The metabolic transition during disease following infection of Arabidopsis thaliana by Pseudomonas syringae pv. tomato.

Awduron Sefydliadau
  • Jane L. Ward(Awdur)
  • Silvia Forcat(Awdur)
  • Manfred Beckmann(Awdur)
  • Mark Bennett(Awdur)
  • Sonia J. Miller(Awdur)
  • John M. Baker(Awdur)
  • Nathaniel D. Hawkins(Awdur)
  • Cornelia Petronella Vermeer(Awdur)
  • Chuan Lu(Awdur)
  • Wanchang Lin(Awdur)
  • William M. Truman(Awdur)
  • Michael H. Beale(Awdur)
  • John Draper(Awdur)
  • John W. Mansfield(Awdur)
  • Murray Grant(Awdur)
Math Erthygl
Iaith wreiddiolSaesneg
Tudalennau (o-i)443-457
Nifer y tudalennau15
CyfnodolynPlant Journal
Cyfrol63
Rhif y cyfnodolyn3
Dyddiad ar-lein cynnar18 Mai 2010
Dangosyddion eitem ddigidol (DOIs)
StatwsCyhoeddwyd - Awst 2010
Cysylltiadau
Cysylltiad parhaol
Gweld graff cysylltiadau
Fformatau enwi

Crynodeb

The outcome of bacterial infection in plants is determined by the ability of the pathogen to successfully occupy the apoplastic space and deliver a constellation of effectors that collectively suppress basal and effector-triggered immune responses. In this study, we examined the metabolic changes associated with establishment of disease using analytical techniques that interrogated a range of chemistries. We demonstrated clear differences in the metabolome of Arabidopsis thaliana leaves infected with virulent Pseudomonas syringae within 8 h of infection. In addition to confirmation of changes in phenolic and indolic compounds, we identified rapid alterations in the abundance of amino acids and other nitrogenous compounds, specific classes of glucosinolates, disaccharides, and molecules that influence the prevalence of reactive oxygen species. Our data illustrate that, superimposed on defence suppression, pathogens reconfigure host metabolism to provide the sustenance required to support exponentially growing populations of apoplastically localized bacteria. We performed a detailed baseline study reporting the metabolic dynamics associated with bacterial infection. Moreover, we have integrated these data with the results of transcriptome profiling to distinguish metabolomic pathways that are transcriptionally activated from those that are post-transcriptionally regulated.

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