C18 (RRHD) (3.0 mm × 150 mm, i.d., 1.8 μm) (Agilent, Santa Clara, USA), with an injection volume of 2 μL and a flow rate of 0.4 mL/min. Eluent A was 0.1% formic acid in water, and eluent B was acetonitrile. The gradient elution program was established as follows: 0~3 min, 95% A; 3~3.01 min, 95% ~ 20% A; 3.01~5 min, 20% A; 5~5.10 min, 20% ~ 95% A; 5.10~8 min, 95% A. The mass spectrometry programs were optimized as follows: sheath gas temperature, 350°C; flow rate of sheath gas, 9 L/min; dry gas temperature, 290°C; flow rate of dry gas, 11 L/min; nebulizer gas pressure, 310 kPa; fragmentor voltage: 380 V; capillary voltage, 3500 V. A multiple reaction monitoring (MRM) model was selected for sample detection69. The external standard method was exploited, using dopamine hydrochloride (Sigma-Aldrich, Darmstadt, Germany), octopamine hydrochloride (MedChemExpress, Monmouth Junction, USA), and serotonin hydrochloride (MedChemExpress, Monmouth Junction, USA) as the standard samples. Data collection and analysis were carried out using MassHunter software (Agilent, Santa Clara, USA).

Statistical analyses
The normality of the data was analyzed by the Shapiro-Wilk test. The gut bacterial load and the relative abundance of gut microbial communities were compared by the Kruskal-Wallis test with Dunnett’s multiple comparisons. The log-rank (Mantel-Cox) test was applied to analyze the survival rates of honeybees. The number of honeybees that extended SER to IPA was compared by a two-sided Chi-squared test. The SER percentage during aversive learning acquisition was analyzed by a binomial generalized linear mixed model (GLMM) with a logit link function based on the R package lme4 version 1.1-37 (https://CRAN.R-project.org/package=lme4). The group and trial number were set as fixed effects. The cage and honeybee were set as random effects. The acquisition index and the acquisition trial in aversive learning were analyzed by a one-way ANOVA test with Tukey’s multiple comparisons. The comparison of honeybee percentage in the memory retrieval assay was conducted by a two-sided Chi-squared test. The SER percentage in the voltage sensitivity test was compared with a one-way ANOVA test with Tukey’s multiple comparisons. The metabolite differences between MFB and NHB in the hindgut and hemolymph were compared with the two-tailed Student’s t test or the two-tailed Mann-Whitney test. The data regarding the levels of dopamine, octopamine, and serotonin in the brain were compared using the two-tailed Mann-Whitney test, two-tailed Student’s t test, Kruskal-Wallis test with Dunnett’s multiple comparisons, or one-way ANOVA test with Bonferroni’s multiple comparisons. Statistical analyses were conducted with GraphPad Prism 9 (GraphPad Software, San Diego, USA) and R (version 4.3.3, https://www.r-project.org/). Statistically significant differences were defined as *p<0.05, **p<0.01, and ***p<0.001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I enjoyed reading this paper. It is a nice scope of work, it is well-written, and this is a timely and exciting topic. I have a few suggestions.\nMy biggest concern is that you refer to sting extension associative learning as a measure of \"defensive ability\" or \"defensive behavior\" throughout the study. I think this is problematic for a few reasons - first, this is an associative learning assay. Therefore, you are showing variation in learning and memory, but not necessarily variation in defensive behavior. It is also possible that you are showing variation in olfactory sensitivity more generally, not variation in defensive behavior. If you also had a non-defensive learning paradigm like PER included, and that showed no change, I would be more convinced that these effects are specific to defensive behavior. Similarly, if you had shown a change to alarm pheromone conditioning versus general odorant conditioning, that would also be more convincing. The second issue with the SER is that variation in response can be influenced by peripheral neurohormones like octopamine, even in the absence of the brain or head of the bee (e.g., in abdominal isolation preparations). While you have included the L&M paradigm here, which suggests the brain is involved in modulating the responses you are seeing, you can't rule out that there are other peripheral processes that are playing a role in modulating the sting extension, or the defensive components of the