Metabolites inducers of cross-tolerance to biotic stress
Ensuring food security and accessibility represents a global challenge since world population grows exponentially while crop quality and productivity is restricted by limitations on arable areas and increasing incidence of stress conditions due to climate change. Plant pathogens are major threats for optimal crop yield and preservation of the harvest. Although plant immune responses are well characterised in isolation, pathogen infections frequently occur in combination with other stress conditions. Importantly, the outcome of plant-pathogen interactions under combinatorial stress is unpredictable as the operating molecular mechanisms differ from those triggered under each individual stressor. Certain abiotic stressors ultimately confer resilience to subsequent pathogen infection, i.e. cross-tolerance, however the network of molecular events involved remains undeciphered. Recent studies suggested that metabolites could retain stress memory and modulate combinatorial stress responses, but their potential role in cross-tolerance has been barely investigated. Hence, this MSCA is aimed at identifying changing metabolites after abiotic stress periods that enhance plant tolerance to pathogens. To this end, comprehensive conditions and time-courses for sequential stress will be defined to cultivate Arabidopsis thaliana plants under frequent abiotic stressors, namely drought and temperature shifts, followed by pathogen challenge. Subsequently, metabolome changes will be profiled and systemically analysed to retrieve central features. The biological relevance of candidates will be assessed in planta. Finally, artificial intelligence algorithms will be applied to select species of agronomic interest that metabolically respond to sequential stress as Arabidopsis does and thus initiate translation of cross-tolerance strategies into crops. Consequently, this MSCA will contribute to the development of new strategies to ensure food security under current climate change conditions.