Global RNA interactome of Salmonella discovers a 5' UTR sponge for the MicF small RNA that connects membrane permeability to transport capacity.

The envelope of Gram-negative bacteria is a vital barrier that must balance protection and nutrient uptake. Small RNAs are crucial regulators of the envelope composition and function. Here, using RIL-seq to capture the Hfq-mediated RNA-RNA interactome in Salmonella enterica, we discover envelope-related riboregulators, including OppX. We show that OppX acts as an RNA sponge of MicF sRNA, a prototypical porin repressor. OppX originates from the 5' UTR of oppABCDF, encoding the major inner-membrane oligopeptide transporter, and sequesters MicF's seed region to derepress the synthesis of the porin OmpF. Intriguingly, OppX operates as a true sponge, storing MicF in an inactive complex without affecting its levels or stability. Conservation of the opp-OppX-MicF-ompF axis in related bacteria suggests that it serves an important mechanism, adjusting envelope porosity to specific transport capacity. These data also highlight the resource value of this Salmonella RNA interactome, which will aid in unraveling RNA-centric regulation in enteric pathogens.

RIL-seq reveals extensive involvement of small RNAs in virulence and capsule regulation in hypervirulent Klebsiella pneumoniae.

Hypervirulent Klebsiella pneumoniae (hvKp) can infect healthy individuals, in contrast to classical strains that commonly cause nosocomial infections. The recent convergence of hypervirulence with carbapenem-resistance in K. pneumoniae can potentially create 'superbugs' that are challenging to treat. Understanding virulence regulation of hvKp is thus critical. Accumulating evidence suggest that posttranscriptional regulation by small RNAs (sRNAs) plays a role in bacterial virulence, but it has hardly been studied in K. pneumoniae. We applied RIL-seq to a prototypical clinical isolate of hvKp to unravel the Hfq-dependent RNA-RNA interaction (RRI) network. The RRI network is dominated by sRNAs, including predicted novel sRNAs, three of which we validated experimentally. We constructed a stringent subnetwork composed of RRIs that involve at least one hvKp virulence-associated gene and identified the capsule gene loci as a hub target where multiple sRNAs interact. We found that the sRNA OmrB suppressed both capsule production and hypermucoviscosity when overexpressed. Furthermore, OmrB base-pairs within kvrA coding region and partially suppresses translation of the capsule regulator KvrA. This agrees with current understanding of capsule as a major virulence and fitness factor. It emphasizes the intricate regulatory control of bacterial phenotypes by sRNAs, particularly of genes critical to bacterial physiology and virulence.

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.

A defense-offense multi-layered regulatory switch in a pathogenic bacterium.

Cells adapt to environmental changes by efficiently adjusting gene expression programs. Staphylococcus aureus, an opportunistic pathogenic bacterium, switches between defensive and offensive modes in response to quorum sensing signal. We identified and studied the structural characteristics and dynamic properties of the core regulatory circuit governing this switch by deterministic and stochastic computational methods, as well as experimentally. This module, termed here Double Selector Switch (DSS), comprises the RNA regulator RNAIII and the transcription factor Rot, defining a double-layered switch involving both transcriptional and post-transcriptional regulations. It coordinates the inverse expression of two sets of target genes, immuno-modulators and exotoxins, expressed during the defensive and offensive modes, respectively. Our computational and experimental analyses show that the DSS guarantees fine-tuned coordination of the inverse expression of its two gene sets, tight regulation, and filtering of noisy signals. We also identified variants of this circuit in other bacterial systems, suggesting it is used as a molecular switch in various cellular contexts and offering its use as a template for an effective switching device in synthetic biology studies.

Interactions between distant ceRNAs in regulatory networks.

Competing endogenous RNAs (ceRNAs) were recently introduced as RNA transcripts that affect each other's expression level through competition for their microRNA (miRNA) coregulators. This stems from the bidirectional effects between miRNAs and their target RNAs, where a change in the expression level of one target affects the level of the miRNA regulator, which in turn affects the level of other targets. By the same logic, miRNAs that share targets compete over binding to their common targets and therefore also exhibit ceRNA-like behavior. Taken together, perturbation effects could propagate in the posttranscriptional regulatory network through a path of coregulated targets and miRNAs that share targets, suggesting the existence of distant ceRNAs. Here we study the prevalence of distant ceRNAs and their effect in cellular networks. Analyzing the network of miRNA-target interactions deciphered experimentally in HEK293 cells, we show that it is a dense, intertwined network, suggesting that many nodes can act as distant ceRNAs of one another. Indeed, using gene expression data from a perturbation experiment, we demonstrate small, yet statistically significant, changes in gene expression caused by distant ceRNAs in that network. We further characterize the magnitude of the propagated perturbation effect and the parameters affecting it by mathematical modeling and simulations. Our results show that the magnitude of the effect depends on the generation and degradation rates of involved miRNAs and targets, their interaction rates, the distance between the ceRNAs and the topology of the network. Although demonstrated for a miRNA-mRNA regulatory network, our results offer what to our knowledge is a new view on various posttranscriptional cellular networks, expanding the concept of ceRNAs and implying possible distant cross talk within the network, with consequences for the interpretation of indirect effects of gene perturbation.

A genome-wide view of the expression and processing patterns of Thermus thermophilus HB8 CRISPR RNAs.

The CRISPR-Cas system represents an RNA-based adaptive immune response system in prokaryotes and archaea. CRISPRs (clustered regularly interspaced short palindromic repeats) consist of arrays of short repeat-sequences interspaced by nonrepetitive short spacers, some of which show sequence similarity to foreign phage genetic elements. Their cistronic transcripts are processed to produce the mature CRISPR RNAs (crRNAs), the elements that confer immunity by base-pairing with exogenous nucleic acids. We characterized the expression and processing patterns of Thermus thermophilus HB8 CRISPRs by using differential deep-sequencing, which differentiates between 5' monophosphate and 5' non-monophosphate-containing RNAs and/or between 3' hydroxyl and 3' non-hydroxyl-containing RNAs. The genome of T. thermophilus HB8 encodes 11 CRISPRs, classified into three distinct repeat-sequence types, all of which were constitutively expressed without deliberately infecting the bacteria with phage. Analysis of the differential deep sequencing data suggested that crRNAs are generated by endonucleolytic cleavage, leaving fragments with 5' hydroxyl and 3' phosphate or 2',3'-cyclic phosphate termini. The 5' ends of all crRNAs are generated by site-specific cleavage 8 nucleotides upstream of the spacer first position; however, the 3' ends are generated by two alternative, repeat-sequence-type-dependent mechanisms. These observations are consistent with the operation of multiple crRNA processing systems within a bacterial strain.

In vivo mapping of RNA-RNA interactions in Staphylococcus aureus using the endoribonuclease III.

Ribonucleases play key roles in gene regulation and in the expression of virulence factors in Staphylococcus aureus. Among these enzymes, the double-strand specific endoribonuclease III (RNase III) is a key mediator of mRNA processing and degradation. Recently, we have defined, direct target sites for RNase III processing on a genome-wide scale in S. aureus. Our approach is based on deep sequencing of cDNA libraries obtained from RNAs isolated by in vivo co-immunoprecipitation with wild-type RNase III and two cleavage-defective mutants. The use of such catalytically inactivated enzymes, which still retain their RNA binding capacity, allows the identification of novel RNA substrates of RNase III. In this report, we will summarize the diversity of RNase III functions, discuss the advantages and the limitations of the approach, and how this strategy identifies novel mRNA targets of small non-coding RNAs in S. aureus.

RepTar: a database of predicted cellular targets of host and viral miRNAs.

Computational identification of putative microRNA (miRNA) targets is an important step towards elucidating miRNA functions. Several miRNA target-prediction algorithms have been developed followed by publicly available databases of these predictions. Here we present a new database offering miRNA target predictions of several binding types, identified by our recently developed modular algorithm RepTar. RepTar is based on identification of repetitive elements in 3'-UTRs and is independent of both evolutionary conservation and conventional binding patterns (i.e. Watson-Crick pairing of 'seed' regions). The modularity of RepTar enables the prediction of targets with conventional seed sites as well as rarer targets with non-conventional sites, such as sites with seed wobbles (G-U pairing in the seed region), 3'-compensatory sites and the newly discovered centered sites. Furthermore, RepTar's independence of conservation enables the prediction of cellular targets of the less evolutionarily conserved viral miRNAs. Thus, the RepTar database contains genome-wide predictions of human and mouse miRNAs as well as predictions of cellular targets of human and mouse viral miRNAs. These predictions are presented in a user-friendly database, which allows browsing through the putative sites as well as conducting simple and advanced queries including data intersections of various types. The RepTar database is available at http://reptar.ekmd.huji.ac.il.

