ABSTRACT
Genetic improvement of coffee plants represents a great challenge for breeders. Conventional breeding takes a too long time for responding timely to market demands, climatic variations and new biological threads. The correlation of genetic markers with the plant phenotype and final product quality is usually poor. Additionally, the creation and use of genetically modified organisms (GMOs) are often legally restricted and rejected by customers that demand natural products. Therefore, we developed a non-targeted metabolomics approach to accelerate conventional breeding. Our main idea was to identify highly heritable metabolites in Coffea canephora seedlings, which are linked to coffee cup quality. We employed a maternal half-sibs approach to estimate the metabolites heritability in open-pollinated plants in both leaves and fruits at an early plant development stage. We evaluated the cup quality of roasted beans and correlated highly heritable metabolites with sensory quality traits of the coffee beverage. Our results provide new insights about the heritability of metabolites of C. canephora plants. Furthermore, we found strong correlations between highly heritable metabolites and sensory traits of coffee beverage. We revealed metabolites that serve as predictive metabolite markers at an early development stage of coffee plants. Informed decisions can be made on plants of six months old, compared to 3.5 to 5 years using conventional selection methods. The metabolome-wide association study (MWAS) drastically accelerates the selection of C. canephora plants with desirable characteristics and represents a novel approach for the focused breeding of crops.
ABSTRACT
High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.
