New function of the myostatin/activin type I receptor (ALK4) as a mediator of muscle atrophy and muscle regeneration.

Skeletal muscle fibrosis and impaired muscle regeneration are major contributors to muscle wasting in Duchenne muscular dystrophy (DMD). Muscle growth is negatively regulated by myostatin (MSTN) and activins. Blockage of these pathways may improve muscle quality and function in DMD. Antisense oligonucleotides (AONs) were designed specifically to block the function of ALK4, a key receptor for the MSTN/activin pathway in skeletal muscle. AON-induced exon skipping resulted in specific Alk4 down-regulation, inhibition of MSTN activity, and increased myoblast differentiation in vitro Unexpectedly, a marked decrease in muscle mass (10%) was found after Alk4 AON treatment in mdx mice. In line with in vitro results, muscle regeneration was stimulated, and muscle fiber size decreased markedly. Notably, when Alk4 was down-regulated in adult wild-type mice, muscle mass decreased even more. RNAseq analysis revealed dysregulated metabolic functions and signs of muscle atrophy. We conclude that ALK4 inhibition increases myogenesis but also regulates the tight balance of protein synthesis and degradation. Therefore, caution must be used when developing therapies that interfere with MSTN/activin pathways.-Pasteuning-Vuhman, S., Boertje-van der Meulen, J. W., van Putten, M., Overzier, M., ten Dijke, P., Kielbasa, S. M., Arindrarto, W., Wolterbeek, R., Lezhnina, K. V., Ozerov, I. V., Aliper, A. M., Hoogaars, W. M., Aartsma-Rus, A., Loomans, C. J. M. New function of the myostatin/activin type I receptor (ALK4) as a mediator of muscle atrophy and muscle regeneration.

Intrasubtype reassortments cause adaptive amino acid replacements in H3N2 influenza genes.

Reassortments and point mutations are two major contributors to diversity of Influenza A virus; however, the link between these two processes is unclear. It has been suggested that reassortments provoke a temporary increase in the rate of amino acid changes as the viral proteins adapt to new genetic environment, but this phenomenon has not been studied systematically. Here, we use a phylogenetic approach to infer the reassortment events between the 8 segments of influenza A H3N2 virus since its emergence in humans in 1968. We then study the amino acid replacements that occurred in genes encoded in each segment subsequent to reassortments. In five out of eight genes (NA, M1, HA, PB1 and NS1), the reassortment events led to a transient increase in the rate of amino acid replacements on the descendant phylogenetic branches. In NA and HA, the replacements following reassortments were enriched with parallel and/or reversing replacements; in contrast, the replacements at sites responsible for differences between antigenic clusters (in HA) and at sites under positive selection (in NA) were underrepresented among them. Post-reassortment adaptive walks contribute to adaptive evolution in Influenza A: in NA, an average reassortment event causes at least 2.1 amino acid replacements in a reassorted gene, with, on average, 0.43 amino acid replacements per evolving post-reassortment lineage; and at least ~9% of all amino acid replacements are provoked by reassortments.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.