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

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.