RegPrecise web services interface: programmatic access to the transcriptional regulatory interactions in bacteria reconstructed by comparative genomics.

Web services application programming interface (API) was developed to provide a programmatic access to the regulatory interactions accumulated in the RegPrecise database (http://regprecise.lbl.gov), a core resource on transcriptional regulation for the microbial domain of the Department of Energy (DOE) Systems Biology Knowledgebase. RegPrecise captures and visualize regulogs, sets of genes controlled by orthologous regulators in several closely related bacterial genomes, that were reconstructed by comparative genomics. The current release of RegPrecise 2.0 includes >1400 regulogs controlled either by protein transcription factors or by conserved ribonucleic acid regulatory motifs in >250 genomes from 24 taxonomic groups of bacteria. The reference regulons accumulated in RegPrecise can serve as a basis for automatic annotation of regulatory interactions in newly sequenced genomes. The developed API provides an efficient access to the RegPrecise data by a comprehensive set of 14 web service resources. The RegPrecise web services API is freely accessible at http://regprecise.lbl.gov/RegPrecise/services.jsp with no login requirements

RegTransBase—a database of regulatory sequences and interactions in a wide range of prokaryotic genomes

RegTransBase is a manually curated database of regulatory interactions in prokaryotes that captures the knowledge in public scientific literature using a controlled vocabulary. Although several databases describing interactions between regulatory proteins and their binding sites are already being maintained, they either focus mostly on the model organisms Escherichia coli and Bacillus subtilis or are entirely computationally derived. RegTransBase describes a large number of regulatory interactions reported in many organisms and contains the following types of experimental data: the activation or repression of transcription by an identified direct regulator, determining the transcriptional regulatory function of a protein (or RNA) directly binding to DNA (RNA), mapping or prediction of a binding site for a regulatory protein and characterization of regulatory mutations. Currently, RegTransBase content is derived from about 3000 relevant articles describing over 7000 experiments in relation to 128 microbes. It contains data on the regulation of about 7500 genes and evidence for 6500 interactions with 650 regulators. RegTransBase also contains manually created position weight matrices (PWM) that can be used to identify candidate regulatory sites in over 60 species. RegTransBase is available at

RegPrecise: a database of curated genomic inferences of transcriptional regulatory interactions in prokaryotes

The RegPrecise database (http://regprecise.lbl.gov) was developed for capturing, visualization and analysis of predicted transcription factor regulons in prokaryotes that were reconstructed and manually curated by utilizing the comparative genomic approach. A significant number of high-quality inferences of transcriptional regulatory interactions have been already accumulated for diverse taxonomic groups of bacteria. The reconstructed regulons include transcription factors, their cognate DNA motifs and regulated genes/operons linked to the candidate transcription factor binding sites. The RegPrecise allows for browsing the regulon collections for: (i) conservation of DNA binding sites and regulated genes for a particular regulon across diverse taxonomic lineages; (ii) sets of regulons for a family of transcription factors; (iii) repertoire of regulons in a particular taxonomic group of species; (iv) regulons associated with a metabolic pathway or a biological process in various genomes. The initial release of the database includes ∼11 500 candidate binding sites for ∼400 orthologous groups of transcription factors from over 350 prokaryotic genomes. Majority of these data are represented by genome-wide regulon reconstructions in Shewanella and Streptococcus genera and a large-scale prediction of regulons for the LacI family of transcription factors. Another section in the database represents the results of accurate regulon propagation to the closely related genomes

Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria

BACKGROUND: In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels. An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis. RESULTS: A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations. CONCLUSIONS: The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/)

Regulon inference without arbitrary thresholds: three levels of sensitivity

Reconstruction of transcriptional regulatory networks is one of the major challenges facing the bioinformatics community in view of constantly growing number of complete genomes. The comparative genomics approach has been successfully used for the analysis of the transcriptional regulation of many metabolic systems in various bacteria taxa. The key step in this approach is given a position weight matrix, find an optimal threshold for the search of potential binding sites in genomes. In our previous work we proposed an approach for automatic selection of TFBS score threshold coupled with inference of regulon content. In this study we developed two modifications of this approach providing two additional levels of sensitivity

Comparative genomics and evolution of transcriptional regulons in Proteobacteria

Comparative genomics approaches are broadly used for analysis of transcriptional regulation in bacterial genomes. In this work, we identified binding sites and reconstructed regulons for 33 orthologous groups of transcription factors (TFs) in 196 reference genomes from 21 taxonomic groups of Proteobacteria. Overall, we predict over 10,600 TF binding sites and identified more than 15,600 target genes for 1,896 TFs constituting the studied orthologous groups of regulators. These include a set of orthologs for 21 metabolism-associated TFs from Escherichia coli and/or Shewanella that are conserved in five or more taxonomic groups and several additional TFs that represent non-orthologous substitutions of the metabolic regulators in some lineages of Proteobacteria. By comparing gene contents of the reconstructed regulons, we identified the core, taxonomy-specific and genome-specific TF regulon members and classified them by their metabolic functions. The detailed analysis of ArgR, TyrR, TrpR, HutC, HypR and other amino acid-specific regulons demonstrated remarkable differences in regulatory strategies used by various lineages of Proteobacteria. The obtained genomic collection of in silico reconstructed TF regulons contains a large number of new regulatory interactions that awaits future experimental validation. It provides a framework for future evolutionary studies of transcriptional regulatory networks in Bacteria. It can be also used for functional annotation of putative metabolic transporters and enzymes that are abundant in the reconstructed regulons

Regulon inference without arbitrary thresholds: three levels of sensitivity

Reconstruction of transcriptional regulatory networks is one of the major challenges facing the bioinformatics community in view of constantly growing number of complete genomes. The comparative genomics approach has been successfully used for the analysis of the transcriptional regulation of many metabolic systems in various bacteria taxa. The key step in this approach is given a position weight matrix, find an optimal threshold for the search of potential binding sites in genomes. In our previous work we proposed an approach for automatic selection of TFBS score threshold coupled with inference of regulon content. In this study we developed two modifications of this approach providing two additional levels of sensitivity

Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria

Background: Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group. Results: A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported. Conclusions: The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov)

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence▿†

Transcriptional regulatory networks are fine-tuned systems that help microorganisms respond to changes in the environment and cell physiological state. We applied the comparative genomics approach implemented in the RegPredict Web server combined with SEED subsystem analysis and available information on known regulatory interactions for regulatory network reconstruction for the human pathogen Staphylococcus aureus and six related species from the family Staphylococcaceae. The resulting reference set of 46 transcription factor regulons contains more than 1,900 binding sites and 2,800 target genes involved in the central metabolism of carbohydrates, amino acids, and fatty acids; respiration; the stress response; metal homeostasis; drug and metal resistance; and virulence. The inferred regulatory network in S. aureus includes ∼320 regulatory interactions between 46 transcription factors and ∼550 candidate target genes comprising 20% of its genome. We predicted ∼170 novel interactions and 24 novel regulons for the control of the central metabolic pathways in S. aureus. The reconstructed regulons are largely variable in the Staphylococcaceae: only 20% of S. aureus regulatory interactions are conserved across all studied genomes. We used a large-scale gene expression data set for S. aureus to assess relationships between the inferred regulons and gene expression patterns. The predicted reference set of regulons is captured within the Staphylococcus collection in the RegPrecise database (http://regprecise.lbl.gov)