Quantitative analysis of processive RNA degradation by the archaeal RNA exosome

Abstract

RNA exosomes are large multisubunit assemblies involved in controlled RNA processing. The archaeal exosome possesses a heterohexameric processing chamber with three RNase-PH-like active sites, capped by Rrp4- or Csl4-type subunits containing RNA-binding domains. RNA degradation by RNA exosomes has not been studied in a quantitative manner because of the complex kinetics involved, and exosome features contributing to efficient RNA degradation remain unclear. Here we derive a quantitative kinetic model for degradation of a model substrate by the archaeal exosome. Markov Chain Monte Carlo methods for parameter estimation allow for the comparison of reaction kinetics between different exosome variants and substrates. We show that long substrates are degraded in a processive and short RNA in a more distributive manner and that the cap proteins influence degradation speed. Our results, supported by small angle X-ray scattering, suggest that the Rrp4-type cap efficiently recruits RNA but prevents fast RNA degradation of longer RNAs by molecular friction, likely by RNA contacts to its unique KH-domain. We also show that formation of the RNase-PH like ring with entrapped RNA is not required for high catalytic efficiency, suggesting that the exosome chamber evolved for controlled processivity, rather than for catalytic chemistry in RNA decay.

Figure 1.

Visualization of RNase activity of the archaeal exosome on denaturing polyacrylamide gels: the input (I) is a 30-mer polyA RNA radioactively labelled at the 5′-end that is degraded from the 3′-end to a final product (FP) of a 3-mer. Time points were taken in increasing intervals [in minutes: 0:10; 0:20; 0:30; 0:40; 0:50; 1:00; 1:10; 1:20; 1:40; 2:00; 2:20; 2:40; 3:00; 3:30; 4:00; 4:30; 5:00; 5:50; 6:00; 6:30; 7:00; 7:30; 8:00; 9:00; 10:00; 12:00; 14:00; 16:00; 18:00; 20:00; 25:00; 30:00; 35:00; 40:00; (B) ends at 8:00 min]. RNA degradation does clearly not occur with constant speed and the (Csl4:Rrp41:Rrp42)₃ exosome (A) degrades RNA with a different time dependency than the (Rrp4:Rrp41:Rrp42)₃ exosome (B).

Figure 2.

Crystal structure of 6-mer RNA bound to the active site of the archaeal exosome. Rrp41 is shown in light and Rrp42 in dark green. The 2F_o–F_c electron density is contoured at 1.0σ and only shown for the RNA and the side chain of Y70^Rrp41. (A) In the wild-type exosome Y70 is stacking with the fourth base of the bound RNA, and only weak density can be seen for the fifth and sixth base. (B) Electron density for the fourth base of the RNA is much weaker in the Y70A^Rrp41 mutant compared to the wild-type and no density can be detected at this contour level for additional nucleotides.

Figure 3.

Three different models to describe the kinetics of RNA degradation by the exosome were tested: (A) scheme for the general kinetic model, which includes cleavage and polymerization rates k_c and k_p as well as association and dissociation rates k_a and k_d for all RNAs from 30–4 nt. (B–D) Quantified concentrations of RNA intermediates from Figure 1A, along with least square fits to different kinetic models. (B) Strict processivity considers only 27 different cleavage rates k_c,30 –k_c,4. (C) cleavage-and-polymerization considers 27 different cleavage rates k_c,30 –k_c,4, 27 different polymerization rates k_p,30–k_p,4 and one initial association rate k_a,30 (=55 rates). With models (C) and (B), no reasonable fit could be obtained. (D) By including association, dissociation and cleavage and making rational simplifications (see text) we can convincingly fit the data with a model containing 28 different rate constants.

Figure 4.

Catalytic efficiency v_i for all RNA intermediates present during the degradation of a 30-mer RNA by the Csl4-Rrp41-Rrp42 exosome was determined with MCMC simulations. (A) shows the traceplot and (B) the final parameter set (burnin = 150 000). It can be seen that the MCMC chains vary in convergence speed as well as in variability. The boxplots in (B) illustrate the main advantage of the MCMC approach: it not only offers a set of parameters that best describe the measured data, but it also yields a posterior distribution for each catalytic efficiency parameter and thus provides a more comprehensive summary of the data.

Figure 5.

Comparisons of the catalytic efficiency v_i of different exosome variants versus RNA lengths: (A) differences in the cap proteins influence catalytic activity. This is shown by comparison of v_i from the cap-less exosome (Rrp41:Rrp42)₃ in magenta, the Csl4 capped exosome (Csl4:Rrp41:Rrp42)₃ in red and the Rrp4 capped exosome (Rrp4:Rrp41:Rrp42)₃ in blue. (B) Tyr70^Rrp42 close to the active site is especially important to efficiently degrade small RNAs. The wild-typ Csl4 exosome is shown in red and the Y70A^Rrp42 mutant in green. (C) The role of the ring architecture and dynamics for catalytic activity is shown by comparing wild-type cap-less exosome (Rrp41:Rrp42)₃ in magenta with the dimeric and open interface mutant (Rrp41:Rrp42)₁ and a rigidified crosslinked variant that is less dynamic in yellow. A total of 1000 parameter sets have been randomly drawn from the stationary phase of the Markov chain. Thus for each RNA length and each timepoint, we obtained 1000 estimates whose distribution is displayed by boxplots.

Figure 6.

SAXS structure of the Rrp4 exosome with endogenously purified bacterial RNA. (A) SAXS data of the Rrp4 exosome (green) and the Rrp4 exosome with RNA (orange) (curves show the scattering intensity I(q) as a function of the scattering angle 2θ and X-ray wavelength λ, where q = (4sin/θ)) and the pair-distribution function describing intramolecular distances; in the presence of RNA longer distances occur and the radius of gyration increases. (B) Average of 10 independent ab initio models for the apo exosome and the RNA-bound complex superimposed with the crystal structure. The additional density for the RNA is clearly visible.