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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. TRS is a new method for determining 3’ transcript termini in bacteria, using data generated by the RNAtag-seq protocol. This methodology opens the door to study the evolution of transcription termini and their condition-dependent dynamics.

Compilations of domain-domain interactions based on solved structures suggest there are distinct domain pairs that are used repeatedly in different protein contexts to mediate protein-protein interactions. However, not all protein pairs with the corresponding domains that can potentially mediate interaction do interact, even when they are colocalized and coexpressed. It is conceivable that there are structural and sequence features, below the domain level, that play a role in determining the potential of domains to mediate protein-protein interactions. Here, we discover such features by comparing domains that, on the one hand, mediate homodimerization of proteins and, on the other, occur in different proteins that are documented as monomers. Intriguingly, this comparison uncovered surface loops that can be considered as determinants of the interactions. There are enabling loops, which mediate the domain interactions, and disabling loops that prevent the interactions. The presence of the enabling/disabling loops is consistent with the fulfillment/prevention of the interaction and is highly preserved in evolution. This suggests that, along with the preservation of structural elements that enable interaction, evolution maintains elements intended to prevent unwanted interactions. The enabling and disabling loops discovered in this study have implications in prediction of protein-protein interactions, by pointing to the protein regions that determine the interaction. Our results extend the hierarchy of attributes that collectively establish the modularity of domain-mediated protein-protein interactions.

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.

The RNA-binding protein CsrA regulates the expression of hundreds of genes in several bacterial species, thus controlling virulence and other processes. However, the outcome of the CsrA-mRNA interactions is modulated by competing small RNAs and other factors through mechanisms that are only partially understood. Here, we show that CsrA accumulates in a dynamic membraneless compartment in cells of E. coli and other pathogenic species. In addition to CsrA, the compartment contains components of the RNA-degrading complex (degradosome), regulatory small RNAs, and selected mRNAs. Formation of the compartment is associated with a switch between promoting and repressing virulence gene expression by CsrA. We suggest that similar CsrA switches may be widespread in diverse bacteria. The RNA-binding protein CsrA switches from upregulation to downregulation of virulence genes in bacteria through unclear mechanisms. Here, the authors show that the switch between promoting and repressing virulence gene expression involves formation of a membraneless compartment containing CsrA, mRNAs, small regulatory RNAs and degradosome components.

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.

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. 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.

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. Synopsis Living cells are self‐regulated by interactions between different molecules. Until very recently, most research has focused on transcription regulation interactions and on protein–protein interactions, which in many cases are involved in post‐translational regulation. During the last years it has become evident that another type of interaction plays a prominent role in the regulation of cellular processes, manifested by small RNA (sRNA) molecules that base pair with the mRNA and regulate gene expression post‐transcriptionally, influencing translation or mRNA stability. This mode of regulation was found in both pro‐ and eukaryotes (for review see Storz et al , 2005 ). In this paper, we focus on bacterial sRNAs, and in particular on regulatory interactions found in Escherichia coli , for which most experimental data on sRNAs are available. At present there are about 80 known sRNAs in E. coli (for review see Gottesman, 2005 ; Storz et al , 2005 ). These molecules are 50–400 nucleotides long and many of them are evolutionary conserved (Hershberg et al , 2003 ). Most of those for which some functional knowledge has been acquired were often shown to act as inhibitors of translation by base pairing with the mRNA in the ribosome‐binding site (for review see Gottesman, 2005 ). We describe the network of sRNA–target regulatory interactions in E. coli , and study the kinetics of this regulation mechanism in comparison to transcription regulation mediated by protein–DNA interaction and post‐translational regulation mediated by protein–protein interaction. For this we describe the three regulatory mechanisms by mathematical models, followed by simulations using average kinetic parameters based on experimental data (Altuvia et al , 1997 ; Altuvia and Wagner, 2000 ; Alon, 2006 ). We show that there are measurable qualitative differences between the three regulation mechanisms, both in response time and in effectiveness. In regard to effectiveness, we find that transcriptional regulation is generally the most effective mechanism in the activation or repression of the target gene. It requires the least copies of the regulating molecule in order to achieve the same level of regulation, as achieved by the two other mechanisms. The regulation by sRNA has stoichiometric properties. We found that it may be effective for the regulation of a few dozens of genes, depending on the relative production rates of the sRNA and target RNA molecules. 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. In regard to response time, protein–protein interaction provides the fastest response to external stimuli, transcription regulation provides the slowest response and sRNA regulation is intermediate. However, when the regulator's synthesis is induced upon an external signal, there is a time interval where regulation by sRNA provides the fastest response to external stimuli (Figure 1 ). This stems from its relatively quick synthesis relative to protein synthesis. We also analyzed the recovery time upon shutoff of the external signal. Two parameters determine the recovery time in case of regulation by sRNA: (1) the ratio between the production rates of the regulatory sRNA and target mRNA and (2) the degradation rate of the sRNA. For a wide range of these parameters, our analysis indicates that regulation by sRNA leads to a fast recovery. Thus, for stimuli that require fast responses in a short time interval, regulation by sRNA may be advantageous, as, for example, under transient stress conditions. Indeed, most of the regulatory sRNAs with known function are induced in response to various stress conditions. As some of the sRNAs are known to regulate the translation of transcriptional regulators, and the production of the sRNAs themselves is transcriptionally regulated, it is conceivable that there are regulatory modules that integrate these two levels of regulation. Indeed, our integrative analysis of the transcription regulation network and post‐transcriptional regulation network identified interesting combinations of the two levels of regulation in regulatory circuits (Figure 6 ). These include mixed feed‐forward loops (Figure 6A ) and mixed feedback loops (Figure 6B ). It is intriguing to understand the advantage of a regulatory circuit that involves the two levels of regulation in comparison to an equivalent circuit that involves only transcription regulation. To this end we examined the feed‐forward loop OmpR‐MicF‐ ompF (Figure 6 ) in comparison to an equivalent feed‐forward loop containing only transcriptional regulators. In this feed‐forward loop, a regulator A activates a second regulator B, and they both repress the target gene c . In loops composed of transcriptional regulation, both A and B are transcriptional regulators, while in the mixed feed‐forward loop found in the E. coli network A is a transcriptional regulator (OmpR) and B is an sRNA (MicF). The mixed feed‐forward loop provides tighter regulation, because it blocks both transcription and translation of the target gene. Thus, if a few transcripts escape the repression by the transcriptional repressor, they will be blocked post‐transcriptionally by the sRNA. It is advantageous also in regard to response time, because for a wide range of parameters it provides both a fast shutdown of the target gene upon an external signal and a fast recovery when the signal has terminated. These properties make this circuit a preferred regulatory module when fast responses to changes in the environment are needed, and further emphasize the advantages of sRNAs in responses to stress conditions. We use dynamical simulations to characterize post‐transcriptional regulation by small non‐coding RNAs (sRNAs), and compare it 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. We find that the halflife 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 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, which provide tight repression, guaranteed by the combination of transcriptional and post‐transcriptional regulations.

Legionella pneumophila is a Gram-negative opportunistic human pathogen that infects and multiplies in a broad range of phagocytic protozoan and mammalian phagocytes. Based on the observation that small regulatory RNAs (sRNAs) play an important role in controlling virulence-related genes in several pathogenic bacteria, we attempted to identify sRNAs expressed by L. pneumophila. We used computational prediction followed by experimental verification to identify and characterize sRNAs encoded in the L. pneumophila genome. A 50-mer probe microarray was constructed to test the expression of predicted sRNAs in bacteria grown under a variety of conditions. This strategy successfully identified 22 expressed RNAs, out of which 6 were confirmed by northern blot and RACE. One of the identified sRNAs is highly expressed in postexponential phase, and computational prediction of its secondary structure reveals a striking similarity to the structure of 6S RNA, a widely distributed prokaryotic sRNA, known to regulate the activity of σ⁷⁰-containing RNA polymerase. A 70-mer probe microarray was used to identify genes affected by L. pneumophila 6S RNA in stationary phase. The 6S RNA positively regulates expression of genes encoding type IVB secretion system effectors, stress response genes such as groES and recA, as well as many genes involved in acquisition of nutrients and genes with unknown or hypothetical functions. Deletion of 6S RNA significantly reduced L. pneumophila intracellular multiplication in both protist and mammalian host cells, but had no detectable effect on growth in rich media.

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

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. By tracing the evolutionary history of transcriptional networks across 23 fungi, two seemingly contradictory trends are observed: rapid target turnover and conserved function. This is reconciled by a model that invokes strong selection to conserve the overall function of a motif, but not its individual targets. Synopsis By tracing the evolutionary history of transcriptional networks across 23 fungi, two seemingly contradictory trends are observed: rapid target turnover and conserved function. This is reconciled by a model that invokes strong selection to conserve the overall function of a motif, but not its individual targets. The vast majority of cis ‐regulatory elements in genes’ promoters are rapidly gained and lost across species. Despite this rapid turnover, most transcription factors are associated with a conserved function even at great evolutionary distances. A functional selection turnover model reconciles these two phenomena by invoking a preference to conserve the overall function of the motif but not the individual target genes. Our model fits the variation in measured transcription factor binding profiles across species in both yeasts and mammals.