Genome Scale Metabolic Model Of Helicobacter Pylori 26695-Books Pdf

Genome Scale Metabolic Model of Helicobacter pylori 26695
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VOL 184 2002 METABOLIC MODEL OF H PYLORI 4583, 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, characterized organisms when analyzed in conjunction with an terization of precise biochemical functions in the cell and general nutrient uptake. in silico model characteristics 5 7 9 11 13 15 17 22 24 26 32 34 35 37 39 51 53 54 55. Pathway analysis The reaction index of H pylori can be defined mathemati. MATERIALS AND METHODS cally in the form of a stoichiometric matrix S which has dimensions of m by n. The overall procedures discussed below for model development and simula where m is the number of metabolites in the reaction network and n is the. tion are summarized in Fig 1 The detailed computational methods and theory number of reactions Therefore each column represents a reaction and each row. behind much of the work presented herein have been published in a collection of represents the stoichiometric participation of a specific metabolite in each of the. recent primary technical manuscripts and reviews 14 21 57 59 61 reactions A particular flux distribution of the network v indicates the flux levels. Metabolic reconstruction Beginning from genomic sequence data relevant through each of the reactions Based on principles of conservation of mass and. genes in primary metabolism are identified to assist in reconstructing the meta the assumption of a steady state the flux distribution through a reaction network. bolic network of H pylori This information is complemented with biochemical can be characterized by the following equation. information and cell physiology data and is used to construct an organism. specific reaction index the set of all reactions included in the reconstruction S v 0 1. The result of this process is a metabolic map illustrated in Fig 1A The reaction. index underlying such a map represents the best determination of the biochem From this equation it is possible to determine all of the chemically balanced. ical reactions that the organism is believed to be capable of carrying out based on metabolic routes through the network that span the entire range of metabolic. all available data The detailed process of metabolic reconstruction and model capabilities for the network These pathways are referred to as extreme path. development has been recently reviewed 14 ways and their method of calculation is based on principles of convex analysis. The metabolic genotype of H pylori was determined from its annotated ge 59 Previously an algorithm for extreme pathway calculation has been pub. nome sequence 66 as provided by the TIGR Microbial Database as well as lished along with a pathway classification scheme 59 Figure 1B illustrates the. from GenBank 4 To begin establishing the metabolic reaction index for H extreme pathways for the hypothetical reaction network Note that these extreme. pylori the annotation of all the open reading frames ORFs was carefully pathways can each have multiple input substrates and multiple output products. examined and only gene assignments based on a high level of sequence similarity For large networks such as for H pylori or H influenzae it is more manageable. and pertaining to metabolic enzymes or membrane transporters were selected for to subdivide the network into smaller subsystems for extreme pathway calcula. inclusion in the metabolic genotype From general biochemistry the complete set tion The strategy for extreme pathway enumeration in subdivided networks is. of potential metabolic reactions carried out by the associated gene products was discussed in the literature 60 and was used in this study to determine the. determined This list of reactions was augmented using additional experimental pathway structure of the H pylori metabolic network as it was previously used to. information from the literature pertaining to the genetic and enzymatic charac study H influenzae. 4584 SCHILLING ET AL J BACTERIOL, Briefly the metabolic network of H pylori was subdivided into six discrete TABLE 1 Forty seven metabolites considered to be required for. subsystems and each of the reactions was assigned to one of the six subsystems biomass generation and maintenance in H pylori. These subsystems include amino acid biosynthesis and degradation nucleotide. biosynthesis and degradation vitamin and cofactor biosynthesis lipid and cell Classification Abbreviation Complete name. envelope biosynthesis central metabolism and transport and energy redox me. Amino acids ALA Alanine, tabolism including the immediate assimilation and dissimilation of various car. ARG Arginine, bon sources The extreme pathways for each of the subsystems were then calcu ASN Asparagine.
