A Computational Knowledge-Base Elucidates the Response of Staphylococcus Aureus to Different Media Types

S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. It has been observed to exhibit differential antibiotic resistance profiles when cultured in different media. Despite growing knowledge of S. aureus metabolic capabilities, our understanding of its systems-level responses to different media types remains incomplete. Here, we develop a manually reconstructed genome-scale model (GEM-PRO) of metabolism with 3D protein structures for S. aureus USA300 str. JE2 containing 854 genes, 1,440 reactions, 1,327 metabolites and 673 3-dimensional protein structures. Computations were in 85% agreement with gene essentiality data from random barcode transposon site sequencing (RB-TnSeq) and 68% agreement with experimental physiological data. Comparison of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metal cofactor promiscuity; 3) genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; and 4) gaps of knowledge in C2-carbon assimilation; 5) the essentiality of purine and amino acid biosynthesis in synthetic physiological medium; and 6) a switch to aerobic glycolysis upon exposure to extracellular glucose that was elucidated using a time-course of quantitative metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus’ metabolic response to its environment.