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.     

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.     

Genomics of Helicobacter pylori

During this review period, we have definitely entered into the genomic era. The Helicobacter pylori studies reported here illustrate the use of most of the technologies currently available to globally interrogate the genome of a pathogen. Global analysis of the gene content of H. pylori strains gives insight into the extent of its genetic diversity and its in vivo evolution. Our understanding of the particularities of H. pylori as a gastric pathogen colonizing a unique niche has been improved by studies aimed at: (i) the identification of H. pylori-specific genes; (ii) the establishment of correlations between the presence of one or a group of genes (or proteins) with clinical outcome; and (iii) the analysis of global regulatory circuits or responses to the extracellular signals. The response of host cells to H. pylori infection will be developed in the chapter 'H. pylori and gastric malignancies' by Sepulveda and Coehlo. Despite our knowledge of the H. pylori genome, the function of about one third of its total proteins is still unknown. Functional genomics are straightforward approaches for the identification of new gene functions or metabolic pathways as well as for the understanding of cellular processes and the detection of new virulence factors. In silico studies combined with experimental work will undoubtedly continue to develop. To date, the expansion of proteomics with refinements in mass spectrometry technology has illustrated that through immunoproteomics and comparative studies, relevant novel antigens can be identified. Genomics not only provides invaluable information on H. pylori but also opens new perspectives for diagnostic or therapeutic applications.     

L-lactic acid secreted from gastric mucosal cells enhances growth of Helicobacter pylori

BACKGROUND: Helicobacter pylori mainly inhabit the mucus layer in the gastric mucosa. However, mechanisms involving H. pylori colonization and proliferation in gastric mucosa are not well established. This study focuses on elucidating the role of gastric mucosal cells on growth of H. pylori. MATERIALS AND METHODS: H. pylori was co-cultured with the murine gastric surface mucosal cells (GSM06), and the growth of H. pylori on the cells was assessed by enumerating the colony-forming units (CFU). The H. pylori growth factor in the culture media conditioned by GSM06 cell was purified by HPLC, and the chemical structure of the growth factor was identified by analyses of (1)H- and (13)C-NMR spectra. RESULTS: A marked increase in the number of CFU of H. pylori was observed in the GSM06 cells. The enhanced H. pylori growth was also observed when indirectly incubated with GSM06 cells through semi-permeable membrane. In addition, culture media conditioned by GSM06 cell stimulated H. pylori growth approximately one thousand-fold. By bioassay-guided purification, the H. pylori growth factor was isolated from the conditioned medium of GSM06 cells and identified as L-lactic acid. The H. pylori growth-enhancing activity under microaerobic condition was well correlated with L-lactic acid concentrations in the conditioned media. CONCLUSIONS: This study demonstrates that L-lactic acid secreted by gastric mucosal cells enhances the growth of H. pylori, and this L-lactic acid-dependent growth of H. pylori may be important to the long-term colonization of H. pylori in the stomach.     

Reconstruction of the central carbon metabolism of Aspergillus niger.

The topology of central carbon metabolism of Aspergillus niger was identified and the metabolic network reconstructed, by integrating genomic, biochemical and physiological information available for this microorganism and other related fungi. The reconstructed network may serve as a valuable database for annotation of genes identified in future genome sequencing projects on aspergilli. Based on the metabolic reconstruction, a stoichiometric model was set up that includes 284 metabolites and 335 reactions, of which 268 represent biochemical conversions and 67 represent transport processes between the different intracellular compartments and between the cell and the extracellular medium. The stoichiometry of the metabolic reactions was used in combination with biosynthetic requirements for growth and pseudo-steady state mass balances over intracellular metabolites for the quantification of metabolic fluxes using metabolite balancing. This framework was employed to perform an in silico characterisation of the phenotypic behaviour of A. niger grown on different carbon sources. The effects on growth of single reaction deletions were assessed and essential biochemical reactions were identified for different carbon sources. Furthermore, application of the stoichiometric model for assessing the metabolic capabilities of A. niger to produce metabolites was evaluated by using succinate production as a case study.     

A novel in vitro infection model of Helicobacter pylori using mucin-producing murine gastric surface mucous cells

BACKGROUND: Helicobacter pylori is found within the gastric surface mucous gel layer and in the epithelial surface. Gastric cancer cells have been used in experimental H. pylori infection in vitro, although cancer cells have some abnormalities in cellular properties. The aim of this study was to develop an in vitro H. pylori infection model using normal gastric surface cells that produce gastric mucin. MATERIALS AND METHODS: Normal murine gastric surface mucous cells (GSM06) were cultured by the liquid interface method using a serum-free medium and a collagen gel containing a fibroblast cell line (L929) and infected with H. pylori. Infection by H. pylori was assessed by enumerating the colony-forming units (CFU) of H. pylori adhered to GSM06 cells and by transmission electron microscopy. The production of mucin was determined by a lectin binding assay, sugar analysis, and MUC5AC gene expression. RESULTS: GSM06 cells cultured under these conditions produced mucin containing N-acetylgalactosamine and MUC5AC as the core protein. Significantly higher numbers of H. pylori adhered to GSM06 cells under mucin-producing conditions than under nonproducing conditions. Microscopic observation showed a filamentous structure resembling a type IV secretion system apparatus formed between the surface of GSM06 cells and H. pylori. CONCLUSIONS: This study demonstrates a novel in vitro H. pylori infection model using mucin-producing murine GSM06 cells for early stages of infection.     

Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.     

Helicobacter pylori 26695 基因组尺度代谢研究进展

代谢网络在代谢功能研究、生物代谢过程控制、疾病诊断分析和药物靶标设计等方面具有重要理论和实践意义。生物信息学研究利用序列同源、结构模拟、对接等手段与生化实验有效结合促进了生物体代谢网络的进一步完善。本文作者在构建幽门螺杆菌（Helicobacter pylori 26695,H.pylori 26695）代谢网络的工作基础上综合了近年来研究者对H.pylori 26695代谢通路关键酶的研究成果,并结合基因组信息,综述了H.pylori 26695特异性的重要代谢通路。本文从基因组水平阐明代谢通路与基因的关系,并详细分析了关键酶对H.pylori 26695生理的重要作用,最后探讨了重构一个连续、完整的代谢网络面临的困难及其在药物靶标设计方面的研究前景。     

Comparative metabolic capabilities for Micrococcus luteus NCTC 2665, the "Fleming" strain, and actinobacteria

Putative gene predictions of the Gram positive actinobacteria Micrococcus luteus (NCTC 2665, "Fleming strain") was used to construct a genome scale reconstruction of the metabolic network for this organism. The metabolic network comprises 586 reactions and 551 metabolites, and accounts for 21% of the genes in the genome. The reconstruction was based on the annotated genome and available biochemical information. M. luteus has one of the smallest genomes of actinobacteria with a circular chromosome of 2,501,097 base pairs and a GC content of 73%. The metabolic pathways required for biomass production in silico were determined based on earlier models of actinobacteria. The in silico network is used for metabolic comparison of M. luteus with other actinomycetes, and hence provides useful information for possible future biotechnological exploitation of this organism, e.g., for production of biofuels.     

An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.     

Systematic identification of selective essential genes in Helicobacter pylori by genome prioritization and allelic replacement mutagenesis

A comparative genomic approach was used to identify Helicobacter pylori 26695 open reading frames (ORFs) which are conserved in H. pylori J99 but highly diverged in other eubacteria. A survey of selected pathways of central intermediary metabolism was also carried out, and genes with a potentially selective role in H. pylori were identified. Forty-five ORFs identified in these two analyses were screened using a rapid vector-free allelic replacement mutagenesis technique, and 33 were shown to be essential in vitro. Notably, 13 ORFs gave essentiality results which are unexpected in view of their known or proposed functions, and phylogenetic analysis was used to investigate the annotation of 7 such ORFs which are highly diverged. We propose that the products of a number of these H. pylori-specific essential genes may be suitable targets for novel anti-H. pylori therapies.     

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.     

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.     

mRNA expression of cytokines and chemokines in the normal gastric surface mucous epithelial cell line GSM06 during bacterial infection with Helicobacter felis.

BACKGROUND AND AIM: A group of the proinflammatory and chemotactic cytokines (chemokines) has been considered as an important factor in the pathomechanism of different bacterial diseases, among them the common Helicobacter pylori infection. Experimental results obtained with gastric biopsy samples of H. pylori positive patients, and with H. pylori infected tumor originated gastric cell lines indicated that these cytokines have essential roles in the development and maintenance of the immune response and inflammation of the gastric mucosa during H. pylori infection. Although the mRNA expression was shown in these biopsy samples and cell lines, it is not yet proved that the normal gastric mucosal epithelial cells themselves express these cytokines. The establishment of a gastric surface mucous cell line with non-tumor origin (GSM06), and the usage of Helicobacter felis as a model of the classic H. pylori infection gave us the possibility to check this question. MATERIALS AND METHODS: in this study GSM06 cells were infected with different numbers (10(5), 10(6), 10(7), 10(8), 10(9) bacterium/ml medium) of H. felis for two different time periods (2, 4 h). Cells treated with medium only were used as control. Then the mRNA expression of the following cytokines was measured by RT-PCR method in the GSM06 cells: proinflammatory cytokine IL1-beta, and chemokine RANTES, eotaxin, MCP-1, MIP1-alpha and MIP1-beta. RESULTS: we found that neither mRNA of the investigated cytokines was expressed constitutively, however the GSM06 cells expressed the mRNA of each cytokine during H. felis infection. CONCLUSION: our results prove that normal gastric surface mucous epithelial cells express immunologically active peptides during H. felis infection. We may suppose that the epithelial cells of the gastric mucosa contribute to the immune response and inflammation by expressing proinflammatory (IL1-beta) and chemotactic (RANTES, eotaxin, MCP-1, MIP1-alpha and beta) cytokines during H. pylori infection in human.     

Genome-scale metabolic representation of Amycolatopsis balhimycina

Infection caused by methicillin-resistant Staphylococcus aureus (MRSA) is an increasing societal problem. Typically, glycopeptide antibiotics are used in the treatment of these infections. The most comprehensively studied glycopeptide antibiotic biosynthetic pathway is that of balhimycin biosynthesis in Amycolatopsis balhimycina. The balhimycin yield obtained by A. balhimycina is, however, low and there is therefore a need to improve balhimycin production. In this study, we performed genome sequencing, assembly and annotation analysis of A. balhimycina and further used these annotated data to reconstruct a genome-scale metabolic model for the organism. Here we generated an almost complete A. balhimycina genome sequence comprising 10,562,587 base pairs assembled into 2,153 contigs. The high GC-genome (～ 69%) includes 8,585 open reading frames (ORFs). We used our integrative toolbox called SEQTOR for functional annotation and then integrated annotated data with biochemical and physiological information available for this organism to reconstruct a genome-scale metabolic model of A. balhimycina. The resulting metabolic model contains 583 ORFs as protein encoding genes (7% of the predicted 8,585 ORFs), 407 EC numbers, 647 metabolites and 1,363 metabolic reactions. During the analysis of the metabolic model, linear, quadratic and evolutionary programming algorithms using flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and OptGene, respectively were applied as well as phenotypic behavior and improved balhimycin production were simulated. The A. balhimycina model shows a good agreement between in silico data and experimental data and also identifies key reactions associated with increased balhimycin production. The reconstruction of the genome-scale metabolic model of A. balhimycina serves as a basis for physiological characterization. The model allows a rational design of engineering strategies for increasing balhimycin production in A. balhimycina and glycopeptide production in general.     

Robustness analysis of the Escherichia coli metabolic network

Genomic, biochemical, and strain-specific data can be assembled to define an in silico representation of the metabolic network for a select group of single cellular organisms. Flux-balance analysis and phenotypic phase planes derived therefrom have been developed and applied to analyze the metabolic capabilities and characteristics of Escherichia coli K-12. These analyses have shown the existence of seven essential reactions in the central metabolic pathways (glycolysis, pentose phosphate pathway, tricarboxylic acid cycle) for the growth in glucose minimal media. The corresponding seven gene products can be grouped into three categories: (1) pentose phosphate pathway genes, (2) three-carbon glycolytic genes, and (3) tricarboxylic acid cycle genes. Here we develop a procedure that calculates the sensitivity of optimal cellular growth to altered flux levels of these essential gene products. The results indicate that the E. coli metabolic network is robust with respect to the flux levels of these enzymes. The metabolic flux in the transketolase and the tricarboxylic acid cycle reactions can be reduced to 15% and 19%, respectively, of the optimal value without significantly influencing the optimal growth flux. The metabolic network also exhibited robustness with respect to the ribose-5-phosphate isomerase, and the ribose-5-phosephate isomerase flux was reduced to 28% of the optimal value without significantly effecting the optimal growth flux. The metabolic network exhibited limited robustness to the three-carbon glycolytic fluxes both increased and decreased. The development presented another dimension to the use of FBA to study the capabilities of metabolic networks.     

Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.