ARTICLE pubs.acs.org/est
Assessment of Chemical Mixtures and Groundwater Effects on Daphnia magna Transcriptomics Natalia Garcia-Reyero,† B. Lynn Escalon,‡ Po Ru Loh,§ Jennifer G. Laird,‡ Alan J. Kennedy,‡ Bonnie Berger,§ and Edward J. Perkins*,‡ †
Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Starkville, Mississippi, United States Environmental Laboratories, U.S. Army Corps of Engineers, Halls Ferry Road, Vicksburg, Mississippi, United States § Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States ‡
bS Supporting Information ABSTRACT: 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 Daphnia magna genes were calibrated to responses elicited in D. magna 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.
’ INTRODUCTION Munitions constituents (MCs) have been found at Department of Defense installations and training sites. These contaminants include nitroaromatic compounds such as 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), 2,4,6-trinitrotoluene (TNT), and 2,4- and 2,6-dinitrotoluene (2,4-DNT and 2,6-DNT). RDX and TNT have been detected in different compartments in the environment, achieving very high concentrations (i.e., 126 mg/kg TNT or 51 mg/kg RDX in soil) in or near training sites.1,2 Several studies have reported their toxicity to soil invertebrates.3 TNT degrades in soils and is metabolized in animals to its major metabolites 2-amino-4,6-dinitrotoluene (2-ADNT) and 4-amino-2,6-dinitrotoluene (4-ADNT). TNT can be further degraded to 1,3,5-trinitrobenzene (TNB) and 1,3-dinitrobenzene (DNB) through photolysis or oxidation.4,5 The compound 2,4-DNT and its isomer 2,6-DNT are used in many manufacturing processes such as production of dyes, munitions, and plasticizing agents. Their production and use in military training activities have also resulted in their release into the aquatic environment.6 Several studies have shown that TNT can cause oxidative stress,7,8 while r 2011 American Chemical Society
RDX can affect the nervous system causing seizures in vertebrates and invertebrates.9,10 In addition, 2,4-DNT affects oxygen transport and lipid metabolism in liver11 and 2,6-DNT has been related to gastrointestinal impacts, anemia, lethargy, and emaciation.9 One of the main challenges when dealing with pollutants in environmental compartments, especially in aquatic environments, is the fact that chemicals are likely to be present in mixtures of parent compounds and their degradation products and at variable concentrations. Munitions-related pollution is no exception to that fact.4 Mixtures of different compounds greatly complicate causal determination of which compound (or combination of compounds) might be responsible for inducing Special Issue: Ecogenomics: Environmental Received: April 12, 2011 Accepted: July 11, 2011 Revised: July 7, 2011 Published: July 11, 2011 42
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toxicity. Biomarkers, a measurement of a biochemical, physiological, or histological change or aberration in an organism that can be used to estimate either exposure to stressors or resultant effects, can be especially useful in assessing chemical presence and effects.12 Although biomarkers show some potential for use in risk assessment, concerns have been raised due to dose responsiveness and specificity in complex environments.13 The present study had two principal goals. First, we examined if gene expression responses in the freshwater cladoceran Daphnia magna behaved in an additive manner by relating individual chemical exposure responses to mixtures of the same chemicals. Second, we assessed the performance of a panel of potential gene biomarkers previously developed to detect exposure to MCs using D. magna.14 The specificity of the gene assay panel was tested across multiple concentrations of RDX, TNT, 2,4-DNT, 2,6-DNT, TNB, and DNB as well as its utility in predicting the chemical composition of MC mixtures. The gene panel was also examined for its ability to detect and discriminate MCs in contaminated groundwater from the Louisiana Army Ammunition Plant (LAAP). The LAAP is an inactive Army Depot facility east of Shreveport (LA, USA) that was listed as a Superfund site on the National Priorities List in 1989 due to explosive wastes in the groundwater.
Table 1. Summary of All Exposures: Exposure to Concentration Gradients of Six Individual Chemicals (a), Exposure to Eight Different Laboratory Mixtures (b), and Exposure to Field-Collected Groundwaters (c) (a) exposure to concentration gradients of six individual chemicals treatment (mg/L) TNT 2,4-DNT 2,6-DNT DNB TNB RDX control
NDa
ND
ND
ND
ND
ND
1% 1/10th LC50
ND
ND
ND
0.254
ND
0.137
10% 1/10th LC50
ND
0.208
0.279
1.054 0.209
35% 1/10th LC50 0.225 70% 1/10th LC50 0.498
0.837 1.586
0.659 1.152
3.206 0.259 14.4 9.51 0.386 29.8
1/10th LC50
2.486
1.797
1.664
13.68
2.9
0.496 31.1
(b) exposure to eight different laboratory mixtures compound (mg/L)
Mix 1 Mix 2 Mix 3 Mix 4 Mix 5 Mix 6 Mix 7 Mix 8
TNT 2,4-DNT
1.012 1.061 0.999 0.972 2.061 1.245 0 1.242 0 0.306
0.145 1.465 1.269 2.779 0.242 0.044
2,6-DNT
1.13
0
0
0
0
DNB
1.107 1.106 0
0
0.053
0.046 0.204 0.157
TNB
0.67
0.58
0.185
’ EXPERIMENTAL SECTION
RDX
0.334 0.563 0
0
0
Chemicals. Reagent-grade TNT, 2,4-DNT, 2,6-DNT, DNB, TNB, and RDX were used in this study. Details can be found in the Supporting Information (SI). Analytical Chemistry. All samples were analyzed by USEPA method 8330B and included standard quality control samples where required.15 Exposure media were examined for actual chemical concentrations. Field analyses were performed on site with a portable GC-MS (see SI). Collection of Contaminated Groundwater. Groundwater was collected from several established monitoring wells at the LAAP. A detailed description is available in the SI section. Briefly, wells were purged with at least three well volumes of groundwater prior to sample collection. Water samples of 4-L were collected at each well and split into 1-L aliquots. One aliquot was immediately analyzed with a field portable GC-MS. A second aliquot was analyzed in the laboratory using an HPLC/UV method as described above. The two remaining 1-L aliquots were reserved for D. magna exposures and replicate analysis. Daphnia magna Exposures. The study consisted of three different experiments (Table 1): (1) exposure to a concentration gradient of six individual MCs (TNT, 2,4-DNT, 2,6-DNT, DNB, TNB, RDX) to determine a dose response to six concentrations (100%, 70%, 30%, 10%, and 1% of 1/10th of the LC50 value, and a control) (Table 1a); (2) exposure to eight different laboratory mixtures of the previously mentioned MCs. Different combinations of MCs including four mixtures (Mixtures 5, 6, 7, and 8) representative of field collected groundwater from LAAP were created (Table 1b); and (3) exposure to MC-contaminated groundwater field-collected from 3 different wells (85, 108, and 141) at the LAAP (Table 1c). Acetone was used as solvent carrier. Exposures were done with moderately hard reconstituted water.16 An equivalent volume of clean acetone (e1 mL/L) was spiked into control medium to ensure acetone did not confound results. Exposures were conducted in quadruplicated 1-L glass beakers containing 750 mL of exposure water and 60 D. magna (6 8 days old) for 24 h at 24 ( 1 °C using a 16:8 photoperiod.
(c) exposure to field-collected groundwaters compounds (mg/L) LAAP 85 LAAP 108
a
0 0
0
0
0
0.242 0.144 0.159 12.118 5.229 0.093
LAAP 141
HMX
0.59
0.15
ND
RDX
3.23
3.08
2.68
TNB
6.08
1.08
1.30
DNB
ND
ND
0.30
TNT
1.35
1.60
3.77
4A-DNT
ND
ND
1.69
2,6-DNT
ND
0.23
1.35
ND: not detected.
The LAAP exposure was conducted using five replicates containing 115 D. magna each to increase statistical power. At the conclusion of each exposure, D. magna in each replicate and exposure were counted, weighed, and flash-frozen then stored at 80 °C until needed. RNA Extraction. Total RNA was isolated from D. magna samples using RNeasy kits (Qiagen, Valencia, CA, USA). RNA quality was assessed with an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) and quantified using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). Total RNA was stored at 80 °C until analyzed using real-time quantitative polymerase chain reaction (QPCR) or oligonucleotide microarrays. Real-Time PCR. Complementary DNA was synthesized using 800 ng of total RNA in a 20-μL reaction containing 250 ng of random primers and SuperScript III reverse transcriptase (Invitrogen, Carlsbad, CA) following the manufacturer’s protocol. The synthesized cDNA was diluted to 10 ng/μL. Quantitative real-time polymerase chain reaction (QPCR) assays were performed on an ABI Sequence Detector 7900 (Applied Biosystems, Foster City, CA). Each 20-μL reaction was run in duplicate and contained 6-μL of synthesized cDNA template along with 2 μL of each forward and reverse primer (5 μM/μL) and 500 nM 43
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Sybr Green PCR Master Mix (Applied Biosystems). Cycling parameters were 95 °C for 15 min, 40 cycles of 95 °C for 15 s, and 60 °C for 1 min. Primers were designed using Primer Express (Applied Biosystems) and synthesized by Operon Biotechnologies (Huntsville, AL, USA). The sequences for all the primers can be found in Table S1. Additive Modeling of Expression Changes. Noise in QPCR data was removed by discarding outliers (biological replicates for which the variation among three technical replicates exceeded the mean variation by two standard errors). For the remaining 97.6% of the biological replicates, the three technical replicates were then averaged to produce one ΔΔCt value per biological replicate. Biological replicates for each measurement combination (i.e., chemical, treatment concentration, and gene of interest) were further averaged and subtracted from the mean ΔΔCt of 18S control primers to obtain one mean ΔΔCt per measurement combination. To further ameliorate experimental variation in expression change, mean ΔΔCt values for the five treatment concentrations of each individual chemical were combined to produce a single piecewise-linear model of expression change for each chemical and gene as follows: first, each mean 2ΔΔCt was cropped to the interval [ 1.5, 1.5] to reduce the impact of outliers. Second, simple linear regression was performed on six points: the five experimental data points with x-values being the treatment concentrations and y-values being the cropped mean ΔΔCt’s, plus one additional point (0, 0) corresponding to no chemical present. The final piecewise-linear model for logfold expression change was obtained by taking this linear fit on the interval 0.01 e x e 1, continuously interpolating to (0, 0) for x e 0.01, and maintaining a constant value for x g 1 to prevent extrapolation. As a first approximation to modeling expression changes caused by mixtures of chemicals, a list of predictions (one per biomarker gene) was made for each mixture by summing individual ΔΔCt values corresponding to its components; each individual ΔΔCt was obtained by evaluating the piecewise-linear model described above at the known component chemical concentration. To evaluate the efficacy of this approach, a correlation coefficient between the actual and predicted ΔΔCt values (over the set of biomarker genes) was computed for each mix and tested for significance. To determine the feasibility of imputing mixture components from expression changes using the data available, correlation coefficients of actual and predicted ΔΔCt values from different mixes were also computed. Microarrays. Custom D. magna 15,000 probe arrays (GPL13761) were purchased from Agilent (Palo Alto, CA, USA). All mixture exposures (Mixtures 1 8), LAAP-85, LAAP-108, and the second highest dose (70% of 1/10th of the LC50) from each individual compound exposure were analyzed using microarrays. Sample LAAP-141 was not included due to insufficient RNA. Four to five replicates were run per each condition. One μg of total RNA was used for all hybridizations, and cDNA synthesis, cRNA labeling, amplification, and hybridization were performed following the manufacturer’s kits and protocols (Quick Amp Labeling kit; Agilent, Palo Alto, CA). The Agilent One-Color Microarray-Based Gene Expression Analysis v6.5 was used for microarray hybridizations according to manufacturer’s recommendations. An Agilent high-resolution C microarray scanner was used to scan microarray images. Data were resolved from microarray images using Agilent Feature Extraction software v10.7. Raw microarray data from this study have been deposited at the
