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Regulation of clock-controlled genes in mammals.

Bozek K, Relógio A, Kielbasa SM, Heine M, Dame C, Kramer A, Herzel H - PLoS ONE (2009)

Bottom Line: We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms.Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found.Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.

ABSTRACT
The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs.Our study is based on a meta-analysis of DNA-array data from rodent tissues. We searched in the promoter regions of 2065 CCGs for highly overrepresented transcription factor binding sites. In order to compensate the relatively high GC-content of CCG promoters, a novel background model to avoid a bias towards GC-rich motifs was employed. We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms. Among the predicted factors are known regulators such as CLOCKratioBMAL1, DBP, HLF, E4BP4, CREB, RORalpha and the recently described regulators HSF1, STAT3, SP1 and HNF-4alpha. As additional promising candidates of circadian transcriptional regulators PAX-4, C/EBP, EVI-1, IRF, E2F, AP-1, HIF-1 and NF-Y were identified. Moreover, GC-rich motifs (SP1, EGR, ZF5, AP-2, WT1, NRF-1) and AT-rich motifs (MEF-2, HMGIY, HNF-1, OCT-1) are significantly overrepresented in promoter regions of CCGs. Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found. The regulation of the erythropoietin (Epo) gene was analysed, which exhibits many binding sites for circadian regulators. We provide experimental evidence for its circadian regulated expression in the adult murine kidney. Basing on a comprehensive literature search we integrate our predictions into a regulatory network of core clock and clock-controlled genes. Our large scale analysis of the CCG promoters reveals the complexity and extensiveness of the circadian regulation in mammals. Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

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Sequential procedure of our study.The data collection consisted of a CCG study search, meta-analysis of the genes and promoter sequence collection. The meta-analysis allowed hierarchical separation of the gene list into subsets of genes expressed in 4 different tissues (heart, liver, SCN, skeletal muscle) and within each tissue into genes with the peak of circadian expression falling into 1 of 4 defined time intervals. Together with the full list of genes and a subset of genes robustly oscillating, those 22 lists were used in a TFBS overrepresentation search. The total number of predicted sites in a promoter set of interest was compared to the mean number of predictions in an iteratively sampled background promoter set (see Materials and Methods). Z-score has been used as a measure of a motif overrepresentation.
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pone-0004882-g001: Sequential procedure of our study.The data collection consisted of a CCG study search, meta-analysis of the genes and promoter sequence collection. The meta-analysis allowed hierarchical separation of the gene list into subsets of genes expressed in 4 different tissues (heart, liver, SCN, skeletal muscle) and within each tissue into genes with the peak of circadian expression falling into 1 of 4 defined time intervals. Together with the full list of genes and a subset of genes robustly oscillating, those 22 lists were used in a TFBS overrepresentation search. The total number of predicted sites in a promoter set of interest was compared to the mean number of predictions in an iteratively sampled background promoter set (see Materials and Methods). Z-score has been used as a measure of a motif overrepresentation.

Mentions: Our promoter analysis of CCGs is a large scale in silico approach to the question of regulatory mechanisms of the clock output pathways. We based our study on a meta-analysis of DNA-array data from rodent tissues. As illustrated in Figure 1 we selected six microarray studies containing complete gene annotation and full information on phases and levels of expression of genes with an oscillating circadian pattern [5], [6], [15], [16], [17], [18]. We noticed that the promoter regions of the assembled 2065 CCGs are relatively GC-rich (Figure 2). In order to avoid a bias towards GC-rich motifs we employed a novel background model. Previous promoter studies without compensation of the GC-content detected primarily GC-rich motifs [19], [20]. Using a stringent control of the false discovery rate [21] we predicted transcription factor binding sites (TFBSs) in the annotated promoter regions for all available TRANSFAC matrices. The frequencies of predicted binding sites in promoters of CCGs were compared with promoters of randomly sampled sets of mouse genes with the same GC-content which allows the use of z-scores as a measure of overrepresentation. This procedure resulted in relatively large lists of overrepresented motifs. We focus our study on transcription factors that are themselves reported as circadian expressed and on factors whose known target genes belong to our list of 2065 CCGs. By applying the analysis on lists of CCGs separated according to their tissue-specific expression, we found candidate factors involved in tissue-specific gene regulation.


Regulation of clock-controlled genes in mammals.

Bozek K, Relógio A, Kielbasa SM, Heine M, Dame C, Kramer A, Herzel H - PLoS ONE (2009)

Sequential procedure of our study.The data collection consisted of a CCG study search, meta-analysis of the genes and promoter sequence collection. The meta-analysis allowed hierarchical separation of the gene list into subsets of genes expressed in 4 different tissues (heart, liver, SCN, skeletal muscle) and within each tissue into genes with the peak of circadian expression falling into 1 of 4 defined time intervals. Together with the full list of genes and a subset of genes robustly oscillating, those 22 lists were used in a TFBS overrepresentation search. The total number of predicted sites in a promoter set of interest was compared to the mean number of predictions in an iteratively sampled background promoter set (see Materials and Methods). Z-score has been used as a measure of a motif overrepresentation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2654074&req=5

pone-0004882-g001: Sequential procedure of our study.The data collection consisted of a CCG study search, meta-analysis of the genes and promoter sequence collection. The meta-analysis allowed hierarchical separation of the gene list into subsets of genes expressed in 4 different tissues (heart, liver, SCN, skeletal muscle) and within each tissue into genes with the peak of circadian expression falling into 1 of 4 defined time intervals. Together with the full list of genes and a subset of genes robustly oscillating, those 22 lists were used in a TFBS overrepresentation search. The total number of predicted sites in a promoter set of interest was compared to the mean number of predictions in an iteratively sampled background promoter set (see Materials and Methods). Z-score has been used as a measure of a motif overrepresentation.
Mentions: Our promoter analysis of CCGs is a large scale in silico approach to the question of regulatory mechanisms of the clock output pathways. We based our study on a meta-analysis of DNA-array data from rodent tissues. As illustrated in Figure 1 we selected six microarray studies containing complete gene annotation and full information on phases and levels of expression of genes with an oscillating circadian pattern [5], [6], [15], [16], [17], [18]. We noticed that the promoter regions of the assembled 2065 CCGs are relatively GC-rich (Figure 2). In order to avoid a bias towards GC-rich motifs we employed a novel background model. Previous promoter studies without compensation of the GC-content detected primarily GC-rich motifs [19], [20]. Using a stringent control of the false discovery rate [21] we predicted transcription factor binding sites (TFBSs) in the annotated promoter regions for all available TRANSFAC matrices. The frequencies of predicted binding sites in promoters of CCGs were compared with promoters of randomly sampled sets of mouse genes with the same GC-content which allows the use of z-scores as a measure of overrepresentation. This procedure resulted in relatively large lists of overrepresented motifs. We focus our study on transcription factors that are themselves reported as circadian expressed and on factors whose known target genes belong to our list of 2065 CCGs. By applying the analysis on lists of CCGs separated according to their tissue-specific expression, we found candidate factors involved in tissue-specific gene regulation.

Bottom Line: We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms.Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found.Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.

ABSTRACT
The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs.Our study is based on a meta-analysis of DNA-array data from rodent tissues. We searched in the promoter regions of 2065 CCGs for highly overrepresented transcription factor binding sites. In order to compensate the relatively high GC-content of CCG promoters, a novel background model to avoid a bias towards GC-rich motifs was employed. We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms. Among the predicted factors are known regulators such as CLOCKratioBMAL1, DBP, HLF, E4BP4, CREB, RORalpha and the recently described regulators HSF1, STAT3, SP1 and HNF-4alpha. As additional promising candidates of circadian transcriptional regulators PAX-4, C/EBP, EVI-1, IRF, E2F, AP-1, HIF-1 and NF-Y were identified. Moreover, GC-rich motifs (SP1, EGR, ZF5, AP-2, WT1, NRF-1) and AT-rich motifs (MEF-2, HMGIY, HNF-1, OCT-1) are significantly overrepresented in promoter regions of CCGs. Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found. The regulation of the erythropoietin (Epo) gene was analysed, which exhibits many binding sites for circadian regulators. We provide experimental evidence for its circadian regulated expression in the adult murine kidney. Basing on a comprehensive literature search we integrate our predictions into a regulatory network of core clock and clock-controlled genes. Our large scale analysis of the CCG promoters reveals the complexity and extensiveness of the circadian regulation in mammals. Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

Show MeSH
Related in: MedlinePlus