Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation

Background  

Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties.     

乳酸乳球菌NZ9000基因组规模代谢网络模型的构建与验证

随着后基因组时代的到来,工业微生物的代谢工程改造在工业生产上发挥着越来越重要的作用。而基因组规模代谢网络模型(Genome-scalemetabolicmodel,GSMM)将生物体体内所有已知代谢信息进行整合,为全局理解生物体的代谢状态、理性指导代谢工程改造提供了最佳的平台。乳酸乳球菌NZ9000(Lactococcuslactis NZ9000)作为工业发酵领域的重要菌株之一,由于其遗传背景清晰且几乎不分泌蛋白,是基因工程改造和外源蛋白表达的理想模式菌株。文中基于基因组功能注释和比较基因组学构建了L.lactisNZ9000的首个基因组规模代谢网络模型iWK557,包含557个基因、668个代谢物、840个反应,并进一步在定性和定量两个层次验证了iWK557的准确性,以期为理性指导L. lactis NZ9000代谢工程改造提供良好工具。     

The small MbtH-like protein encoded by an internal gene of the balhimycin biosynthetic gene cluster is not required for glycopeptide production

The balhimycin biosynthetic gene cluster of the glycopeptide producer Amycolatopsis balhimycina includes a gene (orf1) with unknown function. orf1 shows high similarity to the mbtH gene from Mycobacterium tuberculosis. In almost all nonribosomal peptide synthetase (NRPS) biosynthetic gene clusters, we could identify a small mbtH-like gene whose function in peptide biosynthesis is not known. The mbtH-like gene is always colocalized with the NRPS genes; however, it does not have a specific position in the gene cluster. In all glycopeptide biosynthetic gene clusters the orf1-like gene is always located downstream of the gene encoding the last module of the NRPS. We inactivated the orf1 gene in A. balhimycina by generating a deletion mutant. The balhimycin production is not affected in the orf1-deletion mutant and is indistinguishable from that of the wild type. For the first time, we show that the inactivation of an mbtH-like gene does not impair the biosynthesis of a nonribosomal peptide.     

Reconstructing genome-scale metabolic models with merlin

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin.     

Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data

In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes.     

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Background  

In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.     

Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001

Ketogulonicigenium vulgare WSH001 is an industrial strain commonly used in the vitamin C producing industry. In order to acquire a comprehensive understanding of its physiological characteristics, a genome-scale metabolic model of K. vulgare WSH001, iWZ663, including 830 reactions, 649 metabolites, and 663 genes, was reconstructed by genome annotation and literature mining. This model was capable of predicting quantitatively the growth of K. vulgare under L-sorbose fermentation conditions and the results agreed well with experimental data. Furthermore, phenotypic features, such as the defect in sulfate metabolism hampering the syntheses of L-cysteine, L-methionine, coenzyme A (CoA), and glutathione, were investigated and provided an explanation for the poor growth of K. vulgare in monoculture. The model presented here provides a validated platform that can be used to understand and manipulate the phenotype of K. vulgare to further improve 2-KLG production efficiency.     

Semi-automated Curation of Metabolic Models via Flux Balance Analysis: A Case Study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.     

A genome-scale, constraint-based approach to systems biology of human metabolism

The recent sequencing and annotation of the human genome enables a new era in biomedicine that will be based on an interdisciplinary, systemic approach to the elucidation and treatment of human disease. Reconstruction of genome-scale metabolic networks is an important part of this approach since networks represent the integration of diverse biological data such as genome annotations, high-throughput data, and legacy biochemical knowledge. This article will describe Homo sapiens Recon 1, a functionally tested, genome-scale reconstruction of human cellular metabolism, and its capabilities for facilitating the understanding of physiological and disease metabolic states.     

Genome-scale metabolic model of Helicobacter pylori 26695

A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.     

PseudoCyc,a pathway-genome database for Pseudomonas aeruginosa

A pathway-genome database (PGDB) describes the entire genome of an organism, as well as its biochemical pathways, reactions, and enzymes. Our PathoLogic software can generate a PGDB from an annotated genome of an organism, predicting the metabolic reactions and pathways corresponding to the enzymes present in the annotation. We have used PathoLogic to generate a PGDB for PSEUDOMONAS AERUGINOSA, strain PAO1, called 'PseudoCyc', which includes 139 predicted pathways and 800 predicted reactions involving 623 chemical species and 718 enzymes. Analysis of the PathoLogic predictions of arginine metabolism and the beta-ketoadipate pathway, which are landmark pathways in P. AERUGINOSA, showed that they were for the most part correctly predicted. These studies also provided possible locations for two genes involved in the beta-ketoadipate pathway, PCAI and PCAJ, which are missing from the PAO1 annotation. PseudoCyc adds an extended dimension to the genome of P. AERUGINOSA, providing researchers with a helpful tool for the analysis of the genomic, proteomic, and metabolic information of the organism. The finding of the probable location of the PCAI and PCAJ genes is but one example of the discoveries facilitated by such a PGDB. PseudoCyc, along with PGDBs for 12 other organisms, is available at http://BioCyc.org/.     

Increased glycopeptide production after overexpression of shikimate pathway genes being part of the balhimycin biosynthetic gene cluster

Amycolatopsis balhimycina produces the vancomycin-analogue balhimycin. The strain therefore serves as a model strain for glycopeptide antibiotic production. Previous characterisation of the balhimycin biosynthetic cluster had shown that the border sequences contained both, a putative 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (dahp), and a prephenate dehydrogenase (pdh) gene. In a metabolic engineering approach for increasing the precursor supply for balhimycin production, the dahp and pdh genes from the biosynthetic cluster were overexpressed both individually and together and the resulting strains were subjected to quantitative physiological characterisation. The constructed strains expressing an additional copy of the dahp gene and the strain carrying an extra copy of both dahp and pdh showed improved specific glycopeptide productivities by approximately a factor three, whereas the pdh overexpression strain showed a production profile similar to the wild type strain. In addition to the overexpression strains, corresponding deletion mutants, Δdahp and Δpdh, were constructed and characterised. Deletion of dahp resulted in significant reduction in balhimycin production whereas the Δpdh strain had production levels similar to the parent strain. Based on these results the relation between primary and secondary metabolism with regards to Dahp and Pdh is discussed.     

Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.     

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.     

Integrative network analysis reveals active microRNAs and their functions in gastric cancer

Background

Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO₂ and H₂ as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results

We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion

The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.     

The pathway tools pathway prediction algorithm

The PathoLogic component of the Pathway Tools software performs prediction of metabolic pathways in sequenced and annotated genomes. This article provides a detailed presentation of the PathoLogic algorithm. The algorithm consists of two phases. The reactome inference phase infers the reactions catalyzed by the organism from the set of enzymes present in the annotated genome. The pathway inference phase infers the metabolic pathways present in the organism from the reactions catalyzed by the organism. Both phases draw on the MetaCyc database of metabolic reactions and pathways. MetaCyc contains two data fields to support pathway inference: the expected taxonomic range of each pathway, and a list of key reactions for pathways. These fields have significantly increased the predictive accuracy of PathoLogic.