Deconvolving ageing

Abstract

Ageing is a common feature of living organisms, showing shared molecular features called hallmarks of ageing. Usually they are quantified in groups of individuals as a function of their chronological age (time passed since birth) and display continuous and progressive changes. Such approaches are based on the assumption that individuals taken at a given chronological age are biological replicates. However, even in genetically homogeneous and synchronised populations individuals do die at different chronological ages. This highlights the difference between chronological age and biological age, the latter being defined by the actual mortality risk of the organism, reflecting its physiology. The Smurf assay, previously described by Rera and colleagues, allows the identification of individuals at higher risk of death from natural causes amongst a population of a given chronological age. We found that the categorization of individuals as Smurf or non-Smurf, permits to distinguish transcriptional changes associated with either chronological or biological age. We show that transcriptional heterogeneity increases with chronological age, while four out of the six currently defined transcriptional hallmarks of ageing are associated with the biological age of individuals, i.e. their Smurf state. In conclusion, we demonstrate that studying properties of ageing by applying the Smurf classification allows us to differentiate the effect of time from the effect of a physiological response triggering an end-of-life switch (i.e. Smurf phase). More specifically, we show that the ability to isolate a pre-death phase of life in vivo enables us not only to study late life mechanisms preceding death, but also investigate early physiological changes triggering such phase. This allowed the identification of novel pro-longevity genetic interventions.

We anticipate that the use of the evolutionary conserved Smurf phenotype in ageing studies will allow significant advances in our comprehension of the underlying mechanisms of ageing.

Aging Cell : Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature