Quantifying the influence of 5'-RNA modifications on RNA polymerase I activity

Reversible Kinetics in Multi-nucleotide Addition Catalyzed by S. cerevisiae RNA polymerase II Reveal Slow Pyrophosphate Release

Eukaryotes express at least three nuclear DNA dependent RNA polymerases (Pols). Pols I, II, and III synthesize ribosomal (r) RNA, messenger (m) RNA, and transfer (t) RNA, respectively. Pol I and Pol III have intrinsic nuclease activity conferred by the A12.2 and C11 subunits, respectively. In contrast, Pol II requires the transcription factor (TF) IIS to confer robust nuclease activity. We recently reported that in the absence of the A12.2 subunit Pol I reverses bond formation by pyrophosphorolysis in the absence of added PPi, indicating slow PPi release. Thus, we hypothesized that Pol II, naturally lacking TFIIS, would reverse bond formation through pyrophosphorolysis. Here we report the results of transient-state kinetic experiments to examine the addition of nine nucleotides to a growing RNA chain catalyzed by Pol II. Our results indicate that Pol II reverses bond formation by pyrophosphorolysis in the absence of added PPi. We propose that, in the absence of endonuclease activity, this bond reversal may represent kinetic proofreading. Thus, given the hypothesis that Pol I evolved from Pol II through the incorporation of general transcription factors, pyrophosphorolysis may represent a more ancient form of proofreading that has been evolutionarily replaced with nuclease activity.

Quantifying the impact of initial RNA primer length on nucleotide addition by RNA polymerase I

Transient state kinetic studies of eukaryotic DNA-dependent RNA polymerases (Pols) in vitro provide quantitative characterization of enzyme activity at the level of individual nucleotide addition events. Previous work revealed heterogeneity in the rate constants governing nucleotide addition by yeast RNA polymerase I (Pol I) for each position on a template DNA. In contrast, the rate constants that described nucleotide addition by yeast RNA polymerase II (Pol II) were more homogeneous. This observation led to the question, what drives the variability of rate constants governing RNA synthesis by Pol I? Are the kinetics of nucleotide addition dictated by the position of the nascent RNA within the polymerase or by the identity of the next encoded nucleotide? In this study, we examine the impact of nucleotide position (i.e. nascent RNA primer length) on the rate constants governing nine sequential nucleotide addition events catalyzed by Pol I. The results reveal a conserved trend in the observed rate constants at each position for all primer lengths used, and highlight that the 9-nucleotide, or 9-mer, RNA primer provides the fastest observed rate constants. These findings suggest that the observed heterogeneity of rate constants for RNA synthesis by Pol I in vitro is driven primarily by the template sequence.

Transient-State Kinetic Analysis of the RNA Polymerase II Nucleotide Incorporation Mechanism

Eukaryotic RNA polymerase II (Pol II) is an essential enzyme that lies at the core of eukaryotic biology. Due to its pivotal role in gene expression, Pol II has been subjected to a substantial number of investigations. We aim to further our understanding of Pol II nucleotide incorporation by utilizing transient-state kinetic techniques to examine Pol II single nucleotide addition on the millisecond time scale. We analyzed Saccharomyces cerevisiae Pol II incorporation of ATP or an ATP analog, Sp-ATP-α-S. Here we have measured the rate constants governing individual steps of the Pol II transcription cycle in the presence of ATP or Sp-ATP-α-S. These results suggest that Pol II catalyzes nucleotide incorporation by binding the next cognate nucleotide and immediately catalyzes bond formation and bond formation is either followed by a conformational change or pyrophosphate release. By comparing our previously published RNA polymerase I (Pol I) and Pol I lacking the A12 subunit (Pol I ΔA12) results that we collected under the same conditions with the identical technique, we show that Pol II and Pol I ΔA12 exhibit similar nucleotide addition mechanisms. This observation indicates that removal of the A12 subunit from Pol I results in a Pol II like enzyme. Taken together, these data further our collective understanding of Pol II's nucleotide incorporation mechanism and the evolutionary divergence of RNA polymerases across the three domains of life.

Transient-state kinetic analysis of multi-nucleotide addition catalyzed by RNA polymerase I

RNA polymerases execute the first step in gene expression: transcription of DNA into RNA. Eukaryotes, unlike prokaryotes, express at least three specialized nuclear multisubunit RNA polymerases (Pol I, Pol II, and Pol III). RNA polymerase I (Pol I) synthesizes the most abundant RNA, ribosomal RNA. Nearly 60% of total transcription is devoted to ribosomal RNA synthesis, making it one of the cell's most energy consuming tasks. While a kinetic mechanism for nucleotide addition catalyzed by Pol I has been reported, it remains unclear to what degree different nucleotide sequences impact the incorporation rate constants. Furthermore, it is currently unknown if the previous investigation of a single-nucleotide incorporation was sensitive to the translocation step. Here, we show that Pol I exhibits considerable variability in both k_max and K_1/2values using an in vitro multi-NTP incorporation assay measuring AMP and GMP incorporations. We found the first two observed nucleotide incorporations exhibited faster k_max-values (∼200 s^-1) compared with the remaining seven positions (∼60 s^-1). Additionally, the average K_1/2 for ATP incorporation was found to be approximately threefold higher compared with GTP, suggesting Pol I has a tighter affinity for GTP compared with ATP. Our results demonstrate that Pol I exhibits significant variability in the observed rate constant describing each nucleotide incorporation. Understanding of the differences between the Pol enzymes will provide insight on the evolutionary pressures that led to their specialized roles. Therefore, the findings resulting from this work are critically important for comparisons with other polymerases across all domains of life.

Multi-start Evolutionary Nonlinear OpTimizeR (MENOTR): A hybrid parameter optimization toolbox

Parameter optimization or "data fitting" is a computational process that identifies a set of parameter values that best describe an experimental data set. Parameter optimization is commonly carried out using a computer program utilizing a non-linear least squares (NLLS) algorithm. These algorithms work by continuously refining a user supplied initial guess resulting in a systematic increase in the goodness of fit. A well-understood problem with this class of algorithms is that in the case of models with correlated parameters the optimized output parameters are initial guess dependent. This dependency can potentially introduce user bias into the resultant analysis. While many optimization programs exist, few address this dilemma. Here we present a data analysis tool, MENOTR, that is capable of overcoming the initial guess dependence in parameter optimization. Several case studies with published experimental data are presented to demonstrate the capabilities of this tool. The results presented here demonstrate how to effectively overcome the initial guess dependence of NLLS leading to greater confidence that the resultant optimized parameters are the best possible set of parameters to describe an experimental data set. While the optimization strategies implemented within MENOTR are not entirely novel, the application of these strategies to optimize parameters in kinetic and thermodynamic biochemical models is uncommon. MENOTR was designed to require minimal modification to accommodate a new model making it immediately accessible to researchers with a limited programming background. We anticipate that this toolbox can be used in a wide variety of data analysis applications. Prototype versions of this toolbox have been used in a number of published investigations already, as well as ongoing work with chemical-quenched flow, stopped-flow, and molecular tweezers data sets. STATEMENT OF SIGNIFICANCE: Non-linear least squares (NLLS) is a common form of parameter optimization in biochemistry kinetic and thermodynamic investigations These algorithms are used to fit experimental data sets and report corresponding parameter values. The algorithms are fast and able to provide good quality solutions for models involving few parameters. However, initial guess dependence is a well-known drawback of this optimization strategy that can introduce user bias. An alternative method of parameter optimization are genetic algorithms (GA). Genetic algorithms do not have an initial guess dependence but are slow at arriving at the best set of fit parameters. Here, we present MENOTR, a parameter optimization toolbox utilizing a hybrid GA/NLLS algorithm. The toolbox maximizes the strength of each strategy while minimizing the inherent drawbacks.