TRS: a method for determining transcript termini from RNAtag-seq sequencing data

In bacteria, determination of the 3' termini of transcripts plays an essential role in regulation of gene expression, affecting the functionality and stability of the transcript. Several experimental approaches were developed to identify the 3' termini of transcripts, however, these were applied only to a limited number of bacteria and growth conditions. Here we present a straightforward approach to identify 3' termini from widely available RNA-seq data without the need for additional experiments. Our approach relies on the observation that the RNAtag-seq sequencing protocol results in overabundance of reads mapped to transcript 3' termini. We present TRS (Termini by Read Starts), a computational pipeline exploiting this property to identify 3' termini in RNAtag-seq data, and show that the identified 3' termini are highly reliable. Since RNAtag-seq data are widely available for many bacteria and growth conditions, our approach paves the way for studying bacterial transcription termination in an unprecedented scope.

Prolonged survival of a patient with active MDR-TB HIV co-morbidity: insights from a Mycobacterium tuberculosis strain with a unique genomic deletion

Coinfection of HIV and multidrug-resistant tuberculosis (MDR-TB) presents significant challenges in terms of the treatment and prognosis of tuberculosis, leading to complexities in managing the disease and impacting the overall outcome for TB patients. This study presents a remarkable case of a patient with MDR-TB and HIV coinfection who survived for over 8 years, despite poor treatment adherence and comorbidities. Whole genome sequencing (WGS) of the infecting Mycobacterium tuberculosis (Mtb) strain revealed a unique genomic deletion, spanning 18 genes, including key genes involved in hypoxia response, intracellular survival, immunodominant antigens, and dormancy. This deletion, that we have called "Del-X," potentially exerts a profound influence on the bacterial physiology and its virulence. Only few similar deletions were detected in other non-related Mtb genomes worldwide. In vivo evolution analysis identified drug resistance and metabolic adaptation mutations and their temporal dynamics during the patient's treatment course.

Convergent Within-Host Adaptation of Pseudomonas aeruginosa through the Transcriptional Regulatory Network

Bacteria adapt to their host by mutating specific genes and by reprogramming their gene expression. Different strains of a bacterial species often mutate the same genes during infection, demonstrating convergent genetic adaptation. However, there is limited evidence for convergent adaptation at the transcriptional level. To this end, we utilize genomic data of 114 Pseudomonas aeruginosa strains, derived from patients with chronic pulmonary infection, and the P. aeruginosa transcriptional regulatory network. Relying on loss-of-function mutations in genes encoding transcriptional regulators and predicting their effects through the network, we demonstrate predicted expression changes of the same genes in different strains through different paths in the network, implying convergent transcriptional adaptation. Furthermore, through the transcription lens we associate yet-unknown processes, such as ethanol oxidation and glycine betaine catabolism, with P. aeruginosa host adaptation. We also find that known adaptive phenotypes, including antibiotic resistance, which were identified before as achieved by specific mutations, are achieved also through transcriptional changes. Our study has revealed novel interplay between the genetic and transcriptional levels in host adaptation, demonstrating the versatility of the adaptive arsenal of bacterial pathogens and their ability to adapt to the host conditions in a myriad of ways. IMPORTANCE Pseudomonas aeruginosa causes significant morbidity and mortality. The pathogen's remarkable ability to establish chronic infections greatly depends on its adaptation to the host environment. Here, we use the transcriptional regulatory network to predict expression changes during adaptation. We expand the processes and functions known to be involved in host adaptation. We show that the pathogen modulates the activity of genes during adaptation, including genes implicated in antibiotic resistance, both directly via genomic mutations and indirectly via mutations in transcriptional regulators. Furthermore, we detect a subgroup of genes whose predicted changes in expression are associated with mucoid strains, a major adaptive phenotype in chronic infections. We propose that these genes constitute the transcriptional arm of the mucoid adaptive strategy. Identification of different adaptive strategies utilized by pathogens during chronic infection has major promise in the treatment of persistent infections and opens the door to personalized tailored antibiotic treatment in the future.

Inferring the contribution of small RNAs to changes in gene expression in response to stress

A main strategy of bacteria adapting to environmental changes is the remodeling of their transcriptome. Changes in the transcript levels of specific genes are due to combined effects of various regulators, including small RNAs (sRNAs). sRNAs are post-transcriptional regulators of gene expression that mainly control translation, but also directly and indirectly affect the levels of their target transcripts. Yet, the relative contribution of an sRNA to the total change in the transcript level of a gene upon an environmental change has not been assessed. We present a design of differential gene expression analysis by RNA-seq that allows extracting the contribution of an sRNA to the total change in the transcript level of each gene in response to an environmental change by fitting a linear model to the data. We exemplify this for the sRNA RyhB in cells growing under iron limitation and show a variation among genes in the relative contribution of RyhB to the change in their transcript level upon iron limitation, from subtle to very substantial. Extracting the relative contribution of an sRNA to the total change in expression of genes is important for understanding the integration of regulation by sRNAs with other regulatory mechanisms in the cell.

