Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: Kinetics and metagenomics analysis

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Abstract
Overusing triclosan (TCS) endangered ecological safety and human health, and the pandemic of COVID-19 aggravates the accumulation of TCS in the aquatic environment. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. An aerobic bacterium, Bacillus sp. DL4 was isolated with the capability of TCS biodegradation. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables. All the variables were linear and the interaction of the three factors significantly affected TCS removal at the quadratic level (p < 0.001). Under the optimal conditions (35℃, initial pH 7.31, and 5% strain DL4), the TCS removal rate of 95.89 ± 0.68% was observed and found to be consistent with the predicted values from RSM and ANN models. In addition, statistical comparisons between the models indicated that the ANN model had a stronger predictive capability than the RSM model. Kinetic studies showed that TCS degradation was consistent with a pseudo-first-order kinetic model. Whole genome sequencing indicated that many functional genes were involved in and facilitated TCS degradation. Main metabolite products were detected and identified during the biodegradation process by LC-MS, and a possible degradation pathway was tentatively hypothesized. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

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