Omega-3 and alpha-tocopherol provide more protection against contaminants in novel feeds for Atlantic salmon (Salmo salar L.) than omega-6 and gamma tocopherol

Abstract

Extended use of plant ingredients in Atlantic salmon farming has increased the need for knowledge on the effects of new nutrients and contaminants in plant based feeds on fish health and nutrient-contaminant interactions. Primary Atlantic salmon hepatocytes were exposed to a mixture of PAHs and pesticides alone or in combination with the nutrients ARA, EPA, α-tocopherol, and γ-tocopherol according to a factorial design. Cells were screened for effects using xCELLigence cytotoxicity screening, NMR spectroscopy metabolomics, mass spectrometry lipidomics and RT-qPCR transcriptomics. The cytotoxicity results suggest that adverse effects of the contaminants can be counteracted by the nutrients. The lipidomics suggested effects on cell membrane stability and vitamin D metabolism after contaminant and fatty acid exposure. Co-exposure of the contaminants with EPA or α-tocopherol contributed to an antagonistic effect in exposed cells, with reduced effects on the VTG and FABP4 transcripts. ARA and γ-tocopherol strengthened the contaminant-induced response, ARA by contributing to an additive and synergistic induction of CYP1A, CYP3A and CPT2, and γ-tocopherol by synergistically increasing ACOX1. Individually EPA and α-tocopherol seemed more beneficial than ARA and γ-tocopherol in preventing the adverse effects induced by the contaminant mixture, though a combination of all nutrients showed the greatest ameliorating effect.

Keywords: Arachidonic acid; Benzo(a) pyrene; Chlorpyrifos; Eicosapentaenoic acid; Endosulfan; Interactions; Lipidomics; Metabolomics; Nutrients; PAH; Pesticides; Phenanthrene; RT-qPCR transcriptomics; α-tocopherol; γ-tocopherol.

Fig. 1

A Simplified scaled and centered PLS regression coefficients with 95% confidence intervals for Normalized cell index (NCI) levels measured in primary Atlantic salmon hepatocytes exposed to eicosapentaenoic acid (EPA), arachidonic acid (ARA) and α-tocopherol (αT), γ-tocopherol (γT) and contaminant mixture (CM) accordingly to the factorial design (N = 5). The CM was composed of 100 μM of benzo(a) pyrene and phenanthrene and 1 μM of chlorpyrifos and endosulfan. The model is based on 33 experimental objects, and had one PLS component. The model containing five linear terms and eight interaction terms (R² = 0.85 and Q² = 0.55). Only linear (CM) and interaction terms representing contaminant-nutrient interactions (C-EPA, C-ARA, C-γT) and important nutrient-nutrient interaction (EPA-ARA, αT-γT) were included in the figure (confidence level = 0.95). Significant PLS regression coefficients are indicated with a *(p < 0.05). The complete PLS regression model equation is described in the supplementary A1. The regression coefficients reflecting the impact of the factors on the PLS model. B 4D contour plot of xCELLigence cytotoxicity NCI levels as a function of EPA and ARA with increasing levels of CM and γT on the X- and Y-axis, respectively, keeping αT constant at 100 μM. The highlighted values in the plot represent NCI levels for the different stratification beddings (isoboles).

Fig. 2

PCA scores plots from lipidomics data of salmon hepatocytes treated with different nutrients combined with a contaminant mixture (CM) versus control DMSO and the CM alone. A Effect of eicosapentaenoic acid (EPA) compared to the C-EPA, B arachidonic acid (ARA) compared to C-ARA, C α-tocopherol (αT) compared to C-αT, D γ-tocopherol (γT) compared to C-γT. E The dose related effect on the lipidomic profile when a low dose (C-All low) or a high dose (C-All high) of combination of CM and all nutrients were used.

Fig. 3

Multivariate analyses of NMR metabolic fingerprints for Atlantic salmon hepatocyte responses to exposure shown by A PCA analysis of the response of the contaminant mixture (CM) exposure versus control (DMSO). B The effects of eicosapentaenoic acid (EPA) and C-EPA, C arachidonic acid (ARA) and C-ARA, D α-tocopherol (αT) and C-αT, E γ-tocopherol (γT) and C-γT, and F C-All low and C-All high (low or high concentration of the CM and all nutrients) compared to the effect of CM.

Fig. 4

Simplified scaled and centered PLS regression coefficient models for different transcripts measured in primary Atlantic salmon hepatocytes exposed to eicosapentaenoic acid (EPA), arachidonic acid (ARA), α-tocopherol (αT), γ-tocopherol (γT) and contaminant mixture (CM) using mean normalized expression (MNE) and a factorial design (N = 5). CM contained 100 μM of benzo(a) pyrene and phenanthrene and 1 μM of chlorpyrifos and endosulfan. The combined effects identified with contour plot analysis like additivity, synergism or antagonism are presented in the different PLS regression coefficient models (confidence level = 0.95). Significant PLS regression coefficients are indicated with a *(p < 0.05). A Cytochrome P450 1A (CYP1A), R² = 0.90, Q² = 0.76. B Cytochrome P450 3A (CYP3A), R² = 0.84, Q² = 0.62. C Peroxisome proliferator-activated receptors (PPARα), R² = 0.76, Q² = 0.53. D Carnitine palmitoyltransferase 2 (CPT2), R² = 0.78, Q² = 0.52. E Peroxisomal acyl-coenzyme A oxidase 1 (ACOX1), R² = 0.79, Q² = 0.55. F Fatty acid-binding protein 4 (FABP4), R² = 0.74, Q² = 0.47. G Vitellogenin (VTG), R² = 0.77, Q² = 0.58. The complete PLS regression model equations are described in the supplementary A1. Only important liner and interaction terms representing contaminant-nutrient and nutrient–nutrient interactions were presented in the figures. The regression coefficients reflecting the impact of the factors on the PLS model.