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Mining for novel candidate clock genes in the circadian regulatory network

BACKGROUND: Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein exp...

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Autores principales: Bhargava, Anuprabha, Herzel, Hanspeter, Ananthasubramaniam, Bharath
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4650315/
https://www.ncbi.nlm.nih.gov/pubmed/26576534
http://dx.doi.org/10.1186/s12918-015-0227-2
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author Bhargava, Anuprabha
Herzel, Hanspeter
Ananthasubramaniam, Bharath
author_facet Bhargava, Anuprabha
Herzel, Hanspeter
Ananthasubramaniam, Bharath
author_sort Bhargava, Anuprabha
collection PubMed
description BACKGROUND: Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta-analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction data starting from a published list of 1000 genes with robust transcriptional rhythms and circadian phenotypes of knockdowns. RESULTS: We identified 20 candidate genes including nine known clock genes that received significantly high scores and were also robust to the relative weights assigned to different data types. Our scoring was consistent with the original ranking of the 1000 genes, but also provided novel complementary insights. Candidate genes were enriched for genes expressed in a circadian manner in multiple tissues with regulation driven mainly by transcription factors BMAL1 and REV-ERB α,β. Moreover, peak transcription of candidate genes was remarkably consistent across tissues. While peaks of the 1000 genes were distributed uniformly throughout the day, candidate gene peaks were strongly concentrated around dusk. Finally, we showed that binding of specific transcription factors to a gene promoter was predictive of peak transcription at a certain time of day and discuss combinatorial phase regulation. CONCLUSIONS: Combining complementary publicly-available data targeting different levels of regulation within the circadian network, we filtered the original list and found 11 novel robust candidate clock genes. Using the criteria of circadian proteomic expression, circadian expression in multiple tissues and independent gene knockdown data, we propose six genes (Por, Mtss1, Dgat2, Pim3, Ppp1r3b, Upp2) involved in metabolism and cancer for further experimental investigation. The availability of public high-throughput databases makes such meta-analysis a promising approach to test consistency between sources and tap their entire potential. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0227-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-46503152015-11-19 Mining for novel candidate clock genes in the circadian regulatory network Bhargava, Anuprabha Herzel, Hanspeter Ananthasubramaniam, Bharath BMC Syst Biol Research Article BACKGROUND: Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta-analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction data starting from a published list of 1000 genes with robust transcriptional rhythms and circadian phenotypes of knockdowns. RESULTS: We identified 20 candidate genes including nine known clock genes that received significantly high scores and were also robust to the relative weights assigned to different data types. Our scoring was consistent with the original ranking of the 1000 genes, but also provided novel complementary insights. Candidate genes were enriched for genes expressed in a circadian manner in multiple tissues with regulation driven mainly by transcription factors BMAL1 and REV-ERB α,β. Moreover, peak transcription of candidate genes was remarkably consistent across tissues. While peaks of the 1000 genes were distributed uniformly throughout the day, candidate gene peaks were strongly concentrated around dusk. Finally, we showed that binding of specific transcription factors to a gene promoter was predictive of peak transcription at a certain time of day and discuss combinatorial phase regulation. CONCLUSIONS: Combining complementary publicly-available data targeting different levels of regulation within the circadian network, we filtered the original list and found 11 novel robust candidate clock genes. Using the criteria of circadian proteomic expression, circadian expression in multiple tissues and independent gene knockdown data, we propose six genes (Por, Mtss1, Dgat2, Pim3, Ppp1r3b, Upp2) involved in metabolism and cancer for further experimental investigation. The availability of public high-throughput databases makes such meta-analysis a promising approach to test consistency between sources and tap their entire potential. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0227-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-14 /pmc/articles/PMC4650315/ /pubmed/26576534 http://dx.doi.org/10.1186/s12918-015-0227-2 Text en © Bhargava et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Bhargava, Anuprabha
Herzel, Hanspeter
Ananthasubramaniam, Bharath
Mining for novel candidate clock genes in the circadian regulatory network
title Mining for novel candidate clock genes in the circadian regulatory network
title_full Mining for novel candidate clock genes in the circadian regulatory network
title_fullStr Mining for novel candidate clock genes in the circadian regulatory network
title_full_unstemmed Mining for novel candidate clock genes in the circadian regulatory network
title_short Mining for novel candidate clock genes in the circadian regulatory network
title_sort mining for novel candidate clock genes in the circadian regulatory network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4650315/
https://www.ncbi.nlm.nih.gov/pubmed/26576534
http://dx.doi.org/10.1186/s12918-015-0227-2
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