Limits...
Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

Gu Q, Nagaraj SH, Hudson NJ, Dalrymple BP, Reverter A - BMC Genomics (2011)

Bottom Line: To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine.Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational and Systems Biology, CSIRO Food Futures Flagship and CSIRO Livestock Industries, 306 Carmody Rd, St. Lucia, Brisbane, Queensland 4067, Australia.

ABSTRACT

Background: Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.

Results: We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively.

Conclusion: The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Show MeSH
Simulation result on the linking co-expression and co-regulation. (A) Distribution of the correlation coefficients as a function of the % of TFs with dual or bipotential activity; (B) Simulation results at varying percentages of transcription factors (TF) operating with bipotential activity: 100% (black), 90% (brown), 80% (pink), 70% (purple), 60% (red), 50% (blue), 40% (jade), 30% (green), 20% (cyan), 10% (yellow), 0% (grey).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3025955&req=5

Figure 3: Simulation result on the linking co-expression and co-regulation. (A) Distribution of the correlation coefficients as a function of the % of TFs with dual or bipotential activity; (B) Simulation results at varying percentages of transcription factors (TF) operating with bipotential activity: 100% (black), 90% (brown), 80% (pink), 70% (purple), 60% (red), 50% (blue), 40% (jade), 30% (green), 20% (cyan), 10% (yellow), 0% (grey).

Mentions: As discussed by Yu et al. [21], there are two main reasons for regulation type to impact on the relationship of the expression of their targets. One is that a sizeable proportion of TFs act both as activators and repressors, in some cases for the same target. The other is that the combined effect of multiple TFs can have an unpredictable effect on target expression. Figure 3 illustrates the results from our simulation analyses. One prominent feature is that in order to observe a relationship between co-expression and co-regulation there must be a sizeable proportion of TFs acting as either activators or repressors, but not both. In fact, the relationship between co-expression and co-regulation quickly diminishes with increasing proportion of TFs with bipotential activity (activators and repressors) (Figure 3B). Most interestingly, in the extreme scenario where all TFs have bipotential activity, no relationship between co-expression and co-regulation could be observed and the resulting distribution of the correlations would be perfectly centred at zero (Figure 3A, density shown in black). Instead, such density sees its mass shifted towards the positive space with an increasing proportion of TFs having a single regulation type (Figure 3A, densities shown in colours other than black). We conclude that the higher reliability attributed to positive correlations is a phenomenon of the presence of significant number of TFs that act as either general activators or general repressors and that the co-expression to co-regulation pattern observed from the real expression skeletal muscle dataset is consistent with the presence of 70 to 80% of TFs having a bipotential activity (Figure 2C for real data versus Figure 3B, purple and pink trends, for simulated data). In agreement with our findings, while on a smaller scale, the recent work of Ouyang et al. [34] with mouse embryonic stem cells, revealed that a remarkably high proportion of variation in gene expression can be explained by the binding signals of 12 TFs of which 7 (or 58%) serve as either activator or repressor depending on the target.


Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

Gu Q, Nagaraj SH, Hudson NJ, Dalrymple BP, Reverter A - BMC Genomics (2011)

Simulation result on the linking co-expression and co-regulation. (A) Distribution of the correlation coefficients as a function of the % of TFs with dual or bipotential activity; (B) Simulation results at varying percentages of transcription factors (TF) operating with bipotential activity: 100% (black), 90% (brown), 80% (pink), 70% (purple), 60% (red), 50% (blue), 40% (jade), 30% (green), 20% (cyan), 10% (yellow), 0% (grey).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3025955&req=5

Figure 3: Simulation result on the linking co-expression and co-regulation. (A) Distribution of the correlation coefficients as a function of the % of TFs with dual or bipotential activity; (B) Simulation results at varying percentages of transcription factors (TF) operating with bipotential activity: 100% (black), 90% (brown), 80% (pink), 70% (purple), 60% (red), 50% (blue), 40% (jade), 30% (green), 20% (cyan), 10% (yellow), 0% (grey).
Mentions: As discussed by Yu et al. [21], there are two main reasons for regulation type to impact on the relationship of the expression of their targets. One is that a sizeable proportion of TFs act both as activators and repressors, in some cases for the same target. The other is that the combined effect of multiple TFs can have an unpredictable effect on target expression. Figure 3 illustrates the results from our simulation analyses. One prominent feature is that in order to observe a relationship between co-expression and co-regulation there must be a sizeable proportion of TFs acting as either activators or repressors, but not both. In fact, the relationship between co-expression and co-regulation quickly diminishes with increasing proportion of TFs with bipotential activity (activators and repressors) (Figure 3B). Most interestingly, in the extreme scenario where all TFs have bipotential activity, no relationship between co-expression and co-regulation could be observed and the resulting distribution of the correlations would be perfectly centred at zero (Figure 3A, density shown in black). Instead, such density sees its mass shifted towards the positive space with an increasing proportion of TFs having a single regulation type (Figure 3A, densities shown in colours other than black). We conclude that the higher reliability attributed to positive correlations is a phenomenon of the presence of significant number of TFs that act as either general activators or general repressors and that the co-expression to co-regulation pattern observed from the real expression skeletal muscle dataset is consistent with the presence of 70 to 80% of TFs having a bipotential activity (Figure 2C for real data versus Figure 3B, purple and pink trends, for simulated data). In agreement with our findings, while on a smaller scale, the recent work of Ouyang et al. [34] with mouse embryonic stem cells, revealed that a remarkably high proportion of variation in gene expression can be explained by the binding signals of 12 TFs of which 7 (or 58%) serve as either activator or repressor depending on the target.

Bottom Line: To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine.Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational and Systems Biology, CSIRO Food Futures Flagship and CSIRO Livestock Industries, 306 Carmody Rd, St. Lucia, Brisbane, Queensland 4067, Australia.

ABSTRACT

Background: Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information.

Results: We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively.

Conclusion: The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Show MeSH