Honey bees are universally known to play a critical role in mediating pollination. Recently, attention has been drawn to the influence of gut microbiota on bee health. Studies on the honey bee (Apis mellifera) gut community have become widely appreciated due to conserved phylotypes of gut microbiota. Here, we focused on profiling the gut microbiota of two honey bee species native to Thailand, Apis florea (dwarf honey bee; LB) and Apis dorsata (giant honey bee; GB). Despite inhabiting the same landscape, different Apis species might have different gut microbial profiles. Analysis of gut bacterial diversity and community composition of LB and GB honey bees using deep 16S rRNA gene sequencing revealed major differences in bacterial diversity (OTUs) and abundance of core members in the gut of the two species. Lower species evenness was observed in LB than in GB (P < 0.01). Our results also show that gut microbial communities within LB were dominated by Lactobacillus (P < 0.001), while Gilliamella (P < 0.01) and Snodgrassella (P < 0.05) were predominantly associated with GB. Overall, our study has provided a preliminary overview of the gut microbiota of two honey bee species in Thailand. Further study should therefore focus on the association of host ecology and behavior with the bee gut microbiome.
Honey bees (genus Apis) are highly eusocial insects and important plant pollinators (Cardinal and Danforth 2011). Common members of the group include the western honey bee (Apis mellifera) (Woodard et al. 2011), as well as, Asian species of Apis (e.g., A. cerena, A. dorsata, and A. florea) (Hepburn and Radloff 2011). Among the known indigenous species of honey bees that are widely distributed in Asia (Oldroyd and Nanork 2009), A. florea and A. dorsata are commonly seen throughout Thailand, including the northern part of the country (Rattanawannee et al. 2013; Meemongkolkiat et al. 2019). These two bee species differ in morphology, behavior and phylogenetic placement (Koeniger and Vorwohl 1979; Arias and Sheppard 2005; Abrol 2011; Streinzer et al. 2013; Meemongkolkiat et al. 2019; Oppenheim et al. 2019). Specifically, the size A. dorsata (about 17 mm in length) (Teguh Nagir et al. 2016) is much larger than that of A. florea (7–10 mm in length) (Koeniger et al. 2011; Ghassemi-Khademi 2014). Flight-distance also corresponds to body size of honey bees. Accordingly, morphological differences are thought to play a key role in bee competition for nectar and pollen resources (Ming-Xian Yang et al. 2011).
Honey bee colonies are sensitive to environmental perturbations, which include both biotic and abiotic stressors (Neov et al. 2019). Gut bacterial communities are critical for maintaining bee health by modulating nutrition, protecting against pathogens as well as regulating host immune function (Zheng et al. 2018). Composition of gut microbiota of bees has been examined in A. mellifera and other corbiculate bees (Corby-Harris et al. 2014; Kwong et al. 2017b). Studies based on 16S rRNA gene sequencing have demonstrated that species of eusocial corbiculate bees, including honey bees, stingless bees, and bumble bees have distinct gut microbial profiles, but they have a few taxa in common (Koch et al. 2013; Meeus et al. 2015; Kwong et al. 2017b). Gilliamella, Bifidobacterium, Lactobacillus, and Snodgrassella have been identified as highly conserved core members of gut microbiota among social bees collected from four continents (Kwong et al. 2017b). Acquisition of these core gut bacteria seems to correspond to the appearance of social bees (Koch and Schmid-Hempel 2011; Kwong et al. 2017b). In turn, social interactions within the bee colony, such as oral trophallaxis or even fecal matter transfer within the hive, maintain distinct gut microbiota (Martinson et al. 2012; Powell et al. 2014).
Several factors affect composition and diversity of honey bee gut microbiota (Kwong and Moran 2016). For instance, diversity of gut microbiota of honey bees differed according to developmental stages (Hroncova et al. 2015). Core gut microbial taxa were differentially abundant among worker honey bees engaging in different tasks (nurses and foragers) (Jones et al. 2018b). Season is also a factor affecting microbiota as shown by major compositional shifts of gut bacterial communities detected between winter and summer bees (Kešnerová et al. 2020). Diet is the main driver of gut microbiota as indicated by gnotobiotic bee experiments, whereby pollen diet significantly increased bacterial abundance in the gut of bees (Kešnerová et al. 2020). Taken together, these findings illustrate the importance of host and environmental factors in shaping gut microbiota.
