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PLoS Biol. 2014 Apr 15;12(4):e1001840. doi: 10.1371/journal.pbio.1001840. eCollection 2014 Apr.

Machine learning helps identify CHRONO as a circadian clock component.

Author information

1
Division of Sleep Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America; Center for Sleep and Circadian Neurobiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
2
Department of Pharmacology and the Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
3
Department of Biological Sciences, University of Memphis, Memphis, Tennessee, United States of America.
4
Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey.
5
Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, United States of America.
6
Department of Pharmacology, Morehouse School of Medicine, Atlanta, Georgia, United States of America.
7
Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
8
Center for Sleep and Circadian Neurobiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America; Department of Pharmacology and the Institute for Translational Medicine and Therapeutics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.

Abstract

Over the last decades, researchers have characterized a set of "clock genes" that drive daily rhythms in physiology and behavior. This arduous work has yielded results with far-reaching consequences in metabolic, psychiatric, and neoplastic disorders. Recent attempts to expand our understanding of circadian regulation have moved beyond the mutagenesis screens that identified the first clock components, employing higher throughput genomic and proteomic techniques. In order to further accelerate clock gene discovery, we utilized a computer-assisted approach to identify and prioritize candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experimental screen to characterize the top candidates. We found that several physically interact with known clock components in a mammalian two-hybrid screen and modulate in vitro cellular rhythms in an immortalized mouse fibroblast line (NIH 3T3). One candidate, Gene Model 129, interacts with BMAL1 and functionally represses the key driver of molecular rhythms, the BMAL1/CLOCK transcriptional complex. Given these results, we have renamed the gene CHRONO (computationally highlighted repressor of the network oscillator). Bi-molecular fluorescence complementation and co-immunoprecipitation demonstrate that CHRONO represses by abrogating the binding of BMAL1 to its transcriptional co-activator CBP. Most importantly, CHRONO knockout mice display a prolonged free-running circadian period similar to, or more drastic than, six other clock components. We conclude that CHRONO is a functional clock component providing a new layer of control on circadian molecular dynamics.

PMID:
24737000
PMCID:
PMC3988006
DOI:
10.1371/journal.pbio.1001840
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist.

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