Literature DB >> 18347662

Muscling in on microarrays.

Carl Virtanen1, Mark Takahashi.   

Abstract

Adaptations that are the result of exercise require a multitude of changes at the level of gene expression. The mechanisms involved in regulating these changes are many, and can occur at various points in the pathways that affect gene expression. The completion of the human genome sequence, along with the genomes of related species, has provided an enormous amount of information to help dissect and understand these pathways. High-throughput methods, such as DNA microarrays, were the first on the scene to take advantage of this wealth of information. A new generation of microarrays has now taken the next step in revealing the mechanisms controlling gene expression. Analysis of the regulation of gene expression can now be profiled in a high-throughput fashion. However, the application of this technology has yet to be fully realized in the exercise physiology community. This review will highlight some of the latest advances in microarrays and briefly discuss some potential applications to the field of exercise physiology.

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Mesh:

Year:  2008        PMID: 18347662     DOI: 10.1139/H07-150

Source DB:  PubMed          Journal:  Appl Physiol Nutr Metab        ISSN: 1715-5312            Impact factor:   2.665


  4 in total

1.  Immune response and mitochondrial metabolism are commonly deregulated in DMD and aging skeletal muscle.

Authors:  Daniel Baron; Armelle Magot; Gérard Ramstein; Marja Steenman; Guillemette Fayet; Catherine Chevalier; Philippe Jourdon; Rémi Houlgatte; Frédérique Savagner; Yann Pereon
Journal:  PLoS One       Date:  2011-11-09       Impact factor: 3.240

Review 2.  Assessing the Role of Muscle Protein Breakdown in Response to Nutrition and Exercise in Humans.

Authors:  Kevin D Tipton; D Lee Hamilton; Iain J Gallagher
Journal:  Sports Med       Date:  2018-03       Impact factor: 11.136

3.  Effects of Training Status and Exercise Mode on Global Gene Expression in Skeletal Muscle.

Authors:  Daniel A Bizjak; Martina Zügel; Gunnar Treff; Kay Winkert; Achim Jerg; Jens Hudemann; Frank C Mooren; Karsten Krüger; Andreas Nieß; Jürgen M Steinacker
Journal:  Int J Mol Sci       Date:  2021-11-22       Impact factor: 5.923

4.  Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature.

Authors:  Cynthia Stretch; Sheehan Khan; Nasimeh Asgarian; Roman Eisner; Saman Vaisipour; Sambasivarao Damaraju; Kathryn Graham; Oliver F Bathe; Helen Steed; Russell Greiner; Vickie E Baracos
Journal:  PLoS One       Date:  2013-06-03       Impact factor: 3.240

  4 in total

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