Transcriptional response of Atlantic salmon families to Piscirickettsia salmonis infection highlights the relevance of the iron-deprivation defence system
© Pulgar et al. 2015
Received: 8 August 2014
Accepted: 23 June 2015
Published: 4 July 2015
Piscirickettsiosis or Salmonid Rickettsial Septicaemia (SRS) is a bacterial disease that has a major economic impact on the Chilean salmon farming industry. Despite the fact that Piscirickettsia salmonis has been recognized as a major fish pathogen for over 20 years, the molecular strategies underlying the fish response to infection and the bacterial mechanisms of pathogenesis are poorly understood. We analysed and compared the head kidney transcriptional response of Atlantic salmon (Salmo salar) families with different levels of susceptibility to P. salmonis infection in order to reveal mechanisms that might confer infection resistance.
We ranked forty full-sibling Atlantic salmon families according to accumulated mortality after a challenge with P. salmonis and selected the families with the lowest and highest cumulative mortalities for microarray gene expression analysis. A comparison of the response to P. salmonis infection between low and high susceptibility groups identified biological processes presumably involved in natural resistance to the pathogen. In particular, expression changes of genes linked to cellular iron depletion, as well as low iron content and bacterial load in the head kidney of fish from low susceptibility families, suggest that iron-deprivation is an innate immunity defence mechanism against P. salmonis. To complement these results, we predicted a set of iron acquisition genes from the P. salmonis genome. Identification of putative Fur boxes and expression of the genes under iron-depleted conditions revealed that most of these genes form part of the Fur regulon of P. salmonis.
This study revealed, for the first time, differences in the transcriptional response to P. salmonis infection among Atlantic salmon families with varied levels of susceptibility to the infection. These differences correlated with changes in the abundance of transcripts encoding proteins directly and indirectly involved in the immune response; changes that highlighted the role of nutritional immunity through iron deprivation in host defence mechanisms against P. salmonis. Additionally, we found that P. salmonis has several mechanisms for iron acquisition, suggesting that this bacterium can obtain iron from different sources, including ferric iron through capturing endogenous and exogenous siderophores and ferrous iron. Our results contribute to determining the underlying resistance mechanisms of Atlantic salmon to P. salmonis infection and to identifying future treatment strategies.
The aquaculture industry has confronted severe economic losses due to infectious diseases in recent years [1, 2]. Therefore, control methods against infections and a thorough comprehension of resistance mechanisms are needed. Salmonid Rickettsial Septicaemia (SRS) is a bacterial disease that accounts for annual losses of over US$100 million in the Chilean salmon farming industry [3, 4]. Piscirickettsia salmonis, the causative agent of SRS, was first identified as a pathogenic agent in disease outbreaks among farmed Chilean Coho salmon (Oncorhynchus kisutch) in 1989, and since then, infectivity has been demonstrated in all cultured salmonid species, from the south of Chile to the Northern Hemisphere [5–7]. This pathogen has the ability to infect, replicate, and propagate in salmonid monocytes/macrophages [8, 9], and it produces a systemic infection characterized by the colonization of several organs, including the kidney, liver, spleen, intestine, brain, ovary, and gills [5, 10]. To date, the mechanisms employed by P. salmonis to infect, survive, and proliferate within host cells remain unknown.
Studies on the fish response to this pathogen have principally analysed changes in gene expression following bacterial infection [11, 12]. Rise et al.  reported altered expression levels of transcripts encoding proteins with roles in the antioxidant response and innate immunity in Atlantic salmon (Salmo salar) macrophages (24 h post-injection, hpi) and head kidney cells (14 days post-injection, dpi) with P. salmonis infection . Likewise, Tacchi et al.  analysed the early response (48 hpi) to P. salmonis in the liver, head kidney cells, and muscle of Atlantic salmon and found activated inflammatory and oxidative defence response mechanisms. Thus, transcriptome analyses have provided evidence of cellular processes that may play a role in the host response to P. salmonis infection.
Recent studies have reported significant genetic variation for resistance to P. salmonis infection in Atlantic salmon families [13, 14], and heritability estimates range from 0.11 to 0.41 (medium/high magnitude), indicating that this trait might be genetically improved. Therefore it is of particular interest to examine the transcriptomic response of Atlantic salmon to P. salmonis infection considering genetic background. This approach will provide insight on the biological mechanisms that lead to genetic differences in resistance. In other economically important fish species, variances in gene expression between resistant and susceptible genetic groups have identified genes and pathways underlying differences in infection resistance [15–17].
In the present study, we analysed the transcriptional response to P. salmonis in groups of Atlantic salmon families with low or high susceptibility to infection. Our study aims to contribute to the current understanding of resistance mechanisms to P. salmonis infection.
