The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12

Global transcriptome response in Lactobacillus sakei during growth on ribose

BACKGROUND

Lactobacillus sakei is valuable in the fermentation of meat products and exhibits properties that allow for better preservation of meat and fish. On these substrates, glucose and ribose are the main carbon sources available for growth. We used a whole-genome microarray based on the genome sequence of L. sakei strain 23K to investigate the global transcriptome response of three L. sakei strains when grown on ribose compared with glucose.

RESULTS

The function of the common regulated genes was mostly related to carbohydrate metabolism and transport. Decreased transcription of genes encoding enzymes involved in glucose metabolism and the L-lactate dehydrogenase was observed, but most of the genes showing differential expression were up-regulated. Especially transcription of genes directly involved in ribose catabolism, the phosphoketolase pathway, and in alternative fates of pyruvate increased. Interestingly, the methylglyoxal synthase gene, which encodes an enzyme unique for L. sakei among lactobacilli, was up-regulated. Ribose catabolism seems closely linked with catabolism of nucleosides. The deoxyribonucleoside synthesis operon transcriptional regulator gene was strongly up-regulated, as well as two gene clusters involved in nucleoside catabolism. One of the clusters included a ribokinase gene. Moreover, hprK encoding the HPr kinase/phosphatase, which plays a major role in the regulation of carbon metabolism and sugar transport, was up-regulated, as were genes encoding the general PTS enzyme I and the mannose-specific enzyme II complex (EIIman). Putative catabolite-responsive element (cre) sites were found in proximity to the promoter of several genes and operons affected by the change of carbon source. This could indicate regulation by a catabolite control protein A (CcpA)-mediated carbon catabolite repression (CCR) mechanism, possibly with the EIIman being indirectly involved.

CONCLUSIONS

Our data shows that the ribose uptake and catabolic machinery in L. sakei is highly regulated at the transcription level. A global regulation mechanism seems to permit a fine tuning of the expression of enzymes that control efficient exploitation of available carbon sources.

The operonic location of auto-transcriptional repressors is highly conserved in bacteria

Bacterial genes are commonly encoded in clusters, known as operons, which share transcriptional regulatory control and often encode functionally related proteins that take part in certain biological pathways. Operons that are coregulated are known to colocalize in the genome, suggesting that their spatial organization is under selection for efficient expression regulation. However, the internal order of genes within operons is believed to be poorly conserved, and hence expression requirements are claimed to be too weak to oppose gene rearrangements. In light of these opposing views, we set out to investigate whether the internal location of the regulatory genes within operons is under selection. Our analysis shows that transcription factors (TFs) are preferentially encoded as either first or last in their operons, in the two diverged model bacteria Escherichia coli and Bacillus subtilis. In a higher resolution, we find that TFs that repress transcription of the operon in which they are encoded (autorepressors), contribute most of this signal by specific preference of the first operon position. We show that this trend is strikingly conserved throughout highly diverged bacterial phyla. Moreover, these autorepressors regulate operons that carry out highly diverse biological functions. We propose a model according to which autorepressors are selected to be located first in their operons in order to optimize transcription regulation. Specifically, the first operon position helps autorepressors to minimize leaky transcription of the operon structural genes, thus minimizing energy waste. Our analysis provides statistically robust evidence for a paradigm of bacterial autorepressor preferential operonic location. Corroborated with our suggested model, an additional layer of operon expression control that is common throughout the bacterial domain is revealed.

Unraveling networks of co-regulated genes on the sole basis of genome sequences

With the growing number of available microbial genome sequences, regulatory signals can now be revealed as conserved motifs in promoters of orthologous genes (phylogenetic footprints). A next challenge is to unravel genome-scale regulatory networks. Using as sole input genome sequences, we predicted cis-regulatory elements for each gene of the yeast Saccharomyces cerevisiae by discovering over-represented motifs in the promoters of their orthologs in 19 Saccharomycetes species. We then linked all genes displaying similar motifs in their promoter regions and inferred a co-regulation network including 56,919 links between 3171 genes. Comparison with annotated regulons highlights the high predictive value of the method: a majority of the top-scoring predictions correspond to already known co-regulations. We also show that this inferred network is as accurate as a co-expression network built from hundreds of transcriptome microarray experiments. Furthermore, we experimentally validated 14 among 16 new functional links between orphan genes and known regulons. This approach can be readily applied to unravel gene regulatory networks from hundreds of microbial genomes for which no other information is available except the sequence. Long-term benefits can easily be perceived when considering the exponential increase of new genome sequences.

Isolation and characterization of Ehrlichia chaffeensis RNA polymerase and its use in evaluating p28 outer membrane protein gene promoters

Background

Ehrlichia chaffeensis is a tick-transmitted rickettsial pathogen responsible for an important emerging disease, human monocytic ehrlichiosis. To date how E. chaffeensis and many related tick-borne rickettsial pathogens adapt and persist in vertebrate and tick hosts remain largely unknown. In recent studies, we demonstrated significant host-specific differences in protein expression in E. chaffeensis originating from its tick and vertebrate host cells. The adaptive response of the pathogen to different host environments entails switch of gene expression regulated at the level of transcription, possibly by altering RNA polymerase activity.

Results

In an effort to understand the molecular basis of pathogen gene expression differences, we isolated native E. chaffeensis RNA polymerase using a heparin-agarose purification method and developed an in vitro transcription system to map promoter regions of two differentially expressed genes of the p28 outer membrane protein locus, p28-Omp14 and p28-Omp19. We also prepared a recombinant protein of E. chaffeensis σ70 homologue and used it for in vitro promoter analysis studies. The possible role of one or more proteins presents in E. chaffeensis lysates in binding to the promoter segments and on the modulation of in vitro transcription was also assessed.

Conclusions

Our experiments demonstrated that both the native and recombinant proteins are functional and have similar enzyme properties in driving the transcription from E. chaffeensis promoters. This is the first report of the functional characterization of E. chaffeensis RNA polymerase and in vitro mapping of the pathogen promoters using the enzyme. This study marks the beginning to broadly characterize the mechanisms controlling the transcription by Anaplasmataceae pathogens.

Novel sequence-based method for identifying transcription factor binding sites in prokaryotic genomes

MOTIVATION

Computational techniques for microbial genomic sequence analysis are becoming increasingly important. With next-generation sequencing technology and the human microbiome project underway, current sequencing capacity is significantly greater than the speed at which organisms of interest can be studied experimentally. Most related computational work has been focused on sequence assembly, gene annotation and metabolic network reconstruction. We have developed a method that will primarily use available sequence data in order to determine prokaryotic transcription factor (TF) binding specificities.

RESULTS

Specificity determining residues (critical residues) were identified from crystal structures of DNA-protein complexes and TFs with the same critical residues were grouped into specificity classes. The putative binding regions for each class were defined as the set of promoters for each TF itself (autoregulatory) and the immediately upstream and downstream operons. MEME was used to find putative motifs within each separate class. Tests on the LacI and TetR TF families, using RegulonDB annotated sites, showed the sensitivity of prediction 86% and 80%, respectively.

AVAILABILITY

http://ural.wustl.edu/∼gsahota/HTHmotif/

Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes

Background

Our current understanding of transcription factor binding sites (TFBSs) in sequenced prokaryotic genomes is very limited due to the lack of an accurate and efficient computational method for the prediction of TFBSs at a genome scale. In an attempt to change this situation, we have recently developed a comparative genomics based algorithm called GLECLUBS for de novo genome-wide prediction of TFBSs in a target genome. Although GLECLUBS has achieved rather high prediction accuracy of TFBSs in a target genome, it is still not efficient enough to be applied to all the sequenced prokaryotic genomes.

Results

Here, we designed a new algorithm based on GLECLUBS called extended GLECLUBS (eGLECLUBS) for simultaneous prediction of TFBSs in a group of related prokaryotic genomes. When tested on a group of γ-proteobacterial genomes including E. coli K12, a group of firmicutes genomes including B. subtilis and a group of cyanobacterial genomes using the same parameter settings, eGLECLUBS predicts more than 82% of known TFBSs in extracted inter-operonic sequences in both E. coli K12 and B. subtilis. Because each genome in a group is equally treated, it is highly likely that similar prediction accuracy has been achieved for each genome in the group.

Conclusions

We have developed a new algorithm for genome-wide de novo prediction of TFBSs in a group of related prokaryotic genomes. The algorithm has achieved the same level of accuracy and robustness as its predecessor GLECLUBS, but can work on dozens of genomes at the same time.

Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach

In prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called transcription factors (TFs). In this study, we map the complete repertoire of ∼300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of nonredundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug-induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, defined as those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions such as drug induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and, in general, transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic data sets.

A novel nucleoid protein of Escherichia coli induced under anaerobiotic growth conditions

A systematic search was performed for DNA-binding sequences of YgiP, an uncharacterized transcription factor of Escherichia coli, by using the Genomic SELEX. A total of 688 YgiP-binding loci were identified after genome-wide profiling of SELEX fragments with a high-density microarray (SELEX-chip). Gel shift and DNase-I footprinting assays indicated that YgiP binds to multiple sites along DNA probes with a consensus GTTNATT sequence. Atomic force microscope observation indicated that at low concentrations, YgiP associates at various sites on DNA probes, but at high concentrations, YgiP covers the entire DNA surface supposedly through protein–protein contact. The intracellular concentration of YgiP is very low in growing E. coli cells under aerobic conditions, but increases more than 100-fold to the level as high as the major nucleoid proteins under anaerobic conditions. An E. coli mutant lacking ygiP showed retarded growth under anaerobic conditions. High abundance and large number of binding sites together indicate that YgiP is a nucleoid-associated protein with both architectural and regulatory roles as the nucleoid proteins Fis and IHF. We then propose that YgiP is a novel nucleoid protein of E. coli under anaerobiosis and propose to rename it Dan (DNA-binding protein under anaerobic conditions).

Identification and genomic analysis of transcription factors in archaeal genomes exemplifies their functional architecture and evolutionary origin

Archaea, which represent a large fraction of the phylogenetic diversity of organisms, are prokaryotes with eukaryote-like basal transcriptional machinery. This organization makes the study of their DNA-binding transcription factors (TFs) and their transcriptional regulatory networks particularly interesting. In addition, there are limited experimental data regarding their TFs. In this work, 3,918 TFs were identified and exhaustively analyzed in 52 archaeal genomes. TFs represented less than 5% of the gene products in all the studied species comparable with the number of TFs identified in parasites or intracellular pathogenic bacteria, suggesting a deficit in this class of proteins. A total of 75 families were identified, of which HTH_3, AsnC, TrmB, and ArsR families were universally and abundantly identified in all the archaeal genomes. We found that archaeal TFs are significantly small compared with other protein-coding genes in archaea as well as bacterial TFs, suggesting that a large fraction of these small-sized TFs could supply the probable deficit of TFs in archaea, by possibly forming different combinations of monomers similar to that observed in eukaryotic transcriptional machinery. Our results show that although the DNA-binding domains of archaeal TFs are similar to bacteria, there is an underrepresentation of ligand-binding domains in smaller TFs, which suggests that protein–protein interactions may act as mediators of regulatory feedback, indicating a chimera of bacterial and eukaryotic TFs’ functionality. The analysis presented here contributes to the understanding of the details of transcriptional apparatus in archaea and provides a framework for the analysis of regulatory networks in these organisms.

In silico analysis of transcription factor repertoire and prediction of stress responsive transcription factors in soybean

Sequence-specific DNA-binding transcription factors (TFs) are often termed as 'master regulators' which bind to DNA and either activate or repress gene transcription. We have computationally analysed the soybean genome sequence data and constructed a proper set of TFs based on the Hidden Markov Model profiles of DNA-binding domain families. Within the soybean genome, we identified 4342 loci encoding 5035 TF models which grouped into 61 families. We constructed a database named SoybeanTFDB (http://soybeantfdb.psc.riken.jp) containing the full compilation of soybean TFs and significant information such as: functional motifs, full-length cDNAs, domain alignments, promoter regions, genomic organization and putative regulatory functions based on annotations of gene ontology (GO) inferred by comparative analysis with Arabidopsis. With particular interest in abiotic stress signalling, we analysed the promoter regions for all of the TF encoding genes as a means to identify abiotic stress responsive cis-elements as well as all types of cis-motifs provided by the PLACE database. SoybeanTFDB enables scientists to easily access cis-element and GO annotations to aid in the prediction of TF function and selection of TFs with functions of interest. This study provides a basic framework and an important user-friendly public information resource which enables analyses of transcriptional regulation in soybean.

Ribose utilization by the human commensal Bifidobacterium breve UCC2003

Growth of Bifidobacterium breve UCC2003 on ribose leads to the transcriptional induction of the rbsACBDK gene cluster. Generation and phenotypic analysis of an rbsA insertion mutant established that the rbs gene cluster is essential for ribose utilization, and that its transcription is likely regulated by a LacI‐type regulator encoded by rbsR, located immediately upstream of rbsA. Gel mobility shift assays using purified RbsR_His indicate that the promoter upstream of rbsABCDK is negatively controlled by RbsR_His binding to an 18 bp inverted repeat and that RbsR_His binding activity is modulated by d‐ribose. The rbsK gene of the rbs operon of B. breve UCC2003 was shown to specify a ribokinase (EC 2.7.1.15), which specifically directs its phosphorylating activity towards d‐ribose, converting this pentose sugar to ribose‐5‐phosphate.

Mechanisms and evolution of control logic in prokaryotic transcriptional regulation

A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

Plasticity of transcriptional machinery in bacteria is increased by the repertoire of regulatory families

Escherichia coli K12 and Bacillus subtilis 168 are two of the best characterized bacterial organisms with a long history in molecular biology for understanding various mechanisms in prokaryotic species. However, at the level of transcriptional regulation little is known on a comparative scale. Here we address the question of the degree to which transcription factors (TFs) and their evolutionary families are shared between them. We found that 59 proteins and 28 families are shared between these two bacteria, whereas different subsets were lineage specific. We demonstrate that majority of the common families expand in a lineage-specific manner. More specifically, we found that AraC, ColD, Ebp, LuxR and LysR families are over-represented in E. coli, while ArsR, AsnC, MarR, MerR and TetR families have significantly expanded in B. subtilis. We introduce the notion of regulatory superfamilies based on an empirical number of functional categories regulated by them and show that these families are essentially different in the two bacteria. We further show that global regulators seem to be constrained to smaller regulatory families and generally originate from lineage-specific families. We find that although TF families may be conserved across genomes their functional roles might evolve in a lineage-specific manner and need not be conserved, indicating convergence to be an important phenomenon involved in the functional evolution of TFs of the same family. Although topologically the networks of transcriptional interactions among TF families are similar in both the genomes, we found that the players are different, suggesting different evolutionary origins for the transcriptional regulatory machinery in both bacteria. This study provides evidence from complete repertoires that not only novel families originate in different lineages but conserved TF families expand/contrast in a lineage-specific manner, and suggests that part of the global regulatory mechanisms might originate independently in different lineages.

Limited transcriptional responses of Rickettsia rickettsii exposed to environmental stimuli

Rickettsiae are strict obligate intracellular pathogens that alternate between arthropod and mammalian hosts in a zoonotic cycle. Typically, pathogenic bacteria that cycle between environmental sources and mammalian hosts adapt to the respective environments by coordinately regulating gene expression such that genes essential for survival and virulence are expressed only upon infection of mammals. Temperature is a common environmental signal for upregulation of virulence gene expression although other factors may also play a role. We examined the transcriptional responses of Rickettsia rickettsii, the agent of Rocky Mountain spotted fever, to a variety of environmental signals expected to be encountered during its life cycle. R. rickettsii exposed to differences in growth temperature (25 degrees C vs. 37 degrees C), iron limitation, and host cell species displayed nominal changes in gene expression under any of these conditions with only 0, 5, or 7 genes, respectively, changing more than 3-fold in expression levels. R. rickettsii is not totally devoid of ability to respond to temperature shifts as cold shock (37 degrees C vs. 4 degrees C) induced a change greater than 3-fold in up to 56 genes. Rickettsiae continuously occupy a relatively stable environment which is the cytosol of eukaryotic cells. Because of their obligate intracellular character, rickettsiae are believed to be undergoing reductive evolution to a minimal genome. We propose that their relatively constant environmental niche has led to a minimal requirement for R. rickettsii to respond to environmental changes with a consequent deletion of non-essential transcriptional response regulators. A minimal number of predicted transcriptional regulators in the R. rickettsii genome is consistent with this hypothesis.

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.

A census of human transcription factors: function, expression and evolution

Transcription factors are key cellular components that control gene expression: their activities determine how cells function and respond to the environment. Currently, there is great interest in research into human transcriptional regulation. However, surprisingly little is known about these regulators themselves. For example, how many transcription factors does the human genome contain? How are they expressed in different tissues? Are they evolutionarily conserved? Here, we present an analysis of 1,391 manually curated sequence-specific DNA-binding transcription factors, their functions, genomic organization and evolutionary conservation. Much remains to be explored, but this study provides a solid foundation for future investigations to elucidate regulatory mechanisms underlying diverse mammalian biological processes.

Characterization of the LacI-type transcriptional repressor RbsR controlling ribose transport in Corynebacterium glutamicum ATCC 13032

The gene products of the rbsRACBD (rbs) operon of C. glutamicum (cg1410-cg1414) encode a ribose-specific ATP-binding cassette (ABC) transport system and its corresponding regulatory protein (RbsR). Deletion of the structural genes rbsACBD prohibited ribose uptake. Deletion of the regulatory gene rbsR resulted in an increased mRNA level of the whole operon. Analysis of the promoter region of the rbs operon by electrophoretic mobility shift assays identified a catabolite-responsive element (cre)-like sequence as the RbsR-binding site. Additional RbsR-binding sites were identified in front of the recently characterized uriR operon (uriR-rbsK1-uriT-uriH) and the ribokinase gene rbsK2. In vitro, the repressor RbsR bound to its targets in the absence of an effector. A probable negative effector of RbsR in vivo is ribose 5-phosphate or a derivative thereof, since in a ribokinase (rbsK1 rbsK2) double mutant, no derepression of the rbs operon in the presence of ribose was observed. Analysis of the ribose stimulon in the C. glutamicum wild-type revealed transcriptional induction of the uriR and rbs operons as well as of the rbsK2 gene. The inconsistency between the existence of functional RbsR-binding sites upstream of the ribokinase genes, their transcriptional induction during growth on ribose, and the missing induction in the rbsR mutant suggested the involvement of a second transcriptional regulator. Simultaneous deletion of the regulatory genes rbsR and uriR finally demonstrated a transcriptional co-control of the rbs and uriR operons and the rbsK2 gene by both regulators, RbsR and UriR, which were furthermore shown to recognize the same cognate DNA sequences in the operators of their target genes.

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Structure and function of the LysR-type transcriptional regulator (LTTR) family proteins

The LysR family of transcriptional regulators represents the most abundant type of transcriptional regulator in the prokaryotic kingdom. Members of this family have a conserved structure with an N-terminal DNA-binding helix-turn-helix motif and a C-terminal co-inducer-binding domain. Despite considerable conservation both structurally and functionally, LysR-type transcriptional regulators (LTTRs) regulate a diverse set of genes, including those involved in virulence, metabolism, quorum sensing and motility. Numerous structural and transcriptional studies of members of the LTTR family are helping to unravel a compelling paradigm that has evolved from the original observations and conclusions that were made about this family of transcriptional regulators.

Functional models for large-scale gene regulation networks: realism and fiction

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

Genomics of bacteria and archaea: the emerging dynamic view of the prokaryotic world

The first bacterial genome was sequenced in 1995, and the first archaeal genome in 1996. Soon after these breakthroughs, an exponential rate of genome sequencing was established, with a doubling time of approximately 20 months for bacteria and approximately 34 months for archaea. Comparative analysis of the hundreds of sequenced bacterial and dozens of archaeal genomes leads to several generalizations on the principles of genome organization and evolution. A crucial finding that enables functional characterization of the sequenced genomes and evolutionary reconstruction is that the majority of archaeal and bacterial genes have conserved orthologs in other, often, distant organisms. However, comparative genomics also shows that horizontal gene transfer (HGT) is a dominant force of prokaryotic evolution, along with the loss of genetic material resulting in genome contraction. A crucial component of the prokaryotic world is the mobilome, the enormous collection of viruses, plasmids and other selfish elements, which are in constant exchange with more stable chromosomes and serve as HGT vehicles. Thus, the prokaryotic genome space is a tightly connected, although compartmentalized, network, a novel notion that undermines the ‘Tree of Life’ model of evolution and requires a new conceptual framework and tools for the study of prokaryotic evolution.

Quaternary structural transitions in the DeoR-type repressor UlaR control transcriptional readout from the L-ascorbate utilization regulon in Escherichia coli

UlaR is a DNA binding protein of the DeoR family of eubacterial transcriptional repressors which maintains the utilization of the L-ascorbate ula regulon in a repressed state. The availability of L-ascorbate in the growth medium releases UlaR-mediated repression on the ula regulon, thereby activating transcription. The molecular details of this induction by L-ascorbate have remained elusive to date. Here we have identified L-ascorbate 6-phosphate as a direct effector of UlaR; using a combination of site-directed mutagenesis, gel retardation, isothermal titration calorimetry, and analytical ultracentrifugation studies, we have identified the key amino acid residues that mediate L-ascorbate 6-phosphate binding and constructed the first model of regulation of a DeoR family member, establishing the basis of the ula regulon transcription control by UlaR. In this model, specific quaternary rearrangements of the DeoR-type repressor are the molecular underpinning of the activating and repressing forms. A DNA-bound UlaR tetramer establishes repression, whereas an L-ascorbate-6-phosphate-induced breakdown of the tetrameric configuration in favor of an UlaR dimeric state results in dissociation of UlaR from DNA and allows transcription of ulaG and ula ABCDEF structural genes. Despite the fact that similar changes have been described for other unrelated repressor factors, this is the first report to demonstrate that specific oligomerization changes are responsible for the activating and repressing forms of a DeoR-type eubacterial transcriptional repressor.

The transcriptional factors MurR and catabolite activator protein regulate N-acetylmuramic acid catabolism in Escherichia coli

The MurNAc etherase MurQ of Escherichia coli is essential for the catabolism of the bacterial cell wall sugar N-acetylmuramic acid (MurNAc) obtained either from the environment or from the endogenous cell wall (i.e., recycling). High-level expression of murQ is required for growth on MurNAc as the sole source of carbon and energy, whereas constitutive low-level expression of murQ is sufficient for the recycling of peptidoglycan fragments continuously released from the cell wall during growth of the bacteria. Here we characterize for the first time the expression of murQ and its regulation by MurR, a member of the poorly characterized RpiR/AlsR family of transcriptional regulators. Deleting murR abolished the extensive lag phase observed for E. coli grown on MurNAc and enhanced murQ transcription some 20-fold. MurR forms a stable multimer (most likely a tetramer) and binds to two adjacent inverted repeats within an operator region. In this way MurR represses transcription from the murQ promoter and also interferes with its own transcription. MurNAc-6-phosphate, the substrate of MurQ, was identified as a specific inducer that weakens binding of MurR to the operator. Moreover, murQ transcription depends on the activation by cyclic AMP (cAMP)-catabolite activator protein (CAP) bound to a class I site upstream of the murQ promoter. murR and murQ are divergently orientated and expressed from nonoverlapping face-to-face (convergent) promoters, yielding transcripts that are complementary at their 5' ends. As a consequence of this unusual promoter arrangement, cAMP-CAP also affects murR transcription, presumably by acting as a roadblock for RNA polymerase.

Incorporating gene functions into regression analysis of DNA-protein binding data and gene expression data to construct transcriptional networks

Useful information on transcriptional networks has been extracted by regression analyses of gene expression data and DNA-protein binding data. However, a potential limitation of these approaches is their assumption on the common and constant activity level of a transcription factor (TF) on all the genes in any given experimental condition; for example, any TF is assumed to be either an activator or a repressor, but not both, while it is known that some TFs can be dual regulators. Rather than assuming a common linear regression model for all the genes, we propose using separate regression models for various gene groups; the genes can be grouped based on their functions or some clustering results. Furthermore, to take advantage of the hierarchical structure of many existing gene function annotation systems, such as Gene Ontology (GO), we propose a shrinkage method that borrows information from relevant gene groups. Applications to a yeast dataset and simulations lend support for our proposed methods. In particular, we find that the shrinkage method consistently works well under various scenarios. We recommend the use of the shrinkage method as a useful alternative to the existing methods.

The role of DNA-binding specificity in the evolution of bacterial regulatory networks

Understanding the mechanisms by which transcriptional regulatory networks (TRNs) change through evolution is a fundamental problem.Here, we analyze this question using data from Escherichia coli and Bacillus subtilis, and find that paralogy relationships are insufficient to explain the global or local role observed for transcription factors (TFs) within regulatory networks. Our results provide a picture in which DNA-binding specificity, a molecular property that can be measured in different ways, is a predictor of the role of transcription factors. In particular, we observe that global regulators consistently display low levels of binding specificity, while displaying comparatively higher expression values in microarray experiments. In addition, we find a strong negative correlation between binding specificity and the number of co-regulators that help coordinate genetic expression on a genomic scale. A close look at several orthologous TFs,including FNR, a regulator found to be global in E. coli and local in B.subtilis, confirms the diagnostic value of specificity in order to understand their regulatory function, and highlights the importance of evaluating the metabolic and ecological relevance of effectors as another variable in the evolutionary equation of regulatory networks. Finally, a general model is presented that integrates some evolutionary forces and molecular properties,aiming to explain how regulons grow and shrink, as bacteria tune their regulation to increase adaptation.

Functional organisation of Escherichia coli transcriptional regulatory network

Taking advantage of available functional data associated with 115 transcription and 7 sigma factors, we have performed a structural analysis of the regulatory network of Escherichia coli. While the mode of regulatory interaction between transcription factors (TFs) is predominantly positive, TFs are frequently negatively autoregulated. Furthermore, feedback loops, regulatory motifs and regulatory pathways are unevenly distributed in this network. Short pathways, multiple feed-forward loops and negative autoregulatory interactions are particularly predominant in the subnetwork controlling metabolic functions such as the use of alternative carbon sources. In contrast, long hierarchical cascades and positive autoregulatory loops are overrepresented in the subnetworks controlling developmental processes for biofilm and chemotaxis. We propose that these long transcriptional cascades coupled with regulatory switches (positive loops) for external sensing enable the coexistence of multiple bacterial phenotypes. In contrast, short regulatory pathways and negative autoregulatory loops enable an efficient homeostatic control of crucial metabolites despite external variations. TFs at the core of the network coordinate the most basic endogenous processes by passing information onto multi-element circuits. Transcriptional expression data support broader and higher transcription of global TFs compared to specific ones. Global regulators are also more broadly conserved than specific regulators in bacteria, pointing to varying functional constraints.

Linking Cytoscape and the corynebacterial reference database CoryneRegNet

Background

Recently, the research community has seen an influx of data relating to transcriptional regulatory interactions of Corynebacteria, organisms that are highly relevant to fields of systems biology, biotechnology, and human medicine. Information derived from DNA microarray experiments, computational predictions, and literature has opened the way for the graph-based analysis, visualization, and reconstruction of transcriptional regulatory networks across entire organisms. The reference database for corynebacterial gene regulatory networks CoryneRegNet provides methods for data storage and data exchange in a well-structured manner. Additional information on the model organism Escherichia coli K12 obtained from RegulonDB has been integrated. Generally, gene regulatory networks can be visualized as graphs by drawing directed edges between nodes, where a node represents a gene and an edge corresponds to a typed regulatory interaction. Cytoscape is an open-source software project whose aim is to provide graph-based visualization and analysis for biological networks. Its architecture allows the development and integration of user-made plugins to enhance core functionalities.

Results

We introduce two novel plugins for the Cytoscape environment designed to enhance in silico studies of procaryotic transcriptional regulatory networks. Our plugins leverage the information from the cornyebacterial reference database CoryneRegNet with the graph analysis capabilities of Cytoscape. CoryneRegNetLoader queries the CoryneRegNet database to extract a gene regulatory network represented as a directed graph. Additional information is stored as node/edge attributes within the graph. COMA facilitates consistency checks for gene expression studies given a gene regulatory network. COMA tests whether all gene expression levels correlate properly with the given network topology.

Conclusion

The plugins facilitate in silico studies of procaryotic transcriptional gene regulation, particularly in Corynebacteria and E. coli, by combining the knowledge from the corynebacterial reference database with the graph analysis capabilities of Cytoscape, which is one of the most-widely used tools for biological network analyses.

Evolvability and hierarchy in rewired bacterial gene networks

Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily moulded the contents of a given genome. Though the effect of knocking out or overexpressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or sigma-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that approximately 95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage.

Discovery of conserved motifs in promoters of orthologous genes in prokaryotes

We present a method to predict cis-acting elements for a given gene by detecting over-represented motifs in promoters of a set of ortholo gous genes in prokaryotes (single-gene, multiple-genomes approach). The method has been used successfully to detect regulatory elements at various taxonomical levels in prokaryotes. A web interface is available at the Regulatory Sequence Analysis Tools site (http://rsat.scmbb.ulb.ac.be/rsat/).

Development of software facilities to characterize regulatory binding motifs and application to streptococcaceae

Gene expression regulation often involves the recognition of particular DNA or RNA sequences, called motifs. Detection and characterization of such motifs together with biological expertise allow to build gene expression regulatory maps that facilitate the comprehension of complex cellular processes. In this frame, we developed a software integrating relevant information for the detection and characterization of conserved motifs in genomic sequences. A relational database was built up to host data related to genomic information and transcriptional experiments. A user-friendly interface was designed to allow a convenient representation of these data and to run the detection motif program. A set of complementary utilities was also developed to improve the determination of motif consensus sequences and the detection of additional potential regulator targets in the genome.

Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution

Background

The detection of conserved motifs in promoters of orthologous genes (phylogenetic footprints) has become a common strategy to predict cis-acting regulatory elements. Several software tools are routinely used to raise hypotheses about regulation. However, these tools are generally used as black boxes, with default parameters. A systematic evaluation of optimal parameters for a footprint discovery strategy can bring a sizeable improvement to the predictions.

Results

We evaluate the performances of a footprint discovery approach based on the detection of over-represented spaced motifs. This method is particularly suitable for (but not restricted to) Bacteria, since such motifs are typically bound by factors containing a Helix-Turn-Helix domain. We evaluated footprint discovery in 368 Escherichia coli K12 genes with annotated sites, under 40 different combinations of parameters (taxonomical level, background model, organism-specific filtering, operon inference). Motifs are assessed both at the levels of correctness and significance. We further report a detailed analysis of 181 bacterial orthologs of the LexA repressor. Distinct motifs are detected at various taxonomical levels, including the 7 previously characterized taxon-specific motifs. In addition, we highlight a significantly stronger conservation of half-motifs in Actinobacteria, relative to Firmicutes, suggesting an intermediate state in specificity switching between the two Gram-positive phyla, and thereby revealing the on-going evolution of LexA auto-regulation.

Conclusion

The footprint discovery method proposed here shows excellent results with E. coli and can readily be extended to predict cis-acting regulatory signals and propose testable hypotheses in bacterial genomes for which nothing is known about regulation.

Affinity isolation and I-DIRT mass spectrometric analysis of the Escherichia coli O157:H7 Sakai RNA polymerase complex

Bacteria contain a single multisubunit RNA polymerase that is responsible for the synthesis of all RNA. Previous studies of the Escherichia coli K-12 laboratory strain identified a group of effector proteins that interact directly with RNA polymerase to modulate the efficiency of transcription initiation, elongation, or termination. Here we used a rapid affinity isolation technique to isolate RNA polymerase from the pathogenic Escherichia coli strain O157:H7 Sakai. We analyzed the RNA polymerase enzyme complex using mass spectrometry and identified associated proteins. Although E. coli O157:H7 Sakai contains more than 1,600 genes not present in the K-12 strain, many of which are predicted to be involved in transcription regulation, all of the identified proteins in this study were encoded on the "core" E. coli genome.

Sigma factors in Pseudomonas aeruginosa

In Pseudomonas aeruginosa, as in most bacterial species, the expression of genes is tightly controlled by a repertoire of transcriptional regulators, particularly the so-called sigma (sigma) factors. The basic understanding of these proteins in bacteria has initially been described in Escherichia coli where seven sigma factors are involved in core RNA polymerase interactions and promoter recognition. Now, 7 years have passed since the completion of the first genome sequence of the opportunistic pathogen P. aeruginosa. Information from the genome of P. aeruginosa PAO1 identified 550 transcriptional regulators and 24 putative sigma factors. Of the 24 sigma, 19 were of extracytoplasmic function (ECF). Here, basic knowledge of sigma and ECF proteins was reviewed with particular emphasis on their role in P. aeruginosa global gene regulation. Summarized data are obtained from in silico analysis of P. aeruginosasigma and ECF including rpoD (sigma(70)), RpoH (sigma(32)), RpoF (FliA or sigma(28)), RpoS (sigma(S) or sigma(38)), RpoN (NtrA, sigma(54) or sigma(N)), ECF including AlgU (RpoE or sigma(22)), PvdS, SigX and a collection of uncharacterized sigma ECF, some of which are implicated in iron transport. Coupled to systems biology, identification and functional genomics analysis of P. aeruginosasigma and ECF are expected to provide new means to prevent infection, new targets for antimicrobial therapy, as well as new insights into the infection process.

Structure and evolution of gene regulatory networks in microbial genomes

With the availability of genome sequences for hundreds of microbial genomes, it has become possible to address several questions from a comparative perspective to understand the structure and function of regulatory systems, at least in model organisms. Recent studies have focused on topological properties and the evolution of regulatory networks and their components. Our understanding of natural networks is paving the way to embedding synthetic regulatory systems into organisms, allowing us to expand the natural diversity of living systems to an extent we had never before anticipated.

Ribbon-helix-helix transcription factors: variations on a theme

The ribbon-helix-helix (RHH) superfamily of transcription factors uses a conserved three-dimensional structural motif to bind to DNA in a sequence-specific manner. This functionally diverse protein superfamily regulates the transcription of genes that are involved in the uptake of metals, amino-acid biosynthesis, cell division, the control of plasmid copy number, the lytic cycle of bacteriophages and, perhaps, many other cellular processes. In this Analysis, the structures of different RHH transcription factors are compared in order to evaluate the sequence motifs that are required for RHH-domain folding and DNA binding, as well as to identify conserved protein-DNA interactions in this superfamily.

Coordination logic of the sensing machinery in the transcriptional regulatory network of Escherichia coli

The active and inactive state of transcription factors in growing cells is usually directed by allosteric physicochemical signals or metabolites, which are in turn either produced in the cell or obtained from the environment by the activity of the products of effector genes. To understand the regulatory dynamics and to improve our knowledge about how transcription factors (TFs) respond to endogenous and exogenous signals in the bacterial model, Escherichia coli, we previously proposed to classify TFs into external, internal and hybrid sensing classes depending on the source of their allosteric or equivalent metabolite. Here we analyze how a cell uses its topological structures in the context of sensing machinery and show that, while feed forward loops (FFLs) tightly integrate internal and external sensing TFs connecting TFs from different layers of the hierarchical transcriptional regulatory network (TRN), bifan motifs frequently connect TFs belonging to the same sensing class and could act as a bridge between TFs originating from the same level in the hierarchy. We observe that modules identified in the regulatory network of E. coli are heterogeneous in sensing context with a clear combination of internal and external sensing categories depending on the physiological role played by the module. We also note that propensity of two-component response regulators increases at promoters, as the number of TFs regulating a target operon increases. Finally we show that evolutionary families of TFs do not show a tendency to preserve their sensing abilities. Our results provide a detailed panorama of the topological structures of E. coli TRN and the way TFs they compose off, sense their surroundings by coordinating responses.

RutR is the uracil/thymine-sensing master regulator of a set of genes for synthesis and degradation of pyrimidines

Using the genomic SELEX, a total of six Escherichia coli DNA fragments have been identified, which formed complexes with transcription factor RutR. The RutR regulon was found to include a large number of genes encoding components for not only degradation of pyrimidines but also transport of glutamate, synthesis of glutamine, synthesis of pyrimidine nucleotides and arginine, and degradation of purines. DNase I footprinting indicated that RutR recognizes a palindromic sequence of TTGACCAnnTGGTCAA. The RutR box in P1 promoter of carAB encoding carbamoyl phosphate synthetase, a key enzyme of pyrimidine synthesis, overlaps with the PepA (CarP) repressor binding site, implying competition between RutR and PepA. Adding either uracil or thymine abolished RutR binding in vitro to the carAB P1 promoter. Accordingly, in the rutR-deletion mutant or in the presence of uracil, the activation in vivo of carAB P1 promoter was markedly reduced. Northern blot analysis of the RutR target genes indicated that RutR represses the Gad system genes involved in glutamate-dependent acid resistance and allantoin degradation. Altogether we propose that RutR is the pyrimidine sensor and the master regulator for a large set of the genes involved in the synthesis and degradation of pyrimidines.

Interplay between network structures, regulatory modes and sensing mechanisms of transcription factors in the transcriptional regulatory network of E. coli

Though the bacterial transcription regulation apparatus is distinct in terms of several structural and functional features from its eukaryotic counterpart, the gross structure of the transcription regulatory network (TRN) is believed to be similar in both superkingdoms. Here, we explore the fine structure of the bacterial TRN and the underlying "co-regulatory network" (CRN) to show that despite the superficial similarities to the TRN of the eukaryotic model organism yeast, the bacterial networks display entirely different organizational principles. In particular unlike in eukaryotes, hubs of the bacterial networks are both global regulators and integrators of diverse disparate transcriptional responses. These and other organizational differences might correlate with the fundamental differences in gene and promoter organization in the two superkingdoms, especially the presence of operons and regulons in bacteria. Further we explored to find the interplay, if any, between network structures, mode of regulatory interactions and signal sensing of transcription factors (TFs) in shaping up the bacterial transcriptional regulatory responses. For this purpose, we first classified TFs according to their regulatory mode (activator, repressor or dual regulator) and sensory mechanism (one-component systems responding to internal or external signals, TFs from two-component systems and chromosomal structure modifying TFs) in the bacterial model organism Escherichia coli and then we studied the overall evolutionary optimization of network structures. The incorporation of TFs in different hierarchical elements of the TRN appears to involve on a multi-dimensional selection process depending on regulatory and sensory modes of TFs in motifs, co-regulatory associations between TFs of different functional classes and transcript half-lives. As a result it appears to have generated circuits that allow intricately regulated physiological state changes. We identified the biological significance of most of these optimizations, which can be further used as the basis to explore similar controls in other bacteria. We also show that, though on the larger evolutionary scale, unrelated TFs have evolved to become hubs, within lineages like gamma-proteobacteria there is strong tendency to retain hubs, as well as certain higher-order network modules that have emerged through lineage specific paralog duplications.

The DNA-binding domain as a functional indicator: the case of the AraC/XylS family of transcription factors

The AraC/XylS family of transcription factors, which include proteins that are involved in the regulation of diverse biological processes, has been of considerable interest recently and has been constantly expanding by means of in silico predictions and experimental analysis. In this work, using a HMM based on the DNA binding domain of 58 experimentally characterized proteins from the AraC/XylS (A/X), 1974 A/X proteins were found in 149 out of 212 bacterial genomes. This domain was used as a template to generate a phylogenetic tree and as a tool to predict the putative regulatory role of the new members of this family based on their proximity to a particular functional cluster in the tree. Based on this approach we assigned a functional regulatory role for 75% of the TFs dataset. Of these, 33.7% regulate genes involved in carbon-source catabolism, 9.6% global metabolism, 8.3% nitrogen metabolism, 2.9% adaptation responses, 8.9% stress responses, and 11.7% virulence. The abundance of TFs involved in the regulation of metabolic processes indicates that bacteria have optimized their regulatory systems to control energy uptake. In contrast, the lower percentage of TFs required for stress, adaptation and virulence regulation reflects the specialization acquired by each subset of TFs associated with those processes. This approach would be useful in assigning regulatory roles to uncharacterized members of other transcriptional factor families and it might facilitate their experimental analysis.

Conservation of transcriptional sensing systems in prokaryotes: a perspective from Escherichia coli

The activity of transcription factors is usually governed by allosteric physicochemical signals or metabolites, which are in turn produced in the cell or obtained from the environment by the activity of the products of effector genes. Previously, we identified a collection of more than 110 transcription factors and their corresponding effector genes in Escherichia coli K-12. Here, we introduce the notion of "triferog", which relates to the identification of orthologous transcription factors and effector genes across genomes and show that transcriptional sensing systems known in E. coli are poorly conserved beyond Salmonella. We also find that enzymes that act as effector genes for the production of endogenous effector metabolites are more conserved than their corresponding effector genes encoding for transport and two-component systems for sensing exogenous signals. Finally, we observe that on an evolutionary scale enzymes are more conserved than their respective TFs, suggesting a homogenous cellular metabolism across genomes and the conservation of transcriptional control of critical cellular processes like DNA replication by a common endogenous signal. We hypothesize that extensive variation in the domain architecture of TFs and changes in endogenous conditions at large phylogenetic distances could be the major contributing factors for the observed differential conservation of TFs and their corresponding effector genes encoding for enzymes, causing variations in transcriptional responses across organisms.