Sarcopenia is an age-related disease characterized by the loss of muscle mass and muscle function. A proper understanding of its pathogenesis and mechanisms may lead to new strategies for diagnosis and treatment of the disease. This study aims to discover the underlying genes, proteins, and pathways associated with sarcopenia in both genders. Integrated analysis of microarray datasets has been performed to identify differentially expressed genes (DEGs) between old and young skeletal muscles. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed to uncover the functions of the DEGs. Moreover, a protein–protein interaction (PPI) network was constructed based on the DEGs. We have identified 41,715 DEGs, including 19 downregulated and 41,696 upregulated ones, in men. Among women, 3,015 DEGs have been found, with 2,874 of them being upregulated and 141 downregulated genes. Among the top up-regulated and downregulated genes, the ribosome biogenesis genes and genes involved in lipid storage may be closely related to aging muscles in men and women respectively. Also, the DEGs were enriched in the pathways including those of ribosome and Peroxisome proliferator-activated receptor (PPAR) in men and women, respectively. In the PPI network, Neurotrophic Receptor Tyrosine Kinase 1 (NTRK1), Cullin 3 (CUL3) and P53 have been identified as significant hub proteins in both genders. Using the integrated analysis of multiple gene expression profiles, we propose that the ribosome biogenesis genes and those involved in lipid storage would be promising markers for sarcopenia in men and women, respectively. In the reconstructed PPI network, neurotrophic factors expressed in skeletal muscle are essential for motoneuron survival and muscle fiber innervation during development. Cullin E3 ubiquitin ligase (Cul3) is an important component of the ubiquitin–proteasome system—it regulates the proteolysis. P53 is recognized as a central regulator of the cell cycle and apoptosis. These proteins, which have been identified as the most significant hubs, may be involved in aging muscle and sarcopenia.

The objective of this study was to develop a system-level understanding of acidosis biology. Therefore, the genes expression differences between the normal and acidosis rumen epithelial tissues were first examined using the RNA-seq data in order to understand the molecular mechanisms involved in the disease and then their corresponding metabolic networks constructed. A number of 1074 genes, 978 isoforms, 1049 transcription start sites (TSS), 998 coding DNA sequence (CDS) and 2 promoters were identified being differentially expressed in the rumen tissue between the normal and acidosis samples (p < 0.05). The functional analysis of 627 up-regulated genes revealed their involvement in ion transmembrane transport, filament organization, regulation of cell adhesion, regulation of the actin cytoskeleton, ATP binding, glucose transmembrane transporter activity, carbohydrate binding, growth factor binding and cAMP metabolic process. Additionally, 111 differentially expressed enzymes were identified between the rumen epithelial tissue of the normal and acidosis steers with 46 up-regulated and 65 down-regulated ones in the acidosis group. The pathways and reactions analyses associated with the up-regulated enzymes indicate that most of these enzymes are involved in the fatty acid metabolism, biosynthesis of amino acids, pyruvate and carbon metabolism while most of the down-regulated ones are involved in purine and pyrimidine, vitamin B6 and antibiotics metabolisms. The degree distribution of both metabolic networks follows a power-law one, hence displaying a scale-free property. The top 15 hub metabolites were determined in the acidosis metabolic network with most of them involved in the fatty acid oxidation, VFA biosynthesis, amino acid biogenesis and glutathione metabolism which plays an important role in the stress condition. The limitations of this study were low number of animals and using only epithelial tissue (ventral sac) for RNA-seq.

We described an integrated analysis of gene expression data including tissue-specific metabolic modeling and co-expression networks to identify new cancer biomarkers.