Mapping the small RNA interactome in bacteria using RIL-seq

Abstract

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.

Reprogramming of gene expression in Escherichia coli cultured on pyruvate versus glucose

“…Further analysis of microarray data disclosed that higher levels of RyeB, CyaR, GcvB and RyhB in cells grown on pyruvate correlated with a decrease in the abundance of their known target mRNAs and/or their products of translation (for details, see Table S4). In addition, our gene expression data also revealed that a number of putative sRNA targets (see Table S5) envisaged in previous studies (Melamed et al 2018) had likewise lower abundance in cells grown on pyruvate and therefore were potentially controlled by new antisense mechanisms mediated by RyeB, CyaR, GcvB or RyhB in vivo. Moreover, as the post-transcriptional control of some genes by these sRNAs might affect protein abundance (e.g., regulation of SodB level by RyhB, Table S4), we additionally analyze 2D protein electrophoresis data to search for correlation between the level of the differentially expressed polypeptides and sRNAs predicted to control the corresponding protein-coding genes (Melamed et al 2018;Chao et al 2017).…”

“…In addition, our gene expression data also revealed that a number of putative sRNA targets (see Table S5) envisaged in previous studies (Melamed et al 2018) had likewise lower abundance in cells grown on pyruvate and therefore were potentially controlled by new antisense mechanisms mediated by RyeB, CyaR, GcvB or RyhB in vivo. Moreover, as the post-transcriptional control of some genes by these sRNAs might affect protein abundance (e.g., regulation of SodB level by RyhB, Table S4), we additionally analyze 2D protein electrophoresis data to search for correlation between the level of the differentially expressed polypeptides and sRNAs predicted to control the corresponding protein-coding genes (Melamed et al 2018;Chao et al 2017). This analysis made it possible to expand the number of putative genes and their products (see Table S6) whose level might potentially be controlled by RyeB, CyaR, GcvB, RyhB via new post-transcriptional mechanisms during E. coli growth on alternative carbon sources.…”

Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

“…1 ). This observation is consistent with the RNA-seq characterization of Hfq-dependent sRNA-mRNA interactions described by Melamed et al [ 9 , 21 ], in which approximately 60% of all described interactions occur in the middle of the coding regions.

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Transcriptomic Approaches for Studying Quorum Sensing in Vibrio cholerae

“…Most sRNAs require an RNA-binding "matchmaker" protein, e.g., Hfq, to facilitate base pairing of the sRNA with its targets, a feature that is harnessed in a related technology, called RIL-seq (RNA interaction by ligation and sequencing). Here, sRNA-target pairs are cross-linked on Hfq, trimmed by ribonucleases, and ligated using T4 RNA Ligase (Melamed et al, 2018(Melamed et al, , 2016. Generation of cDNA and sequencing of the chimeric RNA allow for the global identification of sRNA-target interactions under selected conditions.…”