Tropism within the bee gut has been associated with specific functional properties of their gut microbiota (Engel et al. 2012). The activities of the bee gut bacteria in modulating nutrition (e.g., breakdown of plant polysaccharides and organic acid production) have been linked to host weight gain (Zheng et al. 2017; Lee et al. 2018). Other benefits provided by gut microbiota include inducing production of key compounds, such as the antimicrobial peptide (AMP) apidaecin, which is required for bee immune response against pathogens (Kwong et al. 2017a). Thus, nutritional intake and pathogen-resistance mediated by gut microbiota are crucial in maintaining bee colony survival (Engel and Moran 2013; Zheng et al. 2018; Negri et al. 2019).
Previous studies have mostly focused on the contribution of geographic location, environmental area, and developmental stages to variation of bee gut bacterial diversity including dwarf (A. florea) and giant (A. dorsata) honey bees (Saraithong et al. 2014, 2015; Kwong et al. 2017b; Jones et al. 2018a). Herein, we focused on a single geographic area in northern Thailand and employed 16S rRNA gene sequencing in order to compare gut microbial diversity and composition between A. florea and A. dorsata.
Material and methods
Specimen collection and DNA extraction
All bees used herein came from individual colonies of A. florea (20°02′35.3”N 99°53′18.2″E) and A. dorsata (20°02′48.3”N 99°53′36.2″E). Both colonies were located on the grounds of Mae Fah Luang University campus, Chiang Rai, Thailand. The honey bees were sampled in March. Two hundred workers of unknown age from each species were randomly collected from each colony by using an insect net and scooping bees from the curtain of each brood nest. Specimens were immediately placed on ice and then kept at −20 °C until further processing. Of the 200 workers, 50 were randomly selected and assigned into four subsets (A. florea: LB.g.1, LB.g.2, LB.g.3, LB.g.4; A. dorsata: GB.g.1, GB.g.2, GB.g.3, and GB.g.4) to explore the gut microbiota profiles at the population level as well as to determine general species associated with dwarf and giant honey bees inhabiting a single geographic area. Each subset contained the pools of 50 honey bees. Workers in each subset were subjected to surface sterilization using 10% solution of Clorox (NaOCl) for 10 min, followed by washing with sterile (autoclaved) distilled water twice. Subsequently, guts were gently removed from the abdomen by pulling the bee sting with sterile forceps as previously described (Carreck et al. 2013). Genomic DNA (gDNA) was then extracted using QIAamp DNA Stool Mini Kit (Qiagen) following the manufacturer’s protocol for the DNA isolation for pathogen detection. A mechanical disruption step using a microcentrifuge pestle was included during the step of treating gut tissues with the ASL lysis buffer for five minutes on ice prior to homogenization by vortexing. The remaining steps were then carried out according to the manufacturer’s guidance. DNA concentration and quality were measured using a NanoDrop™ spectrophotometer (ThermoFisher Scientific).
PCR amplification and sequencing of 16S rRNA gene fragments
The hypervariable V3 and V4 regions of the 16S rRNA gene were amplified by PCR using specific barcoded primers (V3-V4: 341F: 5′-CCTAYGGGRBGCASCAG-3′, 806R: 5′-GGACTACNNGGGTATCT AAT-3) (Klindworth et al. 2013). The PCR reactions were carried out using Phusion® High-Fidelity PCR Master Mix (New England Biolabs). The PCR products were checked with a 2% agarose gel electrophoresis and those at the expected band size of 400 bp to 450 bp were selected for further experiments. Libraries were generated using the Ion Plus Fragment Library Kit and quantified with Qubit and Q-PCR. Sequencing was performed on an Ion S5™ XL System (Thermo Fisher Scientific, Waltham, MA, USA).
