Stochastic sequence-level model of coupled transcription and translation in prokaryotes

Abstract

BackgroundIn prokaryotes, transcription and translation are dynamically coupled, as the latter starts before the former is complete. Also, from one transcript, several translation events occur in parallel. To study how events in transcription elongation affect translation elongation and fluctuations in protein levels, we propose a delayed stochastic model of prokaryotic transcription and translation at the nucleotide and codon level that includes the promoter open complex formation and alternative pathways to elongation, namely pausing, arrests, editing, pyrophosphorolysis, RNA polymerase traffic, and premature termination. Stepwise translation can start after the ribosome binding site is formed and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation.ResultsFirst, we show that the model accurately matches measurements of sequence-dependent translation elongation dynamics. Next, we characterize the degree of coupling between fluctuations in RNA and protein levels, and its dependence on the rates of transcription and translation initiation. Finally, modeling sequence-specific transcriptional pauses, we find that these affect protein noise levels.ConclusionsFor parameter values within realistic intervals, transcription and translation are found to be tightly coupled in Escherichia coli, as the noise in protein levels is mostly determined by the underlying noise in RNA levels. Sequence-dependent events in transcription elongation, e.g. pauses, are found to cause tangible effects in the degree of fluctuations in protein levels.

Effects of multimerization on the temporal variability of protein complex abundance

“…If this delay is taken to be a random variable, it also results in increased fluctuations of the protein numbers. For the long-term behavior the time-shift is irrelevant, and the estimations of the contribution to noise are considerable smaller than those from other sources [28]. We tested adding such noise (by setting τ P to follow a normally distributed delay) and found no qualitative differences in our conclusions.…”

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

“…Stepwise translation can begin after the formation of the ribosome binding site and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation [3,15]. …”

“…The number of nucleotides (2 Δ P + 1) occupied by the RNAP on the strand while elongating is 25 [24]. Finally, mRNA can undergo degradation (reaction 13) [15]. …”

“…After elongation completion, it follows protein folding (reaction 22). The model accounts for codon-specific translation rates [37] and for the ribonucleotides occupied by a ribosome when on the RNA strand [15]. Finally, protein molecules undergo degradation (reaction 23).…”

“…Additionally, we aim to estimate, at least by comparison, the mean duration of a pause and its location relative to the transcription start site. For that, we simulate stochastic gene expression dynamics at the nucleotide and codon levels [3,15-17] of genes whose sequence includes long-duration pause sites that promote the occurrence of pauses with identical kinetics to that of the his pause [5]. Namely, we produce temporal series of RNA and protein numbers assuming that these molecules can be detected at the single-molecule level, as soon as they are produced, which is possible using MS2-GFP tagging [18,19] for RNA, and tsr-venus sequences for proteins [20].…”