LivAge: An Online Aging Clock for Murine Transcriptomic Age Estimation

Abstract

The increase in life expectancy over the past century has been accompanied by the recognition of age as the primary risk factor for a wide range of pathologies, including cardiovascular and neurodegenerative diseases, as well as cancer. Thus, the development of interventions that slow the underlying biological processes of aging could have beneficial effects on the prevention or progression of these diseases. To evaluate such geroprotective interventions, quantifying biological damage via aging clocks—particularly those based on transcriptomic biomarkers—has become highly relevant, as they provide estimations with strong biological interpretability. However, the limited availability of transcriptomic clocks for murine experimental models has hindered the implementation of these tools in aging research and their use in evaluating interventions that may have geroprotective effects. Here, we have developed an accurate, accessible, and ready‐to‐use murine transcriptomic clock that provides robust age predictions for healthy mice using hepatic RNA‐seq data. Applying the clock to accelerated‐aging models revealed an increased transcriptomic age in progeroid syndromes, demonstrating its ability to capture aging‐related biological processes. Finally, considering the main interest of these tools, we show that well‐established interventions with geroprotective potential, both genetic (Snell Dwarf, Ames Dwarf, and growth hormone receptor‐deficient mice) and environmental (caloric, protein, or methionine restriction, as well as rapamycin supplementation in specific contexts), reduce the transcriptomic age estimated by the clock. These findings indicate that the proposed transcriptomic clock could be a valuable tool for studying the biology of aging and for designing and evaluating potential geroprotective interventions.