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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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Model equations: protein–protein interactions.The model equations for coactivation (Equation 5), short-range quenching (Equation 6), direct repression (Equation 7), adaptor factor recruitment (Equation 8 and 9) and activation synergy (Equation 10) 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.
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pgen-1003243-g003: Model equations: protein–protein interactions.The model equations for coactivation (Equation 5), short-range quenching (Equation 6), direct repression (Equation 7), adaptor factor recruitment (Equation 8 and 9) and activation synergy (Equation 10) 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.

Mentions: We represent coactivation as shown in Figure 3, Equation 5, where represents the coactivation efficiency of a coactivator and the dependence of coactivation on distance is given by . We constrain the activating and repressing activity of a coactivation target to sum to the physical fractional occupancy. The gap genes are short range repressors that act when bound within 150 bp of activators [36]–[38], a fact that we represent by convolving the fractional occupancies of all activators with those of quenchers as shown in Figure 3, Equation 6 to obtain activator fractional occupancies corrected for quenching, where represents the repressive strength of TF and the function represents its range of action (Figure S2). When quenchers are bound within quenching range of the TSS they can prevent activators from acting at any range, a phenomenon described by Arnosti and coworkers as direct repression [36], [38]. Although longer range interactions of repressors with the TSS have been referred to as “direct repression” [39], [40], we limit ourselves to the short range interaction of Arnosti. This form of direct repression is represented in the model (Figure 3, Equation 7) in the same way as Equation 6 except that in this equation is the distance between the repressor binding site and TSS, and that the repressor does not act on but on . is associated with the transcription machinery that binds to the TSS, as we now describe.


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: protein–protein interactions.The model equations for coactivation (Equation 5), short-range quenching (Equation 6), direct repression (Equation 7), adaptor factor recruitment (Equation 8 and 9) and activation synergy (Equation 10) 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.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1003243-g003: Model equations: protein–protein interactions.The model equations for coactivation (Equation 5), short-range quenching (Equation 6), direct repression (Equation 7), adaptor factor recruitment (Equation 8 and 9) and activation synergy (Equation 10) 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.
Mentions: We represent coactivation as shown in Figure 3, Equation 5, where represents the coactivation efficiency of a coactivator and the dependence of coactivation on distance is given by . We constrain the activating and repressing activity of a coactivation target to sum to the physical fractional occupancy. The gap genes are short range repressors that act when bound within 150 bp of activators [36]–[38], a fact that we represent by convolving the fractional occupancies of all activators with those of quenchers as shown in Figure 3, Equation 6 to obtain activator fractional occupancies corrected for quenching, where represents the repressive strength of TF and the function represents its range of action (Figure S2). When quenchers are bound within quenching range of the TSS they can prevent activators from acting at any range, a phenomenon described by Arnosti and coworkers as direct repression [36], [38]. Although longer range interactions of repressors with the TSS have been referred to as “direct repression” [39], [40], we limit ourselves to the short range interaction of Arnosti. This form of direct repression is represented in the model (Figure 3, Equation 7) in the same way as Equation 6 except that in this equation is the distance between the repressor binding site and TSS, and that the repressor does not act on but on . is associated with the transcription machinery that binds to the TSS, as we now describe.

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