Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants

Role of non-coding RNAs in quality improvement of horticultural crops: computational tools, databases, and algorithms for identification and analysis

Horticultural crops, including fruits, vegetables, flowers, and herbs, are essential for food security and economic sustainability. Advances in biotechnology, including genetic modification and omics approaches, have significantly improved these crops'traits. While initial transgenic efforts focused on protein-coding genes, recent research highlights the crucial roles of non-coding RNAs (ncRNAs) in plant growth, development, and gene regulation. ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), influence key biological processes through transcriptional and post-transcriptional regulation. This review explores the classification, functions, and regulatory mechanisms of ncRNAs, emphasizing their potential in enhancing horticultural crop quality. This growing understanding offers promising avenues for enhancing crop performance and developing new horticultural varieties with improved traits. Additionally, we elucidate the role of ncRNA databases and predictive bioinformatics tools into modern horticultural crop improvement strategies.

LMFE: A Novel Method for Predicting Plant LncRNA Based on Multi-Feature Fusion and Ensemble Learning

Background/Objectives: Long non-coding RNAs (lncRNAs) play a crucial regulatory role in plant trait expression and disease management, making their accurate prediction a key research focus for guiding biological experiments. While extensive studies have been conducted on animals and humans, plant lncRNA research remains relatively limited due to various challenges, such as data scarcity and genomic complexity. This study aims to bridge this gap by developing an effective computational method for predicting plant lncRNAs, specifically by classifying transcribed RNA sequences as lncRNAs or mRNAs using multi-feature analysis. Methods: We propose the lncRNA multi-feature-fusion ensemble learning (LMFE) approach, a novel method that integrates 100-dimensional features from RNA biological properties-based, sequence-based, and structure-based features, employing the XGBoost ensemble learning algorithm for prediction. To address unbalanced datasets, we implemented the synthetic minority oversampling technique (SMOTE). LMFE was validated across benchmark datasets, cross-species datasets, unbalanced datasets, and independent datasets. Results: LMFE achieved an accuracy of 99.42%, an F1score of 0.99, and an MCC of 0.98 on the benchmark dataset, with robust cross-species performance (accuracy ranging from 89.30% to 99.81%). On unbalanced datasets, LMFE attained an average accuracy of 99.41%, representing a 12.29% improvement over traditional methods without SMOTE (average ACC of 87.12%). Compared to state-of-the-art methods, such as CPC2 and PLEKv2, LMFE consistently outperformed them across multiple metrics on independent datasets (with an accuracy ranging from 97.33% to 99.21%), with redundant features having minimal impact on performance. Conclusions: LMFE provides a highly accurate and generalizable solution for plant lncRNA prediction, outperforming existing methods through multi-feature fusion and ensemble learning while demonstrating robustness to redundant features. Despite its effectiveness, variations in performance across species highlight the necessity for future improvements in managing diverse plant genomes. This method represents a valuable tool for advancing plant lncRNA research and guiding biological experiments.

MicroRNA gatekeepers: Orchestrating rhizospheric dynamics

The rhizosphere plays a crucial role in plant growth and resilience to biotic and abiotic stresses, highlighting the complex communication between plants and their dynamic rhizosphere environment. Plants produce a wide range of signaling molecules that facilitate communication with various rhizosphere factors, yet our understanding of these mechanisms remains elusive. In addition to protein-coding genes, increasing evidence underscores the critical role of microRNAs (miRNAs), a class of non-coding single-stranded RNA molecules, in regulating plant growth, development, and responses to rhizosphere stresses under diverse biotic and abiotic factors. In this review, we explore the crosstalk between miRNAs and their target mRNAs, which influence the development of key plant structures shaped by the belowground environment. Moving forward, more focused studies are needed to clarify the functions and expression patterns of miRNAs, to uncover the common regulatory mechanisms that mediate plant tolerance to rhizosphere dynamics. Beyond that, we propose that using artificial miRNAs and manipulating the expression of miRNAs and their targets through overexpression or knockout/knockdown approaches could effectively investigate their roles in plant responses to rhizosphere stresses, offering significant potential for advancing crop engineering.

Whole-transcriptome profiling and identification of cold tolerance-related ceRNA networks in japonica rice varieties

Introduction

Low-temperature stress negatively impacts rice yield, posing a significant risk to food security. While previous studies have explored the physiological and linear gene expression alterations in rice under low-temperature conditions, the changes in competing endogenous RNA (ceRNA) networks remain largely unexamined.

Methods

We conducted RNA sequencing on two japonica rice varieties with differing cold-tolerance capabilities to establish ceRNA networks. This enabled us to investigate the transcriptional regulatory network and molecular mechanisms that rice employs in response to low-temperature stress.

Results

We identified 364 differentially expressed circular RNAs (circRNAs), 224 differentially expressed microRNAs (miRNAs), and 12,183 differentially expressed messenger RNAs (mRNAs). WRKY family was the most prominent transcription factor family involved in cold tolerance. Based on the expression patterns and targeted relationships of these differentially expressed RNAs, we discerned five potential ceRNA networks related to low-temperature stress in rice: osa-miR166j-5p from the miR166 family was associated with cold tolerance; osa-miR528-3p and osa-miR156j-3p were linked to stress response; and osa-miR156j-3p was involved in the antioxidant system. In addition, Os03g0152000 in the antioxidant system, as well as Os12g0491800 and Os05g0381400, correlated with the corresponding stress response and circRNAs in the network. A gene sequence difference analysis and phenotypic validation of Os11g0685700 (OsWRKY61) within the WRKY family suggested its potential role in regulating cold tolerance in rice.

Discussion and conclusion

We identified Os11g0685700 (OsWRKY61) as a promising candidate gene for enhancing cold tolerance in japonica rice. The candidate miRNAs, mRNAs, and circRNAs uncovered in this study are valuable targets for researchers and breeders. Our findings will facilitate the development of cold-tolerant rice varieties from multiple angles and provide critical directions for future research into the functions of cold-tolerance-related miRNAs, mRNAs, and circRNAs in rice.

BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

Identification of Specific Pathogen-Infected sRNA-Mediated Interactions between Turnip Yellows Virus and Arabidopsis thaliana

Virus infestation can seriously harm the host plant's growth and development. Turnip yellows virus (TuYV) infestation of host plants can cause symptoms, such as yellowing and curling of leaves and root chlorosis. However, the regulatory mechanisms by which TuYV affects host growth and development are unclear. Hence, it is essential to mine small RNA (sRNA) and explore the regulation of sRNAs on plant hosts for disease control. In this study, we analyzed high-throughput data before and after TuYV infestation in Arabidopsis using combined genetics, statistics, and machine learning to identify 108 specifically expressed and critical functional sRNAs after TuYV infection. First, comparing the expression levels of sRNAs before and after infestation, 508 specific sRNAs were significantly up-regulated in Arabidopsis after infestation. In addition, the results show that AI models, including SVM, RF, XGBoost, and CNN using two-dimensional convolution, have robust classification features at the sequence level, with a prediction accuracy of about 96.8%. A comparison of specific sRNAs with genome sequences revealed that 247 matched precisely with the TuYV genome sequence but not with the Arabidopsis genome, suggesting that TuYV viruses may be their source. The 247 sRNAs predicted target genes and enrichment analysis, which identified 206 Arabidopsis genes involved in nine biological processes and three KEGG pathways associated with plant growth and viral stress tolerance, corresponding to 108 sRNAs. These findings provide a reference for studying sRNA-mediated interactions in pathogen infection and are essential for establishing a vital resource of regulation network for the virus infecting plants and deepening the understanding of TuYV virus infection patterns. However, further validation of these sRNAs is needed to gain a new understanding.

High-Throughput Sequencing Reveals Novel microRNAs Involved in the Continuous Flowering Trait of Longan (Dimocarpus longan Lour.)

A major determinant of fruit production in longan (Dimocarpus longan Lour.) is the difficulty of blossoming. In this study, high-throughput microRNA sequencing (miRNA-Seq) was carried out to compare differentially expressed miRNAs (DEmiRNAs) and their target genes between a continuous flowering cultivar 'Sijimi' (SJ), and a unique cultivar 'Lidongben' (LD), which blossoms only once in the season. Over the course of our study, 1662 known miRNAs and 235 novel miRNAs were identified and 13,334 genes were predicted to be the target of 1868 miRNAs. One conserved miRNA and 29 new novel miRNAs were identified as differently expressed; among them, 16 were upregulated and 14 were downregulated. Through the KEGG pathway and cluster analysis of DEmiRNA target genes, three critical regulatory pathways, plant-pathogen interaction, plant hormone signal transduction, and photosynthesis-antenna protein, were discovered to be strongly associated with the continuous flowering trait of the SJ. The integrated correlation analysis of DEmiRNAs and their target mRNAs revealed fourteen important flowering-related genes, including COP1-like, Casein kinase II, and TCP20. These fourteen flowering-related genes were targeted by five miRNAs, which were novel-miR137, novel-miR76, novel-miR101, novel-miR37, and csi-miR3954, suggesting these miRNAs might play vital regulatory roles in flower regulation in longan. Furthermore, novel-miR137 was cloned based on small RNA sequencing data analysis. The pSAK277-miR137 transgenic Arabidopsis plants showed delayed flowering phenotypes. This study provides new insight into molecular regulation mechanisms of longan flowering.

Epigenetic stress memory: A new approach to study cold and heat stress responses in plants

Understanding plant stress memory under extreme temperatures such as cold and heat could contribute to plant development. Plants employ different types of stress memories, such as somatic, intergenerational and transgenerational, regulated by epigenetic changes such as DNA and histone modifications and microRNAs (miRNA), playing a key role in gene regulation from early development to maturity. In most cases, cold and heat stresses result in short-term epigenetic modifications that can return to baseline modification levels after stress cessation. Nevertheless, some of the modifications may be stable and passed on as stress memory, potentially allowing them to be inherited across generations, whereas some of the modifications are reactivated during sexual reproduction or embryogenesis. Several stress-related genes are involved in stress memory inheritance by turning on and off transcription profiles and epigenetic changes. Vernalization is the best example of somatic stress memory. Changes in the chromatin structure of the Flowering Locus C (FLC) gene, a MADS-box transcription factor (TF), maintain cold stress memory during mitosis. FLC expression suppresses flowering at high levels during winter; and during vernalization, B3 TFs, cold memory cis-acting element and polycomb repressive complex 1 and 2 (PRC1 and 2) silence FLC activation. In contrast, the repression of SQUAMOSA promoter-binding protein-like (SPL) TF and the activation of Heat Shock TF (HSFA2) are required for heat stress memory. However, it is still unclear how stress memory is inherited by offspring, and the integrated view of the regulatory mechanisms of stress memory and mitotic and meiotic heritable changes in plants is still scarce. Thus, in this review, we focus on the epigenetic regulation of stress memory and discuss the application of new technologies in developing epigenetic modifications to improve stress memory.