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

Structural and functional map of a bacterial nucleoid

Mapping global protein binding in the E. coli genome reveals extended domains of high protein occupancy

Regulatory Design Governing Progression of Population Growth Phases in Bacteria

It has long been noted that batch cultures inoculated with resting bacteria exhibit a progression of growth phases traditionally labeled lag, exponential, pre-stationary and stationary. However, a detailed molecular description of the mechanisms controlling the transitions between these phases is lacking. A core circuit, formed by a subset of regulatory interactions involving five global transcription factors (FIS, HNS, IHF, RpoS and GadX), has been identified by correlating information from the well- established transcriptional regulatory network of Escherichia coli and genome-wide expression data from cultures in these different growth phases. We propose a functional role for this circuit in controlling progression through these phases. Two alternative hypotheses for controlling the transition between the growth phases are first, a continuous graded adjustment to changing environmental conditions, and second, a discontinuous hysteretic switch at critical thresholds between growth phases. We formulate a simple mathematical model of the core circuit, consisting of differential equations based on the power-law formalism, and show by mathematical and computer-assisted analysis that there are critical conditions among the parameters of the model that can lead to hysteretic switch behavior, which – if validated experimentally – would suggest that the transitions between different growth phases might be analogous to cellular differentiation. Based on these provocative results, we propose experiments to test the alternative hypotheses

The comprehensive updated regulatory network of Escherichia coli K-12

BACKGROUND: Escherichia coli is the model organism for which our knowledge of its regulatory network is the most extensive. Over the last few years, our project has been collecting and curating the literature concerning E. coli transcription initiation and operons, providing in both the RegulonDB and EcoCyc databases the largest electronically encoded network available. A paper published recently by Ma et al. (2004) showed several differences in the versions of the network present in these two databases. Discrepancies have been corrected, annotations from this and other groups (Shen-Orr et al., 2002) have been added, making the RegulonDB and EcoCyc databases the largest comprehensive and constantly curated regulatory network of E. coli K-12. RESULTS: Several groups have been using these curated data as part of their bioinformatics and systems biology projects, in combination with external data obtained from other sources, thus enlarging the dataset initially obtained from either RegulonDB or EcoCyc of the E. coli K12 regulatory network. We kindly obtained from the groups of Uri Alon and Hong-Wu Ma the interactions they have added to enrich their public versions of the E. coli regulatory network. These were used to search for original references and curate them with the same standards we use regularly, adding in several cases the original references (instead of reviews or missing references), as well as adding the corresponding experimental evidence codes. We also corrected all discrepancies in the two databases available as explained below. CONCLUSION: One hundred and fifty new interactions have been added to our databases as a result of this specific curation effort, in addition to those added as a result of our continuous curation work. RegulonDB gene names are now based on those of EcoCyc to avoid confusion due to gene names and synonyms, and the public releases of RegulonDB and EcoCyc are henceforth synchronized to avoid confusion due to different versions. Public flat files are available providing direct access to the regulatory network interactions thus avoiding errors due to differences in database modelling and representation. The regulatory network available in RegulonDB and EcoCyc is the most comprehensive and regularly updated electronically-encoded regulatory network of E. coli K-12

Transcriptional profile of Pseudomonas syringae pv. phaseolicola NPS3121 in response to tissue extracts from a susceptible Phaseolus vulgaris L. cultivar

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas syringae </it>pv. phaseolicola is a Gram-negative plant-pathogenic bacterium that causes "halo blight" disease of beans (<it>Phaseolus vulgaris </it>L.). This disease affects both foliage and pods, and is a major problem in temperate areas of the world. Although several bacterial genes have been determined as participants in pathogenesis, the overall process still remains poorly understood, mainly because the identity and function of many of the genes are largely unknown. In this work, a genomic library of <it>P. syringae </it>pv. phaseolicola NPS3121 was constructed and PCR amplification of individual fragments was carried out in order to print a DNA microarray. This microarray was used to identify genes that are differentially expressed when bean leaf extracts, pod extracts or apoplastic fluid were added to the growth medium.</p> <p>Results</p> <p>Transcription profiles show that 224 genes were differentially expressed, the majority under the effect of bean leaf extract and apoplastic fluid. Some of the induced genes were previously known to be involved in the first stages of the bacterial-plant interaction and virulence. These include genes encoding type III secretion system proteins and genes involved in cell-wall degradation, phaseolotoxin synthesis and aerobic metabolism. On the other hand, most repressed genes were found to be involved in the uptake and metabolism of iron.</p> <p>Conclusion</p> <p>This study furthers the understanding of the mechanisms involved, responses and the metabolic adaptation that occurs during the interaction of <it>P. syringae </it>pv. phaseolicola with a susceptible host plant.</p

RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions

RegulonDB is the internationally recognized reference database of Escherichia coli K-12 offering curated knowledge of the regulatory network and operon organization. It is currently the largest electronically-encoded database of the regulatory network of any free-living organism. We present here the recently launched RegulonDB version 5.0 radically different in content, interface design and capabilities. Continuous curation of original scientific literature provides the evidence behind every single object and feature. This knowledge is complemented with comprehensive computational predictions across the complete genome. Literature-based and predicted data are clearly distinguished in the database. Starting with this version, RegulonDB public releases are synchronized with those of EcoCyc since our curation supports both databases. The complex biology of regulation is simplified in a navigation scheme based on three major streams: genes, operons and regulons. Regulatory knowledge is directly available in every navigation step. Displays combine graphic and textual information and are organized allowing different levels of detail and biological context. This knowledge is the backbone of an integrated system for the graphic display of the network, graphic and tabular microarray comparisons with curated and predicted objects, as well as predictions across bacterial genomes, and predicted networks of functionally related gene products. Access RegulonDB at

Effect of Different Cytokinins on Shoot Outgrowth and Bioactive Compounds Profile of Lemograss Essential Oil

Lemongrass (Cymbopogon citratus) essential oil (EO) is a major source of bioactive compounds (BC) with anticancer activity such as α-citral, limonene, geraniol, geranyl acetate, and β-caryophyllene. Comparative studies about cytokinin effects on BC profiles in lemongrass are missing. Here, we evaluated four cytokinins (2iP, tZ, BAP, and KIN) in two different osmotic media, MS-N (3% sucrose, 3 g L−1 Gelrite™) and MS-S (5% sucrose, 5 g L−1 Gelrite™). It results in a higher multiplication rate in BAP containing medium compared to tZ, KIN, and 2iP (p ≤ 0.05). While shoots grown on MS-N/BAP, tZ, and KIN exhibited a highly branching morphology, MS-N/2iP produced a less branching architecture. BC profile analysis of established plants in pots revealed that their maxima production depends on the in vitro shoot growth conditions: i.e., highest content (80%) of α-citral in plants that were cultured in MS-S/BAP (p ≤ 0.05), limonene (41%) in MS-N/2iP, or geranyl acetate (25.79%) in MS-S/2iP. These results indicate that it is possible to increase or address the production of BC in lemongrass by manipulating the cytokinin type and osmotic pressure in culture media. The culture protocol described here is currently successfully applied for somatic embryogenesis induction and genetic transformation in lemongrass

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

Transcriptional regulation shapes the organization of genes on bacterial chromosomes

Transcription factors (TFs) are the key elements responsible for controlling the expression of genes in bacterial genomes and when visualized on a genomic scale form a dense network of transcriptional interactions among themselves and with other protein coding genes. Although the structure of transcriptional regulatory networks (TRNs) is well understood, it is not clear what constrains govern them. Here, we explore this question using the TRNs of model prokaryotes and provide a link between the transcriptional hierarchy of regulons and their genome organization. We show that, to drive the kinetics and concentration gradients, TFs belonging to big and small regulons, depending on the number of genes they regulate, organize themselves differently on the genome with respect to their targets. We then propose a conceptual model that can explain how the hierarchical structure of TRNs might be ultimately governed by the dynamic biophysical requirements for targeting DNA-binding sites by TFs. Our results suggest that the main parameters defining the position of a TF in the network hierarchy are the number and chromosomal distances of the genes they regulate and their protein concentration gradients. These observations give insights into how the hierarchical structure of transcriptional networks can be encoded on the chromosome to drive the kinetics and concentration gradients of TFs depending on the number of genes they regulate and could be a common theme valid for other prokaryotes, proposing the role of transcriptional regulation in shaping the organization of genes on a chromosome