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. 2018 Aug;25(8):677-686.
doi: 10.1038/s41594-018-0091-z. Epub 2018 Jul 30.

Analyses of mRNA Structure Dynamics Identify Embryonic Gene Regulatory Programs

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Free PMC article

Analyses of mRNA Structure Dynamics Identify Embryonic Gene Regulatory Programs

Jean-Denis Beaudoin et al. Nat Struct Mol Biol. .
Free PMC article

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.

Conflict of interest statement

Competing interests

The authors declare no competing interests.

Figures

Fig. 1 |
Fig. 1 |. Relationship between mRNA structure and translation during the MZT.
a, Schematic view of the transcriptomic remodeling that occurs during the MZT. After a transcriptionally silent period (gray), the maternal program (pink) transitions to a zygotic program (blue). b, Correlation between the per-transcript changes at 6 hpf versus 2 hpf (6v2) in translation efficiency (ΔTE, 6-2 hpf) and CDS accessibility (6/2 hpf) for 1,337 coding sequences with high coverage at both stages. The Spearman correlation coefficient (ρ) and the corresponding asymptotic P value are shown. c, Methodology used to identify local RNA structure changes between conditions. Differentially structured (DS) sliding windows are defined as those that are significantly changing (P < 0.05) on the basis of the Kolmogorov–Smirnov test, and show an increase (> 1.1) or decrease (< 0.9) in their Gini-index ratio. d, Left, structure changes of each 100-nt window (Gini ratio 6/2 hpf) along each transcript (total of 1,273). Transcripts are ranked according to their changes in translation efficiency during the MZT (6 - 2 hpf). Each transcript region (5′ UTR, CDS and 3′ UTR) is normalized to its length. Right, cumulative distributions of differentially structured windows for transcripts with increased (top 20%, top) and decreased (bottom 20%, bottom) translation. e, Cumulative distribution of global CDS accessibility in vivo, showing that highly translated mRNAs exhibit increased CDS accessibility in vivo at 2 hpf, a stage at which the rate of translation spans ~10,000-fold between highly and weakly translated mRNAs. Ss, single-stranded; ds, double-stranded. Transcripts (n = 2,526) were binned into quintiles according to their translation efficiency. P value was computed with a one-sided Mann–Whitney U test.
Fig. 2 |
Fig. 2 |. Globally, mRNA intrinsic structure is not a main driver of translation.
a, Schematic representation of the two models that might explain the observed anticorrelation between translation efficiency and RNA structure in CDS regions in vivo. In the first model, preexisting CDS RNA structures guide translation (top; this is not supported by our analysis). In a second model, translating ribosomes are responsible for the unfolding of CDS RNA structures (bottom; consistent with our analysis). b, Cumulative distributions of global CDS RNA accessibility in vitro at 2 hpf, displaying no difference among mRNAs with different translation efficiency. Transcripts (n = 2,526) were binned into quintiles according to their translation efficiency. P value was computed with a one-sided Mann–Whitney U test. Although P was < 0.05, we considered this change nonsignificant, given the magnitude of the change and the large sample size; this conclusion is further supported by the scatter-plot analysis in Supplementary Fig. 2c. c, Scatter plot between in vivo and in vitro CDS accessibility at 2 hpf. In vivo, the accessibility was higher for highly translated mRNAs (red dots) and lower for weakly translated mRNAs (blue dots). d, Schematic view of the two models in which RNA structure at the AUG initiation codon is either the cause (top) or the result (bottom) of mRNA translation. e, Correlation between translation efficiency and the structure at AUG regions (n = 2,360), for both in vitro (left) and in vivo (right) conditions. Spearman correlation coefficients (ρ) and the corresponding asymptotic P values are shown.
Fig. 3 |
Fig. 3 |. I Ribosomes promote alternative mRNA structures in the early embryo.
a, Schematic view of the PatA translation-inhibition mechanism. PatA decreases the levels of functional eIF4F initiation complex, which is essential for cap-dependent translation, by trapping eIF4A (4A) on mRNAs and ectopically enhancing its RNA helicase activity. b, Distribution of translation efficiencies of PatA-treated (blue) and untreated (gray) embryos. c, Correlation between 5′ -UTR length and sensitivity to PatA treatment. The mRNA subgroups least and most sensitive to PatA correspond to the top (n = 394 transcripts) and bottom (n = 394 transcripts) quintiles of the change in translation efficiency (PatA - untreated), respectively. P value was computed with a one-sided Mann–Whitney U test. Box, first to last quartiles; whiskers, 1.5x interquartile range; center line, median. d, Scatter plot of the per-transcript ribosome footprints (ribo-seq ribosome profiling) in PatA-treated versus untreated embryos. Results for mitochondrial genes (red), which were unaffected by PatA treatment, remained unchanged. RPKM, reads per kilobase per million reads. e, Schematic representation depicting the effect of PatA, along with its corresponding cumulative distribution of CDS accessibility ratios (untreated/PatA). Accessibility (red arrow) was lower in highly translated mRNAs (red, n = 392) in PatA-treated than untreated embryos, an effect not observed for weakly translated mRNAs (blue, n = 392). P value was computed with a one-sided Mann–Whitney U test. f, Arc plots of DMS-seq-guided RNA secondary-structure predictions of a high-ribosome-occupancy region found in the bsg (basigin) gene. Each arc represents a base-pair interaction g, Arc plots of DMS-seq-guided RNA secondary-structure predictions of the full-length cnot8 mRNA from SeqFold. The CDS region is shadowed in gray. h,i, Principal component (PC)-analysis biplots of the SeqFold mRNA structure predictions of the four tested conditions (untreated, in vitro, PatA treated and CHX treated) for CDS (h, n = 1,130) and 3′ -UTR (i, n = 1,085) regions. Source data for h and i are available online.
Fig. 4 |
Fig. 4 |. Polyadenylation and miR-430 activity influence mRNA structure.
a, Schematic view of the effects of poly(A)-tail length and miRNA-mediated repression on translation during early embryogenesis. b, Left, structure changes along each transcript (total of 965 mRNAs with sufficient coverage in DMS-seq and poly(A)-tail-length profiling experiments at both 2 and 6 hpf), ranked by changes in poly(A)-tail length (6/2 hpf). Structure changes were identified by computation of the Gini ratio for each 100-nt sliding window (6/2 hpf). Right, cumulative distribution of differentially structured windows along the transcripts, for both the top 20% (top) and bottom 20% (bottom) transcripts, binned by changes in poly(A)-tail length. c, Correlation between changes in poly(A)-tail length and accessibility (6/2 hpf), for both CDS (left; n = 939) and 3′ UTR (right, n = 724). Spearman correlation coefficients (ρ) and the corresponding asymptotic P values are shown. d, mRNA structure changes along each of the miR-430 targets (n = 483) or a random set of transcripts (n = 483) taken from the non-miR-430 targets. Regions with structure changes were identified by computation of the Gini ratio for each 100-nt sliding window (4/2 hpf). e, Metaplot of the differentially structured regions along the transcripts, comparing 2 and 4 hpf (4v2), for both miR-430 targets (n = 483) and non-miR-430 targets (n = 1,495).
Fig. 5 |
Fig. 5 |. 3′-UTR regions are more structured in the cell than in vitro.
a, Distribution of differentially structured windows along the transcripts, comparing in vivo and in vitro conditions. Regions with increased structure in vivo are orange, whereas those with decreased structure in vivo are turquoise. b,c, Distribution of differentially structured windows, comparing in vivo and in vitro conditions, for transcripts with either high translation efficiency (top 20%; b) or low translation efficiency (bottom 20%; c).
Fig. 6 |
Fig. 6 |. Cellular-specific 3′-UTR structures modulate miR-430 activity and gene expression.
a, Cartoon representation of the RNA secondary structure found at a miR-430 target site. b, Correlation between the decay strength and the stability of the RNA structure of 18 endogenous miR-430-binding sites (colored according to their seep type), where the RNA secondary structure was predicted with an in silico approach (left) or DMS-seq guided by in vitro (middle) or in vivo structural data (right). Spearman correlation coefficients (ρ), asymptotic P values and regression models (shaded region) are shown (n = 18 different miR-430-binding sites). c,g, In vivo predicted secondary structure and stability (ΔG) of the miR430-target site found in the rab33ba 3′ UTR (c) and in the fam171a1 3′ UTR (g). d,h, Changes in mRNA abundance during the MZT of the endogenous rab33ba (d) and fam171a1 (h) transcripts in wild-type (WT) conditions (black, average of two independent RNA-seq replicates) or in conditions in which miR-430 activity is inhibited (green, one RNA-seq replicate). e,i, Fluorescence microscopy at 24 hpf of GFP reporters containing either a wild-type sequence of the rab33ba (rab-wt) or fam171a1 (fam-wt) target site, compared with a destabilized version of rab33ba (rab33ba single-stranded, rab-ss (e)) or a stabilized version of fam171a1 (fam171a1 double-stranded, fam-ds, (i)) target site in the 3′ UTR. The specific mutations of the rab-ss and fam-ds constructs are highlighted in purple and with arrows. DsRed mRNA was co-injected as a control. Reporters were injected in both the wild type and the maternal-zygotic mutant of miR-430 (MZmir430) lacking the entire miR-430 locus. f,j, miR-430 activity calculated from the fluorescence of the GFP normalized to DsRed and the GFP/DsRed ratio of the MZmir430 mutants for each construct. Data are represented as mean ± s.d. (f: wt, n = 6; ss, n = 6; j: wt, n = 4; ds, n = 3 independent replicates). Student t-test P values (two tailed) are indicated as ***P < 0.001 and ****P < 0.0001. Source data for b, d, h, f and j are provided online.
Fig. 7 |
Fig. 7 |. Dynamic 3′-UTR structures are enriched in decay regulatory elements.
a, Sequence conservation of differentially structured 3′ -UTR regions between 4 and 6 hpf (6v4), compared with all 3′ -UTR regions analyzed (gray). P values were computed with one-sided Mann–Whitney U tests. Box, first to last quartiles; whiskers, 1.5× interquartile range; center line, median. b, Cartoon representation of the RESA experiment used to validate the regulatory activity of 3′ -UTR regions that were identified as changing (red) or not changing (gray) in their RNA structure during the MZT. c, Cumulative distribution of the decay activity measured by the RESA validation experiment for 3′ -UTR regions with dynamic structures (n = 53, red) or with no structural change (n = 18, black) during the MZT. P value computed by one-sided Mann–Whitney U test. d, RESA-validated 3′ -UTR sequences with identified decay or stabilizing elements. e, Endogenous mRNA expression of ccna1 and nanog at 2, 4 and 6 hpf, as well as for α-amanitin (aAm)-treated embryos collected at 6 hpf. f, Quantification of the decay activity of ccna1 measured with RESA (mean ± s.d., 6v2 n = 3; 6v6 aAm n = 4 independent replicates), focusing on reporter-level changes between 2 and 6 hpf (6v2, light gray), and between 6 hpf untreated and aAm-treated samples (6v6 aAm, dark gray). P values were calculated with Student two-tailed t test; **P < 0.01. g, Predicted RNA secondary structures of the 542-nt nanog 3′ UTR at 2 and 6 hpf. Structural domains that are similar at both developmental stages (I and III) are boxed in gray, whereas the one changing (II) is boxed in red. Cyan lines highlight the 20-nt deletion that disrupts a stem region in both structures. h, Location of the 20-nt deletion and the 200-nt RESA fragment with decay activity within the nanog 3′ UTR (top). Decay activity of the nanog wild-type 3′ UTR (black) and with the 20-nt deletion (cyan), quantified by qPCR (6v2), compared with the activity of the 200-nt fragment from the RESA experiment (brown). Data are represented as mean ± s.d. (n = 4 independent replicates for qPCR and RESA). **P < 0.01, ***P < 0.001 and ****P < 0.0001 (Student two-tailed t test). Source data for c, d and f and are available online.

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