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Database: The Journal of Biological Databases and Curation logoLink to Database: The Journal of Biological Databases and Curation
. 2019 Jun 24;2019:baz070. doi: 10.1093/database/baz070

MepmiRDB: a medicinal plant microRNA database

Dongliang Yu 1,†, Jiangjie Lu 1,2,†, Weishan Shao 1,2,†, Xiaoxia Ma 1,†, Tian Xie 3,4,†,✉, Hidetaka Ito 5,†, Tingzhang Wang 6,†, Min Xu 1,2,†, Huizhong Wang 1,2,†,✉, Yijun Meng 1,2,†,✉
PMCID: PMC6589547  PMID: 31231773

Abstract

MicroRNAs (miRNAs) have been recognized as a key regulator in plant development and metabolism. Recent reports showed that the miRNAs of medicinal plants not only act as a critical modulator in secondary metabolism but also had a great potential of performing cross-kingdom gene regulation. Although several plant miRNA repositories have been publicly available, no miRNA database specific for medicinal plants has been reported to date. Here, we report the first version of MepmiRDB (medicinal plant microRNA database), which is freely accessible at http://mepmirdb.cn/mepmirdb/index.html. This database accommodates thousands of miRNA candidates belonging to 29 medicinal plant species. The miRNA information on sequences, expression patterns and regulatory networks has been included in the functional modules of the database. Specifically, the ‘Sequence’ module provides the sequences of the mature miRNAs and their precursors, and the structure information of the precursors. Moreover, the processing and small RNA accumulation signals on the miRNA precursors are also included in the ‘Sequence’ module. The organ/growth condition-specific expression information of the mature miRNAs has been stored in the ‘Expression’ module. The ‘Interaction’ module offers the information of the degradome-validated miRNA—target pairs of eight plant species. The ‘Search’ module enables users to search for the miRNAs by plant species and miRNA families, or by sequences. All data in this database are available for download. Taken together, the functional modules of MepmiRDB ensure its importance and timeliness for mechanistic and functional studies on the medicinal plant miRNAs.

Introduction

MicroRNAs (miRNAs) play critical roles in diverse biological processes of plants, such as growth and development, stress response and metabolism (1). Currently, there are several databases accommodating information of plant miRNAs, such as miRBase (2), PMRD (3), miRNEST (4), miREX (5), PmiRKB (6) and PNRD (7). However, to date, no database has been established specifically for the miRNAs of medicinal plants. Here, we report the first version of MepmiRDB (medicinal plant microRNA database) including 29 medicinal plant species, most of which are listed in the pharmacopoeia of the People’s Republic of China (8).

Increasing pieces of evidence point to the pivotal role of plant miRNAs in modulating the biosynthesis and accumulation of secondary metabolites (9, 10). It is particularly important for the medicinal plants since a dominant portion of the bioactive ingredients are produced through the secondary metabolic pathways (11–14). On the other hand, miRNAs have become a charming candidate for therapeutic management of cancer and other intractable diseases (15). Notably, several reports indicated the ability of plant miRNAs in cross-kingdom communication (16–19), and some studies showed the high stability of certain ingested miRNAs during circulation in human or animal bloodstream (20–22). These lines of evidence point to the possibility that the exogenous, plant-derived miRNAs could perform target regulation in human body. In this regard, the miRNAs emerged as the bioactive components of medicinal plants (23–25). Taken together, the above-mentioned points formed the driving force for design and construction of MepmiRDB, which has been freely available online (http://mepmirdb.cn/mepmirdb/index.html).

Database construction and contents

The database structure has been illustrated in Figure S1 and S2. Five major functional modules, including ‘Sequence’, ‘Expression’, ‘Interaction’, ‘Search’ and ‘Download’, are available in MepmiRDB (Figure 1A) and are described below.

Figure 1.

