Concepts in Plant Metabolomics

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MeJa induced nicotine production in our tobacco cells in a similar manner as previously shown for N. We also observed that nicotine biosynthesis was induced by IAA but moderately suppressed by 2,4-D The biosynthesis of monoterpenoid glycosides appeared to be influenced by auxins and cytokinins, perhaps reflecting the antagonistic crosstalk between these two phytohormone classes Our experimental results support the idea that phytohormones function in a complex network involving the different hormonal pathways but that there is also elaborate crosstalk with nutrients and elicitors.

The auxin-sensitive signalling protein SHY2 is regulated by the cytokinin-induced protein ARR1 Arabidopsis response regulator , which in turn is repressed by gibberellin thus connecting three hormones in one network This may explain why our GA 3 S-plot contained ion traits that were also affected by auxins and cytokinins.

Light has a potent effect on monoterpenoid metabolism by modulating the expression of monoterpenoid synthase genes, controlling precursor synthesis, and affecting constitutive promoter activity 43 , Geraniol glycosides were also influenced by light in our cell cultures. Light induced the formation of malonyl-hexosyl-geranidiol 7 but suppressed the formation of pentosyl-hexosyl-geraniol 2 , hexosyl-geraniol 3 and malonyl-hexosyl-geraniol 4.

We have developed a systematic approach, which implements an experimental design strategy in the context of metabolomics to account for the diverse factors applied simultaneously to plant cells. This is a valuable method for the investigation of complex environmental stress and its impact on plant metabolism by optimizing the number of experiments needed to assess the factors. Our approach significantly reduces the time and effort required for testing by using consensus OPLS-DA models to evaluate and interpret metabolic changes caused by the simultaneous application of diverse ecological factors.

This systematic workflow may facilitate the discovery and characterization of factor—nutrient—elicitor networks and appropriate biomarkers. Finally, we conclude that this novel approach should be able to streamline process optimization for the reproducible production of any secondary metabolite in plant cell cultures by the simultaneous exploration of multiple factors rather than the assessment of one factor at a time. We used tobacco N.

Samsun NN transgenic cell suspension cultures, expressing stably V.

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The cell cultures were initiated and maintained as previously described 8. The low level of each macronutrient in the plant cell culture medium was based on classical Murashige and Skoog MS medium 11 , 45 whereas the high level was based on our recent medium optimization study, although the concentrations of NH 4 NO 3 were reversed 11 , Finally, the low and high levels of light were set to The factor levels are summarized in Table S1.

The cultures were elicited with phytohormones and plant growth regulators 6 days post-inoculation. The samples were extracted as previously described The flow rate was 0. A pool was created by adding equal volumes from all samples to serve as a QC injection. Nine QC injections in total were distributed at regular intervals in the analytical batch. The following method parameters were set: retention time window 1. The experimental design was based on an orthogonal array with 96 runs created with the free open-source R package DoE.

The design was optimized to screen 14 factors by keeping the confounding of low-order effects minimal: all main effects are orthogonal to each other orthogonal array , the design was based on an array with the lowest possible number of squared canonical correlations from three-factor sets equal to 1 48 and the factors were accommodated on columns of the base array such that confounding between main effects and two-factor interactions, and subsequently among two-factor interactions, was minimized 8.

This fractional factorial design, with a randomized run order, was used to screen 12 two-level factors, one three-level factor and one four-level factor: we thus screened for the effects of light and 18 diverse substances representing macronutrients, auxins, cytokinins and elicitors. The experimental design with 96 runs is summarized in coded values in Table S2. The remaining potentially relevant confounding between main effects and two-factor interactions in terms of triples of factor comparisons are summarized in Fig.

Structured plant metabolomics for the simultaneous exploration of multiple factors

For each such triple, the comparison between the levels of each factor in the triple might be affected by an interaction between the other two factors e. DH-z comparison for cytokinins. Sceptics might argue that this possibility for confounding is a reason to refrain from using an experimental design approach in favour of only changing one factor at a time OFAT approach. However, if two-factor interactions are indeed relevant — as would be necessary for the experimental design approach to suffer from misleading conclusions in terms of factor level comparisons — the conclusions from an OFAT approach are also limited in the same manner and would be valid only for the exact settings at which the other factors have been fixed.

Furthermore, to achieve a reasonable amount of replication, the OFAT approach would need a much larger number of experimental runs — e. For each experimental factor, a consensus OPLS-DA model was built to relate the experimental metabolomics data X to a class matrix consisting of zeros and ones, filled according to the levels of each factor Y. The columns of the experimental design were therefore used individually as a response matrix in the context of supervised analysis.

For auxins and cytokinins, a response vector was generated individually for each hormone and filled with zero when the corresponding hormone was absent, whereas a value of one indicated its presence. The consensus OPLS algorithm implements data fusion based on multiple kernel learning. The joint analysis of multiple data tables is achieved by the combination of association matrices computed for each block. Therefore, requirements in terms of memory resources and computation time are minimized without information loss even if the experimental data include a large number of signals. A block-scaling step ensures fairness between blocks by offering equal starting chances to contribute to the model.

RV coefficients are then computed to build a consensus matrix and orientate the model towards better prediction performance. A common subspace is built using a kernel version of the OPLS algorithm and the optimal number of orthogonal components is estimated by cross-validation.


Because systematic variations are summarized using Y-predictive and Y-orthogonal components OPLS framework , the interpretation of the multiblock model is straightforward. Like classical multivariate methods, a consensus score plot allows the distribution of the observations to be evaluated. Because linearity is maintained, variable loadings can easily be computed for biomarker discovery. The weight of each block in the projection also allows the role of each data source to be evaluated Subsets of metabolites sharing similar patterns were investigated using cluster analysis.

For that purpose, the contribution loading of each ion feature associated with an identified metabolite was collected across all significant consensus OPLS-DA models and displayed in a dendrogram and a heat map. This strategy highlights upregulation and downregulation. Cluster analysis was carried out with the Bioinformatics Toolbox v4. Auxins and cytokinins were two factors in our design associated with three and four levels, respectively.

Exactly one auxin and one cytokinin were included in each run of the experimental design. Consequently, the four Y j indicator columns corresponding to auxins and the three Y j indicator columns corresponding to cytokinins are linearly dependent, as stated above. Our analysis included all indicator columns, because each is treated separately.

Introduction to Metabolomics

This implies that downregulation or upregulation must be interpreted within the linearly-dependent groups, e. This behaviour is clearly shown in the heat map and also implies analogous dependencies among the S-plots of the auxins and cytokinins, respectively. How to cite this article : Vasilev, N. Structured plant metabolomics for the simultaneous exploration of multiple factors.

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Experimental designs suitable for testing many factors with limited number of explants in tissue culture. Plant Cell Tiss Organ Cult 81, — Vasilev, N. Assessment of cultivation factors that affect biomass and geraniol production in transgenic tobacco cell suspension cultures.

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