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.

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-seq–based 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.

Common Adaptive Strategies Underlie Within-Host Evolution of Bacterial Pathogens

Within-host adaptation is a hallmark of chronic bacterial infections, involving substantial genomic changes. Recent large-scale genomic data from prolonged infections allow the examination of adaptive strategies employed by different pathogens and open the door to investigate whether they converge toward similar strategies. Here, we compiled extensive data of whole-genome sequences of bacterial isolates belonging to miscellaneous species sampled at sequential time points during clinical infections. Analysis of these data revealed that different species share some common adaptive strategies, achieved by mutating various genes. Although the same genes were often mutated in several strains within a species, different genes related to the same pathway, structure, or function were changed in other species utilizing the same adaptive strategy (e.g., mutating flagellar genes). Strategies exploited by various bacterial species were often predicted to be driven by the host immune system, a powerful selective pressure that is not species specific. Remarkably, we find adaptive strategies identified previously within single species to be ubiquitous. Two striking examples are shifts from siderophore-based to heme-based iron scavenging (previously shown for Pseudomonas aeruginosa) and changes in glycerol-phosphate metabolism (previously shown to decrease sensitivity to antibiotics in Mycobacterium tuberculosis). Virulence factors were often adaptively affected in different species, indicating shifts from acute to chronic virulence and virulence attenuation during infection. Our study presents a global view on common within-host adaptive strategies employed by different bacterial species and provides a rich resource for further studying these processes.

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.

Hierarchy in Hfq Chaperon Occupancy of Small RNA Targets Plays a Major Role in Their Regulation

Bacterial small RNAs (sRNAs) are posttranscriptional regulators of gene expression that base pair with complementary sequences on target mRNAs, often in association with the chaperone Hfq. Here, using experimentally identified sRNA-target pairs, along with gene expression measurements, we assess basic principles of regulation by sRNAs. We show that the sRNA sequence dictates the target repertoire, as point mutations in the sRNA shift the target set correspondingly. We distinguish two subsets of targets: targets showing changes in expression levels under overexpression of their sRNA regulator and unaffected targets that interact more sporadically with the sRNA. These differences among targets are associated with their Hfq occupancy, rather than with the sRNA-target base-pairing potential. Our results suggest that competition among targets over Hfq binding plays a major role in the regulatory outcome, possibly awarding targets with higher Hfq binding efficiency an advantage in the competition over binding to the sRNA.

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Basic concepts of regulation by small RNAs are revealed from large-scale data

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Small changes in the small RNA sequence shift the target repertoire accordingly

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A regulatory sRNA affects the expression levels of only a subset of its targets

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Competition among targets over Hfq binding plays a major role in their regulation

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 Ubiquitous Platform for Bacterial Nanotube Biogenesis

We have previously described the existence of membranous nanotubes, bridging adjacent bacteria, facilitating intercellular trafficking of nutrients, cytoplasmic proteins, and even plasmids, yet components enabling their biogenesis remain elusive. Here we reveal the identity of a molecular apparatus providing a platform for nanotube biogenesis. Using Bacillus subtilis (Bs), we demonstrate that conserved components of the flagellar export apparatus (FliO, FliP, FliQ, FliR, FlhB, and FlhA), designated CORE, dually serve for flagellum and nanotube assembly. Mutants lacking CORE genes, but not other flagellar components, are deficient in both nanotube production and the associated intercellular molecular trafficking. In accord, CORE components are located at sites of nanotube emergence. Deleting COREs of distinct species established that CORE-mediated nanotube formation is widespread. Furthermore, exogenous COREs from diverse species could restore nanotube generation and functionality in Bs lacking endogenous CORE. Our results demonstrate that the CORE-derived nanotube is a ubiquitous organelle that facilitates intercellular molecular trade across the bacterial kingdom.

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Conserved flagellar CORE components dually serve for flagella and nanotube assembly

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CORE mutants are deficient in nanotube formation and intercellular molecular trade

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CORE-dependent nanotube production is conserved among distinct bacterial species

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The CORE-nanotube organelle can provide a common path for bacterial molecular trade

Assessing the functional association of intronic miRNAs with their host genes

In human, nearly half of the known microRNAs (miRNAs) are encoded within the introns of protein-coding genes. The embedment of these miRNA genes within the sequences of protein-coding genes alludes to a possible functional relationship between intronic miRNAs and their hosting genes. Several studies, using predicted targets, suggested that intronic miRNAs influence their hosts' function either antagonistically or synergistically. New experimental data of miRNA expression patterns and targets enable exploring this putative association by relying on actual data rather than on predictions. Here, our analysis based on currently available experimental data implies that the potential functional association between intronic miRNAs and their hosting genes is limited. For host-miRNA examples where functional associations were detected, it was manifested by either autoregulation, common targets of the miRNA and hosting gene, or through the targeting of transcripts participating in pathways in which the host gene is involved. This low prevalence of functional association is consistent with our observation that many intronic miRNAs have independent transcription start sites and are not coexpressed with the hosting gene. Yet, the intronic miRNAs that do show functional association with their hosts were found to be more evolutionarily conserved compared to other intronic miRNAs. This might suggest a selective pressure to maintain this architecture when it has a functional consequence.

Post-transcriptional 3´-UTR cleavage of mRNA transcripts generates thousands of stable uncapped autonomous RNA fragments

The majority of mammalian genes contain one or more alternative polyadenylation sites. Choice of polyadenylation sites was suggested as one of the underlying mechanisms for generating longer/shorter transcript isoforms. Here, we demonstrate that mature mRNA transcripts can undergo additional cleavage and polyadenylation at a proximal internal site in the 3′-UTR, resulting in two stable, autonomous, RNA fragments: a coding sequence with a shorter 3′-UTR (body) and an uncapped 3′-UTR sequence downstream of the cleavage point (tail). Analyses of the human transcriptome has revealed thousands of such cleavage positions, suggesting a widespread post-transcriptional phenomenon producing thousands of stable 3′-UTR RNA tails that exist alongside their transcripts of origin. By analyzing the impact of microRNAs, we observed a significantly stronger effect for microRNA regulation at the body compared to the tail fragments. Our findings open a variety of future research prospects and call for a new perspective on 3′-UTR-dependent gene regulation.

Most mammalian genes contain alternative polyadenylation sites. Here, the authors provide evidence that mRNA can be cleaved post-transcriptionally to generate mRNAs with shorter 3-´UTRs and stable autonomous uncapped 3´-UTR sequences.

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.

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A widely applicable method for in vivo global mapping of small RNA interactome

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Substantial re-wiring of the network upon changes in cellular conditions

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Regulatory circuits involving two regulators derived from the same transcript

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sRNAs acting in trans are encoded within almost every possible genomic element

Integration of Bacterial Small RNAs in Regulatory Networks

Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA-target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

Degradation of Ndd1 by APC/C(Cdh1) generates a feed forward loop that times mitotic protein accumulation

Ndd1 activates the Mcm1-Fkh2 transcription factor to transcribe mitotic regulators. The anaphase-promoting complex/cyclosome activated by Cdh1 (APC/C(Cdh1)) mediates the degradation of proteins throughout G1. Here we show that the APC/C(Cdh1) ubiquitinates Ndd1 and mediates its degradation, and that APC/C(Cdh1) activity suppresses accumulation of Ndd1 targets. We confirm putative Ndd1 targets and identify novel ones, many of them APC/C(Cdh1) substrates. The APC/C(Cdh1) thus regulates these proteins in a dual manner—both pretranscriptionally and post-translationally, forming a multi-layered feedforward loop (FFL). We predict by mathematical modelling and verify experimentally that this FFL introduces a lag between APC/C(Cdh1) inactivation at the end of G1 and accumulation of genes transcribed by Ndd1 in G2. This regulation generates two classes of APC/C(Cdh1) substrates, early ones that accumulate in S and late ones that accumulate in G2. Our results show how the dual state APC/C(Cdh1) activity is converted into multiple outputs by interactions between its substrates.

Approaches and developments in studying the human microbiome network

The human microbiome is dynamic and unique to each individual, and its role is being increasingly recognized in healthy physiology and in disease, including gastrointestinal and neuropsychiatric disorders. Therefore, characterizing the human microbiome and the factors that shape its bacterial population, how they are related to host-specific attributes, and understanding the ways in which it can be manipulated and the phenotypic consequences of such manipulations are of great importance. Characterization of the microbiome so far has been mostly based on compositional studies alone, where relative abundances of different species are compared in different conditions, such as health and disease. However, inter-relationships among the bacterial species, such as competition and cooperation over metabolic resources, may be an important factor that affects the structure and function of the microbiome. Here we review the network-based approaches in answering such questions and explore the first attempts that focus on the interactions facet, complementing compositional studies, towards understanding the microbiome structure and its complex relationship with the human host.

Stochastic analysis of bistability in coherent mixed feedback loops combining transcriptional and posttranscriptional regulations

Mixed feedback loops combining transcriptional and posttranscriptional regulations are common in cellular regulatory networks. They consist of two genes, encoding a transcription factor and a small noncoding RNA (sRNA), which mutually regulate each other's expression. We present a theoretical and numerical study of coherent mixed feedback loops of this type, in which both regulations are negative. Under suitable conditions, these feedback loops are expected to exhibit bistability, namely, two stable states, one dominated by the transcriptional repressor and the other dominated by the sRNA. We use deterministic methods based on rate equation models, in order to identify the range of parameters in which bistability takes place. However, the deterministic models do not account for the finite lifetimes of the bistable states and the spontaneous, fluctuation-driven transitions between them. Therefore, we use stochastic methods to calculate the average lifetimes of the two states. It is found that these lifetimes strongly depend on rate coefficients such as the transcription rates of the transcriptional repressor and the sRNA. In particular, we show that the fraction of time the system spends in the sRNA-dominated state follows a monotonically decreasing sigmoid function of the transcriptional repressor transcription rate. The biological relevance of these results is discussed in the context of such mixed feedback loops in Escherichia coli. It is shown that the fluctuation-driven transitions and the dependence of some rate coefficients on the biological conditions enable the cells to switch to the state which is better suited for the existing conditions and to remain in that state as long as these conditions persist.

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.

Evolutionary patterns of Escherichia coli small RNAs and their regulatory interactions

Most bacterial small RNAs (sRNAs) are post-transcriptional regulators of gene expression, exerting their regulatory function by base-pairing with their target mRNAs. While it has become evident that sRNAs play central regulatory roles in the cell, little is known about their evolution and the evolution of their regulatory interactions. Here we used the prokaryotic phylogenetic tree to reconstruct the evolutionary history of Escherichia coli sRNAs and their binding sites on target mRNAs. We discovered that sRNAs currently present in E. coli mainly accumulated inside the Enterobacteriales order, succeeding the appearance of other types of noncoding RNAs and concurrently with the evolution of a variant of the Hfq protein exhibiting a longer C-terminal region. Our analysis of the evolutionary ages of sRNA-mRNA interactions revealed that while all sRNAs were evolutionarily older than most of their known binding sites on mRNA targets, for quite a few sRNAs there was at least one binding site that coappeared with or preceded them. It is conceivable that the establishment of these first interactions forced selective pressure on the sRNAs, after which additional targets were acquired by fitting a binding site to the active region of the sRNA. This conjecture is supported by the appearance of many binding sites on target mRNAs only after the sRNA gain, despite the prior presence of the target gene in ancestral genomes. Our results suggest a selective mechanism that maintained the sRNAs across the phylogenetic tree, and shed light on the evolution of E. coli post-transcriptional regulatory network.

Auxiliary tRNAs: large-scale analysis of tRNA genes reveals patterns of tRNA repertoire dynamics

Decoding of all codons can be achieved by a subset of tRNAs. In bacteria, certain tRNA species are mandatory, while others are auxiliary and are variably used. It is currently unknown how this variability has evolved and whether it provides an adaptive advantage. Here we shed light on the subset of auxiliary tRNAs, using genomic data from 319 bacteria. By reconstructing the evolution of tRNAs we show that the auxiliary tRNAs are highly dynamic, being frequently gained and lost along the phylogenetic tree, with a clear dominance of loss events for most auxiliary tRNA species. We reveal distinct co-gain and co-loss patterns for subsets of the auxiliary tRNAs, suggesting that they are subjected to the same selection forces. Controlling for phylogenetic dependencies, we find that the usage of these tRNA species is positively correlated with GC content and may derive directly from nucleotide bias or from preference of Watson-Crick codon-anticodon interactions. Our results highlight the highly dynamic nature of these tRNAs and their complicated balance with codon usage.

Global regulation of transcription by a small RNA: a quantitative view

Small RNAs are integral regulators of bacterial gene expression, the majority of which act posttranscriptionally by basepairing with target mRNAs, altering translation or mRNA stability. 6S RNA, however, is a small RNA that is a transcriptional regulator, acting by binding directly to σ(70)-RNA polymerase (σ(70)-RNAP) and preventing its binding to gene promoters. At the transition from exponential to stationary phase, 6S RNA accumulates and globally downregulates the transcription of hundreds of genes. At the transition from stationary to exponential phase (outgrowth), 6S RNA is released from σ(70)-RNAP, resulting in a fast increase in free σ(70)-RNAP and transcription of many genes. The transition from stationary to exponential phase is sharp, and is thus accessible for experimental study. However, the transition from exponential to stationary phase is gradual and complicated by changes in other factors, making it more difficult to isolate 6S RNA effects experimentally at this transition. Here, we use mathematical modeling and simulation to study the dynamics of 6S RNA-dependent regulation, focusing on transitions in growth mediated by altered nutrient availability. We first show that our model reproduces the sharp increase in σ(70)-RNAP at outgrowth, as well as the behavior of two experimentally tested mutants, thus justifying its use for characterizing the less accessible dynamics of the transition from exponential to stationary phase. We characterize the dynamics of the two transitions for Escherichia coli wild-type, as well as for mutants with various 6S RNA-RNAP affinities, demonstrating that the 6S RNA regulation mechanism is generally robust to a wide range of such mutations, although the level of regulation at single promoters and their resulting expression fold change will be altered with changes in affinity. Our results provide insight into the potential advantage of transcription regulation by 6S RNA, as it enables storage and efficient release of σ(70)-RNAP during transitions in nutrient availability, which is likely to give a competitive advantage to cells encountering diverse environmental conditions.

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.

Competition between small RNAs: a quantitative view

Two major classes of small regulatory RNAs--small interfering RNAs (siRNAs) and microRNA (miRNAs)--are involved in a common RNA interference processing pathway. Small RNAs within each of these families were found to compete for limiting amounts of shared components, required for their biogenesis and processing. Association with Argonaute (Ago), the catalytic component of the RNA silencing complex, was suggested as the central mechanistic point in RNA interference machinery competition. Aiming to better understand the competition between small RNAs in the cell, we present a mathematical model and characterize a range of specific cell and experimental parameters affecting the competition. We apply the model to competition between miRNAs and study the change in the expression level of their target genes under a variety of conditions. We show quantitatively that the amount of Ago and miRNAs in the cell are dominant factors contributing greatly to the competition. Interestingly, we observe what to our knowledge is a novel type of competition that takes place when Ago is abundant, by which miRNAs with shared targets compete over them. Furthermore, we use the model to examine different interaction mechanisms that might operate in establishing the miRNA-Ago complexes, mainly those related to their stability and recycling. Our model provides a mathematical framework for future studies of competition effects in regulation mechanisms involving small RNAs.

Codon usage bias in prokaryotic pyrimidine-ending codons is associated with the degeneracy of the encoded amino acids

Synonymous codons are unevenly distributed among genes, a phenomenon termed codon usage bias. Understanding the patterns of codon bias and the forces shaping them is a major step towards elucidating the adaptive advantage codon choice can confer at the level of individual genes and organisms. Here, we perform a large-scale analysis to assess codon usage bias pattern of pyrimidine-ending codons in highly expressed genes in prokaryotes. We find a bias pattern linked to the degeneracy of the encoded amino acid. Specifically, we show that codon-pairs that encode two- and three-fold degenerate amino acids are biased towards the C-ending codon while codons encoding four-fold degenerate amino acids are biased towards the U-ending codon. This codon usage pattern is widespread in prokaryotes, and its strength is correlated with translational selection both within and between organisms. We show that this bias is associated with an improved correspondence with the tRNA pool, avoidance of mis-incorporation errors during translation and moderate stability of codon–anticodon interaction, all consistent with more efficient translation.

Reduced polymorphism in domains involved in protein-protein interactions

Genome sequencing of various individuals or isolates of the same species allows studying the polymorphism level of specific proteins and protein domains. Here we ask whether domains that are known to be involved in mediating protein-protein interactions show lower polymorphism than other domains. To this end we take advantage of a recent genome sequence dataset of 39 Saccahromyces cerevisiae strains and the experimentally determined protein interaction network of the laboratory strain. We analyze the polymorphism in domain residues involved in interactions at various levels of resolution, depending on their likelihood to be interaction mediators. We find that domains involved in interactions are less polymorphic than other domains. Furthermore, as the likelihood of a residue to be involved in interaction increases, its polymorphism decreases. Our results suggest that purifying selection operates on domains capable of mediating protein interactions to maintain their function.

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.

A dynamic view of domain-motif interactions

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.

Domain-motif interactions are instrumental for many central cellular processes, and are therefore tightly regulated. Phosphorylation events are known modulators of protein-protein interactions in general, including domain-motif interactions. Here, we addressed the association of phosphorylation and domain-motif interaction taking a motif-centred view. We integrated human domain-motif interaction and phosphorylation data for four representative domains (SH2, WW, SH3 and PDZ), and showed that the adjacency between phosphorylation and domain-motif interactions is extensive, suggesting interesting functional links between them that extend the classical and widely studied phospho-regulation of SH2 or WW domain-motif interactions. Furthermore, we show that such interaction-regulation units may function as double switches, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. These latter interaction-regulation units are more conserved in evolution than the individual units comprising them. Assuming that the four analyzed domain-motif interaction types are reliable representatives of such interactions, our results support the existence of units comprising motifs and associated phosphorylation sites, in which the regulation of domain-motif interaction is inherent.

A functional selection model explains evolutionary robustness despite plasticity in regulatory networks

Evolutionary rewiring of regulatory networks is an important source of diversity among species. Previous evidence suggested substantial divergence of regulatory networks across species. However, systematically assessing the extent of this plasticity and its functional implications has been challenging due to limited experimental data and the noisy nature of computational predictions. Here, we introduce a novel approach to study cis-regulatory evolution, and use it to trace the regulatory history of 88 DNA motifs of transcription factors across 23 Ascomycota fungi. While motifs are conserved, we find a pervasive gain and loss in the regulation of their target genes. Despite this turnover, the biological processes associated with a motif are generally conserved. We explain these trends using a model with a strong selection to conserve the overall function of a transcription factor, and a much weaker selection over the specific genes it targets. The model also accounts for the turnover of bound targets measured experimentally across species in yeasts and mammals. Thus, selective pressures on regulatory networks mostly tolerate local rewiring, and may allow for subtle fine-tuning of gene regulation during evolution.

Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles

Background

It is widely acknowledged that synonymous codons are used unevenly among genes in a genome. In organisms under translational selection, genes encoding highly expressed proteins are enriched with specific codons. This phenomenon, termed codon usage bias, is common to many organisms and has been recognized as influencing cellular fitness. This suggests that the global extent of codon usage bias of an organism might be associated with its phenotypic traits.

Results

To test this hypothesis we used a simple measure for assessing the extent of codon bias of an organism, and applied it to hundreds of sequenced prokaryotes. Our analysis revealed a large variability in this measure: there are organisms showing very high degrees of codon usage bias and organisms exhibiting almost no differential use of synonymous codons among different genes. Remarkably, we found that the extent of codon usage bias corresponds to the lifestyle of the organism. Especially, organisms able to live in a wide range of habitats exhibit high extents of codon usage bias, consistent with their need to adapt efficiently to different environments. Pathogenic prokaryotes also demonstrate higher extents of codon usage bias than non-pathogenic prokaryotes, in accord with the multiple environments that many pathogens occupy. Our results show that the previously observed correlation between growth rate and metabolic variability is attributed to their individual associations with codon usage bias.

Conclusions

Our results suggest that the extent of codon usage bias of an organism plays a role in the adaptation of prokaryotes to their environments.

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.

Comparative functional genomics of the fission yeasts

The fission yeast clade--comprising Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus--occupies the basal branch of Ascomycete fungi and is an important model of eukaryote biology. A comparative annotation of these genomes identified a near extinction of transposons and the associated innovation of transposon-free centromeres. Expression analysis established that meiotic genes are subject to antisense transcription during vegetative growth, which suggests a mechanism for their tight regulation. In addition, trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response. Differences in gene content and regulation also explain why, unlike the budding yeast of Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source. These analyses elucidate the genome structure and gene regulation of fission yeast and provide tools for investigation across the Schizosaccharomyces clade.

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.

Modularity and directionality in genetic interaction maps

Motivation: Genetic interactions between genes reflect functional relationships caused by a wide range of molecular mechanisms. Large-scale genetic interaction assays lead to a wealth of information about the functional relations between genes. However, the vast number of observed interactions, along with experimental noise, makes the interpretation of such assays a major challenge.

Results: Here, we introduce a computational approach to organize genetic interactions and show that the bulk of observed interactions can be organized in a hierarchy of modules. Revealing this organization enables insights into the function of cellular machineries and highlights global properties of interaction maps. To gain further insight into the nature of these interactions, we integrated data from genetic screens under a wide range of conditions to reveal that more than a third of observed aggravating (i.e. synthetic sick/lethal) interactions are unidirectional, where one gene can buffer the effects of perturbing another gene but not vice versa. Furthermore, most modules of genes that have multiple aggravating interactions were found to be involved in such unidirectional interactions. We demonstrate that the identification of external stimuli that mimic the effect of specific gene knockouts provides insights into the role of individual modules in maintaining cellular integrity.

Availability: We designed a freely accessible web tool that includes all our findings, and is specifically intended to allow effective browsing of our results (http://compbio.cs.huji.ac.il/GIAnalysis).

Contact:maya.schuldiner@weizmann.ac.il; hanahm@ekmd.huji.ac.il; nir@cs.huji.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

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.

