Non-coding RNAs (ncRNAs) in plant: Master regulators for adapting to extreme temperature conditions

Unusual daily temperature fluctuations caused by climate change and climate variability adversely impact agricultural crop production. Since plants are immobile and constantly receive external environmental signals, such as extreme high (heat) and low (cold) temperatures, they have developed complex molecular regulatory mechanisms to cope with stressful situations to sustain their natural growth and development. Among these mechanisms, non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), small-interfering RNAs (siRNAs), and long-non-coding RNAs (lncRNAs), play a significant role in enhancing heat and cold stress tolerance. This review explores the pivotal findings related to miRNAs, siRNAs, and lncRNAs, elucidating how they functionally regulate plant adaptation to extreme temperatures. In addition, this review addresses the challenges associated with uncovering these non-coding RNAs and understanding their roles in orchestrating heat and cold tolerance in plants.

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Elucidation of transfer RNAs as stress regulating agents and the experimental strategies to conceive the functional role of tRNA-derived fragments in plants

In plants, the transfer RNAs (tRNAs) exhibit their profound influence in orchestrating diverse physiological activities like cell growth, development, and response to several surrounding stimuli. The tRNAs, which were known to restrict their function solely in deciphering the codons, are now emerging as frontline defenders in stress biology. The plants that are constantly confronted with a huge panoply of stresses rely on tRNA-mediated stress regulation by altering the tRNA abundance, curbing the transport of tRNAs, fragmenting the mature tRNAs during stress. Among them, the studies on the generation of transfer RNA-derived fragments (tRFs) and their biological implication in stress response have attained huge interest. In plants, the tRFs hold stable expression patterns and regulate biological functions under diverse environmental conditions. In this review, we discuss the fate of plant tRNAs upon stress and thereafter how the tRFs are metamorphosed into sharp ammunition to wrestle with stress. We also address the various methods developed to date for uncovering the role of tRFs and their function in plants.

Web Services for RNA-RNA Interaction Prediction

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

The Multiverse of Plant Small RNAs: How Can We Explore It?

Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.

Analysis of Small RNA Sequencing Data in Plants

Over the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

MicroRNA Databases and Tools

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

Plant RNA-mediated gene regulatory network

Not all transcribed RNAs are protein-coding RNAs. Many of them are non-protein-coding RNAs in diverse eukaryotes. However, some of them seem to be non-functional and are resulted from spurious transcription. A lot of non-protein-coding transcripts have a significant function in the translation process. Gene expressions depend on complex networks of diverse gene regulatory pathways. Several non-protein-coding RNAs regulate gene expression in a sequence-specific system either at the transcriptional level or post-transcriptional level. They include a significant part of the gene expression regulatory network. RNA-mediated gene regulation machinery is evolutionarily ancient. They well-evolved during the evolutionary time and are becoming much more complex than had been expected. In this review, we are trying to summarizing the current knowledge in the field of RNA-mediated gene silencing.

TarDB: an online database for plant miRNA targets and miRNA-triggered phased siRNAs

Background

In plants, microRNAs (miRNAs) are pivotal regulators of plant development and stress responses. Different computational tools and web servers have been developed for plant miRNA target prediction; however, in silico prediction normally contains false positive results. In addition, many plant miRNA target prediction servers lack information for miRNA-triggered phased small interfering RNAs (phasiRNAs). Creating a comprehensive and relatively high-confidence plant miRNA target database is much needed.

Results

Here, we report TarDB, an online database that collects three categories of relatively high-confidence plant miRNA targets: (i) cross-species conserved miRNA targets; (ii) degradome/PARE (Parallel Analysis of RNA Ends) sequencing supported miRNA targets; (iii) miRNA-triggered phasiRNA loci. TarDB provides a user-friendly interface that enables users to easily search, browse and retrieve miRNA targets and miRNA initiated phasiRNAs in a broad variety of plants. TarDB has a comprehensive collection of reliable plant miRNA targets containing previously unreported miRNA targets and miRNA-triggered phasiRNAs even in the well-studied model species. Most of these novel miRNA targets are relevant to lineage-specific or species-specific miRNAs. TarDB data is freely available at http://www.biosequencing.cn/TarDB.

Conclusions

In summary, TarDB serves as a useful web resource for exploring relatively high-confidence miRNA targets and miRNA-triggered phasiRNAs in plants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07680-5.

Up-regulation of microRNA targets correlates with symptom severity in Citrus sinensis plants infected with two different isolates of citrus psorosis virus

MAIN CONCLUSION: miRNA targets from Citrus sinensis are predicted and validated using degradome data. They show an up-regulation upon infection with CPsV, with a positive correlation between target expression and symptom severity. Sweet orange (Citrus sinensis) may suffer from disease symptoms induced by virus infections, thus resulting in drastic economic losses. Infection of sweet orange plants with two isolates of citrus psorosis virus (CPsV), expressing different symptomatologies, alters the accumulation of a set of endogenous microRNAs (miRNAs). Here, we predicted ten putative targets from four down-regulated miRNAs: three belonging to the CCAAT-binding transcription factor family (CBFAs); an Ethylene-responsive transcription factor (RAP2-7); an Integrase-type DNA-binding superfamily protein (AP2B); Transport inhibitor response 1 (TIR1); GRR1-like protein 1-related (GRR1); Argonaute 2-related (AGO2), Argonaute 7 (AGO7), and a long non-coding RNA (ncRNA). We validated six of them through analysis of leaf degradome data. Expressions of the validated targets increase in infected samples compared to healthy tissue, showing a more striking up-regulation those samples with higher symptom severity. This study contributes to the understanding of the miRNA-mediated regulation of important transcripts in Citrus sinensis through target validation and shed light in the manner a virus can alter host regulatory mechanisms leading to symptom expression.

