Small RNAs and Hfq capture unfolded RNA target sites during transcription.

Small ribonucleoproteins (sRNPs) target nascent precursor RNAs to guide folding, modification, and splicing during transcription. Yet, rapid co-transcriptional folding of the RNA can mask sRNP sites, impeding target recognition and regulation. To examine how sRNPs target nascent RNAs, we monitored binding of bacterial Hfq⋅DsrA sRNPs to rpoS transcripts using single-molecule co-localization co-transcriptional assembly (smCoCoA). We show that Hfq⋅DsrA recursively samples the mRNA before transcription of the target site to poise it for base pairing with DsrA. We adapted smCoCoA to precisely measure when the target site is synthesized and revealed that Hfq⋅DsrA often binds the mRNA during target site synthesis close to RNA polymerase (RNAP). We suggest that targeting transcripts near RNAP allows an sRNP to capture a site before the transcript folds, providing a kinetic advantage over post-transcriptional targeting. We propose that other sRNPs may also use RNAP-proximal targeting to hasten recognition and regulation.

Quorum-sensing based small RNA regulation for dynamic and tuneable gene expression.

OBJECTIVES: Developing a dynamic regulation strategy is an essential step in establishing an automatic control system for manipulating metabolic fluxes and cellular behaviors. To broaden the extent of the application, a system that can generally control any gene of interest is demanded. RESULTS: Through characterization and optimization, the strategy repressed the immediate expression incrementally from 0 to 90% during culturing. Moreover, by changing single base pair in the lux box of the Plux promoter, the degree of repression of the target genomic gene was tuned to a difference of 70%. This strategy is expected to control metabolic flux without disrupting cell growth. CONCLUSIONS: We engineered bacterial small RNA to develop a pathway-independent strategy that can dynamically repress the expression of any gene at the posttranscription level.

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

BACKGROUND: Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class. METHODS: In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy. RESULTS: We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae. CONCLUSION: The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

Kinetic modeling reveals additional regulation at co-transcriptional level by post-transcriptional sRNA regulators.

Small RNAs (sRNAs) are important gene regulators in bacteria. Many sRNAs act post-transcriptionally by affecting translation and degradation of the target mRNAs upon base-pairing interactions. Here we present a general approach combining imaging and mathematical modeling to determine kinetic parameters at different levels of sRNA-mediated gene regulation that contribute to overall regulation efficacy. Our data reveal that certain sRNAs previously characterized as post-transcriptional regulators can regulate some targets co-transcriptionally, leading to a revised model that sRNA-mediated regulation can occur early in an mRNA's lifetime, as soon as the sRNA binding site is transcribed. This co-transcriptional regulation is likely mediated by Rho-dependent termination when transcription-coupled translation is reduced upon sRNA binding. Our data also reveal several important kinetic steps that contribute to the differential regulation of mRNA targets by an sRNA. Particularly, binding of sRNA to the target mRNA may dictate the regulation hierarchy observed within an sRNA regulon.

Uncovering Transcriptional Regulators and Targets of sRNAs Using an Integrative Data-Mining Approach: H-NS-Regulated RseX as a Case Study.

Bacterial small RNAs (sRNAs) play a vital role in pathogenesis by enabling rapid, efficient networks of gene attenuation during infection. In recent decades, there has been a surge in the number of proposed and biochemically-confirmed sRNAs in both Gram-positive and Gram-negative pathogens. However, limited homology, network complexity, and condition specificity of sRNA has stunted complete characterization of the activity and regulation of these RNA regulators. To streamline the discovery of the expression of sRNAs, and their post-transcriptional activities, we propose an integrative in vivo data-mining approach that couples DNA protein occupancy, RNA-seq, and RNA accessibility data with motif identification and target prediction algorithms. We benchmark the approach against a subset of well-characterized E. coli sRNAs for which a degree of in vivo transcriptional regulation and post-transcriptional activity has been previously reported, finding support for known regulation in a large proportion of this sRNA set. We showcase the abilities of our method to expand understanding of sRNA RseX, a known envelope stress-linked sRNA for which a cellular role has been elusive due to a lack of native expression detection. Using the presented approach, we identify a small set of putative RseX regulators and targets for experimental investigation. These findings have allowed us to confirm native RseX expression under conditions that eliminate H-NS repression as well as uncover a post-transcriptional role of RseX in fimbrial regulation. Beyond RseX, we uncover 163 putative regulatory DNA-binding protein sites, corresponding to regulation of 62 sRNAs, that could lead to new understanding of sRNA transcription regulation. For 32 sRNAs, we also propose a subset of top targets filtered by engagement of regions that exhibit binding site accessibility behavior in vivo. We broadly anticipate that the proposed approach will be useful for sRNA-reliant network characterization in bacteria. Such investigations under pathogenesis-relevant environmental conditions will enable us to deduce complex rapid-regulation schemes that support infection.

A dynamic and multilocus metabolic regulation strategy using quorum-sensing-controlled bacterial small RNA.

Metabolic regulation strategies have been developed to redirect metabolic fluxes to production pathways. However, it is difficult to screen out target genes that, when repressed, improve yield without affecting cell growth. Here, we report a strategy using a quorum-sensing system to control small RNA transcription, allowing cell-density-dependent repression of target genes. This strategy is shown with convenient operation, dynamic repression, and availability for simultaneous regulation of multiple genes. The parameters Ai, Am, and RA (3-oxohexanoyl-homoserine lactone [AHL] concentrations at which half of the maximum repression and the maximum repression were reached and value of the maximum repression when AHL was added manually, respectively) are defined and introduced to characterize repression curves, and the variant LuxRI58N is identified as the most suitable tuning factor for shake flask culture. Moreover, it is shown that dynamic overexpression of the Hfq chaperone is the key to combinatorial repression without disruptions on cell growth. To show a broad applicability, the production titers of pinene, pentalenene, and psilocybin are improved by 365.3%, 79.5%, and 302.9%, respectively, by applying combinatorial dynamic repression.

An sRNA Screen for Reversal of Quinolone Resistance in Escherichia coli.

In light of the rising prevalence of antimicrobial resistance (AMR) and the slow pace of new antimicrobial development, there has been increasing interest in the development of adjuvants that improve or restore the effectiveness of existing drugs. Here, we use a novel small RNA (sRNA) screening approach to identify genes whose knockdown increases ciprofloxacin (CIP) sensitivity in a resistant strain of Escherichia coli 5000 sRNA constructs were initially screened on a gyrA S83L background, ultimately leading to 30 validated genes whose disruption reduces CIP resistance. This set includes genes involved in DNA replication, repair, recombination, efflux, and other regulatory systems. Our findings increase understanding of the functional interactions of DNA Gyrase, and may aid in the development of new therapeutic approaches for combating AMR.

Sibling rivalry: related bacterial small RNAs and their redundant and non-redundant roles.

Small RNA molecules (sRNAs) are now recognized as key regulators controlling bacterial gene expression, as sRNAs provide a quick and efficient means of positively or negatively altering the expression of specific genes. To date, numerous sRNAs have been identified and characterized in a myriad of bacterial species, but more recently, a theme in bacterial sRNAs has emerged: the presence of more than one highly related sRNAs produced by a given bacterium, here termed sibling sRNAs. Sibling sRNAs are those that are highly similar at the nucleotide level, and while it might be expected that sibling sRNAs exert identical regulatory functions on the expression of target genes based on their high degree of relatedness, emerging evidence is demonstrating that this is not always the case. Indeed, there are several examples of bacterial sibling sRNAs with non-redundant regulatory functions, but there are also instances of apparent regulatory redundancy between sibling sRNAs. This review provides a comprehensive overview of the current knowledge of bacterial sibling sRNAs, and also discusses important questions about the significance and evolutionary implications of this emerging class of regulators.