Plant lncRNAs Research Articles

Identification of Long Non-Coding RNAs Associated with Tomato Fruit Expansion and Ripening by Strand-Specific Paired-End RNA Sequencing

As emerging essential regulators in plant development, long non-coding RNAs (lncRNAs) have been extensively investigated in multiple horticultural crops, as well as in different tissues of plants. Tomato fruits are an indispensable part of people’s diet and are consumed as fruits and vegetables. Meanwhile, tomato is widely used as a model to study the ripening mechanism in fleshy fruit. Although increasing evidence shows that lncRNAs are involved in lots of biological processes in tomato plants, the comprehensive identification of lncRNAs in tomato fruit during its expansion and ripening and their functions are partially known. Here, we performed strand-specific paired-end RNA sequencing (ssRNA-seq) of tomato Heinz1706 fruits at five different developmental stages, as well as flowers and leaves. We identified 17,674 putative lncRNAs by referencing the recently released SL4.0 and annotation ITAG4.0 in tomato plants. Many lncRNAs show different expression patterns in fleshy fruit at different developmental stages compared with leaves or flowers. Our results indicate that lncRNAs play an important role in the regulation of tomato fruit expansion and ripening, providing informative lncRNA candidates for further studies in tomato fruits. In addition, we also summarize the recent advanced progress in lncRNAs mediated regulation on horticultural fruits. Hence, our study updates the understanding of lncRNAs in horticultural plants and provides resources for future studies relating to the expansion and ripening of tomato fruits.

Data integration and evolutionary analysis of long non-coding RNAs in 25 flowering plants

BackgroundLong non-coding RNAs (lncRNAs) play vital roles in many important biological processes in plants. Currently, a large fraction of plant lncRNA studies center at lncRNA identification and functional analysis. Only a few plant lncRNA studies focus on understanding their evolutionary history, which is crucial for an in-depth understanding of lncRNAs. Therefore, the integration of large volumes of plant lncRNA data is required to deeply investigate the evolution of lncRNAs.ResultsWe present a large-scale evolutionary analysis of lncRNAs in 25 flowering plants. In total, we identified 199,796 high-confidence lncRNAs through data integration analysis, and grouped them into 5497 lncRNA orthologous families. Then, we divided the lncRNAs into groups based on the degree of sequence conservation, and quantified the various characteristics of 756 conserved Arabidopsis thaliana lncRNAs. We found that compared with non-conserved lncRNAs, conserved lncRNAs might have more exons, longer sequence length, higher expression levels, and lower tissue specificities. Functional annotation based on the A. thaliana coding-lncRNA gene co-expression network suggested potential functions of conserved lncRNAs including autophagy, locomotion, and cell cycle. Enrichment analysis revealed that the functions of conserved lncRNAs were closely related to the growth and development of the tissues in which they were specifically expressed.ConclusionsComprehensive integration of large-scale lncRNA data and construction of a phylogenetic tree with orthologous lncRNA families from 25 flowering plants was used to provide an oversight of the evolutionary history of plant lncRNAs including origin, conservation, and orthologous relationships. Further analysis revealed a differential characteristic profile for conserved lncRNAs in A. thaliana when compared with non-conserved lncRNAs. We also examined tissue specific expression and the potential functional roles of conserved lncRNAs. The results presented here will further our understanding of plant lncRNA evolution, and provide the basis for further in-depth studies of their functions.

From "Dark Matter" to "Star": Insight Into the Regulation Mechanisms of Plant Functional Long Non-Coding RNAs.

Long non-coding RNAs (lncRNAs) play a vital role in a variety of biological functions in plant growth and development. In this study, we provided an overview of the molecular mechanisms of lncRNAs in interacting with other biomolecules with an emphasis on those lncRNAs validated only by low-throughput experiments. LncRNAs function through playing multiple roles, including sponger for sequestering RNA or DNA, guider or decoy for recruiting or hijacking transcription factors or peptides, and scaffold for binding with chromatin modification complexes, as well as precursor of microRNAs or small interfering RNAs. These regulatory roles have been validated in several plant species with a comprehensive list of 73 lncRNA–molecule interaction pairs in 16 plant species found so far, suggesting their commonality in the plant kingdom. Such initial findings of a small number of functional plant lncRNAs represent the beginning of what is to come as lncRNAs with unknown functions were found in orders of magnitude more than proteins.

PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions

BackgroundPlant long non-coding RNAs (lncRNAs) play vital roles in many biological processes mainly through interactions with RNA-binding protein (RBP). To understand the function of lncRNAs, a fundamental method is to identify which types of proteins interact with the lncRNAs. However, the models or rules of interactions are a major challenge when calculating and estimating the types of RBP.ResultsIn this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC. PRPI-SC predicts interactions between lncRNAs and proteins based on the k-mer features of RNAs and proteins. Experiments proved good results on Arabidopsis thaliana and Zea mays datasets (ATH948 and ZEA22133). The accuracy rates of ATH948 and ZEA22133 datasets were 88.9% and 82.6%, respectively. PRPI-SC also performed well on some public RNA protein interaction datasets.ConclusionsPRPI-SC accurately predicts the interaction between plant lncRNA and protein, which plays a guiding role in studying the function and expression of plant lncRNA. At the same time, PRPI-SC has a strong generalization ability and good prediction effect for non-plant data.

PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

BackgroundLong noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary.ResultsTo boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet).ConclusionsPlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

LncRNADetector: a bioinformatics pipeline for long non-coding RNA identification and MAPslnc: a repository of medicinal and aromatic plant lncRNAs

ABSTRACT Long non-coding RNAs (lncRNAs) are an emerging class of non-coding RNAs and potent regulatory elements in the living cells. High throughput RNA sequencing analyses have generated a tremendous amount of transcript sequence data. A large proportion of these transcript sequences does not code for proteins and are known as non-coding RNAs. Among them, lncRNAs are a unique class of transcripts longer than 200 nucleotides with diverse biological functions and regulatory mechanisms. Recent emerging studies and next-generation sequencing technologies show a substantial amount of lncRNAs within the plant genome, which are yet to be identified. The computational identification of lncRNAs from these transcripts is a challenging task due to the involvement of a series of filtering steps. We have developed lncRNADetector, a bioinformatics pipeline for the identification of novel lncRNAs, especially from medicinal and aromatic plant (MAP) species. The lncRNADetector has been utilized to analyse and identify more than 88,459 lncRNAs from 21 species of MAPs. To provide a knowledge resource for the plant research community towards elucidating the diversity of biological roles of lncRNAs, the information generated about MAP lncRNAs (post-filtering steps) through lncRNADetector has been stored and organized in MAPslnc database (MAPslnc, https://lncrnapipe.cimap.res.in). The lncRNADetector web server and MAPslnc database have been developed in order to facilitate researchers for accurate identification of lncRNAs from the next-generation sequencing data of different organisms for downstream studies. To the best of our knowledge no such MAPslnc database is available till date.

Plant Long Noncoding RNAs: New Players in the Field of Post-Transcriptional Regulations.

The first reference to the “C-value paradox” reported an apparent imbalance between organismal genome size and morphological complexity. Since then, next-generation sequencing has revolutionized genomic research and revealed that eukaryotic transcriptomes contain a large fraction of non-protein-coding components. Eukaryotic genomes are pervasively transcribed and noncoding regions give rise to a plethora of noncoding RNAs with undeniable biological functions. Among them, long noncoding RNAs (lncRNAs) seem to represent a new layer of gene expression regulation, participating in a wide range of molecular mechanisms at the transcriptional and post-transcriptional levels. In addition to their role in epigenetic regulation, plant lncRNAs have been associated with the degradation of complementary RNAs, the regulation of alternative splicing, protein sub-cellular localization, the promotion of translation and protein post-translational modifications. In this review, we report and integrate numerous and complex mechanisms through which long noncoding transcripts regulate post-transcriptional gene expression in plants.

