Evolutionary conservation of domain-domain interactions

BACKGROUND: Recently, there has been much interest in relating domain-domain interactions (DDIs) to protein-protein interactions (PPIs) and vice versa, in an attempt to understand the molecular basis of PPIs. RESULTS: Here we map structurally derived DDIs onto the cellular PPI networks of different organisms and demonstrate that there is a catalog of domain pairs that is used to mediate various interactions in the cell. We show that these DDIs occur frequently in protein complexes and that homotypic interactions (of a domain with itself) are abundant. A comparison of the repertoires of DDIs in the networks of Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens shows that many DDIs are evolutionarily conserved. CONCLUSION: Our results indicate that different organisms use the same 'building blocks' for PPIs, suggesting that the functionality of many domain pairs in mediating protein interactions is maintained in evolution

Cross-Regulation between Bacteria and Phages at a Posttranscriptional Level.

The study of bacteriophages (phages) and prophages has provided key insights into almost every cellular process as well as led to the discovery of unexpected new mechanisms and the development of valuable tools. This is exemplified for RNA-based regulation. For instance, the characterization and exploitation of the anti-phage CRISPR systems is revolutionizing molecular biology. Phage-encoded proteins such as the RNA binding MS2 protein, which is broadly used to isolate tagged RNAs, also have been developed as valuable tools. Hfq, the RNA chaperone protein central to the function of many base pairing small RNAs (sRNAs), was first characterized as a bacterial host factor required for Qβ phage replication. The ongoing studies of RNAs are continuing to reveal regulatory connections between infecting phages, prophages and bacteria and to provide novel insights. There are bacterial and prophage sRNAs that regulate prophage genes, which impact bacterial virulence as well as bacterial cell killing. Conversely, phage- and prophage-encoded sRNAs modulate the expression of bacterial genes modifying metabolism. An interesting subcategory of the prophage-encoded sRNAs are sponge RNAs that inhibit the activities of bacterial-encoded sRNAs. Phages also affect post-transcriptional regulation in bacteria through proteins that inhibit or alter the activities of key bacterial proteins involved in posttranscriptional regulation. However, what is most exciting about phage and prophage research, given the millions of phage-encoded genes that have not yet been characterized, is the vast potential for discovering new RNA regulators and novel mechanisms and for gaining insight into the evolution of regulatory RNAs

Small RNAs Establish Delays and Temporal Thresholds in Gene Expression

Non-coding RNAs are crucial regulators of gene expression in prokaryotes and eukaryotes, but it remains poorly understood how they affect the dynamics of transcriptional networks. We analyzed the temporal characteristics of the cyanobacterial iron stress response by mathematical modeling and quantitative experimental analyses, and focused on the role of a recently discovered small non-coding RNA, IsrR. We found that IsrR is responsible for a pronounced delay in the accumulation of isiA mRNA encoding the late-phase stress protein, IsiA, and that it ensures a rapid decline in isiA levels once external stress triggers are removed. These kinetic properties allow the system to selectively respond to sustained (as opposed to transient) stimuli, and thus establish a temporal threshold, which prevents energetically costly IsiA accumulation under short-term stress conditions. Biological information is frequently encoded in the quantitative aspects of intracellular signals (e.g., amplitude and duration). Our simulations reveal that competitive inhibition and regulated degradation allow intracellular regulatory networks to efficiently discriminate between transient and sustained inputs

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

Design of simple synthetic RNA thermometers for temperature-controlled gene expression in Escherichia coli

RNA thermometers are thermosensors that regulate gene expression by temperature-induced changes in RNA conformation. Naturally occurring RNA thermometers exhibit complex secondary structures which are believed to undergo a series of gradual structural changes in response to temperature shifts. Here, we report the de novo design of considerably simpler RNA thermometers that provide useful RNA-only tools to regulate bacterial gene expression by a shift in the growth temperature. We show that a single small stem-loop structure containing the ribosome binding site is sufficient to construct synthetic RNA thermometers that work efficiently at physiological temperatures. Our data suggest that the thermometers function by a simple melting mechanism and thus provide minimum size on/off switches to experimentally induce or repress gene expression by temperature

Gifsy-1 Prophage IsrK with Dual Function as Small and Messenger RNA Modulates Vital Bacterial Machineries

While an increasing number of conserved small regulatory RNAs (sRNAs) are known to function in general bacterial physiology, the roles and modes of action of sRNAs from horizontally acquired genomic regions remain little understood. The IsrK sRNA of Gifsy-1 prophage of Salmonella belongs to the latter class. This regulatory RNA exists in two isoforms. The first forms, when a portion of transcripts originating from isrK promoter reads-through the IsrK transcription-terminator producing a translationally inactive mRNA target. Acting in trans, the second isoform, short IsrK RNA, binds the inactive transcript rendering it translationally active. By switching on translation of the first isoform, short IsrK indirectly activates the production of AntQ, an antiterminator protein located upstream of isrK. Expression of antQ globally interferes with transcription termination resulting in bacterial growth arrest and ultimately cell death. Escherichia coli and Salmonella cells expressing AntQ display condensed chromatin morphology and localization of UvrD to the nucleoid. The toxic phenotype of AntQ can be rescued by co-expression of the transcription termination factor, Rho, or RNase H, which protects genomic DNA from breaks by resolving R-loops. We propose that AntQ causes conflicts between transcription and replication machineries and thus promotes DNA damage. The isrK locus represents a unique example of an island-encoded sRNA that exerts a highly complex regulatory mechanism to tune the expression of a toxic protein

TargetRNA: a tool for predicting targets of small RNA action in bacteria

Many small RNA (sRNA) genes in bacteria act as posttranscriptional regulators of target messenger RNAs. Here, we present TargetRNA, a web tool for predicting mRNA targets of sRNA action in bacteria. TargetRNA takes as input a genomic sequence that may correspond to an sRNA gene. TargetRNA then uses a dynamic programming algorithm to search each annotated message in a specified genome for mRNAs that evince basepair-binding potential to the input sRNA sequence. Based on the calculated basepair-binding potential of each message with the given sRNA regulator, TargetRNA outputs a ranked list of candidate mRNA targets along with the predicted basepairing interaction of each target to the sRNA. The predictive performance of TargetRNA has been validated experimentally in several bacterial organisms. TargetRNA is freely available at http://snowwhite.wellesley.edu/targetRNA

Clusters of microRNAs emerge by new hairpins in existing transcripts

Genetic linkage may result in the expression of multiple products from a polycistronic transcript, under the control of a single promoter. In animals, protein-coding polycistronic transcripts are rare. However, microRNAs are frequently clustered in the genomes of animals, and these clusters are often transcribed as a single unit. The evolution of microRNA clusters has been the subject of much speculation, and a selective advantage of clusters of functionally related microRNAs is often proposed. However, the origin of microRNA clusters has not been so far explored. Here, we study the evolution of microRNA clusters in Drosophila melanogaster. We observed that the majority of microRNA clusters arose by the de novo formation of new microRNA-like hairpins in existing microRNA transcripts. Some clusters also emerged by tandem duplication of a single microRNA. Comparative genomics show that these clusters are unlikely to split or undergo rearrangements. We did not find any instances of clusters appearing by rearrangement of pre-existing microRNA genes. We propose a model for microRNA cluster evolution in which selection over one of the microRNAs in the cluster interferes with the evolution of the other linked microRNAs. Our analysis suggests that the study of microRNAs and small RNAs must consider linkage associations

One Decade of Development and Evolution of MicroRNA Target Prediction Algorithms

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA-mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future direction

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.National Institutes of Health. National Centers for Biomedical Computing (Informatics for Integrating Biology and the Bedside)National Institutes of Health (U.S.) (grant U54LM008748