The microsporidia parasite Nosema contributes to the steep global decline of honey bees that are critical pollinators of food crops. There are two species of Nosema that have been found to infect honey bees, Nosema apis and N. ceranae. Genome sequencing of N. apis and comparative genome analysis with N. ceranae, a fully sequenced microsporidia species, reveal novel insights into host-parasite interactions underlying the parasite infections. We applied the whole-genome shotgun sequencing approach to sequence and assemble the genome of N. apis which has an estimated size of 8.5 Mbp. We predicted 2,771 protein- coding genes and predicted the function of each putative protein using the Gene Ontology. The comparative genomic analysis led to identification of 1,356 orthologs that are conserved between the two Nosema species and genes that are unique characteristics of the individual species, thereby providing a list of virulence factors and new genetic tools for studying host-parasite interactions. We also identified a highly abundant motif in the upstream promoter regions of N. apis genes. This motif is also conserved in N. ceranae and other microsporidia species and likely plays a role in gene regulation across the microsporidia. The availability of the N. apis genome sequence is a significant addition to the rapidly expanding body of microsprodian genomic data which has been improving our understanding of eukaryotic genome diversity and evolution in a broad sense. The predicted virulent genes and transcriptional regulatory elements are potential targets for innovative therapeutics to break down the life cycle of the parasite.

Background: Disease is a major factor driving the evolution of many organisms. In honey bees, selection for social behavioral responses is the primary adaptive process facilitating disease resistance. One such process, hygienic behavior, enables bees to resist multiple diseases, including the damaging parasitic mite Varroa destructor. The genetic elements and biochemical factors that drive the expression of these adaptations are currently unknown. Proteomics provides a tool to identify proteins that control behavioral processes, and these proteins can be used as biomarkers to aid identification of disease tolerant colonies. Results: We sampled a large cohort of commercial queen lineages, recording overall mite infestation, hygiene, and the specific hygienic response to V. destructor. We performed proteome-wide correlation analyses in larval integument and adult antennae, identifying several proteins highly predictive of behavior and reduced hive infestation. In the larva, response to wounding was identified as a key adaptive process leading to reduced infestation, and chitin biosynthesis and immune responses appear to represent important disease resistant adaptations. The speed of hygienic behavior may be underpinned by changes in the antenna proteome, and chemosensory and neurological processes could also provide specificity for detection of V. destructor in antennae. Conclusions: Our results provide, for the first time, some insight into how complex behavioural adaptations manifest in the proteome of honey bees. The most important biochemical correlations provide clues as to the underlying molecular mechanisms of social and innate immunity of honey bees. Such changes are indicative of potential divergence in processes controlling the hive-worker maturation.

We present a novel way to select for highly polygenic traits. For millennia, humans have used observable phenotypes to selectively breed stronger or more productive livestock and crops. Selection on genotype, using single-nucleotide polymorphisms (SNPs) and genome profiling, is also now applied broadly in livestock breeding programs; however, selection on protein/peptide or mRNA expression markers has not yet been proven useful. Here we demonstrate the utility of protein markers to select for disease-resistant hygienic behavior in the European honey bee (Apis mellifera L.). Robust, mechanistically-linked protein expression markers, by integrating cis- and trans- effects from many genomic loci, may overcome limitations of genomic markers to allow for selection. After three generations of selection, the resulting marker-selected stock outperformed an unselected benchmark stock in terms of hygienic behavior, and had improved survival when challenged with a bacterial disease or a parasitic mite, similar to bees selected using a phenotype–based assessment for this trait. This is the first demonstration of the efficacy of protein markers for industrial selective breeding in any agricultural species, plant or animal.

The Western honey bee (Apis mellifera L.) is a critical component of human agriculture through its pollination activities. For years, beekeepers have controlled deadly pathogens such as Paenibacillus larvae, Nosema spp. and Varroa destructor with antibiotics and pesticides but widespread chemical resistance is appearing and most beekeepers would prefer to eliminate or reduce the use of in-hive chemicals. While such treatments are likely to still be needed, an alternate management strategy is to identify and select bees with heritable traits that allow them to resist mites and diseases. Breeding such bees is difficult as the tests involved to identify disease-resistance are complicated, time-consuming, expensive and can misidentify desirable genotypes. Additionally, we do not yet fully understand the mechanisms behind social immunity. Here we have set out to discover the molecular mechanism behind hygienic behavior (HB), a trait known to confer disease resistance in bees. After confirming that HB could be selectively bred for, we correlated measurements of this behavior with protein expression over a period of three years, at two geographically distinct sites, using several hundred bee colonies. By correlating the expression patterns of individual proteins with HB scores, we identified seven putative biomarkers of HB that survived stringent control for multiple hypothesis testing. Intriguingly, these proteins were all involved in semiochemical sensing (odorant binding proteins), nerve signal transmission or signal decay, indicative of the series of events required to respond to an olfactory signal from dead or diseased larvae. We then used recombinant versions of two odorant-binding proteins to identify the classes of ligands that these proteins might be helping bees detect. Our data suggest that neurosensory detection of odors emitted by dead or diseased larvae is the likely mechanism behind a complex and important social immunity behavior that allows bees to co-exist with pathogens.

Many pathogens and parasites have evolved to overwhelm and suppress their host’s immune system. Nevertheless, the interactive effects of these agents on colony productivity and wintering success have been relatively unexplored, particularly in large-scale phenomic studies. As a defense mechanism, honey bees have evolved remarkable social behaviors to defend against pathogen and parasite challenges, which reduce the impact of disease and improve colony health. To investigate the complex role of pathogens, parasites and social immunity behaviors in relation to colony productivity and outcomes, we extensively studied colonies at several locations across Canada for two years. In 2016 and 2017, colonies founded with 1-year-old queens of diverse genetic origin were evaluated, which represented a generalized subset of the Canadian bee population. During each experimental year (May through April), we collected phenotypic data and sampled colonies for pathogen analysis in a standardized manner. Measures included: colony size and productivity (colony weight, cluster size, honey production, and sealed brood population), social immunity traits (hygienic behavior, instantaneous mite population growth rate, and grooming behavior), as well as quantification of gut parasites (Nosema spp., and Lotmaria passim), viruses (DWV-A, DWV-B, BQCV and SBV) and external parasites (Varroa destructor). Our goal was to examine: 1) correlations between pathogens and colony phenotypes; 2) the dynamics of pathogens and parasites on colony phenotypes and productivity traits; and 3) the effects of social immunity behaviors on colony pathogen load. Our results show that colonies expressing high levels of some social immunity behaviors were associated with low levels of pathogens/parasites, including viruses, Nosema spp., and V. destructor. In addition, we determined that elevated viral and Nosema spp. levels were associated with low levels of colony productivity, and that five out of six pathogenic factors measured were negatively associated with colony size and weight in both fall and spring periods. Finally, this study also provides information about the incidence and abundance of pathogens, colony phenotypes, and further disentangles their inter-correlation, so as to better understand drivers of honey bee colony health and productivity.

The ideal-free distribution and central-place foraging are important ecological models that can explain the distribution of foraging organisms in their environment. However, this model ignores distance-based foraging costs from a central place (hive, nest), whereas central-place foraging ignores competition. Different foraging currencies and cooperation between foragers also create different optimal distributions of foragers, but are limited to a simple two-patch model. We present a hybrid model of the ideal-free distribution that uses realistic competitive effects although accounting for distance-based foraging, and test it using honey bees (Apis mellifera L.) foraging in canola fields (Brassica napus L.). Our simulations show that foragers maximizing efficiency $(energyprofits÷losses)$ prioritize distance to their aggregation more than those maximizing net-rate $(energyprofits÷time),$ and that social foragers move to more distant patches to maximize group benefits, meaning that social foragers do not approach an ideal-free distribution. Simulated efficiency-maximizers had a hump-shaped relationship of trip times with distance, spending shorter amounts of time in both nearby and far-away patches. Canola fields were far more attractive to simulated foragers than semi-natural areas, suggesting limited foraging on semi-natural lands during the bloom period of canola. Finally, we found that the observed distribution of honey bees in canola fields most closely resembled the optimal distribution of solitary efficiency-maximizers. Our model has both theoretical and practical uses, as it allows us to model central-place forager distributions in complex landscapes as well as providing information on appropriate hive stocking rates for agricultural pollination.