Citrus phenylpropanoids and defence against pathogens. Part I: Metabolic profiling in elicited fruits

Penicillium spp. are among the major postharvest pathogens of citrus fruit. Induction of natural resistance in fruits constitutes one of the alternatives to chemical fungicides. Here, we investigated the involvement of the phenylpropanoid pathway in the induction of resistance in Navelate oranges by examining changes in the metabolic profile of upon eliciting citrus fruits. By using both HPLC-PDA-FD and HPLC-PDA-QTOF-MS allowed the identification of several compounds that seem to be relevant for induced resistance. In elicited fruits, a greater diversity of phenolic compounds was observed in the flavedo (outer coloured part of the peel) when compared to the albedo (inner white part). Moreover, only small changes were detected in the most abundant citrus flavonoids. The coumarin scoparone was among the compounds with the highest induction upon elicitation. Two other highly induced compounds were identified as citrusnin A and drupanin aldehyde. All three compounds are known to exert antimicrobial activity. Our results suggest that phenylpropanoids and their derivatives play an important role in the induction of resistance in citrus fruit.This work was supported by Research Grants AGL2008-04828-C03-02, AGL2009-11969 and CONSOLIDER FUNC-FOOD from the Spanish Ministry of Science and Technology, and PROMETEO/2010/010 from the Generalitat Valenciana.Peer Reviewe

High-precision prostate cancer irradiation by clinical application of an offline patient setup verification procedure, using portal imaging

Purpose: To investigate in three institutions, The Netherlands Cancer Institute (Antoni van Leeuwenhoek Huis [AvL]), Dr. Daniel den Hoed Cancer Center (DDHC), and Dr. Bernard Verbeeten Institute (BVI), how much the patient setup accuracy for irradiation of prostate cancer can be improved by an offline setup verification and correction procedure, using portal imaging. Methods and Materials: The verification procedure consisted of two stages. During the first stage, setup deviations were measured during a number (N(max)) of consecutive initial treatment sessions. The length of the average three dimensional (3D) setup deviation vector was compared with an action level for corrections, which shrunk with the number of setup measurements. After a correction was applied, N(max) measurements had to be performed again. Each institution chose different values for the initial action level (6, 9, and 10 mm) and N(max) (2 and 4). The choice of these parameters was based on a simulation of the procedure, using as input preestimated values of random and systematic deviations in each institution. During the second stage of the procedure, with weekly setup measurements, the AvL used a different criterion ('outlier detection') for corrective actions than the DDHC and the BVI ('sliding average'). After each correction the first stage of the procedure was restarted. The procedure was tested for 151 patients (62 in AvL, 47 in DDHC, and 42 in BVI) treated for prostate carcinoma. Treatment techniques and portal image acquisition and analysis were different in each institution. Results: The actual distributions of random and systematic deviations without corrections were estimated by eliminating the effect of the corrections. The percentage of mean (systematic) 3D deviations larger than 5 mm was 26% for the AvL and the DDHC, and 36% for the BVI. The setup accuracy after application of the procedure was considerably improved (percentage of mean 3D deviations larger than 5 mm was 1.6% in the AvL and 0% in the DDHC and BVI), in agreement with the results of the simulation. The number of corrections (about 0.7 on the average per patient) was not larger than predicted. Conclusion: The verification procedure appeared to be feasible in the three institutions and enabled a significant reduction of mean 3D setup deviations. The computer simulation of the procedure proved to be a useful tool, because it enabled an accurate prediction of the setup accuracy and the required number of corrections

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust—a software tool for analysis GC–MS and LC–MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC–MS and LC–MS alignment data set