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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Main Authors: 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
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-04-01
Series:PLoS Biology
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24737000/?tool=EBI
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spelling doaj-9524308b57ad4b38a1489326a7186b212021-07-02T17:10:25ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852014-04-01124e100184010.1371/journal.pbio.1001840Machine learning helps identify CHRONO as a circadian clock component.Ron C AnafiYool LeeTrey K SatoAnand VenkataramanChidambaram RamanathanIbrahim H KavakliMichael E HughesJulie E BaggsJacqueline GroweAndrew C LiuJunhyong KimJohn B HogeneschOver 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24737000/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
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
spellingShingle 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.
PLoS Biology
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)
series PLoS Biology
issn 1544-9173
1545-7885
publishDate 2014-04-01
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.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24737000/?tool=EBI
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