Employing machine learning for reliable miRNA target identification in plants

<p>Abstract</p> <p>Background</p> <p>miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.</p> <p>Result</p> <p>In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like <it>Arabidopsis</it>, <it>Medicago</it>, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.</p> <p>Conclusion</p> <p>A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.</p

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.</p> <p>Results</p> <p>The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in <it>Arabidopsis</it>, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening.</p> <p>Conclusion</p> <p>Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.</p

A computational-based update on microRNAs and their targets in barley (Hordeum vulgare L.)

<p>Abstract</p> <p>Background</p> <p>Many plant species have been investigated in the last years for the identification and characterization of the corresponding miRNAs, nevertheless extensive studies are not yet available on barley (at the time of this writing). To extend and to update information on miRNAs and their targets in barley and to identify candidate polymorphisms at miRNA target sites, the features of previously known plant miRNAs have been used to systematically search for barley miRNA homologues and targets in the publicly available ESTs database. Matching sequences have then been related to Unigene clusters on which most of this study was based.</p> <p>Results</p> <p>One hundred-fifty-six microRNA mature sequences belonging to 50 miRNA families have been found to significantly match at least one EST sequence in barley. As expected on the basis of phylogenetic relations, miRNAs putatively orthologous to those of <it>Triticum </it>are significantly over-represented inside the set of identified barley microRNA mature sequences. Many previously known and several putatively new miRNA/target pairs have been identified. When the predicted microRNA targets were grouped into functional categories, biological processes previously known to be regulated by miRNAs, such as development and response to biotic and abiotic stress, have been highlighted and most of the target molecular functions were related to transcription regulation. Candidate microRNA coding genes have been reported and genetic variation (SNPs/indels) both in functional regions of putative miRNAs (mature sequence) and at miRNA target sites has been found.</p> <p>Conclusions</p> <p>This study has provided an update of the information on barley miRNAs and their targets representing a foundation for future studies. Many of previously known plant microRNAs have homologues in barley with expected important roles during development, nutrient deprivation, biotic and abiotic stress response and other important physiological processes. Putative polymorphisms at miRNA target sites have been identified and they can represent an interesting source for the identification of functional genetic variability.</p

Identification of miRNA from Porphyra yezoensis by High-Throughput Sequencing and Bioinformatics Analysis

BACKGROUND: miRNAs are a class of non-coding, small RNAs that are approximately 22 nucleotides long and play important roles in the translational level regulation of gene expression by either directly binding or cleaving target mRNAs. The red alga, Porphyra yezoensis is one of the most important marine economic crops worldwide. To date, only a few miRNAs have been identified in green unicellar alga and there is no report about Porphyra miRNAs. METHODOLOGY/PRINCIPAL FINDINGS: To identify miRNAs in Porphyra yezoensis, a small RNA library was constructed. Solexa technology was used to perform high throughput sequencing of the library and subsequent bioinformatics analysis to identify novel miRNAs. Specifically, 180,557,942 reads produced 13,324 unique miRNAs representing 224 conserved miRNA families that have been identified in other plants species. In addition, seven novel putative miRNAs were predicted from a limited number of ESTs. The potential targets of these putative miRNAs were also predicted based on sequence homology search. CONCLUSIONS/SIGNIFICANCE: This study provides a first large scale cloning and characterization of Porphyra miRNAs and their potential targets. These miRNAs belong to 224 conserved miRNA families and 7 miRNAs are novel in Porphyra. These miRNAs add to the growing database of new miRNA and lay the foundation for further understanding of miRNA function in the regulation of Porphyra yezoensis development

Identification and characterization of maize microRNAs involved in the very early stage of seed germination

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a new class of endogenous small RNAs that play essential regulatory roles in plant growth, development and stress response. Extensive studies of miRNAs have been performed in model plants such as rice, <it>Arabidopsis thaliana </it>and other plants. However, the number of miRNAs discovered in maize is relatively low and little is known about miRNAs involved in the very early stage during seed germination.</p> <p>Results</p> <p>In this study, a small RNA library from maize seed 24 hours after imbibition was sequenced by the Solexa technology. A total of 11,338,273 reads were obtained. 1,047,447 total reads representing 431 unique sRNAs matched to known maize miRNAs. Further analysis confirmed the authenticity of 115 known miRNAs belonging to 24 miRNA families and the discovery of 167 novel miRNAs in maize. Both the known and the novel miRNAs were confirmed by sequencing of a second small RNA library constructed the same way as the one used in the first sequencing. We also found 10 miRNAs that had not been reported in maize, but had been reported in other plant species. All novel sequences had not been earlier described in other plant species. In addition, seven miRNA* sequences were also obtained. Putative targets for 106 novel miRNAs were successfully predicted. Our results indicated that miRNA-mediated gene expression regulation is present in maize imbibed seed.</p> <p>Conclusions</p> <p>This study led to the confirmation of the authenticity of 115 known miRNAs and the discovery of 167 novel miRNAs in maize. Identification of novel miRNAs resulted in significant enrichment of the repertoire of maize miRNAs and provided insights into miRNA regulation of genes expressed in imbibed seed.</p

Unique and conserved MicroRNAs in wheat chromosome 5D revealed by next-generation sequencing

