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      Gene Expression and Fatty Acid Profiling in Longissimus thoracis Muscle, Subcutaneous Fat, and Liver of Light Lambs in Response to Concentrate or Alfalfa Grazing

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          Abstract

          A better understanding of gene expression and metabolic pathways in response to a feeding system is critical for identifying key physiological processes and genes associated with polyunsaturated fatty acid (PUFA) content in lamb meat. The main objective of this study was to investigate transcriptional changes in L. thoracis (LT) muscle, liver, and subcutaneous fat (SF) of lambs that grazed alfalfa (ALF) and concentrate-fed (CON) slaughtered at 23 kg and using the Affymetrix Ovine Gene 1.1 ST whole-genome array. The study also evaluated the relationship between meat traits in LT muscle, including color, pigments and lipid oxidation during 7 days of display, α-tocopherol content, intramuscular fat (IMF) content and the fatty acid (FA) profile. Lambs that grazed on alfalfa had a greater α-tocopherol concentration in plasma than CON lambs (P < 0.05). The treatment did not affect the IMF content, meat color or pigments (P > 0.05). Grazing increased the α-tocopherol content (P < 0.001) and decreased lipid oxidation on day 7 of display (P < 0.05) in LT muscle. The ALF group contained a greater amount of conjugated linoleic acid (CLA), C18:3 n−3, C20:5 n−3, C22:5 n−3, and C22:6 n−3 than did the CON group (P < 0.05). We identified 41, 96 and four genes differentially expressed in LT muscle, liver, and subcutaneous fat, respectively. The most enriched biological processes in LT muscle were skeletal muscle tissue development, being the genes related to catabolic and lipid processes downregulated, except for CPT1B, which was upregulated in the ALF lambs. Animals grazing alfalfa had lower expression of desaturase enzymes in the liver ( FADS1 and FADS2), which regulate unsaturation of fatty acids and are directly involved in the metabolism of n−3 PUFA series. The results found in the current study showed that ingesting diets richer in n−3 PUFA might have negative effects on the de novo synthesis of n−3 PUFA by downregulating the FADS1 and FADS2 expression. However, feeding diets poorer in n−3 PUFA can promote fatty acid desaturation, which makes these two genes attractive candidates for altering the content of PUFAs in meat.

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          MetaboAnalyst: a web server for metabolomic data analysis and interpretation

          Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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            Structure, function, and dietary regulation of delta6, delta5, and delta9 desaturases.

            Fatty acid desaturases introduce a double bond in a specific position of long-chain fatty acids, and are conserved across kingdoms. Degree of unsaturation of fatty acids affects physical properties of membrane phospholipids and stored triglycerides. In addition, metabolites of polyunsaturated fatty acids are used as signaling molecules in many organisms. Three desaturases, Delta9, Delta6, and Delta5, are present in humans. Delta-9 catalyzes synthesis of monounsaturated fatty acids. Oleic acid, a main product of Delta9 desaturase, is the major fatty acid in mammalian adipose triglycerides, and is also used for phospholipid and cholesteryl ester synthesis. Delta-6 and Delta5 desaturases are required for the synthesis of highly unsaturated fatty acids (HUFAs), which are mainly esterified into phospholipids and contribute to maintaining membrane fluidity. While HUFAs may be required for cold tolerance in plants and fish, the primary role of HUFAs in mammals is cell signaling. Arachidonic acid is required as substrates for eicosanoid synthesis, while docosahexaenoic acid is required in visual and neuronal functions. Desaturases in mammals are regulated at the transcriptional level. Reflecting overlapping functions, three desaturases share a common mechanism of a feedback regulation to maintain products in membrane phospholipids. At the same time, regulation of Delta9 desaturase differs from Delta6 and Delta5 desaturases because its products are incorporated into more diverse lipid groups. Combinations of multiple transcription factors achieve this sophisticated differential regulation.
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              Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.

              Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. metscape-help@umich.edu; akarnovs@umich.edu Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                31 October 2019
                2019
                : 10
                : 1070
                Affiliations
                [1] 1Livestock Gentec, University of Alberta , Edmonton, AB, Canada
                [2] 2Unidad de Producción y Sanidad Animal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA)-Instituto Agroalimentario de Aragón (IA2) (CITA-Universidad de Zaragoza) , Zaragoza, Spain
                [3] 3Precision Nutrition and Obesity, IMDEA-Alimentación , Madrid, Spain
                [4] 4Jean Mayer-USDA Human Nutrition Research Center on Aging, Tufts University , Boston, MA, United States
                [5] 5Departamento de Mejora Genética Animal, INIA , Madrid, Spain
                [6] 6ARAID , Zaragoza, Spain
                Author notes

                Edited by: Robert J. Schaefer, University of Minnesota Twin Cities, United States

                Reviewed by: Andrea Serra, University of Pisa, Italy; Paula Alexandra Lopes, University of Lisbon, Portugal

                *Correspondence: Jorge H. Calvo, jhcalvo@ 123456aragon.es

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.01070
                6834778
                31737049
                c9ee0dd6-039a-4962-8628-c4bbcb351d49
                Copyright © 2019 Dervishi, González-Calvo, Blanco, Joy, Sarto, Martin-Hernandez, Ordovás, Serrano and Calvo

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 April 2019
                : 04 October 2019
                Page count
                Figures: 6, Tables: 6, Equations: 1, References: 72, Pages: 16, Words: 8587
                Categories
                Genetics
                Original Research

                Genetics
                concentrate,alfalfa,microarray,ovine,muscle,subcutaneous fat,liver,meat quality
                Genetics
                concentrate, alfalfa, microarray, ovine, muscle, subcutaneous fat, liver, meat quality

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