Yael Altuvia

Clustering and conservation patterns of human microRNAs

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.

A STRUCTURE-BASED ALGORITHM TO PREDICT POTENTIAL BINDING PEPTIDES TO MHC MOLECULES WITH HYDROPHOBIC BINDING POCKETS

Binding of peptides to MHC class I molecules is a prerequisite for their recognition by cytotoxic T cells. Consequently, identification of peptides that will bind to a given MHC molecule must constitute a central part of any algorithm for prediction of T-cell antigenic peptides based on the amino acid sequence of the protein. Binding motifs, defined by anchor positions only, have proven to be insufficient to ensure binding, suggesting that other positions along the peptide sequence also affect peptide-MHC interaction. The second phase of prediction schemes therefore take into account the effect of all positions along the peptide sequence, and are based on position-dependent-coefficients that are used in the calculation of a peptide score. These coefficients can be extracted from a large ensemble of binding sequences that were tested experimentally, or derived from structural considerations, as in the algorithm developed by us recently. This algorithm uses the coordinates of solved complexes to evaluate the interactions of peptide amino acids with MHC contact residues, and results in a peptide score that reflects its binding energy. Here we present our analysis for peptide binding to four MHC alleles (HLA-A2, HLA-A68, HLA-B27 and H-2Kb), and compare the predictions of the algorithm to experimental binding data. The algorithm performs successfully in predicting peptide binding to MHC molecules with hydrophobic binding pockets but not when MHC molecules with hydrophilic, charged pockets are considered. For MHC molecules with hydrophobic pockets it is demonstrated how the algorithm succeeds in distinguishing binding from non-binding peptides, and in high ranking of immunogenic peptides within all overlapping same-length peptides spanning their respective protein sequences. The latter property of the algorithm makes it a useful tool in the rational design of peptide vaccines aimed at T-cell immunity.

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.

Global Mapping of Small RNA-Target Interactions in Bacteria

Summary 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.

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.

Sequence features that correlate with MHC restriction

Identification of common sequence motifs in antigenic peptides restricted to a specific class II molecule has not been easy due to the large variation in length and sequence that is observed in these peptides. The goal of this study is to develop an automated computerized method for the identification of sequence features and structural determinants that play a role in the MHC restriction of helper T-cell antigenic peptides. For this, we compiled an extended database of helper T-cell sites, including the information on MHC restriction, when available. Two groups of peptides are assigned to each MHC type: (1) peptides that bind to that MHC molecule to elicit a T-cell response, and (2) peptides that were shown experimentally either not to bind to or not to elicit a T-cell proliferative response in association with that MHC molecule. We search for common motifs in the group of binding peptides, and identify significant motifs that are frequent among these peptides but almost absent in the group of non-binding peptides. A motif consists of physical-chemical and structural properties that may be responsible for binding specificity and can be extracted from sequence data, such as, hydrophobicity, charge, hydrogen bonding capability, etc. The first search is performed on the non-aligned binding peptides. Next, the sequences are aligned according to an identified motif and a search for additional, conserved, properties is performed. The statistical significance of the motifs is evaluated as well as their compatibility with published experimental results on substitution effects. Here we demonstrate the general scheme of the analysis and results for I-Ek and I-Ak associated peptides.

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 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.

Examination of possible structural constraints of MHC-binding peptides by assessment of their native structure within their source proteins

Antigenic peptides bind to major histocompatibility complex (MHC) molecules as a prerequisite for their presentation to T cells. In this study, we investigate possible structural preferences of MHC‐binding peptides by examining the conformation space defined by the structures of these peptides within their native source proteins. Comparison of the conformation space of the native structures of MHC‐binding nonamers and a corresponding conformation space defined by a random set of nonamers showed no significant difference. This suggests that the environment of the MHC binding groove has evolved to bind peptides with essentially any “structural background.” A slight tendency for an extended β‐conformation at positions 8 and 9 was observed for the set of native structures. We suggest that such a preference may facilitate the binding of the C‐terminal anchor position of processed peptides into the corresponding specificity pocket. MHC‐binding peptides represent examples of short subsequences that are present in two different structural environments: within their native protein and within the MHC binding groove. Comparison of the native and of the bound structure of the peptides showed that peptides up to 14 residues long may adopt different conformations within different protein environments. This has direct implications for structure prediction algorithms. Proteins 2001;45:47–54.

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.