Myostatin and the skeletal muscle atrophy and hypertrophy signaling pathways

Myostatin, a member of the transforming growth factor-β superfamily, is a potent negative regulator of skeletal muscle growth and is conserved in many species, from rodents to humans. Myostatin inactivation can induce skeletal muscle hypertrophy, while its overexpression or systemic administration causes muscle atrophy. As it represents a potential target for stimulating muscle growth and/or preventing muscle wasting, myostatin regulation and functions in the control of muscle mass have been extensively studied. A wealth of data strongly suggests that alterations in skeletal muscle mass are associated with dysregulation in myostatin expression. Moreover, myostatin plays a central role in integrating/mediating anabolic and catabolic responses. Myostatin negatively regulates the activity of the Akt pathway, which promotes protein synthesis, and increases the activity of the ubiquitin–proteasome system to induce atrophy. Several new studies have brought new information on how myostatin may affect both ribosomal biogenesis and translation efficiency of specific mRNA subclasses. In addition, although myostatin has been identified as a modulator of the major catabolic pathways, including the ubiquitin–proteasome and the autophagy–lysosome systems, the underlying mechanisms are only partially understood. The goal of this review is to highlight outstanding questions about myostatin-mediated regulation of the anabolic and catabolic signaling pathways in skeletal muscle. Particular emphasis has been placed on (1) the cross-regulation between myostatin, the growth-promoting pathways and the proteolytic systems; (2) how myostatin inhibition leads to muscle hypertrophy; and (3) the regulation of translation by myostatin.     

Key role of integrin αIIbβ3 signaling to Syk kinase in tissue factor-induced thrombin generation

The fibrin(ogen) receptor, integrin α(IIb)β(3), has a well-established role in platelet spreading, aggregation and clot retraction. How α(IIb)β(3) contributes to platelet-dependent coagulation is less well resolved. Here, we demonstrate that the potent suppressing effect of clinically used α(IIb)β(3) blockers on tissue factor-induced thrombin generation is linked to diminished platelet Ca(2+) responses and phosphatidylserine (PS) exposure. The same blockers suppress these responses in platelets stimulated with collagen and thrombin receptor agonists, whereas added fibrinogen potentiates these responses. In platelets spreading on fibrinogen, outside-in α(IIb)β(3) signaling similarly enhances thrombin-induced Ca(2+) rises and PS exposure. These responses are reduced in α(IIb)β(3)-deficient platelets from patients with Glanzmann's thrombasthenia. Furthermore, the contribution of α(IIb)β(3) to tissue factor-induced platelet Ca(2+) rises, PS exposure and thrombin generation in plasma are fully dependent on Syk kinase activity. Tyrosine phosphorylation analysis confirms a key role of Syk activation, which is largely but not exclusively dependent on α(IIb)β(3) activation. It is concluded that the majority of tissue factor-induced procoagulant activity of platelets relies on Syk activation and ensuing Ca(2+) signal generation, and furthermore that a considerable part of Syk activation relies on α(IIb)β(3) signaling. These results hence point to a novel role of Syk in integrin-dependent thrombin generation.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.     

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.     

Mutations in NMNAT1 cause Leber congenital amaurosis and identify a new disease pathway for retinal degeneration

Leber congenital amaurosis (LCA) is a blinding retinal disease that presents within the first year after birth. Using exome sequencing, we identified mutations in the nicotinamide adenine dinucleotide (NAD) synthase gene NMNAT1 encoding nicotinamide mononucleotide adenylyltransferase 1 in eight families with LCA, including the family in which LCA was originally linked to the LCA9 locus. Notably, all individuals with NMNAT1 mutations also have macular colobomas, which are severe degenerative entities of the central retina (fovea) devoid of tissue and photoreceptors. Functional assays of the proteins encoded by the mutant alleles identified in our study showed that the mutations reduce the enzymatic activity of NMNAT1 in NAD biosynthesis and affect protein folding. Of note, recent characterization of the slow Wallerian degeneration (Wld(s)) mouse model, in which prolonged axonal survival after injury is observed, identified NMNAT1 as a neuroprotective protein when ectopically expressed. Our findings identify a new disease mechanism underlying LCA and provide the first link between endogenous NMNAT1 dysfunction and a human nervous system disorder.     

Maize HapMap2 identifies extant variation from a genome in flux

Whereas breeders have exploited diversity in maize for yield improvements, there has been limited progress in using beneficial alleles in undomesticated varieties. Characterizing standing variation in this complex genome has been challenging, with only a small fraction of it described to date. Using a population genetics scoring model, we identified 55 million SNPs in 103 lines across pre-domestication and domesticated Zea mays varieties, including a representative from the sister genus Tripsacum. We find that structural variations are pervasive in the Z. mays genome and are enriched at loci associated with important traits. By investigating the drivers of genome size variation, we find that the larger Tripsacum genome can be explained by transposable element abundance rather than an allopolyploid origin. In contrast, intraspecies genome size variation seems to be controlled by chromosomal knob content. There is tremendous overlap in key gene content in maize and Tripsacum, suggesting that adaptations from Tripsacum (for example, perennialism and frost and drought tolerance) can likely be integrated into maize.