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Genome-scale deconvolution of RNA structure ensembles

Nature Methods volume 18, pages 249–252 (2021)Cite this article

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Abstract

RNA structure heterogeneity is a major challenge when querying RNA structures with chemical probing. We introduce DRACO, an algorithm for the deconvolution of coexisting RNA conformations from mutational profiling experiments. Analysis of the SARS-CoV-2 genome using dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) and DRACO, identifies multiple regions that fold into two mutually exclusive conformations, including a conserved structural switch in the 3′ untranslated region. This work may open the way to dissecting the heterogeneity of the RNA structurome.

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Fig. 1: In silico validation of DRACO.
Fig. 2: In vitro validation of DRACO.
Fig. 3: A conserved structural switch in the 3′ UTR of SARS-CoV-2.

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Data availability

Sequencing data have been deposited to the Gene Expression Omnibus (GEO) database under the accession GSE158052. Additional processed files are available at http://www.incarnatolab.com/datasets/DRACO_Morandi_2021.php. Source data are provided with this paper.

Code availability

The source code of DRACO is freely available from GitHub under the GPLv3 license (https://github.com/dincarnato/draco). A complete list of the software used for data analysis is available from the Nature Research Reporting Summary.

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Acknowledgements

D.I. was supported by the Dutch Research Council (Netherlands Organisation for Scientific Research, NWO) as part of the research programme NWO Open Competitie ENW-XS (project number OCENW.XS3.044), and by the Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen. S.O. was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC), grant AIRC IG 2017 Id. 20240 and PRIN 2017. M.J.H. was supported by the Leiden University Fund (LUF), the Bontius Foundation, and donations from the crowdfunding initiative ‘wake up to corona’.

Author information

Author notes

  1. These authors contributed equally: Edoardo Morandi, Ilaria Manfredonia, Lisa M. Simon.

Authors and Affiliations

  1. Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands

    Edoardo Morandi, Ilaria Manfredonia & Danny Incarnato

  2. Dipartimento di Scienze della Vita e Biologia dei Sistemi, Università di Torino, Torino, Italy

    Edoardo Morandi, Lisa M. Simon, Francesca Anselmi & Salvatore Oliviero

  3. Department of Medical Microbiology, Molecular Virology Laboratory, Leiden University Medical Center, Leiden, the Netherlands

    Martijn J. van Hemert

  4. Italian Institute for Genomic Medicine (IIGM), Candiolo, Italy

    Salvatore Oliviero

Authors
  1. Edoardo Morandi
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  2. Ilaria Manfredonia
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  3. Lisa M. Simon
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  4. Francesca Anselmi
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  5. Martijn J. van Hemert
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  6. Salvatore Oliviero
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  7. Danny Incarnato
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Contributions

E.M. and D.I. conceived the project; I.M., L.M.S. and F.A. carried out the wet-lab work; M.J.H. carried out SARS-CoV-2 manipulations; E.M. and D.I. designed and implemented the DRACO algorithm; E.M. and D.I. carried out bioinformatics, structure modeling and data analysis; D.I. and S.O. wrote the manuscript.

Corresponding authors

Correspondence to Salvatore Oliviero or Danny Incarnato.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Walter N. Moss and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Overview of the DRACO algorithm.

By default, a window of a size equal to 90% of the median read is slid along the transcript, in 5% increments. For each window, a mutation map is generated using only the reads covering the entire window. Bases that are mutated with respect to the reference are assigned a value of 1, while not mutated bases are assigned a value of 0. By using this map, a graph is built, in which each vertex is a base of the transcript, and edges connecting two vertices are weighted proportionally to the number of reads in which the two connected bases have been observed to co-mutate. Starting from the adjacency matrix of the graph, the normalized Laplacian matrix is calculated and used for spectral deconvolution. A null-model is derived by repeating the same procedure after shuffling the mutations in the original mutation map. Analysis of the distance between consecutive eigenvalues (eigengaps) for the experimental data with respect to the null model allows identifying the number of informative eigengaps, corresponding to the number of coexisting RNA conformations (clusters). Once the number of clusters has been defined, fuzzy clustering is performed using a custom graph cut approach, that enables the weighting of vertices in accordance with their affinity to each cluster. This analysis is repeated across the whole transcript. Consecutive windows showing a compatible number of clusters are merged. Then, reads are re-assigned to the respective cluster, allowing the deconvolution of the cluster reactivity profiles and relative abundances.

Extended Data Fig. 2 In silico validation of DRACO.

a, Maximum number of conformations detected for 10 sets of 100 simulated RNAs, with length ranging from 600 to 1,500 nt, expected to form 1 to 4 conformations, at a coverage of 5,000X and a read length of 150 nt. Data are presented as mean values ± SD of the 10 sets. The individual data points, representing the mean of each set, are shown. b, Box-plot of median Pearson correlation coefficients (PCC) of reconstructed reactivity profiles for 10 sets of 100 simulated RNAs, with length ranging from 600 to 1,500 nt, expected to form 1 to 4 conformations, at a coverage of 5,000X and a read length of 150 nt. When DRACO detected more than one window with different numbers of clusters, only the largest window, spanning >50% of the RNA length, was considered. Boxes span the 25th to the 75th percentile. The center represents the median. Whiskers span from the 25th percentile – 1.5 times the IQR, to above the 75th percentile + 1.5 times the IQR. Data points falling outside of this range represent outliers and are reported as dots. c, Violin plot depicting the distribution of expected versus reconstructed conformation abundances for 10 sets of 100 simulated RNAs, with length ranging from 600 to 1,500 nt, expected to form 2 conformations with varying relative abundances, at a coverage of 5,000X and with a read length of 150 nt. When multiple windows were detected, only the largest window was considered. The Pearson correlation is indicated in the bottom-right corner of each plot. Whiskers span the 25th to the 75th percentile. The central dot represents the median.

Extended Data Fig. 3 Validation of DRACO on in silico-merged in vitro-generated profiles.

a, DRACO-deconvoluted profiles for cspA RNA folded and probed in vitro at either 37 °C or 10 °C (from Zhang et al., 2018), pooled at different percentages. The percentage of pooling is indicated next to each reconstructed profile. b, Heatmap of Pearson correlation coefficient for DRACO-deconvoluted profiles for each pool, compared to the expected profiles at 37 °C or 10 °C.

Supplementary information

Supplementary Information

Supplementary Figs. 1–19 and Notes 1 and 2.

Reporting Summary

Source data

Source Data Fig. 1

Maximum number of detected conformations, Pearson correlations for reconstructed profiles and estimated conformation abundances for simulated data

Source Data Fig. 2

Normalized reactivity values for DRACO-deconvoluted cspA and add conformations

Source Data Fig. 3

Relative abundance and normalized reactivity values for DRACO-deconvoluted SARS-CoV-2 3′ UTR conformations

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Morandi, E., Manfredonia, I., Simon, L.M. et al. Genome-scale deconvolution of RNA structure ensembles. Nat Methods 18, 249–252 (2021). https://doi.org/10.1038/s41592-021-01075-w

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