The dramatic increase in the global population’s age is good news. However, society must prevent the financial burden and personal suffering of unhealthy ageing. But how can we study a complex problem such as ageing? Using simple model organisms - such as the nematode C.elegans - has proven to be a good strategy because many genes that influence ageing are shared with humans. Researchers have found that the disruption of certain genes causes worms to live a very long-life. In this way, researchers have found the so-called “longevity-pathways”.
The discovery that genes control longevity has been quite significant for the understanding of ageing because it changed the view from a gradual stochastic process, to a genetically controlled process that we can interfere with and potentially slow down. Such findings underscore two key issues of the ageing process. First, it is a highly complex problem that involves thousands of genes. Second, the interplay between genes and the environment can probably explain the high degree of discordance in lifespan among identical twins.
The two main goals of my laboratory are: to understand the non-genetic and environmental influences on lifespan and stress related phenotypes using genetically identical lab strains of C. elegans as a model organism; to develop in silico tools to simply the complexity of the process and develop predictive algorithms that facilitate ageing research.
In line with the first goal, we discovered that ectotherms co-opted ancient heat sensing cellular responses as a thermostat that centrally and cell non-autonomously coordinate complex adaptive responses to warming temperatures, not unlike mechanisms engrained in endotherms. Further work should investigate if a conserved role has been co-opted in mammals, which can be important in the context of ageing and obesity.
In line with the second goal, we have used network analysis to simplify the genetic complexity of a long-lived pathway and narrowed thousands of genes to a few key players. We discovered that the information distribution in a longevity network resembles an hourglass structure where complex input signals are integrated by a small core layer and de-coded into a large output layer. We demonstrated that the genes that have the largest influence in the lifespan of worms are all concentrated at the core of the network. This strategy allowed us to discover 50 new ageing genes, 86% of them with human orthologues. We have established a framework with predictive power that can accelerate gene discovery in the question of ageing in C. elegans and potentially in humans.