Abstract

In spite of the large arsenal of methodologies developed for amino acid assessment in complex matrices, their implementation in metabolomics studies involving wide-ranging mutant screening is hampered by their lack of high-throughput, sensitivity, reproducibility, and/or wide dynamic range. In response to the challenge of developing amino acid analysis methods that satisfy the criteria required for metabolomic studies, improved reverse-phase high-performance liquid chromatography-mass spectrometry (RPHPLC-MS) methods have been recently reported for large-scale screening of metabolic phenotypes. However, these methods focus on the direct analysis of underivatized amino acids and, therefore, problems associated with insufficient retention and resolution are observed due to the hydrophilic nature of amino acids. It is well known that derivatization methods render amino acids more amenable for reverse phase chromatographic analysis by introducing highly-hydrophobic tags in their carboxylic acid or amino functional group. Therefore, an analytical platform that combines the 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) pre-column derivatization method with ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) is presented in this article. For numerous reasons typical amino acid derivatization methods would be inadequate for large scale metabolic projects. However, AQC derivatization is a simple, rapid and reproducible way of obtaining stable amino acid adducts amenable for UPLC-ESI-MS/MS and the applicability of the method for high-throughput metabolomic analysis in Arabidopsis thaliana is demonstrated in this study. Overall, the major advantages offered by this amino acid analysis method include high-throughput, enhanced sensitivity and selectivity; characteristics that showcase its utility for the rapid screening of the preselected plant metabolites without compromising the quality of the metabolic data. The presented method enabled thirty-eight metabolites (proteinogenic amino acids and related compounds) to be analyzed within 10 min with detection limits down to 1.02 × 10−11 M (i.e., atomole level on column), which represents an improved sensitivity of 1 to 5 orders of magnitude compared to existing methods. Our UPLC-ESI-MS/MS method is one of the seven analytical platforms used by the Arabidopsis Metabolomics Consortium. The amino acid dataset obtained by analysis of Arabidopsis T-DNA mutant stocks with our platform is captured and open to the public in the web portal PlantMetabolomics.org. The analytical platform herein described could find important applications in other studies where the rapid, high-throughput and sensitive assessment of low abundance amino acids in complex biosamples is necessary.

Highlights

  • In the post genomics era where the plant biology community is facing the challenge of identifying the functionalities of genes of unknown function (GUFs), metabolomics offers a link between biochemical phenotype and gene function [1]

  • Technology can be combined with UV, fluorescence (FL), or photodiode array (PDA) detection of aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) amino acid derivatives thanks to the commercial availability of the AccQTag Ultra Chemistry package (Waters Corp.) [46,47], the possibility of using UPLC-MS technology has not received enough attention even though the eluents used for AccQTag UPLC amino acid analysis and the AQC

  • This paper focuses on the results obtained by targeted amino acid analysis on leaf extracts of 69 mutant lines selected for three metabolomic experiments (E1, E2, and E3) designed by the consortium

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Summary

Introduction

In the post genomics era where the plant biology community is facing the challenge of identifying the functionalities of genes of unknown function (GUFs), metabolomics offers a link between biochemical phenotype and gene function [1]. The use of metabolomics for the prediction of the function of plant genes faces technical challenges due to the large size (between 100,000 to 200,000 biocompounds) [2,3,4], the chemical complexity and the different abundance levels of a plant metabolic pool. These challenges are currently being tackled by combining targeted and non-targeted metabolic analyses to characterize and compare changes in metabolic networks [1,2,5,6,7]. Identification of metabolites that are discriminatory between the knockout plant compared to the wild-type help fill up the gaps in our understanding of plant-specific regulatory and biosynthetic pathways and determine the function of the GUFs [1,7,9]

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