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What does it take to evolve an enhancer? A simulation-based study of factors influencing the emergence of combinatorial regulation.

Duque T, Sinha S - Genome Biol Evol (2015)

Bottom Line: There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression.We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors.Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign.

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Features of CRMs that influence time-to-evolve. (A) A scatter plot relating the estimated occupancy of HB in a real CRM (x axis) and the median estimated time to evolve a CRM for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is of 0.7, which is significant at a P value of . The best fit line is also shown (solid line). (B) A scatter plot relating the estimated TF occupancy in a CRM, summed over all TFs used in the model (x axis), and the median estimated time-to-evolve for the corresponding pattern (y axis). Pearson CC = 0.47, P = 0.005. However, the partial correlation, discounting the contribution of HB sites, is not significant (P = 0.45). The best fit line is also shown (solid line). (C) Time-to-evolve estimates of CRMs are highly negatively correlated with expression level in anterior parts of the embryo. The y axis shows for each position along the A/P axis (“%egg length,” x axis) the Spearman’s correlation coefficient between a target pattern’s expression level at that axial position and the time-to-evolve estimate for that pattern. The concentration profile of HB across the axis is also shown (dashed line). (D) Scatter plot relating the number of TFs with at least one binding site present in the D. melanogaster CRM (x axis) and the median estimated time-to-evolve for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is 0.49, P-value = 0.004, indicating the number TFs acting in a pattern correlates with the time-to-evolve that pattern.
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evv080-F3: Features of CRMs that influence time-to-evolve. (A) A scatter plot relating the estimated occupancy of HB in a real CRM (x axis) and the median estimated time to evolve a CRM for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is of 0.7, which is significant at a P value of . The best fit line is also shown (solid line). (B) A scatter plot relating the estimated TF occupancy in a CRM, summed over all TFs used in the model (x axis), and the median estimated time-to-evolve for the corresponding pattern (y axis). Pearson CC = 0.47, P = 0.005. However, the partial correlation, discounting the contribution of HB sites, is not significant (P = 0.45). The best fit line is also shown (solid line). (C) Time-to-evolve estimates of CRMs are highly negatively correlated with expression level in anterior parts of the embryo. The y axis shows for each position along the A/P axis (“%egg length,” x axis) the Spearman’s correlation coefficient between a target pattern’s expression level at that axial position and the time-to-evolve estimate for that pattern. The concentration profile of HB across the axis is also shown (dashed line). (D) Scatter plot relating the number of TFs with at least one binding site present in the D. melanogaster CRM (x axis) and the median estimated time-to-evolve for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is 0.49, P-value = 0.004, indicating the number TFs acting in a pattern correlates with the time-to-evolve that pattern.

Mentions: We first tested for a correlation between time-to-evolve for a target pattern and each TF’s binding site count (or estimated occupancy) in the real CRM associated with that pattern. We found that binding site content of the TF HB has a strong positive correlation with time-to-evolve estimates (Pearson CC = 0.70, P = ; fig. 3A). We also found that total binding site content of a CRM, aggregated over all six TFs, significantly positively correlates with time-to-evolve estimates (fig. 3B); however, this effect can be attributed mostly to HB site content, as indicated by a weak partial correlation coefficient (Johnson et al. 1992) with P of 0.45.


What does it take to evolve an enhancer? A simulation-based study of factors influencing the emergence of combinatorial regulation.

Duque T, Sinha S - Genome Biol Evol (2015)

Features of CRMs that influence time-to-evolve. (A) A scatter plot relating the estimated occupancy of HB in a real CRM (x axis) and the median estimated time to evolve a CRM for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is of 0.7, which is significant at a P value of . The best fit line is also shown (solid line). (B) A scatter plot relating the estimated TF occupancy in a CRM, summed over all TFs used in the model (x axis), and the median estimated time-to-evolve for the corresponding pattern (y axis). Pearson CC = 0.47, P = 0.005. However, the partial correlation, discounting the contribution of HB sites, is not significant (P = 0.45). The best fit line is also shown (solid line). (C) Time-to-evolve estimates of CRMs are highly negatively correlated with expression level in anterior parts of the embryo. The y axis shows for each position along the A/P axis (“%egg length,” x axis) the Spearman’s correlation coefficient between a target pattern’s expression level at that axial position and the time-to-evolve estimate for that pattern. The concentration profile of HB across the axis is also shown (dashed line). (D) Scatter plot relating the number of TFs with at least one binding site present in the D. melanogaster CRM (x axis) and the median estimated time-to-evolve for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is 0.49, P-value = 0.004, indicating the number TFs acting in a pattern correlates with the time-to-evolve that pattern.
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Related In: Results  -  Collection

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evv080-F3: Features of CRMs that influence time-to-evolve. (A) A scatter plot relating the estimated occupancy of HB in a real CRM (x axis) and the median estimated time to evolve a CRM for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is of 0.7, which is significant at a P value of . The best fit line is also shown (solid line). (B) A scatter plot relating the estimated TF occupancy in a CRM, summed over all TFs used in the model (x axis), and the median estimated time-to-evolve for the corresponding pattern (y axis). Pearson CC = 0.47, P = 0.005. However, the partial correlation, discounting the contribution of HB sites, is not significant (P = 0.45). The best fit line is also shown (solid line). (C) Time-to-evolve estimates of CRMs are highly negatively correlated with expression level in anterior parts of the embryo. The y axis shows for each position along the A/P axis (“%egg length,” x axis) the Spearman’s correlation coefficient between a target pattern’s expression level at that axial position and the time-to-evolve estimate for that pattern. The concentration profile of HB across the axis is also shown (dashed line). (D) Scatter plot relating the number of TFs with at least one binding site present in the D. melanogaster CRM (x axis) and the median estimated time-to-evolve for the corresponding pattern (y axis). The Pearson’s correlation coefficient between the two variables is 0.49, P-value = 0.004, indicating the number TFs acting in a pattern correlates with the time-to-evolve that pattern.
Mentions: We first tested for a correlation between time-to-evolve for a target pattern and each TF’s binding site count (or estimated occupancy) in the real CRM associated with that pattern. We found that binding site content of the TF HB has a strong positive correlation with time-to-evolve estimates (Pearson CC = 0.70, P = ; fig. 3A). We also found that total binding site content of a CRM, aggregated over all six TFs, significantly positively correlates with time-to-evolve estimates (fig. 3B); however, this effect can be attributed mostly to HB site content, as indicated by a weak partial correlation coefficient (Johnson et al. 1992) with P of 0.45.

Bottom Line: There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression.We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors.Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign.

Show MeSH