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

Yara Seif, Jonathan M. Monk, Nathan Mih, Hannah Tsunemoto, Saugat Poudel, Cristal Zuniga, Jared Broddrick, Karsten Zengler, Bernhard O. Palsson

Rannsóknarafurð: Framlag til fræðitímaritsGreinritrýni

11 Tilvitnanir (Scopus)

Útdráttur

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.

Upprunalegt tungumálEnska
Númer greinare1006644
FræðitímaritPLoS Computational Biology
Bindi15
Númer tölublaðs1
DOI
ÚtgáfustaðaÚtgefið - 2019

Athugasemd

Publisher Copyright:
© 2019 Seif et al.

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