Zebrafish models for human acute organophosphorus poisoning

Terrorist use of organophosphorus-based nerve agents and toxic industrial chemicals against civilian populations constitutes a real threat, as demonstrated by the terrorist attacks in Japan in the 1990 s or, even more recently, in the Syrian civil war. Thus, development of more effective countermeasures against acute organophosphorus poisoning is urgently needed. Here, we have generated and validated zebrafish models for mild, moderate and severe acute organophosphorus poisoning by exposing zebrafish larvae to different concentrations of the prototypic organophosphorus compound chlorpyrifos-oxon. Our results show that zebrafish models mimic most of the pathophysiological mechanisms behind this toxidrome in humans, including acetylcholinesterase inhibition, N-methyl-D-aspartate receptor activation, and calcium dysregulation as well as inflammatory and immune responses. The suitability of the zebrafish larvae to in vivo high-throughput screenings of small molecule libraries makes these models a valuable tool for identifying new drugs for multifunctional drug therapy against acute organophosphorus poisoning

Targeted Gene Expression in Zebrafish Exposed to Chlorpyrifos-Oxon Confirms Phenotype-Specific Mechanisms Leading to Adverse Outcomes

Zebrafish models for mild, moderate, and severe acute organophosphorus poisoning were previously developed by exposing zebrafish larvae to chlopyrifos-oxon. The phenotype of these models was characterized at several levels of biological organization. Oxidative stress and mitochondrial dysfunction were found to be involved in the development of the more severe phenotype. Here we used targeted gene expression to understand the dose-responsiveness of those two pathways and their involvement on generating the different zebrafish models. As the severe phenotype is irreversible after only 3 h of exposure, we also analyzed the response of the oxidative stress pathway at 3 and 24 h. Some of the genes related to oxidative stress were already differentially expressed at 3 h. There was an increase in differentially expressed genes related to both oxidative stress and mitochondrial function from the more mild to the more severe phenotype, suggesting the involvement of these mechanisms in increasing phenotype severity. Temporal data suggest that peroxynitrite leading to lipid peroxidation might be involved in phenotype transition and irreversibility. © 2016, The Author(s).This work was supported in part by the US Army ERDC-IRO (W912HZ-13-BAA-01; D.R., N.G.R.) and Environmental Quality Research Program (N.G.R.), the NATO SfP project MD.SFPP 984777 (D.R., N.G.R.), and the National Science Foundation EPSCoR Grant EPS-0903787 (N.G.R.).Peer reviewe

Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic processes including uptake, transformation, and assimilation. The availability of high-dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), in the earthworm Eisenia fetida. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wilcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios

Assessment of Chemical Mixtures and Groundwater Effects on <i>Daphnia magna</i> Transcriptomics

Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, fifteen polymerase chain reaction assays targeting <i>Daphnia magna</i> genes were calibrated to responses elicited in <i>D. magna</i> exposed for 24 h to five different doses each of the munitions constituents 2,4,6-trinitrotoluene, 2,4-dinitrotoluene, 2,6-dinitrotoluene, trinitrobenzene, dinitrobenzene, or 1,3,5-trinitro-1,3,5-triazacyclohexane. A piecewise-linear model for log-fold expression changes in gene assays was used to predict response to munitions mixtures and contaminated groundwater under the assumption that chemical effects were additive. The correlations of model predictions with actual expression changes ranged from 0.12 to 0.78 with an average of 0.5. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. Whereas mixtures and groundwater exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the nonadditivity of chemical effects in these mixtures. These results suggest that, while gene behavior in response to chemical exposure can be partially predicted based on chemical exposure, estimation of the composition of mixtures from chemical responses is difficult without further understanding of gene behavior in mixtures. Future work will need to examine additive and nonadditive mixture effects using a much greater range of different chemical classes in order to clarify the behavior and predictability of complex mixtures

The Good, the Bad, and the Toxic: Approaching Hormesis in <i>Daphnia magna</i> Exposed to an Energetic Compound

A hormetic response is characterized by an opposite effect in small and large doses of chemical exposure, often resulting in seemingly beneficial effects at low doses. Here, we examined the potential mechanisms underlying the hormetic response of <i>Daphnia magna</i> to the energetic trinitrotoluene (TNT). <i>Daphnia magna</i> were exposed to TNT for 21 days, and a significant increase in adult length and number of neonates was identified at low concentrations (0.002–0.22 mg/L TNT), while toxic effects were identified at high concentrations (0.97 mg/L TNT and above). Microarray analysis of <i>D. magna</i> exposed to 0.004, 0.12, and 1.85 mg/L TNT identified effects on lipid metabolism as a potential mechanism underlying hormetic effects. Lipidomic analysis of exposed <i>D. magna</i> supported the hypothesis that TNT exposure affected lipid and fatty acid metabolism, showing that hormetic effects could be related to changes in polyunsaturated fatty acids known to be involved in <i>Daphnia</i> growth and reproduction. Our results show that <i>Daphnia</i> exposed to low levels of TNT presented hormetic growth and reproduction enhancement, while higher TNT concentrations had an opposite effect. Our results also show how a systems approach can help elucidate potential mechanisms of action and adverse outcomes

Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic processes including uptake, transformation, and assimilation. The availability of high-dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), in the earthworm <i>Eisenia fetida</i>. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wilcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios

Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic processes including uptake, transformation, and assimilation. The availability of high-dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), in the earthworm <i>Eisenia fetida</i>. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wilcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios