par Martineau, Céline 
;Brown, André E X;Laurent, Patrick
Référence PLoS computational biology, 16, 7, page (e1008002)
Publication Publié, 2020-12-01
;Brown, André E X;Laurent, Patrick Référence PLoS computational biology, 16, 7, page (e1008002)
Publication Publié, 2020-12-01
Article révisé par les pairs
| Résumé : | Ageing affects a wide range of phenotypes at all scales, but an objective measure of ageing remains challenging, even in simple model organisms. To measure the ageing process, we characterized the sequence of alterations of multiple phenotypes at organismal scale. Hundreds of morphological, postural, and behavioral features were extracted from high-resolution videos. Out of the 1019 features extracted, 896 are ageing biomarkers, defined as those that show a significant correlation with relative age (age divided by lifespan). We used support vector regression to predict age, remaining life and lifespan of individual C. elegans. The quality of these predictions (age R2 = 0.79; remaining life R2 = 0.77; lifespan R2 = 0.72) increased with the number of features added to the model, supporting the use of multiple features to quantify ageing. We quantified the rate of ageing as how quickly animals moved through a phenotypic space; we quantified health decline as the slope of the declining predicted remaining life. In both ageing dimensions, we found that short lived-animals aged faster than long-lived animals. In our conditions, for isogenic wild-type worms, the health decline of the individuals was scaled to their lifespan without significant deviation from the average for short- or long-lived animals. |
