Metabolomics to Characterize Adaptive and Signaling Responses in Legume Crops under Abiotic Stresses

• Published in: ACS omega 2020-02, Vol.5 (4), p.1752-1763
• Main Authors: Bueno, Paula C. P, Lopes, Norberto P
• Format: Article
• Language: English
• Subjects: Mini-Review
• ISSN: 2470-1343; 2470-1343
• DOI: 10.1021/acsomega.9b03668

Legume species are an important source of protein and other nutrients for human and livestock consumption, playing a central role in food security. Besides, legumes benefit agriculture because of their ability to establish symbiotic interactions with nitrogen-fixing bacteria, providing nitrogen for subsequent crops, which is very much appreciated for sustainable agricultural practices. However, like other food crops, legumes are highly vulnerable to climate variations, water stresses being the main constraint that negatively affects both crop quality and productivity. Because of this, the development of strategies to improve the tolerance of such cultivars against water stresses, as well as the study of effective approaches to monitor these improvements, have gained special attention during the last years. Among these strategies, metabolomics has been considered one of the most promising approaches for the detection and/or quantification of primary and secondary stress-responsive metabolites in abiotic stresses. In plant science, many research groups have been using metabolomics to evaluate the success of genetic modifications by the analysis of chemical markers that can be altered in breeding programs. In addition, metabolomics is a powerful tool for the evaluation and selection of wild specimens with desirable traits that can be used in the development of improved new cultivars. Therefore, the aim of the present paper is to review the recent progress made in the field of metabolomics and plant breeding, especially concerning the adaptive responses of legume species to abiotic stresses as well as to point out the key primary and secondary metabolites involved in the adaptation and sensing mechanisms.