Results and discussion
Differential susceptibility of Atlantic salmon families to P. salmonis infection
The influence of genetic factors on fish susceptibility to P. salmonis [13, 14] and other bacterial infections has been previously reported in other fish species [15, 17, 19, 20]. For example, Camp et al.  challenged fifteen full-sibling families of juvenile channel catfish (Ictalurus punctatus) with the bacterium Edwardsiella ictaluri, the causative agent of enteric septicemia of catfish. Cumulative mortalities among families ranged from 5 to 35 %. Using the most resistant and sensitive families, they also detected differences between families in macrophage aggregations and in the amount of lymphocytes (B and T) in peripheral blood. In a different work, the expression of transcripts encoding for complement component 3 (C3) and lysozyme C II were induced to a greater extent in a resistant rainbow trout (Oncorhynchus mykiss) line compared to a susceptible line in response to Flavobacterium psychrophilum infection . Moreover, changes in the expression of transcripts encoding several toll-like receptors and innate immune components were observed in genetic groups of catfish (Ictalurus punctatus) and Japanese flounder (Paralichthys olivaceus) with different susceptibilities to Edwardsiella spp infection [16, 17]. Thus, the results of these and other studies indicate an association between infection susceptibility levels in fish and changes in the relative abundances of transcripts involved in immune and stress responses, among other processes.
Gene expression analysis of LS and HS families
For gene expression analysis, a second group of naïve fish belonging to the six most resistant and six most susceptible families (LS and HS, respectively) was IP injected with P. salmonis. Fish from each family were divided into control (non-infected fish, n = 10) and treated (infected fish, n = 10) groups, and head kidney samples from each fish were collected at 14 dpi, before the onset of mortality, and analysed using microarrays. No differences in fish weight between LS (487.5 ± 104.5 g) and HS (485.5 ± 122.6 g) or between control and infected groups were observed. Head kidney samples were also examined using qPCR assays to confirm the presence of P. salmonis in all IP-injected fish and its absence in control fish.
Three LS and three HS families were selected for microarray assays. For each assay, head kidney RNA from groups of five non-infected and five infected individuals were pooled, reverse transcribed, and hybridized to microarrays. In order to produce a general description of the transcriptional response to P. salmonis in fish families with different susceptibilities to the infection, each of the three LS families and each of the three HS families were regarded as biological replicates of the high and low susceptibility groups, respectively (experimental design in Additional file 1).
Common transcriptional response of LS and HS families to P. salmonis infection
Representative probe sets differentially expressed between infected and non-infected fish
Adj. P valued
Up-regulated in LS and HS families
Lysozyme C II
MHC class I (hla-UBA)
Canopy 4 (cnpy4)
Microsomal glutathione S-transferase 2
ATP synthase subunit e, mitochondrial
ATP synthase subunit epsilon, mitochondrial
NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 1
NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 8
Succinyl-CoA ligase beta-chain
Mitochondrial import receptor subunit TOM7
Organization and regulation of actin cytoskeleton
Actin, cytoplasmic 1
Tropomyosin alpha-3 chain
Myosin light chain 3
Myosin light chain 4
Ras GTPase-activating protein nGAP (ngap)
Ral guanine nucleotide dissociation stimulator
Cell metabolism and regulation
Cell division protein kinase 10
Anaphase-promoting complex subunit 13
Casein kinase II subunit alpha
Trafficking protein particle complex subunit 5
Charged multivesicular body protein 3
Vacuolar ATP synthase subunit S1
Cytosolic 5-nucleotidase III
Alcohol dehydrogenase class-3
Microsomal glutathione S-transferase 3
Histone deacetylase complex subunit SAP30L
Neurogenic differentiation factor 1
Small nuclear ribonucleoprotein-associated protein B
Eukaryotic translation initiation factor 1A
Eukaryotic translation initiation factor 5A-1
Down-regulated in LS and HS families
Synthesis of proteins
40S ribosomal protein S27
40S ribosomal protein S5
40S ribosomal protein S19
60S ribosomal protein L27
60S ribosomal protein L38
60S ribosomal protein L36
Glycolysis, Oxidative Phosphorylation and mitochondrial transport
Phosphoglycerate mutase 1
Cytochrome oxidase subunit 2
Cytochrome oxidase subunit 3
NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 6
NADH-ubiquinone oxidoreductase chain 1
NADH-ubiquinone oxidoreductase chain 2
NADH-ubiquinone oxidoreductase chain 3
NADH-ubiquinone oxidoreductase chain 4
ADP/ATP translocase 2
ADP/ATP translocase 2
Mitochondrial import inner membrane translocase subunit Tim23
Heat shock cognate 70 kDa protein
Plasma transport of oxygen and selenium
Hemoglobin subunit alpha-4
Hemoglobin subunit beta
Hemoglobin subunit beta-1
Hemoglobin subunit beta-4
Extracellular matrix organization and collagen biosynthesis
Collagen alpha-1(I) a chain
Collagen alpha-1(I) a chain
Collagen alpha-1(I) b chain
Collagen alpha-2(I) chain
Secreted protein acidic and rich in cysteine (SPARC)
Homeostasis of metals (iron and copper)
Ferritin, heavy subunit
Ferritin, middle subunit
On the other hand, genes that had a significantly lower expression in response to infection were associated with different cellular processes and provided some insights on how this pathogen modulates the host response. Thus, transcripts that showed decreased relative abundance following bacterial infection were mainly involved in the processes of protein synthesis (ribosomal proteins), transport of oxygen and selenium, and homeostasis of metals. Twenty-four probe sets encoding 40S ribosomal subunit proteins and 38 encoding 60S ribosomal subunit proteins were down-regulated in response to infection, suggesting that the transcriptional repression of translation machinery might be an antibacterial response or part of a general reduction in host metabolic activity. It has been suggested that the shut-down of translation machinery is a bacterial and viral strategy to control the translation of pathogenic proteins [27–30] and to suppress innate host defences by inhibiting the capacity of infected cells to synthesize immune system proteins [31, 32]. This strategy may be used by P. salmonis to control the host response in order to survive and replicate inside infected cells . Since large numbers of probe sets represent the same protein or processes, these results suggest a coordinated gene expression response to P. salmonis infection.
The relative abundance of transcripts encoding for hemoglobin subunits and selenoprotein P was also significantly decreased in response to infection, suggesting that P. salmonis might impact the plasma transport of oxygen/iron and selenium. The down-regulation of hemoglobin subunits may be part of a host defence mechanism to limit the availability of hemic-iron, an important source of iron for intracellular bacteria [33–35]. Moreover, decreased relative abundance of transcripts encoding for proteins involved in intracellular non-hemic iron binding (ferritin middle and heavy subunits) and in hemic binding (hemopexin) suggest that P. salmonis infection induces changes in iron metabolism in Atlantic salmon. These changes may affect the expression of genes directly involved in the synthesis of hemoglobin, as has been reported for other pathogens . Finally, Selenoprotein P, an extracellular protein that transports most plasma selenium , was consistently down-regulated following infection. Selenoprotein P has been associated with oxidative  and immune defence  mechanisms, and it has been proposed as a viable candidate molecular marker for responses to P. salmonis  and anaemia virus (ISAv)  infections. Thus, the down-regulation of Selenoprotein P transcripts seems to be a conserved response to different types of pathogens infecting Atlantic salmon.
Functional annotation of common probes significantly up- or down-regulated in LS and HS groups of families
Up-regulated in LS and HS families
eNumber of genes
microtubule cytoskeleton organization
hexose metabolic process
cellular response to organic substance
proteolysis involved in cellular protein catabolic process
Toll-like receptor signaling pathway
metabolism of lipids and lipoproteins
Down-regulated in LS and HS families
eNumber of genes
protein complex assembly
regulation of apoptotic process
mRNA Splicing - Major Pathway
Eukaryotic Translation Elongation
Gene expression differences in LS and HS families following the P. salmonis infection
We hierarchically clustered the complete list of probes differentially expressed between infected and non-infected fish from the LS and HS families (Additional file 2 and Additional file 4), and a correlation analysis was applied to measure the degree of association among the gene expression patterns of the six families. A Pearson correlation (Additional file 5: Figure S1A) and Euclidian distance (Additional file 5: Figure S1B) were used as metrics, and an average linkage clustering described the data. Both clustering analyses clearly separated the three LS families from the three HS families in two distinct branches and showed a close proximity among families with similar levels of susceptibility to infection. These results suggest that salmon families with different levels of susceptibility to the infection differentially modulate transcript abundance in response to the pathogen.
Functional annotation of genes differentially expressed between LS and HS in response to P. salmonis infection
Up-regulated in LS families but not modulated in HS Families
eNumber of genes
carbohydrate catabolic process
actin cytoskeleton organization
Fatty acid, triacylglycerol, and ketone body metabolism
Innate Immune System
Down-regulated in LS families but not modulated in HS Families
eNumber of genes
[2, 5, 6]
cellular metal ion homeostasis
mTOR signaling pathway
Phosphatidylinositol signaling system
Natural killer cell mediated cytotoxicity
Platelet activation, signaling and aggregation
Up-regulated in LS families and Down-regulated in HS Families
eNumber of genes
Cellular carbohydrate metabolic process
proton transport (ATP synthesis)
iron ion binding
Metabolism of xenobiotics by cytochrome P450
Adaptive Immune System
Class I MHC mediated antigen processing & presentation
Endosomal Sorting Complex Required For Transport (ESCRT)
Metabolism of amino acids and derivatives
Post-translational protein modification
Regulation of Apoptosis
It is worth mentioning that the characterization of host transcriptional changes at a late stage of P. salmonis infection (14 dpi) included genes directly involved in fighting infection, as well as genes involved in general physiological processes. Among these were genes with predicted functions in histone modification, protein folding, and carbohydrate and fatty acid metabolism (Table 3). This observation suggests that potential infection biomarkers could be involved in more general cellular processes and may not be limited to genes directly involved in the immune response.
Differential activation of the iron-depletion system in LS and HS families after P. salmonis infection
The functional classification of genes differentially expressed between infected and non-infected fish revealed that part of the core response to P. salmonis infection included the down-regulation of several probes representing transcripts that encode for heme-proteins (hemoglobin and cytochrome), heme-binding proteins (hemopexin), and non-hemic iron binding proteins (ferritin, middle and heavy subunits). Moreover, transferrin, the iron-binding glycoprotein that transports iron in the plasma , was up-regulated in LS but down-regulated in HS families. However, hepcidin, the principal regulator of iron efflux in vertebrates, which controls access of iron into circulation [44, 45], was significantly up-regulated in HS families but not in LS families (Additional file 2). These results suggest that the regulation of iron homeostasis could be crucial for the natural resistance of Atlantic salmon to P. salmonis infection.
