Discovering differences in gender-related skeletal muscle aging through the majority voting-based identification of differently expressed genes

Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive interpretation of the relationship between genes and differences in both age and gender. Although there are several thousands of genes, very few differentially expressed genes play an active role in understanding the age and gender differences. The core aim of this study is to uncover new biomarkers that can contribute towards distinguishing between male and female according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system (MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and Receiver Operating Characteristic (ROC)). Later, important genes are acquired using majority voting based on the principle that combining multiple models can improve the generalization of the system. Experiments were conducted on Micro Array gene expression dataset and results have indicated a significant increase in classification accuracy when compared with existing system

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca2+ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies

microRNAs: Modulators of the underlying pathophysiology of sarcopenia?

Skeletal muscle homeostasis depends on an intricate balance between muscle hypertrophy, atrophy and regeneration. As we age, maintenance of muscle homeostasis is perturbed, resulting in a loss of muscle mass and function, termed sarcopenia. Individuals with sarcopenia exhibit impaired balance, increased falls (leading to subsequent injury) and an overall decline in quality of life. The mechanisms mediating sarcopenia are still not fully understood but clarity in our understanding of the precise pathophysiological changes occurring during skeletal muscle ageing has improved dramatically. Advances in transcriptomics has highlighted significant deregulation in skeletal muscle gene expression with ageing, suggesting epigenetic alterations may play a crucial and potentially causative role in the skeletal muscle ageing process. microRNAs (miRNAs, miRs), novel regulators of gene expression, can modulate many processes in skeletal muscle, including myogenesis, tissue regeneration and cellular programming. Expression of numerous evolutionary conserved miRNAs is disrupted in skeletal muscle with age. Given that a single miRNA can simultaneously affect the functionality of multiple signaling pathways, miRNAs are potent modulators of pathophysiological changes. miRNA-based interventions provide a promising new therapeutic strategy against alterations in muscle homeostasis. The aim of this review is two-fold; firstly to outline the latest understanding of the pathophysiological alterations impacting the deregulation of skeletal muscle mass and function with ageing, and secondly, to highlight the mounting evidence for a role of miRNAs in modulating muscle mass, and the need to explore their specific role in sarcopenia