Transcriptome Responses of Atlantic Salmon (

Transcriptomics provides valuable data for functional annotations of genes, the discovery of biomarkers, and quantitative assessment of responses to challenges. Meta-analysis of Nofima's Atlantic salmon microarray database was performed for the selection of genes that have shown strong and reproducible expression changes. Using data from 127 experiments including 6440 microarrays, four transcription modules (TM) were identified with a total of 902 annotated genes: 161 virus responsive genes - VRG (activated with five viruses and poly I:C), genes that responded to three pathogenic bacteria (523 up and 33 down-regulated genes), inflammation not caused by infections - wounds, melanized foci in skeletal muscle and exposure to PAMP (180 up and 72 down-regulated genes), and stress by exercise, crowding and cortisol implants (33 genes). To assist the selection of gene markers, genes in each TM were ranked according to the scale of expression changes. In terms of functional annotations, association with diseases and stress was unknown or not reflected in public databases for a large part of genes, including several genes with the highest ranks. A set of multifunctional genes was discovered. Cholesterol 25-hydroxylase was present in all TM and 22 genes, including most differentially expressed matrix metalloproteinases 9 and 13 were assigned to three TMs. The meta-analysis has improved understanding of the defense strategies in Atlantic salmon. VRG have demonstrated equal or similar responses to RNA (SAV, IPNV, PRV, and ISAV), and DNA (gill pox) viruses, injection of bacterial DNA (plasmid) and exposure of cells to PAMP (CpG and gardiquimod) and relatively low sensitivity to inflammation and bacteria. Genes of the highest rank show preferential expression in erythrocytes. This group includes multigene families (gig and several trim families) and many paralogs. Of pathogen recognition receptors, only RNA helicases have shown strong expression changes. Most VRG (82%) are effectors with a preponderance of ubiquitin-related genes, GTPases, and genes of nucleotide metabolism. Many VRG have unknown roles. The identification of TMs makes possible quantification of responses and assessment of their interactions. Based on this, we are able to separate pathogen-specific responses from general inflammation and stress.