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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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Sensitivity of time-to-evolve estimates to simulation parameters. (A) Sensitivity to the scaled mutation rate (2Nµ). Shown are the average time-to-evolve (median of all simulations for a pattern, averaged over 28 target expression patterns) for three values of 2Nµ. (B) Sensitivity to selection scale parameter (K). (C) The effect of indels. The plot shows time-to-evolve estimates (y axis) for each of the 28 target expression patterns (x axis) for an evolutionary model without insertions or deletions (black circles) and an evolutionary model that includes indels (red triangles). Adding indels significantly increases time-to-evolve estimates (P = 0.0002). (D) Same as (C), except that the initial sequences for simulations are real CRMs that drive a pattern anticorrelated with the target pattern. The trend is that including indels in these simulations reduces the time-to-evolve estimates.
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evv080-F5: Sensitivity of time-to-evolve estimates to simulation parameters. (A) Sensitivity to the scaled mutation rate (2Nµ). Shown are the average time-to-evolve (median of all simulations for a pattern, averaged over 28 target expression patterns) for three values of 2Nµ. (B) Sensitivity to selection scale parameter (K). (C) The effect of indels. The plot shows time-to-evolve estimates (y axis) for each of the 28 target expression patterns (x axis) for an evolutionary model without insertions or deletions (black circles) and an evolutionary model that includes indels (red triangles). Adding indels significantly increases time-to-evolve estimates (P = 0.0002). (D) Same as (C), except that the initial sequences for simulations are real CRMs that drive a pattern anticorrelated with the target pattern. The trend is that including indels in these simulations reduces the time-to-evolve estimates.

Mentions: All our simulations used a time-rescaling heuristic (Hoggart et al. 2007; He et al. 2012) for speeding up simulations, with a scaling factor λ = 1,000, a time-scaled population size 2N = 1,000, and a time-scaled mutation rate µ = 10−5 (mutations per generation per base pair), resulting in a scaled mutation rate 2Nµ = 10−2, which is within the estimated range of (Drake et al. 1998; Thornton and Andolfatto 2006) for Drosophila (see Materials and Methods). We note however that this mutation rate is higher than that used in Duque et al. (2014). The higher mutation rate reduces the computational time required for a simulation and as mentioned is still within the estimated range for Drosophila. However, to understand the effect of mutation rate on our results, we repeated the time-to-evolve estimation procedure with values of 2Nµ that are an order of magnitude greater or lesser than 10−2. Figure 5A shows how the time to evolve a CRM, averaged over the 28 target patterns, changes with the values of 2Nµ. Changing the scaled mutation rate 2Nµ by a factor of 10 results in time-to-evolve estimates that change by less than 10 times, which is not unexpected because different values of 2Nµ result in different balances between selection and drift. In particular, reducing 2Nµ from 0.01 to 0.001 (a factor of 10) results in average time-to-evolve increasing about 7-fold from approximately 2.1 to approximately 18 Myr, with estimates for individual target patterns ranging between 2.1 and 25 Myr. (As a comparison point, we note that the estimated divergence time between D. melanogaster and Drosophila pseudoobscura to be 25–55 Myr; Richards et al. 2005.)


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)

Sensitivity of time-to-evolve estimates to simulation parameters. (A) Sensitivity to the scaled mutation rate (2Nµ). Shown are the average time-to-evolve (median of all simulations for a pattern, averaged over 28 target expression patterns) for three values of 2Nµ. (B) Sensitivity to selection scale parameter (K). (C) The effect of indels. The plot shows time-to-evolve estimates (y axis) for each of the 28 target expression patterns (x axis) for an evolutionary model without insertions or deletions (black circles) and an evolutionary model that includes indels (red triangles). Adding indels significantly increases time-to-evolve estimates (P = 0.0002). (D) Same as (C), except that the initial sequences for simulations are real CRMs that drive a pattern anticorrelated with the target pattern. The trend is that including indels in these simulations reduces the time-to-evolve estimates.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4494070&req=5

evv080-F5: Sensitivity of time-to-evolve estimates to simulation parameters. (A) Sensitivity to the scaled mutation rate (2Nµ). Shown are the average time-to-evolve (median of all simulations for a pattern, averaged over 28 target expression patterns) for three values of 2Nµ. (B) Sensitivity to selection scale parameter (K). (C) The effect of indels. The plot shows time-to-evolve estimates (y axis) for each of the 28 target expression patterns (x axis) for an evolutionary model without insertions or deletions (black circles) and an evolutionary model that includes indels (red triangles). Adding indels significantly increases time-to-evolve estimates (P = 0.0002). (D) Same as (C), except that the initial sequences for simulations are real CRMs that drive a pattern anticorrelated with the target pattern. The trend is that including indels in these simulations reduces the time-to-evolve estimates.
Mentions: All our simulations used a time-rescaling heuristic (Hoggart et al. 2007; He et al. 2012) for speeding up simulations, with a scaling factor λ = 1,000, a time-scaled population size 2N = 1,000, and a time-scaled mutation rate µ = 10−5 (mutations per generation per base pair), resulting in a scaled mutation rate 2Nµ = 10−2, which is within the estimated range of (Drake et al. 1998; Thornton and Andolfatto 2006) for Drosophila (see Materials and Methods). We note however that this mutation rate is higher than that used in Duque et al. (2014). The higher mutation rate reduces the computational time required for a simulation and as mentioned is still within the estimated range for Drosophila. However, to understand the effect of mutation rate on our results, we repeated the time-to-evolve estimation procedure with values of 2Nµ that are an order of magnitude greater or lesser than 10−2. Figure 5A shows how the time to evolve a CRM, averaged over the 28 target patterns, changes with the values of 2Nµ. Changing the scaled mutation rate 2Nµ by a factor of 10 results in time-to-evolve estimates that change by less than 10 times, which is not unexpected because different values of 2Nµ result in different balances between selection and drift. In particular, reducing 2Nµ from 0.01 to 0.001 (a factor of 10) results in average time-to-evolve increasing about 7-fold from approximately 2.1 to approximately 18 Myr, with estimates for individual target patterns ranging between 2.1 and 25 Myr. (As a comparison point, we note that the estimated divergence time between D. melanogaster and Drosophila pseudoobscura to be 25–55 Myr; Richards et al. 2005.)

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
Related in: MedlinePlus