The circadian clock regulates physiology and behavior and impinges on many aspects of our daily life. Nowhere is this more obvious than control of the sleep wake cycle, where clock genes have been shown to play a role in both the timing of sleep and its quality. For example, mutations in PER2 cause familial advanced sleep phase syndrome (FASPS), while mutations in CSNK1E and CSNK1D cause FASPS and delayed sleep phase syndrome (DSPS), respectively. However, while we have learned much about the clock and how it regulates sleep, the picture is incomplete. Behavioral studies in mice and studies in human cells show that dozens to hundreds of loci impact circadian clock function. However, only a dozen genes have been investigated for their roles in regulating behavior. Testing dozens to hundreds of mice isn't practical, so a new approach is needed. Here we seek to address this gap with a novel strategy that uses, i) integrative bioinformatics to prioritize putative core clock factors, ii) new experimental methods to determine whether they interact with known clock genes and regulate clock function in several cellular or tissue slice models, and, finally, iii) for a subset of promising candidates, generate mouse models and test them for their roles in regulating circadian behavior and sleep. Completion of this research will improve our understanding of circadian rhythms and sleep and may point the way to new therapeutic targets for related disorders in humans.

Public Health Relevance

Our internal biological clocks control many important aspects of our physiology and behavior such as the sleep-wake cycle. Genetics and large-scale genomic studies have implicated the role of a dozen canonical and hundreds of additional new genes in the circadian clock. However, almost none of the new genes have been studied in animal models for their ability to regulate sleep onset or quality. We will address this gap using bioinformatics, experimental biology, and finally through behavioral analysis. Completion of this research will improve our understanding of circadian rhythms and sleep and may point the way to new therapeutic targets for related disorders in humans.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-MDCN-T (05))
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He, Janet
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University of Pennsylvania
Schools of Medicine
United States
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Love, Michael I; Hogenesch, John B; Irizarry, Rafael A (2016) Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat Biotechnol 34:1287-1291
Wu, Gang; Anafi, Ron C; Hughes, Michael E et al. (2016) MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics 32:3351-3353
Xu, Lili; Ruan, Guoxiang; Dai, Heng et al. (2016) Mammalian retinal Müller cells have circadian clock function. Mol Vis 22:275-83
Banerjee, Santanu; Hayer, Katharina; Hogenesch, John B et al. (2015) Zebrafish foxc1a drives appendage-specific neural circuit development. Development 142:753-62
Wolman, Marc A; Jain, Roshan A; Marsden, Kurt C et al. (2015) A genome-wide screen identifies PAPP-AA-mediated IGFR signaling as a novel regulator of habituation learning. Neuron 85:1200-11
Li, Jiajia; Grant, Gregory R; Hogenesch, John B et al. (2015) Considerations for RNA-seq analysis of circadian rhythms. Methods Enzymol 551:349-67
Cao, Ruifeng; Gkogkas, Christos G; de Zavalia, Nuria et al. (2015) Light-regulated translational control of circadian behavior by eIF4E phosphorylation. Nat Neurosci 18:855-62
Jang, Christopher; Lahens, Nicholas F; Hogenesch, John B et al. (2015) Ribosome profiling reveals an important role for translational control in circadian gene expression. Genome Res 25:1836-47
Ma, Di; Liu, Tongyu; Chang, Lin et al. (2015) The Liver Clock Controls Cholesterol Homeostasis through Trib1 Protein-mediated Regulation of PCSK9/Low Density Lipoprotein Receptor (LDLR) Axis. J Biol Chem 290:31003-12
Olarerin-George, Anthony O; Hogenesch, John B (2015) Assessing the prevalence of mycoplasma contamination in cell culture via a survey of NCBI's RNA-seq archive. Nucleic Acids Res 43:2535-42

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