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      Transcriptome analyses reveal reduced hepatic lipid synthesis and accumulation in more feed efficient beef cattle

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          Abstract

          The genetic mechanisms controlling residual feed intake (RFI) in beef cattle are still largely unknown. Here we performed whole transcriptome analyses to identify differentially expressed (DE) genes and their functional roles in liver tissues between six extreme high and six extreme low RFI steers from three beef breed populations including Angus, Charolais, and Kinsella Composite (KC). On average, the next generation sequencing yielded 34 million single-end reads per sample, of which 87% were uniquely mapped to the bovine reference genome. At false discovery rate (FDR) < 0.05 and fold change (FC) > 2, 72, 41, and 175 DE genes were identified in Angus, Charolais, and KC, respectively. Most of the DE genes were breed-specific, while five genes including TP53INP1, LURAP1L, SCD, LPIN1, and ENSBTAG00000047029 were common across the three breeds, with TP53INP1, LURAP1L, SCD, and LPIN1 being downregulated in low RFI steers of all three breeds. The DE genes are mainly involved in lipid, amino acid and carbohydrate metabolism, energy production, molecular transport, small molecule biochemistry, cellular development, and cell death and survival. Furthermore, our differential gene expression results suggest reduced hepatic lipid synthesis and accumulation processes in more feed efficient beef cattle of all three studied breeds.

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          Most cited references44

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

            , , (2013)
            RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.
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              Regulation of enzymes of the urea cycle and arginine metabolism.

              The urea cycle is comprised of five enzymes but also requires other enzymes and mitochondrial amino acid transporters to function fully. The complete urea cycle is expressed in liver and to a small degree also in enterocytes. However, highly regulated expression of several enzymes present in the urea cycle occurs also in many other tissues, where these enzymes are involved in synthesis of nitric oxide, polyamines, proline and glutamate. Glucagon, insulin, and glucocorticoids are major regulators of the expression of urea cycle enzymes in liver. In contrast, the "urea cycle" enzymes in nonhepatic cells are regulated by a wide range of pro- and antiinflammatory cytokines and other agents. Regulation of these enzymes is largely transcriptional in virtually all cell types. This review emphasizes recent information regarding roles and regulation of urea cycle and arginine metabolic enzymes in liver and other cell types.
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                Author and article information

                Contributors
                changxi.li@agr.gc.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 May 2018
                8 May 2018
                2018
                : 8
                : 7303
                Affiliations
                [1 ]GRID grid.17089.37, Department of Agricultural, Food and Nutritional Science, , University of Alberta, ; Edmonton, Alberta T6G 2P5 Canada
                [2 ]ISNI 0000 0001 1302 4958, GRID grid.55614.33, Lacombe Research and Development Centre, , Agriculture and Agri-Food Canada, ; Lacombe, Alberta, T4L 1W1 Canada
                [3 ]ISNI 0000 0001 1512 9569, GRID grid.6435.4, Animal and Bioscience Research Department, , Teagasc, Grange, Dunsany, ; County Meath, Ireland
                Article
                25605
                10.1038/s41598-018-25605-3
                5940658
                29740082
                1749eb0a-10e3-42f7-b2f1-795b618cdef2
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 January 2018
                : 12 April 2018
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