MSBased Metabolomic Profiling of Stilbenes and Other Defence Induced Compounds

In order to study variations in the metabolomes of wines and grapevines, Agroscope-ACW, in collaboration with researchers at the University of Geneva, have developed a sensitive MS-based metabolomic approach for the detection of stress-induced biomarkers. The approach is based on the use of ultra high performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-TOF-MS) for a two-step analysis of crude plant extracts [128].

Among the different techniques enlisted for metabolome analysis, UHPLC-TOF-MS represents a powerful platform indeed [123]. UHPLC compared to HPLC affords increases sample throughput for fast fingerprinting of biological matrices as well as important resolution enhancements (when used with long gradients) for detailed profiling and precise localisation of biomarkers [129]. On the other hand, time-of-flight mass spectrometry (TOF-MS) provides sensitive detection of a large number of plant metabolites. Due to its high resolution and high mass accuracy capabilities, TOF-MS provides molecular formula information on all detected compounds for dereplication or preliminary identification of the biomarker of interest [130]. Combination of these two methods gives very reproducible LC-MS datasets, a prerequisite for further data analysis of the high number of extract fingerprints typically recorded in metabolomics [123].

The metabolomics strategy used is based on the following steps: (i) high throughput metabolite fingerprinting involving rapid UPLC-TOF-MS gradients on numerous control and stressed samples for group discrimination and determination of ions (m/z) responsible for the main differences after adequate data treatment; and (ii) high resolution metabolite profiling of selected pool samples on high peak capacity UHPLC columns after efficient gradient transfer for the localisation and deconvolution of the putative biomarkers. Biomarkers can then be identified by searching natural products in MS databases, comparison with standards (when available) or de novo structure identification [128, 130]. In the last case, a generic approach for the rapid isolation of unknown biomarkers has been developed [128]. It consists of (i) targeted LC-MS-triggered microfractionation of the biomarkers of interest at the semi-preparative level, based on computed LC conditions from UHPLC gradients, and (ii) complete structural determination of the unknown biomarkers based on at-line capillary LC-NMR (CapNMR) experiments. This last method has been shown to provide key structural information on natural products (1D and 2D NMR) at the low-microgram scale [130, 131]. In relation to plant defence, this strategy has already been proven efficient for the discovery of important new low-abundance stress-induced phytohormones belonging to the jasmonate family and for the study of their induction dynamics from a holistic perspective [128].

In our laboratory, the same technology has been used for the study of stress biomarker induction in two wood-decaying fungi involved in esca disease (Eutypa lata and Botryosphaeria obtusa). Differential UHPLC-TOF-MS profiling from extracts of these fungi co-cultivated in Petri dishes was performed. Comparison of the high-resolution metabolite profiles showed a strong induction of many stress-induced fungal metabolites (mycoalexins) in the confrontation zones between the pure fungal strains. Microisolation and further CapNMR measurements enabled their de novo characterisation as O-methylmellein derivatives. The same microfractiona-tion procedure also provided enough material to assay the fungitoxic and phytotoxic activities of the compounds that were induced as a result of the confrontation [123].

Our global sequential metabolomic approach [128] was recently applied to the study of stress-induced metabolites in the resistant grapevine cultivar selected in ACW (2091). To obtain preliminary results on the largest possible set of stress-induced compounds that can be produced in this variety, UV-C irradiation (Philips TUV 30 W, 92 ^W cm-2, 253 nm, distance: 13 cm from leaves) was applied. This type of abiotic stress has been shown (in our earlier experiments) to elicit a large range of altered constituents that are characteristic of either natural abiotic or biotic stresses [79].

This study is illustrated in detail in Fig. 2.4. For a rapid estimation of the metabolome changes, 5 leaves of the resistant 2091 cultivar [Gamaret x Bronner] selected in ACW were used as controls, and 5 leaves were exposed to UV light for 10 min. Each fresh leaf sample (300 mg) was crushed in liquid nitrogen and extracted independently with methanol (3 mL) in an ultrasonic bath for 20 min. After a rapid SPE C18 enrichment procedure, the crude extracts (1 ^g) were directly analysed by UHPLC-TOF-MS in both positive and negative ion modes using a rapid gradient (5 min per analysis) and a short UHPLC column (1 x 50 mm). The results presented here are those derived from the negative ion LC-MS datasets, which were the most informative. In the fingerprints obtained (Fig. 2.4a), all metabolites eluted in the first 3 min of elution. The ion maps generated (m/z ions recorded as a function of gradient time) indicated induction of different ions after UV stress (indicated in dashed ring and square on Fig. 2.4a). These ion maps clearly demonstrate the power of mass spectrometry for such a rapid fingerprinting procedure. As shown, if a complete resolution of the constituents of the extract cannot be obtained in 3 min of elution by UHPLC (see the short gradient total ion chromatogram insets in the ion maps in Fig. 2.4a), TOF-MS detection can provide good resolution in the second dimension for most of the metabolites present. This UHPLC-TOF-MS fingerprinting enabled the analysis of numerous replicates in various time series in a high throughput and reproducible manner. For a good estimation of all observed variation in the metabolomes, the LC-MS datasets obtained were submitted to multivariate data analysis (MVDA).

Because the combination of UHPLC separation and MS detection produces large sets of three-dimensional information (retention time x m/z x intensity), preprocessing of the data prior to MVDA was required. In a first step, noise filtering, peak detection and matching were concomitantly performed. The final data table

Control leaves

Control leaves

UHPLC-TOF-MS fingerprinting UV leaves

2D map

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