Freshwater periphyton, playing a key role in ecosystem functions and services, are increasingly used in ecotoxicology to better understand the link between chemical exposure and ecosystem disturbance. To this end, one key challenge is to understand better how environmental conditions interact and modulate the community dynamics in situ. In this context, this project aims to gain understanding of the spatial heterogeneity of the natural periphyton metabolome assembly under various environmental conditions through the implementation of statistic and predictive meta-metabolomics approach. This attempts to characterize the assembling rules (stockastic vs deterministic) of metabolomes in periphyton and to identify the main environmental driving forces of meta-metabolome assemblages that contributes to its spatial heterogeneity. To do so, autochthonous periphytons were collected in 100 sites widely distributed in France encompassing various water physico-chemistry and chemical/ecological status (water agency data). The photosynthetic yield of the periphyton was characterized in situ in parallel of the measurement of the water physico-chemistry (nutrients, micropollutants) by the water agency. Physico-chemical results confirmed the heterogeneity between the sites. The global biomass parameters (e.g. proteins) will be soon characterized while untargeted metametabolomics based on LC-HRMS is ongoing. Those metametabolomics data will provide a comprehensive picture of the chemical landscape of the periphyton, potentially highlighting the existence of a core meta-metabolome. Then meta-community ecology metrics (α/β diversity) will be used to define assembling mechanisms. In parallel, statistics and machine-learning will be implemented in order to identify features or groups of features that can predict specific environmental conditions and water quality status. In addition, by linking metabolites to biomass or photosynthesis, COMBO would support the discovery of predictive effect biomarkers at the community level that may have impair ecosystem functions. Overall, this knowledge will support the translation of metametabolome responses of periphyton to chemical stress under controlled conditions into real applications for biomonitoring purposes.< Leer menos