16S rRNA fragment analysis
Reads obtained from high-throughput sequencing were demultiplexed to remove barcodes and primer sequences. To obtain high-quality sequence data, quality filtering of raw reads with a threshold of 20 (Q20) was performed with QIIME (version 1.7.0, http://qiime.org/ scripts/split_ libraries_fastq. html) (Bokulich et al. 2013) using previously defined filtering conditions (Magoč and Salzberg 2011). The remaining sequences were compared with the GOLD reference database (Gold database, http://drive5.com/uchime/uchime_download. html) using the UCHIME algorithm (http://www.drive5.com/usearch/ manual/uchime_algo.html) (Caporaso et al. 2010) for chimera removal (Edgar et al. 2011). The raw reads of the 16S rRNA gene of honey bee gut microbiome have been deposited at the NCBI SRA database under the Bioproject accession number PRJNA625047 (BioSample accession numbers SAMN14591903–SAMN14591910).
Bacterial OTU clustering and annotation
All filtered reads were subjected to sequence analysis using the UPARSE software (version 7.0.1001, http://drive5.com/uparse/) (Edgar 2013). OTU (operational taxonomic unit) assignment was based on sequences with ≥97% similarity. Then, OTU representative sequences were classified under six taxonomic ranks (phylum, class, order, family, genus, species) (confidence threshold: 0.8–1) (Wang et al. 2007) using the QIIME script assign_taxonomy.py with MOTHUR. Taxonomic information was annotated against the SILVA (version 128) (http://www.arb-silva.de/) SSUrRNA database (Quast et al. 2013). The MUSCLE software (version 3.8.31, http://www.drive5.com/muscle/) was used to align all representative OTU sequences (Quast et al. 2013). OTU tables and reference sequences for each OTU are provided in Supplementary File. A taxonomic tree of annotated OTUs was visualized using GraPhlAn (Graphical Phylogenetic Analysis) (Asnicar et al. 2015). Normalization of OTU abundance was performed using a standard sequence number corresponding to the sample with the least sequences (samples rarefied to a minimum of 80,633 reads per sample).
Gut bacterial diversity analysis
Alpha diversity analysis was conducted using QIIME (version 1.7.0). Bacterial diversity was assessed using ACE, Chao1, Shannon, and Simpson indices. The sum of total phylogenetic branch length for a sample was calculated by phylogenetic diversity (PD whole tree). Good’s coverage (the index of sequencing depth) was used to determine the proportion of total bacterial OTUs represented in a sample. Data assumption for normal distribution and homogeneity of variance were assessed by Shapiro-Wilk test and Levene’s test, respectively. Comparison of means of alpha diversity indices between groups was evaluated using the unpaired two-samples Wilcoxon test and Welch’s t-test. Results were visualized as a box plot with ggplot2 (Wickham 2009) using R software (version 3.6.1). Differences in gut bacterial community structure between dwarf (LB) and giant (GB) honey bees were determined by beta diversity analysis based on weighted and unweighted Unifrac distances matrices using QIIME software (version 1.7.0). Principal coordinates were obtained from Principal Coordinate Analysis (PCoA) and clustering results were visualized with WGCNA package, stat package, and ggplot2 package (Wickham 2009) in R software (version 2.15.3). Hierarchical clustering method, Unweighted Pair-group Method with Arithmetic Means (UPGMA) clustering was performed to illustrate the distance matrix using average linkage (QIIME software version 1.7.0). A non-linear model (non-metric multi- dimensional scaling analysis, NMDS) based on Bray–Curtis dissimilarities was used to calculate the extent of variation based on a distance matrix. LEfSe (linear discriminant analysis (LDA) Effect Size) analysis (Segata et al. 2011) was used to depict the OTUs that were most likely to explain group differences, given their relative abundances. Comparison of bacterial relative abundances between groups was assessed by Metastat by adjusting the false discovery rate (FDR) using Benjamini-Hochberg procedure (q < 0.05) (White et al. 2009). Average percentages of relative abundances of gut microbiota at phylum, genus, and species levels were visualized as heat maps with ggplot2. Dissimilarities of the gut microbial community structure between groups were evaluated using ANOSIM (analysis of similarity) and MRPP (multi- response permutation procedure) on Bray-Curtis distance matrices. Homogeneity of group dispersions (variances) was tested with betadisper and a permutation test (999 permutations) was used to compare distances of samples to group centroids (medians) (Anderson 2006; Anderson et al. 2006). The variation analyses of community structure between groups were carried out using the vegan R package (version 2.5–6) (Oksanen et al. 2016).
