Description
Organic micropollutants (OMPs) generated by human activities are threatening ecosystems and human health. Water treatment systems, that rely on microbial communities to remove pollutants, have a low efficiency for removing OMPs.Nitrifying communities were observed to degrade well some OMPs; yet the conditions that could favor them in treatment systems are still unknown. Indeed, identifying OMP degraders and their cellular mechanisms remains challenging.
Our project aims to predict in silico the conditions and the microorganisms favoring OMP degradation by simulating the microbial communities. We previously ran a 24h-batch experiment with 4 nitrifying communities in 3 conditions, from ammonia and oxygen to anoxic acetate. These communities harbor autotrophic bacteria feeding on ammonia, inorganic carbon and oxygen, building biomass consumed by a big share of diverse heterotrophs.
First, we correlate pathway activity from metatranscriptomics to OMP removal rates. Second, using metagenomics, we reconstruct the metabolic models of microorganisms enriched in correlated pathways and constrained by metatranscriptomics and rates of main metabolites. Third, we simulate a subset of these models and identify the conditions that maximize metabolic fluxes through the correlated pathways. These steps are repeated with various subsets of microorganisms.
Our approach enables us to explore the microbial ecology of complex microbial communities, guiding experimental design. It is the first step towards the continuous process optimization of water treatment plant by practitioners.
| Period | 28 Aug 2024 |
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| Held at | Water Technology & Processes |