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Ecotoxicology and Human Environmental Health

Thiol Reactivity Analyses to Predict Mammalian Cell Cytotoxicity of Water Samples Shengkun Dong, Martin Anthony Page, Elizabeth D. Wagner, and Michael J Plewa Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01675 • Publication Date (Web): 02 Jul 2018 Downloaded from http://pubs.acs.org on July 7, 2018

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Environmental Science & Technology

Thiol Reactivity Analyses to Predict Mammalian Cell Cytotoxicity of Water Samples Shengkun Dong,†,‡ Martin A. Page,§ Elizabeth D. Wagner,⁑,‡ Michael J. Plewa * ⁑,‡ † Department of Civil and Environmental Engineering, ⁑ Department of Crop Sciences, ‡ Safe Global Water Institute, University of Illinois at Urbana-Champaign, 1101 West Peabody Dr., Urbana, Illinois 61801, United States of America § US Army Engineer Research and Development Center, 2902 Newmark Dr., Champaign, Illinois 61822, United States of America

*

Author to whom correspondence should be addressed: Michael J. Plewa; e-mail:

[email protected]

1 ACS Paragon Plus Environment


TOC Art


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ABSTRACT An in chemico high throughput assay based on N-acetylcysteine was developed and used in conjunction with previous and new mammalian cell cytotoxicity data. Our objective was to derive an empirical equation with confidence levels for mammalian cell cytotoxicity prediction. Modeling data included 16 unique sources of waters and wastewaters of distinct water qualities to encompass a wide range of real environmental samples. This approach provides a quick screen to identify those water and wastewaters that could be prioritized for in depth analytical biological analyses and toxicity. The resulting model can serve as a preliminary convenient tool to screen for potential mammalian cell cytotoxicity in organic extracts of a wide variety of water samples.



INTRODUCTION The generation of high quality safe and palatable tap water is a crucial public health service; the reduction of waterborne disease by the disinfection of drinking water was perhaps the greatest public health achievement of the twentieth century.1 With approximately 30% of the global population living under water stress, reuse of treated wastewater as an alternate water source is increasingly necessary.2 Wastewater reuse for up to potable applications has been effective in some cases for sustaining freshwater resources in water stressed environments.3 In addition to conventional wastewater treatment that renders water safe for environmental discharge, advanced chemical and physical processes are applied to control pathogens, chemicals, and other trace contaminants. Some of these processes include strong oxidants, and an unintended consequence may be the reaction of disinfectants with trace natural and anthropogenic organic matter, as well as bromide/iodide to generate known and unknown disinfection by-products (DBPs).4-14 While typically formed at relatively low concentrations, DBPs are toxic agents and are associated with adverse biological and public health outcomes.15, 16 A review of the literature demonstrated that many DBPs are cytotoxic, neurotoxic, mutagenic, genotoxic, carcinogenic and teratogenic.5, 15, 17-23 Epidemiological research demonstrated low but significant associations between disinfected drinking water and adverse health effects. These encompass weak association with adverse pregnancy outcomes,24-30 as well as cancer of the bladder,31-34 colon,35, 36

and rectum.37 Of the over 700 DBPs identified, only 11 DBPs are regulated in the United

States.38 Both disinfected source waters and reclaimed wastewaters contain a wide diversity of DBPs; these complex mixtures are difficult to characterize chemically. As such, it is critical that potential toxicity arising from formation of DBPs and other by-products by direct portable reuse processes be studied.


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Quantitative, comparative biological assays offer a highly sensitive approach to characterize and identify levels of toxicity of disinfected water and wastewater samples. Importantly, this approach captures composite effects, including those from known and unknown contaminants. We previously developed, calibrated and employed several analytical biological assays for use in water research and established the largest mammalian cell cytotoxicity and genotoxicity database for individual DBPs based on Chinese hamster ovary (CHO) cells.18, 39 We employed these assays to study the quantitative toxicity of source waters, drinking waters,12, 40, 41 wastewaters,13, 14, 42-45

and recreational pool waters.46, 47

Analytical in vitro mammalian cell assays are quantitative and provide precise, high quality data as compared to qualitative kit assays, however, they are laborious, time consuming and require specialized facilities and training. With a series of water and wastewater samples we developed and calibrated a cysteine thiol (-SH) high-throughput assay for the initial screening of complex mixtures founded on the work of Ellman.48 The cysteine thiol in glutathione is the major reductant against reactive toxicants, which can induce adverse biological responses if the thiol pool is overwhelmed or depleted.49, 50 A cysteine thiol can be oxidized to a disulfide, or can follow stepwise reactions to form sulfenic, sulfinic, and eventually sulfonic acids. In living systems, the predominant soft nucleophile is the thiol moiety found in the amino acid L-cysteine. Glutathione is the primary intracellular tripeptide (cysteine, glycine, glutamate) that provides a thiol buffer to remediate soft electrophile toxicity.50 We previously employed N-acetyl-Lcysteine (NAC) to measure activated primary alkyl halide functional groups present in individual DBPs. Pals et al., found that soft electrophile DBPs reacting with NAC could be an important



predictor of additive toxicity.51, 52 Additionally, thiol reactivity and aquatic toxicity were correlated among a series of α-halo-carbonyl containing compounds in the order of I > Br > Cl > F.53, 54 A similar relationship amongst the halogen leaving group and mammalian cell and human cell cytotoxicity, genotoxicity and toxicogenomic endpoints were reported.8, 11, 18, 55

We hypothesized that thiol reactivity may be a dominant predictor of mammalian toxicity. To test this hypothesis, the objectives of this study were, (1) to develop, calibrate and standardize an in chemico NAC-based thiol reactivity assay, (2) to analyze a series of source, drinking and wastewaters using this assay, (3) to compare thiol reactivity of these water samples with their in vitro chronic CHO cell cytotoxicity, and (4) to devise a predictive model for mammalian cell cytotoxicity using the NAC thiol reactivity assay.

MATERIALS AND METHODS Water Samples. A description of the water samples used in this study is presented in Table 1.

Chemical and Biological Reagents. Ellman’s reagent, 5, 5’-dithiobis (2-nitrobenzoic acid), (DTNB) was purchased from Sigma Aldrich (St. Louis, MO). DTNB was dissolved in a 100 mM potassium phosphate buffer with 0.1 mM EDTA, pH 8 for a final concentration of 1 mM DTNB. The solution was filter-sterilized and the finished working solution was stored for no more than 4 months at 4° C in the dark. NAC was purchased from Sigma Aldrich (St. Louis, MO). A 100 mM stock solution of NAC was prepared in 200 mM Tris (Fisher Scientific, Hampton, NH) buffer, pH 8 and stored at 4° C. 2,5-Pyrroledione (maleimide) was purchased from Acros


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Organics (Geel, Belgium). A 100 mM stock maleimide solution was prepared in absolute ethanol. Fetal bovine serum (FBS) was purchased from Fisher Scientific.



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Table 1. Description and a list of NAC thiol reactivity EC50 CF1 values and cytotoxicity LC50 CF values for the concentrated water samples used in this study. NAC Thiol CHO Cell DOC Description of Water Sample Reactivity Cytotoxicity Reference (mg C/L) EC50 ± SE1 LC50 ± SE1 CERL-2XAD: Tap water from Army Installation This 1.3 857.0 ± 33.2 104.9 ± 2.8 in Kansas study CERL-4XAD: Ultra-filtered AnMBR-pilot This 7.8 36.7 ± 0.17 3.9 ± 0.2 system effluent from Army Installation in Kansas study CERL-5XAD: Ultra-filtered wastewater from This 35.4 46.5 ± 1.18 4.6 ± 0.1 Army Installation in Kanas study CERL-6XAD: Tap water from Army industrial This 1.2 660.3 ± 32.4 104.8 ± 1.9 site in PA study CERL-7XAD: Ultra-filtered source water from This 1.1 889.9 ± 62.3 116.9 ± 4.0 Army industrial site in PA study CERL-8XAD: Ultra-filtered wastewater effluent This 2.4 1028.5 ± 54.3 82.5 ± 2.3 from Army industrial site in PA study CERL-9XAD: Ultra-filtered gray water from Fort This 34.0 57.3 ± 1.1 6.8 ± 0.1 Leonard Wood Training Area, MO study CERL-10XAD: Advanced oxidative treated This effluent (BF/UF/UV/RO/HOCl) from Fort 2.1 1091 ± 2.9 186. ± 3.3 study Leonard Wood Training Area, MO, gray water CERL-11XAD: On-site treated groundwater from This 6.0 1240.9 ± 16.5 242.9 ± 0.7 DPR site in OH. study CERL-12XAD: On-site advanced treated wastewater from DPR site in OH, aerobic This 2.8 5957.2 ± 12.5 442.6 ± 3.0 biological treatment, softening, UF, RO, UVstudy H2O2, stabilization, hypochlorite) CERL-13XAD: On-site biologically treated wastewater from DPR site in OH.

14.4

271.8 ± 4.0

Ultra-filtered Municipal wastewater secondary effluent disinfected by chloramination (with Br⁻ 7.3 103.3 ± 3.5 and I⁻ ) Ultra-filtered Municipal wastewater secondary 7.3 141.4 ± 2.7 effluent without disinfection (with Br⁻ and I⁻ ) Ultra-filtered Municipal wastewater secondary effluent disinfected by chlorination (with Br⁻ 7.3 191.3 ± 3.9 and I⁻ ) Ultra-filtered Municipal wastewater secondary 7.3 362.8 ± 5.11 effluent without disinfection Ultra-filtered Municipal wastewater secondary effluent disinfected by ozonation (with Br⁻ and 7.3 481.3 ± 4.6 I⁻ ) 1 CF = concentration factor. 2 SE represents standard error of the mean.


79.5 ± 0.4

This study

10.2 ± 0.7

13, 14

64.2 ± 4.6

13, 14

12.9 ± 0.7

13, 14

10.2 ± 0.8

13, 14

54.5 ± 5.8

13, 14

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Water Sample Concentration. Dissolved water or wastewater organics were adsorbed and concentrated by passing the water samples through a chromatography column with a mixture of 110 mL each of XAD-2 (Amberlite XAD-2, Sigma-Aldrich, MO) and XAD-8 (Supelite DAX-8, Sigma-Aldrich, MO) resins. For 72 h, the resins were Soxhlet-cleaned with spectroscopy-grade methanol, followed by spectroscopy-grade ethyl acetate and again with methanol. Details regarding the use of XAD resins, Soxhlet cleaning, conditioning, and the extracting of the adsorbed organics were published.43, 56-59 The XAD-2/XAD-8 resin mixture was packed in glass chromatography columns. The resin packing was conditioned by acid (1N HCl) and base (1N NaOH) before the water samples were passed through the columns. After passing the acidified samples (40-50 mL H2SO4 per 20 L of sample, pH= ~1) through the columns, the organics were eluted with spectroscopy-grade ethyl acetate, the residual water was removed by passing the eluate over ACS reagent grade anhydrous sodium sulfate. The ethyl acetate eluate was vacuum evaporated and the organics concentrated over nitrogen gas. The organic agents were dissolved in ACS reagent grade, dimethyl sulfoxide to a concentration factor of 1×105 fold. The samples were kept in sealed amber HPLC glass vials at −20° C.

CHO Cell Chronic Cytotoxicity Analyses. Chinese hamster ovary cell line K1, AS52, clone 11-4-8 were used for the biological assays.18, 60, 61 CHO cells were maintained on glass culture plates in Ham’s F12 medium containing 5% FBS, 1% antibiotics (100 U/mL sodium penicillin G, 100 µg/mL streptomycin sulfate, 0.25 µg/mL amphotericin B in 0.85% saline), and 1% glutamine at 37° C in a humidified atmosphere of 5% CO2. The cells exhibit adherent, normal morphology, express cell contact inhibition and grow as a monolayer without expression of neoplastic foci.62 The CHO cell cytotoxicity assay was described in detail and published.18



The CHO cell chronic cytotoxicity assay measured the viability of cells as the reduction in cell density as a function of the concentration of the test agent over a duration of 72 h (12 h for cell adhesion with 3-4 cell cycles). The details of the assay were published.18, 39 A known amount of each XAD water extract was dissolved in F12 +5% FBS medium. Each microplate well represented an independent measurement. Eight wells on the microplate served as the negative controls comprising the F12 +FBS medium and 3×103 CHO cells; eight additional wells on the same plate served as the blank controls comprising only the F12 +FBS medium. The remaining wells contained a known concentration of the water sample extract, F12 +FBS medium, and 3×103 CHO cells for a total volume of 200 µL. The microplate was covered with a sheet of sterile AlumnaSeal film to prevent cross contamination and evaporation and was gently shaken on a rocker platform for 10 min to distribute evenly cells on the plate before being incubated for 72 h in a humidified environment of 37° C and 5% CO2. After the 72 h period, the medium was aspirated, and the CHO cells were fixed for 10 min in methanol and stained for 10 min with 1% crystal violet in a 50% methanol solution. The microplate was washed in tap water to remove extracellular crystal violet dye, tapped dry, and 50 µL of 75% (v/v) dimethyl sulfoxide and 25% (v/v) methanol solution was added to each well. The plate was incubated in the dark at room temperature for 10 min before being analyzed at 595 nm with a SpectraMax microplate reader (Molecular Devices, CA). The absorbance value of each well was recorded, and the averaged absorbance value of the eight blank wells was subtracted from the absorbance value from each well on the microplate. The blank-subtracted absorbance values of the concurrent negative controls were defined as 100% viability. The absorbance value of each treatment well was converted into a percentage of the negative control. This process not only normalized the data but also allowed the combination and comparison of data from multiple microplates and the


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generation of concentration-response curves. Regression analysis was applied to each water sample concentration-response curve, which was used to calculate the LC50 value. The LC50 value was the calculated concentration factor of the water sample, from the regression analyses, that induced a cell density that was 50% of the negative control.

NAC Thiol Reactivity Assay Calibration with Negative and Positive Controls. We developed the assay using a 96 well microplate platform to quantify free available thiols spectrophotometrically using the Ellman’s test.48 Ellman’s reagent, DTNB follows a 1:1 stoichiometric ratio to react with the thiol group as illustrated below.

NO2 R

S

OH

S

NO2 OH

HS

O Mixed disulfide

O NTB

NAC represents the thiol in the reaction illustrated above. NTB (2-nitro-5-thiobenzoic acid) a product of this reaction, can be quantified by absorbance at 412 nm (A412). Stock solutions of NAC were prepared in Tris buffer (pH 8) and stock solutions of DTNB were prepared in potassium phosphate buffer (pH 8). For the NAC calibration experiments each well contained 50 µL of 1 mM DTNB plus varying concentrations of NAC plus Tris buffer (pH 8) in a total volume of 100 µL. The blank wells contained 50 µL Tris buffer and 50 µL DTNB. After ° min incubation on a rocker platform at room temperature in the dark, A412 was measured; blank corrected, and normalized by the A412 of the 400 µM NAC concentration (the maximum concentration of NAC).



Maleimide (2,5-pyrroledione) reacts with NAC thiols following a 1:1 stoichiometric ratio, which renders it a suitable compound for serving as the positive control. A serial dilution of the maleimide stock solution up to 400 µM was prepared, which was allowed to react with NAC and subsequently DTNB.

