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Machine learning helps identify CHRONO as a circadian clock component

Anafi, Ron C ; Lee, Yool ; Sato, Trey K ; Venkataraman, Anand ; Ramanathan, Chidambaram ; Kavakli, Ibrahim H ; Hughes, Michael E ; Baggs, Julie E ; Growe, Jacqueline ; Liu, Andrew C ; Kim, Junhyong ; Hogenesch, John B Schibler, Ueli

PLoS Biology, 2014-04, Vol.12 (4), p.e1001840 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Machine learning helps identify CHRONO as a circadian clock component
Autor: Anafi, Ron C ; Lee, Yool ; Sato, Trey K ; Venkataraman, Anand ; Ramanathan, Chidambaram ; Kavakli, Ibrahim H ; Hughes, Michael E ; Baggs, Julie E ; Growe, Jacqueline ; Liu, Andrew C ; Kim, Junhyong ; Hogenesch, John B
Schibler, Ueli
Assuntos: 3T3 Cells ; Amino Acid Sequence ; Animals ; ARNTL Transcription Factors - metabolism ; Artificial Intelligence ; Biology and Life Sciences ; Cell Line ; Circadian Clocks - genetics ; Circadian Clocks - physiology ; Circadian rhythm ; Circadian Rhythm - genetics ; Circadian Rhythm - physiology ; Circadian Rhythm Signaling Peptides and Proteins - biosynthesis ; Circadian Rhythm Signaling Peptides and Proteins - genetics ; Circadian Rhythm Signaling Peptides and Proteins - metabolism ; Circadian rhythms ; Cryptochromes - genetics ; Experiments ; Gene expression ; Genes ; Genetic aspects ; Genetic research ; Genomes ; HEK293 Cells ; Histone Deacetylases - metabolism ; Humans ; Kinases ; Machine learning ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Knockout ; Molecular Sequence Data ; Mutagenesis ; Nuclear Receptor Subfamily 1, Group D, Member 1 - genetics ; Proteins ; Receptors, Cytoplasmic and Nuclear - genetics ; Receptors, Glucocorticoid - metabolism ; Repressor Proteins - biosynthesis ; Repressor Proteins - genetics ; Repressor Proteins - metabolism ; Rodents ; Sequence Alignment ; Transcription, Genetic - genetics
É parte de: PLoS Biology, 2014-04, Vol.12 (4), p.e1001840
Notas: The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: RCA YL TKS ACL JK JBH. Performed the experiments: RCA YL TKS AV CR IHK MEH JEB JPG. Analyzed the data: RCA YL TKS AV CR IHK JEB JPG ACL JK JBH. Contributed reagents/materials/analysis tools: ACL JBH. Wrote the paper: RCA YL TKS AV CR IHK MEH JEB ACL JK JBH.
The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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Machine learning helps identify CHRONO as a circadian clock component

Anafi, Ron C ; Lee, Yool ; Sato, Trey K ; Venkataraman, Anand ; Ramanathan, Chidambaram ; Kavakli, Ibrahim H ; Hughes, Michael E ; Baggs, Julie E ; Growe, Jacqueline ; Liu, Andrew C ; Kim, Junhyong ; Hogenesch, John B Schibler, Ueli

United States: Public Library of Science

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