Probing mechanisms of transcription elongation through cell-to-cell variability of RNA polymerase

Abstract

The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. While biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNAP molecules engaged in the process of transcribing a gene of interest. In this manuscript, we use Gillespie simulations to show that measurements of cellto-cell variability of RNAP numbers and inter-polymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures which can further be used to discern between them. Intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in S. cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes. Author SummaryThe process of transcription comprises many distinct steps and understanding the regulation of each of these steps provides insight into how the levels of gene expression are controlled in the cell. In this manuscript, we use stochastic simulations to explore how regulation at the level of elongation and initiation together influences the distribution of actively transcribing RNAP on a gene. We find that each of these steps of regulation leaves a distinct imprint on the gene-to-gene variability of number of actively transcribing RNAP on the gene and their inter-RNAP distances. Using these results, we analyze recent experimental data of transcribing RNAP distributions and find that the perturbations in these studies primarily impact the transcription initiation dynamics of the genes.

Predictive landscapes hidden beneath biological cellular automata

“…Gene-expression dynamics, whose more natural description is in terms of phenomenological variables such as the rates of production, degradation, binding, and unbinding, is just one example that remains challenging to model without resorting to exhaustive numerical simulations in many cases. Researchers have made some progress by using equilibrium statistical mechanics in which one uses the binding energies of transcription factors to determine steady-state gene-expression levels in microbes [31][32][33][34][35][36][37][38]. The main reason for the success of this approach is that certain processes, such as the binding and unbinding of a transcription factor at a target locus on DNA, occur much faster than the other processes involved in gene expression.…”

A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting

“…control of transcriptional initiation [56] and elongation [57,58], measure translational dynamics [59], and even count molecules [60]. Note that, while appropriate for qualitatively estimating the order of magnitude of bursting timescales, raw fluorescence measurements from MS2 and PP7 experiments such as those in Figure 1A-C do not directly report on the promoter state.…”

Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications

“…Depending on the timescales of the initiation and elongation processes, they will affect the polymerase measurements differently. Recent studies [26,47,86,87] explored this interplay of initiation and elongation dynamics in determining polymerase number distribution using numerical simulations. By considering a wide range (1/s to 1/min [81]) of initiation rates for genes in E.coli, it was shown that if the initiation timescales are much longer (one order of magnitude) than the elongation timescales associated with pausing, backtracking etc, the polymerase counts and interpolymerase distances remain largely unaffected by the elongation dynamics, as in the case of most of the mRNA promoters in it E. coli and yeast [81].…”

“…By considering a wide range (1/s to 1/min [81]) of initiation rates for genes in E.coli, it was shown that if the initiation timescales are much longer (one order of magnitude) than the elongation timescales associated with pausing, backtracking etc, the polymerase counts and interpolymerase distances remain largely unaffected by the elongation dynamics, as in the case of most of the mRNA promoters in it E. coli and yeast [81]. For genes that are highly transcribed such as the ribosomal genes, the initiation and elongation timescales become comparable, and hence the elongation dynamics can significantly alter the distribution of polymerases on a gene [49,50,86].…”