Base-pair conformational switch modulates miR-34a targeting of Sirt1 mRNA

Abstract

MicroRNAs (miRNAs) regulate the levels of translation of messenger RNAs (mRNAs). At present, the major parameter that can explain the selection of the target mRNA and the efficiency of translation repression is the base pairing between the ‘seed’ region of the miRNA and its counterpart mRNA1. Here we use R relaxation-dispersion nuclear magnetic resonance2 and molecular simulations3 to reveal a dynamic switch—based on the rearrangement of a single base pair in the miRNA–mRNA duplex—that elongates a weak five-base-pair seed to a complete seven-base-pair seed. This switch also causes coaxial stacking of the seed and supplementary helix fitting into human Argonaute 2 protein (Ago2), reminiscent of an active state in prokaryotic Ago4,5. Stabilizing this transient state leads to enhanced repression of the target mRNA in cells, revealing the importance of this miRNA–mRNA structure. Our observations tie together previous findings regarding the stepwise miRNA targeting process from an initial ‘screening’ state to an ‘active’ state, and unveil the role of the RNA duplex beyond the seed in Ago2.

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Fig. 1: Conformational dynamics in the seed of miR-34a–Sirt1 mRNA.
Fig. 2: Structure and conformation of the excited state of miR-34a–mSirt1.
Fig. 3: Biophysical and functional characterization of wild-type and trapped excited state miR-34a–mSirt1 duplexes.
Fig. 4: Proposed mechanism of downregulation for GC–GU switches in miR-34a-loaded RISC.

Data availability

NMR sequence-specific resonance assignments have been deposited in the Biological Magnetic Resonance Data Bank under accesssion numbers 27225 (hsa-miR-34a-5p), 27226 (the miR34a–mSirt1 bulge) and 27229 (the miR34a–mSirt1 trapped excited state). The plasmids used for the DLR assay were a gift from J. Weidhaas (Addgene plasmids 78258 and 78259). All data and code used for data analysis are available upon request. The ensembles of REMD simulations have been deposited in Model Archive (www.modelarchive.org) under accession numbers ma-bc9uo, ma-z54y4 and ma-g8e5z.

References

  1. 1.

    Bartel, D. P. Metazoan microRNAs. Cell 173, 20–51 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Marušič, M., Schlagnitweit, J. & Petzold, K. RNA dynamics by NMR spectroscopy. ChemBioChem 20, 2685–2710 (2019).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Ebrahimi, P., Kaur, S., Baronti, L., Petzold, K. & Chen, A. A. A two-dimensional replica-exchange molecular dynamics method for simulating RNA folding using sparse experimental restraints. Methods 162-163, 96–107 (2019).

    CAS  PubMed  Google Scholar 

  4. 4.

    Wang, Y. et al. Nucleation, propagation and cleavage of target RNAs in Ago silencing complexes. Nature 461, 754–761 (2009). 

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sheng, G. et al. Structure-based cleavage mechanism of Thermus thermophilus Argonaute DNA guide strand-mediated DNA target cleavage. Proc. Natl Acad. Sci. USA 111, 652–657 (2014).

    ADS  CAS  PubMed  Google Scholar 

  6. 6.

    Elkayam, E. et al. The structure of human argonaute-2 in complex with miR-20a. Cell 150, 100–110 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Schirle, N. T. & MacRae, I. J. The crystal structure of human Argonaute2. Science 336, 1037–1040 (2012) e

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Nakanishi, K., Weinberg, D. E., Bartel, D. P. & Patel, D. J. Structure of yeast Argonaute with guide RNA. Nature 486, 368–374 (2012).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Nielsen, C. B. et al. Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. RNA 13, 1894–1910 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Klum, S. M., Chandradoss, S. D., Schirle, N. T., Joo, C. & MacRae, I. J. Helix-7 in Argonaute2 shapes the microRNA seed region for rapid target recognition. EMBO J. 37, 75–88 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Schirle, N. T., Sheu-Gruttadauria, J. & MacRae, I. J. Structural basis for microRNA targeting. Science 346, 608–613 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Filipowicz, W., Bhattacharyya, S. N. & Sonenberg, N. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat. Rev. Genet. 9, 102–114 (2008).

    CAS  PubMed  Google Scholar 

  14. 14.

    Sheu-Gruttadauria, J., Xiao, Y., Gebert, L. F. & MacRae, I. J. Beyond the seed: structural basis for supplementary microRNA targeting by human Argonaute2. EMBO J. 38, e101153 (2019).

    PubMed  Google Scholar 

  15. 15.

    Wee, L. M., Flores-Jasso, C. F., Salomon, W. E. & Zamore, P. D. Argonaute divides its RNA guide into domains with distinct functions and RNA-binding properties. Cell 151, 1055–1067 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    He, L. et al. A microRNA component of the p53 tumour suppressor network. Nature 447, 1130–1134 (2007).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Yamakuchi, M., Ferlito, M. & Lowenstein, C. J. miR-34a repression of SIRT1 regulates apoptosis. Proc. Natl Acad. Sci. USA 105, 13421–13426 (2008).

    ADS  CAS  PubMed  Google Scholar 

  18. 18.

    Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. elife 4, e05005 (2015).

    PubMed Central  Google Scholar 

  19. 19.

    Vecenie, C. J. & Serra, M. J. Stability of RNA hairpin loops closed by AU base pairs. Biochemistry 43, 11813–11817 (2004).

    CAS  PubMed  Google Scholar 

  20. 20.

    Ulrich, E. L. et al. BioMagResBank. Nucleic Acids Res. 36, D402–D408 (2008).

    CAS  PubMed  Google Scholar 

  21. 21.

    Parisien, M. & Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55 (2008).

    ADS  CAS  PubMed  Google Scholar 

  22. 22.

    Dethoff, E. A., Petzold, K., Chugh, J., Casiano-Negroni, A. & Al-Hashimi, H. M. Visualizing transient low-populated structures of RNA. Nature 491, 724–728 (2012).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Xue, Y. et al. in Laboratory Methods in Enzymology: RNA vol. 558 (eds Woodson, S. A. & Allain, F. H. T.) 39–73 (Academic Press, 2015).

  24. 24.

    Clay, M. C., Ganser, L. R., Merriman, D. K. & Al-Hashimi, H. M. Resolving sugar puckers in RNA excited states exposes slow modes of repuckering dynamics. Nucleic Acids Res. 45, e134 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Broughton, J. P., Lovci, M. T., Huang, J. L., Yeo, G. W. & Pasquinelli, A. E. Pairing beyond the seed supports microRNA targeting specificity. Mol. Cell 64, 320–333 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Brancati, G. & Großhans, H. An interplay of miRNA abundance and target site architecture determines miRNA activity and specificity. Nucleic Acids Res. 46, 3259–3269 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Lingel, A., Simon, B., Izaurralde, E. & Sattler, M. Nucleic acid 3′-end recognition by the Argonaute2 PAZ domain. Nat. Struct. Mol. Biol. 11, 576–577 (2004).

    CAS  PubMed  Google Scholar 

  28. 28.

    Wang, Y., Li, Y., Ma, Z., Yang, W. & Ai, C. Mechanism of microRNA-target interaction: molecular dynamics simulations and thermodynamics analysis. PLOS Comput. Biol. 6, e1000866 (2010).

    ADS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gebert, L. & MacRae, I. J. Regulation of microRNA function in animals. Nat. Rev. Mol. Cell Biol. 20, 21–37 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Park, J. H., Shin, S.-Y. & Shin, C. Non-canonical targets destabilize microRNAs in human Argonautes. Nucleic Acids Res. 45, 1569–1583 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    De, N. et al. Highly complementary target RNAs promote release of guide RNAs from human Argonaute2. Mol. Cell 50, 344–355 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Beckert, B. & Masquida, B. in Rna 29–41 (Springer, 2011).

  33. 33.

    Baronti, L., Karlsson, H., Marušič, M. & Petzold, K. A guide to large-scale RNA sample preparation. Anal. Bioanal. Chem. 410, 3239–3252 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Kao, C., Zheng, M. & Rüdisser, S. A simple and efficient method to reduce nontemplated nucleotide addition at the 3′ terminus of RNAs transcribed by T7 RNA polymerase. RNA 5, 1268–1272 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Helmling, C. et al. Rapid NMR screening of RNA secondary structure and binding. J. Biomol. NMR 63, 67–76 (2015).

    CAS  PubMed  Google Scholar 

  36. 36.

    De, N. & MacRae, I. J. in Argonaute Proteins 107–119 (Springer, 2011).

  37. 37.

    Pall, G. S. & Hamilton, A. J. Improved northern blot method for enhanced detection of small RNA. Nat. Protocols 3, 1077–1084 (2008).

    CAS  PubMed  Google Scholar 

  38. 38.

    Rio, D. C. Northern blots for small RNAs and microRNAs. Cold Spring Harbor Protocols, https://doi.org/10.1101/pdb.prot080838 (2014).

  39. 39.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Fürtig, B., Richter, C., Wöhnert, J. & Schwalbe, H. NMR spectroscopy of RNA. ChemBioChem 4, 936–962 (2003).

    PubMed  Google Scholar 

  41. 41.

    Schlagnitweit, J., Steiner, E., Karlsson, H. & Petzold, K. Efficient detection of structure and dynamics in unlabeled RNAs: the SELOPE approach. Chemistry 24, 6067–6070 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Hansen, A. L., Nikolova, E. N., Casiano-Negroni, A. & Al-Hashimi, H. M. Extending the range of microsecond-to-millisecond chemical exchange detected in labeled and unlabeled nucleic acids by selective carbon R NMR spectroscopy. J. Am. Chem. Soc. 131, 3818–3819 (2009).

    CAS  PubMed  Google Scholar 

  43. 43.

    Nikolova, E. N., Gottardo, F. L. & Al-Hashimi, H. M. Probing transient Hoogsteen hydrogen bonds in canonical duplex DNA using NMR relaxation dispersion and single-atom substitution. J. Am. Chem. Soc. 134, 3667–3670 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Steiner, E., Schlagnitweit, J., Lundström, P. & Petzold, K. Capturing excited states in the fast-intermediate exchange limit in biological systems using 1H NMR spectroscopy. Angew. Chem. Int. Edn 55, 15869–15872 (2016).

    CAS  Google Scholar 

  45. 45.

    Metropolis, N. & Ulam, S. The Monte Carlo method. J. Am. Stat. Assoc. 44, 335–341 (1949).

    CAS  PubMed  MATH  Google Scholar 

  46. 46.

    Palmer, A. G., III & Massi, F. Characterization of the dynamics of biomacromolecules using rotating-frame spin relaxation NMR spectroscopy. Chem. Rev. 106, 1700–1719 (2006).

    CAS  PubMed  Google Scholar 

  47. 47.

    Popenda, M. et al. RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures. BMC Bioinformatics 11, 231 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2, 19–25 (2015).

    ADS  Google Scholar 

  49. 49.

    Chen, A. A. & García, A. E. High-resolution reversible folding of hyperstable RNA tetraloops using molecular dynamics simulations. Proc. Natl Acad. Sci. USA 110, 16820–16825 (2013).

    ADS  CAS  PubMed  Google Scholar 

  50. 50.

    Steinbrecher, T., Latzer, J. & Case, D. A. Revised AMBER parameters for bioorganic phosphates. J. Chem. Theory Comput. 8, 4405–4412 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Horn, H. W. et al. Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J. Chem. Phys. 120, 9665–9678 (2004).

    ADS  CAS  PubMed  Google Scholar 

  52. 52.

    Joung, I. S. & Cheatham, T. E., III. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J. Phys. Chem. B 112, 9020–9041 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Daura, X. et al. Peptide folding: when simulation meets experiment. Angew. Chem. Int. Edn 38, 236–240 (1999).

    CAS  Google Scholar 

  54. 54.

    Hu, H., Yun, R. H. & Hermans, J. Reversibility of free energy simulations: slow growth may have a unique advantage (with a note on use of Ewald summation). Mol. Simul. 28, 67–80 (2002).

    CAS  Google Scholar 

  55. 55.

    Grentzmann, G., Ingram, J. A., Kelly, P. J., Gesteland, R. F. & Atkins, J. F. A dual-luciferase reporter system for studying recoding signals. RNA 4, 479–486 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Salzman, D. W. et al. miR-34 activity is modulated through 5′-end phosphorylation in response to DNA damage. Nat. Commun. 7, 10954 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sun, F. et al. Downregulation of CCND1 and CDK6 by miR-34a induces cell cycle arrest. FEBS Lett. 582, 1564–1568 (2008).

    CAS  PubMed  Google Scholar 

  58. 58.

    Huang, J. et al. miR-34a modulates angiotensin II-induced myocardial hypertrophy by direct inhibition of ATG9A expression and autophagic activity. PLoS One 9, e94382 (2014).

    ADS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the members of the Petzold and the E. R. Andersson laboratories for insightful discussions; N. H. Hopkins for providing the curated chemical shifts and for contributing to the MC-Fold secondary-structure-prediction script; I. MacRae for the gift of plasmid expressing human Ago2; and the Lund University Protein Production Platform (LP3) (Lund, Sweden) and the Protein Science Facility at the Karolinska Institute Department of Medical Biochemistry and Biophysics (Stockholm, Sweden) for help with human Ago2. K.P. acknowledges funding from the Swedish Research Council (grant number 2014-04303), the Swedish Foundation for Strategic Research (project number ICA14-0023), Harald och Greta Jeansson Stiftelse (JS20140009), Carl Tryggers stiftelse (CTS14-383 and 15-383), Eva och Oscar Ahréns Stiftelse, Åke Wiberg Stiftelse (467080968 and M14-0109), Cancerfonden (CAN 2015/388), the Karolinska Institute Department of Medical Biochemistry and Biophysics (grant number KID 2-3707/2013 and support for the purchase of a 600-MHz Bruker NMR spectrometer) and Ragnar Söderberg Stiftelse (M91/14). A.A.C. acknowledges support from National Science Foundation (grant MCB1651877). B.F. acknowledges funding from the Strategic Research Area (SFO) program of the Swedish Research Council (VR) through Stockholm University. This work used the computational resources of the Extreme Science and Engineering Discovery Environment (XSEDE) (allocation TG-MCB140273 to A.A.C.), which is supported by National Science Foundation grant number ACI-1548562. J.S. acknowledges funding through a Marie Sklodowska-Curie Individual Fellowship (EU H2020/project number 747446).

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Authors

Contributions

L.B. and K.P. conceived the project. L.B. carried out most of the experiments and data analysis, with assistance from I.G., E.S., S.F.S., K.P. and L.S. P.E. and A.A.C. carried out the REMD simulations and slow-growth docking. I.G. and B.F. performed the target screening of GC-to-GU switches. J.S. and C.F. provided advice on design and analysis. A.A.C. supervised computational work and K.P. supervised the overall work. L.B., K.P. and A.A.C. wrote the manuscript and all authors contributed to the final version.

Corresponding author

Correspondence to Katja Petzold.

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Extended data figures and tables

Extended Data Fig. 1 Secondary-structure prediction using MC-Fold.

Secondary-structure rearrangements among the ten lowest-energy structures were calculated using MC-Fold21. Ranking (numbers in parentheses) according to the predicted energy difference, based on the minimum free energy (MFE), is indicated in each label (ΔΔG(n) in units of unreferenced kcal mol−1, as described21). Secondary structures with a single base-pair opening in the cUUCGg region are omitted. a, The miR-34a–mSirt1 duplex connected by a cUUCGg loop (black). The MFE corresponds to a 7-mer–A1 binding site. Suboptimal structures (3) and (5) suggest possible modulation of the binding site to a 8-mer–GU and an 6-mer–A1 configuration, respectively. b, miR-34a–mSirt1 bulge construct, comprising a cUUCGg loop and a closing stem (black). The secondary-structure distribution of the miR-34a–mSirt1 bulge follows the same trend as the full-length duplex; dashed lines connect identical bulge structures. Suboptimal structures were used to validate or reject models of excited-state (ES) secondary structures on the basis of R NMR relaxation-dispersion data. Structure (1), with the MFE, corresponds to the assigned ground-state structure (GS). Structure (3) satisfies the 1H1 and 15N1 R NMR relaxation-dispersion data on gG6(G24), being G:U base paired with tU20(U9). Structure (5) is mutually exclusive with (3) in structural terms and satisfies the 13C R NMR relaxation-dispersion data measured on tA19 that indicate this residue adopting a base-paired conformation. Therefore we propose structure (3) as ES1 and structure (5) as ES2. Conformations (6) and (7) do not agree, and partially clash, with our R NMR relaxation-dispersion data and can therefore be excluded as excited states. c, miR-34a–mSirt1 trapped excited-state duplex connected by a cUUCGg loop (black). Substituted nucleotides used to trap the excited state are in yellow. The MFE corresponds to a 8-mer binding site. d, miR-34a–mSirt1 (turquoise) trapped excited-state construct comprising an cUUCGg loop and a closing stem (black). Substituted nucleotides used to trap the excited state are in yellow. The secondary-structure distribution of the miR-34a–mSirt1 trapped excited state follows a similar trend as the full-length duplex; dashed lines connect identical bulge structures.

Extended Data Fig. 2 Mg2+ titration of the miR-34a–mSirt1 bulge followed by NMR.

Shown are HSQC overlays of different Mg2+ titration steps. a, 1H–13C aromatic 2/6/8 HSQC. b, 1H–13C sugar 1′ HSQC. c, 1H–15N imino 1/3 HSQC. The titration steps are colour-coded (a, top left). Additional overlay of the miR-34a–mSirt1 trapped ES is shown in grey in a, b. Arrows indicate the chemical-shift trajectory during titration. Dashed lines connect equivalent peaks in the miR-34a–mSirt1 bulge and trapped ES constructs.

Extended Data Fig. 3 MCSF analysis of the miR-34a–mSIRT1 bulge and trapped excited state, and analysis of  13C tA22C8 outliers.

a, b, We used the MCSF approach22 to cross-validate our candidate excited state (ES1), modelled using R-derived ground-state-to-excited-state chemical-shift differences (a, 13C R Δω data, blue dots; b, left). pES refers to the excited-state population (popES in the main text). We also generated an ES1 mimic (trapped ES1) using a two-point substitution, predicted to stabilize the proposed conformation (b, bottom). For each reporter atom, we compared 13C R Δω data with the chemical-shift differences derived from the assignment of the bulge and the trapped ES constructs (a, 13C Δω trapped ES (tES) data, turquoise dots). In a, The MCSF analysis validates our ES1 model (green shading), with exceptions arising from the limitations of the mimic (orange shading) and from the presence (violet shading) of a second ES (ES2, b, right). Errors for R relaxation-dispersion-derived Δω represent 1 s.d. from fitting (see also Supplementary Methods). In b, the proposed model for ES2 satisfies the 13C R Δω data measured for tA19 and gG6. GS, ground state. c, The free-energy landscape for the entire star-like three-state exchange process. (The MCSF analysis and ES2 are discussed further in the Supplementary Information, Discussion 5.) The transition coeffient (κ), is assumed to be 1 (ref. 23), so the transition-state energies (TS1 and TS2), calculated using Supplementary equation (11), must be considered an upper limit of this exchange process. d, e, The substitution site (tU21 to tC21) perturbs the chemical environment of tA22C8 that is directly neighbouring the substituted nucleobase (orange sphere in e). Conversely, tA22C2 (green sphere), pointing towards the miR-34a strand (red), experiences an equivalent chemical environment in the bulge (blue) and trapped ES (turquoise) constructs. This explains the inconsistency in the MCSF profile for tA22C8 (Supplementary Fig. 12a, orange box). d, Secondary structure environment of tA22 in the miR-34a–mSirt1 bulge excited state (left) and trapped ES (right) constructs. The substitution site (tU21 to tC21) is highlighted. e, Overlay of average structures of the bulge ES (blue) and trapped ES (turquoise) from REMD ensembles, aligned according to residues gU7 and tA22. Residues gU7, gG8, tU21 and tA22 are shown. tA22C8 and tA22C2 13C atoms are in orange and green respectively.

