Reconstructing the temporal landscape of gene regulation in aging

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PROJECT SUMMARY / ABSTRACT The circadian rhythm orchestrates a vast repertoire of biochemical, physiological, and behavioral processes with a 24-hour period. A clock circuit, driven by the oscillatory activation and repression of a set of core clock genes, is present in every cell and regulates the expression of nearly half the genome across all tissues. Abundant epi- demiological evidence links circadian regulation to human health. In particular, sleep and circadian disruption is associated with neurodegeneration, where it is thought to be both a consequence and a risk factor for Alzheimer's disease (AD). As the most common form of dementia in the elderly, AD is rapidly becoming a public health cri- sis, affecting an estimated 5.5M people in the United States (24M world-wide) and is expected to double over the next 20 years. Addressing this crisis requires a deep understanding of disease mechanisms and the identification of diagnostic/therapeutic targets. A wealth of existing transcriptomic data, coupled with the development of sophisticated computational tools for temporal reconstruction and analysis, provide an exciting opportunity to identify dysregulation in oscillatory patterns of gene expression associated with aging and AD. We propose to develop novel computational strategies to decipher the oscillatory patterns of gene expression using existing untimed transcriptomic datasets; and to apply them to existing data from studies of AD to identify dysregulation in the oscillatory patterns associated with AD. Our methods address a number of analytical chal- lenges, including the need to reconstruct temporal information from untimed samples; ensuring generalizability and accuracy across different transcriptomic profiling technologies; addressing the fact that untimed samples may not span the full day; and identifying changes in complex, non-sinusoidal rhythms. Together, these studies will reveal oscillatory patterns of gene expression associated with aging and Alzheimers disease, and will provide innovative new methods for the circadian analysis of untimed data that can be applied to other phenotypes.