Analysis of Myogenic and Candidate Disease Biomarkers in FSHD Muscle Cells

The UMMS Wellstone Program is a foundation and NIH-funded cooperative research center focusing on identifying biomarkers for facioscapulohumeral muscular dystrophy (FSHD) to gain insight into the molecular pathology of the disease and to develop potential therapies. FSHD is characterized by progressive wasting of skeletal muscles, with weakness often initiating in facial muscles and muscles supporting the scapula and upper arms. While the genetics associated with FSHD are complex, the major form of the disease, FSHD1, is linked to contraction of the D4Z4 repeat region located at chromosome 4q. Recently, a transcript encoded at the distal end of the repeat region, Dux4-fl, normally expressed in embryonic stem cells and germ cells, was also detected in differentiated muscle cells and biopsies from FSHD subjects, giving rise to the hypothesis that DUX4-FL function contributes to muscle weakness. We established a repository of high quality, well-characterized primary and immortalized muscle cell strains from FSHD and control subjects in affected families to provide biomaterials for cell and molecular studies to the FSHD research community. qPCR and immunostaining analyses demonstrate similar growth and differentiation characteristics in cells from FSHD and control subjects within families. We detected Dux4-fl transcript and protein in FSHD cells as recently described; interestingly, we also detected Dux4-fl in muscle cells from a subset of control individuals, suggesting that any Dux4-fl-mediated myopathy would require additional modifying elements. Microarray analysis of FSHD and control muscle cells demonstrated that several genes were upregulated in FSHD cells, including genes that were concurrently identified as downstream targets of Dux4-fl and as candidate FSHD disease genes. Future studies will further characterize the RNA and protein expression of candidate disease genes in cells from FSHD and control subjects, including nonmanifesting subjects with the D4Z4 lesion but no muscle weakness, and utilizing whole transcriptome sequencing (RNAseq) to identify additional candidates

Maternal protein-energy malnutrition during early pregnancy in sheep impacts the fetal ornithine cycle to reduce fetal kidney microvascular development

This paper identifies a common nutritional pathway relating maternal through to fetal protein-energy malnutrition (PEM) and compromised fetal kidney development. Thirty-one twin-bearing sheep were fed either a control (n=15) or low-protein diet (n=16, 17 vs. 8.7 g crude protein/MJ metabolizable energy) from d 0 to 65 gestation (term, ∼ 145 d). Effects on the maternal and fetal nutritional environment were characterized by sampling blood and amniotic fluid. Kidney development was characterized by histology, immunohistochemistry, vascular corrosion casts, and molecular biology. PEM had little measureable effect on maternal and fetal macronutrient balance (glucose, total protein, total amino acids, and lactate were unaffected) or on fetal growth. PEM decreased maternal and fetal urea concentration, which blunted fetal ornithine availability and affected fetal hepatic polyamine production. For the first time in a large animal model, we associated these nutritional effects with reduced micro- but not macrovascular development in the fetal kidney. Maternal PEM specifically impacts the fetal ornithine cycle, affecting cellular polyamine metabolism and microvascular development of the fetal kidney, effects that likely underpin programming of kidney development and function by a maternal low protein diet

Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-016-3286-1This paper examines the forecasting skill of eight Global Climate Models (GCMs) from the North-American Multi-Model Ensemble (NMME) project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecasted than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales