(591b) Omics-Informed Metabolic Modeling Identifies Regulatory Mechanisms in Staphylococcus Aureus mutants

Staphylococcus aureus is a versatile human pathogen that can colonize almost every human organ resulting in multiple pathologic outcomes. Recent trends have recorded a significant rise in community-associated methicillin-resistant S. aureus(CA-MRSA) infections, which can often develop into recalcitrant multiple-antibiotic-resistant and host immune-tolerant biofilm communities. Therefore, there has been a steady interest and focus towards understanding how staphylococcal metabolism relates to antibiotic resistance and pathogenesis. While previous efforts have been made to understand the regulatory networks for the antibiotic mechanism of action to S. aureus, genetic perturbations can also play a major role in understanding the effectiveness of antibiotic killing and inhibition functions. Some key questions in this regard are still unanswered, including i) what are the core carbon and nitrogen metabolic pathways that are crucial for staphylococcal growth and survival; ii) how specific mutations alter the metabolic landscape, growth rate and survival of S. aureus; iii) would a systems-level metabolic model be able to accurately predict growth and metabolite changes that occur within defined mutants.

To answer these questions, we performed an integrative transcriptomic and metabolomic analyses of 8 mutants in the central metabolism of Staphylococcus aureus. These mutants include pyc (pyruvate carboxylase), citZ (citrate synthase), sucA (2-oxoglutarate dehydrogenase), ackA (acetate kinase), gudB (glutamate dehydrogenase), ndhA (NADH dehydrogenase), menD (menaquinone biosynthesis protein), and atpA (ATPase subunit). These mutants were selected for their potential in identifying carbon and nitrogen redirection pathways as they affect important physiological processes including glycolysis, TCA cycle activity, gluconeogenesis, Electron Transport Chain (ETC), cellular redox potential, overflow metabolism and nitrogen metabolism. Quantitative metabolite levels and gene expression of the S. aureus mutants grown in chemically defined media (CDM) were compared to the wild-type S. aureus strain to identify the statistically significant fold changes. The metabolite levels and gene expression data were also collected from the wild-type strain grown in aerobic, anaerobic and micro-aerobic conditions. Differential gene expression and metabolic concentration analysis revealed the genome-wide shifts in carbon and nitrogen metabolism as well as regulatory mechanism in perturbed genetic and environmental conditions.

We integrated the transcriptomic and metabolomic data with our recently published genome-scale metabolic model of S. aureus¹ that combines genome annotation data, reaction stoichiometry and thermodynamic information, and regulation information from biochemical databases and previous strain-specific models ^2-8, which were validated though existing and new experimental results on gene essentiality, auxotrophy, and metabolite excretion. The transcriptomic data was utilized to estimate thermodynamically plausible limits on reactions rates, which were estimated by a newly developed regression method to normalize gene expression by thermodynamic driving force. Implementing the reaction rate limits this way significantly improved the model predictability since it is based on the assumption that a system synthesizes the minimum amount of cellular machinery required to maintain the maximal growth rate⁹. From the metabolite pools estimated from the concentration data, we performed a maximum and minimum flux-sum analysis¹⁰ for each of the reactions in the model upon each of the mutations. From the deviation of flux-sum ranges of reaction in the mutants compared to wild-type, we identified a set of reactions that are modulated by each of the mutations. We then employed a multi-level optimization framework to identify a minimal number or reactions which must be regulated in the each of these mutations. By studying the metabolic consequences of these different mutations, we achieved greater resolution of the known pathways utilized by S. aureus, as well as pinpointed to the regulatory influences caused by these mutations. This will allow us to identify the core metabolic pathways and enzymes that are used by S. aureus, but also fringe pathways that are activated following a block in the core metabolic pathways. The results from our analysis highlight the adaptive capabilities of S. aureus and identify the metabolic bottlenecks in wild-type and mutant growth. This will lead to the identification of additional growth-inhibiting single- and double-knockouts that can potentially provide a solution to the prevailing antibiotic resistance of this organism.

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