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