Succinate induces skeletal muscle fiber remodeling via SUNCR1 signaling.

The conversion of skeletal muscle fiber from fast twitch to slow-twitch is important for sustained and tonic contractile events, maintenance of energy homeostasis, and the alleviation of fatigue. Skeletal muscle remodeling is effectively induced by endurance or aerobic exercise, which also generates several tricarboxylic acid (TCA) cycle intermediates, including succinate. However, whether succinate regulates muscle fiber-type transitions remains unclear. Here, we found that dietary succinate supplementation increased endurance exercise ability, myosin heavy chain I expression, aerobic enzyme activity, oxygen consumption, and mitochondrial biogenesis in mouse skeletal muscle. By contrast, succinate decreased lactate dehydrogenase activity, lactate production, and myosin heavy chain IIb expression. Further, by using pharmacological or genetic loss-of-function models generated by phospholipase Cß antagonists, SUNCR1 global knockout, or SUNCR1 gastrocnemius-specific knockdown, we found that the effects of succinate on skeletal muscle fiber-type remodeling are mediated by SUNCR1 and its downstream calcium/NFAT signaling pathway. In summary, our results demonstrate succinate induces transition of skeletal muscle fiber via SUNCR1 signaling pathway. These findings suggest the potential beneficial use of succinate-based compounds in both athletic and sedentary populations.

Systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient model for real-life counter-current chromatography separation.

Solvent system selection is the first step toward a successful counter-current chromatography (CCC) separation. This paper introduces a systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model, which is efficient in predicting the solute partition coefficient. Firstly, the application of the NRTL-SAC method was extended to the ethyl acetate/n-butanol/water and chloroform/methanol/water solvent system families. Moreover, the versatility and predictive capability of the NRTL-SAC method were investigated. The results indicate that the solute molecular parameters identified from hexane/ethyl acetate/methanol/water solvent system family are capable of predicting a large number of partition coefficients in several other different solvent system families. The NRTL-SAC strategy was further validated by successfully separating five components from Salvia plebeian R.Br. We therefore propose that NRTL-SAC is a promising high throughput method for rapid solvent system selection and highly adaptable to screen suitable solvent system for real-life CCC separation.

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.