Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity

May 22, 2019 - Finally, a mechanism-based model for predicting the hTTR binding affinity was developed. ... (11) Thus, further studies are necessary t...
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Article Cite This: Environ. Sci. Technol. 2019, 53, 7019−7028

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Emerging Polar Phenolic Disinfection Byproducts Are High-Affinity Human Transthyretin Disruptors: An in Vitro and in Silico Study Xianhai Yang,*,†,‡ Wang Ou,† Yue Xi,† Jingwen Chen,§ and Huihui Liu*,†

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†

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 ‡ Nanjing Institute of Environmental Science, Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210042, China § Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China S Supporting Information *

ABSTRACT: Phenolic disinfection byproducts (phenolicDBPs) have been identified in recent years. However, the toxicity data for phenolic-DBPs are scarce, hampering their risk assessment and the development of regulations on the acceptable concentration of phenolic-DBPs in water. In this study, the binding potency and underlying interaction mechanism between human transthyretin (hTTR) and five groups of representative phenolic-DBPs (2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids) were determined and probed by competitive fluorescence displacement assay integrated with in silico methods. Experimental results implied that 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, and 3,5-dihalo-4-hydroxybenzaldehydes have a high binding affinity with hTTR. The hTTR binding potency of the chemicals with electron-withdrawing groups on their molecular structures were higher than that with electron-donor groups. Molecular modeling methods were used to decipher the binding mechanism between model compounds and hTTR. The results documented that ionic pair, hydrogen bonding and hydrophobic interactions were dominant interactions. Finally, a mechanismbased model for predicting the hTTR binding affinity was developed. The determination coefficient (R2), leave-one-out cross validation Q2 (Q2LOO), bootstrapping coefficient (Q2BOOT), external validation coefficient (Q2EXT) and concordance correlation coefficient (CCC) of the developed model met the acceptable criteria (Q2 > 0.600, R2 > 0.700, CCC > 0.850), implying that the model had good goodness-of-fit, robustness, and external prediction performances. All the results indicated that the phenolicDBPs have the hTTR disrupting effects, and further studies are needed to investigate their other mechanism of endocrine disruption.

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INTRODUCTION Disinfection byproducts (DBPs) are formed by the reaction of chemical disinfectants (e.g., chlorine, chlorine dioxide, chloramine, and ozone) with natural organic matter, anthropogenic contaminants, and halogen, in the disinfection processes for drinking water, swimming pool water, wastewater, aquaculture water, etc.1−6 Since the 1970s, over 700 DBPs have been identified.7,8 However, it was estimated that this number of DBPs represents merely a fraction of the total organic halogens generated in the disinfection processes.9 In addition, only ∼100 DBPs have been evaluated by systematic toxicological analyses,10 meaning there are great knowledge gaps for the toxicological information on DBPs.11 Thus, further studies are necessary to identify emerging DBPs, test their toxicity, and probe underlying toxic mechanisms of action. Recently, dozens of polar phenolic DBPs (phenolic-DBPs), including halogenated-phenols, halogenated-4-nitrophenol, © 2019 American Chemical Society

halogenated-4-hydroxybenzaldehydes, halogenated-4-hydroxybenzoic acids, and halogenated-salicylic acids, were identified.5,12−16 Although the toxicity information for those DBPs were scarce up to now, an concern is increasing because their toxicity profile is distinct from the commonly known aliphatic halogenated DBPs. For example, Yang et al.,13 and Pan et al.,17 evaluated the developmental toxicity of these emerging pollutants on marine polychaete Platynereis dumerilii. Their results indicated that the developmental toxicity of phenolicDBPs was higher than that of aliphatic halogenated DBPs. Moreover, this phenomenon was also observed in the studies of algae toxicity,18,19 and CHO cells cytotoxicity.15 Besides the Received: Revised: Accepted: Published: 7019

January 11, 2019 May 14, 2019 May 20, 2019 May 22, 2019 DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028

Article

Environmental Science & Technology Table 1. Information of Model Compounds and Their IC50, Kd, and log RP Values no.

log D

compound name

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

IC50 (nM) 685 ± 10

L-thyroxine

2,4,6-Trihalo-phenols 2.14 1410 ± 100 2.92 370 ± 30 3.81 260 ± 10 2.36 890 ± 10 2.68 510 ± 20 2,6-Dihalo-4-nitrophenols 2,6-dichloro-4-nitrophenol (2,6-diCl-4-NP) 0.84 780 ± 80 2,6-dibromo-4-nitrophenol (2,6-diBr-4-NP) 1.17 340 ± 40 3,5-Dihalo-4-hydroxybenzaldehydes 3,5-dichloro-4-hydroxybenzaldehyde (3,5-diCl-4-HBA) 0.59 8010 ± 290 3,5-dibromo-4-hydroxybenzaldehyde (3,5-diBr-4-HBA) 0.93 1970 ± 40 3-bromo-5-chloro-4-hydroxybenzaldehyde (3-Br-5-Cl-4-HBA) 0.76 2360 ± 100 Halo-salicylic Acids 5-chlorosalicylic acid (5-Cl-SA) −0.93 211000 ± 25000 5-bromosalicylic acid (5-Br-SA) −0.76 639000 ± 156000 3,5-dichlorosalicylic acid (3,5-diClSA) −0.33 40900 ± 4900 3-bromo-5-chlorosalicylic acid (3-Br-5-Cl-SA) −0.16 26400 ± 1300 3,5-dibromosalicylic acid (3,5-diBr-SA) 0 14300 ± 700 3,5-Dihalo-4-hydroxybenzoic Acids 3,5-dichloro-4-hydroxybenzoic acid (3,5-diCl-4-HB) −1.28 257000 ± 29000 3,5-dibromo-4-hydroxybenzoic acid (3,5-diBr-4-HB) −0.72 208000 ± 43000 Compounds Resembling the Structure of 2,4,6-Tribromophenol 2,6-dibromo-4-methylphenol (2,6-diBr-4-MP) 3.29 1060 ± 170 3,5-dibromo-4-hydroxyacetophenone (3,5-diBr-4-HAP) 0.9 2320 ± 120 3,5-dibromo-4-hydroxybenzonitrile (3,5-diBr-4-HBN) 1.19 670 ± 20 4-amino-2,6-dibromophenol (4-A-2,6-DBP) 2.19 3170 ± 340 3,5-dibromo-4-hydroxybenzotrifluoride (3,5-diBr-4-HBTF) 3.27 420 ± 30 3,5-dibromo-4-hydroxybenzoic acid methyl ester (3,5-diBr-4-HBME) 1.75 2240 ± 80 2,4,6-trichlorophenol (2,4,6-TCP) 2,4,6-tribromophenol (2,4,6-TBP) 2,4,6-triiodophenol (2,4,6-TIP) 4-bromo-2,6-dichlorophenol (4-Br-2,6-DCP) 4-chloro-2,6-dibromophenol (4-Cl-2,6-DBP)

Kd (nM)

log RP

31.2 ± 3.55 8.56 2.34 1.41 4.60 2.78

−0.314 ± 0.0314 0.267 ± 0.0358 0.421 ± 0.0179 −0.114 ± 0.00800 0.128 ± 0.0182

35.6 ± 5.42 15.5 ± 2.53

−0.0564 ± 0.0450 0.304 ± 0.0515

365 ± 43.2 89.9 ± 10.3 108 ± 13.0

−1.07 ± 0.0169 −0.459 ± 0.0109 −0.537 ± 0.0195

9622 ± 1573 29138 ± 7835 1865 ± 307 1204 ± 148 652 ± 80.1

−2.49 −2.97 −1.78 −1.59 −1.32

11719 ± 1869 9485 ± 2233

−2.57 ± 0.0494 −2.48 ± 0.0899

48.3 ± 9.48 106 ± 13.1 30.6 ± 3.56 145 ± 22.5 19.2 ± 2.56 102 ± 12.1

−0.190 ± 0.0699 −0.530 ± 0.0233 0.00962 ± 0.0144 −0.665 ± 0.0470 0.212 ± 0.0316 −0.515 ± 0.0167

64.3 16.9 11.9 40.6 23.3

± ± ± ± ±

± ± ± ± ±

0.0518 0.106 0.0524 0.0223 0.0222

the biological in vivo and/or in vitro testing of all potentially DBPs is unrealistic. It is therefore necessary to employ an efficient method to analyze their capacity to induce adverse biological effects and reveal underlying mechanisms. In silico methods are considered as a fast, cost-efficient, and powerful tool to predict the potential toxicity and decipher the mechanism of action.32−36 To date, only few in silico models were developed to predict the toxicity of DBPs. The endpoints included the developmental toxicity, carcinogenicity, mutagenicity, reactive toxicities, and genotoxicity.11,37,38 However, no predictive models are available for the endpoints of serum proteins binding. Therefore, development of a predictive model is also meaningful to fill the data gap of other phenolicDBPs with similar structures on their serum proteins binding potency. In this study, the human transthyretin (hTTR) binding potency of phenolic-DBPs was investigated by in vitro integrated with in silico methods. The reasons why hTTR is selected as model serum proteins are as follows: (a) hTTR is the major thyroid hormones (THs) carrier in the brain and fetal tissue and it is also responsible for transporting THs across protective physiological barriers, such as the placenta and the blood−brain barrier.39 Disrupting the hTTR transport process may result in transporting the pollutant to normally inaccessible sites of action and eliciting deleterious health effects.40 (b) Previous experimental results indicated that the most potent hTTR binders were aromatic, hydroxylated, and halogenated.41 In view of the molecular structure, the phenolicDBPs may be potential hTTR binders. Herein, we first

observed toxicology effects, other potential adverse biological effects for those emerging DBPs are still unknown. What are the underlying molecular mechanisms of action? The limited information has hampered the risk assessment and development of regulations on acceptable concentrations of the emerging DBPs in water. It was reported that DBPs could be absorbed into human bodies through several routes, such as ingestion, dermal absorption, or inhalation,20−22 which resulted in many DBPs being detected in human blood, urinary, and alveolar air samples, etc.4,23−25 The DBPs in human sera may bind to various serum proteins. It is well-known that many serum proteins are critical deliverers of endocrine hormones.26,27 Thus, DBPs binding to the serum proteins may result in the decrease of available protein binding sites for the endocrine signaling molecules, even altering their homeostasis. However, research for this point has not been reported. Although no previous study documented that the phenolic-DBPs had been detected in human blood, it has been reported that the phenolic-DBPs could permeate across human skin.28 Since halo-phenols from other sources were detected in human blood,29−31 it is a conceivable hypothesis that phenolic-DBPs could find their way into our blood. In this regard, it is of vital importance to determine the potential binding ability of the emerging phenolic-DBPs to serum proteins, as well as to reveal underlying mechanism of binding action. As mentioned above, there are big data gaps for more than 85% (∼600) identified DBPs, let alone the new DBPs discovered continually. Because of time and cost limitations, 7020

DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028

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Environmental Science & Technology determined the binding affinity of 17 phenolic-DBPs with hTTR by competitive fluorescence displacement assay. Then, the underlying binding mechanisms were deciphered using molecular modeling methods. Lastly, a mechanism-based quantitative structure−activity relationship (QSAR) model was developed and validated.

