Toward the Design of Less Hazardous Chemicals: Exploring

Oct 17, 2016 - Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States. ∥ C...
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Toward the Design of Less Hazardous Chemicals: Exploring Comparative Oxidative Stress in Two Common Animal Models Jone Corrales,† Lauren A. Kristofco,† W. Baylor Steele,† Gavin N. Saari,† Jakub Kostal,‡ E. Spencer Williams,† Margaret Mills,§ Evan P. Gallagher,§ Terrance J. Kavanagh,§ Nancy Simcox,§ Longzhu Q. Shen,∥ Fjodor Melnikov,∥ Julie B. Zimmerman,∥,⊥ Adelina M. Voutchkova-Kostal,‡ Paul T. Anastas,∥ and Bryan W. Brooks*,† †

Department of Environmental Science, Baylor University, Waco, Texas 76798, United States Department of Chemistry, George Washington University, Washington, D.C. 20052, United States § Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States ∥ Center for Green Chemistry and Green Engineering, Yale University, New Haven, Connecticut 06520, United States ⊥ Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06520, United States ‡

S Supporting Information *

ABSTRACT: Sustainable molecular design of less hazardous chemicals presents a potentially transformative approach to protect public health and the environment. Relationships between molecular descriptors and toxicity thresholds previously identified the octanol−water distribution coefficient, log D, and the HOMO−LUMO energy gap, ΔE, as two useful properties in the identification of reduced aquatic toxicity. To determine whether these two property-based guidelines are applicable to sublethal oxidative stress (OS) responses, two common aquatic in vivo models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio), were employed to examine traditional biochemical biomarkers (lipid peroxidation, DNA damage, and total glutathione) and antioxidant gene activation following exposure to eight structurally diverse industrial chemicals (bisphenol A, cumene hydroperoxide, dinoseb, hydroquinone, indene, perfluorooctanoic acid, R-(−)-carvone, and tert-butyl hydroperoxide). Bisphenol A, cumene hydroperoxide, dinoseb, and hydroquinone were consistent inducers of OS. Glutathione was the most consistently affected biomarker, suggesting its utility as a sensitivity response to support the design of less hazardous chemicals. Antioxidant gene expression (changes in nrf 2, gclc, gst, and sod) was most significantly (p < 0.05) altered by R-(−)-carvone, cumene hydroperoxide, and bisphenol A. Results from the present study indicate that metabolism of parent chemicals and the role of their metabolites in molecular initiating events should be considered during the design of less hazardous chemicals. Current empirical and computational findings identify the need for future derivation of sustainable molecular design guidelines for electrophilic reactive chemicals (e.g., SN2 nucleophilic substitution and Michael addition reactivity) to reduce OS related adverse outcomes in vivo.

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INTRODUCTION

less hazardous chemicals to protect public health and the environment. These efforts are particularly timely and critical because limited to no toxicity data exist for the ∼85,000

One of the grand challenges to achieving sustainable environmental quality is the reduction of adverse health and ecological outcomes from chemicals in commerce. Rational molecular design presents a potentially transformational approach to address this challenge.1 It is inspired by the fourth principle of green chemistry,2 which aims to identify, develop, and select © 2016 American Chemical Society

Special Issue: Systems Toxicology II Received: July 14, 2016 Published: October 17, 2016 893

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Chemical Research in Toxicology Table 1. Structures, Properties, and Mechanistic Domain Designations of Study Chemicals

toxicity strategies in response to chemical regulation (e.g., REACH).6 Unfortunately, because these chemical classification approaches often rely on existing information from standardized toxicity studies with descriptive endpoints (e.g., an acute LC50 value), mechanistic linkages to specific adverse outcome pathways (AOP) of nonstandardized toxicity endpoints associated with specific molecular initiating events (MIE) are rare.7 To enable sustainable molecular design, it is critical to (1) identify attributes of chemicals eliciting specific MIE and (2) support the design and selection of alternative chemicals with minimal biological activity. Our research team has previously reported an in silico approach to advance the rational molecular design of chemicals with reduced standardized aquatic acute and chronic toxicity. These initial design guidelines are based on relationships among molecular descriptors of organic industrial compounds and toxicity thresholds for common standardized aquatic toxicity models.8−10 Two properties, also known as the “Rule of 2,” were identified to describe a less hazardous chemical space associated with bioavailability (the octanol−water partition coefficient, log P) and the energy gap between the highest occupied and lowest unoccupied frontier orbitals, ΔE.8,9 Compounds with log P values 9 eV were found to be significantly less likely to be hazardous to aquatic organisms. For ionizable chemicals, Kostal et al.10 further examined the octanol−water distribution coefficient (log D, rather than log P); identified the utility of molecular volume (V) as an additional descriptor; and proposed new log D and ΔE cutoffs based on density functional theory calculations (log D7.4 < 1.7; ΔE > 6). The potential implications of these observations appear clear. Using probabilistic hazard assessment (PHA) modeling, we predicted that the percent of

industrial chemicals in commerce. Such paucity of basic hazard information has resulted from a large number of new chemicals routinely introduced to commerce and a backlog of ∼62,000 chemical substances grandfathered when the Toxic Substances Control Act (TSCA) was passed by the US Congress in 1976 (http://www.epa.gov/tsca-inventory). High cost and limited mechanistic insights on chemical toxicity from descriptive regulatory toxicity testing further challenge efficient and effective assessments of industrial chemicals.3 Consequently, the US Environmental Protection Agency (EPA) initiated the Toxicity Forecaster (ToxCast) program, which uses in vitro high-throughput screening (HTS) methods and predictive computational toxicology models with a goal to rapidly identify and prioritize chemicals without the need for whole animal studies.4,5 Various in silico approaches have been developed and implemented by international regulatory agencies to rapidly predict toxicity thresholds (e.g., LC50 values) of new and unstudied chemicals using quantitative structure−activity relationships (QSARs). However, QSAR predictions are not universally applicable to the entire chemical space and have variable accuracy of prediction due in part to uncertainties in empirical data. As a result, regulatory agencies apply larger modifying factors (e.g., 10−1,000) to these toxicity predictions during tiered testing schemes. Outputs from related regulatory activities include identifying and classifying the relative toxicity of organic compounds (“very highly toxic,” “highly toxic,” “moderately toxic,” “slightly toxic,” or “practically nontoxic”). Chemicals are also categorized based on structural similarity, which is increasingly used for “read across” (i.e., toxicity data interpolation for related substances when toxicity data for a specific chemical is absent) during integrated or intelligent 894

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Chemical Research in Toxicology chemicals in commerce classified as “highly toxic” and “very highly toxic” for aquatic toxicity could be appreciably diminished if they were replaced with substances that follow the Rule of 2 design guidelines.11 However, Connors et al.11 identified several classes of potential outliers, including aldehydes, alcohols, and ketones. Because aldehydes readily hydrolyze in water, the resulting hydrolyzed form of aldehydes would have different log P (or log D) and ΔE values, thus explaining why this class of compounds did not strongly follow the Rule of 2. Alcohols were only found to follow the log P criteria for reducing aquatic toxicity possibly because baseline narcosis is the MOA of this class of compounds in the standardized aquatic model organisms and endpoints examined. For ketones, the analysis included a limited number of compounds. Connors et al.11 further identified a need to advance sustainable molecular design guidelines for chemicals sublethally eliciting adverse outcomes through specific and nonspecific MIEs, such as receptor-mediated responses and oxidative stress (OS), respectively. OS results from an imbalance between production of reactive oxygen species (ROS) and cellular antioxidant defenses; this imbalance leads to cellular damage.12−15 The transcription factor nrf 2 (nuclear factor erythroid 2-like 2, also known as NFE2L2) is recognized as a key oxidative stress sensor that binds to DNA antioxidant response elements (ARE) leading to the activation of antioxidant genes such as certain glutathione peroxidases and glutathione S-transferase isoforms, glutamatecysteine ligase catalytic subunit, superoxide dismutase, and catalase, as well as increasing cellular glutathione concentrations.12,16−18 Other cellular damage mechanisms resulting from OS include lipid peroxidation (e.g., oxidants damage lipids containing carbon−carbon double bonds)13,19 and DNA damage (e.g., formation of 8-hydroxy-2-deoxyguanosine and 8-hydroxyguanine due to oxygen radicals).13,20 Environmental exposure to many organic chemicals resulting in OS has been linked to a wide range of adverse outcomes from cancer to diabetes, atherosclerosis, and neurodegenerative disorders such as Alzheimer’s, Parkinson’s, and Huntington’s diseases.12,18 The primary objective of the present study was to determine whether current two property-based guidelines for the design of less hazardous chemicals to aquatic organisms can be applied to sublethal OS responses caused by chemical exposure. We selected eight model industrial chemicals with different traditional chemical classifications known to cause OS. We employed the two most common fish toxicology models, the fathead minnow (Pimephales promelas) and zebrafish (Danio rerio), to determine whether (1) previously reported design guidelines for aquatic toxicity could be applied to standardized and sublethal responses and to understudied chemicals; and (2) differences in sensitivity exist among common OS biomarkers (lipid peroxidation, DNA damage, glutathione, or antioxidant gene activation) to specific chemical categories.

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Louis, MO, USA). A common stock solution of each compound from a specific chemical batch was used for experimental treatment levels in concurrent toxicology experiments with both fish models. Fish Model Cultures. Fathead Minnow. Fathead minnows (P. promelas) were maintained following standard culture conditions at Baylor University. Individuals were housed in a flow-through system supplied with aged, dechlorinated tap water at a constant temperature of 25 ± 1 °C under a 16:8 light/dark photoperiod. Fish were fed twice daily with brine shrimp (Artemia sp. nauplii; Pentair AES, Apopka, FL) and TetraMin Tropical Flakes (Pentair AES, Apopka, FL, USA). Individuals were aged to at least 120 d before breeding at which time they were placed in tanks in a 1:4−5 male to female ratio. Embryos were collected, and within 24 h posthatched larvae were used for toxicity studies. Zebrafish. Tropical 5D wild type zebrafish (D. rerio) were maintained following standard culture conditions at Baylor University as previously described.21,22 Briefly, adult fish were kept at a density of 100 mg/L).27 Statistical Analysis. Mean lethal concentrations of initial 96 h experiments (96h LC50) were estimated in R (version 3.0.1) using the package “drc” (R Core Team 2014). Relationships between mortality and sublethal (biochemical biomarkers, gene expression) toxicity data and chemical properties were examined using Sigma Plot (La Jolla, CA, USA). Data sets were first analyzed by the Kolmogorov−Smirnov test to determine normal distribution assumptions. Lipid peroxidation, DNA damage, and glutathione data were analyzed by one-way ANOVA followed by Dunnet’s posthoc test. Statistical differences in gene expression between treatments were determined on the linearized 2−ΔCt values by one-way ANOVA followed by Tukey’s posthoc test. For bisphenol A, cumene hydroperoxide, dinoseb, and indene, solvent controls were used for statistical analysis after determining that no significant differences were observed for any response between the negative and solvent control groups. Statistically significant responses were accepted at p ≤ 0.05. Computational Approach. The importance of frontier orbitals on chemical reactivity is well-known and is summarized in the frontier molecular orbital (FMO) theory pioneered by Kenichi Fukui.28 Energy separation between the highest occupied and lowest unoccupied molecular orbitals (HOMO−LUMO gap) has long served as a simple measure of kinetic stability. A molecule with a small or no HOMO− LUMO gap is considered chemically reactive for covalent bonding. HOMO−LUMO energies and reactivity parameters were computed as previously described.10 Briefly, Monte Carlo (MC) simulations in aqueous solution (TIP4P water model) were used to sample the conformational space of each compound in the data set and locate the ground (i.e., lowest energy) states.29 HOMO and LUMO energies were derived from density functional theory, DFT (mPW1PW91/ MIDIX+), and the Universal solvation model (SMD) was used in orbital calculations to estimate the influence of hydration.30,31 Bond dissociation energies for phenols and peroxides were calculated at the M06-HF/6-31+G* level of theory in the gas phase. MC simulations were carried out using BOSS 4.7, and DFT calculations were performed using Gaussian 09 Rev.D.01.32,33

natural product of lipid peroxidation in living organisms. Thiobarbituric acid (TBA) was added to the samples, and an MDA-TBA adduct was formed. Elevated MDA present in a sample is proportional to elevated adducts formed, which indicates lipid peroxidation. DNA Damage. Oxidative damage to nucleic acids has been associated with a variety of diseases. We used a commercially available enzyme immunoassay (EIA) to measure DNA oxidative damage (Cayman Chemical Company, Ann Arbor, MI, USA). Prior to performing the DNA oxidative damage immunoassay, we extracted DNA from fathead minnows or zebrafish by homogenizing tissue in DNAzol (Molecular Research Center, Cincinnati, OH, USA) following the manufacturer’s instructions. After the DNA was quantitated using the NanoDrop2000 (Thermo Scientific, Wilmington, DE, USA), 5 μg of DNA per sample was used to determine DNA damage following the manufacturer’s instructions (Cayman Chemical Company). The oxidatively damaged guanine species in DNA and an added 8-OHdG-acetylcholinesterase conjugate competed for an oxidative damage monoclonal antibody. This antibody oxidatively damaged guanine complex then bound to a goat polyclonal antimouse IgG. The intensity of the signal was inversely proportional to the amount of free 8-OHdG (8-hydroxy-2-deoxyguanosine) or damaged DNA. Glutathione. Glutathione (GSH) was determined by a modified Tietze assay using a commercially available kit (Cayman Chemical Company, Ann Arbor, MI, USA). GSH is a tripeptide (γglutamylcysteinylglycine) widely distributed in organisms and serves as a cosubstrate to glutathione transferases in the detoxification of xenobiotics and is an essential electron donor to glutathione peroxidases in the reduction of hydroperoxides. Samples were first deproteinated with 1.25 M metaphosphoric acid and 0.2 M triethanolamine. DTNB (5,5-dithio-bis-2-nitrobenzoic acid, Ellman’s reagent) was then added. The sulfhydryl group of GSH present in the fish homogenates reacted with DTNB to produce 5-thio-2-nitrobenzoic acid (TNB). The rate of TNB production was measured, which is directly proportional to the concentration of GSH in a sample. Antioxidant Gene Expression Using qPCR. To determine how exposure to each of the eight chemicals affected genomic activity associated with OS, changes in mRNA abundance were measured for gclc, gst, nrf 2, and sod (specific isoforms measured in zebrafish were gst p1, nrf 2a, and sod1, though due to the poor annotation of this gene family in fathead minnows we were not able to identify the specific isoforms in this species). RNA from fish larvae was isolated with RNAzol (Molecular Research Center, Cincinnati, OH, USA) and purified with RNeasy Mini Kit (Qiagen, Valencia, CA, USA). Total RNA (500 ng) was reverse transcribed to cDNA by using TaqMan Reverse Transcription Reagents (Applied Biosystems by Life Technologies, Carlsbad, CA, USA) to yield a 25 ng/μL cDNA reaction. Next, relative abundance of target genes was determined by real-time reverse transcription polymerase chain reaction (quantitative PCR, qPCR). The reaction included 1 μL of cDNA, 300 nM final concentration of each forward and reverse primers, and 1× Power SYBR Green PCR Master Mix (Applied Biosystems by Life Technologies, Carlsbad, CA, USA). Primer sequences and amplicon size are listed in Table S3. Prior to performing assays, amplification efficiencies of all primer pairs were determined at ≥90%. Using a StepOnePlus Real-Time PCR System (Applied Biosystems by Life Technologies, Carlsbad, CA, USA), gene amplification reaction conditions were 95 °C for 10 min, followed by 40 cycles of 95 °C for 10 s, and 60 °C for 1 min. Reaction of each sample was performed in triplicate. After studying the results of well-recognized reference genes such as beta-actin (actb), glyceraldehyde-3-phosphate dehydrogenase (gapdh), and hypoxanthine phosphoribosyltransferase 1 (hprt1), the geometric mean of actb1 and gapdh for zebrafish and that of act, gapdh and hprt1 for fathead minnow were generally used as controls to normalize the starting quantity of mRNA in target genes with three exceptions in zebrafish (only actb1 was used as a reference following exposure to cumene hydroperoxide, indene, and R(−)-carvone due to variation in gapdh) and three exceptions in fathead minnow (only gapdh was used as reference gene following exposure to cumene hydroperoxide, only gapdh and hprt1 following

