No abstract

Myostatin plays a major role in muscle growth and development and animals with disruption of this gene display marked increases in muscle mass. Little is known about muscle physiological adaptations in relation to this muscle hypertrophy. To provide a more comprehensive view, we analyzed bovine muscles from control, heterozygote and homozygote young Belgian blue bulls for myostatin deletion, which results in a normal level of inactive myostatin. Heterozygote and homozygote animals were characterized by a higher proportion of fast-twitch glycolytic fibers in Semitendinosus muscle. Differential proteomic analysis of this muscle was performed using two-dimensional gel electrophoresis followed by mass spectrometry. Thirteen proteins, corresponding to 28 protein spots, were significantly altered in response to the myostatin deletion. The observed changes in protein expression are consistent with an increased fast muscle phenotype, suggesting that myostatin negatively controls mainly fast-twitch glycolytic fiber number. Finally, we demonstrated that differential mRNA splicing of fast troponin T is altered by the loss of myostatin function. The structure of mutually exclusive exon 16 appears predominantly expressed in muscles from heterozygote and homozygote animals. This suggests a role for exon 16 of fast troponin T in the physiological adaptation of the fast muscle phenotype.

Hierarchical clustering methodology is a powerful data mining approach for a first exploration of proteomic data. It enables samples or proteins to be grouped blindly according to their expression profiles. Nevertheless, the clustering results depend on parameters such as data preprocessing, between-profile similarity measurement, and the dendrogram construction procedure. We assessed several clustering strategies by calculating the F-measure, a widely used quality metric. The combination, on logged matrix, of Pearson correlation and Ward's methods for data aggregation is among the best clustering strategies, at least with the data sets we studied. This study was carried out using PermutMatrix, a freely available software derived from transcriptomics.