G protein-coupled receptor 56 regulates mechanical overload-induced muscle hypertrophy.

Peroxisome proliferator-activated receptor gamma coactivator 1-alpha 4 (PGC-1α4) is a protein isoform derived by alternative splicing of the PGC1α mRNA and has been shown to promote muscle hypertrophy. We show here that G protein-coupled receptor 56 (GPR56) is a transcriptional target of PGC-1α4 and is induced in humans by resistance exercise. Furthermore, the anabolic effects of PGC-1α4 in cultured murine muscle cells are dependent on GPR56 signaling, because knockdown of GPR56 attenuates PGC-1α4-induced muscle hypertrophy in vitro. Forced expression of GPR56 results in myotube hypertrophy through the expression of insulin-like growth factor 1, which is dependent on Gα12/13 signaling. A murine model of overload-induced muscle hypertrophy is associated with increased expression of both GPR56 and its ligand collagen type III, whereas genetic ablation of GPR56 expression attenuates overload-induced muscle hypertrophy and associated anabolic signaling. These data illustrate a signaling pathway through GPR56 which regulates muscle hypertrophy associated with resistance/loading-type exercise.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells.

BACKGROUND: Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events. RESULTS: Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3ß) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3ß, with a significant increase at 100 µM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3ß and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3ß, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. CONCLUSIONS: These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3ß phosphorylation and glycogen synthesis.

Significance analysis of microarray transcript levels in time series experiments.

BACKGROUND: Microarray time series studies are essential to understand the dynamics of molecular events. In order to limit the analysis to those genes that change expression over time, a first necessary step is to select differentially expressed transcripts. A variety of methods have been proposed to this purpose; however, these methods are seldom applicable in practice since they require a large number of replicates, often available only for a limited number of samples. In this data-poor context, we evaluate the performance of three selection methods, using synthetic data, over a range of experimental conditions. Application to real data is also discussed. RESULTS: Three methods are considered, to assess differentially expressed genes in data-poor conditions. Method 1 uses a threshold on individual samples based on a model of the experimental error. Method 2 calculates the area of the region bounded by the time series expression profiles, and considers the gene differentially expressed if the area exceeds a threshold based on a model of the experimental error. These two methods are compared to Method 3, recently proposed in the literature, which exploits splines fit to compare time series profiles. Application of the three methods to synthetic data indicates that Method 2 outperforms the other two both in Precision and Recall when short time series are analyzed, while Method 3 outperforms the other two for long time series. CONCLUSION: These results help to address the choice of the algorithm to be used in data-poor time series expression study, depending on the length of the time series.

A quantization method based on threshold optimization for microarray short time series.

BACKGROUND: Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes. RESULTS: A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks. CONCLUSION: The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes.