Researchers at the Luxembourg Centre for Systems Biomedicine of the University of Luxembourg, together with colleagues from Harvard Medical School and CIC bioGUNE, have developed a systems approach to searching for genes capable of rejuvenating cells at the molecular level.
Instead of chronological age, the scientists focused on biological age: they created a machine-learning model — a transcriptomic age predictor — that uses gene expression data in skin cells to determine how "young" molecular processes appear. The model was trained on samples from donors ranging from children to the elderly, after which dozens of candidates were tested for their ability to shift age-related signatures toward youth.
Among all the genes, SRSF1 stood out; it encodes a protein involved in RNA processing and splicing. Its expression is higher in young cells; artificially raising its level in aged human cells shifted their transcriptomic profile into a significantly younger state, while the cells retained their identity and functionality — a problem encountered by many previous reprogramming approaches.
To test whether the effect works outside the Petri dish, the researchers conducted experiments on model organisms — old worms and mice. Inducible expression of SRSF1 slowed the development of frailty and was associated with an increase in lifespan of up to 14 percent. At the same time, indicators of biological function improved: tissue repair, resilience and physiological activity in late age.
According to Professor Antonio del Sol, who heads the computational biology groups at LCSB and CIC bioGUNE, the goal was to create a framework for the systematic search for regulators of aging. "We now have a strategy that allows us to find other factors that will help us better understand the aging process and identify ways to promote healthy aging in the future," he notes.
The discovery of SRSF1 is presented as a proof-of-concept: in the future, the model can be expanded to a larger number of genes, pathways and cell types, including those affected by age-related diseases. The work has been published in the journal Nature Aging and relies on data from international collaboration with the support of specialized foundations.
Thus, the approach opens the way to a targeted search for interventions that can maintain health and independence in old age while preserving cellular identity and function.
