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

Autor: Seif, Yara, Monk, Jonathan M., Mih, Nathan, Tsunemoto, Hannah, Poudel, Saugat, Zuniga, Cristal, Broddrick, Jared, Zengler, Karsten, Palsson, Bernhard O.
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Zdroj: PLoS Computational Biology; 1/9/2019, Vol. 15 Issue 01, p1-27, 27p, 4 Diagrams, 2 Charts, 1 Graph
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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