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. Author manuscript; available in PMC: 2017 Aug 1.
Published in final edited form as: Environ Mol Mutagen. 2016 Jul 1;57(7):546–558. doi: 10.1002/em.22028

γH2AX and p53 Responses in TK6 Cells Discriminate Promutagens and Non-genotoxicants in the Presence of Rat Liver S9

Derek T Bernacki 1,#, Steven M Bryce 1,#, Jeffrey C Bemis 1, David Kirkland 2, Stephen D Dertinger 1,†
PMCID: PMC4980245  NIHMSID: NIHMS791120  PMID: 27364561

Abstract

Previous work with a diverse set of reference chemicals suggests that an in vitro multiplexed flow cytometry-based assay (MultiFlow™ DNA Damage Kit—p53, γH2AX, Phospho-Histone H3) can distinguish direct-acting clastogens and aneugens from non-genotoxicants [Environ Mol Mutagen 57(2016)171-189]. The current work extends this line of investigation to include compounds that require metabolic activation to form reactive electrophiles. For these experiments TK6 cells were exposed to 11 promutagens and 37 presumed non-genotoxicants in 96 well plates. Unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. Exposure occurred for 4 hrs after which time cells were washed to remove S9 and test article. Immediately following the wash and again at 24 hrs, cell aliquots were added to wells of a microtiter plate containing the working detergent/stain/antibody cocktail. After a brief incubation robotic sampling was employed for walk-away flow cytometric data acquisition. Univariate logistic regression analyses indicated that γH2AX induction and p53 activation provide the greatest degree of discrimination between clastogens and non-genotoxicants. Multivariate prediction algorithms that incorporated both of these endpoints, in each combination of time points, were evaluated. The best performing models correctly predicted 9 clastogens out of 11 and 36 non-genotoxicants out of 37. These results are encouraging as they suggest that an efficient and highly scalable multiplexed assay can effectively identify clastogenic chemicals that require bioactivation. More work is planned with a broader range of chemicals, additional cell lines, and other laboratories in order to further evaluate the merits and limitations of this approach.

Keywords: genotoxicity, γH2AX, p53, metabolic activation, flow cytometry

Introduction

A large portion of work directed at improving in vitro genotoxicity assays relates to the low throughput and assay specificity of current methods, lack of information relating to mode of action (MoA), and/or their competence to activate promutagens to in vivo relevant DNA-reactive metabolites [Audebert et al., 2010; Smart et al., 2011; Hendriks et al., 2012; Tsamou et al., 2012; Garcia-Canton et al., 2013; Nikolova et al., 2014; Bryce et al., 2008, 2014; Nagel et al., 2014; Khoury et al., 2015; Cheung et al., 2015; Yauk et al., 2016]. A previous report by this laboratory addressed several of these issues through the use of a multiplexed p53, γH2AX, and phospho-histone H3 (p-H3) assay that uses flow cytometry as the analytical platform [Bryce et al., 2016]. That work described experiments with TK6 cells, and the resulting multiplexed measurements were converted into a prediction of group membership (clastogen, aneugen, or non-genotoxicant) based on a four-factor multinomial logistic regression model.

While initial work with this multiplexed assay generated promising results—assay sensitivity and specificity values >90%—only 4 of the 84 chemicals studied required metabolic activation to form DNA-reactive electrophiles. It was therefore of interest to more extensively evaluate the utility of the assay to detect genotoxicants that require metabolic activation using an exogenous activation system. We utilized Aroclor-induced rat liver S9 given its widespread use in the field of genetic toxicology. Furthermore, in contrast to our previous report in which we sought to discriminate clastogens, aneugens and non-genotoxicants, we limited this investigation to discrimination of clastogens and non-genotoxicants. This decision was based on current literature that to the best of our knowledge reports few, if any, compelling examples of metabolically activated aneugens.

As with the previous report, the work described herein was conducted with TK6 cells. However, for the present experiments exposure was for 4 hrs in the presence of S9 as opposed to 24 continuous hrs without a metabolic activation system. After washing cells at 4 hrs to remove S9 and test article, sample processing and flow cytometric analysis occurred at one early (4 hr) and one late (24 hr) time point, consistent with the work that was done previously. Univariate logistic regression (L-R) was used to identify promising endpoints. Subsequently several candidate multifactorial computation models were used to predict group membership. The resulting data are discussed in terms of the sensitivity and specificity of the assay to discriminate metabolically activated clastogens from non-genotoxicants.