MicroRNAs are a class of short, non-coding, single-stranded RNAs that act as post-transcriptional regulators in gene expression. miRNA analysis of Triticum aestivum chromosome 5D was performed on 454 GS FLX Titanium sequences of flow sorted chromosome 5D with a total of 3,208,630 good quality reads representing 1.34x and 1.61x coverage of the short (5DS) and long (5DL) arms of the chromosome respectively. In silico and structural analyses revealed a total of 55 miRNAs; 48 and 42 miRNAs were found to be present on 5DL and 5DS respectively, of which 35 were common to both chromosome arms, while 13 miRNAs were specific to 5DL and 7 miRNAs were specific to 5DS. In total, 14 of the predicted miRNAs were identified in wheat for the first time. Representation (the copy number of each miRNA) was also found to be higher in 5DL (1,949) compared to 5DS (1,191). Targets were predicted for each miRNA, while expression analysis gave evidence of expression for 6 out of 55 miRNAs. Occurrences of the same miRNAs were also found in Brachypodium distachyon and Oryza sativa genome sequences to identify syntenic miRNA coding sequences. Based on this analysis, two other miRNAs: miR1133 and miR167 were detected in B. distachyon syntenic region of wheat 5DS. Five of the predicted miRNA coding regions (miR6220, miR5070, miR169, miR5085, miR2118) were experimentally verified to be located to the 5D chromosome and three of them : miR2118, miR169 and miR5085, were shown to be 5D specific. Furthermore miR2118 was shown to be expressed in Chinese Spring adult leaves. miRNA genes identified in this study will expand our understanding of gene regulation in bread wheat

Regulation of Gene Expression in Plants through miRNA Inactivation

Eukaryotic organisms possess a complex RNA-directed gene expression regulatory network allowing the production of unique gene expression patterns. A recent addition to the repertoire of RNA-based gene regulation is miRNA target decoys, endogenous RNA that can negatively regulate miRNA activity. miRNA decoys have been shown to be a valuable tool for understanding the function of several miRNA families in plants and invertebrates. Engineering and precise manipulation of an endogenous RNA regulatory network through modification of miRNA activity also affords a significant opportunity to achieve a desired outcome of enhanced plant development or response to environmental stresses. Here we report that expression of miRNA decoys as single or heteromeric non-cleavable microRNA (miRNA) sites embedded in either non-protein-coding or within the 3′ untranslated region of protein-coding transcripts can regulate the expression of one or more miRNA targets. By altering the sequence of the miRNA decoy sites, we were able to attenuate miRNA inactivation, which allowed for fine regulation of native miRNA targets and the production of a desirable range of plant phenotypes. Thus, our results demonstrate miRNA decoys are a flexible and robust tool, not only for studying miRNA function, but also for targeted engineering of gene expression in plants. Computational analysis of the Arabidopsis transcriptome revealed a number of potential miRNA decoys, suggesting that endogenous decoys may have an important role in natural modulation of expression in plants

A Combined Approach of High-Throughput Sequencing and Degradome Analysis Reveals Tissue Specific Expression of MicroRNAs and Their Targets in Cucumber

MicroRNAs (miRNAs) are endogenous small RNAs playing an important regulatory function in plant development and stress responses. Among them, some are evolutionally conserved in plant and others are only expressed in certain species, tissue or developmental stages. Cucumber is among the most important greenhouse species in the world, but only a limited number of miRNAs from cucumber have been identified and the experimental validation of the related miRNA targets is still lacking. In this study, two independent small RNA libraries from cucumber leaves and roots were constructed, respectively, and sequenced with the high-throughput Illumina Solexa system. Based on sequence similarity and hairpin structure prediction, a total of 29 known miRNA families and 2 novel miRNA families containing a total of 64 miRNA were identified. QRT-PCR analysis revealed that some of the cucumber miRNAs were preferentially expressed in certain tissues. With the recently developed ‘high throughput degradome sequencing’ approach, 21 target mRNAs of known miRNAs were identified for the first time in cucumber. These targets were associated with development, reactive oxygen species scavenging, signaling transduction and transcriptional regulation. Our study provides an overview of miRNA expression profile and interaction between miRNA and target, which will help further understanding of the important roles of miRNAs in cucumber plants

Functional Specialization of the Plant miR396 Regulatory Network through Distinct MicroRNA–Target Interactions

MicroRNAs (miRNAs) are ∼21 nt small RNAs that regulate gene expression in animals and plants. They can be grouped into families comprising different genes encoding similar or identical mature miRNAs. Several miRNA families are deeply conserved in plant lineages and regulate key aspects of plant development, hormone signaling, and stress response. The ancient miRNA miR396 regulates conserved targets belonging to the GROWTH-REGULATING FACTOR (GRF) family of transcription factors, which are known to control cell proliferation in Arabidopsis leaves. In this work, we characterized the regulation of an additional target for miR396, the transcription factor bHLH74, that is necessary for Arabidopsis normal development. bHLH74 homologs with a miR396 target site could only be detected in the sister families Brassicaceae and Cleomaceae. Still, bHLH74 repression by miR396 is required for margin and vein pattern formation of Arabidopsis leaves. MiR396 contributes to the spatio-temporal regulation of GRF and bHLH74 expression during leaf development. Furthermore, a survey of miR396 sequences in different species showed variations in the 5′ portion of the miRNA, a region known to be important for miRNA activity. Analysis of different miR396 variants in Arabidopsis thaliana revealed that they have an enhanced activity toward GRF transcription factors. The interaction between the GRF target site and miR396 has a bulge between positions 7 and 8 of the miRNA. Our data indicate that such bulge modulates the strength of the miR396-mediated repression and that this modulation is essential to shape the precise spatio-temporal pattern of GRF2 expression. The results show that ancient miRNAs can regulate conserved targets with varied efficiency in different species, and we further propose that they could acquire new targets whose control might also be biologically relevant

Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments