An improved method for surface immobilisation of RNA: application to small Non-Coding RNA - mRNA pairing

Characterisation of RNA and its intermolecular interactions is increasing in importance as the inventory of known RNA functions continues to expand. RNA-RNA interactions are central to post-transcriptional gene regulation mechanisms in bacteria, and the interactions of bacterial small non-coding RNAs (sRNAs) with their mRNA targets are the subject of much current research. The technology of surface plasmon resonance (SPR) is an attractive approach to studying these interactions since it is highly sensitive, and allows interaction measurements to be recorded in real-time. Whilst a number of approaches exist to label RNAs for surface-immobilisation, the method documented here is simple, quick, efficient, and utilises the high-affinity streptavidin-biotin interaction. Specifically, we ligate a biotinylated nucleotide to the 3' end of RNA using T4 RNA ligase. Although this is a previously recognised approach, we have optimised the method by our discovery that the incorporation of four or more adenine nucleotides at the 3' end of the RNA (a poly-A-tail) is required in order to achieve high ligation efficiencies. We use this method within the context of investigating small non-coding RNA (sRNA)-mRNA interactions through the application of surface technologies, including quantitative SPR assays. We first focus on validating the method using the recently characterised Escherichia coli sRNA-mRNA pair, MicA-ompA, specifically demonstrating that the addition of the poly-A-tail to either RNA does not affect its subsequent binding interactions with partner molecules. We then apply this method to investigate the novel interactions of a Vibrio cholerae Qrr sRNA with partner mRNAs, hapR and vca0939; RNA-RNA pairings that are important in mediating pathogenic virulence. The calculated binding parameters allow insights to be drawn regarding sRNA-mRNA interaction mechanisms

Hfq binding changes the structure of Escherichia coli small noncoding RNAs OxyS and RprA, which are involved in the riboregulation of rpoS

OxyS and RprA are two small noncoding RNAs (sRNAs) that modulate the expression of rpoS, encoding an alternative sigma factor that activates transcription of multiple Escherichia coli stress-response genes. While RprA activates rpoS for translation, OxyS down-regulates the transcript. Crucially, the RNA binding protein Hfq is required for both sRNAs to function, although the specific role played by Hfq remains unclear. We have investigated RprA and OxyS interactions with Hfq using biochemical and biophysical approaches. In particular, we have obtained the molecular envelopes of the Hfq–sRNA complexes using small-angle scattering methods, which reveal key molecular details. These data indicate that Hfq does not substantially change shape upon complex formation, whereas the sRNAs do. We link the impact of Hfq binding, and the sRNA structural changes induced, to transcript stability with respect to RNase E degradation. In light of these findings, we discuss the role of Hfq in the opposing regulatory functions played by RprA and OxyS in rpoS regulation

Probing of surface-immobilised biotinylated-sRNA with partner mRNA-Cy3.

<p>(a) Streptavidin-coated microarray slide with control spots of (1) biotin-OxyS, (2) blank surface, (3) sRNA MicA, and test spot of (4) biotin-MicA. The surface was probed with Cy3-labelled <i>ompA</i>. The specific <i>ompA i</i>nteraction with surface-immobilised biotin-MicA is shown by the green spot. (b) As for (a) but in this case the test spot (4) is biotin-Qrr1 and the control sRNA spot (3) is Qrr1. The surface was probed with Cy3-labelled <i>hapR</i>. The specific <i>hapR</i> interaction with surface-immobilised biotin-Qrr1 is seen by the green spot. Schematic illustrations of the interactions occurring in (a) and (b) are shown beneath the microarray slides with the streptavidin surface in yellow, sRNAs in brown and Cy3-labelled mRNA in green.</p

Kinetic data for sRNA-mRNA interactions from SPR analyses.

<p>Kinetic data for sRNA-mRNA interactions from SPR analyses.</p

Analysis of the ligation reaction for Qrr2 sRNA (a) with and (b) without A-tails.

<p>Gels were stained with the SYBR-Gold, whereas blots were probed with streptavidin-HRP to detect biotin-labelled RNA. Schematic representations of RNA species identified on the gels/blots are shown. The sequences of the RNAs are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079142#pone.0079142.s001" target="_blank">Table S1 in File S1</a>.</p

SPR analysis of RNA-RNA interactions.

<p>(a) Surface-immobilised biotin-<i>ompA</i>. Example sensorgrams of sequential injections of MicA (red) or OxyS (blue) from 0–10 µM; MicA data fit (black) with chi² = 0.20 RU². (b) Control sensorgrams of sequential injections of MicA from 0–10 µM over surface-immobilised <i>rpoS</i> mRNA (orange) or U-biotin reagent (green).</p

SPR analysis of Qrr3-mRNA interactions.

<p>(a) Surface-immobilised biotin-<i>hapR</i>. Example sensorgram of sequential injections of Qrr3 from 0–0.25 µM; data fit (black) with chi² = 0.28 RU². (b) Surface-immobilised biotin-<i>vca0939</i>. Example sensorgram of sequential injections of Qrr3 from 0–0.25 µM; data fit (black) with chi² = 0.41 RU².</p

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe