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Visualizing a protonated RNA state that modulates microRNA-21 maturation

Abstract

MicroRNAs are evolutionarily conserved small, noncoding RNAs that regulate diverse biological processes. Due to their essential regulatory roles, microRNA biogenesis is tightly regulated, where protein factors are often found to interact with specific primary and precursor microRNAs for regulation. Here, using NMR relaxation dispersion spectroscopy and mutagenesis, we reveal that the precursor of oncogenic microRNA-21 exists as a pH-dependent ensemble that spontaneously reshuffles the secondary structure of the entire apical stem-loop region, including the Dicer cleavage site. We show that the alternative excited conformation transiently sequesters the bulged adenine into a noncanonical protonated A+-G mismatch, conferring a substantial enhancement in Dicer processing over its ground conformational state. These results indicate that microRNA maturation efficiency may be encoded in the intrinsic dynamic ensemble of primary and precursor microRNAs, providing a potential means of regulating microRNA biogenesis in response to environmental and cellular stimuli.

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Fig. 1: NMR characterization of preE-miR-21.
Fig. 2: PreE-miR-21 populates a protonated excited state with a neutral-shifted pKa.
Fig. 3: Excited-state structure of preE-miR-21.
Fig. 4: NMR characterization of protonated preE-miR-21 at low pH.
Fig. 5: Excited state of preE-miR-21 enhances Dicer processing.
Fig. 6: Modulation of miR-21 maturation with a protonation-dependent structural ensemble.

Data availability

All data supporting the findings of this study are available in this published article, extended data and source data files.

Code availability

The in-house OriginLab scripts for data analyses are available upon request.

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Acknowledgements

We thank G. Young and S. Parnham for maintenance of NMR instruments and members of the Zhang laboratory for critical comments. This work was supported by a start-up fund and Jefferson Pilot Fellowship from the University of North Carolina at Chapel Hill and an NSF grant (MCB1652676).

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Contributions

J.T.B. and Q.Z. conceived the project and experimental design. J.T.B., J.A.B., B.Z., S.M.H. and Q.Z. prepared the samples. J.T.B., J.A.B., B.Z. and Q.Z. carried out NMR experiments, analyzed the data and wrote the paper.

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Correspondence to Qi Zhang.

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Extended data

Extended Data Fig. 1 13C R RD profiles of preE-miR-21 residues without apparent chemical exchange.

On- and off-resonance 13C RD profiles depicting spin-lock power (ωeff/2π) and offset (Ω/2π) dependence of R2 + Rex measured at pH 6.45. Solid lines represent the best fits to a single-state model using the Bloch-McConnell equation. Shown are best-fit values and experimental uncertainties (s.d.) estimated from mono-exponential fitting of n = 3 independently measured peak intensities.

Extended Data Fig. 2 13C R RD profiles of preE-miR-21 residues that undergo chemical exchange at pH 6.45.

Solid lines represent the best fits to a global two-state exchange model (kex = 1445 ± 17 s−1 and pE = 15.2 ± 0.3%) using the Bloch-McConnell equation. Shown are best-fit values and experimental uncertainties (s.d.) estimated from mono-exponential fitting of n = 3 independently measured peak intensities.

Extended Data Fig. 3 Comparison of GS, ES, mutant carbon chemical shifts.

Shown are differences between the observed chemical shifts (black) and the chemical shifts of GS (blue) and ES (red) extracted from 13 C R1ρ RD profiles at pH 6.45, G38U mutant at pH 6.45, and preE-miR-21 at pH 4.81. The apparent discrepancies between ES base carbon chemical shifts of A-C2/C8s and C-C5/C6s and their corresponding chemical shifts at pH 4.81 are likely due to intrinsic protonation at A-N1s and C-N3s at pH 4.81.

Extended Data Fig. 4 13C R RD characterization of preE-miR-21 at pH 8.04.

a, On- and off-resonance 13C RD profiles of residues that are exchange-broadened at lower pH points, including A22-C2, G38-C8, and G38-C1’. Solid lines represent the best fits to a global two-state exchange (kex = 1228 ± 51 s−1 and pE = 1.1 ± 0.1%) using the Bloch-McConnell equation. b, Comparison of GS, ES, mutant carbon chemical shifts. Shown are differences between observed chemical shifts (black) and chemical shifts of GS (blue) and ES (red) extracted from 13C R RD profiles at pH 8.04, G38U mutant at pH 6.45, and preE-miR-21 at pH 4.81. Shown are best-fit values and experimental uncertainties (s.d.) estimated from mono-exponential fitting of n = 3 independently measured peak intensities.

Extended Data Fig. 5 NMR RD characterization of pH-dependent chemical exchange of residue A22.

Shown are 13C R RD profiles of A22-C2/C8/C1’ and 15N CEST profile of A22-N1 at a-b, pH 8.04, c-d, pH 7.47, and e-f, pH 6.96. Solid lines represent the best fits to a global two-state exchange model at each individual pH condition using the Bloch-McConnell equation, resulting in pE = 1.1 ± 0.1% at pH 8.04, pE = 3.4 ± 0.1% at pH 7.47, and pE = 6.4 ± 0.1% at pH 6.96. Shown are best-fit values and experimental uncertainties (s.d.) estimated from n = 3 independently measured peak intensities for CEST profiles and mono-exponential fitting of n = 3 independently measured peak intensities for R RD profiles.

Extended Data Fig. 6 NMR characterization of pH-dependent changes of preE-miR-21.

a, 13C-1H HSQC spectra of base carbons (C6 and C8) of uniformly 13C/15N-labeled preE-miR-21 at pHs 4.35 and 8.04 with mapping of chemical shift changes. b, 13C-1H HSQC spectra of base carbons (C8) of adenine-specifically 13C/15N-labeled preE-miR-21 with pH ranging from 4.35 to 8.04. c, 13C-1H HSQC spectra of base carbons (C2) of adenine-specifically 13C/15N-labeled preE-miR-21 with pH ranging from 4.35 to 8.04.

Extended Data Fig. 7 NMR characterization of preE-miR-21 mutants.

Shown are 13C-1H HSQC spectra of base carbons (C6 and C8) (black) that overlay with 13C-1H HSQC spectrum of WT preE-miR-21 at pH 8.04 (green) at 35oC, 1H-1H imino NOESY and 15N-1H HSQC spectra at 10oC, and 13C R RD profiles of uniformly 13C/15N-labeled a, ∆A22 “bulge”, b, GC “Stem”, and c, UUCG “Loop” mutants at pH 6.45. Solid lines represent the best fits to a single-state model using the Bloch-McConnell equation. Shown are best-fit values and experimental uncertainties (s.d.) estimated from mono-exponential fitting of n = 3 independently measured peak intensities.

Extended Data Fig. 8 Secondary structure prediction of preE-miR-21 and G38U mutant by Mfold and MC-Fold.

a, M-fold predicted secondary structure of preE-miR-21, which contains G-U base pairs as observed in NMR data of the ground state. b, MC-Fold predicted secondary structures of preE-miR-21. Shown are the top 5 lowest-energy structures, ranked with ∆G relative to the lowest predicted structure. The A22-G38 base pair is predicted in all MC-Fold structures. c, M-fold predicted secondary structure of preE-miR-21 G38U mutant. d, MC-Fold predicted secondary structures of preE-miR-21 G38U mutant. Shown are the top 5 lowest-energy structures, ranked with ∆G relative to the lowest predicted structure. Both M-fold and MC-Fold predict the same lower stem structure of preE-miR-21 G38U mutant, which is also consistent with NMR data.

Extended Data Fig. 9 NMR characterization of preE-miR-21 excited-state mimic.

Shown are a, 13C-1H HSQC spectrum of base carbons (C6 and C8) (black) that overlays with 13C-1H HSQC spectrum of WT preE-miR-21 at pH 8.04 (green) at 35oC, b, 1H-1H imino NOESY at 75 ms mixing time and 15N-1H HSQC spectra at 10oC, and c, 13C R RD profiles of uniformly 13C/15N-labeled G38U mutant at pH 6.45. Solid lines represent the best fits to a single-state model using the Bloch-McConnell equation. Shown are best-fit values and experimental uncertainties (s.d.) estimated from mono-exponential fitting of n = 3 independently measured peak intensities.

Extended Data Fig. 10 Single-turnover pre-miR-21 processing with 10-fold, 25-fold, and 250-fold excess Dicer enzyme.

a, Single-turnover processing assays of recombinant human Dicer (500 nM) with pre-miR-21 at 50, 20, and 2 nM concentrations at pH 7.77 and 35 °C. b, Quantification of time-dependent Dicer processing of wild-type pre-miR-21 using ImageQuant. Solid lines represent best fits to a single exponential equation as described in Methods. Shown are mean values and standard deviations (s.d.) from n = 3 independent assays.

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Source Data Fig. 5b–d

Full-length, unprocessed gel images.

Source Data Fig. 5e–g

Statistical source data: Dicer processing assay.

Source Data Extended Data Fig. 10a

Full-length, unprocessed gel images.

Source Data Extended Data Fig. 10b

Statistical source data: Dicer processing assay.

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Baisden, J.T., Boyer, J.A., Zhao, B. et al. Visualizing a protonated RNA state that modulates microRNA-21 maturation. Nat Chem Biol 17, 80–88 (2021). https://doi.org/10.1038/s41589-020-00667-5

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