A wide repertoire of miRNA binding sites: prediction and functional implications.

MOTIVATION: Over the past decade, deciphering the roles of microRNAs (miRNAs) has relied heavily upon the identification of their targets. Most of the targets that were computationally and experimentally characterized were evolutionarily conserved 'seed' targets, containing a perfect 6-8 nt match between the miRNA 5(')-region and the messenger RNA (mRNA). Gradually, it has become evident that other types of miRNA binding can confer target regulation, but their characterization has been lagging behind. RESULTS: Here, we complement the putative evolutionarily-conserved seed-containing targets by a wide repertoire of putative targets exhibiting a variety of miRNA binding patterns, predicted by our algorithm RepTar. These include non-conserved sites, 'seed' binding sites with G:U-wobbles within the seed, '3(') compensatory' sites and 'centered' sites. Apart from the centered sites, we demonstrate the functionality of these sites and characterize the target profile of a miRNA by the types of binding sites predicted in its target 3(') UTRs. We find that different miRNAs have individual target profiles, with some more inclined to seed binding and others more inclined to binding through 3(') compensatory sites. This diversity in targeting patterns is also evident within several miRNA families (defined by common seed sequences), leading to divergence in the target sets of members of the same family. The prediction of non-conventional miRNA targets is also beneficial in the search for targets of the non-conserved viral miRNAs. Analyzing the cellular targets of viral miRNAs, we show that viral miRNAs use various binding patterns to exploit cellular miRNA binding sites and suggest roles for these targets in virus-host interactions.

Cytomegalovirus microRNAs facilitate persistent virus infection in salivary glands.

Micro (mi)RNAs are small non-coding RNAs that regulate the expression of their targets' messenger RNAs through both translational inhibition and regulation of target RNA stability. Recently, a number of viruses, particularly of the herpesvirus family, have been shown to express their own miRNAs to control both viral and cellular transcripts. Although some targets of viral miRNAs are known, their function in a physiologically relevant infection remains to be elucidated. As such, no in vivo phenotype of a viral miRNA knock-out mutant has been described so far. Here, we report on the first functional phenotype of a miRNA knock-out virus in vivo. During subacute infection of a mutant mouse cytomegalovirus lacking two viral miRNAs, virus production is selectively reduced in salivary glands, an organ essential for virus persistence and horizontal transmission. This phenotype depends on several parameters including viral load and mouse genetic background, and is abolished by combined but not single depletion of natural killer (NK) and CD4+ T cells. Together, our results point towards a miRNA-based immunoevasion mechanism important for long-term virus persistence.

Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA-RNA Interaction Data.

The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and "other RNAs". We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3' untranslated regions, but also 5' untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions.

The impact of Hfq-mediated sRNA-mRNA interactome on the virulence of enteropathogenic Escherichia coli.

Small RNAs (sRNAs) exert their regulation posttranscriptionally by base pairing with their target mRNAs, often in association with the RNA chaperone protein Hfq. Here, integrating RNA-seq­based technologies and bioinformatics, we deciphered the Hfq-mediated sRNA-target interactome of enteropathogenic Escherichia coli (EPEC). The emerging network comprises hundreds of sRNA-mRNA pairs, including mRNAs of virulence-associated genes interacting with known sRNAs encoded within the core genome, as well as with newly found sRNAs encoded within pathogenicity islands. Some of the sRNAs affect multiple virulence genes, suggesting they function as hubs of virulence control. We further analyzed one such sRNA hub, MgrR, and one of its targets identified here, the major virulence-associated chaperon, cesT. We show that MgrR adjusts the level of EPEC cytotoxicity via regulation of CesT expression. Our results reveal an elaborate sRNA-mRNA interactome controlling the pathogenicity of EPEC and reinforce a role for sRNAs in the control of pathogen-host interaction.

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.

A melanoma multiepitope polypeptide induces specific CD8+ T-cell response.

Strategies using epitope-based vaccination are being considered for melanoma immunotherapy, in an attempt to overcome failure of other modalities. In the present study, we designed and produced a multiepitope polypeptide for melanoma (MEP-mel), which contains three repeats of four antigenic epitopes (gp100: 209-217 (210M); gp100: 280-288 (288V); Mart1: 26-35 (27L); tyrosinase: 368-376 (370D). The peptides were attached to each other by linkers containing sequences recognized by the proteasome, to improve protein cleavage and antigen presentation. The results show that peptide-specific T cells produced IFN-gamma when stimulated with MEP-mel-transfected dendritic cells. The presentation of peptides by MEP-mel-transfected dendritic cells was proteasome-dependent and was more long-lasting than the presentation of exogenously delivered native peptides. When dendritic cells were loaded with MEP-mel protein, weak cross presentation was induced. The production of multiepitope molecules based on several peptides linked by sequences sensitive to proteasomal cleavage represents a promising new tool for the improvement of cancer immunotherapy.

Clustering and conservation patterns of human microRNAs.

MicroRNAs (miRNAs) are approximately 22 nt-long non-coding RNA molecules, believed to play important roles in gene regulation. We present a comprehensive analysis of the conservation and clustering patterns of known miRNAs in human. We show that human miRNA gene clustering is significantly higher than expected at random. A total of 37% of the known human miRNA genes analyzed in this study appear in clusters of two or more with pairwise chromosomal distances of at most 3000 nt. Comparison of the miRNA sequences with their homologs in four other organisms reveals a typical conservation pattern, persistent throughout the clusters. Furthermore, we show enrichment in the typical conservation patterns and other miRNA-like properties in the vicinity of known miRNA genes, compared with random genomic regions. This may imply that additional, yet unknown, miRNAs reside in these regions, consistent with the current recognition that there are overlooked miRNAs. Indeed, by comparing our predictions with cloning results and with identified miRNA genes in other mammals, we corroborate the predictions of 18 additional human miRNA genes in the vicinity of the previously known ones. Our study raises the proportion of clustered human miRNAs that are <3000 nt apart to 42%. This suggests that the clustering of miRNA genes is higher than currently acknowledged, alluding to its evolutionary and functional implications.

Insights from MHC-bound peptides.

Cytotoxic T cells recognize short antigenic peptides, the processing products of protein antigens, when they are bound to major histocompatibility complex (MHC) class I molecules. Peptide binding to MHC molecules has been studied extensively in numerous laboratories, providing vast amounts of sequence and structure data that have been used as a rich source for bioinformatic research. MHC-bound peptides and their flanking sequences provide information about the sequence requirements of the different processing stages, in particular, the cleavage by the proteasome and the binding to MHC molecules. Elucidation of these sequence requirements sheds light on the evolutionary forces that have shaped and designed these peptides, and should lead to the development of an integrative predictive algorithm. Remarkably, the peptide sequence and structure data are also valuable for the study of biological questions that are apparently unrelated to cellular immunity, namely, sequence-structure relationship and genome annotation. Here we describe our computational analyses of MHC-bound peptides, applied to all these biological topics.

Host immune system gene targeting by a viral miRNA.

Virally encoded microRNAs (miRNAs) have recently been discovered in herpesviruses. However, their biological roles are mostly unknown. We developed an algorithm for the prediction of miRNA targets and applied it to human cytomegalovirus miRNAs, resulting in the identification of the major histocompatibility complex class I-related chain B (MICB) gene as a top candidate target of hcmv-miR-UL112. MICB is a stress-induced ligand of the natural killer (NK) cell activating receptor NKG2D and is critical for the NK cell killing of virus-infected cells and tumor cells. We show that hcmv-miR-UL112 specifically down-regulates MICB expression during viral infection, leading to decreased binding of NKG2D and reduced killing by NK cells. Our results reveal a miRNA-based immunoevasion mechanism that appears to be exploited by human cytomegalovirus.

Characterization and prediction of protein-protein interactions within and between complexes.

Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.