Genome-wide dynamics of RNA synthesis, processing and degradation without RNA metabolic labeling

Abstract

11The kinetic rates of RNA synthesis, processing and degradation determine the dynamics of 12 transcriptional regulation by governing both the abundance and the responsiveness to modulations of 13 premature and mature RNA species. The study of RNA dynamics is largely based on the integrative 14 analysis of total and nascent transcription, with the latter being quantified through RNA metabolic 15 labeling. We describe here INSPEcT-, a computational method based on mathematical modeling of 16 intronic and exonic expression, able to derive the dynamics of transcription from steady state or time 17 course profiling of just total RNA, without requiring any information on nascent transcripts. Our 18 approach closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves 19 the ability to detect changes in transcripts half-lives, reduces the cost and complexity of the 20 experiments, and can be adopted to study experimental conditions where nascent transcription cannot 21 be readily profiled. Finally, we applied INSPEcT-to the characterization of post-transcriptional 22 102 First, these data indicate that measurements of mature RNA are in themselves poorly informative 103 of the real transcriptional state of genes. For example, the detection of a mature RNA is typically 104 taken as indication that the corresponding gene is transcriptionally active. This is not necessarily the 105 4 case for highly stable RNAs, which might persist long after the gene has become silent. Second, these 106 data illustrate how difficult it is to decipher the mechanism responsible for modulating mature RNAs 107 without determining the corresponding RNA dynamics. For example, the modulation of mature RNA 108 species is typically seen as indication of transcriptional regulation, while it could originate from 109 changes in the dynamics of processing and/or degradation, without any change in the rate of synthesis 110 taking place. Ultimately, these data show the necessity to develop methods for the quantification of 111 RNA kinetic rates, in order to fully disclose the mechanisms behind complex responses in gene 112 expression. 113

Direct RNA Sequencing for the Study of Synthesis, Processing, and Degradation of Modified Transcripts

“…Mathematical modeling is then used for the gene-level quantification of RNA kinetic rates, for example as implemented and documented in the INSPEcT R/Bioconductor library (de Pretis et al, 2015;Furlan et al, 2019a). Briefly, when short labeling times are adopted (<1 h), the quantification of nascent RNA for each gene provides a proxy for the rate of synthesis of premature RNA.…”

“…Then, total RNA-seq reads are used to measure the abundance of premature and mature transcripts: reads that entirely map to one or more exons are used to quantify mature RNA species, and the remaining mapped reads (entirely, or partially, covering introns) are used for the quantification of premature species. Finally, the combination of synthesis rate and premature RNA abundance is used to quantify the rate of processing, while the combination of synthesis rate and mature RNA abundance allows the quantification of degradation rates (Furlan et al, 2019a).…”

“…The joint analysis of the information gained from RNA metabolic labeling experiments, together with the profiling of specific RNA modifications, would be extremely powerful for the study of the functional consequences of these marks on specific RNA life cycle steps. However, while the application of metabolic labeling for the profiling of nascent RNA (Dolken et al, 2008) and for the quantification of the RNA kinetic rates (Dolken et al, 2008;Miller et al, 2011;Rabani et al, 2011Rabani et al, , 2014de Pretis et al, 2015;Furlan et al, 2019a) is an established approach, its combination with the profiling of RNA modifications is more problematic. In fact, the joint profiling of nascent and modified RNA requires the identification of at least two RNA modifications: the endogenous mark (e.g., m 6 A), and the exogenous modification used for the labeling (e.g., 4sU).…”

Ontology-driven integrative analysis of omics data through Onassis

“…www.nature.com/scientificreports www.nature.com/scientificreports/ Landscapes of post-transcriptional regulation from the integrative analysis of large-scale gene expression datasets. With the aim of determining the extent of post-transcriptional regulation in human we analyzed large-scale 35 RNA-seq data from 620 publicly available samples. Metadata of samples, which were generated with the removal of ribosomal RNA (rather than selection of polyA + RNAs), were retrieved through GEOmetadb queries.…”

“…The annotation procedure resulted in 26 tissue and 24 disease semantic sets. RNA-seq samples within each semantic set were then used to identify genes that were post-transcriptionally regulated for a given tissue and disease as described in 35 . Without the use of Onassis we could only have independently identified genes within each of the 620 samples.…”