Modeling Neisseria meningitidis metabolism: from genome to metabolic fluxes

Abstract

Background: Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years.

Results: Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested successfully in batch and chemostat cultures.

Conclusion: The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature.

Figure 1

Schematic representation of model construction. The genome can be classified as the first-level database. holding the potential functions of an organism. The transcriptome can be classified as the second-level database of functions describing the actual expression of genes, and the proteome can be classified as the third-level database of functions describing the actual expressed proteins. The metabolome (and fluxome) can be classified as the fourth-level database holding the complete collection of metabolites and reactions in which the metabolites participate. The metabolome, and to a lesser extent the proteome, determine the functionality of the cell [146]. In principle, all databases can be used as source of input for construction (or extension) of a genome-scale model (white arrows). In our study, information provided by the genome and the literature was used for model construction (black boxes). A minimal medium for growth was derived from the genome-scale model (upper gray box). The genome-scale model was simplified as descibed in the text, resulting in the 'putative model'. The measured specific metabolic rates and the corresponding measurement variances used in flux balance analysis (FBA) were calculated using Monte Carlo Simulation (MCS) with the measured experimental data and their standard deviation as input. The final model, verified by FBA, can be used for process development purposes (for example, optimization of growth medium, lower gray box). Subsequently, the model can be extended to the desired informative level using all available sources of information (light gray circle).

Figure 2

Simplified metabolic model of N. meningitidis. As described in the text, the simplified model was obtained by simplification of the genome-scale model. For ease of understanding, only the main pathways were admitted into the diagram illustrated here. A complete overview of the model including a list of all abbreviations used is in Additional data file 1.

Figure 3

Oxidation of cysteine to cystine. Cysteine (CYS) is oxidized to cystine (CYST), forming reactive oxygen O₂^- (step 1), which can reduce cytochrome c (step 4). The electron is used by cytochrome cbb₃, which reduces oxygen to water and causes the concomitant generation of a protonmotive force, Δμ^H+ (step 5). The protonmotive force is in turn used to form ATP (step 6). Step 2 involves the formation of hydrogen peroxide (H₂O₂) from O₂^- by superoxide dismutase followed by catalase to regenerate oxygen (step 3). Cysteine can be converted to glutathione (GSH), via glutamate-cysteine ligase and glutathione synthetase (step 8). In turn, GSH can be converted to cysteine via gamma-glutamyltranspeptidase and aminopeptidase N (step 9), yielding a functional γ-glutamyl cycle. GSH can be oxidized to glutathione disulfide (GSSG), by glutathione peroxidase (step 7).

Figure 4

Determination of measurement variance using Monte Carlo simulation. When an arbitraty value (r) for the production rate of a hypothetical product of 1.00 with standard deviation of 0.01 was used as input for Monte Carlo simulation, 10⁴ simulations were required to obtain the original input value (1.00 ± 0.01), showing that accurate results for the actual measured input values can be expected after 10⁴ simulations.

Figure 5

Growth of N. meningitidis in a minimal medium designed from the model. Growth of N. meningitidis strain HB-1 in minimal medium in a bioreactor operated in chemostat mode after an initial batch phase of approximately 36 h were assessed by optical density. Two experiments in identical medium and growth conditions are shown here. Conditions were as described in Materials and methods.