Uncovering the exercise-related proteome signature in skeletal muscle.

Abstract:

:Exercise training has been recommended as a nonpharmacological strategy for the prevention and attenuation of skeletal muscle atrophy in distinct pathophysiological conditions. Despite the well-established phenotypic alterations, the molecular mechanisms underlying exercise-induced skeletal muscle remodeling are poorly characterized. Proteomics based on mass spectrometry have been successfully applied for the characterization of skeletal muscle proteome, representing a pivotal approach for the wide characterization of the molecular networks that lead to skeletal muscle remodeling. Nevertheless, few studies were performed to characterize the exercise-induced proteome remodeling of skeletal muscle, with only six research papers focused on the cross-talk between exercise and pathophysiological conditions. In order to add new insights on the impact of distinct exercise programs on skeletal muscle proteome, molecular network analysis was performed with bioinformatics tools. This analysis highlighted an exercise-related proteome signature characterized by the up-regulation of the capacity for ATP generation, oxygen delivery, antioxidant capacity and regulation of mitochondrial protein synthesis. Chronic endurance training up-regulates the tricarboxylic acid cycle and oxidative phosphorylation system, whereas the release of calcium ion into cytosol and amino acid metabolism are the biological processes up-regulated by a single bout of exercise. Other issues as exercise intensity, load, mode and regimen as well as muscle type also influence the exercise-induced proteome signature. The comprehensive analysis of the molecular networks modulated by exercise training in health and disease, taking in consideration all these variables, might not only support the therapeutic effect of exercise but also highlight novel targets for the development of enhanced pharmacological strategies.

journal_name

Proteomics

journal_title

Proteomics

authors

Padrão AI,Ferreira R,Amado F,Vitorino R,Duarte JA

doi

10.1002/pmic.201500382

subject

Has Abstract

pub_date

2016-03-01 00:00:00

pages

816-30

issue

5

eissn

1615-9853

issn

1615-9861

journal_volume

16

pub_type

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