Zep-4

Antibiotics, as emerging pollutants, are increasingly detected in various water bodies at low-doses. The hormesis effect observed at these low-doses presents a challenge for toxicity prediction. Accurately predicting the key parameters of the hormesis effect is crucial. However, current methods for predicting the key parameters of hormesis mixtures (EC_min and ZEP) are limited. This study introduces machine learning-based QSAR (quantitative structure-activity relationship) models designed to predict these parameters. We conducted a binary mixture toxicity experiment using 10 quinolone antibiotics, with Q67 as the indicator organism, to obtain experimental data. Molecular structure descriptors of the antibiotics were calculated, and the optimal descriptors were selected. Additionally, molecular docking was used to convert the relative 3D conformation of antibiotic-protein complexes into SMILES strings. QSAR models were developed using the GA-MLR (genetic algorithms multivariate linear regression) method and the Transformer-CNN (Transformer model and convolutional neural network) method with the mixture descriptors and SMILES strings as independent variables and the toxic effect values (EC₅₀, EC_min, and ZEP) as dependent variables. The models were validated internally and externally, demonstrating reliable prediction of the toxic effect values of EC₅₀, EC_min, and ZEP at three different exposure times (4 h, 12 h, and 24 h), model quality is better with longer exposure times. The QSAR model exhibited strong internal stability and external predictive ability. A comparison of the two modelling approaches showed that the Transformer-CNN method produced QSAR models with a coefficient of determination (R²) ranging from 0.8458 to 0.9853, and a root mean square error (RMSE) ranging from 0.0409 to 0.1496, indicating higher accuracy in predicting time-dependent toxicity. This study offers a novel approach to exploring and predicting the key parameters of the hormesis effect.

Growth of the toxic alga Prymnesium parvum is hormetically stimulated with environmentally relevant concentrations of glyphosate. The mechanisms of glyphosate hormesis in this species, however, are unknown. We evaluated the transcriptomic response of P. parvum to glyphosate at concentrations that stimulate maximum growth and where growth is not different from control values, the zero-equivalent point (ZEP). Maximum growth occurred at 0.1 mg l^-1 and the ZEP was 2 mg l^-1. At 0.1 mg l^-1, upregulated transcripts outnumbered downregulated transcripts by one order of magnitude. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that the upregulated transcriptome is primarily associated with metabolism and biosynthesis. Transcripts encoding heat shock proteins and co-chaperones were among the most strongly upregulated, and several others were associated with translation, Redox homeostasis, cell replication, and photosynthesis. Although most of the same transcripts were also upregulated at concentrations ≥ZEP, the proportion of downregulated transcripts greatly increased as glyphosate concentrations increased. At the ZEP, downregulated transcripts were associated with photosynthesis, cell replication, and anion transport, indicating that specific interference with these processes is responsible for the nullification of hormetic growth. Transcripts encoding the herbicidal target of glyphosate, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), were upregulated at concentrations ≥ZEP but not at 0.1 mg l^-1, indicating that disruption of EPSPS activity occurred at high concentrations and that nullification of hormetic growth involves the direct interaction of glyphosate with this enzyme. Results of this study may contribute to a better understanding of glyphosate hormesis and of anthropogenic factors that influence P. parvum biogeography and bloom formation.