Structure and evolution of gene regulatory networks in microbial genomes

Unravelling microbial efflux through mathematical modelling

AbstractMathematical modelling is a useful tool that is increasingly used in the life sciences to understand and predict the behaviour of biological systems. This review looks at how this interdisciplinary approach has advanced our understanding of microbial efflux, the process by which microbes expel harmful substances. The discussion is largely in the context of antimicrobial resistance, but applications in synthetic biology are also touched upon. The goal of this paper is to spark further fruitful collaborations between modellers and experimentalists in the efflux community and beyond.

PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest

Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

Synthetic biology aims to apply engineering principles for the rational, systematical design and construction of biological systems displaying functions that do not exist in nature or even building a cell from scratch. Understanding how molecular entities interconnect, work, and evolve in an organism is pivotal to this aim. Here, we summarize and discuss some historical organizing principles identified in bacterial gene regulatory networks. We propose a new layer, the concilion, which is the group of structural genes and their local regulators responsible for a single function that, organized hierarchically, coordinate a response in a way reminiscent of the deliberation and negotiation that take place in a council. We then highlight the importance that the network structure has, and discuss that the natural decomposition approach has unveiled the system-level elements shaping a common functional architecture governing bacterial regulatory networks. We discuss the incompleteness of gene regulatory networks and the need for network inference and benchmarking standardization. We point out the importance that using the network structural properties showed to improve network inference. We discuss the advances and controversies regarding the consistency between reconstructions of regulatory networks and expression data. We then discuss some perspectives on the necessity of studying regulatory networks, considering the interactions’ strength distribution, the challenges to studying these interactions’ strength, and the corresponding effects on network structure and dynamics. Finally, we explore the ability of evolutionary systems biology studies to provide insights into how evolution shapes functional architecture despite the high evolutionary plasticity of regulatory networks.

iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters

Motivation

Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge.

Results

We present iPromoter-BnCNN for identification and accurate classification of six types of promoters—σ24,σ28,σ32,σ38,σ54,σ70. It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset.

Availability and implementation

Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework

Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.

An energy model for recognizing the prokaryotic promoters based on molecular structure

Promoter is an important functional elements of DNA sequences, which is in charge of gene transcription initiation. Recognizing promoter have important help for understanding the relative life phenomena. Based on the concept that promoter is mainly determined by its sequence and structure, a novel statistical physics model for predicting promoter in Escherichia coli K-12 is proposed. The total energies of DNA local structure of sequence segments in the three benchmark promoter sequence datasets, the sole prediction parameter, are calculated by using principles from statistical physics and information theory. The better results are obtained. And a web-server PhysMPrePro for predicting promoter is established at http://202.207.14.87:8032/bioinformation/PhysMPrePro/index.asp, so that other scientists can easily get their desired results by our web-server.

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Promoters are short regions at specific locations of DNA sequences, which are playing key roles in directing gene transcription. They can be grouped into six types (σ²⁴,σ²⁸,σ³²,σ³⁸,σ⁵⁴,σ⁷⁰). Recently, a predictor called "iPromoter-2L" was constructed to predict the promoters and their six types, which is the first approach to predict all the six types of promoters. However, its predictive quality still needs to be further improved for real-world application requirement. In this study, we proposed the smoothing cutting window algorithm to find the window fragments of the DNA sequences based on the conservation scores to capture the sequence patterns of promoters. For each window fragment, the discriminative features were extracted by using kmer and PseKNC. Combined with support vector machines (SVMs), different predictors were constructed and then clustered into several groups based on their distances. Finally, a new predictor called iPromoter-2L2.0 was constructed to identify the promoters and their six types, which was developed by ensemble learning based on the key predictors selected from the cluster groups. The results showed that iPromoter-2L2.0 outperformed other existing methods for both promoter prediction and identification of their six types, indicating that iPromoter-2L2.0 will be helpful for genomics analysis.

RAIL: A new tool for defining bacterial promoter regions

In this issue of the Journal of Bacteriology, Hustmyer and colleagues describe a new method for rapidly generating reporter libraries (Hustmyer citation). This RAIL technique (Rapid Arbitrary PCR Insertion Libraries) uses arbitrary PCR and isothermal DNA assembly to insert random fragments of promoter regions into reporter plasmids, resulting in libraries that can be screened to identify regions required for gene expression. This technique will likely be useful for a number of different genetic applications.

Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome

Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial pangenomes.

The influence of transcriptional regulatory networks on the evolution of bacterial pangenomes has not yet been elucidated, even though the role of transcriptional regulation is widely recognized. Using the model symbiont Sinorhizobium meliloti we have predicted the regulatory targets of 41 transcription factors in 51 strains and 5 other rhizobial species, showing a correlation between regulon diversity and pangenome evolution, through upstream sequence diversity and accessory genome composition. We have also shown that genes not wired to the regulatory network are more likely to belong to the accessory genome, thus suggesting that inclusion in the regulatory circuits may be an indicator of gene conservation. We have also highlighted a series of transcription factors that preferentially regulate genes belonging to one of the three replicons of this species, indicating the presence of replicon-specific regulatory modules, with peculiar functional signatures. At the same time the chromid shares a significant part of the regulatory network with the chromosome, indicating an additional way by which this replicon integrates itself in the pangenome.

Adaptive Evolution of Thermotoga maritima Reveals Plasticity of the ABC Transporter Network

Thermotoga maritima is a hyperthermophilic anaerobe that utilizes a vast network of ABC transporters to efficiently metabolize a variety of carbon sources to produce hydrogen. For unknown reasons, this organism does not metabolize glucose as readily as it does glucose di- and polysaccharides. The leading hypothesis implicates the thermolability of glucose at the physiological temperatures at which T. maritima lives. After a 25-day laboratory evolution, phenotypes were observed with growth rates up to 1.4 times higher than and glucose utilization rates exceeding 50% those of the wild type. Genome resequencing revealed mutations in evolved cultures related to glucose-responsive ABC transporters. The native glucose ABC transporter, GluEFK, has more abundant transcripts either as a result of gene duplication-amplification or through mutations to the operator sequence regulating this operon. Conversely, BglEFGKL, a transporter of beta-glucosides, is substantially downregulated due to a nonsense mutation to the solute binding protein or due to a deletion of the upstream promoter. Analysis of the ABC2 uptake porter families for carbohydrate and peptide transport revealed that the solute binding protein, often among the transcripts detected at the highest levels, is predominantly downregulated in the evolved cultures, while the membrane-spanning domain and nucleotide binding components are less varied. Similar trends were observed in evolved strains grown on glycerol, a substrate that is not dependent on ABC transporters. Therefore, improved growth on glucose is achieved through mutations favoring GluEFK expression over BglEFGKL, and in lieu of carbon catabolite repression, the ABC transporter network is modulated to achieve improved growth fitness.

Interplay between copper and zinc homeostasis through the transcriptional regulator Zur in Enterococcus faecalis

By integrating the microarray expression data and a global E. faecalis transcriptional network we identified a sub-network activated by zinc and copper. Our analyses indicated that the transcriptional response of the bacterium to copper and zinc exposure involved the activation of two modules, module I that contains genes implicated in zinc homeostasis, including the Zur transcriptional repressor, and module II containing a set of genes associated with general stress response and basal metabolism. Bacterial exposure to zinc and copper led to the repression of the zinc uptake systems of module I. Upon deletion of Zur, exposure to different zinc and copper conditions induced complementary homeostatic mechanisms (ATPase efflux proteins) to control the intracellular concentrations of zinc. The transcriptional activation of zinc homeostasis genes by zinc and copper reveals a functional interplay between these two metals, in which exposure to copper also impacts on the zinc homeostasis. Finally, we present a new zinc homeostasis model in E. faecalis, positioning this bacterium as one of the most complete systems biology model in metals described to date.

Microbial biofilm as a smart material

Microbial biofilm colonies will in many cases form a smart material capable of responding to external threats dependent on their size and internal state. The microbial community accordingly switches between passive, protective, or attack modes of action. In order to decide which strategy to employ, it is essential for the biofilm community to be able to sense its own size. The sensor designed to perform this task is termed a quorum sensor, since it only permits collective behaviour once a sufficiently large assembly of microbes have been established. The generic quorum sensor construct involves two genes, one coding for the production of a diffusible signal molecule and one coding for a regulator protein dedicated to sensing the signal molecules. A positive feedback in the signal molecule production sets a well-defined condition for switching into the collective mode. The activation of the regulator involves a slow dimerization, which allows low-pass filtering of the activation of the collective mode. Here, we review and combine the model components that form the basic quorum sensor in a number of Gram-negative bacteria, e.g., Pseudomonas aeruginosa.

Collective sensing and collective responses in quorum-sensing bacteria

Bacteria often face fluctuating environments, and in response many species have evolved complex decision-making mechanisms to match their behaviour to the prevailing conditions. Some environmental cues provide direct and reliable information (such as nutrient concentrations) and can be responded to individually. Other environmental parameters are harder to infer and require a collective mechanism of sensing. In addition, some environmental challenges are best faced by a group of cells rather than an individual. In this review, we discuss how bacteria sense and overcome environmental challenges as a group using collective mechanisms of sensing, known as 'quorum sensing' (QS). QS is characterized by the release and detection of small molecules, potentially allowing individuals to infer environmental parameters such as density and mass transfer. While a great deal of the molecular mechanisms of QS have been described, there is still controversy over its functional role. We discuss what QS senses and how, what it controls and why, and how social dilemmas shape its evolution. Finally, there is a growing focus on the use of QS inhibitors as antibacterial chemotherapy. We discuss the claim that such a strategy could overcome the evolution of resistance. By linking existing theoretical approaches to data, we hope this review will spur greater collaboration between experimental and theoretical researchers.

IntergenicDB: a database for intergenic sequences

A whole genome contains not only coding regions, but also non-coding regions. These are located between the end of a given coding region and the beginning of the following coding region. For this reason, the information about gene regulation process underlies in intergenic regions. There is no easy way to obtain intergenic regions from current available databases. IntergenicDB was developed to integrate data of intergenic regions and their gene related information from NCBI databases. The main goal of INTERGENICDB is to offer friendly database for intergenic sequences of bacterial genomes.

Enterococcus faecalis reconfigures its transcriptional regulatory network activation at different copper levels

A global transcriptional regulatory network was generated in the pathogenic bacterium Enterococcus faecalis in order to understand how this organism can activate and coordinate its expression at different copper concentrations. The topological evaluation of the network showed common patterns described in other organisms. Integrating microarray experiments allowed the identification of two sub-networks activated at low (0.05 mM CuSO4) and high (0.5 mM CuSO4) concentrations of copper. The analysis indicates the presence of specific functionally activated modules induced by copper levels, highlighting the regulons LysR and ArgR as global regulators and CopY, Fur and LexA as local regulators. Taking advantage of the fact that E. faecalis presented a homeostatic module, we produced an in vivo intervention by removing this system from the cell without affecting the connectivity of the global transcriptional network. This strategy led us to find that this bacterium can reconfigure its gene expression to maintain cellular homeostasis, activating new modules principally related to glucose metabolism and transcriptional processes. Finally, these results position E. faecalis as the most complete and controllable systemic model organism for copper homeostasis available to date.

DNA duplex stability as discriminative characteristic for Escherichia coli σ(54)- and σ(28)- dependent promoter sequences

The advent of modern high-throughput sequencing has made it possible to generate vast quantities of genomic sequence data. However, the processing of this volume of information, including prediction of gene-coding and regulatory sequences remains an important bottleneck in bioinformatics research. In this work, we integrated DNA duplex stability into the repertoire of a Neural Network (NN) capable of predicting promoter regions with augmented accuracy, specificity and sensitivity. We took our method beyond a simplistic analysis based on a single sigma subunit of RNA polymerase, incorporating the six main sigma-subunits of Escherichia coli. This methodology employed successfully re-discovered known promoter sequences recognized by E. coli RNA polymerase subunits σ(24), σ(28), σ(32), σ(38), σ(54) and σ(70), with highlighted accuracies for σ(28)- and σ(54)- dependent promoter sequences (values obtained were 80% and 78.8%, respectively). Furthermore, the discrimination of promoters according to the σ factor made it possible to extract functional commonalities for the genes expressed by each type of promoter. The DNA duplex stability rises as a distinctive feature which improves the recognition and classification of σ(28)- and σ(54)- dependent promoter sequences. The findings presented in this report underscore the usefulness of including DNA biophysical parameters into NN learning algorithms to increase accuracy, specificity and sensitivity in promoter beyond what is accomplished based on sequence alone.

Signal correlations in ecological niches can shape the organization and evolution of bacterial gene regulatory networks

Transcriptional regulation plays a significant role in the biological response of bacteria to changing environmental conditions. Therefore, mapping transcriptional regulatory networks is an important step not only in understanding how bacteria sense and interpret their environment but also to identify the functions involved in biological responses to specific conditions. Recent experimental and computational developments have facilitated the characterization of regulatory networks on a genome-wide scale in model organisms. In addition, the multiplication of complete genome sequences has encouraged comparative analyses to detect conserved regulatory elements and infer regulatory networks in other less well-studied organisms. However, transcription regulation appears to evolve rapidly, thus, creating challenges for the transfer of knowledge to nonmodel organisms. Nevertheless, the mechanisms and constraints driving the evolution of regulatory networks have been the subjects of numerous analyses, and several models have been proposed. Overall, the contributions of mutations, recombination, and horizontal gene transfer are complex. Finally, the rapid evolution of regulatory networks plays a significant role in the remarkable capacity of bacteria to adapt to new or changing environments. Conversely, the characteristics of environmental niches determine the selective pressures and can shape the structure of regulatory network accordingly.

From specific to global analysis of posttranscriptional regulation in eukaryotes: posttranscriptional regulatory networks

Regulation of gene expression occurs at several levels in eukaryotic organisms and is a highly controlled process. Although RNAs have been traditionally viewed as passive molecules in the pathway from transcription to translation, there is mounting evidence that their metabolism is controlled by a class of proteins called RNA-binding proteins (RBPs), as well as a number of small RNAs. In this review, I provide an overview of the recent developments in our understanding of the repertoire of RBPs across diverse model systems, and discuss the computational and experimental approaches currently available for the construction of posttranscriptional networks governed by them. I also present an overview of the different roles played by RBPs in the cellular context, based on their cis-regulatory modules identified in the literature and discuss how their interplay can result in the dynamic, spatial and tissue-specific expression maps of RNAs. I finally present the concept of posttranscriptional network of RBPs and their cognate RNA targets and discuss their cross-talk with other important posttranscriptional regulatory molecules such as microRNAs s, resulting in diverse functional network motifs. I argue that with rapid developments in the genome-wide elucidation of posttranscriptional networks it would not only be possible to gain a deeper understanding of regulation at a level that has been under-appreciated in the past, but would also allow us to use the newly developed high-throughput approaches to interrogate the prevalence of these phenomena in different states, and thereby study their relevance to physiology and disease across organisms.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

A model for Escherichia coli chromosome packaging supports transcription factor-induced DNA domain formation

What physical mechanism leads to organization of a highly condensed and confined circular chromosome? Computational modeling shows that confinement-induced organization is able to overcome the chromosome's propensity to mix by the formation of topological domains. The experimentally observed high precision of separate subcellular positioning of loci (located on different chromosomal domains) in Escherichia coli naturally emerges as a result of entropic demixing of such chromosomal loops. We propose one possible mechanism for organizing these domains: regulatory control defined by the underlying E. coli gene regulatory network requires the colocalization of transcription factor genes and target genes. Investigating this assumption, we find the DNA chain to self-organize into several topologically distinguishable domains where the interplay between the entropic repulsion of chromosomal loops and their compression due to the confining geometry induces an effective nucleoid filament-type of structure. Thus, we propose that the physical structure of the chromosome is a direct result of regulatory interactions. To reproduce the observed precise ordering of the chromosome, we estimate that the domain sizes are distributed between 10 and 700 kb, in agreement with the size of topological domains identified in the context of DNA supercoiling.

The nucleoid-associated proteins H-NS and FIS modulate the DNA supercoiling response of the pel genes, the major virulence factors in the plant pathogen bacterium Dickeya dadantii

Dickeya dadantii is a pathogen infecting a wide range of plant species. Soft rot, the visible symptom, is mainly due to the production of pectate lyases (Pels) that can destroy the plant cell walls. Previously we found that the pel gene expression is modulated by H-NS and FIS, two nucleoid-associated proteins (NAPs) modulating the DNA topology. Here, we show that relaxation of the DNA in growing D. dadantii cells decreases the expression of pel genes. Deletion of fis aggravates, whereas that of hns alleviates the negative impact of DNA relaxation on pel expression. We further show that H-NS and FIS directly bind the pelE promoter and that the response of D. dadantii pel genes to stresses that induce DNA relaxation is modulated, although to different extents, by H-NS and FIS. We infer that FIS acts as a repressor buffering the negative impact of DNA relaxation on pel gene transcription, whereas H-NS fine-tunes the response of virulence genes precluding their expression under suboptimal conditions of supercoiling. This novel dependence of H-NS effect on DNA topology expands our understanding of the role of NAPs in regulating the global bacterial gene expression and bacterial pathogenicity.

Benchmarks for flexible and rigid transcription factor-DNA docking

BACKGROUND

Structural insight from transcription factor-DNA (TF-DNA) complexes is of paramount importance to our understanding of the affinity and specificity of TF-DNA interaction, and to the development of structure-based prediction of TF binding sites. Yet the majority of the TF-DNA complexes remain unsolved despite the considerable experimental efforts being made. Computational docking represents a promising alternative to bridge the gap. To facilitate the study of TF-DNA docking, carefully designed benchmarks are needed for performance evaluation and identification of the strengths and weaknesses of docking algorithms.

RESULTS

We constructed two benchmarks for flexible and rigid TF-DNA docking respectively using a unified non-redundant set of 38 test cases. The test cases encompass diverse fold families and are classified into easy and hard groups with respect to the degrees of difficulty in TF-DNA docking. The major parameters used to classify expected docking difficulty in flexible docking are the conformational differences between bound and unbound TFs and the interaction strength between TFs and DNA. For rigid docking in which the starting structure is a bound TF conformation, only interaction strength is considered.

CONCLUSIONS

We believe these benchmarks are important for the development of better interaction potentials and TF-DNA docking algorithms, which bears important implications to structure-based prediction of transcription factor binding sites and drug design.

Rational design of an artificial genetic switch: Co-option of the H-NS-repressed proU operon by the VirB virulence master regulator

The H-NS protein represses the transcription of hundreds of genes in Gram-negative bacteria. Derepression is achieved by a multitude of mechanisms, many of which involve the binding of a protein to DNA at the repressed promoter in a manner that compromises the maintenance of the H-NS-DNA nucleoprotein repression complex. The principal virulence gene promoters in Shigella flexneri, the cause of bacillary dysentery, are repressed by H-NS. VirB, a protein that closely resembles members of the ParB family of plasmid-partitioning proteins, derepresses the operons that encode the main structural components and the effector proteins of the S. flexneri type III secretion system. Bioinformatic analysis suggests that VirB has been co-opted into its current role as an H-NS antagonist in S. flexneri. To test this hypothesis, the potential for VirB to act as a positive regulator of proU, an operon that is repressed by H-NS, was assessed. Although VirB has no known relationship with the osmoregulated proU operon, it could relieve H-NS-mediated repression when the parS-like VirB binding site was placed appropriately upstream of the RpoD-dependent proU promoter. These results reveal the remarkable facility with which novel regulatory circuits can evolve, at least among those promoters that are repressed by H-NS.

Interplay between posttranscriptional and posttranslational interactions of RNA-binding proteins

RNA-binding proteins (RBPs) play important roles in the posttranscriptional control of gene expression. However, our understanding of how RBPs interact with each other at different regulatory levels to coordinate the RNA metabolism of the cell is rather limited. Here, we construct the posttranscriptional regulatory network among 69 experimentally studied RBPs in yeast to show that more than one-third of the RBPs autoregulate their expression at the posttranscriptional level and demonstrate that autoregulatory RBPs show reduced protein noise with a tendency to encode for hubs in this network. We note that in- and outdegrees in the posttranscriptional RBP-RBP regulatory network exhibit gaussian and scale-free distributions, respectively. This network was also densely interconnected with extensive cross-talk between RBPs belonging to different posttranscriptional steps, regulating varying numbers of cellular RNA targets. We show that feed-forward loops and superposed feed-forward/feedback loops are the most significant three-node subgraphs in this network. Analysis of the corresponding protein-protein interaction (posttranslational) network revealed that it is more modular than the posttranscriptional regulatory network. There is significant overlap between the regulatory and protein-protein interaction networks, with RBPs that potentially control each other at the posttranscriptional level tending to physically interact and being part of the same ribonucleoprotein (RNP) complex. Our observations put forward a model wherein RBPs could be classified into those that can stably interact with a limited number of protein partners, forming stable RNP complexes, and others that form transient hubs, having the ability to interact with multiple RBPs forming many RNPs in the cell.

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.

Evolution of gene regulatory networks by fluctuating selection and intrinsic constraints

Various characteristics of complex gene regulatory networks (GRNs) have been discovered during the last decade, e.g., redundancy, exponential indegree distributions, scale-free outdegree distributions, mutational robustness, and evolvability. Although progress has been made in this field, it is not well understood whether these characteristics are the direct products of selection or those of other evolutionary forces such as mutational biases and biophysical constraints. To elucidate the causal factors that promoted the evolution of complex GRNs, we examined the effect of fluctuating environmental selection and some intrinsic constraining factors on GRN evolution by using an individual-based model. We found that the evolution of complex GRNs is remarkably promoted by fixation of beneficial gene duplications under unpredictably fluctuating environmental conditions and that some internal factors inherent in organisms, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for the evolution of GRNs. The results indicate that various biological properties observed in GRNs could evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves. Our study emphasizes that evolutionary models considering such intrinsic constraining factors should be used as null models to analyze the effect of selection on GRN evolution.

Various organismal traits, including the morphology of multicellular species and metabolism in unicellular species, are determined by the amount and combinations of proteins in the cell. The complex regulatory network plays an important role in controlling the protein profiles in a cell. Recent studies have revealed that gene regulatory networks have many interesting structural and mutational features such as their scale-free structure, mutational robustness, and evolvability. However, why and how these features have emerged from evolution is unknown. In this paper, we constructed an evolutionary model of gene regulatory networks and simulated its evolution under various environmental conditions. The results show that most features of known gene regulatory networks evolve as a result of adaptation to unpredictable environmental fluctuations. In addition, some internal organismal factors, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for GRN evolution observed in real organisms. Thus, these GRN features appear to evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves.

Fractal topology of gene promoter networks at phase transitions

Much is known regarding the structure and logic of genetic regulatory networks. Less understood is the contextual organization of promoter signals used during transcription initiation, the most pivotal stage during gene expression. Here we show that promoter networks organize spontaneously at a dimension between the 1-dimension of the DNA and 3-dimension of the cell. Network methods were used to visualize the global structure of E. coli sigma (σ) recognition footprints using published promoter sequences (RegulonDB). Footprints were rendered as networks with weighted edges representing bp-sharing between promoters (nodes). Serial thresholding revealed phase transitions at positions predicted by percolation theory, and nuclei denoting short steps through promoter space with geometrically constrained linkages. The network nuclei are fractals, a power-law organization not yet described for promoters. Genome-wide promoter abundance also scaled as a power-law. We propose a general model for the development of a fractal nucleus in a transcriptional grammar.

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.

Reconstructing genome trees of prokaryotes using overlapping genes

BACKGROUND

Overlapping genes (OGs) are defined as adjacent genes whose coding sequences overlap partially or entirely. In fact, they are ubiquitous in microbial genomes and more conserved between species than non-overlapping genes. Based on this property, we have previously implemented a web server, named OGtree, that allows the user to reconstruct genome trees of some prokaryotes according to their pairwise OG distances. By analogy to the analyses of gene content and gene order, the OG distance between two genomes we defined was based on a measure of combining OG content (i.e., the normalized number of shared orthologous OG pairs) and OG order (i.e., the normalized OG breakpoint distance) in their whole genomes. A shortcoming of using the concept of breakpoints to define the OG distance is its inability to analyze the OG distance of multi-chromosomal genomes. In addition, the amount of overlapping coding sequences between some distantly related prokaryotic genomes may be limited so that it is hard to find enough OGs to properly evaluate their pairwise OG distances.

RESULTS

In this study, we therefore define a new OG order distance that is based on more biologically accurate rearrangements (e.g., reversals, transpositions and translocations) rather than breakpoints and that is applicable to both uni-chromosomal and multi-chromosomal genomes. In addition, we expand the term "gene" to include both its coding sequence and regulatory regions so that two adjacent genes whose coding sequences or regulatory regions overlap with each other are considered as a pair of overlapping genes. This is because overlapping of regulatory regions of distinct genes suggests that the regulation of expression for these genes should be more or less interrelated. Based on these modifications, we have reimplemented our OGtree as a new web server, named OGtree2, and have also evaluated its accuracy of genome tree reconstruction on a testing dataset consisting of 21 Proteobacteria genomes. Our experimental results have finally shown that our current OGtree2 indeed outperforms its previous version OGtree, as well as another similar server, called BPhyOG, significantly in the quality of genome tree reconstruction, because the phylogenetic tree obtained by OGtree2 is greatly congruent with the reference tree that coincides with the taxonomy accepted by biologists for these Proteobacteria.

CONCLUSIONS

In this study, we have introduced a new web server OGtree2 at http://bioalgorithm.life.nctu.edu.tw/OGtree2.0/ that can serve as a useful tool for reconstructing more precise and robust genome trees of prokaryotes according to their overlapping genes.

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.

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.