RFλ =

Fλ − 0 Fλ − i

(1)

where Fλ‑0 and Fλ‑i are the maximum fluorescence intensity without and with i-ligand concentration of model compounds, respectively. Then, the average relative fluorescence intensity of three independent repeats for each compound was plotted as a function of ligand concentration. The competition curves were fitted with a sigmoidal model to derive an IC50 value by Origin (Northampton, MA, USA). Then the binding constants Kd,ligand of the compounds with hTTR were calculated as

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MATERIALS AND METHODS Reagents and Chemicals. A total of 17 phenolic-DBPs including five 2,4,6-trihalo-phenols, two 2,6-dihalo-4-nitrophenols, three 3,5-dihalo-4-hydroxybenzaldehydes, five halosalicylic acids, and two 3,5-dihalo-4-hydroxybenzoic acids were selected as model compounds (Table 1). Furthermore, to clarify the influence of substituent groups on the hTTR binding affinity, another six compounds resembling the structure of 2,4,6-tribromophenol were also selected. The model compounds are dissolved in dimethyl sulfoxide (DMSO). Thyroxine (T4) is dissolved in 10 mM NaOH solution. hTTR and 8-anilino-1-naphthalenesulfonate ammonium (ANSA) were prepared in 100 mM NaCl/50 mM TrisHCl, pH 7.40 ± 0.02. All chemicals and solvents are analytical grade. The molecular structures and reagent purchase information on chemicals and solvents are listed in Figure S2 and Table S1 of the Supporting Information, respectively. ANSA-Based Competitive Fluorescence Displacement Assay. The competitive fluorescence displacement assay has been considered as a powerful and promising tool to assess the hTTR binding capacity of compounds by the scientific community,26,42−45 and the Organization for Economic Cooperation and Development (OECD).46,47 The explanation to justify the selection of ANSA-based competitive fluorescence displacement assay is given in the Supporting Information, Text S1. We determined the binding constant (Kd,ligand) and 50% inhibition concentration (IC50) of model compounds with hTTR by employing a modified procedure. Fluorescence measurements were carried out by an INESA 970CRT fluorescence spectrometer (Shanghai Instrument Electric Analytical Instrument Co., Ltd., China). For each test, three independent repeats were performed. A direct fluorescent ligand binding measurement was performed to measure the binding constant (Kd,probe) of ANSA with hTTR. Different volumes of ANSA were added from the stock solution to 1.82 μM hTTR (total volume 2000 μL) to obtain the required concentration. After the solutions were allowed to stand for 5 min at room temperature, the fluorescence emission spectrum of each was recorded. The excitation wavelength and emission wavelength of the fluorescence spectrophotometer were set at 380 and 470 nm, respectively. The Kd,probe value of ANSA with hTTR was 2280 ± 257 nM, calculated by nonlinear regression curve-fitting of the binding data using the Graphpad Prism software (Figure S3). The competitive binding assay was carried out to measure the binding ability of T4 and other compounds with hTTR. hTTR (0.5 μM ) and ANSA (50 μM) were mixed in a total volume of 2000 μL and incubated for 5 min at room temperature. Then, the ligand was continually added every 5 min with a guaranteed total volume change of less than 3%. The solvent effect was investigated, and the results indicated that there is no effect on the fluorescence intensity (Fλ) of the system when the DMSO concentration is below 5% (Figure S4). The Fλ at 470 nm of the solution after ligand addition was measured. The relative fluorescence intensity (RFλ) of each ligand concentration was expressed as

Kd,ligand =

IC50,ligandKd,probe [ANSA]

(2)

where IC50,ligand is the half-maximal inhibitory concentration of model compounds; [ANSA] is the concentration of ANSA. To reduce the bias in different laboratories and compare with the data in other literature, we also selected the logarithm of the relative competitive potential with T4 to evaluate a chemical binding capacity (log RP). log RP = log

