RNA-directed DNA methylation in plants: Where to start?

Epigenetic Phenomenon of Paramutation in Plants and Animals

The phenomenon of paramutation describes the interaction between two alleles, in which one allele initiates inherited epigenetic conversion of another allele without affecting the DNA sequence. Epigenetic transformations due to paramutation are accompanied by the change in DNA and/or histone methylation patterns, affecting gene expression. Studies of paramutation in plants and animals have identified small non-coding RNAs as the main effector molecules required for the initiation of epigenetic changes in gene loci. Due to the fact that small non-coding RNAs can be transmitted across generations, the paramutation effect can be inherited and maintained in a population. In this review, we will systematically analyze examples of paramutation in different living systems described so far, highlighting common and different molecular and genetic aspects of paramutation between organisms, and considering the role of this phenomenon in evolution.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Barley AGO4 proteins show overlapping functionality with distinct small RNA-binding properties in heterologous complementation

KEY MESSAGE

Barley AGO4 proteins complement expressional changes of epigenetically regulated genes in Arabidopsis ago4-3 mutant and show a distinct affinity for the 5' terminal nucleotide of small RNAs, demonstrating functional conservation and divergence. The function of Argonaute 4 (AGO4) in Arabidopsis thaliana has been extensively characterized; however, its role in monocots, which have large genomes abundantly supplemented with transposable elements (TEs), remains elusive. The study of barley AGO4 proteins can provide insights into the conserved aspects of RNA-directed DNA methylation (RdDM) and could also have further applications in the field of epigenetics or crop improvement. Bioinformatic analysis of RNA sequencing data identified two active AGO4 genes in barley, HvAGO4a and HvAGO4b. These genes function similar to AtAGO4 in an Arabidopsis heterologous complementation system, primarily binding to 24-nucleotide long small RNAs (sRNAs) and triggering methylation at specific target loci. Like AtAGO4, HvAGO4B exhibits a preference for binding sRNAs with 5' adenine residue, while also accepting 5' guanine, uracil, and cytosine residues. In contrast, HvAGO4A selectively binds only sRNAs with a 5' adenine residue. The diverse binding capacity of barley AGO4 proteins is reflected in TE-derived sRNAs and in their varying abundance. Both barley AGO4 proteins effectively restore the levels of extrachromosomal DNA and transcript abundancy of the heat-activated ONSEN retrotransposon to those observed in wild-type Arabidopsis plants. Our study provides insight into the distinct binding specificities and involvement in TE regulation of barley AGO4 proteins in Arabidopsis by heterologous complementation.

sORFPred: A Method Based on Comprehensive Features and Ensemble Learning to Predict the sORFs in Plant LncRNAs

Long non-coding RNAs (lncRNAs) are important regulators of biological processes. It has recently been shown that some lncRNAs include small open reading frames (sORFs) that can encode small peptides of no more than 100 amino acids. However, existing methods are commonly applied to human and animal datasets and still suffer from low feature representation capability. Thus, accurate and credible prediction of sORFs with coding ability in plant lncRNAs is imperative. This paper proposes a new method termed sORFPred, in which we design a model named MCSEN by combining multi-scale convolution and Squeeze-and-Excitation Networks to fully mine distinct information embedded in sORFs, integrate and optimize multiple sequence-based and physicochemical feature descriptors, and built a two-layer prediction classifier based on Bayesian optimization algorithm and Extra Trees. sORFPred has been evaluated on sORFs datasets of three species and experimentally validated sORFs dataset. Results indicate that sORFPred outperforms existing methods and achieves 97.28% accuracy, 97.06% precision, 97.52% recall, and 97.29% F1-score on Arabidopsis thaliana, which shows a significant improvement in prediction performance compared to various conventional shallow machine learning and deep learning models.

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs by biological experimenrts. However, there is a lack of methods for identifying SEPs directly. In this study, a machine learning method to identify SEPs of plant lncRNA (ISPL) is proposed. Hybrid features including sequence features and physicochemical features are extracted manually or adaptively to construct different modal features. In order to keep the stability of feature selection, the non-linear correction applied in Max-Relevance-Max-Distance (nocRD) feature selection method is proposed, which integrates multiple feature ranking results and uses the iterative random forest for different modal features dimensionality reduction. Classification models with different modal features are constructed, and their outputs are combined for ensemble classification. The experimental results show that the accuracy of ISPL is 89.86% percent on the independent test set, which will have important implications for further studies of functional genomic.