Figure 1

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Brief summary of MepmiRDB (medicinal plant microRNA database). (A) Functional flow chart of MepmiRDB. The database provides four major functional modules, ‘Sequence’, ‘Expression’, ‘Interaction’ and ‘Search’. All of the sequence and expression data could be retrieved from ‘Download’. (B) 5′ first nucleotide composition of the miRNAs deposited in MepmiRDB. (C) Sequence length distribution of the miRNAs deposited in MepmiRDB. For (B) and (C), the 29 plant species are indicated by different colors, as shown on the bottom.

The ‘Sequence’ module

Sequence information of thousands of the miRNAs belonging to 29 plant species was included in this module. All of the miRNAs were predicted by using the recently published workflow PmiRDiscVali (26). These miRNAs were generally divided into conserved and non-conserved ones. By comparing to all of the plant miRNAs registered in miRBase (release 21), the sRNAs with identical sequences were extracted from the sRNA-seq (small RNA sequencing) data sets, and were regarded as the conserved miRNAs existed in specific medicinal plants (Figure 2A for example). Besides, 86 RNA-seq datasets of 23 plant species were employed for transcriptome assembly by Trinity (trinityrnaseq_r20140717, default parameters) (27). The assembled transcripts were served as the basic data for miRNA precursor discovery by using miRDeep-P (v1.3, default parameters) (28). We noticed that a large portion of the miRDeep-P predictions belonged to the non-conserved miRNAs (Table 1), suggesting that these miRNA candidates might be evolutionarily young or species-specific.

Figure 2.

Figure 2

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Sample results of different functional modules. (A) Through the ‘Sequence’ module, the sequence information of the mature miRNA (miRNAs; MeP-smi-miR12-5p for example here) of S. miltiorrhiza (smi), along with its precursors (MeP-smi-pri-MIR12 and MeP-smi-pre-MIR5), could be obtained. Based on sequence conservation, the miRNA has been assigned to the miR164 family. The ‘miRBase ID’ provides external links of miRNA homologs. (B) Through the ‘Expression’ module, miRNA expression levels in different organs (root, stem, leaf and flower) of S. miltiorrhiza could be browsed as a table, which is downloadable. The ‘Mature ID’ provides links for visiting the ‘Sequence’ module. (C) Processing and small RNA (sRNA) expression signals on MeP-pgi-pri-MIR81. Two regions encoding mature miRNAs (MeP-pgi-miR81-5p and MeP-pgi-miR81-3p) on the predicted secondary structure were marked by green and red lines respectively. The processing signal detected by degradome-seq data analysis was denoted by a blue arrow. The expression level of the sRNAs on the pri-miRNA was measured by RPM (reads per million). (D) Processing and sRNA expression signals on MeP-nnu-pri-MIR3. Two regions encoding mature miRNAs (MeP-nnu-miR3-5p and MeP-nnu-miR3-3p) on the predicted secondary structure were marked by green and red lines respectively. The processing signal detected by degradome-seq data analysis was denoted by a blue arrow. The expression level of the sRNAs on the pri-miRNA was measured by RPM. (E) Example of regulatory network constituted by a miRNA (MeP-csi-miR66-5p), its targets and the interacting transcripts. (F) T-plot (target plot) showing the cleavage signals of the miRNA–target regulatory pair. The upper panel provides a global view of the degradome signals (the leaf degradome library) on the full-length target transcript (Dof-2leafdata-115 128). The light green shadow indicates the binding site of the regulatory miRNAs (MeP-dof-miR135-3p and MeP-dof-miR136-3p). The lower panel provides the local view of the degradome signals (indicated by blue bars) within the miRNA binding region. The cleavage signal was highlighted by red color. The positions of the miRNA binding region and the cleavage signal were also shown on the lower panel. The degradome signal intensity was measured by RPM (reads per million).

Table 1.

The number of microRNAs reported by the current version of MepmiRDB.

