Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo

By Silvi Rouskin, Meghan M. Zubradt, Stefan Washietl, Manolis Kellis, Jonathan S. Weissman. Nature, 2013

825 citations recorded by the source corpus.

RNA has a dual role as an informational molecule and a direct effector of biological tasks. The latter function is enabled by RNA’s ability to adopt complex secondary and tertiary folds and thus has motivated extensive computational and experimental efforts for determining RNA structures. Existing approaches for evaluating RNA structure have been largely limited to in vitro systems, yet the thermodynamic forces which drive RNA folding in vitro may not be sufficient to predict stable RNA structures in vivo. Indeed, the presence of RNA-binding proteins and ATP-dependent helicases can influence which structures are present inside cells. Here we present an approach for globally monitoring RNA structure in native conditions in vivo with single-nucleotide precision. This method is based on in vivo modification with dimethyl sulphate (DMS), which reacts with unpaired adenine and cytosine residues, followed by deep sequencing to monitor modifications. Our data from yeast and mammalian cells are in excellent agreement with known messenger RNA structures and with the high-resolution crystal structure of the Saccharomyces cerevisiae ribosome. Comparison between in vivo and in vitro data reveals that in rapidly dividing cells there are vastly fewer structured mRNA regions in vivo than in vitro. Even thermostable RNA structures are often denatured in cells, highlighting the importance of cellular processes in regulating RNA structure. Indeed, analysis of mRNA structure under ATP-depleted conditions in yeast shows that energy-dependent processes strongly contribute to the predominantly unfolded state of mRNAs inside cells. Our studies broadly enable the functional analysis of physiological RNA structures and reveal that, in contrast to the Anfinsen view of protein folding whereby the structure formed is the most thermodynamically favourable, thermodynamics have an incomplete role in determining mRNA structure in vivo.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f8af2ae955e661155a5ec2efb02de61b2023853

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Citations in the corpus

RNA duplex map in living cells reveals higher order transcriptome structure (2016, methodology): “Compared with icSHAPE and DMS-seq, which measure nucleotide flexibility (Ding et al., 2014; Rouskin et al., 2014; Spitale et al., 2015), PARIS directly determines the locations of long-range duplexes and can resolve complex structures such as pseudoknots and alternative structures.”
RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP) (2014, positive): “…to read out the results of enzymatic, SHAPE or dimethyl sulfate (DMS) probing have provided comprehensive support for large-scale comparative trends in transcript structure but have not been shown to yield accurate models of complex secondary structures or enable discovery of new motifs 4–11 .”
3D RNA and Functional Interactions from Evolutionary Couplings. (2015, neutral): “…et al., 1987; Latham and Cech, 1989; Moazed and Noller, 1986), have identified transcriptome-wide RNA base-pairing in vivo (Ding et al., 2014; Rouskin et al., 2014; Spitale et al., 2015; Wan et al., 2014) including within the coding region of mRNAs, suggesting a function independent of…”
Recent advances in RNA structurome (2022, methodology): “The main difference between DMS-seq and Structure-seq is that DMSseq requires ligation of priming adapters to fragment RNAs prior to PCR and sequencing, whereas Structure-seq uses random hexanucleotide sequences (N6) to directly prime RNA during RT, where cDNA is derived from and then ligated to DNA”
Structural imprints in vivo decode RNA regulatory mechanisms (2015, methodology): “Metagene analysis of translation sites, pause site, m6A sites and protein binding sites—We calculated metagene structure profile around different functional sits by averaging all valid reactivity score R: (1), 10 nts upstream and downstream of the RNA methylation m6A site, as determined in by our la”
Single-cell RNA-seq: advances and future challenges (2014, neutral): “Moreover, variants of the RNA-seq technique globally determine many other RNA-related aspects in the cell, for example, secondary structures of transcripts (32), editing sites (33), transcript stability (34), translation rates (35) and the protein–RNA interactome (36).”
NONCODEV5: a comprehensive annotation database for long non-coding RNAs (2017, methodology): “DMS-seq or Structure-seq uses the small molecule dimethyl sulfate (DMS) to modify adenines and cytosines in single-stranded states both in vivo and in vitro (64,65).”
Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences (2017, neutral): “We assessed the effect of secondary structure, which can influence ribosome initiation, scanning, and elongation (Rouskin et al. 2013; Pop et al. 2014).”