response. Similarly, serotonin and octopamine have been variably associated with defensive behaviors in honey bees, and these are not addressed in the current study at all. The focus on dopamine specifically and singly is not well-motivated (for either its impacts on defensive behavior, or SER more generally). For example - octopamine levels could differ between colony-reared and lab-reared bees, and are associated with variation in the SER and aggression. Serotonin is also produced at high levels by the microbiome. Third - defensive behavior involves much more than stinging, which is the most severe form of defense, and observed more rarely than other lower level defensive behaviors, especially in the context of the response to non-nest mate honey bees, and even alarm pheromone. I do not support equating the sting extension assay and conditioning to \"defensive behavior\" and \"defensive ability\", as it is an oversimplification. For this last concern, I would be satisfied if you changed this language to something like \"stinging response\" or even \"stinging behavior\".\nGiven the links between gut microbiome and disease response, I think you should add some interpretation of your results that addresses the conflicting evidence that aggression and/or accelerated aging (which is correlated with higher aggression) is associated with better versus worse honey bee health outcomes:\nWray, M.K., H.R. Mattila, and T.D. Seeley, Collective personalities in honeybee colonies are linked to colony fitness. Animal Behaviour, 2011. 81(3): p. 559-568.\n-positive relationship between defensive behavior, foraging, and colony growth\nRittschof, C.C., B.E.R. Rubin, and J.H. Palmer, The transcriptomic signature of low aggression in honey bees resembles a response to infection. BMC Genomics, 2019. 20(1): p. 1029.\n-molecular evidence that low aggression individuals resemble individuals that are in the diseased state (positive relationship between aggression and health state/stress response).\nFujiyuki, T., et al., Novel Insect Picorna-Like Virus Identified in the Brains of Aggressive Worker Honeybees. Journal of Virology, 2004. 78(3): p. 1093-1100.\n-more aggressive individuals are sick with a virus\nPerry, C.J., et al., Rapid behavioral maturation accelerates failure of stressed honey bee colonies. Proc Natl Acad Sci U S A, 2015. 112(11): p. 3427-3432.\n-stressed colonies show accelerated aging (a profile associated with increased aggression) and are more prone to collapse\nRivera-Marchand, B., D. Oskay, and T. Giray, Gentle Africanized bees on an oceanic island. Evol Appl, 2012. 5(7): p. 746-56.\n-Aggressive Africanized bees are more resistant to Varroa mites, but these traits are decoupled in an island population", "reviewer_2": "Feng, Zhang, Li and colleagues investigated how the honeybee gut microbiota affects aversive learning and memory, which they term defensive behavior, as well as the molecular mechanisms underlying these effects. They analyzed hive bees and generated germ-free bees, recolonizing them with either the full gut microbiota or mono-associating them with individual Enterococcus strains. These bacteria were previously suggested to convert tyrosine into dopamine, which the authors propose as the underlying mechanism. Defensive behavior was assessed using the stinger extension reflex (SER) assay, where an initially neutral odor is paired with an electric shock to form a Pavlovian association. The authors also generated gut and hemolymph metabolomic datasets from a subset of experimental treatments to infer candidate mechanisms for further testing. Additionally, they examined the effects of supplementing tyrosine alongside the inoculation of Enterococcus strains, and used pharmacological treatments to reduce brain dopamine levels. They found that colonized bees exhibited increased aversive learning and memory compared to germ-free bees, that over two thousand metabolites differed between these groups in the gut and over a thousand in the hemolymph, that Enterococcus strains replicated the effects of the native gut microbiota on SER phenotypes, and that fluphenazine reduced the SER response and brain dopamine levels.\nThis study is potentially exciting, but I have serious concerns about its framing, experimental design, statistical analyses, and interpretation of molecular mechanisms, which dampen my initial enthusiasm.\nFirst, the study’s framing is problematic. The title and main conclusion suggest that the gut microbiota regulates defensive behaviors, but only the learning and memory components of a defensive response were tested. Defensive behaviors encompass both learning-related components and direct aggressive responses (e.g., stinging intruders). Learning and memory components may involve neural circuits distinct from those controlling aggression toward intruders. To support the claim that gut microbes regulate defensive behaviors more broadly, further tests, such as intruder assays, responses to a rotating dummy, or direct SER responses to a noxious stimulus, would be necessary (see Nouvian et al. 2016, J. Exp. Biol."}, "author_response": "Point-by-point response to the reviewers’ comments\nManuscript No.: NCOMMS-25-03037\nThe responses to the editor’s and reviewers’ comments are provided herein. We sincerely\nappreciate the opportunity to revise the manuscript based on the reviewers’ feedback. In this\ndocument, the reviewers' comments are “italicized”, and our responses are “indented” and “in\nregular font”. The line numbers corresponding to the changes made in the revised manuscript\n(submitted Manuscript_Clean version) in response to each comment are provided in “bold and\nitalic text”. All revisions in the manuscript (submitted Manuscript_Clean version) were\nhighlighted in “yellow”.\nreviewer #1 (Remarks to the Author):\nI enjoyed reading this paper. It is a nice scope of work, it is well-written, and this is a timely and\nexciting topic. I have a few suggestions.\nMy biggest concern is that you refer to sting extension associative learning as a measure of\n\"defensive ability\" or \"defensive behavior\" throughout the study. I think this is problematic for a\nfew reasons - first, this is an associative learning assay. Therefore, you are showing variation in\nlearning and memory, but not necessarily variation in defensive behavior. It is also possible that\nyou are showing variation in olfactory sensitivity more generally, not variation in defensive\nbehavior. If you also had a non-defensive learning paradigm like PER included, and that showed\nno change, I would be more convinced that these effects are specific to defensive behavior.\nSimilarly, if you had shown a change to alarm pheromone conditioning versus general odorant\nconditioning, that would also be more convincing. The second issue with the SER is that variation\nin response can be influenced by peripheral neurohormones like octopamine, even in the absence\nof the brain or head of the bee (e.g., in abdominal isolation preparations). While you have\nincluded the L&M paradigm here, which suggests the brain is involved in modulating the\nresponses you are seeing, you can't rule out that there are other peripheral processes that are\nplaying a role in modulating the sting extension, or the defensive components of the response.\nSimilarly, serotonin and octopamine have been variably associated with defensive behaviors in\nhoney bees, and these are not addressed in the current study at all. The focus on dopamine\nspecifically and singly is not well-motivated (for either its impacts on defensive behavior, or SER\nmore generally). For example - octopamine levels could differ between colony-reared and\nlab-reared bees, and are associated with variation in the SER and aggression. Serotonin is also\nproduced at high levels by the microbiome. Third - defensive behavior involves much more than\nstinging, which is the most severe form of defense, and observed more rarely than other lower\nlevel defensive behaviors, especially in the context of the response to non-nest mate honey bees,\nand even alarm pheromone. I do not support equating the sting extension assay and conditioning\nto \"defensive behavior\" and \"defensive ability\", as it is an oversimplification. For this last concern,\nI would be satisfied if you changed this language to something like \"stinging response\" or even\n\"stinging behavior\".\nWe sincerely thank the reviewer for your encouraging and positive comments on our work.\nWe are grateful for your recognition of the scope, writing quality, and relevance of our work.\nYour thoughtful and constructive suggestions have helped us substantially improve the clarity\nand rigor of our manuscript. Your supportive remarks were truly motivating for our team.\nBelow, we address each concern in detail, with corresponding revisions highlighted in the\nrevised manuscript.