Experimental Procedures Using Water Samples. We adapted the assay to a 96-well plate format with a working volume of 100 µL per well. The assay is divided into two parts, first the test sample is reacted with NAC for 20 min in a volume of 50 µL followed with the addition of 50 µL DTNB for resolution at A412. An experiment form for this assay is included in the Supporting Information (SI) (Table S1). Each microplate contained a concurrent negative control, positive control, sample concentrations, and their corresponding blanks. For the negative control, each well contained 40 µL Tris buffer pH 8, 10 µL of 2 mM NAC, and 50 µL of 1 mM DTNB. For the positive control, each well contained 38 µL Tris buffer pH 8, 10 µL of 4 mM NAC, 2 µL of 10 mM maleimide and 50 µL of 1 mM DTNB. For the treatment groups, each well typically contained 10 µL of 4 mM NAC, 50 µL of 1 mM DTNB, a serial dilution of the concentrated water XAD extract and Tris buffer at pH 8. The total volume of the sample and Tris buffer was 40 µL. Sample blanks are important because they correct for the background A412. Each corresponding blank well typically contained 50 µL of 1 mM DTNB, an identical volume of sample to which this blank corresponds, and Tris buffer at pH 8 for a total volume of 100 µL.

Because it is important to keep the reaction solution at pH 8, Tris buffer was added first followed by the test sample (or maleimide positive control) and the NAC was added last. After 20 min incubation on a rocker platform in the dark, 50 µL of 1 mM DTNB was added to quantify the


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available thiols. Directly after the addition of DTNB, the plate was analyzed at 412 nm on a microplate reader after linear shaking of 10 sec (Figure S1). The data were saved in an Excel spreadsheet. For unknown test samples of complex mixtures, an initial range-finder experiment with a concentration range of at least 1000-fold was conducted.

Data Analysis. After confirming the positive control, the A412 values for each well were blankcorrected by subtracting the A412 values of the blanks from the corresponding A412 values for each treatment group. The blank-corrected negative control data were averaged and this value was divided into the individual A412 values for each treatment group ×100 and the data expressed as the percent of the concurrent negative controls. Using this normalized data, we generated concentration-response curves and regression analyses to calculate the EC50 values, the effective concentration of the test sample that induced a reduction in the NAC thiol concentration by 50% compared to the concurrent negative controls.

RESULTS AND DISCUSSION Assay Calibration. The NAC thiol reactivity assay was first calibrated to determine the NAC concentration to employ in the experiments with the XAD2/8 organic extracts of the water samples. Figure 1 illustrates the results. A Pearson’s correlation analysis demonstrated a highly linear and significant correlation between the A412 value and the NAC concentration (Figure 1, r = 0.99, P < 0.001). A positive control of maleimide was also evaluated. Maleimide reacted with NAC thiols in a linear fashion until the free thiols were fully consumed (Figure 2). A maleimide concentration of 200 µM was chosen for the concurrent positive control for this study.


Control (400 µM N-Acetyl-L-cysteine) (±SE)

Free Thiol (A 412) as the Average Percent of the


100

80

60

40

20

0 0

100

200

300

400

N-Acetyl-L-cysteine (µM) Figure 1. The absorbance value at 412 nm vs. N-acetylcysteine (NAC) concentration. The data were normalized to the control value of 400 µM NAC. Error bars represent standard error of the mean encompassing 12-24 replicates per NAC concentration and the response was linear (R2 = 0.997). The figure insert illustrates the chemical structure of NAC


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Free Thiol as the Mean Percent of the Control (400 µM NAC) (±SE)

100

80

60

40

20

0

100

200

300

400

Maleimide (µM) Figure 2. The absorbance value of 400 µM NAC at 412 nm vs. maleimide concentration. Error bars represent standard error of the mean of 4-19 replicates per maleimide concentration and the response was linear (R2 = 0.979).



NAC Thiol Reactivity of Water Samples. After these calibrations, the NAC thiol reactivity assay was used to evaluate the thiol reactivity of the XAD2/8 organic extracts of water and wastewater samples. These samples encompassed 16 independent sources including municipal wastewaters before and after disinfection, tap waters, source water, gray water, industrial wastewaters, and treated industrial wastewaters. The different treatment technologies included anaerobic membrane bioreactors and a series of advanced oxidation processes. We analyzed for associations between the NAC thiol reactivity EC50 values and the CHO cell cytotoxicity LC50 values for each water sample (Table 1). An example of the thiol reactivity (A) and CHO cell cytotoxicity (B) concentration-response curves for a representative water sample is illustrated in Figure 3.

Correlation of Organic Content of Water Samples, Cytotoxicity and Thiol Reactivity. The use of the total organic carbon or dissolved organic carbon (DOC) as a metric to determine water quality was discussed in the literature and no definite conclusions were resolved. 63, 64 We determined the DOC for the water samples analyzed in this study. After conducting a Pearson’s Product Moment Correlation test with the data presented in Table 1, we found that DOC is not correlated with LC50 values or EC50 values, r = −0.407; P > 0.117 and r = −0.320; P > 0.227, respectively. There was a strong, highly significant correlation between LC50 and EC50 values (r = 0.920; P < 0.001). We concluded from these data that DOC is not a predictive metric for the toxicity of the analyzed water samples.


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NAC-Thiol Assay: Response as the Mean Percent of the Negative Control (±SE)

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100

A

80

60

40

20 0

20

40

60

80

100

120

CHO Cell Cytotoxicity: Mean Cell Density as the Percent of the Negative Control (±SE)

CERL-9XAD (Concentration Factor) Fort Leonard Wood Training Area, Gray Water

100

B

80

60

40

20 0

2

4

6

8

CERL-9XAD (Concentration Factor) Fort Leonard Wood Training Area, Gray Water

Figure 3. (A) Concentration-response curve illustrating the thiol reactivity of sample CERL9XAD (A). The mean NAC EC50 value was 57.3±1.06 concentration factor (R2=0.99. The error bars represent standard error of the mean with six replicates/concentration. (B) The mammalian cell chronic cytotoxicity expressed a mean LC50 value of 6.75 ± 0.05 concentration factor (R2=0.98). The error bars represent standard error of the mean with 4-24 replicates/ concentration. 17 ACS Paragon Plus Environment


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Development of CHO Cell Cytotoxicity Prediction Equations. The data presented in Table 1 were used to fit the data and generate Figure 4. We used equation 1 to obtain the X value that corresponds to a value of 50% for Y, also known as the EC50 value.

= +

(1)

A1, A2, log X0, and p are all fitting parameters (log is 10 based). X is the concentration factor of the water sample. Y is the percent reduction in A412 compared to the negative control.

We obtained 16 independent water and wastewater samples (Table 1) which were concentrated over XAD2/8 columns. For all samples, we analyzed both the CHO cell chronic cytotoxicity and the NAC thiol reactivity. The goal was to evaluate if a significant correlation existed between NAC thiol reactivity and CHO cell cytotoxicity. A good correlation would lead to the development of a CHO cell cytotoxicity prediction equation. Among all samples, a linear association was observed between the LC50 values versus the EC50 values (Figure 4, R2 = 0.74), where the fitting equation was:

= 0.676 + 0.110

(2)

The standard error for the intercept and slope were ± 2.878 and ± 0.015, respectively. A simple linear model was used because of the distribution of data with the fewest assumptions. Since thiol reactivity may not account for all of the cytotoxicity, when LC50 (or EC50) equals zero, the


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physical meaning of the other variable becomes irrelevant, and the fitting was therefore not forced through the origin.

For samples that were suspected to have a higher cytotoxicity, such as highly contaminated wastewaters compared to drinking waters, this prediction model worked well, as shown in the inset in Figure 4. The coefficient of determination (R2) for the model with these data points (LC50 lower than 10 and EC50 lower than 120) was 0.77. A Pearson’s correlation analysis of all 16 samples revealed a highly significant correlation coefficient between EC50 and LC50 values (r = 0.92; P < 0.001).

