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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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Estimating the time necessary for a CRM to evolve. (A, B) Methodology. A schematic representation of the PEBCRES framework describing how it is used to estimate the time necessary for a CRM to evolve from genomic background. Expression readout of the evolving CRM is predicted using GEMSTAT, producing a fitness value (A), which is then plugged into a Wright–Fisher simulation with selection (B). (C) Top panel: Time-to-evolve estimates (y axis), in Myr, for each of the 28 target expression patterns (x axis). Bottom panel: A representation of the 28 A/P expression patterns that serve as target patterns in our simulations, sorted by time-to-evolve estimate (same order as in top panel). Each expression pattern is represented by a column in the heatmap, with red representing high expression and white representing absent expression. The anterior end of the embryo is at the top and posterior end at the bottom. Only 20–80% egg length interval is shown.
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evv080-F1: Estimating the time necessary for a CRM to evolve. (A, B) Methodology. A schematic representation of the PEBCRES framework describing how it is used to estimate the time necessary for a CRM to evolve from genomic background. Expression readout of the evolving CRM is predicted using GEMSTAT, producing a fitness value (A), which is then plugged into a Wright–Fisher simulation with selection (B). (C) Top panel: Time-to-evolve estimates (y axis), in Myr, for each of the 28 target expression patterns (x axis). Bottom panel: A representation of the 28 A/P expression patterns that serve as target patterns in our simulations, sorted by time-to-evolve estimate (same order as in top panel). Each expression pattern is represented by a column in the heatmap, with red representing high expression and white representing absent expression. The anterior end of the embryo is at the top and posterior end at the bottom. Only 20–80% egg length interval is shown.

Mentions: Repeat the experiment to determine median time-to-evolve per CRM using the baseline model as the fitness function, but targeting the merged expression pattern. This is only done for the 28 CRMs shown in figure 1.


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)

Estimating the time necessary for a CRM to evolve. (A, B) Methodology. A schematic representation of the PEBCRES framework describing how it is used to estimate the time necessary for a CRM to evolve from genomic background. Expression readout of the evolving CRM is predicted using GEMSTAT, producing a fitness value (A), which is then plugged into a Wright–Fisher simulation with selection (B). (C) Top panel: Time-to-evolve estimates (y axis), in Myr, for each of the 28 target expression patterns (x axis). Bottom panel: A representation of the 28 A/P expression patterns that serve as target patterns in our simulations, sorted by time-to-evolve estimate (same order as in top panel). Each expression pattern is represented by a column in the heatmap, with red representing high expression and white representing absent expression. The anterior end of the embryo is at the top and posterior end at the bottom. Only 20–80% egg length interval is shown.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494070&req=5

evv080-F1: Estimating the time necessary for a CRM to evolve. (A, B) Methodology. A schematic representation of the PEBCRES framework describing how it is used to estimate the time necessary for a CRM to evolve from genomic background. Expression readout of the evolving CRM is predicted using GEMSTAT, producing a fitness value (A), which is then plugged into a Wright–Fisher simulation with selection (B). (C) Top panel: Time-to-evolve estimates (y axis), in Myr, for each of the 28 target expression patterns (x axis). Bottom panel: A representation of the 28 A/P expression patterns that serve as target patterns in our simulations, sorted by time-to-evolve estimate (same order as in top panel). Each expression pattern is represented by a column in the heatmap, with red representing high expression and white representing absent expression. The anterior end of the embryo is at the top and posterior end at the bottom. Only 20–80% egg length interval is shown.
Mentions: Repeat the experiment to determine median time-to-evolve per CRM using the baseline model as the fitness function, but targeting the merged expression pattern. This is only done for the 28 CRMs shown in figure 1.

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