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RESULTS AND DISCUSSION Acute Toxicity of Model Chemicals. Prior to initiating the present study, very limited aquatic toxicity information was available for the study chemicals, with the exception of bisphenol A, which has been the subject of extensive study because of concerns related to endocrine disruption and modulation.34 The zebrafish 96 h LC50 value of 12.8 mg/L for bisphenol A in the present study is similar to LC50 values (8.6, 8.04 mg/L) previously reported for zebrafish embryos.35,36 In addition, the fathead minnow LC50 for bisphenol A (4.2 mg/L) in the present study is also consistent with a previously reported 96 h LC50 (4.6 mg/L).37 LC50 values of the eight study chemicals varied over multiple orders of magnitude for fathead minnow (0.06−413.2 mg/L) and zebrafish (0.19−176.9 mg/L) models. Acute toxicity was similar for some compounds between the fathead minnow and zebrafish models, while they were very different for others 896

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highly toxic (0−0.1 mg/L) to the fathead minnow and zebrafish, respectively (Table 2). Bisphenol A fell in the category of moderately toxic (1−10 mg/L) for both fish models. As noted above, markedly different toxicity thresholds were observed between fish models for hydroquinone; a very highly toxic classification was observed for the fathead minnow though this substance was considered slightly toxic to zebrafish (Table 2). To maximize comparisons of study findings, we employed standardized experimental designs from regulatory or international organizations for both the fathead minnow and zebrafish. It is important to note the initiation of toxicity studies with these fish models focuses on early life stages when organisms are believed to be more susceptible to contaminants. The OECD FET method for zebrafish used in the present study initiates toxicity studies with organisms within 24 h of fertilization; however, the fathead minnow toxicity studies are initiated with organisms 24 h following hatching from eggs. Thus, the developmental age of the two common fish models employed in this study differed during initiation of toxicity experiments. Though very few studies have examined comparative toxicology between the zebrafish and fathead minnow models, the effect of chemical exposure and developmental timing has previously been examined in select fish models. For example, the toxicity of organophosphate (OP) insecticides increases during developmental age in the fish model Japanese medaka, apparently due to increased metabolism with age.41,42 Our more recent studies with embryonic zebrafish observed similar increases in LC50 values with developmental age during exposure to the OP diazinon.22 Acute Toxicity Data and the “Rule of 2” Design Guidelines for Safer Aquatic Chemicals. We compared acute toxicity observations for the study chemicals to the “Rule of 2” design guidelines. This rule suggests that chemicals with octanol/water distribution coefficients at pH 7.4 (log D7.4) less than 1.7 log units and HOMO−LUMO energy gap (ΔE) greater than 6 eV are five times more likely to be acutely safer to the fathead minnow (e.g., LC50 > 500 mg/L) (Figure 1).

(Figure 2 and Figure S1). For example, LC50 values were quite similar between fathead minnow and zebrafish models for dinoseb (0.16 and 0.19 mg/L, respectively), indene (50.9 and 48.8 m/L, respectively), and R-(−)-carvone (58.6 and 58.2 mg/ L, respectively) (Table 2 and Figure S1). Interestingly, Table 2. LC50 (mg/L) Values for Fathead Minnow and Zebrafish Following 96 h Exposure to Eight Compounds and Corresponding Acute Toxicity Classification 96 h LC50 (mg/L) fathead minnow

zebrafish

acute toxicity classificationa fathead minnow

bisphenol A

4.2

12.8

cumene hydroperoxide dinoseb

12.7

23.4

moderately toxic slightly toxic

0.16

0.19

highly toxic

hydroquinone

0.06

14.3

indene

50.9

48.9

very highly toxic slightly toxic

perfluorooctanoic acid R-(−)-carvone

413.2

24.6

nontoxic

58.6

58.2

slightly toxic

77.1

175.9

slightly toxic

tert-butyl hydroperoxide

zebrafish slightly toxic slightly toxic highly toxic slightly toxic slightly toxic slightly toxic slightly toxic nontoxic

a

Acute toxicity categories as outlined by the US Environmental Protection Agency guidelines (USEPA 2010): very highly toxic (0−0.1 mg/L), highly toxic (0.1−1 mg/L), moderately toxic (1−10 mg/L), slightly toxic (10−100 mg/L), and practically nontoxic (>100 mg/L).

bisphenol A, cumene hydroperoxide, and tert-butyl hydroperoxide were two to three times more acutely toxic to fathead minnow than zebrafish. Further, the fathead minnow model was over 2 orders of magnitude more sensitive to hydroquinone than the zebrafish model (Table 2). Because CYP-mediated metabolism changes during fish development and fathead minnows were at a more advanced developmental stage than zebrafish in the present study, increased toxicity of bisphenol A and hydroquinone to the fathead minnow model may have resulted from increased biotransformation of the parent molecules to more active metabolites.38 Interestingly, perfluorooctanoic acid was ∼17 times more toxic to zebrafish than to the fathead minnow model (Table 2). Because water pH can alter chemical ionization state, bioavailability, and aquatic toxicity, our previous research identified the importance of accounting for pH influences on the bioavailability of ionizable chemicals during aquatic toxicology studies.39,40 For all toxicity experiments in the present study with both fathead minnow and zebrafish, experimental conditions were maintained at pH 7.5; thus, any observed differences in toxicity responses between model organisms for specific ionizable chemicals were not related to pH influences on bioavailability. When toxicity observations from the present study were classified by level of concern for acute toxicity data,27 differences among chemicals and between models were observed. For example, cumene hydroperoxide, indene, perfluorooctanoic acid, R-(−)-carvone, and tert-butyl hydroperoxide were considered either slightly toxic (10−100 mg/L) or nontoxic (>100 mg/L) for both fish models (Table 2). Dinoseb was classified as highly toxic (0.1−1 mg/L) or very

Figure 1. Scatter plot of the octanol−water distribution coefficient (logD7.4) vs energy difference between the highest occupied and the lowest unoccupied frontier orbitals (ΔE) for the compounds under study, colored by category of concern for acute aquatic toxicity: red, high concern (LC50 < 1 mg/L); orange, medium concern (LC50 1− 100 mg/L); yellow, low concern (LC50 100−500 mg/L); green, no concern, LC50 > 500 mg/L).

