Trace organic compounds (TOrCs) such as pharmaceuticals, personal care products, pesticides, has become emerging concerns in aquatic environment. These anthropogenic and xenobiotic compounds are frequently detected in surface water, ground water and even drinking water at the concentration ranging from few ng·L−1 to several μg·L−1. Biological treatment is a promising technology as microbial communities bear a high potential to eliminate TOrCs via enzymatic degradation processes. However, the complexity of microbial interactions based on numerous species in realistic environment impedes the exploration of TOrCs biotransformation mechanisms, making it remain elusive to date. The aim of this study is to uncover the TOrCs biotransformation “black box” by simplified “model communities”. Model communities with reduced complexity, either due to low species richness or dominance by one or a few populations, have been commonly used to overcome the challenge. A model community is defined as a closed assembly of microorganisms that represents or mimics the systemic behavior of ecological communities under controlled conditions.

Model communities were derived from natural environment including sediment core (Osterseen, Bavaria), technical sand (lab-scale biofiltration column, Garching), tap water (Garching) and soil (Garching). These inoculum were preadapted to a high concentration of mixed TOrCs for six months. Afterwards, we used dilution-to-extinction method to dilute both pre-adapted and non-adapted inoculum in 96 deep well plates for model communities. Communities which can grow up successfully were selected to identify taxonomy, which was achieved by 16S Nanopore sequencing. Model communities with different diversity were used to conduct biotransformation experiment to find links between TOrCs removal and diversity or taxa. In future work, metagenome sequencing and bioinformatics analysis will be applied to investigate the functional genes and enzymes in TOrCs biotransformation process as well as the microbial interactions in model communities.