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Quantitative analysis of processive RNA degradation by the archaeal RNA exosome.

Hartung S, Niederberger T, Hartung M, Tresch A, Hopfner KP - Nucleic Acids Res. (2010)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Integrated Protein Sciences, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany.

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.

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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 kc and kp as well as association and dissociation rates ka and kd 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 kc,30–kc,4. (C) cleavage-and-polymerization considers 27 different cleavage rates kc,30–kc,4, 27 different polymerization rates kp,30–kp,4 and one initial association rate ka,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.
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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 kc and kp as well as association and dissociation rates ka and kd 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 kc,30–kc,4. (C) cleavage-and-polymerization considers 27 different cleavage rates kc,30–kc,4, 27 different polymerization rates kp,30–kp,4 and one initial association rate ka,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.

Mentions: Kinetic models are shown in Figure 3A. They are described by four parameters: association rate ka,i, dissociation rate kd,i, cleavage rate kc,i and polymerization rate kp,i, one for each RNA of length i = 4, 5, …, 30. The corresponding set of differential equations that quantitatively describe RNA degradation is shown in the supplement Data (Chapter 1). Since the reaction takes place in an excess of inorganic phosphate (10 mM phosphate compared to only 3.6 μM ADP at the time all RNA molecules are totally degraded), we may assume no polymerization takes place, i.e. kp,i = 0 for all i. Consistently, we saw no synthesis of longer RNAs in our reactions. To obtain empirical estimates of the posterior parameter distribution, we implemented a MCMC approach based on the Metropolis–Hastings algorithm. The key ingredients are the likelihood function, the prior, and the proposal distribution. The likelihood function penalizes the estimation error produced by a given model. More precisely, it penalizes the residuals, i.e. the deviation of the measured RNA amounts at each time point from the amounts that have been predicted from the current parameter set. We assume that the residuals are independent realizations of Gaussian distributions with zero mean. Since the variances of these Gaussians are not known a priori, we assume a two-parameter error model with an additive and a multiplicative error component which has been proposed (39) in the context of spot quantification on arrays. We initialize the error model very conservatively (presuming large measurement errors). During the MCMC run, the error model is updated continuously by replacing it with an empirical estimate derived from the residuals that occurred in the Markov chain so far.Figure 1.


Quantitative analysis of processive RNA degradation by the archaeal RNA exosome.

Hartung S, Niederberger T, Hartung M, Tresch A, Hopfner KP - Nucleic Acids Res. (2010)

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 kc and kp as well as association and dissociation rates ka and kd 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 kc,30–kc,4. (C) cleavage-and-polymerization considers 27 different cleavage rates kc,30–kc,4, 27 different polymerization rates kp,30–kp,4 and one initial association rate ka,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.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2926604&req=5

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 kc and kp as well as association and dissociation rates ka and kd 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 kc,30–kc,4. (C) cleavage-and-polymerization considers 27 different cleavage rates kc,30–kc,4, 27 different polymerization rates kp,30–kp,4 and one initial association rate ka,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.
Mentions: Kinetic models are shown in Figure 3A. They are described by four parameters: association rate ka,i, dissociation rate kd,i, cleavage rate kc,i and polymerization rate kp,i, one for each RNA of length i = 4, 5, …, 30. The corresponding set of differential equations that quantitatively describe RNA degradation is shown in the supplement Data (Chapter 1). Since the reaction takes place in an excess of inorganic phosphate (10 mM phosphate compared to only 3.6 μM ADP at the time all RNA molecules are totally degraded), we may assume no polymerization takes place, i.e. kp,i = 0 for all i. Consistently, we saw no synthesis of longer RNAs in our reactions. To obtain empirical estimates of the posterior parameter distribution, we implemented a MCMC approach based on the Metropolis–Hastings algorithm. The key ingredients are the likelihood function, the prior, and the proposal distribution. The likelihood function penalizes the estimation error produced by a given model. More precisely, it penalizes the residuals, i.e. the deviation of the measured RNA amounts at each time point from the amounts that have been predicted from the current parameter set. We assume that the residuals are independent realizations of Gaussian distributions with zero mean. Since the variances of these Gaussians are not known a priori, we assume a two-parameter error model with an additive and a multiplicative error component which has been proposed (39) in the context of spot quantification on arrays. We initialize the error model very conservatively (presuming large measurement errors). During the MCMC run, the error model is updated continuously by replacing it with an empirical estimate derived from the residuals that occurred in the Markov chain so far.Figure 1.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Integrated Protein Sciences, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany.

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.

Show MeSH
Related in: MedlinePlus