Interactions and links among the noncoding RNAs in plants under stresses

Regulatory Peptide Encoded by the Primary Transcript of miR396a Influences Gene Expression and Root Development in Solanum lycopersicum

MicroRNAs (miRNAs) are the processing products of primary miRNAs (pri-miRNAs) that regulate the expression of target genes. Recent studies have demonstrated that some pri-miRNAs can encode small peptides (miPEPs) that perform significant biological functions. The function of miPEPs in tomatoes, an important model horticultural crop, remains to be investigated. Here, we characterized the primary sequence of tomato miR396a using 5' RACE and confirmed the presence of miPEP396a in tomato by verifying the translational activity of the start codon. It primarily resides in the nucleus to exert its function and additionally regulates the expression of pri-miR396a, miR396a, and its target genes. Transcriptomic and metabolomic analyses showed that in vitro synthesis of miPEP396a significantly increased the expression of genes related to phenylpropanoid biosynthesis and hormones in tomato. Meanwhile, our in vitro application of miPEP396a in tomato significantly inhibited the elongation of tomato primary roots. In conclusion, our results indicate that miPEP396a regulates root growth in tomato by specifically promoting miR396a expression, provide insight into the function of miPEPs in tomato and potential applications.

Single-Cell Transcriptome Sequencing Reveals Molecular Expression Differences and Marker Genes in Testes during the Sexual Maturation of Mongolian Horses

This study aimed to investigate differences in testicular tissue morphology, gene expression, and marker genes between sexually immature (1-year-old) and sexually mature (10-year-old) Mongolian horses. The purposes of our research were to provide insights into the reproductive physiology of male Mongolian horses and to identify potential markers for sexual maturity. The methods we applied included the transcriptomic profiling of testicular cells using single-cell sequencing techniques. Our results revealed significant differences in tissue morphology and gene expression patterns between the two age groups. Specifically, 25 cell clusters and 10 cell types were identified, including spermatogonial and somatic cells. Differential gene expression analysis highlighted distinct patterns related to cellular infrastructure in sexually immature horses and spermatogenesis in sexually mature horses. Marker genes specific to each stage were also identified, including APOA1, AMH, TAC3, INHA, SPARC, and SOX9 for the sexually immature stage, and PRM1, PRM2, LOC100051500, PRSS37, HMGB4, and H1-9 for the sexually mature stage. These findings contribute to a deeper understanding of testicular development and spermatogenesis in Mongolian horses and have potential applications in equine reproductive biology and breeding programs. In conclusion, this study provides valuable insights into the molecular mechanisms underlying sexual maturity in Mongolian horses.

The lncRNA20718-miR6022-RLPs module regulates tomato resistance to Phytophthora infestans

KEY MESSAGE

Sl-lncRNA20718 acts as an eTM of Sl-miR6022 regulating its expression thereby affecting SlRLP6/10 expression. SlRLP6/10 regulate PRs expression, ROS accumulation, and JA/ET content thereby affecting tomato resistance to P. infestans. Tomato (Solanum lycopersicum) is an important horticultural and cash crop whose yield and quality can be severely affected by Phytophthora infestans (P. infestans). Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are widely involved in plant defense responses against pathogens. The involvement of Sl-lncRNA20718 and Sl-miR6022 in tomato resistance to P. infestans as well as the targeting of Sl-miR6022 to receptor-like protein genes (RLPs) were predicted in our previous study. However, uncertainty exists regarding their potential interaction as well as the molecular processes regulating tomato resistance. Here, we found that Sl-lncRNA20718 and Sl-miR6022 are positive and negative regulators of tomato resistance to P. infestans by gain- and loss-of-function experiments, respectively. Overexpression of Sl-lncRNA20718 decreased the expression of Sl-miR6022, induced the expression of PRs, reduced the diameter of lesions (DOLs), thereby enhanced disease resistance. A six-point mutation in the binding region of Sl-lncRNA20718 to Sl-miR6022 disabled the interaction, indicating that Sl-lncRNA20718 acts as an endogenous target mimic (eTM) of Sl-miR6022. We demonstrated that Sl-miR6022 cleaves SlRLP6/10. Overexpression of Sl-miR6022 decreases the expression levels of SlRLP6/10, induces the accumulation of reactive oxygen species (ROS) and reduces the content of JA and ET, thus inhibiting tomato resistance to P. infestans. In conclusion, our study provides detailed information on the lncRNA20718-miR6022-RLPs module regulating tomato resistance to P. infestans by affecting the expression of disease resistance-related genes, the accumulation of ROS and the phytohormone levels, providing a new reference for tomato disease resistance breeding.

Sl-lncRNA47980, a positive regulator affects tomato resistance to Phytophthora infestans

Emerging evidence suggests that long non-coding RNAs (lncRNAs) involve in defense respond against pathogen attack and show great potentials to improve plant resistance. Tomato late blight, a destructive plant disease, is caused by the oomycete pathogen Phytophthora infestans, which seriously affects the yield and quality of tomato. Our previous research has shown that Sl-lncRNA47980 is involved in response to P. infestans infection, but its molecular mechanism is unknown. Gain- and loss-of-function experiments revealed that Sl-lncRNA47980 as a positive regulator, played a crucial role in enhancing tomato resistance to P. infestans. The Sl-lncRNA47980-overexpressing transgenic plants exhibited an improved ability to scavenge reactive oxygen species (ROS), decreased contents of endogenous gibberellin (GA) and salicylic acid (SA), and increased contents of jasmonic acid (JA), while silencing of Sl-lncRNA47980 showed an opposite trend in the levels of these hormones. Furthermore, it was found that Sl-lncRNA47980 could upregulate the expression of SlGA2ox4 gene through activation of the promoter of SlGA2ox4 to affect GA content. The increased expression of the tomato GA signaling repressor SlDELLA could activate JA-related genes and inhibit SA-related genes to varying degrees respectively. In addition, exogenous application of GA3 and GA synthesis inhibitor uniconazole could increase disease susceptibility of Sl-lncRNA47980-overexpressing plants and the resistance of Sl-lncRNA47980-silenced plants, respectively, to P. infestans. From thus, it was speculated that Sl-lncRNA47980 conferred tomato resistance to P. infestans, which was related to the decrease in endogenous GA content. Our study provided information to link Sl-lncRNA47980 with changes in ROS accumulation and phytohormone levels in plant immunity, thus providing a new candidate gene for tomato breeding.

Recent perspective of non-coding RNAs at the nexus of plant-pathogen interaction

In natural habitats, plants are exploited by pathogens in biotrophic or necrotrophic ways. Concurrently, plants have evolved their defense systems for rapid perception of pathogenic effectors and begin concerted cellular reprogramming pathways to confine the pathogens at the entry sites. During the reorganization of cellular signaling mechanisms following pathogen attack, non-coding RNAs serves an indispensable role either as a source of resistance or susceptibility. Besides the well-studied functions of non-coding RNAs related to plant development and abiotic stress responses, previous and recent discoveries have established that non-coding RNAs like miRNAs, siRNAs, lncRNAs and phasi-RNAs can fine tune plant defense responses by targeting various signaling pathways. In this review, recapitulation of previous reports associated with non-coding RNAs as a defense responder against virus, bacteria and fungus attacks and insightful discussion will lead us to conceive innovative ideas to fight against approaching threats of resistant breaking pathogens.

PmliHFM: Predicting Plant miRNA-lncRNA Interactions with Hybrid Feature Mining Network

Due to the crucial role of interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in biological processes, the study of their biological functions is necessary. So far, the various computational methods have been employed to make predictions of the miRNA-lncRNA interaction, which compensate for the inadequacy of biological experiments. However, the existing methods do not consider the differences between miRNA and lncRNA in feature extraction. In this paper, we propose a hybrid feature mining network, named PmliHFM, for predicting plant miRNA-lncRNA interactions. Firstly, miRNA and lncRNA with different sequence lengths are encoded by different encodings, which can reduce the loss of information caused by using the same coding approach. Then, a hybrid feature mining network is designed to adapt to different encoding methods and extract more useful feature information than a single network. Finally, an ensemble module is utilized to integrate the training results of the hybrid feature mining network, while a prediction module is employed to determine whether there are interactions. By testing on multiple test sets, PmliHFM outperforms several state-of-the-art approaches. The results show that the AUC of PmliHFM achieves 0.8[Formula: see text], 3.1[Formula: see text] and 0.4[Formula: see text] improvement respectively on three balanced datasets, and achieves 2.1[Formula: see text] and 1.8[Formula: see text] improvement respectively on two imbalanced datasets. These experiments demonstrate the feasibility of the proposed method.

Identification of Competing Endogenous RNAs (ceRNAs) Network Associated with Drought Tolerance in Medicago truncatula with Rhizobium Symbiosis

Drought, bringing the risks of agricultural production losses, is becoming a globally environmental stress. Previous results suggested that legumes with nodules exhibited superior drought tolerance compared with the non-nodule group. To investigate the molecular mechanism of rhizobium symbiosis impacting drought tolerance, transcriptome and sRNAome sequencing were performed to identify the potential mRNA-miRNA-ncRNA dynamic network. Our results revealed that seedlings with active nodules exhibited enhanced drought tolerance by reserving energy, synthesizing N-glycans, and medicating systemic acquired resistance due to the early effects of symbiotic nitrogen fixation (SNF) triggered in contrast to the drought susceptible with inactive nodules. The improved drought tolerance might be involved in the decreased expression levels of miRNA such as mtr_miR169l-5p, mtr_miR398b, and mtr_miR398c and its target genes in seedlings with active nodules. Based on the negative expression pattern between miRNA and its target genes, we constructed an mRNA-miR169l-ncRNA ceRNA network. During severe drought stress, the lncRNA alternative splicings TCONS_00049507 and TCONS_00049510 competitively interacted with mtr_miR169l-5p, which upregulated the expression of NUCLEAR FACTOR-Y (NF-Y) transcription factor subfamily NF-YA genes MtNF-YA2 and MtNF-YA3 to regulate their downstream drought-response genes. Our results emphasized the importance of SNF plants affecting drought tolerance. In conclusion, our work provides insight into ceRNA involvement in rhizobium symbiosis contributing to drought tolerance and provides molecular evidence for future study.

Comprehensive Genome-Wide Analysis and Expression Pattern Profiling of PLATZ Gene Family Members in Solanum Lycopersicum L. under Multiple Abiotic Stresses

PLATZ (plant AT-rich sequence and zinc-binding) family proteins with two conserved zinc-dependent DNA-binding motifs are transcription factors specific to the plant kingdom. The functions of PLATZ proteins in growth, development, and adaptation to multiple abiotic stresses have been investigated in various plant species, but their role in tomato has not been explored yet. In the present work, 20 non-redundant Solanum lycopersicum PLATZ (SlPLATZ) genes with three segmentally duplicated gene pairs and four tandemly duplicated gene pairs were identified on eight tomato chromosomes. The comparative modeling and gene ontology (GO) annotations of tomato PLATZ proteins indicated their probable roles in defense response, transcriptional regulation, and protein metabolic processes as well as their binding affinity for various ligands, including nucleic acids, peptides, and zinc. SlPLATZ10 and SlPLATZ17 were only expressed in 1 cm fruits and flowers, respectively, indicating their preferential involvement in the development of these organs. The expression of SlPLATZ1, SlPLATZ12, and SlPLATZ19 was up- or down-regulated following exposure to various abiotic stresses, whereas that of SlPLATZ11 was induced under temperature stresses (i.e., cold and heat stress), revealing their probable function in the abiotic stress tolerance of tomato. Weighted gene co-expression network analysis corroborated the aforementioned findings by spotlighting the co-expression of several stress-associated genes with SlPLATZ genes. Confocal fluorescence microscopy revealed the localization of SlPLATZ−GFP fusion proteins in the nucleus, hinting at their functions as transcription factors. These findings provide a foundation for a better understanding of the structure and function of PLATZ genes and should assist in the selection of potential candidate genes involved in the development and abiotic stress adaptation in tomato.

Non-coding RNAs fine-tune the balance between plant growth and abiotic stress tolerance

To survive in adverse environmental conditions, plants have evolved sophisticated genetic and epigenetic regulatory mechanisms to balance their growth and abiotic stress tolerance. An increasing number of non-coding RNAs (ncRNAs), including small RNAs (sRNAs) and long non-coding RNAs (lncRNAs) have been identified as essential regulators which enable plants to coordinate multiple aspects of growth and responses to environmental stresses through modulating the expression of target genes at both the transcriptional and posttranscriptional levels. In this review, we summarize recent advances in understanding ncRNAs-mediated prioritization towards plant growth or tolerance to abiotic stresses, especially to cold, heat, drought and salt stresses. We highlight the diverse roles of evolutionally conserved microRNAs (miRNAs) and small interfering RNAs (siRNAs), and the underlying phytohormone-based signaling crosstalk in regulating the balance between plant growth and abiotic stress tolerance. We also review current discoveries regarding the potential roles of ncRNAs in stress memory in plants, which offer their descendants the potential for better fitness. Future ncRNAs-based breeding strategies are proposed to optimize the balance between growth and stress tolerance to maximize crop yield under the changing climate.

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.

Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions

Long non-coding RNAs (lncRNAs) have attracted extensive attention due to their important roles in various biological processes, among which lncRNA-protein interaction plays an important regulatory role in plant immunity and life activities. Laboratory methods are time consuming and labor-intensive, so that many computational methods have gradually emerged as auxiliary tools to assist relevant research. However, there are relatively few methods to predict lncRNA-protein interaction of plant. Due to the lack of experimentally verified interactions data, there is an imbalance between known and unknown interaction samples in plant data sets. In this study, a multi-feature fusion method based on linear neighborhood propagation is developed to predict plant unobserved lncRNA-protein interaction pairs through known interaction pairs, called MPLPLNP. The linear neighborhood similarity of the feature space is calculated and the results are predicted by label propagation. Meanwhile, multiple feature training is integrated to better explore the potential interaction information in the data. The experimental results show that the proposed multi-feature fusion method can improve the performance of the model, and is superior to other state-of-the-art approaches. Moreover, the proposed approach has better performance and generalization ability on various plant datasets, which is expected to facilitate the related research of plant molecular biology.

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.

Cryo-Treatment Enhances the Embryogenicity of Mature Somatic Embryos via the lncRNA-miRNA-mRNA Network in White Spruce

In conifers, somatic embryogenesis is uniquely initiated from immature embryos in a narrow time window, which is considerably hindered by the difficulty to induce embryogenic tissue (ET) from other tissues, including mature somatic embryos. In this study, the embryogenic ability of newly induced ET and DNA methylation levels was detected, and whole-transcriptome sequencing analyses were carried out. The results showed that ultra-low temperature treatment significantly enhanced ET induction from mature somatic embryos, with the induction rate from 0.4% to 15.5%, but the underlying mechanisms remain unclear. The newly induced ET showed higher capability in generating mature embryos than the original ET. DNA methylation levels fluctuated during the ET induction process. Here, WGCNA analysis revealed that OPT4, TIP1-1, Chi I, GASA5, GST, LAX3, WRKY7, MYBS3, LRR-RLK, PBL7, and WIN1 genes are involved in stress response and auxin signal transduction. Through co-expression analysis, lncRNAs MSTRG.505746.1, MSTRG.1070680.1, and MSTRG.33602.1 might bind to pre-novel_miR_339 to promote the expression of WRKY7 genes for stress response; LAX3 could be protected by lncRNAs MSTRG.1070680.1 and MSTRG.33602.1 via serving as sponges for novel_miR_495 to initiate auxin signal transduction; lncRNAs MSTRG.505746.1, MSTRG.1070680.1, and MSTRG.33602.1 might serve as sponges for novel_miR_527 to enhance the expression of Chi I for early somatic embryo development. This study provides new insight into the area of stress-enhanced early somatic embryogenesis in conifers, which is also attributable to practical applications.

preMLI: a pre-trained method to uncover microRNA-lncRNA potential interactions

The interaction between microribonucleic acid and long non-coding ribonucleic acid plays a very important role in biological processes, and the prediction of the one is of great significance to the study of its mechanism of action. Due to the limitations of traditional biological experiment methods, more and more computational methods are applied to this field. However, the existing methods often have problems, such as inadequate acquisition of potential features of the sequence due to simple coding and the need to manually extract features as input. We propose a deep learning model, preMLI, based on rna2vec pre-training and deep feature mining mechanism. We use rna2vec to train the ribonucleic acid (RNA) dataset and to obtain the RNA word vector representation and then mine the RNA sequence features separately and finally concatenate the two feature vectors as the input of the prediction task. The preMLI performs better than existing methods on benchmark datasets and has cross-species prediction capabilities. Experiments show that both pre-training and deep feature mining mechanisms have a positive impact on the prediction performance of the model. To be more specific, pre-training can provide more accurate word vector representations. The deep feature mining mechanism also improves the prediction performance of the model. Meanwhile, The preMLI only needs RNA sequence as the input of the model and has better cross-species prediction performance than the most advanced prediction models, which have reference value for related research.

Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method

The interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) play important roles in biological activities. Specially, lncRNAs as endogenous target mimics (eTMs) can bind miRNAs to regulate the expressions of target messenger RNAs (mRNAs). A growing number of studies focus on animals, but the studies on plants are scarce and many functions of plant eTMs are unknown. This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various plant species. A total of 17 potential eTMs are verified by biological experiment to involve in 22 regulations, and 14 potential eTMs are inferred by Gene Ontology enrichment analysis to involve in 63 functions, which is significant for further research.

Function identification of miR394 in tomato resistance to Phytophthora infestans

MiR394 plays a negative role in tomato resistance to late blight. The lncRNA40787 severing as an eTM for miR394 to regulate LCR and exerting functions in tomato resistance. Tomato (Solanum lycopersicum), which was used as model species for studying the mechanism of plant disease defense, is susceptible to multiple pathogens. Non-coding RNA (ncRNA) has a pivotal role in plants response to biological stresses. It has previously been observed that the expression level of miR394 changed significantly after the infection of various pathogens. However, there has been no detailed investigation of the accumulated or suppressed mechanism of miR394. Our previous study predicted three lncRNAs (lncRNA40787, lncRNA27177, and lncRNA42566) that contain miR394 endogenous target mimics (eTM), which may exist as the competitive endogenous RNAs (ceRNAs) of miR394. In our study, the transcription levels of these three lncRNAs were strongly up-regulated in tomato upon infection with P. infestans. In contrast with the three lncRNAs, the accumulation of miR394 was significantly suppressed. Based on the expression pattern, and value of minimum free energy (mfes) that represents the binding ability between lncRNA and miRNA, lncRNA40787 was chosen for further investigation. Results showed that overexpression of lncRNA40787 reduced the expression of miR394 along with decreased lesion area and enhanced disease resistance. Overexpression of miR394, however, decreased the expression of its target gene Leaf Curling Responsiveness (LCR), and suppressed the synthesis components genes of jasmonic acid (JA), depressing the resistance of tomato to P. infestans infection. Taken together, our findings indicated that miR394 can be decoyed by lncRNA40787, and negatively regulated the expression of LCR to enhance tomato susceptibility under P. infestans infection. Our study provided detailed information on the lncRNA40787-miR394-LCR regulatory network and serves as a reference for future research.

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

BACKGROUND

Plant 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.

RESULTS

In 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.

CONCLUSIONS

PRPI-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.

Editing miR482b and miR482c Simultaneously by CRISPR/Cas9 Enhanced Tomato Resistance to Phytophthora infestans

Late blight, caused by Phytophthora infestans, is severely damaging to the global tomato industry. Micro-RNAs (miRNAs) have been widely demonstrated to play vital roles in plant resistance by repressing their target genes. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) method has been continuously improved and extensively applied to edit plant genomes. However, editing multiplex miRNAs by CRISPR/Cas9 in tomato has not been studied yet. We knocked out miR482b and miR482c simultaneously in tomato through the multiplex CRISPR/Cas9 system. Two transgenic plants with silenced miR482b and miR482c simultaneously and one transgenic line with silenced miR482b alone were obtained. Compared with wild-type plants, the disease symptoms of three transgenic plants upon infection were reduced, accompanied by increased expression of their common target nucleotide binding site-leucine-rich repeat genes and decreased levels of reactive oxygen species. Furthermore, silencing miR482b and miR482c simultaneously was more resistant than silencing miR482b alone in tomato. More importantly, we found that knocking out miR482b and miR482c can elicit expression perturbation of other miRNAs, suggesting cross-regulation between miRNAs. Our study demonstrated that editing miR482b and miR482c simultaneously with CRISPR/Cas9 is an efficient strategy for generating pathogen-resistant tomatoes, and cross-regulation between miRNAs may reveal the novel mechanism in tomato-P. infestans interactions.

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.

Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA-lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This paper proposes an ensemble deep learning model based on multi-level information enhancement and greedy fuzzy decision (PmliPEMG) for plant miRNA-lncRNA interaction prediction. The fusion complex features, multi-scale convolutional long short-term memory networks, and attention mechanism are adopted to enhance the sample information at the feature, scale, and model levels, respectively. An ensemble deep learning model is built based on a novel method (greedy fuzzy decision) which greatly improves the efficiency. The multi-level information enhancement and greedy fuzzy decision are verified to have the positive effects on prediction performance. PmliPEMG can be applied to the cross-species prediction. It shows better performance and stronger generalization ability than state-of-the-art predictors and may provide valuable references for related research.

Interplay between miRNAs and lncRNAs: Mode of action and biological roles in plant development and stress adaptation

Plants employ sophisticated mechanisms to control developmental processes and to cope with environmental changes at transcriptional and post-transcriptional levels. MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs), two classes of endogenous noncoding RNAs, are key regulators of gene expression in plants. Recent studies have identified the interplay between miRNAs and lncRNAs as a novel regulatory layer of gene expression in plants. On one hand, miRNAs target lncRNAs for the production of phased small interfering RNAs (phasiRNAs). On the other hand, lncRNAs serve as origin of miRNAs or regulate the accumulation or activity of miRNAs at transcription and post-transcriptional levels. Theses lncRNA-miRNA interplays are crucial for plant development, physiology and responses to biotic and abiotic stresses. In this review, we summarize recent advances in the biological roles, interaction mechanisms and computational predication methods of the interplay between miRNAs and lncRNAs in plants.

Genome-Wide Identification and Characterization of Potato Long Non-coding RNAs Associated With Phytophthora infestans Resistance

Long non-coding RNA (lncRNA) is a crucial regulatory mechanism in the plant response to biotic and abiotic stress. However, their roles in potato (Solanum tuberosum L.) resistance to Phytophthora infestans (P. infestans) largely remain unknown. In this study, we identify 2857 lncRNAs and 33,150 mRNAs of the potato from large-scale published RNA sequencing data. Characteristic analysis indicates a similar distribution pattern of lncRNAs and mRNAs on the potato chromosomes, and the mRNAs were longer and had more exons than lncRNAs. Identification of alternative splicing (AS) shows that there were a total of 2491 lncRNAs generated from AS and the highest frequency (46.49%) of alternative acceptors (AA). We performed R package TCseq to cluster 133 specific differentially expressed lncRNAs from resistance lines and found that the lncRNAs of cluster 2 were upregulated. The lncRNA targets were subject to KEGG pathway enrichment analysis, and the interactive network between lncRNAs and mRNAs was constructed by using GENIE3, a random forest machine learning algorithm. Transient overexpression of StLNC0004 in Nicotiana benthamiana significantly suppresses P. infestans growth compared with a control, and the expression of extensin (NbEXT), the ortholog of the StLNC0004 target gene, was significantly upregulated in the overexpression line. Together, these results suggest that lncRNAs play potential functional roles in the potato response to P. infestans infection.

Optimized combination methods for exploring and verifying disease-resistant transcription factors in melon

A large amount of omics data and number of bioinformatics tools has been produced. However, the methods for further exploring omics data are simple, in particular, to mine key regulatory genes, which are a priority concern in biological systems, and most of the specific functions are still unknown. First, raw data of two genotypes of melon (susceptible and resistant) were obtained by transcriptome analysis. Second, 391 transcription factors (TFs) were identified from the plant transcription factor database and cucurbit genomics database. Then, functional enrichment analysis indicated that these genes were mainly annotated in the process of transcription regulation. Third, 243 and 230 module-specific TFs were screened by weighted gene coexpression network analysis and short time series expression miner, respectively. Several TF genes, such as WRKYs and bHLHs, were regarded as key regulatory genes according to the values of significantly different modules. The coexpression network showed that these TF genes were significant correlated with resistance (R) genes, such as DRP2, RGA3, DRP1 and NB-ARC. Fourth, cis-acting element analysis illustrated that these R genes may bind to WRKY and bHLH. Finally, the expression of WRKY genes was verified by quantitative reverse transcription PCR (RT-qPCR). Phylogenetic analysis was carried out to further confirm that these TFs may play a critical role in Curcurbitaceae disease resistance. This study provides a new optimized combination strategy to explore the functions of TFs in a wide spectrum of biological processes. This strategy may also effectively predict potential relationships in the interactions of essential genes.