(35c) Optimization-Based Analysis of Comparative Metabolomic Data Identifies Regulatory Mechanisms in Staphylococcus Aureus mutants (Faculty Candidate)

Recent trends have recorded a significant rise in community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections. Additionally, most clinical isolates can develop into recalcitrant multiple-antibiotic-resistant and host immune-tolerant biofilm communities, necessitating novel therapeutic strategies for combating S. aureus infections. 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 a metabolomic analysis 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 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. three mutants (menD, atpA, ackA) in exponential phase and seven mutants (pyc, citZ, sucA, gudB, ndhA, atpA, ackA) in the post-exponential phase, along with the wild-type following growth in CDM. The metabolite levels were also estimated in the wild-type strain in aerobic, anaerobic and micro-aerobic conditions. In total, 53 intermediate metabolites in the central carbon metabolism were measured by LC-MS/MS analysis.

We integrated the 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. From the metabolite pools, we performed a maximum and minimum flux-sum analysis⁹ 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.

References:

1. Islam MM, Thomas VC, Van Beek M, et al. An integrated computational and experimental study to investigate Staphylococcus aureus metabolism. NPJ Syst Biol Appl. Jan 30 2020;6(1):3.
2. Becker SA, Palsson BO. Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC microbiology. Mar 07 2005;5:8.
3. Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson BO. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A. Jun 28 2016;113(26):E3801-3809.
4. Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature biotechnology. Sep 2010;28(9):977-982.
5. Schellenberger J, Park JO, Conrad TM, Palsson BO. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC bioinformatics. Apr 29 2010;11:213.
6. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research. Jan 1 2000;28(1):27-30.
7. Pence HE, Williams A. ChemSpider: An Online Chemical Information Resource. J Chem Educ. Nov 2010;87(11):1123-1124.
8. Ren Q, Chen K, Paulsen IT. TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res. // 2007;35:D274-D279.
9. Chung BK, Lee DY. Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network. Bmc Syst Biol. 2009;3:117.