The skeletal muscle sncRNA-metabolite interactome as a biomolecular marker and mediator of sarcopenia

As we age, muscle mass, strength, and function decline. Some individuals, however, experience more severe and accelerated muscle loss, leading to the age-associated disease sarcopenia, resulting in increased falls, fractures, and mortality. Sarcopenia is the major cause of lost independence in later life; approximately 2–2.5 million people in the UK are living with sarcopenia, with this number expected to rise as life expectancy increases. With no effective treatments, there is urgent need for novel diagnostic tools and therapeutic interventions. Epigenetic processes regulate gene expression. Small non-coding RNAs (sncRNAs) are epigenetic regulators, of which microRNAs (miRNAs) (the most characterised) have been shown to control important processes in muscle. However, miRNAs are one part of the larger sncRNA family that includes PIWI-interacting RNAs, transfer RNAs and novel tRNA-derived fragments. Despite increasing evidence that these sncRNA subfamilies are associated with disease, research has neglected their roles in muscle dysregulation. My recent study suggests specific sncRNA populations in blood may report on muscle function, and their dysregulation is linked to age-associated pathologies including sarcopenia. In parallel, sncRNAs regulate key metabolic pathways by modulating expression of metabolism-related genes and pathways, with metabolic signatures associated with severe sarcopenia. However, despite sncRNAs levels being reflective of upstream transcriptomic processes, their links to downstream metabolite changes in ageing and sarcopenic muscle are unknown. Thus, the question remains: i) ‘Do sarcopenia-linked blood sncRNA profiles associate with sncRNA changes in muscle and with circulating metabolomic signatures of sarcopenia, and ii) Can sncRNA-metabolite signatures be used as biomarkers of muscle dysregulation?’ This study proposes to establish the first sncRNA-metabolomic interactome signature of sarcopenia by combining sncRNA sequencing (sncRNAseq), gene expression analysis (RNAseq) and metabolomic profiling (NMR/LCMS) with AI-linked topological analysis in human muscle tissue and serum, offering unprecedented insight into epigenetic-gene-metabolomic interactions driving sarcopenia and identifying new avenues for diagnosis and targets for interventions. RNAseq/sncRNAseq will be conducted on muscle tissue and serum from older individuals (age 72-83) with/without sarcopenia (228 male/female participants, 36 with sarcopenia). Bioinformatics will compare sarcopenic sncRNA and gene expression profiles, along with associated regulatory networks, to identify novel muscle sncRNAs, their gene targets and relationships to altered serum sncRNAs. Furthermore, state-of-the-art machine learning applied to sequencing data will identify sncRNA/gene signatures and molecular endotypes of sarcopenia. Serum metabolomic profiles (~1000 bioactive compounds) will be characterised and integrated with sncRNAs, their gene targets, and biological pathways modulated in sarcopenia. Critical dysregulated pathways will be validated and targeted using natural/pharmacological compounds in primary myoblasts from individuals with/without sarcopenia. Functional analyses will involve siRNA knockdown, overexpression (miRNA-mimics), immunohistochemistry (fibre staining), gene expression assays and Seahorse mitochondrial stress tests. This research proposal combines the latest advances in multi-omics to elucidate the role of sncRNAs in ageing muscle and will generate the first high-resolution molecular signature of sarcopenia. Findings will pinpoint and predict muscle health trajectories with high power, aiding generation of minimally invasive diagnostic tools. Furthermore, identification of molecular targets and endotypes of sarcopenia will allow disease stratification and inform future personalised prevention and combined therapeutic strategies for muscle ageing/sarcopenia.