Extended Data Fig. 4 Biophysical and biochemical characterization of the constructs.

a, Individual A260 UV melting profiles for the constructs used here. The miR-34a–mSirt1 duplex, miR-34a–mSirt1 trapped ES duplex, miR-34a–complementary-strand duplex and miR-34a single-stranded RNA (ssRNA) were each measured as three technical independent replicates (shown in different colours; n = 1). Individual technical replicates are plotted. Tm values are shown as means ± s.d. of fitted Tm values in individual technical replicates (n = 3). The other ssRNAs (bottom row) were measured and plotted as individual technical replicates; fitted Tm values are shown with associated confidence intervals of 95% (n = 1) as an estimate of the experimental error. Normalized differential melting curves (δA260T) are plotted as a function of temperature (in K) (circles) and fitted to Supplementary equation (1a) or (1b) (curves), depending on the molecularity of the system. b, EMSA titration profiles for the miR-34a–mSirt1 duplex, miR-34a–mSirt1 trapped ES duplex and miR-34a–complementary-strand duplex, measured as three independent technical replicates. The ratio of bound to total miR-34a 3′-Cy3 is plotted as a function of titrand concentration (circles) and fits a standard binding isotherm (line) (Supplementary equation (2)). The plot centre is the mean; error bars represent 1 s.d. from the three independent replicates. Fitted Kd values along with confidence intervals of 95% are shown as an estimate of the experimental error (n = 3). Gel images were acquired by detection of Cy3 fluorescence. During the titration, miR-34a 3′-Cy3 was kept at a constant concentration of 24 nM, setting the sensitivity limit for estimating Kd (Supplementary Fig. 1a–c). mSirt1 and its trapped-ES counterpart are equivalent in their ability to form a stable RNA–RNA duplex with miR-34a. Tighter binding is observed for the complementary strand (48.4 ± 9.5 nM) than for the mSirt1 (124.3 ± 21.7 nM) and trapped-ES mSirt1 (110.3 ± 23.0 nM), providing a control for the dynamic range of Kd estimation. c, Equilibrium FBA profiles for mSirt1, mSirt1 trapped ES and a scrambled control, binding to miR-34a-loaded Ago2. The three targets were each measured as three independent replicates and fitted to a standard binding isotherm (line) (Supplementary equation (2)). The plot centre is the mean; error bars represent 1 s.d. from three independent replicates. Fitted Kd values are shown with confidence intervals of 95% (an estimate of the experimental error). As in c, mSirt1 and mSirt1 trapped ES are equivalent in their ability to form a stable ternary complex within RISC. The simulated data set (dotted lines) indicate curves corresponding to Kd values ten times lower (red) or ten times higher (green) than the average value for mSirt1 and mSirt1 trapped ES, providing a frame for the amplitude of our experimental error. d, Top, northern blot showing the detection of miR-34a loaded in Ago2. Bottom, a standard calibration curve (using naked miR-34a), used to obtain an estimate of miR-34a in RISC. The centre calibration curve was used to calculate R2. The two outer curves indicate the 95% confidence interval of the calibration-line fit (from a single repeated experiment). The average ratio of Ago2 and miR-34a-loaded Ago2 (both in pmole) was used to obtain the fraction of Ago2 loaded with our guide (roughly 1.5%). The complete lists of fitted parameters for UV melting, EMSA titration, FBA titration and northern blot are in Supplementary Table 1a–d. The complete fitting analyses of UV melting, EMSA titration and FBA titration are in Supplementary Tables 79.

Extended Data Fig. 5 Crystal structure of Ago2 overlaid with REMD ensembles.

