Borchert, Erik , Hammerschmidt, Katrin , Hentschel, Ute and Deines, Peter (2021) Enhancing Microbial Pollutant Degradation by Integrating Eco-Evolutionary Principles with Environmental Biotechnology. Trends in Microbiology, 29 (10). pp. 908-918. DOI 10.1016/j.tim.2021.03.002.

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Abstract

Highlights:
We advocate to shift research efforts in environmental biotechnology from searching for desired traits of monocultures to that of microbial communities. As these traits will be hard to identify with classical genome mining approaches, we recommend using artificial community selection as a tool to identify and to select for novel and/or enhanced functions.

Bioremediation and biodegradation with artificially selected microbial communities harbors great potential to become a fast, cost-effective, eco-friendly, and socially acceptable way to remove pollutants without prior knowledge of the involved species and degradation pathways needed.

The use of highly integrated multispecies microbial communities instead of monocultures in biodegradation processes will result in more stable and more productive cultures.

The novelty of our proposed approach lies in the combination of eco-evolutionary principles with applied biotechnology. This will stimulate new advancements in environmental biotechnology, and will likely result in the discovery of novel metabolic degradation pathways.
Environmental accumulation of anthropogenic pollutants is a pressing global issue. The biodegradation of these pollutants by microbes is an emerging field but is hampered by inefficient degradation rates and a limited knowledge of potential enzymes and pathways. Here, we advocate the view that significant progress can be achieved by harnessing artificial community selection for a desired biological process, an approach that makes use of eco-evolutionary principles. The selected communities can either be directly used in bioremediation applications or further be analyzed and modified, for instance through a combination of systems biology, synthetic biology, and genetic engineering. This knowledge can then inform machine learning and enhance the discovery of novel biodegradation pathways.

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