Functional characterization of bacterial sRNAs using a network biology approach

Role of Small Non-Coding RNA in Gram-Negative Bacteria: New Insights and Comprehensive Review of Mechanisms, Functions, and Potential Applications

Small non-coding RNAs (sRNAs) are a key part of gene expression regulation in bacteria. Many physiologic activities like adaptation to environmental stresses, antibiotic resistance, quorum sensing, and modulation of the host immune response are regulated directly or indirectly by sRNAs in Gram-negative bacteria. Therefore, sRNAs can be considered as potentially useful therapeutic options. They have opened promising perspectives in the field of diagnosis of pathogens and treatment of infections caused by antibiotic-resistant organisms. Identification of sRNAs can be executed by sequence and expression-based methods. Despite the valuable progress in the last two decades, and discovery of new sRNAs, their exact role in biological pathways especially in co-operation with other biomolecules involved in gene expression regulation such as RNA-binding proteins (RBPs), riboswitches, and other sRNAs needs further investigation. Although the numerous RNA databases are available, including 59 databases used by RNAcentral, there remains a significant gap in the absence of a comprehensive and professional database that categorizes experimentally validated sRNAs in Gram-negative pathogens. Here, we review the present knowledge about most recent and important sRNAs and their regulatory mechanism, strengths and weaknesses of current methods of sRNAs identification. Also, we try to demonstrate the potential applications and new insights of sRNAs for future studies.

Multiscale regulation of nutrient stress responses in Escherichia coli from chromatin structure to small regulatory RNAs

Recent research has indicated the presence of heterochromatin-like regions of extended protein occupancy and transcriptional silencing of bacterial genomes. We utilized an integrative approach to track chromatin structure and transcription in E. coli K-12 across a wide range of nutrient conditions. In the process, we identified multiple loci which act similarly to facultative heterochromatin in eukaryotes, normally silenced but permitting expression of genes under specific conditions. We also found a strong enrichment of small regulatory RNAs (sRNAs) among the set of differentially expressed transcripts during nutrient stress. Using a newly developed bioinformatic pipeline, the transcription factors regulating sRNA expression were bioinformatically predicted, with experimental follow-up revealing novel relationships for 36 sRNA-transcription factors candidates. Direct regulation of sRNA expression was confirmed by mutational analysis for five sRNAs of metabolic interest: IsrB, CsrB and CsrC, GcvB, and GadY. Our integrative analysis thus reveals additional layers of complexity in the nutrient stress response in E. coli and provides a framework for revealing similar poorly understood regulatory logic in other organisms.

Beyond membrane permeability: A role for the small RNA MicF in regulation of chromosome replication and partitioning

Small regulatory RNAs (sRNA) have been shown to play a large role in the management of stress responses in Escherichia coli and other bacteria. sRNAs act post-transcriptionally on target mRNA through an imperfect base pairing mechanism to regulate downstream protein expression. The imperfect base pairing allows a single sRNA to bind and regulate a variety mRNA targets which can form intricate regulatory networks that connect different physiological processes for the cell's response. Upon exposure to antimicrobials and superoxide generating agents, the MicF sRNA in E. coli has been shown to regulate a small set of genes involved in the management of membrane permeability. Currently, it is unknown whether MicF acts on other processes to mediate the response to these agents. Using an sRNA interaction prediction tool, we identified genes in E. coli that are potentially regulated by MicF. Through subsequent analysis using a sfGFP-based reporter-gene fusion, we have validated two novel targets of MicF regulation: SeqA, a negative modulator of DNA replication, and ObgE, a GTPase crucial for chromosome partitioning. Importantly, the interaction between MicF and these target mRNAs is contingent upon the presence of the RNA chaperone protein, Hfq. Furthermore, our findings affirm the role of MicF's conserved 5' seed pairing region in initiating these regulatory interactions. Our study suggests that, beyond its established role in membrane permeability management, MicF exerts control over chromosome dynamics in response to distinct environmental cues, implicating a more multifaceted regulatory function in bacterial stress adaptation.

Multilayered regulation of amino acid metabolism in Escherichia coli

Amino acid metabolism in Escherichia coli has long been studied and has established the basis for regulatory mechanisms at the transcriptional, posttranscriptional, and posttranslational levels. In addition to the classical signal transduction cascade involving posttranslational modifications (PTMs), novel PTMs in the two primary nitrogen assimilation pathways have recently been uncovered. The regulon of the master transcriptional regulator NtrC is further expanded by a small RNA derived from the 3´UTR of glutamine synthetase mRNA, which coordinates central carbon and nitrogen metabolism. Furthermore, recent advances in sequencing technologies have revealed the global regulatory networks of transcriptional and posttranscriptional regulators, Lrp and GcvB. This review provides an update of the multilayered and interconnected regulatory networks governing amino acid metabolism in E. coli.

Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes

Accurate determination of causalities between genes is a challenge in the inference of gene regulatory networks (GRNs) from the gene expression profile. Although many methods have been developed for the reconstruction of GRNs, most of them are insufficient in determining causalities or regulatory directions. In this work, we present a novel method, namely, DDTG, to improve the accuracy of causality determination in GRN inference by dissecting downstream target genes. In the proposed method, the topology and hierarchy of GRNs are determined by mutual information and conditional mutual information, and the regulatory directions of GRNs are determined by Taylor formula-based regression. In addition, indirect interactions are removed with the sparseness of the network topology to improve the accuracy of network inference. The method is validated on the benchmark GRNs from DREAM3 and DREAM4 challenges. The results demonstrate the superior performance of the DDTG method on causality determination of GRNs compared to some popular GRN inference methods. This work provides a useful tool to infer the causal gene regulatory network.

Using genome-wide expression compendia to study microorganisms

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

Synthetic Genetic Interactions Reveal a Dense and Cryptic Regulatory Network of Small Noncoding RNAs in Escherichia coli

Over the past 20 years, we have learned that bacterial small noncoding RNAs (sRNAs) can rapidly effect changes in gene expression in response to stress. However, the broader role and impact of sRNA-mediated regulation in promoting bacterial survival has remained elusive. Indeed, there are few examples where disruption of sRNA-mediated gene regulation results in a discernible change in bacterial growth or survival. The lack of phenotypes attributable to loss of sRNA function suggests that either sRNAs are wholly dispensable or functional redundancies mask the impact of deleting a single sRNA. We investigated synthetic genetic interactions among sRNA genes in Escherichia coli by constructing pairwise deletions in 54 genes, including 52 sRNAs. Some 1,373 double deletion strains were studied for growth defects under 32 different nutrient stress conditions and revealed 1,131 genetic interactions. In one example, we identified a profound synthetic lethal interaction between ArcZ and CsrC when E. coli was grown on pyruvate, lactate, oxaloacetate, or d-/l-alanine, and we provide evidence that the expression of ppsA is dysregulated in the double deletion background, causing the conditionally lethal phenotype. This work employs a unique platform for studying sRNA-mediated gene regulation and sheds new light on the genetic network of sRNAs that underpins bacterial growth.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Mining RNA-seq data reveals the massive regulon of GcvB small RNA and its physiological significance in maintaining amino acid homeostasis in Escherichia coli

Bacterial small RNAs regulate the expression of multiple genes through imperfect base-pairing with target mRNAs mediated by RNA chaperone proteins such as Hfq. GcvB is the master sRNA regulator of amino acid metabolism and transport in a wide range of Gram-negative bacteria. Recently, independent RNA-seq approaches identified a plethora of transcripts interacting with GcvB in Escherichia coli. In this study, the compilation of RIL-seq, CLASH, and MAPS data sets allowed us to identify GcvB targets with high accuracy. We validated 21 new GcvB targets repressed at the posttranscriptional level, raising the number of direct targets to >50 genes in E. coli. Among its multiple seed sequences, GcvB utilizes either R1 or R3 to regulate most of these targets. Furthermore, we demonstrated that both R1 and R3 seed sequences are required to fully repress the expression of gdhA, cstA, and sucC genes. In contrast, the ilvLXGMEDA polycistronic mRNA is targeted by GcvB through at least four individual binding sites in the mRNA. Finally, we revealed that GcvB is involved in the susceptibility of peptidase-deficient E. coli strain (Δpeps) to Ala-Gln dipeptide by regulating both Dpp dipeptide importer and YdeE dipeptide exporter via R1 and R3 seed sequences, respectively.

Small RNA GcvB Regulates Oxidative Stress Response of Escherichia coli

Small non-translated regulatory RNAs control plenty of bacterial vital activities. The small RNA GcvB has been extensively studied, indicating the multifaceted roles of GcvB beyond amino acid metabolism. However, few reported GcvB-dependent regulation in minimal medium. Here, by applying a high-resolution RNA-seq assay, we compared the transcriptomes of a wild-type Escherichia coli K-12 strain and its gcvB deletion derivative grown in minimal medium and identified putative targets responding to GcvB, including flu, a determinant gene of auto-aggregation. The following molecular studies and the enhanced auto-aggregation ability of the gcvB knockout strain further substantiated the induced expression of these genes. Intriguingly, the reduced expression of OxyR (the oxidative stress regulator) in the gcvB knockout strain was identified to account for the increased expression of flu. Additionally, GcvB was characterized to up-regulate the expression of OxyR at the translational level. Accordingly, compared to the wild type, the GcvB deletion strain was more sensitive to oxidative stress and lost some its ability to eliminate endogenous reactive oxygen species. Taken together, we reveal that GcvB regulates oxidative stress response by up-regulating OxyR expression. Our findings provide an insight into the diversity of GcvB regulation and add an additional layer to the regulation of OxyR.

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.

