Triclosan is a widely used antibacterial agent and disinfectant, and its overuse endangered ecological safety and human health. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. Bacillus sp. DL4, an aerobic bacterium with TCS biodegradability, was isolated from pharmaceutical wastewater samples. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables, and the optimal growth conditions of strain DL4 were obtained (35 °C, initial pH 7.31, and 5% v/v). After 48 h of cultivation under the optimal conditions, the removal efficiency of strain DL4 on TCS was 95.89 ± 0.68%, which was consistent with the predicted values from RSM and ANN models. In addition, higher R2 value and lower MSE and ADD values indicated that the ANN model had a stronger predictive capability than the RSM model. Whole genome sequencing results showed that many functional genes were annotated in metabolic pathways related to TCS degradation (e.g., amino acid metabolism, xenobiotics biodegradation and metabolism, carbohydrate metabolism). Main intermediate metabolites were identified during the biodegradation process by liquid chromatography-mass spectrometry (LC-MS), and a possible pathway was hypothesized based on the metabolites. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.
Keywords: Bacillus sp.; ANN; Genome analysis; RSM; Triclosan.
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.