Limits...
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.

Show MeSH

Related in: MedlinePlus

Distribution of the number of predicted TFBS.The histograms illustrate the distributions of number of predicted hits of several motifs in sampled background promoter sets with a GC-content matched to the selected subset of 167 CCGs. X-axis indicates numbers of predicted sites, the height of vertical bars corresponds to the percentage of sampled background sets containing the given number of predictions. The number of hits in the foreground set of 167 CCGs is marked with an arrow. All shown motifs are overrepresented in the analyzed CCG set (have z-scores over 2). The corresponding z-score values are given in Tables 1 and 2.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2654074&req=5

pone-0004882-g003: Distribution of the number of predicted TFBS.The histograms illustrate the distributions of number of predicted hits of several motifs in sampled background promoter sets with a GC-content matched to the selected subset of 167 CCGs. X-axis indicates numbers of predicted sites, the height of vertical bars corresponds to the percentage of sampled background sets containing the given number of predictions. The number of hits in the foreground set of 167 CCGs is marked with an arrow. All shown motifs are overrepresented in the analyzed CCG set (have z-scores over 2). The corresponding z-score values are given in Tables 1 and 2.

Mentions: This way, the randomly obtained control gene sets had identical GC-content distribution of their promoter sequences as the analysed CCG set. We repeated this GC-matched background sampling procedure 100 times and calculated mean numbers of predicted binding sites of each transcription factor along with their standard deviations. Next, we contrasted the random sampling results with the number of predicted TFBS in the set of CCG promoters. The overrepresentation of binding sites was quantified using z-scores (Table 1). Figure 3 shows representative histograms of the number of predicted TFBSs in the background and the number of predictions in our set of 167 selected CCGs.


Regulation of clock-controlled genes in mammals.

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

Distribution of the number of predicted TFBS.The histograms illustrate the distributions of number of predicted hits of several motifs in sampled background promoter sets with a GC-content matched to the selected subset of 167 CCGs. X-axis indicates numbers of predicted sites, the height of vertical bars corresponds to the percentage of sampled background sets containing the given number of predictions. The number of hits in the foreground set of 167 CCGs is marked with an arrow. All shown motifs are overrepresented in the analyzed CCG set (have z-scores over 2). The corresponding z-score values are given in Tables 1 and 2.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0004882-g003: Distribution of the number of predicted TFBS.The histograms illustrate the distributions of number of predicted hits of several motifs in sampled background promoter sets with a GC-content matched to the selected subset of 167 CCGs. X-axis indicates numbers of predicted sites, the height of vertical bars corresponds to the percentage of sampled background sets containing the given number of predictions. The number of hits in the foreground set of 167 CCGs is marked with an arrow. All shown motifs are overrepresented in the analyzed CCG set (have z-scores over 2). The corresponding z-score values are given in Tables 1 and 2.
Mentions: This way, the randomly obtained control gene sets had identical GC-content distribution of their promoter sequences as the analysed CCG set. We repeated this GC-matched background sampling procedure 100 times and calculated mean numbers of predicted binding sites of each transcription factor along with their standard deviations. Next, we contrasted the random sampling results with the number of predicted TFBS in the set of CCG promoters. The overrepresentation of binding sites was quantified using z-scores (Table 1). Figure 3 shows representative histograms of the number of predicted TFBSs in the background and the number of predictions in our set of 167 selected CCGs.

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