Materials and Methods

Chemicals

The identity of the 48 chemicals is provided in Table I, along with our a priori predictions of genotoxic potential, based on published literature. Eleven clastogens that require metabolic activation were selected to represent a broad range of genotoxic activities, most of which have been studied in multiple laboratories and in both in vitro and in vivo systems. Thirty-seven nongenotoxicants were selected for their diverse range of activities/toxic mechanisms, and many are part of an ECVAM list of agents that should be negative in genotoxicity testing [Kirkland et al., 2008, 2015]. Collectively these chemicals represented reference agents that were useful for identifying the time points and biomarkers that can best discriminate clastogens and nongenotoxicants and for developing machine learning-based prediction algorithms.

Table I.

Chemicals and a priori Classifications.

Chemical CAS No. a priori Clasification, Notes about Biotransformation, Misc. References
2-Acetylaminofluorene 53-96-3 Clastogen, requires metabolic activation (CYP1A2), forms C8 adduct on guanine; top non-precipitating conc. 250 μM Otteneder et al., 1999; Kirkland et al., 2015
2-Aminoanthracene 613-13-8 Clastogen, aromatic amine, requires metabolic activation (CYP1B1, 2A family); top non-precipitating conc. 500 μM Carriére et al., 1992
7,12-Dimethylbenzanthracene 57-97-6 Clastogen, requires metabolic activation (CYP1B1), forms bulky adducts Kirkland et al., 2015
2-Nitrofluorene 607-57-8 Clastogen, nitrated polycyclic aromatic hydrocarbon, requires metabolic activation; top non-precipitating conc. 177 μM Wierckx et al., 1990; Matsuoka et al., 1991
Benzo[a]pyrene 50-32-8 Clastogen, polycyclic aromatic hyrocarbon, requires metabolic activation (CYP1A1, 1B1, epoxide hydrolase), forms bulky adducts; top non-precipitating conc. 250 μM Kirkland et al., 2015
Cyclophosphamide monohydrate 6055-19-2 Clastogen, nitrogen mustard, requires metabolic activation (CYP2B6, CYP2C19, CYP2C9 and CYP3A4/5) Kirkland et al., 2015; Rodriguez-Antona and Inglelman-Sundberg, 2006
Dibenzo[a,l]pyrene 191-30-0 Clastogen, polycyclic aromatic hydrocarbon, requires metabolic activation (thought to be primarily activated by CYP1A1); 10 μM highest tested conc. due to cytotoxicity Arif and Gupta, 1997
Hexamethylphosphoramide 680-31-9 Clastogen, phosphoramide, requires metabolic activation to form proximate genotoxicant (presumably formaldehyde) Sarrif et al., 1997; Doherty et al., 2012
3,4-Dimethylimidazo[4,5-f]quinolin-2-amine (MeIQ) 77094-11-2 Clastogen, heterocyclic amine, requires metabolic activation (CYP1A2); while clastogenic in vivo, often not clearly or only weakly clastogenic in vitro; weak inducer of MN in HepG2 cells Ramsey et al., 1998; Sasaki et al., 1992; Aeschbacher et al., 1989; Thompson et al., 1987; Rueff et al., 1996; Knasmüller et al., 1999; Kim and Guengerich, 2004
N-Nitrosodiethylamine (DEN) 55-18-5 Clastogen, requires metabolic activation to form alkylating agent (likely involves CYP2E1 which is not highly expressed in rat liver S9); often only positive at high concentrations (≥10 mM) Ishidate et al., 1988; Yamazaki et al., 1992;
2-amino-1-methy l-6-phenylimidazo [4,5-b]pyridine (PhIP) 105650-23-5 Clastogen, heterocyclic amine, requires metabolic activation (CYP1A family); top non-precipitating conc. 250 μM Kirkland et al., 2015; Krais et al., 2016
Amitrole 61-82-5 Non-genotoxicant Kirkland et al., 2015