From Trash to Luxury: The Potential Role of Plant LncRNA in DNA Methylation During Abiotic Stress.

Remarkable progress has been made in elucidating important roles of plant non-coding RNAs. Among these RNAs, long noncoding RNAs (lncRNAs) have gained widespread attention, especially their role in plant environmental stress responses. LncRNAs act at different levels of gene expression regulation, and one of these mechanisms is by recruitment of DNA methyltransferases or demethylases to regulate the target gene transcription. In this mini-review, we highlight the function of lncRNAs, including their potential role in RNA-directed DNA Methylation (RdDM) silencing pathway and their potential function under abiotic stresses conditions. Moreover, we also present and discuss studies of lncRNAs in crops. Finally, we propose a path outlook for future research that may be important for plant breeding.

Large DNA fragment deletion in lncRNA77580 regulates neighboring gene expression in soybean (Glycine max)

Long non-coding RNAs (lncRNAs) affect gene expressions via a wide range of mechanisms and are considered important regulators of numerous essential biological processes, including abiotic stress responses. However, the biological functions of most lncRNAs are yet to be determined. Moreover, to date, no effective methods have been developed to study the function of plant lncRNAs. We previously discovered a salt stress-related lncRNA, lncRNA77580 in soybean (Glycine max L.). In this study, we cloned the full-length lncRNA77580 and found that it shows nuclear-specific localisation. Furthermore, we employed CRISPR/Cas9 technology to induce large DNA fragment deletions in lncRNA77580 in soybean using a dual-single guide RNA/Cas9 design. As a result, we obtained deletion mutant soybean roots with targeted genomic fragment deletion in lncRNA77580. Deletion and overexpression of lncRNA77580 were found to alter the expression of several neighboring protein-coding genes associated with the response to salt stress. The longer the deleted DNA fragment in lncRNA77580, the greater the influence on the expression of lncRNA77580 itself and neighboring genes. Collectively, the findings of this study revealed that large DNA fragment deletion in lncRNAs using the CRISPR/Cas9 system is a powerful method to obtain functional mutations of soybean lncRNAs that could benefit future research on lncRNA function in soybean.

Long Non-Coding RNAs, the Dark Matter: An Emerging Regulatory Component in Plants.

Long non-coding RNAs (lncRNAs) are pervasive transcripts of longer than 200 nucleotides and indiscernible coding potential. lncRNAs are implicated as key regulatory molecules in various fundamental biological processes at transcriptional, post-transcriptional, and epigenetic levels. Advances in computational and experimental approaches have identified numerous lncRNAs in plants. lncRNAs have been found to act as prime mediators in plant growth, development, and tolerance to stresses. This review summarizes the current research status of lncRNAs in planta, their classification based on genomic context, their mechanism of action, and specific bioinformatics tools and resources for their identification and characterization. Our overarching goal is to summarize recent progress on understanding the regulatory role of lncRNAs in plant developmental processes such as flowering time, reproductive growth, and abiotic stresses. We also review the role of lncRNA in nutrient stress and the ability to improve biotic stress tolerance in plants. Given the pivotal role of lncRNAs in various biological processes, their functional characterization in agriculturally essential crop plants is crucial for bridging the gap between phenotype and genotype.

NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants.

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

Functional classification of plant long noncoding RNAs: a transcript is known by the company it keeps.

The extraordinary maturation in high-throughput sequencing technologies has revealed the existence of a complex network of transcripts in eukaryotic organisms, including thousands of long noncoding (lnc) RNAs with little or no protein-coding capacity. Subsequent discoveries have shown that lncRNAs participate in a wide range of molecular processes, controlling gene expression and protein activity though direct interactions with proteins, DNA or other RNA molecules. Although significant advances have been achieved in the understanding of lncRNA biology in the animal kingdom, the functional characterization of plant lncRNAs is still in its infancy and remains a major challenge. In this review, we report emerging functional and mechanistic paradigms of plant lncRNAs and partner molecules, and discuss how cutting-edge technologies may help to identify and classify yet uncharacterized transcripts into functional groups.

PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs.

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein coding potential. The expanding list of lncRNAs and accumulating evidence of their functions in plants have necessitated the creation of a comprehensive database for lncRNA research. However, currently available plant lncRNA databases have some deficiencies, including the lack of lncRNA data from some model plants, uneven annotation standards, a lack of visualization for expression patterns, and the absence of epigenetic information. To overcome these problems, we upgraded our Plant Long noncoding RNA Database (PLncDB, http://plncdb.tobaccodb.org/), which was based on a uniform annotation pipeline. PLncDB V2.0 currently contains 1 246 372 lncRNAs for 80 plant species based on 13 834 RNA-Seq datasets, integrating lncRNA information from four other resources including EVLncRNAs, RNAcentral and etc. Expression patterns and epigenetic signals can be visualized using multiple tools (JBrowse, eFP Browser and EPexplorer). Targets and regulatory networks for lncRNAs are also provided for function exploration. In addition, PLncDB V2.0 is hierarchical and user-friendly and has five built-in search engines. We believe PLncDB V2.0 is useful for the plant lncRNA community and data mining studies and provides a comprehensive resource for data-driven lncRNA research in plants.

Long Noncoding RNAs in Plant Viroids and Viruses: A Review.

Infectious long-noncoding (lnc) RNAs related to plants can be of both viral and non-viral origin. Viroids are infectious plant lncRNAs that are not related to viruses and carry the circular, single-stranded, non-coding RNAs that replicate with host enzymatic activities via a rolling circle mechanism. Viroids interact with host processes in complex ways, emerging as one of the most productive tools for studying the functions of lncRNAs. Defective (D) RNAs, another category of lnc RNAs, are found in a variety of plant RNA viruses, most of which are noncoding. These are derived from and are replicated by the helper virus. D RNA-virus interactions evolve into mutually beneficial combinations, enhancing virus fitness via competitive advantages of moderated symptoms. Yet the satellite RNAs are single-stranded and include either large linear protein-coding ss RNAs, small linear ss RNAs, or small circular ss RNAs (virusoids). The satellite RNAs lack sequence homology to the helper virus, but unlike viroids need a helper virus to replicate and encapsidate. They can attenuate symptoms via RNA silencing and enhancement of host defense, but some can be lethal as RNA silencing suppressor antagonists. Moreover, selected viruses produce lncRNAs by incomplete degradation of genomic RNAs. They do not replicate but may impact viral infection, gene regulation, and cellular functions. Finally, the host plant lncRNAs can also contribute during plant-virus interactions, inducing plant defense and the regulation of gene expression, often in conjunction with micro and/or circRNAs.

The expression of long non-coding RNAs is associated with H3Ac and H3K4me2 changes regulated by the HDA6-LDL1/2 histone modification complex in Arabidopsis.

In recent years, eukaryotic long non-coding RNAs (lncRNAs) have been identified as important factors involved in a wide variety of biological processes, including histone modification, alternative splicing and transcription enhancement. The expression of lncRNAs is highly tissue-specific and is regulated by environmental stresses. Recently, a large number of plant lncRNAs have been identified, but very few of them have been studied in detail. Furthermore, the mechanism of lncRNA expression regulation remains largely unknown. Arabidopsis HISTONE DEACETYLASE 6 (HDA6) and LSD1-LIKE 1/2 (LDL1/2) can repress gene expression synergistically by regulating H3Ac/H3K4me. In this research, we performed RNA-seq and ChIP-seq analyses to further clarify the function of HDA6-LDL1/2. Our results indicated that the global expression of lncRNAs is increased in hda6/ldl1/2 and that this increased lncRNA expression is particularly associated with H3Ac/H3K4me2 changes. In addition, we found that HDA6-LDL1/2 is important for repressing lncRNAs that are non-expressed or show low-expression, which may be strongly associated with plant development. GO-enrichment analysis also revealed that the neighboring genes of the lncRNAs that are upregulated in hda6/ldl1/2 are associated with various developmental processes. Collectively, our results revealed that the expression of lncRNAs is associated with H3Ac/H3K4me2 changes regulated by the HDA6-LDL1/2 histone modification complex.

Identification and characterization of long non-coding RNA (lncRNA) in the developing seeds of Jatropha curcas

Long non-coding RNAs (lncRNAs) play critical roles in plant development. However, the information of lncRNAs in Jatropha curcas remains largely unexplored. Thus, an attempt has been made in J. curcas to identify 1,850 lncRNAs based on deep sequencing of developing seeds at three typical stages. About ten percent lncRNAs (196 lncRNAs) were differentially expressed lncRNAs during seed developing process. Together with reverse transcription quantitative real-time PCR, the lncRNA expression analyses revealed the stage-specific expression patterns of some novel lncRNAs in J. curcas. The target genes of lncRNAs were annotated for their roles in various biological processes such as gene expression, metabolism, and cell growth. Besides, 10 lncRNAs were identified as the precursors of microRNAs and 26 lncRNAs were predicted to be the targets of Jatropha miRNAs. A total of 31 key lncRNAs play critical roles in the seed developing process in the context of cell growth and development, lipid metabolism, and seed maturation. Our study provides the first systematic study of lncRNAs in the developing seeds of J. curcas and facilitates the functional research of plant lncRNAs and the regulation of seed development.

A deep learning model for plant lncRNA-protein interaction prediction with graph attention.

Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l2-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods.

Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction

Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate prediction of plant lncRNA–protein interaction is imperative for subsequent functional studies. We present an integrative model, namely DRPLPI. Its uniqueness is that it predicts by multi-feature fusion. Structural and four groups of sequence features are used, including tri-nucleotide composition, gapped k-mer, recursive complement and binary profile. We design a multi-head self-attention long short-term memory encoder-decoder network to extract generative high-level features. To obtain robust results, DRPLPI combines categorical boosting and extra trees into a single meta-learner. Experiments on Zea mays and Arabidopsis thaliana obtained 0.9820 and 0.9652 area under precision/recall curve (AUPRC) respectively. The proposed method shows significant enhancement in the prediction performance compared with existing state-of-the-art methods.

The Emerging Role of Long Non-Coding RNAs in Plant Defense Against Fungal Stress.

Growing interest and recent evidence have identified long non-coding RNA (lncRNA) as the potential regulatory elements for eukaryotes. LncRNAs can activate various transcriptional and post-transcriptional events that impact cellular functions though multiple regulatory functions. Recently, a large number of lncRNAs have also been identified in higher plants, and an understanding of their functional role in plant resistance to infection is just emerging. Here, we focus on their identification in crop plant, and discuss their potential regulatory functions and lncRNA-miRNA-mRNA network in plant pathogen stress responses, referring to possible examples in a model plant. The knowledge gained from a deeper understanding of this colossal special group of plant lncRNAs will help in the biotechnological improvement of crops.

Genome-wide identification of Arabidopsis long noncoding RNAs in response to the blue light

Long non-coding RNAs (lncRNAs) have been shown in animals to play roles in a wide range of biological processes. In plant, light modulates the growth and development as a key external signal. However, little is known about the role of plant lncRNA in response to light. In this study, we sequenced the messenger RNAs (mRNAs), lncRNAs and microRNAs (miRNAs) in Arabidopsis seedlings under blue light for 2 h and 8 h. Compared to dark, we identified 4197 mRNAs, 375 miRNAs and 481 lncRNAs, or 5207 mRNAs, 286 miRNAs and 545 lncRNAs of differential expressions under blue light treatments for 2 h or 8 h respectively. Subsequently, a total of 407 competing endogenous RNA (ceRNA) pairs (lncRNA-mRNA-miRNA) were constructed. We identified a blue light-induced lncRNA which plays roles in blue light-directed plant photomorphogenesis and response to mannitol stress by serving as a ceRNA to sequester miR167 in a type of target mimicry. These results revealed previously unknown roles of the lncRNA in blue light signaling and mannitol stress, and provided useful resources of lncRNAs associated with miRNAs in response to blue light.