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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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The effect of uniformly expressed activators on time-to-evolve for each target pattern. (A) Comparison of time-to-evolve estimates between the baseline model and a model that includes ZLD as a uniform activator. Expression patterns are sorted based on time-to-evolve estimates from the baseline model. (B) Comparison of time-to-evolve estimates between the baseline model and a model that includes DSTAT as a uniform activator. (C) Concentration profiles of seven TFs across the A/P axis. Activators are indicated with an A and repressors with an R. (D) Target expression pattern for the CRM eve_37ext_ru. (E) Number of sites present in the eve_37ext_ru CRM in D. melanogaster, for each of the six TFs (other than DSTAT). Sites are called at relative strength of 0.25 following the procedure described in Materials and Methods.
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evv080-F4: The effect of uniformly expressed activators on time-to-evolve for each target pattern. (A) Comparison of time-to-evolve estimates between the baseline model and a model that includes ZLD as a uniform activator. Expression patterns are sorted based on time-to-evolve estimates from the baseline model. (B) Comparison of time-to-evolve estimates between the baseline model and a model that includes DSTAT as a uniform activator. (C) Concentration profiles of seven TFs across the A/P axis. Activators are indicated with an A and repressors with an R. (D) Target expression pattern for the CRM eve_37ext_ru. (E) Number of sites present in the eve_37ext_ru CRM in D. melanogaster, for each of the six TFs (other than DSTAT). Sites are called at relative strength of 0.25 following the procedure described in Materials and Methods.

Mentions: To explore this hypothesis, we repeated the time-to-evolve simulations from above with a GEMSTAT (CRM function) model specification that includes a ubiquitous activator, and compared the results with those from the original model. We designed a methodology that ensures that there exists a fit solution (CRM) for the target pattern under either function model, with and without the ubiquitous activator, so that any difference in time-to-evolve can be attributed to the evolutionary ramifications of the ubiquitous activator (see Materials and Methods). We tested the effects of two well-characterized ubiquitous activators, ZLD (Liang et al. 2008; Harrison et al. 2011) and DSTAT (Tsurumi et al. 2011), separately. As shown in figure 4, each of these TFs reduces the median time-to-evolve for several target patterns, with the effect of ZLD being clearly more prominent. A two-way analysis of variance supported these observations, with P value of (ZLD) and 0.03 (DSTAT) (supplementary tables S1 and S2, Supplementary Material online), indicating that adding either ubiquitous activator to the model has a statistically significant effect of decreasing time-to-evolve.


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)

The effect of uniformly expressed activators on time-to-evolve for each target pattern. (A) Comparison of time-to-evolve estimates between the baseline model and a model that includes ZLD as a uniform activator. Expression patterns are sorted based on time-to-evolve estimates from the baseline model. (B) Comparison of time-to-evolve estimates between the baseline model and a model that includes DSTAT as a uniform activator. (C) Concentration profiles of seven TFs across the A/P axis. Activators are indicated with an A and repressors with an R. (D) Target expression pattern for the CRM eve_37ext_ru. (E) Number of sites present in the eve_37ext_ru CRM in D. melanogaster, for each of the six TFs (other than DSTAT). Sites are called at relative strength of 0.25 following the procedure described in Materials and Methods.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evv080-F4: The effect of uniformly expressed activators on time-to-evolve for each target pattern. (A) Comparison of time-to-evolve estimates between the baseline model and a model that includes ZLD as a uniform activator. Expression patterns are sorted based on time-to-evolve estimates from the baseline model. (B) Comparison of time-to-evolve estimates between the baseline model and a model that includes DSTAT as a uniform activator. (C) Concentration profiles of seven TFs across the A/P axis. Activators are indicated with an A and repressors with an R. (D) Target expression pattern for the CRM eve_37ext_ru. (E) Number of sites present in the eve_37ext_ru CRM in D. melanogaster, for each of the six TFs (other than DSTAT). Sites are called at relative strength of 0.25 following the procedure described in Materials and Methods.
Mentions: To explore this hypothesis, we repeated the time-to-evolve simulations from above with a GEMSTAT (CRM function) model specification that includes a ubiquitous activator, and compared the results with those from the original model. We designed a methodology that ensures that there exists a fit solution (CRM) for the target pattern under either function model, with and without the ubiquitous activator, so that any difference in time-to-evolve can be attributed to the evolutionary ramifications of the ubiquitous activator (see Materials and Methods). We tested the effects of two well-characterized ubiquitous activators, ZLD (Liang et al. 2008; Harrison et al. 2011) and DSTAT (Tsurumi et al. 2011), separately. As shown in figure 4, each of these TFs reduces the median time-to-evolve for several target patterns, with the effect of ZLD being clearly more prominent. A two-way analysis of variance supported these observations, with P value of (ZLD) and 0.03 (DSTAT) (supplementary tables S1 and S2, Supplementary Material online), indicating that adding either ubiquitous activator to the model has a statistically significant effect of decreasing time-to-evolve.

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