Modelling translation initiation under the influence of sRNA

Abstract

Bacterial small non-coding RNA (sRNA) plays an important role in post-transcriptional gene regulation. Although the number of annotated sRNA is steadily increasing, their functional characterization is still lagging behind. Various computational strategies for finding sRNA−mRNA interactions, and thus putative sRNA targets, were developed. Most of them suffer from a high false positive rate. Here, we present a qualitative model to simulate the effect of an sRNA on the translation initiation of a potential target. Information about the ribosome−mRNA interaction, sRNA−mRNA interaction and expression information from deep sequencing experiments is integrated to calculate the change in translation initiation complex formation, as a proxy for translational activity. This model can be used to post-evaluate predicted targets, hence condensing the list of potential targets. We show that our translation initiation model, under the influence of an sRNA, can successfully simulate thirteen out of fifteen tested sRNA−mRNA interactions in a qualitative manner. To show the gain in specificity, we applied our method to a target search for the Escherichia coli sRNA RyhB. Compared with simple target prediction without post-evaluation, we reduce the number of targets to less than one fourth potential targets, considerably reducing the burden of experimental validation.

Figure 1

Graphical illustration of all reactions and species considered in the reaction network. The RNA species are depicted with black backbones, blue intra-molecular and orange inter-molecular base-pairs. The ribosome with its anti-RRS sequence is shown as a green sphere. The RDS is highlighted in gray. The RRS, the start codon and the RNA binding site are marked with green, red and yellow, respectively. Reactions are symbolized with ↔ arrows, their corresponding equilibrium constants and a reference to the reaction equation in the main text. (A) In the case where the RDS and the RNA binding site overlap, two reaction branches from M_F compete with each other. One leads to sRNA bound mRNA M_S, the other leads via M_F^* to ribosome bound mRNA M_R; (B) In the case where the RNA binding-site and RDS are spatially separated, there are two routes from free mRNA to translationally active M_TA. One leads as before via M_F^* to M_R. The other route first leads to an sRNA·mRNA complex, which can further expose its RDS M_S^*, and eventually ends in the active ribosome·mRNA·sRNA complex M_SR.

Figure 2

Illustration of the work-flow for the classification of whether sRNA binding can influence the mRNA’s translation initiation. RNAplex is used to calculate possible sRNA– mRNA interaction sites. RNAduplex calculates the ribosome–mRNA interaction, hence determining the position of the RRS and RDS, and the hybridization energy ΔG_R. The position of the RDS and the sRNA binding site (sRNA-BS) is used with RNAup to determine the exposing probabilities P_EF and P_ES. The concentrations of all reactants are deduced from RNA-seq data. All this information is integrated in the Translation Initiation Model to calculate the amount of mRNA that is bound by the initiation complex assuming the presence (m_R(s_T )) and the absence (m_R(0)) of sRNA. The ratio α of these serves as a descriptor to classify the potential of the sRNA to influence translation initiation.

Figure 3

The distribution of hybridization energy. The blue curve shows the minimal hybridization energy for each gene with a calculated binding site from −150 nt upstream to +20 nt downstream of the translation start site and ΔG ≤ −7 kcal/mol. The experimental validated genes are marked with ⋄. In contrast, the red curve shows the hybridization energy for all genes that are potentially altered in their expression by RyhB, according to our Translation Initiation Model (TIM).