The Role of the Regenerating Protein Family on Skeletal Muscle Regeneration

Skeletal muscle regeneration is dependent upon the influences of intrinsic and extrinsic factors that stimulate satellite cells. Regenerating proteins are upregulated at the onset of trauma or inflammation in the pancreas, gastrointestinal tract, liver, neural cells and other tissues. Studies have shown that Reg proteins have a mitogenic, anti-apoptotic and anti-inflammatory function in damaged tissues and is necessary for normal progression of regeneration. As skeletal muscle is also able to regenerate itself at a rapid rate, it seems highly likely that Reg proteins function to promote myogenesis in skeletal muscle regeneration. Therefore, the goal of our research was to characterize the expression of the Reg proteins and receptor in regenerating skeletal muscle and satellite cells, investigate the effect of exogenous Reg protein on myogenesis, and to examine direct Reg protein effect on satellite cell activity. To determine whether Reg proteins participate in skeletal muscle regeneration, mice were injected with marcaine in their tibialis anterior muscles to induce skeletal muscle damage. The gene expression analysis of undamaged and marcaine-damaged tibialis anterior muscles and mice satellite cells showed that Reg I, II, IIIα, IIIγ, IV and EXTL3 genes are present during skeletal muscle regeneration and satellite cells significantly express Reg I, IIIα, IIIγ and EXTL3. As Reg I and IIIα are most prevalent in vivo and in vitro respectively, we advocate these isoforms as the predominant candidates in skeletal muscle regeneration. To determine the effect of exogenous Reg protein on myogenesis, we performed gene expression and muscle morphometry analysis of Reg IIIα or PBS injected tibialis anterior muscles. Interestingly, our results indicate that the addition of Reg IIIα to damaged muscles inhibited myogenesis. To determine the direct effect of Reg protein on myogenic stem cell activity, Reg proteins were added to mice satellite cells and C2C12 cells. Results from these studies were inconclusive due to the failure of known positive and negative controls. Overall, our studies suggest that Reg proteins contribute to skeletal muscle regeneration; however, as an overabundance of Reg IIIα in regenerating tissues may have inhibited myogenesis, it is imperative that other isoforms or lower concentrations be investigated

Greater Colo-Rectal Activation Phenotype in Exercised mdx Mice.

Introduction: Duchenne Muscular Dystrophy is a genetic disease that is caused by a deficiency of dystrophin protein. Both Duchenne Muscular Dystrophy patients and dystrophic mice suffer from intestinal dysfunction. Methods: The present study arose from a chance observation of differences in fecal output of dystrophic vs. normal mice during 20-minutes of forced continuous treadmill exercise. Here, we report on the effects of exercise on fecal output in two different dystrophic mutants and their normal background control strains. All fecal materials evacuated during exercise were counted, dried and weighed. Results: Mice of both mutant dystrophic strains produced significantly more fecal material during the exercise bout than the relevant control strains. iscussion: We propose that exercise--induced Colo--Rectal Activation Phenotype test could be used as a simple, highly sensitive, non-invasive biomarker to determine efficacy of dystrophin replacement therapies

Greater Colo­Rectal Activation Phenotype (CRAP) in Exercised Dystrophic Mice.csv

Fecal output data for exercised and resting dystrophic and normal mice

TGF-β-driven muscle degeneration and failed regeneration underlie disease onset in a DMD mouse model

Duchenne muscular dystrophy (DMD) is a chronic muscle disease characterized by poor myogenesis and replacement of muscle by extracellular matrix. Despite the shared genetic basis, severity of these deficits varies among patients. One source of these variations is the genetic modifier that leads to increased TGF-β activity. While anti-TGF-β therapies are being developed to target muscle fibrosis, their effect on the myogenic deficit is underexplored. Our analysis of in vivo myogenesis in mild (C57BL/10ScSn-mdx/J and C57BL/6J-mdxΔ52) and severe DBA/2J-mdx (D2-mdx) dystrophic models reveals no defects in developmental myogenesis in these mice. However, muscle damage at the onset of disease pathology, or by experimental injury, drives up TGF-β activity in the severe, but not in the mild, dystrophic models. Increased TGF-β activity is accompanied by increased accumulation of fibroadipogenic progenitors (FAPs) leading to fibro-calcification of muscle, together with failure of regenerative myogenesis. Inhibition of TGF-β signaling reduces muscle degeneration by blocking FAP accumulation without rescuing regenerative myogenesis. These findings provide in vivo evidence of early-stage deficit in regenerative myogenesis in D2-mdx mice and implicates TGF-β as a major component of a pathogenic positive feedback loop in this model, identifying this feedback loop as a therapeutic target

TGF-β–driven muscle degeneration and failed regeneration underlie disease onset in a DMD mouse model

Duchenne muscular dystrophy (DMD) is a chronic muscle disease characterized by poor myogenesis and replacement of muscle by extracellular matrix. Despite the shared genetic basis, severity of these deficits varies among patients. One source of these variations is the genetic modifier that leads to increased TGF-β activity. While anti-TGF-β therapies are being developed to target muscle fibrosis, their effect on the myogenic deficit is underexplored. Our analysis of in vivo myogenesis in mild (C57BL/10ScSn-mdx/J and C57BL/6J-mdxΔ52) and severe DBA/2J-mdx (D2-mdx) dystrophic models reveals no defects in developmental myogenesis in these mice. However, muscle damage at the onset of disease pathology, or by experimental injury, drives up TGF-β activity in the severe, but not in the mild, dystrophic models. Increased TGF-β activity is accompanied by increased accumulation of fibroadipogenic progenitors (FAPs) leading to fibro-calcification of muscle, together with failure of regenerative myogenesis. Inhibition of TGF-β signaling reduces muscle degeneration by blocking FAP accumulation without rescuing regenerative myogenesis. These findings provide in vivo evidence of early-stage deficit in regenerative myogenesis in D2-mdx mice and implicates TGF-β as a major component of a pathogenic positive feedback loop in this model, identifying this feedback loop as a therapeutic target

Hybrid forecasting: blending climate predictions with AI models

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes