Defining genetic and chemical diversity in wheat grain by 1H-NMR spectroscopy of polar metabolites

Scope The application of high‐throughput 1H nuclear magnetic resonance (1H‐NMR) of unpurified extracts to determine genetic diversity and the contents of polar components in grain of wheat. Methods and results Milled whole wheat grain was extracted with 80:20 D2O:CD3OD containing 0.05% d4–trimethylsilylpropionate. 1H‐NMR spectra were acquired under automation at 300°K using an Avance Spectrometer operating at 600.0528 MHz. Regions for individual metabolites were identified by comparison to a library of known standards run under identical conditions. The individual 1H‐NMR peaks or levels of known metabolites were then compared by Principal Component Analysis using SIMCA‐P software. Conclusions High‐throughput 1H‐NMR is an excellent tool to compare the extent of genetic diversity within and between wheat species, and to quantify specific components (including glycine betaine, choline, and asparagine) in individual genotypes. It can also be used to monitor changes in composition related to environmental factors and to support comparisons of the substantial equivalence of transgenic lines

Enhancement of plant metabolite fingerprinting by machine learning

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted