Open Access

Hepatic transcriptome analysis of inter-family variability in flesh n-3 long-chain polyunsaturated fatty acid content in Atlantic salmon

  • Sofia Morais1Email author,
  • John B Taggart1,
  • Derrick R Guy2,
  • J Gordon Bell1 and
  • Douglas R Tocher1
BMC Genomics201213:410

DOI: 10.1186/1471-2164-13-410

Received: 21 November 2011

Accepted: 18 July 2012

Published: 20 August 2012

Abstract

Background

Genetic selection of Atlantic salmon families better adapted to alternative feed formulations containing high levels of vegetable ingredients has been suggested to ensure sustainable growth of aquaculture. The present study aimed to identify molecular pathways that could underlie phenotypic differences in flesh n-3 long-chain polyunsaturated fatty acid (LC-PUFA) levels when fish are fed vegetable oil diets. Liver transcriptome was analyzed and compared in four families presenting higher or lower n-3 LC-PUFA contents at two contrasting flesh total lipid levels.

Results

The main effect of n-3 LC-PUFA contents was in the expression of immune response genes (38% of all significantly affected genes), broadly implicated in the modulation of inflammatory processes and innate immune response. Although genetic evaluations of traits used in the breeding program revealed that the chosen families were not balanced for viral disease resistance, this did not fully explain the preponderance of immune response genes in the transcriptomic analysis. Employing stringent statistical analysis no lipid metabolism genes were detected as being significantly altered in liver when comparing families with high and low n-3 LC-PUFA flesh contents. However, relaxing the statistical analysis enabled identification of potentially relevant effects, further studied by RT-qPCR, in cholesterol biosynthesis, lipoprotein metabolism and lipid transport, as well as eicosanoid metabolism particularly affecting the lipoxygenase pathway. Total lipid level in flesh also showed an important effect on immune response and 8% of significantly affected genes related to lipid metabolism, including a fatty acyl elongase (elovl2), an acyl carrier protein and stearoyl-CoA desaturase.

Conclusions

Inter-family differences in n-3 LC-PUFA content could not be related to effects on lipid metabolism, including transcriptional modulation of the LC-PUFA biosynthesis pathway. An association was found between flesh adiposity and n-3 LC-PUFA in regulation of cholesterol biosynthesis, which was most likely explained by variation in tissue n-3 LC-PUFA levels regulating transcription of cholesterol metabolism genes through srebp2. A preponderance of immune response genes significantly affected by n-3 LC-PUFA contents could be potentially associated with disease resistance, possibly involving anti-inflammatory actions of tissue n-3 LC-PUFA through eicosanoid metabolism. This association may have been fortuitous, but it is important to clarify if this trait is included in future salmon breeding programmes.

Background

Aquaculture is the fastest growing animal production activity worldwide, supplying an increasing proportion of fish for human consumption, estimated at around 50% of total supply in 2008 [1]. However, the growth of marine aquaculture is threatened by its excessive reliance on fishmeal (FM) and fish oil (FO) from wild stocks for the production of fish feeds, which is also an ecologically unsound practice. Almost 89% of the total global production of FO is currently used by aquaculture [2] and the future of this activity strongly depends on the reduction of dependency on FM and FO and its replacement with alternative ingredients, such as vegetable oils (VO) and plant meals, while maintaining fish welfare and health benefits for the human consumer. Fish are highly nutritious components of the human diet and the main source of essential n-3 long-chain polyunsaturated fatty acids (LC-PUFA). The beneficial effects of fatty acids, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are numerous and important, including protection against a range of cardiovascular and inflammatory diseases, as well as neurological disorders [3]. Atlantic salmon (Salmo salar) can grow well on diets where FO has been completely replaced by VO but this results in lower levels of n-3 LC-PUFA in their flesh, compromising their nutritional value and health-promoting effects to the human consumer [4].

The use of selective breeding programs to enhance traits of commercial importance is becoming increasingly common in aquaculture [5]. It has been suggested that combining genetic selection for fish that are more efficient in retaining and/or biosynthesising n-3 LC-PUFA with changes in commercial diet formulations (i.e., higher levels of inclusion of VO) might be a viable strategy to meet growing worldwide demands for aquaculture products, without loss of nutritional value. Previous studies have shown wide individual variability in the capacity of Atlantic salmon to retain or synthesize n-3 LC-PUFA when fed VO diets [6]. Following this, Leaver et al. [7] demonstrated that deposition and/or retention in flesh of dietary n-3 LC-PUFA, EPA and DHA, is a highly heritable trait (h2 = 0.77) in salmon. These results have prompted further interest in large-scale in-depth studies exploring genotype × nutrient interactions in salmon, analysing whether the genetic background of the fish could affect the physiological response to complete dietary replacement of FO by VO [8, 9]. In the present study we investigated this further by analyzing the transcriptome from liver, the primary site of synthesis and export of lipids to extra-hepatic tissues including flesh, from four Atlantic salmon families phenotyped for different levels of flesh n-3 LC-PUFA content in response to a VO diet. The objective was to identify gene pathways and molecular mechanisms that might underlie differences in flesh n-3 LC-PUFA contents when salmon families were fed the same low LC-PUFA diet. Furthermore, because n-3 LC-PUFA level is a component of, and associated with total lipid content in a tissue, a factorial design was chosen in which families containing higher and lower proportions of flesh n-3 LC-PUFA were compared at similar flesh total lipid contents.

Results

Family lipid contrasts

Lipid analysis of fifty Atlantic salmon families showed flesh lipid levels ranging from 2.3 to 5.7% of wet weight, with relative and absolute n-3 LC-PUFA contents varying from 71 to 136 (μg/mg lipid) and 314 to 554 (mg/100 g flesh), respectively. As expected, high correlations between lipid level and n-3 LC-PUFA content were observed (r = −0.65 or 0.70 for relative and absolute contents, respectively), indicating that only families with near identical lipid levels should be compared to avoid confounding effects associated with the lipid level factor (additional file 1). Using these results, four families were identified; two with high (H) levels of lipid (5 g/100 g flesh), and two with low (L) levels of lipid (3.5 g/100 g flesh) and, within each level of total lipid, the two families had significantly contrasting relative n-3 LC-PUFA contents (similarly termed H and L). Therefore, the four families constituted a 2 x 2 factorial design, labelling each family by the total lipid/n-3 LC-PUFA contrasts as LL, LH, HL and HH, respectively (Table 1), which allowed comparisons of n-3 LC-PUFA contents at a constant lipid level and, similarly, comparisons of total lipid at constant n-3 LC-PUFA levels.
Table 1

Lipid phenotypes of families chosen for molecular analysis

Family

Total Lipid (g/100 g flesh)

n-3 LC-PUFA

ARA

  

Relative (μg/mg lipid)

Absolute (mg/100 g flesh)

Relative (μg/mg lipid)

Absolute (mg/100 g flesh)

LL

3.5 ± 0.4

105.1 ± 3.8 *

363.0 ± 30.3

3.45 ± 0.13 *

11.93 ± 1.00

LH

3.5 ± 0.7

133.8 ± 4.8 *

468.0 ± 92.9

4.25 ± 0.06 *

14.87 ± 2.94

HL

5.1 ± 0.8

83.7 ± 14.0 *

426.9 ± 103.2

2.70 ± 0.53

13.81 ± 3.80

HH

5.0 ± 0.7

112.0 ± 7.9 *

554.3 ± 50.7

3.67 ± 0.63

18.04 ± 2.09

Indicated are levels of total lipid (g/100 g flesh, wet weight), and relative and absolute contents of total n-3 LC-PUFA and of the n-6 LC-PUFA arachidonic acid (ARA) in the flesh (n = 3 pools) of the 4 Atlantic salmon families used in the transcriptomic analysis. Asterisks signify significant differences between the two families with the same total lipid content.

Microarray analysis

A two-way ANOVA analysis employing the Benjamini-Hochberg multiple testing correction (at a significance level of 0.05 and fold change cut-off of 1.2) was performed to assess significant effects of the factors 'n-3 LC-PUFA' and 'total lipid', which returned lists with 43, 109 and 66 entities for each factor and their interaction, respectively. These significant lists were then analyzed in detail and genes were categorized according to their biological function, in some cases inferred from mammalian homolog genes (Tables 2 and 3). Because the focus of this work was to identify genes that are specifically affected by the trait n-3 LC-PUFA content without the interference of total lipid level, the interaction between the two factors is not presented. Distribution of genes by categories of biological function (excluding 12-18% non-annotated probes, those representing the same gene or with a miscellaneous function) revealed that there was a preponderance of immune response genes significantly affected by both factors: 38% by 'n-3 LC-PUFA' and 29% by 'total lipid'. Gene Ontology (GO) enrichment analysis, which enables the identification of GO terms significantly enriched in the input entity list when compared to the whole array dataset, revealed that this is a true over-representation in the list of genes significantly affected by the 'total lipid' factor (Additional file 2). In contrast, genes involved in the broad category of metabolism only corresponded to 21% of genes significantly affected by n-3 LC-PUFA content and 30% by the 'total lipid' factor. Surprisingly, no lipid metabolism genes were significantly altered in liver when comparing families with higher and lower contents of n-3 LC-PUFA in their flesh, while about 8% were significantly affected by flesh lipid level. Within these, noteworthy was the down-regulation of fatty acyl elongase (elovl2) and of acyl carrier protein transcripts in salmon having a higher lipid level in their flesh, independent of LC-PUFA content. On the other hand, stearoyl-CoA desaturase was significantly up-regulated in fish with higher lipid levels in their flesh. The interaction between both factors is not presented but it did not substantially affect lipid metabolism genes. Finally, and in general, genes involved in regulation of transcription and signalling were also prevalent, 17% in response to 'n-3 LC-PUFA' and 12-13% to 'total lipid'.
Table 2

Liver transcripts differentially expressed when examining the explanatory power of the factor 'n-3 LC-PUFA' content in flesh of four families of Atlantic salmon fed the same low FM/high VO diet

Probe name

Gene

High/Low LC-PUFA

p-value

  

L Lipid (LH/LL)

H Lipid (HH/HL)

 

Metabolism (21%)

Energy metabolism (4%)

Ssa#S31995754

Cytochrome c oxidase subunit 2

7.1

- 1.0

0.0001

Protein and amino acid metabolism (13%)

Ssa#CB502423

N-acetylated alpha-linked acidic dipeptidase-like 1

3.7

79.8

0.0011

Ssa#STIR03710

Proteasome subunit beta type-9 precursor

- 14.9

- 1.1

0.0026

Ssa#S31993738_S

Ubiquitin-conjugating enzyme E2

- 3.3

- 1.4

0.0188

Xenobiotic and oxidant metabolism (4%)

Ssa#S18892279

Cytochrome P450 1A

1.8

1.4

0.0096

Ssa#STIR00161_2

Cytochrome P450 1A

1.9

1.4

0.0160

Ssa#STIR00161_3

Cytochrome P450 1A

2.5

1.8

0.0213

Con_CANDS_13

Cytochrome P450 1A

2.0

1.4

0.0494

Translation (8%)

Ssa#STIR26031

Mitochondrial 28 S ribosomal protein S34

- 11.1

1.5

0.0017

Ssa#S18867312

Ribonuclease UK114

1.4

1.4

0.0450

Regulation of transcription (17%)

