Exercise, diet and skeletal muscle gene expression

Skeletal muscle, as a consequence of its mass and great capacity for altered metabolism, has a major impact on whole-body metabolic homeostasis and is capable of remarkable adaptation in response to various physiological stimuli, including exercise and dietary intervention. Exercise-induced increases in skeletal muscle mRNA levels of a number of genes have been reported, due to transcriptional activation and/or increased mRNA stability. The cellular adaptations to exercise training appear to be due to the cumulative effects of transient increases in gene transcription after repeated exercise bouts. The relative importance of transcriptional (mRNA synthesis) and translational (mRNA stability or translational efficiency) mechanisms for the training-induced increases in skeletal muscle protein abundance remains to be fully elucidated. Dietary manipulation, and the associated alterations in nutrient availability and hormone levels, can also modify skeletal muscle gene expression, although fewer studies have been reported. A major challenge is to understand how exercise and diet exert their effects on gene and protein expression in skeletal muscle. In relation to exercise, potential stimuli include stretch and muscle tension, the pattern of motor nerve activity and the resultant calcium transients, the energy charge of the cell and substrate availability, oxygen tension and circulating hormones. These are detected by various cellular signaling mechanisms, acting on a range of downstream targets and a wide range of putative transcription factors. A key goal in the years ahead is to identify how alterations at the level of gene expression are coupled to the changes in skeletal muscle phenotype. It is clear that gene expression, although representing a specific site of regulation, is only one step in a complex cascade from the initial stimulus to the final phenotypic adaptation and integrated physiological response.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Inhibition of HDAC3- and HDAC6-promoted survivin expression plays an important role in SAHA-induced autophagy and viability reduction in breast cancer cells

SAHA is a class I HDAC/HDAC6 co-inhibitor and an autophagy inducer currently undergoing clinical investigations in breast cancer patients. However, the molecular mechanism of action of SAHA in breast cancer cells remains unclear. In this study, we found that SAHA is equally effective in targeting cells of different breast cancer subtypes and tamoxifen sensitivity. Importantly, we found that down-regulation of survivin plays an important role in SAHA-induced autophagy and cell viability reduction in human breast cancer cells. SAHA decreased survivin and XIAP gene transcription, induced survivin protein acetylation and early nuclear translocation in MCF7 and MDA-MB-231 breast cancer cells. It also reduced survivin and XIAP protein stability in part through modulating the expression and activation of the 26S proteasome and heat-shock protein 90. Interestingly, targeting HDAC3 and HDAC6, but not other HDAC isoforms, by siRNA/pharmacological inhibitors mimicked the effects of SAHA in modulating the acetylation, expression, and nuclear translocation of survivin and induced autophagy in MCF7 and MDA-MB-231 cancer cells. Targeting HDAC3 also mimicked the effect of SAHA in up-regulating the expression and activity of proteasome, which might lead to the reduced protein stability of survivin in breast cancer cells. In conclusion, this study provides new insights into SAHA's molecular mechanism of actions in breast cancer cells. Our findings emphasize the complexity of the regulatory roles in different HDAC isoforms and potentially assist in predicting the mechanism of novel HDAC inhibitors in targeted or combinational therapies in the future.