Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

Abstract

The rate at which RNA molecules are degraded is a key determinant of cellular RNA concentrations, yet current approaches for measuring RNA half-lives are generally laborintensive, 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 and sequencing (PRO-seq) and RNA sequencing (RNA-seq). Our method treats PRO-seq as a measure of transcription rate and RNAseq as a measure of RNA concentration, and estimates the rate of RNA degradation required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA halflives genome-wide, with reasonable accuracy and good 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, including intron length, are positively correlated with RNA stability, whereas features related to miRNA binding, DNA methylation, and G+C-richness are negatively correlated with RNA stability. Furthermore, we find that a measure of predicted stability based on U1 binding sites 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 after controlling for their correlations with transcription. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

Histone hyperacetylation disrupts core gene regulatory architecture in rhabdomyosarcoma

“…3f). Furthermore, we calculated relative mRNA stability using combined analysis of ChRO-seq and total RNA-seq 22 , and find indeed that CR TF transcripts are much less stable ( Supplementary Fig. 3f-h), allowing relatively tight kinetics between transcriptional changes and net mRNA reduction, especially for the least stable transcripts such as MYCN (as reported for MYC in previous studies 23 ).…”

Interdependence between histone marks and steps in Pol II transcription

“…We show here that nascent transcription measured using PRO-seq provides at least as much information about chromatin state as the combination of multiple ChIP-seq datasets. In addition to chromatin state, nascent transcription is also known to provide direct information about gene expression (71), transcription factor binding (72), the location of transcription start sites, and the grammar of transcription initiation domains (11,15,53,73). Moreover, the introduction of new biochemical tools that allow the application of PRO-seq techniques with greater ease in solid tissue samples and other samples that have proven challenging to measure using conventional genomic techniques has the potential to further democratize these technologies (51,74).…”

One-shot analysis of translated mammalian lncRNAs with AHARIBO

“…Numerous IncRNAs are misannotated as non-coding but contain short ORFs encoding for micropeptides with biological relevance in cancer (D’Lima et al, 2017; Huang et al, 2017), bones development (Galindo et al, 2007), macrophage activity (van Solingen et al, 2018), metabolism (Magny et al, 2013; Nelson et al, 2016) and DNA repair (Slavoff et al, 2014). Different methodological approaches have been developed to quantify the variations of mRNA abundance (e.g., RNA-seq, RNA imaging) (Amit Blumberg et. al, 2019; Jao and Salic, 2008; Morisaki et al, 2016; Wu et al, 2016), RNA engagement with the translational machinery (e.g., ribosomal profiling, polysomal profiling) (Arava et al, 2003; Clamer et al, 2018; Eden et al, 2011; Taniguchi et al, 2010), and protein synthesis (e.g., pSILAC, PUNCH-P, BONCAT) (Aviner et al, 2013; Dieterich et al, 2006; Schwanhäusser et al, 2009; Yan et al, 2016).…”