Abstract
BackgroundMost 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.ResultsWe 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.ConclusionsCombining 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 materialThe online version of this article (doi:10.1186/s12918-015-0227-2) contains supplementary material, which is available to authorized users.
Highlights
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
Anafi and colleagues compiled this master list by combining Bayesian scores representing five features necessary in a clock gene: (i) oscillating transcripts in liver, pituitary and NIH3T3 cells; (ii) a circadian phenotype in response to RNA interference (RNAi) of the gene; (iii) significant number of functional genetic interactions with an exemplar list of known “core” clock genes based on radiation hybrid mapping; (iv) ubiquity of expression of the gene across multiple tissues based on expressed sequence tags (EST); (v) phylogenic conservation across fruit flies (Drosophila melanogaster) and mammals
Based on the ChIP-seq data sets, genes in the master list were mostly bound by transcription factors (TFs) via ROR response element (RRE) and to a lesser extent at E-boxes and D-boxes (Additional file 3: Figure S1)
Summary
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. 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. The daily and seasonal geophysical variations have driven the evolution of a circadian clock system in most organisms These biological timekeepers permit organisms to maintain near 24 h rhythms in most physiological processes and anticipate periodic changes in their environments. While Clock was discovered by costly and laborious forward genetic screens by Joseph Takahashi and colleagues [6], current high-throughput data from genetics, transcriptomics and proteomics and availability of the entire genome combined with system biological approaches have tremendously accelerated our ability to find new putative members of the TTFL [8]. Similar bioinformatic approaches were used to identify novel circadian genes from microarray data [10] and using coexpression data and text-mining [11], to find circadian genes disrupted in cancer cell lines [12] and to find health implications of disrupted clock genes [13]
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