Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage

doi:10.21203/rs.3.rs-4727319/v1

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Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage

https://doi.org/10.21203/rs.3.rs-4727319/v1

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Honey bee gut microbiota play a number of specific roles in promoting host growth and physiology. While the gut microbiota of honey bee queens, workers, and larvae has been extensively studied, less is known about the role and composition of gut microbiota in the winter worker bees. This study investigates the dynamics of the gut microbiota in overwintering adult worker bees, focusing on two commercial bee strains: Bolton™ bees and Mann Lake™ bees. The strains were investigated under different storage conditions (indoor storage at 6°C and outdoor storage in natural conditions) during the winter months (October, November, and December). Utilizing 16S rRNA gene amplicon sequencing, we characterized the microbial composition of the whole gut. We observed a stable microbiota with slight variations in host-associated bacterial communities with Lactobacillus genus dominating in all the overwintering honey bee guts. Bolton bees exhibited higher abundance levels of Bartonella, Bifidobacterium, and Wolbachia, while Mann Lake bees showed increased abundance of Commensalibacter. Our results suggest that alpha diversity is influenced by the month rather than by the bee strain or storage conditions. We also found significant differences in beta diversity by month. Overall, in this study a stable gut microbiota was observed in both indoor and outdoor environmental conditions across different months with slight differences in abundance between bee strains. Considering all potential benefits of the honey bee gut microbiome to health and nutrition, this study suggests that beekeepers need not be concerned about whether overwintering storage conditions affect the gut microbiome.

Biological sciences/Microbiology/Communities

Biological sciences/Microbiology/Communities/Microbial ecology

Honey bees

gut microbiota

temperature stress

climate change

Lactobacillus

overwintering storage

Winter presents a significant challenge for honey bee colonies in temperate regions. Over the last two decades, beekeepers have reported high colony losses during winters. These losses have been associated with low temperature stress, geographic relocation due to climate change, genotype, diseases, pesticides, and poor nutrition ^1–4. In preparation for overwintering, a honey bee colony undergoes notable alterations in both the behavior and physiology of the worker bees. These changes include shifts in endocrine profiles, decreased individual activity, increased nutrient reserves, extended longevity, and thermoregulating clustering at the colony level ¹. Low temperatures affect honey bee survival(Q. Wang et al., 2016) through higher larval mortality and reduced life expectancy in adulthood ⁶. To mitigate these deleterious effects, honey bees have evolved life history strategies like cessation of brood rearing during the winters, increased immunity, and increased antioxidant expression ⁷. Additionally, honey bees form a dense winter cluster to withstand harsh winter conditions. The thermoregulation of this winter cluster relies heavily on the insulation provided by the tightly packed outer layer of bees, known as the mantle bees, which is crucial for survival at low temperatures ⁸. Moreover, to reduce the cold temperature stress on the bee colonies, and to improve overwintering survival, bee keepers often store the hives indoors. Storing hives under controlled cold climate conditions of 5°C − 7°C and 25% relative humidity has been shown to improve survival(Degrandi-Hoffman et al., 2019,Owens et al., 1971, Meikle et al., 2023).

The symbiotic gut microbiota of honey bees is vulnerable to temperature stress ¹². The loss of honey bee workers during the winter is due to weather conditions as the primary stress factor, with starvation and diseases being secondary factors ¹³. Moreover, honey bees experiencing cold stress have an increased risk of diseases and infections, which in turn raises the likelihood of colony losses. A recent transcriptomic study provides insight into the molecular mechanisms of cold resistance in Apis cerana. This study identified various temperature-sensitive proteins, such as heat shock proteins and zinc finger proteins, which were upregulated when bees were exposed to 0°C and are considered candidate genes for honey bees’ tolerance to cold stress ¹⁴. Apart from the host physiology, the role played by the honey bee gut microbiome has also emerged as one of the most important physiological aspects to investigate in relation to honey bee cold tolerance. The gut bacterial species within honey bees undergo alterations due to various environmental and developmental stresses, with temperature being a crucial one ¹⁵.

The honey bee gut microbiome contains five core host-specific bacterial species that are highly conserved and comprise 95% of the total microbes ^16,17. The hindgut of every adult worker contains Snodgrassella alvi, Gilliamella apicola, Lactobacillus Firm-5, Lactobacillus Firm-4, and Bifidobacterium species. Along with these core bacteria, there are non-core species including Bartonella apis, Commensalibacter spp., and some other identified and several unidentified species that have been collected from the surrounding environment like hives and plants ^18,19. These bacterial species promote honey bee growth, physiology, facilitate breakdown of toxic dietary compounds, and modulate immune functions ²⁰.Dynamic changes to the microbiota composition have been observed throughout the seasons ^21–23. The gut microbiota has been reported to differ between winter and summer honey bees, with reduced α-diversity and higher levels of Bartonella and Commensalibacter during winter ²⁴. Moreover, temperature during different months affects the composition of the gut microbiota by gradually changing the bacterial diversity between the seasons. In subtropical conditions, precipitation affects the composition of the honey bee gut microbiota ²⁵. Numerous studies have been conducted to understand the seasonal dynamics of honey bee gut microbiota. However, very few studies have explored the effects of prolonged exposure to harsh climatic conditions on honey bee gut microbiome. For instance, direct exposure to high temperatures has been found to influence the gut microbiota in both honey bees and bumble bees, rendering them more resistant to heat stress ²⁶. Additionally, elevated temperature has shown to affect nectar microbes where the abundance of bacteria increased in the warmer temperature, influencing bumble bee forager preference ²⁷.

Beyond seasonality and temperature, vast differences in gut microbiota at the strain level has also been observed in two closely related honey bee species, Apis mellifera and Apis cerana ²⁸. The gut composition exhibits greater divergence among genetically distinct bees at both phylotype- and sequence-discrete population levels ²⁹. Additionally, significant variation in both composition and function among diverse Asian honey bee populations has been reported, showing distinct patterns of their gut microbiota ³⁰. A previous study showed genetic divergence and functional convergence of gut bacteria in the eastern honey bee, Apis cerana and the western honey bee, Apis mellifera³¹ and between Apis cerana and Apis florea ³². Differences in gut microbes between strains of Apis mellifera may influence the performance of those honey bee strains under different climate conditions.

The goal of this study was to assess whether overwintering storage conditions disrupt the stability and diversity of the gut microbes in overwintering honey bees. To understand long term storage effects on gut microbiota, we compared the whole gut microbiota of two commercial strains of Apis mellifera i.e., claimed cold hardy bees https://boltonbees.com/pages/mn-hardy-hives (Bolton Bees, MN) that are bred and reared in Minnesota and Italian bees (Mann lake Bees, MN) of unknown rearing. Hives were stored either at a constant 6°C indoor or kept outside in natural conditions during winter. We predicted that hives stored outside in natural fluctuating temperatures will have different gut bacterial communities due to cold stress compared to hives stored in constant temperatures. We also predicted that bacterial communities would change when sampled over a three-month period of falling temperatures, viz. October, November, and December.

Alpha diversity

To investigate alpha diversity patterns, generalized linear mixed models (LMM) that included Shannon diversity as a response variable was applied. (Table 1). The most parsimonious, best-fit model included month as a fixed effect, and random effects for hive ID (different hives in each treatment) (Table 2). In contrast to the predictions, alternative models incorporating bee strain and storage treatment did not significantly improve the best-fit model. The random effects structure in the best fit model showed relatively little variation among hives (For hives and replications, SD = 0.1604 & 0.0406, respectively), when compared to the residual error of the model (SD = 0.3560). This indicates variation between hives and the replications do not account for the majority of the variation in Shannon diversity. Furthermore, Shannon diversity index in October did not differ significantly from November (p = 0.1275), and December (p = 0.398), whereas it was significantly lower in November compared to December (p = 0.0045). Month explained only 8% of the variation in Shannon diversity, whereas the full model explained 23% of the variation in Shannon diversity. Overall, results indicate a temporal variation in Shannon diversity, but the strain of bee and whether the hives were stored inside or outside had no effect (Fig. 1).

Table 1

Comparisons of Akaike Information Criterion (AIC) value for different Generalized Linear Mixed Models (GLMMs) with relevant predictor and random variables. Best fit GLMM with the lowest AIC value is highlighted in bold. ∆ AIC ≤ 2 indicated similar models, which leads to the selection of simpler model (i.e. with less parameters).
Model	AIC
Model: Shannon diversity index~
Storage status + month + bee strain + 1\|hive ID	110.0
Storage status * month * bee strain + 1\|hive ID	112.8
Storage status * month + bee strain + 1\|hive ID	106.8
Storage status + month * bee strain + 1\|hive ID	114.6
Storage status * bee type + strain + 1\|hive ID	113.7
Storage status + month + 1\|hive ID	108.0
Month + bee strain + 1\|hive ID	108.1
Storage status + bee strain + 1\|hive ID	116.2
Month * bee strain + 1\|hive ID	110.9
Storage status * bee strain + 1\|hive	118.2
Storage status + 1\|hive ID	114.3
Bee strain + 1\|hive ID	116.4
Month + 1\|hive ID	106.1
1\|hive ID	112.5

Table 2

Results of best fit Generalized Linear Mixed Model from Shannon diversity index.
Model		β Estimate ± SE	df	t value	p value	Variance	SD
Shannon diversity index ~ Month + 1\|hive ID
Fixed Effects	(Intercept)	3.33555 ± 0.07521	33.48419	44.352	< 2×10^{–16 a}
	Month (Reference: October
	November	− 0.16872 ± 0.08512	95.20089	-1.982	0.0503
	December)	0.11101 ± 0.08447	95.08469	1.314	0.1920
Random Effects	Hive (Intercept)					0.02506	0.1583
	Residual					0.1284	0.3584
Number of observations = 107; groups: Hive, 12;
^a Statistically significant at p ≤ 0.05. SE = standard error; SD = standard deviation

Beta diversity

To investigate beta diversity patterns, we conducted PERMANOVA using the adonis2 package in R, testing various models with Bray-Curtis distance matrices (phyloseq) as a response variable. The simplest and best-fitting model included storage status and month in interaction with bee strain as a fixed effect, and block (strata) as bee hive ID; Distance ~ Storage status + Month * Bee strain, strata = hive ID (Table 3). The overall composition of bee gut microbiomes differed by bee type (adonis F = 3.19, df = 1, R² = 0.0242, P = 0.002), month (adonis F = 5.09, df = 2, R² = 0.0872, P = 0.001), but not storage status (adonis F = 0.57, df = 1, R² = 0.0047, P = 0.887). The interaction between month and bee strain was significant (adonis F = 1.57, df = 2, R² = 0.0269, P = 0.001). Month explained only 8.7% of the variation, whereas the bee type, and interaction between month and bee strain explained 2.4%, and 2.6% of the variation, respectively. Overall, these findings suggest that the microbial dynamics in overwintering honey bee workers are influenced by bee strain over the months, with stability observed in storage conditions (Fig. 2). Pairwise comparison showed significant differences in gut microbiota when compared October – November (p value = 0.002), November – December (p value = 0.001), and October - December (p value = 0.001).

Table 3

Best fit permutation analysis of variance (PERMANOVA) model applied using the adonis function with distance matrices with 999 permutations and bee hives accounted as a blocks – strata.
Model		Df	Sum of squares	R²	F	Pr (> F)
Distance ~ Storage status + Month Bee strain, strata = hive ID)*
Fixed Effects	Storage Status	1	0.0894	0.00487	0.5701	0.887
	Month	2	1.5975	0.08702	5.0950	0.001
	Bee stain	1	0.5001	0.02724	3.1903	0.001
	Month * bee strain	2	0.4945	0.02694	1.5771	0.021
	Residual	100	15.6767	0.85394
	Total	106	18.3581	1.00000
a Statistically significant at p ≤ 0.05.

Taxonomical abundance

Following the removal of barcodes and primer sequences through QIIME2 (Demultiplexing), we obtained a collective sum of 34,879,494 paired-end 16S rRNA gene amplicon reads. The average read count per sample was 317,086, with an overall average quality score of 34. Across the 108 samples analyzed, the range of reads per sample varied from a minimum of 56,533 to a maximum of 1,314,819. Analyzing the taxonomic profile revealed a predominance of Firmicutes (70.96%), Proteobacteria (20.18%), and Actinobacteriota (5.83%) at the phylum level. We investigated the presence of the core gut microbiota and identified only a limited number of reads for Snodgrassella. Lactobacillus Firm-5 and Lactobacillus Firm-4 were not detected in our samples. We focused on examining the relative abundance of two non-core (Bartonella and Commensalibacter) and two core (Gilliamella and Bifidobacterium) microbes, along with the endosymbiont Wolbachia. The bacterial species-specific analysis for relative abundance was performed using mixed model with formula: Abundance ~ Storage status + Month + Bee strain + (1 | hive ID).

Considering the importance of genus Bartonella in a previous study ²⁴, we analyzed the abundance of two Bartonella species: Bartonella apis and another denoted as uncultured Bartonella. Bartonella apis abundance did not differ by month (df = 99.11, F value = 2.27, p-value = 0.1085), bee strain (df = 38.26, F value = 0.1824, p-value = 0.6717), or storage status (df = 38.26, F value = .1824, p-value = 0.6717). The Bartonella uncultured ASV significantly differed by month ( df = 98.72, F value = 4.74, p-value = 0.0107) while storage status (df = 34.58, F value = 0.0528, p-value = 0.81954) and bee strain (df = 11.559, F value = 0.1322, p-value = 0.72274) were not significant. We found a significant difference in abundance between December and November (SE = 1.03, df = 97, p-value: 0.0085), but not between October and December (SE = 1.21, df = 112, Tuckey HSD, p-value = 0.358) or October and November (SE = 1.21, df = 112, Tuckey HSD, p-value = 0.456). For Commensalibacter (Commensalibacter Acetobacteraceae_bacterium), bee strain (SE = 1.48, df = 102, Tukey HSD, p-value < 0.0001) was the only factor significantly influencing abundance with Mann Lake bees having a higher abundance than Bolton bees. The effect of storage status (df = 107, F value = 0.261, p-value = 0.6097) and month (df = 107, F value = 1.6663, p-value = 0.1938) was not statistically significant.

Bee strain is a significant predictor of Bifidobacterium (Bifidobacterium uncultured_Bifidobacterium) abundance where Bifidobacterium showed higher abundance in Bolton bees compared to Mann Lake bees (SE = 0.077, df =102, Tuckey HSD, p-value = 0.0014). The storage status (df = 107, F value = 0.0682, p-value = 0.7944) and month (df = 107, F value = 0.8069, p-value = 0.4489) variables did not contribute significantly to the variation in this model. The storage status (df = 37.34, F value = 6.67, p-value = 0.013) and month (df = 99.15, F value = 14.18, p-value = 3.808×10⁻⁶) significantly affected the abundance of Gilliamella (Unclassified Gilliamella), while Bee strain did not show a significant effect (df = 12.10, F value = 0.675, p-value = 0.426). Pairwise comparison showed significant differences in abundance during October and December (SE = 0.197, df =112, Tuckey HSD, p-value < 0.0001), November and December (SE = 0.166, df =97.5, Tuckey HSD, p-value = 0.002), but no significant difference during November and October (SE = 0.197, df =112, Tuckey HSD, p-value = 0.5361). A significant effect of the storage status on the abundance of Gilliamella was seen between the Inside and Outside bees (SE = 0.215, df =45, Tuckey HSD, p-value = 0.0272). storage status (df = 48.66, F value = 0.0377, p-value = 0.846) and Month (df = 99.42, F value = 0.97, p-value = 0.38) is not a significant predictor of abundance in Wolbachia (Unclassified Wolbachia). Wolbachia abundance was significantly different between the bee strains (df = 11.78, F value = 5.352 p-value = 0.03959). The results suggest a potential difference in gut microbial abundance between Bolton bees and Mann lake bees, but the significance was marginal (SE = 0.243, df =14, Tuckey HSD, p-value = 0.0570). Overall, this taxonomic data emphasizes the marginal differences in gut microbiome composition between bee types, revealing a temporally stable microbial composition that is not influenced by storage conditions in overwintering worker honey bee (Figure 3).

We used a 16s rRNA gene amplicon sequencing approach to study/characterize the gut microbiota of overwintering adult worker bees between (a) two commercial bees (Bolton Bees and Mann Lake bees), (b) different storage status/conditions and, (c) different months during storage/ a season. In this study, we hypothesized that temperature would be one of the most important environmental stressors affecting the gut microbiota in overwintering honey bees. Surprisingly, we found no changes in the diversity, composition, or abundance of the microbiota when the bees were stored at constant 6°C or outside during the winter. Despite the physiological stress induced by cold temperatures on honey bee physiology, this study demonstrates that honey bees have the potential to acclimatize their gut microbiota despite the cold stress conditions. One possible explanation for these findings is that honey bees rely on specific bacterial species to maintain their health and utilize the functions performed by the gut microbiota, and therefore the bees retain and maintain these bacterial species to extract sufficient resources from them.

Interestingly, the overwintering bees’ gut microbiome was dominated by Lactobacillus genus, which contributed to an increased bacterial load across all samples, regardless of storage conditions. As the most important genus in the guts of honey bees ^33,34, Lactobacillus plays an important role in the health of bees by showing probiotic characteristics. Lactobacilli have shown an ability to digest flavonoids and other compounds present in the pollen wall ³⁵, to inhibit pathogens^36–38 and to release short chain fatty acids and vitamins used by midgut cells as an energy resource ³⁹. A higher abundance of Lactobacillus during overwintering phase may support heat production by worker bees, stabilizing colonies temperature. The only differences in bacterial composition at the species level were observed between both bee types. Bolton bees exhibited significantly higher abundance of Bartonella (in some instances), Bifidobacterium, and Wolbachia, whereas Mann lake showed significantly higher abundance of Commensalibacter when compared to Bolton bees.

The bees obtained from Bolton Bees represent specific genetic lines known for their resilience in Minnesota's harsh climate. This genetic line, named after the location, where the parent Queen overwintered (Bolton, Minnesota), is derived from the base stock called MN Hygienic. The queens from the MN-Hardy line, used as grafts for queen production, have successfully endured the challenges of a prolonged and frigid Minnesota winter. Typically, such severe winters could affect honey stores, cleansing flights, and brood-laying, but these MN-Hardy Queens are claimed to have demonstrated ability to thrive under these cold winter conditions (https://boltonbees.com/pages/mn-hardy-hives). On the other hand, “OHB Italian” Mann Lake bees, sourced from Olivarez Honey Bees (OHB) in Northern California, are accompanied by claims for their hive performance, disease resistance, and overall robust health. The Italian Queens from OHB have reportedly undergone extensive breeding and careful genetic selection. The incorporation of traits such as "Minnesota Hygienic" and "VSH" (Varroa Sensitive Hygiene) further strengthens their innate ability to resist diseases and combat mite infestations. These two distinct types of bees have been specifically employed for their resilience to withstand the challenging winters typical of the Midwest, making them interesting subjects to study the gut microbiome during the overwintering period.

Among the social bees, the transmission of symbiotic bacteria in the gut occurs among successive generations of siblings through social interactions. In a previous study on honey bees, the colonization of specific sets of bacteria, such as Lactobacillus Firm5 and Bifidobacterium Bifido-1.2, in genetically varied hosts strongly suggests that the genotype significantly influences the microbiota structure ²⁹. Another study on honey bees has identified marked differences in the core gut microbial community when comparing different lineages, which include Maltese honey bees (lineage A) to the Italian honey bees (lineage C). Notably, Maltese honey bees exhibited an inverse proportion of Lactobacillaceae and Bartonellaceae when compared to Italian honey bees ⁴⁰. The functionality of these differences between bee strains have not been studied. This finding underscores the extent of strain-level diversity within the bacterial communities. Our study showed extensive overlapping of the gut microbial strains among both bee types and differences in specific bacterial species. The results of the current study raises several fundamental questions regarding the evolution and maintenance of stable microbiota with slight change in specific host-associated bacterial communities.

The findings in this study reveal varying degrees of presence of bacterial species in overwintering honeybees across different months. There were significant differences in the richness and evenness (Shannon Diversity Index) between October and November but no differences were noted in the bees sampled in November when compared to December samples. Additionally, beta diversity shows difference in species diversity in different months. Before overwintering, foragers collect pollen, water, nectar, and propolis. Remarkably, the overwintering honeybee samples exhibited consistent dominance in species composition with no significant difference in their abundance across different months. However, a significant difference in beta diversity was observed and this is consistent with findings from prior studies ^41,42. These findings could be interpreted as that during the timeframe when the honey bee workers are confined to their hives without access to foraging, there is a lack of exposure to new environmental microorganisms to colonize and populate the bee gut.

Previous studies have noted that the gut microbiota differs between winter and summer honey bees, with the long-lived winter bees exhibiting a stable microbiota with reduced α-diversity and higher levels of Bartonella and Commensalibacter ²⁴. This lower community alpha diversity with Bartonella and Commensalibacter as dominant bacterial species may confer certain physiological benefits. However, these studies also collectively reveal minor variations in gut microbial communities in temperate honey bee colonies during winter, indicating a shift in dominance, with the non-core bacterium Bartonella surpassing the core bacterial species ^24,42,43. Another recent study also highlighted the significance of Bartonella, highlighting its expanded capability to convert metabolic wastes such as lactate and ethanol into pyruvate, which potentially provides energy for the host as well as other symbionts ⁴⁴. Considering the importance of Bartonella in previous studies, we analyzed two different species of Bartonella for their taxonomic abundance. However, we found the Lactobacillus genus as dominant, present in higher abundance, contrasting with the findings of a previous study where the Bartonella genus showed dominance in winter bees. Lower diversity in the gut microbiota was observed in both summer and winter, with predominance of Gilliamella apicola and Snodgrasella alvi ²⁵. Overall, we observed that the honey bee gut microbiota remains stable across different months whether under storage of natural conditions in mid-western part of the United States.

In this study, it is possible that seasonal- or storage condition-dependent variations in the hind gut core bacteria are either not detectable, or are being overridden by the non-core, non-stable midgut bacteria. It is possible that slight differences in the relative abundance of bee-associated bacterial species are influenced by changes in other non-core hive-associated bacteria. A previous study ⁴⁵ identified co-evolutionary relationship of core gut bacteria and honey bee that is generally very similar and share the same core bacterial species. Measuring slight differences between bee strains in terms of absolute abundance would be challenging due to costs associated with increasing the depth of 16S sequencing. Additionally, 16S rRNA data cannot measure absolute abundances, only the relative abundance and general compositional nature of the microbiome. To enhance bacterial species detection resolution and functional profiling at the gene level, future studies could employ shotgun sequencing instead of 16S amplicon sequencing. Our study was also limited in timeframe, only sampling in the fall and early winter. We observed beta diversity drastically decline in the month of November, and it is likely that this pattern could change if bees were sampled during subsequent months, such as January, February, and March.

In conclusion, our study shows that the gut microbiota of overwintering honey bees is dominated by one specific bacterial genus, Lactobacillus, which contribute to the bees’ ability to withstand environmental stressors due to known functionality ^33–39. Our study of the gut microbiota in overwintering honey bees reveals significant stability across different months and storage conditions. However, there was a significant difference in alpha and beta diversity, which was influences by month irrespective of storage conditions and different bee strain. Furthermore, our analysis reveals minor differences in the gut microbiota between different bee types, viz. Bolton and Mann Lake bees. Despite the microbial differences among the bee types, the overall stability of the gut microbiota across different months and storage conditions suggests a remarkable resilience of honey bee gut bacteria to environmental changes. This resilience may be attributed to the co-evolutionary relationship between honey bees and their gut microbiota, which has developed over millions of years ⁴⁵. Finally, our findings suggest that beekeepers need not be overly concerned about changes in the gut microbiota during the winter months, as the bacteria appear to remain stable regardless of storage conditions. Overall, our study contributes to the growing body of research on the gut microbiota of honey bees and its role in bee health during winter. By demonstrating the stability of the gut microbiota across different months of storage and storage conditions, this study provides important insights for beekeepers and researchers alike. Further research is needed to explore the mechanisms underlying the resilience of honey bee gut bacteria and its implications for bee health.

Insect samples

Honey bee hives were managed outside in the field from the first week of May to the last week of October and then 3 hives from each bee strain were shifted to refrigerated cargo containers for overwintering storage (6°C) in November while 3 hives from each bee strain were left outdoors in the field. The first sampling was done on 10/17/2022 (base sampling of outdoor bees), followed by moving some hives inside storage on 10/21/2022. The second sampling was done on 11/15/2022 from hives both inside and outside. The third sampling was done 12/14/2022, again for both inside and outside hives (Fig. 4, Table. 4)

Table 4

Representation of honey bee sample collected from various storage environments across different months.
	Bee strain
Months	Mann lake Bees: Outside (6 hives)		Bolton Bees: Outside (6 hives)
October	3 bees/hive	3 bees/hive	3 bees/hive	3 bees/hive
	Mann lake Bees		Bolton Bees
	Storage - Constant 6°C (3 hives)	Outside (3 hives)	Storage - Constant 6°C (3 hives)	Outside (3 hives)
November	3 bees/hive	3 bees/hive	3 bees/hive	3 bees/hive
December	3 bees/hive	3 bees/hive	3 bees/hive	3 bees/hive

DNA extraction

Three bees from each treatment were used for whole gut dissection after surface sterilizing bees using 1% sodium hypochlorite followed by three washes using sterilized water in sterile conditions. Whole gut samples were prepared by bead-beating the samples on a Qiagen Tissuelyser for 6 min at 30 Hz to disrupt recalcitrant cells. Samples for cell lysis were prepared by combining two 3-mm chromium steel beads and approximately 50 µl of 0.1 & 0.5 mm ZR BashingBeads™ inside lysis tube (Biospec, Bartlesville, OK) with 750 µl of ZymoBIOMICS™ lysis solution and 20 µl of proteinase K. A subsequent round of bead beating involved rotating the samples for 6 minutes at 30 Hz, followed by an incubation period at 56°C for one hour. DNA was extracted using ZymoBIOMICS™ DNA microprep kit collection (Zymo research, Irvine, California) including 2 blank extractions as a no template control for further downstream analysis.

PCR Amplification and Illumina Sequencing

16S rRNA gene libraries for paired-end reads were generated following a previously published protocol (McFrederick & Rehan, 2016; Russell & McFrederick, 2022). The V5-V6 region of the 16S rRNA gene was amplified using 16S rRNA gene primers (799F mod3: CMGGATTAGATACCCKGG and 1115R: AGGGTTGCGCTCGTTG), each incorporating a unique barcode sequence. PCR1 reactions were conducted with 2 µl of DNA, 10 µl of 2× Pfusion High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA), 10 µl of ultrapure water, and 0.5 µl of 10 µM 799F-mod3 and 0.5 µl of 10 µM 1115R primers. The reaction condition for PCR1 was 94°C for 3 minutes, 94°C for 45 seconds, 52°C for 1 minute, 70°C for 1:30 minutes, repeated step2 29X, and 72°C for 10 minutes to amplify this region. We ran gel electrophoresis to confirmed if 16s rRNA primers were successfully attached to our samples. To complete the Illumina adapter sequence, we initiated the process by cleaning the PCR product with exonuclease and shrimp alkaline phosphatase. Exonuclease was applied to remove excess primers, while shrimp alkaline phosphatase was used to eliminate residual deoxynucleoside triphosphates (dNTPs). Four µL of the 1x ExoSAP was mixed with each 7µL PCR1 sample. This reaction was incubated at 37˚C for 30 minutes and then 95˚C for 5 minutes. Following this initial cleanup, the purified PCR products were employed as templates for a second PCR. This subsequent PCR (PCR2) utilized 1 µl of the cleaned PCR product as a template, using the same primers (PCR2F and PCR2R), and was conducted under conditions identical to the initial PCR. For PCR2, a 1 µl aliquot of Exo-Sap PCR1 product was utilized to conduct the second step of Illumina library preparation. In PCR2, the Exo-Sap PCR1 products were further amplified, incorporating linker poly-A primers for recognition on the sequencing platform. The PCR2 reaction conditions included an initial denaturation at 94°C for 3 minutes, followed by cycles of 94°C for 45 seconds, 58°C for 1 minute, 72°C for 1 minute and 30 seconds, with step 2 repeated 14 times, and a final extension at 72°C for 10 minutes, with final volume of 25 µl. To ensure uniform DNA input quantities, Invitrogen DynaMag TM-96 Side Skirted kit was employed for the normalization step across all samples prior to submitting a final volume of 10 µl for sequencing. The 16 rRNA gene sequencing was performed on NextSeq2000 P2 600 cycle kit (2x300xN/A) that produced total of 382.53 M reads at Q30 of 88.96%.

Bioinformatics

QIIME 2-2019 was used for the visualization and trimming of low-quality ends in reads from raw 16S rRNA sequence libraries. Subsequently, DADA2 was utilized for the assignment of sequences to amplicon sequence variants (ASVs), which represent 16S rRNA gene sequences with 100% matches. This process involved the removal of chimeras and reads with more than two expected errors. Taxonomy was assigned to the ASVs through the sklearn classifier, trained specifically for the 799-1,115 region of the 16S rRNA gene, utilizing the SILVA database. Additionally, local BLASTn searches against the NCBI 16S microbial database (accessed October 2023) were conducted. Features were filtered from the resultant ASV table, corresponding to contaminants identified in the blanks using the R package ‘decontam’ (version 1.10.0) at a conservative threshold of 0.5. This process aimed to identify contaminants while also eliminating ASVs identified as chloroplast and mitochondria. To standardize the number of sequences per library, alpha rarefaction in QIIME2 was employed, and 4000 reads per sample were selected. This approach ensured the retention of 107 out of 108 samples while capturing most of the diversity.

