Literature DB >> 16985009

A gene coexpression network for bovine skeletal muscle inferred from microarray data.

Antonio Reverter1, Nicholas J Hudson, Yonghong Wang, Siok-Hwee Tan, Wes Barris, Keren A Byrne, Sean M McWilliam, Cynthia D K Bottema, Adam Kister, Paul L Greenwood, Gregory S Harper, Sigrid A Lehnert, Brian P Dalrymple.   

Abstract

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.

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Year:  2006        PMID: 16985009     DOI: 10.1152/physiolgenomics.00105.2006

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


  14 in total

1.  Muscle-specific gene expression is underscored by differential stressor responses and coexpression changes.

Authors:  Natalia Moreno-Sánchez; Julia Rueda; Antonio Reverter; María Jesús Carabaño; Clara Díaz
Journal:  Funct Integr Genomics       Date:  2011-09-01       Impact factor: 3.410

2.  Functional classification of skeletal muscle networks. I. Normal physiology.

Authors:  Yu Wang; Jack Winters; Shankar Subramaniam
Journal:  J Appl Physiol (1985)       Date:  2012-10-18

3.  Skeletal muscle specific genes networks in cattle.

Authors:  Natalia Moreno-Sánchez; Julia Rueda; María J Carabaño; Antonio Reverter; Sean McWilliam; Carmen González; Clara Díaz
Journal:  Funct Integr Genomics       Date:  2010-06-04       Impact factor: 3.410

4.  Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

Authors:  Quan Gu; Shivashankar H Nagaraj; Nicholas J Hudson; Brian P Dalrymple; Antonio Reverter
Journal:  BMC Genomics       Date:  2011-01-12       Impact factor: 3.969

5.  Identification of candidate genes related to bovine marbling using protein-protein interaction networks.

Authors:  Dajeong Lim; Nam-Kuk Kim; Hye-Sun Park; Seung-Hwan Lee; Yong-Min Cho; Sung Jong Oh; Tae-Hun Kim; Heebal Kim
Journal:  Int J Biol Sci       Date:  2011-08-13       Impact factor: 6.580

6.  Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

Authors:  Nicholas J Hudson; Antonio Reverter; YongHong Wang; Paul L Greenwood; Brian P Dalrymple
Journal:  PLoS One       Date:  2009-10-01       Impact factor: 3.240

7.  Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism.

Authors:  Khuram Shahzad; Juan J Loor
Journal:  Curr Genomics       Date:  2012-08       Impact factor: 2.236

8.  Using a 3D virtual muscle model to link gene expression changes during myogenesis to protein spatial location in muscle.

Authors:  Ashley J Waardenberg; Antonio Reverter; Christine A Wells; Brian P Dalrymple
Journal:  BMC Syst Biol       Date:  2008-10-22

9.  Bioinformatics analysis of transcriptome dynamics during growth in angus cattle longissimus muscle.

Authors:  Sonia J Moisá; Daniel W Shike; Daniel E Graugnard; Sandra L Rodriguez-Zas; Robin E Everts; Harris A Lewin; Dan B Faulkner; Larry L Berger; Juan J Loor
Journal:  Bioinform Biol Insights       Date:  2013-08-04

10.  Characterization of genes for beef marbling based on applying gene coexpression network.

Authors:  Dajeong Lim; Nam-Kuk Kim; Seung-Hwan Lee; Hye-Sun Park; Yong-Min Cho; Han-Ha Chai; Heebal Kim
Journal:  Int J Genomics       Date:  2014-01-30       Impact factor: 2.326

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