Genome-scale metabolic model of Helicobacter pylori 26695

Abstract

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.

FIG. 1.

Overview of the in silico modeling methods and their conceptual basis. (A) Hypothetical network describing a set of metabolic reactions. Based on genomic, biochemical, and physiological data, such a reaction network can be reconstructed to represent the set of chemical reactions predicted to occur within an organism. (B) Extreme pathways are calculated for the reconstructed reaction network. Reactions that do not occur in any of the extreme pathways constitute unused reactions. Certain reactions always participate together when active in an extreme pathway. These groups of concomitantly occurring reactions constitute reaction subsets, as illustrated. (C) FBA is used to determine what input substrates and balanced reaction fluxes are required to meet the demand of producing metabolites I and J simultaneously in a fixed ratio. In this example only substrate B is available. (D) The reaction from metabolite B to E is eliminated, requiring the use of substrate A to meet the production demands of I and J. This situation also leads to the production of metabolite H as a by-product. Note that this reaction is essential if substrate A is not present.

FIG. 2.

Reconstructed central metabolic subsystem for H. pylori. The different metabolite colors indicate the ability of the metabolite to leave the subsystem for participation in reactions in other subsystems. (Supplementary data are available at http://gcrg.ucsd.edu/supplementary_data/JBACT/Hpylori.xls.)

FIG. 3.

Minimal substrate requirements for H. pylori to generate the 47 required biomass constituents which can be synthesized. Lines connecting substrates to biomass constituents indicate that the network is unable to generate the product without the substrate. The dashed lines indicate that either alanine or arginine is required for the production of the connected biomass requirement. See Table 1 for abbreviations.