Bacterial load in infected fish
Mean relative abundance
As iron content between LS and HS families was similar without infection, the correlation between decreased metal content, reduced bacterial load, and pathogen resistance might be explained by an ability of LS families to reduce iron content in the head kidney in response to infection, and not by the iron status preceding infection. This suggests that decreased cellular iron content is a physiological response to infection.
Although HS families were also able to modify the abundance of transcripts encoding iron transporters after infection, in some cases, modulation was opposite to that observed in LS families. For example, the HS1 family (that most susceptible to infection, with 64.3 % cumulative mortality) showed a significant increase in the abundance of dmt1 transcripts and a decrease in the abundance of transcripts encoding for Ireg1. The HS3 family, the only HS family that showed a significant decrease in iron content, displayed increased Ireg1 while no changes were detected for uptake transporters. On the other hand, the HS2 family had a decreased abundance of uptake transporters without alterations in the abundance of Ireg1. Since the HS2 family was not able to reduce cellular iron content in response to infection, this suggests that Ireg1activity may be pivotal to reduce iron availability in infected fish. Interestingly, the transcript encoding for the peptide hepcidin, which causes ferroportin internalization and degradation , was up-regulated only in those families that did not reduce iron abundance following infection, thus supporting the idea that the regulation of iron efflux is fundamental for reducing the intracellular content of this metal.
Finally, the effect of P. salmonis infection on the abundance of transferrin (trf) and ferritin light chain (ferl) transcripts was evaluated. These encode for ubiquitous proteins that bind and store extracellular and intracellular iron, respectively . Results showed a significant increase in the abundance of trf and ferl in all LS families and a reduction of trf in two HS families, suggesting that infected fish were able to trigger strategies to limit the access of P. salmonis to cellular iron with different efficiencies between LS and HS families. In view of this, our results support that the iron-deprivation mechanism of nutritional immunity  could be an important defence mechanism against P. salmonis infection.
Genome sequence of P. salmonis reveals the presence of iron-acquisition genes
Iron acquisition systems of P. salmonis
dClosest homolog in Genbank (blastx)
fProtein identity (%)
WP_018274636.1 Fur family transcriptional regulator [Teredinibacter turnerae]
YP_004591266.1 ferrous iron transport, protein A [Enterobacter aerogenes]
YP_003038990.1 ferrous iron transport, protein B [Photorhabdus asymbiotica]
YP_002799440.1 ferrous iron transport, protein protein C [Azotobacter vinelandii]
YP_001184687.1 tonB-dependent hemin/siderophore receptor [Shewanella sp.]
YP_003459692.1 tonB family protein [Thioalkalivibrio sp.]
WP_021695817.1 biopolymer transport protein ExbD/TolR [Loktanella cinnabarina]
YP_006295784.1biopolymer transport protein ExbB [Methylophaga nitratireducenticrescens]
NP_484432.1 periplasmic iron (III) -binding protein, ABC transporter [Nostoc sp.]
YP_323336.1 iron-hydroxamate transporter permease [Anabaena variabilis]
WP_006640164.1 iron-hydroxamate ABC transporter ATP-binding protein [Bacillus sonorensis]
YP_159259.1 siderophore receptor, TonB-dependent [Aromatoleum aromaticum]
WP_018314298.1 siderophore biosynthesis [Cupriavidus sp.]
WP_018077816.1 siderophore biosynthesis, (carboxylate-amine ligase) [Thiobacillus denitrificans]
WP_005449425.1 Multi-drug efflux pump PvsC [Vibrio harveyi]
YP_747943.1 siderophore biosynthesis, IucA/IucC family protein [Nitrosomonas eutropha]
AHI32018.1 siderophore biosynthesis (diaminopimelate decarboxylase) [Marinobacter sp.]
From the genome sequence, three orthologous genes were predicted encoding for synthetases of vibrioferrin, an unusual marine carboxylate siderophore (pvsA, pvsB, pvsD); its membrane-spanning exporter (pvsC); and its TonB-dependent siderophore receptor (PvuA) . All classic components of the ExbB − ExbD − TonB energy system were found, supporting the TonB-dependent active transport of iron siderophores across the outer bacterial membrane . Furthermore, components of both fhu and feo operons, required for the acquisition of hydroxamate siderophores (or heme)  and ferrous iron  respectively, were predicted from the P. salmonis genome. Finally, an orthologue of Fur was also identified. Fur controls the intracellular concentration of iron in bacteria, thus in the presence of intracellular iron, Fur binds DNA and represses the transcription of genes involved in siderophore biosynthesis and iron acquisition .
To explore whether the predicted iron gene clusters were regulated by Fur, we sought putative Fur-binding sites in the whole genome of P. salmonis. For this purpose, the Fur-binding site motif of γ-proteobacteria was reconstructed from 656 manually curated intergenic sequences, and this information was used to search for this motif in the entire P. salmonis genome. The sequence logo for the predicted Fur-binding sites in P. salmonis is shown in Fig. 6b.
The results indicated that two hundred open-reading frames (ORFs) had at least one putative Fur-binding site in the intergenic region upstream of the respective start codon (data not shown). More importantly, a putative Fur-binding site was found upstream of the three iron gene clusters described above (Fig. 6a), suggesting that these gene clusters might be regulated by Fur and become activated during iron deficiency.
The expression of predicted iron acquisition genes under supplemented and non-supplemented iron conditions was also measured. A significant increase in the relative abundance of all predicted transcripts occurred in response to iron deficiency (0 mM Fe-NTA) as compared to both supplemented conditions (Fig. 7c). This behaviour was supported through a putative Fur-binding site found present upstream of the three iron gene clusters described.
Although fhuA/hemeR was not predicted as part of an iron gene cluster, and a Fur box was not identified in its upstream region as has been described in other bacterial species [56, 65], the transcriptional behaviour of fhuA/hemeR indicates that it might be part of a still un-annotated polycistronic operon regulated by Fur. However, future assays are necessary to confirm this hypothesis.
Interestingly, the abundance of transcripts that encoded a putative Fur transcription factor was significantly reduced in the iron-restricted condition. The absence of a predicted Fur-binding site, and the role of Furas a repressor of iron acquisition genes, support its transcriptional behaviour, which has also been reported in other bacterial species [66, 67]. Finally, from previously published information and the present results, we formulated a model for the possible spatial organization of the predicted proteins involved in iron acquisition in P. salmonis (Additional file 7). In light of the severity of P. salmonis infection and the risk of acquiring antibiotic-resistant bacterial strains, fully describing iron acquisition systems and the roles of these in P. salmonis pathogenesis is a crucial step towards developing therapeutic agents.
To the best of our knowledge, this is the first study that compares the transcriptional response to P. salmonis infection among Atlantic salmon families with different levels of susceptibility. Nevertheless, this study has some limitations, for instance, fish were infected by intra-peritoneal injection, a method that does not represent the natural form of P. salmonis infection. Moreover, for gene expression analysis, a single tissue (head kidney) was sampled and only at one time-point, so we could have missed other differentially expressed transcripts that appear in other tissues affected by the infection and before and after 14 dpi. Such issues can be addressed by using different infection protocols, multiple tissues and time-point measurements, and in future studies, by using RNA Sequencing in order to identify potential splicing variants and polymorphisms among salmon families, information that could be relevant to understand fish resistance to infection. Another limitation of our study is that the complexity of P. salmonis genome only permitted us the assembly of a draft genome (198 scaffolds). Thus, in order to identify the complete set of genes involved in iron metabolism and acquisition and their genetic contexts, further studies are necessary to assembly the complete P. salmonis genome.
We carried out experimental infection challenges against P. salmonis in Atlantic salmon families. Results revealed a heterogeneous distribution of cumulative mortality among families that may be explained by the genotype of the challenged families. To further explore these differences, we compared the transcriptional response between groups of families with different susceptibilities to P. salmonis infection. Our results revealed distinctive gene expression profiles between LS and HS groups of families, and this led to the identification of biological processes possibly involved in natural resistance. In particular, the ability of fish from LS families to limit iron availability to the bacterium suggests that iron-deprivation represents a mechanism of innate immunity and resistance against P. salmonis. These data were complemented with predictions of iron acquisition systems in the P. salmonis genome. Identification of putative Fur boxes and the expression of these genes under iron-depleted conditions revealed that most form part of the Fur regulon of P. salmonis. This information contributes to current understands on the varied responses of fish with different levels of susceptibility to P. salmonis infection and supports the development of future treatment strategies.
Fish and infection experiments
Forty unvaccinated F1 full-sibling families of Atlantic salmon (mean weight 276.9 ± 78.3 g and length 283.6 ± 25.4 at start), were obtained from Agua Buena Center, Puerto Montt, Chile. Fish were implanted with a Passive Integrated Transponder (PIT) tag containing a specific code to identify each individual fish and thus the family to which they belong. Fish were maintained in 7 m3 tanks at enterprise SGS Chile Ltda., Puerto Montt, Chile, with seawater in a recirculation system at 14.7 ± 0.1 °C water temperature, 33.92 ± 0.04 ppm salinity, and 93.35 ± 1.28 % oxygen saturation, sharing the same nutritional (frequency, amount and type of food) and environmental (same tanks) conditions. Fish were randomly tested and screened to ensure that they were pathogen-free (SRS, IPNv, BKD and ISAv).
Two challenges with P. salmonis were performed. For the first challenge up to around twenty fish from each family were randomly transferred to ten tanks of 0.75 m3 and kept for four weeks in acclimatization under the conditions listed above. Then, all fish were anesthetized with benzocaine (Sigma) (20 % w/v; 50 mg/L) and immediately infected by intra-peritoneal injection with 0.2 ml of P. salmonis (PS889, isolated from Oncorhynchus kisutch on 2003) (1 × 104 PFU/ml) in minimal essential media (MEM). P. salmonis inoculum was grown in CHSE-214 cell cultures (ATCC catalog number: CRL-1681) with 80-90 % cell confluence, according to standard operating procedures of SGS Chile Ltda. During the challenge, experimental fish were observed daily and mortality per family group was recorded daily for 40 days post-infection. After the forty days, fish were harvested and the percentage of cumulative mortality (dead fish/total fish*100) for each family was calculated. For the second challenge, families with the highest cumulative mortality (greater than 30 %) levels were considered of relatively high susceptibility (HS) and families with the lowest cumulative mortality (0 %) levels were considered of relatively low susceptibility (LS) to P. salmonis infection. For the second challenge, representative naive fish from six LS and six HS families were selected. Then, ten fish from each family were anesthetized and inoculated by intra-peritoneal (IP) injection of P. salmonis (experimental condition, infected fish), using the conditions previously described. The remaining ten fish were injected with sterile culture media, representing the control condition (non-infected fish). Fish were homogeneously distributed in three tanks of 0.7 m3, and acclimatized as described above. Fourteen days post-injection (dpi) all fish were sacrificed by overdose of benzocaine (100 mg/L during 10 min) and their head kidneys were removed using aseptic techniques, stabilised in RNAlater (Ambion) at 4 °C for 24 h and then stored at −80 °C until RNA extraction. To confirm the presence of P. salmonis in Atlantic salmon tissues, DNA was extracted from head kidney of all fish by DNEasy kit (Qiagen) and used as template in a qPCR reaction as described by Karatas et al. . The trials were approved by the Ethics Committee of the Institute of Nutrition and Food Technology, University of Chile.
For microarrays hybridizations, total RNA was extracted from 50 to 100 mg of head kidney using TRIZOL Reagent (Invitrogen), and incubated for 30 min at 37 °C with 20 units of RQ1 RNase-Free DNase (Promega) to remove residual genomic DNA. Then, RNA was purified using RNeasy mini kit (Qiagen) according to the manufacturer’s instructions. Even though we selected six LS and six HS families for the second challenge, we were able to obtain five individual samples of good RNA quality only from fish of three LS and three HS families of those six families.
For qPCR of iron acquisition genes of P. salmonis, total RNA was extracted with the RiboPure Bacteria Kit (Ambion). Pellets form liquid cultures of bacterium were re-suspended in 350 μl of RNAwiz solution and transferred to a 0.5 ml RNAse-free skirted screw-cap microcentrifuge tube containing 250 μl of 0.1 mm ice-cold Zirconia Beads. Then, tubes were placed horizontally on the vortex adapter and processed at maximum speed during 15 min. Remaining steps were performed according to the manufacturer’s instructions.
All RNA samples were treated with Dnase I (Ambion) and purified by RNeasy column (Qiagen) The quantity of the total RNA were determined using a Qubit Fluorometric Quantitation System (Life Technologies) and the purity (absorbance 260/280 nm) using a NanoQuant Spectrophotometer (Tecan Technologies), while integrity was confirmed by RNA Integrity Number (RIN) using a 2200 TapeStation Instrument (Agilent Technologies, Inc). Only samples of high quality (Absorbance 260/280 nm ≥ 1.8 and RIN ≥ 8.0) were used for gene expression analyses. After quality analysis of the RNAs, the best individual samples from both infected and non-infected fish of each family were selected.
cDNA synthesis and labelling
For microarrays hybridizations, pools of RNA from five control and five infected fish of each one of the six families were prepared separately by mixing the same mass of RNA from each individual sample. Three μg of the pooled RNA were reverse transcribed using a SuperScript® Indirect cDNA Labelling System (Invitrogen). Briefly, cDNA synthesis was performed at 46 °C for 3 h in a 12 μl reaction volume, followed by RNA degradation with 1 M NaOH at 37 °C for 15 min and neutralization with 1 M HCl. Then, cDNA was precipitated with ethanol and sodium acetate (3 M, pH 5.2) overnight at −20 °C and the pellets obtained were washed in 70 % ethanol and air dried before resuspension in 5 μl 2X coupling buffer. Once the cDNA was fully dissolved, 2 μl of Cyanine 3 (Cy3-dUTP) or Cyanine 5 (Cy5-dUTP) (GE HealthCare; PA23001, PA25001) dyes was added and incubated in the dark overnight at 25 °C. To remove unincorporated dye, labeled cDNA was extracted with phenol:chloroform:isoamyl alcohol (25:24:1) and then precipitated with ethanol and sodium acetate (3 M, pH 5.2) overnight at −20 °C. The pellets were washed with 70 % ethanol and air dried before resuspension in 25 μl of hybridization buffer (50 % Formamide, 8X SSC, 0.5 % SDS, 4X Denhardt). Dye incorporation was checked by spectrophotometry.
