Abstract-The effects of independently altering chloride, calcium, and dissolved organic carbon (DOC) on the toxicity of silver (presented as AgNO 3 ) were compared between rainbow trout (Oncorhynchus mykiss) and fathead minnows (Pimephales promelas). The 96-h median lethal concentration (LC50) toxicity tests for both species were performed under the same conditions, within the same containers. In addition, the effect of altering [Cl Ϫ ] on silver-induced perturbations to body Na ϩ influx and gill silver load was studied. Toxicity tests were conducted in synthetic soft water (50 M Na, and [DOC] were adjusted by the addition of NaCl, CaNO 3 , or humic acid, respectively. On the basis of total silver, increasing [Cl Ϫ ] over a range of 50 M to 1,500 M resulted in a 4.3-fold increase in the 96-h LC50 values (decrease in toxicity) for rainbow trout, but did not significantly affect the 96-h LC50 values for fathead minnows. Increasing water [Ca 2ϩ ] (from 50 to 2,000 M) had only a small influence (1.5-fold increase) on the 96-h LC50 values in either species. However, increasing DOC levels (from 0.3 to 5.8 mg DOC/L) significantly increased the 96-h LC50 values (2.7-to 4.1-fold increases) in both species. If the 96-h LC50 values are calculated on the basis of ionic silver, Ag ϩ (utilizing the aquatic geochemical computer program MINEQLϩ), then, in the case of rainbow trout, toxicity correlates to Ag ϩ . However, this correlation does not exist for fathead minnows. Increasing [Cl Ϫ ] did not affect the degree of perturbation of Na ϩ influx during acute exposure (first 4 h) to 8 g Ag/L in either species, nor did it affect the whole-body silver uptake rates, but it did reduce the gill silver load. These results demonstrate that differences exist in the way in which water chemistry ameliorates silver toxicity between rainbow trout and fathead minnows.
The branchial uptake mechanism of the nonessential heavy metal silver from very dilute media by the gills of freshwater rainbow trout was investigated. At concentrations >36 nM AgNO(3), silver rapidly entered the gills, reaching a peak at 1 h, after which time there was a steady decline in gill silver concentration and a resulting increase in body silver accumulation. Below 36 nM AgNO(3), there was only a very gradual increase in gill and body silver concentration over the 48-h exposure period. Increasing water sodium concentration ([Na(+)]; 0.05 to 21 mM) significantly reduced silver uptake, although, in contrast, increasing ambient [Ca(2+)] or [K(+)] up to 10 mM did not reduce silver uptake. Kinetic analysis of silver uptake at varying [Na(+)] showed a significant decrease in maximal silver transport capacity (173 +/- 34 pmol. g(-1). h(-1) at 0.1 mM [Na(+)] compared with 35 +/- 9 at 13 mM [Na(+)]) and only a slight decrease in the affinity for silver transport (K(m); 55 +/- 27 nM at 0.1 mM [Na(+)] compared with 91 +/- 47 nM at 13 mM [Na(+)]). Phenamil (a specific blocker of Na(+) channels), at a concentration of 100 microM, blocked Na(+) uptake by 78% of control values (58% after washout), and bafilomycin A(1) (a specific blocker of V-type ATPase), at a concentration of 2 microM, inhibited Na(+) uptake by 57% of control values, demonstrating the presence of a proton-coupled Na(+) channel in the apical membrane of the gills. Phenamil (after washout) and bafilomycin A(1) also blocked silver uptake by 62 and 79% of control values, respectively, indicating that Ag(+) is able to enter the apical membrane via the proton-coupled Na(+) channel.
Abstract-The influence of different water Cl Ϫ (50-600 M), Ca 2ϩ (50-1,500 M), Na ϩ (50-1,500 M), or dissolved organic carbon (DOC, 0.31-5 mg/L) levels on silver-induced physiological and biochemical perturbations of rainbow trout were investigated. Fish were acclimated to soft water (50 M; Cl Ϫ , Ca 2ϩ , and Na ϩ ), then exposed to 3.7 g/L Ag (as AgNO 3 ) for 6 h, which resulted in a reduction in Na ϩ influx from the water, an inhibition of gill sodium-and potassium-activated adenosine triphosphatase (Na ϩ / K ϩ -ATPase) activity, and an accumulation of silver on the gills. Increasing the water Cl Ϫ or DOC levels ameliorated the silver toxicity. However, increasing water Ca 2ϩ or Na ϩ concentration did not reduce the silver-induced physiological and biochemical perturbations. The free silver ion (Ag ϩ ) concentrations (calculated from MINEQLϩ, a geochemical speciation computer program) showed a negative correlation with the Na ϩ influx rates and gill Na ϩ /K ϩ -ATPase activity. However, gill silver levels did not correlate to Ag ϩ concentrations and no correlation was found between gill silver levels and either Na ϩ influx rates or gill Na ϩ /K ϩ -ATPase activity. These results support the notion that the [Ag ϩ ] concentration is of major importance when assessing silver toxicity in fish, and that this should be taken into account in regulatory strategies for silver in the natural environment.
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23-0.73 and 0.34-1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.