The datasets generated during and/or analysed during the current study are available at the NCBI SRA, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA625047.
Dwarf honey bees (LB) have similar species richness but lower evenness than giant honey bees (GB)
The diversity of gut microbiota in two species of honey bees was characterized using ACE, Chao1, observed OTUs, PD, Shannon, and Simpson indices. The data obtained from sequencing analysis and alpha diversity indices are presented in Supplementary Table 1 and 2. The rarefaction curves of microbial diversity estimators for eight samples reached a plateau phase, confirming that most microbial species had been captured in all samples (Supplementary Fig .1). The observed OTUs in LB were not significantly different from those in GB (Fig. 1c). Consistent results were also detected when using the abundance-based coverage estimators (ACE and Chao1) and phylogenetic diversity (PD) (Fig. 1a, b, and d, respectively). However, when considering species richness (Shannon) and evenness (Simpson) parameters, LB honey bees showed a significant reduction in gut bacterial diversity (P < 0.05, P < 0.01, respectively) (Fig. 1e and f). The lower value of diversity indices was driven by a small number of dominant OTUs in LB as compared to GB honey bees with higher numbers of OTUs that were more evenly distributed in the community.
Beta diversity comparison of gut microbiota communities of dwarf (LB) and giant honey bees (GB)
Analysis of beta diversity using weighted and unweighted Unifrac distance matrices revealed marked differences in bacterial communities between the two bee types. UPGMA cluster tree and PCoA based both on weighted and unweighted Unifrac distances showed that, only weighted Unifrac separated the gut microbiota profiles between LB and GB honey bees (P < 0.05, Wilcox rank-sum test; Supplementary Fig. 2) (Fig. 2a, c), while profiles were indistinguishable when using unweighted Unifrac (P = 0.065, Wilcox rank-sum test) (Fig. 2b, d). Moreover, NMDS based on Bray−Curtis dissimilarity index showed a clear cluster separation of LB and GB honey bees, with the latter being more dispersed (stress factor < 0.2, confirming the reliability of NMDS analysis results, Supplementary Fig. 3). However, the difference was not significantly influenced by community dispersal between groups (weighted Unifrac; Permutest F(1,8) = 3.60, P = 0.17) and unweighted Unifrac; Permutest F(1,8) = 2.85, P = 0.14). Variation analysis of community structure between groups of honey bees using ANOSIM (R = 0.688, P = 0.03, Supplementary Fig. 4) and MRPP (P = 0.026) confirmed that inter-group variation of microbial community structure significantly exceeded intra-group variation. No difference in distances to group centroids (medians) between groups was detected based on Bray–Curtis dissimilarities (Permutest F (1,8) = 1.86, P = 0.29). Therefore, statistical tests for beta diversity demonstrated differences in microbial communities between LB and GB, and between them, differences in relative abundance of bacterial OTUs (weighted Unifrac) influenced the overall microbial community structure rather than the presence or absence of specific gut bacteria (unweighted Unifrac).
Differences between major gut microbiota members of dwarf (LB) and giant honey bees (GB)
A total of 1193 OTUs were obtained and classified into 224 genera, 107 families, 60 orders, 37 classes, and 19 phyla. The OTU annotation trees combined with taxon abundances of the top 40 bacterial taxa were obtained (Supplementary Fig. 5). The richness of unique OTUs in GB was six times higher than that of LB. The two species shared 278 OTUs including core members of the bee gut microbiota belonging to Gilliamella, Bifidobacterium, Lactobacillus, Snodgrassella genera (Fig. 3a). OTUs representing other noncore members (Frischella, Apibacter, Commensalibacter) and environmental bacteria (Pseudomonas) also distributed in both LB and GB. Firmicutes, Bacteroidetes, Cyanobacteria, Proteobacteria, Actinobacteria, and Tenericutes dominated the microbial communities of both LB and GB, but their proportions differed in the two species of honey bees (Fig. 3b). Firmicutes was significantly more abundant in LB (P < 0.001; Fig. 3c), while Proteobacteria (P < 0.05; Fig. 3d) and Bacteroidetes (P < 0.001; Fig. 3e) were significantly increased in GB.
Firmicutes almost completely dominated the gut microbial community of LB, and this result was consistent at both the genus and species levels. Bacterial loads of major gut microbiota taxa and differences between LB and GB were calculated (Table 1). At the genus level, Lactobacillus was 14-fold more abundant in LB compared to GB (P < 0.001). The abundances of core members in the gut of GB differed from those in LB (Fig. 4a). In particular, two members of Bacteroidetes (Dysgonomonas and Apibacter) as well as five members related to Proteobacteria (Gilliamella, Commensalibacter, Frischella, Snodgrassella, and Pseudomonas) were significantly more abundant in GB (P < 0.05). This was reflected by a significant reduction in species richness and evenness in LB compared to GB (P < 0.05, P < 0.01, respectively). The abundance of Lactobacillus remained significantly higher at the species level in LB (Fig. 4b). In comparison, Lactobacillus spp. in GB had low abundance. The gut community of LB was largely dominated by Lactobacillus spp. including Lactobacillus mellis (Firm−4) (P < 0.05) and unclassified Lactobacillus (Lactobacillus sp. Afpoto13, Lactobacillus sp. Aahmto12, and Lactobacillus sp. G5_12_4MO2) (q < 0.05). Bifidobacterium asteroides was also prevalent in LB (P < 0.01). Taken together, the results indicated that the overall gut microbiota of LB and GB honey bees differed in terms of relative abundance.
LEfSe analysis confirmed abundance differences of specific taxa between LB and GB. The specific bacterial taxa associated with each group of honey bees are presented in Fig. 4c, as a cladogram and histogram of the LDA scores representing structure of the microbiota. Four bacterial taxa (Lactobacillus sp. Afpoto13, Lactobacillus sp. Aahmto12, Lactobacillus mellis (Firm-4), and Bifidobacterium asteroides) were significantly emphasized as specific species in LB (LDA > 4). The gut of GB had more specific bacterial taxa related to genera Frischella, Apibacter, Dysgonomonas, and Commensalibacter. With the exception of Frischella, the rest of the genera belonged to three families (Flavobacteriaceae, Porphyromonadaceae, and Acetobacteraceae, respectively). The LDA scores indicated that Firmicutes contributed to the microbial community of LB, while Bacteroidetes contributed to GB (score larger than 4.8).
The 16S rRNA gene-based profiling of gut microbiota in honey bees revealed variation in composition and relative abundances of common OTUs between open-nesting dwarf (LB) and giant (GB) honey bees in Thailand. Even though both species in this study came from the same geographical region and habitat, their gut microbiome diversity differed in terms of species abundance distribution. The observed difference may be explained by the phylogenetically distant relationship of the two bee hosts. Host phylogeny has recently been used to illustrate microbiota variation among bee species, particularly social corbiculate bees. Previous studies of genetic diversity of Apis species using 28 s rRNA and cytb gene fragments (Meemongkolkiat et al. 2019) have consistently shown the close phylogenetic affinity of A. mellifera and A. cerana, as well as A. florea and A. andreniformis. However, the phylogenetic placement of A. dorsata was distant to these four. Kwong et al. showed that closely related corbiculate bees (e.g. Apis, Bombus, and Meliponini) had more similar microbiomes (Kwong et al. 2017b). Analysis of gut microbial communities sampled from Singapore revealed a distinct profile of A. dorsata compared to A. florea, A. cerana, and A. andreniformis. Differences between A. dorsata and A. cerana were also observed in samples taken from Malaysia (Kwong et al. 2017b). These findings imply that host genetics might contribute to the observed variation in gut microbial diversity between LB and GB herein. Nevertheless, the greater variation in alpha diversity (ACE, Chao, observed OTUs, PD and Shannon indices) detected in GB when compared to LB could be due to the small sample size (four samples per group), pooling of samples, and/or the use of worker bees of unknown age. Any of these could have led to inadequate statistical power for detecting significant differences between LB and GB, particularly in ACE, Chao1, and observed OTUs indices. Likewise, high variation of gut microbiota was observed among subsets of GB based on weighted Unifrac with Proteobacteria being more abundant in GB.g.3 and GB.g.4 than in GB.g.1 and GB.g.2 (Fig. 2a). We speculate that a pool of honey bee gut samples from unknown age workers may account for variability hidden among individuals within the GB population. Changes in bacterial abundances were previously detected across four stages of development of A. dorsata and even varied between newly emerged workers and older workers (Saraithong et al. 2015). Future studies with additional samples from defined growth stages, multiple colonies as well as various geographic locations will facilitate our understanding of gut microbiota profiles in these two honey bee species.
Host ecology based on habitat scale and colony size might influence gut microbiota diversity. Though community and host size were not found to have an effect on bacterial diversity across corbiculate bees, a weak positive correlation was noted between bacterial diversity and abundance, when each bee species was considered separately (Kwong et al. 2017b). Specifically, a strong correlation was shown in Apis (Kwong et al. 2017b). The concept of species-area relationship (diversity measurement) has been proposed to include habitat size and host characteristics (e.g., body size and colony size) when estimating gut microbiota diversity (Kwong et al. 2017b). Given the lack of these relevant data in our study, it remains uncertain whether the greater diversity observed in GB is linked to these factors. Nonetheless, several lines of evidence have shown that giant honey bees have larger colonies (Phiancharoen et al. 2011; Buawangpong et al. 2014) than dwarf honey bees (Rinderer et al. 1996; Phiancharoen et al. 2011; Chuttong and Burgett 2018). Thus, future studies should include relevant ecological observations along with information on colony size and bee body length. Collectively these data may aid in disentangling relationships between host factors and gut microbiome.
Considering the core members of honey bee gut microbiota (Fig. 4a), Lactobacillus and Bifidobacterium were predominantly associated with LB, while Gilliamella and other noncore members (Frischella, Apibacter, Commensalibacter) dominated in GB. Our results regarding the presence of these bacterial taxa in the gut of LB and GB honey bees are similar to those of Kwong et al. (2017b), suggesting that host relatedness might influence the gut microbial profiles of these two honey bee species. The apparent differences of some core members in terms of their relative abundance were particularly striking in our study. Specifically, in this study, the abundance of Lactobacillus was 89% and 6% in LB and GB, respectively. When compared to Kwong et al. (2017b), abundance of Lactobacillus was 8% in A. florea: 8% and 1% in A. dorsata, while abundance of Firm-5 was 45% in A. florea and 23% in A. dorsata. The abundance of Bifidobacterium in the gut of honey bees showed an opposite profile, whereby a lower proportion was detected in LB (5%) and GB (2%) compared to A. florea (31%) and A. dorsata (10%) studied by Kwong et al. (2017b). The differences observed difference between our study and that of Kwong et al. might be due to influence of environmental factors involved in each geographic location (Saraithong et al. 2014, 2015). Moreover, a contrasting pattern regarding the relative abundance of Snodgrassella between our study and Kwong et al. deserves further investigation as the fluctuations in bacterial abundance could be linked to bee age or season (Corby-Harris et al. 2014; Kwong et al. 2017b). Lactobacillus, Bifidobacterium, and Gilliamella are generally known as fermenters in the honey bee gut (Kwong and Moran 2016; Wang et al. 2018), where they utilize carbohydrates derived from the host diet (e.g., pollen, nectar, and honey) (Haydak 1970; Taylor et al. 2019). The flight-distance of A. dorsata and A. mellifera is longer than that of A. florea and A. cerana. Despite being smaller in size, A. florea, has been suggested to be a successful competitor of floral resources within the same area, as the larger bees are limited to selecting flowers relative to their size (Punchihewa et al. 1985). The long flight distance and large foraging range of A. dorsata is conducive to exploiting multiple nectar and pollen resources over a large geographic area (Oldroyd et al. 2000; Oldroyd and Nanork 2009). Morphological variations induced by genetic background between honey bee species might dictate their foraging behavior and dietary preferences. Previous findings have hinted that host behaviors, size, and diet might shape the composition of gut microbiota. Future studies will therefore need to include these host-related factors.
