Radiological characterization of gilthead seabream (Sparus aurata) by X-ray computed tomography

Wound-Induced Changes in Antioxidant Enzyme Activities in Skin Mucus and in Gene Expression in the Skin of Gilthead Seabream (Sparus aurata L.)

This study investigated the antioxidant enzyme activities in the skin mucus of gilthead seabream (Sparus aurata L.) at 3 and 7 days post-wounding (dpw). The expression levels of the genes that encode stress proteins (grp170, grp94, grp75, sod and hsp70) and skin regeneration-related proteins (tf, igf1, tgfb1, der1, apo1 and erdj3) in the skin also were determined. Mucus and skin samples were obtained from the left and right flanks of non-wounded and wounded fish. In both flanks of the wounded fish, catalase and glutathione reductase activities in the skin mucus increased (p < 0.05) at 3 and 7 dpw (100 ± 31% and 111 ± 25%, respectively), whereas superoxide dismutase activity increased (p < 0.05) only at 7 dpw (135 ± 15%). The expression levels of stress proteins in the skin of the wounded flank of the wounded fish mainly increased at 7 dpw (grp170 increased to 288 ± 85%, grp94 to 502 ± 143%, grp75 to 274 ± 69%, sod to 569 ± 99%, and hsp70 increased to 537 ± 14%) (p < 0.05). However, the expression levels of the tissue regeneration-related genes varied depending on the flank investigated, on the experimental time, and on the gene studied. To the best of our knowledge, this is the first work to determine the effect of a wound in different skin parts of the same fish.

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1-4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.