Comparative transcriptomics reveal conserved impacts of rearing density on immune response of two important aquaculture species

doi:10.21203/rs.2.14499/v1

Download PDF

Research article

Comparative transcriptomics reveal conserved impacts of rearing density on immune response of two important aquaculture species

https://doi.org/10.21203/rs.2.14499/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 01 Sep, 2020

Read the published version in Fish & Shellfish Immunology →

Version 1

posted

You are reading this latest preprint version

Background: Infectious diseases represent an important barrier to sustainable aquaculture development. Rearing density can substantially impact fish productivity, health and welfare in aquaculture, including growth rates, behaviour and, crucially, immune activity. Given the current emphasis on aquaculture diversification, stress-related indicators broadly applicable across species are needed.

Results: Utilising an interspecific comparative transcriptomic (RNAseq) approach, we compared gill gene expression responses of Atlantic salmon (Salmo salar) and Nile tilapia (Oreochromis niloticus) to rearing density and Saprolegnia parasitica infection. Salmon reared at high-density showed increased expression of stress-related markers (e.g. c-fos and hsp70), and downregulation of innate immune genes. Upon pathogen challenge, only salmon reared at low density exhibited increased expression of inflammatory interleukins and lymphocyte-related genes. Tilapia immunity, in contrast, was impaired at low-density. Using overlapping gene ontology enrichment and gene ortholog analyses, we found that density-related stress similarly impacted salmon and tilapia in key immune pathways, altering the expression of genes vital to inflammatory and Th17 responses to pathogen challenge.

Conclusions: Given the challenges posed by ectoparasites and gill diseases in fish farms, this study underscores the importance of optimal rearing densities for immunocompetence, particularly for mucosal immunity. Our comparative transcriptomics analyses identified density stress impacted immune markers common across different fish taxa, providing key molecular targets with potential for monitoring and enhancing aquaculture resilience in a wide range of farmed species.

Animal Science

Epigenetics & Genomics

Molecular Genetics

rearing density

stress

immunity

transcriptome

comparative transcriptomics

Atlantic salmon

Nile tilapia

Saprolegnia parasitica

Th17 responses

Sustainable aquaculture development continues to be at the forefront of priorities for meeting protein demands of a growing human population (1, 2) and therefore remains the fastest growing food sector (3). A staggering 598 aquatic species are commercially cultured worldwide today, up by 26.7% in the last 10 years alone (3), vastly outweighing the diversity of terrestrial animal production (4). Arguably one of the greatest challenges to the current level of farmed aquatic species diversity and future diversification of aquaculture is to identify and reliably assess the optimum conditions for each species’ health, welfare, and productivity.

Rearing density is considered one of the pivotal factors determining aquaculture productivity and profitability (5–7). While overcrowding and/or under-stocking can significantly impact overt measures of fish performance such as growth rate (8, 9), size uniformity (10), and aggressive/unwanted behaviours (11–13), it can also adversely affect less obvious physiological parameters such as stress levels (9, 14), circulating hormones (14, 15), and flesh quality/composition (16). It is increasingly apparent that suboptimal rearing densities have negative consequences for fish immunity (17–19) and thus increase susceptibility to pathogens (10, 20). Infectious disease is currently one of the greatest barriers to sustainable aquaculture intensification (21), and a substantial economic burden on the industry (22). Therefore, it is important to know if the underlying effects of rearing density on fish health are conserved across fish species, and whether broadly applicable key stress indicators can be applied for management of density-related stress in aquaculture.

RNAseq methods have proved valuable tools for assessing the wider impacts of environmental stressors and pathogens on animal health at the functional genomic level (23–25). Interspecific comparative transcriptomics (comparison of gene expression responses across multiple species) has, as yet, been little used in the context of aquaculture. However, the utility of interspecific comparative transcriptomics to address fundamental questions in fish biology and evolution (26, 27), and reveal key species differences in response to shared pathogens in vertebrates (28, 29), indicates its potential value as a tool for refining aquaculture practices.

Nile tilapia (Oreochromis niloticus) and Atlantic salmon (Salmo salar) are two of the most important farmed finfish species worldwide, accounting for 8% and 4% of global annual production, respectively (3). Suboptimal rearing densities have been shown to negatively affect salmon and tilapia welfare (30–32), health (33, 34) and productivity (35). Salmon tend to experience greater stress at high rearing densities (31, 36). Conversely, tilapia show increased aggression, and are therefore considered more stressed, at low densities (20, 32). For both tilapia and salmon production, outbreaks of Saprolegnia parasiticaa fungal-like pathogen that parasitizes the skin, fins and gills of fish (37)—are a substantial economic burden (37–39), with limited effective treatment options approved for aquaculture (37).

This study compares the impact of rearing density on the functional genomic responses of salmon and tilapia to Saprolegnia parasitica challenge, utilising an interspecific comparative transcriptomic (RNAseq) approach. We assess the commonalities of density-specific pathogen responses of these two key—yet highly divergent (ca. 225 MYA; (40))—aquaculture species, with the aim of identifying stress-related transcriptomic-level markers broadly applicable across the diversity of cultured fish.

This study first examines Atlantic salmon (Salmo salar) transcriptome-wide expression responses to rearing density and infection challenge. This data was then analysed alongside comparable, previously reported data (20) on Nile Tilapia (Oreochromis niloticus) to assess commonalities of density-specific immune responses across divergent fish species, which respond differently to stocking-density.

Salmon transcriptomic responses to rearing density and Saprolegnia challenge

No salmon developed visible signs of Saprolegnia infection (i.e. mycelial growths or lesions) during the current experiment. Illumina RNAseq achieved an average 31.6 million reads per sample (range 29.3 to 32.1 million). In the gills, comparison of healthy salmon at high and low density (i.e. unchallenged controls) gene expression found 1,163 genes significantly differentially expressed. We found significant enrichment of genes with higher expression in low-density salmon (568 genes) for 124 biological process GO terms including several immune-related functions such as “innate immune response” (GO:0045087, e.g. hck),, “neutrophil mediated immunity” (GO:0002446, e.g. rac2),, and “dendritic cell differentiation” (GO:0097028, e.g. lyn).. These genes were also enriched for “cortisol metabolic process” (GO:0034650, e.g. hsd11b2).. Genes with higher expression in healthy high-density salmon (compared to healthy low-density fish, 595 genes) were enriched for 93 biological process GO terms including a number related to physiological stress such as osmotic (e.g. GO:0042538, GO:0042539) and starvation responses (e.g. GO:0042594). This gene set also included hormonal responses, including “response to growth hormone” (GO:0060416), “thyroid hormone transport” (GO:0070327), and “response to estradiol” (GO:0032355). Full lists of differentially expressed genes and GO terms are provided in Supplementary File S1.

Comparison of gill tissues in healthy (unchallenged control) and Saprolegnia-challenged salmon at high and low density revealed 1,859 and 1,649 differentially expressed genes respectively. Genes exhibiting increased expression in Saprolegnia-challenged salmon at both densities were highly enriched for GO terms related to immune functions including “inflammatory response” (GO:0006954), “lymphocyte chemotaxis” (GO:0048247), “T cell proliferation” (GO:0042098), and “cellular response to interleukin–1” (GO:0071347). Enrichment of downregulated genes in Saprolegnia-challenged salmon (59 GO terms) was predominantly developmental processes (e.g. GO:0048706 embryonic skeletal system development, GO:0050793 regulation of developmental process, GO:0048538 thymus development). However, this gene set also included immune (GO:0045624 positive regulation of T-helper cell differentiation, GO:0072679 thymocyte migration) and circadian rhythm functions (GO:0032922 circadian regulation of gene expression, GO:0045475 locomotor rhythm). Upregulated expression specific to high-density challenged salmon was rich in GO terms related to mast cell responses, including mast cell mediator production (e.g. leukotriene: GO:0006691, prostaglandin: GO:0001516), chemotaxis (GO:0071624) and activation (GO:0033004, GO:0043303). In contrast, genes found only to be significantly increased in low-density challenged salmon were enriched for adaptive immune functions including lymphocyte differentiation and aggregation (GO:0030098, GO:0071593), T helper cell development (GO:0045064, GO:0072540), and B cell chemotaxis (GO:0035754). Full lists of differentially expressed genes and GO terms are provided in Supplementary File S1.

Interspecific comparison of density and Saprolegnia transcriptomic responses

Impact of density on uninfected salmon and tilapia

Comparison of functional enrichment of differential expression between high- and low-density treatments in unchallenged (healthy) salmon and tilapia reveal a small number of biological processes shared between these species (Supplementary File S2), including “innate immune response” (GO:0045087) in genes showing higher expression in high-density tilapia and low-density salmon. Using a reciprocal best hit approach, we found 21,745 1:1 orthologs between published salmon and tilapia transcriptomes, of which 13,364 were expressed in gill tissues of both species. Twenty-three gene orthologs were found to be differentially expressed between healthy high- and low-density fish in both species (Table 1), including several genes involved in regulation of transcription (dlx3, mef2d, npas2, med12)..

Saprolegnia responses in salmon and tilapia

To assess overlap of functional responses to challenge with Saprolegnia, we compared differential expression GO term enrichment between salmon (unchallenged control vs Saprolegnia challenged) and tilapia (20). All GO terms found to be shared among salmon and tilapia treatment groups are summarized in Table 2. For the purposes of this study, we focussed our attention to those related to immunity. Salmon challenged with Saprolegnia (though no visible signs of saprolegniasis) showed increased expression of genes associated with “immune response” (GO:0006955) at both densities. In contrast, this GO term was enriched in genes with decreased expression in tilapia at both densities challenged with Saprolegnia (and exhibiting signs of saprolegniasis). Increased expression of genes involved in Fcε receptor signalling (GO:0038095) was found at both densities in both species (Table 2). Mast cell degranulation genes (GO:0043303) were upregulated in Saprolegnia-challenged tilapia in both density treatments, but only observed in high-density salmon (Table 2). Genes with increased expression in low-density salmon and high-density tilapia shared GO enrichment for “T-helper 2 cell differentiation” (GO:0045064), “T-helper 17 cell lineage commitment” (GO:0072540), “myeloid dendritic cell differentiation” (GO:0043011), “defence response to protozoan” (GO:0042832), and “isotype switching” (GO:0045190) (Table 2).

Examining expression patterns of 1:1 gene orthologs, we found 41 genes with significantly higher expression in Saprolegnia-challenged salmon and tilapia. These included genes involved in antigen presentation (ap1m1, ap1s2, ap1s3),, neutrophil activity (lect2, serpinb1) and inflammation (il1rap) (Table 3). Twenty-two genes were found to have increased expression in challenged fish in both species at only low densities, including two tumor necrosis factor superfamily members involved in immune responses (tnfsf9, tnfsf15).. Mucin genes also exhibited similar responses with density in both species; muc5ac had higher expression at low density, whilst “integumentary mucin” (XP_014011243.1) was significantly lower in expression in high density challenged fish of both species. Several genes exhibited contrasting density-specific Saprolegnia challenge responses (22 increased in high-density salmon/low-density tilapia, 21 increased in low-density salmon/high-density tilapia) were related to T helper cell activity/maintenance (e.g. il17c, lag3, tnfaip8l2, batf; Figure 1).

An alternative to looking at the overlap of individual gene differential expression and gene ontology enrichment, is to examine cross-species preservation of weighted gene co-expression networks (28, 41). WGCNA of expressed tilapia genes with salmon 1:1 orthologs (n = 13,364) found 18 genes modules, of which 4 were associated with Saprolegnia-challenge status and significantly preserved in salmon (Supplementary File S2). By defining gene co-expression networks (13 modules) in salmon, six modules were significantly associated with Saprolegnia status and were preserved in tilapia (Supplementary File S2). Although in both species single gene modules were found to be associated with density and Saprolegnia (Supplementary File S2), neither were significantly preserved in the other species.

Rearing density is a critical factor for intensive aquaculture productivity, with suboptimal conditions known to impact fish, from growth (8, 9) and quality (16) to health (17, 19) and welfare (9, 30, 31, 42). Here, we used a transcriptome-wide approach to assess the effect of rearing density on pathogen responses in Atlantic salmon, revealing suppression of immunologically-important gill gene expression responses at high density. Strikingly, we identified conserved disruption of Th₁₇ responses in salmon and Nile tilapia (43) when subject to density stress. This study highlights the potential of interspecific comparative transcriptomics to identify broadly-applicable indicators of fish health.

In our study, comparison of expression profiles of unchallenged (i.e. “healthy”) salmon reared at high and low densities revealed a substantial number of differentially expressed genes in the gills (n = 1,163). High density salmon (four-fold higher than “low-density” treatment, though still within recommended welfare limits (44)) had increased expression of key markers for stress in vertebrates including c-fos (45–47) and hsp70 (48, 49). Previous studies on the effects of rearing density on immunity in salmonids have primarily focussed on levels of serum antibodies (typically IgM) or antibody-producing cells, with suppression generally found at higher densities (8, 36). In addition, non-specific innate immune markers such as serum lysozyme activity have been shown to be influenced by rearing density (42). Here, we found that salmon raised at high density had lower expression of genes related to immune responses including neutrophil (e.g. rac2),, dendritic cell (e.g. cd209, ctss),, and B cell immunity (e.g. lyn, cd22, blnk) plus inflammatory interleukins (il–12, il–17) (Supplementary File S1). A previous study of rainbow trout head-kidney gene expression during crowding stress also showed increased hsp70 stress marker expression, but found different immune gene suppression (lyzII, tnf–1α, il–1β, il–8 and ifn-γ1) (18). This suggests that crowding stress in salmonids may result in tissue-specific suppression of immune factors, which must be considered for their potential impacts on disease susceptibility. Clearly overcrowding in salmonids has a wider impact on their immune system expression than previously reported. The effects of crowding stress on immune health caused by high rearing densities may explain in part the failure of supportive breeding in salmon conservation (50–52).

We found over 1,500 genes differentially expressed in the gills of Saprolegnia-challenged and control (sham-challenged) fish at both densities, although no salmon at either density showed visual signs of saprolegniasis (e.g. mycelial growth). Fish at both densities exhibited expression profiles indicating initiation of inflammatory (particularly interleukin–1β mediated) responses and lymphocyte migration (Supplementary File S1), in line with previous studies of salmonids (53–55). A previous targeted immune gene study of Saprolegnia-challenged Atlantic salmon also showed differential expression profiles in gills despite an absence of visible signs of infection (53). Our results, however, indicate a far wider impact of sublethal Saprolegnia challenge; in addition to altered expression of immune genes, we found disruption of expression related to a wide range of physiological processes including development and circadian functioning (Supplementary File S1). Disruption of circadian rhythms is increasingly recognised as detrimental to vertebrate health (56), yet we are only beginning to consider this in the context of teleost immunity in aquaculture (20, 57, 58). Furthermore, Saprolegnia species are considered ubiquitous in freshwaters (37) and our results suggest that in aquaculture facilities even sublethal levels of Saprolegnia parasitica (or other related pathogenic species) may substantially impact fish health and productivity.

A key factor to Saprolegnia virulence is the pathogens’ ability to suppress fish adaptive immunity such as T helper cell responses and immunoglobulin production (53). While we found transcriptomic evidence of adaptive immunosuppression at both densities (e.g. downregulation of T helper cell differentiation, Supplementary File S1), the extent of suppression appeared to be density-specific. Genes upregulated in response to Saprolegnia found only in salmon reared at the lower density included those important to lymphocyte development and migration (including both B and T cells). In contrast, infection responses of fish raised at high density suggest a greater reliance on mast cells (Supplementary File S1), mediators of acute inflammation and non-specific antimicrobial production in teleosts (59, 60). Given the dramatic impact Saprolegniasis has on salmon aquaculture, accounting for at least 1 in 10 reported mortalities (61), our findings highlight the potential importance for optimal rearing densities to mitigate against this devastating fish pathogen.

The great diversity of fish species now cultured (3) poses a challenge to identify biomarkers broadly applicable to monitor optimal husbandry conditions and/or fish health and welfare status in aquaculture. Cortisol is most commonly used as an indicator of stress in fish and is widely implicated in suppression of immunity (62). Cortisol levels do not, however, necessarily correlate with immune function and parasite susceptibility (63), and cortisol effects on immune levels can be inconsistent (62). Our approach to uncover common biomarkers for the impacts of suboptimal husbandry conditions and their effects on fish immunity, was to compare the full transcriptional responses of two highly divergent fish species to density and pathogen challenge. Examining gene ontology (GO) enrichment in density responses from our salmon data and a previous, comparable study of Nile tilapia (20) revealed a number of GO terms shared between healthy salmon and tilapia (Supplementary File S2). Importantly, we found low-density tilapia and high-density salmon both have lower expression of genes classed as GO term “innate immune response”, indicating a broad-scale signal of innate immune suppression due to density-dependent stress. Although we found only a small number of salmon-tilapia gene orthologs sharing differential expression due to density alone (Table 1), these included a homolog of dok1, a known negative regulator of inflammatory pathways and innate lymphocytes in vertebrates (64, 65). This gene had higher expression in high-density salmon and low-density tilapia, further indicating that suboptimal rearing density suppresses innate immune levels.

Comparing expression responses to Saprolegnia between salmon and tilapia revealed an upregulation of genes related to Fcε receptor signalling in both species at both densities (Table 2). Moreover, “mast cell degranulation” was found in upregulated genes of both densities of Saprolegnia-challenged tilapia, and high-density salmon (Table 2). Mast cells release mediators thought to be critical in responses against the fungal-like pathogen Saprolegnia (53). There is increasing recognition for the involvement of mast cells in fungal infections in other vertebrates (66), and more generally their importance in fish immune systems (60). We propose this cell set should be considered more closely for understanding innate resistance/susceptibility to saprolegniasis, particularly in salmonids where their functioning appears to be impacted by rearing density.

We found intriguing conservation in the impacts of rearing density on immune responses of fish. At the rearing density least stressful for each species (salmon; low-density, tilapia; high-density), we found expression patterns consistent with enhanced T helper cell activity. GO term enrichment for Th₂ cell differentiation and Th₁₇ cell lineage commitment were found in genes with increased expression in both these groups. In addition, batfa transcription factor crucial to Th₁₇ cell differentiation (67) - was significantly upregulated in response to Saprolegnia challenge only at these “non-stressful” densities (Figure 1). In contrast, the “stressed” fish density groups (high-density salmon, low-density tilapia) both exhibited increased expression of lag3, a negative regulator of T cell expansion (68), whose activity has been implicated in reduced parasite clearance in other vertebrates (69). Moreover, these fish also had increased expression of tnfaip8l2 (Table 3), a suppressor of inflammation that is typically downregulated (i.e. to induce inflammation) during pathogen challenge in vertebrates including fish (70, 71). Taken together, these results indicate suboptimal rearing densities can disrupt beneficial Th₁₇/inflammatory transcriptional responses to pathogens, and these genes provide potential new markers for measures of health under different rearing densities, across a wide range of teleost species. Interestingly, in mammals, Th₁₇ responses are increasingly recognised for their importance in mucosal (72) and vaccine-induced immunity (73). This appears to hold true for fish (74), which raises the question as to whether optimising rearing densities in aquaculture may in turn increase vaccine efficacy.

Rearing density of Atlantic salmon can significantly impact their immune status with suboptimal rearing density broadly suppressing gill innate immune gene expression, but also key adaptive immune responses to pathogen challenge. In addition, we found density-driven disruption of Th₁₇responses—key to mucosal immunity—to be similar between Atlantic salmon and Nile tilapia, suggesting these genes may be useful transcriptional indicators of rearing density impacted immunity across a broad range of fish species. We propose maintaining fish at suitable densities may not only improve natural immunocompetence, but could improve vaccination efficacy, and recommend this as a valuable line of future research for mitigating disease in aquaculture. As the species diversity of aquaculture increases, whilst disease remains a barrier to sustainable intensification of the industry, the key molecular targets identified here have the potential for monitoring and enhancing aquaculture resilience across the range of farmed species.

Salmon rearing conditions

Salmon fry (average weight = 2.55 g, average standard length = 6.52 cm), obtained from Landcatch Natural Selection (10 families; 1:1 crosses), were maintained in a re-circulating aquaculture system in CSAR, Swansea University (water temperature 10.5 ± 0.5 °C, pH 7.5 ± 0.2). Fry were fed with a commercial salmon feed (Nutraparr, Skretting, UK) and kept under a 12:12h photoperiod. Water oxygen saturation (>90%), ammonia (<0.02 mg/L), nitrite (<0.01 mg L^–1) and nitrate (<15 mg L^–1) were maintained within an appropriate range. The density experiment was conducted for 16 weeks. Fry were randomly assigned to low- and high-density groups, within two replicate 260 L tanks per treatment. Each low-density tank contained 130 fish (initial density 1.3 g L^–1, final density 3.6 g L^–1), and each high-density tank contained 520 fish (initial density 5.1 g L^–1, final density 14.6 g L^–1). These densities fall within current farming practices and UK welfare recommendations (up to 30 g L^–1 for 5 to 30 g juvenile fish (44)). All experiments were performed with the approval of the Swansea Animal Welfare and Ethical Review Body (Approval Number IP–1415–2), and infection challenges were approved by Cardiff University Animal Ethics Committee and conducted under UK Home Office License PPL 302876.

Saprolegnia challenges

Saprolegnia parasitica maintenance and zoopsore production followed Ellison et al (43) and zoospore suspensions were equilibrated to 10.5 °C before use (75). Fish were simultaneously challenged with S. parasitica within their treatment groups to avoid the masking effects of acute stress due to confinement and individual isolation (76). The exposure trials were conducted in 22L tanks, with 2 replicate tanks per group containing 96 fish/tank (4 fish L^–1, 29.2 g L^–1) for high density groups (2 control tanks, 2 Saprolegnia-challenge tanks), and 24 fish/tank (1 fish L^–1, 7.3 g L^–1) for low density groups (2 control tanks, 2 Saprolegnia-challenge tanks).

Following Ellison et al (43), all fish were net shaken to facilitate infection (77) and live zoospores were added directly to high-density aquaria to achieve a concentration of 5 × 10⁶zoospores L^− 1. A mixture of 1:3 live:heat-killed zoospores was added directly to each low-density aquarium to achieve a concentration of 5 × 10⁶ zoospores L^− 1, controlling for equivalent 1) number of infective zoospores per individual and 2) concentration of organic matter between density treatment groups. Water and zoospore solutions were changed every 6 h during 24 h exposure period and fish in unchallenged control groups received the same handling and maintenance regime.

Fish were visually inspected hourly (under red light during dark periods) throughout the experiment and those challenged with S. parasitica from both density groups displayed signs of lethargy (reduced swimming activity, increased resting on the bottom of the tank) ~12 h after infection, but there were no signs of mycelial growth. At 24 h post-exposure, six fish per treatment tank (high/low density, challenged/sham-challenged, 2 tank replicates) were euthanised with an overdose of Phenoxyethanol (0.5 ml L^–1) and samples of gill tissues (all arches) were immediately preserved in RNAlater and stored at − 80 °C until RNA extraction. Gill tissues were chosen as they are one of the primary sites of infection of S. parasitica (54), and critical to fish mucosal immunity and antibody-producing cell production (78).

Salmon transcriptome sequencing and gene expression analyses

RNA extractions, Illumina TruSeq library preparation, Illumin NextSeq sequencing, raw sequence read quality assessment and preparation followed Ellison et al. (43). Raw reads are available at the NCBI Short Read Archive under Accession Number PRJNA552428. Trimmed reads were mapped to the Salmo salar genome (International Cooperation to Sequence the Atlantic Salmon Genome, version 2) using HISAT2 version 2.0.5 (80) and quantified using RSEM version 1.2.30 (81). Transcripts were filtered to include only those with at least two counts per million mapped reads (TPM) in at least two individuals. Differential expression tests were performed using the R package limma (82), comparing 1) high- and low-density control (uninfected) fish, and 2) infected and uninfected (control) fish. Potential tank effects were explicitly accounted using the duplicateCorrelation function (82). Overlap of differentially expressed genes between salmon treatment groups were determined using Venny version 2.1 (83). Gene ontology (GO) functional enrichment tests (with FDR correction) were carried out via the R package TopGO (84) to detect significantly overrepresented biological processes of groups of differentially expressed genes shared/unique to particular treatment groups.

Interspecific comparisons of gill transcriptomic responses to Saprolegnia challenge

To examine the similarity of rearing density impacts on transcriptomic responses to pathogen challenge across divergent fish species, we compared salmon gene expression profiles to those previously characterised in Nile tilapia (Oreochromis niloticus) by Ellison et al. (2018, 24 h sample data only). For this, we used three methods: 1) overlap of GO term enrichment of differentially expressed genes using the full transcriptome of both species, 2) overlap of differentially expressed 1:1 gene orthologs, and 3) preservation of weighted gene co-expression networks defined using 1:1 gene orthologs. These two datasets were broadly comparable as in both 1) “high-density” rearing treatments were 4 times that of the “low-density” treatments (4 fish L^–1 and 1 fish L^–1 respectively), 2) the same tissue (gill) was studied, 3) tissue samples were taken 24 h post-Saprolegnia exposure, and 4) the same S. parasitica isolate, inoculation dose and challenge procedures were used.

Biological processes GO term lists from functional enrichment tests comparing healthy and Saprolegnia-infected tilapia at 24 h post-exposure were compared to those in salmon using Venny version 2.1 (83). We performed a reciprocal best-hit analysis to identify 1:1 gene orthologs between the two species. We used BLASTP with an E-value threshold of 1 × 10⁻⁶ to search all protein sequences from one species against the other (85). Only sequences that were the reciprocal best hit between both species were retained for further analyses.

Weighted gene co-expression networks (gene modules) were defined and correlated with treatments following methods of Ellison et al. (2018) in tilapia and salmon using only genes with a 1:1 ortholog in the other species. To assess the degree to which gene modules were conserved in the other species, module preservation statistics were computed using the modulePreservation function (500 permutations) (41, 86). Network module preservation statistics quantify how density and connectivity patterns of modules defined in a reference data set are preserved in a test data. A Z_summary score of 2.0 to 10.0 was considered weak to moderately preserved, and Z_summaryabove 10.0 was considered highly preserved among species (41, 86).

FDR: false discovery rate

GO: gene ontology

MYA: million years ago

RNAseq: RNA sequencing

Th₂: T helper cell type 2

Th₁₇: T helper cell type 17

TPM: transcripts per million mapped reads

WGCNA: weighted gene coexpression network analysis

Ethics approval & consent to participate: All experiments were performed with the approval of the Swansea Animal Welfare and Ethical Review Body (Approval Number IP–1415–2), and infection challenges were approved by Cardiff University Animal Ethics Committee and conducted under UK Home Office License PPL 302876. Consent to participate: not applicable.

Consent for publication: Not applicable.

Availability of data and materials: All sequence data have been submitted to the NCBI Sequence Read Archive (Accession: PRJNA552428) and will be made publicly available upon acceptance of this manuscript for publishing. All other data are available as additional files with this article.

Competing interests: The authors declare that they have no competing interests.

Funding: The study was part-funded by a grant from BBSRC (BB/M026469/1) to CGL, and a grant from the Welsh Government and Higher Education Funding Council for Wales through the Sêr Cymru National Research Network for Low Carbon, Energy and the Environment (NRN-LCEE) AquaWales project. AE was additionally supported by a BBSRC Future Leader Fellowship (BB/R010609/1).

Author contributions: All authors designed the study. SC, CG, JC and POW organized funding. AE, TUW and DRB collected data. AE performed analyses. AE wrote the manuscript with contributions and edits from all authors.

Acknowledgments: We thank staff at CSAR Swansea University for maintaining the fish, staff at Cardiff Biosciences Genomics Hub for assistance in sequencing, and Landcatch, Hendrix Genetics (Alastair Hamilton) for the provision of pedigree fish.

1.Froehlich HE, Runge CA, Gentry RR, Gaines SD, Halpern BS. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proceedings of the National Academy of Sciences. 2018:201801692.

2.Béné C, Barange M, Subasinghe R, Pinstrup-Andersen P, Merino G, Hemre G-I, et al. Feeding 9 billion by 2050–Putting fish back on the menu. Food Security. 2015;7(2):261–74.

3.Nations FaAOotU. The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals. Rome; 2018.

4.Troell M, Naylor RL, Metian M, Beveridge M, Tyedmers PH, Folke C, et al. Does aquaculture add resilience to the global food system? Proceedings of the National Academy of Sciences. 2014;111(37):13257–63.

5.Engle CR, McNevin A, Racine P, Boyd CE, Paungkaew D, Viriyatum R, et al. Economics of Sustainable Intensification of Aquaculture: Evidence from Shrimp Farms in Vietnam and Thailand. Journal of the World Aquaculture Society. 2017;48(2):227–39.

6.Johnson K, Engle C, Wagner B. Comparative Economics of US Catfish Production Strategies: Evidence from a Cross‐sectional Survey. Journal of the World Aquaculture Society. 2014;45(3):279–89.

7.Bondad-Reantaso MG, Subasinghe RP, Arthur JR, Ogawa K, Chinabut S, Adlard R, et al. Disease and health management in Asian aquaculture. Veterinary parasitology. 2005;132(3):249–72.

8.Liu B, Liu Y, Sun G. Effects of stocking density on growth performance and welfare‐related physiological parameters of Atlantic salmon S almo salar L. in recirculating aquaculture system. Aquaculture Research. 2017;48(5):2133–44.

9.Calabrese S, Nilsen TO, Kolarevic J, Ebbesson LOE, Pedrosa C, Fivelstad S, et al. Stocking density limits for post-smolt Atlantic salmon (Salmo salar L.) with emphasis on production performance and welfare. Aquaculture. 2017;468:363–70.

10.Garcia F, Romera DM, Gozi KS, Onaka EM, Fonseca FS, Schalch SHC, et al. Stocking density of Nile tilapia in cages placed in a hydroelectric reservoir. Aquaculture. 2013;410:51–6.

11.Jones HAC, Noble C, Damsgård B, Pearce GP. Social network analysis of the behavioural interactions that influence the development of fin damage in Atlantic salmon parr (Salmo salar) held at different stocking densities. Applied Animal Behaviour Science. 2011;133(1–2):117–26.

12.Manley CB, Rakocinski CF, Lee PG, Blaylock RB. Stocking density effects on aggressive and cannibalistic behaviors in larval hatchery-reared spotted seatrout, Cynoscion nebulosus. Aquaculture. 2014;420:89–94.

13.Champneys T, Castaldo G, Consuegra S, Garcia de Leaniz C. Density-dependent changes in neophobia and stress-coping styles in the world’s oldest farmed fish. Royal Society open science. 2018;5(12):181473.

14.De las Heras V, Martos-Sitcha JA, Yúfera M, Mancera JM, Martínez-Rodríguez G. Influence of stocking density on growth, metabolism and stress of thick-lipped grey mullet (Chelon labrosus) juveniles. Aquaculture. 2015;448:29–37.

15.Laiz-Carrión R, Fuentes J, Redruello B, Guzmán JM, del Río MPM, Power D, et al. Expression of pituitary prolactin, growth hormone and somatolactin is modified in response to different stressors (salinity, crowding and food-deprivation) in gilthead sea bream Sparus auratus. General and comparative endocrinology. 2009;162(3):293–300.

16.Suárez MD, García-Gallego M, Trenzado CE, Guil-Guerrero JL, Furné M, Domezain A, et al. Influence of dietary lipids and culture density on rainbow trout (Oncorhynchus mykiss) flesh composition and quality parameter. Aquacultural engineering. 2014;63:16–24.

17.Jia R, Liu B-L, Feng W-R, Han C, Huang B, Lei J-L. Stress and immune responses in skin of turbot (Scophthalmus maximus) under different stocking densities. Fish & shellfish immunology. 2016;55:131–9.

18.Yarahmadi P, Miandare HK, Fayaz S, Caipang CMA. Increased stocking density causes changes in expression of selected stress-and immune-related genes, humoral innate immune parameters and stress responses of rainbow trout (Oncorhynchus mykiss). Fish & shellfish immunology. 2016;48:43–53.

19.Sun P, Bao P, Tang B. Transcriptome analysis and discovery of genes involved in immune pathways in large yellow croaker (Larimichthys crocea) under high stocking density stress. Fish & shellfish immunology. 2017;68:332–40.

20.Ellison AR, Webster TMU, Rey O, de Leaniz CG, Consuegra S, Orozco-terWengel P, et al. Transcriptomic response to parasite infection in Nile tilapia (Oreochromis niloticus) depends on rearing density. BMC genomics. 2018;19(1):723.

21.Stentiford GD, Sritunyalucksana K, Flegel TW, Williams BAP, Withyachumnarnkul B, Itsathitphaisarn O, et al. New paradigms to help solve the global aquaculture disease crisis. PLoS pathogens. 2017;13(2):e1006160.

22.Bank W. Reducing disease risks in aquaculture. 2014. Contract No.: #88257-GLB.

23.Field KA, Johnson JS, Lilley TM, Reeder SM, Rogers EJ, Behr MJ, et al. The white-nose syndrome transcriptome: activation of anti-fungal host responses in wing tissue of hibernating little brown myotis. PLoS pathogens. 2015;11(10):e1005168.

24.Ellison AR, Savage AE, DiRenzo GV, Langhammer P, Lips KR, Zamudio KR. Fighting a losing battle: vigorous immune response countered by pathogen suppression of host defenses in the chytridiomycosis-susceptible frog Atelopus zeteki. G3: Genes Genomes Genetics. 2014.

25.Robledo D, Gutiérrez AP, Barría A, Yáñez JM, Houston RD. Gene expression response to sea lice in Atlantic salmon skin: RNA sequencing comparison between resistant and susceptible animals. Frontiers in genetics. 2018;9:287.

26.Santos ME, Baldo L, Gu L, Boileau N, Musilova Z, Salzburger W. Comparative transcriptomics of anal fin pigmentation patterns in cichlid fishes. BMC genomics. 2016;17(1):712.

27.Marra NJ, Richards VP, Early A, Bogdanowicz SM, Bitar PDP, Stanhope MJ, et al. Comparative transcriptomics of elasmobranchs and teleosts highlight important processes in adaptive immunity and regional endothermy. BMC genomics. 2017;18(1):87.

28.Ellison AR, Tunstall T, DiRenzo GV, Hughey MC, Rebollar EA, Belden LK, et al. More than skin deep: functional genomic basis for resistance to amphibian chytridiomycosis. Genome biology and evolution. 2015;7(1):286–98.

29.Valenzuela-Muñoz V, Boltaña S, Gallardo-Escárate C. Comparative immunity of Salmo salar and Oncorhynchus kisutch during infestation with the sea louse Caligus rogercresseyi: An enrichment transcriptome analysis. Fish & shellfish immunology. 2016;59:276–87.

30.Adams CE, Turnbull JF, Bell A, Bron JE, Huntingford FA. Multiple determinants of welfare in farmed fish: stocking density, disturbance, and aggression in Atlantic salmon (Salmo salar). Canadian journal of fisheries and aquatic sciences. 2007;64(2):336–44.

