CoryneRegNet 6.0—Updated database content, new analysis methods and novel features focusing on community demands

Post-genomic analysis techniques such as next-generation sequencing have produced vast amounts of data about micro organisms including genetic sequences, their functional annotations and gene regulatory interactions. The latter are genetic mechanisms that control a cell's characteristics, for instance, pathogenicity as well as survival and reproduction strategies. CoryneRegNet is the reference database and analysis platform for corynebacterial gene regulatory networks. In this article we introduce the updated version 6.0 of CoryneRegNet and describe the updated database content which includes, 6352 corynebacterial regulatory interactions compared with 4928 interactions in release 5.0 and 3235 regulations in release 4.0, respectively. We also demonstrate how we support the community by integrating analysis and visualization features for transiently imported custom data, such as gene regulatory interactions. Furthermore, with release 6.0, we provide easy-to-use functions that allow the user to submit data for persistent storage with the CoryneRegNet database. Thus, it offers important options to its users in terms of community demands. CoryneRegNet is publicly available at http://www.coryneregnet.de

RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions

RegulonDB is the internationally recognized reference database of Escherichia coli K-12 offering curated knowledge of the regulatory network and operon organization. It is currently the largest electronically-encoded database of the regulatory network of any free-living organism. We present here the recently launched RegulonDB version 5.0 radically different in content, interface design and capabilities. Continuous curation of original scientific literature provides the evidence behind every single object and feature. This knowledge is complemented with comprehensive computational predictions across the complete genome. Literature-based and predicted data are clearly distinguished in the database. Starting with this version, RegulonDB public releases are synchronized with those of EcoCyc since our curation supports both databases. The complex biology of regulation is simplified in a navigation scheme based on three major streams: genes, operons and regulons. Regulatory knowledge is directly available in every navigation step. Displays combine graphic and textual information and are organized allowing different levels of detail and biological context. This knowledge is the backbone of an integrated system for the graphic display of the network, graphic and tabular microarray comparisons with curated and predicted objects, as well as predictions across bacterial genomes, and predicted networks of functionally related gene products. Access RegulonDB at

Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.</p> <p>Results</p> <p>Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using <it>E. coli </it>expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for <it>E. coli </it>was integrated with 266 <it>E. coli </it>gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.</p> <p>Conclusion</p> <p>POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.</p

A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor–gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor–gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic–anaerobic shift interface

COLOMBOS: Access Port for Cross-Platform Bacterial Expression Compendia

Background: Microarrays are the main technology for large-scale transcriptional gene expression profiling, but the large bodies of data available in public databases are not useful due to the large heterogeneity. There are several initiatives that attempt to bundle these data into expression compendia, but such resources for bacterial organisms are scarce and limited to integration of experiments from the same platform or to indirect integration of per experiment analysis results. Methodology/Principal Findings: We have constructed comprehensive organism-specific cross-platform expression compendia for three bacterial model organisms (Escherichia coli, Bacillus subtilis, and Salmonella enterica serovar Typhimurium) together with an access portal, dubbed COLOMBOS, that not only provides easy access to the compendia, but also includes a suite of tools for exploring, analyzing, and visualizing the data within these compendia. It is freely available at http://bioi.biw.kuleuven.be/colombos. The compendia are unique in directly combining expression information from different microarray platforms and experiments, and we illustrate the potential benefits of this direct integration with a case study: extending the known regulon of the Fur transcription factor of E. coli. The compendia also incorporate extensive annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the COLOMBOS analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc. Conclusions/Significance: We have created cross-platform expression compendia for several bacterial organisms and developed a complementary access port COLOMBOS, that also serves as a convenient expression analysis tool to extract useful biological information. This work is relevant to a large community of microbiologists by facilitating the use of publicly available microarray experiments to support their research

Self-organization of gene regulatory network motifs enriched with short transcript's half-life transcription factors

Network motifs, the recurring regulatory structural patterns in networks, are able to self-organize to produce networks. Three major motifs, feedforward loop, single input modules and bi-fan are found in gene regulatory networks. The large ratio of genes to transcription factors (TFs) in genomes leads to a sharing of TFs by motifs and is sufficient to result in network self-organization. We find a common design principle of these motifs: short transcript's half-life (THL) TFs are significantly enriched in motifs and hubs. This enrichment becomes one of the driving forces for the emergence of the network scale-free topology and allows the network to quickly adapt to environmental changes. Most feedforward loops and bi-fans contain at least one short THL TF, which can be seen as a criterion for self-assembling these motifs. We have classified the motifs according to their short THL TF content. We show that the percentage of the different motif subtypes varies in different cellular conditions.Comment: Trends Genet (in press), main text 1, supplementary notes 1, 40 pages, 7 tables, 4 figs, minor modification

Adaptive laboratory evolution of a genome-reduced Escherichia coli.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain's systems biology

Methods for analysis of derivative strains from metabolic evolution experiments

One of the largest challenges in genomics studies is determining the relationship between genotype and phenotype and then applying this knowledge to design principles. Metabolic engineering of bacteria can introduce targeted genomic interventions to well-characterized genes for the purpose of modifying cellular metabolism, but in some cases, even for the model organism Escherichia coli, alternative strategies are required to achieve a desired phenotype. Metabolic evolution involves applying selective pressure to a population, and over time advantageous mutations will arise that improve organism fitness. To understand what mutations occurred during these experiments and how they affect phenotype, whole genome sequencing is required, followed by mutation analysis and strain characterization. Genome sequencing generates a large amount of data for researchers to examine and traditionally mutation analysis focuses only on gene variations. Supporting mutation analysis with computational tools and using a systems-level approach that utilizes public databases describing gene regulation and cellular metabolism improves upon existing analysis techniques and advances our understanding of how genotype relates to phenotype. Using our mutation analysis software, E. coli Variant Analysis (EVA), we examine antibiotic resistance, benzoate tolerance, and octanoic acid tolerance in E. coli. Our analysis pipeline includes a defined set of rules for mutation categorization. Prioritization of mutations supports efforts to reverse-engineer evolved strains and focus on the variants most likely to be damaging or relevant to phenotype. From mutation analysis results, we construct biological networks for visualization of mutations and possible downstream effects. This allows for improved mutation interpretation and identification of possible mutation interactions. Furthermore, we integrate RNA-seq data into our analysis to investigate the effects of variant regulators on the transcriptome. In contrast to existing methods which focus on mutated genes, we incorporate annotations for binding sites and other regulatory features on the genome for the most complete interpretation based on the available genome and gene regulatory models

Making connections between novel transcription factors and their DNA motifs

The key components of a transcriptional regulatory network are the connections between trans-acting transcription factors and cis-acting DNA-binding sites. In spite of several decades of intense research, only a fraction of the estimated ∼300 transcription factors in Escherichia coli have been linked to some of their binding sites in the genome. In this paper, we present a computational method to connect novel transcription factors and DNA motifs in E. coli. Our method uses three types of mutually independent information, two of which are gleaned by comparative analysis of multiple genomes and the third one derived from similarities of transcription-factor-DNA-binding-site interactions. The different types of information are combined to calculate the probability of a given transcription-factor-DNA-motif pair being a true pair. Tested on a study set of transcription factors and their DNA motifs, our method has a prediction accuracy of 59% for the top predictions and 85% for the top three predictions. When applied to 99 novel transcription factors and 70 novel DNA motifs, our method predicted 64 transcription-factor-DNA-motif pairs. Supporting evidence for some of the predicted pairs is presented. Functional annotations are made for 23 novel transcription factors based on the predicted transcription-factor-DNA-motif connections