Intact RNA structurome reveals mRNA structure-mediated regulation of miRNA cleavage in vivo.

MicroRNA (miRNA)-mediated cleavage is involved in numerous essential cellular pathways. miRNAs recognize target RNAs via sequence complementarity. In addition to complementarity, in vitro and in silico studies have suggested that RNA structure may influence the accessibility of mRNAs to miRNA-induced silencing complexes (miRISCs), thereby affecting RNA silencing. However, the regulatory mechanism of mRNA structure in miRNA cleavage remains elusive. We investigated the role of in vivo RNA secondary structure in miRNA cleavage by developing the new CAP-STRUCTURE-seq method to capture the intact mRNA structurome in Arabidopsis thaliana. This approach revealed that miRNA target sites were not structurally accessible for miRISC binding prior to cleavage in vivo. Instead, we found that the unfolding of the target site structure plays a key role in miRISC activity in vivo. We found that the single-strandedness of the two nucleotides immediately downstream of the target site, named Target Adjacent nucleotide Motif, can promote miRNA cleavage but not miRNA binding, thus decoupling target site binding from cleavage. Our findings demonstrate that mRNA structure in vivo can modulate miRNA cleavage, providing evidence of mRNA structure-dependent regulation of biological processes.

A computational approach for inferring the cell wall properties that govern guard cell dynamics.

Guard cells dynamically adjust their shape in order to regulate photosynthetic gas exchange, respiration rates and defend against pathogen entry. Cell shape changes are determined by the interplay of cell wall material properties and turgor pressure. To investigate this relationship between turgor pressure, cell wall properties and cell shape, we focused on kidney-shaped stomata and developed a biomechanical model of a guard cell pair. Treating the cell wall as a composite of the pectin-rich cell wall matrix embedded with cellulose microfibrils, we show that strong, circumferentially oriented fibres are critical for opening. We find that the opening dynamics are dictated by the mechanical stress response of the cell wall matrix, and as the turgor rises, the pectinaceous matrix stiffens. We validate these predictions with stomatal opening experiments in selected Arabidopsis cell wall mutants. Thus, using a computational framework that combines a 3D biomechanical model with parameter optimization, we demonstrate how to exploit subtle shape changes to infer cell wall material properties. Our findings reveal that proper stomatal dynamics are built on two key properties of the cell wall, namely anisotropy in the form of hoop reinforcement and strain stiffening.

PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing.

Small RNAs (sRNAs) are a class of short (20-25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets.

SUMMARY: RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods. However, many bioinformatics tools do not scale accordingly, are cumbersome, or require extensive support from bioinformatics experts. Therefore, researchers need user-friendly, robust tools, capable of not only processing large sRNA datasets in a reasonable time frame but also presenting the results in an intuitive fashion and visualizing sRNA genomic features. Herein, we present the UEA sRNA workbench, a suite of tools that is a successor to the web-based UEA sRNA Toolkit, but in downloadable format and with several enhanced and additional features. AVAILABILITY: The program and help pages are available at http://srna-workbench.cmp.uea.ac.uk. CONTACT: vincent.moulton@cmp.uea.ac.uk.

Models and Mechanisms of Stomatal Mechanics.

The mechanism of stomatal function (control of gas flux through the plant surface via regulation of pore size) is fundamentally mechanical. The material properties of the pore-forming guard cells must play a key role in setting the dynamics and degree of stomatal opening/closure, but our understanding of the molecular players involved and resultant mechanical performance has remained limited. The application of indentation techniques and computational modelling, combined with molecular tools for imaging and manipulating guard cells and their constituent cell walls, has opened the way to a systems approach to analysing this problem. The outcomes of these investigations have led to a reassessment of accepted paradigms and are providing a new understanding of the mechanism of stomatal mechanics.

Towards the Physics of Calcium Signalling in Plants.

Calcium is an abundant element with a wide variety of important roles within cells. Calcium ions are inter- and intra-cellular messengers that are involved in numerous signalling pathways. Fluctuating compartment-specific calcium ion concentrations can lead to localised and even plant-wide oscillations that can regulate downstream events. Understanding the mechanisms that give rise to these complex patterns that vary both in space and time can be challenging, even in cases for which individual components have been identified. Taking a systems biology approach, mathematical and computational techniques can be employed to produce models that recapitulate experimental observations and capture our current understanding of the system. Useful models make novel predictions that can be investigated and falsified experimentally. This review brings together recent work on the modelling of calcium signalling in plants, from the scale of ion channels through to plant-wide responses to external stimuli. Some in silico results that have informed later experiments are highlighted.

Modeling the effect of copper availability on bacterial denitrification.

When denitrifying bacteria such as Paracoccus denitrificans respire anaerobically they convert nitrate to dinitrogen gas via a pathway which includes the potent greenhouse gas, nitrous oxide (N2 O). The copper-dependent enzyme Nitrous Oxide reductase (Nos) catalyzes the reduction of N2 O to dinitrogen. In low-copper conditions, recent experiments in chemostats have demonstrated that Nos efficiency decreases resulting in significant N2 O emissions. For the first time, a chemostat-based mathematical model is developed that describes the anaerobic denitrification pathway based on Michaelis-Menten kinetics and published kinetic parameters. The model predicts steady-state enzyme levels from experimental data. For low copper concentrations, the predicted Nos level is significantly reduced, whereas the levels for the non copper-dependent reductases in the pathway remain relatively unaffected. The model provides time courses for the pathway metabolites that accurately reflect previously published experimental data. In the absence of experimental data purely predictive analyses can also be readily performed by calculating the relative Nos level directly from the copper concentration. Here, the model quantitatively estimates the increasing level of emitted N2 O as the copper level decreases.