Structured plant metabolomics for the simultaneous exploration of multiple factors.

Nikolay Vasilev,Simon Goepfert,Rainer Fischer,Serge Rudaz,Gerhard Lang,Ulrike Grömping,Stefan Schillberg,Julien Boccard

doi:10.1038/srep37390

Abstract

Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor–metabolite crosstalk. However, unravelling all factor–metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset.

Highlights

Metabolomics generates large, multi-dimensional datasets using automated analytical procedures such as gas chromatography or high-pressure liquid chromatography coupled to mass spectrometry (GC-Murashige and Skoog (MS) and HPLC-MS)
The importance of multiple simultaneous metabolic effects has been underestimated in the past and here we addressed this challenge by combining several orthogonal techniques: reversed-phase ultra-high-pressure liquid chromatography (RP-UHPLC) with positive and negative electrospray ionization (ESI) modes, and hydrophilic interaction liquid chromatography (HILIC), both coupled to time of flight mass spectrometry (TOF-MS) to achieve greater coverage of the metabolome
The UHPLC-QTOF-MS gradient conditions we applied allowed us to monitor more than 3500 features in the m/z range 100–1200 based on RP-UHPLC (ESI+ and ESI− ion detection modes) and HILIC (ESI+ ion detection mode)

Summary

Introduction

Metabolomics generates large, multi-dimensional datasets using automated analytical procedures such as gas chromatography or high-pressure liquid chromatography coupled to mass spectrometry (GC-MS and HPLC-MS). This is the first systematic investigation of metabolic remodelling in plants following simultaneous multi-factorial treatment This novel combination of metabolomics and experimental design, associated with the simultaneous analysis of multiblock omics data, is a powerful approach that allows us to unravel the metabolic responses in transgenic tobacco cells at a global level when diverse input factors such as macronutrients, plant growth regulators and light are varied simultaneously. This high-throughput screening system can be used for process optimization with metabolically engineered cell lines. We hypothesize that product optimization using the simultaneous exploration of multiple factors may achieve more accurate and reproducible results than the assessment of one factor at a time

Methods

Results

Conclusion