miRkwood: a tool for the reliable identification of microRNAs in plant genomes

Potential role of the regulatory miR1119-MYC2 module in wheat (Triticum aestivum L.) drought tolerance

MicroRNA (miRNA)-target gene modules are essential components of plants' abiotic stress signalling pathways Little is known about the drought-responsive miRNA-target modules in wheat, but systems biology approaches have enabled the prediction of these regulatory modules and systematic study of their roles in responses to abiotic stresses. Using such an approach, we sought miRNA-target module(s) that may be differentially expressed under drought and non-stressed conditions by mining Expressed Sequence Tag (EST) libraries of wheat roots and identified a strong candidate (miR1119-MYC2). We then assessed molecular and physiochemical differences between two wheat genotypes with contrasting drought tolerance in a controlled drought experiment and assessed possible relationships between their tolerance and evaluated traits. We found that the miR1119-MYC2 module significantly responds to drought stress in wheat roots. It is differentially expressed between the contrasting wheat genotypes and under drought versus non-stressed conditions. We also found significant associations between the module's expression profiles and ABA hormone content, water relations, photosynthetic activities, H₂O₂ levels, plasma membrane damage, and antioxidant enzyme activities in wheat. Collectively, our results suggest that a regulatory module consisting of miR1119 and MYC2 may play an important role in wheat's drought tolerance.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

MicroRNA miR1118 contributes to wheat (Triticum aestivum L.) salinity tolerance by regulating the Plasma Membrane Intrinsic Proteins1;5 (PIP1;5) gene

microRNAs (miRNAs) are important regulators of various adaptive stress responses in crops; however, many details about associations among miRNAs, their target genes and physiochemical responses of crops under salinity stress remain poorly understood. We designed this study in a systems biology context and used a collection of computational, experimental and statistical procedures to uncover some regulatory functions of miRNAs in the response of the important crop, wheat, to salinity stress. Accordingly, under salinity conditions, wheat roots' Expressed Sequence Tag (EST) libraries were computationally mined to identify the most reliable differentially expressed miRNA and its related target gene(s). Then, molecular and physiochemical evaluations were carried out in a separate salinity experiment using two contrasting wheat genotypes. Finally, the association between changes in measured characteristics and wheat salinity tolerance was determined. From the results, miR1118 was assigned as a reliable salinity-responsive miRNA in wheat roots. The expression profiles of miR1118 and its predicted target gene, Plasma Membrane Intrinsic Proteins1,5 (PIP1;5), significantly differed between wheat genotypes. Moreover, results revealed that expression profiles of miR1118 and PIP1;5 significantly correlate to Relative Water Content (RWC), root hydraulic conductance (Lp), photosynthetic activities, plasma membrane damages, osmolyte accumulation and ion homeostasis of wheat. Our results suggest a plausible regulatory node through miR1118 adjusting the wheat water status, maintaining ion homeostasis and mitigating membrane damages, mainly through the PIP1;5 gene, under salinity conditions. To our knowledge, this is the first report on the role of miR1118 and PIP1;5 in wheat salinity response.

Evidence that miR168a contributes to salinity tolerance of Brassica rapa L. via mediating melatonin biosynthesis

Melatonin is a master regulator of diverse biological processes, including plant's abiotic stress responses and tolerance. Despite the extensive information on the role of melatonin in response to abiotic stress, how plants regulate endogenous melatonin content under stressful conditions remains largely unknown. In this study, we computationally mined Expressed Sequence Tag (EST) libraries of salinity-exposed Chinese cabbage (Brassica rapa) to identify the most reliable differentially expressed miRNA and its target gene(s). In light of these analyses, we found that miR168a potentially targets a key melatonin biosynthesis gene, namely O-METHYLTRANSFERASE 1 (OMT1). Accordingly, molecular and physiochemical evaluations were performed in a separate salinity experiment using contrasting B. rapa genotypes. Then, the association between B. rapa salinity tolerance and changes in measured molecular and physiochemical characteristics was determined. Results indicated that the expression profiles of miR168a and OMT1 significantly differed between B. rapa genotypes. Moreover, the expression profiles of miR168a and OMT1 significantly correlated with more melatonin content, robust antioxidant activities, and better ion homeostasis during salinity stress. Our results suggest that miR168a plausibly mediates melatonin biosynthesis, mainly through the OMT1 gene, under salinity conditions and thereby contributes to the salinity tolerance of B. rapa. To our knowledge, this is the first report on the role of miR168a and OMT1 in B. rapa salinity response.

Integrated sRNA-seq and RNA-seq Analyses Reveal a microRNA Regulation Network Involved in Cold Response in Pisum sativum L

(1) Background: Cold stress affects growth and development in plants and is a major environmental factor that decreases productivity. Over the past two decades, the advent of next generation sequencing (NGS) technologies has opened new opportunities to understand the molecular bases of stress resistance by enabling the detection of weakly expressed transcripts and the identification of regulatory RNAs of gene expression, including microRNAs (miRNAs). (2) Methods: In this study, we performed time series sRNA and mRNA sequencing experiments on two pea (Pisum sativum L., Ps) lines, Champagne frost-tolerant and Térèse frost-sensitive, during a low temperature treatment versus a control condition. (3) Results: An integrative analysis led to the identification of 136 miRNAs and a regulation network composed of 39 miRNA/mRNA target pairs with discordant expression patterns. (4) Conclusions: Our findings indicate that the cold response in pea involves 11 miRNA families as well as their target genes related to antioxidative and multi-stress defense mechanisms and cell wall biosynthesis.

Regulatory role of microRNAs (miRNAs) in the recent development of abiotic stress tolerance of plants

MicroRNAs (miRNAs) are a distinct groups of single-stranded non-coding, tiny regulatory RNAs approximately 20-24 nucleotides in length. miRNAs negatively influence gene expression at the post-transcriptional level and have evolved considerably in the development of abiotic stress tolerance in a number of model plants and economically important crop species. The present review aims to deliver the information on miRNA-mediated regulation of the expression of major genes or Transcription Factors (TFs), as well as genetic and regulatory pathways. Also, the information on adaptive mechanisms involved in plant abiotic stress responses, prediction, and validation of targets, computational tools, and databases available for plant miRNAs, specifically focus on their exploration for engineering abiotic stress tolerance in plants. The regulatory function of miRNAs in plant growth, development, and abiotic stresses consider in this review, which uses high-throughput sequencing (HTS) technologies to generate large-scale libraries of small RNAs (sRNAs) for conventional screening of known and novel abiotic stress-responsive miRNAs adds complexity to regulatory networks in plants. The discoveries of miRNA-mediated tolerance to multiple abiotic stresses, including salinity, drought, cold, heat stress, nutritional deficiency, UV-radiation, oxidative stress, hypoxia, and heavy metal toxicity, are highlighted and discussed in this review.

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.

Detecting MicroRNAs in Plant Genomes with miRkwood

We present miRkwood, a comprehensive software tool developed to identify microRNAs and their precursor in plant genomes, with or without small-RNA-seq sequencing data. We describe how to install the software, how to set up and run it, and how to explore and analyse the results: genomic annotations, secondary structure of the precursor, alignments, reads distribution.

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.

Biotic stress-associated microRNA families in plants

Plants and animals utilize various regulatory mechanisms for control of gene expression during development in different tissues and cell types. About 30 years ago, a new mechanism of gene regulation, termed RNA interference (RNAi), was discovered and proved revolutionary for the mechanistic understanding of gene regulation. Noncoding RNAs, including short, 21-24 nucleotide (nt) long microRNAs (miRNAs), endogenously-generated from MIR genes, are key components of RNAi processes, by post-transcriptionally controlling transcripts with antisense complementarity through either translational repression or mRNA degradation. Since their discovery, important roles in regulation of ontogenetic development, cell differentiation, proliferation, and apoptosis in eukaryotes have been elucidated. In plants, miRNAs are known regulatory elements of basic endogenous functions and responses to the environmental stimuli. While the role of miRNAs in regulation of nutrient uptake, circadian clock and general response to abiotic stress is already well understood, a comprehensive understanding of their immune-regulatory roles in response to various biotic stress factors has not yet been achieved. This review summarizes the current understanding of the function of miRNAs and their targets in plants during interaction with microbial pathogens and symbionts. Additionally, we provide a consensus conclusion regarding the typical induction or repression response of conserved miRNA families to pathogenic and beneficial fungi, bacteria, and oomycetes, as well as an outlook of agronomic application of miRNAs in plants. Further investigation of plant miRNAs responsive to microbes, aided with novel sequencing and bioinformatics approaches for discovery and prediction in non-model organisms holds great potential for development of new forms of plant protection.

Evaluation and application of tools for the identification of known microRNAs in plants

MicroRNAs (miRNAs), endogenous non-coding RNA regulators, post-transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep-P2, miRExpress, miRkwood, miRDeep2, miR-PREFeR, and sRNAbench) were selected for evaluation. High-throughput small RNA sequencing data from four plant species (including C3 and C4 species, and both monocots and dicots, i.e., Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep-P2 was the most sensitive, miRDeep-P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (Triticum aestivum). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work.