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. 2019 Jun 25:10:1283.
doi: 10.3389/fmicb.2019.01283. eCollection 2019.

iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6

Oscar Dias  1 , João Saraiva  1 , Cristiana Faria  1 , Mario Ramirez  2 , Francisco Pinto  3 , Isabel Rocha  1   4
Affiliations

iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6

Oscar Dias et al. Front Microbiol. .

Abstract

A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.

Keywords: Streptococcus pneumoniae R6; avirulent; genome-scale metabolic model; iDS372; metabolic reconstruction; phenotypical reconciliation.

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Figures

FIGURE 1
FIGURE 1
Workflow for metabolic network reconstruction of S. pneumoniae R6 (EC, Enzyme Commission Number; TC, Transporter Classification). A draft of the network is reconstructed semi-automatically by merlin. The genome re-annotation, compartmentalization, and manual curation are performed using this user-friendly’s graphical user interfaces. Next, the biomass equation is formulated, resorting to available experimental data for S. pneumoniae R6 or closely related organisms (as determined via 16s rRNA analysis). Additionally, literature is also analyzed to improve the biomass equation. Environmental conditions, to mimic experimental data, are defined next. Using the software Optflux, simulations are performed using the model under the previously established environmental conditions. The simulated growth is compared to experimental data. If the results are distinct then the model is further reviewed and curated and the process is repeated until no significant differences exist between the experimental results and those obtained in silico.
FIGURE 2
FIGURE 2
Streptococcus pneumoniae R6 pyruvate metabolism under different environmental conditions. Pyruvate is fully converted into lactate by lactate dehydrogenase (Ldh) when glucose is present, and oxygen is absent. In the presence of oxygen, S. pneumoniae R6 switches to a heterofermentative profile by activating the spxB (pyruvate oxidase) gene to produce acetate and H2O2 together with lactate (in minor quantities). In the presence of n-acetylglucosamine or mannose (without O2), the ldh gene is fully activate leading to the production of lactate as major fermentation product and pfl (pyruvate formate-lyase) genes are only partially activated, as shown by the minor quantities of acetate, ethanol, and formate. In the presence of galactose, formate is the major product of fermentation revealing a complete activation of the pfl genes. Ethanol and acetate are also produced, ethanol by the action of alcohol dehydrogenase (Adh) and acetate from acetyl-CoA production by phosphate acetyltransferase (Pta) and acetate kinase (AckA).
FIGURE 3
FIGURE 3
Model assessment in anaerobic conditions, for several levels of expression of the pfl genes, in silico, against experimental data. Left panel corresponds to a maximization of growth rate as objective function. Right panel corresponds to simulations with fixed maximum growth rate (μ = 0.78 h−1). Blue lines represent experimental data, the orange line represents the maximization of the specific growth rate, and the shadowed areas represent the products’ flux variability analysis. All simulations were performed with environmental conditions inferred from Carvalho et al. (2013).
FIGURE 4
FIGURE 4
Assessment of the model simulations performed under aerobic conditions for different glucose uptake rates (qGlucose) and comparison with experimental results. (A) Production of H2O2, acetate, and lactate using a qGlucose of 21.02 mmol g−1 h−1. (B) Production of H2O2, acetate, and lactate using a qGlucose of 31.71 mmol g−1 h−1. Growth rate was limited to 1.07 h−1.
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