Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Parallel degradome-seq and DMS-MaPseq substantially revise the miRNA biogenesis atlas in Arabidopsis

Nature Plants volume 10, pages 1126–1143 (2024)Cite this article

Subjects

Abstract

MicroRNAs (miRNAs) are produced from highly structured primary transcripts (pri-miRNAs) and regulate numerous biological processes in eukaryotes. Due to the extreme heterogeneity of these structures, the initial processing sites of plant pri-miRNAs and the structural rules that determine their processing have been predicted for many miRNAs but remain elusive for others. Here we used semi-active DCL1 mutants and advanced degradome-sequencing strategies to accurately identify the initial processing sites for 147 of 326 previously annotated Arabidopsis miRNAs and to illustrate their associated pri-miRNA cleavage patterns. Elucidating the in vivo RNA secondary structures of 73 pri-miRNAs revealed that about 95% of them differ from in silico predictions, and that the revised structures offer clearer interpretation of the processing sites and patterns. Finally, DCL1 partners Serrate and HYL1 could synergistically and independently impact processing patterns and in vivo RNA secondary structures of pri-miRNAs. Together, our work sheds light on the precise processing mechanisms of plant pri-miRNAs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Identification of bona fide pri-miRNAs by degradome-seq of semi-active DCL1 mutants in Arabidopsis.
Fig. 2: Degradome-seq reveals that 37, 16, 38, 13 and 43 pri-miRNAs display the BTL, SBTL, LTB, SLTB and bidirectional processing patterns, respectively.
Fig. 3: SE and HYL1 show different impacts on pri-miRNA processing.
Fig. 4: DMS-MaPseq reveals bona fide RSS of pri-miRNAs in vivo.
Fig. 5: DRS provides more meaningful interpretation for determination of the initial cleavages of pri-miRNAs than RPS.
Fig. 6: DCL1, SE and HYL1 maintain the proper pri-miRNA secondary structures for processing.
Fig. 7: Atlas of miRNA biogenesis in Arabidopsis drawn from degradome-seq and DMS-MaPseq.

Similar content being viewed by others

Data availability

The degradome-seq and DMS-MaPseq data have been deposited in the NCBI Sequence Read Archive under the BioProject database with accession code PRJNA1092576. The Arabidopsis genome reference was obtained from TAIR (https://www.arabidopsis.org) and the NCBI Nucleotide database (CP002684–CP002688). Information on the 326 previously annotated pri-miRNAs was from the miRBase (https://www.mirbase.org/browse/results/?organism=ath). sRNA-seq and AGOs-IP sRNA-seq data were obtained from the NCBI website with Gene Expression Omnibus accession codes GSE78090, GSE66599 and GSM707678–GSM707691. All other data supporting the findings of the study are present in the main text and/or the Supplementary Information.

Code availability

The code (CountMismatch2Bed.py) used for mismatch calling of DMS-MaPseq generated in this study is accessible via GitHub at https://github.com/changhaoli/TAMU_02RSS.

References

  1. Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Song, X. W., Li, Y., Cao, X. F. & Qi, Y. J. MicroRNAs and their regulatory roles in plant–environment interactions. Annu. Rev. Plant Biol. 70, 489–525 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Li, S., Castillo‐González, C., Yu, B. & Zhang, X. The functions of plant small RNA s in development and in stress responses. Plant J. 90, 654–670 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Ma, Z. & Zhang, X. Actions of plant Argonautes: predictable or unpredictable? Curr. Opin. Plant Biol. 45, 59–67 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zhu, H. et al. Bidirectional processing of pri-miRNAs with branched terminal loops by Arabidopsis Dicer-like1. Nat. Struct. Mol. Biol. 20, 1106–1115 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nguyen, T. A. et al. Functional anatomy of the human Microprocessor. Cell 161, 1374–1387 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Partin, A. C. et al. Heme enables proper positioning of Drosha and DGCR8 on primary microRNAs. Nat. Commun. 8, 1737 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jin, W., Wang, J., Liu, C. P., Wang, H. W. & Xu, R. M. Structural basis for pri-miRNA recognition by Drosha. Mol. Cell 78, 423–433 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Partin, A. C. et al. Cryo-EM structures of human Drosha and DGCR8 in complex with primary microRNA. Mol. Cell 78, 411–422 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Park, J. E. et al. Dicer recognizes the 5′ end of RNA for efficient and accurate processing. Nature 475, 201–205 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bologna, N. G. et al. Multiple RNA recognition patterns during microRNA biogenesis in plants. Genome Res. 23, 1675–1689 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Chorostecki, U. et al. Evolutionary footprints reveal insights into plant microRNA biogenesis. Plant Cell 29, 1248–1261 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moro, B. et al. Efficiency and precision of microRNA biogenesis modes in plants. Nucleic Acids Res. 46, 10709–10723 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Gonzalo, L. et al. R-loops at microRNA encoding loci promote co-transcriptional processing of pri-miRNAs in plants. Nat. Plants 8, 402–418 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mencia, R., Gonzalo, L., Tossolini, I. & Manavella, P. A. Keeping up with the miRNAs: current paradigms of the biogenesis pathway. J. Exp. Bot. 74, 2213–2227 (2023).

    Article  CAS  PubMed  Google Scholar 

  17. Wang, Z. et al. SWI2/SNF2 ATPase CHR2 remodels pri-miRNAs via Serrate to impede miRNA production. Nature 557, 516–521 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 47, D155–D162 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Meyers, B. C. et al. Criteria for annotation of plant microRNAs. Plant Cell 20, 3186–3190 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Axtell, M. J. & Meyers, B. C. Revisiting criteria for plant microRNA annotation in the era of big data. Plant Cell 30, 272–284 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lobbes, D., Rallapalli, G., Schmidt, D. D., Martin, C. & Clarke, J. SERRATE: a new player on the plant microRNA scene. EMBO Rep. 7, 1052–1058 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Han, M. H., Goud, S., Song, L. & Fedoroff, N. The Arabidopsis double-stranded RNA-binding protein HYL1 plays a role in microRNA-mediated gene regulation. Proc. Natl Acad. Sci. USA 101, 1093–1098 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Vazquez, F., Gasciolli, V., Crété, P. & Vaucheret, H. J. C. B. The nuclear dsRNA binding protein HYL1 is required for microRNA accumulation and plant development, but not posttranscriptional transgene silencing. Curr. Biol. 14, 346–351 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Wei, X. et al. Structural basis of microRNA processing by Dicer-like 1. Nat. Plants 7, 1389–1396 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Dong, Z., Han, M. H. & Fedoroff, N. The RNA-binding proteins HYL1 and SE promote accurate in vitro processing of pri-miRNA by DCL1. Proc. Natl Acad. Sci. USA 105, 9970–9975 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kurihara, Y., Takashi, Y. & Watanabe, Y. The interaction between DCL1 and HYL1 is important for efficient and precise processing of pri-miRNA in plant microRNA biogenesis. RNA 12, 206–212 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xie, D. et al. Phase separation of SERRATE drives dicing body assembly and promotes miRNA processing in Arabidopsis. Nat. Cell Biol. 23, 32–39 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Wang, Z., Wang, M., Wang, T., Zhang, Y. & Zhang, X. Genome-wide probing RNA structure with the modified DMS-MaPseq in Arabidopsis. Methods 155, 30–40 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Zhang, Z., Liu, X., Guo, X., Wang, X. J. & Zhang, X. Arabidopsis AGO3 predominantly recruits 24-nt small RNAs to regulate epigenetic silencing. Nat. Plants 2, 16049 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Barciszewska-Pacak, M. et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front. Plant Sci. 6, 410 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bologna, N. G., Mateos, J. L., Bresso, E. G. & Palatnik, J. F. A loop-to-base processing mechanism underlies the biogenesis of plant microRNAs miR319 and miR159. EMBO J. 28, 3646–3656 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ma, Z. et al. Arabidopsis Serrate coordinates histone methyltransferases ATXR5/6 and RNA processing factor RDR6 to regulate transposon expression. Dev. Cell 45, 769–784 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Curtin, S. J. et al. The roles of plant dsRNA-binding proteins in RNAi-like pathways. FEBS Lett. 582, 2753–2760 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Reis, R. S., Hart-Smith, G., Eamens, A. L., Wilkins, M. R. & Waterhouse, P. M. Gene regulation by translational inhibition is determined by Dicer partnering proteins. Nat. Plants 1, 14027 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Zubradt, M. et al. DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo. Nat. Methods 14, 75–82 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Merchan, F., Boualem, A., Crespi, M. & Frugier, F. Plant polycistronic precursors containing non-homologous microRNAs target transcripts encoding functionally related proteins. Genome Biol. 10, R136 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Cuperus, J. T., Fahlgren, N. & Carrington, J. C. Evolution and functional diversification of MIRNA genes. Plant Cell 23, 431–442 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Stepien, A. et al. Posttranscriptional coordination of splicing and miRNA biogenesis in plants. WIREs RNA 8, e1403 (2017).

    Article  Google Scholar 

  39. Luo, Q. J. et al. RNA structure probing reveals the structural basis of Dicer binding and cleavage. Nat. Commun. 12, 3397 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rojas, A. M. L. et al. Identification of key sequence features required for microRNA biogenesis in plants. Nat. Commun. 11, 5320 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hirata, R. et al. Unpaired nucleotides on the stem of microRNA precursor are important for precise cleavage by Dicer-like 1 in Arabidopsis. Genes Cells 27, 280–292 (2022).

