Background

Social organisms, including honey bees (Apis mellifera L.), have defense mechanisms to control the multiplication and transmission of parasites and pathogens within their colonies. Self-grooming, a mechanism of behavioral immunity, seems to contribute to restraining the population growth of the ectoparasitic mite Varroa destructor in honey bee colonies. Because V. destructor is the most damaging parasite of honey bees, breeding them for resistance against the mite is a high priority of the beekeeping industry. We conducted a bidirectional breeding program to select honey bee colonies with low and high varroa population growth (LVG and HVG, respectively). Having high and low lines of bees allowed the study of genetic mechanisms underlying self-grooming behavior between the extreme genotypes. Worker bees were classified into two categories: ‘light groomers’ and ‘intense groomers’. The brains of bees from the different categories (LVG-intense, LVG-light, HVG-intense, and HVG-light) were used for gene expression and viral quantification analyses.

Results

Differentially expressed genes (DEGs) associated with the LVG and HVG lines were identified, including four odorant-binding proteins and a gustatory receptor. A functional enrichment analysis showed 19 enriched pathways from a list of 219 down-regulated DEGs in HVG bees, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) term of oxidative phosphorylation. Additionally, bees from the HVG line showed higher levels of Apis rhabdovirus 1 and 2, Varroa destructor virus -1 (VDV-1), and Deformed wing virus-A (DWV-A) compared to bees of the LVG line.

Conclusions

The difference in expression of odorant-binding protein genes and a gustatory receptor between bee lines suggests a possible link between them and the perception of irritants to trigger rapid self-grooming instances that require the activation of energy metabolic pathways. Therefore, our results provide new insights into the molecular mechanisms involved in honey bee grooming behavior. Differences in viral levels in the brains of LVG and HVG bees showed the importance of investigating the pathogenicity and potential impacts of neurotropic viruses on behavioral immunity. The results of this study advance the understanding of a trait used for selective breeding, self-grooming, and the potential of using genomic-assisted selection to improve breeding programs.

A total of 2,496 worker bees were collected from the brood chambers of three colonies selected for LVG, and from three colonies selected for HVG at the Honey Bee Research Centre, University of Guelph (43° 32' 11.292"N, -80° 12' 50.9898"W; De la Mora et al., 2020). Briefly, for each colony, three frames with bees from the brood chamber were shaken into a 5 L plastic container, and a scoop of bees (from different ages) was collected and transported into the lab. The bees used for self-grooming assays were randomly selected. The bees were subjected to self-grooming assays as per Guzman-Novoa et al. (2012) with modifications by Morfin et al. (2020). For each self-grooming assay, each worker bee was placed inside a Petri dish (100 mm x 15 mm; Fisher Scientific, Mississauga, ON, Canada) covered with a perforated lid, the worker bee was left for 2 min to become used to the environment. After that, approximately 20 mg of wheat flour (Robin Hood®, Markham, ON, Canada) was put on her thorax using a fine paint brush (6 mm x 11 mm; DeSerres®, Oakville, ON, Canada) to stimulate grooming instances. Each worker bee was observed for 3 min and the time of first response to the stimulus and the intensity with which the worker bee removed the flour by grooming instances was recorded. Worker bees were classified as ‘light’ groomers if slow movements were noted and no more than one or two legs were used to remove the irritant, or as ‘intense groomers’, if vigorous shaking and wiping were observed and if the bee used three or more legs to remove the irritant. After the self-grooming trials, each bee was flash-frozen on dry ice.

The brains of 50 randomly selected worker bees from each category (HVG-intense, HVG-light, LVG-intense, LVG-light) were pooled to extract RNA. There were three biological repetitions (three colonies of each genotype) and two technical replicates totalling 24 total RNA extractions of pooled brains and a total of 1,200 dissections (performed as per Morfin et al. (2021). Total RNA was extracted using TRIzol™ (Invitrogen, California, USA) following the manufacturer’s instructions (see Figure S1). A spectrophotometer was used to determine the absorbance ratio of the RNA; values between 1.8 and 2.0 for 260/280 nm and values between 2.0 and 2.2 for 260/230 nm were considered acceptable for purity. The samples were kept at -70°C until sequencing.

A total of 24 RNA samples were sent to McGill University (Génome Québec Innovation Centre, Montreal, QC, Canada) to perform a high throughput sequencing analysis. A second quality assessment of the RNA was done using a Bioanalyzer prior to cDNA library construction with NEB kit Illumina (San Diego, CA, USA). RNA sequencing was performed as 150 bp paired end reads using NovaSeq 6000 S4 (Illumina, San Diego, CA, USA).

FastQC was used to assess the quality of the raw sequence data (Andrews, 2010). A transcriptome index based on the latest honey bee transcriptome (Amel_HAV3.1 genome build; Wallberg et al. 2019) was built with Kallisto (Bray et al., 2016). The output produced by Kallisto was processed using Sleuth (R studio Team, 2020; Pimentel et al., 2017). BioMart package was used to match gene names to transcripts (Durinck et al., 2009). The exploratory analysis of the RNAseq data was done using Shiny (Potter et al., 2016). A functional enrichment analysis was done with g:Profiler (Raudvere et al., 2019), using cumulative hypergeometric test to evaluate the functional enrichment of the gene list and perform multiple test corrections with g:SCS (set counts and sizes).

The FastVirome pipeline (Tithi et al., 2018) was used to identify and quantify viral transcript abundance, using a precomputed Kallisto index containing the sequences of 20 viruses known to infect honey bees, retrieved from the National Center for Biotechnology Information (NCBI, 2021). Degust (Powell, 2019) was used to compare the viral transcript abundance between bees from LVG and HVG colonies.