Functional characterization of bacterial sRNAs using a network biology approach

Abstract

Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.microbiology | network inference | sRNA regulation S mall noncoding RNAs (sRNAs) are ubiquitous in all kingdoms of life. These molecules range in length from a few nucleotides to a few hundred nucleotides, and are involved in the regulation of a wide range of physiological processes (1-3). Bacterial sRNAs, some of which have been studied extensively (4), have been implicated in the regulation of bacterial stress responses, iron uptake, quorum sensing, virulence, and biofilm formation (5-8).The most widely studied class of bacterial sRNAs act as posttranscriptional regulators by base-pairing to target mRNAs. This interaction is facilitated by the Hfq protein, a bacterial Sm-like protein with chaperone function, which acts as a general cofactor in RNA interactions (9). Binding of an sRNA to its mRNA target can result in changes in translational efficiency as well as transcript instability, a process dependent on the RNase-E-including degradosome complex (10). To date, w80 sRNAs have been identified in Escherichia coli, 30 of which are Hfq-dependent. A number of these sRNAs have been well characterized, such as RyhB, which is known to regulate iron homeostasis (8).Small RNAs and their targets are being discovered and identified with greater efficiency (11-15); however, the functions of many sRNAs remain unknown. In the present study, we were interested in exploring the possibility of developing and using a network biology approach to elucidate sRNA functional roles. We applied a network inference algorithm to a compendium of E. coli microarray expression profiles to reconstruct an sRNA regulatory network. Functional enrichment of the resulting sRNA subnetworks confirmed known functions for some sRNAs and identified putative functions for others. We experimentally validated predicted functional roles for three sRNAs, and an indepth analysis of the inferred network led...

A computational strategy for the search of regulatory small RNAs in Actinobacillus pleuropneumoniae

“…As a comparison, in E. coli, whose genome is about twice as big as that of A. pleuropneumoniae's genome, around 80 sRNAs have already been described (Modi et al 2011). We would therefore expect roughly half the regulatory RNAs in our model microorganism.…”

“…Here, we used TargetRNA2 as a target predictor for being the algorithm with the best correlation of targets predicted and actually confirmed, among the programs widely used for this purpose (Kery et al 2014). Because many of the mRNAs predicted are potential targets of more than one sRNA, these regulators may share some of their targets, placing them in a characteristic entangled network of gene regulation (Modi et al 2011). Our predictions are strongly corroborated by the fact that several of the targets predicted for the GcvB (Arrc01) sRNA had been shown for other microorganisms and are consistent with its role (Sharma et al 2011).…”

Non-coding RNAs in marine Synechococcus and their regulation under environmentally relevant stress conditions

“…Especially noticeable is the pronounced complementarity in the region directly 5 0 of the mRNA AUG (boldface letters and arrow). (Wassarman et al, 2001) and regulates probably more than 20 iron-storage, iron-using and other proteins when iron becomes limiting (Masse and Gottesman, 2002;Modi et al, 2011;Salvail and Masse, 2011). Functional homologs of RyhB were also identified in several non-enterobacteria, among them PrrF1 and PrrF2 in Pseudomonas aeruginosa (Wilderman et al, 2004).…”

Regulatory RNAs

“…In addition, recent innovative ways of characterizing sRNA have relied on a biology network approach to infer regulatory relationships between sRNAs and mRNAs. 93 A creative approach to bridge the gap between discovery of bacterial regulatory RNAs and functional characterization is to use a holistic whole cell approach. To this end, Modi et al 93 proposed a network biology approach in which a computational algorithm was applied to the study of sRNAs.…”

“…93 A creative approach to bridge the gap between discovery of bacterial regulatory RNAs and functional characterization is to use a holistic whole cell approach. To this end, Modi et al 93 proposed a network biology approach in which a computational algorithm was applied to the study of sRNAs. They fed extensive gene expression profiles into the algorithm to infer regulatory relationships between IsrA, GlmZ, GcvB, and their mRNA targets that were experimentally confirmed.…”