Tractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: , and . This database contains a collection of computationally predicted Transcription Factors' binding sites in gamma-proteobacterial genomes. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms. In this paper we describe the main improvements incorporated to the database in the past year and a half which include incorporating information on the regulatory networks of 13—increasing to 30—new gamma-proteobacteria and developing a new computational strategy to complement the putative sites identified by the original weight matrix-based approach. We have also added dynamically generated navigation tabs to the navigation interfaces. Moreover, we developed a new interface that allows users to directly retrieve information on the conservation of regulatory interactions in the 30 genomes included in the database by navigating a map that represents a core of the known Escherichia coli regulatory network

ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks

ProdoNet is a web-based application for the mapping of prokaryotic genes and the corresponding proteins to common gene regulatory and metabolic networks. For a given list of genes, the system detects shared operons, identifies co-expressed genes and deduces joint regulators. In addition, the contribution to shared metabolic pathways becomes visible on KEGG maps. Furthermore, the co-occurrence of genes of interest in gene expression profiles can be added to the visualization of the global network. In this way, ProdoNet provides the basis for functional genomics approaches and for the interpretation of transcriptomics and proteomics data. As an example, we present an investigation of an experimental membrane subproteome analysis of Pseudomonas aeruginosa with ProdoNet. The ProdoNet dataset on transcriptional regulation is based on the PRODORIC Prokaryotic Database of Gene Regulation and the Virtual Footprint tool. ProdoNet is accessible at http://www.prodonet.tu-bs.de

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

On the power and limits of evolutionary conservation—unraveling bacterial gene regulatory networks

The National Center for Biotechnology Information (NCBI) recently announced ‘1000 prokaryotic genomes are now completed and available in the Genome database’. The increasing trend will provide us with thousands of sequenced microbial organisms over the next years. However, this is only the first step in understanding how cells survive, reproduce and adapt their behavior while being exposed to changing environmental conditions. One major control mechanism is transcriptional gene regulation. Here, striking is the direct juxtaposition of the handful of bacterial model organisms to the 1000 prokaryotic genomes. Next-generation sequencing technologies will further widen this gap drastically. However, several computational approaches have proven to be helpful. The main idea is to use the known transcriptional regulatory network of reference organisms as template in order to unravel evolutionarily conserved gene regulations in newly sequenced species. This transfer essentially depends on the reliable identification of several types of conserved DNA sequences. We decompose this problem into three computational processes, review the state of the art and illustrate future perspectives

Effect of genomic distance on coexpression of coregulated genes in E. coli

In prokaryotes, genomic distance is a feature that in addition to coregulation affects coexpression. Several observations, such as genomic clustering of highly coexpressed small regulons, support the idea that coexpression behavior of coregulated genes is affected by the distance between the coregulated genes. However, the specific contribution of distance in addition to coregulation in determining the degree of coexpression has not yet been studied systematically. In this work, we exploit the rich information in RegulonDB to study how the genomic distance between coregulated genes affects their degree of coexpression, measured by pairwise similarity of expression profiles obtained under a large number of conditions. We observed that, in general, coregulated genes display higher degrees of coexpression as they are more closely located on the genome. This contribution of genomic distance in determining the degree of coexpression was relatively small compared to the degree of coexpression that was determined by the tightness of the coregulation (degree of overlap of regulatory programs) but was shown to be evolutionary constrained. In addition, the distance effect was sufficient to guarantee coexpression of coregulated genes that are located at very short distances, irrespective of their tightness of coregulation. This is partly but definitely not always because the close distance is also the cause of the coregulation. In cases where it is not, we hypothesize that the effect of the distance on coexpression could be caused by the fact that coregulated genes closely located to each other are also relatively more equidistantly located from their common TF and therefore subject to more similar levels of TF molecules. The absolute genomic distance of the coregulated genes to their common TF-coding gene tends to be less important in determining the degree of coexpression. Our results pinpoint the importance of taking into account the combined effect of distance and coregulation when studying prokaryotic coexpression and transcriptional regulation

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in BioengineeringThe reconstruction of genome-scale metabolic models (GEMs) from genome functional annotations is, nowadays, a routine practice in Systems Biology (SB) research. The models have been successfully used to predict organisms’ behavior, gene essentiality, growth phenotypes and to aid strain optimization via metabolic engineering strategies. As the community acknowledges the usefulness of GEMs, they also present limitations, most notably the inability to account for the impact of regulation on the metabolic activity. The overall objective of this thesis was to reconstruct and perform in silico phenotype simulations for integrated models of metabolism and regulation. The number of genomes available in the public domain increased exponentially in the last decade. The overwhelming amount of data led to the introduction of automated pipelines for genome annotation, also facilitating the propagation of annotation inconsistencies from public repositories. In this work, we explore the use of GEMs as tools for annotation curation. A protocol for annotation curation with metabolic network reconstructions was designed and applied to the genus Brucella. The high-throughput reconstruction and analysis of genome-scale transcriptional regulatory networks is a current challenge in SB research. In this work, the model organism Bacillus subtilis was chosen as a case study and a new manually curated network for its transcriptional regulation was introduced. We proposed a new methodology for the inference of regulatory interactions from gene expression data. The newly proposed methodology dubbed “atomic regulon inference” was shown to capture many sets of genes corresponding to regulatory units in the manually curated network. Following this line of work, based on the proposed regulatory transcriptional regulatory network for B. subtilis, we introduced an integrated genome-scale model for the metabolism and transcriptional regulation in B. subtilis. Model validation was performed with in silico growth phenotype simulations for mutant strains described in the literature. The integrated model was able to predict transcription factor knockouts for growth in multiple environmental conditions, expanding the predictive capabilities of the metabolic model by itself.A reconstrução de modelos metabólicos à escala genómica (MMEGs) a partir de anotações funcionais do genoma é, hoje em dia, uma prática comum na investigação em Biologia de Sistemas (BS). Estes modelos foram usados com sucesso para prever o comportamento de organismos, essencialidade de genes, fenótipos de crescimento e na optimização estirpes bacterianas com estratégias de engenharia metabólica. Com o reconhecimento pela comunidade da utilidade de MMEGs, várias limitações foram identificadas, principalmente a incapacidade destes modelos explicarem o impacto da regulação de genes na actividade metabólica. O objetivo global desta tese foi a reconstrução e execução de simulações de fenótipo in silico para modelos que integram metabolismo e regulação. O número de genomas disponíveis no domínio público aumentou exponencialmente na última década. Com este aumento exponencial de dados, plataformas para anotação automática de genomas tornaram-se uma necessidade, o que facilita a propagação de inconsistências nas anotações em repositórios públicos. Neste trabalho exploramos o uso MMEGs como ferramentas para melhoramento de anotações. Um protocolo para o melhoramento de anotações com o uso de MMEGs foi desenvolvido neste trabalho e testado na melhoria de anotações do género Brucella. A reconstrução e análise de redes regulatórias de fenómenos de transcrição à escala genómica é um desafio actual na investigação em BS. Neste trabalho, foi efectuada a reconstrução manual da rede regulatória da transcrição para o microrganismo Bacillus subtilis. Um novo método para inferência automática de interações de regulação, a partir de dados de expressão de genes, foi igualmente desenvolvido. Este novo método mostrou ser capaz de inferir interações regulatórias comparáveis às observadas na rede reconstruída manualmente. Com base na rede regulatória proposta para a regulação da transcrição de B. subtilis, desenvolvemos um modelo à escala genómica que integra o metabolismo e regulação da transcrição em B. subtilis. O modelo foi validado com simulações de fenótipo de crescimento in silico para estirpes mutantes descritas na literatura. O modelo integrado foi capaz de prever o efeito da deleção de factores de transcrição no crescimento em várias condições ambientais, ampliando as capacidades de previsão do modelo metabólico por si só.Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH / BD / 70824 / 2010), co-financiada pelo POPH - QREN - Tipologia 4.1 -Formação Avançada - e comparticipados pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)