Dataset Information

Parallel Epigenomic and Transcriptomic Responses to Viral Infection in Honey Bees (Apis mellifera)

ABSTRACT: Here, we examined the transcriptional and epigenetic (DNA methylation) responses to viral infection in honey bee workers. One-day old worker honey bees were fed solutions containing Israeli Acute Paralysis Virus (IAPV), a virus which causes muscle paralysis and death and has previously been associated with colony loss. Uninfected control and infected, symptomatic bees were collected within 20-24 hours after infection. Worker fat bodies, the primary tissue involved in metabolism, detoxification and immune responses, were collected for analysis. We performed transcriptome- and bisulfite-sequencing of the worker fat bodies to identify genome-wide gene expression and DNA methylation patterns associated with viral infection. There were 753 differentially expressed genes (FDR<0.05) in infected versus control bees, including several genes involved in epigenetic and antiviral pathways. DNA methylation status of 156 genes (FDR<0.1) changed significantly as a result of the infection, including those involved in antiviral responses in humans. There was no significant overlap between the significantly differentially expressed and significantly differentially methylated genes, and indeed, the genomic characteristics of these sets of genes were quite distinct. Our results indicate that honey bees have two distinct molecular pathways, mediated by transcription and methylation, that modulate protein levels and/or function in response to viral infections.

ORGANISM(S): Apis mellifera

PROVIDER: GSE65659 | GEO | 2015/02/06

SECONDARY ACCESSION(S): PRJNA274674

REPOSITORIES: GEO

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Project description:Purpose: Parts of Europe and the United States have witnessed dramatic losses in commercially managed honey bees over the past decade to what is considered an unsustainable extent. The large-scale loss of honey bees has considerable implications for the agricultural economy because honey bees are one of the leading pollinators of numerous crops. Honey bee declines have been associated with several interactive factors. Poor nutrition and viral infection are two environmental stressors that pose heightened dangers to honey bee health. Methods: We used RNA-sequencing to examine how monofloral diets (Rockrose and Chestnut) and Israeli acute paralysis virus inoculation influence gene expression patterns in honey bees. Results: We found a considerable nutritional response, with almost 2,000 transcripts changing with diet quality. The majority of these genes were over-represented for nutrient signaling (insulin resistance) and immune response (Notch signaling and JaK-STAT pathways). Somewhat unexpectedly, the transcriptomic response to viral infection was fairly limited. We only found 43 transcripts to be differentially expressed, some with known immune functions (argonaute-2), transcriptional regulation, and muscle contraction. We created contrasts to determine if any protective mechanisms of good diet were due to direct effects on immune function (resistance) or indirect effects on energy availability (tolerance). A similar number of resistance and tolerance candidate differentially expressed genes were found, suggesting both processes may play significant roles in dietary buffering from pathogen infection. We also compared the virus main effect in our study (polyandrous colonies) to that obtained in a previous study (single-drone colonies) and verified significant overlap in differential expression despite visualization methods showing differences in the noisiness levels between these two datasets. Conclusions: Through transcriptional contrasts and functional enrichment analysis, we add to evidence of feedbacks between diet and disease in honey bees. We also show that comparing results derived from polyandrous colonies (which are typically more natural) and single-drone colonies (which usually yield more signal) may allow researchers to identify transcriptomic patterns in honey bees that are concurrently less artificial and less noisy. Altogether, we hope this work underlines possible merits of using data visualization techniques and multiple datasets when interpreting RNA-sequencing studies.

Dataset Information

Parallel Epigenomic and Transcriptomic Responses to Viral Infection in Honey Bees (Apis mellifera)

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