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

The leucine-responsive regulatory proteins/feast-famine regulatory proteins: an ancient and complex class of transcriptional regulators in bacteria and archaea

Since the discovery of the Escherichia coli leucine-responsive regulatory protein (Lrp) almost 50 years ago, hundreds of Lrp homologs have been discovered, occurring in 45% of sequenced bacteria and almost all sequenced archaea. Lrp-like proteins are often referred to as the feast/famine regulatory proteins (FFRPs), reflecting their common regulatory roles. Acting as either global or local transcriptional regulators, FFRPs detect the environmental nutritional status by sensing small effector molecules (usually amino acids) and regulate the expression of genes involved in metabolism, virulence, motility, nutrient transport, stress tolerance, and antibiotic resistance to implement appropriate behaviors for the specific ecological niche of each organism. Despite FFRPs' complexity, a significant role in gene regulation, and prevalence throughout prokaryotes, the last comprehensive review on this family of proteins was published about a decade ago. In this review, we integrate recent notable findings regarding E. coli Lrp and other FFRPs across bacteria and archaea with previous observations to synthesize a more complete view on the mechanistic details and biological roles of this ancient class of transcription factors.

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2 .

An ArcA-Modulated Small RNA in Pathogenic Escherichia coli K1

Escherichia coli K1 is the leading cause of meningitis in newborns. Understanding the molecular basis of E. coli K1 pathogenicity will help develop treatment of meningitis and prevent neurological sequelae. E. coli K1 replicates in host blood and forms a high level of bacteremia to cause meningitis in human. However, the mechanisms that E. coli K1 employs to sense niche signals for survival in host blood are poorly understood. We identified one intergenic region in E. coli K1 genome that encodes a novel small RNA, sRNA-17. The expression of sRNA-17 was downregulated by ArcA in microaerophilic blood. The ΔsRNA-17 strain grew better in blood than did the wild-type strain and enhanced invasion frequency in human brain microvascular endothelial cells. Transcriptome analyses revealed that sRNA-17 regulates tens of differentially expressed genes. These data indicate that ArcA downregulates the sRNA-17 expression to benefit bacterial survival in blood and penetration of the blood–brain barrier. Our findings reveal a signaling mechanism in E. coli K1 for host adaptation.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

Impact of Different Trace Elements on the Growth and Proteome of Two Strains of Granulicella, Class "Acidobacteriia"

Acidobacteria represents one of the most dominant bacterial groups across diverse ecosystems. However, insight into their ecology and physiology has been hampered by difficulties in cultivating members of this phylum. Previous cultivation efforts have suggested an important role of trace elements for the proliferation of Acidobacteria, however, the impact of these metals on their growth and metabolism is not known. In order to gain insight into this relationship, we evaluated the effect of trace element solution SL10 on the growth of two strains (5B5 and WH15) of Acidobacteria belonging to the genus Granulicella and studied the proteomic responses to manganese (Mn). Granulicella species had highest growth with the addition of Mn, as well as higher tolerance to this metal compared to seven other metal salts. Variations in tolerance to metal salt concentrations suggests that Granulicella sp. strains possess different mechanisms to deal with metal ion homeostasis and stress. Furthermore, Granulicella sp. 5B5 might be more adapted to survive in an environment with higher concentration of several metal ions when compared to Granulicella sp. WH15. The proteomic profiles of both strains indicated that Mn was more important in enhancing enzymatic activity than to protein expression regulation. In the genomic analyses, we did not find the most common transcriptional regulation of Mn homeostasis, but we found candidate transporters that could be potentially involved in Mn homeostasis for Granulicella species. The presence of such transporters might be involved in tolerance to higher Mn concentrations, improving the adaptability of bacteria to metal enriched environments, such as the decaying wood-rich Mn environment from which these two Granulicella strains were isolated.

Plugging Small RNAs into the Network

Small RNAs (sRNAs) have been discovered in every bacterium examined and have been shown to play important roles in the regulation of a diverse range of behaviors, from metabolism to infection. However, despite a wide range of available techniques for discovering and validating sRNA regulatory interactions, only a minority of these molecules have been well characterized. In part, this is due to the nature of posttranscriptional regulation: the activity of an sRNA depends on the state of the transcriptome as a whole, so characterization is best carried out under the conditions in which it is naturally active. In this issue of mSystems, Arrieta-Ortiz and colleagues (M. L. Arrieta-Ortiz, C. Hafemeister, B. Shuster, N. S. Baliga, et al., mSystems 5:e00057-20, 2020, https://doi.org/10.1128/mSystems.00057-20) present a network inference approach based on estimating sRNA activity across transcriptomic compendia. This shows promise not only for identifying new sRNA regulatory interactions but also for pinpointing the conditions in which these interactions occur, providing a new avenue toward functional characterization of sRNAs.

Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.

IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Applying a New REFINE Approach in Zymomonas mobilis Identifies Novel sRNAs That Confer Improved Stress Tolerance Phenotypes

As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes. However, a general challenge prevents the more widespread use of sRNA engineering strategies: mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery. This makes it difficult to understand how to efficiently identify useful sRNAs to engineer a phenotype of interest. To help address this, we developed a forward systems approach to identify naturally occurring sRNAs relevant to a desired phenotype: RNA-seq Examiner for Phenotype-Informed Network Engineering (REFINE). This pipeline uses existing RNA-seq datasets under different growth conditions. It filters the total transcriptome to locate and rank regulatory-RNA-containing regions that can influence a metabolic phenotype of interest, without the need for previous mechanistic characterization. Application of this approach led to the uncovering of six novel sRNAs related to ethanol tolerance in non-model ethanol-producing bacterium Zymomonas mobilis. Furthermore, upon overexpressing multiple sRNA candidates predicted by REFINE, we demonstrate improved ethanol tolerance reflected by up to an approximately twofold increase in relative growth rate compared to controls not expressing these sRNAs in 7% ethanol (v/v) RMG-supplemented media. In this way, the REFINE approach informs strain-engineering strategies that we expect are applicable for general strain engineering.

