Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells

Abstract

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions, leading to a complex dynamic equilibrium in RNAP occupancy of the ~100 bp immediately downstream of the transcription start site. In this article, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations over a broad range of parameters, we show that this model enables accurate estimation of initiation and pause-escape rates even in the presence of collisions between RNAPs and variable elongation rates. Furthermore, we show by simulation and analysis of data for human cell lines that pause-escape is often more strongly rate-limiting than conventional “pausing indices” would suggest, that occupancy of the pause site is elevated at many genes, and that steric hindrance of initiation can lead to a pronounced reduction in apparent initiation rates. Our modeling framework is generally applicable for all types of nascent RNA sequencing data and can be applied to a variety of inference problems.

Evolution of promoter-proximal pausing enabled a new layer of transcription control

“…9B ). Interestingly, the main difference between clusters 1 and 3 was that genes in cluster 3 had higher initiation rates, as determined by both TT-seq ( 33 ) and a computational modeling approach analyzing steady-state PRO-seq data ( 17 ) ( Fig. 3H-I; fig.…”

“…The plant A. thaliana and yeast S. cerevisiae showed no evidence of pausing. To reveal more subtle differences in the dynamics and gene-by-gene variation at the pause site than observed in meta profiles, we computed pausing indexes which quantify the duration Pol II spends in a promoter-proximal paused state ( 3, 17 ). Pausing indexes revealed that the residence time of Pol II at the pause site is, on average, 1-2 orders of magnitude higher in metazoans than unicellular eukaryotes or plants ( fig.…”

Evolution of promoter-proximal pausing enabled a new layer of transcription control

“…9B). Interestingly, the main difference between clusters 1 and 3 was that genes in cluster 3 had higher initiation rates, as determined by both TT-seq 33 and a computational modeling approach analyzing steadystate PRO-seq data 17 (Fig. 3H-I; fig.…”

“…The plant A. thaliana and yeast S. cerevisiae showed no evidence of pausing. To reveal more subtle differences in the dynamics and gene-by-gene variation at the pause site than observed in meta profiles, we computed pausing indexes which quantify the duration Pol II spends in a promoterproximal paused state 3,17 . Pausing indexes revealed that the residence time of Pol II at the pause site is, on average, 1-2 orders of magnitude higher in metazoans than unicellular eukaryotes or plants (fig.…”

DNA-sequence and epigenomic determinants of local rates of transcription elongation

“…In this study, we revisit these questions using a fundamentally different statistical modeling approach. Our method is based on a recently developed “unified model” for nascent RNA sequencing (NRS) data, which describes both the kinetics of Pol II movement on the DNA template and the generation of NRS read counts [24, 25]. We adapt this model to allow for a continuously variable elongation rate along the genome, using a generalized linear model to capture the relationship between the local rate and nearby genomic features.…”