None of the chemicals in this study meets the Rule of 2, and none has LC50 > 500 mg/L in fathead minnow. The only chemical close to following the Rule of 2 was perfluorooctanoic acid, which had a LC50 of 413.2 mg/L for the fathead minnow and thus met the log D7.4 guideline (log D7.4 = 1.57, less than 1.7) while approaching the guideline for ΔE (ΔE = 5.33 eV, not greater than 6 eV). The example of perfluorooctanoic acid underscores the benefit of using log D7.4, which takes into 897

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Chemical Research in Toxicology account ionization, versus log P, which does not. The log P of perfluorooctanoic acid is 6.44, which is not an accurate reflection of the bioavailability of the chemical as it is almost entirely ionized at the pH the assays were performed (7.5). The majority of the other chemicals fail to meet both criteria (e.g., log D7.4 > 1.7 and ΔE < 6 eV); according to the Rule of 2, these properties make the chemicals likely to be more toxic to the fathead minnow and potentially zebrafish. Though the Rule of 2 was consistent with the acute toxicity data for the eight chemicals in terms of correctly predicting that none of these have LC50 > 500 mg/L, it is severely limited in that it cannot further inform about the relative toxicity of the chemicals. The reason the Rule of 2 is limited is that it uses ΔE, a nonspecific parameter that is associated broadly with acid− base reactivity only to gauge the tendency for “reactive” MOAs (beyond narcosis). The eight chemicals in the present study likely exert OS by specific molecular mechanisms. Thus, we sought to probe whether MOA classifications would help us group the chemicals in a manner reflective of the relative acute toxicity. Classification by Modes/Mechanisms of Action. We first applied the well-established Verhaar et al.25 classification scheme to the eight chemicals. The Verhaar classifications of the compounds are listed in Table 1. The eight chemicals examined in the present study did not follow a narcosis pattern (no relationship between log D and LC50; Figure 3) for either

Figure 3. Relationship between log P and 96 h-LC50 values (mg/L) of eight industrial chemicals in fathead minnow and zebrafish models.

Verhaar classifications are informative but do not help distinguish chemicals based on relative hazard. They also do not distinguish between electrophiles, pro-electrophiles, and peroxide-forming chemicals, all of which have distinct molecular mechanisms of interaction with cellular targets.46 Unfortunately, the current Verhaar classification scheme was not designed to identify different types of specific MOAs within class 3 or 4 categories. Given the inadequacy of the Verhaar classification to address chemicals that cause OS, we applied a structural screen recently developed by the authors to distinguish the key types of molecular interaction (Table 1). This classification distinguishes electrophiles (and identifies different types), proelectrophiles, and peroxides based on substructural features. Hydroquinone, bisphenol A, and dinoseb were classified as proMichael Acceptors (PRO-MA), meaning that they are likely metabolized to reactive Michael acceptors. This classification is consistent with literature reports that indicate bisphenol A is metabolized in humans to BPA-glucuronide and to bisphenol oquinone, which is a Michael acceptor that covalently modifies DNA.47,48 Similarly, hydroquinone is expected to be metabolized to 1,4-benzoquinone, which is highly reactive and toxic.49 Two of the chemicals were classified as reactive peroxides (cumene hydroperoxide, t-butyl hydroperoxide), while R(−)-carvone was classified as a direct Michael acceptor (MA). It should be noted though that R-(−)-carvone is not expected to be a highly reactive MA due to the steric hindrance at the gamma carbon. Oxidative Stress: Comparative Biochemical and Transcriptional Responses. For decades, indices such as cellular lipid peroxidation, DNA damage, and glutathione depletion have been considered gold standard biomarkers of toxicity in mammalian50−53 and aquatic animal models54−58 in studies of chemical-induced OS. In addition to these common biochemical biomarkers, transcriptional changes in antioxidant genes such as superoxide dismutase (sod), nrf 2, glutathione Stransferase (gst), and glutamate cysteine ligase catalytic (gclc) subunit are now considered important genotypic indicators of OS.59 However, the majority of previous OS studies have evaluated one or several of these biochemical or molecular responses in a single in vivo model system following exposure to a single chemical. Unfortunately, it is very rare to find comparative studies in the refereed literature that examined several of these biomarkers using multiple model systems. In

Figure 2. Relationship between log 96 h-LC50 toxicity values (mg/L) of eight industrial compounds for fathead minnow and zebrafish. BPA, bispnenol A; CHP, cumene hydroperoxide; DNS, dinoseb; HQ, hydroquinone; IND, indene; PFOA, perfluorooctanoic acid; RCar, R(−)-carvone; and tBHP, tert-butyl hydroperoxide.

fish model. This observation is consistent with the fact that only one compound (indene) was classified as Verhaar class 1, while the remaining compounds included two chemicals in class 2 (polar narcosis), four in class 3 (electrophile), and one in class 4 (specific mechanism; Table 1). Dinoseb, the only class 4 chemical in the group, is a herbicide for which MOA include inhibition of photosynthesis, respiration, energy transfer, and lipid, protein and RNA synthesis.43,44 Bisphenol A and hydroquinone exert acute toxicity through polar narcosis, a Verhaar class slightly more potent than class 1 narcosis.45 The remaining compounds (cumene hydroperoxide, perfluorooctanoic acid, R-(−)-carvone, and tert-butyl hydroperoxide) have electrophile, proelectrophile, or peroxide-forming activity. The 898

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Table 3. No Observed Effect Concentration (NOEC) and Lowest Observed Effect Concentration (LOEC) (mg/L) Values to Elicit Lipid Peroxidation, DNA Damage, and Glutathione Response in Fathead Minnow and Zebrafish Following a 96 h Exposure to Eight Compoundsa fathead minnow lipid peroxidation

DNA damage

zebrafish glutathione

lipid peroxidation

DNA damage

glutathione

Chemical

NOEC (mg/L)

LOEC (mg/L)

NOEC (mg/L)

LOEC (mg/L)

NOEC (mg/L)

LOEC (mg/L)

NOEC (mg/L)

LOEC (mg/L)

NOEC (mg/L)

LOEC (mg/L)

NOEC (mg/L)

LOEC (mg/L)

BPA CHP DSN HQ IND PFOA RCar tBHP

1.69 5.09 0.064 1.69 >5.09 >0.064 0.0032 >20.36 >165.28 >23.44 >30.80

1.69 0.64 0.064 0.0256 20.36 165.28 23.44 30.80

>1.69 1.27 >0.064 >0.0256 >20.36 >165.28 >23.44 >30.80

1.69 5.09 5.09 0.008 >0.0256 10.18 >165.28 2.93 >30.80

0.64 0.077 9.85 5.82 >70.00

5.12 9.35 0.077 1.43 9.78 9.85 23.28 70.00

>5.12 >9.35 >0.077 9.85 >23.28 >70.00

5.12 9.35 0.077 1.43 19.56 9.85 5.12 >9.35 >0.077 19.56 >9.85 2.91 >70.00

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Chemical abbreviations: BPA, bisphenol A; CHP, cumene hydroperoxide; DNS, dinoseb; HQ, hydroquinone; IND, indene; PFOA, perfluorooctanoic acid; RCar, R-(−)-carvone; and tBHP, tert-butyl hydroperoxide.

fact, comparative in vivo model investigations of multiple biomarkers for multiple chemicals are lacking. Thus, in the present study we selected eight model industrial compounds to determine whether OS responses are consistent among several chemical classifications and between the two most common fish models used in toxicology studies. Response patterns (induction or depletion) observed in the present study were generally consistent with the refereed literature (Table S2). For example, cumene hydroperoxide was previously reported to increase lipid peroxidation in a rat model,60 which is consistent with zebrafish responses in the present study (Figure S2). Lipid peroxidation results from the formation of lipid peroxides that are generated when ROS binds methylene groups between or adjacent to ethylene bonds of polyunsaturated fatty acids; cellular phospholipid membranes are predominantly susceptible to peroxidation.61,62 The reaction involves hydrogen abstraction from a carbon, with oxygen insertion resulting in lipid peroxyl radicals and hydroperoxides, mainly alkanes and carbonylic compounds (ketones and aldehydes),63 which we previously identified as outliers for the Rule of 2.11 Cumene hydroperoxide has also previously been reported to cause DNA damage in fish sperm.64 In the present study, similar observations were observed with significant (p < 0.05; Figure S2) DNA damage elicited to the fathead minnow, but not in zebrafish. DNA-reactive aldehydes, such as those formed during lipid peroxidation, can damage DNA either by reacting directly with DNA bases or by generating more reactive bifunctional intermediates, which form DNA adducts.19,65 In a similar fashion to lipid peroxidation, hydroxyl radicals bind to double bonds of heterocyclic DNA bases and abstract a hydrogen atom from the methyl group of thymine and from each of the C−H bonds of 2-deoxyribose.66 DNA, unlike proteins and lipids, once modified, cannot be replaced and can only be repaired; this process has gained much attention in its involvement in the pathogenesis of a number of diseases. We observed significant (p < 0.05) glutathione depletion following exposure to a number of chemicals, including R(−)-carvone (Figure S2). Glutathione is a well-recognized biomarker for OS responses.67 It is the thiol group of GSH that is important in antioxidant defense, xenobiotic and eicosanoid metabolism, and cell cycle regulation.68 Glutathione does not directly react with hydroperoxides, instead it serves as the substrate for glutathione peroxidase, and this is the

predominant mechanism by which H2O2 and lipid peroxides are reduced. Cellular glutathione concentrations are known to decrease in response to oxidative stress and many pathological conditions.69 However, tert-butyl hydroperoxide did not significantly (p > 0.05) alter glutathione levels of either in vivo model in this study (Figure S2). Such observations are also consistent with previous research in the zebrafish model.70 In the present study, no single biochemical response was found to be more sensitive than other biomarkers across all compounds, and similarly no chemical significantly (p < 0.05) affected all three biomarkers (lipid peroxidation, DNA damage, and glutathione) in either of the fish models (Table 3 and Figure S2). However, several consistencies were observed among chemical toxicity thresholds across fish models and biochemical biomarkers (Table 3 and Figure S2). Bisphenol A, cumene hydroperoxide, dinoseb, and hydroquinone elicited more pronounced lipid peroxidation responses in both the fathead minnow and zebrafish models. Bisphenol A, dinoseb, and hydroquinone also more consistently caused DNA damage and glutathione depletion in both fish species. In addition, perfluorooctanoic acid and tert-butyl hydroperoxide exerted the least significant adverse effects on these biochemical biomarkers. R-(−)-carvone differed among the chemicals examined because while it did not significantly affect DNA damage, it caused significant (p < 0.05) changes in lipid peroxidation, and it also caused glutathione depletion in both fish models at relatively low concentrations. In fact, glutathione was the biochemical biomarker more consistently significantly affected by the eight chemicals examined, which highlights the utility of this biomarker as a sensitivity response to support the design of less hazardous chemicals. Control and regulation of antioxidant genes are part of the oxidative/electrophilic stress defense mechanism that includes the coordinated induction of hundreds of genes in response to xenobiotics, antioxidants, heavy metals, and UV light.59 Both constitutive and inducible expressions of these genes are regulated by a DNA element known as the antioxidant response element (ARE/core sequence GTGACNNNGC), which is located upstream of the antioxidant genes. The transcription factor nrf 2 binds to ARE resulting in the activation of antioxidant genes. Nrf 2 is ubiquitously expressed in a wide range of tissues and cell types and resides in the cytoplasm forming a heterodimer with the inhibitor Keap1 (Kelch-like ECH associating protein 1).12,13 Under OS, nrf 2 dissociates 899

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900

0.0032 (0.064 (0.064) 5.86b (2.93) >165.28 (165.28) >1.69 (1.69) >5.09 (5.09) >30.80 (30.80) >0.72 (0.72) >19.56 (19.56) >0.077 (0.077) 11.64b (5.82) >9.85 (9.85) >5.12 (5.12) >9.35 (9.35) >70.00 (70.00) >0.0256 (0.0256) >10.19 (10.19) >0.064 (0.064) 23.44b (11.72) >165.28 (165.28) >1.69 (1.69) 1.27b (30.80 (30.80) 4.07 4.20 5.12 8.44 21.08 24.65 no value no value hydroquinone indene dinoseb R-(−)-carvone perfluorooctanoic acid bisphenol A cumene hydroperoxide tert-butyl hydroperoxide

Abbreviations: FHM, fathead minnow; ZF, zebrafish; nrf 2, nuclear factor erythroid 2-like 2; gst, glutathione S-transferase; gclc, glutamate-cysteine ligase catalytic subunit; and sod, superoxide dismutase. Concentration that elicited a significant change in gene expression. cAC50 (mg/L) was obtained from EPA ToxCast database. The AC50 represents the concentration at which there was nrf 2 activity in HepG2 human liver cells. An AC50 value for cumene hydroperoxide was not provided in the database, and tert-butyl hydroperoxide was not listed among the chemicals that were tested. dNOEC is shown in parentheses. eChemicals are ordered starting with most toxic as per the AC50 concentration that elicited nrf2 activity. fSpecific isoforms of these genes measured in zebrafish were nrf 2a, gst p1, and sod1.

>0.0256 (0.0256) 5.1b (2.55) >0.064 (0.064) >23.44 (23.44) 20.66b (1.69 (1.69) 2.55b (30.80 (30.80) >0.72 (0.72) 9.78b (4.89) >0.077 (0.077) 11.64b (5.82) >9.85 (9.85) >5.12 (5.12) >9.35 (9.35) 70.00b (35.00)

FHM ZF FHM ZF HepG2 cells chemical

nrf2

FHM

nrf 2 e

b

a

b

>0.72 (0.72) >19.56 (19.56) >0.077 (0.077) >23.28 (23.28) >9.85 (9.85) 1.28b (0.64) 2.34b (1.17) >70.00 (70.00) >0.0256 (0.0256) >10.19 (10.19) >0.064 (0.064) >23.44 (23.44) >165.28 (165.28) >1.69 (1.69) 1.27b(30.80 (30.80) >0.72 (0.72) 4.89b (0.077 (0.077) 11.64b (5.82) >9.85 (9.85) 1.28b (0.64) >9.35 (9.35) >70.00 (70.00)

ZF

FHM

sod gclc f

gst f

gene expression (qPCR) of in vivo studies as a function of LOEC and NOECd ToxCast AC50c

Table 4. Comparison of the Concentration (AC50, mg/L) That Elicited nrf 2 Activity in Vitro to No Observed Effect Concentrations (NOEC) and Lowest Observed Effect Concentrations (LOEC) (mg/L) Corresponding to the Expression of the nrf 2 Pathway-Associated Genes (nrf 2, gst, gclc, and sod) in Fathead Minnow and Zebrafish Following a 96 h Exposure to Eight Compoundsa

