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Duarte AM, Silva F, Mendes S, Pinto FR, Barroso S, Silva E, Neves A, Sequeira V, Magalhães M, Rebelo R, Assis C, Vieira AR, Gordo LS, Gil MM. Seasonal study of the nutritional composition of unexploited and low commercial value fish species from the Portuguese coast. Food Sci Nutr 2022; 10:3368-3379. [PMID: 36249977 PMCID: PMC9548369 DOI: 10.1002/fsn3.2937] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Target species diversification is essential for fisheries sustainability and fish market revitalization. Fish discards are a widely recognized problem resulting from fisheries worldwide, and are of major concern for all sector players, from administrations, to fishermen, and scientists. However, non-target species are seldom studied, and information on nutritional profiles and seasonal changes in nutritional properties is generally lacking. This study assessed the seasonal nutritional composition of two unexploited (Serranus cabrilla, Capros aper) and three low commercial value fish species (Trachurus picturatus, Spondyliosoma cantharus, and Trigla lyra), captured on the Portuguese coast over 1 year. Significant seasonal variations were observed in the nutritional composition of all the species studied. Moisture and ash contents varied from 70% to 81% and from 5% to 13%, respectively. The maximum fat contents were 5% for C. aper and 4% for T. picturatus, allowing to classify all studied fishes as lean. The highest protein contents were recorded for C. aper (25%) and S. cantharus (20%). The unexploited and low commercial value fish species studied were shown to be good fat and protein sources, comparable to commonly consumed species, such as cod and salmon, having a great potential to become commonly consumed fish in Portugal.
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Affiliation(s)
- Ana M. Duarte
- MARE – Marine and Environmental Sciences CentrePolytechnic of LeiriaPenichePortugal
| | - Frederica Silva
- MARE – Marine and Environmental Sciences CentrePolytechnic of LeiriaPenichePortugal
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Susana Mendes
- MARE – Marine and Environmental Sciences CentreESTM, Polytechnic of LeiriaPenichePortugal
| | - Filipa R. Pinto
- MARE – Marine and Environmental Sciences CentrePolytechnic of LeiriaPenichePortugal
| | - Sónia Barroso
- MARE – Marine and Environmental Sciences CentrePolytechnic of LeiriaPenichePortugal
| | - Elisabete Silva
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Ana Neves
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Vera Sequeira
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Maria Filomena Magalhães
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- CE3C – Centre for Ecology, Evolution and Environmental ChangesFaculdade de Ciências, Universidade de LisboaLisbonPortugal
| | - Rui Rebelo
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- CE3C – Centre for Ecology, Evolution and Environmental ChangesFaculdade de Ciências, Universidade de LisboaLisbonPortugal
| | - Carlos Assis
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Ana Rita Vieira
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Leonel Serrano Gordo
- MARE – Marine and Environmental Sciences Centre, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
- Departamento de Biologia Animal, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Maria Manuel Gil
- MARE – Marine and Environmental Sciences CentreESTM, Polytechnic of LeiriaPenichePortugal
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Ahmed RO, Ali A, Al-Tobasei R, Leeds T, Kenney B, Salem M. Weighted Single-Step GWAS Identifies Genes Influencing Fillet Color in Rainbow Trout. Genes (Basel) 2022; 13:genes13081331. [PMID: 35893068 PMCID: PMC9332390 DOI: 10.3390/genes13081331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 02/04/2023] Open
Abstract
The visual appearance of the fish fillet is a significant determinant of consumers' purchase decisions. Depending on the rainbow trout diet, a uniform bright white or reddish/pink fillet color is desirable. Factors affecting fillet color are complex, ranging from the ability of live fish to accumulate carotenoids in the muscle to preharvest environmental conditions, early postmortem muscle metabolism, and storage conditions. Identifying genetic markers of fillet color is a desirable goal but a challenging task for the aquaculture industry. This study used weighted, single-step GWAS to explore the genetic basis of fillet color variation in rainbow trout. We identified several SNP windows explaining up to 3.5%, 2.5%, and 1.6% of the additive genetic variance for fillet redness, yellowness, and whiteness, respectively. SNPs are located within genes implicated in carotenoid metabolism (β,β-carotene 15,15'-dioxygenase, retinol dehydrogenase) and myoglobin homeostasis (ATP synthase subunit β, mitochondrial (ATP5F1B)). These genes are involved in processes that influence muscle pigmentation and postmortem flesh coloration. Other identified genes are involved in the maintenance of muscle structural integrity (kelch protein 41b (klh41b), collagen α-1(XXVIII) chain (COL28A1), and cathepsin K (CTSK)) and protection against lipid oxidation (peroxiredoxin, superoxide dismutase 2 (SOD2), sestrin-1, Ubiquitin carboxyl-terminal hydrolase-10 (USP10)). A-to-G single-nucleotide polymorphism in β,β-carotene 15,15'-dioxygenase, and USP10 result in isoleucine-to-valine and proline-to-leucine non-synonymous amino acid substitutions, respectively. Our observation confirms that fillet color is a complex trait regulated by many genes involved in carotenoid metabolism, myoglobin homeostasis, protection against lipid oxidation, and maintenance of muscle structural integrity. The significant SNPs identified in this study could be prioritized via genomic selection in breeding programs to improve fillet color in rainbow trout.
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Affiliation(s)
- Ridwan O. Ahmed
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (R.O.A.); (A.A.)
| | - Ali Ali
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (R.O.A.); (A.A.)
| | - Rafet Al-Tobasei
- Computational Science Program, Middle Tennessee State University, Murfreesboro, TN 37132, USA;
| | - Tim Leeds
- United States Department of Agriculture Kearneysville, National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, Kearneysville, WV 25430, USA;
| | - Brett Kenney
- Division of Animal and Nutritional Sciences, West Virginia University, Morgantown, WV 26506, USA;
| | - Mohamed Salem
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA; (R.O.A.); (A.A.)
- Correspondence:
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Identification of candidate genes on the basis of SNP by time-lagged heat stress interactions for milk production traits in German Holstein cattle. PLoS One 2021; 16:e0258216. [PMID: 34648531 PMCID: PMC8516222 DOI: 10.1371/journal.pone.0258216] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 09/21/2021] [Indexed: 01/22/2023] Open
Abstract
The aim of this study was to estimate genotype by time-lagged heat stress (HS) variance components as well as main and interaction SNP-marker effects for maternal HS during the last eight weeks of cow pregnancy, considering milk production traits recorded in the offspring generation. The HS indicator was the temperature humidity index (THI) for each week. A dummy variable with the code = 1 for the respective week for THI ≥ 60 indicated HS, otherwise, for no HS, the code = 0 was assigned. The dataset included test-day and lactation production traits from 14,188 genotyped first parity Holstein cows. After genotype quality control, 41,139 SNP markers remained for the genomic analyses. Genomic animal models without (model VC_nHS) and with in-utero HS effects (model VC_wHS) were applied to estimate variance components. Accordingly, for genome-wide associations, models GWA_nHS and GWA_wHS, respectively, were applied to estimate main and interaction SNP effects. Common genomic and residual variances for the same traits were very similar from models VC_nHS and VC_wHS. Genotype by HS interaction variances varied, depending on the week with in-utero HS. Among all traits, lactation milk yield with HS from week 5 displayed the largest proportion for interaction variances (0.07). For main effects from model GWA_wHS, 380 SNPs were suggestively associated with all production traits. For the SNP interaction effects from model GWA_wHS, we identified 31 suggestive SNPs, which were located in close distance to 62 potential candidate genes. The inferred candidate genes have various biological functions, including mechanisms of immune response, growth processes and disease resistance. Two biological processes excessively represented in the overrepresentation tests addressed lymphocyte and monocyte chemotaxis, ultimately affecting immune response. The modelling approach considering time-lagged genotype by HS interactions for production traits inferred physiological mechanisms being associated with health and immunity, enabling improvements in selection of robust animals.
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Blay C, Haffray P, Bugeon J, D’Ambrosio J, Dechamp N, Collewet G, Enez F, Petit V, Cousin X, Corraze G, Phocas F, Dupont-Nivet M. Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss. Front Genet 2021; 12:639223. [PMID: 33692832 PMCID: PMC7937956 DOI: 10.3389/fgene.2021.639223] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/03/2021] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- Carole Blay
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | | | | | - Jonathan D’Ambrosio
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
- SYSAAF, Station LPGP-INRAE, Rennes, France
| | - Nicolas Dechamp
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | | | | | | | - Xavier Cousin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
- MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Palavas-les-Flots, France
| | - Geneviève Corraze
- INRAE, University of Pau & Pays Adour, E2S UPPA, UMR 1419 NuMéA, Saint-Pée-sur-Nivelle, France
| | - Florence Phocas
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
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