A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps

Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSO_FCM-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSO_FCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSO_FCM-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency.

Synthetic negative feedback circuits using engineered small RNAs

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.

Meta-analytic framework for modeling genetic coexpression dynamics

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third “coordinator” gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Mimicry, deception and competition: The life of competing endogenous RNAs

Since their discovery, small regulatory RNAs (sRNAs) were thought to be regulated exclusively at the transcriptional level. However, accumulating data from recent reports indicate that posttranscriptional signals can also modulate the function and stability of sRNAs. One of these posttranscriptional signals are competing endogenous RNAs (ceRNAs). Commonly called RNA sponges, ceRNAs can effectively sequester sRNAs and prevent them from binding their cognate target messenger RNAs (mRNAs). Subsequently, they prevent sRNA-dependent regulation of translation and stability of mRNA targets. While some ceRNAs seem to be expressed constitutively, others are intricately regulated according to environmental conditions. The outcome of ceRNA binding to a sRNA reaches beyond simple sequestration. Various effects observed on sRNA functions extend from reducing transcriptional noise to promote RNA turnover. Here, we present a historical perspective of the discovery of ceRNAs in eukaryotic organisms and mainly focus on the synthesis and function of select, well-described, ceRNAs in bacterial cells. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Translation > Translation Regulation RNA Turnover and Surveillance > Regulation of RNA Stability.

Bacterial Small RNAs in Mixed Regulatory Networks

Small regulatory RNAs are now recognized as key regulators of gene expression in bacteria. They accumulate under specific conditions, most often because their synthesis is directly controlled by transcriptional regulators, including but not limited to alternative sigma factors and response regulators of two-component systems. In turn, small RNAs regulate, mostly at the posttranscriptional level, expression of multiple genes, among which are genes encoding transcriptional regulators. Small RNAs are thus embedded in mixed regulatory circuits combining transcriptional and posttranscriptional controls, and whose properties are discussed here.

GcvB small RNA uses two distinct seed regions to regulate an extensive targetome

GcvB small RNA is described as post-transcriptional regulator of 1-2% of all mRNAs in Escherichia coli and Salmonella Typhimurium. At least 24 GcvB:mRNA interactions have been validated in vivo, establishing the largest characterized sRNA targetome. By performing MS2-affinity purification coupled with RNA sequencing (MAPS) technology, we identified seven additional mRNAs negatively regulated by GcvB in E. coli. Contrary to the vast majority of previously known targets, which pair to the well-conserved GcvB R1 region, we validated four mRNAs targeted by GcvB R3 region. This indicates that base-pairing through R3 seed sequence seems relatively common. We also noticed unusual GcvB pairing sites in the coding sequence of two target mRNAs. One of these target mRNAs has a pairing site displaying a unique ACA motif, suggesting that GcvB could hijack a translational enhancer element. The second target mRNA is likely regulated via an active RNase E-mediated mRNA degradation mechanism. Remarkably, we confirmed the importance of the sRNA sponge SroC in the fine-tuning control of GcvB activity in function of growth conditions such as growth phase and nutrient availability.

Identification of New Bacterial Small RNA Targets Using MS2 Affinity Purification Coupled to RNA Sequencing

Small regulatory RNAs (sRNAs) are ubiquitous regulatory molecules expressed in living cells. In prokaryotes, sRNAs usually bind to target mRNAs to either promote their degradation or interfere with translation initiation. Because a single sRNA can regulate a considerable number of target mRNAs, we seek to identify those targets rapidly and reliably. Here, we present a robust method based on the co-purification of target mRNAs bound to MS2-tagged sRNAs expressed in vivo. After purification of the tagged-sRNA, we use RNAseq to determine the identity of all RNA interacting partners and their enrichment level. We describe how to analyze the RNAseq data through the Galaxy Project Platform bioinformatics tools to identify new mRNA targets. This technique is applicable to most sRNAs of E. coli and Salmonella.

Unraveling the evolution and coevolution of small regulatory RNAs and coding genes in Listeria

BACKGROUND

Small regulatory RNAs (sRNAs) are widely found in bacteria and play key roles in many important physiological and adaptation processes. Studying their evolution and screening for events of coevolution with other genomic features is a powerful way to better understand their origin and assess a common functional or adaptive relationship between them. However, evolution and coevolution of sRNAs with coding genes have been sparsely investigated in bacterial pathogens.

RESULTS

We designed a robust and generic phylogenomics approach that detects correlated evolution between sRNAs and protein-coding genes using their observed and inferred patterns of presence-absence in a set of annotated genomes. We applied this approach on 79 complete genomes of the Listeria genus and identified fifty-two accessory sRNAs, of which most were present in the Listeria common ancestor and lost during Listeria evolution. We detected significant coevolution between 23 sRNA and 52 coding genes and inferred the Listeria sRNA-coding genes coevolution network. We characterized a main hub of 12 sRNAs that coevolved with genes encoding cell wall proteins and virulence factors. Among them, an sRNA specific to L. monocytogenes species, rli133, coevolved with genes involved either in pathogenicity or in interaction with host cells, possibly acting as a direct negative post-transcriptional regulation.

CONCLUSIONS

Our approach allowed the identification of candidate sRNAs potentially involved in pathogenicity and host interaction, consistent with recent findings on known pathogenicity actors. We highlight four sRNAs coevolving with seven internalin genes, some of which being important virulence factors in Listeria.

Impact of bacterial sRNAs in stress responses

Bacterial life is harsh and involves numerous environmental and internal challenges that are perceived as stresses. Consequently, adequate responses to survive, cope with, and counteract stress conditions have evolved. In the last few decades, a class of small, non-coding RNAs (sRNAs) has been shown to be involved as key players in stress responses. This review will discuss — primarily from an enterobacterial perspective — selected stress response pathways that involve antisense-type sRNAs. These include themes of how bacteria deal with severe envelope stress, threats of DNA damage, problems with poisoning due to toxic sugar intermediates, issues of iron homeostasis, and nutrient limitation/starvation. The examples discussed highlight how stress relief can be achieved, and how sRNAs act mechanistically in regulatory circuits. For some cases, we will propose scenarios that may suggest why contributions from post-transcriptional control by sRNAs, rather than transcriptional control alone, appear to be a beneficial and universally selected feature.

The sibling sRNAs NgncR_162 and NgncR_163 of Neisseria gonorrhoeae participate in the expression control of metabolic, transport and regulatory proteins

Neisseria gonorrhoeae is the causative agent of gonorrhoea, the second most common bacterial sexually transmitted disease. Riboregulation mediated by small regulatory RNAs (sRNAs) is increasingly recognized as an important means of gene expression control in this human-restricted pathogen. sRNAs act at the post-transcriptional level by base-pairing with their target mRNAs which affects translation initiation and/or mRNA stability. In this study we initiated the characterization of a pair of highly conserved sRNAs of N. gonorrhoeae which exhibit redundant functions in the control of a common set of target genes. The identified targets of the sibling sRNAs NgncR_162 and NgncR_163 participate in basic metabolic processes including the methylcitrate and citrate cycle, aa uptake and degradation, and also in transcription regulation. Our data indicate that the sibling sRNAs control their targets via direct base-pairing between the same single-stranded domain(s) of the sRNA and the ribosome binding site in the 5'-untranslated region of the mRNA.

Genome-Wide Detection of Small Regulatory RNAs in Deep-Sea Bacterium Shewanella piezotolerans WP3

Shewanella are one of the most abundant Proteobacteria in the deep-sea and are renowned for their versatile electron accepting capacities. The molecular mechanisms involved in their adaptation to diverse and extreme environments are not well understood. Small non-coding RNAs (sRNAs) are known for modulating the gene expression at transcriptional and posttranscriptional levels, subsequently playing a key role in microbial adaptation. To understand the potential roles of sRNAs in the adaptation of Shewanella toward deep-sea environments, here an in silico approach was utilized to detect the sRNAs in the genome of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant deep-sea iron reducing bacterium. After scanning 3673 sets of 5' and 3' UTRs of orthologous genes, 209 sRNA candidates were identified with high confidence in S. piezotolerans WP3. About 92% (193 out of 209) of these putative sRNAs belong to the class trans-encoded RNAs, suggesting that trans-regulatory RNAs are the dominant class of sRNAs in S. piezotolerans WP3. The remaining 16 cis-regulatory RNAs were validated through quantitative polymerase chain reaction. Five cis-sRNAs were further shown to act as cold regulated sRNAs. Our study provided additional evidence at the transcriptional level to decipher the microbial adaptation mechanisms to extreme environmental conditions.

Engineering bacterial translation initiation - Do we have all the tools we need?

BACKGROUND

Reliable tools that allow precise and predictable control over gene expression are critical for the success of nearly all bioengineering applications. Translation initiation is the most regulated phase during protein biosynthesis, and is therefore a promising target for exerting control over gene expression. At the translational level, the copy number of a protein can be fine-tuned by altering the interaction between the translation initiation region of an mRNA and the ribosome. These interactions can be controlled by modulating the mRNA structure using numerous approaches, including small molecule ligands, RNAs, or RNA-binding proteins. A variety of naturally occurring regulatory elements have been repurposed, facilitating advances in synthetic gene regulation strategies. The pursuit of a comprehensive understanding of mechanisms governing translation initiation provides the framework for future engineering efforts.

SCOPE OF REVIEW

Here we outline state-of-the-art strategies used to predictably control translation initiation in bacteria. We also discuss current limitations in the field and future goals.

MAJOR CONCLUSIONS

Due to its function as the rate-determining step, initiation is the ideal point to exert effective translation regulation. Several engineering tools are currently available to rationally design the initiation characteristics of synthetic mRNAs. However, improvements are required to increase the predictability, effectiveness, and portability of these tools.