    Article  CAS  PubMed  Google Scholar 

  42. Li, Y. et al. Degradation of SERRATE via ubiquitin-independent 20S proteasome to survey RNA metabolism. Nat. Plants 6, 970–982 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, L. et al. PRP4KA phosphorylates SERRATE for degradation via 20 S proteasome to fine-tune miRNA production in Arabidopsis. Sci. Adv. 8, eabm8435 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Shan, L. et al. Nucleolar URB1 ensures 3′ ETS rRNA removal to prevent exosome surveillance. Nature 615, 526–534 (2023).

    Article  CAS  PubMed  Google Scholar 

  45. Xiang, Y. et al. Pervasive downstream RNA hairpins dynamically dictate start-codon selection. Nature 621, 423–430 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang, X., Henriques, R., Lin, S. S., Niu, Q. W. & Chua, N. H. Agrobacterium-mediated transformation of Arabidopsis thaliana using the floral dip method. Nat. Protoc. 1, 641–646 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Zhu, H. et al. Arabidopsis Argonaute10 specifically sequesters miR166/165 to regulate shoot apical meristem development. Cell 145, 242–256 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Martin, M. J. E. J. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Article  Google Scholar 

  49. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Cheng, C. Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Wang, H. et al. Deep sequencing of small RNAs specifically associated with Arabidopsis AGO1 and AGO4 uncovers new AGO functions. Plant J. 67, 292–304 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dai, X., Zhuang, Z. & Zhao, P. X. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res. 46, W49–W54 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Xu, Z. Z. & Mathews, D. H. Experiment-assisted secondary structure prediction with RNAstructure. Methods Mol. Biol. 1490, 163–176 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all the members of the Zhang laboratory for their help and careful proofreading of this paper. We thank the TAMU High Performance Research Computing group for supercomputing support. This work was supported by grants from NIH (no. R35GM151976), NSF MCB (no. 2139857) and the Welch Foundation (no. A-2177-20230405) to X.Z. X.Y. was partially supported by a China Scholar Council fellowship.

Author information

Author notes

  1. Kaiye Liu

    Present address: National Key Laboratory for Tropical Crop Breeding, School of Tropical Agriculture and Forestry, Hainan University, Haikou, China

  2. Ziying Wang

    Present address: McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA

  3. Anikah Yusuf

    Present address: Doctor of Osteopathic Medicine Program, Des Moines University, West Des Moines, IA, USA

  4. These authors contributed equally: Xingxing Yan, Changhao Li.

Authors and Affiliations

  1. Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA

    Xingxing Yan, Changhao Li, Kaiye Liu, Tianru Zhang, Xindi Li, Jiaying Zhu, Anikah Yusuf & Xiuren Zhang

  2. Molecular and Environmental Plant Sciences, Texas A&M University, College Station, TX, USA

    Xingxing Yan, Tianru Zhang & Xiuren Zhang

  3. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA

    Qian Xu & James J. Cai

  4. Department of Biology, Texas A&M University, College Station, TX, USA

    Ziying Wang & Xiuren Zhang

  5. School of Food and Biological Engineering, Hefei University of Technology, Hefei, China

    Shuqing Cao

  6. Department of Medical Physiology, School of Medicine, Texas A&M University, Bryan, TX, USA

    Xu Peng

  7. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    James J. Cai

Authors
  1. Xingxing Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changhao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaiye Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianru Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xindi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiaying Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ziying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anikah Yusuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shuqing Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xu Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. James J. Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Xiuren Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.Z. conceived the project. K.L. initially started the project and generated genetic materials. K.L. and T.Z. contributed equally to performing degradome-seq. J.Z. and X.L. contributed to the library construction. X.Y. performed DMS-MaPseq. C.L., X.Y., Q.X. and K.L. analysed the degradome-seq data. Z.W. and A.Y. helped pinpoint partial cleavage sites for the degradome-seq data. C.L. and X.Y. analysed the DMS-MaPseq data. S.C., X.P. and J.J.C. provided guidance and intellectual input. X.Y. and C.L. wrote the initial draft of the paper. X.Z. thoroughly edited the paper, and all authors contributed to the proofreading of the paper.

Corresponding author

Correspondence to Xiuren Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Hiro-oki Iwakawa, Peter Waterhouse and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Quality control of DMS-MaPseq library and overall patterns of DMS-MaPseq signals cross pri-miRNA backbones.

(a) Average mismatch ratios of A/C/G/U caused by DMS reactivities in Col-0, dcl1-9, hyl1-2 and se-1. The data are from 67 commonly detected pri-miRNAs from three biological replicates for Col-0, dcl1-9 and hyl1-2, but two biological replicates for se-1. P (dcl1-9 vs Col-0) = 0.132, P (hyl1-2 vs Col-0) = 0.06494, P (se-1 vs Col-0) = 0.1714. P value by Wilcoxon test. (b) Boxplots show the DMS reactivities for 16 SBTL (left panel) and 12 SLTB (right panel) pri-miRNAs around base/top and duplex regions in Col-0, from three biological replicates. In both top and bottom panels, position ‘0’ is defined as the first nucleotides of duplex region, the purple and yellow arrowheads labeled in the pri-miRNA cartoon represent the first cleavage sites. The blue and pink regions represent miRNA/* duplex. Centres of the boxes represent the median values. Upper bound and lower bound show the first and the third quartiles respectively. Whiskers indicate data within 1.5× the interquartile range of both quartiles. Data points at the ends of whiskers represent outliers. (c) Pri-miR156a, pri-miR168a, pri-miR844a and pri-miR856a show identical structures in DRS (right) compared to RPS (left). Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures.

Extended Data Fig. 2 Pri-miR156c, d, pri-miR157a, pri-miR158a, pri-miR159a, b, pri-miR161, pri-miR162a, b, pri-miR163, and pri-miR164a, c show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 3 Pri-miR165a, b, pri-miR166a, e, f, and pri-miR167a-d show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 4 Pri-miR168b, pri-miR169a, d, and pri-miR171a-c show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 5 Pri-miR172a-e, pri-miR319a, b, pri-miR390a, b and pri-miR391 show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 6 Pri-miR393a, pri-miR394b, pri-miR395c, f, pri-miR396a, b, pri-miR397a, pri-miR398b, c and pri-miR400 show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 7 Pri-miR403, pri-miR408, pri-miR447b, pri-miR771a, pri-miR779a, pri-miR780a, pri-miR781a, pri-miR823a and pri-miR825a show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 8 Pri-miR828a, pri-miR833a, pri-miR849a, pri-miR851a, pri-miR853a, pri-miR1888b, pri-miR2112, pri-miR3434 and pri-miR4245 show structural differences in DRS (right) compared to RPS (left).

Black and gray arrows indicated first cutting sites for BTL and LTB directions, respectively. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures. Blue dotted boxes indicated structural differences in DRS.

Extended Data Fig. 9 In vivo RSS of pri-miRNAs can better explain the first cleavage sites than in silico predicted structures.

(a) Barchart shows around 5% additional BTL-typed pri-miRNAs have internal loops/bulges that are 9-11 nt and 15-17 nt away from the first cleavage sites obtained in DRS vs RPS. (b) Barchart shows around 13% additional LTB-typed pri-miRNAs have internal loops/bulges that are 9-11 nt and 15-17 nt away from the first cleavage sites obtained in DRS vs RPS. (c) Venn diagram shows that both BTL- and LTB-typed pri-miRNAs concurrently present internal loops/bulges that are ~9-11 nt and ~15-17 nt away from the first cutting sites. RPS: RNAfold Predicted Structures. DRS: DMS Reactivity based Structures.

Extended Data Fig. 10 DCL1, SE and HYL1 impact RSS of pri-miRNAs.

(a) Gini index of 67 common pri-miRNAs in Col-0, dcl1-9, hyl1-2 and se-1. P value by Wilcoxon test. The data are from three biological replicates for Col-0, dcl1-9 and hyl1-2, but two biological replicates for se-1. P (dcl1-9 vs Col-0) = 0.43, P (hyl1-2 vs Col-0) = 0.073, P (se-1 vs Col-0) = 0.0015. P value by Wilcoxon test. Centres of the boxes represent the median values. Upper bound and lower bound show the first and the third quartiles respectively. Whiskers indicate data within 1.5× the interquartile range of both quartiles. (b-d) Examples of SBTL-processed pri-miR447b (b), SLTB-processed pri-miR319a (c) and bidirectional-processed pri-miR166a (d) that show structural difference of pri-miRNAs in dcl1-9, hyl1-2 and se-1 compared to Col-0. Dotted boxes indicated structural differences in mutants. (e) Re-design of a known amiR backbone from pri-miR159a. An existing amiR backbone of pri-miR159a (top panel). Re-designing of the amiR backbone of pri-miR159a (bottom panel). amiR sequence is labelled with purple.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and unprocessed western blots for Supplementary Fig. 1a.

Reporting Summary

Supplementary Data

Supplementary Tables 1–6.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, X., Li, C., Liu, K. et al. Parallel degradome-seq and DMS-MaPseq substantially revise the miRNA biogenesis atlas in Arabidopsis. Nat. Plants 10, 1126–1143 (2024). https://doi.org/10.1038/s41477-024-01725-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-024-01725-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing