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Concentration and length dependence of DNA looping in transcriptional regulation.

Han L, Garcia HG, Blumberg S, Towles KB, Beausang JF, Nelson PC, Phillips R - PLoS ONE (2009)

Bottom Line: This action at a distance is often mediated by the formation of DNA loops: Binding at two or more sites on the DNA results in the formation of a loop, which can bring the transcription factor into the immediate neighborhood of the relevant promoter.We find that loops form even at interoperator spacings considerably shorter than the DNA persistence length, without the intervention of any other proteins to prebend the DNA.The concentration measurements also permit us to use a simple statistical mechanical model of DNA loop formation to determine the free energy of DNA looping, or equivalently, the for looping.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Physics, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
In many cases, transcriptional regulation involves the binding of transcription factors at sites on the DNA that are not immediately adjacent to the promoter of interest. This action at a distance is often mediated by the formation of DNA loops: Binding at two or more sites on the DNA results in the formation of a loop, which can bring the transcription factor into the immediate neighborhood of the relevant promoter. These processes are important in settings ranging from the historic bacterial examples (bacterial metabolism and the lytic-lysogeny decision in bacteriophage), to the modern concept of gene regulation to regulatory processes central to pattern formation during development of multicellular organisms. Though there have been a variety of insights into the combinatorial aspects of transcriptional control, the mechanism of DNA looping as an agent of combinatorial control in both prokaryotes and eukaryotes remains unclear. We use single-molecule techniques to dissect DNA looping in the lac operon. In particular, we measure the propensity for DNA looping by the Lac repressor as a function of the concentration of repressor protein and as a function of the distance between repressor binding sites. As with earlier single-molecule studies, we find (at least) two distinct looped states and demonstrate that the presence of these two states depends both upon the concentration of repressor protein and the distance between the two repressor binding sites. We find that loops form even at interoperator spacings considerably shorter than the DNA persistence length, without the intervention of any other proteins to prebend the DNA. The concentration measurements also permit us to use a simple statistical mechanical model of DNA loop formation to determine the free energy of DNA looping, or equivalently, the for looping.

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Effective J-factor from different experiments.Although the  factor obtained from cyclization experiments is not directly comparable to the looping  factor studied in this paper (due to the differences in geometry), we present the two quantities together as functions of loop length to summarize the work from many groups. Error bars have been omitted for clarity. The filter binding data is an order of magnitude estimate.
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pone-0005621-g012: Effective J-factor from different experiments.Although the factor obtained from cyclization experiments is not directly comparable to the looping factor studied in this paper (due to the differences in geometry), we present the two quantities together as functions of loop length to summarize the work from many groups. Error bars have been omitted for clarity. The filter binding data is an order of magnitude estimate.

Mentions: The data shown in fig. 4 can be fit in several different ways as suggested by the three different formulae characterizing the looping probability given above. The fit shown in fig. 4 is a full nonlinear fit in which the parameters , and are treated as fitting parameters. Alternatively, using this same data of fig. 4, we can actually obtain the looping free energy, as well as the binding energies by fitting the data to eqn. 1. Note that these two descriptions are equivalent and each depends upon three unknown parameters. Once one set of parameters is known, in principle, the complementary parameters are also known. We find it convenient to work in terms of both languages because in some discussions it is useful to talk in terms of looping free energies, and in other contexts, in terms of the looping J-factor. Finally, we can fit the data corresponding to LacI concentrations of 10 pM and higher using the linear model from eqn. 4. The results of these different fits are shown in Table 1. These results are usefully contrasted with results of other experiments on the lac operon, which are also summarized in Table 1. We see from the table that the nonlinear model fails to constrain the value of reliably. In the case of the binding constants we see a difference of almost two orders of magnitude with published dissociation constants, which translates into a difference of roughly in the binding energy.


Concentration and length dependence of DNA looping in transcriptional regulation.

Han L, Garcia HG, Blumberg S, Towles KB, Beausang JF, Nelson PC, Phillips R - PLoS ONE (2009)

Effective J-factor from different experiments.Although the  factor obtained from cyclization experiments is not directly comparable to the looping  factor studied in this paper (due to the differences in geometry), we present the two quantities together as functions of loop length to summarize the work from many groups. Error bars have been omitted for clarity. The filter binding data is an order of magnitude estimate.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2682762&req=5

pone-0005621-g012: Effective J-factor from different experiments.Although the factor obtained from cyclization experiments is not directly comparable to the looping factor studied in this paper (due to the differences in geometry), we present the two quantities together as functions of loop length to summarize the work from many groups. Error bars have been omitted for clarity. The filter binding data is an order of magnitude estimate.
Mentions: The data shown in fig. 4 can be fit in several different ways as suggested by the three different formulae characterizing the looping probability given above. The fit shown in fig. 4 is a full nonlinear fit in which the parameters , and are treated as fitting parameters. Alternatively, using this same data of fig. 4, we can actually obtain the looping free energy, as well as the binding energies by fitting the data to eqn. 1. Note that these two descriptions are equivalent and each depends upon three unknown parameters. Once one set of parameters is known, in principle, the complementary parameters are also known. We find it convenient to work in terms of both languages because in some discussions it is useful to talk in terms of looping free energies, and in other contexts, in terms of the looping J-factor. Finally, we can fit the data corresponding to LacI concentrations of 10 pM and higher using the linear model from eqn. 4. The results of these different fits are shown in Table 1. These results are usefully contrasted with results of other experiments on the lac operon, which are also summarized in Table 1. We see from the table that the nonlinear model fails to constrain the value of reliably. In the case of the binding constants we see a difference of almost two orders of magnitude with published dissociation constants, which translates into a difference of roughly in the binding energy.

Bottom Line: This action at a distance is often mediated by the formation of DNA loops: Binding at two or more sites on the DNA results in the formation of a loop, which can bring the transcription factor into the immediate neighborhood of the relevant promoter.We find that loops form even at interoperator spacings considerably shorter than the DNA persistence length, without the intervention of any other proteins to prebend the DNA.The concentration measurements also permit us to use a simple statistical mechanical model of DNA loop formation to determine the free energy of DNA looping, or equivalently, the for looping.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Physics, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
In many cases, transcriptional regulation involves the binding of transcription factors at sites on the DNA that are not immediately adjacent to the promoter of interest. This action at a distance is often mediated by the formation of DNA loops: Binding at two or more sites on the DNA results in the formation of a loop, which can bring the transcription factor into the immediate neighborhood of the relevant promoter. These processes are important in settings ranging from the historic bacterial examples (bacterial metabolism and the lytic-lysogeny decision in bacteriophage), to the modern concept of gene regulation to regulatory processes central to pattern formation during development of multicellular organisms. Though there have been a variety of insights into the combinatorial aspects of transcriptional control, the mechanism of DNA looping as an agent of combinatorial control in both prokaryotes and eukaryotes remains unclear. We use single-molecule techniques to dissect DNA looping in the lac operon. In particular, we measure the propensity for DNA looping by the Lac repressor as a function of the concentration of repressor protein and as a function of the distance between repressor binding sites. As with earlier single-molecule studies, we find (at least) two distinct looped states and demonstrate that the presence of these two states depends both upon the concentration of repressor protein and the distance between the two repressor binding sites. We find that loops form even at interoperator spacings considerably shorter than the DNA persistence length, without the intervention of any other proteins to prebend the DNA. The concentration measurements also permit us to use a simple statistical mechanical model of DNA loop formation to determine the free energy of DNA looping, or equivalently, the for looping.

Show MeSH