\n1. Sting extension reflex (SER) as a measure of “defensive behavior”\nWe thank the reviewer for this important comment and fully agree that equating SER with\n“defensive ability or defensive behavior” may oversimplify its biological interpretation. As\nthe reviewer correctly pointed out, the SER in our study reflects a conditioned stinging\nresponse under associative learning, and does not encompass the full range of natural\nhoneybee holistic defensive behavior such as guarding, biting, or responsing to nestmate\nintruders1. In our original manuscript, we regarded SER as representative of defensive\nbehavior because stinging is the most severe and recognizable form of defense1. However, we\nnow acknowledge that this interpretation was overly broad. The term “defensive behavior”\nmay imply a range of colony-level and context-dependent actions beyond the specific\nstinging response elicited in our paradigm. To address this concern, we have revised the\nmanuscript to replace “defensive behavior”, “defensive ability”, or “defensive response” with\nmore precise terms such as “stinging behavior”, “stinging ability”, or “stinging response”\nthroughout the manuscript.\n2. The stinging response of honeybees to the alarm pheromone\nWe fully agree with the reviewer’s consideration that the behavioral paradigm used in our\nstudy (i.e. SER) represents an associative conditioning task and reflects variation in learning\nand memory components, rather than holistic defensive behavior. To further confirm that the\nobserved microbiota-dependent differences in stinging behavior are specific to associative\nlearning contexts, we refer to prior findings showing that microbiota-free honeybees (MFB)\nexhibit reduced proboscis extension reflex (PER) under odor conditioning compared to\nconventional bees2,3. These findings suggest that the gut microbiota broadly modulate\nassociative learning, not limited to stinging responses or defensive paradigms.\nGiven that stinging behavior was the focus of our study, we additionally explored the\nunconditioned stinging response to isopentyl acetate (IPA), a principal component of\nhoneybee stinger alarm pheromone known to induce innate defensive reactions. MFB showed\na slightly reduced tendency to exhibit SER in response to IPA compared to normal-hive\nhoneybees (NHB), guard hindgut-colonized honeybees (HCB_Guard), and nurse\nhindgut-colonized honeybees (HCB_Nurse), but the difference did not reach statistical\nsignificance (Supplementary Fig. 3, p > 0.05). One possible explanation, as mentioned by the\nreviewer, is that stinging behavior represents the most extreme form of defensive behavior in\nhoneybees and thus occurs at a relatively low baseline frequence (~20%) even under strong\nalarm stimulation. This low baseline may limit the sensitivity of SER as a readout for innate\ndefensive behavior in this context.\nIn light of these results and the reviewer’s constructive feedback, we have included additional\nexperimental details in the Methods section (lines 662-671), incorporated relevant data into\nthe Results section (lines 99-102, 126-129, 244-247, 268-272, 288-292, 303-306), and\nupdated the Introduction (lines 72-73) and Discussion accordingly (lines 371-372, 381-387).\nThe corresponding data have been presented in Supplementary Fig. 3 in the revised\nsupplementary information.\nSupplementary Fig. 3 Unconditioned stinging responses of honeybees to isopentyl\nacetate (IPA). a, b, c The honeybees used for the unconditioned stinging behavior assay\nincluded microbiota-free honeybees (MFB), normal-hive honeybees (NHB), guard\nhindgut-colonized honeybees (HCB_Guard), nurse hindgut-colonized honeybees\n(HCB_Nurse), Enterococcus faecium-colonized honeybees (EfmB), Enterococcus\nfaecalis-colonized honeybees (EfsB), MFB supplied with tyrosine (MFB & Tyr),\nEnterococcus faecium-colonized honeybees supplied with tyrosine (EfmB & Tyr),\nEnterococcus faecalis-colonized honeybees supplied with tyrosine (EfsB & Tyr),\n6,7-ADTN-treated honeybees (6,7-ADTN), and fluphenazine-treated honeybees\n(Fluphenazine). n = 60 in each group. The same MFB data are presented in a and b, and the\nsame NHB data are shown in a and c. The number of honeybees exhibiting the sting\nextension reflex (SER) in response to IPA was recorded. Group differences were analyzed\nusing the Chi-squared test (p > 0.05).