Internal versus external effector and transcription factor gene pairs differ in their relative chromosomal position in Escherichia coli

Using RegulonDB, the Escherichia coli K-12 Gene Regulatory Transcriptional Network Database

In RegulonDB, for over 25 years, we have been gathering knowledge by manual curation from original scientific literature on the regulation of transcription initiation and genome organization in transcription units of the Escherichia coli K-12 genome. This unit describes six basic protocols that can serve as a guiding introduction to the main content of the current version (v9.4) of this electronic resource. These protocols include general navigation as well as searching for specific objects such as genes, gene products, transcription units, promoters, transcription factors, coexpression, and genetic sensory response units or GENSOR Units. In these protocols, the user will find an initial introduction to the concepts pertinent to the protocol, the content obtained when performing the given navigation, and the necessary resources for carrying out the protocol. This easy-to-follow presentation should help anyone interested in quickly seeing all that is currently offered in RegulonDB, including position weight matrices of transcription factors, coexpression values based on published microarrays, and the GENSOR Units unique to RegulonDB that offer regulatory mechanisms in the context of their signals and metabolic consequences. © 2018 by John Wiley & Sons, Inc.

Distance matters: the impact of gene proximity in bacterial gene regulation

Following recent discoveries of colocalization of downstream-regulating genes in living cells, the impact of the spatial distance between such genes on the kinetics of gene product formation is increasingly recognized. We here show from analytical and numerical analysis that the distance between a transcription factor (TF) gene and its target gene drastically affects the speed and reliability of transcriptional regulation in bacterial cells. For an explicit model system, we develop a general theory for the interactions between a TF and a transcription unit. The observed variations in regulation efficiency are linked to the magnitude of the variation of the TF concentration peaks as a function of the binding site distance from the signal source. Our results support the role of rapid binding site search for gene colocalization and emphasize the role of local concentration differences.

Early Career Research Award Lecture. Structure, evolution and dynamics of transcriptional regulatory networks

The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

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.

Time-resolved transcriptome analysis of Bacillus subtilis responding to valine, glutamate, and glutamine

Microorganisms can restructure their transcriptional output to adapt to environmental conditions by sensing endogenous metabolite pools. In this paper, an Agilent customized microarray representing 4,106 genes was used to study temporal transcript profiles of Bacillus subtilis in response to valine, glutamate and glutamine pulses over 24 h. A total of 673, 835, and 1135 amino-acid-regulated genes were identified having significantly changed expression at one or more time points in response to valine, glutamate, and glutamine, respectively, including genes involved in cell wall, cellular import, metabolism of amino-acids and nucleotides, transcriptional regulation, flagellar motility, chemotaxis, phage proteins, sporulation, and many genes of unknown function. Different amino acid treatments were compared in terms of both the global temporal profiles and the 5-minute quick regulations, and between-experiment differential genes were identified. The highlighted genes were analyzed based on diverse sources of gene functions using a variety of computational tools, including T-profiler analysis, and hierarchical clustering. The results revealed the common and distinct modes of action of these three amino acids, and should help to elucidate the specific signaling mechanism of each amino acid as an effector.

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.

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.

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Principles of transcriptional regulation and evolution of the metabolic system in E. coli

Organisms must adapt to make optimal use of the metabolic system in response to environmental changes. In the long-term, this involves evolution of the genomic repertoire of enzymes; in the short-term, transcriptional control ensures that appropriate enzymes are expressed in response to transitory extracellular conditions. Unicellular organisms are particularly susceptible to environmental changes; however, genome-scale impact of these modulatory effects has not been explored so far in bacteria. Here, we integrate genome-scale data to investigate the evolutionary trends and transcriptional control of metabolism in Escherichia coli K12. Globally, the regulatory system is organized in a clear hierarchy of general and specific transcription factors (TFs) that control differing ranges of metabolic functions. Further, catabolic, anabolic, and central metabolic pathways are targeted by distinct combinations of these TFs. Locally, enzymes catalyzing sequential reactions in a metabolic pathway are co-regulated by the same TFs. Regulation is more complex at junctions: General TFs control the overall activity of all connecting reactions, whereas specific TFs control individual enzymes. Divergent junctions play a special role in delineating metabolic pathways and decouple the regulation of incoming and outgoing reactions. We find little evidence for differential usage of isozymes, which are generally co-expressed in similar conditions, and thus are likely to reinforce the metabolic system through redundancy. Finally, we show that enzymes controlled by the same TFs have a strong tendency to co-evolve, suggesting a significant constraint to maintain similar regulatory regimes during evolution. Catabolic, anabolic, and central energy pathways evolve differently, emphasizing the role of the environment in shaping the metabolic system. Many of the observations also occur in yeast, and our findings may apply across large evolutionary distances.

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.

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.

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.