Gene Expression Omnibus Web site (http://www.ncbi.nlm.nih. gov/geo/; GSE30163). Gene Expression Analysis. Microarray data were analyzed using the GeneSpring GX v4.0 software (Agilent) to normalize data by quantile normalization and conduct baseline transformation to the median of all samples. Individual chemical exposures were analyzed separately using the t test and filtered based on expression levels (p < 0.05; cutoff 1.5 fold change). Mixture exposures were analyzed using ANOVA followed by the pairwise Tukey’s HSD (Honestly Significant Difference) test (p < 0.05; mixtures and LAAP). A list of differentially expressed genes (DEGs) for each of the treatments can be found in Supporting Information File 1. Network and functional analysis of the DEGs were performed using Ingenuity Pathway Analysis (IPA, Redwood City, CA) and the Database for Annotation and Integrated Discovery (DAVID17,18). Due to the poor annotation of the array, only 30% of the genes (e-value 4 mg/L) were within recommended ranges.16 Although no significant mortality was observed in the LAAP-108 exposure, the mean pH for this water (6.68 ( 0.19) approached the low end of the above range. The specific conductivity values for LAAP-85 (70 ( 16 μS/cm) were substantially lower than conductivity values recorded for the other laboratory and field water exposures (>280 μS/cm). A 24-h experiment was conducted to determine the lower tolerance threshold for D. magna to conductivity, with concentrations ranging from 1 to 101 μS/cm. The 24-h NOEC and LC50 values were 11.2 and 1.1 μS/cm; thus, it is unlikely the conductivity of LAAP-85 (70 ( 16 μS/cm) alone induced the observed mortality, there is potential that organisms were stressed since this conductivity is at the lower threshold of standard reconstituted water conductivity levels (75 1200 μS/cm). Relatively low mortality (98%), an increasing percentage of D. magna at 30% of 1/10th of the LC50 (48 ( 13%), 70% of 1/10th LC50 (51 ( 35%), and the 1/10th of the LC50 (100 ( 0%) were bleached in coloration and entrained in the surface tension of the water; this was not observed in lower concentrations. Visibly affected daphnids were not used for gene expression analysis. Significantly reduced survival was observed in the field water LAAP-85 (78 ( 7%) relative to the control. Surviving D. magna in this exposure appeared unhealthy with a deteriorated carapace. Therefore, the exposure was rerun with diluting the groundwater 30%. In addition to low conductivity, the TNB concentration in LAAP-85 was higher (6.08 mg/L) relative to the other field sites ( LAAP-85 in order of decreasing plasticizer content. N-butylbenzene has been reported to immobilize 50% D. magna (EC50) after 24-h exposure at concentrations ranging from 0.52 to 0.69 mg/L.29,30 Although the field chromatograms do not allow for quantification of the compound, the relative concentration of N-butylbenzene in well LAAP-141 could be quite high (Russell, personal communication). Use of Gene Expression to Assess Mixture Composition. We have demonstrated that, to a limited extent, it is possible to predict gene expression changes in the higher eukaryote D. magna given the chemical mixture to which it has been exposed. Mixture effects prevented use of gene expression values to predict the composition of simple or complex mixtures. Examination of both PCR and microarray results suggests that while additivity is an adequate first approximation to model chemical interaction 48
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effects on gene expression, many interactions occur in a nonadditive manner where both common and different genes/functions are affected by closely related chemicals and mixtures. The current predictive model was limited not only by nonadditive effects, but by use of chemicals in a narrow chemical class with too few examples on which to model gene behavior. As a result, no one assay was specific for a single chemical at all concentrations. To apply this approach, either in microarray format or simple PCR panels, future work will need to examine a much greater range of different chemical classes with investigations into how mixtures cause additive and nonadditive effects in order to clarify the behavior and predictability of complex mixtures.
4,6-dinitrobenzofuroxan (DNBF): Implications for its toxic action. Acta Biochem. Pol. 2004, 51 (4), 1081–1086. (9) Gust, K. A.; Pirooznia, M.; Quinn, M. J., Jr.; Johnson, M. S.; Escalon, B. L.; Indest, K. J.; Guan, X.; Clarke, J.; Deng, Y.; Gong, P.; Perkins, E. J. Neurotoxicogenomic investigations to assess mechanisms of action of the munitions constituents RDX and 2,6-DNT in Northern Bobwhite (Colinus virginianus). Toxicol. Sci. 2009, 110, 168–180. (10) Garcia-Reyero, N.; Habib, T.; Pirooznia, M.; Gust, K. A.; Gong, P.; Warner, C.; Wilbanks, M.; Perkins, E. Conserved toxic responses across divergent phylogenetic lineages: A meta-analysis of the neurotoxic effects of RDX among multiple species using toxicogenomics. Ecotoxicology 2011, 20 (3), 580–94. (11) Wintz, H.; Yoo, J. L.; Loguinov, A.; Wu, Y. Y.; Steevens, J. A.; Holland, R. D.; Beger, R. D.; Perkins, E. J.; Hughes, O.; Vulpe, C. D. Gene expression profiles in fathead minnows exposed to 2,4-DNT: Correlation with toxicity in mammals. Toxicol. Sci. 2006, 94, 71–82. (12) Villeneuve, D. L.; Garcia-Reyero, N. Vision & strategy: Predictive ecotoxicology in the 21st century. Environ. Toxicol. Chem. 2011, 30, 1–8. (13) Forbes, V. E.; Palmqvist, A.; Bach, L. The use and misuse of biomarkers in ecotoxicology. Environ. Toxicol. Chem. 2006, 25, 272–280. (14) Garcia-Reyero, N.; Poynton, H. C.; Kennedy, A. J.; Guan, X.; Escalon, B. L.; Chang, B.; Varshavsky, J.; Loguinov, A. V.; Vulpe, C. D.; Perkins, E. J. Biomarker discovery and transcriptomic responses in Daphnia magna exposed to munitions constituents. Environ. Sci. Technol. 2009, 43, 4188–4193. (15) U.S. Environmental Protection Agency. Nitroaromatics, Nitroamines, and Nitrate Esters by High Performance Liquid Chromatography (HPLC) Method 8330B, Revision 2; Washington, DC, 2006. (16) Methods for Measuring the Acute Toxicity of Effluents and Receiving Waters to Freshwater and Marine Organisms; EPA/812/R/02/012; U.S. Environmental Protection Agency, Office of Water: Washington, DC, 2002. (17) Huang, D. W.; Sherman, B. T.; Tan, Q.; Collins, J. R.; Alvord, W. G.; Roayaei, J.; Stephens, R.; Baseler, M. W.; Lane, H. C.; Lempicki, R. A. The DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007, 8 (9), R183. (18) Huang, D. W.; Sherman, B. T.; Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat. Protoc. 2009, 4, 44–57. (19) Hosack, D. A.; Dennis, G., Jr.; Sherman, B. T.; Lane, H. C.; Lempicki, R. A. Identifying biological themes within lists of genes with EASE. Genome Biol. 2003, 4, R70. (20) Bentley, R. E., Dean, J. W.; Ells, S. J.; Hollister, T. A.; LeBlanc, G. A.; Sauter, S.; Sleight, B. H. Laboratory Evaluation of the Toxicity of Cyclotrimethylene Trinitramine (RDX) to Aquatic Organisms; U.S.NTIS AD-A061730; U.S.Army Medical Res. Develop. Command: Frederick, MD, 1977; p 86. (21) Bringmann, G.; Kuhn, R. The Effects of Water Pollutants on Daphnia magna. Wasser-Abwasser-Forsch 1977, 10 (5), 161–166. (22) Liu, D. H.; Bailey, H. C.; Pearson, J. G. Toxicity of a complex munitions wastewater to aquatic organisms. In Bishop, W. E., Cardwell, R. D., Heidolph, B. B., Eds.; Aquatic Toxicology and Hazard Assessment: Sixth Symposium; ASTM STP 802; Philadelphia, PA, 1983; pp 135 150. (23) Calvano, S. E.; Xiao, W.; Richards, D. R.; Felciano, R. M.; Baker, H. V.; Cho, R. J.; Chemn, R. O.; Brownstein, B. H.; Cobb, J. P.; Tschoeke, S. K.; Miller-Graziano, C.; Moldawer, L. L.; Mindrinos, M. N.; Davis, R. W.; Tompkins, R. G.; Lowry, S. F. A network-based analysis of systemic inflammation in humans. Nature 2005, 437, 1032–1037. (24) Martins, R. N.; Harper, C. G.; Stokes, G. B.; Masters, C. L. Increased cerebral glucose-6-phosphate dehydrogenase activity in Alzheimer’s disease may reflect oxidative stress. J. Neurochem. 1986, 46 (4), 1042–1045. (25) Rawat, A.; Gust, K. A.; Deng, Y.; Garcia-Reyero, N.; Quinn, M. J., Jr.; Johnson, M. S.; Indest, K. J.; Elasri, M. O.; Perkins, E. J. From raw materials to validated system: The construction of a genomic library
’ ASSOCIATED CONTENT
bS
Supporting Information. Additional text, tables and figures. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]; phone: +1 601 634 2782; fax: +1 601 634 4002.
’ ACKNOWLEDGMENT This work was funded by the U.S. Army Environmental Quality Research Program (including BAA 08-4379). Permission for publishing this information has been granted by the Chief of Engineers. We thank Amber Russell for chemical analysis of the LAAP samples. ’ REFERENCES (1) Jenkins, T. F.; Hewitt, A. D.; Grant, C. L.; Thiboutot, S.; Ampleman, G.; Walsh, M. E.; Ranney, T. A.; Ramsey, C. A.; Palazzo, A. J.; Pennington, J. C. Identity and distribution of residues of energetic compounds at army live-fire training ranges. Chemosphere 2006, 63 (8), 1280–1290. (2) Juhasz, A. L.; Naidu, R. Explosives: Fate, dynamics, and ecological impact in terrestrial and marine environments. Rev. Environ. Contam. Toxicol. 2007, 191, 163–215. (3) Kuperman, R. G.; Simini, M.; Siciliano, S.; Gong, P. Effects of Energetic Materials on Soil Organisms. In Ecotoxicology of Explosives; Sunahara, G. I., Hawari, J., Lotufo, G., Kuperman, R. G., Eds.; CRC Press: Boca Raton, FL, 2008. (4) Talmage, S. S.; Opresko, D. M.; Maxwell, C. J.; Welsh, C. J.; Cretella, F. M.; Reno, P. H.; Daniel, F. B. Nitroaromatic munition compounds: Environmental effects and screening values. Rev. Environ. Contam. Toxicol. 1999, 161, 1–156. (5) Richter-Torres, P.; Dorsey, A.; Hodes, C. S. Toxicological Profile for 2,4,6-Trinitrotoluene; Agency for Toxic Substances and Disease Registry: Atlanta, GA, 1995. (6) Simini, M.; Wentsel, R. S.; Checkai, R. T.; Phillips, C. T.; Chester, N. A.; Major, M. A.; Amos, J. C. Evaluation of soil toxicity at Joliet Army Ammunition Plant. Environ. Toxicol. Chem. 1995, 14, 623–630. (7) Cenas, N.; Nemeikaite-Ceniene, A.; Sergediene, E.; Nivinskas, H.; Anusevicius, Z.; Sarlauskas, J. Quantitative structure-activity relationships in enzymatic single-electron reduction of nitroaromatic explosives: Implications for their cytotoxicity. Biochem. Biophys. Acta 2001, 1528 (1), 31–38. (8) Nemeikaite-Ceniene, A.; Sarlauskas, J.; Miseviciene, L.; Anusevicius, Z.; Maroziene, A.; Cenas, N. Enzymatic redox reactions of the explosive 49
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