Loss of ACTN3 gene function alters mouse muscle metabolism and shows evidence of positive selection in humans

More than a billion humans worldwide are predicted to be completely deficient in the fast skeletal muscle fiber protein alpha-actinin-3 owing to homozygosity for a premature stop codon polymorphism, R577X, in the ACTN3 gene. The R577X polymorphism is associated with elite athlete status and human muscle performance, suggesting that alpha-actinin-3 deficiency influences the function of fast muscle fibers. Here we show that loss of alpha-actinin-3 expression in a knockout mouse model results in a shift in muscle metabolism toward the more efficient aerobic pathway and an increase in intrinsic endurance performance. In addition, we demonstrate that the genomic region surrounding the 577X null allele shows low levels of genetic variation and recombination in individuals of European and East Asian descent, consistent with strong, recent positive selection. We propose that the 577X allele has been positively selected in some human populations owing to its effect on skeletal muscle metabolism.     

Advances in anti-viral immune defence: revealing the importance of the IFN JAK/STAT pathway

Interferon-alpha (IFN-α) is a potent anti-viral cytokine, critical to the host immune response against viruses. IFN-α is first produced upon viral detection by pathogen recognition receptors. Following its expression, IFN-α embarks upon a complex downstream signalling cascade called the JAK/STAT pathway. This signalling pathway results in the expression of hundreds of effector genes known as interferon stimulated genes (ISGs). These genes are the basis for an elaborate effector mechanism and ultimately, the clearance of viral infection. ISGs mark an elegant mechanism of anti-viral host defence that warrants renewed research focus in our global efforts to treat existing and emerging viruses. By understanding the mechanistic role of individual ISGs we anticipate the discovery of a new “treasure trove” of anti-viral mediators that may pave the way for more effective, targeted and less toxic anti-viral therapies. Therefore, with the aim of highlighting the value of the innate type 1 IFN response in our battle against viral infection, this review outlines both historic and recent advances in understanding the IFN-α JAK/STAT pathway, with a focus on new research discoveries relating to specific ISGs and their potential role in curing existing and future emergent viral infections.     

Enhanced Model-Based Clustering,Density Estimation,and Discriminant Analysis Software: MCLUST

MCLUST is a software package for model-based clustering, density estimation and discriminant analysis interfaced to the S-PLUS commercial software and the R language. It implements parameterized Gaussian hierarchical clustering algorithms and the EM algorithm for parameterized Gaussian mixture models with the possible addition of a Poisson noise term. Also included are functions that combine hierarchical clustering, EM and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation, and discriminant analysis. MCLUST provides functionality for displaying and visualizing clustering and classification results. A web page with related links can be found at .     

Model-Based Clustering for Image Segmentation and Large Datasets via Sampling

The rapid increase in the size of data sets makes clustering all the more important to capture and summarize the information, at the same time making clustering more difficult to accomplish. If model-based clustering is applied directly to a large data set, it can be too slow for practical application. A simple and common approach is to first cluster a random sample of moderate size, and then use the clustering model found in this way to classify the remainder of the objects. We show that, in its simplest form, this method may lead to unstable results. Our experiments suggest that a stable method with better performance can be obtained with two straightforward modifications to the simple sampling method: several tentative models are identified from the sample instead of just one, and several EM steps are used rather than just one E step to classify the full data set. We find that there are significant gains from increasing the size of the sample up to about 2,000, but not from further increases. These conclusions are based on the application of several alternative strategies to the segmentation of three different multispectral images, and to several simulated data sets.     

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of components and the model parameterization, we propose a modified version of BIC, where the likelihood is evaluated at the MAP instead of the MLE. We use a highly dispersed proper conjugate prior, containing a small fraction of one observation's worth of information. The resulting method avoids degeneracies and singularities, but when these are not present it gives similar results to the standard method using MLE, EM and BIC.