Comparative genomics boosts target prediction for bacterial small RNAs

Abstract

Significance This study presents a unique approach (CopraRNA, for Comparative Prediction Algorithm for sRNA Targets) towards reliably predicting the targets of bacterial small regulatory RNAs (sRNAs). These molecules are important regulators of gene expression. Their detailed analysis thus far has been hampered by the lack of reliable algorithms to predict their mRNA targets. CopraRNA integrates phylogenetic information to predict sRNA targets at the genomic scale, reconstructs regulatory networks upon functional enrichment and network analysis, and predicts the sRNA domains for target recognition and interaction. Our results demonstrate that CopraRNA substantially improves the bioinformatic prediction of target genes and opens the field for the application to nonmodel bacteria.

A game of tag: MAPS catches up on RNA interactomes

“…The first one is erpA mRNA, which was previously shown to be a target during the course of our study. 29 The second is the grxD mRNA that is negatively regulated by RyhB. In the case of the other sRNA, RybB, we were able to demonstrate that yifE mRNA is stabilized following expression of RybB, validating yifE as a positive target.…”

The sRNA NsiR4 is involved in nitrogen assimilation control in cyanobacteria by targeting glutamine synthetase inactivating factor IF7

“…This type of regulatory motif results in a delayed response to the transcription factor offset (49). Mixed feed-forward loops involving transcription factors and sRNAs are frequent regulatory elements in bacterial gene regulation and have also been described or discussed for other sRNAs, e.g., Spot42 or FnrS (38,50). A delay in GS inactivation might be of importance in habitats showing strong fluctuations in nitrogen availability.…”

“…To predict potential targets of NsiR4, we used the CopraRNA algorithm, considering the folding, hybridization, and conservation of a particular sRNA (38). The highest interaction probability was predicted for the gifA (ssl1911) mRNA (Table 1).…”

“…The selection of the reporter strains and bioluminescence measurements were performed as previously described (52). Target prediction was performed using CopraRNA (38,42) with the NsiR4 homologs of Synechocystis 6803, Microcystis aeruginosa NIES-843, Cyanothece sp. ATCC 51142, Cyanothece sp.…”

An assessment of bacterial small RNA target prediction programs

“…RNAup 19 and IntaRNA 7 are the major programs in this class. Finally, a recent generation of programs including CopraRNA 20,21 and TargetRNA2 22 combine the benefits of the "inter-RNA" class of programs to the use of conservation information. While TargetRNA2 requires conservation of the sRNA region involved in target binding, based on observations by Peer and Margalit, 23 CopraRNA uses the conservation of the interaction itself.…”