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RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP)


Many biological processes are RNA-mediated, but higher-order structures for most RNAs are unknown, which makes it difficult to understand how RNA structure governs function. Here we describe selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) that makes possible de novo and large-scale identification of RNA functional motifs. Sites of 2′-hydroxyl acylation by SHAPE are encoded as noncomplementary nucleotides during cDNA synthesis, as measured by massively parallel sequencing. SHAPE-MaP–guided modeling identified greater than 90% of accepted base pairs in complex RNAs of known structure, and we used it to define a new model for the HIV-1 RNA genome. The HIV-1 model contains all known structured motifs and previously unknown elements, including experimentally validated pseudoknots. SHAPE-MaP yields accurate and high-resolution secondary-structure models, enables analysis of low-abundance RNAs, disentangles sequence polymorphisms in single experiments and will ultimately democratize RNA-structure analysis.

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Figure 1: SHAPE-MaP overview.
Figure 2: Nucleotide-resolution interrogation of RNA structure and ligand-induced conformational changes.
Figure 3: Accuracy of SHAPE-MaP–directed modeling of secondary structure.
Figure 4: SHAPE-MaP analysis of the HIV-1 NL4-3 genome.
Figure 5: Functional and structural validation of newly discovered HIV-1 RNA motifs.

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We thank R.J. Gorelick for expert preparation of HIV-1 genomic RNA and many insightful discussions, C.E. Hajdin for extensive discussion and for initial pseudoknot mutant design, R. Swanstrom for critical discussions, K. Compliment for expert technical assistance and W. Resch for competition calculations. TZM-bl cells were obtained from J. Kappes and X. Wu (Tranzyme Inc.) via the US National Institutes of Health (NIH) AIDS Reagent Program. This work was supported by the NIH (AI068462 to K.M.W.) and the University of North Carolina Center for AIDS Research (P30 AI50410). N.A.S. was funded as a Lineberger Postdoctoral Fellow in the Basic Sciences and by a Ruth L. Kirschstein NRSA Fellowship (F32 GM010169). G.M.R. was supported in part by an NIH training grant in molecular and cellular biophysics (T32 GM08570).

Author information




The SHAPE-MaP strategy was conceived and designed by N.A.S. and K.M.W. SHAPE experiments were performed by N.A.S. and G.M.R. HIV-1 replication assays were designed and performed by N.A.S. and J.A.E.N. The SHAPE-MaP data analysis pipeline was created by S.B. RNA folding and motif discovery analyses were conceived and created by G.M.R. and K.M.W. All authors collaborated in interpreting the experiments and writing the manuscript.

Corresponding author

Correspondence to Kevin M Weeks.

Ethics declarations

Competing interests

N.A.S., S.B. and K.M.W. are listed as inventors on a US provisional patent application based on elements of this work.

Integrated supplementary information

Supplementary Figure 1 SHAPE-MaP data analysis pipeline.

Outline of software pipeline that fully automates calculations of per-nucleotide mutation rates, SHAPE reactivities, and standard error estimates given high-throughput sequencing data and at least one reference sequence. The software is executable on Unix-based platforms. See Online Methods for full description. This strategy is implemented in the ShapeMapper software.

Supplementary Figure 2 Overview of multiscale RNA secondary structure modeling.

Genome-scale RNA secondary structure modeling was performed in steps to increase computational efficiency and model accuracy and to facilitate incorporation of pseudoknot prediction into a global model. (top) The first step involved searching for pseudoknots in short windows. For all later steps, pseudoknot pairs were prohibited from forming base pairs. In the second step, the partition function was calculated in overlapping windows and averaged for Shannon entropy and pairing probability evaluations (see Online Methods). Base pairs with probabilities ≥99% were forced to form during calculation of a minimum free energy structure using Fold47, using overlapping 4000-nt windows. In the third step, a consensus structure from the overlapping windows was generated by retaining base pairs that appeared in more than half of possible windows. Finally, pseudoknotted helices were added to the final model. (bottom) Comparison of windowed folding versus one-step folding for calculating the partition functions and minimum free energy structures for RNAs of 1500 to 9200 nts. Wall-time for modeling the entire HIV-1 was estimated (asterisk). A small performance penalty is observed for splitting an RNA into overlapping windows. However, computation time for RNAs over 3000 nts will scale approximately linearly with sequence length. Folding times are reported both as wall clock times and as cpu cycles (2012 iMac with 3.1 GHz Intel Core i7 and 16 GB RAM). This strategy is implemented in the SHAPE-MaP Folding Pipeline.

Supplementary Figure 3 Strategies for the SHAPE-MaP experiment using either gene-specific primers or random fragmentation for sample analysis and sequencing library preparation.

SHAPE-MaP can be performed using gene-specific primers (for small RNAs or targeted areas in large RNAs and for analysis of scarce and low concentration RNAs) or random primers (for comprehensive analysis of large RNAs or complete transcriptomes) to create the initial cDNA pool. For both approaches, RNA is treated with a SHAPE reagent or with solvent under conditions of interest, and a sample of RNA is modified under denaturing conditions. For gene-specific samples, reverse transcription and PCR primers are designed based on the known target sequence. Large RNAs are randomly fragmented in a buffered Mg2+ solution. Single-stranded cDNA was synthesized using mutation-prone reverse transcription; misincorporation events in the nascent cDNA mark the location of SHAPE adducts in the subject RNA. Double-stranded cDNAs were created either by PCR (gene-specific approach) or second-strand synthesis (randomly fragmented samples). Sequence platform-specific sequences (including multiplexing barcodes) were added to the dsDNA libraries, either directly through a second PCR (gene-specific approach) or by a DNA-DNA ligation of adaptor sequences (random fragmented samples). Libraries prepared by either method were then sequenced, producing data that were processed into SHAPE reactivity profiles used in structure modeling applications. SHAPE-MaP is fully independent of sequencing platform and library generation scheme (once the initial cDNA has been synthesized). Thus, any platform and any library generation scheme can be used.

Supplementary Figure 4 Mutation rate histograms for paired and non-paired nucleotides in the 16S rRNA.

Nucleotides were separated into paired (upper panels) and non-paired (lower panels) groups based on their observed pairing in the E. coli 16S rRNA38. Mutation rate histograms for each experimental sample (SHAPE, untreated, and denatured) were calculated based on pairing status (left-hand panels). Distributions of mutation rates for the SHAPE-modified and untreated samples are similar for base-paired nucleotides; whereas nucleotides in non-paired conformations are much more reactive towards SHAPE probing. (right-hand panels) SHAPE-MaP reactivities are independent of nucleotide type.

Supplementary Figure 5 SHAPE-MaP replicates of E. coli 16S rRNA.

Data correspond to full biological replicates performed six months apart by different individuals. The inset for nucleotides 1350-1450 (bottom right) shows standard errors.

Supplementary Figure 6 Error analysis for SHAPE-MaP.

Deep bootstrapping of highly sequenced TPP riboswitch samples (see Fig. 2). Individual sequencing reads from a large pool (150,000) were sampled with replacement 100 times per simulated depth. The standard error of the SHAPE reactivity was calculated at each depth from each bootstrap. Consistent with a Poisson model, the standard error of the SHAPE measurement decreased as the -1/2 power of read depth across all nucleotides.

Supplementary Figure 7 Secondary structure models for regions of HIV-1NL4-3, identified de novo, with low SHAPE reactivities and low Shannon entropies.

Nucleotides are colored by SHAPE reactivity. Structures are the same as shown in Fig. 4c, except that nucleotide identities are shown explicitly.

Supplementary Figure 8 Pseudoknot mutants.

Green arrows indicate sites of disruptive mutations.

Supplementary Figure 9 Deconvolution of profiles for two alleles of the U3PK in a single SHAPE-MaP experiment.

RNAs with nearly identical sequences can be computationally separated and analyzed using data generated from a single experiment (see Online Methods). Yellow bars indicate significant SHAPE reactivity differences (and highlight the same regions shown in Fig. 5).

Supplementary Figure 10 Pseudoknot SHAPE-MaP profiles for ENVPK and CAPK.

(upper panel) SHAPE-MaP and structure profiles for ENVPK and direct growth competition and viral spread data. (lower panel) SHAPE-MaP and structure profiles for CAPK, located in a high entropy region of the RNA genome and thus served as a negative control. Also displayed are competition and viral spread assay data.

Supplementary Figure 11 Detection of 2'-O-adducts by mutational profiling.

Shown are rates for sequence changes and unambiguously aligned deletions, above background for the E. coli 16S rRNA. Nucleotides were defined as non-paired or paired based on the accepted secondary structure. The letter in the lower right of each panel indicates the expected nucleotide based on the coding strand, and the letters on the vertical axes indicate the nucleotide detected by sequencing or “del” for deletion. Rates are shown for the (a) 1M7, (b) 1M6, and (c) NMIA reagents. Nucleotide misincorporation and deletion rates were similar for the three SHAPE reagents.

Supplementary Figure 12 Primer design for SHAPE-MaP.

Sequences with low or unevenly distributed GC-content benefit from the newly designed LNA-based primers used to analyze HIV-1 sequences in this work.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 and Supplementary Tables 1–3 (PDF 5911 kb)

Supplementary Data 1

Full SHAPE dataset for the HIV-1 RNA genome. (XLSX 752 kb)

Supplementary Data 2

Structure models for each well-defined region in the HIV-1 RNA genome, in connect-table format. (ZIP 46 kb)

Supplementary Data 3

Pairing probabilities for HIV-1 nucleotides, in tab-delimited text format. (TXT 190 kb)

Supplementary Data 4

Complete differential SHAPE-Map data for the model RNAs reported in Figure 3. (ZIP 233 kb)

Supplementary Software 1

Software pipeline for analyzing MaP data. Illustrated in Supplementary Figure 1. (ZIP 76 kb)

Supplementary Software 2

Pipeline for automated windowed modeling approach for folding long RNAs and calculating pairing probabilities and Shannon entropies. Illustrated in Supplementary Figure 2. (ZIP 205 kb)

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Siegfried, N., Busan, S., Rice, G. et al. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat Methods 11, 959–965 (2014).

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