TarHunter, a tool for predicting conserved microRNA targets and target mimics in plants

Summary

In plants, the targets of deeply conserved microRNAs (miRNAs) were comprehensively studied. Evidence is emerging that targets of less conserved miRNAs, endogenous target mimics (eTM) and non-canonical targets play functional roles. Existing plant miRNA prediction tools lack a cross-species conservation filter and eTM prediction function. We developed a tool named TarHunter that features a strict cross-species conservation filter and capability of predicting eTMs. TarHunter has higher recall or precision rate as compared with other tools, and the conservation filter effectively increases prediction precision. TarHunter prediction combined with degradome analysis uncovered previously neglected miRNA targets including non-canonical target sites from various plant species, which are available at the TarHunter website (http://tarhunter.genetics.ac.cn/).

Availability and implementation

The code of TarHunter is available on Github (https://github.com/XMaBio).

Contact

xuemei.chen@ucr.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exploring miRNAs for developing climate-resilient crops: A perspective review

Climate changes and environmental stresses have significant implications on global crop production and necessitate developing crops that can withstand an array of climate changes and environmental perturbations such as irregular water-supplies leading to drought or water-logging, hyper soil-salinity, extreme and variable temperatures, ultraviolet radiations and metal stress. Plants have intricate molecular mechanisms to cope with these dynamic environmental changes, one of the most common and effective being the reprogramming of expression of stress-responsive genes. Plant microRNAs (miRNAs) have emerged as key post-transcriptional and translational regulators of gene-expression for modulation of stress implications. Recent reports are establishing their key roles in epigenetic regulations of stress/adaptive responses as well as in providing plants genome-stability. Several stress responsive miRNAs are being identified from different crop plants and miRNA-driven RNA-interference (RNAi) is turning into a technology of choice for improving crop traits and providing phenotypic plasticity in challenging environments. Here we presents a perspective review on exploration of miRNAs as potent targets for engineering crops that can withstand multi-stress environments via loss-/gain-of-function approaches. This review also shed a light on potential roles plant miRNAs play in genome-stability and their emergence as potent target for genome-editing. Current knowledge on plant miRNAs, their biogenesis, function, their targets, and latest developments in bioinformatics approaches for plant miRNAs are discussed. Though there are recent reviews discussing primarily the individual miRNAs responsive to single stress factors, however, considering practical limitation of this approach, special emphasis is given in this review on miRNAs involved in responses and adaptation of plants to multi-stress environments including at epigenetic and/or epigenomic levels.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Prediction of Plant miRNA Targets

microRNAs (miRNAs) are the central component of an important layer of regulation of gene expression at posttranscriptional level. In plants, miRNAs target the transcripts in a highly complementary sequence-dependent manner. Extensive research is being made to study genome-wide miRNA-mediated regulation of gene expression, which has resulted in the development of many tools for in silico prediction of miRNA targets. Although several tools have been developed for predicting miRNA targets in model plants, genome-wide analysis of miRNA targets is still a challenge for non-model species that lack dedicated tools. Here, we describe an in silico procedure for studying miRNA-mediated interactions in plants, which is based on the fact that canonical miRNA-target sites are highly complementary, the miRNAs negatively regulate the expression of their target genes, and miRNAs may form regulatory networks as one miRNA may target more than one transcript and vice versa to modulate and fine-tune expression of the genome.

A compilation of Web-based research tools for miRNA analysis

Since the discovery of microRNAs (miRNAs), a class of noncoding RNAs that regulate the gene expression posttranscriptionally in sequence-specific manner, there has been a release of number of tools useful for both basic and advanced applications. This is because of the significance of miRNAs in many pathophysiological conditions including cancer. Numerous bioinformatics tools that have been developed for miRNA analysis have their utility for detection, expression, function, target prediction and many other related features. This review provides a comprehensive assessment of web-based tools for the miRNA analysis that does not require prior knowledge of any computing languages.

A comprehensive review of web-based resources of non-coding RNAs for plant science research

Non-coding RNAs (ncRNAs) are transcribed from genome but not translated into proteins. Many ncRNAs are key regulators of plants growth and development, metabolism and stress tolerance. In order to make the web-based ncRNA resources for plant science research be more easily accessible and understandable, we made a comprehensive review for 83 web-based resources of three types, including genome databases containing ncRNA data, microRNA (miRNA) databases and long non-coding RNA (lncRNA) databases. To facilitate effective usage of these resources, we also suggested some preferred resources of miRNAs and lncRNAs for performing meaningful analysis.

Computational Methods for MicroRNA Target Prediction

MicroRNAs (miRNAs) have been identified as one of the most important molecules that regulate gene expression in various organisms. miRNAs are short, 21–23 nucleotide-long, single stranded RNA molecules that bind to 3' untranslated regions (3' UTRs) of their target mRNAs. In general, they silence the expression of their target genes via degradation of the mRNA or by translational repression. The expression of miRNAs, on the other hand, also varies in different tissues based on their functions. It is significantly important to predict the targets of miRNAs by computational approaches to understand their effects on the regulation of gene expression. Various computational methods have been generated for miRNA target prediction but the resulting lists of candidate target genes from different algorithms often do not overlap. It is crucial to adjust the bioinformatics tools for more accurate predictions as it is equally important to validate the predicted target genes experimentally.