Anthranilic acid 118-92-3 Non-genotoxicant Kirkland et al., 2015
Brefeldin A 20350-15-6 Non-genotoxicant; ER-golgi transporter inhibitor, ER stress-induced apoptosis; top conc. 500 μM due to solubility Moon et al., 2012
Caffeine 58-08-2 Non-genotoxicant; mitochondria-dependent apoptosis, ROS involvement likely Lu et al., 2008
Carbonyl cyanide m-chlorophenyl hydrazone (CCCP) 555-60-2 Non-genotoxicant; uncoupler of oxidative phosphorylation de Graaf et al., 2004
Clofibrate 637-07-0 Non-genotoxicant; antilipidemic agent IARC monograph
Cyclohexanone 108-94-1 Non-genotoxicant; industrial chemical Kirkland et al., 2008
Cycloheximide 66-81-9 Non-genotoxicant; protein synthesis inhibitor Youngblom et al., 1989
D-Mannitol 69-65-8 Non-genotoxicant; polyol Kirkland et al., 2015
Dexamethasone 50-02-2 Non-genotoxicant; glucocorticoid receptor agonist Krishna et al., 1995
Dextrose 50-99-7 Non-genotoxicant; sugar Lotz et al., 2009
Di-(2-ethylhexyl)phthalate (DEHP) 117-81-7 Non-genotoxicant; organic plasticizer Kirkland et al., 2015
Diethanolamine 111-42-2 Non-genotoxicant; secondary amine Kirkland et al., 2015
Erythromycin 114-07-8 Non-genotoxicant; antibiotic Kirkland et al., 2015
Pepcid® (Famotidine) 76824-35-6 Non-genotoxicant; histamine H2 receptor antagonist FDA approved label
Gleevec® (imatinib mesylate) 152459-95-5 Non-genotoxicant; protein-tyrosine kinase inhibitor FDA approved label
Hexachloroethane 67-72-1 Non-genotoxicant; industrial chemical Kirkland et al., 2015
Lidoderm® (Lidocaine) 137-58-6 Non-genotoxicant; amide local anesthetic FDA approved label
Mevacor® (Lovastatin) 75330-75-5 Non-genotoxicant; HMG-CoA reductase inhibitor FDA approved label
Melamine 108-78-1 Non-genotoxicant: industrial organic base Kirkland et al., 2015
Methyl carbamate 598-55-0 Non-genotoxicant; industrial intermediate Kirkland et al., 2015
N-Butyl chloride 109-69-3 Non-genotoxicant; fumigant Kirkland et al., 2015
Floxin® (Ofloxacin) 82419-36-1 Non-genotoxicant; fluoroquinoline antibiotic; top conc. 500 μM due to solubility FDA approved label
Paxil® (Paroxetine) 61869-08-7 Non-genotoxicant; SSRI antidepressant FDA approved label
Phenanthrene 85-01-8 Non-genotoxicant; polycyclic aromatic hydrocarbon Kirkland et al., 2008
Phenformin HCl 834-28-6 Non-genotoxicant; biguanide antidiabetic Kirkland et al., 2015
Progesterone 57-83-0 Non-genotoxicant; steroid hormone Kirkland et al., 2008
Pyridine 110-86-1 Non-genotoxicant; heterocyclic organic compound Kirkland et al., 2015
Sodium chloride 7647-14-5 Non-genotoxicant; prepared in RPMI medium Matsushima et al., 1999
Sodium dodecyl sulfate 151-21-3 Non-genotoxicant; ionic detergent NTP website
Sucrose 57-50-1 Non-genotoxicant Diaz et al., 2007
Tert-butyl alcohol 75-65-0 Non-genotoxicant Kirkland et al., 2015
Thapsigargin 67526-95-8 Non-genotoxicant; ER stress-induced apoptosis Futami et al., 2005
Tunicamycin 11089-65-9 Non-genotoxicant; glycosylation inhibitor, ER stress-mediated apoptosis; top conc. 500 μM due to solubility Han et al., 2008
Alosetron HCl 122852-42-0 Non-genotoxicant; 5-HT3 antagonist; top conc. 250 μM due to solubility Kirkland et al., 2015
D-Limonene 5989-27-5 Non-genotoxicant; male rat kidney tumors due to α2μ-globulin nephropathy Kirkland et al., 2015
Tolterodine L-tartrate 124937-52-6 Non-genotoxicant; muscarinic receptor antagonist Kirkland et al., 2015
Zonisamide 68291-97-4 Non-genotoxicant; sulfonamide anticonvulsant Kirkland et al., 2015
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DNA Damage Assay