GENERAL SIGNIFICANCE

Predictable and reliable control over translation initiation will allow greater predictability when designing, constructing, and testing genetic circuits. The ability to build more complex circuits predictably will advance synthetic biology and contribute to our fundamental understanding of the underlying principles of these processes. "This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue.

Increased production of L-serine in Escherichia coli through Adaptive Laboratory Evolution

L-serine is a promising building block biochemical with a high theoretical production yield from glucose. Toxicity of L-serine is however prohibitive for high-titer production in E. coli. Here, E. coli lacking L-serine degradation pathways was evolved for improved tolerance by gradually increasing L-serine concentration from 3 to 100g/L using adaptive laboratory evolution (ALE). Genome sequencing of isolated clones revealed multiplication of genetic regions, as well as mutations in thrA, thereby showing a potential mechanism of serine inhibition. Additional mutations were evaluated by MAGE combined with amplicon sequencing, revealing role of rho, lrp, pykF, eno, and rpoB on tolerance and fitness in minimal medium. Production using the tolerant strains resulted in 37g/L of L-serine with a 24% mass yield. The resulting titer is similar to the highest production reported for any organism thereby highlighting the potential of ALE for industrial biotechnology.

Small Non-Coding RNAs: New Insights in Modulation of Host Immune Response by Intracellular Bacterial Pathogens

Pathogenic bacteria possess intricate regulatory networks that temporally control the production of virulence factors and enable the bacteria to survive and proliferate within host cell. Small non-coding RNAs (sRNAs) have been identified as important regulators of gene expression in diverse biological contexts. Recent research has shown bacterial sRNAs involved in growth and development, cell proliferation, differentiation, metabolism, cell signaling, and immune response through regulating protein–protein interactions or via their ability to base pair with RNA and DNA. In this review, we provide a brief overview of mechanism of action employed by immune-related sRNAs, their known functions in immunity, and how they can be integrated into regulatory circuits that govern virulence, which will facilitate our understanding of pathogenesis and the development of novel, more effective therapeutic approaches to treat infections caused by intracellular bacterial pathogens.

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

Bacterial regulatory small RNAs (sRNAs) play important roles in gene regulation and are frequently connected to the expression of virulence factors in diverse bacteria. Only a few sRNAs have been described for Pasteurellaceae pathogens and no in-depth analysis of sRNAs has been described for Actinobacillus pleuropneumoniae, the causative agent of porcine pleuropneumonia, responsible for considerable losses in the swine industry. To search for sRNAs in A. pleuropneumoniae, we developed a strategy for the computational analysis of the bacterial genome by using four algorithms with different approaches, followed by experimental validation. The coding strand and expression of 17 out of 23 RNA candidates were confirmed by Northern blotting, RT-PCR, and RNA sequencing. Among them, two are likely riboswitches, three are housekeeping regulatory RNAs, two are the widely studied GcvB and 6S sRNAs, and 10 are putative novel trans-acting sRNAs, never before described for any bacteria. The latter group has several potential mRNA targets, many of which are involved with virulence, stress resistance, or metabolism, and connect the sRNAs in a complex gene regulatory network. The sRNAs identified are well conserved among the Pasteurellaceae that are evolutionarily closer to A. pleuropneumoniae and/or share the same host. Our results show that the combination of newly developed computational programs can be successfully utilized for the discovery of novel sRNAs and indicate an intricate system of gene regulation through sRNAs in A. pleuropneumoniae and in other Pasteurellaceae, thus providing clues for novel aspects of virulence that will be explored in further studies.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Inference of Gene Regulatory Network Based on Local Bayesian Networks

The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.

Mechanical Genomics Identifies Diverse Modulators of Bacterial Cell Stiffness

Bacteria must maintain mechanical integrity to withstand the large osmotic pressure differential across the cell membrane and wall. Although maintaining mechanical integrity is critical for proper cellular function, a fact exploited by prominent cell-wall-targeting antibiotics, the proteins that contribute to cellular mechanics remain unidentified. Here, we describe a high-throughput optical method for quantifying cell stiffness and apply this technique to a genome-wide collection of ∼4,000 Escherichia coli mutants. We identify genes with roles in diverse functional processes spanning cell-wall synthesis, energy production, and DNA replication and repair that significantly change cell stiffness when deleted. We observe that proteins with biochemically redundant roles in cell-wall synthesis exhibit different stiffness defects when deleted. Correlating our data with chemical screens reveals that reducing membrane potential generally increases cell stiffness. In total, our work demonstrates that bacterial cell stiffness is a property of both the cell wall and broader cell physiology and lays the groundwork for future systematic studies of mechanoregulation.

sRNA roles in regulating transcriptional regulators: Lrp and SoxS regulation by sRNAs

Post-transcriptional regulation of transcription factors contributes to regulatory circuits. We created translational reporter fusions for multiple central regulators in Escherichia coli and examined the effect of Hfq-dependent non-coding RNAs on these fusions. This approach yields an 'RNA landscape,' identifying Hfq-dependent sRNAs that regulate a given fusion. No significant sRNA regulation of crp or fnr was detected. hns was regulated only by DsrA, as previously reported. Lrp and SoxS were both found to be regulated post-transcriptionally. Lrp, ' L: eucine-responsive R: egulatory P: rotein,' regulates genes involved in amino acid biosynthesis and catabolism and other cellular functions. sRNAs DsrA, MicF and GcvB each independently downregulate the lrp translational fusion, confirming previous reports for MicF and GcvB. MicF and DsrA interact with an overlapping site early in the lrp ORF, while GcvB acts upstream at two independent sites in the long lrp leader. Surprisingly, GcvB was found to be responsible for significant downregulation of lrp after oxidative stress; MicF also contributed. SoxS, an activator of genes used to combat oxidative stress, is negatively regulated by sRNA MgrR. This study demonstrates that while not all global regulators are subject to sRNA regulation, post-transcriptional control by sRNAs allows multiple environmental signals to affect synthesis of the transcriptional regulator.

RNA-Seq analysis uncovers non-coding small RNA system of Mycobacterium neoaurum in the metabolism of sterols to accumulate steroid intermediates

BACKGROUND

Understanding the metabolic mechanism of sterols to produce valuable steroid intermediates in mycobacterium by a noncoding small RNA (sRNA) view is still limited. In the work, RNA-seq was implemented to investigate the noncoding transcriptome of Mycobacterium neoaurum (Mn) in the transformation process of sterols to valuable steroid intermediates, including 9α-hydroxy-4-androstene-3,17-dione (9OHAD), 1,4-androstadiene-3,17-dione (ADD), and 22-hydroxy-23, 24-bisnorchola-1,4-dien-3-one (1,4-BNA).

RESULTS

A total of 263 sRNA candidates were predicted from the intergenic regions in Mn. Differential expression of sRNA candidates was explored in the wide type Mn with vs without sterol addition, and the steroid intermediate producing Mn strains vs wide type Mn with sterol addition, respectively. Generally, sRNA candidates were differentially expressed in various strains, but there were still some shared candidates with outstandingly upregulated or downregulated expression in these steroid producing strains. Accordingly, four regulatory networks were constructed to reveal the direct and/or indirect interactions between sRNA candidates and their target genes in four groups, including wide type Mn with vs without sterol addition, 9OHAD, ADD, and BNA producing strains vs wide type Mn with sterol addition, respectively. Based on these constructed networks, several highly focused sRNA candidates were discovered to be prevalent in the networks, which showed comprehensive regulatory roles in various cellular processes, including lipid transport and metabolism, amino acid transport and metabolism, signal transduction, cell envelope biosynthesis and ATP synthesis. To explore the functional role of sRNA candidates in Mn cells, we manipulated the overexpression of candidates 131 and 138 in strain Mn-9OHAD, which led to enhanced production of 9OHAD from 1.5- to 2.3-fold during 6 d' fermentation and a slight effect on growth rate.

CONCLUSIONS

This study revealed the complex and important regulatory roles of noncoding small RNAs in the metabolism of sterols to produce steroid intermediates in Mn, further analysis of which will promote the better understanding about the molecular metabolism of these sRNA candidates and open a broad range of opportunities in the field.

Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli

As antibiotic resistance is increasingly becoming a public health concern, an improved understanding of the bacterial DNA damage response (DDR), which is commonly targeted by antibiotics, could be of tremendous therapeutic value. Although the genetic components of the bacterial DDR have been studied extensively in isolation, how the underlying biological pathways interact functionally remains unclear. Here, we address this by performing systematic, unbiased, quantitative synthetic genetic interaction (GI) screens and uncover widespread changes in the GI network of the entire genomic integrity apparatus of Escherichia coli under standard and DNA-damaging growth conditions. The GI patterns of untreated cultures implicated two previously uncharacterized proteins (YhbQ and YqgF) as nucleases, whereas reorganization of the GI network after DNA damage revealed DDR roles for both annotated and uncharacterized genes. Analyses of pan-bacterial conservation patterns suggest that DDR mechanisms and functional relationships are near universal, highlighting a modular and highly adaptive genomic stress response.

Ribonucleoprotein particles of bacterial small non-coding RNA IsrA (IS61 or McaS) and its interaction with RNA polymerase core may link transcription to mRNA fate

Coupled transcription and translation in bacteria are tightly regulated. Some small RNAs (sRNAs) control aspects of this coupling by modifying ribosome access or inducing degradation of the message. Here, we show that sRNA IsrA (IS61 or McaS) specifically associates with core enzyme of RNAP in vivo and in vitro, independently of σ factor and away from the main nucleic-acids-binding channel of RNAP. We also show that, in the cells, IsrA exists as ribonucleoprotein particles (sRNPs), which involve a defined set of proteins including Hfq, S1, CsrA, ProQ and PNPase. Our findings suggest that IsrA might be directly involved in transcription or can participate in regulation of gene expression by delivering proteins associated with it to target mRNAs through its interactions with transcribing RNAP and through regions of sequence-complementarity with the target. In this eukaryotic-like model only in the context of a complex with its target, IsrA and its associated proteins become active. In this manner, in the form of sRNPs, bacterial sRNAs could regulate a number of targets with various outcomes, depending on the set of associated proteins.

