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.
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.
Abstract 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.
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.
MicroRNAs (miRNAs) are ∼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.
Abstract 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. Availability: All RepTar's predictions are available for simple and advanced querying at http://reptar.ekmd.huji.ac.il Contact: hanahm@ekmd.huji.ac.il Supplementary Information: Supplementary data are available at Bioinformatics online.
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.
