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. Although 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 singlecell measurements of RNA polymerase (RNAP) molecules engaged in the process of transcribing a gene of interest. In this article, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and interpolymerase 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. Most 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 Saccharomyces cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

“…Like cell-to-cell variability in transcription initiation, single-cell distributions of elongation rates can provide crucial insights into, for example, promoter-proximal pausing [ 54 ], traffic jams [ 93 , 94 ], transcriptional bursting [ 95 , 96 ], and noise propagation [ 59 ]. While genome-wide approaches have had huge success in measuring mean properties of elongation [ 16 , 97 ], they remain unable to resolve single-cell distributions of elongation rates.…”

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

“…Like cell-to-cell variability in transcription initiation, single-cell distributions of elongation rates can provide crucial insights into, for example, promoter-proximal pausing (Serov et al, 2017), traffic jams (Klumpp and Hwa, 2008;Klumpp, 2011), transcriptional bursting (Choubey et al, 2015(Choubey et al, , 2018, and noise propagation (Ali et al, 2020). While genome-wide approaches have had huge success in measuring mean properties of elongation (Core et al, 2008;Carrillo Oesterreich et al, 2010), they remain unable to resolve single-cell distributions of elongation rates.…”

“…From elucidating the nature of mutations (Luria and Delbruck, 1943) and revealing mechanisms of transcription initiation (Zenklusen et al, 2008;Sanchez et al, 2011;So et al, 2011;Sanchez and Golding, 2013;Little et al, 2013;Hocine et al, 2013;Jones et al, 2014;Xu et al, 2015;Choubey et al, 2015;Zoller et al, 2018;Choubey et al, 2018;Filatova et al, 2020), transcription elongation (Boettiger et al, 2011;Serov et al, 2017;Ali et al, 2020), and translational control (Cai et al, 2006), to enabling the calibration of fluorescent proteins in absolute units (Rosenfeld et al, 2005(Rosenfeld et al, , 2006Teng et al, 2010;Brewster et al, 2014;Kim et al, 2016;Bakker and Swain, 2019), examining single-cell distributions through the lens of theoretical models has made it possible to extract molecular insights about biological function that are inaccessible through the examination of averaged quantities. The single-cell measurements afforded by our approach made it possible to infer full distributions of transcription parameters (Fig.…”

“…Crucially, our analysis also delivered novel single-cell statistics of the whole transcription cycle that were previously unmeasurable using genome-wide approaches, making it possible to generate distributions of parameter values necessary for investigations that go beyond simple populationaveraged analyses (Raj et al, 2006;Zenklusen et al, 2008;Sanchez et al, 2011;Coulon et al, 2013;Little et al, 2013;Sanchez and Golding, 2013;Jones et al, 2014;Serov et al, 2017;Lucas et al, 2018;Zoller et al, 2018;Ali et al, 2020;Filatova et al, 2020). We show that, by taking advantage of time-resolved data, our inference is able to filter out uncorrelated noise, such as that originating from random measurement error, in these distributions and retain sources of correlated variability (such as biological and systematic noise.…”

Statistics of Nascent and Mature RNA Fluctuations in a Stochastic Model of Transcriptional Initiation, Elongation, Pausing, and Termination

“…A special case of such non-constitutive behaviour is bursty expression, whereby transcripts are produced in short bursts that are separated by long silent intervals (Suter et al. 2011 ; Halpern et al. 2015 ).…”