Machine learning helps identify CHRONO as a circadian clock component.

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 understa...

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Autores principales:	Ron C Anafi, Yool Lee, Trey K Sato, Anand Venkataraman, Chidambaram Ramanathan, Ibrahim H Kavakli, Michael E Hughes, Julie E Baggs, Jacqueline Growe, Andrew C Liu, Junhyong Kim, John B Hogenesch
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9524308b57ad4b38a1489326a7186b21
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id	oai:doaj.org-article:9524308b57ad4b38a1489326a7186b21
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spelling	oai:doaj.org-article:9524308b57ad4b38a1489326a7186b212021-11-18T05:37:29ZMachine learning helps identify CHRONO as a circadian clock component.1544-91731545-788510.1371/journal.pbio.1001840https://doaj.org/article/9524308b57ad4b38a1489326a7186b212014-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24737000/?tool=EBIhttps://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885Over 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.Ron C AnafiYool LeeTrey K SatoAnand VenkataramanChidambaram RamanathanIbrahim H KavakliMichael E HughesJulie E BaggsJacqueline GroweAndrew C LiuJunhyong KimJohn B HogeneschPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 12, Iss 4, p e1001840 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ron C Anafi Yool Lee Trey K Sato Anand Venkataraman Chidambaram Ramanathan Ibrahim H Kavakli Michael E Hughes Julie E Baggs Jacqueline Growe Andrew C Liu Junhyong Kim John B Hogenesch Machine learning helps identify CHRONO as a circadian clock component.
description	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.
format	article
author	Ron C Anafi Yool Lee Trey K Sato Anand Venkataraman Chidambaram Ramanathan Ibrahim H Kavakli Michael E Hughes Julie E Baggs Jacqueline Growe Andrew C Liu Junhyong Kim John B Hogenesch
author_facet	Ron C Anafi Yool Lee Trey K Sato Anand Venkataraman Chidambaram Ramanathan Ibrahim H Kavakli Michael E Hughes Julie E Baggs Jacqueline Growe Andrew C Liu Junhyong Kim John B Hogenesch
author_sort	Ron C Anafi
title	Machine learning helps identify CHRONO as a circadian clock component.
title_short	Machine learning helps identify CHRONO as a circadian clock component.
title_full	Machine learning helps identify CHRONO as a circadian clock component.
title_fullStr	Machine learning helps identify CHRONO as a circadian clock component.
title_full_unstemmed	Machine learning helps identify CHRONO as a circadian clock component.
title_sort	machine learning helps identify chrono as a circadian clock component.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/9524308b57ad4b38a1489326a7186b21
work_keys_str_mv	AT roncanafi machinelearninghelpsidentifychronoasacircadianclockcomponent AT yoollee machinelearninghelpsidentifychronoasacircadianclockcomponent AT treyksato machinelearninghelpsidentifychronoasacircadianclockcomponent AT anandvenkataraman machinelearninghelpsidentifychronoasacircadianclockcomponent AT chidambaramramanathan machinelearninghelpsidentifychronoasacircadianclockcomponent AT ibrahimhkavakli machinelearninghelpsidentifychronoasacircadianclockcomponent AT michaelehughes machinelearninghelpsidentifychronoasacircadianclockcomponent AT julieebaggs machinelearninghelpsidentifychronoasacircadianclockcomponent AT jacquelinegrowe machinelearninghelpsidentifychronoasacircadianclockcomponent AT andrewcliu machinelearninghelpsidentifychronoasacircadianclockcomponent AT junhyongkim machinelearninghelpsidentifychronoasacircadianclockcomponent AT johnbhogenesch machinelearninghelpsidentifychronoasacircadianclockcomponent
_version_	1718424865123336192

Machine learning helps identify CHRONO as a circadian clock component.

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