From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters

Abstract

Background: The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription Factor (TF) binding sites, and their location with respect to promoters. While variations of binding sequences modulate the strength of the interaction between the TF and its binding sites, the distance between binding sites and promoters alter the interaction between the TF and the RNA polymerase (RNAP).

Results: In this paper we estimated the dissociation constants (K(d)) of several E. coli TFs in their interaction with variants of their binding sequences from the scores resulting from aligning them to Positional Weight Matrices. A correlation coefficient of 0.78 was obtained when pooling together sites for different TFs. The theoretically estimated K(d) values were then used, together with the dissociation constants of the RNAP-promoter interaction to analyze activated and repressed promoters. The strength of repressor sites -- i.e., the strength of the interaction between TFs and their binding sites -- is slightly higher than that of activated sites. We explored how different factors such as the variation of binding sequences, the occurrence of more than one binding site, or different RNAP concentrations may influence the promoters' response to the variations of TF concentrations. We found that the occurrence of several regulatory sites bound by the same TF close to a promoter -- if they are bound by the TF in an independent manner -- changes the effect of TF concentrations on promoter occupancy, with respect to individual sites. We also found that the occupancy of a promoter will never be more than half if the RNAP concentration-to-K(p) ratio is 1 and the promoter is subject to repression; or less than half if the promoter is subject to activation. If the ratio falls to 0.1, the upper limit of occupancy probability for repressed drops below 10%; a descent of the limits occurs also for activated promoters.

Conclusion: The number of regulatory sites may thus act as a versatility-producing device, in addition to serving as a source of robustness of the transcription machinery. Furthermore, our results show that the effects of TF concentration fluctuations on promoter occupancy are constrained by RNAP concentrations.

Figure 1

Correlation between -log(K_d) and PWM scores of DNA sequences downloaded from ProNIT. The equation of the fitting line, the Pearson's correlation coefficient, and its associated p-value, resulting from 1000 randomizations of the original set are shown at the upper corner of the graph.

Figure 2

Distribution of regulatory sites according to their K_d values and the K_p values of their corresponding promoter sequences. The vertical and horizontal lines correspond to the mean -log(K_d), 7.63 and mean -log(K_p), 6.91 respectively.

Figure 3

Probability of TF-site interaction and transcription initiation (RNAP-promoter interaction) as a function of TF concentration at four simple promoters. Panels A and B correspond to activator sites; C and D represent repressor sites.

Figure 4

Probability of RNAP-promoter binding vs TF concentration for several promoters repressed by LexA. RNAP concentration: 5E-08 mol/L. Graphs' legend: Transcription Unit name (gene names concatenated by underscores): position of the regulatory site, -log(Kd), -log(Kp).

Figure 5

Probability of RNAP-promoter interaction. A, activator sites, at RNAP concentration equal to K_p of promoters, at four TF concentrations; B, repressor sites at RNAP concentration equal to K_p of promoters, at four TF concentrations; C, activator sites, at TF concentration equal to K_d of sites, at four RNAP concentrations; D, repressor sites, at TF concentration equal to the K_d of sites, at four RNAP concentrations.

Figure 6

Probabilities of lexA_dinF promoter occupancy calculated only on the basis of two of the LexA binding sites located within the regulatory region of the TU. Panel A: The blue dots represent probability of promoter occupancy calculated considering only occupation of the -50.5 LexA binding site. The red dots represent probability of promoter occupancy calculated considering only occupation of the -9 site. The green dots represent probabilities calculated relying on the possibility of occupancy of either site by LexA. (RNAP concentration: 1E-08). Panel B: The blue and green dots represent exactly the same calculations as in panel A; the red dots represent the probability of lexA_dinF promoter occupancy calculated assuming that the -9 site mutates to produce a sequence whose binding to LexA is weaker by one order of magnitude than the wild type.