Development of metabolite-based biomarkers for modeling/predicting complex traits
In contrast to genetic markers metabolite based biomarkers are largely neglected in plant breeding. This is very different to the situation in the medicinal field, where clinical biochemistry has since long been the basis for developing metabolite based biomarkers that, often due to a lack of understanding of the underlying causality, serve as surrogate markers for a physiological disorder.
A predictive model of maize hybrid performance in the field
Heterosis has been extensively exploited for yield gain in maize (Zea mays L.). In this project we use a comparative metabolomics-based analysis of young roots from in vitro germinating seedlings and from leaves of field-grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F1 hybrids. Metabolite levels in hybrids appear to be more robust than in inbred lines. We use state-of-the-art modeling techniques, to predict plant biomass from the metabolite data.
Principal component analysis (PCA) of metabolic profiles from inbreds and hybrids. (a) Root metabolites. PC1 and PC2 do not separate hybrids (red triangles) from inbreds (Flint and Dents represented by blue and green circles, respectively). (b) Leaf metabolites. PC1 separates hybrids from inbreds.[less]
Principal component analysis (PCA) of metabolic profiles from inbreds and hybrids. (a) Root metabolites. PC1 and PC2 do not separate hybrids (red triangles) from inbreds (Flint and Dents represented by blue and green circles, respectively). (b) Leaf metabolites. PC1 separates hybrids from inbreds.
Predicting rice plant height from seed metabolism
In a second project, we aim to build a mathematical model for the prediction of plant height from the metabolic composition of rice seeds and seedlings. The analyses are key to our understanding of rice seed metabolism, and can be used to aid and focus future breeding programs.
This project is realized in close collaboration with the group of Prof. Aaron Fait at the French Associates Institute for Agriculture and Biotechnology of Drylands (Ben-Gurion University) in Sede Boquer, Israel.
Meyer, R. C., Steinfath, M., Lisec, J., Becher, M., Witucka-Wall, H., Törjék, O., Fiehn, O., Eckardt, A., Willmitzer, L., Selbig, J. & Altmann, T. The metabolic signature related to high plant growth rate in Arabidopsis thaliana PNAS 104 (11) 4759-4764 (2007)
Lisec, J., Romisch-Margl, L., Nikoloski, Z., Piepho, H.P., Giavalisco, P., Selbig, J., Gierl, A. & Willmitzer, L. Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. Plant Journal 68, 326-336 (2011).
Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., Altmann, T., Stitt, M., Willmitzer, L. & Melchinger, A.E. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics 44, 217-220 (2012).
de Abreu e Lima, F., Westhues, M., Cuadros-Inostroza, Á., Willmitzer, L., Melchinger, A. E. and Nikoloski, Z. (2017), Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field. Plant Journal doi:10.1111/tpj.13495 (2017)