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Rearrangements of 2.5 kilobases of noncoding DNA from the Drosophila even-skipped locus define predictive rules of genomic cis-regulatory logic.

Kim AR, Martinez C, Ionides J, Ramos AF, Ludwig MZ, Ogawa N, Sharp DH, Reinitz J - PLoS Genet. (2013)

Bottom Line: The most radical effects are generated by juxtaposing the minimal stripe enhancers MSE2 and MSE3 for stripes 2 and 3 with and without small "spacer" segments less than 360 bp in length.The model was highly constrained by the training data, which it described within the limits of experimental error.The model, so constrained, was able to correctly predict expression patterns driven by enhancers for other Drosophila genes; even-skipped enhancers not included in the training set; stripe 2, 3, and 7 enhancers from various Drosophilid and Sepsid species; and long segments of even-skipped regulatory DNA that contain multiple enhancers.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolution, Chicago Center for Systems Biology, University of Chicago, Chicago, Illinois, USA.

ABSTRACT
Rearrangements of about 2.5 kilobases of regulatory DNA located 5' of the transcription start site of the Drosophila even-skipped locus generate large-scale changes in the expression of even-skipped stripes 2, 3, and 7. The most radical effects are generated by juxtaposing the minimal stripe enhancers MSE2 and MSE3 for stripes 2 and 3 with and without small "spacer" segments less than 360 bp in length. We placed these fusion constructs in a targeted transformation site and obtained quantitative expression data for these transformants together with their controlling transcription factors at cellular resolution. These data demonstrated that the rearrangements can alter expression levels in stripe 2 and the 2-3 interstripe by a factor of more than 10. We reasoned that this behavior would place tight constraints on possible rules of genomic cis-regulatory logic. To find these constraints, we confronted our new expression data together with previously obtained data on other constructs with a computational model. The model contained representations of thermodynamic protein-DNA interactions including steric interference and cooperative binding, short-range repression, direct repression, activation, and coactivation. The model was highly constrained by the training data, which it described within the limits of experimental error. The model, so constrained, was able to correctly predict expression patterns driven by enhancers for other Drosophila genes; even-skipped enhancers not included in the training set; stripe 2, 3, and 7 enhancers from various Drosophilid and Sepsid species; and long segments of even-skipped regulatory DNA that contain multiple enhancers. The model further demonstrated that elevated expression driven by a fusion of MSE2 and MSE3 was a consequence of the recruitment of a portion of MSE3 to become a functional component of MSE2, demonstrating that cis-regulatory "elements" are not elementary objects.

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Related in: MedlinePlus

Model equations: TF binding to DNA.The model equations for binding site prediction (Equation 1 and 2), cooperative and competitive binding (Equation 3) and fractional occupancy calculation (Equation 4) are shown together in a flow diagram with cartoons of each mechanism on the left and an example application in blue with 5 TF binding sites. Subgrouping process partitioning the binding sites into independent binding groups allows faster computation without losing accuracy. In the example, we set the range of quenching to 20 bp.
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pgen-1003243-g002: Model equations: TF binding to DNA.The model equations for binding site prediction (Equation 1 and 2), cooperative and competitive binding (Equation 3) and fractional occupancy calculation (Equation 4) are shown together in a flow diagram with cartoons of each mechanism on the left and an example application in blue with 5 TF binding sites. Subgrouping process partitioning the binding sites into independent binding groups allows faster computation without losing accuracy. In the example, we set the range of quenching to 20 bp.

Mentions: The central players of transcriptional regulation are sequence-specific TFs that bind to DNA. The position of a TF binding site and its binding affinity are determined by a frequency matrix normalized to a position weight matrix (PWM; Figure 2, Equation 1). In this equation, is the probability of finding base () at the th position of a possible binding site for ligand that extends from base on the 5′ side to base on the 3′ side, and is the expected frequency of base in D. melanogaster. When convolved with sequence, the score of the PWM on the sequence is proportional to the free energy of binding [27], and can be exponentiated to obtain the binding affinity of ligand at site . This is shown in Figure 2, Equation 2, where is the maximum possible score and is the proportionality constant to free energy. We include a binding site in a calculation when its score is above a certain threshold. This threshold can be determined with different degrees of accuracy for each TF depending on the quality of the data used to construct its PWM (Materials and Methods).


Rearrangements of 2.5 kilobases of noncoding DNA from the Drosophila even-skipped locus define predictive rules of genomic cis-regulatory logic.

Kim AR, Martinez C, Ionides J, Ramos AF, Ludwig MZ, Ogawa N, Sharp DH, Reinitz J - PLoS Genet. (2013)

Model equations: TF binding to DNA.The model equations for binding site prediction (Equation 1 and 2), cooperative and competitive binding (Equation 3) and fractional occupancy calculation (Equation 4) are shown together in a flow diagram with cartoons of each mechanism on the left and an example application in blue with 5 TF binding sites. Subgrouping process partitioning the binding sites into independent binding groups allows faster computation without losing accuracy. In the example, we set the range of quenching to 20 bp.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585115&req=5

pgen-1003243-g002: Model equations: TF binding to DNA.The model equations for binding site prediction (Equation 1 and 2), cooperative and competitive binding (Equation 3) and fractional occupancy calculation (Equation 4) are shown together in a flow diagram with cartoons of each mechanism on the left and an example application in blue with 5 TF binding sites. Subgrouping process partitioning the binding sites into independent binding groups allows faster computation without losing accuracy. In the example, we set the range of quenching to 20 bp.
Mentions: The central players of transcriptional regulation are sequence-specific TFs that bind to DNA. The position of a TF binding site and its binding affinity are determined by a frequency matrix normalized to a position weight matrix (PWM; Figure 2, Equation 1). In this equation, is the probability of finding base () at the th position of a possible binding site for ligand that extends from base on the 5′ side to base on the 3′ side, and is the expected frequency of base in D. melanogaster. When convolved with sequence, the score of the PWM on the sequence is proportional to the free energy of binding [27], and can be exponentiated to obtain the binding affinity of ligand at site . This is shown in Figure 2, Equation 2, where is the maximum possible score and is the proportionality constant to free energy. We include a binding site in a calculation when its score is above a certain threshold. This threshold can be determined with different degrees of accuracy for each TF depending on the quality of the data used to construct its PWM (Materials and Methods).

Bottom Line: The most radical effects are generated by juxtaposing the minimal stripe enhancers MSE2 and MSE3 for stripes 2 and 3 with and without small "spacer" segments less than 360 bp in length.The model was highly constrained by the training data, which it described within the limits of experimental error.The model, so constrained, was able to correctly predict expression patterns driven by enhancers for other Drosophila genes; even-skipped enhancers not included in the training set; stripe 2, 3, and 7 enhancers from various Drosophilid and Sepsid species; and long segments of even-skipped regulatory DNA that contain multiple enhancers.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolution, Chicago Center for Systems Biology, University of Chicago, Chicago, Illinois, USA.

ABSTRACT
Rearrangements of about 2.5 kilobases of regulatory DNA located 5' of the transcription start site of the Drosophila even-skipped locus generate large-scale changes in the expression of even-skipped stripes 2, 3, and 7. The most radical effects are generated by juxtaposing the minimal stripe enhancers MSE2 and MSE3 for stripes 2 and 3 with and without small "spacer" segments less than 360 bp in length. We placed these fusion constructs in a targeted transformation site and obtained quantitative expression data for these transformants together with their controlling transcription factors at cellular resolution. These data demonstrated that the rearrangements can alter expression levels in stripe 2 and the 2-3 interstripe by a factor of more than 10. We reasoned that this behavior would place tight constraints on possible rules of genomic cis-regulatory logic. To find these constraints, we confronted our new expression data together with previously obtained data on other constructs with a computational model. The model contained representations of thermodynamic protein-DNA interactions including steric interference and cooperative binding, short-range repression, direct repression, activation, and coactivation. The model was highly constrained by the training data, which it described within the limits of experimental error. The model, so constrained, was able to correctly predict expression patterns driven by enhancers for other Drosophila genes; even-skipped enhancers not included in the training set; stripe 2, 3, and 7 enhancers from various Drosophilid and Sepsid species; and long segments of even-skipped regulatory DNA that contain multiple enhancers. The model further demonstrated that elevated expression driven by a fusion of MSE2 and MSE3 was a consequence of the recruitment of a portion of MSE3 to become a functional component of MSE2, demonstrating that cis-regulatory "elements" are not elementary objects.

Show MeSH
Related in: MedlinePlus