IC50,T4 IC50,ligand

(3)

where IC50,T4 and IC50,ligand are the half-maximal inhibitory concentration of T4 and model compounds, respectively. Molecular Modeling. Molecular modeling was used to reveal underlying interaction mechanisms between model compounds and hTTR. The CDOCKER protocol in Discovery Studio 2.5.5 (Accelrys Software Inc.) was adopted to determine the initial potential bioactive conformations of the model compounds binding to hTTR. The hTTR crystal structure was obtained from RCSB Protein Data Bank (http:// www.rcsb.org/pdb/home/home.do) with PDB ID 1ICT (3 Å). The ionizable function groups in both hTTR and model compounds were protonated or deprotonated under pH = 7.40 condition.48 The conformation determined by molecular docking was further optimized by molecular dynamic (MD) simulation. All the calculations related to the MD simulation and the topology, and coordinate files were generated by using AMBER 12 package and AMBER tools 12.49 R.E.D. Server Development with restrained electrostatic potentials (RESP) method was used to derive the atomic partial charges of the model compounds.50 The atom types, bonds, and angle parameters of ligands and complexes were built using the GAFF Force Field and ff12SB Force Field, respectively.51 Each complex was neutralized by the counterion (e.g., Na+) and was solvated into a 9.0 Å cuboid TIP3P water box.52 The total number of atoms for 23 simulated systems is listed in Table S2. The MD simulations were performed with a time step of 2 fs. The long-range electrostatics was treatment by Particle Mesh Ewald (PME) with a nonbonded cutoff of 8 Å. Hydrogen atoms in each complex were constrained with the SHAKE algorithm.53 During the MD simulations, each complex was minimized with 2000 cycles of steepest descent and conjugates gradient minimization, respectively, and was gradually heated from 0 to 300 K in the NPT ensemble over a period of 50 ps. Finally, 5 ns MD simulations were performed. The atom coordinates were saved every 10 ps (100 frames/ ns).54 On the basis of the results from MD simulation, we analyzed the binding pattern of DBPs in the activity site of hTTR. The binding pattern includes orientation of ionizable 7021

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function groups and noncovalent interaction.48,54 The orientation of the ionizable function groups was illustrated by Discovery Studio 2.5.5. The ionic pair interaction, halogen bonds, and hydrogen bonds were analyzed by an in-house program. For ionic pair interaction, we analyzed the oxygen− nitrogen distances (dO−N) between the ionized groups in the model compounds and the −NH3+ group of Lys 15 in hTTR. If there exists a stable ionic pair interaction, the dO−N is ≤ 5 Å.55 For a hydrogen bond, the criteria are as follows: (a) the distance between a hydrogen atom and an electron acceptor atom (dA···H) is less than their sum of van der Waals radii; (b) the D−H···A angle is greater than 135°.56 For a halogen bond, the criteria are as follows: (a) the distance between halogen atom and electron donor atom (dX···D) is less than their sum of van der Waals radii; (b) the C−X···D angle is greater than 140°.57 The hydrophobic interaction was illustrated by the LigPlot+ program.58 QSAR Modeling. According to the interaction mechanism analysis results, 15 molecular descriptors were selected to describe the interactions of the model compounds with hTTR (Table S3). Fourteen chemical-form-adjusted quantum chemical descriptors were selected to characterize the hydrogen bonding and ionic pair interaction.48,54 The logarithm of the noctanol/water distribution coefficient (log D) characterizes the hydrophobic interaction.59 The model compounds’ geometry was optimized at the B3LYP/6-31+G(d,p) level using Gaussian 16 program package.60 Then, the quantum chemical descriptors were extracted from the Gaussian 16 output files; the log D values were calculated by the Calculator Plugins from MarvinSketch 15.6.29.0, 2015, ChemAxon (http://www. chemaxon.com) at the pH = 7.40 condition. In the QSAR modeling, the observed data set was divided into a training set of 18 compounds and a validation set of 5 compounds at random. Stepwise multiple linear regression (MLR) analysis was used to select the predictive variable and construct the QSAR model employing the SPSS 19.0 software. The internal and external prediction performances and the applicability domain of the derived model were assessed by following the OECD QSAR model validation guideline.61,62 The determination coefficient (R2), leave-one out cross validation Q2 (Q2LOO) and bootstrapping coefficient (Q2BOOT) were used to evaluate the goodness-of-fit and robustness. The external predictive ability was assessed by the external validation coefficient (Q2EXT) and the concordance correlation coefficient (CCC) of the validation set. The Y-scrambling technique was adopted to check the chance correlation of the model. In addition, the root-mean-square error (RMSE), standard errors (SE), and the mean absolute error (MAE) were also used to evaluate the prediction error. The variance inflation factor (VIF) was calculated and used to quantify the severity of multicollinearity.63 The applicability domain (AD) of the developed model was assessed using the Euclidean distance-based method and the Williams plot. The Euclidean distance-based method was incorporated into the AMBIT Discovery (version 0.04) (http://ambit.sourceforge.net/download_ambitdiscovery. html). The Williams plot of standardized residuals (δ) versus leverage values (h) was illustrated, in which compounds with the absolute values of standardized residual |δ| > 3 were recognized as outliers. The definition of δ and h were detailed in our previous studies.64