Generalized Linear Mixed-effects Models (GLMMs) was used to model the Shannon diversity index. All GLMMs were fitted using the lme4 package ⁴⁸ and lmer Test package ⁴⁹ in R ⁵⁰, using R studio version 4.2.2. Bee strain, storage treatment (exposure to natural vs. storage at 6°C temperature), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measuring of each hive ⁵¹. A stepwise backward selection process was employed for model selection. Initially, a full model was run with all fixed effects and random effects. Then all possible interactions among fixed effects were examined, followed by the systematic removal of one fixed effect at a time. The process continued until no further model improvement (e.g., lower AIC) could be observed. A difference of ≤ 2 in the AIC indicated that models were similar, in which case the simpler model (i.e., with fewer parameters) was selected as the best-fit model ⁵². The best fit model was diagnosed for model assumptions through residual diagnostic plots using the ‘DHARMa’ package ⁵³. Finally, package ‘ggeffects’ version 0.16.0 ⁵⁴ was used to extract means, standard deviations, and 95% confidence intervals for the predicted values of Shannon diversity index for different sampling times. Tukey HSD tests for multiple comparisons between treatments was employed with estimated marginal means comparisons (EMMs) using the ‘emmeans’ function (Russell V. Lenth, 2024).

For beta diversity analysis, Adonis Bray–Curtis distance dissimilarities and nonmetric multidimensional scaling (NMDS) ordination were conducted in R v4.3.0 with ‘vegan’ package. The 'phyloseq’ (ver 1.34.0) R package was used for beta diversity analysis. Permutation analysis of variance (PERMANOVA) was applied using the ‘adonis’ function on distance matrices with 999 permutations. The differences between bee gut microbiomes including distance as a dependent variable and months (October, November, and December), bee strain (Bolton bees and Mann lake bees) and Storage Status (Inside and Outside) as independent variable were assessed using separate Adonis models. In all models, bee hives were accounted as a block- strata, where permutations were allowed among levels of all the variable, but within each level of bee hives (random variable), no permutations occurred.

To understand the effect of different variables on the abundance of candidate bacterial species, Generalized Linear Mixed-effects Models (GLMMs) was used to model the abundance of particular bacterial species. Bee strain, storage treatment (exposure to natural vs. storage at 6°C), and sampling month were incorporated as fixed effects. Hive ID was incorporated as a random effect to account for the repeated measures of each hive ⁵¹. The fitted GLMM and lm was diagnosed for issues with heterogeneity, overdispersion or missing co-variates using the residual plots. To evaluate the significance of fixed effect, analysis of variance tables for the GLMM was calculated using Wald chi-square tests. Tukey HSD tests was employed for multiple comparisons between treatments with estimated marginal means comparisons (EMMs) using the ‘emmeans’ function (Lenth, 2024).

Data availability and benefit-sharing statement:

https://datadryad.org/stash/share/ycQx06SicJFuCPsZ0XMkwLVX9F6DetlucISxvTQoQF0

Acknowledgments: The authors thank Melanie Tischleder and Nyle Jonason for their technical assistance through this project. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.

Author contributions: Gagandeep Brar (GB) is the first author, with Lyna Ngor (LN), Quinn McFrederick (QSM), Preetpal Singh (PS), Arun Rajamohan (AR), Alex Torson (AT), Joseph Rinehart (JR), and Julia Bowsher (JB) as co-authors. GB, JR, and JB conceived and designed the study. GB and LN processed samples, and GB and PS analyzed the data. GB wrote the first version of the manuscript. All authors contributed to reviewing the final manuscript.

Funding: Funding was provided by NSF RII Track-2 FEC 1826834, NSF-IOS-1557940, and USDA-ARS 3060-21220-032-00D

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“All authors affirm that the study confirms to the ethical standards and integrity policies of the Scientific Reports”

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No competing interests reported.

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You are reading this latest preprint version

Stability of the Gut Microbiome in Honey Bees During Harsh Winters and Overwintering Storage

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Abstract

Figures

Introduction

Results

Alpha diversity

Beta diversity

Taxonomical abundance

Discussion

Methods

Insect samples

DNA extraction

PCR Amplification and Illumina Sequencing

Bioinformatics

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References

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Status:

Version 1