Functional organisation of Escherichia coli transcriptional regulatory network

<p>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 controllingmetabolic 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.</p

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

Scaling relationship in the gene content of transcriptional machinery in bacteria

<p>Here, we show that sigma, transcription factors (TFs) and the number of protein coding genes occur in different magnitudes across 291 non-redundant eubacterial genomes. We suggest that these differences can be explained based on the fact that the universe of TFs, in contrast to sigma factors, exhibits a greater flexibility for transcriptional regulation, due to their ability to sense diverse stimuli through a variety of ligand-binding domains by discriminating over longer regions on DNA, through their diverse DNA-binding domains, and by their combinatorial role with other sigmas and TFs. We also note that the diversity of extra-cytoplasmic sigma factors and TF families is constrained in larger genomes. Our results indicate that most widely distributed families across eubacteria are small in size, while large families are relatively limited in their distribution across genomes. Clustering of the distribution of transcription and sigma families across genomes suggests that functional constraints could force their co-evolution, as was observed in sigma54, IHF and EBP families. Our results also indicate that large families might be a consequence of lifestyle, as pathogens and free-living organisms were found to exhibit a major proportion of these expanded families. Our results suggest that understanding proteomes from an integrated perspective, as presented in this study, can be a general framework for uncovering the relationships between different classes of proteins.</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

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

Network of up-regulated miRs and the targets (genes) controlled by them.

<p>Red/orange nodes represent miRNA, with number corresponding to the table; the larger the node the more genes targeted. The green/yellow nodes represent genes regulated by these miRNA and the black lines the connectivity between miRNa and target genese. The highest degree which was observed to be 381 targets in this network. Network is generated using Cytoscape.</p

Network of down-regulated miRs and the targets (genes) controlled by them.

<p>Color transition of the nodes is based on the degree (number of connections) of the nodes. Here nodes comprise both miRs and their targets. A table in the figure provides information about the nodes corresponding to the miRs. 0 represents lowest degree that is 1 and 1 corresponds to highest degree which was observed to be 262 targets in this network. Red color corresponds to the highest degree and green color corresponds to lowest degree. Network is generated using cytoscape.</p

RT-PCR validation of selected miRNAs n MV and VSMC.

<p>To validate the miRNA identified by the arrays that regulate multiple genes, we performed Real time PCR on VSMc and MV to determine the expression of miR-667, miR-702, miR-3562, mir-3568 and miR-3584 and normalized by U6. Each sample (n = 3 with MV and VSMC isolated from 3 CKD rats, same samples as arrays) was assayed in triplicate. The results demonstrated increased expression in MV compared to VSMC for each of these miRNAs, confirming the array results. Data were expressed as mean ± SEM. * p<0.05, MV vs. VSMC.</p