Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

International audienceBACKGROUND: Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. RESULTS: We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities). We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. CONCLUSION: This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis

BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism

SPABBATS: A pathway-discovery method based on Boolean satisfiability that facilitates the characterization of suppressor mutants

BACKGROUND: Several computational methods exist to suggest rational genetic interventions that improve the productivity of industrial strains. Nonetheless, these methods are less effective to predict possible genetic responses of the strain after the intervention. This problem requires a better understanding of potential alternative metabolic and regulatory pathways able to counteract the targeted intervention. RESULTS: Here we present SPABBATS, an algorithm based on Boolean satisfiability (SAT) that computes alternative metabolic pathways between input and output species in a reconstructed network. The pathways can be constructed iteratively in order of increasing complexity. SPABBATS allows the accumulation of intermediates in the pathways, which permits discovering pathways missed by most traditional pathway analysis methods. In addition, we provide a proof of concept experiment for the validity of the algorithm. We deleted the genes for the glutamate dehydrogenases of the Gram-positive bacterium Bacillus subtilis and isolated suppressor mutant strains able to grow on glutamate as single carbon source. Our SAT approach proposed candidate alternative pathways which were decisive to pinpoint the exact mutation of the suppressor strain. CONCLUSIONS: SPABBATS is the first application of SAT techniques to metabolic problems. It is particularly useful for the characterization of metabolic suppressor mutants and can be used in a synthetic biology setting to design new pathways with specific input-output requirements

Understanding the Evolutionary Relationships and Major Traits of \u3cem\u3eBacillus\u3c/em\u3e through Comparative Genomics

Background: The presence of Bacillus in very diverse environments reflects the versatile metabolic capabilities of a widely distributed genus. Traditional phylogenetic analysis based on limited gene sampling is not adequate for resolving the genus evolutionary relationships. By distinguishing between core and pan-genome, we determined the evolutionary and functional relationships of known Bacillus. Results: Our analysis is based upon twenty complete and draft Bacillus genomes, including a newly sequenced Bacillus isolate from an aquatic environment that we report for the first time here. Using a core genome, we were able to determine the phylogeny of known Bacilli, including aquatic strains whose position in the phylogenetic tree could not be unambiguously determined in the past. Using the pan-genome from the sequenced Bacillus, we identified functional differences, such as carbohydrate utilization and genes involved in signal transduction, which distinguished the taxonomic groups. We also assessed the genetic architecture of the defining traits of Bacillus, such as sporulation and competence, and showed that less than one third of the B. subtilis genes are conserved across other Bacilli. Most variation was shown to occur in genes that are needed to respond to environmental cues, suggesting that Bacilli have genetically specialized to allow for the occupation of diverse habitats and niches. Conclusions: The aquatic Bacilli are defined here for the first time as a group through the phylogenetic analysis of 814 genes that comprise the core genome. Our data distinguished between genomic components, especially core vs. pan-genome to provide insight into phylogeny and function that would otherwise be difficult to achieve. A phylogeny may mask the diversity of functions, which we tried to uncover in our approach. The diversity of sporulation and competence genes across the Bacilli was unexpected based on previous studies of the B. subtilis model alone. The challenge of uncovering the novelties and variations among genes of the non-subtilis groups still remains. This task will be best accomplished by directing efforts toward understanding phylogenetic groups with similar ecological niches

Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis

The genetic regulatory network (GRN) plays a key role in controlling the response of the cell to changes in the environment. Although the structure of GRNs has been the subject of many studies, their large scale structure in the light of feedbacks from the metabolic network (MN) has received relatively little attention. Here we study the causal structure of the GRNs, namely the chain of influence of one component on the other, taking into account feedback from the MN. First we consider the GRNs of E. coli and B. subtilis without feedback from MN and illustrate their causal structure. Next we augment the GRNs with feedback from their respective MNs by including (a) links from genes coding for enzymes to metabolites produced or consumed in reactions catalyzed by those enzymes and (b) links from metabolites to genes coding for transcription factors whose transcriptional activity the metabolites alter by binding to them. We find that the inclusion of feedback from MN into GRN significantly affects its causal structure, in particular the number of levels and relative positions of nodes in the hierarchy, and the number and size of the strongly connected components (SCCs). We then study the functional significance of the SCCs. For this we identify condition specific feedbacks from the MN into the GRN by retaining only those enzymes that are essential for growth in specific environmental conditions simulated via the technique of flux balance analysis (FBA). We find that the SCCs of the GRN augmented by these feedbacks can be ascribed specific functional roles in the organism. Our algorithmic approach thus reveals relatively autonomous subsystems with specific functionality, or regulatory modules in the organism. This automated approach could be useful in identifying biologically relevant modules in other organisms for which network data is available, but whose biology is less well studied.Comment: 15 figure

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275We introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same ON and OFF gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.AG and VF acknowledge funding from the European Union Basynthecproject(BaSynthecFP7-244093)

Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models

Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High throughput annotation and metabolic modeling of these genomes is now a reality. The high throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represents the next frontier in microbial bioinformatics. The fruition of this next frontier will depend upon the integration of numerous data sources relating to mechanisms, components, and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data, and binding site locations, and we explore how these data are being used for the reconstruction of new regulatory networks. From template network based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. Finally, we explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.J.P.F. acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. The work was supported in part by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), National Funds through the FCT within projects [FCOMP-01-0124-FEDER015079] (ToMEGIM—Computational Tools for Metabolic Engineering using Genome-scale Integrated Models) and FCOMP-01-0124-FEDER009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori), the U.S. Department of Energy under contract [DE-ACO2-06CH11357] and the National Science Foundation under [0850546]

Reconstruction of xylose utilization pathway and regulons in Firmicutes

<p>Abstract</p> <p>Background</p> <p>Many Firmicutes bacteria, including solvent-producing clostridia such as <it>Clostridium acetobutylicum</it>, are able to utilize xylose, an abundant carbon source in nature. Nevertheless, homology searches failed to recognize all the genes for the complete xylose and xyloside utilization pathway in most of them. Moreover, the regulatory mechanisms of xylose catabolism in many Firmicutes except <it>Bacillus </it>spp. still remained unclear.</p> <p>Results</p> <p>A comparative genomic approach was used to reconstruct the xylose and xyloside utilization pathway and analyze its regulatory mechanisms in 24 genomes of the Firmicutes. A novel xylose isomerase that is not homologous to previously characterized xylose isomerase, was identified in <it>C. acetobutylicum </it>and several other Clostridia species. The candidate genes for the xylulokinase, xylose transporters, and the transcriptional regulator of xylose metabolism (XylR), were unambiguously assigned in all of the analyzed species based on the analysis of conserved chromosomal gene clustering and regulons. The predicted functions of these genes in <it>C. acetobutylicum </it>were experimentally confirmed through a combination of genetic and biochemical techniques. XylR regulons were reconstructed by identification and comparative analysis of XylR-binding sites upstream of xylose and xyloside utilization genes. A novel XylR-binding DNA motif, which is exceptionally distinct from the DNA motif known for <it>Bacillus </it>XylR, was identified in three Clostridiales species and experimentally validated in <it>C. acetobutylicum </it>by an electrophoretic mobility shift assay.</p> <p>Conclusions</p> <p>This study provided comprehensive insights to the xylose catabolism and its regulation in diverse Firmicutes bacteria especially Clostridia species, and paved ways for improving xylose utilization capability in <it>C. acetobutylicum </it>by genetic engineering in the future.</p

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)

Blueprint: descrição da complexidade da regulação metabólica através da reconstrução de modelos metabólicos e regulatórios integrados

Tese de doutoramento em Biomedical EngineeringUm modelo metabólico consegue prever o fenótipo de um organismo. No entanto, estes modelos podem obter previsões incorretas, pois alguns processos metabólicos são controlados por mecanismos reguladores. Assim, várias metodologias foram desenvolvidas para melhorar os modelos metabólicos através da integração de redes regulatórias. Todavia, a reconstrução de modelos regulatórios e metabólicos à escala genómica para diversos organismos apresenta diversos desafios. Neste trabalho, propõe-se o desenvolvimento de diversas ferramentas para a reconstrução e análise de modelos metabólicos e regulatórios à escala genómica. Em primeiro lugar, descreve-se o Biological networks constraint-based In Silico Optimization (BioISO), uma nova ferramenta para auxiliar a curação manual de modelos metabólicos. O BioISO usa um algoritmo de relação recursiva para orientar as previsões de fenótipo. Assim, esta ferramenta pode reduzir o número de artefatos em modelos metabólicos, diminuindo a possibilidade de obter erros durante a fase de curação. Na segunda parte deste trabalho, desenvolveu-se um repositório de redes regulatórias para procariontes que permite suportar a sua integração em modelos metabólicos. O Prokaryotic Transcriptional Regulatory Network Database (ProTReND) inclui diversas ferramentas para extrair e processar informação regulatória de recursos externos. Esta ferramenta contém um sistema de integração de dados que converte dados dispersos de regulação em redes regulatórias integradas. Além disso, o ProTReND dispõe de uma aplicação que permite o acesso total aos dados regulatórios. Finalmente, desenvolveu-se uma ferramenta computacional no MEWpy para simular e analisar modelos regulatórios e metabólicos. Esta ferramenta permite ler um modelo metabólico e/ou rede regulatória, em diversos formatos. Esta estrutura consegue construir um modelo regulatório e metabólico integrado usando as interações regulatórias e as ligações entre genes e proteínas codificadas no modelo metabólico e na rede regulatória. Além disso, esta estrutura suporta vários métodos de previsão de fenótipo implementados especificamente para a análise de modelos regulatórios-metabólicos.Genome-Scale Metabolic (GEM) models can predict the phenotypic behavior of organisms. However, these models can lead to incorrect predictions, as certain metabolic processes are controlled by regulatory mechanisms. Accordingly, many methodologies have been developed to extend the reconstruction and analysis of GEM models via the integration of Transcriptional Regulatory Network (TRN)s. Nevertheless, the perspective of reconstructing integrated genome-scale regulatory and metabolic models for diverse prokaryotes is still an open challenge. In this work, we propose several tools to assist the reconstruction and analysis of regulatory and metabolic models. We start by describing BioISO, a novel tool to assist the manual curation of GEM models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype predictions. Hence, this tool can reduce the number of artifacts in GEM models, decreasing the burdens of model refinement and curation. A state-of-the-art repository of TRNs for prokaryotes was implemented to support the reconstruction and integration of TRNs into GEM models. The ProTReND repository comprehends several tools to extract and process regulatory information available in several resources. More importantly, this repository contains a data integration system to unify the regulatory data into standardized TRNs at the genome scale. In addition, ProTReND contains a web application with full access to the regulatory data. Finally, we have developed a new modeling framework to define, simulate and analyze GEnome-scale Regulatory and Metabolic (GERM) models in MEWpy. The GERM model framework can read a GEM model, as well as a TRN from different file formats. This framework assembles a GERM model using the regulatory interactions and Genes-Proteins-Reactions (GPR) rules encoded into the GEM model and TRN. In addition, this modeling framework supports several methods of phenotype prediction designed for regulatory-metabolic models.I would like to thank Fundação para a Ciência e Tecnologia for the Ph.D. studentship I was awarded with (SFRH/BD/139198/2018)