Summary
SARS-CoV-2 is a betacoronavirus with a single-stranded, positive-sense, 30-kilobase RNA genome responsible for the ongoing COVID-19 pandemic. Currently, there are no antiviral drugs with proven efficacy, and development of these treatments are hampered by our limited understanding of the molecular and structural biology of the virus. Like many other RNA viruses, RNA structures in coronaviruses regulate gene expression and are crucial for viral replication. Although genome and transcriptome data were recently reported, there is to date little experimental data on native RNA structures in SARS-CoV-2 and most putative regulatory sequences are functionally uncharacterized. Here we report secondary structure ensembles of the entire SARS-CoV-2 genome in infected cells at single nucleotide resolution using dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) and the algorithm ‘detection of RNA folding ensembles using expectation–maximization’ clustering (DREEM). Our results reveal previously undescribed alternative RNA conformations across the genome, including structures of the frameshift stimulating element (FSE), a major drug target, that are drastically different from prevailing in vitro population average models. Importantly, we find that this structural ensemble promotes frameshifting rates (~40%) similar to in vivo ribosome profiling studies and much higher than the canonical minimal FSE (~20%). Overall, our result highlight the value of studying RNA folding in its native, dynamic and cellular context. The genomic structures detailed here lays the groundwork for coronavirus RNA biology and will guide the design of SARS-CoV-2 RNA-based therapeutics.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We show new functional data that the rate of ribosomal frameshifting is nearly twice as high when the frameshifting stimulation element (FSE) is surrounded by ~3kb of its native sequence context (~40%) than when it is studied in isolation (~20%) (Fig. 6). We have updated the genome-wide structure model of SARS-CoV-2 based on new deep sequencing data (Fig. 1 and Supplemental File 1). These deeper data also enable us to quantify structural heterogeneity genome-wide (Fig. 3). To pinpoint the differences between alternative structures in the FSE, we show the difference in DMS signal between alternative structures in Fig. 5. We also present a new metric, the data-structure correlation index (DSCI), to quantify the agreement between structure and SHAPE/DMS reactivities, and show that our genome-wide model has a high agreement of 0.891 (Fig. 2).
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153851
