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RNA structure maps across mammalian cellular compartments


RNA structure is intimately connected to each step of gene expression. Recent advances have enabled transcriptome-wide maps of RNA secondary structure, called ‘RNA structuromes’. However, previous whole-cell analyses lacked the resolution to unravel the landscape and also the regulatory mechanisms of RNA structural changes across subcellular compartments. Here we reveal the RNA structuromes in three compartments, chromatin, nucleoplasm and cytoplasm, in human and mouse cells. The cytotopic structuromes substantially expand RNA structural information and enable detailed investigation of the central role of RNA structure in linking transcription, translation and RNA decay. We develop a resource with which to visualize the interplay of RNA–protein interactions, RNA modifications and RNA structure and predict both direct and indirect reader proteins of RNA modifications. We also validate a novel role for the RNA-binding protein LIN28A as an N6-methyladenosine modification ‘anti-reader’. Our results highlight the dynamic nature of RNA structures and its functional importance in gene regulation.

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Fig. 1: Chromatin fractions are enriched for pre-mRNA and lncRNA structures.
Fig. 2: RNA structure plays a central role in connecting transcription, translation and RNA degradation.
Fig. 3: RNA structure differences in cellular contexts.
Fig. 4: RNA modification and RBP binding underlie RNA structural changes.
Fig. 5: Structural analysis dissects different types of m6A readers.
Fig. 6: Validation of IGF2BP3 as an indirect m6A reader and LIN28A as an anti-reader.

Code availability

All scripts can be found on GitHub at the following site online:

Data availability

All sequencing data are available through the Gene Expression Omnibus (GEO) under accession GSE117840. icSHAPE reactivity scores and Lin28A CLIP peaks can be found on the UCSC Genome Browser at the following sites: human,; mouse, Source data for Figs. 4d and 5a are provided with the paper.


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We thank members of the Chang and Zhang labs for discussion. We thank R. Flynn for experimental advice. We thank C. Dai, Y. Li, and Y. Yang for computational advice, and we acknowledge sequencing support from the genomics and synthesis biology core facility. This work is supported by National Institutes (NIH) grants R01-HG004361 (to H.Y.C.), R35-CA209919 (to H.Y.C.) and R01GM127295 (to E.T.K.), and by the National Natural Science Foundation of China (Grants 31671355, 91740204, and 31761163007), and the National Thousand Young Talents Program of China (to Q.C.Z.). F.M.F. was supported by a NIH T32 Stanford Genome Training Program (SGTP) Fellowship and the Arnold O. Beckman Postdoctoral Fellowship. Some sequencing data was generated on an Illumina Hiseq 4000 that was purchased with funds from NIH (award number S10OD018220). H.Y.C. is an Investigator of the Howard Hughes Medical Institute.

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Authors and Affiliations



H.Y.C. and Q.C.Z. conceived the project. L.S. and F.M.F. performed icSHAPE experiments in human and mouse cell lines respectively. E.T.K. supplied reagents. P.L. and L.S. analyzed all the results. J.P.B. performed the Lin28A CLIP-seq experiments. B.L. and L.T. assisted with experiments, and W.H. assisted with analysis. Q.C.Z. and H.Y.C. supervised the project. Q.C.Z., L.S., F.M.F. and H.Y.C. wrote the manuscript with inputs from all authors.

Corresponding author

Correspondence to Qiangfeng Cliff Zhang.

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Competing interests

H.Y.C. is co-founder and serves on the SAB of Epinomics and Accent Therapeutics.

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Integrated supplementary information

Supplementary Figure 1 Subcellular fractions are enriched for distinct protein and RNA markers.

a, Widefield imaging of intact mES cells (left), mES nuclei after cytosol extraction, but excluding nonidet P-40 (NP-40) detergent in buffer (middle, control), and mES nuclei after cytosol extraction and ER removal (right). Nuclear (DAPI) stain shown in blue, and ER tracker dye in red. b, Western blots of protein markers for different subcellular compartments for HEK293 cells. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) for the cytoplasmic fraction (cy), small nuclear ribonucleoprotein U1 subunit 70 (SNRP70) for the nucleoplasmic fraction (np), and histone H3 for the chromatin fraction (ch). Protein from the whole cell (wc) is shown for comparison. c, RT-qPCR of RNA markers of different cellular compartments. For GAPDH and U1, the RT (reverse transcription) primers target spliced (mature) mRNAs, whereas for ACTIN mRNA, an intron is targeted. d-e, Western blots of protein markers for mES cells with (d) DMSO treatment and (e) NAI-N3 treatment. ACTIN for the cytoplasmic and nucleoplasmic fractions (cy and np), small nuclear ribonucleoprotein U1 subunit 70 (SNRP70) for the nucleoplasmic fraction (np), and histone H3 for the chromatin fraction (ch).

Supplementary Figure 2 Reverse transcription stops in the icSHAPE experiments are highly correlated in replicates.

Cumulative distribution plots of Pearson correlation coefficient of reverse transcription stops in replicates of RNA fractions in chromatin (ch), nucleoplasm (np), and cytoplasm (cy). Data from DMSO replicates are shown in red, in vitro NAI-N3 replicates in green, and in vivo NAI-N3 replicates in blue.

Supplementary Figure 3 Quality control of icSHAPE reactivity across replicates, fractions and different abundance of RNAs.

a,b, Scatter plot and Pearson correlation coefficients of icSHAPE reactivities between each pair of replicates and fractions in mouse (a) and human (b). c, Cumulative density plot of Pearson correlation coefficient of the number of RT stops between replicates at different RNA expression level thresholds. d, Scatter plots of NAI reads number versus RNA-seq RPKM * Length (nt).

Supplementary Figure 4 Chromatin fractions are enriched for lncRNA and pre-mRNA (intron) structural information.

a-b, Structural models of (a) Ribonuclease P RNA, and (b) Signal recognition particle (SRP) RNA. Models are generated from the ViennaRNA web service based on data from the RNASTRAND database. Nucleotides are colored with icSHAPE scores from the nucleoplasm fraction. c, Histograms of ratios of lncRNAs versus all transcripts in different cellular compartments for human and mouse. d, Histograms of ratios of the sequencing reads mapped to introns versus all transcripts in different cellular compartments for human and mouse. e-g, Violin plot of Gini index of icSHAPE data in exon versus in intron in (e) mRNAs from mES cells, (f) mRNAs from HEK293 cells depleted of RBP-binding sites (20nt) and (g) mRNAs from HEK293 cells depleted of RBP-binding sites (100nt).

Supplementary Figure 5 RNA structure plays a central role in connecting transcription, translation and RNA degradation.

a-i, Kernel density estimation (KDE) plots (a,c,e,g), scatter plots (b,d,f,h), and scatter plots with a higher read depth cutoff (read depth =200) (i) of transcription rate versus 5’UTR RNA structure in chromatin, translational efficiency versus 5’UTR RNA structure in cytoplasm, RNA half-life versus full-length-transcript RNA structure in nucleoplasm, and RNA half-life versus RNA structure in cytoplasm. The two-tailed p-value was calculated by python package function scipy.stats.pearsonr. j-l, Scatter plots of transcription rate versus 5’UTR RNA structure in chromatin. Transcription rates are from published datasets: Min, I. M. et al. Genes & development. 25, 742–754, 2011 (j) and Tastemel, M. et al. Stem Cell Res. 25, 250–255, 2017 (k). translational efficiency versus 5’UTR RNA structure in cytoplasm (l). Translational efficiency data are from a published report (Yoshikawa, H. et al. eLife. 7, 2018).

Supplementary Figure 6 Inverse correlations of RNA half-lives with RNA structures in different transcript regions and positive correlations of RNA translation efficiency with transcription rate.

a, Density plot of Gini index with half-life in 5’UTR, CDS and 3’UTR regions in cytoplasm and nucleoplasm. b, Density plot of translation efficiency versus transcription rate in mES cells. Figure is generated using published data (Ingolia, N. T. et al. Cell 147, 789–802, 2011, and Jonkers, I. et al. eLife. 3, 2014). The two-sided p-value was calculated by python package function scipy.stats.pearsonr.

Supplementary Figure 7 RNA is less structured in chromatin than in cytoplasm.

Heatmaps of average icSHAPE scores of different RNA types in various RNA fractions in human (including and excluding all known RNA binding protein (RBP) binding sites) and mouse. Dashed lines represent insufficient data.

Supplementary Figure 8 M6A, pseudouridylation (Ψ) and HNRNPC binding sites are more single-stranded.

a-c, Metagene profiles of icSHAPE scores at (a) m6A sites versus control (unmodified) sites with the m6A motif, (b) pseudouridylation (Ψ) sites versus random U sites, and (c) HNRNPC binding sites versus control sites with the polyU motif. P-values were calculated by single-sided Mann-Whitney U test, red stars mean p-values are less than 0.01.

Supplementary Figure 9 Many RBP-binding sites are enriched in RNA structurally changes regions.

The number of RBP-binding sites in the identified changes regions (red triangle) versus shuffled changes regions (blue dots).

Supplementary Figure 10 The binding of LIN28A and IGF2BP3 to their target RNAs is influenced by m6A modification.

a,b, RNA pull-down assays and RBP Western blots using RNA probes that contain unmodified A, m6A, and U mutation respectively from (a) transcripts IGF2BP3 binds to, and (b) LIN28A binds to m6A sites are marked with red ‘m’. Histograms show RNA pull down with three replicates. Western blots are done with (a) anti-IGF2BP3 antibody or anti-LIN28A antibody, after RNA pull down. The error bars represent standard deviation of replicates. c, Overlap of Lin28a binding sites (including both in Mettl3 knock-out (KO) and in wild-type (WT) mES cells) and m6A sites. For those overlapped sites, Lin28a binds more strongly at 45 out of 68 sites in Mettl3 KO, versus 23 out of 68 sites in WT mES cells.

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Sun, L., Fazal, F.M., Li, P. et al. RNA structure maps across mammalian cellular compartments. Nat Struct Mol Biol 26, 322–330 (2019).

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