Mechanism of time-dependent toxicity of quinolone antibiotics on luminescent bacteria Vibrio qinghaiensis sp.-Q67.

Four quinolone antibiotics (ciprofloxacin (CIP), enrofloxacin (ENR), sparfloxacin (SPA), gatifloxacin (GAT)) and their binary mixtures at environmentally relevant concentrations exhibited time-dependent hormesis on Vibrio qinghaiensis sp.-Q67 (Q67). The study aims to investigate the time-dependent toxicity of low-dose pollutants and the occurrence of hormesis. These indicators, total protein (TP), reactive oxygen species (ROS), superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA) and luminescence-related chemicals flavin mononucleotide (FMN), nicotinamide adenine dinucleotide (NADH), were measured to explore the mechanism of hormesis. The results showed a trend of increases in all indicators after 12 h of exposure, reaching maximal effects at 60 h and then decreasing as time progressed. At 36 h, 60 h and 84 h, the results showed a gradual increase followed by a decreasing trend in TP, FMN and NADH as the concentration in the group increased, whereas ROS, CAT, SOD and MDA showed the opposite trend. Notably, the degree of changes was related to the magnitude of hormesis. At low concentrations, the content of ROS and MDA decreased, the activity of CAT and SOD was lower, but the content of TP, FMN, NADH gradually increased, positively correlated with the promotion of Q67. At high concentrations, ROS and MDA content in Q67 increased, triggering the antioxidant defense mechanism (CAT and SOD activity increased), but TP, FMN, NADH content decreased, negatively correlated with the inhibited Q67. Therefore, our findings demonstrated two common patterns in these seven biochemical indicators on Q67. These findings have important practical implications for the ecological risk assessment of antibiotics in aquatic environment.

Molecular dynamics simulations on interactions of five antibiotics with luciferase of Vibrio Qinghaiensis sp.-Q67.

A large number of antibiotics have been used in the medical industry, agriculture, and animal husbandry industry in recent years. It may cause pollution to the aquatic environment and ultimately threaten to human health due to their prolonged exposure to the environment. We aim to study the toxicity mechanism of enrofloxacin (ENR), chlortetracycline hydrochloride (CTC), trimethoprim (TMP), chloramphenicol (CMP), and erythromycin (ETM) to luciferase of Vibrio Qinghaiensis sp.-Q67 (Q67) by using toxicity testing combined with molecular docking, molecular dynamics, and binding free energy analysis. The curve categories for ENR were different from the other four antibiotics, with ENR being J-type and the rest being S-type, and the toxicity of these five antibiotics (pEC50) followed the order of ENR (7.281) > ETM (6.814) > CMP (6.672) > CTC (6.400) > TMP (6.123), the order of toxicity value is consistent with the the magnitude of the binding free energy (ENR (-47.759 kcal/mol), ETM (-46.821 kcal/mol), CMP (-42.905 kcal/mol), CTC (-40.946 kcal/mol), TMP (-28.251 kcal/mol)). The van der Waals force provided the most important contribution to the binding free energy of the five antibiotics in the binding system with Q67 luciferase. Therefore, the dominant factor for the binding of antibiotics to luciferase was shape compensation. The face-to-face π-π stacking interaction between the diazohexane structure outside the active pocket region and the indoles structure of Phe194 and Phe250 in the molecular structure was the main reason for the highest toxicity value of antibiotic ENR. The hormesis effect of ENR has a competitive binding relationship with the α and ß subunits of luciferase. Homology modeling, molecular docking, molecular dynamics simulations and binding free energy calculations were used to derive the toxicity magnitude of different antibiotics against Q67, and insights at the molecular level. The conclusion of toxicological experiments verified the correctness of the simulation results. This study contributes to the understanding of toxicity mechanisms of five antibiotics and facilitates risk assessment of antibiotic contaminants in the aquatic environment.

Toxicity interactions of azole fungicide mixtures on Chlorella pyrenoidosa.

It is acknowledged that azole fungicides may release into the environment and pose potential toxic risks. The combined toxicity interactions of azole fungicide mixtures, however, are still not fully understood. The combined toxicities and its toxic interactions of 225 binary mixtures and 126 multi-component mixtures on Chlorella pyrenoidosa were performed in this study. The results demonstrated that the negative logarithm 50% effect concentration (pEC50 ) of 10 azole fungicides to Chlorella pyrenoidosa at 96 h ranged from 4.23 (triadimefon) to 7.22 (ketoconazole), while the pEC50 values of the 351 mixtures ranged from 3.91 to 7.44. The high toxicities were found for the mixtures containing epoxiconazole. According to the results of the model deviation ratio (MDR) calculated from the concentration addition (MDRCA ), 243 out of 351 (69.23%) mixtures presented additive effect at the 10% effect, while the 23.08% and 7.69% of mixtures presented synergistic and antagonistic effects, respectively. At the 30% effect, 47.29%, 29.34%, and 23.36% of mixtures presented additive effects, synergism, and antagonism, respectively. At the 50% effect, 44.16%, 34.76%, and 21.08% of mixtures presented additive effects, synergism, and antagonism, respectively. Thus, the toxicity interactions at low concentration (10% effect) were dominated by additive effect (69.23%), whereas 55.84% of mixtures induced synergism and antagonism at high concentration (50% effect). Climbazole and imazalil were the most frequency of components presented in the additive mixtures. Epoxiconazole was the key component induced the synergistic effects, while clotrimazole was the key component in the antagonistic mixtures.

Efficient Adsorption Capacity of MgFe-Layered Double Hydroxide Loaded on Pomelo Peel Biochar for Cd (II) from Aqueous Solutions: Adsorption Behaviour and Mechanism.

A novel pomelo peel biochar/MgFe-layered double hydroxide composite (PPBC/MgFe-LDH) was synthesised using a facile coprecipitation approach and applied to remove cadmium ions (Cd (II)). The adsorption isotherm demonstrated that the Cd (II) adsorption by the PPBC/MgFe-LDH composite fit the Langmuir model well, and the adsorption behaviour was a monolayer chemisorption. The maximum adsorption capacity of Cd (II) was determined to be 448.961 (±12.3) mg·g-1 from the Langmuir model, which was close to the actual experimental adsorption capacity 448.302 (±1.41) mg·g-1. The results also demonstrated that the chemical adsorption controlled the rate of reaction in the Cd (II) adsorption process of PPBC/MgFe-LDH. Piecewise fitting of the intra-particle diffusion model revealed multi-linearity during the adsorption process. Through associative characterization analysis, the adsorption mechanism of Cd (II) of PPBC/MgFe-LDH involved (i) hydroxide formation or carbonate precipitation; (ii) an isomorphic substitution of Fe (III) by Cd (II); (iii) surface complexation of Cd (II) by functional groups (-OH); and (iv) electrostatic attraction. The PPBC/MgFe-LDH composite demonstrated great potential for removing Cd (II) from wastewater, with the advantages of facile synthesis and excellent adsorption capacity.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R²) > 0.7, explained variance in leave-one-out prediction (Q²LOO) and in leave-many-out prediction (Q²LMO) > 0.6, variance explained in external prediction (Q²F1, Q²F2, and Q²F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Two-Stage Prediction on Effects of Mixtures Containing Phenolic Compounds and Heavy Metals on Vibrio qinghaiensis sp. Q67.

Two-stage prediction (TSP) model had been developed to predict toxicities of mixtures containing complex components, but its prediction power need to be further validated. Six phenolic compounds and six heavy metals were selected as mixture components. One mixture (M1) was built with equivalent-effect concentration ratio and four mixtures (M2-M5) were designed with fixed concentration ratio. In M1-M5, the toxicities were well predicted by TSP model, while CA overestimated and IA underestimated the toxicities. In M1-M5, compared with the actual mixture EC50 value, the prediction errors of TSP model (13.9%, 17.9%, 19.2%, and 17.3% and 15.8%, respectively) were significantly lower than those in the CA (higher than 30%) and IA models (20.9%, 33.0%, 20.6%, 21.8% and 12.5%, respectively). Thus, the TSP model performed better than the CA and IA model.

Key Component Analysis of the Time Toxicity Interaction of Five Antibiotics to Q67.

Antibiotics are considered as persistent emerging contaminants. The phenomenon of mixed exposure to the environment is a common occurrence causing serious harm to human health and the environment. Therefore, we employed enrofloxacin (ENR), chlortetracycline (CTC), methotrexate (TMP), chloramphenicol (CMP), and erythromycin (ETM) in this study. Nine treatments were designed using the uniform design concentration ratio (UDCR) method to systematically determine the toxicity of individual contaminants and their mixtures on Vibrio qinghaiensis sp.-Q67 through the time-dependent microplate toxicity assay. The combinatorial index (CI) method and the dose reduction index (DRI) were used to analyze the toxic interactions of the mixtures and the magnitude of the contribution of each component to the toxic interactions. The results showed that the toxicities of ENR, CTC, TMR, CMP, and ETM and their mixtures were time-dependent, with toxic effects being enhanced except when exposure time was prolonged. The types of toxic interactions in the ENR-CTC-TMR-CMP-ETM mixtures were found to be correlated with the proportion of each component's concentration, where the proportion of the components exerted the most significant influence. Through DRI extrapolation, it was determined that the primary components of the mixture exhibited a pronounced dependency on time. Specifically, at the 4 h mark, TMP emerged as the predominant component, gradually giving way to ENR as time advanced. Upon analyzing the frequency of mixture interactions under specified effects, the additive effect appeared most frequently (66.6%), while the antagonist effect appeared the least frequently (15.9%) among the nine rays.

Mechanism of the Synergistic Toxicity of Ampicillin and Cefazoline on Selenastrum capricornutum.

Ampicillin (AMP) and cefazolin (CZO) are commonly used ß-lactam antibiotics which are extensively globally produced. Additionally, AMP and CZO are known to have relatively high ecotoxicity. Notably, the mix of AMP and CZO creates a synergistic effect that is more harmful to the environment, and how exposure to AMP-CZO can induce synergism in algae remains virtually unknown. To yield comprehensive mechanistic insights into chemical toxicity, including dose-response relationships and variations in species sensitivity, the integration of multiple endpoints with de novo transcriptomics analyses were used in this study. We employed Selenastrum capricornutum to investigate its toxicological responses to AMP and CZO at various biological levels, with the aim of elucidating the underlying mechanisms. Our assessment of multiple endpoints revealed a significant growth inhibition in response to AMP at the relevant concentrations. This inhibition was associated with increased levels of reactive oxygen species (ROS) and perturbations in nitrogen metabolism, carbohydrate metabolism, and energy metabolism. Growth inhibition in the presence of CZO and the AMP-CZO combination was linked to reduced viability levels, elevated ROS production, decreased total soluble protein content, inhibited photosynthesis, and disruptions in the key signaling pathways related to starch and sucrose metabolism, ribosome function, amino acid biosynthesis, and the production of secondary metabolites. It was concluded from the physiological level that the synergistic effect of Chlorophyll a (Chla) and Superoxide dismutase (SOD) activity strengthened the growth inhibition of S. capricornutum in the AMP-CZO synergistic group. According to the results of transcriptomic analysis, the simultaneous down-regulation of LHCA4, LHCA1, LHCA5, and sodA destroyed the functions of the photosynthetic system and the antioxidant system, respectively. Such information is invaluable for environmental risk assessments. The results provided critical knowledge for a better understanding of the potential ecological impacts of these antibiotics on non-target organisms.

LC-QTOF/MS-based non-targeted metabolomics to explore the toxic effects of di(2-ethylhexyl) phthalate (DEHP) on Brassica chinensis L.

Di(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer known to pose health risks to humans upon exposure. Recognizing the toxic nature of DEHP, our study aimed to elucidate the response mechanisms in Brassica chinensis L. (Shanghai Qing) when subjected to varying concentrations of DEHP (2 mg kg-1, 20 mg kg-1, and 50 mg kg-1), particularly under tissue stress. The findings underscored the substantial impact of DEHP treatment on the growth of Brassica chinensis L., with increased DEHP concentration leading to a notable decrease in chlorophyll levels and alterations in the content of antioxidant enzyme activities, particularly superoxide dismutase (SOD) and peroxidase (POD). Moreover, elevated DEHP concentrations correlated with increased malondialdehyde (MDA) levels. Our analysis detected a total of 507 metabolites in Brassica chinensis L., with 331 in shoots and 176 in roots, following DEHP exposure. There was a significant difference in the number of metabolites in shoots and roots, with 79 and 64 identified, respectively (VIP > 1, p < 0.05). Metabolic pathway enrichment in Brassica chinensis L. shoots revealed significant perturbations in valine, leucine, and isoleucine biosynthesis and degradation, aminoacyl-tRNA, and glucosinolate biosynthesis. In the roots of Brassica chinensis L., varying DEHP levels exerted a substantial impact on the biosynthesis of zeatin, ubiquinone terpenoids, propane, piperidine, and pyridine alkaloids, as well as glutathione metabolic pathways. Notably, DEHP's influence was more pronounced in the roots than in the shoots, with higher DEHP concentrations affecting a greater number of metabolic pathways. This experimental study provides valuable insights into the molecular mechanisms underlying DEHP-induced stress in Brassica chinensis L., with potential implications for human health and food safety.

Toxic interactions at the physiological and biochemical levels of green algae under stress of mixtures of three azole fungicides.

Assessing the interactions between environmental pollutants and these mixtures is of paramount significance in understanding their negative effects on aquatic ecosystems. However, existing research often lacks comprehensive investigations into the physiological and biochemical mechanisms underlying these interactions. This study aimed to reveal the toxic mechanisms of cyproconazole (CYP), imazalil (IMA), and prochloraz (PRO) and corresponding these mixtures on Auxenochlorella pyrenoidosa by analyzing the interactions at physiological and biochemical levels. Higher concentrations of CYP, IMA, and PRO and these mixtures resulted in a reduction in chlorophyll (Chl) content and increased total protein (TP) suppression, and malondialdehyde (MDA) content exhibited a negative correlation with algal growth. The activity of catalase (CAT) and superoxide dismutase (SOD) decreased with increasing azole fungicides and their mixture concentrations, correlating positively with growth inhibition. Azole fungicides induced dose-dependent apoptosis in A. pyrenoidosa, with higher apoptosis rates indicative of greater pollutant toxicity. The results revealed concentration-dependent toxicity effects, with antagonistic interactions at low concentrations and synergistic effects at high concentrations within the CYP-IMA mixtures. These interactions were closely linked to the interactions observed in Chl-a, carotenoid (Car), CAT, and cellular apoptosis. The antagonistic effects of CYP-PRO mixtures on A. pyrenoidosa growth inhibition can be attributed to the antagonism observed in Chl-a, Chl-b, Car, TP, CAT, SOD, and cellular apoptosis. This study emphasized the importance of gaining a comprehensive understanding of the physiological and biochemical interactions within algal cells, which may help understand the potential mechanism of toxic interaction.

Occurrence and fate of N-nitrosamines in full-scale domestic wastewater treatment plants and their impact on receiving waters along the Lijiang River, China.

Domestic wastewaters contaminated with N-nitrosamines pose a significant threat to river ecosystems worldwide, particularly in urban areas with riparian cities. Despite widespread concern, the precise impact of these contaminants on receiving river waters remains uncertain. This study investigated eight N-nitrosamines in wastewater treatment plants (WWTPs) and their adjacent receiving river, the Lijiang River in Guilin City, Southwest China. By analyzing thirty wastewater samples from five full-scale WWTPs and twenty-three river water samples from Guilin, we quantified the mass loads of N-nitrosamines discharged into the surrounding watershed via domestic effluents. The results revealed that N-nitrosodimethylamine (10-60 ng/L), N-nitrosodiethylamine (3.4-22 ng/L), and N-nitrosopyrrolidine (not detected-4.5 ng/g) were predominant in influents, effluents, and sludge, respectively, with the overall removal efficiencies ranging from 17.7 to 65.6% during wastewater treatment. Cyclic activated sludge system and ultraviolet disinfection were effective in removing N-nitrosamines (rates of 59.6% and 24.3%), while chlorine dioxide disinfection promoted their formation. A total of 30.4 g/day of N-nitrosamine mass loads were observed in the Lijiang River water, with domestic effluents contributing about 31.3% (19.4 g/day), followed by livestock breeding wastewater (34.5%, 12.0 g/day), and unknown sources (24.7%, 7.5 g/day). These findings highlight the critical role of WWTPs in transporting N-nitrosamines to watersheds and emphasize the urgent need for further investigation into other potential sources of N-nitrosamine pollution within watersheds.

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures.

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

Development of validated quantitative structure-retention relationship models for retention indices of plant essential oils.

Quantitative structure-retention relationship (QSRR) models were developed for the retention indices of 505 frequently reported components of plant essential oils. Multiple linear regression was used to build QSRR models for the dimethyl silicone, dimethyl silicone with 5% phenyl groups, and polyethylene glycol stationary phases. We tried to improve the variable selection and modeling method based on prediction method for selecting the optimum descriptors from the molecular weight, 75 topological indices, and 170 atom-type E-state indices. The three-variable QSRR models perform high correlation coefficients of 0.937 for dimethyl silicone and 0.933 for dimethyl silicone with 5% phenyl groups stationary phase. Four variables were selected to developed QSRR model for the polyethylene glycol stationary phase. The leave-one-out and leave-many-out cross-validations, bootstrapping, and y-randomization test showed the three models are robust and have no chance correlation. The external validation with the test set showed the three models present high externally predictive power. The three models presented high-quality fit, internally, and externally predictive power. It is expected that the models can effectively predict retention indices of essential oils components without experimental value.

Modeling non-monotonic dose-response relationships: model evaluation and hormetic quantities exploration.

Non-monotonic (biphasic) dose-response relationships, known as hormetic relationships, have been observed across multiple experimental systems. Several models were proposed to describe non-monotonic relationships. However, few studies provided comprehensive description of hermetic quantities and their potential application. In this study, five biphasic models were used to fit five hormetic datasets from three different experimental systems of our lab. The bisection algorithm based on individual monotone functions was proposed to calculate arbitrary hormetic quantities instead of traditional methods (e.g., model reparameterization) which need complex mathematical manipulation. Results showed that all the five biphasic models could describe those datasets fairly well with coefficient of determination ( R(2) adj) greater than 0.95 and root mean square error (RMSE) smaller than 0.10. The best-fit model could be selected based on EC(R10), RMSE, and a supplemental criterion of Akaike information criterion (AIC). Hormetic quantities that trigger 10% stimulation at the left (EC(L10)) and right (EC(R10)) side of stimulatory peak were calculated and emphasized for their implication in hormesis exploration for the first time. Furthermore, the EC(L10), proposed as an alarm threshold for hormesis, was expected to be useful in risk assessment of environmental chemicals. This study lays a foundation in the quantitative description of the low dose hormetic effect and the investigation of hormesis in environmental risk assessment.

Degradation Characteristics and Remediation Ability of Contaminated Soils by Using ß-HCH Degrading Bacteria.

Three degradation strains that can utilize ß-Hexachlorocyclohexanes (ß-HCH) as the sole carbon source were isolated from the soil substrate of constructed wetland under long-term ß-HCH stress, and they were named A1, J1, and M1. Strains A1 and M1 were identified as Ochrobactrum sp. and strain J1 was identified as Microbacterium oxydans sp. by 16S rRNA gene sequence analysis. The optimum conditions for degradation with these three strains, A1, J1, and M1, were pH = 7, 30 °C, and 5% inoculum amount, and the degradation rates of 50 µg/L ß-HCH under these conditions were 58.33%, 51.96%, and 50.28%, respectively. Degradation characteristics experiments showed that root exudates could increase the degradation effects of A1 and M1 on ß-HCH by 6.95% and 5.82%, respectively. In addition, the degradation bacteria A1 and J1 mixed in a ratio of 1:1 had the highest degradation rate of ß-HCH, which was 69.57%. An experiment on simulated soil remediation showed that the compound bacteria AJ had the best effect on promoting the degradation of ß-HCH in soil within 98 d, and the degradation rate of ß-HCH in soil without root exudates was 60.22%, whereas it reached 75.02% in the presence of root exudates. The addition of degradation bacteria or degradation bacteria-root exudates during soil remediation led to dramatic changes in the community structure of the soil microorganisms, as well as a significant increase in the proportion of aerobic and Gram-negative bacterial groups. This study can enrich the resources of ß-HCH degrading strains and provided a theoretical basis for the on-site engineering treatment of ß-HCH contamination.

Effective adsorption of Pb(ii) from wastewater using MnO2 loaded MgFe-LD(H)O composites: adsorption behavior and mechanism.

Pb(ii) adsorption by MnO2/MgFe-layered double hydroxide (MnO2/MgFe-LDH) and MnO2/MgFe-layered metal oxide (MnO2/MgFe-LDO) materials was experimentally studied in lab-scale batches for remediation property and mechanism analysis. Based on our results, the optimum adsorption capacity for Pb(ii) was achieved at the calcination temperature of 400 °C for MnO2/MgFe-LDH. Langmuir and Freundlich adsorption isotherm models, pseudo-first-order and pseudo-second-order kinetics, Elovich model, and thermodynamic studies were used for exploring the Pb(ii) adsorption mechanism of the two composites. In contrast to MnO2/MgFe-LDH, MnO2/MgFe-LDO400 °C has a stronger adsorption capacity and the Freundlich adsorption isotherm model (R2 > 0.948), the pseudo-second-order kinetic model (R2 > 0.998), and the Elovich model (R2 > 0.950) provide great fits to the experimental data, indicating that the adsorption occurs predominantly via chemisorption. The thermodynamic model suggests that MnO2/MgFe-LDO400 °C is spontaneously heat-absorbing during the adsorption process. The maximum adsorption capacity of MnO2/MgFe-LDO400 °C for Pb(ii) was 531.86 mg g-1 at a dosage of 1.0 g L-1, pH of 5.0, and temperature of 25 °C. Through characterization analysis, the main mechanisms involved in the adsorption process were precipitation action, complexation with functional groups, electrostatic attraction, cation exchange and isomorphic replacement, and memory effect. Besides, MnO2/MgFe-LDO400 °C has excellent regeneration ability in five adsorption/desorption experiments. The above results highlight the powerful adsorption capacity of MnO2/MgFe-LDO400 °C and may inspire the development of new types of nanostructured adsorbents for wastewater remediation.

Time-Dependent Toxicity and Health Effects Mechanism of Cadmium to Three Green Algae.

As algae are extremely sensitive to heavy-metal ions and can be critical biological indicators in the heavy-metal toxicity analyses conducted by environmental health researchers, this paper explores the sensitivity to temporal toxicity of three species of green algae: Scenedesmus obliquus, Chlorella pyrenoidosa, and Selenastrum capricornutum. The method of time-dependent microplate toxicity analysis was used to systematically investigate the changes in the toxicities of the three green-algae species induced by different concentrations of cadmium (Cd). The chlorophyll a content, antioxidant enzyme activity, and malondialdehyde (MDA) content in the algae were analyzed to explore the mechanism of Cd toxicity after 96 h of exposure. The results showed that the toxic effects of Cd on the three algae species were time-dependent. By comparing the toxic effect of Cd, indicated by pEC50 (the negative logarithm of EC50), on the algae species at four durations of exposure (24, 48, 72, and 96 h), this study found that the indicator organisms had different sensitivities to Cd. The order of sensitivity was C. pyrenoidosa > S. obliquus > S. capricornutum. Cd exposure had significant effects on the chlorophyll a and MDA content and on the enzyme activity of superoxide dismutase (SOD) and catalase (CAT) in the algae species. The chlorophyll a content in the cells of the algae decreased with increasing Cd concentration. The enzyme activity of CAT and content of MDA increased with increasing Cd concentration, which indicated that Cd had an oxidative stress effect on the three algae species.

Toxic mechanism of three azole fungicides and their mixture to green alga Chlorella pyrenoidosa.

Currently, few studies have investigated the joint toxicity mechanism of azole fungicides at different exposure times and mixed at the relevant environmental concentrations. In this study, three common azole fungicides, namely, myclobutanil (MYC), propiconazole (PRO), and tebuconazole (TCZ), were used in studying the toxic mechanisms of a single substance and its ternary mixture exposed to ambient concentrations of Chlorella pyrenoidosa. Superoxide dismutase (SOD), catalase (CAT), chlorophyll a (Chla), and total protein (TP), were used as physiological indexes. Results showed that three azole fungicides and ternary mixture presented obvious time-dependent toxicities at high concentrations. MYC induced a hormetic effect on algal growth, whereas PRO and TCZ inhibit algal growth in the entire range of the tested concentrations. The toxicities of the three azole fungicides at 7 days followed the order PRO > TCZ > MYC. Three azole fungicides and their ternary mixture induced different levels of SOD and CAT activities in algae at high concentrations. The ternary mixture showed additive effects after 4 and 7 days exposure, but no effect was observed at actual environmental concentrations. The toxic mechanisms may be related to the continuous accumulation of reactive oxygen species, which not only affected protein structures and compositions but also damaged thylakoid membranes, hindered the synthesis of proteins and chlorophyll a, and eventually inhibited algal growth. These findings increase the understanding of the ecotoxicity of azole fungicides and use of azole fungicides in agricultural production.

Concentration Addition, Independent Action, and Quantitative Structure-Activity Relationships for Chemical Mixture Toxicities of the Disinfection By products of Haloacetic Acids on the Green Alga Raphidocelis subcapitata.

The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pKa ). The toxicities of synergetic mixtures can be interpreted with the total energy (ET ) and pKa of the mixtures. Dipole moment and ET are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.