CYCLOPS reveals human transcriptional rhythms in health and disease
Abstract
Circadian rhythms influence most aspects of physiology and behavior. However, how do we apply this knowledge in medicine? Identifying molecular mechanisms in humans is challenging as existing large-scale datasets rarely include time of day. To address this problem, we combine understanding of periodic structure, evolutionary conservation, and unsupervised machine learning to order unordered human biopsy data along a periodic cycle. We show this works using ordered mouse and human data and that it gives consistent results when applied to populations on different continents. Then, we investigate molecular rhythms in normal human lung and liver and cancerous liver. Finally, we demonstrate proof of concept by finding the best time to administer a chemotherapeutic drug in an animal model.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 24, 2017
- Source ID
- 10.1073/pnas.1619320114
Entities
People
- John B. Hogenesch
- Junhyong Kim
- Lauren J. Francey
- Ron C. Anafi
Organizations
- Cincinnati Children's Hospital Medical Center
- National Institute of Neurological Disorders and Stroke
- University of Pennsylvania