\n3. Role of octopamine and serotonin, as well as justification for focusing on dopamine\nWe thank the reviewer for the insightful comment regarding the potential roles of other\nneurotransmitters, such as octopamine and serotonin, in modulating SER, particularly via\nperipheral mechanisms. To address this concern, we conducted additional experiments to\nquantify octopamine and serotonin levels in the hindgut, hemolymph, and brain of MFB and\nNHB, as detailed in the Methods section of the revised manuscript (lines 767-768, 785-787,\n808-811). The results (Results section, lines 214-217, 322-324) indicated that octopamine\nwas undetectable in the hindgut and hemolymph of both MFB and NHB. No significant\ndifferences in serotonin levels were observed in the hindgut (Supplementary Fig. 6a, p =\n0.194) and hemolymph (Supplementary Fig. 6b, p = 0.556) between MFB and NHB. In the\nbrain, neither octopamine nor serotonin levels differed significantly between MFB and NHB\n(Supplementary Fig. 8a, p = 0.807; Supplementary Fig. 8b, p = 0.310). While we can not\nentirely rule out the possibility that octopamine in other peripheral tissues may contribute to\nvariation in SER, our data suggest that the gut microbiota do not substantially modulate\noctopamine or serotonin levels in the gut- or circulation-associated compartments.\nIn response to the reviewer’s concern that our focus on dopamine was not sufficiently\nmotivated, we would like to emphasize that our decision was based on converging lines of\nevidence. First, previous studies have shown that dopamine plays a critical role in modulating\nconditioned stinging behavior in honeybees4,5. Second, and importantly, our metabolomic\nanalysis revealed that tyrosine metabolism, a direct pathway of dopamine biosynthesis, was\nsignificantly altered in the hindgut and hemolymph of MFB. These metabolic alterations\nfurther support dopamine as a microbiota-sensitive neuromodulator relevant to SER\nbehavioral regulation. Taken together, these findings support our focus on dopamine as the\nprimary target in understanding the gut-brain modulation of conditioned stinging behavior.\nNevertheless, we have now included measurements of octopamine and serotonin, and have\nexpanded the Discussion (lines 460-468) to acknowledge their potential contributions to SER\nregulation.\nWe again thank the reviewer for your valuable suggestions, which have helped us refine the\ninterpretation of our findings. All changes have been implemented in the revised manuscript,\nand we hope these revisions have addressed the concerns.\nSupplementary Fig. 6 Serotonin levels in the hindgut and hemolymph of\nmicrobiota-free honeybees (MFB) and normal-hive honeybees (NHB). Levels of\nserotonin were measured in the (a) hindgut and (b) hemolymph. No significant differences\nwere observed between groups. Statistical analysis was performed by two-tailed student’s\nt-test (hindgut, n = 8 in each group, NHB vs. MFB, t = 1.365, p = 0.194; hemolymph, n = 8\nin each group, NHB vs. MFB, t = 0.604, p = 0.556).\nSupplementary Fig. 8 Octopamine and serotonin levels in the brains of microbiota-free\nhoneybees (MFB) and normal-hive honeybees (NHB). Levels of (a) octopamine and (b)\nserotonin were measured in the brain. No significant differences were observed between\ngroups. Statistical analysis was performed by two-tailed student’s t-test (octopamine, n = 15\nin each group, NHB vs. MFB, t = 0.247, p = 0.807; serotonin, n = 10 in each group, NHB vs.\nMFB, t = 1.044, p = 0.310).\nGiven the links between gut microbiome and disease response, I think you should add some\ninterpretation of your results that addresses the conflicting evidence that aggression and/or\naccelerated aging (which is correlated with higher aggression) is associated with better versus\nworse honey bee health outcomes:\nWray, M.K., H.R. Mattila, and T.D. Seeley, Collective personalities in honeybee colonies are\nlinked to colony fitness. Animal Behaviour, 2011. 81(3): p. 559-568.\n-positive relationship between defensive behavior, foraging, and colony growth\nRittschof, C.C., B.E.R. Rubin, and J.H. Palmer, The transcriptomic signature of low aggression in\nhoney bees resembles a response to infection. BMC Genomics, 2019. 20(1): p. 1029.\n-molecular evidence that low aggression individuals resemble individuals that are in the diseased\nstate (positive relationship between aggression and health state/stress response).\nFujiyuki, T., et al., Novel Insect Picorna-Like Virus Identified in the Brains of Aggressive Worker\nHoneybees. Journal of Virology, 2004. 78(3): p. 1093-1100.\n-more aggressive individuals are sick with a virus\nPerry, C.J., et al., Rapid behavioral maturation accelerates failure of stressed honey bee colonies.\nProc Natl Acad Sci U S A, 2015. 112(11): p. 3427-3432.\n-stressed colonies show accelerated aging (a profile associated with increased aggression) and\nare more prone to collapse\nRivera-Marchand, B., D. Oskay, and T. Giray, Gentle Africanized bees on an oceanic island. Evol\nAppl, 2012. 5(7): p. 746-56.\n-Aggressive Africanized bees are more resistant to Varroa mites, but these traits are decoupled in\nan island population\nWe sincerely thank the reviewer for raising this critical point regarding the complex and\nsometimes contradictory links between aggression and honeybee health. As the cited studies\nsuggest, aggression may confer health benefits in certain contexts, for example, by enhancing\ncolony defense and foraging performance6, increasing parasite resistance (e.g., Africanized\nhoneybees resisting Varroa mites)7, or reflecting stronger stress responses, as individuals with\nlow-aggression show gene expression patterns resembling infection8. Conversely, elevated\naggression has also been associated with adverse outcomes, such as viral infections affecting\nbrain function9 and accelerated aging under chronic stress, which may predispose colonies to\ncollapse10. These seemingly conflicting findings point to a context-dependent duality in how\naggression relates to health, shaped by pathogen type (viruses inducing behavioral anomalies\nvs. behavioral immunity suppressing parasites), environmental stress (short-term adaptation\nvs. long-term physiological costs), and genetic background (decoupling of\naggression-resistance traits in island populations of Africanized bees). Moreover, emerging\nevidence suggests that gut microbiota modulate behavior and health via the gut-brain axis,\nfurther complicating this relationship2,3,11. This highlights the need to integrate ecological,\nphysiological, and molecular dynamics to fully unravel the biological significance of\naggression in honeybee health.\nIn our study, we focus on how gut microbiota influences stinging behavior in an associative\nlearning context, rather than measuring innate aggression or colony-level defense. To\nminimize confounding variables, we randomly selected three healthy honeybee colonies at\nthe beginning of the experiment. We found no significant differences in the survival rates\nbetween microbiota-loaded honeybees and microbiota-free honeybees (Supplementary Fig. 4,\np > 0.05), suggesting comparable baseline health. As suggested by the reviewer, we have\nadded a related discussion regarding aggression and honeybee health in the revised\nmanuscript (Discussion section, lines 411-417) to explicitly address the context-dependent\nnature of aggression-health relationships and propose testable hypotheses to resolve existing\ncontradictions, with particular emphasis on microbiome-mediated mechanisms. Future\nstudies should integrate behavioral assays, metagenomic profiling of gut microbiota, and\nimmune marker quantification in populations to explore the relationships between aggression\nand health. For instance, targeted manipulation of gut microbiota (e.g., antibiotic treatment,\nfecal microbiota transplantation) in aggressive or gentle honeybees could directly test\nmicrobiome contributions to phenotypic decoupling.\nWe again thank the reviewer for prompting this thoughtful and constructive discussion, which\nhas helped significantly strengthen the conceptual framework of the manuscript.\nReferences\n1. Nouvian, M., Reinhard, J. & Giurfa, M. The defensive response of the honeybee Apis\nmellifera. J. Exp. Biol. 219, 3505−3517 (2016).\n2. Zhang, Z., et al. Honeybee gut Lactobacillus modulates host learning and memory\nbehaviors via regulating tryptophan metabolism. Nat. Commun. 13, 2037 (2022).\n3. Zhong, Z., et al. Gut symbiont-derived anandamide promotes reward learning in\nhoneybees by activating the endocannabinoid pathway. Cell Host Microbe 32,\n1944−1958.e7 (2024).\n4. Vergoz, V., et al. Aversive learning in honeybees revealed by the olfactory conditioning\nof the sting extension reflex. PLoS One 2, e288 (2007).\n5. Jarriault, D., et al. Dopamine release in mushroom bodies of the honey bee (Apis\nmellifera L.) in response to aversive stimulation. Sci. Rep. 8, 16277 (2018).\n6. Wray, M. K., Mattila, H. R., & Seeley, T. D. Collective personalities in honeybee\ncolonies are linked to colony fitness. Anim. Behav. 81, 559−568 (2011).\n7. Rivera-Marchand, B., Oskay, D. & Giray, T. Gentle Africanized bees on an oceanic\nisland. Evol. Appl. 5, 746−756 (2012).\n8. Rittschof, C. C., Rubin, B. E. R. & Palmer, J. H. The transcriptomic signature of low\naggression in honey bees resembles a response to infection. BMC Genomics 20, 1029\n(2019).\n9. Fujiyuki, T., et al. Novel insect picorna-like virus identified in the brains of aggressive\nworker honeybees. J. Virol. 78, 1093−1100 (2004).\n10. Perry, C. J., et al., Rapid behavioral maturation accelerates failure of stressed honey bee\ncolonies. Proc. Natl. Acad. Sci. U. S. A. 112, 3427−3432 (2015).\n11. Motta, E. V. S. & Moran, N. A. The honeybee microbiota and its impact on health and\ndisease. Nat. Rev. Microbiol. 22, 122−137 (2024).\nreviewer #2 (Remarks to the Author):\nFeng, Zhang, Li and colleagues investigated how the honeybee gut microbiota affects aversive\nlearning and memory, which they term defensive behavior, as well as the molecular mechanisms\nunderlying these effects. They analyzed hive bees and generated germ-free bees, recolonizing them\nwith either the full gut microbiota or mono-associating them with individual Enterococcus strains.\nThese bacteria were previously suggested to convert tyrosine into dopamine, which the authors\npropose as the underlying mechanism. Defensive behavior was assessed using the stinger\nextension reflex (SER) assay, where an initially neutral odor is paired with an electric shock to\nform a Pavlovian association. The authors also generated gut and hemolymph metabolomic\ndatasets from a subset of experimental treatments to infer candidate mechanisms for further\ntesting. Additionally, they examined the effects of supplementing tyrosine alongside the inoculation\nof Enterococcus strains, and used pharmacological treatments to reduce brain dopamine levels.\nThey found that colonized bees exhibited increased aversive learning and memory compared to\ngerm-free bees, that over two thousand metabolites differed between these groups in the gut and\nover a thousand in the hemolymph, that Enterococcus strains replicated the effects of the native\ngut microbiota on SER phenotypes, and that fluphenazine reduced the SER response and brain\ndopamine levels.\nThis study is potentially exciting, but I have serious concerns about its framing, experimental\ndesign, statistical analyses, and interpretation of molecular mechanisms, which dampen my initial\nenthusiasm.\nWe sincerely thank the reviewer for your thoughtful evaluation and constructive feedback on\nour manuscript. We greatly appreciate the recognition of the potential significance of our\nwork. We acknowledge your concern and have carefully revised the manuscript to improve\nclarity, address methodological limitations, and provide a more rigorous justification for our\nconclusions. Specifically, we have: (1) refined our use of terminology (e.g., replacing\n“defensive behavior” with “stinging behavior”); (2) clarified the experimental design and\nrationale in the methods; (3) reanalyzed data with stricter statistics; (4) added additional\nbehavioral and neurotransmitter data; and (5) revised our interpretation of the mechanistic\nfindings with more caution and improved logic. We hope these extensive revisions have\naddressed your concerns and strengthened the overall quality and clarity of the manuscript.\nWe are grateful for the reviewer’s insightful comments, which have helped us improve our\nwork considerably.\nFirst, the study’s framing is problematic. The title and main conclusion suggest that the gut\nmicrobiota regulates defensive behaviors, but only the learning and memory components of a\ndefensive response were tested. Defensive behaviors encompass both learning-related components\nand direct aggressive responses (e.g., stinging intruders). Learning and memory components may\ninvolve neural circuits distinct from those controlling aggression toward intruders. To support the\nclaim that gut microbes regulate defensive behaviors more broadly, further tests, such as intruder\nassays, responses to a rotating dummy, or direct SER responses to a noxious stimulus, would be\nnecessary (see Nouvian et al. 2016, J. Exp. Biol."}]

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