ADAAPT

The agroecological transitions of current agri-food systems are taking place in a context of increasing pressure due to climate change. To improve the sustainability of our agricultural systems in this context, it is essential to understand how individuals can adapt to environmental changes.

Genetic selection represents only a part of phenotypic variation, and current models assume that the ranking of individuals is not affected by environmental conditions. However, it has been demonstrated in various species that environmental disturbances can influence the epigenome and phenotypic traits of organisms.

It is also crucial to study the role of the microbiome in explaining phenotypic variability. Therefore, the holobiont, meaning the host organism (animal or plant) and the various microorganisms (such as bacteria, viruses, protozoa, and fungi) that live inside and on the host organism, must be considered.

Facing these challenges, which concern both cultivated plant species and farm animals, new phenotyping tools need to be developed to monitor the adequacy of the individual (plant or animal) and its environment (exposome). Epigenetic modifications, such as DNA methylation, are molecular markers that impact the phenotypic diversity of organisms.

The scientific challenge of this project is to use machine learning technologies to leverage epigenetic data in order to optimize the use of animal and plant genetic resources, including their microbiota (holobiont), in an agroecological perspective. This will also allow the modeling of the phenotypic trajectories of livestock or cultivated plants in agroecological pathways.

Its specific objectives include:

• Characterizing the epigenetic profiles of genetic resources conserved in France in various exposomes related to the agroecological transition.
• The integration of genetic, epigenetic, and microbiome data for agricultural modeling using numerical methods.
• The study of epigenetic variability and its implications for adaptive capacities in response to significant environmental changes such as heat stress.
• Studying the capacity of an epigenetic clock to accurately predict phenotypes and serve as a good biomarker of past exposures. The methylation data generated during the project will contribute to the development of a high-throughput epigenotyping tool that could be used in the medium term by breeders to integrate genetic and epigenetic information, thus improving the accuracy of phenotypic predictions.

The ADAAPT project aims to tackle these challenges by combining expertise on livestock, crops, their microbiota, physiology, genetics, epigenetics, epigenomics, and digital technologies for agriculture from 13 different INRAE laboratories, two teams from the Institut Agro, one team from the University of Orléans, and one team from Inria. Such a consortium brings together biologists with solid expertise in various fields (geneticists, physiologists, microbiologists) and data scientists (statisticians and bioinformaticians).