Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Abstract

SummaryBiomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments.Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods.By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits.The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.

Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions

“…Natural genetic diversity provides a powerful tool to analyze complex networks because it allows the study of thousands of genetic perturbations that vary independently between different genotypes. Profiling of populations of Arabidopsis natural accessions or inbred lines and application of multivariate analysis tools has allowed sets of metabolites to be identified that are predictive of biomass (Meyer et al, 2007;Sulpice et al, 2009;Steinfath et al, 2010aSteinfath et al, , 2010bCuadros-Inostroza et al, 2010;Carreno-Quintero et al, 2012) and in some cases has allowed hypotheses to be formulated with respect to which aspects of metabolism play a key role in the determination of growth . However, metabolite levels depend on the growth condition (Caldana et al, 2011;Obata and Fernie, 2012; see the introduction for further references).…”

“…Metabolite profiling of large populations of Arabidopsis (Arabidopsis thaliana) natural accessions or inbred lines and the application of multivariate analysis tools, such as canonical correlation analysis and partial least squares (PLS) regression, has allowed the identification of descriptor sets of metabolites that are predictive of biomass (Meyer et al, 2007;Sulpice et al, 2009;Cuadros-Inostroza et al, 2010;Steinfath et al, 2010a;Carreno-Quintero et al, 2012) and physiological traits such as freezing tolerance (Korn et al, 2010) and herbivore resistance (Kliebenstein, 2012;Züst et al, 2012). The advantage of surveying a wide range of metabolites is underlined by the fact that multivariate analysis allows predictions to be made from data matrices in which no individual metabolite significantly correlates with biomass (Meyer et al, 2007).…”

Untargeted Metabolic Quantitative Trait Loci Analyses Reveal a Relationship between Primary Metabolism and Potato Tuber Quality

“…An approach using canonical correlation analysis to test the predictive power of metabolic composition for biomass traits in Arabidopsis revealed a number of metabolites related to biomass and growth (Meyer et al, 2007). In potato, a partial leastsquares analysis was used to discover metabolites that function as predictors for susceptibility to black spot bruising and chip quality (Steinfath et al, 2010). The validity of these results was tested in a collection of potato cultivars and in a set of individuals of a segregating population where metabolic and phenotypic information obtained from a first environment was used to predict phenotypic properties from the metabolic data obtained from a second environment.…”

Phenotypic and metabolic variation among spring Brassica napus genotypes during heat stress

“…Exploring this metabolic variation may identify metabolic markers that could then be utilised to increase the accuracy and/or capacity of phenotypic screening (Peng et al 2015). In a review, Fernandez et al (2016) list the several metabolic markers from grain yield under drought stress in maize (Obata and Fernie 2012) to chip quality in potatoes (Steinfath et al 2010) that have the potential to enhance phenotypic data and enable earlier selection decisions.…”