The origin of short transcriptional pauses

Abstract

RNA polymerases are protein molecular machines that transcribe genetic information from DNA into RNA. The elongation of the RNA molecule is frequently interrupted by pauses, the detailed nature of which remains controversial. Here we ask whether backtracking, the central mechanism behind long pauses, could also be responsible for short pauses normally attributed to the ubiquitous pause state. To this end, we model backtracking as a force-biased random walk, giving rise to a broad distribution of pause durations as observed in experiments. Importantly, we find that this single mechanism naturally generates two populations of pauses that are distinct both in duration and trajectory: long-time pauses with the expected behavior of diffusive backtracks, and a new class of short-time backtracks with characteristics similar to those of the ubiquitous pause. These characteristics include an apparent force insensitivity and immobility of the polymerase. Based on these results and a quantitative comparison to published pause trajectories measured with optical tweezers, we suggest that a significant fraction of short pauses are simply due to backtracking.

Figure 1

(a) Schematic drawing of RNAP with an applied assisting force f. The integer n corresponds to the number of RNA bases that protrude past the active site. (b) Corresponding schematic free-energy landscape. (c) The return time distribution normalized to the particles that return within a finite time. Due to this normalization and the specific assumption that the transition state is centrally positioned, δ = a/2, the distribution is invariant with respect to f → −f. (Inset) Chance of returning within a finite time, $p_{\text{ret}}={\int }_0^{\mathrm{\infty }}\Psi \left(t\right)\mathrm{d}t$, as a function of force. For negative forces, only the fraction exp {f/f₀} returns, where f₀ = kT/a ≈ 12 pN at physiological conditions.

Figure 2

(a) The maximum depth of the average backtrack as a function of pause duration (computed numerically; solid line) and the corresponding short-time (dashed) and long-time (dash-dot) asymptotic behaviors. (b) An illustration of the average trajectory in the two regimes. Short trajectories remain shallow throughout the backtrack (left-hand panel), whereas long trajectories display the three regimes reported in Shaevitz et al. (10) (right-hand panel). They start diffusively in regime I with a steep entrance into the backtrack, remain at a distance in regime II, and return in regime III in a manner reverse to that of regime I.

Figure 3

Single parameter fit of the asymptotic long-pause shape function for regime I to data from Shaevitz et al. (10), giving a characteristic time t₀ = 0.54 s (solid line). Also shown are the corresponding asymptotic curves of Eq. 4 (dashed lines). The best fit to a line has a mean-square error that is 3.5 times larger than the fit to the full theory presented here.