Literature DB >> 21472852

Staphylococcus aureus physiological growth limitations: insights from flux calculations built on proteomics and external metabolite data.

Chunguang Liang1, Manuel Liebeke, Roland Schwarz, Daniela Zühlke, Stephan Fuchs, Leonhard Menschner, Susanne Engelmann, Christiane Wolz, Sarah Jaglitz, Jörg Bernhardt, Michael Hecker, Michael Lalk, Thomas Dandekar.   

Abstract

Comparing proteomics and metabolomics allows insights into Staphylococcus aureus physiological growth. We update genome and proteome information and deliver strain-specific metabolic models for three S. aureus strains (COL, N315, and Newman). We find a number of differences in metabolism and enzymes. Growth experiments (glucose or combined with oxygen limitation) were conducted to measure external metabolites. Fluxes of the central metabolism were calculated from these data with low error. In exponential phase, glycolysis is active and amino acids are used for growth. In later phases, dehydroquinate synthetase is suppressed and acetate metabolism starts. There are strain-specific differences for these phases. A time series of 2-D gel protein expression data on COL strain delivered a second data set (glucose limitation) on which fluxes were calculated. The comparison with the metabolite-predicted fluxes shows, in general, good correlation. Outliers point to different regulated enzymes for S. aureus COL under these limitations. In exponential growth, there is lower activity for some enzymes in upper glycolysis and pentose phosphate pathway and stronger activity for some in lower glycolysis. In transition phase, aspartate kinase is expressed to meet amino acid requirements and in later phases there is high expression of glyceraldehyde-3-phosphate dehydrogenase and lysine synthetase. Central metabolite fluxes and protein expression of their enzymes correlate in S. aureus.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2011        PMID: 21472852     DOI: 10.1002/pmic.201000151

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  10 in total

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5.  Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence.

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Review 6.  Identification of Antifungal Targets Based on Computer Modeling.

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Journal:  Front Mol Biosci       Date:  2016-06-17
  10 in total

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