The lactic acid bacteria (LAB), G. apicola and S. alvi have roles in defending against pathogens (Raymann and Moran 2018). In this study, Lactobacillus was significantly more abundant in LB. LAB have a long coevolutionary history with their bee hosts (Moran 2015). This symbiotic relationship has provided the bees with substantial benefits, including pollen fermentation (Hroncova et al. 2015) and improved immune responses (Vásquez et al. 2012). Thus, we speculate that Lactobacillus might possess disparate strategies to tackle external perturbations in bees. Furthermore, LB might require a wider variety of advantageous compounds produced by its symbiont microbes (e.g., antimicrobial substances (Olofsson et al. 2016)) than GB, which harbored diverse Proteobacteria. The inhibitory effects of LAB against human and honey bee pathogens have been previously demonstrated (Olofsson et al. 2016; Daisley et al. 2020). Studies using germ-free and conventional honey bees have shown that short-chain fatty acids (SCFAs) are key gut metabolites, which are produced by the fermenters G. apicola and Lactobacillus spp. and which enhance host growth (Zheng et al. 2017). This study highlights the need to further examine metabolic profiles of gut microbiota, which would be useful to disentangle functions of each community member within the bee gut.
In this study, Pseudomonas was also detected, which was likely an environmental contaminant (Kwong et al. 2017b; Ribière et al. 2019). This bacterial genus is generally found in the environment including plants (Peix et al. 2009). In GB, other bacteria including unknown bacteria accounted for 94% of the relative abundance of OTUs at species level. Using a longer fragment of the 16S rRNA genes will be invaluable for more accurate taxonomic identification of honey bee gut microbiota at the genus/species level.
Our findings are in agreement with a previous study reporting presence of core gut bacteria across Apis species and other corbiculate bees (Kwong et al. 2017b). However, differences in gut microbial profiles between host species (from single colonies) may not be enough for addressing factors that drive the observed patterns. As this is a descriptive study, the comparisons between the honey bee species are at best a hypothesis to be tested by future studies. Future work should test a larger sample size to determine if the differences are statistically significant. For instance, normalize individual factors (such as age of honey bees) would reduce variability of samples. Assumptions underlying sample pooling may need to be further validated when selecting the size of the pool. Alternatively, samples could be analyzed individually to determine variations of bacterial diversity among individuals and community structure. An additional study should also concentrate on examining the association of ecological and behavioral features, such as colony size, number of colony members, their behavioral tasks, and diets, with the honey bee gut microbiota. More information on these aspects would provide new insights into host-bacterial relationships.
In this study, the gut microbiota profiles of Apis dorsata (GB) and Apis florea (LB), collected from a single geographic location in Thailand were compared. Deep sequencing targeting the 16S rRNA gene revealed differences in the relative abundances of core gut bacteria were found between the two honey bee species. Specifically, Firmicutes (especially Lactobacillus) was highly associated with LB, while Bacteroidetes and Proteobacteria dominated the gut of GB. As such, host genetic lineage might contribute to the variation observed in gut microbial diversity within these corbiculate bee species. This pilot study has provided insight into the distinct gut microbiota of two honey bee species. Prospective studies focusing on observing bee ecology along with behavior, diet, and analyzing metagenomes and metabolomes of bee gut microbiota are necessary to comprehend the interactions between host and gut bacteria in honey bees.
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The authors would like to thank Ms. Nantinee Moontan for technical assistance. We would like to thank Dr. Eleni Gentekaki for useful discussions and editing of this manuscript.
This work was financially supported by Mae Fah Luang University.
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The authors declare that they have no conflict of interest.
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Gruneck, L., Khongphinitbunjong, K. & Popluechai, S. Gut microbiota associated with two species of domesticated honey bees from Thailand. Symbiosis (2021). https://doi.org/10.1007/s13199-021-00754-8
- Apis dorsata
- Apis florea
- Gut microbiota
- 16S rRNA gene sequencing