Ssa#S35510106

Zinc finger protein 367

- 1.4

6.5

0.0026

Omy#S18104058

Zinc finger protein 235

4.1

2.3

0.0058

Ssa#TC111702

Reverse transcriptase-like protein

- 1.3

4.6

0.0104

Ssa#TC112002

Retinoid X receptor beta

1.0

- 19.1

0.0134

Signalling and protein modification (17%)

Ssa#STIR15776

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 1.7

123.3

0.0000

Ssa#STIR23530

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 1.9

115.6

0.0000

Ssa#STIR03642

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 1.7

68.1

0.0000

Ssa#STIR01857

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 1.5

125.5

0.0001

Ssa#STIR31840

Sphingomyelin phosphodiesterase acid transcript variant 1

2.1

2.3

0.0303

Ssa#STIR07369

RAF1 proto-oncogene serine/threonine-protein kinase

1.8

1.3

0.0343

Ssa#S35552908

Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide

- 1.6

- 2.1

0.0455

Immune response (38%)

Ssa#S35536179

similar to novel NACHT domain containing protein

2.4

1.8

0.0026

Ssa#S35516341

Tripartite motif-containing protein 25 (trim25)

5.2

6.7

0.0066

Ssa#S30241035

MHC class I

- 1.3

- 26.0

0.0079

Ssa#STIR02298

c-c motif chemokine 13 precursor (ccl13)

4.4

3.0

0.0100

Ssa#S35581943

Myelin and lymphocyte protein (mal)

- 22.2

- 25.0

0.0100

Ssa#KSS3969

Leukocyte cell-derived chemotaxin 2 precursor (lect2)

4.6

7.1

0.0134

Ssa#STIR15577

Tissue factor pathway inhibitor a

22.3

1.7

0.0135

Omy#S15332652

Pentraxin

- 1.1

199.4

0.0172

Ssa#TC70262

Cathepsin K

1.4

2.3

0.0279

Ssa#S35558945

Tripartite motif-containing protein 25 (trim25)

4.8

6.5

0.0347

Miscellaneous/unknown function

Ssa#STIR09736

Transmembrane protein 42

2.3

1.9

0.0009

Ssa#S35519407

Tetratricopeptide repeat protein 23

3.7

1.0

0.0011

Ssa#STIR02307

Family with sequence similarity member a (FAM36A)

1.3

3.6

0.0026

Omy#BX309274

X-ray repair complementing defective repair in Chinese hamster cells 3

- 1.0

- 8.0

0.0100

Ssa#STIR21287

Solute carrier family 30 (zinc transporter) member 7

2.1

1.5

0.0100

Ssa#S35521859_S

Family with sequence similarity member a (FAM36A)

- 1.1

4.8

0.0358

Ssa#S18842295

Alveolin

1.7

4.6

0.0422

Results were obtained by two-way ANOVA analysis (p < 0.05; fold change cut-off of 1.2) with Benjamini-Hochberg multiple testing correction. Transcripts (43 probes; 12% unknowns) are arranged by categories of biological function and, within these, by p-value. Indicated are also the probe names and the expression ratio determined separately between families with high vs low n-3 LC-PUFA contents, for each total lipid level. Percentages of distribution of genes within each category do not include non-annotated probes, those representing the same gene or with a miscellaneous function.

Table 3

Liver transcripts differentially expressed when examining the explanatory power of the variable 'total lipid' level in flesh of four families of Atlantic salmon fed the same low FM/high VO diet

Probe name

Gene

High/Low Lipid

p-value

  

Lo LC-PUFA (HL/LL)

Hi LC-PUFA (HH/LH)

 

Metabolism (30%)

Lipid metabolism (8%)

Ssa#STIR02479

Butyrophilin subfamily 2 member A2 precursor

7.76

1.23

0.0001

Ssa#STIR03356

Acyl carrier protein, mitochondrial precursor

- 5.17

- 1.55

0.0004

Ssa#STIR00151_3

Polyunsaturated fatty acid elongase (elovl2)

- 1.40

- 1.87

0.0190

Ssa#STIR00151_2

Polyunsaturated fatty acid elongase (elovl2)

- 1.28

- 1.97

0.0238

Con_CANDS_03

Polyunsaturated fatty acid elongase (elovl2)

- 1.33

- 1.82

0.0310

Ssa#STIR24266

Acyl carrier protein, mitochondrial precursor

- 2.88

- 1.52

0.0312

Ssa#STIR21802

Stearoyl-CoA desaturase

3.53

3.17

0.0324

Ssa#STIR00151_4

Polyunsaturated fatty acid elongase (elovl2)

- 1.41

- 1.82

0.0393

Ssa#S35501441_S

Acyl carrier protein, mitochondrial precursor

- 2.65

- 1.44

0.0457

Ssa#KSS4634

Stearoyl-CoA desaturase

3.52

2.82

0.0486

Energy metabolism and generation of precursor metabolites (11%)

Ssa#S31995754

Cytochrome c oxidase subunit 2

6.57

- 1.12

0.0002

Ssa#STIR03496

LYR motif-containing protein 5

1.20

1.48

0.0233

Ssa#STIR03740

6-Phosphogluconate dehydrogenase, decarboxylating

1.60

1.93

0.0238

Ssa#TC106663

Creatine kinase, testis isozyme

1.76

1.46

0.0380

Ssa#STIR19155

NADH dehydrogenase iron-sulfur protein 7

1.21

2.30

0.0409

Ssa#STIR12872

V-type ATPase B subunit

- 1.19

- 1.24

0.0419

Protein and amino acid metabolism (4%)

Ssa#STIR03710

Proteasome subunit beta type-9 precursor

1.94

25.52

0.0001

Ssa#S30294677

Serine protease HTRA1 precursor (Serine protease 11)

3.42

2.44

0.0089

Carbohydrate metabolism (4%)

Ssa#TC106766

Glycogenin

8.64

1.67

0.0093

Ssa#S30290426

Serine dehydratase-like

1.60

1.72

0.0393

Xenobiotic and oxidant metabolism (4%)

Ssa#S35671757

Extracellular superoxide dismutase

2.18

1.09

0.0061

Ssa#STIR25620

Microsomal glutathione S-transferase 1

2.45

2.72

0.0096

Omy#S18159333

Microsomal glutathione S-transferase 1

2.47

3.15

0.0233

Transport (2%)

Ssa#S35599996

ATPase, H + transporting, lysosomal, V1 subunit H

45.10

15.16

0.0000

Translation (4%)

Ssa#STIR26031

Mitochondrial 28 S ribosomal protein S34

1.19

19.22

0.0000

Ssa#S30241612

39 S ribosomal protein L16

1.30

1.51

0.0324

Regulation of transcription (12%)

Ssa#STIR06878

Cytosolic iron-sulfur protein assembly 1

- 1.16

- 1.56

0.0106

Ssa#TC112002

Retinoid X receptor beta

1.20

- 16.00

0.0254

Omy#S15320037

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin a4

- 1.97

- 1.53

0.0324

Ssa#CN181280

alpha thalassaemia mental retardation X-linked protein

- 3.05

- 6.90

0.0419

Ssa#S35697153

YLP motif containing 1

- 1.49

- 1.32

0.0428

Ssa#S35486480

Zinc finger protein 492

- 1.08

- 2.29

0.0452

Signalling and protein modification (13%)

Ssa#STIR15776

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 84.27

2.44

0.0000

Ssa#STIR23530

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 83.34

2.60

0.0000

Ssa#STIR03642

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 49.76

2.34

0.0001

Ssa#STIR01857

Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4

- 74.19

2.51

0.0011

Ssa#S35516167

14 kDa phosphohistidine phosphatase

1.57

1.34

0.0019

Ssa#STIR11086

Lunatic fringe

- 5.47

- 2.00

0.0154

Ssa#S30263209

HtrA serine peptidase 3

3.71

2.33

0.0154

Ssa#STIR22920

HCLS1-associated protein X-1

1.73

2.33

0.0390

Ssa#S35701148

Phosphatase and actin regulator 3

- 1.52

- 65.53

0.0404

Omy#S15290792

Serine/threonine-protein kinase PLK2

1.70

2.03

0.0468

Immune response (29%)

Ssa#STIR00130_4

Complement factor H precursor (cfh)

- 1.43

- 1.67

0.0012

Ssa#S35536179

novel NACHT domain containing protein

- 1.89

- 2.41

0.0013

Ssa#S30241035

MHC class I

- 1.55

- 30.59

0.0024

Ssa#S35516341

Tripartite motif-containing protein 25 (trim25)

- 5.64

- 4.34

0.0125

Ssa#S18834140

Complement factor H precursor (cfh)

- 1.34

- 1.57

0.0131

Omy#utu04b09

Complement factor H precursor (cfh)

- 1.56

- 1.70

0.0134

Ssa#STIR15577

Tissue factor pathway inhibitor a

21.03

1.59

0.0151

Ssa#S35558236

C-type lectin domain family 16, member A

4.59

2.25

0.0190

Ssa#STIR10409

CD83 antigen precursor

1.98

1.68

0.0238

Omy#S15332652

Putative pentraxin

- 1.33

159.84

0.0246

Ssa#S35685271

GTPase IMAP family member 7

−168.44

- 4.09

0.0263

Ssa#S35551959

Duodenase-1

- 2.94

- 2.77

0.0349

Ssa#S35558945

Tripartite motif-containing protein 25 (trim25)

- 5.74

- 4.28

0.0373

Ssa#S35685273

Lactose-binding lectin l-2 precursor putative

1.56

4.04

0.0380

Ssa#S31977617

Scavenger receptor cysteine-rich type 1 protein m130

- 1.31

- 2.05

0.0404

Ssa#STIR04893

Lactose-binding lectin l-2 precursor putative

1.57

4.15

0.0419

Ssa#S30264865

Indoleamine-pyrrole 2,3-dioxygenase

2.83

1.06

0.0444

Ssa#S31981622

Granzyme A

- 5.31

- 4.33

0.0444

Ssa#S35685718

CD83 antigen precursor

2.03

1.58

0.0462

Ssa#KSS3969

Leukocyte cell-derived chemotaxin 2 precursor (lect2)

3.16

4.86

0.0486

Structural proteins (10%)

Ssa#STIR03004

Troponin I, slow skeletal muscle

- 202.28

- 135.03

0.0000

Ssa#CK891024

Vitelline envelope protein gamma

1.07

- 15.43

0.0002

Ssa#STIR02053

Troponin I, slow skeletal muscle

- 70.21

- 10.07

0.0005

Omy#S34312003

similar to Titin (Connectin)

- 44.29

- 29.86

0.0013

Omy#S15317515

Type I keratin E7

5.08

2.43

0.0167

Ssa#STIR05140

Troponin I, slow skeletal muscle

- 13.16

- 14.20

0.0124

Ssa#STIR08802

Myosin regulatory light chain 2, smooth muscle isoform

1.55

1.34

0.0324

Miscellaneous/unknown function

Ssa#S35519407

Tetratricopeptide repeat protein 23

3.64

1.02

0.0012

Ssa#S35584894

NCK-associated protein 1-like

- 1.82

- 1.70

0.0019

Ssa#S35521859_S

FAM36A

1.11

5.75

0.0047

Ssa#STIR02307

FAM36A

1.16

3.31

0.0058

Ssa#TC87798

Envelope polyprotein

- 1.83

- 1.03

0.0233

Ssa#STIR20319

TPA-induced transmembrane protein

- 1.09

1.75

0.0233

Ssa#TC110493_S

Beta-3-galactosyltransferase

6.80

1.80

0.0233

Ssa#S30270166

Transmembrane protein 37

2.13

1.90

0.0254

Ssa#STIR08658

Tetraspanin-3 putative

- 3.20

- 1.47

0.0312

Ssa#S30293470

Deoxyribonuclease gamma precursor

2.19

2.85

0.0366

Ssa#S35667723

Dynein, cytoplasmic 1, light intermediate chain 2

- 1.90

- 1.48

0.0366

Ssa#CX354464

Retinol dehydrogenase 12

2.37

2.35

0.0373

Ssa#S35582016

Type I iodothyronine deiodinase

1.51

1.74

0.0380

Ssa#S35515630

C7orf57

1.32

1.13

0.0380

Ssa#STIR15617

FAM36A

1.02

4.60

0.0403

Ssa#STIR31448

osteopontin-like

- 4.84

- 1.58

0.0419

Ssa#STIR26263

Anterior gradient protein 2 homolog precursor

3.72

1.68

0.0419

Ssa#TC65497_S

Adipocyte plasma membrane-associated protein

2.50

1.64

0.0419

Ssa#TC93681

Ring finger protein 44

- 1.23

- 1.17

0.0455

Ssa#EG819142

Glutaminyl-peptide cyclotransferase-like

- 1.11

- 1.49

0.0455

Results were obtained by two-way ANOVA analysis (p < 0.05; fold change cut-off of 1.2) with Benjamini-Hochberg multiple testing correction. Transcripts (109 probes; 18% unknowns) are arranged by categories of biological function and, within these, by p-value. Indicated are also the probe names and the expression ratio determined separately between families with high vs low total lipid level, for each n-3 LC-PUFA grouping. Percentages of distribution of genes within each category do not include non-annotated probes, those representing the same gene or with a miscellaneous function.

Therefore, the results did not identify lipid metabolism pathways that might underlie differences in flesh n-3 LC-PUFA composition between families. However, previous studies demonstrated that hepatic metabolism genes typically show only low fold changes, even when comparing highly contrasting nutritional compositions (e.g., inclusion of 100% FO versus 100% VO in diets [8, 9]), compared to immune response genes that tend to be regulated with higher magnitudes of change [10]. Hence, nutritional data such as the present data have been analysed previously without multiple testing correction and this was found to result in relevant biological interpretations, when validated by reverse transcription real time quantitative PCR (RT-qPCR) [9, 11]. For this reason, we examined the significant effects of 'n-3 LC-PUFA' without the correction, and from within the list containing 1951 features (significance level, 0.05; fold change cut-off, 1.2), we identified and categorized all 48 lipid metabolism transcripts present (Table 4). An effect on cholesterol metabolism was apparent for the factor 'n-3 LC-PUFA', with several genes of the biosynthesis pathway and its regulation being down-regulated in fish with a high n-3 LC-PUFA phenotype. In addition, glycerophospholipid synthesis, lipid hydrolysis and eicosanoid synthesis and metabolism were also affected, while other genes were associated with lipid and fatty acid transport, fatty acid synthesis and regulation of lipid metabolism.
Table 4

Lipid metabolism genes differentially expressed in the liver of fish having high or low flesh n-3 LC-PUFA contents

Probe name

Gene

High/Low LC-PUFA

p- value

  

L Lipid (LH/LL)

H Lipid (HH/HL)

 

Cholesterol biosynthesis, metabolism and regulation

Omy#S15288895

Sterol-C4-methyl oxidase-like

1.84

1.32

0.0033

Ssa#STIR00031_3

7-dehydrocholesterol reductase (7dchr)

- 1.55

- 1.48

0.0036

Ssa#S30286041

7-dehydrocholesterol reductase (7dchr)

- 1.48

- 1.42

0.0120

Omy#CF752841

Sterol regulatory element-binding transcription factor 2 (srebp2)

- 1.71

- 1.31

0.0170

Ssa#TC102141

Cytochrome P450, family 27, subfamily A, polypeptide 1

- 3.97

- 1.04

0.0192

Ssa#STIR16974

7-dehydrocholesterol reductase (7dchr)

- 1.46

- 1.31

0.0195

Ssa#AM402497

Hydroxymethylglutaryl-CoA synthase 1

- 2.39

1.21

0.0199

Ssa#STIR00031_4

7-dehydrocholesterol reductase (7dchr)

- 1.41

- 1.46

0.0212

Ssa#STIR00098_4

Isopentenyl-diphosphate delta isomerise (ipi)

- 2.12

- 1.15

0.0228

Ssa#S18867829

7-dehydrocholesterol reductase (7dchr)

- 1.33

- 1.25

0.0228

Ssa#DY741343

Lanosterol 14-alpha demethylase

- 1.42

- 1.28

0.0346

Omy#S22913656

Acetoacetyl-CoA synthetase

- 1.77

- 2.52

0.0358

Ssa#STIR00033_3

Mevalonate kinase (mev)

- 1.54

- 1.08

0.0393

Ssa#CA064135

Vigilin

2.80

- 1.22

0.0423

Ssa#DW582478

Cytochrome P450, family 8, subfamily B, polypeptide 1

- 1.26

- 1.50

0.0449

Ssa#STIR00102_3

Squalene epoxidase

- 2.11

- 1.36

0.0468

Glycerophospholipid synthesis

Ssa#STIR39152_S

Lipid phosphate phosphohydrolase 2 (lpp2)

1.20

1.19

0.0177

Ssa#KSS4003

Chka protein

1.18

1.53

0.0264

Ssa#S35538062_S

Monoacylglycerol O-acyltransferase 1 (mgat)

1.20

1.51

0.0303

Ssa#S31963704

Diacylglycerol O-acyltransferase homolog 2

1.41

1.02

0.0358

Ssa#S48418830

Phosphatidylglycerophosphate synthase 1

- 1.52

- 1.72

0.0369

Fatty acid synthesis

Ssa#KSS4155

Trans-2-enoyl-CoA reductase, mitochondrial precursor

- 1.26

- 1.25

0.0440

Intracellular fatty acid transport

Ssa#S35585414

Acyl-CoA-binding protein

10.47

1.65

0.0081

Ssa#DY703528

Fatty acid-binding protein, intestinal

- 1.25

- 1.46

0.0331

Ssa#CB509140

Fatty acid-binding protein, intestinal

- 1.20

- 1.35

0.0375

Ssa#STIR04578

Fatty acid-binding protein, heart

1.22

1.47

0.0401

Lipid transport

Ssa#CK898816

Low density lipoprotein receptor-related protein 1 (lrp1)

- 1.14

- 1.39

0.0341

Ssa#S32008850

Apolipoprotein A-IV precursor (apoa4a)

- 1.43

- 1.01

0.0377

Omy#BX318293

Low density lipoprotein receptor-related protein 1 (lrp1)

- 3.61

- 1.67

0.0404

Ssa#S18866963

Apolipoprotein A-IV precursor (apoa4b)

- 1.39

- 1.43

0.0428

Lipid hydrolysis

Ssa#S18887340

Acyl-coenzyme A thioesterase 5

1.64

1.18

0.0007

Ssa#STIR02708

Isoamyl acetate-hydrolyzing esterase 1 homolog

1.54

1.12

0.0013

Ssa#DW007099

Similar to patatin-like phospholipase domain containing 7

- 3.75

- 2.37

0.0070

Ssa#S31963297

Acyl-CoA thioesterase 11

1.35

1.38

0.0097

Ssa#STIR07750

N-acylsphingosine amidohydrolase 1

- 1.14

- 1.34

0.0122

Ssa#STIR05034

Isoamyl acetate-hydrolyzing esterase 1 homolog

1.74

1.01

0.0208

Omy#CA366823

Acyl-coenzyme A thioesterase 3

1.33

2.17

0.0248

Ssa#STIR22551

Lipoprotein lipase (lpl)

1.31

1.31

0.0346

Eicosanoid synthesis and metabolism

Ssa#TC68569

Thromboxane-A synthase (thas)

- 1.33

- 1.67

0.0269

Ssa#TC110080

Phospholipase A2 (pla2g4)

1.26

1.08

0.0296

Omy#TC147730

Prostaglandin I2 (prostacyclin) synthase (ptgis)

- 2.53

- 4.25

0.0349

Ssa#S35581706

15-hydroxyprostaglandin dehydrogenase

- 1.51

- 1.09

0.0400

Ssa#EG930234

Arachidonate 5-lipoxygenase (alox5)

1.91

1.55

0.0449

Regulation of lipid metabolism

Ssa#TC112002

Retinoid X receptor beta

1.00

- 19.09

0.0000

Ssa#KSS2129

Adiponectin receptor protein 1

- 1.30

- 1.31

0.0098

Ssa#CA056493

Angiopoietin-like 6

- 1.63

- 1.07

0.0290

Ssa#S35490606

Adiponectin, C1Q and collagen domain containing, like

1.34

1.66

0.0441

Ssa#S18888608

Adiponectin receptor protein 1

- 1.38

- 1.40

0.0451

Results were obtained by two-way ANOVA analysis (p < 0.05; fold change cut-off of 1.2) without multiple testing correction (n = 1951 total features). Transcripts are arranged by functional categories and, within these, by p-value. Indicated are also the probe names and the expression ratio between families determined separately for each total lipid level.

Validation of results by RT-qPCR

To validate the microarray analysis results, expression of selected genes was quantified by RT-qPCR. These genes were chosen from lipid metabolism pathways that were more highly affected by the factor 'n-3 LC-PUFA', and also included immune response genes, which was the category most highly affected by both 'n-3 LC-PUFA' and 'total lipid' factors. In addition, the expression of two fatty acyl desaturases (Δ5fad and Δ6fad) and one elongase (elovl2), which are typically responsive to dietary levels of n-3 LC-PUFA were also determined. The LC-PUFA biosynthesis pathway was not identified by the microarray analysis as being differentially expressed in families with different n-3 LC-PUFA flesh contents but, given the potential importance of this pathway in determining n-3 PUFA phenotypes, we specifically aimed to verify this result. The RT-qPCR results confirmed that genes involved in LC-PUFA biosynthesis were not differentially expressed in families with higher and lower levels of n-3 LC-PUFA (Table 5). Furthermore, the RT-qPCR results confirmed significant down-regulation of genes involved in hepatic cholesterol biosynthesis, such as isopentenyl-diphosphate isomerase (ipi), 7-dehydrocholesterol reductase (7dchr) and sterol regulatory element-binding protein 2 (srebp2) in families containing higher levels of n-3 LC-PUFA in their flesh although this was only observed when this phenotype was also associated with low lipid level, except for 7dchr, which was significantly down-regulated irrespective of lipid level. With regards to lipoprotein metabolism (lipid transport) genes, general trends such as the magnitude and direction of change were broadly similar between the microarray and the RT-qPCR analysis for the high versus low n-3 LC-PUFA comparison at low lipid contents, although RT-qPCR results were not significant. In the case of high lipid contents, the match between microarray and RT-qPCR data was less consistent, except for lipoprotein lipase (lpl), which was similarly up-regulated albeit non-significantly. Up-regulation of the glycerophospholipid biosynthesis pathway in fish with higher n-3 LC-PUFA contents was also indicated when associated with high lipid levels, significant for monoacylglycerol O-acyltransferase 1 (mgat). With regards to the eicosanoid biosynthesis pathway, the microarray results could only be confirmed for arachidonic 5-lipoxygenase (alox5). Validation of lipid metabolism genes affected by the 'total lipid' factor (Table 6) confirmed the lower expression of elovl2 in salmon presenting higher lipid levels in their flesh, independent of LC-PUFA content. Finally, good agreement was found between the microarray and RT-qPCR results for immune response genes in response to both 'n-3 LC-PUFA' (Table 5) and 'total lipid' (Table 6) factors.
Table 5

Validation of microarray results and expression of genes of interest in relation to the factor 'n-3 LC-PUFA level'

Gene

Low Lipid LH/LL

High Lipid HH/HL

 

Microarray

RT-qPCR

Microarray

RT-qPCR

LC-PUFA biosynthesis

Δ5fad

 

−1.19

 

1.17

Δ6fad

 

1.13

 

−1.21

elovl2

 

1.14

 

1.06

Cholesterol biosynthesis

ipi

−2.13

−3.92

−1.15

1.32

mev

−1.54

−1.51

−1.08

1.06

7dchr

−1.33 to −1.54

−1.47

−1.25 to −1.47

−1.34

srebp2

−1.72

−1.68

−1.30

1.60

Lipid transport and lipoprotein metabolism

lrp1

−1.14 to −3.57

−1.36

−1.39 to −1.67

1.24

apoA4a

−1.43

−1.09

1.00

1.32

apoA4b

−1.39

−1.48

−1.43

−1.10

lpl

1.31

1.23

1.31

1.38

Glycerophospholipid synthesis

lpp2

1.20

−1.19

1.19

1.30

mgat

1.20

1.04

1.51

1.78

Eicosanoid biosynthesis

alox5

1.91

1.48

1.55

1.62

pla2g4

1.26

−1.08

1.08

1.06

thas

−1.33

−1.08

−1.67

1.34

ptgis

−2.53

−1.25

−4.25

1.27

Immune response

mal

−22.20

−3.70

−25.00

−5.00

ccl13

4.40

5.98

3.00

2.14

trim25

5.20

2.80

6.70

2.51

lect2

4.60

1.92

7.10

7.57

Values represent the expression ratios between high PUFA / low PUFA, for fish containing either low or high total lipid levels in their flesh, obtained by microarray analysis or RT-qPCR. Expression ratios in bold were significant by REST2008 analysis of RT-qPCR results.

Delta5 and 6 fatty acyl desaturases (Δ5fad and Δ6fad); fatty acyl elongase (elovl2); isopentenyl-diphosphate isomerase (ipi); mevalonate kinase (mev); 7-dehydrocholesterol reductase (7dchr); sterol regulatory element-binding protein 2 (srebp2); low density lipoprotein receptor-related protein 1 (lrp1); apolipoprotein A-IV (apoa4a and apoa4b); lipoprotein lipase (lpl); lipid phosphate phosphohydrolase 2 (lpp2); monoacylglycerol O-acyltransferase 1 (mgat); arachidonate 5-lipoxygenase (alox5); phospholipase A2 (pla2g4); thromboxane-A synthase (thas); prostaglandin I2 (prostacyclin) synthase (ptgis); myelin and lymphocyte protein (mal); c-c motif chemokine 13 precursor (ccl13); tripartite motif-containing protein 25 (trim25); leukocyte cell-derived chemotaxin 2 precursor (lect2).

Table 6

Validation of microarray results and expression of genes of interest in relation to the factor 'Lipid level'

Gene

Low n-3 LC-PUFA HL/LL

High n-3 LC-PUFA HH/LH

 

Microarray

RT-qPCR

Microarray

RT-qPCR

LC-PUFA biosynthesis

Δ5fad

 

−1.03

 

1.35

Δ6fad

 

1.04

 

−1.32

elovl2

−1.28 to −1.41

−1.51

−1.82 to −1.97

−1.62

Cholesterol biosynthesis

  

ipi

 

−3.95

 

1.31

mev

 

−1.40

 

1.14

7dchr

 

−1.01

 

1.09

srebp2

 

−2.09

 

1.29

Lipid transport and lipoprotein metabolism

 

lrp1

 

−1.82

 

−1.08

apoA4a

 

−1.37

 

−0.95

apoA4b

 

−2.43

 

−1.80

lpl

 

−1.18

 

−1.05

Immune response

cfh

−1.33 to −1.56

−1.24

−1.56 to −1.69

−1.31

trim25

−5.64

−2.09

−4.34

−2.33

lect2

3.16

1.23

4.86

4.84

Values represent the expression ratios between high lipid / low lipid, for fish containing either low or high n-3 LC-PUFA levels in their flesh, obtained by microarray analysis or RT-qPCR. Expression ratios in bold were significant by REST2008 analysis of RT-qPCR results.

Delta5 and 6 fatty acyl desaturases (Δ5fad and Δ6fad); fatty acyl elongase (elovl2); isopentenyl-diphosphate isomerase (ipi); mevalonate kinase (mev); 7-dehydrocholesterol reductase (7dchr); sterol regulatory element-binding protein 2 (srebp2); low density lipoprotein receptor-related protein 1 (lrp1); apolipoprotein A-IV (apoa4a and apoa4b); lipoprotein lipase (lpl); complement factor H precursor (cfh); tripartite motif-containing protein 25 (trim25); leukocyte cell-derived chemotaxin 2 precursor (lect2).

Genetic evaluations

Subsequent to the dietary trial and microarray analyses, genetic evaluations (estimated breeding values, EBVs) became available for a range of traits upon which the families are under active selection in the breeding program. Given the unexpectedly high preponderance of immune response genes identified by transcriptomic analysis, we investigated associations with traits that could potentially explain the gene expression data. In this respect, one of the most relevant traits was ‘survival to infectious pancreatic necrosis (IPN) virus’, known to be almost entirely controlled by a major QTL [12]. Genetic evaluations included data collected from a freshwater experimental IPN challenge on full-sibs from the same families as the trial fish. Examining the families, selected on their lipid phenotypes, used for transcriptomic analysis it was seen that family HH, containing both high total lipid and high n-3 LC-PUFA flesh contents, also showed a high EBV for survival to IPN (selection differential on a standardized normal distribution = 1.86 standard deviations), contrasting with −0.83 (LL) -0.99 (LH) and −1.28 (HL) for the other families, that could introduce a potential for bias in interpretation of the transcriptomic responses. However, no such imbalance was present in the lower lipid grouping, comparing families LL and LH (additional file 3).

Discussion

The present study which ascertained lipid profiles of 50 Atlantic salmon families confirmed previous results showing important inter-family variation in the ability to retain n-3 LC-PUFA in the flesh when fish are fed diets with low levels of these fatty acids [7]. Furthermore, even though a high correlation was found between flesh lipid levels and n-3 LC-PUFA contents, families with the same total lipid level varied significantly in n-3 LC-PUFA contents. In the present study we did not examine whether these differences have a genetic basis, as this was established previously [7], but instead aimed to identify molecular pathways whose transcriptional regulation might underlie the phenotypic differences, independent of lipid content.

LC-PUFA biosynthesis

Differences in flesh n-3 LC-PUFA content in individuals fed the same diet is likely to arise from either selective incorporation and retention of fatty acids supplied by the diet or from biosynthesis from precursors in tissues such as the liver. In the present study we performed a transcriptomic study to identify molecular mechanisms potentially underlying flesh n-3 LC-PUFA phenotypes. Expression of candidate genes of the LC-PUFA biosynthesis pathway were also quantified as there was good evidence that these genes are transcriptionally regulated and that mRNA levels correlate with enzymatic activity of this pathway [13, 14], and so this appeared a likely mechanism that required specific investigation. Flesh was the target tissue for analysis of the n-3 LC-PUFA retention trait because salmon accumulate lipid reserves in muscle and this is the main product for human consumption, and so its composition will affect the health-promoting properties of salmon. However, hepatic tissue was analyzed for effects on gene expression since the production of both LC-PUFA and the lipoproteins that transport them to the tissues takes place mainly in the liver [15].

The transcriptomic analysis revealed few effects of the n-3 LC-PUFA factor on metabolism in general and, in particular, a lack of effect on lipid metabolism genes, when the statistical analysis employed multiple testing correction. However, this correction is typically not used when examining effects of diet and genetic background on metabolic genes, which tend to show subtle, but physiologically relevant, changes [9, 11, 16]. Without multiple testing correction we were able to identify pathways of lipid metabolism that might be altered in response to this factor, although a clear mechanism for the observed inter-family differences in n-3 LC-PUFA content was not identified. Potential effects on lipid transport and lipoprotein metabolism were indicated by the presence of two apolipoprotein A4 transcripts (apoa4a and b), a low density lipoprotein (LDL) receptor-related protein (lrp1) and a lipoprotein lipase (lpl) transcript in the microarray analysis, albeit these were not validated by RT-qPCR. In contrast, the RT-qPCR results clearly confirmed that the flesh n-3 LC-PUFA phenotype cannot be explained by transcriptional modulation of genes of LC-PUFA biosynthesis and so other mechanisms must be in operation. One hypothesis might be that phenotypic differences between families originates from the presence of different alleles of fatty acyl desaturases and/or elongases encoding proteins with altered biological activity or specificity, as described for the nematode Caenorhabditis elegans[17].

Effects of n-3 LC-PUFA flesh contents on hepatic cholesterol biosynthesis

Within the lipid metabolism genes that were differentially expressed in the liver between fish showing higher or lower n-3 LC-PUFA contents in flesh, the category of cholesterol biosynthesis and its regulation was the most apparent, based on the number of probes for interrelated genes present in this list, all with coordinated regulation indicating reduced cholesterol biosynthesis in salmon having higher flesh n-3 LC-PUFA. In addition, and inferred by the magnitude of change (i.e., fold-changes), effects were more pronounced in fish containing lower flesh lipid levels. These results were confirmed by quantifying the expression of three enzymes catalyzing steps in cholesterol biosynthesis (mev, ipi and 7dchr) as well as srebp2, a transcription factor that regulates cholesterol synthesis [18]. Furthermore, the RT-qPCR analysis indicated that this regulation was only associated with lower flesh lipid levels given that in the high lipid group only 7dchr was down-regulated. Therefore, this experiment confirmed previous studies suggesting an association between flesh adiposity and n-3 LC-PUFA in the regulation of cholesterol biosynthesis in Atlantic salmon families, with lean fish showing a higher responsiveness to n-3 LC-PUFA [8]. However, an important novel outcome of the present study was the demonstration that the previous results were not solely a consequence of a higher dietary intake of cholesterol supplied by a FO diet in contrast to a VO diet [11] but also resulted from higher incorporation and increased tissue levels of n-3 LC-PUFA. The likely explanation for these results is the role of n-3 LC-PUFA as regulators of gene transcription, including some implicated in cholesterol biosynthesis, mediated by srebp2[1820]. Nonetheless, the mechanism for why this response was only observed when associated with low flesh lipid levels requires clarification. Recent studies showed that lean humans are also more responsive, in terms of plasma lipid and lipoprotein composition, to cholesterol-reducing diets containing lower levels of saturated fatty acids and cholesterol than obese individuals, and several mechanisms have been proposed to explain this [21]. In the present case, the absolute, rather than the relative, level of n-3 LC-PUFA may be the determinant factor affecting gene transcription and, in the high lipid group, absolute levels of these fatty acids might have been sufficiently high to repress cholesterol biosynthesis genes, even at lower relative n-3 LC-PUFA contents (i.e., group HL). This hypothesis is supported by the RT-qPCR analysis comparing the families with regards to lipid level, HL/LL and HH/LH. In the HL/LL comparison, contrasting absolute n-3 LC-PUFA levels of 427 versus 363 mg/100 g flesh, there was down-regulation of both ipi and srebp2 (−3.95 and −2.09, respectively), whereas comparison of the families HH/LH, containing 554 versus 468 mg/100 g flesh, showed no difference in the expression of the genes. Similarly, genes involved in lipoprotein metabolism, which are also regulated by LC-PUFA through different mechanisms [20], also showed more significant changes when comparing fatter and leaner salmon with lower LC-PUFA levels, indicating that a similar regulatory mechanism might occur. Therefore, the present study is consistent with previous work identifying cholesterol and lipoprotein metabolism as pathways significantly and differentially affected by n-3 LC-PUFA depending on flesh adiposity [8].

Effects of total lipid level on lipid metabolism

Lipid level significantly affected expression of lipid metabolism genes, although effects were still relatively small (8% of all genes assigned to a biological function category). A noteworthy result was the down-regulation of elovl2 (confirmed by RT-qPCR) in salmon presenting higher flesh lipid, independent of LC-PUFA content. Elovl2 has substrate specificity towards LC-PUFA and is highly responsive to dietary n-3 LC-PUFA levels in salmon [22]. However, the expression of this gene is often co-ordinately regulated with other genes of LC-PUFA biosynthesis, such as Δ5fad and Δ6fad[9], which was not the case here. Hence, the biological significance of this result is not clear and may indicate other roles of elovl2 in lipid metabolism. For instance, an association between overexpression of elovl2 and enhanced triacylglycerol synthesis and lipid droplet accumulation, as well as induction of PPARγ target genes, was shown in mouse preadipocyte cell lines [23]. In addition, elovl2 was up-regulated in the liver transcriptome of rats with nephrotic syndrome, a condition characterized by hyperlipidemia [24]. Elovl2 was only recently characterized in salmon [22], and this is the first indication of an association between its expression and lipid accumulation in a non-mammalian vertebrate, with results suggesting that increased lipid level in salmon flesh repressed elovl2 expression independent of n-3 LC-PUFA level although this requires further investigation. Another gene down-regulated at higher lipid levels was a mitochondrial acyl carrier protein, involved in acyl transfer steps, including roles in fatty acid synthesis and functioning of the electron transport chain [25], which could conceptually be responding to similar regulatory mechanisms affecting elovl2. In contrast, stearoyl-CoA desaturase, responsible for the synthesis of monounsaturated fatty acids from saturated precursors, was up-regulated in salmon with higher flesh lipid levels. This gene was positively correlated with fat accumulation in bovine skeletal muscle [26], consistent with up-regulation in salmon families with increased fat stores.

Possible association between flesh n-3 LC-PUFA contents and immune response

The predominance of immune response genes responding to total lipid level and, particularly, n-3 LC-PUFA contents in salmon flesh was unexpected. This was a true over-representation as GO enrichment analysis enabled identification of several GO terms related to regulation of immune and inflammatory responses in relation to the total lipid factor. However, as mentioned above, the transcriptomic comparison, although balanced for total lipid, was not balanced for viral disease resistance (specifically IPN in this case) and, as a consequence, higher contrast between families was imposed on the high lipid group (families HL and HH) due to the fortuitous selection of family HH presenting a much higher viral resistance EBV. Nonetheless, if family HH biased the results of the two-way ANOVA we would expect a preponderance of immune-related genes to occur only when comparing these two families, presenting higher and lower flesh n-3 LC-PUFA contents at the higher lipid level. In order to assess this, t-tests were performed comparing separately the higher versus lower n-3 LC-PUFA families at each total lipid level, i.e., LH/LL and HH/HL. A Venn diagram contrasting the two t-test significant lists was then performed and when analyzing the genes that were similarly affected by n-3 LC-PUFA contents at both higher and lower total lipid level, a similar preponderance (33%) of immune response genes was observed (Additional file 4). Finally, examination of the fold changes of immune-related genes, indicating magnitude of effects, between families with higher and lower contents of n-3 LC-PUFA at either higher or lower total lipid levels (Tables 2 and 5), showed no clear evidence of the effect being more marked for the high lipid comparison, which is what would be expected if results were caused simply by inclusion of family HH in the transcriptomic analysis.

Hence, there is evidence to suggest that there may be some correlation between flesh n-3 LC-PUFA contents and immune response in the families analysed. An anti-inflammatory role of n-3 LC-PUFA is well established in mammals and fish [2729]. Immune cells are typically rich in arachidonic acid (ARA), the precursor for eicosanoids with a pro-inflammatory action, whereas EPA and DHA give rise to eicosanoids that are less biologically active, as well as to resolvins and protectins presenting anti-inflammatory properties [30]. Higher incorporation of n-3 LC-PUFA in biological membranes of immune cells can modulate immune responses in several ways [reviewed in [15, 3033]. They alter the production of inflammatory eicosanoid mediators of which they are precursors, directly affect the organization and properties of the immune cell membranes with effects on signalling pathways, phagocytic capacity and antigen presenting capability, and activate transcription of various genes involved in inflammatory responses. Therefore, families with higher tissue levels of n-3 LC-PUFA may show differential expression of immune response and inflammation-related genes, as well as of genes involved in signalling and regulation of transcription (as observed in the present study). Furthermore, although liver is chiefly a metabolic organ, it has other physiological functions including removal of pathogens and antigens from the blood and modulation of immune responses, as well as the production of inflammatory mediators [34, 35].

Related to the above, microarray analysis revealed the presence of several genes that intervene in eicosanoid synthesis and metabolism including phospholipase A2 (pla2), arachidonate 5-lipoxygenase (alox5), thromboxane-A synthase (thas), prostaglandin I2 synthase (ptgis) and 15-hydroxyprostaglandin dehydrogenase [36]. However, RT-qPCR only confirmed up-regulation of hepatic alox5 in families presenting higher flesh n-3 LC-PUFA and, given that alox5 acts on LC-PUFA of both n-3 and n-6 series and that ARA levels generally accompanied the n-3 LC-PUFA phenotype (Table 1), it cannot be ascertained whether this transcript was responding to higher levels of membrane ARA or EPA and hence if it would result in increased pro-inflammatory 4-series, or less potent 5-series, leukotrienes [37].

The immune response genes whose expression was correlated with 'n-3 LC-PUFA' are mainly involved in the modulation of inflammatory processes and innate immune response to pathogens, which are particularly important in fish species and that can be easily compromised in aquaculture conditions [38]. We could speculate that the changes in expression may give enhanced protection from inflammation or pathological conditions in fish with higher n-3 LC-PUFA in their tissues. Up-regulation associated with high flesh n-3 LC-PUFA was noted in expression of NACHT domain containing protein, tripartite motif-containing protein 25 (trim25), c-c motif chemokine 13 precursor (ccl13), leukocyte cell-derived chemotaxin 2 precursor (lect2), tissue factor pathway inhibitor a, pentraxin and cathepsin K. In contrast, down-regulation in the high n-3 LC-PUFA families was observed for MHC class I (mostly in the high total lipid group), and for myelin and lymphocyte protein (mal). NACHT domain containing proteins are pathogen-sensing molecules (recognizing intracellular pathogen-associated molecular patterns – PAMPs) implicated in early host defence, inflammation and innate immune signalling pathways in mammals [39], by activating transcription of MHC class II and the apoptotic pathway. The trim25 protein is involved in antiviral innate immune responses through activation of signalling pathways leading to production of interferons and in teleost cells TRIM genes are induced in response to viral infections [40, 41]. The ccl13 (also known as monocyte chemotactic protein 4) and lect2 proteins are both involved in inflammation, having roles in attracting monocytes and T lymphocytes in tissues exposed to exogenous pathogens, and have neutrophil chemotactic function [42, 43]. Expression of lect2 was increased in fish liver and spleen after bacterial infections [43]. Tissue factor pathway inhibitor inhibits the initial reactions of the blood coagulation cascade and modulates cell proliferation, and may protect vascular tissue in inflammatory conditions in mammals [44]. Cathepsin K mediates immune responses in cells, having a critical role in signalling events proximal to the Toll-like receptor 9 (TLR9) that has a fundamental role in pathogen recognition (recognizing PAMPs) and activation of mammalian innate immunity [45]. Finally, pentraxins are pattern recognition proteins of the innate immune system that play a role in the acute phase response, activating complement pathways to clear pathogens in both mammals and fish [46, 47]. In this case, up-regulation of pentraxin in salmon with higher n-3 LC-PUFA in their flesh was only observed with high lipid levels. Similarly, down-regulation of the MHC class I transcript was observed only in the high lipid group. In mammalian studies, high LC-PUFA contents (EPA, DHA and ARA) reduced cell surface expression of MHC I, decreasing antigen presentation and altering T-cell signalling [34, 35]. Therefore, the high IPN resistance genotype observed in family HH in later genetic evaluations of the families could potentially involve effects on both the complement pathway and T-cell mediated immunity, and involve co- or post-translational modification of proteins by N-linked glycosylation through up-regulation of dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 4 (Table 2; [48]). Given the high economic impact of IPN in salmonid culture, identification of genes potentially involved in the progression of the disease using transcriptomic approaches is already in progress [49]. Finally, down-regulation of mal, associated with T-cell differentiation and signal transduction [50], was observed at higher n-3 LC-PUFA levels.

As mentioned above, several immune response-related genes were also affected by the total lipid factor with results validated by RT-qPCR. However, we cannot exclude the possibility that this results from the strong correlation between total lipid levels and absolute LC-PUFA contents, which makes it difficult to dissociate both factors.

Conclusions

It has been demonstrated earlier that LC-PUFA flesh content is a highly heritable trait [7], but the present study has shown that the underlying mechanisms do not appear to involve changes in the expression of lipid metabolism genes, including the LC-PUFA biosynthesis pathway. Other possible mechanisms, such as alleles with different biological activity, require investigation. The present study revealed an association between flesh adiposity and n-3 LC-PUFA in the regulation of cholesterol biosynthesis, which was down-regulated by higher n-3 LC-PUFA levels but only in the lean families. This response was not caused by dietary factors, given that the fish were all fed the same VO-based diet, and is most likely explained by variation in tissue n-3 LC-PUFA levels, regulating transcription of cholesterol metabolism genes through srebp2. Furthermore, the transcriptional repression of these genes may be sensitive to the absolute levels of these fatty acids in the tissues, which could explain the lack of regulation when comparing the families containing higher flesh lipid levels. It is likely that n-3 LC-PUFA exert similar roles in regulation of gene expression in fish as in mammals and, furthermore, fish might be a useful model to study important relationships between genetics, diet, adiposity/obesity and lipoprotein/cholesterol metabolism. However, unexpected differences were found in the expression of genes implicated in the modulation of inflammatory processes and innate immune response between families differing in lipid composition, both in terms of total lipid level and, particularly, n-3 LC-PUFA contents. Although the evidence is generally circumstantial it is important to clarify this association if flesh n-3 LC-PUFA level is included as a trait for genetic selection in Atlantic salmon breeding programmes. If such a relationship is confirmed, the data suggest that the underlying mechanism might involve anti-inflammatory actions of tissue n-3 LC-PUFA on the eicosanoid biosynthesis pathway (particularly affecting the lipoxygenase pathway), although direct effects through regulation of transcription of immune genes or more indirectly through changes in architecture and properties of immune cell membranes are also possible.

Methods

Feeding trial and sampling

Fifty full-sib families selected from the 200 broodstock families of the Landcatch Natural Selection (LNS) Atlantic salmon breeding program (2005-strip year-group) were specifically selected for the feeding trial. On the basis of parental genetic evaluations, 25 high flesh lipid contrasting with 25 low flesh lipid families were identified, and 35 fish (initial weight, ~100 g) from each family were transferred and grown in communal seawater pens (Marine Harvest, Ardnish, Scotland). All fish were tagged with electronic transponders (PIT tags) to allow family identification while rearing in a common environment. After acclimation, the fish were grown for 12 weeks on the same low FM/high VO diet (Nutreco ARC, Stavanger, Norway) containing 25% FM and 44% plant meals and a VO blend including rapeseed oil/palm oil/camelina oil (2.5:1.5:1). At the end of the trial (378 g average weight), flesh samples (Norwegian Quality Cut) were collected, frozen on dry ice and stored at −20°C until lipid analysis. Liver samples were also taken and stored at −70°C for subsequent molecular analyses.

Lipid analysis and choice of families for transcriptomic comparisons

The 50 selected families were screened for their ability to retain and/or synthesize n-3 LC-PUFA when fed a low FM/high VO diet. De-boned and skinned flesh samples were combined into 3 pools per family for lipid analysis. Total lipids were extracted and determined gravimetrically from 1–2 g of pooled flesh [51]. Fatty acid methyl esters (FAME) were prepared by acid-catalyzed transesterification of total lipids [52]. Following purification, FAME were separated and quantified by gas–liquid chromatography as described in [9]. These data were used to select four families for transcriptomic analysis: two with equivalent high levels of lipid ‘H’, and two with equivalent low levels of lipid ‘L’. Within each level of total lipid, two families with significantly contrasting (p < 0.05 on Student’s t-test; Graphpad Prism™, version 4.0, Graphpad Software, San Diego, CA) relative n-3 LC-PUFA levels (similarly termed H and L) were identified (Table 1).

RNA extraction and purification

Hepatic tissue (200 mg) from ten individuals per family was rapidly homogenized in 2 ml TRI Reagent (Ambion, Applied Biosystems, Warrington, U.K.). Total RNA was isolated, following manufacturer’s instructions, and RNA quality and quantity was assessed by gel electrophoresis and spectrophotometry (NanoDrop ND-1000, Thermo Scientific, Wilmington, U.S.A.), respectively. Equal amounts (50 μg) of total RNA were pooled from two individuals to produce five biological replicates per family, which were further purified by mini spin-column purification (RNeasy Mini Kit, Qiagen, Crawly, U.K.).

Microarray hybridization and analysis

A custom-made Atlantic salmon oligoarray with 44 K features per array on a four-array-per-slide format (Agilent Technologies, Cheshire, U.K.), with experimental features printed singly was used [described more fully in [53]. The probes were co-designed at the Institute of Aquaculture, University of Stirling, U.K. and Nofima, Norway, with array design available in the EBI ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/arrays/browse.html) under accession number A-MEXP-2065. The features were mainly derived from a core set of Atlantic salmon Unigenes (NCBI) supplemented with other unique cDNAs derived from Genbank and the Atlantic Salmon Gene Index (http://compbio.dfci.harvard.edu/tgi/tgipage.html). Probe annotations were derived from Blastx comparisons across four protein databases, as detailed elsewhere [54]. The entire experiment comprised 20 hybridizations (5 slides): 4 groups (families) × 5 biological replicates (pools of 2 individuals each).

Indirect labelling was employed in preparing the microarray targets, as described in detail previously [8]. Antisense amplified RNA (aRNA) was produced from 500 ng of each total RNA purification reaction using the Amino Allyl MessageAmpTM II aRNA Amplification Kit (Ambion, Applied Biosystems), following the manufacturer’s methodology followed by Cy3 or Cy5 fluor (PA23001 or PA25001, GE HealthCare) incorporation through a dye-coupling reaction.

The hybridizations were performed using SureHyb hybridisation chambers (Agilent) in a DNA Microarray Hybridisation Oven (Agilent). Sample order was semi-randomized, with one replicate per experimental group being loaded into each slide. Each biological replicate pool was co-hybridized in a two-dye experiment with a single pooled reference sample. This pooled reference comprised equal quantitites of aRNA from all 20 biological replicate pools. Microarry manufacturer’s instructions were followed. Briefly, for each hybridization, 825 ng of Cy3-labelled experimental biological replicate and Cy5-labelled reference pool were combined. A fragmentation master mix containing 10× blocking agent (Agilent), 25× fragmentation buffer (Agilent) and nuclease-free water, was dispensed into the Cy-dyes mix. After incubating in the dark at 60°C for 30 mins, 2× GE Hybridization buffer (pre-heated to 37°C; Agilent) was added, contents gently mixed, spun at 16 K g for 1 min and finally kept on ice until loaded onto the microarray slides. Hybridization was carried out in the oven rotator (Agilent) at 65°C and 10 rpm for 17 h. Post-hybridization washes were carried out in EasyDipTM Slide staining containers (Canemco Inc., Quebec, Canada). After disassembling the array-gasket sandwiches submersed in wash buffer 1 (Agilent) at room temperature, the microarray slides were incubated in wash buffer 1 for 1 min at 31°C in a Stuart Orbital Incubator S150 rotating at 150 rpm, and then a further 1 min at 31°C at 150 rpm in wash buffer 2 (Agilent). A final dip in wash buffer 2 at room temperature was performed, after which the slides were dried by centrifugation (500 xg for 6 mins) and kept in a desiccator and in the dark until scanned, the same day.

Scanning was performed at 5 μm resolution using an Axon GenePix 4200AL Scanner (MDS Analytical Technologies, Wokingham, Berkshire, U.K.). Laser power was kept constant (50%) and the “auto PMT” function within the acquisition software (v.4) was enabled to adjust PMT for each channel such that less than 0.1% of features were saturated and that the mean intensity ratio of the Cy3 and Cy5 signals was close to one. Agilent Feature Extraction Software (v 9.5) was used to identify features and extract fluorescence intensity values from the resultant TIF images. Analysis of the intensity values was performed in the GeneSpring GX version 11 analysis platform (Agilent Technologies, Wokingham, Berkshire, U.K.). All intensity values <0.1 were set to equal 0.1 followed by a Lowess normalization. After removing control features, four quality filtering steps were carried out sequentially using a range of quality control metrics produced by the Agilent Feature Extraction software to remove features that were saturated, non uniform, population outliers and spots non-significantly different from background. This gave a final list of 32,566 probes that were eligible for statistical analysis. Experimental annotation complied fully with minimum information about a microarray experiment (MIAME) guidelines [55]. The experimental hybridizations and further methodological details are archived on the EBI ArrayExpress database under accession number E-TABM-1204.

Normalized and quality-filtered fluorescence intensity data was analysed in GeneSpring GX v11 by two-way ANOVA, which examined the explanatory power of the variables ‘total lipid’ and ‘n-3 LC-PUFA’ and the interaction between the two, at a significance level of 0.05 and expression ratio (i.e., fold change) cut-off of 1.2. Two sets of analysis were performed, with or without Benjamini-Hochberg multiple testing correction. In the set with multiple testing correction, GO enrichment analysis was performed at a significance level of 0.05.

RT-qPCR

Expression of selected genes found by microarray analysis to be significantly affected by either ‘total lipid’ or ‘n-3 LC-PUFA’ content was quantified by RT-qPCR. In addition, the expression of two fatty acyl desaturases (Δ5fad and Δ6fad) and one elongase (elovl2) that are typically responsive to dietary n-3 LC-PUFA was determined. Primers were designed using Primer3 software (http://biotools.umassmed.edu/bioapps/primer3_www.cgi) (Table 7). Two reference genes, elongation factor-1α (elf-1α) and β-actin, were also quantified.
Table 7

Primers used for RT-qPCR analyses

Transcript

Primer sequence (5’-3’)

Fragment

Ta

Efficiency

Accession No.

Source

Δ5fad

GTGAATGGGGATCCATAGCA

192 bp

56°C

0,945

AF478472 1

[9]

 

AAACGAACGGACAACCAGA

     

Δ6fad_a

CCCCAGACGTTTGTGTCAG

181 bp

56°C

0,928

AY458652 1

[9]

 

CCTGGATTGTTGCTTTGGAT

     

elovl2

CGGGTACAAAATGTGCTGGT

145 bp

60°C

0,926

TC91192 2

[24]

 

TCTGTTTGCCGATAGCCATT

     

ipi

ACAGCCCTATGGTTATGTGTCATCTC

230 bp

60°C

0,985

CK875291 1

[11]

 

CAAGGTGAGGCGAATGTTTGAAC

     

mev

CCCTTAATCAGGGTCCCAAT

247 bp

60°C

0,910

DW005667 1

[11]

 

GGTGCTGGTTGATGTCAATG

     

7dchr

CTTCTGGAATGAGGCATGGT

230 bp

60°C

0,977

TC99602 2

[11]

 

ACAGGTCCTTCTGGTGGTTG

     

srebp2

GACAGGCACAACACAAGGTG

215 bp

60°C

0,887

DY733476 1

[11]

 

CAGCAGGGGTAAGGGTAGGT

     

lrp1

ACCAACCGCATCTACTGGAC

204 bp

60°C

0,996

CK898816 1

New design

 

CAGATTACCAGCCACCCAGT

     

apoA4a

CCCAAACCAACACCACTCCT

150 bp

60°C

0,997

BT047465 1

New design

 

GGTTTATATTTCTCACCCTGCAC

     

apoA4b

CTCTTGCCCTCTTGATGACTG

154 bp

60°C

0,918

BT047267 1

New design

 

TGACTCATCAGAGCCAATTCA

     

lpl

AGGGCGTTAATCCATGTCAG

223 bp

60°C

0,917

TC84899 2

[8]

 

GACCTTTCAAAAGGGCATGA

     

lpp2

TCCGGAAGAACTCGCAATAC

174 bp

60°C

0,926

NM_001140716 1

[9]

 

ACATCACGTCCACCAAGACA

     

mgat

TTAACCCAAAGATGCTGCAA

157 bp

60°C

0,977

EG824440 1

New design

 

CACGCAGTTGTCAGTGGTTT

     

alox5

TATCTCCCTCTCCCTCAGTCC

155 bp

56°C

0,987

CX727592 1

[57]

 

GGTCAGCAGTGCCATCA

     

pla2g4

GTCGCTGGCTGGAGCTGTGG

138 bp

60°C

0,998

NM_001141333 1

New design

 

AGCCCTATGGGCCCTGGTCA

     

thas

TGTTCACACGGACCTGATTC

150 bp

60°C

0,986

NM_001165312 1

New design

 

GACCGGATCGTCATTCTGTT

     

ptgis

GCGTGTTTGTGGTCATTACG

247 bp

60°C

0,836

GE778709 1

New design

 

TTCCCTTAGCAAGGTCTGGA

     

mal

GGCCTCAGTCAAAGAGGAGA

156 bp

60°C

0,946

NM_001141320 1

New design

 

GGGGAGTGCACACTTTAGGA

     

ccl13

CGAGGATCCCTCTTCAACAA

178 bp

60°C

0,996

EG831431 1

New design

 

ATCGTCGACTAGGCAGCAGT

     

trim25

GCAGGGTCCTATCTCATCCA

215 bp

60°C

0,951

BT048046 1

New design

 

GGACTGGACCTTTTTATTCTCTCA

     

lect2

CTGTGTTGTCAGAGTGCGAGATGGT

150 bp

60°C

0,996

BT050009 1

[58]

 

TACACACAATGTCCAGGCCCTGA

     

cfh

TGTGATGATGGAGAGATGCAG

193 bp

60°C

0,966

TC141997 2

New design

 

CAAGCGACAAAGAAACCACA

     

Reference genes*

     

elf-1α

CTGCCCCTCCAGGACGTTTACAA

175 bp

60°C

1.000

AF321836 1

[11]

 

CACCGGGCATAGCCGATTCC

     

β-actin

ACATCAAGGAGAAGCTGTGC

141 bp

56°C

0.939

AF012125 1

[11]

 

GACAACGGAACCTCTCGTTA

     

1 GenBank (http://www.ncbi.nlm.nih.gov/).

2 Atlantic salmon Gene Index (http://compbio.dfci.harvard.edu/tgi/).

* geNorm average stability (M value) of reference genes = 0.514 [59].

For RT-qPCR, 2 μg of column-purified total RNA per sample was reverse transcribed into cDNA using the High-Capacity cDNA RT kit (Applied Biosystems, Paisley, U.K.), following manufacturer’s instructions, but using a mixture of the random primers (1.5 μl as supplied) and anchored oligo-dT (0.5 μl at 400 ng/μl, Eurofins MWG Operon, Ebersberg, Germany). Negative controls (containing no enzyme) were performed to check for genomic DNA contamination. A similar amount of cDNA was pooled from all samples and the remaining cDNA was then diluted 20-fold with water. RT-qPCR analysis used relative quantification with the amplification efficiency of the primer pairs being assessed by serial dilutions of the cDNA pool. Amplifications were carried out in duplicate (Quantica, Techne, Cambridge, U.K.) in a final volume of 20 μl containing 5 μl or 2 μl (for more highly expressed genes) diluted (1/20) cDNA, 0.5 μM of each primer (0.4 μM for lect2) and 10 μl AbsoluteTM QPCR SYBR® Green mix (ABgene). Amplifications were carried out with a systematic negative control (NTC-non template control, containing no cDNA). The RT-qPCR profiles contained an initial activation step at 95°C for 15 min, followed by 30 to 40 cycles: 15 s at 95°C, 15 s at the specific primer pair annealing temperature (Ta; Table 7) and 15 s at 72°C. After the amplification phase, a melt curve of 0.5°C increments from 75°C to 90°C was performed, enabling confirmation of the amplification of a single product in each reaction. Non-occurrence of primer-dimer formation in the NTC was verified. RT-qPCR product sizes and presence of single bands were checked by agarose gel electrophoresis. Additionally, sequencing of amplicons corresponding to new primer designs enabled the confirmation of identities and presence of single sequences for all genes except for trim25, as the sequencing result was of insufficient quality to conclude on the presence of a single gene product, and lrp1, for which results were indicative of quantification of a highly similar, recently duplicated, gene.

Results were analyzed by the ΔΔCt method using the relative expression software tool (REST 2009, http://www.gene-quantification.info/), which employs a pair wise fixed reallocation randomization test (10,000 randomizations) with efficiency correction [56], to determine the statistical significance of expression ratios (or gene expression fold-changes) between two treatments.

Genetic evaluations of traits used in the salmon breeding program

Parental evaluations were confirmed by subsequent analysis of family sibs (at harvest weight, some 1 year after the present study) for a range of traits upon which the breeding program families are under active selection including flesh lipid composition parameters (total lipid) as well as EBVs for weight at harvest, precocious maturation, flesh colour, sealice resistance and resistance to a viral infection (IPN).

Author’s contributions

SM performed laboratory analyses and data analysis. DRG was responsible for family selection. JBT supported the microarray analysis. SM wrote the first draft of the manuscript, followed by contributions from remaining authors. SM, JBT and DRT planned and coordinated the research. DRG, JGB and DRT were project leaders. All authors read and approved the final manuscript.

Declarations

Acknowledgements

This study was funded by the EU FP6 IP “AQUAMAX” (Sustainable Aquafeeds to Maximise the Health Benefits of Farmed Fish for Consumers; 016249–2). SM was supported by Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/64679/2009). Technical assistance from Jacquie Ireland in microarray hybridizations is deeply appreciated.

Authors’ Affiliations

(1)
Institute of Aquaculture, University of Stirling
(2)
Landcatch Natural Selection Ltd

References

  1. FAO: The State of World Fisheries and Aquaculture 2008. 2009, Rome: Food and Agriculture Organization of the United NationsGoogle Scholar
  2. Tacon AGJ, Metian M: Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: Trends and future prospects. Aquaculture. 2008, 285: 146-158. 10.1016/j.aquaculture.2008.08.015.View ArticleGoogle Scholar
  3. Calder PC, Yaqoob P: Omega-3 polyunsaturated fatty acids and human health outcomes. BioFactors. 2009, 35: 266-272. 10.1002/biof.42.View ArticlePubMedGoogle Scholar
  4. Menoyo D, López-Bote CJ, Obach A, Bautista JM: Effect of dietary fish oil substitution with linseed oil on the performance, tissue fatty acid profile, metabolism, and oxidative stability of Atlantic salmon. J Anim Sci. 2005, 83: 2853-2862.PubMedGoogle Scholar
  5. Gjedrem T, Baranski M: Selective Breeding in Aquaculture: an Introduction [Series: Reviews: Methods and Technologies in Fish Biology and Fisheries, Vol. 10]. 2009, London: Springer Science + Business Media B.VGoogle Scholar
  6. Schlechtriem C, Bron JE, Tocher DR: Inter-individual variation in total lipid fatty acid compositions of flesh of Atlantic salmon smolts fed diets containing fish oil or vegetable oil. Aquaculture Res. 2007, 38: 1045-1055. 10.1111/j.1365-2109.2007.01759.x.View ArticleGoogle Scholar
  7. Leaver MJ, Taggart JB, Villeneuve L, Bron JE, Guy DR, Bishop SC, Houston RD, Matika O, Tocher DR: Heritability and mechanisms of n-3 long chain polyunsaturated fatty acid deposition in the flesh of Atlantic salmon. Comp Biochem Physiol Part D Genomics Proteomics. 2011, 6: 62-69. 10.1016/j.cbd.2010.04.002.View ArticlePubMedGoogle Scholar
  8. Morais S, Pratoomyot J, Torstensen BE, Taggart JB, Guy DR, Bell JG, Tocher DR: Diet × genotype interactions in hepatic cholesterol and lipoprotein metabolism in Atlantic salmon (Salmo salar) in response to replacement of dietary fish oil with vegetable oil. Br J Nutr. 2011, 106: 1457-1469. 10.1017/S0007114511001954.View ArticlePubMedGoogle Scholar
  9. Morais S, Pratoomyot J, Taggart JB, Bron JE, Guy DR, Bell JG, Tocher DR: Genotype-specific responses in Atlantic salmon (Salmo salar) subject to dietary fish oil replacement by vegetable oil: a liver transcriptomic analysis. BMC Genomics. 2011, 12: 255-10.1186/1471-2164-12-255.PubMed CentralView ArticlePubMedGoogle Scholar
  10. LeBlanc F, Laflamme M, Gagné N: Genetic markers of the immune response of Atlantic salmon (Salmo salar) to infectious salmon anemia virus (ISAV). Fish Shellfish Immunol. 2010, 29: 217-232. 10.1016/j.fsi.2010.03.007.View ArticlePubMedGoogle Scholar
  11. Leaver MJ, Villeneuve LA, Obach A, Jensen L, Bron JE, Tocher DR, Taggart JB: Functional genomics reveals increases in cholesterol biosynthetic genes and highly unsaturated fatty acid biosynthesis after dietary substitution of fish oil with vegetable oils in Atlantic salmon (Salmo salar). BMC Genomics. 2008, 9: 299-10.1186/1471-2164-9-299.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Houston RD, Haley CS, Hamilton A, Guy DR, Tinch AE, Taggart JB, McAndrew BJ, Bishop SC: Major quantitative trait loci affect resistance to infectious pancreatic necrosis in Atlantic salmon (Salmo salar). Genetics. 2008, 178: 1109-1115. 10.1534/genetics.107.082974.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Zheng X, Tocher DR, Dickson CA, Bell JG, Teale AJ: Effects of diets containing vegetable oil on expression of genes involved in highly unsaturated fatty acid biosynthesis in liver of Atlantic salmon (Salmo salar). Aquaculture. 2004, 236: 467-483. 10.1016/j.aquaculture.2004.02.003.View ArticleGoogle Scholar
  14. Zheng X, Torstensen BE, Tocher DR, Dick JR, Henderson RJ, Bell JG: Environmental and dietary influences on highly unsaturated fatty acid biosynthesis and expression of fatty acyl desaturase and elongase genes in liver of Atlantic salmon (Salmo salar). Biochim Biophys Acta. 2005, 1734: 13-24. 10.1016/j.bbalip.2005.01.006.View ArticlePubMedGoogle Scholar
  15. Galli C, Calder PC: Effects of fat and fatty acid intake on inflammatory and immune responses: a critical review. Ann Nutr Metab. 2009, 55: 123-139. 10.1159/000228999.View ArticlePubMedGoogle Scholar
  16. Kolditz CI, Paboeuf G, Borthaire M, Esquerré D, SanCristobal M, Lefèvre F, Médale F: Changes induced by dietary energy intake and divergent selection for muscle fat content in rainbow trout (Oncorhynchus mykiss), assessed by transcriptome and proteome analysis of the liver. BMC Genomics. 2008, 9: 506-10.1186/1471-2164-9-506.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Watts JL, Browse J: Genetic dissection of polyunsaturated fatty acid synthesis in Caenorhabditis elegans. Proc Natl Acad Sci U S A. 2002, 99: 5854-5859. 10.1073/pnas.092064799.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Sakakura Y, Shimano H, Sone H, Takahashi A, Inoue K, Toyoshima H, Suzuki S, Yamada N: Sterol regulatory element–binding proteins induce an entire pathway of cholesterol synthesis. Biochem Biophys Res Commun. 2001, 286: 176-183. 10.1006/bbrc.2001.5375.View ArticlePubMedGoogle Scholar
  19. Jump DB, Botolin D, Wang Y, Xu J, Christian B, Demeure O: Fatty acid regulation of hepatic gene transcription. J Nutr. 2005, 135: 2503-2506.PubMedGoogle Scholar
  20. Sampath H, Ntambi JM: Polyunsaturated fatty acid regulation of genes of lipid metabolism. Annu Rev Nutr. 2005, 25: 317-340. 10.1146/annurev.nutr.25.051804.101917.View ArticlePubMedGoogle Scholar
  21. Flock MR, Green MH, Kris-Etherton PM: Effects of adiposity on plasma lipid response to reductions in dietary saturated fatty acids and cholesterol. Adv Nutr. 2011, 2: 261-274.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Morais S, Monroig O, Zheng X, Leaver MJ, Tocher DR: Highly unsaturated fatty acid synthesis in Atlantic salmon: characterization of ELOVL5- and ELOVL2-like elongases. Mar Biotechnol. 2009, 1: 627-639.View ArticleGoogle Scholar
  23. Kobayashi T, Zadravec D, Jacobsson A: ELOVL2 overexpression enhances triacylglycerol synthesis in 3 T3-L1 and F442A cells. FEBS Lett. 2007, 581: 3157-3163. 10.1016/j.febslet.2007.05.081.View ArticlePubMedGoogle Scholar
  24. Zhou Y, Zhang X, Chen L, Wu J, Dang H, Wei M, Fan Y, Zhang Y, Zhu Y, Wang N, Breyer MD, Guan Y: Expression profiling of hepatic genes associated with lipid metabolism in nephrotic rats. Am J Physiol Renal Physiol. 2008, 295: F662-671. 10.1152/ajprenal.00046.2008.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Feng D, Witkowski A, Smith S: Down-regulation of mitochondrial acyl carrier protein in mammalian cells compromises protein lipoylation and respiratory complex I and results in cell death. J Biol Chem. 2009, 284: 11436-11445.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Jiang Z, Michal JJ, Tobey DJ, Daniels TF, Rule DC, Macneil MD: Significant associations of stearoyl-CoA desaturase (SCD1) gene with fat deposition and composition in skeletal muscle. Int J Biol Sci. 2008, 4: 345-351.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Montero D, Grasso V, Izquierdo MS, Ganga R, Real F, Tort L, Caballero MJ, Acosta F: Total substitution of fish oil by vegetable oils in gilthead sea bream (Sparus aurata) diets: effects on hepatic Mx expression and some immune parameters. Fish Shellfish Immunol. 2008, 24: 147-155. 10.1016/j.fsi.2007.08.002.View ArticlePubMedGoogle Scholar
  28. Oxley A, Jolly C, Eide T, Jordal AE, Svardal A, Olsen RE: The combined impact of plant-derived dietary ingredients and acute stress on the intestinal arachidonic acid cascade in Atlantic salmon (Salmo salar). Br J Nutr. 2010, 103: 851-861. 10.1017/S0007114509992467.View ArticlePubMedGoogle Scholar
  29. Estensoro I, Benedito-Palos L, Palenzuela O, Kaushik S, Sitjà-Bobadilla A, Pérez-Sánchez J: The nutritional background of the host alters the disease course in a fish–myxosporean system. Vet Parasitol. 2011, 175: 141-150. 10.1016/j.vetpar.2010.09.015.View ArticlePubMedGoogle Scholar
  30. Yaqoob P: Mechanisms underlying the immunomodulatory effects of n-3 PUFA. Proc Nutr Soc. 2010, 69: 311-315. 10.1017/S0029665110001837.View ArticlePubMedGoogle Scholar
  31. Calder PC: Dietary modification of inflammation with lipids. Proc Nutr Soc. 2002, 61: 345-358. 10.1079/PNS2002166.View ArticlePubMedGoogle Scholar
  32. Shaikh SR, Edidin M: Polyunsaturated fatty acids, membrane organization, T cells, and antigen presentation. Am J Clin Nutr. 2006, 84: 1277-1289.PubMedGoogle Scholar
  33. Yaqoob P, Calder PC: Fatty acids and immune function: new insights into mechanisms. Br J Nutr. 2007, 98 (Suppl 1): S41-45.PubMedGoogle Scholar
  34. Kmieć Z: Cooperation of liver cells in health and disease. Adv Anat Embryol Cell Biol. 2001, 161: III-XIII. 1–151PubMedGoogle Scholar
  35. Knolle PA, Gerken G: Local control of the immune response in the liver. Immunol Rev. 2000, 174: 21-34. 10.1034/j.1600-0528.2002.017408.x.View ArticlePubMedGoogle Scholar
  36. Smith WL: The eicosanoids and their biochemical mechanisms of action. Biochem J. 1989, 259: 315-324.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Lee TH, Mencia-Huerta JM, Shih C, Corey EJ, Lewis RA, Austen KF: Effects of exogenous arachidonic, eicosapentaenoic, and docosahexaenoic acids on the generation of 5-lipoxygenase pathway products by ionophore-activated human neutrophils. J Clin Invest. 1984, 74: 1922-1933. 10.1172/JCI111612.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Ellis AE: Innate host defense mechanisms of fish against viruses and bacteria. Dev Comp Immunol. 2001, 25: 827-839. 10.1016/S0145-305X(01)00038-6.View ArticlePubMedGoogle Scholar
  39. Stein C, Caccamo M, Laird G, Leptin M: Conservation and divergence of gene families encoding components of innate immune response systems in zebrafish. Genome Biol. 2007, 8: R251-10.1186/gb-2007-8-11-r251.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Munir M: TRIM proteins: another class of viral victims. Sci Signal. 2010, 3: jc2-10.1126/scisignal.3118jc2.View ArticlePubMedGoogle Scholar
  41. van der Aa LM, Levraud JP, Yahmi M, Lauret E, Briolat V, Herbomel P, Benmansour A, Boudinot P: A large new subset of TRIM genes highly diversified by duplication and positive selection in teleost fish. BMC Biol. 2009, 7: 7-10.1186/1741-7007-7-7.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Uguccioni M, Loetscher P, Forssmann U, Dewald B, Li H, Lima SH, Li Y, Kreider B, Garotta G, Thelen M, Baggiolini M: Monocyte chemotactic protein 4 (MCP-4), a novel structural and functional analogue of MCP-3 and eotaxin. J Exp Med. 1996, 183: 2379-2384. 10.1084/jem.183.5.2379.View ArticlePubMedGoogle Scholar
  43. Li MY, Chen J, Shi YH: Molecular cloning of leucocyte cell-derived chemotaxin-2 gene in croceine croaker (Pseudosciaena crocea). Fish Shellfish Immunol. 2008, 24: 252-256. 10.1016/j.fsi.2007.09.003.View ArticlePubMedGoogle Scholar
  44. Kato H: Regulation of functions of vascular wall cells by tissue factor pathway inhibitor: basic and clinical aspects. Arterioscler Thromb Vasc Biol. 2002, 22: 539-548. 10.1161/01.ATV.0000013904.40673.CC.View ArticlePubMedGoogle Scholar
  45. Asagiri M, Hirai T, Kunigami T, Kamano S, Gober HJ, Okamoto K, Nishikawa K, Latz E, Golenbock DT, Aoki K, Ohya K, Imai Y, Morishita Y, Miyazono K, Kato S, Saftig P, Takayanagi H: Cathepsin K-dependent toll-like receptor 9 signaling revealed in experimental arthritis. Science. 2008, 319: 624-627. 10.1126/science.1150110.View ArticlePubMedGoogle Scholar
  46. Agrawal A, Singh PP, Bottazzi B, Garlanda C, Mantovani A: Pattern recognition by pentraxins. Adv Exp Med Biol. 2009, 653: 98-116. 10.1007/978-1-4419-0901-5_7.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Gisladottir B, Gudmundsdottir S, Brown L, Jonsson ZO, Magnadottir B: Isolation of two C-reactive protein homologues from cod (Gadus morhua L.) serum. Fish Shellfish Immunol. 2009, 26: 210-219. 10.1016/j.fsi.2008.03.015.View ArticlePubMedGoogle Scholar
  48. Larkin A, Imperiali B: The expanding horizons of asparagine-linked glycosylation. Biochemistry. 2011, 50: 4411-4426. 10.1021/bi200346n.PubMed CentralView ArticlePubMedGoogle Scholar
  49. Cepeda V, Cofre C, González R, MacKenzie S, Vidal R: Identification of genes involved in immune response of Atlantic salmon (Salmo salar) to IPN virus infection, using expressed sequence tag (EST) analysis. Aquaculture. 2011, 318: 54-60. 10.1016/j.aquaculture.2011.04.045.View ArticleGoogle Scholar
  50. Alonso MA, Weissman SM: cDNA cloning and sequence of MAL, a hydrophobic protein associated with human T-cell differentiation. Proc Natl Acad Sci U S A. 1987, 84: 1997-2001. 10.1073/pnas.84.7.1997.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Folch J, Lees M, Sloane-Stanley GH: A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 1957, 226: 497-509.PubMedGoogle Scholar
  52. Christie WW: Lipid analysis. 2003, Bridgewater: The Oily PressGoogle Scholar
  53. Tacchi L, Bron JE, Taggart JB, Secombes CJ, Bickerdike R, Adler MA, Takle H, Martin SAM: 2011 Multiple tissue transcriptomic responses to Piscirickettsia salmonis in Atlantic salmon (Salmo salar). Physiol Genomics. 2011, 43: 1241-1254. 10.1152/physiolgenomics.00086.2011.View ArticlePubMedGoogle Scholar
  54. Krasnov A, Timmerhaus G, Afanasyev S, Jørgensen S-M: Development and assessment of oligonucleotide microarrays for Atlantic salmon (Salmo salar L.). Comp Biochem Physiol Part D Genomics Proteomics. 2011, 6: 31-38. 10.1016/j.cbd.2010.04.006.View ArticlePubMedGoogle Scholar
  55. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001, 29: 365-371. 10.1038/ng1201-365.View ArticlePubMedGoogle Scholar
  56. Pfaffl MW, Horgan GW, Dempfle L: Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002, 30: e36-10.1093/nar/30.9.e36.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Haugland O, Torgersen J, Syed M, Evensen O: Expression profiles of inflammatory and immune-related genes in Atlantic salmon (Salmo salar L.) at early time post vaccination. Vaccine. 2005, 23: 5488-5499. 10.1016/j.vaccine.2005.07.034.View ArticlePubMedGoogle Scholar
  58. Todorcević M, Skugor S, Krasnov A, Ruyter B: Gene expression profiles in Atlantic salmon adipose-derived stromo-vascular fraction during differentiation into adipocytes. BMC Genomics. 2010, 11: 39-10.1186/1471-2164-11-39.PubMed CentralView ArticlePubMedGoogle Scholar
  59. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002, 3: 34.1-34.11.View ArticleGoogle Scholar

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