Multiomic longitudinal analysis of lifespan predictors in the short-lived killifish Nothobranchius furzeri

• Applicant: Professor Dr. Alessandro Cellerino, Ph.D.
• Subject Area: General Genetics and Functional Genome Biology ; Bioinformatics and Theoretical Biology ; Biogerontology and Geriatric Medicine
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 513826488

Understanding the molecular and genetic underpinnings of lifespan determination and aging is of paramount importance for biomedical research. An understudied aspect of aging relates to inter-individual differences in aging phenotypes that are best revealed by longitudinal investigations of age-related molecular and physiological changes to correlate their modulation with differences in lifespan. Canonical vertebrate models, such as rodents and small teleosts, however, have lifespans of several years that represent a biological limit for their practical use as models of aging. The killifish Nothobranchius furzeri inhabits habitats in the African savannah that are subject to seasonal desiccation and shows a captive lifespan of few months replicating many key aspects of vertebrate aging. This organism is now established as an alternative experimental model to investigate vertebrate aging. In our preliminary work, we performed a longitudinal RNA-seq study and correlated gene expression in fin biopsies obtained at young adult age with age at death and revealed that measureable differences in gene expression at young age can be detected between short- and long-lived individuals. In our preliminary work, we also developed an epigenetic clock for N. furzeri using a cross-sectional dataset and also developed a neural network for prediction of lifespan based on RNA-seq. The first aim of the present project is to extend this longitudinal approach to correlated splicing, DNA methylation and microbiome with individual lifespan to unravel the connections between these different layers. The second aim of the project is to use artificial intelligence to provide a surrogate measure of lifespan based on the above data and validate it by experimental manipulations. This lifespan predictor will enable to test effects of pharmacological intervention reducing both the number of animals and the time required and the cost of the experimentation with important implications also for animal welfare. More specifically, the predictor is supposed to be applied before and after a pharmacological treatment to test whether the treatment results in an extension of predicted lifespan on an individual basis.

• DFG Programme: Research Grants
• Co-Investigator: Privatdozent Dr. Alessandro Ori