Regulatory network of Escherichia coli: consistency between literature knowledge and microarray profiles

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields

MOTIVATION

Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning.

RESULTS

To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena.

AVAILABILITY AND IMPLEMENTATION

All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

The transcription factor network of E. coli steers global responses to shifts in RNAP concentration

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

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.

On the Consistency between Gene Expression and the Gene Regulatory Network of Corynebacterium glutamicum

Background: Transcriptional regulation of gene expression is crucial for the adaptation and survival of bacteria. Regulatory interactions are commonly modeled as Gene Regulatory Networks (GRNs) derived from experiments such as RNA-seq, microarray and ChIP-seq. While the reconstruction of GRNs is fundamental to decipher cellular function, even GRNs of economically important bacteria such as Corynebacterium glutamicum are incomplete. Materials and Methods: Here, we analyzed the predictive power of GRNs if used as in silico models for gene expression and investigated the consistency of the C. glutamicum GRN with gene expression data from the GEO database. Results: We assessed the consistency of the C. glutamicum GRN using real, as well as simulated, expression data and showed that GRNs alone cannot explain the expression profiles well. Conclusion: Our results suggest that more sophisticated mechanisms such as a combination of transcriptional, post-transcriptional regulation and signaling should be taken into consideration when analyzing and constructing GRNs.

Regulation of biofilm formation by marT in Salmonella Typhimurium

In this study, we aimed at identifying the regulatory role of marT gene, known as the regulator of misL, on 15 different biofilm-related genes in S. Typhimurium 14028 strain. We also tested the strains for their ability to form biofilm and determined the adherence characteristics of the wild type and the mutant strains of the organism on Caco-2 and HEp-2 cells. For comparative analyses of the candidate genes, individual gene mutations were created via antibiotic gene cassette insertion into each gene of interest. marT gene was cloned behind an arabinose inducible BAD promoter in order to control marT expression. This recombinant plasmid was transfer into each of the 15 mutant strains to investigate the level of expression of each single gene in the presence and absence of marT induction. Besides determination of variations in biofilm formation by each mutant strain, the attachment characteristics of them onto Caco-2 and HEp-2 cell lines were also reported. As a result of attachments experiments on polystyrene surfaces, it was determined that the biofilm production capacity of each mutant strain decreased in a statistically significant manner (p < 0.05). QRT-PCR trials indicated that the marT gene regulates the expression of 14 genes, namely fimA, fimD, fimF, fimH, stjB, stjC, csgA, csgD, ompC, sthB, sthE, rmbA, fliZ and yaiC, in a positive manner. QRT-PCR studies were also revealed that the MarT protein positively regulates its own promoter. When the adherence characteristics of the mutant strains and the wild-type were investigated by using Caco-2 and HEp-2 cells, it was determined that the single gene mutations did have no effect on bacterial adhesion. In view of our mutational analyses and biofilm formation studies, it was concluded that fliZ, ompC, rmbA, stjB and stjC genes are related with biofilm formation in Salmonella, besides other cellular functions of them. Taken together, our data suggested that the regulatory role of MarT protein is not only restricted to the regulation of misL gene expression, but it rather acts as a general regulator on the biofilm-related genes in Salmonella.

Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes

Transcription factors (TFs) are important drivers of cellular decision-making. When bacteria encounter a change in the environment, TFs alter the expression of a defined set of genes in order to adequately respond. It is commonly assumed that genes regulated by the same TF are involved in the same biological process. Examples of this are methods that rely on coregulation to infer function of not-yet-annotated genes. We have previously shown that only 21% of TFs involved in metabolism regulate functionally homogeneous genes, based on the proximity of the gene products’ catalyzed reactions in the metabolic network. Here, we provide more evidence to support the claim that a 1-TF/1-process relationship is not a general property. We show that the observed functional heterogeneity of regulons is not a result of the quality of the annotation of regulatory interactions, nor the absence of protein–metabolite interactions, and that it is also present when function is defined by Gene Ontology terms. Furthermore, the observed functional heterogeneity is different from the one expected by chance, supporting the notion that it is a biological property. To further explore the relationship between transcriptional regulation and metabolism, we analyzed five other types of regulatory groups and identified complex regulons (i.e. genes regulated by the same combination of TFs) as the most functionally homogeneous, and this is supported by coexpression data. Whether higher levels of related functions exist beyond metabolism and current functional annotations remains an open question.

Reconstruction and inference of the Lactococcus lactis MG1363 gene co-expression network

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process.

E. coli gene regulatory networks are inconsistent with gene expression data

Gene regulatory networks (GRNs) and gene expression data form a core element of systems biology-based phenotyping. Changes in the expression of transcription factors are commonly believed to have a causal effect on the expression of their targets. Here we evaluated in the best researched model organism, Escherichia coli, the consistency between a GRN and a large gene expression compendium. Surprisingly, a modest correlation was observed between the expression of transcription factors and their targets and, most noteworthy, both activating and repressing interactions were associated with positive correlation. When evaluated using a sign consistency model we found the regulatory network was not more consistent with measured expression than random network models. We conclude that, at least in E. coli, one cannot expect a causal relationship between the expression of transcription and factors their targets, and that the current static GRN does not adequately explain transcriptional regulation. The implications of this are profound as they question what we consider established knowledge of the systemic biology of cells and point to methodological limitations with respect to single omics analysis, static networks and temporality.

Construction and Analysis of Gene Co-Expression Networks in Escherichia coli

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Escherichia coli (E. coli) has long been a popular model organism for basic biological research. In this paper, weighted gene co-expression network analysis (WGCNA) algorithm was applied to construct gene co-expression networks in E. coli. Thirty-one gene co-expression modules were detected from 1391 microarrays of E. coli data. Further characterization of these modules with the database for annotation, visualization, and integrated discovery (DAVID) tool showed that these modules are associated with several kinds of biological processes, such as carbohydrate catabolism, fatty acid metabolism, amino acid metabolism, transportation, translation, and ncRNA metabolism. Hub genes were also screened by intra-modular connectivity. Genes with unknown functions were annotated by guilt-by-association. Comparison with a previous prediction tool, EcoliNet, suggests that our dataset can expand gene predictions. In summary, 31 functional modules were identified in E. coli, 24 of which were functionally annotated. The analysis provides a resource for future gene discovery.

A general mechanism of ribosome dimerization revealed by single-particle cryo-electron microscopy

Bacteria downregulate their ribosomal activity through dimerization of 70S ribosomes, yielding inactive 100S complexes. In Escherichia coli, dimerization is mediated by the hibernation promotion factor (HPF) and ribosome modulation factor. Here we report the cryo-electron microscopy study on 100S ribosomes from Lactococcus lactis and a dimerization mechanism involving a single protein: HPF^long. The N-terminal domain of HPF^long binds at the same site as HPF in Escherichia coli 100S ribosomes. Contrary to ribosome modulation factor, the C-terminal domain of HPF^long binds exactly at the dimer interface. Furthermore, ribosomes from Lactococcus lactis do not undergo conformational changes in the 30S head domains upon binding of HPF^long, and the Shine–Dalgarno sequence and mRNA entrance tunnel remain accessible. Ribosome activity is blocked by HPF^long due to the inhibition of mRNA recognition by the platform binding center. Phylogenetic analysis of HPF proteins suggests that HPF^long-mediated dimerization is a widespread mechanism of ribosome hibernation in bacteria.

When bacteria enter the stationary growth phase, protein translation is suppressed via the dimerization of 70S ribosomes into inactive complexes. Here the authors provide a structural basis for how the dual domain hibernation promotion factor promotes ribosome dimerization and hibernation in bacteria.

Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering

Improvements in DNA synthesis and sequencing have underpinned comprehensive assessment of gene function in bacteria and eukaryotes. Genome-wide analyses require high-throughput methods to generate mutations and analyze their phenotypes, but approaches to date have been unable to efficiently link the effects of mutations in coding regions or promoter elements in a highly parallel fashion. We report that CRISPR-Cas9 gene editing in combination with massively parallel oligomer synthesis can enable trackable editing on a genome-wide scale. Our method, CRISPR-enabled trackable genome engineering (CREATE), links each guide RNA to homologous repair cassettes that both edit loci and function as barcodes to track genotype-phenotype relationships. We apply CREATE to site saturation mutagenesis for protein engineering, reconstruction of adaptive laboratory evolution experiments, and identification of stress tolerance and antibiotic resistance genes in bacteria. We provide preliminary evidence that CREATE will work in yeast. We also provide a webtool to design multiplex CREATE libraries.

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coli proteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Background

Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur.

Results

Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent.

Conclusions

Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users.

Transcription profile of Escherichia coli: genomic SELEX search for regulatory targets of transcription factors

Bacterial genomes are transcribed by DNA-dependent RNA polymerase (RNAP), which achieves gene selectivity through interaction with sigma factors that recognize promoters, and transcription factors (TFs) that control the activity and specificity of RNAP holoenzyme. To understand the molecular mechanisms of transcriptional regulation, the identification of regulatory targets is needed for all these factors. We then performed genomic SELEX screenings of targets under the control of each sigma factor and each TF. Here we describe the assembly of 156 SELEX patterns of a total of 116 TFs performed in the presence and absence of effector ligands. The results reveal several novel concepts: (i) each TF regulates more targets than hitherto recognized; (ii) each promoter is regulated by more TFs than hitherto recognized; and (iii) the binding sites of some TFs are located within operons and even inside open reading frames. The binding sites of a set of global regulators, including cAMP receptor protein, LeuO and Lrp, overlap with those of the silencer H-NS, suggesting that certain global regulators play an anti-silencing role. To facilitate sharing of these accumulated SELEX datasets with the research community, we compiled a database, ‘Transcription Profile of Escherichia coli’ (www.shigen.nig.ac.jp/ecoli/tec/).

REP sequences: Mediators of the environmental stress response?

Repetitive Extragenic Palindromic (REP) sequences are highly conserved, structured, 35- to 40-nt elements located at ∼500 positions around the Escherichia coli chromosome. They are found in intergenic regions and are transcribed together with their upstream genes. Although their stable stem-loop structures protect messages against exoribonuclease digestion, their primary function has remained unknown. Recently, we found that about half of all REP sequences have the potential to stall ribosomes immediately upstream of the termination codon, leading to endonucleolytic cleavage of the mRNA, and induction of the trans-translation process. As a consequence, the mRNA and almost completed protein are degraded, and protein production from the affected gene is down-regulated. The process is critically dependent on the location of the REP element, with an effect only if it is within 15 nt of the termination codon. Using nrdAB as a model, we found that its down-regulation is affected by RNA helicases. Elimination of 6 helicases lowered NrdA production further, whereas overexpression of any RNA helicase partially reversed the downregulation. UV stress completely reversed down-regulation of NrdA production. Analysis of genes containing a REP sequence within 15 nt of the termination codon revealed that most, if not all, are up-regulated by environmental stress, as are RNA helicases. Based on these findings, we propose that REP-dependent downregulation serves as a mechanism to allow a rapid response to environmental stresses whereby RNA helicases partially open the REP elements enabling ribosomes to complete translation immediately increasing protein production from the affected genes.

Novel gene sets improve set-level classification of prokaryotic gene expression data

Background

Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.

Methods

We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach.

Results

The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers.

Conclusion

Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0786-7) contains supplementary material, which is available to authorized users.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.

Perturbed states of the bacterial chromosome: a thymineless death case study

Spatial patterns of transcriptional activity in the living genome of Escherichia coli represent one of the more peculiar aspects of the E. coli chromosome biology. Spatial transcriptional correlations can be observed throughout the chromosome, and their formation depends on the state of replication in the cell. The condition of thymine starvation leading to thymineless death (TLD) is at the "cross-roads" of replication and transcription. According to a current view, e.g., (Cagliero et al., 2014), one of the cellular objectives is to segregate the processes of transcription and replication in time and space. An ultimate segregation would take place when one process is inhibited and another is not, as it happens during thymine starvation, which results in numerous molecular and physiological abnormalities associated with TLD. One of such abnormalities is the loss of spatial correlations in the vicinity of the origin of replication. We review the transcriptional consequences of replication inhibition by thymine starvation in a context of the state of DNA template in the starved cells and opine about a possible significance of normal physiological coupling between the processes of replication and transcription.

Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions

Living organisms have sophisticated but well-organized regulation system. It is important to understand the metabolic regulation mechanisms in relation to growth environment for the efficient design of cell factories for biofuels and biochemicals production. Here, an overview is given for carbon catabolite regulation, nitrogen regulation, ion, sulfur, and phosphate regulations, stringent response under nutrient starvation as well as oxidative stress regulation, redox state regulation, acid-shock, heat- and cold-shock regulations, solvent stress regulation, osmoregulation, and biofilm formation, and quorum sensing focusing on Escherichia coli metabolism and others. The coordinated regulation mechanisms are of particular interest in getting insight into the principle which governs the cell metabolism. The metabolism is controlled by both enzyme-level regulation and transcriptional regulation via transcription factors such as cAMP-Crp, Cra, Csr, Fis, P(II)(GlnB), NtrBC, CysB, PhoR/B, SoxR/S, Fur, MarR, ArcA/B, Fnr, NarX/L, RpoS, and (p)ppGpp for stringent response, where the timescales for enzyme-level and gene-level regulations are different. Moreover, multiple regulations are coordinated by the intracellular metabolites, where fructose 1,6-bisphosphate (FBP), phosphoenolpyruvate (PEP), and acetyl-CoA (AcCoA) play important roles for enzyme-level regulation as well as transcriptional control, while α-ketoacids such as α-ketoglutaric acid (αKG), pyruvate (PYR), and oxaloacetate (OAA) play important roles for the coordinated regulation between carbon source uptake rate and other nutrient uptake rate such as nitrogen or sulfur uptake rate by modulation of cAMP via Cya.

The bacterial translation stress response

Throughout their life, bacteria need to sense and respond to environmental stress. Thus, such stress responses can require dramatic cellular reprogramming, both at the transcriptional as well as the translational level. This review focuses on the protein factors that interact with the bacterial translational apparatus to respond to and cope with different types of environmental stress. For example, the stringent factor RelA interacts with the ribosome to generate ppGpp under nutrient deprivation, whereas a variety of factors have been identified that bind to the ribosome under unfavorable growth conditions to shut-down (RelE, pY, RMF, HPF and EttA) or re-program (MazF, EF4 and BipA) translation. Additional factors have been identified that rescue ribosomes stalled due to stress-induced mRNA truncation (tmRNA, ArfA, ArfB), translation of unfavorable protein sequences (EF-P), heat shock-induced subunit dissociation (Hsp15), or antibiotic inhibition (TetM, FusB). Understanding the mechanism of how the bacterial cell responds to stress will not only provide fundamental insight into translation regulation, but will also be an important step to identifying new targets for the development of novel antimicrobial agents.

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Using a large-scale knowledge database on reactions and regulations to propose key upstream regulators of various sets of molecules participating in cell metabolism

Background

Most of the existing methods to analyze high-throughput data are based on gene ontology principles, providing information on the main functions and biological processes. However, these methods do not indicate the regulations behind the biological pathways. A critical point in this context is the extraction of information from many possible relationships between the regulated genes, and its combination with biochemical regulations. This study aimed at developing an automatic method to propose a reasonable number of upstream regulatory candidates from lists of various regulated molecules by confronting experimental data with encyclopedic information.

Results

A new formalism of regulated reactions combining biochemical transformations and regulatory effects was proposed to unify the different mechanisms contained in knowledge libraries. Based on a related causality graph, an algorithm was developed to propose a reasonable set of upstream regulators from lists of target molecules. Scores were added to candidates according to their ability to explain the greatest number of targets or only few specific ones. By testing 250 lists of target genes as inputs, each with a known solution, the success of the method to provide the expected transcription factor among 50 or 100 proposed regulatory candidates, was evaluated to 62.6% and 72.5% of the situations, respectively. An additional prioritization among candidates might be further realized by adding functional ontology information. The benefit of this strategy was proved by identifying PPAR isotypes and their partners as the upstream regulators of a list of experimentally-identified targets of PPARA, a pivotal transcriptional factor in lipid oxidation. The proposed candidates participated in various biological functions that further enriched the original information. The efficiency of the method in merging reactions and regulations was also illustrated by identifying gene candidates participating in glucose homeostasis from an input list of metabolites involved in cell glycolysis.

Conclusion

This method proposes a reasonable number of regulatory candidates for lists of input molecules that may include transcripts of genes and metabolites. The proposed upstream regulators are the transcription factors themselves and protein complexes, so that a multi-level description of how cell metabolism is regulated is obtained.

Steady state analysis of the genetic regulatory network incorporating underlying molecular mechanisms for anaerobic metabolism in Escherichia coli

A Gene Regulatory Network (GRN) represents complex connections between genes in a cell which interact with each other through their RNA and protein expression products, thereby determining the expression levels of mRNA and proteins required for functioning of the cell. Microarray experiments yield the log fold change in mRNA abundance and quantify the expression levels for a GRN at the genome level. While Boolean or Bayesian modeling along with expression and location data are useful in analyzing microarray data, they lack underlying mechanistic details present in GRNs. Our objective is to understand the role of molecular mechanisms in quantifying a GRN. To that effect, we analyze under steady state, the complete GRN for the central metabolic pathway during anaerobiosis in Escherichia coli. We simulate the microarray experiments using a steady state gene expression simulator (SSGES) that models molecular mechanistic details such as dimerization, multiple-site binding, auto-regulation and feedback. Given a GRN, the SSGES provided the log fold change in mRNA expression values as the output, which can be compared to data from microarray experiments. We predict the log fold changes for mutants obtained by knocking out crucial transcriptional regulators such as FNR (F), ArcA (A), IHFA-B (I) and DpiA (D) and observe a high degree of correlation with previously reported experimental data. We also predict the microarray expression values for hitherto unknown combinations of deletion mutants. We hierarchically cluster the predicted log fold change values for these mutants and postulate that E. coli has evolved from a predominantly lactate secreting (FAID mutant) into a mixed acid secreting phenotype as seen in the wild type (WT) during anaerobiosis. Upon simulating a model without incorporating the mechanistic details, not only the correlation with the experimental data reduced considerably, but also the clustering of expression data indicated WT to be closer to the quadruple mutant FAID. This clearly demonstrates the significance of incorporating mechanistic data while quantifying the expression profile of a GRN which can help in predicting the effect of a gene mutant and understanding the evolution of transcriptional control.

Regulation Systems of Bacteria such as Escherichia coli in Response to Nutrient Limitation and Environmental Stresses

An overview was made to understand the regulation system of a bacterial cell such as Escherichia coli in response to nutrient limitation such as carbon, nitrogen, phosphate, sulfur, ion sources, and environmental stresses such as oxidative stress, acid shock, heat shock, and solvent stresses. It is quite important to understand how the cell detects environmental signals, integrate such information, and how the cell system is regulated. As for catabolite regulation, F1,6B P (FDP), PEP, and PYR play important roles in enzyme level regulation together with transcriptional regulation by such transcription factors as Cra, Fis, CsrA, and cAMP-Crp. αKG plays an important role in the coordinated control between carbon (C)- and nitrogen (N)-limitations, where αKG inhibits enzyme I (EI) of phosphotransferase system (PTS), thus regulating the glucose uptake rate in accordance with N level. As such, multiple regulation systems are co-ordinated for the cell synthesis and energy generation against nutrient limitations and environmental stresses. As for oxidative stress, the TCA cycle both generates and scavenges the reactive oxygen species (ROSs), where NADPH produced at ICDH and the oxidative pentose phosphate pathways play an important role in coping with oxidative stress. Solvent resistant mechanism was also considered for the stresses caused by biofuels and biochemicals production in the cell.

Lessons from the modular organization of the transcriptional regulatory network of Bacillus subtilis

Background

The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution.

Results

We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the cascades that define relevant cellular processes in this organism. We discussed that some particular functions were distributed in more than one module and that some modules contained more than one related function. We confirm that the presence of paralogous proteins confers advantages to B. subtilis to adapt and select strategies to successfully face the extreme and changing environmental conditions in which it lives.

Conclusions

The intricate organization is the product of a non-random network evolution that primarily follows a hierarchical organization based on the presence of transcription and σ factor, which is reflected in the connections that exist within and between modules.

Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.

Cellular signal transduction is orchestrated by communication networks of signaling proteins commonly depicted on signaling pathway maps. However, each cell type may have distinct variants of signaling pathways, and wiring diagrams are often altered in disease states. The identification of truly active signaling topologies based on experimental data is therefore one key challenge in systems biology of cellular signaling. We present a new framework for training signaling networks based on interaction graphs (IG). In contrast to complex modeling formalisms, IG capture merely the known positive and negative edges between the components. This basic information, however, already sets hard constraints on the possible qualitative behaviors of the nodes when perturbing the network. Our approach uses Integer Linear Programming to encode these constraints and to predict the possible changes (down, neutral, up) of the activation levels of the involved players for a given experiment. Based on this formulation we developed several algorithms for detecting and removing inconsistencies between measurements and network topology. Demonstrated by EGFR/ErbB signaling in hepatocytes, our approach delivers direct conclusions on edges that are likely inactive or missing relative to canonical pathway maps. Such information drives the further elucidation of signaling network topologies under normal and pathological phenotypes.

Transcription factors in Escherichia coli prefer the holo conformation

The transcriptional regulatory network of Escherichia coli K-12 is among the best studied gene networks of any living cell. Transcription factors bind to DNA either with their effector bound (holo conformation), or as a free protein (apo conformation) regulating transcription initiation. By using RegulonDB, the functional conformations (holo or apo) 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 apo 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 holo 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 E. coli. We discuss these ideas within the framework of the demand theory of gene regulation.

Prokaryotic genome regulation: a revolutionary paradigm

After determination of the whole genome sequence, the research frontier of bacterial molecular genetics has shifted to reveal the genome regulation under stressful conditions in nature. The gene selectivity of RNA polymerase is modulated after interaction with two groups of regulatory proteins, 7 sigma factors and 300 transcription factors. For identification of regulation targets of transcription factors in Escherichia coli, we have developed Genomic SELEX system and subjected to screening the binding sites of these factors on the genome. The number of regulation targets by a single transcription factor was more than those hitherto recognized, ranging up to hundreds of promoters. The number of transcription factors involved in regulation of a single promoter also increased to as many as 30 regulators. The multi-target transcription factors and the multi-factor promoters were assembled into complex networks of transcription regulation. The most complex network was identified in the regulation cascades of transcription of two master regulators for planktonic growth and biofilm formation.

Designing logical rules to model the response of biomolecular networks with complex interactions: an application to cancer modeling

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and nonsignificant model predictions, mainly because rules used to propagate information--usually disjunctive constraints--are weak. Here, we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between microarray and interaction data. We use these rules to identify the nodes which are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page.

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.

Reconstruction of the regulatory network of Lactobacillus plantarum WCFS1 on basis of correlated gene expression and conserved regulatory motifs

Gene regulatory networks can be reconstructed by combining transcriptome data from many different experiments to elucidate relations between the activity of certain transcription factors and the genes they control. To obtain insight in the regulatory network of Lactobacillus plantarum, microarray transcriptome data from more than 70 different experimental conditions were combined and the expression profiles of the transcriptional units (TUs) were compared. The TUs that displayed correlated expression were used to identify putative cis‐regulatory elements by searching the upstream regions of the TUs for conserved motifs. Predicted motifs were extended and refined by searching for motifs in the upstream regions of additional TUs with correlated expression. In this way, cis‐acting elements were identified for 41 regulons consisting of at least four TUs (correlation > 0.7). This set of regulons included the known regulons of CtsR and LexA, but also several novel ones encompassing genes with coherent biological functions. Visualization of the regulons and their connections revealed a highly interconnected regulatory network. This network contains several subnetworks that encompass genes of correlated biological function, such as sugar and energy metabolism, nitrogen metabolism and stress response.

Advantages and limitations of current network inference methods

Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.

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.

Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli

The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data, one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with fewer changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: (1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation and (2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.

Bacterial cells can adapt to various genomic mutations and intriguingly many environmental changes. They do this by adjusting their gene expression profile to meet the requirements of a new condition. In this work, we study the interplay of different mechanisms of gene regulatory control driving this adaptation in the bacterium E. coli. We deconstruct the network of all transcription factor mediated regulatory interactions into subnets, topologically defined subgraphs which we expect to act as information processing units. Indeed, we find that many subnets react coordinately to cellular stress, and are used by the cells to account for mutations. In these subnets, we also find many small RNA targets. In contrast, those subnets that do not act in a coordinated fashion are highly enriched with feed-forward loops, a 3-node network motif with important information processing properties. Our approach reveals correlations and anti-correlations of three scales of regulatory control: subnets, feed-forward loops, and small RNA.

Numbers on the edges: a simplified and scalable method for quantifying the gene regulation function

The gene regulation function (GRF) provides an operational description of a promoter behavior as a function of the concentration of one of its transcriptional regulators. Behind this apparently trivial definition lies a central concept in biological control: the GRF provides the input/output relationship of each edge in a transcriptional network, independently from the molecular interactions involved. Here we discuss how existing methods allow direct measurement of the GRF, and how several trade-offs between scalability and accuracy have hindered its application to relatively large networks. We discuss the theoretical and technical requirements for obtaining the GRF. Based on these requirements, we introduce a simplified and easily scalable method that is able to capture the significant parameters of the GRF. The GRF is able to predict the behavior of a simple genetic circuit, illustrating how addressing the quantitative nature of gene regulation substantially increases our comprehension on the mechanisms of gene control.

Ranges of control in the transcriptional regulation of Escherichia coli

BACKGROUND

The positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression.

RESULTS

Here we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances.

CONCLUSIONS

These findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).

FolX and FolM are essential for tetrahydromonapterin synthesis in Escherichia coli and Pseudomonas aeruginosa

Tetrahydromonapterin is a major pterin in Escherichia coli and is hypothesized to be the cofactor for phenylalanine hydroxylase (PhhA) in Pseudomonas aeruginosa, but neither its biosynthetic origin nor its cofactor role has been clearly demonstrated. A comparative genomics analysis implicated the enigmatic folX and folM genes in tetrahydromonapterin synthesis via their phyletic distribution and chromosomal clustering patterns. folX encodes dihydroneopterin triphosphate epimerase, which interconverts dihydroneopterin triphosphate and dihydromonapterin triphosphate. folM encodes an unusual short-chain dehydrogenase/reductase known to have dihydrofolate and dihydrobiopterin reductase activity. The roles of FolX and FolM were tested experimentally first in E. coli, which lacks PhhA and in which the expression of P. aeruginosa PhhA plus the recycling enzyme pterin 4a-carbinolamine dehydratase, PhhB, rescues tyrosine auxotrophy. This rescue was abrogated by deleting folX or folM and restored by expressing the deleted gene from a plasmid. The folX deletion selectively eliminated tetrahydromonapterin production, which far exceeded folate production. Purified FolM showed high, NADPH-dependent dihydromonapterin reductase activity. These results were substantiated in P. aeruginosa by deleting tyrA (making PhhA the sole source of tyrosine) and folX. The DeltatyrA strain was, as expected, prototrophic for tyrosine, whereas the DeltatyrA DeltafolX strain was auxotrophic. As in E. coli, the folX deletant lacked tetrahydromonapterin. Collectively, these data establish that tetrahydromonapterin formation requires both FolX and FolM, that tetrahydromonapterin is the physiological cofactor for PhhA, and that tetrahydromonapterin can outrank folate as an end product of pterin biosynthesis.

From sequence to dynamics: the effects of transcription factor and polymerase concentration changes on activated and repressed promoters

Background

The fine tuning of two features of the bacterial regulatory machinery have been known to contribute to the diversity of gene expression within the same regulon: the sequence of Transcription Factor (TF) binding sites, and their location with respect to promoters. While variations of binding sequences modulate the strength of the interaction between the TF and its binding sites, the distance between binding sites and promoters alter the interaction between the TF and the RNA polymerase (RNAP).

Results

In this paper we estimated the dissociation constants (K_d) of several E. coli TFs in their interaction with variants of their binding sequences from the scores resulting from aligning them to Positional Weight Matrices. A correlation coefficient of 0.78 was obtained when pooling together sites for different TFs. The theoretically estimated K_d values were then used, together with the dissociation constants of the RNAP-promoter interaction to analyze activated and repressed promoters. The strength of repressor sites -- i.e., the strength of the interaction between TFs and their binding sites -- is slightly higher than that of activated sites. We explored how different factors such as the variation of binding sequences, the occurrence of more than one binding site, or different RNAP concentrations may influence the promoters' response to the variations of TF concentrations. We found that the occurrence of several regulatory sites bound by the same TF close to a promoter -- if they are bound by the TF in an independent manner -- changes the effect of TF concentrations on promoter occupancy, with respect to individual sites. We also found that the occupancy of a promoter will never be more than half if the RNAP concentration-to-K_p ratio is 1 and the promoter is subject to repression; or less than half if the promoter is subject to activation. If the ratio falls to 0.1, the upper limit of occupancy probability for repressed drops below 10%; a descent of the limits occurs also for activated promoters.

Conclusion

The number of regulatory sites may thus act as a versatility-producing device, in addition to serving as a source of robustness of the transcription machinery. Furthermore, our results show that the effects of TF concentration fluctuations on promoter occupancy are constrained by RNAP concentrations.

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

An Arabidopsis thaliana transcriptional network reveals regulatory mechanisms for the control of genes related to stress adaptation.

Background

Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes.

Results

We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures.

Conclusions

These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote.

Identification of network topological units coordinating the global expression response to glucose in Bacillus subtilis and its comparison to Escherichia coli

Background

Glucose is the preferred carbon and energy source for Bacillus subtilis and Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzymatic activities, in response to the presence of this sugar. We present a comparison of the cellular response to glucose in these two model organisms, using an approach combining global transcriptome and regulatory network analyses.

Results

Transcriptome data from strains grown in Luria-Bertani medium (LB) or LB+glucose (LB+G) were analyzed, in order to identify differentially transcribed genes in B. subtilis. We detected 503 genes in B. subtilis that change their relative transcript levels in the presence of glucose. A similar previous study identified 380 genes in E. coli, which respond to glucose. Catabolic repression was detected in the case of transport and metabolic interconversion activities for both bacteria in LB+G. We detected an increased capacity for de novo synthesis of nucleotides, amino acids and proteins. A comparison between orthologous genes revealed that global regulatory functions such as transcription, translation, replication and genes relating to the central carbon metabolism, presented similar changes in their levels of expression. An analysis of the regulatory network of a subset of genes in both organisms revealed that the set of regulatory proteins responsible for similar physiological responses observed in the transcriptome analysis are not orthologous. An example of this observation is that of transcription factors mediating catabolic repression for most of the genes that displayed reduced transcript levels in the case of both organisms. In terms of topological functional units in both these bacteria, we found interconnected modules that cluster together genes relating to heat shock, respiratory functions, carbon and peroxide metabolism. Interestingly, B. subtilis functions not found in E. coli, such as sporulation and competence were shown to be interconnected, forming modules subject to catabolic repression at the level of transcription.

Conclusion

Our results demonstrate that the response to glucose is partially conserved in model organisms E. coli and B. subtilis, including genes encoding basic functions such as transcription, translation, replication and genes involved in the central carbon metabolism.

Inferring the transcriptional network of Bacillus subtilis

The adaptation of bacteria to the vigorous environmental changes they undergo is crucial to their survival. They achieve this adaptation partly via intricate regulation of the transcription of their genes. In this study, we infer the transcriptional network of the Gram-positive model organism, Bacillus subtilis. We use a data integration workflow, exploiting both motif and expression data, towards the generation of condition-dependent transcriptional modules. In building the motif data, we rely on both known and predicted information. Known motifs were derived from DBTBS, while predicted motifs were generated by a de novo motif detection method that utilizes comparative genomics. The expression data consists of a compendium of microarrays across different platforms. Our results indicate that a considerable part of the B. subtilis network is yet undiscovered; we could predict 417 new regulatory interactions for known regulators and 453 interactions for yet uncharacterized regulators. The regulators in our network showed a preference for regulating modules in certain environmental conditions. Also, substantial condition-dependent intra-operonic regulation seems to take place. Global regulators seem to require functional flexibility to attain their roles by acting as both activators and repressors.

BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks

Background

The method most commonly used to analyse regulatory networks is the in silico simulation of fluctuations in network components when a network is perturbed. Nevertheless, confronting experimental data with a regulatory network entails many difficulties, such as the incomplete state-of-art of regulatory knowledge, the large-scale of regulatory models, heterogeneity in the available data and the sometimes violated assumption that mRNA expression is correlated to protein activity.

Results

We have developed a plugin for the Cytoscape environment, designed to facilitate automatic reasoning on regulatory networks. The BioQuali plugin enhances user-friendly conversions of regulatory networks (including reference databases) into signed directed graphs. BioQuali performs automatic global reasoning in order to decide which products in the network need to be up or down regulated (active or inactive) to globally explain experimental data. It highlights incomplete regions in the network, meaning that gene expression levels do not globally correlate with existing knowledge on regulation carried by the topology of the network.

Conclusion

The BioQuali plugin facilitates in silico exploration of large-scale regulatory networks by combining the user-friendly tools of the Cytoscape environment with high-performance automatic reasoning algorithms. As a main feature, the plugin guides further investigation regarding a system by highlighting regions in the network that are not accurately described and merit specific study.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

DISTILLER, a data integration framework for the inference of transcriptional module networks, is presented and used to investigate the condition dependency and modularity in Escherichia coli networks.

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

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.

YmdB: a stress-responsive ribonuclease-binding regulator of E. coli RNase III activity

The broad cellular actions of RNase III family enzymes include ribosomal RNA (rRNA) processing, mRNA decay, and the generation of noncoding microRNAs in both prokaryotes and eukaryotes. Here we report that YmdB, an evolutionarily conserved 18.8-kDa protein of Escherichia coli of previously unknown function, is a regulator of RNase III cleavages. We show that YmdB functions by interacting with a site in the RNase III catalytic region, that expression of YmdB is transcriptionally activated by both cold-shock stress and the entry of cells into stationary phase, and that this activation requires the sigma-factor-encoding gene, rpoS. We discovered that down-regulation of RNase III activity occurs during both stresses and is dependent on YmdB production during cold shock; in contrast, stationary-phase regulation was unperturbed in YmdB-null mutant bacteria, indicating the existence of additional, YmdB-independent, factors that dynamically regulate RNase III actions during normal cell growth. Our results reveal the previously unsuspected role of ribonuclease-binding proteins in the regulation of RNase III activity.

Regulon organization of Arabidopsis

BACKGROUND

Despite the mounting research on Arabidopsis transcriptome and the powerful tools to explore biology of this model plant, the organization of expression of Arabidopsis genome is only partially understood. Here, we create a coexpression network from a 22,746 Affymetrix probes dataset derived from 963 microarray chips that query the transcriptome in response to a wide variety of environmentally, genetically, and developmentally induced perturbations.

RESULTS

Markov chain graph clustering of the coexpression network delineates 998 regulons ranging from one to 1623 genes in size. To assess the significance of the clustering results, the statistical over-representation of GO terms is averaged over this set of regulons and compared to the analogous values for 100 randomly-generated sets of clusters. The set of regulons derived from the experimental data scores significantly better than any of the randomly-generated sets. Most regulons correspond to identifiable biological processes and include a combination of genes encoding related developmental, metabolic pathway, and regulatory functions. In addition, nearly 3000 genes of unknown molecular function or process are assigned to a regulon. Only five regulons contain plastomic genes; four of these are exclusively plastomic. In contrast, expression of the mitochondrial genome is highly integrated with that of nuclear genes; each of the seven regulons containing mitochondrial genes also incorporates nuclear genes. The network of regulons reveals a higher-level organization, with dense local neighborhoods articulated for photosynthetic function, genetic information processing, and stress response.

CONCLUSION

This analysis creates a framework for generation of experimentally testable hypotheses, gives insight into the concerted functions of Arabidopsis at the transcript level, and provides a test bed for comparative systems analysis.