We also calculated the Upper Confidence Limit (UCL) and the Lower Confidence Limit (LCL) at the 95% confidence level, and presented them in Figure 4. To account for the variability in the LC50 values, the UCL and LCL were calculated as follows, ±

,#$ %& '∑+ !

*, )*

+

--̅ /00

(3)

where is the prediction of y value (LC50) at x by equation (2), α is the significance level, n is the number of samples,

!

,#$

is the one tail critical value for a significance level of α/2, 12 is

the weight factor calculated as 1/42 $ (where 42 is the standard error of y value), %& is the weighted root mean square error of the data set given by the expression, %& = '

∑+ *, )* 5* 5 #$

6̅ is the weighted average of the EC50 values given as

∑+ *, )* -* ∑+ *, )*

squares of the difference between each x and 6̅ given as: 19 ACS Paragon Plus Environment

(4) , and 7-- is the weighted sum of


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7-- = ∑#29 12 62 $ − 6̅ $ ∑#29 12 For this study since = 0.676 + 0.110 , : = 16, ; = 0.05,

.$,=

(5) = −2.145, the

%& = 55.31, 6̅ = 63.49, and 7-- = 7810347.47. We therefore arrive at the following expressions for the UCL and LCL for the current set of data: UCL: = 0.676 + 0.110 + 118.64'0.001735 +

CDE FG.=H

(6)

LCL: = 0.676 + 0.110 − 118.64'0.001735 +

CDE FG.=H

(7)

IJG=I.=I

IJG=I.=I

The data in general fall within the 95% UCL and LCL (Figure 4). Equation (2), (6), and (7) may be used for preliminary screening predictions of CHO cell cytotoxicity rank order. A larger number of samples will improve the model regarding its ability to predict a range of presumptive cytotoxicity values for subsequent analytical biological experiments. A spreadsheet containing the model calculations is presented in Table S2 in the SI.


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Figure 4. LC50 versus EC50 values. The inset graph illustrates the data with higher toxicity (LC50 lower than 10 and EC50 lower than 120). Error bars represent standard error of the mean of six replicates. The shaded area surrounding the prediction line represents the 95% confidence interval.

Limitations. Reactions with thiol groups in biological peptides and other cellular targets is only one pathway to toxicity. This thiol reactivity assay cannot uncover all toxic mechanisms. However, this assay may be a metric for the interaction of thiol-based cellular protectants, such as glutathione, and reactive toxicants.49 50 Experimental error was limited by using the same XAD-2/XAD-8 resin method to process all water samples and to extract and concentrate the organics from each sample. Although this method to extract organics is widely employed, it could be an important source of variation.56, 57



We developed and calibrated a rapid assay that would rank order complex water samples for their toxic potential to mammalian cells. We developed a model to predict CHO cell chronic cytotoxicity with upper and lower confidence limits. The predicted LC50 may be used to rank order predicted mammalian cell cytotoxicity of water samples. The thiol reactivity assay may be employed as a fast, high throughput analysis to perform preliminary screening before conducting analytical biological assays. However, thiol reactivity is only one mechanism of toxicity and therefore it should not be a replacement for analytical biological analyses.

SUPPORTING INFORMATION

In the Supporting Information, a detailed procedure of the NAC thiol reactivity assay is presented. A downloadable form for the assay is included as well as a figure illustrating the response of a positive result of the assay.

ACKNOWLEDGEMENTS We acknowledge funding from the Army Environmental Quality Technology program of the Assistant Secretary of the Army for Installations, Energy and Environment, via grant number CESU W9132T-16-2-0005 administered through the U.S. Army Engineer Research and Development Center. We thank Adam Arnold of Tangent Company LLC, Chris Otto of Fort Riley and Jacob Gogno of Tobyhanna Army Depot for facilitating sampling. We thank Dr. Justin Pals for his thoughtful conversations on the use of the Ellman’s reaction with individual toxic DBPs.


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