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

<p>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.</p

Transcriptional regulation shapes the organization of genes on bacterial chromosomes

<p>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.</p

Internal Versus External Effector and Transcription Factor Gene Pairs Differ in Their Relative Chromosomal Position in Escherichia coli

<p>Here, we analyze the genome organization of the genetic components of these sensing systems, using the classification described earlier. We report the chromosomal proximity of transcription factors and their effector genes to sense periplasmic signals or transported metabolites (i.e. transcriptional sensing systems from the external class) in contrast to the components for sensing internally synthesized metabolites, which tend to be distant on the chromosome. We strengthen our finding that external sensing genetic machinery behaves like chromosomal modules of regulation to respond rapidly to variations in external conditions through co-expression of their genetic components, which is corroborated with microarray data for E. coli. Furthermore, we show several lines of evidence supporting the need for the coordinated activity of external sensing systems in contrast to that of internal sensing machinery, which can explain their close chromosomal organization. The observed functional correlation between the chromosomal organization and the genetic machinery for environmental sensing should contribute to our understanding of the logical functioning and evolution of the transcriptional regulatory networks in bacteria.</p

Internal-sensing machinery directs the activity of the regulatory network in Escherichia coli

<p>Individual cells need to discern and synchronize transcriptional responses according to variations in external and internal conditions. Metabolites and chemical compounds are sensed by transcription factors (TFs), which direct the corresponding specific transcriptional responses. We propose a classification of the currently known TFs of Escherichia coli based on whether they respond to metabolites incorporated from the exterior, to internally produced compounds, or to both. When analyzing the mutual interactions of TFs, the dominant role of internal signal sensing becomes apparent, greatly due to the role of global regulators of transcription. This work encompasses metabolite–TF interactions, bridging the gap between the metabolic and regulatory networks, thus advancing towards an integrated network model for the understanding of cellular behavior.</p

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

<p>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.</p

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing-0

<p><b>Copyright information:</b></p><p>Taken from "Automatic reconstruction of a bacterial regulatory network using Natural Language Processing"</p><p>http://www.biomedcentral.com/1471-2105/8/293</p><p>BMC Bioinformatics 2007;8():293-293.</p><p>Published online 7 Aug 2007</p><p>PMCID:PMC1964768.</p><p></p>nting papers relevant for transcriptional regulation in K-12. Different selection strategies (keyword searches on PubMed and curated databases references) result in diverse document sets, which can contain in some cases groups of the same documents as well as other non-relevant papers

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing-1

<p><b>Copyright information:</b></p><p>Taken from "Automatic reconstruction of a bacterial regulatory network using Natural Language Processing"</p><p>http://www.biomedcentral.com/1471-2105/8/293</p><p>BMC Bioinformatics 2007;8():293-293.</p><p>Published online 7 Aug 2007</p><p>PMCID:PMC1964768.</p><p></p>nting papers relevant for transcriptional regulation in K-12. Different selection strategies (keyword searches on PubMed and curated databases references) result in diverse document sets, which can contain in some cases groups of the same documents as well as other non-relevant papers

Transcription Factors in <i>Escherichia coli</i> Prefer the <i>Holo</i> Conformation

<div><p>The transcriptional regulatory network of <i>Escherichia coli</i> K-12 is among the best studied gene networks of any living cell. Transcription factors bind to DNA either with their effector bound (<i>holo</i> conformation), or as a free protein (<i>apo</i> conformation) regulating transcription initiation. By using RegulonDB, the functional conformations (<i>holo</i> or <i>apo</i>) of transcription factors, and their mode of regulation (activator, repressor, or dual) were exhaustively analyzed. We report a striking discovery in the architecture of the regulatory network, finding a strong under-representation of the <i>apo</i> conformation (without allosteric metabolite) of transcription factors when binding to their DNA sites to activate transcription. This observation is supported at the level of individual regulatory interactions on promoters, even if we exclude the promoters regulated by global transcription factors, where three-quarters of the known promoters are regulated by a transcription factor in <i>holo</i> conformation. This genome-scale analysis enables us to ask what are the implications of these observations for the physiology and for our understanding of the ecology of <i>E. coli</i>. We discuss these ideas within the framework of the demand theory of gene regulation.</p></div

Gene classification based on MultiFun and TF conformational bias.

<p>Each gene was classified by its regulation, on the function of the TF (activator or repressor), the conformation (apo or holo), and functional class (T: transport; O: others; C: catabolic; A: anabolic). This plot represents a contingency table, with each rectangle corresponding to a piece of the plot, with their sizes proportional to the cell entry. The Pearson residuals indicate the fit of a log-linear model. Blue represents the maximum significance of the corresponding residual, and red shows the minimum.</p

Environmental Conditions and Transcriptional Regulation in Escherichia coli: A Physiological Integrative Approach

<p>In this study we describe how the information pertaining to gene expression and associated growth conditions (even with very little knowledge of the associated regulatory mechanisms) is gathered from the literature and incorporated into Regulon-DB, a database on transcriptional regulation and operon organization in E. coli. The link between growth conditions, signal transduction, and transcriptional regulation is modeled in the database in a simple format that highlights biological relevant information. As far as we know, there is no other database that explicitly clarifies the effect of environmental conditions on gene transcription. We discuss how this knowledge constitutes a benchmark that will impact future research aimed at integration of regulatory responses in the cell; for instance, analysis of microarrays, predicting culture behavior in biotechnological processes, and comprehension of dynamics of regulatory networks. This integrated knowledge will contribute to the future goal of modeling the behavior of E. coli as an entire cell.</p