Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

Abstract

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.gene expression | gene transcription | RNA processing | Gaussian process inference | RNA splicing I nduction of transcription through extracellular signaling can yield rapid changes in gene expression for many genes. Establishing the timing of events during this process is important for understanding the rate-limiting mechanisms regulating the response and vital for inferring causality of regulatory events. Several processes influence the patterns of mRNA abundance observed in the cell, including the kinetics of transcriptional initiation, elongation, splicing, and mRNA degradation. It was recently demonstrated that significant delays attributable to the kinetics of splicing can be an important factor in a focused study of genes induced by tumor necrosis factor (TNF-α) (1). Delayed transcription can play an important functional role in the cell, for example, inducing oscillations within negative feedback loops (2) or facilitating "justin-time" transcriptional programs with optimal efficiency (3). It is therefore important to identify such delays and to better understand how they are regulated. In this study, we combine RNA polymerase (pol-II) ChIP-Seq data with RNA-Seq data to study transcription kinetics of estrogen receptor (ER) signaling in breast cancer cells. Using an unbiased genome-wide modeling approach, we find evidence for large delays in mRNA production in 11% of the genes with a quantifiable signal in our data. A statistical analysis of gene...

Transient dynamics and their control in time-delay autonomous Boolean ring networks

“…When comparing time scales to real GRNs, one has to consider various processes within gene expression. Some of them have been characterized as time delays, such as mRNA splicing, which can be on the order of the actual gene product lifetime [30,70]. Additionally, there are many more processes that translate into time delay in our model, such as elongation [71], actual mature mRNA production rate, translational delays and many more.…”

“…Additionally, intermediate steps in gene expression can cause significant time delay in certain types of genes [30], which we include explicitly in our experimental approach. Time delays along network links in related non-Boolean systems, such as neural networks, have been shown to induce oscillations in systems that would otherwise converge to a fixed point [31][32][33].…”

Biophysically Motivated Regulatory Network Inference: Progress and Prospects

“…The most commonly used differential equation models are ordinary differential equation and stochastic differential equation models where the TF effect on the transcription rate is learned using a greedy search (one target gene at a time). Several challenges remain for learning dynamic models including (1) treating the parameterization of these large networks as a proper global system by simultaneously fitting all parameters [50] , (2) modeling latent states such as TF activity [51,52] , (3) explicitly modeling activator, repressor [53] , degradation, and target expression with distinct biophysically correct distributions, and (4) determining correct methods for using these models to design optimal experiments.…”

“…Implicitly, many (among our) TF-centric approaches are based on the assumption that TFs collectively modulate RNA polymerase II recruitment and elongation (could be measured using ChIP) resulting in changes in the rate of gene transcription [51,169,170] . Importantly, traditional mature mRNA sequencing (RNA-seq) results only in snapshots of total mature mRNA levels; that is, the rates of transcription are not directly measured (could be measured using GRO-seq/chromatin associated RNAseq).…”

Time to move on: Modeling transcription dynamics during an embryonic transition away from maternal control

“…39 The additional delay of 1.4 to 2.3 min might represent time needed for mRNA maturation, release and cytoplasmic transport as reported recently. 40 Since hb mRNA concentration changes much more slowly than the promoter status ( Fig. 2A, B), patterning decisions along the AP axis of the Figure 2.…”