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      Comparative Physiological and Transcriptomic Profiling Offers Insight into the Sexual Dimorphism of Hepatic Metabolism in Size-Dimorphic Spotted Scat ( Scatophagus argus)

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          Abstract

          The spotted scat ( Scatophagus argus) is an economically important cultured marine fish that exhibits a typical sexual size dimorphism (SSD). SSD has captivated considerable curiosity for farmed fish production; however, up till now the exact underlying mechanism remains largely unclear. As an important digestive and metabolic organ, the liver plays key roles in the regulation of fish growth. It is necessary to elucidate its significance as a downstream component of the hypothalamic-pituitary-liver axis in the formation of SSD. In this study, the liver physiological differences between the sexes were evaluated in S. argus, and the activity of several digestive and metabolic enzymes were affected by sex. Females had higher amylase, protease, and glucose-6-phosphate dehydrogenase activities, while males exhibited markedly higher hepatic lipase and antioxidant enzymes activities. A comparative transcriptomics was then performed to characterize the responsive genes. Illumina sequencing generated 272.6 million clean reads, which were assembled into 79,115 unigenes. A total of 259 differentially expressed genes were identified and a few growth-controlling genes such as igf1 and igfbp1 exhibited female-biased expression. Further analyses showed that several GO terms and pathways associated with metabolic process, particularly lipid and energy metabolisms, were significantly enriched. The male liver showed a more active mitochondrial energy metabolism, implicating an increased energy expenditure associated with reproduction. Collectively, the female-biased growth dimorphism of S. argus may be partially attributed to sexually dimorphic metabolism in the liver. These findings would facilitate further understanding of the nature of SSD in teleost fish.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              PROTEIN MEASUREMENT WITH THE FOLIN PHENOL REAGENT

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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Life (Basel)
                Life (Basel)
                life
                Life
                MDPI
                2075-1729
                21 June 2021
                June 2021
                : 11
                : 6
                : 589
                Affiliations
                [1 ]Guangdong Research Center on Reproductive Control and Breeding Technology of Indigenous Valuable Fish Species, Key Laboratory of Marine Ecology and Aquaculture Environment of Zhanjiang, Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China; chenhp@ 123456gdou.edu.cn (H.C.); dnjiang@ 123456gdou.edu.cn (D.J.); g_yl903@ 123456163.com (Z.L.); yaorongwang217@ 123456126.com (Y.W.)
                [2 ]College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518052, China; yangxw@ 123456szu.edu.cn (X.Y.); szu_sfli@ 123456163.com (S.L.)
                [3 ]State Key Laboratory of Biocontrol, Sun Yat-sen University, Guangzhou 510275, China; lshuish@ 123456mail.sysu.edu.cn
                [4 ]Guangdong Province Key Laboratory for Aquatic Economic Animals, Sun Yat-sen University, Guangzhou 510275, China
                [5 ]Food and Environmental Engineering Department, Yangjiang Polytechnic, Yangjiang 529566, China
                Author notes
                [* ]Correspondence: yangwei516@ 123456stu.edu.cn (W.Y.); ligl@ 123456gdou.edu.cn (G.L.); Tel.: +86-662-3362800 (W.Y.); +86-759-2383124 (G.L.); Fax: +86-662-3316729 (W.Y.); +86-759-2382459 (G.L.)
                Author information
                https://orcid.org/0000-0002-3566-8671
                https://orcid.org/0000-0003-2675-2043
                Article
                life-11-00589
                10.3390/life11060589
                8233746
                945e99e0-0af2-4efb-95e0-82057ab02f9c
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 28 May 2021
                : 20 June 2021
                Categories
                Article

                scatophagus argus,sexual size dimorphism,liver,rna-seq,metabolism,enzyme activity

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