through a series of phosphorylation events and translocates to the nucleus to bind ARE. The basic region of the molecule is responsible for DNA binding, and the acidic region is required for transcriptional activation. Regulation by nrf 2 is highly conserved across taxa.59 In the present study, we examined expression changes of four different genes (nrf 2, gclc, gst, and sod; the specific gene isoforms measured in zebrafish were nrf 2a, gst p1, and sod1)12,16−18 associated with OS following exposure to eight different compounds using the two common fish models. To our knowledge, this study represents the first time such a comparative in vivo toxicology approach has been attempted. As noted above, R-(−)-carvone and cumene hydroperoxide are categorized as Class 3 electrophiles, while bisphenol A appears to be metabolized to bisphenol o-quinone. These three chemicals elicited relatively more significant transcriptional responses than other chemicals examined. Collectively, these chemicals significantly altered expression of all four genes (nrf 2, gclc, gst, and sod) in at least one of the fish models (Table 4, Figure S3). For example, R-(−)-carvone significantly (p < 0.05) induced nrf 2 expression in both fish models, while bisphenol A and cumene hydroperoxide elicited nrf 2 activity in fathead minnow but not zebrafish (Table 4 and Figure S3). Though nrf 2 gene expression responses to R-(−)-carvone have not previously been examined, bisphenol A was previously reported to induce nrf 2 activity in zebrafish.71 In addition to nrf 2 activity, we examined sod1 responses to each chemical. Cu/Zn superoxide dismutase (sod1) is one of the major cellular defense enzymes that performs a vital role in protecting cells against toxic effects of superoxide radicals through not only ARE but also xenobiotic response elements (XRE), located between nucleotides −400 and −239 upstream of the sod1 gene.72 Cumene hydroperoxide was the only chemical that resulted in significant (p < 0.05) changes in sod or sod1 indicating that the changes exerted by this chemical were consistent between the two fish models. A decline in sod1 gene expression has previously been reported at 96 hpf in zebrafish following exposure to 3,3′,4,4′,5-pentachlorobiphenyl (PCB126); interestingly, the authors argued that an induction was observed at 132 hpf, a longer duration of exposure than the current study.73 However, a shorter 6 h exposure of 96 hpf zebrafish to tertbutyl hydroquinone or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) resulted in no change in sod1 expression.74 Glutathione S-transferase (gst) enzymes protect the cell from contaminant insults by conjugation of glutathione to electrophiles.75 Two supergene families encode proteins with gst activity: one family comprising genes that encode proteins expressed in the cytosol and the second comprising genes that encode proteins expressed in membranes.75 In the present study, we measured the cytosolic family of gst genes, which includes alpha, mu, theta, pi, zeta, sigma, kappa, and chi. We specifically studied gst pi 1 in zebrafish; however, in fathead minnow the annotation is not complete and does not yet extend to specific families of gst. We also examined expression changes of glutamate cysteine ligase (gcl), which catalyzes the rate-limiting step during glutathione synthesis. It is a heterodimeric protein composed of catalytic (gclc) and modifier (gclm) subunits; induction of gclc (glutamate-cysteine ligase catalytic subunit) expression is dependent on nrf 2.76,77 Whereas indene significantly (p < 0.05) induced the expression of gclc and gst in both fish models, hydroquinone and tert-butyl hydroperoxide induced (p < 0.05) gst expression in at least one of these in vivo models. Our observation of significant gst and

ZFf

Chemical Research in Toxicology

DOI: 10.1021/acs.chemrestox.6b00246 Chem. Res. Toxicol. 2017, 30, 893−904

Article

Chemical Research in Toxicology gclc induction (p < 0.05) following exposure to tert-butyl hydroperoxide is consistent with previously reported gst induction in zebrafish.78 Though we are not aware of previous studies examining dinoseb influences on gene expression, no significant (p > 0.05) gene expression responses to dinoseb were observed in the present study. However, acute toxicity results and more traditional biochemical biomarker responses, particularly glutathione, consistently suggest that dinoseb was one of the more toxic substances examined. It appears that exposure duration and developmental age are important considerations in OS responses; however, very few studies have investigated differential gene expression throughout development. For example, Liu et al.73 examined exposure duration and developmental age concurrently. Zebrafish embryos were exposed to PCB126 starting at 3 hpf, and modulation in gene expression was evaluated at 24, 72, 96, and 132 hpf. In this experimental design, expression responses to both variables (exposure duration and age) changed over time.73 By contrast, a 6 h exposure period was always used to look at changes in gene expression at 24, 48, 72, 96, and 120 hpf zebrafish.74 It is apparent that OS associated transcript levels differ both constitutively and with respect to inducibility throughout development. Unfortunately, developmental studies of this nature are limited in zebrafish and lacking in the fathead minnow model. Though the aquatic toxicology literature includes numerous examples comparing sensitivities of various model organisms to specific chemicals (e.g., species sensitivity distributions), very few peer-reviewed studies have compared fish toxicity responses across model species during concurrent exposures to a single chemical. Even fewer studies have directly compared toxicity responses of any type between the two most common fish toxicology models, the fathead minnow and zebrafish. Most of these studies have identified acute toxicity thresholds, while very few efforts have examined whether sublethal responses such as genotoxicity, developmental deformities, or OS are consistent between these models.79−83 Interestingly, Warner et al.81 examined the similarity of transcriptional, morphological, and behavioral responses between three-day old zebrafish and fathead minnow exposed to a neurotoxicant (1,3,5-trinitroperhydro-1,3,5-triazine) for 96 h. The authors concluded that responses between zebrafish and fathead minnow were markedly different. As noted above, we used standardized toxicity methods to facilitate comparison of data from the present study. Because these standardized methods dictate initiating toxicology experiments with the two fish models at different developmental ages, such observations suggest that metabolism of compounds and the physicochemical attributes of the resulting metabolites causing molecular initiating events should be considered. In fact, the limited metabolism capacity of many high throughput assays, including in vitro models employed by Tox21, present challenges for computational toxicology efforts, including sustainable molecular design of less hazardous chemicals. Much work is needed to determine the extent to which various AOPs are conserved across species, given genome similarities among mammals and other vertebrate models.7,83 In future studies, we plan to examine influences of developmental stage to determine whether OS differences or similarities at this early stage in life are species and/or age dependent. Computational Insights. When all compounds in this study were ranked by either LC50 values or LOEC and NOEC values for each OS biochemical marker, bisphenol A, cumene

hydroperoxide, dinoseb, and hydroquinone were consistently the most toxic. The observed trends in acute toxicity and biochemical responses of these four compounds in addition to tert-butyl hydroperoxide (long studied as an OS inducer) were briefly considered by computations. In the presence of Fe(II) or Fe(III), peroxides yield peroxy (ROO) and alkoxy (RO) radicals, which have been implicated in DNA base damage. The O−O and O−H bond dissociation energies (ΔH) were calculated at the M06-HF/6-31+G* level of theory for cumene hydroperoxide and tert-butyl hydroperoxide in the gas phase. While no significant difference was observed in the relative stability of the ROO radicals ( 6 for standardized aquatic toxicity responses could be applied to other standardized and sublethal OS responses for understudied chemicals. Comparative toxicity of study chemicals, which were selected to possess ΔE values