Paper Info
Title
A computational knowledge-base elucidates the response of Staphylococcus aureus to different media types.
Abstract
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.
Year
DOI
Venue
2019
10.1371/journal.pcbi.1006644
PLOS COMPUTATIONAL BIOLOGY
DocType
Volume
Issue
Journal
15
1
ISSN
Citations 
PageRank 
1553-7358
0
0.34
References 
Authors
5
9
Name
Order
Citations
PageRank
Yara Seif101.35
Jonathan Monk203.04
Nathan Mih321.75
Hannah Tsunemoto400.34
Saugat Poudel501.01
Cristal Zuniga610.69
Jared Broddrick700.34
Karsten Zengler8252.39
Bernhard O. Palsson975167.99