H OH metabolites OH Article Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars Chanel J. Pretorius, Fidele Tugizimana , Paul A. Steenkamp , Lizelle A. Piater and Ian A. Dubery * Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa; [email protected] (C.J.P.); [email protected] (F.T.); [email protected] (P.A.S.); [email protected] (L.A.P.) * Correspondence: [email protected]; Tel.: +27-11-5592401 Abstract: The first step in crop introduction—or breeding programmes—requires cultivar identifica- tion and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemi- cal maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high Citation: Pretorius, C.J.; Tugizimana, performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) F.; Steenkamp, P.A.; Piater, L.A.; high-definition mass spectrometer analytical platform. The generated data were processed and Dubery, I.A. Metabolomics for analysed using multivariate statistical methods. Principal component analysis (PCA) models were Biomarker Discovery: Key Signatory computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of Metabolic Profiles for the the samples and pointing to underlying differential metabolic profiles of these cultivars. Further Identification and Discrimination of Oat Cultivars. Metabolites 2021, 11, multivariate analyses were performed to profile differential signatory markers, which included car- 165. https://doi.org/10.3390/ boxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic metabo11030165 acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in Academic Editor: Gilles Comte profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation. Received: 15 February 2021 Accepted: 5 March 2021 Keywords: Avena sativa; cultivar distinction; liquid chromatography; mass spectrometry; metabolomics; Published: 12 March 2021 multivariate data analysis; oat; secondary metabolites Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- 1. Introduction iations. Food demand has been rapidly increasing with the overall growth in the world pop- ulation, which is expected to reach around 9.7 billion by the year 2050 [1]. Now more than ever, crop improvement and plant breeding studies have become imperative in en- suring food security and sustainability [2]. The primary step involved in plant breeding, Copyright: © 2021 by the authors. inspection, registration, trade and seed production requires the identification of cultivars Licensee MDPI, Basel, Switzerland. and varieties, and therefore a rapid and effective method for cultivar fingerprinting is This article is an open access article required [3]. Over the years, plant breeding has been greatly improved for unravelling the distributed under the terms and conditions of the Creative Commons molecular basis of complex traits using genomic analyses and next-generation sequencing Attribution (CC BY) license (https:// methods [4]. Currently, plant breeding methods have integrated phenotypic traits with creativecommons.org/licenses/by/ a range of marker-assisted selection techniques to more efficiently determine trait out- 4.0/). comes [5]. Although genetic markers have been at the forefront of plant breeding efforts, Metabolites 2021, 11, 165. https://doi.org/10.3390/metabo11030165 https://www.mdpi.com/journal/metabolites Metabolites 2021, 11, 165 2 of 23 many limitations have restricted the use thereof in analysing complexities arising from genotype × environment interactions (environmental plasticity), polygenic inheritance and epistasis (which is referred to as the action of one gene on another) [6]. Metabolomics, a systems biology approach to interrogate cellular biochemistry and metabolism, offers unique possibilities that can be incorporated into unravelling these complexities to gain a genotype × metabolite × phenotype understanding that can be applied in plant breeding. The phenotype is an observable reflection that results from complex interactions between the genotype and the environment, with the former also able to prime multiple phenotypes [7]. These interactions can result in various success rates in reproduction and cause subsequent alterations in the genotype, as summarised in the abridged illustration (Figure1). To bridge the gap between the genotype and phenotype, Metabolites 2021, 11, x FOR PEER REVIEW 3 of 25 metabolomics was proposed as a means to provide insight into how genotypic variation affects phenotypic diversity in plants [8–10]. FigureFigure 1.1. Triangular arrangement illustrating illustrating the the genoty genotypepe × ×environmentenvironment × phenotype× phenotype interactions, interactions, with the metabolome at the core, bridging the gap between the genotype and phenotype. The metab- with the metabolome at the core, bridging the gap between the genotype and phenotype. The olome is the final recipient of biological information flow and carries imprints of genetic and envi- metabolomeronmental factors. is the It final is more recipient sensitive of biological to perturba informationtions in both flow metabolic and carries fluxes imprintsand enzyme of geneticactivity and environmentalthan either the transcriptome factors. It is more or proteome sensitive and to is perturbations thus a reflection in both of the metabolic phenotype. fluxes Quantitative, and enzyme activityglobal measurements than either the of transcriptome the metabolome or proteome therefore andprovide is thus an aexploration reflection ofof the cellular phenotype. metabolism, Quantita- tive,revealing global patterns measurements and functional of the metabolome signatures of therefore the biochemical provide landscape an exploration and cellular of cellular physiology metabolism, revealingof the system patterns under and consideration functional signatures[8–10]. of the biochemical landscape and cellular physiology of the system under consideration [8–10]. 2. Results 2.1. DifferentialMetabolomics, Chromatographic–Mass defined as the comprehensive Spectrometric Analyses qualitative of Respective and quantitative Oat Cultivars analysis of allMethanolic metabolites extracts in a biological of leaf and system, root tissue is ans of established the respective omics cultivars technology were separated that holds promiseon an ultra-high in agricultural performance research; liquid therefore, chromatography metabolomics system has becomecoupled an to indispensable a quadrupole tool intime-of-flight various plant high-definition sciences studies mass [11 ,12spectr]. Dueometer to the (UHPLC–qTOF–MS) diverse and large variety and detected of metabolites in foundboth positive in plants, and an negative extensive electrospray array of analytical ionisation techniques(ESI) modes. has Initial been optimisation developed tostud- obtain sufficienties indicated coverage that the for majority plant metabolomics. of extractable metabolites Liquid chromatography–mass ionised more effectively spectrometry in the (LC–MS)-basedESI (−) mode; accordingly, plant metabolomics, only these compareddatasets are to further that of presented gas chromatography–mass and illustrated. The spec- trometrychromatographically (GC–MS) and distinct nuclear base magnetic peak intensity resonance (BPI) (NMR),chromatograms has been of advantageousleaf and root in detectingextracts (Figure a wide 2A,B) range provide of secondary a visual presenta metabolitestion/description with higher of sensitivitythe similarities and and selectivity dif- ferences between the respective cultivars and reflect the complexity of their metabolic profiles. Although chromatography is extremely useful in separating the components based on their polarity, and high-definition mass spectrometry enables accurate mass de- termination in order to generate empirical formulae to aid in compound annotation, fur- ther chemometric analyses were performed to obtain biologically useful information. Metabolites 2021, 11, 165 3 of 23 (compared to NMR) and has the ability to detect and identify a broader range of com- pounds whilst being less time-consuming in the preparation of samples (compared to GC–MS which requires derivatisation) [13–15]. In the past, metabolomics has proven crucial for studying plant x environment interac- tions (e.g., adaptive responses towards biotic and abiotic stresses) and