Fuentealba Valenzuela, Matías Sebastián; (2021) In silico identification of genetic and pharmacological interventions to modulate ageing. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

As life expectancy increases and fertility rates decrease, the growing ageing population poses a significant challenge to the healthcare systems of developed countries. Ageing as the major risk factor for chronic diseases constitutes the primary target to reduce the burden of diseases and improve human health. However, ageing is a complex process and predicting potential interventions into it requires system-level approaches. In this thesis, I present the development of two computational methods using biological data to predict novel genetic and pharmacological interventions to ameliorate ageing. My first study focused on identifying repurposable drugs to delay human ageing. Several computational drug-repurposing studies have been developed, but most of them focus on predicting geroprotectors using animal models data, even though certain aspects of ageing may be human-specific. Using drug-protein interaction information, I searched for drugs targeting a significant proportion of human ageing-related genes and pathways. The top-ranked drugs included a significant number of known geroprotectors, validating the capability of the method to discover drugs to modulate ageing. On the top of the list was tanespimycin, a heat shock protein inhibitor, whose geroprotective properties we validated experimentally. My second study centres on determining the molecular mechanisms associated with healthy lifespan, and how to use this information to find new genetic interventions to delay ageing. In recent years, the number of transcriptomic studies of mouse models of ageing has increased dramatically, providing the opportunity to compare gene expression changes of long- and short-lived strains. I showed that differences in healthy lifespan are associated with expression changes in genes regulating mitochondrial metabolism. Using these gene sets as biomarkers of lifespan, I compared the mouse models of ageing against 51 genetically engineered mice and predicted candidate genetic and pharmacological interventions with the potential to delay ageing. Through computational studies I predicted a narrowed down list of candidate genetic and pharmacological interventions to delay mouse and human ageing and validated several predictions made by other researchers using different methods, confirming the robustness of computational methods to identify new anti-ageing interventions. With the discovery of tanespimycin as a new geroprotector, I revealed that a little proteostatic stress is good for longevity and that we can trigger this hormetic response pharmacologically. I exposed the complexity of ageing as I found multiple mechanisms to delay ageing, most of which were tissue-specific, and found evidence for new candidate hallmarks of ageing and novel biomarkers of lifespan.

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