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Although there have been numerous studies describing plant growth systems for root exudate collection, a common limitation is that these systems require disruption of the plant root system to facilitate exudate collection. Here, we present a newly designed semi-hydroponic system that uses glass beads as solid support to simulate soil impedance, which combined with drip irrigation, facilitates growth of healthy maize plants, collection and analysis of root exudates, and phenotyping of the roots with minimal growth disturbance or root damage.
This system was used to collect root exudates from seven maize genotypes using water or 1 mM CaCl2, and to measure root phenotype data using standard methods and the Digital imaging of root traits (DIRT) software. LC–MS/MS (Liquid Chromatography—Tandem Mass Spectrometry) and GC–MS (Gas Chromatography—Mass Spectrometry) targeted metabolomics platforms were used to detect and quantify metabolites in the root exudates. Phytohormones, some of which are reported in maize root exudates for the first time, the benzoxazinoid DIMBOA (2,4-Dihydroxy-7-methoxy-1,4-benzoxazin-3-one), amino acids, and sugars were detected and quantified. After validating the methodology using known concentrations of standards for the targeted compounds, we found that the choice of the exudate collection solution affected the exudation and analysis of a subset of analyzed metabolites. No differences between collection in water or CaCl2were found for phytohormones and sugars. In contrast, the amino acids were more concentrated when water was used as the exudate collection solution. The collection in CaCl2required a clean-up step before MS analysis which was found to interfere with the detection of a subset of the amino acids. Finally, using the phenotypic measurements and the metabolite data, significant differences between genotypes were found and correlations between metabolites and phenotypic traits were identified.
A new plant growth system combining glass beads supported hydroponics with semi-automated drip irrigation of sterile solutions was implemented to grow maize plants and collect root exudates without disturbing or damaging the roots. The validated targeted exudate metabolomics platform combined with root phenotyping provides a powerful tool to link plant root and exudate phenotypes to genotype and study the natural variation of plant populations.
Foliar stomatal movements are critical for regulating plant water loss and gas exchange. Elevated carbon dioxide (
CO2) levels are known to induce stomatal closure. However, the current knowledge on CO2signal transduction in stomatal guard cells is limited. Here we report metabolomic responses of Brassica napusguard cells to elevated CO2using three hyphenated metabolomics platforms: gas chromatography‐mass spectrometry ( MS); liquid chromatography ( LC)‐multiple reaction monitoring‐ MS; and ultra‐high‐performance LC‐quadrupole time‐of‐flight‐ MS. A total of 358 metabolites from guard cells were quantified in a time‐course response to elevated CO2level. Most metabolites increased under elevated CO2, showing the most significant differences at 10 min. In addition, reactive oxygen species production increased and stomatal aperture decreased with time. Major alterations in flavonoid, organic acid, sugar, fatty acid, phenylpropanoid and amino acid metabolic pathways indicated changes in both primary and specialized metabolic pathways in guard cells. Most interestingly, the jasmonic acid ( JA) biosynthesis pathway was significantly altered in the course of elevated CO2treatment. Together with results obtained from JAbiosynthesis and signaling mutants as well as CO2signaling mutants, we discovered that CO2‐induced stomatal closure is mediated by JAsignaling.
Background: Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. Aim of Review: We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. Key Scientific Concepts of Review: This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.more » « less
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