31.Turnbull J, Bell A, Adams C, Bron J, Huntingford F. Stocking density and welfare of cage farmed Atlantic salmon: application of a multivariate analysis. Aquaculture. 2005;243(1):121–32.

32.Evans JJ, Pasnik DJ, Horley P, Kraeer K, Klesius PH. Aggression and mortality among Nile tilapia (Oreochromis niloticus) maintained in the laboratory at different densities. Res J Anim Sci. 2008;2(2):57–64.

33.Liu B, Liu Y, Wang X. The effect of stocking density on growth and seven physiological parameters with assessment of their potential as stress response indicators for the Atlantic salmon (Salmo salar). Marine and freshwater behaviour and physiology. 2015;48(3):177–92.

34.Qiang J, He J, Yang H, Xu P, Habte-Tsion HM, Ma XY, et al. The changes in cortisol and expression of immune genes of GIFT tilapia Oreochromis niloticus (L.) at different rearing densities under Streptococcus iniae infection. Aquaculture international. 2016;24(5):1365–78.

35.Ridha MT. Comparative study of growth performance of three strains of Nile tilapia, Oreochromis niloticus, L. at two stocking densities. Aquaculture Research. 2006;37(2):172–9.

36.Mazur CF, Iwama GK. Handling and crowding stress reduces number of plaque‐forming cells in Atlantic salmon. Journal of Aquatic Animal Health. 1993;5(2):98–101.

37.Van Den Berg AH, McLaggan D, Diéguez-Uribeondo J, Van West P. The impact of the water moulds Saprolegnia diclina and Saprolegnia parasitica on natural ecosystems and the aquaculture industry. Fungal Biology Reviews. 2013;27(2):33–42.

38.Saad TT, Atallah ST, El-Bana SA. Fish diseases and its economic effect on Egyptian fish farms. Journal of Agriculture and Food Technology 2014;4(5):1–6.

39.Chauhan R. Fungal attack on Tilapia mossambicus in culture pond, leading to mass mortality of fishes. Int J Phram Sci Rev Res. 2014;7.

40.Hughes LC, Ortí G, Huang Y, Sun Y, Baldwin CC, Thompson AW, et al. Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data. Proceedings of the National Academy of Sciences. 2018:201719358.

41.Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLoS computational biology. 2011;7(1):e1001057.

42.North BP, Turnbull JF, Ellis T, Porter MJ, Migaud H, Bron J, et al. The impact of stocking density on the welfare of rainbow trout (Oncorhynchus mykiss). Aquaculture. 2006;255(1–4):466–79.

43.Ellison AR, Uren-Webster TM, Rey O, de Leaniz CG, Consuegra S, Orozco-terWengel P, et al. Transcriptomic response to parasite infection in Nile tilapia (Oreochromis niloticus) depends on rearing density. BMC genomics. 2018;19(1):723.

44.RSPCA. RSPCA welfare standards for farmed Atlantic salmon. Horsham, UK; 2018. Contract No.: 978–0–901098–14–6.

45.Martinez M, Calvo-Torrent A, Herbert J. Mapping brain response to social stress in rodents with c-fos expression: a review. Stress. 2002;5(1):3–13.

46.Liu S, Gao G, Palti Y, Cleveland BM, Weber GM, Rexroad Iii CE. RNA-seq analysis of early hepatic response to handling and confinement stress in rainbow trout. Plos one. 2014;9(2):e88492.

47.Salierno JD, Snyder NS, Murphy AZ, Poli M, Hall S, Baden D, et al. Harmful algal bloom toxins alter c-Fos protein expression in the brain of killifish, Fundulus heteroclitus. Aquatic toxicology. 2006;78(4):350–7.

48.Iwama GK, Afonso LOB, Todgham A, Ackerman P, Nakano K. Are hsps suitable for indicating stressed states in fish? Journal of Experimental Biology. 2004;207(1):15–9.

49.Zlatković J, Bernardi RE, Filipović D. Protective effect of Hsp70i against chronic social isolation stress in the rat hippocampus. Journal of Neural Transmission. 2014;121(1):3–14.

50.Roberts LJ, Taylor J, de Leaniz CG. Environmental enrichment reduces maladaptive risk-taking behavior in salmon reared for conservation. Biological Conservation. 2011;144(7):1972–9.

51.Roberts LJ, Taylor J, Gough PJ, Forman DW, Garcia de Leaniz C. Silver spoons in the rough: can environmental enrichment improve survival of hatchery Atlantic salmon Salmo salar in the wild? Journal of Fish Biology. 2014;85(6):1972–91.

52.Stringwell R, Lock A, Stutchbury CJ, Baggett E, Taylor J, Gough PJ, et al. Maladaptation and phenotypic mismatch in hatchery‐reared Atlantic salmon Salmo salar released in the wild. Journal of Fish Biology. 2014;85(6):1927–45.

53.Belmonte R, Wang T, Duncan GJ, Skaar I, Mélida H, Bulone V, et al. Role of pathogen-derived cell wall carbohydrates and prostaglandin E2 in immune response and suppression of fish immunity by the oomycete Saprolegnia parasitica. Infection and immunity. 2014;82(11):4518–29.

54.de Bruijn I, Belmonte R, Anderson VL, Saraiva M, Wang T, van West P, et al. Immune gene expression in trout cell lines infected with the fish pathogenic oomycete Saprolegnia parasitica. Developmental & Comparative Immunology. 2012;38(1):44–54.

55.Kales SC, DeWitte-Orr SJ, Bols NC, Dixon B. Response of the rainbow trout monocyte/macrophage cell line, RTS11 to the water molds Achlya and Saprolegnia. Molecular immunology. 2007;44(9):2303–14.

56.Dumbell R, Matveeva O, Oster H. Circadian Clocks, Stress, and Immunity. Frontiers in endocrinology. 2016;7.

57.Du LY, Darroch H, Keerthisinghe P, Ashimbayeva E, Astin JW, Crosier KE, et al. The innate immune cell response to bacterial infection in larval zebrafish is light-regulated. Scientific reports. 2017;7(1):12657.

58.Guerra-Santos B, López-Olmeda JF, Pereira DSP, Ruiz CE, Sánchez-Vázquez FJ, Esteban MÁ, et al. Daily rhythms after vaccination on specific and non-specific responses in Nile tilapia (Oreochromis niloticus). Chronobiology international. 2018;35(9):1305–18.

59.Reite OB, Evensen Ø. Inflammatory cells of teleostean fish: a review focusing on mast cells/eosinophilic granule cells and rodlet cells. Fish & shellfish immunology. 2006;20(2):192–208.

60.Sfacteria A, Brines M, Blank U. The mast cell plays a central role in the immune system of teleost fish. Molecular immunology. 2015;63(1):3–8.

61.van West P. Saprolegnia parasitica, an oomycete pathogen with a fishy appetite: new challenges for an old problem. Mycologist. 2006;20(3):99–104.

62.Tort L. Stress and immune modulation in fish. Developmental & Comparative Immunology. 2011;35(12):1366–75.

63.Small BC, Bilodeau AL. Effects of cortisol and stress on channel catfish (Ictalurus punctatus) pathogen susceptibility and lysozyme activity following exposure to Edwardsiella ictaluri. General and comparative endocrinology. 2005;142(1–2):256–62.

64.Downer EJ, Johnston DGW, Lynch MA. Differential role of Dok1 and Dok2 in TLR2-induced inflammatory signaling in glia. Molecular and Cellular Neuroscience. 2013;56:148–58.

65.Celis‐Gutierrez J, Boyron M, Walzer T, Pandolfi PP, Jonjić S, Olive D, et al. Dok1 and Dok2 proteins regulate natural killer cell development and function. The EMBO journal. 2014;33(17):1928–40.

66.Saluja R, Metz M, Maurer M. Role and relevance of mast cells in fungal infections. Frontiers in immunology. 2012;3:146.

67.Schraml BU, Hildner K, Ise W, Lee W-L, Smith WAE, Solomon B, et al. The AP–1 transcription factor Batf controls T H 17 differentiation. Nature. 2009;460(7253):405.

68.Anderson AC, Joller N, Kuchroo VK. Lag–3, Tim–3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity. 2016;44(5):989–1004.

69.Butler NS, Moebius J, Pewe LL, Traore B, Doumbo OK, Tygrett LT, et al. Therapeutic blockade of PD-L1 and LAG–3 rapidly clears established blood-stage Plasmodium infection. Nature immunology. 2012;13(2):188.

70.Umasuthan N, Revathy KS, Whang I, Kim E, Oh M-J, Jung S-J, et al. Genomic identification and molecular characterization of a non-mammalian TNFAIP8L2 gene from Oplegnathus fasciatus. Gene. 2014;542(1):52–63.

71.Li T, Wang W, Gong S, Sun H, Zhang H, Yang A-G, et al. Genome-wide analysis reveals TNFAIP8L2 as an immune checkpoint regulator of inflammation and metabolism. Molecular immunology. 2018;99:154–62.

72.Khader SA, Gaffen SL, Kolls JK. Th17 cells at the crossroads of innate and adaptive immunity against infectious diseases at the mucosa. Mucosal immunology. 2009;2(5):403.

73.Lin Y, Slight SR, Khader SA, editors. Th17 cytokines and vaccine-induced immunity2010: Springer.

74.Zhang H, Fei C, Wu H, Yang M, Liu Q, Wang Q, et al. Transcriptome profiling reveals Th17-like immune responses induced in zebrafish bath-vaccinated with a live attenuated Vibrio anguillarum. PloS one. 2013;8(9):e73871.

75.Stewart A, Jackson J, Barber I, Eizaguirre C, Paterson R, van West P, et al. Hook, Line and Infection: A Guide to Culturing Parasites, Establishing Infections and Assessing Immune Responses in the Three-Spined Stickleback. Advances in parasitology. 2017;98:39.

76.Auperin B, Baroiller J-F, Ricordel M-J, Fostier A, Prunet P. Effect of confinement stress on circulating levels of growth hormone and two prolactins in freshwater-adapted tilapia (Oreochromis niloticus). General and comparative endocrinology. 1997;108(1):35–44.

77.Hoshiai G. Studies on saprolegniasis in cultured coho salmon, Oncorhynchus kisutch Walbaum. Studies on saprolegniasis in cultured coho salmon, Oncorhynchus kisutch Walbaum. 1990(39):154–7.

78.Secombes CJ, Wang T. The innate and adaptive immune system of fish. Infectious disease in aquaculture: Elsevier; 2012. p. 3–68.

79.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

80.Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature methods. 2015;12(4):357–60.

81.Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics. 2011;12(1):323.

82.Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43(7):e47-e.

83.Oliveros JC. VENNY. An interactive tool for comparing lists with Venn Diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html2007.

84.Alexa A, Rahnenfuhrer J. topGO: Enrichment Analysis for Gene Ontology. version 2.34.0 ed: R package; 2018.

85.Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics. 2007;24(3):319–24.

86.Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics. 2008;9(1):559.

Table 1. Summary of Atlantic salmon and Nile tilapia gene orthologs that exhibit density-specific expression in response to rearing density. Shaded boxes indicate the treatment group (H = high-density 4 fish/L, L = low-density 1 fish/L) with higher expression.

Gene	Function	Salmon		Tilapia
Gene	Function	H	L	H	L
dihydropyrimidinase-related	axon repair/development, cell migration, filipodia
large neutral amino acids transporter small subunit 4-like	amino acid transport
aquaporin FA-CHIP-like	ammonium, CO₂, water transport
complement decay-accelerating factor-like	cell adhesion, motility, angiogenesis
GTP-binding 2	GTPase activity
ras-related and estrogen-regulated growth inhibitor	GTPase activity
inositol-trisphosphate 3-kinase C	inositol phosphate biosynthetic process
egl nine homolog 2-like	oxidation-reduction
death-associated kinase 3	protein kinase
transmembrane protease serine 4-like	proteolysis
neuronal PAS domain-containing 2-like	regulation of transcription
homeobox DLX-3	regulation of transcription
myocyte-specific enhancer factor 2D	regulation of transcription
mediator of RNA polymerase II transcription subunit 12	regulation of transcription
endothelin B receptor-like	vasoconstriction, cartilage development
PDZ and LIM domain 4	zinc ion binding, protein binding
docking 1-like	insulin receptor binding
transmembrane 268-like	unknown
poly [ADP-ribose] polymerase 9	NAD+ ADP-ribosyl transferase activity
claudin-4-like	structural molecule activity
aldehyde dehydrogenase family 3 member B1-like	aldehyde metabolic process
non-lysosomal glucosylceramidase	axonogenesis
nucleotide exchange factor SIL1	binding

Table 2. Summary of Gene Ontology (GO) term enrichment overlap of differentially expressed genes compared between control (sham-challenged) and Saprolegnia-challenged Atlantic salmon and Nile tilapia. Arrows indicate treatment groups (H = high-density 4 fish/L, L = low-density 1 fish/L, HL = both densities) in which significant enrichment was found. Arrow direction indicates direction of expression (↓ = decreased expression, ↑ = increased expression).

Salmon			Tilapia			GO biological process
HL	H	L	HL	H	L	GO biological process
↓	↓	↓	↓	↓	↓	regulation of Rho protein signal transduction
↑	↑		↑	↑		spliceosomal snRNP assembly
	↑		↓	↓	↑	regulation of apoptotic process
↑			↑	↑	↑	fructose metabolic process
↓	↓		↓		↓	peptidyl-tyrosine phosphorylation
↓		↓	↓	↑		DNA replication
↓		↓	↓		↓	protein phosphorylation
	↓		↓	↓	↑	regulation of cell growth
↑	↑		↑			isoprenoid biosynthetic process
	↑	↑			↓	spermine biosynthetic process, embryonic neurocranium morphogenesis
	↑		↑	↑		protein peptidyl-prolyl isomerization, protein folding, glycolytic process, pentose-phosphate shunt, oxidation-reduction process
	↑		↑↓			protein methylation
	↑		↑		↑	Arp2/3 complex-mediated actin nucleation
	↑		↓		↑	cellular response to xenobiotic stimulus
↑↓					↓	cell cycle
↑	↓			↓		determination of ventral identity
↑		↑		↑		rRNA modification
↑		↑	↑			defence response to Gram-negative bacterium
↑		↓	↓			convergent extension involved in gastrulation
↑			↑	↑		pseudouridine synthesis
↑			↑		↑	fructose 2,6-bisphosphate metabolic process
↑			↓		↓	immune response
↓		↓		↓		locomotor rhythm
↓		↓			↓	embryonic skeletal system development, transmembrane receptor protein tyrosine kinase signalling pathway
↓			↓	↓		regulation of ARF protein signal transduction
↓			↓		↓	protein kinase C-activating G protein-coupled receptor signalling pathway, retinal ganglion cell axon guidance
	↓	↑		↓		regulation of transcription by RNA polymerase II
	↓	↓		↓		positive regulation of GTPase activity, negative regulation of transcription (DNA-templated)
	↓	↓			↓	negative regulation of angiogenesis
	↓		↓	↓		cell adhesion
	↓		↓		↓	axon guidance
		↑	↑	↑		ribosome biogenesis, nucleoside metabolic process
		↑		↑	↑	protein O-linked mannosylation
		↓	↑	↑		DNA recombination
		↓	↓	↓		signal transduction
	↑			↑		proton transport
	↑		↑			mast cell degranulation, response to lipopolysaccharide, peptidyl-lysine methylation, galactose metabolic process, proton-transporting ATP synthase complex assembly
	↑		↓			embryonic digestive tract morphogenesis, proepicardium development, regulation of vascular endothelial growth factor receptor signalling pathway
	↑			↓		regulation of alternative mRNA splicing via spliceosome
	↑				↑	fucosylation, RNA phosphodiester bond hydrolysis, intestinal cholesterol absorption, peptide cross-linking
	↑				↓	stabilization of membrane potential
↑				↑		spliceosomal complex assembly, mRNA transport, exonucleolytic trimming, positive regulation of cell division
↑			↑			Fc-epsilon receptor signalling pathway, viral entry into host cell, maturation of LSU-rRNA, asparagine biosynthetic process, nuclear import, isocitrate metabolic process, regulation of translational initiation, histone mRNA metabolic process, cyclooxygenase pathway, mitotic sister chromatid cohesion, ribosomal subunit export from nucleus
↑			↓			cell migration involved in gastrulation, spermatid development, cell chemotaxis
↑				↓		positive regulation of gene expression, positive regulation of ERK1 and ERK2 cascade, gastric inhibitory peptide signalling pathway
↑					↑	carbohydrate phosphorylation, melanosome transport, phospholipid transport
↑					↓	NADP biosynthetic process
↓				↑		regulation of skeletal muscle cell differentiation, adherens junction assembly
↓			↓			positive regulation of cell proliferation, negative regulation of canonical Wnt signalling, fibroblast growth factor receptor signalling, smoothened signalling pathway, dorsal root ganglion development, positive regulation of transcription
↓				↓		phosphate ion transmembrane transport, endothelial cell chemotaxis
↓					↓	de novo' actin filament nucleation, lipoprotein metabolic process
	↓		↑			regulation of stress fiber assembly
	↓		↓			proteolysis, heart development, retinol metabolic process, response to axon injury, homophilic cell adhesion via plasma membrane
	↓			↓		regulation of cell proliferation, mesenchyme migration, somatic muscle development, inactivation of MAPK activity, positive regulation of protein kinase A signalling, angiogenesis, actin filament organization
	↓				↑	phagocytosis, protein autophosphorylation, reverse cholesterol transport, negative regulation of ERK1 and ERK2 cascade, Rho protein signal transduction
	↓				↓	Roundabout signalling pathway, notochord morphogenesis, regulation of calcineurin-NFAT signalling, calcium ion import
		↑		↑		T-helper 2 cell differentiation, T-helper 17 cell lineage commitment, myeloid dendritic cell differentiation, defence response to protozoan, isotype switching, rRNA methylation, hematopoietic stem cell differentiation, ribonucleoside monophosphate biosynthetic process, fatty acid biosynthetic process, threonine catabolic process
		↑	↑			regulation of defence response to virus by virus
		↑		↓		NLS-bearing protein import into nucleus, regulation of RNA splicing
		↓	↑			peptidyl-diphthamide biosynthetic process, 'de novo' CTP biosynthetic process
		↓	↓			regulation of protein localization, retinoic acid catabolic process
		↓			↑	regulation of Rho guanyl-nucleotide exchange factor activity, regulation of ephrin receptor signalling, cytoplasmic microtubule organization, peripheral nervous system myelin maintenance, Kit signalling pathway, drug transmembrane transport
		↓			↓	positive regulation of non-canonical Wnt signalling pathway, negative regulation of vascular endothelial growth factor receptor signalling pathway, reelin-mediated signalling pathway, histone H4-K16 acetylation, positive regulation of Wnt signalling pathway

Table 3. Summary of gene orthologs sharing differential expression between control (sham-challenged) and Saprolegnia-challenged Atlantic salmon and Nile tilapia. Arrows indicate treatment groups (H = high-density 4 fish/L, L = low-density 1 fish/L) in which significant enrichment was found. Arrow direction indicates direction of expression (↓ = decreased expression, ↑ = increased expression).

Salmon		Tilapia		ID	Gene
H	L	H	L	ID	Gene
↑			↑	XP_013995411.1	growth differentiation factor 15
↑			↑	XP_014004629.1	interleukin-17C
↑			↑	XP_014009356.1	lymphocyte activation gene 3
↑			↑	XP_014056305.1	tumor necrosis factor alpha-induced protein 8-like 2
	↑	↑		XP_003455658.1	basic leucine zipper transcriptional factor ATF-like
	↓	↓		XP_014027980.1	perforin-1
↑		↑		XP_014068485.1	stimulator of interferon genes
↓		↓		XP_014011243.1	integumentary mucin
	↑		↑	XP_014015941.1	tumor necrosis factor ligand superfamily member 15-like
	↑		↑	XP_014001973.1	tumor necrosis factor receptor superfamily member 9-like
	↑		↑	XP_014064920.1	mucin-5AC-like
↑	↑	↑	↑	NP_001117024.1	interleukin 1 receptor accessory protein
↑	↑	↑	↑	XP_014036616.1	leukocyte elastase inhibitor
↑	↑	↑	↑	XP_014067437.1	leukocyte cell-derived chemotaxin 2 precursor
↑	↑	↑	↑	XP_014071691.1	AP-1 complex subunit mu-1
↑	↑	↑	↑	XP_014047427.1	AP-1 complex subunit sigma-2
↑	↑	↑	↑	NP_001134642.1	AP-1 complex subunit sigma-3

Download PDF

Journal Publication

published 01 Sep, 2020

Read the published version in Fish & Shellfish Immunology →

Version 1

posted

You are reading this latest preprint version

Comparative transcriptomics reveal conserved impacts of rearing density on immune response of two important aquaculture species

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Salmon transcriptomic responses to rearing density and Saprolegnia challenge

Interspecific comparison of density and Saprolegnia transcriptomic responses

Impact of density on uninfected salmon and tilapia

Saprolegnia responses in salmon and tilapia

Discussion

Conclusions

Methods

Salmon rearing conditions

Saprolegnia challenges

Salmon transcriptome sequencing and gene expression analyses

Interspecific comparisons of gill transcriptomic responses to Saprolegnia challenge

List of Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1