Posttranscriptional expression regulation: what determines translation rates?

Abstract

Recent analyses indicate that differences in protein concentrations are only 20%-40% attributable to variable mRNA levels, underlining the importance of posttranscriptional regulation. Generally, protein concentrations depend on the translation rate (which is proportional to the translational activity, TA) and the degradation rate. By integrating 12 publicly available large-scale datasets and additional database information of the yeast Saccharomyces cerevisiae, we systematically analyzed five factors contributing to TA: mRNA concentration, ribosome density, ribosome occupancy, the codon adaptation index, and a newly developed "tRNA adaptation index." Our analysis of the functional relationship between the TA and measured protein concentrations suggests that the TA follows Michaelis-Menten kinetics. The calculated TA, together with measured protein concentrations, allowed us to estimate degradation rates for 4,125 proteins under standard conditions. A significant correlation to recently published degradation rates supports our approach. Moreover, based on a newly developed scoring system, we identified and analyzed genes subjected to the posttranscriptional regulation mechanism, translation on demand. Next we applied these findings to publicly available data of protein and mRNA concentrations under four stress conditions. The integration of these measurements allowed us to compare the condition-specific responses at the posttranscriptional level. Our analysis of all 62 proteins that have been measured under all four conditions revealed proteins with very specific posttranscriptional stress response, in contrast to more generic responders, which were nonspecifically regulated under several conditions. The concept of specific and generic responders is known for transcriptional regulation. Here we show that it also holds true at the posttranscriptional level.

Figure 1. Correlation of Protein Abundance and Different Translation Rates

Shows Spearman rank correlation (r_s) of TA versus reference protein abundance; error bars indicate ± one standard deviation based on random subsamples. TA1 = mRNA × ribocc × ribden; TA2 = TA1 × CAI; TA3 assumes Michaelis–Menten kinetics for the TA (Equation 3). The tRNA–AIs were calculated as described in Methods; tRNA–AI_p indicates the codon–tRNA assignment according to [42] and tRNA–AI _c the assignment according to Crick's wobble rules [43]. All correlations are based on 4,123 ORFs. Accounting for tRNA–AI_p slightly improves correlations compared with TA1 alone. However, TA2 (with CAI) performs better. Overall, considering saturation, (TA3) gave the best results.

Figure 2. Saturation of the Transactional Activity

Shows Spearman rank correlation (r_s) of different models for TA prediction as a function of the Michaelis constant, K_m. All four factors (F) contributing to TA (mRNA, ribocc, ribden, CAI) were tested for saturation individually and in combination. The different colors indicate the models for TA prediction: The product of mRNA × ribocc × ribden in the saturation term (i.e., TA3) yielded the best correlation with reference protein concentrations. The value without any saturation (r_s = 0.68) is approached for K_m → ∞.

Figure 3. Change of Translation Rate and Protein Change in Response to Different Stimuli

Median protein and translation rate ratios are shown for the conditions ± galactose (A), ethanol/galactose (B), minimal medium/normal medium (C), ± mating pheromone (D). Translation rates were calculated according to Equation 3, assuming constant ribden and ribocc (because those changes were unavailable for all but one condition). Proteins were functionally grouped based on MIPS annotation. AR, protein activity regulation; BG, biogenesis; CC, cell cycle/DNA processing; CF, cell fate; CR, cell rescue/defense/virulence; DF, differentiation; IE, interaction with cellular environment; MB, metabolism; PB, protein with binding function; PF, protein fate; ST, cellular communication/signal transduction; TC, transcription; TP, cellular transport.