Decoding the sorghum methylome: understanding epigenetic contributions to agronomic traits

DNA methylation is a chromatin modification that plays an essential role in regulating gene expression and genome stability and it is typically associated with gene silencing and heterochromatin. Owing to its heritability, alterations in the patterns of DNA methylation have the potential to provide for epigenetic inheritance of traits. Contemporary epigenomic technologies provide information beyond sequence variation and could supply alternative sources of trait variation for improvement in crops such as sorghum. Yet, compared with other species such as maize and rice, the sorghum DNA methylome is far less well understood. The distribution of CG, CHG, and CHH methylation in the genome is different compared with other species. CG and CHG methylation levels peak around centromeric segments in the sorghum genome and are far more depleted in the gene dense chromosome arms. The genes regulating DNA methylation in sorghum are also yet to be functionally characterised; better understanding of their identity and functional analysis of DNA methylation machinery mutants in diverse genotypes will be important to better characterise the sorghum methylome. Here, we catalogue homologous genes encoding methylation regulatory enzymes in sorghum based on genes in Arabidopsis, maize, and rice. Discovering variation in the methylome may uncover epialleles that provide extra information to explain trait variation and has the potential to be applied in epigenome-wide association studies or genomic prediction. DNA methylation can also improve genome annotations and discover regulatory elements underlying traits. Thus, improving our knowledge of the sorghum methylome can enhance our understanding of the molecular basis of traits and may be useful to improve sorghum performance.

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

Background

Long 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.

Results

To 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).

Conclusions

PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

Regulation of pri-MIRNA processing: mechanistic insights into the miRNA homeostasis in plant

miRNAs in plant plays crucial role in controlling proper growth, development and fitness by modulating the expression of their target genes. Therefore to modulate the expression of any stress/development related gene specifically, it is better to modulate expression of the miRNA that can target that gene. To modulate the expression level of miRNA, it is prerequisite to uncover the underlying molecular mechanism of its biogenesis. The biogenesis pathway consists of two major steps, transcription of MIR gene to pri-MIRNA and processing of pri-MIRNA into mature miRNA via sequential cleavage steps. Both of these pathways are tightly controlled by several different factors involving structural and functional molecules. This review is mainly focused on different aspects of pri-MIRNA processing mechanism to emphasize on the fact that to modulate the level of a miRNA in the cell only over-expression or knock-down of that MIR gene is not always sufficient rather it is also crucial to take processing regulation into consideration. The data collected from the recent and relevant literatures depicts that processing regulation is controlled by several aspects like structure and size of the pri-MIRNA, presence of introns in MIR gene and their location, interaction of processing factors with the core components of processing machinery etc. These detailed information can be utilized to figure out the particular point which can be utilized to modulate the expression of the miRNA which would ultimately be beneficial for the scientist and researcher working in this field to generate protocol for engineering plant with improved yield and stress tolerance.

PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA-lncRNA interaction prediction

MOTIVATION

The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA-lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant.

RESULTS

This article proposes a new method for fulfilling plant miRNA-lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA-lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time-polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness.

AVAILABILITY AND IMPLEMENTATION

The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Conservation and divergence of small RNA pathways and microRNAs in land plants

Background

As key regulators of gene expression in eukaryotes, small RNAs have been characterized in many seed plants, and pathways for their biogenesis, degradation, and action have been defined in model angiosperms. However, both small RNAs themselves and small RNA pathways are not well characterized in other land plants such as lycophytes and ferns, preventing a comprehensive evolutionary perspective on small RNAs in land plants.

Results

Using 25 representatives from major lineages of lycophytes and ferns, most of which lack sequenced genomes, we characterized small RNAs and small RNA pathways in these plants. We identified homologs of DICER-LIKE (DCL), ARGONAUTE (AGO), and other genes involved in small RNA pathways, predicted over 2600 conserved microRNA (miRNA) candidates, and performed phylogenetic analyses on small RNA pathways as well as miRNAs. Pathways underlying miRNA biogenesis, degradation, and activity were established in the common ancestor of land plants, but the 24-nucleotide siRNA pathway that guides DNA methylation is incomplete in sister species of seed plants, especially lycophytes. We show that the functional diversification of key gene families such as DCL and AGO as observed in angiosperms occurred early in land plants followed by parallel expansion of the AGO family in ferns and angiosperms. We uncovered a conserved AGO subfamily absent in angiosperms.

Conclusions

Our phylogenetic analyses of miRNAs in bryophytes, lycophytes, ferns, and angiosperms refine the time-of-origin for conserved miRNA families as well as small RNA machinery in land plants.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1291-2) contains supplementary material, which is available to authorized users.

Single-base resolution methylomes of upland cotton (Gossypium hirsutum L.) reveal epigenome modifications in response to drought stress

BACKGROUND

DNA methylation, with a cryptic role in genome stability, gene transcription and expression, is involved in the drought response process in plants, but the complex regulatory mechanism is still largely unknown.

RESULTS

Here, we performed whole-genome bisulfite sequencing (WGBS) and identified long non-coding RNAs on cotton leaves under drought stress and re-watering treatments. We obtained 31,223 and 30,997 differentially methylated regions (representing 2.48% of the genome) after drought stress and re-watering treatments, respectively. Our data also showed that three sequence contexts, including ^mCpG, ^mCHG, ^mCHH, all presented a hyper-methylation pattern under drought stress and were nearly restored to normal levels after the re-watering treatment. Among all the methylation variations, asymmetric CHH methylation was the most consistent with external environments, suggesting that methylation/demethylation in a CHH context may constitute a novel epigenetic modification in response to drought stress. Combined with the targets of long non-coding RNAs, we found that long non-coding RNAs may mediate variations in methylation patterns by splicing into microRNAs. Furthermore, the many hormone-related genes with methylation variations suggested that plant hormones might be a potential mechanism in the drought response.

CONCLUSIONS

Future crop-improvement strategies may benefit by taking into account not only the DNA genetic variations in cotton varieties but also the epigenetic modifications of the genome.

RNA Silencing in Plants: Mechanisms, Technologies and Applications in Horticultural Crops

Understanding the fundamental nature of a molecular process or a biological pathway is often a catalyst for the development of new technologies in biology. Indeed, studies from late 1990s to early 2000s have uncovered multiple overlapping but functionally distinct RNA silencing pathways in plants, including the posttranscriptional microRNA and small interfering RNA pathways and the transcriptional RNA-directed DNA methylation pathway. These findings have in turn been exploited for developing artificial RNA silencing technologies such as hairpin RNA, artificial microRNA, intrinsic direct repeat, 3' UTR inverted repeat, artificial trans-acting siRNA, and virus-induced gene silencing technologies. Some of these RNA silencing technologies, such as the hairpin RNA technology, have already been widely used for genetic improvement of crop plants in agriculture. For horticultural plants, RNA silencing technologies have been used to increase disease and pest resistance, alter plant architecture and flowering time, improve commercial traits of fruits and flowers, enhance nutritional values, remove toxic compounds and allergens, and develop high-value industrial products. In this article we aim to provide an overview of the RNA silencing pathways in plants, summarize the existing RNA silencing technologies, and review the current progress in applying these technologies for the improvement of agricultural crops particularly horticultural crops.

RNA Quality Control as a Key to Suppressing RNA Silencing of Endogenous Genes in Plants

RNA quality control of endogenous RNAs is an integral part of eukaryotic gene expression and often relies on exonucleolytic degradation to eliminate dysfunctional transcripts. In parallel, exogenous and selected endogenous RNAs are degraded through RNA silencing, which is a genome defense mechanism used by many eukaryotes. In plants, RNA silencing is triggered by the production of double-stranded RNAs (dsRNAs) by RNA-DEPENDENT RNA POLYMERASEs (RDRs) and proceeds through small interfering (si) RNA-directed, ARGONAUTE (AGO)-mediated cleavage of homologous transcripts. Many studies revealed that plants avert inappropriate posttranscriptional gene silencing of endogenous coding genes by using RNA surveillance mechanisms as a safeguard to protect their transcriptome profiles. The tug of war between RNA surveillance and RNA silencing ensures the appropriate partitioning of endogenous RNA substrates among these degradation pathways. Here we review recent advances on RNA quality control and its role in the suppression of RNA silencing at endogenous genes and discuss the mechanisms underlying the crosstalk among these pathways.

Transcriptional profiling of epigenetic regulators in somatic embryos during temperature induced formation of an epigenetic memory in Norway spruce

A significant number of epigenetic regulators were differentially expressed during embryogenesis at different epitype-inducing conditions. Our results support that methylation of DNA and histones, as well as sRNAs, are pivotal for the establishment of the epigenetic memory. As a forest tree species with long generation times, Norway spruce is remarkably well adapted to local environmental conditions despite having recently, from an evolutionary perspective, recolonized large areas following the last glaciation. In this species, there is an enigmatic epigenetic memory of the temperature conditions during embryogenesis that allows rapid adaptation to changing environment. We used a transcriptomic approach to investigate the molecular mechanisms underlying the formation of the epigenetic memory during somatic embryogenesis in Norway spruce. Nine mRNA libraries were prepared from three epitypes of the same genotype resulting from exposure to epitype-inducing temperatures of 18, 23 and 28 °C. RNA-Seq analysis revealed more than 10,000 differentially expressed genes (DEGs). The epitype-inducing conditions during SE were accompanied by marked transcriptomic changes for multiple gene models related to the epigenetic machinery. Out of 735 putative orthologs of epigenetic regulators, 329 were affected by the epitype-inducing temperatures and differentially expressed. The majority of DEGs among the epigenetic regulators was related to DNA and histone methylation, along with sRNA pathways and a range of putative thermosensing and signaling genes. These genes could be the main epigenetic regulators involved in formation of the epigenetic memory. We suggest considerable expansion of gene families of epigenetic regulators in Norway spruce compared to orthologous gene families in Populus and Arabidopsis. Obtained results provide a solid basis for further genome annotation and studies focusing on the importance of these candidate genes for the epigenetic memory formation.

RNA-directed DNA methylation in plants

In plants, many small interfering RNAs (siRNAs) direct de novo methylation by DNA methyltransferase. DNA methylation typically occurs by RNA-directed DNA methylation (RdDM), which directs transcriptional gene silencing of transposons and endogenous transgenes. RdDM is driven by non-coding RNAs (ncRNAs) produced by DNA-dependent RNA polymerases IV and V (PolIV and PolV). The production of siRNAs is initiated by PolIV and ncRNAs produced by PolIV are precursors of 24-nucleotide siRNAs. In contrast, ncRNAs produced by PolV are involved in scaffolding RNAs. In this review, we summarize recent studies of RdDM. In particular, we focus on the mechanisms involved in chromatin remodeling by PolIV and PolV.

Functions of plants long non-coding RNAs

Long non-coding RNAs (lncRNAs) have been emerged as important players for various biological pathways, including dosage compensation, genomic imprinting, chromatin regulation, alternative splicing and nuclear organization. A large number of lncRNAs had already been identified by different approaches in plants, while the functions of only a few of them have been investigated. This review will summarize our current understanding of a wide range of plant lncRNAs functions, and highlight their roles in the regulation of diverse pathways in plants. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa.

Exploring the secrets of long noncoding RNAs

High-throughput sequencing has revealed that the majority of RNAs have no capacity to encode protein. Among these non-coding transcripts, recent work has focused on the roles of long noncoding RNAs (lncRNAs) of >200 nucleotides. Although many of their attributes, such as patterns of expression, remain largely unknown, lncRNAs have key functions in transcriptional, post-transcriptional, and epigenetic gene regulation; Also, new work indicates their functions in scaffolding ribonuclear protein complexes. In plants, genome-wide identification of lncRNAs has been conducted in several species, including Zea mays, and recent research showed that lncRNAs regulate flowering time in the photoperiod pathway, and function in nodulation. In this review, we discuss the basic mechanisms by which lncRNAs regulate key cellular processes, using the large body of knowledge on animal and yeast lncRNAs to illustrate the significance of emerging work on lncRNAs in plants.