Superposition of ground state (green) and excited state (orange) conformational ensembles on the Ago2 crystal structure (PDB code 4W5T), with seed sequences aligned to crystallographic miRNA–mRNA positions (red). Although the seed orientations are comparable, the ground-state and excited-state conformations sample different space within Ago2.

Extended Data Fig. 6 Slow-growth insertion of excited-state RNA into Ago2 predicts the ability of bulged miRNA–mRNA complexes to access an alternative dsRNA-binding mode of Ago2.

Slow-growth induced-fit Ago2 structures are compared with existing X-ray structures (whose PDB identification codes are shown in the figure) via structural alignment. a, Ago2 after induced fit with ES RNA binds in the PIWI-adjacent groove rather than the PAZ domain. b, The Thermus thermophilus (Tt) Ago crystal structure similarly shows DNA/RNA-duplex binding in the analogous PIWI-adjacent groove. cj, The root mean square deviation (r.m.s.d.) for each indicated pair of Ago structures was measured after structural alignment either of all protein atoms, or excluding the PAZ domain, PIWI loops and helix-7 atoms (‘subset aligned’; these excluded atoms still count towards the r.m.s.d.). The subset-aligned structures show that most of the r.m.s.d. difference arises from pivoting motions of the PAZ domain, coupled with small shifts in helix-7 and PIWI loops to accommodate the inserted ES RNA structures. c, Comparison of slow-growth human Ago2–GS and the existing Ago2 structure (PDB code 4OLA; r.m.s.d. = 2.065 Å (all) and 2.62 Å (subset aligned)). d, Comparison of slow-growth Ago2–ES and the 4OLA structure (r.m.s.d. = 1.4 Å (all) and 1.65 Å (subset aligned)). e, Comparison of the slow-growth Ago2–trapped ES and the 4OLA structure (r.m.s.d. = 1.9 Å (all) and 2.18  Å (subset aligned)). f, Comparison of the slow-growth Ago2–GS with Ago2–ES (r.m.s.d. = 2.1 Å (all) and 2.2 Å (subset aligned)). g, Comparison of the slow-growth Ago2–ES with Ago2–trapped ES (r.m.s.d. = 1.6 Å (all) and 1.33 Å (subset aligned)). h, Comparison of the slow-growth Ago2–GS and the 6N4O structure (r.m.s.d. = 2.05 Å (all) and 2.065 Å (subset aligned)). i, Comparison of the slow-growth Ago2–ES (green) with the 3HM9 structure (r.m.s.d. = 4.52 Å (all)). j, Comparison of the slow-growth Ago2–GS with the 3HM9 structure (r.m.s.d. = 3.85Å (all)).

Extended Data Fig. 7 DLR assay of additional miR-34a targets.

We studied five targets of different bulge sizes (see Methods). Individual replicates are plotted as circles; the centre line is the mean; error bars represent 1 s.d from three independent replicates; nts, nucleotides. a, Standard DLR normalization (relative to the control condition with no miR-34a duplex transfected). Despite the large variability between replicates, a consistent increase in downregulation (connecting lines) is observed for wild-type (WT) and trapped excited-state (tES) constructs. b, When the data sets are internally normalized and the WT condition is set to 100% (mean value), the variation due to experimental replicas is attenuated and the trend observed in a is maintained.

Extended Data Table 1 Tm and Kd fitted parameters

Supplementary information

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This file contains the Supplementary Discussion, Supplementary Methods, Supplementary Figures 1-9, a full description of the Supplementary Data (Supplementary Data file supplied separately) and Supplementary References.

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This file contains the uncropped gels.

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Video 1 Morph of GS-ES Ago2 slow-growth structures.

Morphing transition between GS-ES Ago2 structures shows that the accommodation of the ES state requires pivoting of the PAZ domain.

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Baronti, L., Guzzetti, I., Ebrahimi, P. et al. Base-pair conformational switch modulates miR-34a targeting of Sirt1 mRNA. Nature 583, 139–144 (2020). https://doi.org/10.1038/s41586-020-2336-3

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