Impacts on the metabolome of down-regulating polyphenol oxidase in potato tubers
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Tubers of potato (Solanum tuberosum L. cv. Estima) genetically modified to reduce polyphenol oxidase (PPO) activity and enzymatic discolouration were assessed for changes in the metabolome using Liquid Chromatography–Mass Spectrometry (LC–MS) and Gas Chromatography (GC)–MS. Metabolome changes induced over a 48 hour (h) period by tuber wounding (sliced transverse sections) were also assessed using two PPO antisense lines (asPPO) and a wild-type (WT) control. Data were analysed using Principal Components Analysis and Analysis of Variance to assess differences between genotypes and temporal changes post-tuber wounding (by slicing). The levels of 15 metabolites (out of a total of 134 that were detected) differed between the WT and asPPO lines in mature tubers at harvest. A considerably higher number (63) of these metabolites changed significantly over a 48 h period following tuber wounding. For individual metabolites the magnitude of the differences between the WT and asPPO lines at harvest were small compared with the impacts of tuber wounding on metabolite levels. Some of the observed metabolite changes are explicable in terms of pathways known to be affected by wound responses. Whilst some statistically significant interactions (11 metabolites) were observed between line and time after wounding, very few profiles were consistent when comparing the WT with both asPPO lines, and the underlying metabolites appeared to be random in terms of the pathways they occupy. Overall, mechanical damage to tubers has a considerably greater impact on the metabolite profile than any potential unintended effects resulting from the down-regulation of PPO gene expression.
KeywordsBruising Genetic modification Metabolomics Potato PPO Transgenic
This work was supported by SAFEFOODS (EU FP6 Contract No. Food-CT-2004-506446); QualityLowInputFood (QLIF; EU FP6 Contract No CT-2004-506358) and by the Scottish Government’s Rural and Environment Science and Analytical Services (RESAS) Division. The authors would like to thank James W McNicol (Biomathematics and Statistics Scotland, Invergowrie, Dundee) for general statistical advice. The authors would also like to thank Sean Connor, Gary Dobson and Tom Shepherd for advice with data processing and metabolite identification.
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