Environmental Census: Modeling Synthetic Biology Ecological Risk with Metagenomic Enzymatic Data and High-Performance...

Engineered microorganisms in biotechnology present biosafety and environmental management challenges. As the synthetic biology market develops and deploys new technologies, these engineered organisms may escape into unintended environments. Improved predictive computational tools are necessary to assess the potential establishment risk and environmental location of these escaped engineered microorganisms, assisting their design and management. Here, we present EnCen, a risk assessment Python software package that predicts the environmental range of engineered microorganisms through annotated functional one-hot-encoded similarity between the engineered microorganism and resident microorganisms of a given environment. EnCen utilizes publicly available composite metagenomes as representatives of microbial environments that occur along an agriculture-water cycle and can be customized for any additional target environment. This tool was deployed against case studies reported in the literature and to reassess commercially available bacterial biopesticides, highlighting both the successful recapture of previously reported dynamics and the identification of select commercial products that pose a wider establishment risk in multiple environments. When further utilizing EnCen to investigate the receiving environments comprising the central database, key enzyme classes are mapped as characteristics to select environments, prioritizing certain modifications likely leading to a greater risk (or effectiveness) of establishment. The results demonstrate that EnCen meaningfully summarizes publicly available metagenomic data, prioritizes environments to monitor for adverse effects, and analyzes potential impacts on microbial community composition and functioning. Overall, this study demonstrates a computational approach to managing engineered microorganisms, aiding in the safe deployment and benefit of industrial synthetic biology.