An integrated computational and experimental study to investigate Staphylococcus aureus metabolism

Abstract

Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understand its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 nonessential mutants from the Nebraska Transposon Mutant Library was performed, and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 863 metabolic genes, 1379 metabolites (including 1159 unique metabolites), and 1545 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data, and therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

Fig. 1. The overall view of the model reconstruction.

a The schematic of the reconstruction and curation process for iSA863. b Pathway distribution of metabolic reactions. c Overlap of reactions between recent genome-scale metabolic reconstructions of S. aureus.

Fig. 2. Growth–no-growth (G–NG) prediction matrices and the impact of Growmatch application.

a Before reconciliation of growth–no-growth inconsistency by GrowMatch procedure. b After reconciliation of growth–no-growth inconsistency by GrowMatch procedure. Here, specificity = #NGNG/(#NGNG + #GNG), sensitivity or true viable rate (TVR) = #GG/(#GG + #NGG), false viable rate (FVR) = #GNG/(#GNG + #NGNG), and accuracy = (#GG + #NGNG)/(#GG + #GNG + #NGG + #NGNG). c A case study of GNG inconsistency and the corresponding Growmatch solution.

Fig. 3. Refinements in the central metabolic pathway of the model iSA863.

These include corrections of reaction directionality, additions, and deletions.

Fig. 4. Shifts in flux space for 8 mutants in the central carbon and nitrogen metabolic pathway.

Every row in the table (inset) denotes a reaction as identified in the pathway map. The relative shifts compared with the wild-type flux space are color-coded according to the legend in the figure.