Thu Thi Minh Vo1,2,3, Tuan Viet Nguyen4, Gianluca Amoroso5, Tomer Ventura6,7, Abigail Elizur8. 1. GeneCology Research Centre, University of the Sunshine Coast, Queensland, Sunshine Coast, Australia. 2. School of Science, Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia. 3. School of Biotechnology, International University, Viet Nam National University, 700000, Ho Chi Minh City, Vietnam. 4. Centre for AgriBiosciences, AgriBio, Agriculture Victoria, Victoria, 3083, Bundoora, Australia. 5. Petuna Aquaculture, Tasmania, 7310, East Devonport, Australia. 6. GeneCology Research Centre, University of the Sunshine Coast, Queensland, Sunshine Coast, Australia. tventura@usc.edu.au. 7. School of Science, Technology and Engineering, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia. tventura@usc.edu.au. 8. GeneCology Research Centre, University of the Sunshine Coast, Queensland, Sunshine Coast, Australia. aelizur@usc.edu.au.
Abstract
BACKGROUND: The flesh pigmentation of farmed Atlantic salmon is formed by accumulation of carotenoids derived from commercial diets. In the salmon gastrointestinal system, the hindgut is considered critical in the processes of carotenoids uptake and metabolism. In Tasmania, flesh color depletion can noticeably affect farmed Atlantic salmon at different levels of severity following extremely hot summers. In this study, RNA sequencing (RNA-Seq) was performed to investigate the reduction in flesh pigmentation. Library preparation is a key step that significantly impacts the effectiveness of RNA sequencing (RNA-Seq) experiments. Besides the commonly used whole transcript RNA-Seq method, the 3' mRNA-Seq method is being applied widely, owing to its reduced cost, enabling more repeats to be sequenced at the expense of lower resolution. Therefore, the output of the Illumina TruSeq kit (whole transcript RNA-Seq) and the Lexogen QuantSeq kit (3' mRNA-Seq) was analyzed to identify genes in the Atlantic salmon hindgut that are differentially expressed (DEGs) between two flesh color phenotypes. RESULTS: In both methods, DEGs between the two color phenotypes were associated with metal ion transport, oxidation-reduction processes, and immune responses. We also found DEGs related to lipid metabolism in the QuantSeq method. In the TruSeq method, a missense mutation was detected in DEGs in different flesh color traits. The number of DEGs found in the TruSeq libraries was much higher than the QuantSeq; however, the trend of DEGs in both library methods was similar and validated by qPCR. CONCLUSIONS: Flesh coloration in Atlantic salmon is related to lipid metabolism in which apolipoproteins, serum albumin and fatty acid-binding protein genes are hypothesized to be linked to the absorption, transport and deposition of carotenoids. Our findings suggest that Grp could inhibit the feeding behavior of low color-banded fish, resulting in the dietary carotenoid shortage. Several SNPs in genes involving in carotenoid-binding cholesterol and oxidative stress were detected in both flesh color phenotypes. Regarding the choice of the library preparation method, the selection criteria depend on the research design and purpose. The 3' mRNA-Seq method is ideal for targeted identification of highly expressed genes, while the whole RNA-Seq method is recommended for identification of unknown genes, enabling the identification of splice variants and trait-associated SNPs, as we have found for duox2 and duoxa1.
BACKGROUND: The flesh pigmentation of farmed Atlantic salmon is formed by accumulation of carotenoids derived from commercial diets. In the salmon gastrointestinal system, the hindgut is considered critical in the processes of carotenoids uptake and metabolism. In Tasmania, flesh color depletion can noticeably affect farmed Atlantic salmon at different levels of severity following extremely hot summers. In this study, RNA sequencing (RNA-Seq) was performed to investigate the reduction in flesh pigmentation. Library preparation is a key step that significantly impacts the effectiveness of RNA sequencing (RNA-Seq) experiments. Besides the commonly used whole transcript RNA-Seq method, the 3' mRNA-Seq method is being applied widely, owing to its reduced cost, enabling more repeats to be sequenced at the expense of lower resolution. Therefore, the output of the Illumina TruSeq kit (whole transcript RNA-Seq) and the Lexogen QuantSeq kit (3' mRNA-Seq) was analyzed to identify genes in the Atlantic salmon hindgut that are differentially expressed (DEGs) between two flesh color phenotypes. RESULTS: In both methods, DEGs between the two color phenotypes were associated with metal ion transport, oxidation-reduction processes, and immune responses. We also found DEGs related to lipid metabolism in the QuantSeq method. In the TruSeq method, a missense mutation was detected in DEGs in different flesh color traits. The number of DEGs found in the TruSeq libraries was much higher than the QuantSeq; however, the trend of DEGs in both library methods was similar and validated by qPCR. CONCLUSIONS: Flesh coloration in Atlantic salmon is related to lipid metabolism in which apolipoproteins, serum albumin and fatty acid-binding protein genes are hypothesized to be linked to the absorption, transport and deposition of carotenoids. Our findings suggest that Grp could inhibit the feeding behavior of low color-banded fish, resulting in the dietary carotenoid shortage. Several SNPs in genes involving in carotenoid-binding cholesterol and oxidative stress were detected in both flesh color phenotypes. Regarding the choice of the library preparation method, the selection criteria depend on the research design and purpose. The 3' mRNA-Seq method is ideal for targeted identification of highly expressed genes, while the whole RNA-Seq method is recommended for identification of unknown genes, enabling the identification of splice variants and trait-associated SNPs, as we have found for duox2 and duoxa1.
Authors: E M Hevrøy; R Waagbø; B E Torstensen; H Takle; I Stubhaug; S M Jørgensen; T Torgersen; L Tvenning; S Susort; O Breck; T Hansen Journal: Gen Comp Endocrinol Date: 2011-10-21 Impact factor: 2.822
Authors: Ravi Kumar; Yasunori Ichihashi; Seisuke Kimura; Daniel H Chitwood; Lauren R Headland; Jie Peng; Julin N Maloof; Neelima R Sinha Journal: Front Plant Sci Date: 2012-08-28 Impact factor: 5.753
Authors: Gianluca Amoroso; Tomer Ventura; Jennifer M Cobcroft; Mark B Adams; Abigail Elizur; Chris G Carter Journal: PLoS One Date: 2016-12-15 Impact factor: 3.240