| Summary: | <p>Abstract</p> <p>Background</p> <p>Successful defence of tobacco plants against attack from the oomycete <it>Phytophthora nicotianae </it>includes a type of local programmed cell death called the hypersensitive response. Complex and not completely understood signaling processes are required to mediate the development of this defence in the infected tissue. Here, we demonstrate that different families of metabolites can be monitored in small pieces of infected, mechanically-stressed, and healthy tobacco leaves using direct infrared laser desorption ionization orthogonal time-of-flight mass spectrometry. The defence response was monitored for 1 - 9 hours post infection.</p> <p>Results</p> <p>Infrared laser desorption ionization orthogonal time-of-flight mass spectrometry allows rapid and simultaneous detection in both negative and positive ion mode of a wide range of naturally occurring primary and secondary metabolites. An unsupervised principal component analysis was employed to identify correlations between changes in metabolite expression (obtained at different times and sample treatment conditions) and the overall defence response.</p> <p>A one-dimensional projection of the principal components 1 and 2 obtained from positive ion mode spectra was used to generate a Biological Response Index (BRI). The BRI obtained for each sample treatment was compared with the number of dead cells found in the respective tissue. The high correlation between these two values suggested that the BRI provides a rapid assessment of the plant response against the pathogen infection. Evaluation of the loading plots of the principal components (1 and 2) reveals a correlation among three metabolic cascades and the defence response generated in infected leaves. Analysis of selected phytohormones by liquid chromatography electrospray ionization mass spectrometry verified our findings.</p> <p>Conclusion</p> <p>The described methodology allows for rapid assessment of infection-specific changes in the plant metabolism, in particular of phenolics, alkaloids, oxylipins, and carbohydrates. Moreover, potential novel biomarkers can be detected and used to predict the quality of plant infections.</p>
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