plant microrna

A phosphate switch to fine-tune the protein production in the cells:

Fast-Forward Genetics Identifies Plant CPL Phosphatases as Regulators of miRNA Processing Factor HYL1


Thale cress, Arabidopsis thalianaMicroRNAs are essential regulators of the genetic program in multicellular organisms. Because of their potent effects, the production of these small regulators has itself to be tightly controlled. That is the key finding of a new study performed by Tübingen scientists at the Max Planck Institute for Developmental Biology. They identified a new component that modulates the production of micro RNAs in thale cress, Arabidopsis thaliana, by the removal of phosphate residues from a micro RNA-biogenesis enzyme. This can be as quick as the turn of a switch, allowing the plant to adapt to changing conditions. In this study, the scientists combined advanced imaging for facile detection of plants with defective microRNA activity with whole genome sequencing for rapid identification of new mutations.

The cell seems to thwart itself: Reading the DNA, a mobile messenger RNA is produced in the cell nucleus, exported to the cytoplasm where it serves as a blueprint for the production of proteins. At the same time, the cell is able to produce micro RNAs that, by binding to specific messenger RNAs, can block protein production or even initiate its destruction. But why does the cell start a costly process and immediately stops it? “Well, the answer lies on the fine balance the cell has to achieve between [click to continue…]

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MicroRNAs in Plants vs. Animals

by Doug Dluzen on May 10, 2012

in Publications

It is becoming increasingly clear that microRNA are important regulators of gene expression within the animal kingdom. However, microRNA are also found in plants, behaving more like small inhibiting RNA (siRNA) during target gene knockdown. A recent review published in Genome Biology aims to discuss the differences between animal and plant microRNA and highlights the important role of each within the two kingdoms. Axtell et al. serves to showcase the important similarities and differences between microRNA in separate kingdoms and uses the model plant organism Arabidopsis thaliana as an example of classic plant microRNA function.  In plants, microRNA are transcribed by RNA polymerase II as in animals, but the entire process of microRNA biogenesis is undertaken within the plant nucleus. The mature microRNA are exported out of the nucleus by Hasty, an exportin 5-like protein found in plants. A major difference between plant and animal microRNA falls within target recognition. Axtell et al. reviews the target recognition process between plants and animals; notably the direct mRNA cleavage of a microRNA target in plants due to near-perfect base complementation between the microRNA and its target. This differs vastly in animals where protein repression is believed to occur by translation inhibition as well as mRNA degradation. Hybridization of microRNA to target in animals is less stringent near the 3’ end of the microRNA strand and relies on the canonical 7-8 nucleotide “seed sequence” to drive microRNA target recognition.

After highlighting the similarities and differences between plants and animals, the review dives into some evolutionally perspectives and driving factors of microRNA evolution in plants and animals. Interestingly, Axtell et al. discusses events that lead to the emergence of new microRNA genes in plants and animals. Briefly, it is more common in plants for microRNA genes to emergence via mechanisms of inverted duplication events, where as in animals it is more common for microRNA hairpins to evolve from mutational events in “unstructured” sequences of the genome. These evolutionary driving factors and mechanisms for newly acquired microRNA genes can perhaps help researchers identify novel microRNA targets within gene loci of interest. Even though most research in microRNA regulation of target genes is primarily focused on animal gene regulation, and specifically within human disease states, acknowledging the breadth and scope of microRNA regulation across kingdoms may provide useful insights into microRNA research.

Axtell, MJ., et al.  
Vive la difference: biogenesis and evolution of microRNAs in plants and animals.

Genome Biology. 12(2011): p. 221-234.


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  • The experiment tested a specific microRNA gene that was identified in
    corn and soybean, and confirmed the potential to develop plants capable
    of withstanding intermittent irrigation with seawater and growth in high
    salinity soils
  • During the experiment, plants were intermittently irrigated with salt
    water with three times the salinity level of seawater
  • Rosetta Green has previously demonstrated that microRNA genes are
    capable of improving plants under extreme drought conditions

Rosetta  Green LogoREHOVOT, Israel, Jan. 10, 2012 (GLOBE NEWSWIRE) — The Israeli agro-biotechnology company Rosetta Green, which develops improved crops for the agriculture industry, reports successful experimental results in which plants were grown using seawater irrigation. The experiment was conducted on tobacco plants which are used as model plants for corn and soybean. The plants that were improved by a microRNA gene were found to have an enormous potential to grow under irrigation with seawater.

In the said experiment, which took place in recent months in Rosetta Green’s controlled and unique growth rooms in Rehovot, the effect of the microRNA gene was tested on tobacco plants under conditions of seawater irrigation. For that purpose, plants that were improved by this microRNA gene and control plants that did not undergo such improvement were irrigated with salt water with triple the salinity level of seawater. Subsequently, both plant groups were put back [click to continue…]


A new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA – target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA – target duplex having a large loop, making them undetectable by traditional tools.

The TAPIR web server can be accessed at:

Bonnet E, He Y, Billiau K, Van de Peer Y. (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics [Epub ahead of print]. [abstract]

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microRNA Target Prediction Tools

by Chris on November 16, 2009

in Web Based Tools

miRecords is resource for animal miRNA-target interactions developed at the University of Minnesota. miRecords consists of two components. The Validated Targets component is a large, high-quality database of experimentally validated miRNA targets resulting from meticulous literature curation. The Predicted Targets component of miRecords is an integration of predicted miRNA targets produced by 11 established miRNA target prediction programs

PicTar is an algorithm for the identification of microRNA targets. This searchable website provides details (3′ UTR alignments with predicted sites, links to various public databases etc) regarding microRNA target predictions in vertebrates, several Drosophila species, and C. elegans.

miRanda is an algorithm for finding genomic targets for microRNAs. This algorithm has been written in C and is available as an open-source method under the GPL. MiRanda was developed at the Computational Biology Center of Memorial Sloan-Kettering Cancer Center. This software will be further developed under the open source model, coordinated by Anton Enright and Chris Sander (

TargetScan: Prediction of microRNA targets – These are the most recent TargetScanS predictions (April 2005). They are essentially the 3′UTR targets reported in the Lewis et al., 2005 paper, with a few changes arising from updated gene boundary definitions from the April 2005 UCSC genome browser mapping of RefSeq mRNAs to the hg17 human genome assembly. To avoid difficulties in browser display, the few predictions spanning splice junctions are excluded.   [click to continue…]

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