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Analyses of mRNA structure dynamics identify embryonic gene regulatory programs

Nature Structural & Molecular Biologyvolume 25pages677686 (2018) | Download Citation

Abstract

RNA folding plays a crucial role in RNA function. However, knowledge of the global structure of the transcriptome is limited to cellular systems at steady state, thus hindering the understanding of RNA structure dynamics during biological transitions and how it influences gene function. Here, we characterized mRNA structure dynamics during zebrafish development. We observed that on a global level, translation guides structure rather than structure guiding translation. We detected a decrease in structure in translated regions and identified the ribosome as a major remodeler of RNA structure in vivo. In contrast, we found that 3′ untranslated regions (UTRs) form highly folded structures in vivo, which can affect gene expression by modulating microRNA activity. Furthermore, dynamic 3′-UTR structures contain RNA-decay elements, such as the regulatory elements in nanog and ccna1, two genes encoding key maternal factors orchestrating the maternal-to-zygotic transition. These results reveal a central role of RNA structure dynamics in gene regulatory programs.

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Acknowledgements

We thank K. Bilguvar, S. Mane and C. Castaldi for sequencing support. We thank A. Bazzini and members of the laboratories of A.J.G. and M.K. for intellectual and technical support. This research was supported by the Fonds de Recherche du Québec–Santé (postdoctoral fellowship to J.-D.B.); the Human Frontier Science Program (LT000307/2013-l to E.M.N.); the Australian Research Council (DE170100506 to E.M.N.); and the National Institute of Health (grants R01 HD074078, GM103789, GM102251, GM101108 and GM081602), the Pew Scholars Program in the Biomedical Sciences, the March of Dimes (1-FY12-230), the Yale Scholars Program and Whitman fellowship funds provided by E. E. Just, Lucy B. Lemann, Evelyn and Melvin Spiegel, H. Keffer Hartline and Edward F. MacNichol, Jr. of the Marine Biological Laboratory, Woods Hole, Massachusetts, USA, to A.J.G.

Author information

Author notes

  1. These authors contributed equally: Jean-Denis Beaudoin, Eva Maria Novoa.

Affiliations

  1. Department of Genetics, Yale University School of Medicine, New Haven, CT, USA

    • Jean-Denis Beaudoin
    • , Charles E. Vejnar
    • , Valeria Yartseva
    • , Carter M. Takacs
    •  & Antonio J. Giraldez
  2. Computer Science and Electrical Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Eva Maria Novoa
    •  & Manolis Kellis
  3. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Eva Maria Novoa
    •  & Manolis Kellis
  4. Department of Neuroscience, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia

    • Eva Maria Novoa
  5. School of Medicine, University of New South Wales, Sydney, New South Wales, Australia

    • Eva Maria Novoa
  6. College of Arts and Sciences, University of New Haven, West Haven, CT, USA

    • Carter M. Takacs
  7. Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA

    • Antonio J. Giraldez

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Contributions

J.-D.B. and A.J.G. conceived the project. J.-D.B. performed the experiments. J.-D.B., E.M.N. and C.E.V. performed data processing. V.Y. identified the regulatory element in the nanog 3′ UTR and built the reporter constructs. C.M.T. performed the KHSRP iCLIP experiment. J.-D.B. and E.M.N. performed data analysis and, together with A.J.G., interpreted the results. A.J.G. supervised the project, with the contribution of M.K. J.-D.B., E.M.N. and A.J.G. wrote the manuscript with input from the other authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Jean-Denis Beaudoin or Antonio J. Giraldez.

Integrated supplementary information

  1. Supplementary Figure 1 DMS-seq controls and mRNA structure changes during the MZT.

    a, Schematic view of in vivo and in vitro genome-wide DMS probing. Red stars represent DMS-modified nucleotides. b, Cartoon shows the DMS-seq protocol. Dashed lines highlight the second size selection capturing reverse transcription products that prematurely stop due to DMS modifications. c, Proportion of DMS-seq reads from DMS-modified nucleotides in vivo at 2 hpf (+DMS) (see Methods). d, e, DMS-seq data are reproducible across biological replicates at a per-transcript (d) and per-nucleotide (e) resolutions in vivo at 2 hpf. f, Validated secondary structure of the Tetrahymena ribozyme (Rz) (adapted from Walter, N., Woodson, R. T. & Batey, R. T. Non-Protein Coding RNAs. (Springer Science & Business Media, 2008). In the zoomed panels (corresponding to regions P2, P9 and P4-5-6), DMS-seq accessibilities have been overlaid onto the known secondary structures. In the upper middle part of the figure, box plot represents consistency between accessibilities and nucleotide pairing status. P-value computed using a one-sided Mann-Whitney U-test (ss, n = 66; ds, n = 89). Box spans first to last quartiles and whiskers represent 1.5 × the interquartile range. g, In vivo accessibility profile of the dgcr8 mRNA. Accessibilities corresponding to annotated conserved secondary structures (gray rectangles) are highlighted. The detailed secondary structure is also shown, where in vivo accessibilities have been overlaid for A and C bases, while Gs and Us are depicted in white. h, Box plot comparing the distribution of in vivo accessibilities and the pairing status of A and C bases found in three conserved secondary structures used as controls (selt1a, selt2 and dgcr8). P-value computed using a one-sided Mann-Whitney U-test (ss, n = 41; ds, n = 59). Box spans first to last quartiles and whiskers represent 1.5 × the interquartile range. i, Comparisons of per-transcript global DMS-seq accessibilities between replicates (left, n = 899) and across developmental stages (right, n = 1,309). Spearman correlation coefficients (rho) and P-values are shown. j, Proportion of differentially structured windows (see Methods) across the 3 developmental stages, expressed as the percentage of differentially structured windows -relative to the total windows analysed- (black), and the percentage of analyzed mRNAs that contain these windows (gray). k, Proportion of differentially structured windows that increase (orange) or decrease (turquoise) in RNA structure, for each pairwise comparison, based on the directionality of their change in Gini index. The direction of change is defined with respect to the latest developmental stage, for each pairwise comparison. l, Correlation between the per-transcript changes in translation efficiency and accessibility for each transcript region (5’-UTR (663), CDS (1,337) and 3’-UTR (1,050)) between 2 and 6 hpf. Spearman correlation coefficients (rho) and P-values are shown

  2. Supplementary Figure 2 Ribosomes unwind RNA structures in the 5′ UTR and CDS, including the region surrounding the AUG.

    a, Cumulative distributions of global accessibilities of the 50 nucleotides upstream of the AUG (2,110), CDS (2,526) or 50 nucleotides downstream of the STOP codon (2,364) are shown for in vivo (top), in vitro (middle) and in vivo vs in vitro (bottom) samples at 2 hpf. mRNAs have been binned in quintiles according to their translation efficiency (TE). P-values have been computed using a two-sided Wilcoxon signed-rank test and a one-sided Mann-Whitney U-test, for comparisons within and across translational statuses, respectively. b, c, Correlation between translation efficiency and CDS (left) and 3’-UTR (right) accessibilities, for both in vivo (b) and in vitro (c) conditions (CDS in vivo (n = 2,507) and in vitro (n = 2,523); 3’-UTR in vivo (n = 2,263) and in vitro (n = 2,374)). Spearman correlation coefficients (rho) and P-values are shown. d, Cartoon shows the region surrounding the AUG initiation codon used to predict RNA secondary structure and highlights the Kozak region (5’-NNNNAUGNNN-3’). e, Correlation between translation efficiencies and accessibilities at the AUG initiation codon for both in vivo (left) and in vitro (right) conditions. Spearman correlation coefficients (rho) and P-values are shown. f, The RNA secondary structure and the Gibbs free energy (ΔG’) were computed for each AUG initiation codon. The energy required to unfold the Kozak region (ΔΔG) was calculated by subtracting the ΔG’ from the energy of the structure where the Kozak sequence is forced to be single-stranded (ΔG”). g, Correlation between translation efficiency and ΔΔG for both in vivo (left) and in vitro (right) conditions. No correlation is observed in both cases, suggesting that the structure of the Kozak sequence is not a major factor driving translation in the early embryo. Spearman correlation coefficients (rho) and P-values are shown. Structural analyses of AUG initiation codons (d-g) were performed on a set of 2,360 start codons

  3. Supplementary Figure 3 Ribosomes promote alternative RNA conformations with reduced stability in the cell.

    a, Cumulative distributions of 5’-UTR (659), CDS (763) and 3’-UTR (736) global accessibilities in PatA-treated and untreated samples. P-values have been computed using a two-sided Wilcoxon signed-rank test. b, Arc plots of DMS-seq guided RNA secondary structure predictions of high ribosome occupancy regions found in the nsun2 and ddit4 genes, for different conditions/samples. c, Pairwise comparisons of Gibbs free energy differences (ΔΔG; untreated-in vitro, PatA-in vitro and CHX-in vitro) of windows with high ribosome footprint densities (728 non-overlapping windows covering 332 genes, in gray) and without ribosome footprints (728 non-overlapping windows covering 312 genes, in white) (see Methods). Positive ΔΔG indicates a less stable structure in the first condition (untreated, PatA or CHX), compared to in vitro, whereas negative ΔΔG indicates a more stable structure in the first condition, compared to in vitro. Note the decrease in stability for structures formed by ribosome-rich regions (gray) in the untreated and CHX-treated samples, but not in the ribosome-free PatA-treated sample or in regions without ribosome (white). Violin plot features a kernel density estimation and lines represent the quartiles of the distribution. P-values have been computed using a two-sided Wilcoxon signed-rank test and a one-sided Mann-Whitney U-test, for comparisons across probing conditions for a same group and between groups within a specific condition, respectively. d, Cartoon representation depicting the impact of CHX, along with its corresponding cumulative distribution of CDS accessibility ratios (untreated/CHX) for highly (n = 356) and lowly (n = 356) translated mRNAs at 2 hpf. Note the minor increase in accessibility in lowly translated mRNAs following CHX treatment (blue arrow). P-value was determined using a one-sided Mann-Whitney U-test. e, Arc plots of DMS-seq guided RNA secondary structure predictions of the full-length ctcf, maea and nsun2 mRNAs using SeqFold for different conditions/samples. The CDS region has been shadowed in gray

  4. Supplementary Figure 4 uORF translation remodels 5′-UTR structure.

    a, Schematic view of the 5’-UTR RNA structure remodeling that occurs upon translation of upstream open reading frames (uORF). b, Cumulative distributions of in vivo (top) and in vitro (bottom) uORF accessibilities for highly (red, n = 196) and lowly (blue, n = 196) translated uORFs. P-values were determined using one-sided Mann-Whitney U-tests. c, Distributions of ribo-seq reads (top panel) and accessibility differences (in vivoin vitro) (bottom panel) for the cdc25 5’-UTR, which contains two translated uORFs (highlighted in gray). Increased accessibility in vivo is shown in red, whereas decreased accessibility in vivo is shown in blue. d, Arc plot depicting the predicted cdc25 5’-UTR secondary structure, guided by either in vivo (yellow) or in vitro (green) DMS-seq accessibilities. Each arc represents a base pair interaction

  5. Supplementary Figure 5 Poly(A) tail length–dependent mRNA structure remodeling depends on translation.

    a, Comparison of per-region accessibilities (CDS; top panels and 3’-UTR; bottom panels) and per-transcript poly(A) tail length, at 2 hpf (left panels) and 4 hpf (right panels) (CDS 2 hpf (n = 1,238) and 4 hpf (n = 1,138; 3’-UTR 2 hpf (n = 1,221) and 4 hpf (n = 1,014)). Spearman correlation coefficients (rho) and P-values are shown. b, c, Comparison of CDS accessibilities and per-transcript poly(A) tail lengths in PatA-treated (b, n = 1,671) and in vitro refolded samples (c, n = 1,663) from 2 hpf embryos. Spearman correlation coefficients (rho) and P-values are shown. d, Comparison of differentially structured (DS) windows (2 vs 4 hpf) found in 483 miR-430 targets (red) and 1,000 sets of 483 randomly chosen non miR-430 target mRNAs (gray)

  6. Supplementary Figure 6 Differences between in vivo and in vitro RNA structures are not uniformly distributed along transcripts.

    a, Cumulative distributions of global 5’-UTR (837), CDS (1,122) and 3’-UTR (1,051) accessibilities in vivo and in vitro from the transcriptome of 2 hpf embryos. Only transcripts with a minimum of 85% coverage of As and Cs and with at least 10 reads in average per As and Cs for both in vivo and in vitro DMS-seq experiments are shown. P-values were computed using two-sided Wilcoxon signed-rank tests. b, Distribution of differentially structured windows along the transcripts of 2 hpf embryos, comparing in vivo and in vitro conditions. Windows with increased structure in vivo are depicted in orange, whereas those with decreased structure in vivo are shown in turquoise. Each transcript region (5’-UTR, CDS, 3’-UTR) has been normalized by its length, as well as by total number of windows analyzed in each region. Transcripts have been binned into quintiles based on their translation efficiency, which has been determined using ribosome profiling data

  7. Supplementary Figure 7 DMS-seq signal provides information on the RNA conformation favored upon the binding of two different trans factors.

    a, Sequence logo of KHSRP identified using iCLIP. b, Cartoon depicting the binding status of KHSRP to its target in vitro and in vivo. c, Comparison of the accessibility ratio (in vivo / in vitro) of KHSRP-bound regions (cyan) and matching unbound controls (gray), suggesting that KHSRP binding helps to maintain a single-stranded RNA conformation in the cell. KHSRP binding sites were determined using iCLIP experiments and limited to those found within 3’-UTRs (see Methods). P-value computed using one-sided Mann-Whitney U-test. Box spans first to last quartiles and whiskers represent 1.5 × the interquartile range. d, Schematic representation of the impact of KHSRP binding on RNA structure in vivo keeping bound-RNA in a single-stranded conformation. e, Cartoon depicting the binding status of the Ago2-miR-430 complex to its target in various conditions. f, Comparison of accessibility ratios (across conditions and time points) of miR-430 seeds (cyan) and matching controls (gray). miR-430 seeds correspond to any 8- and 7-mers miR-430 binding sites found in the 3’-UTR of miR-430 targets (see Methods). Control regions of 8-nt were randomly chosen within the miR-430 seed containing 3’-UTRs (see Methods). MiR-430 seed and control accessibilities were calculated by averaging the accessibility of A and C bases found in each 8-nt sequence. P-values computed using one-sided Mann-Whitney U-tests. Box spans first to last quartiles and whiskers represent 1.5 × the interquartile range g, Meta-analysis of average accessibilities at 2 and 4 hpf (top panels) and nucleotide content (bottom panels) at each position of miR-430 seed (n = 74, left panels) and control (n = 75, right panels) regions. Each region corresponds to a 100-nt window centered on a 8-nt miR-430 seed or control sequences highlighted in cyan and gray, respectively. Dotted lines correspond to the average accessibility of the metaplot for each developmental stage. Error bars denote s.e.m

  8. Supplementary Figure 8 In vivo, specific 3′-UTR structures affect miRNA activity and gene expression.

    a, b, In vivo and in vitro predicted secondary structures and stabilities (ΔG) of the 200-nt region centered on the miR430 target site found in non miR-430 targets (rab33ba, zgc:55733 and pgk1; a) and miR-430 targets (fam171a1, znf706 and rtkn2a; b). Regions complementary to the miR-430 seed and its complementary binding region within the 3’-UTR are shown in cyan and red, respectively. Changes in mRNA abundance during the MZT of the endogenous transcripts in wild type conditions (black) or in conditions where miR-430 activity is inhibited by a tinyLNA complementary to the miR-430 seed (green) are shown for each gene. MiR-430 targets are characterized by a gain in stability at 6 hpf when miR-430 activity is reduced (-miR-430) (b). The average of 2 independent RNA-seq time course experiments is shown for WT conditions while values from –miR-430 conditions come from a single RNA-seq time course experiment

  9. Supplementary Figure 9 Dynamic 3′-UTR structures reveal decay elements during the MZT.

    a, Enrichment of conserved sequences in 3’-UTR regions with a dynamic structure between 4 and 6 hpf (6v4), compared to all 3’-UTR regions analyzed. Dynamic structures correspond to those with a KS test P-value < 0.05. Box spans first to last quartiles and whiskers represent 1.5 × the interquartile range. P-value computed using a one-sided Mann-Whitney U-test. b, Decay activity of regions with a dynamic structure (left column, red) and a static structure (right column, black) originated from the same 3’-UTR, calculated from the RESA experiment. c, Decay (blue) and stabilizing (dark orange) activities of all the 200-nt 3’-UTR regions with sufficient read coverage from the RESA experiment (see Methods). Note the enrichment of regions with a dynamic structure among the decay elements (blue). d, Per-transcript KS test P-value profile (top) and conservation (bottom) for the igf2bp3 mRNA, comparing 4 and 6 hpf. The profiles have been computed by analyzing 100-nt sliding windows throughout the transcript (see Methods). Examples of conserved 3’-UTR regions with a dynamic structure (red) and without structural change (black) are highlighted. P-values between 1 and 0.2 are shown in gray, between 0.2 and 0.05 are shown in yellow and <0.05 are shown in red. e, Quantification of the decay activity for the dynamic (red) and non-changing (black) structures found in the igf2bp3 3’-UTR, as calculated by RESA. Data are represented as the mean ± SD (n = 3 independent replicates). Student t-test P-values are indicated as ** < 0.01. f, Genome tracks of RNA-seq time course experiments representing mRNA levels of the ccna1 (top) and nanog (bottom) transcripts at 2, 4 and 6 hpf in wild type (left) and alpha-amanitin (right) conditions. Alpha-amanitin inhibits zygotic genome activation, highlighting the implication of zygotic factors in the clearance of ccna1 and, to a lesser extend, nanog mRNAs

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–9

  2. Reporting Summary

  3. Supplementary Table 1

    List of oligos use in this study

  4. Supplementary Table 2

    Summary and statistics of each high-throughput sequencing experiments

  5. Supplementary Table 3

    List of primer pairs for each insert of the RESA experiment for regions with dynamic structures (insert) and no structural changes (control)

  6. Supplementary Dataset 1

    RNA-seq experiment table including time courses in various conditions and purification methods

  7. Supplementary Dataset 2

    Ribo-seq experiment table including translation efficiencies and RPKMs for untreated and PatA-treated embryos

  8. Supplementary Dataset 3

    Coding sequence annotation and per-transcript DMS-seq count and accessibility profiles for 2 hpf in vivo

  9. Supplementary Dataset 4

    Coding sequence annotation and per-transcript DMS-seq count and accessibility profiles for 4 hpf in vivo

  10. Supplementary Dataset 5

    Coding sequence annotation and per-transcript DMS-seq count and accessibility profiles for 6 hpf in vivo

  11. Source Data, Figure 3

  12. Source Data, Figure 6

  13. Source Data, Figure 7

  14. Source Data, Supplementary Figure 1

  15. Source Data, Supplementary Figure 9

About this article

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DOI

https://doi.org/10.1038/s41594-018-0091-z

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