Organism miR_total miR_miRBase miR_miRDP miR_(miRBase ∩ miRDP) Pri-miR_miRDP Pre-miR_miRDP
Actinidia chinensis (ach) 141 141 0 0 0 0
Ammopiptanthus mongolicus (amo) 456 332 151 27 148 76
Aquilaria sinensis (asi) 235 202 44 11 23 22
Catharanthus roseus (cro) 217 166 68 17 38 37
Citrus sinesis (csi) 948 470 530 52 641 273
Carthamus tinctorius (cti) 287 250 42 5 22 21
Dimocarpus longan (dlo) 195 154 55 14 30 30
Dendranthema morifolium (dmo) 497 199 312 14 317 168
Dendrobium officinale (dof) 669 283 408 22 370 225
Eucommia ulmoides (eul) 313 313 0 0 0 0
Ginkgo biloba (gbi) 438 298 150 10 106 77
Juglans regia (jre) 329 224 148 43 176 88
Litchi chinensis (lch) 298 219 85 6 84 43
Lonicera japonica (lja) 315 161 171 17 140 117
Lycium chinense (lyc) 298 245 67 14 41 35
Nelumbo nucifera (nnu) 627 453 207 33 198 107
Oenanthe javanica (oja) 25 25 0 0 0 0
Panax ginseng (pgi) 249 117 135 3 167 77
Punicai granatum (pgr) 261 258 4 1 2 2
Paeonia lactiflora Dafugui (plaD) 192 192 0 0 0 0
Paeonia lactiflora Hongyanzhenghui (plaH) 154 154 0 0 0 0
Paeonia lactiflora Zifengyu (plaZ) 163 163 0 0 0 0
Panax notoginseng (pno) 339 281 66 8 45 34
Papaver somniferum (pso) 164 138 26 0 14 13
Ricinus communis (rco) 178 159 34 15 29 18
Raphanus sativus (rsa) 124 120 4 0 2 2
Salvia miltiorrhiza (smi) 345 220 143 18 122 75
Taxus mairei (tma) 180 132 49 1 25 25
Taxus x media (tme) 581 315 281 15 257 145
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An overview of the sequence characteristics shows that around 50% of the miRNA candidates start with 5’ U and are 21 nt in length (Figure 1B and C), which is consistent with the known sequence features of plant miRNAs (1).

Degradome-seq data is useful for tracking the processing signals of miRNA precursors (29, 30). For eight plant species with degradome-seq data, the ‘Sequence’ module provides the detectable processing signals on the secondary structures of the pri-miRNAs (Figure 2C and D). Additionally, sRNA expression levels on the miRNA precursors were achieved by mapping the sRNA-seq data onto the pri-miRNAs. In many cases, the highly enriched accumulation regions of the sRNAs correlated well with the mature miRNA-coding regions, indicating the high reliability of the pri-miRNA candidates (Figure 2C and D).

The ‘Expression’ module

The module provides the organ/growth condition-specific expression information of the mature miRNA candidates (Figure 2B).

The ‘Interaction’ module

For the eight species with degradome-seq data, miRNA target prediction was performed by using the psRNATarget algorithm (2017 Update, default parameters) (31), treating the assembled transcripts and the mature miRNA candidates as the input data. Then, the predicted miRNA–target pairs were subject to degradome-seq data-based validation according to the previously reported method (32). As a result, 380 pairs involving 119 miRNAs and 47 target transcripts in Ammopiptanthus mongolicus, 2341 pairs involving 192 miRNAs and 208 targets in Citrus sinesis, 13 023 pairs involving 389 miRNAs and 4506 targets in Dendrobium officinale, 6933 pairs involving 216 miRNAs and 2968 targets in Nelumbo nucifera, 9660 pairs involving 143 miRNAs and 2009 targets in Panax ginseng, 1560 pairs involving 130 miRNAs and 864 targets in Salvia miltiorrhiza, 149 pairs involving 72 miRNAs and 55 targets in Taxus mairei and 262 pairs involving 22 miRNAs and 98 targets in Taxus × media were identified. For each pair, t-plot (target plot) showing target cleavage signal(s) and visual network showing regulatory relationship between the miRNA and its target are available for users (Figure 2E and F). Besides, the transcripts interacting with the miRNA targets at the protein level were retrieved from the STRING database (33) by using BLASTX (34) (v2.2.26, E-value <1e-3, query coverage >50%, identity >35%). As a result, the protein–protein interactions were also visible in the regulatory networks (Figure 2E).

The ‘Search’ module

The module enables users to search for the miRNAs by plant species and miRNA families, or by sequences. For sequence search, it enables users to perform either ‘exact’ or ‘fuzzy’ search.

Other functional modules

The ‘Statistics’ module provides users with statistical information in both table and figure formats. Besides, all of the sequence and expression information reported in MepmiRDB has been made freely available in the ‘Download’ module. A help document is available in the ‘Help’ module for the first-time visitors.

Conclusion

All of the modules provided by MepmiRDB ensure its importance and timeliness for mechanistic and functional studies on the medicinal plant miRNAs. It is foreseeable that MepmiRDB will attract wide attention from medical and pharmaceutical researchers, and plant biologists.

Availability

MepmiRDB database is freely available at http://mepmirdb.cn/mepmirdb/index.html. We recommend the visitors to use the browsers like Mozilla Firefox, Google Chrome and Safari.

Supplementary data

Supplementary data are available at Database Online.

Supplementary Material

Figure_S1_baz070
Click here for additional data file. (196.7KB, pdf)
Figure_S2_baz070
Click here for additional data file. (104.7KB, pdf)

Acknowledgements

The authors would like to thank all the publicly available datasets and the scientists behind them.

Database URL: http://mepmirdb.cn/mepmirdb/index.html

Funding

National Natural Science Foundation of China (81730108, 31601062 and 31771436); Major Increase Or Decrease Program In The Central Finance Level (2060302); Key Project of Zhejiang Province Ministry of Science and Technology (2015C03055); Key Project of Hangzhou Ministry of Science and Technology (20162013A07).

Conflict of interest. None declared.

References

  • 1. Jones-Rhoades M.W., Bartel D.P. and Bartel B. (2006) MicroRNAs and their regulatory roles in plants. Annu. Rev. Plant Biol., 57, 19–53. [DOI] [PubMed] [Google Scholar]
  • 2. Kozomara A. and Griffiths-Jones S. (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res., 42, D68–D73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Zhang Z., Yu J., Li D. et al. (2010) PMRD: plant microRNA database. Nucleic Acids Res., 38, D806–D813. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Szczesniak M.W. and Makalowska I. (2014) miRNEST 2.0: a database of plant and animal microRNAs. Nucleic Acids Res., 42, D74–D77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Bielewicz D., Dolata J., Zielezinski A. et al. (2012) mirEX: a platform for comparative exploration of plant pri-miRNA expression data. Nucleic Acids Res., 40, D191–D197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Meng Y., Gou L., Chen D. et al. (2011) PmiRKB: a plant microRNA knowledge base. Nucleic Acids Res., 39, D181–D187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Yi X., Zhang Z., Ling Y. et al. (2015) PNRD: a plant non-coding RNA database. Nucleic Acids Res., 43, D982–D989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. The Pharmacopoeia Editorial Committee (2015) Pharmacopoeia of the People’s Republic of China. Chemical Technology Press, Shanghai, China. [Google Scholar]
  • 9. Bulgakov V.P. and Avramenko T.V. (2015) New opportunities for the regulation of secondary metabolism in plants: focus on microRNAs. Biotechnol. Lett., 37, 1719–1727. [DOI] [PubMed] [Google Scholar]
  • 10. Gupta O.P., Karkute S.G., Banerjee S. et al. (2017) Contemporary understanding of miRNA-based regulation of secondary metabolites biosynthesis in plants. Front. Plant Sci., 8, 374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Boke H., Ozhuner E., Turktas M. et al. (2015) Regulation of the alkaloid biosynthesis by miRNA in opium poppy. Plant Biotechnol. J., 13, 409–420. [DOI] [PubMed] [Google Scholar]
  • 12. Vashisht I., Mishra P., Pal T. et al. (2015) Mining NGS transcriptomes for miRNAs and dissecting their role in regulating growth, development, and secondary metabolites production in different organs of a medicinal herb, Picrorhiza kurroa. Planta, 241, 1255–1268. [DOI] [PubMed] [Google Scholar]
  • 13. Yu Z.X., Wang L.J., Zhao B. et al. (2015) Progressive regulation of sesquiterpene biosynthesis in arabidopsis and patchouli (Pogostemon cablin) by the miR156-targeted SPL transcription factors. Mol. Plant, 8, 98–110. [DOI] [PubMed] [Google Scholar]
  • 14. Zhang M., Dong Y., Nie L. et al. (2015) High-throughput sequencing reveals miRNA effects on the primary and secondary production properties in long-term subcultured Taxus cells. Front. Plant Sci., 6, 604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Rupaimoole R. and Slack F.J. (2017) MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat. Rev. Drug Discov., 16, 203–222. [DOI] [PubMed] [Google Scholar]
  • 16. Li J., Zhang Y., Li D. et al. (2015) Small non-coding RNAs transfer through mammalian placenta and directly regulate fetal gene expression. Protein Cell, 6, 391–396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Sala-Cirtog M., Marian C. and Anghel A. (2015) New insights of medicinal plant therapeutic activity—the miRNA transfer. Biomed. Pharmacother., 74, 228–232. [DOI] [PubMed] [Google Scholar]
  • 18. Chin A.R., Fong M.Y., Somlo G. et al. (2016) Cross-kingdom inhibition of breast cancer growth by plant miR159. Cell Res., 26, 217–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Zhao Y., Cong L. and Lukiw W.J. (2018) Plant and animal microRNAs (miRNAs) and their potential for inter-kingdom communication. Cell. Mol. Neurobiol., 38, 133–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Arroyo J.D., Chevillet J.R., Kroh E.M. et al. (2011) Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc. Natl. Acad. Sci. U. S. A., 108, 5003–5008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Liang G., Zhu Y., Sun B. et al. (2014) Assessing the survival of exogenous plant microRNA in mice. Food Sci. Nutr., 2, 380–388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Yang J., Farmer L.M., Agyekum A.A. et al. (2015) Detection of dietary plant-based small RNAs in animals. Cell Res., 25, 517–520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Hong M., Wang N., Tan H.Y. et al. (2015) MicroRNAs and Chinese medicinal herbs: new possibilities in cancer therapy. Cancers, 7, 1643–1657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Zhou Z., Li X., Liu J. et al. (2015) Honeysuckle-encoded atypical microRNA2911 directly targets influenza A viruses. Cell Res., 25, 39–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Xie W., Weng A. and Melzig M.F. (2016) MicroRNAs as new bioactive components in medicinal plants. Planta Med., 82, 1153–1162. [DOI] [PubMed] [Google Scholar]
  • 26. Yu D., Wan Y., Ito H. et al. (2019) PmiRDiscVali: an integrated pipeline for plant microRNA discovery and validation. BMC Genomics, 20, 133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Grabherr M.G., Haas B.J., Yassour M. et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnol., 29, 644–652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Yang X. and Li L. (2011) miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics, 27, 2614–2615. [DOI] [PubMed] [Google Scholar]
  • 29. Ma X., Tang Z., Qin J. et al. (2015) The use of high-throughput sequencing methods for plant microRNA research. RNA Biol., 12, 709–719. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Yu D., Ma X., Zuo Z. et al. (2017) Bioinformatics resources for deciphering the biogenesis and action pathways of plant small RNAs. Rice, 10, 38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Dai X. and Zhao P.X. (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res., 39, W155–W159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Shao C., Chen M. and Meng Y. (2013) A reversed framework for the identification of microRNA-target pairs in plants. Brief. Bioinform., 14, 293–301. [DOI] [PubMed] [Google Scholar]
  • 33. Szklarczyk D., Morris J.H., Cook H. et al. (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 45, D362–D368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Altschul S.F., Madden T.L., Schaffer A.A. et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 3389–3402. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure_S1_baz070
Click here for additional data file. (196.7KB, pdf)
Figure_S2_baz070
Click here for additional data file. (104.7KB, pdf)

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