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Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor
Ecotoxicology and Environmental Safety 148 (2018) 211–219
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor ⁎
Junyi Hea, Tao Pengb, Xianhai Yangc, , Huihui Liua,
MARK
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a Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China b State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; University of Chinese Academy of Sciences, Beijing, China c Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Jiang-wang-miao Street, Nanjing 210042, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Endocrine disrupting chemicals (EDCs) Estrogen receptor (ER) Quantitative structure-activity relationship (QSAR) Relative binding affinity (RBA) Fish species
Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17β-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together.
1. Introduction Maintaining the homeostasis of hormones is a prerequisite for organisms ensuring its normal growth and development. However, some naturally occurring or man-made chemicals, named endocrine disrupting chemicals (EDCs), can influence the homeostasis of hormones and disturb the function of endocrine system, thereby causing adverse health effects (Birnbaum, 2013; Zhou et al., 2017). In the past decades, due to the ubiquity and the ever-increasing number of hazardous chemicals in environment, endocrine disrupting effect has become a central point of concern. Especially, estrogenic contaminants, such as alkylphenols, organochlorine pesticides and phthalate esters, were the most detected in environment and have gained notoriety as EDCs (Chou et al., 2015; Miyagawa et al., 2014). Very often, these chemicals find ways (e.g., wastewater input and surface runoff) to enter aquatic environments, and make waterway a major sink of EDCs. Inevitably,
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Corresponding authors. E-mail addresses: [email protected] (X. Yang), [email protected] (H. Liu).
http://dx.doi.org/10.1016/j.ecoenv.2017.10.023 Received 31 July 2017; Received in revised form 6 October 2017; Accepted 9 October 2017 0147-6513/ © 2017 Elsevier Inc. All rights reserved.
aquatic organisms may thus be exposed to various EDCs, and take up EDCs through gill pumping or directly drinking water. As a result, EDCs concentrate on the organisms, get into the food web and finally reach into high level animal bodies. Generally, a chemical can exert its disrupting endocrine function through the following biological mechanisms: (a) impacting the hypothalamic-pituitary-gonad thyroid axis function and regulation, (b) inhibiting hormone synthesis, (c) disrupting hormonal transport protein, (d) activating/inhibiting hormone receptor, and (e) inhibiting hormone metabolism. In our previous studies, we have emphasized the mechanism of disrupting hormonal transport protein, by exploring the binding interaction of EDCs with sex hormone-binding globulin (Liu et al., 2016, 2017). But undeniably, among all these mechanisms, the mechanism of activating/inhibiting hormone receptor is the most focused. There are much more data and models on hormone receptor than those on transporter protein. Moreover, methods related to hormone
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Kloas et al., 2000; Leaños-Castañeda and Van Der Kraak, 2007; Leusch et al., 2006; Marchand-Geneste et al., 2006; Matthews et al., 2000; Molina-Molina et al., 2008; Nakai, 2003; Olsen et al., 2005; Passos et al., 2009; Rider et al., 2009; Schmieder et al., 2004; Secombes et al., 2009; Segner et al., 2013; Tollefsen et al., 2002; Tollefsen and Nilsen, 2008; Yost et al., 2014). The relative binding affinity (RBA) was calculated by comparing the IC50 (half of the inhibition concentration) of a test chemical with that of 17β-estradiol. In this study, the logarithm of relative binding affinity (log RBA) was employed to scale the binding affinity, which is defined as:
receptor also are recommended as standard methods by U.S. EPA, to rapidly ascertain whether a chemical is an endocrine disruptor (EDSTAC; Willett et al., 2011). Hence, exploring the binding interaction of estrogenic chemicals with typical hormone receptor can improve our understanding of endocrine disrupting effect. Especially, estrogen receptor (ER), as a member of the nuclear receptor superfamily, plays a critical role in the organisms’ growth, development and reproduction. ER functions as a ligand-dependent transcription factor, and the common test endpoints of endocrine disrupting effect mainly ascribe to the unnormal activation or inhibition of ER. To date, ER has been well-characterized in a variety of vertebrates, including fish, birds, amphibians, reptiles and mammals (Hawkins et al., 2000). Although the function of ER seems phylogenetically conserved, a significant interspecies variation in the amino acid sequence of ER ligand binding domain has been demonstrated, and thereby deriving a considerable difference in the transactivation ability and ligand preference. For instance, the rainbow trout ER exhibits a tenfold lower affinity for 17β-estradiol than that does the human ER; the pig ER exhibits a significantly greater affinity for α-zearalenol than that does the chicken ER; the rainbow trout ER exhibits a ten-fold higher affinity for 4-hydroxytamoxifen than that does the Atlantic salmon ER (Matthews et al., 2000; Tollefsen et al., 2002). Fish, as a typical representative of aquatic organism, is an important biological indicator of the degree of water pollution. Moreover, fish has been well-studied in characterizing the endocrine disrupting effect. As for the mechanism about ER, most studies focused only one fish species, especially rainbow trout (Benninghoff et al., 2010; Leaños-Castañeda and Van Der Kraak, 2007; Tollefsen and Nilsen, 2008), but less on the direct comparison of ER in different fish species (Miyagawa et al., 2014). Questions remain as to whether a chemical that binds to the ER in one fish species exert similar effects in other fish species, and the validity of extrapolating the estrogenic effects of a chemical between different fish species also has not been tested. Obviously, it is infeasible to experimentally test the binding affinity of each chemical to every fish species, which is usually laborious, time-consuming, expensive and equipment dependent. Computational modeling, especially the quantitative structure-activity relationship (QSAR) modeling, provides a promising tool for rapidly identifying whether a chemical being the ER activator or ER inhibitor. This study, following the Organization for Economic Cooperation and Development (OECD) guidelines on the development and validation of QSAR model (OECD, 2007), developed QSAR models those could predict the ER binding affinity of various chemicals in different fish species, so as to characterize the structural features those governing the binding behavior of chemicals, to screen potential ER disruptors from existing and emerging chemicals, and further to explore the species variation in ER binding affinity specificity of fish.
log RBA = log (
17β − estradiol IC50 × 100) test IC50
17β − estradiol test where IC50 and IC50 are the concentrations of 17β-estradiol and test chemical at 50% inhibition of [3H]−17β-estradiol binding to estrogen receptor, respectively. There were a total of 77, 39, 20, 15, 15, 15, 11 and 7 data points for Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream, respectively. In the modeling, except Sea bream, the datasets for each fish were randomly divided into a training set and a validation set with a ratio of 3:1. The training sets were used to establish QSAR models, while the validation sets were used to assess the external predictive ability of the established models. In this case, there were 19, 10, 5, 4, 4, 4 and 3 data points in validation sets for the aforementioned seven fish species, respectively; while the remaining data points were grouped into the training sets. With Sea bream, due to the small number of data points in its dataset, which including a total of seven chemicals, all the data were grouped into the training set. The names, CAS numbers, corresponding log RBA values of the chemicals are presented in Table S1 of the Supporting materials.
2.2. Calculation of molecular descriptors DRAGON descriptors were used for model development, which encode a series of parameters of a compound in its typical configuration. Before calculating the molecular descriptors, the molecular structures of compounds were preliminarily optimized with the minimize energy method (at the minimum RMS gradient of 0.001), which was contained in the ChemBio3D Ultra (Version 12.0) (Schnur et al., 1991). Then, the molecular structures of model compounds were further optimized by employing MOPAC 2012 software (Keywords: PM6 eps = 78.6 CHARGE = 0 EF GNOPRM = 0.01 POLAR MULLIK SHIFT = 80). Based on the optimized geometric structures from MOPAC, 4885 DRAGON descriptors were calculated by employing the DRAGON software (version 6.0) (Talete and srl, 2012), therein the default methods in the DRAGON 6 software were used to preliminarily select descriptors. Details for the exclusion rules were listed in the homepage of the DRAGON software (http://www.talete.mi.it/help/dragon_help/ index.html). As a result of this prereduction procedure, a final set of 1140, 1175, 796, 724, 804, 771, 659 and 471 DRAGON descriptors for Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream were retained, respectively.
2. Materials and methods 2.1. Data sets Commonly, ER has two subtypes: ERα and ERβ. Previous studies have proved that liver is an abundant source of ER and contains only the ERα isoform. Moreover, when exposure to estrogens, the response signal of ERβ is instable, while that of ERα is stable (Keith et al., 2000; Shen et al., 2013). Hence, the data points used in this study came from the response signal of ER fusion proteins or ERα subtype in liver. In the present study, we selected the binding affinity of chemicals to fish ER, as the test endpoint of endocrine disrupting effect. The fish species used in this study include Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream. The experimentally determined binding affinity of each chemical was collected from the corresponding literatures for the eight fish species (Benninghoff et al., 2010; Burnison et al., 2003; Costache et al., 2005; Cox and Bunce, 1999; Denny et al., 2005; Hawkins and Thomas, 2004;
2.3. Development and validation of QSAR models Stepwise multiple linear regression (MLR) analysis was employed to select variables and develop the QSAR models by using the SPSS software (SPSS 19.0), with the log RBA values (data points in training set) as the dependent variable and the molecular descriptors (DRAGON descriptors) as the predictor variables. In this case, the statistical parameters R2adj (adjusted determination coefficient), Q2LOO (leave-oneout cross-validated coefficient), Q2ext (external explained variance), bootstrap method (Q2BOOT) and RMSE (root mean square error) were 212
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established QSAR models can be used to predict the log RBA value of the untested compounds that fall within the applicability domains of the models.
calculated to assess the performance of the established model. Finally, the best model was selected, which should have the lowest number of molecular descriptors, the maximum values of R2adj, the minimum values of RMSE and the variable inflation factor (VIF) of less than 10 for each predictor variable. Additionally, the models should also comply with the QUIK rules, i.e., KXX (inter correlation of selected descriptors) < KXY (the correlation of the X block with Y), where X is the selected molecular descriptors’ matrix and Y is the response variable vector.
3.2. Applicability domain evaluation The applicability domains of QSAR models were evaluated by Euclidean distance-based approach and Williams plot. With the Euclidean distance-based approach, because only one descriptor was screened for Sea bream, its Euclidean distance plot could not be drawn. For other seven fish species, as shown in Fig. 2, nearly all the chemicals in both the training sets and the validation were in the acceptable region, implying that these training sets for the models represented the domains well. Exceptionally, for Atlantic salmon, there was one chemical (ethynylestradiol) locating outside the acceptable domain, which was identified as an outlier. This chemical has the largest CATS2D_03_DL value among all the chemicals in the dataset for Atlantic salmon. Reasons for the deviant prediction may be from the limited number of chemicals with the similar structure (alkynyl compound) in the dataset; or maybe the experimental value from the original literature was not accurate (Gramatica, 2007). The application domains were also characterized by Williams plots (Fig. 3). Notably, for Atlantic salmon and Sea bream, due to the number of chemicals in validation set less than the number of descriptors in the QSAR models, only the evaluation results for their training sets were displayed in the Williams plot. As shown, all the data points located at the domain of |δ| < 3, implying that there was no chemical that could be identified as outlier. Thus, chemicals in training sets had good representativeness, and the current model has reliable predictive ability for chemicals in validation sets. However, two chemicals (resorcinol sulfide and 4-t-butylbenzoic acid) in the training set of Rainbow trout and a chemical (estriol) in the training set of Zebrafish were found with hi > h*, it indicated that these chemicals had great influence on the models. Moreover, their observed and predicted log RBA agreed well with each other (−3.24 vs. −3.10, −4.82 vs. −4.99 and 1.00 vs. 1.39, respectively; Table S1), highlighting that they could enable the QSAR models to be more stable and precise. Meanwhile, a chemical (4-methylphenol) in the validation set of Rainbow trout was also found with hi > h*, and its predicted log RBA was ten-fold lower than the observed log RBA (−4.28 vs. −5.85, Table S1), it meant that this chemical was structurally distant from those used in the training set, and maybe it was incorrectly predicted by the established model. Overall, the developed models had a good performance, and the log
2.4. Applicability domain The applicability domain (AD) of the models was characterized by the Euclidean distance-based method or Williams plot. Details for the evaluation method were presented in our previous study (Liu et al., 2016). 3. Results and discussion 3.1. QSAR models for each fish species QSAR models, with the log RBA as the dependent variable and the molecular descriptors as the independent variable, were established. Based on the screening principles, the optimum QSAR models and their statistical parameters for each fish species are presented in Table 1. As shown, with the training sets, the adjusted correlation coefficient 2 2 square Rtra and the leave-one-out cross-validated coefficient QLOO ranged from 0.822 to 0.909 and 0.646 to 0.822, respectively. According to the criteria (Q2 > 0.5, R2 > 0.6) proposed by Golbraikh et al. (Golbraikh et al., 2003), these results implied high goodness-of-fit and robustness of models for the eight fish species. With the validation sets, 2 the adjusted correlation coefficient square Rext and the external ex2 plained variance Qext were in the range of 0.699–0.996 and 0.696–0.962, respectively, which showed good predictive ability of the models. Additionally, the models also complied with the QUIK rules, i.e., KXX < KXY for each fish species, which also proved good performance of the developed QSAR models. Description of screened descriptors involved in the models is listed in Table S2. As shown, the VIF values for all the predictor variables were less than 10, indicating that there was no serious multicollinearity among the variables. The plots of observed log RBA versus predicted log RBA are shown in Fig. 1, in which such good consistency illustrated good predictive ability of the developed QSAR models. Hence, the Table 1 Optimum QSAR models and statistical parameters for each fish species. Fish
ntra
Rainbow trout
QSAR model: log RBA = –166 + 0.435piPC09 + 76.9SpDiam_X + 0.422SaasC + 1.59IC1 – 1.20CATS2D_06_NL + 0.676SpMax2_Bh(s)+ 1.62MATS6m + 1.32Mor16u – 0.562F02[C-S] – 5.64TDB09p 58 0.822 0.753 0.804 19 0.794 0.701 0.975 0.296 0.326 QSAR model: log RBA = –23.8 – 2.65Mor23e + 4.23IC1 + 8.28TDB06u – 0.921F09[O-O] + 2.14G3e + 3.89E3s 29 0.844 0.651 0.792 10 0.699 0.696 0.944 0.225 0.280 QSAR model: log RBA = –12.3 + 0.575CATS2D_02_DL + 0.837RDF105m + 0.0428P_VSA_LogP_4 + 9.96PJI2 15 0.840 0.685 0.691 5 0.920 0.831 0.756 0.431 0.445 QSAR model: log RBA = 4.21 – 3.70GATS6s + 0.450C−001 11 0.887 0.813 0.309 4 0.966 0.962 0.175 0.233 0.573 QSAR model: log RBA = 6.94 + 3.38R6u – 17.5×0Av 11 0.857 0.646 0.635 4 0.924 0.894 0.478 0.119 0.516 QSAR model: log RBA = –2.46 + 2.40nArOH – 0.955Mor18u 11 0.908 0.822 0.365 4 0.918 0.854 0.476 0.006 0.477 QSAR model: log RBA = –3.75 + 337R8p + 0.396CATS2D_03_DL 8 0.909 0.692 0.557 3 0.996 0.799 0.555 0.629 0.780 QSAR model: log RBA = 8.49 – 7.99GATS7v 7 0.883 0.748 0.271
Zebrafish Common carp Atlantic croaker Fathead minnow Medaka Atlantic salmon Sea bream
2 Rtra
2 QLOO
RMSEtra
2 R ext
next
2 Qext
RMSEext
KXX
KXY
Notes: ntra and next are the numbers of chemicals in training set and validating set, respectively. RMSEtra and RMSEext are the root mean square errors of data in training set and validating set, 2 2 and R ext are the correlation coefficient square between observed and fitted values in training set and validating set, respectively. respectively. Rtra
213
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Fig. 1. Plots of the predicted versus observed log RBA values for the training sets and validation sets.
RBA values for nearly all the compounds were accurately predicted by the model. The developed QSAR models can be used to predict the log RBA values of structurally diverse chemicals, including natural/
synthetic estrogens, nonsteroidal estrogens, alkylphenols, alkylated non-phenolics, phytotoxins/mycotoxins, benzophenone derivatives, organochlorine pesticide, diphenylmethanes, pharmaceutical, and 214
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Fig. 2. Applicability domains for the developed models characterized by the Euclidean distance-based approach.
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Fig. 3. Plots of standardized residuals versus leverages (h). The transverse shot-dotted lines represent ± 3 standardized residuals, and the vertical shot-dotted line represents warningleverage value (h*).
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information about branching. R6u has a positive sign, while X0Av has a negative sign in the model. With the QSAR model for Medaka, two descriptors (nArOH and Mor18u) were screened. nArOH represents the number of aromatic hydroxyls, which is a crucial factor in many endocrine disrupters. Moreover, Tollefsen et al. paid special attention on alkylphenols and emphasized the importance of phenolic ring structure in ER binding ligands (Tollefsen and Nilsen, 2008). Mor18u is the unweighted signal 18 and it characterizes the molecules with great structural variance. This descriptor had negative contribution to log RBA values. With the QSAR model for Atlantic salmon, two descriptors (R8p and CATS2D_03_DL) were screened. R8p is the R maximal autocorrelation of lag 8 / weighted by polarizability. CATS2D_03_DL is the CATS2D DonorLipophilic at lag 03, which was related to the hydrogen-bond donor (-OH, -NH, -NH2) and lipophilic group. The two positive contributed the increase of log RBA. With Sea bream, owing to the small number of data points in its dataset, only one descriptor (GATS7v) was screened for the QSAR model. GATS7v denotes Geary autocorrelation of lag 7 weighted by van der Waals volume and its negative coefficient in the model showed an inverse relationship with log RBA.
industrial compounds. As a whole, the developed models in this study had a wide range of applicability domain, which especially included many environmental pollutants. 3.3. Mechanism interpretation Descriptions of screened descriptors involved in the developed models are listed in Table S2. With the QSAR model for Rainbow trout, ten descriptors were screened, which were piPC09, SpDiam_X, SaasC, IC1, CATS2D_06_NL, SpMax2_Bh(s), MATS6m, Mor16u, F02[C-S] and TDB09p. Among them, piPC09 is the molecular path count of order 09; SpDiam_X means spectral diameter from chi matrix; SaasC denotes the aromatic carbon to which a substituent is bonded (Hall and Story, 1996); IC1 characterizes the probabilities of finding equivalent atoms or patterns of atoms in a given structural formula (Mousavisafavi et al., 2013); SpMax2_Bh(s) encodes the largest eigenvalue n. 2 of Burden matrix weighted by I-state; MATS6m provides information about the distribution of molecule mass along the topological structure of molecules. Mor16u characterizes the molecules with great structural variance (Saiz-Urraa et al., 2006). The above descriptors produced a positive contribution to the binding affinity of chemicals to ER. CATS2D_06_DL is defined as CATS2D Negative-Lipophilic at lag 06; F02[C-S] means the frequency of occurrence of a carbon and sulfur atom that are two bonds apart (Winkler et al., 2016). TDB09p is the 3D topological distance based descriptors -lag 9 weighted by polarizability. Their negative coefficient indicated that chemicals with more negatively charged/ionizable group, lipophilic group and carbon-sulfur atom and with large 3D topological distance tended not to bind with ER. With the QSAR model for Zebrafish, six descriptors were screened, which were Mor23e, IC1, TDB06u, F09[O-O], G3e and E3s. Among them, Mor23e denotes the signal 23/ weighted by Sanderson electronegativity, which is a 3D-molecule representation of structures based on electron diffraction, and this descriptor contributes to the decrease of log RBA. F09[O-O] reflects the frequency of occurrence of O-O at topological distance 9, and its negative coefficient suggested that the presence of O-O in chemical structures was unfavorable for their binding to ER. IC1 was identical to that in QSAR model for Rainbow trout. TDB06u was also related to 3D topological distance. These two descriptors positively contributes to the increase of log RBA. Both G3e and E3s belong to WHIM descriptors, and the positive signs for the two descriptors indicated that high electronegativity symmetry and I-state accessibility along with the third component in chemical structure would improve their binding to ER. With the QSAR model for Common carp, four descriptors were screened, which were CATS2D_02_DL, RDF105m, P_VSA_LogP_4 and PJI2. CATS2D_02_DL is CATS2D Donor-Lipophilic at lag 02, which was related to the hydrogen-bond donor (-OH, -NH, -NH2) and lipophilic group. Its positive coefficient indicated that chemicals with the two groups had a higher affinity to ER. RDF105m is interpreted as the probability to find atoms inside virtual spheres 10.5 Å of diameter. P_VSA_LogP_4 is related to the atomic contribution to octanol-water coefficient at bin #4. PJI2 denotes the petitjean shape index, which is calculated from the topological radius. The three descriptors had positive signs in the model, which indicated that increase in these descriptor values resulted in an increase of log RBA values. With the QSAR model for Atlantic croaker, two descriptors (GATS6s and C-001) were screened. GATS6s is the Geary autocorrelation of lag 6 weighted by I-state, and it provides information about the distribution of intrinsic state along the topological structure. This descriptor had negative contribution to log RBA values. C-001 denotes the number of CH3R / CH4 in a molecule, and its positive sign implied the important of CH3R / CH4 fragments in chemical structures to their ER affinity. With the QSAR model for Fathead minnow, two descriptors (R6u and X0Av) were screened. R6u is the unweighted R autocorrelation of lag 6. X0Av is the average valence connectivity index of order 0, and it provides
3.4. Activity comparisons for different fish species Experimental log RBA values of the same chemicals for different fish species are presented in Table S3. Obviously, even for the same chemical, the log RBA values may have significantly different affinity for ER, with the variation coefficient ranging from 8.81% to 238% and the average of 83.6%, depending on different fish species. Hence, it is unreasonable to use the response signal of a particular fish species to predict that of another fish species. It is necessary to develop its own QSAR model for each fish species. The data distribution of their experimental log RBA values for the same chemicals are presented in Fig. S1. On the whole, for most compounds, Atlantic croaker always had the a relatively high log RBA value (red dots), indicating this fish species has strong response signal to the tested compounds; while Sea bream, phylogenetically close to Atlantic croaker and both belonging to the same Order Perciformes, had a relatively low log RBA value (green dots). Other fish species showed no significant advantage or disadvantage. Among the eight fish species, Rainbow trout and Atlantic salmon belong to the Order Salmoniformes; Zebrafish, Common carp and Fathead minnow belong to the Order Cypriniformes; Atlantic croaker and Sea bream belong to the Order Perciformes; and Medaka belongs to the Order Cyprinodontiformes. The matrix of linear correlation coefficients (r2) of the experimental log RBA for each pair of fish species is listed in Table S4. Apparently, the largest r2 occurred in the Order Cypriniformes, i.e., 0.935 within Fathead minnow and Common carp, 0.959 within Zebrafish and Fathead minnow, and 0.975 within Zebrafish and Common carp. Moreover, in the Order Cypriniformes, Rainbow trout and Atlantic salmon has the r2 of 0.774, also showing a good linear relationship. These results highlighted that the ligand-binding affinity of ER are approximated in phylogenetically close fish species. Unfortunately, in the Order Perciformes, Atlantic croaker and Sea bream had a very weak relationship with r2 of 0.027, consistent with the significant deviation between their response signals in Fig. S1, which may be ascribed to the limited data points of the same chemicals in their datasets; or maybe the experimental values from the original literatures were not accurate. Even though previous study has also pointed out that different fish species may show differences in their responsiveness and their susceptibility to the effects of environmental estrogens (Lange et al., 2012). According to our results, fish species in the same Order may respond similarly to the same chemicals. QSAR model derived from one fish species can be used to extrapolate other fish species in the same Order. Hence we probed the linear relationship of the QSAR-predicted log RBA values for each pair of fish species. As shown in Fig. S2, a good 217
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Marchand-Geneste, N., Cazaunau, M., Carpy, A.J., Laguerre, M., Porcher, J.-M., Devillers, J., 2006. Homology model of the rainbow trout estrogen receptor (rtERα) and docking of endocrine disrupting chemicals (EDCs)‖. SAR QSAR Environ. Res. 17, 93–105. Matthews, J., Celius, T., Halgren, R., Zacharewski, T., 2000. Differential estrogen receptor binding of estrogenic substances: a species comparison. J. Steroid Biochem. Mol. Biol. 74, 223–234. Miyagawa, S., Lange, A., Hirakawa, I., Tohyama, S., Ogino, Y., Mizutani, T., Kagami, Y., Kusano, T., Ihara, M., Tanaka, H., Tatarazako, N., Ohta, Y., Katsu, Y., Tyler, C.R., Iguchi, T., 2014. Differing species responsiveness of estrogenic contaminants in fish is conferred by the ligand binding domain of the estrogen receptor. Environ. Sci. Technol. 48, 5254–5263. Molina-Molina, J.-M., Escande, A., Pillon, A., Gomez, E., Pakdel, F., Cavaillès, V., Olea, N., Aït-Aïssa, S., Balaguer, P., 2008. Profiling of benzophenone derivatives using fish and human estrogen receptor-specific in vitro bioassays. Toxicol. Appl. Pharmacol. 232, 384–395. Mousavisafavi, S.M., Mirkhani, S.A., Gharagheizi, F., Akbari, J., 2013. A predictive quantitative structure-property relationship for glass transition temperature of 1,3dialkyl imidazolium ionic liquids. Part 1. The linear approach. J. Therm. Anal. Calorim. 111, 235–246. Nakai, M., 2003. Receptor Binding Assay and Reporter Gene Assay of Medaka. Chemical Evaluation and Research Institute, Japan, pp. 21–26. OECD, 2007. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] Models. Organisation for Economic Co-operation and Development, Paris, France. Olsen, C.M., Meussen-Elholm, E.T., Hongslo, J.K., Stenersen, J., Tollefsen, K.-E., 2005. Estrogenic effects of environmental chemicals: an interspecies comparison. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 141, 267–274. Passos, A.L., Pinto, P.I., Power, D.M., Canario, A.V., 2009. A yeast assay based on the gilthead sea bream (teleost fish) estrogen receptor β for monitoring estrogen mimics. Ecotoxicol. Environ. Saf. 72, 1529–1537. Rider, C.V., Hartig, P.C., Cardon, M.C., Wilson, V.S., 2009. Comparison of chemical binding to recombinant fathead minnow and human estrogen receptors alpha in whole cell and cell‐free binding assays. Environ. Toxicol. Chem. 28, 2175–2181. Saiz-Urraa, L., Gonzalez, M.P., Teijeira, M., 2006. QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3DMoRSE descriptors and statistical considerations about variable selection. Bioorg. Med. Chem. 14, 7347–7358. Schmieder, P.K., Tapper, M.A., Denny, J.S., Kolanczyk, R.C., Sheedy, B.R., Henry, T.R., Veith, G.D., 2004. Use of trout liver slices to enhance mechanistic interpretation of estrogen receptor binding for cost-effective prioritization of chemicals within large inventories. Environ. Sci. Technol. 38, 6333–6342. Schnur, D.M., Grieshaber, M.V., Bowen, J.P., 1991. Development of an internal searching algorithm for parameterization of the MM2/MM3 force fields. J. Comput. Chem. 12, 844–849. Secombes, C., Zou, J., Bird, S., 2009. Fish cytokines: discovery, activities and potential
consistency was found for the predicted log RBA values between Zebrafish and Fathead minnow, between Common carp and Fathead minnow, between Zebrafish and Common carp, between Rainbow trout and Atlantic salmon, with the slope of 0.86, 0.97, 0.91 and 0.91, and the r2 of 0.83, 0.85, 0.73 and 0.81, respectively. Thus, it is speculated that fishes in one Order may share the same QSAR model. 4. Conclusions Among the various biological mechanisms for disrupting endocrine functions, the mechanism of activating/inhibiting hormone receptor is the most focused, and many common test endpoints of endocrine disrupting effect all result from the unnormal activation or inhibition of estrogen receptor (ER). This study, following the OECD guidelines, developed QSAR models for predicting the binding affinity of various chemicals to the ER of eight fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability. The model covered the up-to-date data set and a large applicability domain. A brief mechanism interpretation for models was also performed. Comparing the log RBA values, it was found that different fish species showed differences in their responsiveness and their susceptibility to the same chemicals. Therefore, when evaluating the endocrine disrupting effect of aquatic organisms, species diversity should be taken into account. Furthermore, a significant linear correlation of log RBA values was observed for fish species in the same Order, thus to the untested fish species, the ER binding affinity can be calculated by the current QSAR models of fishes in the same Order, thereby saving time and reducing workload. However, it should also be noted that the current models only covered a limited data points, the developed models should be further validated and optimized when additional data is available. These models presented here can be used as a preliminary screening tool until such time that specific models for certain fish species have been developed. Acknowledgements The study was supported by National Natural Science Foundation of China (No. 41671489, No. 21507061 and No. 21507038), Natural Science Foundation of Jiangsu Province (No. BK20150771), the 2017 Specialized Fund for the Basic Research Operating Expenses Program of Central Public Welfare Research Institutes (GYZX170202, 2017). Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2017.10.023. References Benninghoff, A.D., Bisson, W.H., Koch, D.C., Ehresman, D.J., Kolluri, S.K., Williams, D.E., 2010. Estrogen-like activity of perfluoroalkyl acids in vivo and interaction with human and rainbow trout estrogen receptors in vitro. Toxicol. Sci. 120, 42–58. Birnbaum, L.S., 2013. State of the science of endocrine disruptors. Environ. Health Persp. 121 (A107-A107). Burnison, B.K., Hartmann, A., Lister, A., Servos, M.R., Ternes, T., Van Der Kraak, G., 2003. A toxicity identification evaluation approach to studying estrogenic substances in hog manure and agricultural runoff. Environ. Toxicol. Chem. 22, 2243–2250. Chou, P.H., Lin, Y.L., Liu, T.C., Chen, K.Y., 2015. Exploring potential contributors to endocrine disrupting activities in Taiwan's surface waters using yeast assays and chemical analysis. Chemosphere 138, 814–820. Costache, A.D., Pullela, P.K., Kasha, P., Tomasiewicz, H., Sem, D.S., 2005. Homologymodeled ligand-binding domains of zebrafish estrogen receptors α, β1, and β2: from in silico to in vivo studies of estrogen interactions in Danio rerio as a model system. Mol. Endocr. 19, 2979–2990. Cox, B.J., Bunce, N.J., 1999. Gel-filtration chromatographic method for determining relative binding affinities: rat hepatic estrogen receptor as an example system. Anal. Chem. 267, 357–365. Denny, J.S., Tapper, M.A., Schmieder, P.K., Hornung, M.W., Jensen, K.M., Ankley, G.T., Henry, T.R., 2005. Comparison of relative binding affinities of endocrine active compounds to fathead minnow and rainbow trout estrogen receptors. Environ. Toxicol. Chem. 24, 2948–2953.
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salar) and rainbow trout (Oncorhynchus mykiss). Gen. Comp. Endocr. 126, 14–22. Willett, C.E., Bishop, P.L., Sullivan, K.M., 2011. Application of an integrated testing strategy to the US EPA endocrine disruptor screening program. Toxicol. Sci. 123, 15–25. Winkler, D., Breedon, M., White, P., Hughes, A., Sapper, E., Cole, I., 2016. Using high throughput experimental data and in silico models to discover alternatives to toxic chromate corrosion inhibitors. Corros. Sci. 106, 229–235. Yost, E.E., Lee Pow, C., Hawkins, M.B., Kullman, S.W., 2014. Bridging the gap from screening assays to estrogenic effects in fish: potential roles of multiple estrogen receptor subtypes. Environ. Sci. Technol. 48, 5211–5219. Zhou, R., Cheng, W., Feng, Y., Wei, H., Liang, F., Wang, Y., 2017. Interactions between three typical endocrine-disrupting chemicals (EDCs) in binary mixtures exposure on myocardial differentiation of mouse embryonic stem cell. Chemosphere 178, 378–383.
applications. Fish. Def. 1, 1–36. Segner, H., Casanova-Nakayama, A., Kase, R., Tyler, C.R., 2013. Impact of environmental estrogens on Yfish considering the diversity of estrogen signaling. Gen. Comp. Endocr. 191, 190–201. Shen, J., Xu, L., Fang, H., Richard, A.M., Bray, J.D., Judson, R.S., Zhou, G., Colatsky, T.J., Aungst, J.L., Teng, C., 2013. EADB: an estrogenic activity database for assessing potential endocrine activity. Toxicol. Sci. 135, 277–291. Talete, srl, 2012. Dragon (Software for Molecular Descriptor Calculation) Version 6.0. 〈http://www.talete.mi.it/〉. Tollefsen, K.-E., Nilsen, A.J., 2008. Binding of alkylphenols and alkylated non-phenolics to rainbow trout (Oncorhynchus mykiss) hepatic estrogen receptors. Ecotoxicol. Environ. Saf. 69, 163–172. Tollefsen, K.-E., Mathisen, R., Stenersen, J., 2002. Estrogen mimics bind with similar affinity and specificity to the hepatic estrogen receptor in Atlantic salmon (Salmo
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor ⁎
Junyi Hea, Tao Pengb, Xianhai Yangc, , Huihui Liua,
MARK
⁎
a Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China b State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; University of Chinese Academy of Sciences, Beijing, China c Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Jiang-wang-miao Street, Nanjing 210042, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Endocrine disrupting chemicals (EDCs) Estrogen receptor (ER) Quantitative structure-activity relationship (QSAR) Relative binding affinity (RBA) Fish species
Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17β-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together.
1. Introduction Maintaining the homeostasis of hormones is a prerequisite for organisms ensuring its normal growth and development. However, some naturally occurring or man-made chemicals, named endocrine disrupting chemicals (EDCs), can influence the homeostasis of hormones and disturb the function of endocrine system, thereby causing adverse health effects (Birnbaum, 2013; Zhou et al., 2017). In the past decades, due to the ubiquity and the ever-increasing number of hazardous chemicals in environment, endocrine disrupting effect has become a central point of concern. Especially, estrogenic contaminants, such as alkylphenols, organochlorine pesticides and phthalate esters, were the most detected in environment and have gained notoriety as EDCs (Chou et al., 2015; Miyagawa et al., 2014). Very often, these chemicals find ways (e.g., wastewater input and surface runoff) to enter aquatic environments, and make waterway a major sink of EDCs. Inevitably,
⁎
Corresponding authors. E-mail addresses: [email protected] (X. Yang), [email protected] (H. Liu).
http://dx.doi.org/10.1016/j.ecoenv.2017.10.023 Received 31 July 2017; Received in revised form 6 October 2017; Accepted 9 October 2017 0147-6513/ © 2017 Elsevier Inc. All rights reserved.
aquatic organisms may thus be exposed to various EDCs, and take up EDCs through gill pumping or directly drinking water. As a result, EDCs concentrate on the organisms, get into the food web and finally reach into high level animal bodies. Generally, a chemical can exert its disrupting endocrine function through the following biological mechanisms: (a) impacting the hypothalamic-pituitary-gonad thyroid axis function and regulation, (b) inhibiting hormone synthesis, (c) disrupting hormonal transport protein, (d) activating/inhibiting hormone receptor, and (e) inhibiting hormone metabolism. In our previous studies, we have emphasized the mechanism of disrupting hormonal transport protein, by exploring the binding interaction of EDCs with sex hormone-binding globulin (Liu et al., 2016, 2017). But undeniably, among all these mechanisms, the mechanism of activating/inhibiting hormone receptor is the most focused. There are much more data and models on hormone receptor than those on transporter protein. Moreover, methods related to hormone
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Kloas et al., 2000; Leaños-Castañeda and Van Der Kraak, 2007; Leusch et al., 2006; Marchand-Geneste et al., 2006; Matthews et al., 2000; Molina-Molina et al., 2008; Nakai, 2003; Olsen et al., 2005; Passos et al., 2009; Rider et al., 2009; Schmieder et al., 2004; Secombes et al., 2009; Segner et al., 2013; Tollefsen et al., 2002; Tollefsen and Nilsen, 2008; Yost et al., 2014). The relative binding affinity (RBA) was calculated by comparing the IC50 (half of the inhibition concentration) of a test chemical with that of 17β-estradiol. In this study, the logarithm of relative binding affinity (log RBA) was employed to scale the binding affinity, which is defined as:
receptor also are recommended as standard methods by U.S. EPA, to rapidly ascertain whether a chemical is an endocrine disruptor (EDSTAC; Willett et al., 2011). Hence, exploring the binding interaction of estrogenic chemicals with typical hormone receptor can improve our understanding of endocrine disrupting effect. Especially, estrogen receptor (ER), as a member of the nuclear receptor superfamily, plays a critical role in the organisms’ growth, development and reproduction. ER functions as a ligand-dependent transcription factor, and the common test endpoints of endocrine disrupting effect mainly ascribe to the unnormal activation or inhibition of ER. To date, ER has been well-characterized in a variety of vertebrates, including fish, birds, amphibians, reptiles and mammals (Hawkins et al., 2000). Although the function of ER seems phylogenetically conserved, a significant interspecies variation in the amino acid sequence of ER ligand binding domain has been demonstrated, and thereby deriving a considerable difference in the transactivation ability and ligand preference. For instance, the rainbow trout ER exhibits a tenfold lower affinity for 17β-estradiol than that does the human ER; the pig ER exhibits a significantly greater affinity for α-zearalenol than that does the chicken ER; the rainbow trout ER exhibits a ten-fold higher affinity for 4-hydroxytamoxifen than that does the Atlantic salmon ER (Matthews et al., 2000; Tollefsen et al., 2002). Fish, as a typical representative of aquatic organism, is an important biological indicator of the degree of water pollution. Moreover, fish has been well-studied in characterizing the endocrine disrupting effect. As for the mechanism about ER, most studies focused only one fish species, especially rainbow trout (Benninghoff et al., 2010; Leaños-Castañeda and Van Der Kraak, 2007; Tollefsen and Nilsen, 2008), but less on the direct comparison of ER in different fish species (Miyagawa et al., 2014). Questions remain as to whether a chemical that binds to the ER in one fish species exert similar effects in other fish species, and the validity of extrapolating the estrogenic effects of a chemical between different fish species also has not been tested. Obviously, it is infeasible to experimentally test the binding affinity of each chemical to every fish species, which is usually laborious, time-consuming, expensive and equipment dependent. Computational modeling, especially the quantitative structure-activity relationship (QSAR) modeling, provides a promising tool for rapidly identifying whether a chemical being the ER activator or ER inhibitor. This study, following the Organization for Economic Cooperation and Development (OECD) guidelines on the development and validation of QSAR model (OECD, 2007), developed QSAR models those could predict the ER binding affinity of various chemicals in different fish species, so as to characterize the structural features those governing the binding behavior of chemicals, to screen potential ER disruptors from existing and emerging chemicals, and further to explore the species variation in ER binding affinity specificity of fish.
log RBA = log (
17β − estradiol IC50 × 100) test IC50
17β − estradiol test where IC50 and IC50 are the concentrations of 17β-estradiol and test chemical at 50% inhibition of [3H]−17β-estradiol binding to estrogen receptor, respectively. There were a total of 77, 39, 20, 15, 15, 15, 11 and 7 data points for Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream, respectively. In the modeling, except Sea bream, the datasets for each fish were randomly divided into a training set and a validation set with a ratio of 3:1. The training sets were used to establish QSAR models, while the validation sets were used to assess the external predictive ability of the established models. In this case, there were 19, 10, 5, 4, 4, 4 and 3 data points in validation sets for the aforementioned seven fish species, respectively; while the remaining data points were grouped into the training sets. With Sea bream, due to the small number of data points in its dataset, which including a total of seven chemicals, all the data were grouped into the training set. The names, CAS numbers, corresponding log RBA values of the chemicals are presented in Table S1 of the Supporting materials.
2.2. Calculation of molecular descriptors DRAGON descriptors were used for model development, which encode a series of parameters of a compound in its typical configuration. Before calculating the molecular descriptors, the molecular structures of compounds were preliminarily optimized with the minimize energy method (at the minimum RMS gradient of 0.001), which was contained in the ChemBio3D Ultra (Version 12.0) (Schnur et al., 1991). Then, the molecular structures of model compounds were further optimized by employing MOPAC 2012 software (Keywords: PM6 eps = 78.6 CHARGE = 0 EF GNOPRM = 0.01 POLAR MULLIK SHIFT = 80). Based on the optimized geometric structures from MOPAC, 4885 DRAGON descriptors were calculated by employing the DRAGON software (version 6.0) (Talete and srl, 2012), therein the default methods in the DRAGON 6 software were used to preliminarily select descriptors. Details for the exclusion rules were listed in the homepage of the DRAGON software (http://www.talete.mi.it/help/dragon_help/ index.html). As a result of this prereduction procedure, a final set of 1140, 1175, 796, 724, 804, 771, 659 and 471 DRAGON descriptors for Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream were retained, respectively.
2. Materials and methods 2.1. Data sets Commonly, ER has two subtypes: ERα and ERβ. Previous studies have proved that liver is an abundant source of ER and contains only the ERα isoform. Moreover, when exposure to estrogens, the response signal of ERβ is instable, while that of ERα is stable (Keith et al., 2000; Shen et al., 2013). Hence, the data points used in this study came from the response signal of ER fusion proteins or ERα subtype in liver. In the present study, we selected the binding affinity of chemicals to fish ER, as the test endpoint of endocrine disrupting effect. The fish species used in this study include Rainbow trout, Zebrafish, Common carp, Atlantic croaker, Fathead minnow, Medaka, Atlantic salmon and Sea bream. The experimentally determined binding affinity of each chemical was collected from the corresponding literatures for the eight fish species (Benninghoff et al., 2010; Burnison et al., 2003; Costache et al., 2005; Cox and Bunce, 1999; Denny et al., 2005; Hawkins and Thomas, 2004;
2.3. Development and validation of QSAR models Stepwise multiple linear regression (MLR) analysis was employed to select variables and develop the QSAR models by using the SPSS software (SPSS 19.0), with the log RBA values (data points in training set) as the dependent variable and the molecular descriptors (DRAGON descriptors) as the predictor variables. In this case, the statistical parameters R2adj (adjusted determination coefficient), Q2LOO (leave-oneout cross-validated coefficient), Q2ext (external explained variance), bootstrap method (Q2BOOT) and RMSE (root mean square error) were 212
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established QSAR models can be used to predict the log RBA value of the untested compounds that fall within the applicability domains of the models.
calculated to assess the performance of the established model. Finally, the best model was selected, which should have the lowest number of molecular descriptors, the maximum values of R2adj, the minimum values of RMSE and the variable inflation factor (VIF) of less than 10 for each predictor variable. Additionally, the models should also comply with the QUIK rules, i.e., KXX (inter correlation of selected descriptors) < KXY (the correlation of the X block with Y), where X is the selected molecular descriptors’ matrix and Y is the response variable vector.
3.2. Applicability domain evaluation The applicability domains of QSAR models were evaluated by Euclidean distance-based approach and Williams plot. With the Euclidean distance-based approach, because only one descriptor was screened for Sea bream, its Euclidean distance plot could not be drawn. For other seven fish species, as shown in Fig. 2, nearly all the chemicals in both the training sets and the validation were in the acceptable region, implying that these training sets for the models represented the domains well. Exceptionally, for Atlantic salmon, there was one chemical (ethynylestradiol) locating outside the acceptable domain, which was identified as an outlier. This chemical has the largest CATS2D_03_DL value among all the chemicals in the dataset for Atlantic salmon. Reasons for the deviant prediction may be from the limited number of chemicals with the similar structure (alkynyl compound) in the dataset; or maybe the experimental value from the original literature was not accurate (Gramatica, 2007). The application domains were also characterized by Williams plots (Fig. 3). Notably, for Atlantic salmon and Sea bream, due to the number of chemicals in validation set less than the number of descriptors in the QSAR models, only the evaluation results for their training sets were displayed in the Williams plot. As shown, all the data points located at the domain of |δ| < 3, implying that there was no chemical that could be identified as outlier. Thus, chemicals in training sets had good representativeness, and the current model has reliable predictive ability for chemicals in validation sets. However, two chemicals (resorcinol sulfide and 4-t-butylbenzoic acid) in the training set of Rainbow trout and a chemical (estriol) in the training set of Zebrafish were found with hi > h*, it indicated that these chemicals had great influence on the models. Moreover, their observed and predicted log RBA agreed well with each other (−3.24 vs. −3.10, −4.82 vs. −4.99 and 1.00 vs. 1.39, respectively; Table S1), highlighting that they could enable the QSAR models to be more stable and precise. Meanwhile, a chemical (4-methylphenol) in the validation set of Rainbow trout was also found with hi > h*, and its predicted log RBA was ten-fold lower than the observed log RBA (−4.28 vs. −5.85, Table S1), it meant that this chemical was structurally distant from those used in the training set, and maybe it was incorrectly predicted by the established model. Overall, the developed models had a good performance, and the log
2.4. Applicability domain The applicability domain (AD) of the models was characterized by the Euclidean distance-based method or Williams plot. Details for the evaluation method were presented in our previous study (Liu et al., 2016). 3. Results and discussion 3.1. QSAR models for each fish species QSAR models, with the log RBA as the dependent variable and the molecular descriptors as the independent variable, were established. Based on the screening principles, the optimum QSAR models and their statistical parameters for each fish species are presented in Table 1. As shown, with the training sets, the adjusted correlation coefficient 2 2 square Rtra and the leave-one-out cross-validated coefficient QLOO ranged from 0.822 to 0.909 and 0.646 to 0.822, respectively. According to the criteria (Q2 > 0.5, R2 > 0.6) proposed by Golbraikh et al. (Golbraikh et al., 2003), these results implied high goodness-of-fit and robustness of models for the eight fish species. With the validation sets, 2 the adjusted correlation coefficient square Rext and the external ex2 plained variance Qext were in the range of 0.699–0.996 and 0.696–0.962, respectively, which showed good predictive ability of the models. Additionally, the models also complied with the QUIK rules, i.e., KXX < KXY for each fish species, which also proved good performance of the developed QSAR models. Description of screened descriptors involved in the models is listed in Table S2. As shown, the VIF values for all the predictor variables were less than 10, indicating that there was no serious multicollinearity among the variables. The plots of observed log RBA versus predicted log RBA are shown in Fig. 1, in which such good consistency illustrated good predictive ability of the developed QSAR models. Hence, the Table 1 Optimum QSAR models and statistical parameters for each fish species. Fish
ntra
Rainbow trout
QSAR model: log RBA = –166 + 0.435piPC09 + 76.9SpDiam_X + 0.422SaasC + 1.59IC1 – 1.20CATS2D_06_NL + 0.676SpMax2_Bh(s)+ 1.62MATS6m + 1.32Mor16u – 0.562F02[C-S] – 5.64TDB09p 58 0.822 0.753 0.804 19 0.794 0.701 0.975 0.296 0.326 QSAR model: log RBA = –23.8 – 2.65Mor23e + 4.23IC1 + 8.28TDB06u – 0.921F09[O-O] + 2.14G3e + 3.89E3s 29 0.844 0.651 0.792 10 0.699 0.696 0.944 0.225 0.280 QSAR model: log RBA = –12.3 + 0.575CATS2D_02_DL + 0.837RDF105m + 0.0428P_VSA_LogP_4 + 9.96PJI2 15 0.840 0.685 0.691 5 0.920 0.831 0.756 0.431 0.445 QSAR model: log RBA = 4.21 – 3.70GATS6s + 0.450C−001 11 0.887 0.813 0.309 4 0.966 0.962 0.175 0.233 0.573 QSAR model: log RBA = 6.94 + 3.38R6u – 17.5×0Av 11 0.857 0.646 0.635 4 0.924 0.894 0.478 0.119 0.516 QSAR model: log RBA = –2.46 + 2.40nArOH – 0.955Mor18u 11 0.908 0.822 0.365 4 0.918 0.854 0.476 0.006 0.477 QSAR model: log RBA = –3.75 + 337R8p + 0.396CATS2D_03_DL 8 0.909 0.692 0.557 3 0.996 0.799 0.555 0.629 0.780 QSAR model: log RBA = 8.49 – 7.99GATS7v 7 0.883 0.748 0.271
Zebrafish Common carp Atlantic croaker Fathead minnow Medaka Atlantic salmon Sea bream
2 Rtra
2 QLOO
RMSEtra
2 R ext
next
2 Qext
RMSEext
KXX
KXY
Notes: ntra and next are the numbers of chemicals in training set and validating set, respectively. RMSEtra and RMSEext are the root mean square errors of data in training set and validating set, 2 2 and R ext are the correlation coefficient square between observed and fitted values in training set and validating set, respectively. respectively. Rtra
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Fig. 1. Plots of the predicted versus observed log RBA values for the training sets and validation sets.
RBA values for nearly all the compounds were accurately predicted by the model. The developed QSAR models can be used to predict the log RBA values of structurally diverse chemicals, including natural/
synthetic estrogens, nonsteroidal estrogens, alkylphenols, alkylated non-phenolics, phytotoxins/mycotoxins, benzophenone derivatives, organochlorine pesticide, diphenylmethanes, pharmaceutical, and 214
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Fig. 2. Applicability domains for the developed models characterized by the Euclidean distance-based approach.
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Fig. 3. Plots of standardized residuals versus leverages (h). The transverse shot-dotted lines represent ± 3 standardized residuals, and the vertical shot-dotted line represents warningleverage value (h*).
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information about branching. R6u has a positive sign, while X0Av has a negative sign in the model. With the QSAR model for Medaka, two descriptors (nArOH and Mor18u) were screened. nArOH represents the number of aromatic hydroxyls, which is a crucial factor in many endocrine disrupters. Moreover, Tollefsen et al. paid special attention on alkylphenols and emphasized the importance of phenolic ring structure in ER binding ligands (Tollefsen and Nilsen, 2008). Mor18u is the unweighted signal 18 and it characterizes the molecules with great structural variance. This descriptor had negative contribution to log RBA values. With the QSAR model for Atlantic salmon, two descriptors (R8p and CATS2D_03_DL) were screened. R8p is the R maximal autocorrelation of lag 8 / weighted by polarizability. CATS2D_03_DL is the CATS2D DonorLipophilic at lag 03, which was related to the hydrogen-bond donor (-OH, -NH, -NH2) and lipophilic group. The two positive contributed the increase of log RBA. With Sea bream, owing to the small number of data points in its dataset, only one descriptor (GATS7v) was screened for the QSAR model. GATS7v denotes Geary autocorrelation of lag 7 weighted by van der Waals volume and its negative coefficient in the model showed an inverse relationship with log RBA.
