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Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens
(2017)
Background: Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses.
Results: We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse pathogens. We found that genes differentially expressed after infection exhibit a higher evolutionary rate than non-differentially expressed genes. Using our new bioinformatics approach, we unveiled additional pathogen-specific responses of honey bees; we found that apoptosis appeared to be an important response following microsporidian infection, while genes from the immune signalling pathways, Toll and Imd, were differentially expressed after Varroa/virus infection. Finally, we applied our bioinformatics approach and generated a gene co-expression network to identify highly connected (hub) genes that may represent important mediators and regulators of anti-pathogen responses.
Conclusions: Our meta-analysis generated a comprehensive overview of the host metabolic and other biological processes that mediate interactions between insects and their pathogens. We identified key host genes and pathways that respond to phylogenetically diverse pathogens, representing an important source for future functional studies as well as offering new routes to identify or generate pathogen resilient honey bee stocks. The statistical and bioinformatics approaches that were developed for this study are broadly applicable to synthesize information across transcriptomic datasets. These approaches will likely have utility in addressing a variety of biological questions.
The bipartite interactions between insect hosts and their bacterial gut microbiota, or their bacterial pathogens, are empirically and theoretically well-explored. However, direct, and indirect tripartite interactions will also likely occur inside a host. These interactions will almost certainly affect the trajectory of pathogen virulence evolution, an area that is currently under researched. The interactions within tripartite associations can be competitive, that is, exploitative-competition, interference-competition or apparent-competition. Competitive interactions will be significantly influenced by non-competitive effects, for example, immunopathology, immunosuppression, and microbiota-mediated tolerance. Considering a combination of these interactions and effects, will enable an increased understanding of the evolution of pathogen virulence. This new perspective allows us to identify several novel research questions, which we hope will be a useful framework for future research.