Expanded Metabolic Reconstruction of
            <i>Helicobacter pylori</i>
            (
            <i>i</i>
            IT341 GSM/GPR): an In Silico Genome-Scale Characterization of Single- and Double-Deletion Mutants

Thiele, Ines; Vo, Thuy D.; Price, Nathan D.; Palsson, Bernhard Ø.

doi:10.1128/jb.187.16.5818-5830.2005

Cited by 212 publications

(194 citation statements)

References 49 publications

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“…We retrieved the gene sequences for the six draft reconstructions from the Model SEED and KBase platforms. Published reconstructions of the same strains were available for four of these microbes [65][66][67][68] . Their gene sequences were retrieved from the NCBI database 69 .…”

Section: Methodsmentioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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1 r e s o u r c eChanges in the composition of the human gut microbiota have been associated with the development of chronic diseases including type 2 diabetes, obesity, and colorectal cancer 1 . Gut bacterial functions, such as synthesis of amino acids and vitamins 2 , breakdown of indigestible plant polysaccharides 3 , and production of metabolites involved in energy metabolism 4 , have been linked to human health. The use of 'omics approaches to study human microbiome communities has led to the generation of enormous data sets whose interpretations require systems biology tools to shed light on the functional capacity of gut microbiomes and their interactions with the human host 5 .In order to infer the metabolic repertoire of a gut metagenome data set, researchers usually map sequenced genes or organisms onto metabolic networks derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 6 , and functional annotations from KEGG ontologies 7 . However, this approach cannot identify the contribution of each bacterial species to the metabolic repertoire of the whole gut microbiome, nor can it infer the effects of different gut microbial communities on host metabolism.A technique that can bridge this gap is constraint-based reconstruction and analysis (COBRA) 8 using genome-scale metabolic reconstructions (GENREs) of individual human gut microbes. GENREs are assembled using the genome sequence and experimental information 9 . These reconstructions form the basis for the development of condition-specific metabolic models whose functions are simulated and validated by comparison with experimental results. The models can be used to investigate genotype-phenotype relationships 10 , microbe-microbe interactions 11 , and host-microbe interactions 11 . Numerous tools can be used to automatically generate draft GENREs but such models contain errors 12 and are incomplete.Manual curation of draft reconstructions is time consuming because it involves an extensive literature review and experimental validation of metabolic functions 9 .To provide an extensive resource of GENREs for human gut microbes, we developed a comparative metabolic reconstruction method that enables any refinement to one metabolic reconstruction to be propagated to others. This accelerates reconstruction and improves model quality. We generated AGORA, which includes 773 gut microbes, comprising 205 genera and 605 species. All reconstructions were based on literature-derived experimental data and comparative genomics. The metabolic predictions of two AGORA reconstructions and their derived metabolic models were validated against experimental data. RESULTS Metabolic reconstruction pipelineWe devised a comparative metabolic reconstruction method (Fig. 1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http:/...

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Section: Methodsmentioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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“…Construction of host-microbe models We retrieved 11 manually constructed and validated reconstructions of human gut microbes 13,[43][44][45][46][47][48][49][50] and an extensive, highquality reconstruction of human metabolism.…”

Section: Methodsmentioning

confidence: 99%

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

2015

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Keywords: human gut microbiome, in silico, metabolome, metabolic modelingThe gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "exgermfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial cometabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

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“…1b, the agreement between predicted and measured maximal growth rates is remarkably good for various E. coli mutants, providing further evidence for the validity of our approach. It is worth noting that, as with FBA alone (25,26), this analysis is not limited to single-enzyme mutants, but can be carried out for any combination of two or more enzyme deletions as well.…”

Section: Fbawmc Predicts the Relative Maximum Growth Of E Coli Growimentioning

confidence: 99%

Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity

Beg

Vázquez

Ernst

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

366

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The influence of the high intracellular concentration of macromolecules on cell physiology is increasingly appreciated, but its impact on system-level cellular functions remains poorly quantified. To assess its potential effect, here we develop a flux balance model of Escherichia coli cell metabolism that takes into account a systemslevel constraint for the concentration of enzymes catalyzing the various metabolic reactions in the crowded cytoplasm. We demonstrate that the model's predictions for the relative maximum growth rate of wild-type and mutant E. coli cells in single substratelimited media, and the sequence and mode of substrate uptake and utilization from a complex medium are in good agreement with subsequent experimental observations. These results suggest that molecular crowding represents a bound on the achievable functional states of a metabolic network, and they indicate that models incorporating this constraint can systematically identify alterations in cellular metabolism activated in response to environmental change.flux balance analysis ͉ metabolic networks ͉ systems biology A n important aim of systems biology is the identification of the organizing principles and fundamental constraints that characterize the function of molecular interaction networks and the limits of an organism's phenotypic diversity (1-3). Flux balancebased modeling approaches, combining the constraints imposed by the metabolic network's structure with, e.g., mass-or energyconservation principles (3-6), are especially successful in providing experimentally testable predictions on an organism's metabolic flux state and growth rate. A relative shortcoming of these approaches, however, is that they do not take into account the physical and spatial constraints resulting from the cell's unique intracellular milieu (7-9). For example, Ϸ20-30% of the Escherichia coli cytoplasm is occupied by macromolecules, many of them enzymes, whose cytoplasmic concentration cannot be further increased without drastically affecting protein folding, protein-protein association rates, biochemical reaction kinetics, and transport dynamics within a cell (9, 10). This suggests that constraint-based modeling approaches, such as flux balance analysis (FBA) (3, 11), could be improved if we take into account that the enzymes catalyzing each reaction compete for the available cytoplasmic space (12, 13), potentially limiting the attainable flux rates.Current flux balance-based modeling approaches also have limited ability to predict substrate uptake from the environment. Extensive experimental data indicates that when grown in complex medium bacterial cells use the available substrates either preferentially or simultaneously depending on the growth condition (see, e.g., refs. 14-17). Efforts to model mixed-substrate growth have assumed specific kinetic expressions for substrate uptake and biomass growth rates, and their predictions are formulated in terms of known model parameters (15,18). Similarly, FBA predictions are based on previous knowledge of the...

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Expanded Metabolic Reconstruction of Helicobacter pylori ( i IT341 GSM/GPR): an In Silico Genome-Scale Characterization of Single- and Double-Deletion Mutants

Cited by 212 publications

References 49 publications

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity

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