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Skeletal muscle specific genes networks in cattle

While physiological differences across skeletal muscles have been described, the differential gene expression underlying them and the discovery of how they interact to perform specific biological processes are largely to be elucidated. The purpose of the present study was, firstly, to profile by cDN...

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Detalles Bibliográficos
Autores principales: Moreno-Sánchez, Natalia, Rueda, Julia, Carabaño, María J., Reverter, Antonio, McWilliam, Sean, González, Carmen, Díaz, Clara
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990504/
https://www.ncbi.nlm.nih.gov/pubmed/20524025
http://dx.doi.org/10.1007/s10142-010-0175-2
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author Moreno-Sánchez, Natalia
Rueda, Julia
Carabaño, María J.
Reverter, Antonio
McWilliam, Sean
González, Carmen
Díaz, Clara
author_facet Moreno-Sánchez, Natalia
Rueda, Julia
Carabaño, María J.
Reverter, Antonio
McWilliam, Sean
González, Carmen
Díaz, Clara
author_sort Moreno-Sánchez, Natalia
collection PubMed
description While physiological differences across skeletal muscles have been described, the differential gene expression underlying them and the discovery of how they interact to perform specific biological processes are largely to be elucidated. The purpose of the present study was, firstly, to profile by cDNA microarrays the differential gene expression between two skeletal muscle types, Psoas major (PM) and Flexor digitorum (FD), in beef cattle and then to interpret the results in the context of a bovine gene coexpression network, detecting possible changes in connectivity across the skeletal muscle system. Eighty four genes were differentially expressed (DE) between muscles. Approximately 54% encoded metabolic enzymes and structural-contractile proteins. DE genes were involved in similar processes and functions, but the proportion of genes in each category varied within each muscle. A correlation matrix was obtained for 61 out of the 84 DE genes from a gene coexpression network. Different groups of coexpression were observed, the largest one having 28 metabolic and contractile genes, up-regulated in PM, and mainly encoding fast-glycolytic fibre structural components and glycolytic enzymes. In FD, genes related to cell support seemed to constitute its identity feature and did not positively correlate to the rest of DE genes in FD. Moreover, changes in connectivity for some DE genes were observed in the different gene ontologies. Our results confirm the existence of a muscle dependent transcription and coexpression pattern and suggest the necessity of integrating different muscle types to perform comprehensive networks for the transcriptional landscape of bovine skeletal muscle. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10142-010-0175-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-29905042010-11-29 Skeletal muscle specific genes networks in cattle Moreno-Sánchez, Natalia Rueda, Julia Carabaño, María J. Reverter, Antonio McWilliam, Sean González, Carmen Díaz, Clara Funct Integr Genomics Short Communication While physiological differences across skeletal muscles have been described, the differential gene expression underlying them and the discovery of how they interact to perform specific biological processes are largely to be elucidated. The purpose of the present study was, firstly, to profile by cDNA microarrays the differential gene expression between two skeletal muscle types, Psoas major (PM) and Flexor digitorum (FD), in beef cattle and then to interpret the results in the context of a bovine gene coexpression network, detecting possible changes in connectivity across the skeletal muscle system. Eighty four genes were differentially expressed (DE) between muscles. Approximately 54% encoded metabolic enzymes and structural-contractile proteins. DE genes were involved in similar processes and functions, but the proportion of genes in each category varied within each muscle. A correlation matrix was obtained for 61 out of the 84 DE genes from a gene coexpression network. Different groups of coexpression were observed, the largest one having 28 metabolic and contractile genes, up-regulated in PM, and mainly encoding fast-glycolytic fibre structural components and glycolytic enzymes. In FD, genes related to cell support seemed to constitute its identity feature and did not positively correlate to the rest of DE genes in FD. Moreover, changes in connectivity for some DE genes were observed in the different gene ontologies. Our results confirm the existence of a muscle dependent transcription and coexpression pattern and suggest the necessity of integrating different muscle types to perform comprehensive networks for the transcriptional landscape of bovine skeletal muscle. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10142-010-0175-2) contains supplementary material, which is available to authorized users. Springer-Verlag 2010-06-04 2010 /pmc/articles/PMC2990504/ /pubmed/20524025 http://dx.doi.org/10.1007/s10142-010-0175-2 Text en © The Author(s) 2010 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Short Communication
Moreno-Sánchez, Natalia
Rueda, Julia
Carabaño, María J.
Reverter, Antonio
McWilliam, Sean
González, Carmen
Díaz, Clara
Skeletal muscle specific genes networks in cattle
title Skeletal muscle specific genes networks in cattle
title_full Skeletal muscle specific genes networks in cattle
title_fullStr Skeletal muscle specific genes networks in cattle
title_full_unstemmed Skeletal muscle specific genes networks in cattle
title_short Skeletal muscle specific genes networks in cattle
title_sort skeletal muscle specific genes networks in cattle
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990504/
https://www.ncbi.nlm.nih.gov/pubmed/20524025
http://dx.doi.org/10.1007/s10142-010-0175-2
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