Abstract
BackgroundThe concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. Despite the critical importance of RNA decay rates, current approaches for measuring them are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here, we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On sequencing (PRO-seq) and RNA sequencing (RNA-seq).ResultsOur method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding and DNA methylation are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1 binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability.ConclusionWe introduce an approach for estimating the relative half-lives of individual RNAs. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.
Highlights
The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay
The dependency on intronic reads limits the method to intron-containing transcription units that are transcribed at relatively high levels. This simple approach requires no time course, metabolic labeling, transcriptional inhibition, or any experimental innovation beyond standard RNA sequencing (RNA-seq), making it an inexpensive and effective strategy for identifying genes undergoing cell type- or condition-specific decay [12, 21, 22]. We show that this same general approach—but using a measure of nascent transcription based on Precision Run-On sequencing (PRO-seq) rather than intronic RNA-seq reads—results in improved estimates of relative RNA half-life
We found that the PRO-seq and RNA-seq measurements were well correlated overall, with Spearman’s ρ = 0.83 (Fig. 1a), suggesting that transcription explains the majority of the variance in mRNA levels
Summary
The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. RNA concentrations, result from a dynamic equilibrium between the production of new RNA molecules and their decay [1,2,3,4,5,6,7]. Bulk differences in RNA concentrations across types of transcription units (TUs) often result from differences in RNA decay rates rather than differences in production rates. Among protein-coding genes, mRNAs associated with housekeeping functions tend to be stable, whereas those associated with regulation of transcription and apoptosis tend to have much shorter half-lives, probably to enable RNA concentrations to change rapidly in response to changing conditions [4, 6, 7, 10, 11]. RNA decay is accelerated by condition- or cell typespecific expression of microRNAs or RNA-binding proteins [3, 12]
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