Analysis of Translation Elongation Dynamics in the Context of an Escherichia coli Cell

Abstract

Understanding the mechanisms behind translation and its rate-limiting steps is crucial for both the development of drug targets and improvement of heterologous protein production with many biotechnological applications, such as in pharmaceutical and biofuel industries. Despite many advances in the knowledge of the ribosome structure and function, there is still much discussion around the determinants of translation elongation with experiments and computational studies pointing in different directions. Here, we use a stochastic framework to simulate the process of translation in the context of an Escherichia coli cell by gathering the available biochemical data into a ribosome kinetics description. Our results from the study of translation in E. coli at different growth rates contradict the increase of mean elongation rate with growth rate established in the literature. We show that both the level of tRNA competition and the type of cognate binding interaction contribute to the modulation of elongation rate, and that optimization of a heterologous transcript for faster elongation rate is achieved by combining the two. We derive an equation that can accurately predict codon elongation rates based on the abundances of free tRNA in the cell, and can be used to assist transcript design. Finally, we show that non-cognate tRNA-ribosome binding has an important weight in translation, and plays an active role in the modulation of mean elongation rate as shown by our amino-acid starvation/surplus studies.

Figure 1

Schematic representation of the ribosome kinetics of the translation elongation cycle during which a polypeptide is synthesized following the decoding of its corresponding mRNA sequence. The four pathways represent the different types of codon-anticodon interaction (WC, WB, nc, and non). After the tRNA accommodation and peptide bond formation from state 5 to state 6, the subsequent kinetic pathway is assumed to be common for the different types of binding interactions, as the kinetic steps no longer depend on the codon-anticodon recognition. $T_{WC}^{\left(f\right)}$, $T_{WB}^{\left(f\right)}$, $T_{nc}^{\left(f\right)}$, and $T_{\text{non}}^{\left(f\right)}$ are the concentration of free cognate WC, cognate WB, and near-cognate and non-cognate tRNAs, respectively, for the codon being translated. A-site OFF, A-site PROOF, and E-site OFF correspond to the positions where tRNA is released from the mRNA-ribosome complex and P-site ON corresponds to the position where ribosome translocation to the next codon occurs and hence the tRNA in the A-site is placed in the P-site and the one in P-site is placed in the E-site. To see this figure in color, go online.

Figure 2

(a) Distribution of protein synthesis rate (V_p) for the different growth rates. The red bar and number represent the mean V_p among all mRNA species. (b) Elongation rate (υ_r) for each mRNA species in function of the ribosomal density (ρ) for the different growth rates. The red star and text represent the mean (ρ, υ_r) from all the mRNA species in the cell. Vertical and horizontal error bars represent standard deviation from 100 repeated simulations. (c and d) Specific protein synthesis rate (V_s) for each mRNA species in function of the ribosomal density (ρ) (c) and in function of the initiation rate (k_I × R^f) (d) for the different growth rates. Green and blue color-code separates the data points that have a V_p below or above the mean V_p among all mRNA species, respectively. Vertical and horizontal error bars represent standard deviation from 100 repeated simulations. To see this figure in color, go online.

Figure 3

(a) Comparison between simulated (sim) and experimental time-evolution curves of methionine level obtained from experiments in Spencer et al. (12) for WT Luciferase and for two of its synonymous transcripts (WC and tRNA genes and CU-based). Bounds represent the 25th and 75th quartile of the distribution from the in silico pulse-chase curves. Time axis from the experimental data points was adjusted with the same calibration factor used for the methionine labeling time (see Supporting Materials and Methods S8). (b) In silico pulse-chase performed during the translation of seven heterologous transcripts yielding the same amino-acid sequence based on different synonymous codon substitution criteria in E. coli cells at 1.07 h⁻¹. The time-evolution curves of methionine level result from the average of 4000 repeated simulations. The curves are plotted with the bounds representing the 25th and 75th quartile of their sample distribution in Fig. S7. To see this figure in color, go online.

Figure 4

(a) Main and total effect ($S_{k_i},S_{T_{K_i}}$) on the value of υ_r due to a change in rate constant k_i. (Inset) Changes in υ_r in function of the changes on $k_2$ for a range of two orders of magnitude below and above its nominal value (star). (b) Codon elongation rate obtained from stochastic simulations ($k_{\text{stoch}}$) versus the codon elongation rate constant ($k_{\text{eff}}$; open circles). Each data point corresponds to one of the 61 codons taking part in the translation elongation. Linear regression line is represented by a continuous line. The Pearson correlation coefficient (r) and p-value are indicated. The dashed line is the one-to-one function for comparison. The signal/noise from $k_{\text{stoch}}$ corresponding to each codon is represented by the dots and remains higher than 1 for all codons, starting to stabilize for the codons with higher elongation rates. To see this figure in color, go online.

Figure 5

Number of tRNA molecules of species i active in translation ($tRN{A}_i^a$) in function of its respective cognate interaction-based mRNA codon usage frequency ($IBmC{U}_{{\text{tRNA}}_i}^{\text{cogn}}$). The Pearson correlation coefficients (r) and the p-values are indicated. Correlation outliers are underlined in the legend. To see this figure in color, go online.

Figure 6

Relative deviation of the average elongation rate from all mRNA species in the cell, upon combined 50% decrease of the abundance of all tRNA isoacceptors. This is per amino-acid type in function of the relative deviation of the average elongation rate from all mRNA species in the cell upon one-at-a-time 50% increase of the abundance of the same tRNA isoacceptors per amino-acid group. One-to-one line (dashed) plotted for comparison. To see this figure in color, go online.