lated From these extreme pathways a list of reactions that do not appear in any ASP Aspartate. of the pathways can be determined In addition reaction subsets can be identified CYS Cysteine. as sets of reactions that always occur together in pathways at fixed stoichiometric GLU Glutamate. ratios see Fig 1B for examples GLN Glutamine, FBA Using the extreme pathways and or the reaction network to describe the GLY Glycine. connectivity of the system allows for the calculation of network properties HIS Histidine. through the use of flux balance analysis FBA FBA is a method to assess the ILE Isoleucine. production capabilities and systemic properties of a metabolic network The LEU Leucine. fundamentals of FBA have been reviewed elsewhere 6 21 58 67 Extreme LYS Lysine. pathway analysis and FBA share a common theoretical basis which has been MET Methionine. described in detail previously 57 Specifically FBA utilizes the principles of PHE Phenylalanine. linear programming LP which is a subset of convex analysis PRO Proline. Constraints are placed on individual reactions that state the upper and lower SER Serine. bounds on the range of flux values that each of the reactions can have This THR Threonine. constraint is described in the following form TRP Tryptophan. TYR Tyrosine,VAL Valine,Polyamines PTRC Putrescine. where i is the lower bound on flux vi and i is the upper bound If no informa SPMD Spermidine. tion about flux levels is available the value of i is set to zero for irreversible. fluxes and in all other cases i and i are left unconstrained allowing the flux to Ribonucleotides ATP Adenosine 5 triphosphate. take on any value positive or negative The capabilities of the metabolic system GTP Guanine 5 triphosphate. are then explored using LP 12 67 A reaction is first selected as an objective CTP Cytidine 5 triphosphate. function that is to be maximized or minimized A solution is then calculated that UTP Uridine 5 triphosphate. satisfies all of the constraints of equations 1 and 2 The result is the optimal flux. distribution that will allow the highest flux through the chosen objective reaction Deoxyribonucleotides DATP Deoxyadenosine 5 triphosphate. LINDO was used to solve the LP problems LINDO Systems Inc Chicago Ill DGTP Deoxyguanine 5 triphosphate. While FBA can be used to generate highly quantitative results that have been DCTP Deoxycytidine 5 triphosphate. validated against experimental data 19 20 here the approach is used primarily DTTP Deoxythymidine 5 triphosphate. to assess the qualitative metabolic capabilities of the H pylori reaction network. Figure 1C and D provide examples of how FBA was used herein. Phospholipids PS Phosphatidyl serine,PE Phosphatidyl ethanolamine. Objective function To calculate an optimal flux distribution an objective must. PG Phosphatidyl glycerol, be defined For physiologically meaningful results we have defined the objective. as the ability to produce the required components of cellular biomass e g Cell envelope PEPTIDO Peptidoglycan. amino acids nucleotides and phospholipids etc that enable the organism to LPS Lipopolysaccharide. grow and survive This growth objective is mathematically defined as an output. flux utilizing each biomass precursor metabolite as a substrate Forty seven 45 Isoprenoids OPP Octaprenyl pyrophosphate. metabolites were selected as required biomass constituents Table 1 An output UDPP Undecaprenyl pyrophosphate. biomass reaction exchange flux was created that utilized these constituents in. equal stoichiometric ratios to be used as a means to assess the ability of the Vitamins and cofactors NAD Nicotinamide adenine dinucleotide. network to produce all of the required demands based on particular substrate NADP Nicotinamide adenine dinucleotide. availability conditions This reaction was selected as the objective in all of the phosphate. calculations The complete ability of the network to produce all of the biomass FAD Flavin adenine dinucleotide. constituents leads to a positive flux value for this objective reaction CoA Coenzyme A. Minimal medium determination Beginning with all of the extracellular me ACP Acyl carrier protein. tabolites available to the metabolic network each of the metabolites was re PTH Pantothenate. moved individually to determine if they were required for producing all of the Thiamine. biomass constituents This determination was accomplished by constraining the MTHF Methyl tetrahydrofolate. exchange flux or uptake reaction for the metabolite to zero and optimizing for MK Menaquinone. the biomass objective reaction After thorough examination a set of metabolites. DMK Dimethylmenaquinone, was arrived at for which the removal of any one metabolite would render the.
network unable to produce the biomass demands This set of metabolites con. stitutes a defined minimal medium required for the in silico model to support substrates available to the in silico model were screened to determine their. growth of H pylori ability to serve as a major source of carbon to the organism These carbon. Alternative carbon source determination In the minimal medium alanine and sources are referred to as the alternative carbon sources. arginine fulfill the bulk carbon requirements for the network as presented in the Deletion studies All of the reactions in central metabolism were examined to. Results section To determine what other major carbon sources can be used by determine the essentiality of the reaction to the network under a variety of. the network the reactions used to assimilate alanine and arginine into central medium conditions To assess the consequences of deleting a reaction from the. metabolism were simultaneously removed This was accomplished by removing network the constraints on the particular reaction are set to zero A simulation. the D amino acid dehydrogenase alanine dehydrogenase and 1 pyrroline 5 is then run to see if the network can support growth by optimizing for the. carboxylate dehydrogenase With these reactions removed alanine and arginine biomass objective reaction If the network cannot support growth then the. are still utilized by the network to fulfill certain biomass requirements however deleted reaction is deemed essential under the particular environment and me. their broad utilization by the metabolic network is no longer possible with the dium conditions used in the simulation. removal of the above mentioned reactions Under these conditions individual Four different medium conditions are presented in this deletion study These. metabolites were included in the minimal medium composition to determine if include i the in silico minimal medium described in Results ii the minimal. the network could support the use of the biomass reaction All of the possible medium plus glucose iii the minimal medium plus all of the alternative carbon. VOL 184 2002 METABOLIC MODEL OF H PYLORI 4585, TABLE 2 Comparison of genomic characteristics and in silico abolic reconstruction 28 The TCA cycle in the metabolic. metabolic model characteristics of H pylori and H influenzae 63 reconstruction is complete but not in a traditional sense as in. Bacterial strain E coli There are reactions included such as ketoglutarate. Properties oxidase AKO 55 succinyl coenzyme A CoA acetoacetate. H pylori 26695 H influenzae Rd,CoA transferase SCOT 13 and malate quinone oxi. Genome characteristics doreductase MQO 31 which allow complete connectivity. Genome length bp 1 667 867 1 830 135,G C content 39 38. between the metabolites of the TCA cycle although these. No of ORFs 1 590 1 748 reactions are not typically considered to be part of a TCA. No with identified database match 1 091 1 011 cycle Notably the fumarate reductase included in the recon. No with no database match 499 732 struction is reversible as evidenced from experimental litera<. A genome scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence anno tation 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

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