- 1State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
- 2Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- 3State Key Laboratory of Tree Genetics and Breeding, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China
- 4Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University, Yancheng, China
Introduction
Non-coding RNA (ncRNA) is a key regulatory RNA with limited abilities of coding potential (Liu et al., 2015). To date, a variety of ncRNAs have been identified in plants, which can be classified into three groups according to their sequence length: small RNAs <50 nucleotides (nt), such as microRNAs (miRNAs); long non-coding RNAs (lncRNAs) with arbitrarily longer than 200 nt; and intermediate-size ncRNA between small RNAs and lncRNA in length (Wang et al., 2014). In addition, the process of back splicing also produces a class of covalently closed RNA molecules called exonic circular RNAs (circRNAs).
Different types of ncRNAs show diverse mechanisms of action. miRNAs typically degrade their target genes by binding to their transcripts at the post-transcriptional level (Chipman and Pasquinelli, 2019). However, lncRNAs function at transcriptional, post-transcriptional and epigenetic levels by interacting with macromolecules (Wu et al., 2020). Whiles circRNAs can act as miRNA/protein sponges, or regulate alternative splicing or transcription (Lai et al., 2018). These ncRNAs with different mechanisms of action form a complex regulatory network to jointly regulate plant growth, development and stress response. For instance, Vvi-miPEP171d1 regulates adventitious root formation in grapevine (Chen et al., 2020). And miRNA frameworks have been proved to be important for the flower induction in apple (Fan et al., 2018). In addition to regulation of self-life activities, ncRNAs also play a role in plant-to-plant communication. Exogenous miR399 and miR156 can trigger RNA interference to repress the expression of PHOSPHATE OVERACCUMULATOR 2 (PHO2) and SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE 9 (SPL9) in plants (Betti et al., 2021). Moreover, ncRNAs can even act as a bridge between plants and other species. For example, two plant viruses, barley yellow dwarf virus and red clover necrotic mosaic virus, can express small subgenomic (sg) RNAs to attenuate host translation by binding translation initiation factor eIF4G (Miller et al., 2016). Expressing double-strand (ds) RNA in maize can effectively reduce feeding damage of western corn rootworm by triggering its RNA interference (Baum et al., 2007). In summary, ncRNAs play important roles in plant activities, and their regulatory functions are the basis for plant growth, development and survival.
The regulatory roles of ncRNAs make them important tools for adjusting gene expression and studying gene functions. For example, the artificial miRNA pAmiRNA156h-PDSh can effectively decline the expression of phytoene desaturase (MdPDS) in apple (Charrier et al., 2019). Overexpression of amiRNA-319a-HaEcR in tomato can effectively silence the ECDYSONE RECEPTOR (HaEcR) gene to reduce the survival of Helicoverpa armigera (Yogindran and Rajam, 2021). From this point of view, exploring new ncRNAs and elucidating the regulatory mechanism of ncRNAs will have profound impacts on plant research. Here, our emphasis is put on recognition technologies of ncRNAs in plants, including bioinformatic tools and experimental technologies.
Bioinformatics Tools
With the release of more and more genomic information of diverse plants, it provides a key basis for the discovery of novel ncRNAs. In addition, knowledge about the function, structure and conservation of ncRNAs is accumulating, which can help us distinguish different types of ncRNAs. Since ncRNAs were reported, at least 39 bioinformatics websites and softwares have been established, of which 40.5% (13 websites and 4 softwares) were released in the past 5 years (Table 1). Furthermore, 41 deep learning models were developed in the past 3 years (Table 1). These tools can be divided into three classes.
ncRNA Databases
The ncRNA databases collected ncRNA-related information, such as sequences, interactions between ncRNAs and target genes, as well as expression profiles. In addition, some tools contain information that has been supported by experimental evidences. For example, miRbase (Kozomara et al., 2019) integrates published mature sequences of miRNAs and their relevant hairpin precursors of miRNA. miRTarBase (Huang et al., 2020) and NPInter (Teng et al., 2020) not only focus on the interaction information between ncRNAs and other biomolecules, but also provide the relevant experimental evidences. To date, miRNAs, lncRNAs, and circular RNAs (circRNAs) in ncRNAs were collected by different online databases (Chu et al., 2017; Jin et al., 2021). Details of these tools including the application system, input format, and included species are listed in Supplementary Table S1. The development of high-throughput sequencing technologies and the increasing amount of published genomic data have given us the opportunity to collect diverse information from different species, which facilities the analysis of ncRNA evolutional and the construction of conserved models for predicting and exploring novel ncRNAs. However, the application of these databases in cross-species research still faces enormous challenges. On the one hand, there is a lack of identifying species-conserved or species-specific ncRNAs; and on the other hand, it is difficult to determine functional ncRNAs in a predictive manner.
