The promoter-search mechanism of Escherichia coli RNA polymerase is dominated by three-dimensional diffusion

Abstract

Gene expression, DNA replication and genome maintenance are all initiated by proteins that must recognize specific targets from among a vast excess of nonspecific DNA. For example, to initiate transcription, Escherichia coli RNA polymerase (RNAP) must locate promoter sequences, which compose <2% of the bacterial genome. This search problem remains one of the least understood aspects of gene expression, largely owing to the transient nature of search intermediates. Here we visualize RNAP in real time as it searches for promoters, and we develop a theoretical framework for analyzing target searches at the submicroscopic scale on the basis of single-molecule target-association rates. We demonstrate that, contrary to long-held assumptions, the promoter search is dominated by three-dimensional diffusion at both the microscopic and submicroscopic scales in vitro, which has direct implications for understanding how promoters are located within physiological settings.

Fig. 1. Single–molecule DNA curtain assay for promoter–specific binding by RNA polymerase

(a) Double–tethered DNA curtain assay for organizing substrates on surfaces of a microfluidic device. (b) Two–color images of YOYO1–stained DNA (green) bound by QD–RNAP (magenta). (c) Schematic of the λ–phage genome (48.5–kb), including relative locations and orientations of promoters aligned with images of QD–RNAP on single DNA molecules (Supplementary Table 2). As shown in (b–c) most RNAP is bound to the promoters, and the left half of the λ–DNA that lacks promoters is essentially devoid of bound proteins. The finding that RNAP can locate promoters on stretched DNA molecules eliminates intersegmental transfer as an obligatory component of the promoter search (Supplementary Fig. 1).

Fig. 2. Visualizing single molecules of RNA polymerase as they search for and engage promoters

(a) Kymograms of RNAP binding to λ–DNA showing kinetically distinct intermediates. DNA is unlabeled, and RNAP is magenta. (b) Representative example of RNAP binding and initiating transcription from λP_R; for this assay RNAP was premixed with all four rNTPs immediately prior to injection into the sample chamber (also see Supplementary Fig. 4). Initial binding (t = 0 s) is indicated as (), and magenta bars highlight the first 3–9 seconds of the reaction trajectory. (c) Binding distributions of kinetically distinct intermediates, and corresponding lifetime measurements (insets; also see Supplementary Fig. 2 & Supplementary Fig. 2); a schematic showing the relative promoter location is included. Error bars indicate 70% confidence intervals obtained through bootstrap analysis. (d) Kinetic scheme reflecting observed intermediates. NSP, CC, and OC, refer to nonspecifically bound, closed complex, and open complex, respectively; note that CC could also represent another intermediate preceding the open complex. Kinetic parameters are not segregated for individual promoters, rather they are considered collectively, and therefore reported values should be considered an average of all λ promoters. (e) Upper bound of observed diffusion coefficients for promoter–bound RNAP, compared to immobilized dig–QDs and other proteins known to undergo 1D–diffusion (Supplementary Fig. 7–8 & Supplementary Table 4).^,, Diffusion coefficients are gamma distributed, therefore we report the magnitude of the square root of the variance as error bars (n ≥ 50 for all data sets).

Fig. 3. Single–molecule kinetics reveal the promoter search is dominated by 3D–diffusion

(a) Influence of protein orientation on target association. The angle θ₀ defines the effective DNA–binding surface of QD–RNAP, and θ defines the orientation of the effective binding surface relative to the promoter. (b) Illustration of linear target size (a), for example where a = 2–bp: a 1–bp offset (in either direction) results in target recognition, but a 2–bp offset does not result in target recognition. (c) Relationship between θ, a and ψ, and their influence on promoter recognition. (d) Observed promoter assocition rates (k_a). Dashed magenta line corresponds to $k_{\alpha }^{\left(\psi \right)}\left(t\right)$ in the absence of faciliated diffusion (for ψ = 0.75–nm), and experimental values above this line reflect rate enhancement due to facilitated diffusion. The boundary between the shaded and unshaded regions of the graph represents the facilitation threshold (C_thr; as indicated). (e) Effective target size (ψ) versus RNAP concentration. The dashed black line highlights the limiting value of ψ. (f) Rate acceleration (k_a/C₀) versus RNAP concentration. The difference between the experimental values and $k_{\alpha }^{\left(\psi \right)}\left(t\right)$ reflects facilitated diffusion, and the orange shaded region represents the maximum possible acceleration due 1D–sliding and/or hopping. In (d–f) error bars represent S.E.M. (n ≥ 50 for each data point).

Fig. 4. Protein concentration exerts a dominant influence on target searches even for proteins capable of sliding on DNA

(a) DNA schematic showing the location of the 5x lac operator. (b) Two–color image of YOYO1–stained DNA (green) bound by QD–lac repressor (magenta). (c) Kymogram showing an example of lac repressor binding to nonspecific DNA and then diffusing in 1D to the operator; data were collected at 33 pM lac repressor. The distance between the initial binding site and the operator is indicated as Δx. (d) Kymogram showing an example of direct operator binding in the absence of any detectable 1D sliding; data were collected at 800 pM lac repressor. The successful search through 3D binding is highlighted, as are examples of molecules that searched through FD but failed to locate the operator. (e) Graph showing the mean value of Δx as a function of protein concentration for proteins that successfully engage the operator. Inset, percentage of total operator binding events that are attributable to FD (magenta) and 3D (green) at each protein concentration. Error bars represent S.D. of the data (n ≥ 54 for each data point). (f) Graph of Δx for all observed proteins. Blue data points () correspond to proteins that fail to bind the operator, magenta data points () are proteins that bind the operator after undergoing FD, and green data points () correspond 3D binding to the operator. All green data points within each column overlap at zero, but their fractional contribution to operator binding is shown as green bars in the inset of panel (e). These experiments were all conducted in buffer containing 10 mM Tris–HCl (pH 8.0), 1 mM MgCl₂, 1 mM DTT, and 1 mg ml⁻¹ BSA.

Fig. 5. Increasingly complex environments encountered during in vivo searches

Facilitated diffusion (FD) will be favored at concentrations below the facilitation threshold because the initial encounter with the DNA will most often occur at nonspecific sites, so the probability (P) of target engagement through FD exceeds the probability of engagement through 3D (P_FD > P_3D). Concentrations equal to or exceeding the facilitation threshold will favor 3D because the relative increase in protein abundance increases the probability of a direct collision with the target site (P_FD > P_3D). FD–related processes such as sliding/hopping can still occur at high protein concentrations, but those proteins undergoing FD are less likely to reach the target site before those that collide directly with the target. Although the facilitation threshold will vary for different proteins and different conditions, higher protein concentrations will still favor 3D collisions irrespective of the local environment (e.g. the presence of recruitment factors, DNA–bound obstacles, macromolecular crowding, local DNA folding) or global DNA architecture. See the Discussion for additional details.