An Epigenetic Clock for Skeletal Muscle
Epigenetic clocks are multiplying year by year. Each is a weighted algorithmic combination of the status of various methylation sites on the genome, built by analyzing the epigenome of many different people at different ages in order to arrive at correlations with chronological age, or, more usefully, with metrics such as mortality risk that reflect biological age, the burden of molecular damage and its consequences. This process of building a new clocks is the easier half of the challenge, however. The hard part, that still lies ahead, is to determine what exactly it is that these clocks measure. What do these characteristic epigenetic changes of age actually reflect, in terms of the underlying processes of aging? That is a challenging question to answer well, but answers are needed if epigenetic clocks are to be used to speed up development of rejuvenation therapies by measuring biological age before and after a short treatment.
Ageing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue. To address this, we developed a more accurate, muscle-specific epigenetic clock based on the genome-wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets.
In the current study, we aimed to address the poor performance of the pan-tissue clock in muscle by developing a muscle-specific epigenetic clock. We hypothesise that by using a large number of human skeletal muscle DNA methylation profiles, we can develop a muscle-specific epigenetic clock that outperforms the pan-tissue clock and that can estimate chronological age with high accuracy. We utilised DNA methylation data to estimate epigenetic age in a total of 682 male and female skeletal muscle samples aged 18-89. We also conducted an epigenome-wide association study (EWAS) to discover genes whose methylation change with age in skeletal muscle.
The newly developed clock uses 200 cytosine-phosphate-guanine dinucleotides to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 cytosine-phosphate-guanine dinucleotides of the pan-tissue clock. This new clock significantly outperforms the previous pan-tissue clock and can calculate the epigenetic age in skeletal muscle with a mean accuracy of 4.9 ± 4.5 years across 682 samples. This muscle clock will be of interest and potential use to researchers, clinicians, and forensic scientists working in the fields of skeletal muscle, chronic diseases, and ageing. In the future, we intend to evaluate how environmental factors, such as exercise and diet, could influence ageing via this newly developed clock.