Reagents necessary to prepare nuclei for flow cytometric analysis were part of a prototype kit, i.e., MultiFlow™ DNA Damage Kit—p53, γH2AX, Phospho-Histone H3 (Litron Laboratories, Rochester, NY). The proprietary working solution was used to simultaneously digest cytoplasmic membranes, stain chromatin with a fluorescent nucleic acid dye, and label several epitopes with fluorescent antibodies. With this kit, anti-γH2AX-Alexa Fluor® 647 is used as a DNA double strand break marker, anti-phospho-histone H3-PE serves as a mitotic cell marker, and in the context of the detergent-mediated lysis anti-p53-FITC recognizes nuclear accumulation of p53. RNase and propidium iodide provide cell cycle and polyploidization information, and a known concentration of latex microspheres (“counting beads”; i.e., Sphero™ Multi-Fluorophore Particles, cat. no. FP-3057-2; Spherotech, Inc., Lake Forest, IL) is used to calculate nuclei density.

Cell culture and treatments

TK6 cells were obtained from ATCC® (cat. no. CRL-8015), and grown in a humid atmosphere at 37°C with 5% CO2. For routine culturing, the cells were maintained at or below 1 × 106 cells/mL. The culture medium consisted of RPMI 1640 with 200 μg/mL sodium pyruvate, 200 μM L-glutamine, 50 units/mL penicillin, 50 μg/mL streptomycin, and 10% v/v heat-inactivated horse serum.

On the day of treatment, logarithmically growing cells were transferred to round-bottom 96-well plate(s) where they were exposed to 20 concentrations of test chemical. Each concentration was evaluated in one well except for the solvent that was evaluated in 4 replicate wells. The solvent was DMSO except in several instances noted in Table I. As per ICH recommendations [2011], top test chemical concentration was 1000 μM unless precipitation was observed in the 100× stock solution or upon addition to culture medium; in these cases the highest non-precipitating concentration was evaluated. Table I notes instances when less than 1000 μM was used as the top concentration based on precipitation or cytotoxicity considerations. Regardless of the top starting concentration, every successively lower concentration differed by a factor of square root 2, for example 1000, 707, 500 μM, etc.

All data reported herein are based on cell treatments in the presence of an exogenously supplied metabolic activation system via inclusion of Aroclor-induced rat liver S9, final concentration 2% (Mutazyme Lyophilized Rat Liver S9, Molecular Toxicology, Inc., Boone, NC). The S9 mix and cells (2 × 105 cells/mL) were transferred as 198 μL/well. Solvent and test chemicals were added in a volume of 2 μL per well at which time the treated cells were re-incubated in a humid atmosphere at 37°C with 5% CO2. After 4 hrs cells were washed out of exposure medium by two successive centrifugation (340 × g, 5 min) and resuspension steps (200 μL/well). The cells were resuspended with 200 μL/well fresh growth medium and at this point 25 μL of washed cells were removed from each well for the 4 hr time point. The remaining cells were returned to the incubator for an additional 20 hrs when 25 μL of cells were once again removed from each well for flow cytometric analysis.

Flow cytometric analysis

As noted above, cells at 4 and 24 hr time points were added to a new 96-well plate containing 50 μL/well of pre-aliquoted working MultiFlow Kit reagent. Mixing occurred by pipetting the contents of each well several times. After a 30 min room temperature incubation period, flow cytometric analysis was performed using a FACSCanto™ II flow cytometer equipped with a BD™ High Throughput Sampler. The user-defined mixing and fluidics parameters were programmed as follows: 40 μL of sample were mixed 4 times at a mixing speed of 180 μL/sec, and then 20 μL were analyzed at a flow rate of 1 μL/sec until the entire volume was exhausted. Between samples the High Throughput Sampler was programmed to wash the probe with 400 μL of sheath fluid. Each 4 hr sample provided on average approximately 1,750 nuclei with 2n or greater DNA content analysis, and each 24 hr sample provided approximately 4,820 such nuclei.

Two endpoints, γH2AX and p53, were based on median channel fluorescence intensity, and for all graphical representation and statistical analyses these values were expressed as fold-change relative to a plate-specific solvent control arithmetic mean. Gating logic required these events to exhibit propidium iodide-associated fluorescence corresponding to 2n - 4n DNA content. In order to limit the influence that mitotic and apoptotic cells might have on γH2AX measurements, p-H3 positive cells and highly fluorescent γH2AX-positive events were excluded from analysis [McManus and Hendzel, 2005; Huang et al., 2006; Rogakou et al., 2000].

The p-H3 measurements were the proportion of p-H3-positive events that exhibited propidium iodide-associated fluorescence of 4n and greater DNA content relative to the number of total events with 2n and greater DNA content. Polyploidy was quantified as the proportion of 8n-positive events relative to the number of total events with 2n and greater DNA content. For all graphical representations and statistical analyses presented herein the p-H3 and polyploidy data were converted to fold-change relative to a plate-specific solvent control arithmetic mean value.

Latex microspheres were included in the working dye/antibody solution at a known concentration (between 9.5 and 10.0 × 104/mL) and this allowed these particles to serve as counting beads. Nuclei to counting bead ratios were calculated for each specimen, and this was used to determine nuclei densities. These values were used to derive 4 and 24 hr cytotoxicity values relative to plate-specific mean solvent control wells. That is relative nuclei counts (RNC) underwent linear transformations to provide simple cytotoxicity indices, i.e., 100% minus 4 hr RNC and also 100% minus 24 hr RNC.

Electronic compensation was used to eliminate fluorescence spillover of propidium iodide into the adjacent PE channel. A more detailed description of compensation settings can be found in Bryce et al., 2016.

Supervised machine learning

L-R was used to identify predictive biomarkers and to evaluate multifactorial models for categorizing chemicals according to group membership—clastogen versus non-genotoxicant (JMP® software, v12.0.1, SAS Institute Inc., Cary, NC). Initially univariate analyses were performed to identify the most promising biomarkers and time points. Statistics that were useful for this purpose included McFadden's pseudo R2, receiver operator characteristic (ROC) curves, and percentage of correctly categorized chemicals. To evaluate whether multiple biomarker data would improve the model, an iterative forward-stepping approach was used. As covariates identified for inclusion were added to the multivariate model, the importance of each endpoint was assessed in a manner similar to the univariate analyses described above. Then, a range of cytotoxicity limits were considered and an optimal value was chosen in the same way—by empirically studying their impact on model performance. Particularly high performing L-R models included two-factors: 24 hr p53 and either 4 or 24 hr γH2AX in the context of a 70% 24 hr cytotoxicity limit. As described in more detail below, weighting these models on 24 hr cytotoxicity values led to further improvements to R2 and ROC values.

As with our previous report, the current machine learning approach utilized data from every concentration [Bryce et al., 2016]. A consequence of building a L-R model with data from multiple concentrations is that it calculates numerous group membership probabilities. This explains why multiple probability scores are reported for each chemical. Since a prediction as to group membership occurred at every concentration, this also explains the need to synthesize the model output (probability scores) into a final judgment as to group membership, and this is described below.

Call criteria, definitions

For the proof-of-principle work described herein clastogen and non-genotoxicant call criteria were selected to maximize assay sensitivity. With this goal in mind, we provisionally required a clastogen call to exhibit at least one concentration with a clastogen probability score ≥ 80%. A non-genotoxicant call was defined as no concentration exhibiting a clastogen probability score ≥ 80%. Assay sensitivity was calculated by determining the percentage of clastogens that were identified as such. Assay specificity was calculated by determining the percentage of non-genotoxicants that were identified as such. Assay concordance was calculated by determining the percentage of correct calls compared to total calls.

Results and Discussion

Identifying candidate biomarkers

MultiFlow kit reagents were successfully applied to TK6 cells exposed to chemicals in the presence of a rat liver S9 activation system. We found that centrifugation and subsequent resuspension of cells with fresh growth medium effectively reduced the fine microsome particulate matter and resulted in cells that were compatible with the kit's detergent/antibody cocktail. See Figure 1 for flow cytometry-generated p53, γH2aX, and p-H3 profiles.

Figure 1.

Figure 1

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Flow cytometry bivariate plots for TK6 nuclei from solvent (left) or benzo(a)pyrene (right) treated cultures prepared with MultiFlow kit reagents as described herein. Top panel: p53 fluorescence versus nucleic acid dye fluorescence at the 24 hr time point. Note the increased p53-associated fluorescence associated with clastogen exposure. Middle panel: γH2AX fluorescence versus nucleic acid dye fluorescence at the 4 hr time point. Note the increased γH2AX fluorescence associated with clastogen exposure. Bottom panel: Phospho-histone H3 fluorescence versus nucleic acid dye fluorescence at the 4 hr time point. Whereas the proportion of phospho-histone H3 events was not found to be highly informative in terms of genotoxic versus non-genotoxic predictions, it does convey information about cell proliferation.

Figure 2 shows representative biomarker responses for a metabolically-activated genotoxicant, specifically the heterocyclic amine 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) at both early (4 hr) and late (24 hr) time points. PhIP serves as a useful example for several reasons. First, it shows concentration-related increases in γH2AX-associated fluorescence at both time points, a phenomenon that was typical for the genotoxicants that were effectively bioactivated. Exceptions were 2-nitrofluorene and 3,4-dimethylimidazo[4,5-f]quinolin-2-amine, both of which showed robust responses at 4 hrs that were considerably reduced at the later time point. PhIP-induced p53 responses were also prototypical. Whereas γH2AX responses tended to increase or plateau with increasing concentration and cytotoxicity, p53 induction tended to plateau or even downturn with increasing cytotoxicity. Second, PhIP, like most clastogens, induced higher p53 responses at 24 hr relative to the 4 hr time point. This was particularly true for cyclophosphamide, where no p53 response was observed at 4 hr but at 24 hr a peak increase of 3.3-fold occurred. Not surprisingly, some chemicals affected the proportion of mitotic (p-H3 positive) cells, and PhIP is representative of agents that caused concentration-dependent reductions to these events. In this case the p-H3 effect was particularly evident at 24 hrs, and this manifestation of cytotoxicity may provide some insight into why certain endpoints, such as p53, were observed to saturate or even exhibit a downturn at the highest concentrations tested. As seen in Figure 2, polyploid cells were not induced by PhIP. As described below polyploidization was observed for certain compounds, but in all cases it was in the context of high cytotoxicity. Finally, the PhIP data shown in Figure 2 are illustrative of another consistent finding. That is, when reduction to RNC is used to assess treatment-induced cytotoxicity, 24 hr measurements were always more responsive/sensitive than measurements that occurred at the earlier time point.

Figure 2.

Figure 2

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Fold-increase responses for each of five biomarkers at each of two time points are graphed against 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) concentration. PhIP is representative of clastogenic-activity response profiles observed for the metabolically activated genotoxicants studied reported herein.

In an effort to begin evaluating candidate biomarkers for their ability to discriminate clastogens from non-genotoxicants, fold-change (relative to mean solvent control) was graphed against cytotoxicity (Figure 3). This is an aggregate view, where every chemical and every concentration was included, irrespective of cytotoxicity level. These data suggest that γH2AX and p53 responses are promising predictive biomarkers. While the PhIP data described above suggest that a cytotoxicity limit may be useful for maximizing the sensitivity of the p53 endpoint, the aggregate data shown in Figure 3 suggest another advantage. Thus, it appears that a cytotoxicity limit may also be useful for maintaining the specificity of certain biomarkers, for example γH2AX.

Figure 3.

Figure 3

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Fold-increase responses for each of four biomarkers at each of two time points are graphed against a measure of cytotoxicity (i.e., 100% - Relative Nuclei Count at 24 hrs). The graphs are aggregate data for all 48 chemicals studied herein, and are coded according to group membership: clastogens = red circles, non-genotoxicants = green triangles. These graphs provide insight into the most predictive biomarkers, and suggest that testing up to cytotoxic concentrations, to a point, maximizes signal to baseline ratios.

Univariate logistic regression analyses were used to investigate biomarkers and cytotoxicity limits in a more quantitative and systematic manner. R2 and ROC values were calculated for each biomarker/time point combination (no cytotoxicity limit) and are presented in Table II. This table also summarizes logistic regression predictions. These analyses suggest that γH2AX and p53, at both 4 and 24 hrs, are the most promising biomarkers in terms of predicting metabolite clastogenicity.

Table II.

Univariate Logistic Regression Analyses.

Endpoint Time Pseudo R2 ROC Correct Clastogen Predictions Correct Non-Genotoxicant Predictions
γH2aX 4 hrs 0.2264 0.80410 6 / 11 32 / 37
p-H3 0.0048 0.43147 0 / 11 37 / 37
p53 0.0989 0.65948 2 / 11 35 / 37
Polyploidy 0.0090 0.58132 0 / 11 37 / 37
γH2aX 24 hrs 0.1538 0.75507 5 / 11 33 / 37
p-H3 0.0618 0.63035 0 / 11 37 / 37
p53 0.1706 0.73776 6 / 11 34 / 37
Polyploidy 0.0019 0.55785 0 / 11 37 / 37
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Additional analyses considered whether different cytotoxicity limits improved R2 and ROC values, and whether applying a weight function based on cytotoxicity would be beneficial. These results are shown in Figure 4. In general, applying a cytotoxicity limit was useful, and a cytotoxicity cutoff value that worked well for both γH2AX and p53 endpoints was 70% (based on 24 hr RNC). These data also indicate that giving more weight to concentrations that exhibited greater cytotoxicity markedly improved statistics associated with model performance.

Figure 4.

Figure 4

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R2 and ROC values associated with univariate logistic regression analyses show the influence of using a cytotoxicity limit (0, 80, 70, or 60%), as well as weighting the model on %cytotoxicity. These graphs show that the ability of the endpoints γH2AX and p53 to discriminate between clastogens and non-genotoxicants generally improves when a cytotoxicity limit is used, and when the responses are weighted on cytotoxicity.

The 70% cytotoxicity limit is consistent with an observation made in our previous report that noted γH2AX and p53 responses either saturated or exhibited a downturn by 80% cytotoxicity [Bryce et al., 2016]. In any event, when considering the appropriateness of the 70% cytotoxicity limit for short-term treatment in the presence of an S9 system, several points should be kept in mind. First, as described above, it was not simply adopted from other assays/endpoints, but rather was empirically determined by an objective and systematic investigation of data from the endpoints under consideration. Second, the cytotoxicity limit was based on RNC at the 24 hr time point. It is not accurate to think of RNC being equivalent to cell death. Rather, these measurements integrate cell division, loss of cells due to necrosis and/or apoptosis, and also cytostasis. Finally, at this point we consider the 70% cutoff value provisional in nature. While it was effective for the experiments described herein, it is important to recognize that the analyses considered a single cell line, one treatment/harvest schedule, and only 48 chemicals.

Model building, optimization

L-R models were built by combining γH2AX and p53 data associated with concentrations that exhibited ≤ 70% cytotoxicity, and applying a weight function based on %cytotoxicity. For this exercise, both endpoints were considered at each combination of time points. A supplementary file shows fold-increase values for these two endpoints at both time points and for every chemical studied. Figures 5-8 displays the resulting L-R probabilities for the four models: 4hr γH2AX and p53; 24 hr γH2AX and p53; 4 hr γH2AX and 24 hr p53; and 24 hr γH2AX and 4 hr p53, respectively.

Figure 5.

Figure 5

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Logistic regression probabilities for a clastogen classification are graphed for each of 48 chemicals. This logistic regression model was based on 4 hr γH2AX and 4 hr p53 fold-change data. Chemicals are color-coded: clastogens = red circles, non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, as each represents a different concentration. These data show that 8/11 clastogens were correctly classified by this two-factor model, while 36/37 non-genotoxicants were correctly classified.

Figure 8.

Figure 8

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Logistic regression probabilities for a clastogen classification are graphed for each of 48 chemicals. This logistic regression model was based on 24 hr γH2AX and 4 hr p53 fold-change data. Chemicals are color-coded: clastogens = red circles, non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, as each represents a different concentration. These data show that 8/11 clastogens were correctly classified by this two-factor model, while 36/37 non-genotoxicants were correctly classified.

The two highest performing models consisted of (i) 4 hr γH2AX and 24 hr p53 data, and (ii) 24 hr γH2AX and 24 hr p53 data. In both cases, 9 out of 11 total clastogens were correctly identified, and 36 out of 37 total non-genotoxicants were correctly classified. Assay performance values were 82% for sensitivity, 97% for specificity, and 94% for concordance with a priori classifications.

The clastogens that were not predicted correctly were N-nitrosodiethylamine (DEN) and hexamethylphosphoramide (HMPA). In both cases no evidence of cytotoxicity was observed, either in terms of reductions to RNC, or to p-H3 positive events. This suggests that the S9 metabolic activation system was not effective at biotransforming these agents to reactive metabolites. In the case of DEN, this is not surprising. DEN is chiefly activated by CYP2E1, an isoform that does not exhibit high levels of activity in Aroclor-treated rat livers [Burke et al., 1994]. The literature surrounding HMPA is considerably sparser relative to the other progenotoxicants studied herein. Reports by Sarrif et al. [1997] and Doherty and colleagues [2012], suggest that formaldehyde is the proximate genotoxicant. However, the known appreciable cytotoxicity of formaldehyde, and the lack of cytotoxicity observed in our experiments, supports our premise that HMPA was not effectively bioactivated by the S9 reagent that we used, thus the lack of γH2AX and p53 responses was not surprising.

Conclusions

A method that reports on γH2AX, p-H3, p53, polyploidization, as well as cytotoxicity, was successfully applied to TK6 cells treated with chemicals in the presence of an exogenous metabolic activation system. γH2AX induction and p53 activation were found to provide the greatest degree of discrimination between clastogens and non-genotoxicants. Multivariate prediction algorithms based on these endpoints were evaluated and the best performing models correctly predicted 9 clastogens out of 11 total and 36 non-genotoxicants out of 37 total. The misclassification of two clastogens can likely be attributed to deficiencies with the rat liver S9-based metabolic activation system as opposed to issues with the endpoints or time points per se.

These results are encouraging as they suggest data from an efficient and highly scalable multiplexed assay can be used to make predictions about chemicals’ potential to cause S9-mediated clastogenicity. Overall, the characteristics of this assay make it well suited for early screening programs, including high throughput environments that utilize robotic liquid handling instruments. Further work is planned with different treatment/harvest schedules, a broader range of chemicals, additional cell lines, and other laboratories in order to further evaluate the merits and limitations of this approach.

Supplementary Material

Supp Info

Figure 6.

Figure 6

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Logistic regression probabilities for a clastogen classification are graphed for each of 48 chemicals. This logistic regression model was based on 24 hr γH2AX and 24 hr p53 fold-change data. Chemicals are color-coded: clastogens = red circles, non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, as each represents a different concentration. These data show that 9/11 clastogens were correctly classified by this two-factor model, while 36/37 non-genotoxicants were correctly classified.

Figure 7.

Figure 7

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Logistic regression probabilities for a clastogen classification are graphed for each of 48 chemicals. This logistic regression model was based on 4 hr γH2AX and 24 hr p53 fold-change data. Chemicals are color-coded: clastogens = red circles, non-genotoxicants = green triangles. A series of probabilities are plotted for each chemical, as each represents a different concentration. These data show that 9/11 clastogens were correctly classified by this two-factor model, while 36/37 non-genotoxicants were correctly classified.

ACKNOWLEDGMENTS

This work benefited from genetic toxicology experts that contributed to the experimental design used herein, and made valuable suggestions about chemical selection as well as the graphical representation of the data. We are indebted to Maik Schuler, Randy Spellman, Maria Engel, Jennifer Cheung, Andreas Zeller, Melanie Guérard, Valerie Naessens, Andreas Sutter, Sabrina Wilde, Marian Raschke, Miriam Dambowsky, Marlies Nern, Elisabeth Lorge, Véronique Thybaud, Ulrike Hemmann, Michael Ruppert, Birgit Meyerhoefer, and Pekka Heikkinen.

This work was funded by a grant from the National Institute of Health/National Institute of Environmental Health Sciences (NIEHS; grant no. R44ES024039). The contents are solely the responsibility of the authors, and do not necessarily represent the official views of the NIEHS.

Footnotes

AUTHOR CONTRIBUTIONS

All authors contributed to experimental design. DTB and SMB executed various aspects of the experiments. An outline of the paper was developed by SDD, and all authors contributed to the writing of the final version.

CONFLICT OF INTEREST STATEMENT

Several of the authors are employed by Litron Laboratories. Litron has filed a patent covering the flow cytometry-based assay described in this manuscript and plans to sell a commercial kit based on these procedures (i.e., MultiFlow™ DNA Damage Kit—p53, γH2AX, Phospho-Histone H3).

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