4 research outputs found
honeybee workers exhibit conserved molecular responses to diverse pathogens
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
Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens
This is the final version of the article. Available from the publisher via the DOI in this record.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.This article is a joint effort of the working group TRANSBEE and an
outcome of two workshops kindly supported by sDiv, the Synthesis
Centre for Biodiversity Sciences within the German Centre for Integrative
Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Science
Foundation (FZT 118). New datasets were performed thanks to the Insect
Pollinators Initiative (IPI grant BB/I000100/1 and BB/I000151/1), with participation
of the UK-USA exchange funded by the BBSRC BB/I025220/1 (datasets #4,
11 and 14). The IPI is funded jointly by the Biotechnology and Biological
Sciences Research Council, the Department for Environment, Food and Rural
Affairs, the Natural Environment Research Council, the Scottish Government
and the Wellcome Trust, under the Living with Environmental Change
Partnershi
Additional file 2: Table ST1. of Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens
List of 7,077 genes ordered by their rank product after the rank product analysis looking for up-regulated genes. Genes ordered from higher ranks (up-regulated) to lower ranks (non-regulated). Table ST2. List of 7,077 genes ordered by their rank product after the rank product analysis looking for down-regulated genes. Genes ordered from higher ranks (down-regulated) to lower ranks (non-regulated). Table ST3. List of 7,077 genes ordered by their rank product after the rank product analysis looking for differentially-regulated genes. Genes ordered from higher ranks (differentially-regulated) to lower ranks (non-regulated). Table ST4. List of 7,077 genes ordered by their rank product after the directed rank product analysis looking for up-regulated genes in abdominal tissue, after Nosema infection. Table ST5. List of 7,077 genes ordered by their rank product after the directed rank product analysis looking for down-regulated genes in abdominal tissue, after Nosema infection. Table ST6. Functional analysis (GO slim) based on top up-regulated genes in abdominal tissues (gut, fat body or all abdomen) upon infection by Nosema. Cut-offâ<â0.01 uncorrected p-value, genes from S4 Table. Table ST7. Functional analysis (GO slim) based on top down-regulated genes in abdominal tissues (gut, fat body or all abdomen) upon infection by Nosema. Cut-offâ<â0.01 uncorrected p-value, genes from S5 Table. Table ST8. List of 7,077 genes ordered by their rank product after the directed rank product analysis looking for up-regulated genes after RNA virus infection and Varroa infestation. Table ST9. List of 7,077 genes ordered by their rank product after the directed rank product analysis looking for down-regulated genes after RNA virus infection and Varroa infestation. Table ST10. Functional analysis (GO slim) based on top up-regulated genes upon infection by RNA virus and Varroa infestation. Cut-offâ<â0.01 uncorrected p-value, genes from S8 Table. Table ST11. Functional analysis (GO slim) based on top down-regulated genes upon infection by RNA virus and Varroa infestation. Cut-offâ<â0.01 uncorrected p-value, genes from S9 Table. Table ST12. List of the 209 highly connected (hub) genes with at least 34 inter-gene connections. Table ST13. List of genes involved in the immune gene network (Fig. 4C). Table ST14. List of immune genes used to construct the immune gene network (Fig. 4C). Table ST15. Experimental procedure and description of datasets. (XLSX 9947 kb
Additional file 1: Figure S1-S9. of Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens
This file includes supplementary figures documenting our multidimensional scaling analysis results, a heat map of the differential expression of the 7,077 genes across the 19 datasets, a Venn diagram of differentially expressed genes, the expression profile of the gene coding for hymenoptaecin, the distribution of genes according to their number of inter-gene connections, the degree of connectivity of differentially expressed genes, the process of gene selection for this study, the distribution of genesâ differential expression across datasets, and a diagram illustrating our new bioinformatics approach. (PDF 683Â kb