Predictive metabolomics of maritime pine resistance to (a)biotic stresses

Context and challenges

 In this region, maritime pine plantations stretch over some 800,000 hectares, representing a major stake for the forest-wood sector at the regional level, as well as nationally, representing 24% of the French timber harvest. Thanks to the research and development work carried out by the Groupement d’Intérêt Scientifique “Groupe Pin Maritime du Futur”, new improved varieties for wood production and adaptation are regularly introduced (28 million plants/year in a “normal” year, up to 44 million following reforestation after Cyclone Klaus). This cultivated forest also represents the most efficient carbon mobilization per unit of time on a national level.

Today, this forested area is faced with biotic and abiotic stresses and disturbances that are becoming more pronounced as a result of global changes. Most climate change scenarios predict an increase in average temperatures and drought severity. Many pathogens are therefore likely to increase their damage in response to these changes, notably through the increased susceptibility of trees. However, conifers are known to have constitutive and/or induced chemical defenses against many insects and pathogens. It is also likely, and in some cases recognized, that certain metabolic pathways may be shared during these various biotic stresses. Several studies also suggest that water stress may have consequences for the type of compounds expressed.

Goals

The MACARONI project proposes to study the individual and simultaneous effects of multiple hazards such as abiotic hydric stress (drought) and biotic stresses (fungi in the Fomes genus, root rot already established in the forested area, and the pinewood nematode, not yet present, but considered the most potentially dangerous pathogen for European pine forests since its accidental introduction in Portugal in 1999). The project will explore the possible links existing between metabolic profile and resistance to pathogens and water stress, by developing a predictive metabolomics approach that allows phenotypic traits of interest to be predicted on the basis of metabolome analysis using machine learning. This emerging theme could ultimately lead to innovative breeding methodologies by proposing an unprecedented diagnostic tool. 

Partenaires INRAE

Non-INRAE partners