The stem-loop RT-PCR

Plant microRNAs (miRNAs) are a class of endogenous small RNAs that are essential for plant development and survival. They arise from larger precursor RNAs with a characteristic hairpin structure and regulate gene activity by targeting mRNA transcripts for cleavage or translational repression. Efficient and reliable detection and quantification of miRNA expression has become an essential step in understanding their specific roles. The expression levels of miRNAs can vary dramatically between samples and they often escape detection by conventional technologies such as cloning, northern hybridization and microarray analysis. The stem-loop RT-PCR method described here is designed to detect and quantify mature miRNAs in a fast, specific, accurate and reliable manner. First, a miRNA-specific stem-loop RT primer is hybridized to the miRNA and then reverse transcribed. Next, the RT product is amplified and monitored in real time using a miRNA-specific forward primer and the universal reverse primer. This method enables miRNA expression profiling from as little as 10 pg of total RNA and is suitable for high-throughput miRNA expression analysis.

Varkonyi-Gasic E, Hellens RP.Quantitative Stem-Loop RT-PCR for Detection of MicroRNAs. Methods Mol Biol. 2011;744:145-57.

A novel microRNA (miRNA) quantification method has been developed using stem-loop RT followed by TaqMan PCR analysis. Stem-loop RT primers are better than conventional ones in terms of RT efficiency and specificity. TaqMan miRNA assays are specific for mature miRNAs and discriminate among related miRNAs that differ by as little as one nucleotide. Furthermore, they are not affected by genomic DNA contamination. Precise quantification is achieved routinely with as little as 25 pg of total RNA for most miRNAs. In fact, the high sensitivity, specificity and precision of this method allows for direct analysis of a single cell without nucleic acid purification. Like standard TaqMan gene expression assays, TaqMan miRNA assays exhibit a dynamic range of seven orders of magnitude. Quantification of five miRNAs in seven mouse tissues showed variation from less than 10 to more than 30,000 copies per cell. This method enables fast, accurate and sensitive miRNA expression profiling and can identify and monitor potential biomarkers specific to tissues or diseases. Stem-loop RT-PCR can be used for the quantification of other small RNA molecules such as short interfering RNAs (siRNAs). Furthermore, the concept of stem-loop RT primer design could be applied in small RNA cloning and multiplex assays for better specificity and efficiency.

Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, Lao KQ, Livak KJ, Guegler KJ.Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005 Nov 27;33(20):e179

Plant amiRNA vector

Artificial microRNA (amiRNA) technology is a novel tool in reverse genetic research for discovering or validating gene functions in plants. A convenient cloning strategy has been developed to construct plant amiRNA vectors based on lacO reconstruction and mating-assisted, genetically-integrated cloning (MAGIC). The amiRNA precursor fragment was generated by PCR and inserted into a small donor plasmid through reconstruction of integrated lacO sequence. Blue recombinants were selected on plates containing X-gal and the efficiency of successful clones was 100%. The amiRNA expression cassette was transferred from the donor plasmid to the recipient plasmid p1301-gfp through MAGIC and an amiRNA expression plasmid was created. More than 40 plant amiRNA vectors were generated through this method, one of which was transformed into Arabidopsis thaliana and the target gene was silenced efficiently. The approach will be useful for amiRNA expression vectors construction in plants.

Keywords  amiRNA vectors – Blue–white screening –  lacO reconstruction – MAGIC

Yan H, Zhong X, Jiang S, Zhai C, Ma L. (2010) Improved method for constructing plant amiRNA vectors with blue-white screening and MAGIC. Biotechnol Lett. 2011 Apr 9.http://www.springerlink.com/content/v016845p20453373/fulltext.pdf

comprehensive annotation and discovery of small RNAs from transcriptomic data

Advances in high-throughput next-generation sequencing technology have reshaped the transcriptomic research landscape. However, exploration of these massive data remains a daunting challenge. In this study, we describe a novel database, deepBase, which we have developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data. The current release of deepBase contains deep sequencing data from 185 small RNA libraries from diverse tissues and cell lines of seven organisms: human, mouse, chicken, Ciona intestinalis, Drosophila melanogaster, Caenhorhabditis elegans and Arabidopsis thaliana. By analyzing ∼14.6 million unique reads that perfectly mapped to more than 284 million genomic loci, we annotated and identified ∼380 000 unique ncRNA-associated small RNAs (nasRNAs), ∼1.5 million unique promoter-associated small RNAs (pasRNAs), ∼4.0 million unique exon-associated small RNAs (easRNAs) and ∼6 million unique repeat-associated small RNAs (rasRNAs). Furthermore, 2038 miRNA and 1889 snoRNA candidates were predicted by miRDeep and snoSeeker. All of the mapped reads can be grouped into about 1.2 million RNA clusters. For the purpose of comparative analysis, deepBase provides an integrative, interactive and versatile display. A convenient search option, related publications and other useful information are also provided for further investigation. deepBase is available at: http://deepbase.sysu.edu.cn/.

Jian-Hua Yang, Peng Shao, Hui Zhou, Yue-Qin Chen, and Liang-Hu Qu. deepBase: a database for deeply annotating and mining deep sequencing data Nucl. Acids Res. (2010) 38(suppl 1): D123-D130 first published online December 4, 2009 oi:10.1093/nar/gkp943

microRNA-mRNA interaction

MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively. In this study, we introduce a novel database, starBase (sRNA target Base), which we have developed to facilitate the comprehensive exploration of miRNA–target interaction maps from CLIP-Seq and Degradome-Seq data. The current version includes high-throughput sequencing data generated from 21 CLIP-Seq and 10 Degradome-Seq experiments from six organisms. By analyzing millions of mapped CLIP-Seq and Degradome-Seq reads, we identified ∼1 million Ago-binding clusters and ∼2 million cleaved target clusters in animals and plants, respectively. Analyses of these clusters, and of target sites predicted by 6 miRNA target prediction programs, resulted in our identification of approximately 400 000 and approximately 66 000 miRNA-target regulatory relationships from CLIP-Seq and Degradome-Seq data, respectively. Furthermore, two web servers were provided to discover novel miRNA target sites from CLIP-Seq and Degradome-Seq data. Our web implementation supports diverse query types and exploration of common targets, gene ontologies and pathways. The starBase is available at http://starbase.sysu.edu.cn/.

Jian-Hua Yang, Jun-Hao Li, Peng Shao, Hui Zhou, Yue-Qin Chen, and Liang-Hu Qu. starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data Nucl. Acids Res. (2011) 39(suppl 1): D202-D209 first published online October 30, 2010 doi:10.1093/nar/gkq1056

Artificial microRNA

WMD3 - Web app for the automated design of artificial microRNAs.

Artificial microRNAs (amiRNAs) are 21mer small RNAs, which can be genetically engineered and function to specifically silence single or multiple genes of interest in more than 90 plants, according to the previously determined parameters of target gene selection. It uses your favorite gene(s), which you want to silence, and designs 21mer amiRNA sequences. You will retrieve oligo sequences to express the small RNA from endogenous miRNA precursors. Please read the Procedure before you use WMD3 for the first time! More information about miRNAs, design and application of amiRNAs and several guides on all the tools on this page can be found in the Help section. Cloning protocols are available in the Download section.
The artificial miRNA vectors pRS300 and pNW55 are available from Addgene. On their website, search for plasmids associated with the keyword "weigel".

Proof of concept studies and other related publications:

The systematic design of amiRNAs has first been described in:
Rebecca Schwab, Stephan Ossowski, Markus Riester, Norman Warthmann, and Detlef Weigel. (2006) Highly Specific Gene Silencing by Artificial MicroRNAs in Arabidopsis Plant Cell 18: 1121-1133. Link to PubMed

Detailed overview of WMD:
Stephan Ossowski, Rebecca Schwab, Detlef Weigel (2008) Gene silencing in plants using artificial microRNAs and other small RNAs The Plant Journal 53 (4) , 674-690 Link to PubMed

Oryza sativa:
Warthmann N, Chen H, Ossowski S, Weigel D, Hervé P. (2008) Highly Specific Gene Silencing by Artificial miRNAs in Rice PLoS ONE 3(3): e1829. Link to PLoS ONE

Physcomitrella patens:
Khraiwesh B, Ossowski S, Weigel D, Reski R, Frank W. (2008) Specific gene silencing by artificial MicroRNAs in Physcomitrella patens: an alternative to targeted gene knockouts. Plant Physiol. Link to Plant Physiol.

Chlamydomonas reinhardtii:
Molnar A, Bassett A, Thuenemann E, Schwach F, Karkare S, Ossowski S, Weigel D, Baulcombe D. (2008) Highly specific gene silencing by artificial microRNAs in the unicellular alga Chlamydomonas reinhardtii. Plant J. Link to Pubmed

© Copyright 2005-2009 Max Planck Institute for Developmental Biology, Tübingen. http://www.weigelworld.org/
Our work on artificial miRNAs is supported by the SIROCCO EU Integrated Project. http://www.sirocco-project.eu

Role of microRNA in fiber development

Background

Cotton fiber development undergoes rapid and dynamic changes in a single cell type, from fiber initiation, elongation, primary and secondary wall biosynthesis, to fiber maturation. Previous studies showed that cotton genes encoding putative MYB transcription factors and phytohormone responsive factors were induced during early stages of ovule and fiber development. Many of these factors are targets of microRNAs (miRNAs) that mediate target gene regulation by mRNA degradation or translational repression.

Results

Here we sequenced and analyzed over 4 million small RNAs derived from fiber and non-fiber tissues in cotton. The 24-nucleotide small interfering RNAs (siRNAs) were more abundant and highly enriched in ovules and fiber-bearing ovules relative to leaves. A total of 31 miRNA families, including 27 conserved, 4 novel miRNA families and a candidate-novel miRNA, were identified in at least one of the cotton tissues examined. Among 32 miRNA precursors representing 19 unique miRNA families identified, 7 were previously reported, and 25 new miRNA precursors were found in this study. Sequencing, miRNA microarray, and small RNA blot analyses showed a trend of repression of miRNAs, including novel miRNAs, during ovule and fiber development, which correlated with upregulation of several target genes tested. Moreover, 223 targets of cotton miRNAs were predicted from the expressed sequence tags derived from cotton tissues, including ovules and fibers. The cotton miRNAs examined triggered cleavage in the predicted sites of the putative cotton targets in ovules and fibers.

Conclusions

Enrichment of siRNAs in ovules and fibers suggests active small RNA metabolism and chromatin modifications during fiber development, whereas general repression of miRNAs in fibers correlates with upregulation of a dozen validated miRNA targets encoding transcription and phytohormone response factors, including the genes found to be highly expressed in cotton fibers. Rapid and dynamic changes in siRNAs and miRNAs may contribute to ovule and fiber development in allotetraploid cotton.

Pang M, Woodward AW, Agarwal V, Guan X, Ha M, Ramachandran V, Chen X, Triplett BA, Stelly DM and Chen Z J (2009) Genome- wide analysis reveals rapid and dynamic change in miRNA and siRNA sequence and expression during ovule and fiber development I allotetraploid cotton (Gossypium hirsutum L.) Genome Biology 10;R122http://genomebiology.com/2009/10/11/R122

Roles of miRNAs in Tomato leaf curl New Delhi virus (ToLCNDV) induced leaf curling.

 

Background

Tomato leaf curl virus (ToLCV), a constituent of the genus Begomovirus, infects tomato and other plants with a hallmark disease symptom of upward leaf curling. Since microRNAs (miRs) are known to control plants developmental processes, we evaluated the roles of miRNAs in Tomato leaf curl New Delhi virus (ToLCNDV) induced leaf curling.

Results

Microarray analyses of miRNAs, isolated from the leaves of both healthy and ToLCNDV agroinfected tomato cv Pusa Ruby, revealed that ToLCNDV infection significantly deregulated various miRNAs representing ~13 different conserved families (e.g., miR319, miR172, etc.). The precursors of these miRNAs showed similar deregulated patterns, indicating that the transcription regulation of respective miRNA genes was perhaps the cause of deregulation. The expression levels of the miRNA-targeted genes were antagonistic with respect to the amount of corresponding miRNA. Such deregulation was tissue-specific in nature as no analogous misexpression was found in flowers. The accumulation of miR159/319 and miR172 was observed to increase with the days post inoculation (dpi) of ToLCNDV agroinfection in tomato cv Pusa Ruby. Similarly, these miRs were also induced in ToLCNDV agroinfected tomato cv JK Asha and chilli plants, both exhibiting leaf curl symptoms. Our results indicate that miR159/319 and miR172 might be associated with leaf curl symptoms. This report raises the possibility of using miRNA(s) as potential signature molecules for ToLCNDV infection.

Conclusions

The expression of several host miRNAs is affected in response to viral infection. The levels of the corresponding pre-miRs and the predicted targets were also deregulated.

This change in miRNA expression levels was specific to leaf tissues and observed to be associated with disease progression. Thus, certain host miRs are likely indicator of viral infection and could be potentially employed to develop viral resistance strategies.

 http://www.virologyj.com/content/7/1/281/#refs

Afsar R Naqvi, Qazi MR Haq and Sunil K Mukherjee(2010) MicroRNA profiling of tomato leaf curl new delhi virus (tolcndv) infected tomato leaves indicates that deregulation of mir159/319 and mir172 might be linked with leaf curl disease. Virology Journal 2010, 7:281

Modulation of SPL gene expression by miR156/157

In Arabidopsis 11 out of 17 SPL genes have been suggested to be target of the similar microRNA 156/157. Based on degradome analysis, target genes for microRNA 157 include SPL 15, SPL9, SPL2, SPL11 and SPL10. SPL gene were known to express in various tissues, at different developmental stages and regulate multiple important and divergent biological processes including leaf development (Wu and Poethig 2006),  phase transition (Usami et al .,2009), flower and fruit development (Wang et al .,2995 Mannubg et al .,2006), Plant acrhitechture (Becraft et al .,1990, Jiao et al .,2010), GA signalling (Zhang et al .,2007) as well as response to copper and fungal toxin (Eriksson et al .,2004; Stone et al .,2005). Previously, Wang, et al ., 2008 demonstrated that microRNA156/157 modulates expression of SPL in leaf primordia and that SPL activity inhibits initiation of new leaves at the shoot apical meristem. Three closely related members SPL10, SPL11 and SPL2 redundantly control laminar shape at the vegetative phase (Shikata et al .,2009), whereas leaf shape is affected by two other SPL genes SPL9 and SPL15 (Usami et al .,2009). Furthermore, SPL 9 was reported to bind directly to promoter of microRNA172 gene and activates its transcription, regulating downstream APETALA2 (AP2)-like transcrioption factor TARGET OF EAT (TOE1) and TOE2 that repress adult characteristics in the leaf epidermis (Chen et al .,2010). Similarly Degradome sequence analysis showed  SPL3,SPL4 and SPL5 as potential target genes for microRNA156 in addition to SPL 15, SPL9, SPL2, SPL11, SPL10 and SPL6 which were found to be common targets between microRNA156 and microRNA157. ATSPL3, ATSPL4 and ATSPL5 regulates trichome distribution and affects the cell number and cell size in adult leaf although they do not affect leaf shape (Wu and Poethig 2006; Usami et al .,2009).

siRNA vs miRNA

SiRNA vs miRNA

Two classes of small RNAs, small interfering RNAs (siRNAs) and microRNAs (miRNAs), affect gene expression in animals and plants. They interfere with normal gene function on several levels, including promoter activity, mRNA stability, and translational efficiency. Small RNAs are the specificity components of a protein machinery known as RNA-induced silencing complex (RISC), which uses the small RNAs to recognize complementary motifs in target nucleic acids

Basic difference in siRNa and miRNA is that siRNA is a exogenous double stranded RNA taken up by cell, while miRNA is endogenous RNA molecule, encoded by specific miRNA gene and short haiprin pri-miRNA in the nucleus.  The siRNAs are cleaved from dsRNA by a class of RNase III enzymes known as Dicers (Bernstein et al., 2001 ). After cleavage, siRNAs from both strands can then target additional RNA molecules for degradation. Analogous to siRNAs are microRNAs (miRNAs) (Carrington and Ambros, 2003), a class of small RNAs differentiated from siRNAs by several features: nearly all miRNAs are complementary to sites within target mRNAs but generally contain one or more mismatches; miRNAs are processed from larger noncoding RNA precursors that contain stem-loop structures processed by a Dicer; and miRNAs are highly conserved in sequence, expression, and function. In plants, miRNAs act through several possible mechanisms: posttranscriptional cleavage of mRNA :inhibition of translation; and RdRP-mediated second-strand synthesis and trans-acting siRNA (ta-siRNA) production initiated by miRNA action .Cleavage of miRNA target genes has been documented regardless of mode of action. The prevalence of the putative translational block has not been assessed systematically and some evidence indicates that a transcriptional feedback mechanism may also be active

microRNA discovery using Illumina high throughput sequencing

The study of microRNA (miRNA) is growing rapidly as researchers discover new miRNAs and uncover the importance of these small regulatory elements linked to a wide range of biological functions.  The miRBase sequence database¹ is the primary public repository for newly discovered miRNAs and the number of miRBase entries has grown rapidly from a mere 218 in 2002 to almost 10,000 in the latest version, suggesting the existence of many more miRNAs yet to be discovered.

MicroRNA sequencing is a new method and a powerful tool to identify and quantitatively decode the entire population of microRNAs in your sample. LC Sciences now provides a microRNA discovery service using the Illumina high-throughput sequencing technology which enables comprehensive coverage, highly sensitive and specific discovery and profiling of all forms of microRNAs in your sample. Profile microRNA in any species - there is no need for probes based on prior sequence or secondary structure information. Profiling is transcriptome-wide, you can investigate all microRNAs, of any size, known and unknown, in your sample.. Achieve digital transcript expression analysis – Accurate quantitation of expression levels over 5 orders of magnitude of transcript abundance. Detect rare transcripts and transcript variants, such as single nucleotide mutations (SNP).They also provides the comprehensive data analysis facility.

microRNA discovery by microarray analysis

MicroRNA are a class of highly evolutionarily conserved endogenous non coding small RNA with about 21 nucleotides in length (Ambros , 2001; Ambros et al ., 2003). Large number of microRNA  have been report in various plants species (Llave et al ., 2002b; Park et al ., 2002; Reinhart et al .,2002; Palatnik et al ., 2003; Bonnet et al ., 2004; Floyd and Bowman, 2004; Jones-Rhoades and Bartel, 2004; Sunkar and Zhu, 2004; Wang et al ., 2004a, 2004b; Adai et al ., 2005; Bedell et al ., 2005). Increasing evidence suggests that plant microRNA play vital roles in multiple essential biological processes, such as leaf morphogenesis and polarity (Plantinik et al .,2003), floral organ identity (Aukerman and Sakai, 2003; Chen 2004), phase changes from growth to generative growth (Aukerman and Sakai 2003 ; Chen 2004), virus  stress (Kasschau et al, 2003), mineral nutrient and dehydration stress (Sunjar and Zhu 2004).

LC Sciences provides a genome-wide microRNA (miRNA) expression profiling service using µParaflo® technology and proprietary probe design, which enable highly sensitive and specific direct detection of miRNAs. Standard arrays for mature miRNA of all species available in the latest version of the miRBase database (Release 17).Service is comprehensive and includes sample preparation of your total RNA sample, single or dual color labeling, hybridization, image data processing and in-depth data analysis. Two-three weeks after receiving your total RNA samples, the analyzed data, representative and original images of the array, and raw data will be send to you. The in-depth data analysis may include multi-array normalization, t-test, ANOVA, False Discovery Rate calculator, and/or clustering analysis.

microRNA sequencing

MicroRNas initially discovered in C.elegans , are newly identified class of non-protein-coding small (~20nt) RNA that widely exist in animals, plants and in some viruses. Large number of microRNA  have been report in various plants species (Llave et al., 2002b; Park et al., 2002; Reinhart et al.,2002; Palatnik et al., 2003; Bonnet et al., 2004; Floyd and Bowman, 2004; Jones-Rhoades and Bartel, 2004; Sunkar and Zhu, 2004; Wang et al., 2004a, 2004b; Adai et al., 2005; Bedell et al., 2005). There are mounting evidence demonstrating important role of microRNA in various plant biological process, including tissue identity, developmental timing and response to environmental stress . In contrast to animal, the preferred mechanism of action of microRNA in plants is cleavage of target mRNAs by the RNA-induced silencing complex (RISC), guided by the miRNANA (Bartel 2004). Plant microRNA have also been reported to act by repressing translation or by inducing methylation of DNA (Bao et al 2004). Repression of the target transcript by miRNANAs may occur through translational inhibition, accelerated exonucleolytic mRNA decay or slicing within miRNANA-mRNA base pairing (Eulalio et al 2008). Most characterized eukaryotic MIRNA genes are RNA polymerase II transcription units that yield a primary miRNANA transcript called a pri-miRNANA (Lee et al 2004). The pri-miRNANA typically forms an imperfect fold-back structure, which is processed into a stem loop precursor (Kim 2005). This precursor molecule is than cleaved by Dicer-like 1 protein resulting in a miRNA: miRNA* complex which after tansport to cytoplasm separates into the miRNA and miRNA* unit. One strand (miRNA) serve as guide for the  RNA-induced silencing complex  (RISC), which cleave the RNA of target genes at the paired region (Llave et al 2002).

Until recently, most experimental miRNA isolation studies involved cloning and capillary sequencing. Though, the concatemerization of sRNA clones, followed by cloning and cDNA isolation from bacteria before sequencing makes this approach laborious and costly (Barakat et al 2007).  However, lot of researcher used this approach to identify miRNANA in different plant species. Shanfa Lu et al 2005, reported novel and mechanical stress-reponsive microRNA in Populus trichocarpa using such an approach. Similarly using the cloning approach, Sunkar and Zhu (2004) identified a significant number of miRNA from Arabidopsis grown under abiotic stress conditions. However, fact that most of the miRNANA identified using this approach are highly expressed or tissure or development specific, shifted identification efforts towards computational analysis which have resulted in a considerable larger number of identified Arabidopsis miRNA (Bonnet et al 2004, Wang er al 2004, Adai et al 2005). Several investigators have  reported identification of novel as well as conserved miRNA using computational approach in recent past (Zhang et al 2005;2006; 2006b; 2007; 2008; Pan et al 2007; Wang et al 2005; Sunkar and Jagadeeswaran 2008). However this method is limited by the number of nucleotide sequences available in the database. Recently introduced high through put sequencing technology provided better alternative (Abdelali et al 2007) as it generates millions of bases per run and has been used successfully for sequencing the genomes of bacteria (Goldberg et al 2006), chloroplast (Moore et al 2006) and mitochondria( Poinar et al 2006) as well as for transcriptiome analysis (Weber et al 2007). Recently Zhao et al 2010, utilized deep sequencing to identified novel and conserved microRNA in peanut, earlier Rajagopalan et al 2006, Fahlgren et al 2007 and Barakat et al 2007 used ultrahigh throughput sequencing technology for small RNA sequencing in Arabidopsis and basal eudicot Eschscholzia californica.  In these studies, the number of microRNA identified in Arabidopsis doubled the number previously discovered in total of over 30 studies using capillary sequencing. The greater efficiency to detect variants that are expressed at low levels derives from the much deeper coverage of the sRNA and avoidance of cloning, makes deep sequencing technology favorable approach for microRNA discovery, specially in species where genome is not completely sequenced yet.