A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types

• Published in: PLoS computational biology Vol. 15; no. 1; p. e1006644
• Main Authors: Seif, Yara, Monk, Jonathan M., Mih, Nathan, Tsunemoto, Hannah, Poudel, Saugat, Zuniga, Cristal, Broddrick, Jared, Zengler, Karsten, Palsson, Bernhard O.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2019; Public Library of Science (PLoS)
• Subjects: Alkaloids; Amino acids; Antibiotics; Bar codes; Bioengineering; Biology and Life Sciences; Biosynthesis; Computation; Computer applications; Culture Media - metabolism; Culture Media - pharmacology; E coli; Fermentation; Funding; Gene expression; Gene Expression Regulation, Bacterial - drug effects; Gene Expression Regulation, Bacterial - genetics; Gene regulation; Gene sequencing; Genes; Genome, Bacterial - genetics; Genomes; Genomics; Glucose; Knowledge base; Knowledge Bases; Knowledge bases (artificial intelligence); Medicine and Health Sciences; Metabolic Networks and Pathways - drug effects; Metabolic Networks and Pathways - genetics; Metabolic response; Metabolism; Metabolites; Metabolome - drug effects; Metabolome - genetics; Metabolomics; Models, Biological; Online data bases; Pediatrics; Physical Sciences; Physiological aspects; Physiology; Protein turnover; Proteins; Scale models; Staphylococcus aureus; Staphylococcus aureus - drug effects; Staphylococcus aureus - genetics; Staphylococcus aureus - metabolism; Staphylococcus aureus - physiology; Staphylococcus infections; Supervision; Systems Biology - methods; Three dimensional models; Transcription; Transcription (Genetics); Transposons; La Jolla California; United States > US; California
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006644

S. aureus is classified as a serious threat pathogen and is a priority that guides the discovery and development of new antibiotics. 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. Comparisons of computational predictions with experimental observations highlight: 1) cases of non-essential biomass precursors; 2) metabolic genes subject to transcriptional regulation involved in Staphyloxanthin biosynthesis; 3) the essentiality of purine and amino acid biosynthesis in synthetic physiological media; and 4) a switch to aerobic fermentation upon exposure to extracellular glucose elucidated as a result of integrating time-course of quantitative exo-metabolomics data. An up-to-date GEM-PRO thus serves as a knowledge-based platform to elucidate S. aureus' metabolic response to its environment.