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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Sl-lncRNA15492 interacts with Sl-miR482a and affects Solanum lycopersicum immunity against Phytophthora infestans.

Long non-coding RNAs (lncRNAs) are involved in the resistance of plants to infection by pathogens via interactions with microRNAs (miRNAs). Long non-coding RNAs are cleaved by miRNAs to produce phased small interfering RNAs (phasiRNAs), which, as competing endogenous RNAs (ceRNAs), function as decoys for mature miRNAs, thus inhibiting their expression, and contain pre-miRNA sequences to produce mature miRNAs. However, whether lncRNAs and miRNAs mediate other molecular mechanisms during plant resistance to pathogens is unknown. In this study, as a positive regulator, Sl-lncRNA15492 from tomato (Solanum lycopersicum Zaofen No. 2) plants affected tomato resistance to Phytophthora infestans. Gain- and loss-of-function experiments and RNA ligase-mediated 5'-amplification of cDNA ends (RLM-5' RACE) also revealed that Sl-miR482a was negatively involved in tomato resistance by targeting Sl-NBS-LRR genes and that silencing of Sl-NBS-LRR1 decreased tomato resistance. Sl-lncRNA15492 inhibited the expression of mature Sl-miR482a, whose precursor was located within the antisense sequence of Sl-lncRNA15492. Further degradome analysis and additional RLM-5' RACE experiments verified that mature Sl-miR482a could also cleave Sl-lncRNA15492. These results provide a mechanism by which lncRNAs might inhibit precursor miRNA expression through antisense strands of lncRNAs, and demonstrate that Sl-lncRNA15492 and Sl-miR482a mutually inhibit the maintenance of Sl-NBS-LRR1 homeostasis during tomato resistance to P. infestans.

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.

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.

Function identification of miR394 in tomato resistance to Phytophthora infestans.

KEY MESSAGE: 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.

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

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

Genome-wide identification, characterization and expression analysis of the TLP gene family in melon (Cucumis melo L.).

Thaumatin-like proteins (TLPs), which belong to pathogenesis-related (PR) protein family 5 (PR5), are involved in plant host defense and various developmental processes. The functions of the TLP family have been extensively discussed in multiple organisms, whereas the detailed information of this family in melon has not been reported yet. In this study, we identified 28 TLP genes in the melon genome and a N-terminal signal peptide was found highly conserved within each member of this family. Phylogeny analysis indicated that TLPs from melon and other plant species were clustered into ten groups. Twelve segmental and seven tandem duplication gene pairs that underwent purifying selection were identified. TLP genes expressed differentially in different tissues/organs, and were significantly induced after Podosphaera xanthii infection. TLPs in breeding line MR-1 tend to express early after pathogen infection compared with cultivar Top Mark. Our study provides a comprehensive understanding of the melon TLP family and demonstrates their potential roles in disease resistance, therefore provides more reference for further research.

LncRNA33732-respiratory burst oxidase module associated with WRKY1 in tomato- Phytophthora infestans interactions.

Our previous studies indicated that tomato WRKY1 transcription factor acts as a positive regulator during tomato resistance to Phytophthora infestans. However, the molecular mechanism of WRKY1-mediated resistance regulation remains unclear. Here, we used a comparative transcriptome analysis between wild-type and WRKY1-overexpressing tomato plants to identify differentially expressed genes (DEGs) and long non-coding RNAs (DELs), and we examined long non-coding RNA (lncRNA)-gene networks. The promoter sequences of the upregulated DEGs and DELs were analyzed. Among 1073 DEGs and 199 DELs, 1 kb 5'-upstream regions of 59 DEGs and 22 DELs contain the W-box, the target sequence of the WRKY1. The results of promoter-ß-glucuronidase (GUS) fusion and yeast one-hybrid assay showed that lncRNA33732 was activated by WRKY1 through sequence-specific interactions with the W-box element in its promoter. The overexpression and silencing analysis of lncRNA33732 in tomato showed that lncRNA33732 acts as a positive regulator and enhanced tomato resistance to P. infestans by induction of the expression of respiratory burst oxidase (RBOH) and increase in the accumulation of H2 O2 . When the expression of RBOH gene was inhibited in tomato plants, H2 O2 accumulation decreased and resistance were impaired. These findings suggest that lncRNA33732 activated by WRKY1 induces RBOH expression to increase H2 O2 accumulation in early defense reaction of tomato to P. infestans attack. Our results provide insights into the WRKY1-lncRNA33732-RBOH module involved in the regulation of H2 O2 accumulation and resistance to P. infestans, as well as provide candidates to enhance broad-spectrum resistance to pathogens in tomato.

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

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