Shijin Ren

Estimating the toxicities of organic chemicals to bioluminescent bacteria and activated sludge

Toxicity assays based on bioluminescent bacteria have several advantages including a quick response and an easily measured signal. The Shk1 assay is a procedure for wastewater toxicity testing based on the bioluminescent bacterium Shk1. Using the Shk1 assay, the toxicity of 98 organic chemicals were measured and EC50 values were obtained. Quantitative structure-activity relationship (QSAR) models based on the logarithm of the octanol-water partition coefficient (log(Kow)) were developed for individual groups of organic chemicals with different functional groups. The correlation coefficients for different groups of organic compounds varied between 0.69 and 0.99. An overall QSAR model without discriminating the functional groups, which can be used for a quick estimate of the toxicities of organic chemicals, was also developed and model predictions were compared to experimental data. The model accuracy was found to be one order of magnitude from the observed values.

Kinetics of the toxicity of metals to luminescent bacteria

Abstract Bioluminescent bacteria are widely used in the toxicity assessment of aqueous solutions of heavy metals. They have also been used to screen wastewater treatment plant influent for toxicity resulting from the presence of heavy metals in the influent. However, some studies have indicated that certain strains of bioluminescent bacteria are not appropriate for influent toxicity screening for wastewater treatment plants. Shk1 is a bioluminescent bacterial strain genetically engineered for the specific purpose of monitoring influent wastewater to wastewater treatment plants for toxicity. Heavy metals at sufficient concentrations are toxic to Shk1 cells as they are to activated sludge microorganisms, and the exposure of Shk1 cells to heavy metal ions results in bioluminescence repression. The kinetics of the toxic effects of the heavy metals to Shk1 can be mathematically described in a manner similar to the non-competitive inhibition of enzymes. We determined the inhibition coefficients K i of seven heavy metals. We arranged K i and EC 50 values (a frequently used indicator of toxicity) of the seven heavy metals in increasing order and found that the sequences were in good agreement. We also show that under appropriate conditions, predictions of toxicity can be made based on EC 50 values that contain kinetic information similar to that contained in predictions using K i .

Identifying the mechanism of aquatic toxicity of selected compounds by hydrophobicity and electrophilicity descriptors

The most successful quantitative structure-activity relationships (QSARs) have been developed by separating toxicants by their mechanisms of action (MOAs). However, since the activity of a chemical compound on an organism is dependent upon several physical, chemical and biological factors, among which interactions may also exist, the MOA of a compound is not easily determined. In this study, the use of discriminant analysis and logistic regression in distinguishing between narcotic and reactive compounds was investigated. The discriminating variables included hydrophobicity (log(K(ow))) and electrophilicity descriptors (S(av)(N), E(HOMO), and E(LUMO)). Classification results showed that logistic regression gave a smaller total error rate compared to discriminant analysis. Since the value of the descriptors can be calculated, the classification methods can be used in predictive toxicology.

Use of multidimensional scaling in the selection of wastewater toxicity test battery components

In aquatic toxicity testing, no single test species is sensitive to all toxicants. Therefore, test batteries consisting of several individual assays are becoming more common. The organisms in a test battery should be representative of the entire system of interest. The results of the assays should be complementary to other components in the test battery to avoid redundancy. With the aid of multidimensional scaling (MDS), a multivariate statistical method, we examined the toxicity data of five bioassays (the continuous Shk1, Polytox, activated sludge respiration inhibition, Nitrosomonas, and Tetrahymena assays) that could serve as test battery components for the assessment of wastewater toxicity to activated sludge. MDS mapped the five assays into a two-dimensional space and showed that the Nitrosomonas assay should be included in test batteries plus one of the remaining four assays for assessing wastewater toxicity to activated sludge.

Using factorial experiments to study the toxicity of metal mixtures

Two-level factorial experiments were employed in this study for understanding and predicting the toxicity of binary and ternary metal mixtures. Toxicity of metal mixtures with concentrations between the respective EC10 and EC80 values was experimentally measured. Models were fit to the experimental data and the resultant models were of high quality as reflected by R2 (coefficient of determination). Interactions between mixture components were indicated by the existence of statistically significant interaction terms in the models. Toxicity predictions based on the models were compared with observed toxicity for binary and ternary metal mixtures. The models developed did not assume additivity between metals, were simple and interpretable, and gave satisfactory predictions of the toxicity of metal mixtures in aqueous solutions without requiring knowledge on synergism or antagonism.

Predicting three narcosis mechanisms of aquatic toxicity

In this study, the use of solute descriptors (the McGowans characteristic volume V(X), the excess molar refraction R, the dipolarity/dipolarizability pi(H), the effective or summation hydrogen-bond acidity summation operatoralpha(H) and the effective or summation hydrogen-bond basicity summation operatorbeta(H)) in classifying and predicting the non-polar, polar, and ester narcosis toxicity mechanisms for organic compounds was investigated. Discriminant analysis was performed and the significant discriminating variables were found to be R, pi(H), sigma alpha(H), and (sigma beta (H))(2), the latter of which was created to aid the mechanism classifications. Cross-validation of the non-linear discriminant functions showed a small total error rate of approximately 5% which was reduced to approximately 2% when seven compounds with uncertain a priori mechanism designations were removed. Compared with other methods for toxicity mechanism classification and prediction, the method developed in this study has advantages. It relies on the use of objective numerical values of molecular descriptors that can be calculated and does not require additional experimental work when used for new compounds. The descriptor values can also aid the interpretation of the toxicity mechanism classifications and predictions. Because of the possibility of making incorrect mechanism predictions using a single method, it was recommended that several methods be used together to make the most appropriate mechanism designations and to increase the confidence level associated with the mechanism designations.

An exploratory study of the use of multivariate techniques to determine mechanisms of toxic action

The most successful quantitative structure-activity relationships have been developed by separating compounds by their mechanisms of toxic action (MOAs). However, to correctly determine the MOA of a compound is often not easy. We investigated the usefulness of discriminant analysis and logistic regression in determining MOAs. The discriminating variables used were the logarithm of octanol-water partition coefficients (logKow) and the experimental toxicity data obtained from Pimephales promelas and Tetrahymena pyriformis assays. Small total error rates were obtained when separating nonpolar narcotic compounds from other compounds, however, relatively high total error rates were obtained when separating less reactive compounds (polar, ester, and amine narcotics) from more reactive compounds (electrophiles, proelectrophiles, and nucleophiles).

Phenol mechanism of toxic action classification and prediction: a decision tree approach

In this study, the use of decision tree in classifying and predicting the aquatic toxicity mechanisms of phenols was investigated. Four mechanisms including polar narcosis, respiratory uncoupling, pro-electrophilicity, and soft electrophilicity were involved. Using molecular descriptors as splitting variables, a three level decision tree with six terminal nodes was obtained. The tree model first separated polar narcosis/pro-electrophilicity from respiratory uncoupling/soft electrophilicity by E(lumo) in the first level of the tree. In subsequent levels of the tree, polar narcosis was separated from pro-electrophilicity by N(hdon) and E(homo), and respiratory uncoupling was separated from soft electrophilicity by E(lumo) and logK(ow). Validation of the decision tree approach indicated that the overall mechanism prediction accuracy was approximately 85%. The decision tree model had the advantage of easy interpretation.

A comparative study of an accelerated life-test model and a toxicokinetics-based model for the analysis of Porcellio scaber survival data

Statistical models have long been used for reliability analysis and risk assessment. In the present study, an accelerated life-test model was used to analyze a set of dose-time-response data obtained with the terrestrial isopod Porcellio scaber. Survival data were experimentally obtained by exposing P. scaber to diazinon (a nonpersistent insecticide) at six concentrations between 2 and 11.31 microg/g (toxicant/soil). Survival data are presented on a weekly basis. The accelerated life-test model assumed a log-normal distribution and constant variance across all diazinon concentrations. Model parameters were obtained by maximum likelihood estimation. The accelerated life-test model was compared to a toxicokinetics-based model reported in the literature. Survival predictions made by both models were compared with the observed data. Both the accelerated life-test model and the toxicokinetics-based model underestimated toxicity at a diazinon concentration of 8 microg/g. Overall, however, the accelerated life-test model outperformed the toxicokinetics-based model, with survival predictions closer to the observed data in most cases and a stronger correlation between predicted and observed survivals. However, as a statistical model, the accelerated life-test model did not reveal mechanistic information, and only statistical and distributional interpretations of its model parameters could be made.

Research Digest: Toxicity Screening of Influents Using Bioluminescent Reporter Technology

Influent toxicity can be a critical problem for publicly owned treatment works that use the activated sludge process as part of their treatment regime. In this project, the researchers developed two protocols for the screening of wastewater treatment plant influent for toxicity. Both protocols are based on a genetically engineered bioluminescent bacterium designated Shk1. Scientists at the University of Tennessee’s Center for Environmental Biotechnology constructed Shk1 from a host Pseudomonas strain isolated from an industrial wastewater treatment plant (WWTP). The first of the two Shk1-based assays that were developed utilizes a batch-wise sampling technique for analyzing grab-samples from industrial effluent, WWTP influent, and the various operations in an activated sludge WWTP (aeration basin, clarifier, etc.). The second method utilizes a continuous sampling technique and is designed for continuous monitoring of the wastewater treatment plant influent upstream of the activated sludge process. The researchers used the batch Shk1 assay to test the influent, activated sludge, and clarifier supernatant in a bench-scale wastewater treatment plant subjected to shock loads of metals (zinc, copper, nickel, and cadmium) for toxicity. They compared data on the repression of bioluminescence to activated sludge respirometry and conventional measures of plant performance (effluent ammonia and chemical oxygen demand, COD). In general, they found that the Shk1 assay indicated toxicity at levels similar to that indicated by activated sludge respirometry. However, no trend could be established between increasing Shk1 toxicity response and effluent quality. Effluent COD and NH3 data (when available) showed little or no significant effect or were highly varible. The researchers used the Shk1-based continuous toxicity screening method to generate toxicity data for a large suite of metals and synthetic organic compounds. They compared these results to literature data for toxicity as indicated by activated sludge respirometry and by the P. phosphoreum-based assay. In general, the Shk1 system gave EC50 values similar to those found in the literature for activated sludge respirometry for 102 organic compounds and to concentrations found to affect activated sludge for seven metals.The researchers adapted the continuous monitoring system for field application and installed it immediately downstream of the effluent from the primary clarifier in a municipal wastewater treatment system. They compared data from the Shk1-based system to plant performance data. During the time of the field study, no significant event occurred during which the operation of the plant was seriously impaired. Therefore, the researchers compared the Shk1 signal to the operations data provided by the plant personnel to determine if any correlation existed between the signal from Shk1 and minor fluctuations in the operations data. They found no simple quantitative relationship between the signal from the toxicity monitoring system and the plant performance data. They applied principal component and factor analysis to the Shk1data and 20 additional plant variables. The results of these analyses showed that 10 principal components were needed to account for 90% of the variability of the data and that the signal from Shk1 was therefore not sufficient to predict the system state in the absence of a major toxic event without knowledge of the values of other operating variables. In summary, these analyses indicated that the Shk1 signal would be a valuable addition to models to predict the future system state from the influent, operating, and effluent variables but it is not a sufficient variable by itself. This title belongs to WERF Research Report Series ISBN: 9781843396314 (eBook)