Identifying novel targets for cardioprotection with non-traditional animal models

Project Summary/Abstract:Low-cost sequencing has ushered in a new era of drug discovery that can that utilize genomic information from hundreds of thousands of people. However, humans are a limited resource for identifying novel drug targets, and attempts at therapeutic development often rely on an already well-known set of genes and pathways. Greater potential for discovery exists if we broaden our search throughout the animal kingdom. In particular, animal adaptations for disease resistance have great potential to unearth novel biological pathways to counteract human diseases. Hibernating mammals are an especially rich resource to inspire novel therapeutics as they exhibit numerous transient phenotypes that mirror critical human health problems such as ischemia-reperfusion injury, Alzheimer’s disease, osteoporosis, muscle atrophy, and obesity/diabetes, yet they are able to avoid or reverse pathologies. A systematic understanding of the gene networks utilized to generate the protective and healing phenotypes of hibernators has great potential to reveal novel therapeutic avenues; however, targets that reproduce across independent datasets, including associating with the same phenotype across multiple species have higher likelihood of translating to humans. In this proposal, specifically, we propose to (1) obtain high-throughput phenotypic data to test our platform (2) create a better model for identifying targets based on machine learning and (3) grow our approach into new disease areas. Our long-term vision is to develop a genomics discovery platform centered on hibernating animals for all of the diseases discussed above. We believe that our approach will identify novel therapeutic targets that will translate to humans, and we will advance our discoveries through strategic pharmaceutical partnerships.Project Narrative: Genomic drug discovery leverages data from hundreds of thousands of people, but humans are a limited resource for identifying novel drug targets, and drug developers focus too often on well-known genes and pathways. Animal adaptations for disease resistance, particularly in hibernating mammals, have great potential to unearth novel biological pathways to counteract human diseases as they exhibit phenotypes that mirror diseases such as Alzheimer’s disease, ischemia-reperfusion injury, osteoporosis, muscle atrophy, and obesity/diabetes but are able to avoid or reverse these pathologies. Here we propose to obtain high-throughput phenotypic data to test our platform in order to create a better model for identifying targets based on machine learning and grow our approach into new disease areas.