Stochastic analysis of the SOS response in Escherichia coli

BACKGROUND

DNA damage in Escherichia coli evokes a response mechanism called the SOS response. The genetic circuit of this mechanism includes the genes recA and lexA, which regulate each other via a mixed feedback loop involving transcriptional regulation and protein-protein interaction. Under normal conditions, recA is transcriptionally repressed by LexA, which also functions as an auto-repressor. In presence of DNA damage, RecA proteins recognize stalled replication forks and participate in the DNA repair process. Under these conditions, RecA marks LexA for fast degradation. Generally, such mixed feedback loops are known to exhibit either bi-stability or a single steady state. However, when the dynamics of the SOS system following DNA damage was recently studied in single cells, ordered peaks were observed in the promoter activity of both genes (Friedman et al., 2005, PLoS Biol. 3(7):e238). This surprising phenomenon was masked in previous studies of cell populations. Previous attempts to explain these results harnessed additional genes to the system and deployed complex deterministic mathematical models that were only partially successful in explaining the results.

METHODOLOGY/PRINCIPAL FINDINGS

Here we apply stochastic methods, which are better suited for dynamic simulations of single cells. We show that a simple model, involving only the basic components of the circuit, is sufficient to explain the peaks in the promoter activities of recA and lexA. Notably, deterministic simulations of the same model do not produce peaks in the promoter activities.

CONCLUSION/SIGNIFICANCE

We conclude that the double negative mixed feedback loop with auto-repression accounts for the experimentally observed peaks in the promoter activities. In addition to explaining the experimental results, this result shows that including additional regulations in a mixed feedback loop may dramatically change the dynamic functionality of this regulatory module. Furthermore, our results suggests that stochastic fluctuations strongly affect the qualitative behavior of important regulatory modules even under biologically relevant conditions, thus emphasizing the importance of stochastic analysis of regulatory circuits.

A novel Bayesian DNA motif comparison method for clustering and retrieval

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.

Regulation of gene expression plays a central role in the activity of living cells and in their response to internal (e.g., cell division) or external (e.g., stress) stimuli. Key players in determining gene-specific regulation are transcription factors that bind sequence-specific sites on the DNA, modulating the expression of nearby genes. To understand the regulatory program of the cell, we need to identify these transcription factors, when they act, and on which genes. Transcription regulatory maps can be assembled by computational analysis of experimental data, by discovering the DNA recognition sequences (motifs) of transcription factors and their occurrences along the genome. Such an analysis usually results in a large number of overlapping motifs. To reconstruct regulatory maps, it is crucial to combine similar motifs and to relate them to transcription factors. To this end we developed an accurate fully-automated method, termed BLiC, based upon an improved similarity measure for comparing DNA motifs. By applying it to genome-wide data in yeast, we identified the DNA motifs of transcription factors and their putative target genes. Finally, we analyze motifs of transcription factor that alter their target genes under different conditions, and show how cells adjust their regulatory program in response to environmental changes.

Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence

The emergence of pathogenic strains of enteric bacteria and their adaptation to unique niches are associated with the acquisition of foreign DNA segments termed 'genetic islands'. We explored these islands for the occurrence of small RNA (sRNA) encoding genes. Previous systematic screens for enteric bacteria sRNAs were mainly carried out using the laboratory strain Escherichia coli K12, leading to the discovery of approximately 80 new sRNA genes. These searches were based on conservation within closely related members of enteric bacteria and thus, sRNAs, unique to pathogenic strains were excluded. Here we describe the identification and characterization of 19 novel unique sRNA genes encoded within the 'genetic islands' of the virulent strain Salmonella typhimurium. We show that the expression of many of the island-encoded genes is associated with stress conditions and stationary phase. Several of these sRNA genes are induced when Salmonella resides within macrophages. One sRNA, IsrJ, was further examined and found to affect the translocation efficiency of virulence-associated effector proteins into nonphagocytic cells. In addition, we report that unlike the majority of the E. coli sRNAs that are trans regulators, many of the island-encoded sRNAs affect the expression of cis-encoded genes. Our study suggests that the island encoded sRNA genes play an important role within the network that regulates bacterial adaptation to environmental changes and stress conditions and thus controls virulence.

Regulation of gene expression by small non-coding RNAs: a quantitative view

The importance of post-transcriptional regulation by small non-coding RNAs has recently been recognized in both pro- and eukaryotes. Small RNAs (sRNAs) regulate gene expression post-transcriptionally by base pairing with the mRNA. Here we use dynamical simulations to characterize this regulation mode in comparison to transcriptional regulation mediated by protein–DNA interaction and to post-translational regulation achieved by protein–protein interaction. We show quantitatively that regulation by sRNA is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses. Our analysis indicates that the half-life of the sRNA–mRNA complex and the ratio of their production rates determine the steady-state level of the target protein, suggesting that regulation by sRNA may provide fine-tuning of gene expression. We also describe the network of regulation by sRNA in Escherichia coli, and integrate it with the transcription regulation network, uncovering mixed regulatory circuits, such as mixed feed-forward loops. The integration of sRNAs in feed-forward loops provides tight repression, guaranteed by the combination of transcriptional and post-transcriptional regulations.

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.

Co-evolution of transcription factors and their targets depends on mode of regulation

Analysis of transcription regulatory networks in γ-proteobacteria reveals that repressors co-evolve tightly with their target genes, whereas activators can be lost independently of their targets.

Background

Differences in the transcription regulation network are at the root of much of the phenotypic variation observed among organisms. These differences may be achieved either by changing the repertoire of regulators and/or their targets, or by rewiring the network. Following these changes and studying their logic is crucial for understanding the evolution of regulatory networks.

Results

We use the well characterized transcription regulatory network of Escherichia coli K12 and follow the evolutionary changes in the repertoire of regulators and their targets across a large number of fully sequenced γ-proteobacteria. By focusing on close relatives of E. coli K12, we study the dynamics of the evolution of transcription regulation across a relatively short evolutionary timescale. We show significant differences in the evolution of repressors and activators. Repressors are only lost from a genome once their targets have themselves been lost, or once the network has significantly rewired. In contrast, activators are often lost even when their targets remain in the genome. As a result, E. coli K12 repressors that regulate many targets are rarely absent from organisms that are closely related to E. coli K12, while activators with a similar number of targets are often absent in these organisms.

Conclusion

We demonstrate that the mode of regulation exerted by transcription factors has a strong effect on their evolution. Repressors co-evolve tightly with their target genes. In contrast, activators can be lost independently of their targets. In fact, loss of an activator can lead to efficient shutdown of an unnecessary pathway.

Evolutionary conservation of domain-domain interactions

Mapping of domain-domain interactions onto the cellular protein-protein interaction networks of different organisms demonstrates that there is a catalogue of domain pairs that is used for mediating various interactions in the cell

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.

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.

Proteins of the same fold and unrelated sequences have similar amino acid composition

It is well established that there is a relationship between the amino acid composition of a protein and its structural class (i.e., alpha, beta, alpha + beta, or alpha/beta). Several studies have even shown the power of amino acid composition in predicting the secondary structure class of a protein. Herein, we show that significant similarity in amino acid composition exists not only between proteins of the same class, but even between proteins of the same fold. To test conjectural explanations for this phenomenon, we analyzed a set of structurally similar proteins that are dissimilar in sequence. Based on this analysis, we suggest that specific residues that are involved in intramolecular interactions may account for this surprising relationship between composition and structure.

Towards an integrated protein-protein interaction network: a relational Markov network approach

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.

Assigning functions to genes: identification of S-phase expressed genes in Leishmania major based on post-transcriptional control elements

Assigning functions to genes is one of the major challenges of the post-genomic era. Usually, functions are assigned based on similarity of the coding sequences to sequences of known genes, or by identification of transcriptional cis-regulatory elements that are known to be associated with specific pathways or conditions. In trypanosomatids, where regulation of gene expression takes place mainly at the post-transcriptional level, new approaches for function assignment are needed. Here we demonstrate the identification of novel S-phase expressed genes in Leishmania major, based on a post-transcriptional control element that was recognized in Crithidia fasciculata as involved in the cell cycle-dependent expression of several nuclear and mitochondrial S-phase expressed genes. Hypothesizing that a similar regulatory mechanism is manifested in L.major, we have applied a computational search for similar control elements in the genome of L.major. Our computational scan yielded 132 genes, of which 33% are homologues of known DNA metabolism genes and 63% lack any annotation. Experimental testing of seven of these genes revealed that their mRNAs cycle throughout the cell cycle, reaching a maximum level during S-phase or just prior to it. It is suggested that screening for post-transcriptional control elements associated with a specific function provides an efficient method for assigning functions to trypanosomatid genes.

Ab initio prediction of transcription factor targets using structural knowledge

Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid–nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys₂His₂ Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys₂His₂ transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins.

Cells respond to dynamic changes in their environment by invoking various cellular processes, coordinated by a complex regulatory program. A main component of this program is the regulation of transcription, which is mainly accomplished by transcription factors that bind the DNA in the vicinity of genes. To better understand transcriptional regulation, advanced computational approaches are needed for linking between transcription factors and their targets. The authors describe a novel approach by which the binding site of a given transcription factor can be characterized without previous experimental binding data. This approach involves learning a set of context-specific amino acid–nucleotide recognition preferences that, when combined with the sequence and structure of the protein, can predict its specific binding preferences. Applying this approach to the Cys₂His₂ Zinc Finger protein family demonstrated its genome-wide potential by automatically predicting the direct targets of 29 regulators in the genome of the fruit fly Drosophila melanogaster. At present, with the availability of many genome sequences, there are numerous proteins annotated as transcription factors based on their sequence alone. This approach offers a promising direction for revealing the targets of these factors and for understanding their roles in the cellular network.