Intragenomic Matching Reveals a Huge Potential for miRNA-Mediated Regulation in Plants

microRNAs (miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm based on intragenomic matching of potential miRNAs and their targets coupled with support vector machine classification of miRNA precursors, we explore the potential for regulation by miRNAs in three plant genomes: Arabidopsis thaliana, Populus trichocarpa, and Oryza sativa. We find that the intragenomic matching in conjunction with a supervised learning approach contains enough information to allow reliable computational prediction of miRNA candidates without requiring conservation across species. Using this method, we identify ∼1,200, ∼2,500, and ∼2,100 miRNA candidate genes capable of extensive base-pairing to potential target mRNAs in A. thaliana, P. trichocarpa, and O. sativa, respectively. This is more than five times the number of currently annotated miRNAs in the plants. Many of these candidates are derived from repeat regions, yet they seem to contain the features necessary for correct processing by the miRNA machinery. Conservation analysis indicates that only a few of the candidates are conserved between the species. We conclude that there is a large potential for miRNA-mediated regulatory interactions encoded in the genomes of the investigated plants. We hypothesize that some of these interactions may be realized under special environmental conditions, while others can readily be recruited when organisms diverge and adapt to new niches

Computational identification and characterization of putative miRNAs in Nasonia species

MicroRNAs are important at post transcriptional regulation in eukaryotes. Nasonia genus is becoming increasingly popular model in present days due to genetic advantages it possesses over Drosophila. Nasonia species are found distributed throughout the world, expect for N. longicornis, and N. giraulti. In this study, we use the sequential method of blasting all known invertebrate miRNA genes against the Nasonia vitripennis, Nasonia longicornis, and Nasonia giraulti genomes. We identify 40, 31 and 29 putative pre-​miRNAs and mature sequences in N. vitripennis, N. giraulti and N. longicornis resp. A cross species comparison of putative miRNA sequences and their statistical characteristics reveals that there are no huge differences between the species, except for few miRNAs which are reported. We also find that the minimal folding energy index for three Nasonia species pre-​miRNA's av. is around -​0.85 ± 0.11. Further, we report that U is predominant at the 5' end of mature sequence, which being a typical characteristic of plant miRNAs. Using MiRanda, we predict nearly 471 potential sites in the N. vitripennis genome. Thus concluding our study to be the beginning of understanding the Nasonia's non coding RNAs and may play an important role in effective pest management in near future

PMRD: plant microRNA database

MicroRNAs (miRNA) are ∼21 nucleotide-long non-coding small RNAs, which function as post-transcriptional regulators in eukaryotes. miRNAs play essential roles in regulating plant growth and development. In recent years, research into the mechanism and consequences of miRNA action has made great progress. With whole genome sequence available in such plants as Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Glycine max, etc., it is desirable to develop a plant miRNA database through the integration of large amounts of information about publicly deposited miRNA data. The plant miRNA database (PMRD) integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house. This database contains sequence information, secondary structure, target genes, expression profiles and a genome browser. In total, there are 8433 miRNAs collected from 121 plant species in PMRD, including model plants and major crops such as Arabidopsis, rice, wheat, soybean, maize, sorghum, barley, etc. For Arabidopsis, rice, poplar, soybean, cotton, medicago and maize, we included the possible target genes for each miRNA with a predicted interaction site in the database. Furthermore, we provided miRNA expression profiles in the PMRD, including our local rice oxidative stress related microarray data (LC Sciences miRPlants_10.1) and the recently published microarray data for poplar, Arabidopsis, tomato, maize and rice. The PMRD database was constructed by open source technology utilizing a user-friendly web interface, and multiple search tools. The PMRD is freely available at http://bioinformatics.cau.edu.cn/PMRD. We expect PMRD to be a useful tool for scientists in the miRNA field in order to study the function of miRNAs and their target genes, especially in model plants and major crops

Identification of novel maize miRNAs by measuring the precision of precursor processing

<p>Abstract</p> <p>Background</p> <p>miRNAs are known to play important regulatory roles throughout plant development. Until recently, nearly all the miRNAs in maize were identified by comparative analysis to miRNAs sequences of other plant species, such as rice and <it>Arabidopsis</it>.</p> <p>Results</p> <p>To find new miRNA in this important crop, small RNAs from mixed tissues were sequenced, resulting in over 15 million unique sequences. Our sequencing effort validated 23 of the 28 known maize miRNA families, including 49 unique miRNAs. Using a newly established criterion, based on the precision of miRNA processing from precursors, we identified 66 novel miRNAs in maize. These miRNAs can be grouped into 58 families, 54 of which have not been identified in any other species. Five new miRNAs were validated by northern blot. Moreover, we found targets for 23 of the 66 new miRNAs. The targets of two of these newly identified miRNAs were confirmed by 5'RACE.</p> <p>Conclusion</p> <p>We have implemented a novel method of identifying miRNA by measuring the precision of miRNA processing from precursors. Using this method, 66 novel miRNAs and 50 potential miRNAs have been identified in maize.</p

Analysis of Antisense Expression by Whole Genome Tiling Microarrays and siRNAs Suggests Mis-Annotation of Arabidopsis Orphan Protein-Coding Genes

MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation

Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing

<p>Abstract</p> <p>Background</p> <p>MiRNAs play important roles in cellular control and in various disease states such as cancers, where they may serve as markers or possibly even therapeutics. Identifying the whole repertoire of miRNAs and understanding their expression patterns is therefore an important goal.</p> <p>Methods</p> <p>Here we describe the analysis of 454 pyrosequencing of small RNA from four different tissues: Breast cancer, normal adjacent breast, and two teratoma cell lines. We developed a pipeline for identifying new miRNAs, emphasizing extracting and retaining as much data as possible from even noisy sequencing data. We investigated differential expression of miRNAs in the breast cancer and normal adjacent breast samples, and systematically examined the mature sequence end variability of miRNA compared to non-miRNA loci.</p> <p>Results</p> <p>We identified five novel miRNAs, as well as two putative alternative precursors for known miRNAs. Several miRNAs were differentially expressed between the breast cancer and normal breast samples. The end variability was shown to be significantly different between miRNA and non-miRNA loci.</p> <p>Conclusion</p> <p>Pyrosequencing of small RNAs, together with a computational pipeline, can be used to identify miRNAs in tumor and other tissues. Measures of miRNA end variability may in the future be incorporated into the discovery pipeline as a discriminatory feature. Breast cancer samples show a distinct miRNA expression profile compared to normal adjacent breast.</p

Genome-wide profiling of Populus small RNAs

<p>Abstract</p> <p>Background</p> <p>Short RNAs, and in particular microRNAs, are important regulators of gene expression both within defined regulatory pathways and at the epigenetic scale. We investigated the short RNA (sRNA) population (18-24 nt) of the transcriptome of green leaves from the sequenced <it>Populus trichocarpa </it>using a concatenation strategy in combination with 454 sequencing.</p> <p>Results</p> <p>The most abundant size class of sRNAs were 24 nt. Long Terminal Repeats were particularly associated with 24 nt sRNAs. Additionally, some repetitive elements were associated with 22 nt sRNAs. We identified an sRNA hot-spot on chromosome 19, overlapping a region containing both the proposed sex-determining locus and a major cluster of <it>NBS-LRR </it>genes. A number of phased siRNA loci were identified, a subset of which are predicted to target PPR and <it>NBS-LRR </it>disease resistance genes, classes of genes that have been significantly expanded in <it>Populus</it>. Additional loci enriched for sRNA production were identified and characterised. We identified 15 novel predicted microRNAs (miRNAs), including miRNA*sequences, and identified a novel locus that may encode a dual miRNA or a miRNA and short interfering RNAs (siRNAs).</p> <p>Conclusions</p> <p>The short RNA population of <it>P. trichocarpa </it>is at least as complex as that of <it>Arabidopsis thaliana</it>. We provide a first genome-wide view of short RNA production for <it>P. trichocarpa </it>and identify new, non-conserved miRNAs.</p

Discovery of barley miRNAs through deep sequencing of short reads

Background: MicroRNAs are important components of the regulatory network of biological systems and thousands have been discovered in both animals and plants. Systematic investigations performed in species with sequenced genomes such as Arabidopsis, rice, poplar and Brachypodium have provided insights into the evolutionary relationships of this class of small RNAs among plants. However, miRNAs from barley, one of the most important cereal crops, remain unknown. Results: We performed a large scale study of barley miRNAs through deep sequencing of small RNAs extracted from leaves of two barley cultivars. By using the presence of miRNA precursor sequences in related genomes as one of a number of supporting criteria, we identified up to 100 miRNAs in barley. Of these only 56 have orthologs in wheat, rice or Brachypodium that are known to be expressed, while up to 44 appear to be specifically expressed in barley. Conclusions: Our study, the first large scale investigation of small RNAs in barley, has identified up to 100 miRNAs. We demonstrate that reliable identification of miRNAs via deep sequencing in a species whose genome has not been sequenced requires a more careful analysis of sequencing errors than is commonly performed. We devised a read filtering procedure for dealing with errors. In addition, we found that the use of a large dataset of almost 35 million reads permits the use of read abundance distributions along putative precursor sequences as a practical tool for isolating miRNAs in a large background of reads originating from other non-coding and coding RNAs. This study therefore provides a generic approach for discovering novel miRNAs where no genome sequence is available.Andreas W Schreiber, Bu-Jun Shi, Chun-Yuan Huang, Peter Langridge, Ute Bauman

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes\u27 namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response