ncRNA Prediction Tools
Prediction of ncRNA, including recognition of ncRNA and prediction of ncRNA function. At present, there are many prediction tools for ncRNA recognition, but relatively few tools for their function prediction. We classify the existing prediction tools according to their categories (Table 1, Supplementary Table S1). How to accurately predict novel ncRNAs and their target genes has always been the focus of researchers. The current development of prediction tools mainly focuses on three aspects: predicting the sequences of miRNAs and their precursors (Wu et al., 2012; Lei and Sun, 2014; Fei et al., 2021), predicting the binding sites of ncRNAs to targets (Bonnet et al., 2010; Brousse et al., 2014), predicting or visualizing the secondary or three-dimensional structure of ncRNAs (Steffen et al., 2006; Byun and Han, 2009; Biesiada et al., 2016). Sequence alignment is the basis for this prediction, and the divergence of ncRNA sequences is an important factor affecting the accuracy of prediction. In general, ancient ncRNAs (especially those related to plant development) remain highly conserved across species (Willmann and Poethig, 2007). However, recently evolved ncRNAs appear to be highly species-specific (Cuperus et al., 2011). Furthermore, there appears to be variability among different classes of ncRNAs in conservation among different species (Wu et al., 2020). Therefore, how to improve the accuracy of ncRNA prediction is an important problem to be solved. With the accumulation of ncRNA data, building conserved models for each ncRNA family may be a solution for this question.
Application of Deep Learning in ncRNA Study
Deep learning developed in recent years has shown a powerful potential capability in addressing bioinformatics problems in ncRNA study. For example, RPITER model can be used to predict interactions between ncRNAs and proteins based on sequence and structure information (Peng et al., 2019). Compared with traditional approaches, more information can be introduced into the computational process of deep learning to ensure the accuracy of the predicted results (Zhang S. W. et al., 2020). So far, at least 41 deep learning models have been built to predict ncRNA classification (Amin et al., 2019), ncRNA-protein interaction (Peng et al., 2019), ncRNA interaction, as well as ncRNA identification and functions (Khan et al., 2020; Zhang P. et al., 2020) (Table 1, Supplementary Table S1).
The basic processes of applying deep learning are showed in Figure 1A. After choosing an appropriate framework, researchers need to input data to generate relevant models. The resulting model can be used for the next prediction step. Compared with other prediction methods, the advantage of deep learning can effectively reduce the prediction bias caused by imperfect design parameters. Its limitation is that the accuracy of predictions heavily depends on the accuracy of the models, which usually require large enough data to build and train. Therefore, the dataset used to build deep learning models seriously affect the accuracy of prediction and analysis. How to obtain a large amount of ncRNA data from different species for building more accurate models is a serious problem that needs to be solved. In addition, most of them are distributed in Linux system, and the proficient computational skills of users are an important precondition to utilize these models. Developing softwares with a user-friendly interface (Xu et al., 2021) is crucial for the application of these models. However, the cross-operating system adaptability of these models and the differences in fit to different data types are the main obstacles facing the use of new models to build user-friendly softwares.
Figure 1. Deep learning processes (A) and experimental techniques (B) for studying ncRNAs. sRNA-Seq, small RNA sequencing; CLIP-Seq, crosslinking and immunoprecipitation with sequencing; 5′RLM-RACE, 5′ RNA ligase mediated amplification of cDNA ends; ssRNA-Seq, strand-specific RNA sequencing; CAGE-Seq, cap analysis gene expression sequencing.
Experimental Technologies
Although the results of bioinformatics predictions are becoming more and more accurate with the accumulation of ncRNA knowledge, experimental technologies are still needed to further validate the prediction results. According to the characteristics of ncRNAs, at least three aspects need to be verified: firstly, a functional ncRNA should have transcriptional activity (Bazzini et al., 2009); secondly, there should be an expression correlation between ncRNA and target genes (Bai et al., 2018); thirdly, if the ncRNA functions by degrading its target genes, the cleavage site of the ncRNA on the target genes should be verified (Gao et al., 2018). Moreover, experimental strategies should be varied for different types of ncRNAs. Figure 1B summarized the main sequencing and experimental techniques in ncRNA studies. In detail, the combination of sRNA-Seq and RNA-Seq is commonly used for global identification of novel small ncRNAs (Huang et al., 2019). After removed low-quality reads, high-quality reads are subsequently annotated by several databases (such as miRbase and Rfam) (Deforges et al., 2019; Huang et al., 2019). The length of small RNAs is far shorter than protein-coding genes and lncRNAs. Therefore, the extraction of small RNAs unlike other RNA, can be performed using special RNA-extraction kits (Gao et al., 2019) or TRIzol reagent (Tan et al., 2018). For miRNAs, it is necessary to identify the binding sites between miRNAs and their target genes (Shen W. et al., 2021). Degradome sequencing and crosslinking and immunoprecipitation with sequencing (CLIP-Seq) can be used to analyze binding sites between miRNAs and the target genes (Han et al., 2016; Chipman and Pasquinelli, 2019). Furthermore, 5′-RNA ligase mediated amplification of cDNA ends (5′RLM-RACE) assays are directly used for verifying the predicted binding sites (Cui et al., 2020). The detection of novel lncRNAs can be completed by multiple RNA-Seq strategies, such as isoform-sequencing, strand-specific RNA-Seq (ssRNA-Seq), cap analysis gene expression with polyA-Seq technologies (CAGE-Seq) (Zheng et al., 2021). Although lncRNAs are considered as a part of ncRNAs, some of them still remain a weak ability of translating small peptides. Therefore, ribosome profiling become has become one of the strategies to detect lncRNAs (Wu et al., 2019). Meanwhile, the recently developed high-precision single-base CRISPR/Cas9 technology can effectively create ncRNA-related mutants to explore the relationships between ncRNAs and target genes (Jacobs et al., 2015). Currently, in addition to the research on the function of small RNAs, more and more attention is focused on circRNAs in recent years. However, most studies focus more on the discovery of novel circRNAs by sequencing technologies. One of challenges of circRNA sequencing is to improve the accuracy of detection and quantification of circRNAs due to their lack of poly (A) tails and insufficient expression levels (Wang et al., 2019; Zhao et al., 2019). Treatment of total RNA with ribonuclease R or increasing sequencing depth may resolve these issues (Chen et al., 2017). Meanwhile, it is also necessary to develop functional research techniques to further clarify the biological functions of circRNAs.
Conclusion
In conclusion, although many tools and technologies have been developed to study ncRNAs in plants, there are still opportunities and challenges in this field. In bioinformatics, since there are significant differences in ncRNAs between species, it is beneficial for our research on ncRNAs to collect as much data as possible based on different species. Meanwhile, ncRNAs in a same family exhibit high conservation, it is possible for us to build models to discover novel ncRNAs. Moreover, most prediction tools and deep-learning models are developed based on Linux system, and the development of user-friendly Windows versions will help more researchers to analyze different kinds of ncRNA. As ncRNAs play a regulatory role in plants, how to manipulate ncRNAs through genetic engineering to regulate specific biological processes remains to be resolved.
Author Contributions
JZ conceived the study. DX collected and synthesized the data and draft the manuscript. JZ, WY, CF, BL, and M-ZL revised the manuscript. All authors contributed to the article and approved the final version.
Funding
This work was supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02070-1), the National Key Research and Development Program of China (2021YFD2200205 and 2021YFD2200700), the National Science Foundation of China (32171814), the Natural Science Foundation of Zhejiang Province for Distinguished Young Scholars (LR22C160001), and the Zhejiang A&F University Research and Development Fund Talent Startup Project (2021LFR013).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.890663/full#supplementary-material
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Keywords: non-coding RNA, bioinformatics tools, experimental technology, deep learning, miRNA
Citation: Xu D, Yuan W, Fan C, Liu B, Lu M-Z and Zhang J (2022) Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants. Front. Plant Sci. 13:890663. doi: 10.3389/fpls.2022.890663
Received: 06 March 2022; Accepted: 28 March 2022;
Published: 14 April 2022.
Edited by:
Enhui Shen, Zhejiang University, ChinaCopyright © 2022 Xu, Yuan, Fan, Liu, Lu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jin Zhang, emhhbmdqJiN4MDAwNDA7emFmdS5lZHUuY24=; orcid.org/0000-0002-8397-5078