No abstract

The aim of this study was to examine the impact of dispersed crude oil on retinoid metabolism and signaling in developing haddock larvae (Melanogrammus aeglefinus). Retinoids and its active metabolites are involved in the regulation of several developmental processes as cell differentiation, growth and limb patterning. Thus, disruption of retinoid signaling, especially during early developmental stages, may have detrimental effects on the organism. In the present study, crude oil exposure during embryonic development and early larvae development disrupted vitamin A signaling by affecting retinoid levels and genes involved in retinoid metabolism. The disruption of retinoid signaling was also evident when looking at the gene expression pattern at several time-points during and after exposure, demonstrating the complexity and temporal factor of retinoid signaling disruption. Exposure during the embryonic period (pre-hatch) resulted in reduced eye size and increased incidence of abnormal eye morphology. The observed changes in expression of retinoid metabolic genes related to the visual cycle indicates that there is a link between the modulated retinoid levels (alltrans retinoic acid and retinol) and increased incidence of abnormal eye development as a result of crude oil exposure. This is the first study to demonstrate disrupted retinoid signaling in fish following crude oil exposure at environmental relevant levels, giving novel insight in to the mechanism of toxicity.

The pesticide chlorpyrifos-methyl (CLP-m) has been identified in plant ingredients intended for aquaculture feed production. To investigate systemic effects of CLP-m with emphasis on lipid metabolism, post-smolt Atlantic salmon were fed in duplicate (n=2) either diets with no CLP-m (Control) or CLP-m at different concentrations (0.1, 1.0 or 8.0 mg kg-1) for a total of 67 days (Low, Medium, High). Fish in all groups almost doubled their weight during the feeding trial from 262 ± 26g (mean ± SD) to 465 ± 64g (overall mean), with no significant effects on any growth parameters. There was a significant dosedependent inhibition of plasma cholinesterase activity (BuChE) after 67 days. The CLP-m biotransformation metabolite, TCP was detected in liver and bile, with low levels of the parent compound in the organs. Spleen somatic index decreased significantly with increasing dietary CLP-m intake. Hematocrit (%) decreased linearly with increasing dietary exposure to CLP-m after 30 days of exposure, but this decrease was less at 67 days of exposure. A significantly reduced content of arachidonic acid (ARA 20:4n-6), accompanied by a significantly increased content of the saturated fatty acid, palmitic acid (PA 16:0), was observed in liver phospholipids (PLs) with increasing dietary content of CLP-m. Major effects were seen on the PL classes in liver which showed a significantly decreased absolute content, possibly indicating inhibition of PL remodeling pathways or other membrane perturbation effects from CLP-m exposure. In conclusion, this study shows that the pesticide CLP-m is a relatively potent toxicant in Atlantic salmon, especially affecting liver PLs and ARA metabolism.

Although living organisms and the environment may encounter chemical mixtures, the chemical risk assessment has often focused on individual compounds rather than chemical mixtures. However, many scientific findings have reported that toxicological effects can be provoked among substances even at levels below no observed effect concentrations of each toxicant. Conventionally, simple additive models, e.g. concentration addition (CA) and independent action (IA) models, have been frequently employed to estimate the toxicity of chemical mixtures. Since the CA and IA models assumes that mixtures have similar modes of toxic action (MoAs) and dissimilar MoAs, respectively. To develop predictive models explaining both cases which can be substantially occurred in the real world, integrated addition models combining the CA and IA concepts have been developed. The objective of this study was to conduct a comparative validation of simple additive models and integrated addition models via different experimental datasets to evaluate their performance and potential in predicting mixture toxicity.http://dx.

No abstract