industrial compounds. As a whole, the developed models in this study had a wide range of applicability domain, which especially included many environmental pollutants. 3.3. Mechanism interpretation Descriptions of screened descriptors involved in the developed models are listed in Table S2. With the QSAR model for Rainbow trout, ten descriptors were screened, which were piPC09, SpDiam_X, SaasC, IC1, CATS2D_06_NL, SpMax2_Bh(s), MATS6m, Mor16u, F02[C-S] and TDB09p. Among them, piPC09 is the molecular path count of order 09; SpDiam_X means spectral diameter from chi matrix; SaasC denotes the aromatic carbon to which a substituent is bonded (Hall and Story, 1996); IC1 characterizes the probabilities of finding equivalent atoms or patterns of atoms in a given structural formula (Mousavisafavi et al., 2013); SpMax2_Bh(s) encodes the largest eigenvalue n. 2 of Burden matrix weighted by I-state; MATS6m provides information about the distribution of molecule mass along the topological structure of molecules. Mor16u characterizes the molecules with great structural variance (Saiz-Urraa et al., 2006). The above descriptors produced a positive contribution to the binding affinity of chemicals to ER. CATS2D_06_DL is defined as CATS2D Negative-Lipophilic at lag 06; F02[C-S] means the frequency of occurrence of a carbon and sulfur atom that are two bonds apart (Winkler et al., 2016). TDB09p is the 3D topological distance based descriptors -lag 9 weighted by polarizability. Their negative coefficient indicated that chemicals with more negatively charged/ionizable group, lipophilic group and carbon-sulfur atom and with large 3D topological distance tended not to bind with ER. With the QSAR model for Zebrafish, six descriptors were screened, which were Mor23e, IC1, TDB06u, F09[O-O], G3e and E3s. Among them, Mor23e denotes the signal 23/ weighted by Sanderson electronegativity, which is a 3D-molecule representation of structures based on electron diffraction, and this descriptor contributes to the decrease of log RBA. F09[O-O] reflects the frequency of occurrence of O-O at topological distance 9, and its negative coefficient suggested that the presence of O-O in chemical structures was unfavorable for their binding to ER. IC1 was identical to that in QSAR model for Rainbow trout. TDB06u was also related to 3D topological distance. These two descriptors positively contributes to the increase of log RBA. Both G3e and E3s belong to WHIM descriptors, and the positive signs for the two descriptors indicated that high electronegativity symmetry and I-state accessibility along with the third component in chemical structure would improve their binding to ER. With the QSAR model for Common carp, four descriptors were screened, which were CATS2D_02_DL, RDF105m, P_VSA_LogP_4 and PJI2. CATS2D_02_DL is CATS2D Donor-Lipophilic at lag 02, which was related to the hydrogen-bond donor (-OH, -NH, -NH2) and lipophilic group. Its positive coefficient indicated that chemicals with the two groups had a higher affinity to ER. RDF105m is interpreted as the probability to find atoms inside virtual spheres 10.5 Å of diameter. P_VSA_LogP_4 is related to the atomic contribution to octanol-water coefficient at bin #4. PJI2 denotes the petitjean shape index, which is calculated from the topological radius. The three descriptors had positive signs in the model, which indicated that increase in these descriptor values resulted in an increase of log RBA values. With the QSAR model for Atlantic croaker, two descriptors (GATS6s and C-001) were screened. GATS6s is the Geary autocorrelation of lag 6 weighted by I-state, and it provides information about the distribution of intrinsic state along the topological structure. This descriptor had negative contribution to log RBA values. C-001 denotes the number of CH3R / CH4 in a molecule, and its positive sign implied the important of CH3R / CH4 fragments in chemical structures to their ER affinity. With the QSAR model for Fathead minnow, two descriptors (R6u and X0Av) were screened. R6u is the unweighted R autocorrelation of lag 6. X0Av is the average valence connectivity index of order 0, and it provides
3.4. Activity comparisons for different fish species Experimental log RBA values of the same chemicals for different fish species are presented in Table S3. Obviously, even for the same chemical, the log RBA values may have significantly different affinity for ER, with the variation coefficient ranging from 8.81% to 238% and the average of 83.6%, depending on different fish species. Hence, it is unreasonable to use the response signal of a particular fish species to predict that of another fish species. It is necessary to develop its own QSAR model for each fish species. The data distribution of their experimental log RBA values for the same chemicals are presented in Fig. S1. On the whole, for most compounds, Atlantic croaker always had the a relatively high log RBA value (red dots), indicating this fish species has strong response signal to the tested compounds; while Sea bream, phylogenetically close to Atlantic croaker and both belonging to the same Order Perciformes, had a relatively low log RBA value (green dots). Other fish species showed no significant advantage or disadvantage. Among the eight fish species, Rainbow trout and Atlantic salmon belong to the Order Salmoniformes; Zebrafish, Common carp and Fathead minnow belong to the Order Cypriniformes; Atlantic croaker and Sea bream belong to the Order Perciformes; and Medaka belongs to the Order Cyprinodontiformes. The matrix of linear correlation coefficients (r2) of the experimental log RBA for each pair of fish species is listed in Table S4. Apparently, the largest r2 occurred in the Order Cypriniformes, i.e., 0.935 within Fathead minnow and Common carp, 0.959 within Zebrafish and Fathead minnow, and 0.975 within Zebrafish and Common carp. Moreover, in the Order Cypriniformes, Rainbow trout and Atlantic salmon has the r2 of 0.774, also showing a good linear relationship. These results highlighted that the ligand-binding affinity of ER are approximated in phylogenetically close fish species. Unfortunately, in the Order Perciformes, Atlantic croaker and Sea bream had a very weak relationship with r2 of 0.027, consistent with the significant deviation between their response signals in Fig. S1, which may be ascribed to the limited data points of the same chemicals in their datasets; or maybe the experimental values from the original literatures were not accurate. Even though previous study has also pointed out that different fish species may show differences in their responsiveness and their susceptibility to the effects of environmental estrogens (Lange et al., 2012). According to our results, fish species in the same Order may respond similarly to the same chemicals. QSAR model derived from one fish species can be used to extrapolate other fish species in the same Order. Hence we probed the linear relationship of the QSAR-predicted log RBA values for each pair of fish species. As shown in Fig. S2, a good 217
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consistency was found for the predicted log RBA values between Zebrafish and Fathead minnow, between Common carp and Fathead minnow, between Zebrafish and Common carp, between Rainbow trout and Atlantic salmon, with the slope of 0.86, 0.97, 0.91 and 0.91, and the r2 of 0.83, 0.85, 0.73 and 0.81, respectively. Thus, it is speculated that fishes in one Order may share the same QSAR model. 4. Conclusions Among the various biological mechanisms for disrupting endocrine functions, the mechanism of activating/inhibiting hormone receptor is the most focused, and many common test endpoints of endocrine disrupting effect all result from the unnormal activation or inhibition of estrogen receptor (ER). This study, following the OECD guidelines, developed QSAR models for predicting the binding affinity of various chemicals to the ER of eight fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability. The model covered the up-to-date data set and a large applicability domain. A brief mechanism interpretation for models was also performed. Comparing the log RBA values, it was found that different fish species showed differences in their responsiveness and their susceptibility to the same chemicals. Therefore, when evaluating the endocrine disrupting effect of aquatic organisms, species diversity should be taken into account. Furthermore, a significant linear correlation of log RBA values was observed for fish species in the same Order, thus to the untested fish species, the ER binding affinity can be calculated by the current QSAR models of fishes in the same Order, thereby saving time and reducing workload. However, it should also be noted that the current models only covered a limited data points, the developed models should be further validated and optimized when additional data is available. These models presented here can be used as a preliminary screening tool until such time that specific models for certain fish species have been developed. Acknowledgements The study was supported by National Natural Science Foundation of China (No. 41671489, No. 21507061 and No. 21507038), Natural Science Foundation of Jiangsu Province (No. BK20150771), the 2017 Specialized Fund for the Basic Research Operating Expenses Program of Central Public Welfare Research Institutes (GYZX170202, 2017). Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2017.10.023. References Benninghoff, A.D., Bisson, W.H., Koch, D.C., Ehresman, D.J., Kolluri, S.K., Williams, D.E., 2010. Estrogen-like activity of perfluoroalkyl acids in vivo and interaction with human and rainbow trout estrogen receptors in vitro. Toxicol. Sci. 120, 42–58. Birnbaum, L.S., 2013. State of the science of endocrine disruptors. Environ. Health Persp. 121 (A107-A107). Burnison, B.K., Hartmann, A., Lister, A., Servos, M.R., Ternes, T., Van Der Kraak, G., 2003. A toxicity identification evaluation approach to studying estrogenic substances in hog manure and agricultural runoff. Environ. Toxicol. Chem. 22, 2243–2250. Chou, P.H., Lin, Y.L., Liu, T.C., Chen, K.Y., 2015. Exploring potential contributors to endocrine disrupting activities in Taiwan's surface waters using yeast assays and chemical analysis. Chemosphere 138, 814–820. Costache, A.D., Pullela, P.K., Kasha, P., Tomasiewicz, H., Sem, D.S., 2005. Homologymodeled ligand-binding domains of zebrafish estrogen receptors α, β1, and β2: from in silico to in vivo studies of estrogen interactions in Danio rerio as a model system. Mol. Endocr. 19, 2979–2990. Cox, B.J., Bunce, N.J., 1999. Gel-filtration chromatographic method for determining relative binding affinities: rat hepatic estrogen receptor as an example system. Anal. Chem. 267, 357–365. Denny, J.S., Tapper, M.A., Schmieder, P.K., Hornung, M.W., Jensen, K.M., Ankley, G.T., Henry, T.R., 2005. Comparison of relative binding affinities of endocrine active compounds to fathead minnow and rainbow trout estrogen receptors. Environ. Toxicol. Chem. 24, 2948–2953.
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