Leveraging functional profiling datasets with machine learning to uncover proteins and cellular processes important for ageing

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Ageing is the largest risk factor for most human diseases in developed countries, including progressive diseases such as Alzheimer's and Parkinson's, diseases like cancer that show variable rates of onset, and catastrophic system failures such as heart-attack and stroke. While the study of specific disease processes has long been a major focus of research, there is a growing realization of the importance of studying the normal ageing process itself as an essential part of the problem, and of exploring ways to slow or reverse its effects. Ageing is a multi-factorial process that can be seen as an inevitable feature of the ravages of time. Recent discoveries, however, demonstrate that ageing can be modified in dramatic ways by simple interventions. For example, single gene knockouts can delay ageing and improve health late in the life of laboratory animals. The processes involved in ageing are similar in different organisms, and genetic mutations affecting these processes are associated with longevity in humans. A central challenge of ageing research, however, remains to tease out a complete and unified picture of the biological factors and processes determining lifespan. Ageing is highly complex and affected by diverse proteins and processes. Modern biological assays can simultaneously measure properties and interactions of thousands of proteins or genes, but it is challenging to make sense of such large datasets. Advances in computational data-analysis methods, called 'machine learning', provide exciting opportunities to get the most from large biological datasets and thus increase our understanding of complex processes like ageing. Machine Learning can find hidden patterns in data that is too complex for humans to process. Advances in computer power, algorithms and data sizes allow recent machine-learning architectures (known as 'deep learning') to accurately find and classify intricate patterns in combined datasets of different types. We plan to use fission yeast as a model organism, together with multi-step machine learning, to comprehensively identify biological processes with fundamental importance for ageing. Remarkably, many of these processes are similar from yeast to human, but are much easier to study in the simple yeast. Yeast cells enter a dormant, non-dividing state under limiting nutrients. Such dormant cells provide a useful system to analyse proteins and processes affecting the lifespan in this state. In previous studies, we have identified 116 proteins that, when absent, allow the yeast to live longer (long-lived knockout mutants). So these proteins are involved in ageing, and can be used to train machine-learning programs to predict new ageing proteins by a method known as 'guilt by association'. We will combine large systematic data on mutant features (phenotypes) with diverse existing data to empower the machine-learning predictor. We will test the predicted ageing proteins in the laboratory for lifespan effects in yeast, and feed this information back to the computer for it to learn more about ageing proteins. We will then use mutants of the new ageing proteins identified by the computer and confirmed in yeast to measure links with all other mutants. Such 'genetic-interaction' data provide rich information on functional relationships, which will be used to explore other, potentially more powerful deep-learning methods to predict the biological processes that are involved in ageing. We will then test the most attractive predictions with laboratory experiments. Moreover, we will make all the new data, methods and predictions available to interested scientists to help with their research. We anticipate that this project, using intimate cycles of experiments and machine-learning, will provide a valuable platform to better understand all the biological factors involved in ageing, to eventually develop interventions that extend healthy lifespan in humans.