(696e) Data-Driven Discovery of Novel Therapeutic Targets through Metabolic Modeling of Staphylococcus Aureus

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. aureusinfections. Therefore, there has been a steady interest and focus towards understanding how staphylococcal metabolism relates to antibiotic resistance and pathogenesis. In recent years, whole genome sequencing has accelerated the reconstruction of genome-scale metabolic models of several species. However, these models often do not accurately predict biological phenotypes due to inherent insufficiencies, including metabolic gaps, unbalanced reactions, and lack of key physiological information.

We reconstructed the genome-scale metabolic model of S. aureus by combining genome annotation data, reaction stoichiometry and thermodynamic information, and regulation information from biochemical databases and previous strain-specific models ^1-7, which were validated though our experimental observations. We then employed our model to identify metabolic-model driven genetic intervention strategies to combat the pathogenic activity of S. aureus. Reactions in the model were elemental and charge balanced. Upon manual and automated gap-filling, the model has 850 metabolic genes, 1437 metabolites and 1718 reactions that include inter-compartment transport and exchange reactions. To resolve the growth and no-growth inconsistencies in the model, we used an automated procedure called GrowMatch to reconcile the inconsistency predictions by suppressing or adding functionalities in the model while maintaining the already identified correct growth and no-growth predictions ⁸.

The extensive manual curation performed on the draft genome-scale reconstruction resulted in improved prediction capabilities while successfully capturing experimental results on growth inhibition. Our synthetic lethality analyses identified both intuitive and novel multiple gene-knockout strategies for reducing growth of Staphylococcusto a specific predetermined threshold. We performed correlation analyses between the synthetic lethal gene pairs to pinpoint the most probable candidates for genetic manipulations that fight the pathogenicity of S. aureus. We identified some experimentally-observed metabolic bottlenecks in wild-type and mutant growth, and predicted additional growth-inhibiting single- and double-knockouts which can potentially provide a solution to the prevailing antibiotic resistance of this organism.The results serve as a foundation from which to build, modify, and constantly improve, through the incorporation of future “-omics” data, the prediction and evaluation of novel therapeutic targets, thus, enhancing its functional utility and use as a resource to augment staphylococcal research worldwide.

References:

1. 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.
2. 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.
3. 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.
4. 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.
5. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research. Jan 1 2000;28(1):27-30.
6. Pence HE, Williams A. ChemSpider: An Online Chemical Information Resource. J Chem Educ. Nov 2010;87(11):1123-1124.
7. 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.
8. Kumar VS, Maranas CD. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS computational biology. Mar 2009;5(3):e1000308.