Global Mapping of Small RNA-Target Interactions in Bacteria.

Small RNAs (sRNAs) associated with the RNA chaperon protein Hfq are key posttranscriptional regulators of gene expression in bacteria. Deciphering the sRNA-target interactome is an essential step toward understanding the roles of sRNAs in the cellular networks. We developed a broadly applicable methodology termed RIL-seq (RNA interaction by ligation and sequencing), which integrates experimental and computational tools for in vivo transcriptome-wide identification of interactions involving Hfq-associated sRNAs. By applying this methodology to Escherichia coli we discovered an extensive network of interactions involving RNA pairs showing sequence complementarity. We expand the ensemble of targets for known sRNAs, uncover additional Hfq-bound sRNAs encoded in various genomic regions along with their trans encoded targets, and provide insights into binding and possible cycling of RNAs on Hfq. Comparison of the sRNA interactome under various conditions has revealed changes in the sRNA repertoire as well as substantial re-wiring of the network between conditions.

In vivo cleavage rules and target repertoire of RNase III in Escherichia coli.

Bacterial RNase III plays important roles in the processing and degradation of RNA transcripts. A major goal is to identify the cleavage targets of this endoribonuclease at a transcriptome-wide scale and delineate its in vivo cleavage rules. Here we applied to Escherichia coli grown to either exponential or stationary phase a tailored RNA-seq-based technology, which allows transcriptome-wide mapping of RNase III cleavage sites at a nucleotide resolution. Our analysis of the large-scale in vivo cleavage data substantiated the established cleavage pattern of a double cleavage in an intra-molecular stem structure, leaving 2-nt-long 3' overhangs, and refined the base-pairing preferences in the cleavage site vicinity. Intriguingly, we observed that the two stem positions between the cleavage sites are highly base-paired, usually involving at least one G-C or C-G base pair. We present a clear distinction between intra-molecular stem structures that are RNase III substrates and intra-molecular stem structures randomly selected across the transcriptome, emphasizing the in vivo specificity of RNase III. Our study provides a comprehensive map of the cleavage sites in both intra-molecular and inter-molecular duplex substrates, providing novel insights into the involvement of RNase III in post-transcriptional regulation in the bacterial cell.

Mapping the small RNA interactome in bacteria using RIL-seq.

Small RNAs (sRNAs) are major post-transcriptional regulators of gene expression in bacteria. To enable transcriptome-wide mapping of bacterial sRNA-target pairs, we developed RIL-seq (RNA interaction by ligation and sequencing). RIL-seq is an experimental-computational methodology for capturing sRNA-target interactions in vivo that takes advantage of the mutual binding of the sRNA and target RNA molecules to the RNA chaperone protein Hfq. The experimental part of the protocol involves co-immunoprecipitation of Hfq and bound RNAs, ligation of RNAs, library preparation and sequencing. The computational pipeline maps the sequenced fragments to the genome, reveals chimeric fragments (fragments comprising two ligated independent fragments) and determines statistically significant overrepresented chimeric fragments as interacting RNAs. The statistical filter is aimed at reducing the number of spurious interactions resulting from ligation of random neighboring RNA fragments, thus increasing the reliability of the determined sRNA-target pairs. A major advantage of RIL-seq is that it does not require overexpression of sRNAs; instead, it simultaneously captures the in vivo targets of all sRNAs in the native state of the cell. Application of RIL-seq to bacteria grown under different conditions provides distinctive snapshots of the sRNA interactome and sheds light on the dynamics and rewiring of the post-transcriptional regulatory network. As RIL-seq needs no prior information about the sRNA and target sequences, it can identify novel sRNAs, along with their targets. It can be adapted to detect protein-mediated RNA-RNA interactions in any bacterium with a sequenced genome. The experimental part of the RIL-seq protocol takes 7-9 d and the computational analysis takes ∼2 d.