Transcriptome Responses of Atlantic Salmon (Salmo salar L.) to Viral and Bacterial Pathogens, Inflammation, and Stress

Abstract

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.

Expression Analysis of Moritella viscosa-Challenged Atlantic Salmon Identifies Disease-Responding Genes, MicroRNAs and Their Predicted Target Genes and Pathways

“…These results agree well with previous microarray studies on Moritella [ 36 ]. This indicates that gene expression changes both in DE-mRNAs as well as gDE-miRNAs are representative for responses to M. viscosa infection.…”

“…One based on homology-based functional annotation of the genes detected by individual oligos, in the GO and KEGG frameworks, as well as the STARS framework, which is designed specifically for fish [ 38 ]. The other approach was based on the TM/TS framework, as described in the introduction, methods ( Section 4.5 ) and Krasnov et al [ 36 ]. Applying this approach, the DE genes are categorized based on their expression patterns in this study compared to patterns in other similar microarray studies.…”

“…In parallel, expression analysis was also carried out in head-kidney, which is an important immune organ in teleost fish. Unlike miRNAs, mRNAs responding to M. viscosa infection have been previously characterized in Atlantic salmon by microarray analysis or high-throughput sequencing [ 6 , 35 , 36 , 37 ]. The results of these studies (differentially expressed mRNAs) have been used as input in enrichment analyses to reveal biological processes that are affected by infection.…”

“…Such enrichment analyses rely on functional annotation of the differentially expressed mRNAs (DE-mRNAs), which in turn are mostly based on their homology with well-annotated genes from higher vertebrates. Another approach for annotation of differentially expressed genes, introduced by Krasnov et al [ 36 ], does not annotate DE-mRNAs by assumed function inferred from their homology to evolutionary distant species. Instead, genes are grouped into Transcription Modules (TMs) by showing consistent co-regulation in response to different factors (viral infection, bacterial infection, inflammation, or stress) across a large set of microarray studies.…”

Differential Expression of miRNAs and Their Predicted Target Genes Indicates That Gene Expression in Atlantic Salmon Gill Is Post-Transcriptionally Regulated by miRNAs in the Parr-Smolt Transformation and Adaptation to Sea Water

“…A threshold of log2 fold-changes ≤ –0.80 or ≥ 0.80 and p < 0.05 ( t -test) was used to identify differently expressed mRNAs (DE mRNAs). Finally, enrichment analysis of DE mRNAs was carried out as described in Krasnov et al [ 72 ] using GO, STARS and KEGG annotation data sets. Functional and pathway categories with ratio ≥1.5 and Yates’ corrected chi-square ( p ≤ 0.05) were defined as enriched in the DE-mRNA data set.…”