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.

Spontaneous chromosome instability and tissue culture-induced karyotypic alteration in wheat-Thinopyrum intermedium alien addition lines.

MAIN CONCLUSION: Wheat lines harboring wild-relative chromosomes can be karyotypically unstable during long-term maintenance. Tissue culture exacerbates chromosomal instability but appears inefficient to induce somatic homoeologous exchange between alien and wheat chromosomes. We assessed if long-term refrigerator storage with regular renewal via self-fertilization, a widely used practice for crop germplasm maintenance, would ensure genetic fidelity of alien addition lines, and explored the possibility of inducing somatic homoeologues exchange by tissue culture. We cytogenetically characterized sampled stock seeds of originally confirmed 12 distinct wheat-Thinopyrum intermedium alien addition lines (dubbed TAI lines), and subjected immature embryos of the TAI lines to tissue culture. We find eight of the 12 TAI lines were karyotypically departed from their original identity as bona fide disomic alien addition lines due to extensive loss of whole-chromosomes of both Th. intermedium and wheat origins during the ca. 3-decade storage. Rampant numerical chromosome variations (NCVs) involving both alien and wheat chromosomes were detected in regenerated plants of all 12 studied TAI lines, but at variable rates among the wheat sub-genomes and chromosomes. Compared with NCVs, structural chromosome variations (SCVs) occurred at substantially lower rates, and no SCV involving the added alien chromosomes was observed. The NCVs manifested only moderate effects on phenotypes of the regenerated plants under field conditions.

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.

miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning.

MicroRNAs (miRNAs) are an essential type of small molecule RNAs that play significant regulatory roles in organisms. Recent studies have demonstrated that small open reading frames (sORFs) harbored in primary miRNAs (pri-miRNAs) can encode small peptides, known as miPEPs. Plant miPEPs can increase the abundance and activity of cognate miRNAs by promoting the transcription of their corresponding pri-miRNAs, thereby modulating plant traits. Biological experiments are the most effective way to accurately identify miPEPs; however, they are time-consuming and expensive. Hence, an efficient computational method for the identification of miPEPs on a large scale is highly desirable. Up to now, there have been no specialized computational tools for identifying miPEPs. In this work, a novel predictor named miPEPPred-FRL based on an adaptive feature representation learning framework that consists of the feature transformation module and the cascade architecture has been proposed. The feature transformation module integrating a newly designed feature selection method and classifier selection rule is developed to convert sequence-based features into primary class and probabilistic features, which are then fed into the improved cascade architecture to obtain more stable and discriminative augmented features. Finally, the augmented features are utilized to construct the final predictor. Cross-validation experiments illustrate that the novel feature selection method and classifier selection rule contribute to boosting the feature representation ability of the framework. Furthermore, the high accuracy of miPEPPred-FRL on independent testing data suggests that it is a trustworthy and valuable tool for the identification of miPEPs.

PAMPred: A hierarchical evolutionary ensemble framework for identifying plant antimicrobial peptides.

Antimicrobial peptides (AMPs) play a crucial role in plant immune regulation, growth and development stages, which have attracted significant attentions in recent years. As the wet-lab experiments are laborious and cost-prohibitive, it is indispensable to develop computational methods to discover novel plant AMPs accurately. In this study, we presented a hierarchical evolutionary ensemble framework, named PAMPred, which consisted of a multi-level heterogeneous architecture to identify plant AMPs. Specifically, to address the existing class imbalance problem, a cluster-based resampling method was adopted to build multiple balanced subsets. Then, several peptide features including sequence information-based and physicochemical properties-based features were fed into the different types of basic learners to increase the ensemble diversity. For boosting the predictive capability of PAMPred, the improved particle swarm optimization (PSO) algorithm and dynamic ensemble pruning strategy were used to optimize the weights at different levels adaptively. Furthermore, extensive ten-fold cross-validation and independent testing experimental results demonstrated that PAMPred achieved excellent prediction performance and generalization ability, and outperformed the state-of-the-art methods. It also indicated that the proposed method could serve as an effective auxiliary tool to identify plant AMPs, which would be conducive to explore the immune regulatory mechanism of plants.

The effectors of Phytophthora infestans impact host immunity upon regulation of antagonistic hormonal activities.

MAIN CONCLUSION: Phytophthora infestans effectors manipulate the antagonism of host hormones to interfere with the immune response of plants at different infection stages. Phytophthora infestans (P. infestans) poses a serious threat to global crop production, and its effectors play an indispensable role in its pathogenicity. However, the function of these effectors during the switch from biotrophy to necrotrophy of P. infestans remains unclear. Further research on the effectors that manipulate the antagonistic response of host hormones is also lacking. In this study, a coexpression analysis and infection assays were performed to identify distinct gene expression changes in both P. infestans and tomato. During the switch from biotrophy to necrotrophy, P. infestans secretes three types of effectors to interfere with host salicylic acid (SA), jasmonic acid (JA), ethylene (ET), and abscisic acid (ABA) levels. The three aforementioned effectors also regulate the host gene expression including NPR1, TGA2.1, PDF1.2, NDR1, ERF3, NCED6, GAI4, which are involved in hormone crosstalk. The changes in plant hormones are mediated by the three types of effectors, which may accelerate infection and drive completion of the P. infestans lifecycle. Our findings provide new insight into plant‒pathogen interactions that may contribute to the prevention growth of hemibiotrophic pathogens.

Secreted Peptide SpPIP1 Modulates Disease Resistance and Salt Tolerance in Tomato.

Tomato is a globally important horticultural and economic crop, but its productivity is severely affected by various stresses. Plant small secretory peptides have been identified as crucial mediators in plant resistance. Here, we conducted a comparative transcriptome analysis and identified the prePIP1 gene from Solanum pimpinellifolium (SpprePIP1), as an ortholog of Arabidopsis prePIP1 encoding the precursor protein of PAMP-induced SSP 1. The expression level of SpprePIP1 is transcriptionally induced in tomato upon infection with Phytophthora infestans (P. infestans), the pathogen responsible for late blight. Overexpression of SpprePIP1 resulted in enhanced tomato resistance to P. infestans. In addition, exogenous application of SpPIP1, whether through spraying or irrigation, improved tomato resistance by enhancing the transcript accumulations of pathogenesis-related proteins, as well as reactive oxygen species and the jasmonic acid (JA) levels. Integrated analysis of transcriptomics and metabolomics revealed the potential contributions of JA and phenylpropanoid biosynthesis to SpPIP1-induced tomato immunity. Additionally, SpPIP1 may strengthen tomato resistance to salt stress through the ABA signaling pathway. Overall, our findings demonstrate that SpPIP1 positively regulates tomato tolerance to P. infestans and salt stress, making it a potential plant elicitor for crop protection in an environmentally friendly way.

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.

Unveiling the secrets of non-coding RNA-encoded peptides in plants: A comprehensive review of mining methods and research progress.

Non-coding RNAs (ncRNAs) are not conventionally involved in protein encoding. However, recent findings indicate that ncRNAs possess the capacity to code for proteins or peptides. These ncRNA-encoded peptides (ncPEPs) are vital for diverse plant life processes and exhibit significant potential value. Despite their importance, research on plant ncPEPs is limited, with only a few studies conducted and less information on the underlying mechanisms, and the field remains in its nascent stage. This manuscript provides a comprehensive overview of ncPEPs mining methods in plants, focusing on prediction, identification, and functional analysis. We discuss the strengths and weaknesses of various techniques, identify future research directions in the ncPEPs domain, and elucidate the biological functions and agricultural application prospects of plant ncPEPs. By highlighting the immense potential and research value of ncPEPs, we aim to lay a solid foundation for more in-depth studies in plant science.

Identification of small open reading frames in plant lncRNA using class-imbalance learning.

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.

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.

Characterization of lncRNAs in mycorrhizal tomato and elucidation of the role of lncRNA69908 in disease resistance.

Long noncoding RNAs (lncRNAs) have attracted widespread attention because of their meaningful roles in various plant biological processes. However, the potential functions of lncRNAs in the plant-beneficial microorganism interactions have not been fully explored. Arbuscular mycorrhiza (AM) symbiosis is accompanied by the systemic induction of defense responses in the host leaves. In the present study, we globally profiled lncRNA expression and explored their potential regulatory roles in AM fungi-inoculated tomato leaves. Among 851 differentially expressed lncRNAs, a novel lncRNA (lncRNA69908) that was significantly downregulated in the leaves of AM fungi inoculated tomato, affected tomato resistance after pathogen infection. One of the competing endogenous RNA networks, lncRNA69908-sly-miR319c, was verified by using a coexpression system. Silencing of lncRNA69908 or overexpression of sly-miR319c enhanced tomato resistance to Phytophthora infestans, whereas overexpression of lncRNA69908 decreased the reactive oxygen species scavenging. As above, we speculated that lncRNA69908 may be involved in mycorrhiza-induced defense responses. Our findings can broaden the knowledge on the potential regulatory roles of ncRNAs in AM symbiosis.

The lncRNA39896-miR166b-HDZs module affects tomato resistance to Phytophthora infestans.

The yield and quality of tomatoes (Solanum lycopersicum) is seriously affected by Phytophthora infestans. The long non-coding RNA (lncRNA) Sl-lncRNA39896 is induced after P. infestans infection and was previously predicted to act as an endogenous target mimic (eTM) for the microRNA Sl-miR166b, which function in stress responses. Here, we further examined the role of Sl-lncRNA39896 and Sl-miR166b in tomato resistance to P. infestans. Sl-miR166b levels were higher in Sl-lncRNA39896-knockout mutants than in wild-type plants, and the mutants displayed enhanced resistance to P. infestans. A six-point mutation in the region of Sl-lncRNA39896 that binds to Sl-miR166b disabled the interaction, suggesting that Sl-lncRNA39896 acts as an eTM for Sl-miR166b. Overexpressing Sl-miR166b yielded a similar phenotype to that produced by Sl-lncRNA39896-knockout, whereas silencing of Sl-miR166b impaired resistance. We verified that Sl-miR166b cleaved transcripts of its target class III homeodomain-leucine zipper genes SlHDZ34 and SlHDZ45. Silencing of SlHDZ34/45 decreased pathogen accumulation in plants infected with P. infestans. Additionally, jasmonic acid and ethylene contents were elevated following infection in the plants with enhanced resistance. Sl-lncRNA39896 is the first known lncRNA to negatively regulate resistance to P. infestans in tomato. We propose a novel mechanism in which the lncRNA39896-miR166b-HDZ module modulates resistance to P. infestans.

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.

Genome-wide identification and characterization of the tomato F-box associated (FBA) protein family and expression analysis of their responsiveness to Phytophthora infestans.

Late blight caused by Phytophthora infestans brings huge economic losses to the production of tomato (Solanum lycopersicum) every year. F-box proteins participate in plants response to phytohormones and biotic stress, whereas as the largest subfamily of F-box superfamily, the detailed information about F-box associated (SlFBA) family in tomato has been rarely reported. In this study, a total of 46 tomato FBA genes were identified based on the latest genome annotation. Phylogenetic analysis revealed that the FBA proteins from tomato and 6 different plant species were clustered into 7 distinct clades. The SlFBA genes were unevenly distributed on 11 chromosomes of tomato, mainly concentrated in the regions with high gene density. Tandem duplications and purification selection contribute to the expansion and evolution of the SlFBA gene family. Transcriptome analysis revealed that the SlFBA genes were differentially expressed in different tissues with obvious tissue-specific expression patterns. There were 18 SlFBA genes differentially expressed in P. infestans-resistant and -susceptible tomato, among which, 3 SlFBA genes might play positive roles in tomato resistance to P. infestans. Taken together, this study systematically analyzed the SlFBA genes family for the first time and identified the candidate SlFBA genes that affect tomato resistance to P. infestans, which provided important genetic and breeding resources for improving tomato resistance to pathogens.

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.

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.

LncRNA-Encoded Short Peptides Identification Using Feature Subset Recombination and Ensemble Learning.

Long non-coding RNA (lncRNA), which is a type of non-coding RNA, was reported to contain short open reading frames (sORFs). SORFs-encoded short peptides (SEPs) have been demonstrated to play a crucial role in regulating the biological processes such as growth, development, and resistance response. The identification of SEPs is vital to further understanding their function. However, there is still a lack of methods for identifying SEPs effectively and rapidly. In this study, a novel method for lncRNA-encoded short peptides identification based on feature subset recombination and ensemble learning, lncPepid, is developed. lncPepid transforms the data of Zea mays and Arabidopsis thaliana into hybrid features from two aspects including sequence composition and physicochemical properties separately. It optimizes hybrid features by proposing a novel weighted iteration-based feature selection method to recombine a stable subset that characterizes SEPs effectively. Different classification models with different optimized features are constructed and tested separately. The outputs of the optimal models are integrated for ensemble classification to improve efficiency. Experimental results manifest that the geometric mean of sensitivity and specificity of lncPepid is about 70% on the identification of functional SEPs derived from multiple species. It is an effective and rapid method for the identification of lncRNA-encoded short peptides. This study can be extended to the research on SEPs from other species and have crucial implications for further findings and studies of functional genomics.

Overexpression of lncRNA08489 enhances tomato immunity against Phytophthora infestans by decoying miR482e-3p.

LncRNAs are widely involved in various biological processes of plants. Recent evidences indicated that lncRNAs could act as competing endogenous RNAs (ceRNAs) to adsorb complementary miRNAs in a type of target mimicry, thereby indirectly regulating the target genes of miRNAs. In this study, a lncRNA, lncRNA08489 was identified to be the ceRNA of miR482e-3p in tomato plants. The expression patterns of lncRNA08489 and miR482e-3p showed opposite trends after tomato plants infected with Phytophthora infestans. In tomato leaves overexpressing lncRNA08489 (OE08489), the expression level of miR482e-3p decreased and its target gene, NBS-LRR increased. After infection with P. infestans, the resistance of OE08489 plants was stronger than that of the wild type, and the reactive oxygen species (ROS) scavenging ability of OE08489 plants was significantly improved. Taken together, these results indicated that lncRNA08489 acted as a ceRNA to decoy miR482e-3p and regulate the expression of NBS-LRR to enhance tomato resistance through ROS-scavenging system.

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.