Identification and reconstruction of various transcriptional regulons in bacteria using a computational comparative genomics approach is coming of age. During the past decade a large number of manually-curated high quality inferences of transcriptional regulatory interactions were accumulated for diverse taxonomic groups of bacteria. These data pro-vide a good foundation for understanding molecular mechanisms of transcriptional regu-lation, identification of regulatory circuits, and interconnections among circuits within the cell. Traditional experimental methods for regulon analysis have certain limitations both in terms of productivity and feasibility. While the development of high-throughput tran-scriptome approaches allow to obtain genome-scale gene expression patterns, in many cases the complexity of the interactions between regulons makes it difficult to distinguish between direct and indirect effects on transcription. The availability of a large number of closely related genomes allows one to apply comparative genomics to accurately expand already known regulons to yet uncharacterized organisms, and to predict and describe new regulons. Due to fast accumulation of such valuable data, there is a need for a spe-cialized database and associated analysis tools that will compile and present the growing collection of high quality predicted bacterial regulons. The RegPrecise database was developed for capturing, visualization and analysis of tran-scription factor regulons that were reconstructed by the comparative genomic approach. The primary object of the database is a single regulon in a particular genome, which is described by the identified transcription factor, its DNA binding site model (a profile), as well as the set of regulated genes, operons and associated operator sites. Regulons for orthologous transcription factors from closely related genomes are combined into the collections that provide an overview of the conserved and variable components of the regulon. A higher level representation of the regulatory interactions is also provided for orthologous regulons described in several bacterial taxonomic groups enabling compari-son and evolutionary analysis of the transcription factor binding motifs. Another view of complex data in the database is a general overview of multiple regulons inferred in a set of closely related group of genomes. The current version of database covers more than 250 genomes and 180 profiles. Among others, it represents the results of our recent comparative genomic reconstruction of metabolic regulons in 13 Shewanella species that included near 70 transcription factors, approximately 400 binding sites and more than 1000 target genes per each genome. The database gives access to large regulatory networks reconstructed for certain metabolic pathways, e.g. degradation of fatty acids, branch chain amino acids, and aminosugars, homeostasis of biometals, and biosynthesis of NAD cofactor. In the near future we are planning to add a large collection of regulons for the LacI family transcription factors.

Background

Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks.

Findings

We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API.

Conclusions

CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

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 bacterial 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. Here we demonstrate that this problem is tightly bound to a problem of discovering the optimal content of regulon and suggest an approach to solve both problems simultaneously

Previous work on respiratory chlorate reduction has biochemically identified the terminal reductase ClrABC and the chlorite detoxifying enzyme Cld. In Shewanella algae ACDC, genes encoding these enzymes reside on composite transposons whose core we refer to as the chlorate reduction composite transposon interior (CRI). To better understand this metabolism in ACDC, we used RNA-seq and proteomics to predict carbon and electron flow during chlorate reduction and posit that formate is an important electron carrier with lactate as the electron donor, but that NADH predominates on acetate. Chlorate-specific transcription of electron transport chain components or the CRI was not observed, but clr and cld transcription was attenuated by oxygen. The major chlorate-specific response related to oxidative stress and was indicative of reactive chlorine species production. A genetic system based on rpsL-streptomycin counter selection was developed to further dissect the metabolism, but ACDC readily lost the CRI via homologous recombination of the composite transposon's flanking insertion sequences. An engineered strain containing a single chromosomal CRI did not grow on chlorate, but overexpression of cld and its neighbouring cytochrome c restored growth. We postulate that the recently acquired CRI underwent copy-number expansion to circumvent insufficient expression of key genes in the pathway.

Abstract 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/).

One of the major challenges for the bioinformatics community in view of constantly growing number of complete genomes is providing effective tools to enable high-quality reconstruction of transcriptional regulatory networks (TRN). Definition of a particular TRN includes specification of which transcription factors (TF) bind to TF-binding sites (TFBS) in the promoter regions of which genes and what is the integrated effect of all these TFs on the expression of al these genes. Reconstruction of TRNs helps to better understand the metabolism and functions of bacteria. Among different approaches that are used for TRN reconstruction are an expression data-driven approach, and comparative genomic approaches that are either computing-driven, or subsystem (pathway) -driven. DNA microarrays, reporting gene expression, continue to be an important tool for high-throughput measurements on transcriptional levels, and machine-learning approaches were used to identify TRN (without a TFBS component) from a compendium of microarray expression profiles . However, in many cases the complexity of the interactions between regulons makes it difficult to distinguish between direct and indirect effects on transcription. Availability of a large number of complete genomes opens an opportunity to apply modern approaches of comparative genomics to expand the known regulons to yet uncharacterized organisms and to predict and describe new regulons with high precision.

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.

Background

The σ(54) subunit controls a unique class of promoters in bacteria. Such promoters, without exception, require enhancer binding proteins (EBPs) for transcription initiation. Desulfovibrio vulgaris Hildenborough, a model bacterium for sulfate reduction studies, has a high number of EBPs, more than most sequenced bacteria. The cellular processes regulated by many of these EBPs remain unknown.

Results

To characterize the σ(54)-dependent regulome of D. vulgaris Hildenborough, we identified EBP binding motifs and regulated genes by a combination of computational and experimental techniques. These predictions were supported by our reconstruction of σ(54)-dependent promoters by comparative genomics. We reassessed and refined the results of earlier studies on regulation in D. vulgaris Hildenborough and consolidated them with our new findings. It allowed us to reconstruct the σ(54) regulome in D. vulgaris Hildenborough. This regulome includes 36 regulons that consist of 201 coding genes and 4 non-coding RNAs, and is involved in nitrogen, carbon and energy metabolism, regulation, transmembrane transport and various extracellular functions. To the best of our knowledge, this is the first report of direct regulation of alanine dehydrogenase, pyruvate metabolism genes and type III secretion system by σ(54)-dependent regulators.

Conclusions

The σ(54)-dependent regulome is an important component of transcriptional regulatory network in D. vulgaris Hildenborough and related free-living Deltaproteobacteria. Our study provides a representative collection of σ(54)-dependent regulons that can be used for regulation prediction in Deltaproteobacteria and other taxa.