DOI: 10.5061/dryad.59zw3r29q
title: Data for: Breeding honey bees (Apis mellifera L.) for low and high Varroa destructor population growth: gene expression of bees performing grooming behavior

description: 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.
description: 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.