Labeled samples were hybridized to a 32 K cDNA microarray developed at the Consortium for Genomics Research on All Salmonids Project (cGRASP), University of Victoria  (GEO accession number: GPL8904). The cGRASP microarray contains 27,917 Atlantic salmon and 4065 rainbow trout cDNA features from different cDNA libraries. Before hybridization, slides were pre-treated with 3 % BSA fraction V, 5 X SSC, 0.1 % SDS for 90 min at 49 °C and washed with 5X ultrapure water for 30 s at room temperature. cDNA samples from control and infected fish of each family were mixed, heated at 90 °C for 2 min and hybridized to the microarrays for 16 h at 49 °C in a water bath. Four slides were used for each family cDNA and hybridizations used a dye-swap design. Next, hybridization slides were washed five times with washing solution A (2X SSC, 0.2 % SDS) for 10 min at 49 °C, twice with washing solution B (1 X SSC) for 10 min and four times with washing solution C (0.1 X SSC) at room temperature and immediately dried by centrifugation before scanning.
Statistical analysis of microarrays
Microarrays slides were scanned using a Scan array Express system (PerkinElmer), all slides were scanned with set laser power at 90 % and photomultiplier gain at 55 % for Cy5 and Cy3. A group of measures including foreground (FG) and background (BG) values of intensity for each spot in both channels were stored as tab-separated text file for further analysis. Quality of microarray images was examined by inspection of FG and BG distribution of red (R) and green (G) channels in each slide. Spearman correlation between R and G channels from replicate microarrays was calculated in order to select unbiased microarray data for significance analysis. For selected microarrays, a quality index based on local and global BG values (adapted from ) was used to filter and eliminate spots with low quality.
Microarray analyses were conducted separately for fish with high or low susceptibility to infection using each of the three LS families and each of the three HS families as biological replicates. Therefore, for each group of families, LS and HS, we carried out 12 microarray hybridizations, which can be divided into three independent biological replicates (the three different families of each group), each with 4 pseudo-replicates. Prior to significance analysis of data, we transformed the foreground values of red and green channels into a matrix of log intensity ratios (M) and the geometric average between channels intensities (A). Data were background corrected using a normexp procedure which fits a convolution model of normal and exponential distributions to the foreground intensities using the background intensities as a covariate . After this, data were normalized using an intensity dependent normalization procedure (loess) . Independent linear models for LS and HS families were fitted to log2 expression data for each gene across the microarrays . In each case, the coefficient of the model represented the estimated fold change between infected and non-infected samples. These values were used to compute a moderated t-statistics, which has the same interpretation of an ordinary t-statistic except for the standard errors that have been moderated across genes using a simple Bayesian model. Raw p-values were corrected for multiple testing using a false discovery rate (FDR) of 0.05. Genes with an adjusted p-value under this cut-off were acknowledged as differentially expressed. All calculations were performed on R statistical software environment running on Linux or Windows machine, using limma package . Microarray data were submitted to Gene Expression Omnibus (GEO, accession number: GSE43255).
Real time PCR (qPCR)
Reactions were carried out on a Light Cycler System 1.5 (Roche Diagnostics) using LightCycler FastStart DNA Master SYBR Green kit (Roche Applied Science). Pooled RNA of each fish family, previously used for microarray analysis, was used to verify a set of differentially expressed genes from the microarray experiment and to evaluate relative abundance of iron genes between infected and control fish. To accomplish this, 2 μg of total RNA was used as template for reverse transcription reactions to synthesize single strand cDNA using MMLV-RT reverse transcriptase (Promega) and oligo-dT primer (Invitrogen), according to standard procedures. Similarly, cDNA of P. salmonis was synthesized from 2 μg of total RNA incubated by 1 h at 37 °C using High Capacity RNA to cDNA kit (Applied Biosystems), following manufacturer instructions. cDNAs were diluted to 100 ng and used as template for qPCR, with primers designed against genes of interest. PCR conditions were 95 °C for 5 min followed by 94 °C for 15 s, 57–60 °C for 15 s and 72 °C for 20 s for a total of 35 cycles. Melting curves (1 °C steps between 75–95 °C) ensured that a single product was amplified in each reaction. To determine relative expression levels of genes, the method described by Pfaffl , and adapted by Talke et al.  was used. Elongation factor 1 alpha (EF1A) of Atlantic salmon and recombination protein F (RecF) of P. salmonis were selected as internal reference genes. At least five replicates were performed and differences among conditions were analysed using Student’s t-test (p < 0.05) or One-way ANOVA. PCR efficiencies were determined by linear regression analysis performed directly on the sample data using LinRegPCR . Additional file 8 shows the complete list of primers used in this study.
To validate the results from the microarray experiments, 40 genes were randomly selected from the microarray data and their expression levels were determined by qPCR. For each gene, mean log2 ratios (infected/control) determined by microarrays were plotted against mean log2 ratios (infected/control) determined by qPCR assays. Correlation between microarrays and qPCR were calculated by Pearson product moment correlation and a p < 0.01 was considered statistically significant.
Functional analysis and clustering
To carry out the functional analysis, we uploaded the significant differentially expressed probes into Cytoscape (v3.1.1) . Using ClueGO (v2.1.3) plugin,  we performed an enrichment analysis based on functional classifications available at Kyoto Encyclopedia of Genes and Genomes (KEGG) , Reactome  and Gene Ontology (GO) , using the complete set of probes of 32 K cDNA microarray as reference. Probes were previously re-annotated by Danio rerio RefSeq Database (ensembl 9.6 release). Enrichment was tested based on a hypergeometric test with p-value corrected by Benjamini-Hochberg method, and functions with adjusted p < 0.05 were selected.
Hierarchical Clustering Analysis was performed using Multi Experiment Viewer software (MeV v4.4.0) [83, 84]. From the total list of differentially expressed probes in the two groups of families, LS and HS, a gene expression matrix was built using the fold change values of each gene in each one of the six families. When a differentially expressed probe in a family had a missing fold change value (NA) in another family of its group (LS or HS), we used the average of the two remaining families of the group. If a differentially expressed probe had a missing fold change value in two families of its group, the probe was not incorporated into the analysis. Pearson correlation (Additional file 5: Figure S1A) and Euclidian distance (Additional file 5: Figure S1B) were used as distance metrics and an average linkage clustering as a linkage method.
Cellular iron content
For total iron (Fe) content quantification, 50 to 100 mg of five head kidney tissues, representatives of control and infected conditions from LS and HS families, were dried by evaporation (SpeedVac, Thermos) and processed as described in . Briefly, tissues were mechanically disrupted using a homogenizer (Omni International 2000) in concentrated nitric acid Suprapur (Merck, Chemical Co.) and digested at 80 °C overnight. Then, samples were diluted with ultrapure distilled-deionised water (Merck, Chemical Co.) until a final nitric acid concentration of 5 % v/v.
For determining iron content of P. salmonis, bacteria were processed as described in . Briefly, bacteria were collected by centrifugation at 7800 g for 15 min, and washed once with phosphate-buffered saline (pH 7.0) and twice with ultrapure distilled deionised water. Washed cells were mixed with 1 ml of concentrated nitric acid Suprapur and 0.1 ml of concentrated perchloric acid (Ultrapure AA-100; Tama Chemicals). Cells were dissolved into liquid by microwave treatment. Then, samples were dissolved in 1 ml of 5 % v/v nitric acid solution.
Iron content was determined by means of a graphite furnace atomic absorption spectrophotometer (Perkin Elmer, SIMMA 6100, Shelton, CT, USA). Calibration was performed against an iron standard curve and iron content values (μg) were expressed per mg of dry weight tissue (kidney) or mg of total proteins (P. salmonis). Zinc (Zn) content measurement was performed under the same conditions. The difference between conditions was analysed using Student’s t-test (p < 0.05).
P. salmonis was grown aerobically in A broth (AB) medium  supplemented with 0.1 % L-Cys (Sigma), 2.5 % FBS (Gibco), 1.5 % NaCl (Merck) and (0 mM, 0.1 mM or 1 mM of Fe-NTA) (Sigma) at 17 °C with shaking (100 rpm). Growth was measured by monitoring the optical density at 600 nm (OD600) every day during 12 days to build the growth curve. Each point in the curve represents the mean of nine determinations (± SEM), differences among conditions were analysed using Student’s t-test (p < 0.05).
Genome sequencing of P. salmonis
For genome sequencing, the chromosomal DNA of P. salmonis cultured in liquid medium, was extracted using the DNeasy blood and tissue kit (Qiagen). The draft genome sequence of P. salmonis LF-89 strain was determined using a 5 Kb mate pair library with the Roche 454-GS FLX Titanium and a shotgun library with a Roche 454-GS Junior GS, reaching a 17-fold coverage of the genome. Using wgs-assembler (Celera v.7) , with default parameters, were produced a total of 198 scaffolds (N50 scaffold size was ∼ 38 Kb), with an average length of 15 Kb. The longest and shortest scaffolds were 140 Kb and 1 Kb, respectively. Potential Coding DNA Sequences (CDS) were identified with REGANOR , and the genome sequence was annotated in a locally installed annotation system GenDB v2.2 . The draft genome was 2,988,956 bp in length and contained 3173 CDS. The Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AMGC00000000. The version described in this paper is the first version, AMGC01000000.
Bioinformatic prediction of Fur box
Manually curated (n = 656) intergenic sequences of Fur regulons from γ-proteobacteria stored in RegPrecise 3.0 database  were used to reconstruct the Fur motif using the MEME software  (parameters –dna –w 19 –minsites 650). Then we searched this motif against intergenic regions of the annotated genome of P. salmonis, using the MAST software . The results from this analysis were manually curated.
Availability of supporting data
The Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AMGC01000000 (http://www.ncbi.nlm.nih.gov/bioproject/199690)
Microarray gene expression data is available at GEO at the NCBI (GenBank) under the accession GSE43255 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43255)
All nucleotide sequences of iron acquisition genes are available at NCBI GenBank under accession numbers KJ804204 to KJ804223 (http://www.ncbi.nlm.nih.gov/nuccore).
All other data sets supporting the results of this article are included within the article and its additional files.
We thank Dr. Jean Paul Lhorente and the staff of AquaInnovo S.A. for helping us during infection challenges and tissue sampling. This work was supported by INNOVA-Consorcio 206–5047, Fondap 15090007, Fondecyt 1120254 to VC and Fondecyt 1110427 to MG. RP and CH were supported by Fondecyt 3130742 and 3110129, respectively; RP was also supported by an AquaInnovo doctoral fellowship.
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