2-Aminoacrylate Stress Induces a Context-Dependent Glycine Requirement in ridA Strains of Salmonella enterica

The reactive enamine 2-aminoacrylate (2AA) is a metabolic stressor capable of damaging cellular components. Members of the broadly conserved Rid (RidA/YER057c/UK114) protein family mitigate 2AA stress in vivo by facilitating enamine and/or imine hydrolysis. Previous work showed that 2AA accumulation in ridA strains of Salmonella enterica led to the inactivation of multiple target enzymes, including serine hydroxymethyltransferase (GlyA). However, the specific cause of a ridA strain's inability to grow during periods of 2AA stress had yet to be determined. Work presented here shows that glycine supplementation suppressed all 2AA-dependent ridA strain growth defects described to date. Depending on the metabolic context, glycine appeared to suppress ridA strain growth defects by eliciting a GcvB small RNA-dependent regulatory response or by serving as a precursor to one-carbon units produced by the glycine cleavage complex (GCV). In either case, the data suggest that GlyA is the most physiologically sensitive target of 2AA inactivation in S. enterica. The universally conserved nature of GlyA among free-living organisms highlights the importance of RidA in mitigating 2AA stress.

IMPORTANCE

The RidA stress response prevents 2-aminoacrylate (2AA) damage from occurring in prokaryotes and eukaryotes alike. 2AA inactivation of serine hydroxymethyltransferase (GlyA) from Salmonella enterica restricts glycine and one-carbon production, ultimately reducing fitness of the organism. The cooccurrence of genes encoding 2AA production enzymes and serine hydroxy-methyltransferase (SHMT) in many genomes may in part underlie the evolutionary selection for Rid proteins to maintain appropriate glycine and one-carbon metabolism throughout life.

Accelerating Discovery and Functional Analysis of Small RNAs with New Technologies

Over the past decade, bacterial small RNAs (sRNAs) have gone from a biological curiosity to being recognized as a major class of regulatory molecules. High-throughput methods for sampling the transcriptional output of bacterial cells demonstrate that sRNAs are universal features of bacterial transcriptomes, are plentiful, and appear to vary extensively over evolutionary time. With ever more bacteria coming under study, the question becomes how can we accelerate the discovery and functional characterization of sRNAs in diverse organisms. New technologies built on high-throughput sequencing are emerging that can rapidly provide global insight into the numbers and functions of sRNAs in bacteria of interest, providing information that can shape hypotheses and guide research. In this review, we describe recent developments in transcriptomics (RNA-seq) and functional genomics that we expect to help us develop an integrated, systems-level view of sRNA biology in bacteria.

Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks.

sRNA-Mediated Regulation of P-Fimbriae Phase Variation in Uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) are capable of occupying physiologically distinct intracellular and extracellular niches within the urinary tract. This feat requires the timely regulation of gene expression and small RNAs (sRNAs) are known to mediate such rapid adjustments in response to changing environmental cues. This study aimed to uncover sRNA-mediated gene regulation in the UPEC strain UTI89, during infection of bladder epithelial cells. Hfq is an RNA chaperone known to facilitate and stabilize sRNA and target mRNA interactions with bacterial cells. The co-immunoprecipitation and high throughput RNA sequencing of Hfq bound sRNAs performed in this study, revealed distinct sRNA profiles in UPEC in the extracellular and intracellular environments. Our findings emphasize the importance of studying regulatory sRNAs in a biologically relevant niche. This strategy also led to the discovery of a novel virulence-associated trans-acting sRNA—PapR. Deletion of papR was found to enhance adhesion of UTI89 to both bladder and kidney cell lines in a manner independent of type-1 fimbriae. We demonstrate PapR mediated posttranscriptional repression of the P-fimbriae phase regulator gene papI and postulate a role for such regulation in fimbrial cross-talk at the population level in UPEC. Our results further implicate the Leucine responsive protein (LRP) as a transcriptional activator regulating PapR expression. Our study reports, for the first time, a role for sRNAs in regulation of P-fimbriae phase variation and emphasizes the importance of studying pathogenesis-specific sRNAs within a relevant biological niche.

Recent years have seen an increasing emphasis placed on the role of small RNAs (sRNAs) in the regulation of bacterial gene expression and stress adaptation. The advent of high-throughput sequencing methods has now made it possible to directly monitor the appearance of potentially virulence-associated sRNAs that may contribute to rapid adaptation of the pathogen to a changing environment during infection. Uropathogenic Escherichia coli (UPEC) are presumably exposed to a deluge of stimuli from epithelial cell contact, urine and host immune factors and we asked if any regulatory sRNAs would play a role in the transition of UPEC from the extracellular niche to the intracellular one. This study employs co-immunoprecipitation using the RNA chaperone Hfq to identify novel virulence-associated sRNAs in intracellular UPEC, followed by high-throughput RNA-seq. We report the identification of a novel sRNA that we designate PapR (P-fimbriae regulator) and elaborate on this discovery by demonstrating a role for PapR in regulation of P-fimbriae—a UPEC surface virulence factor. The results presented in this study offer new insights into the molecular mechanisms of UPEC pathogenesis and a role for sRNA mediated regulation of virulence factors.