Article

RESULTS AND DISCUSSION hTTR Binding Potency of Phenolic-DBPs. The fluorescence spectra and fluorescence displacement curve of T4 and model compounds are listed in Figure 1 and Figures S5

Figure 1. Fluorescence displacement curves of thyroxine, 2,4,6tribromophenol, 2,6-dibromo-4-nitrophenol, 3,5-dibromo-4-hydroxybenzaldehyde, 3,5-dibromosalicylic acid, and 3,5-dibromo-4-hydroxybenzoic acid titrated into the solution of 50 μM ANSA and 0.5 μM hTTR. The error bars represent the standard deviation of three independent measurements.

and S6. The calculated binding constants Kd,ligand, IC50, and log RP values of model compounds are shown in Table 1. The hTTR binding potency of 2,4,6-trichlorophenol and 2,4,6tribromophenol was tested in a previous study. The log RP values were −0.314 ± 0.0314 and −0.477,65 0.267 ± 0.0358 and 0.30166/0.47767/0.62568 for 2,4,6-trichlorophenol and 2,4,6-tribromophenol in our work and previous study, respectively, indicating the log RP values tested here are agreement with previously published data. As shown in Figure 2, the tested five groups of phenolicDBPs exhibited a distinct hTTR binding potency. The rank

Figure 2. Distribution of the log RP for model compounds. The compound with log RP > −1.26 is a high potency hTTR binder, that with −2.26 < log RP < −1.26 is a moderate potency hTTR binder, and that with log RP < −2.26 is a low potency hTTR binder.

order of the hTTR binding affinity was 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols > 3,5-dihalo-4-hydroxybenzaldehydes > 3,5-dihalo-4-hydroxybenzoic acids, halo-salicylic acids. In addition, the log RP values of 2,4,6-trichlorophenol, 2,4,6-tribromophenol, and 2,4,6-triiodophenol are −0.314 ± 0.0314, 0.267 ± 0.0358, and 0.421 ± 0.0179, respectively, 7022

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six substituent groups were further introduced (Figure S7), that is, −CH3 (2,6-dibromo-4-methylphenol), −COCH3 (3,5dibromo-4-hydroxyacetophenone), −CN (3,5-dibromo-4-hydroxybenzonitrile), −NH2 (4-amino-2,6-dibromophenol), −CF 3 (3,5-dibromo-4-hydroxybenzotrifluoride), and −COOCH3 (3,5-dibromo-4-hydroxybenzoic acid methyl ester). Among the studied substituent groups, there are three electron-donor groups (i.e., −COO−, −NH2, −CH3) and eight electron-withdrawing groups (i.e., −COCH3, −COOCH3, −CHO, −CN, −Cl, −CF3, −Br, −NO2).74 The log RP values of those analogues are −2.48 ± 0.0899 (−COO−), −0.665 ± 0.0470 (−NH2), −0.190 ± 0.0699 (−CH3), −0.530 ± 0.0233 (−COCH3), −0.515 ± 0.0167 (−COOCH3), −0.459 ± 0.0109 (−CHO), 0.00962 ± 0.0144 (−CN), 0.128 ± 0.0182 (−Cl), 0.212 ± 0.0316 (−CF3), 0.267 ± 0.0358 (−Br), 0.304 ± 0.0515 (−NO2). The results indicated that (a) the log RP values for all of the six new introduced compounds is greater than −1.26 (Figure 2), implying that the six compounds are high potency hTTR disruptors; (b) the substituent group in para-position influence the binding interaction between hTTR and compounds; (c) the log RP values of chemicals with electron-withdrawing groups on their structures are higher than that of chemicals with electron donor groups except for −CH3. A quantitative contribution of the substituent groups on the binding interaction was also attempted to establish by employing Hammett substituent constants (σ) (Table S5). However, no significant linear correlation was observed between σ and log RP (Figure S8). The reason why 2,6dibromo-4-methylphenol (−CH3) exhibited high hTTR disrupting affinity is that the interaction between model compounds and hTTR is positively correlated with log D. The log D value of 2,6-dibromo-4-methylphenol (−CH3) is the second largest among the model compounds. Binding Mechanism Analysis. The binding pattern of the studied model compounds in the hTTR ligand binding domain was analyzed. Among the 23 studied compounds, 16 compounds were found to have ionized groups that point toward the mouth of the T4 binding site (Figure S9). Thus, in the dominant conformations of the anionic forms binding with hTTR, the ionized groups point toward the entry port of hTTR. Formation of the orientational noncovalent interactions may explain this phenomenon. According to the noncovalent interaction analysis results, we found that all of the compounds contained the conformation with oxygen−nitrogen distances (dO−N) between the ionized groups in the ligands and the −NH3+ group of Lys 15, ≤5 Å, documenting that the anionic groups of the model compounds form ionic pair interactions with the −NH3+ group of Lys15 in hTTR. To evaluate the stability of the ionic pair interactions, we further measured the cumulative percentage of the distance for the model compounds by calculating the ratio of conformations with ionic pair to the total conformations (500 frames). For the 23 compounds, the occupancy ratio of the ionic pair for 16 compounds was greater than 90% (Figure 3), indicating that the formed ionic pair interactions are stable for those compounds. The hydrogen bond analysis results indicated that all of the compounds can form a hydrogen bond with the amino acid residues in hTTR. The observed total hydrogen bonds’ occupancy ratio for 16 compounds is greater than 80%, implying that the hydrogen bonds between the ionizable groups of the compounds and hTTR is stable for the 16

implying that, for the hTTR binding potency, iodo-phenolicDBPs > bromo-phenolic-DBPs > chloro-phenolic-DBPs. Previous evidence also shows that iodinated DBPs generally are significantly the most cytotoxic and genotoxic.1,13,14 According to the criteria for classification proposed by Hamers et al.,69 the compound with log RP > −1.26 is considered as a high potency hTTR binder, that with −2.26 < log RP < −1.26 is a moderate potency hTTR binder, and that with log RP < −2.26 is a low potency hTTR binder (Table S4). In this case, the 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes are high potency hTTR disruptors. Among the 2,4,6-trihalo-phenols and 2,6dihalo-4-nitrophenols, there are four phenolic-DBPs named 2,4,6-tribromophenol, 2,4,6-triiodophenol, 2,6-dibromo-4chlorophenol, and 2,6-dibromo-4-nitrophenol with their log RP > 0, indicating that the binding affinity of those compounds is higher than that of T4. 3,5-Dichlorosalicylic acid, 3-bromo-5chlorosalicylic acid, and 3,5-dibromosalicylic acid are moderate potency hTTR binders. 5-Chlorosalicylic acid, 5-bromosalicylic acid, 3,5-dichloro-4-hydroxybenzoic acid, and 3,5-dibromo-4hydroxybenzoic acid are low potency hTTR binders. Gales et al.70 determined the binding affinity of salicylic acid, 5iodosalicylic acid, and 3,5-diiodosalicylic acid with hTTR using the radiolabeled ligand displacement method. They observed that salicylic acid does not compete with T4, while 5iodosalicylic acid presents a very low competition with T4; the log RP value of 3,5-diiodosalicylic acid is −0.890. Taken as a whole, our results indicated that 2,4,6-trihalo-phenols, 2,6dihalo-4-nitrophenols, 3,5-dihalo-4-hydroxybenzaldehydes, and polyhalogenated salicylic acid have high priority for further in vivo testing. It is well-known that ionization of a compound makes it more water-soluble and then less lipophilic.71 The log D is usually employed to describe the distribution ability of ionizable compounds from the water to the organic phase (e.g., protein).72 For analogues, a higher log D value usually means a higher distribution ability from water to proteins. As shown in Table 1, the log D values of 3,5-dihalo-4hydroxybenzoic acids and halo-salicylic acids range from −1.28 to 0. While the log D values of 2,4,6-trihalo-phenols, 2,6-dihalo-4-nitrophenols and 3,5-dihalo-4-hydroxybenzaldehydes range from 0.59 to 3.8. In addition, a statistically significant positive linear correlation between our observed log RP values and log D of model compounds (n = 23, r = 0.867, p < 0.0001) was found, indicating that a compound with higher log D value may lead to a higher log RP value. This result implied that the lower distribution ability of 3,5-dihalo4-hydroxybenzoic acids, halo-salicylic acids from water to the hTTR may be responsible for the low hTTR binding potency of the two phenolic-DBPs groups. Recently, our QSAR modeling results indicated that the log D had a positive correlation with the bovine serum albumin−water partition coefficients,73 and the chicken and fish muscle protein−water partition coefficients59 of ionizable compounds, implying that log D can be used to describe the distribution ability of ionizable compounds from water to bovine serum albumin, chicken, and fish muscle protein. Influence of Substituent Groups on the Binding of Compounds with hTTR. As shown in Figure S7, the prominent difference for some phenolic-DBPs is that there is a different substituent group in the para-position compared with 2,4,6-tribromophenol. To clarify the influence of substituent groups on the hTTR binding interaction, another 7023

DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028

Article

Environmental Science & Technology

Figure 3. Cumulative percentage of the oxygen− nitrogen distances (dO−N) for the model compounds. The cumulative percentage of dO−N for each compound was measured from the dO−N of total conformations (500 frames).

Figure 5. Relationship of the observed log RP and orientational noncovalent interactions. The percentages were the ratio of conformations formed ionic pair interactions, total hydrogen bonds, and halogen bonds to the total conformations (500 frames).

anionic compounds. In addition, the oxygen/nitrogen hydrogen bonds occupancy ratio is higher than that of the halogen hydrogen bonds except for 5-chlorosalicylic acid, elucidating that the main hydrogen bonds type is the oxygen/nitrogen hydrogen bonds (Figure 4). Furthermore, the amino acid

Development of the QSAR Model. The optimum QSAR model is log RP = −0.744 ± 0.276 + 0.463 ± 0.0854 log D − 0.0773 ± 0.0236 dipoleadj ntraining = 18,

2 R training = 0.828,

2 Q BOOT = 0.776,

(4)

2 Q LOO = 0.729,

2 RYS = 0.109,

2 Q YS = − 0.284,

RMSEtraining = 0.418, straining = 0.458, MAEtraining = 0.339, nEXT = 5,

2 Q EXT = 0.902,

RMSE EXT = 0.316,

p < 0.0001

CCC = 0.936,

sEXT = 0.499,

MAE EXT = 0.257

where ntraining and nEXT are the number of compounds in the training set and validation set, respectively; R2training is the squared correlation coefficient between observed and fitted values for the training set; Q2LOO and Q2BOOT are leave-one out cross validation Q2 and bootstrapping coefficient, respectively; R2YS and Q2YS are Y-scrambling technique parameters; Q2EXT is the externally explained variance; CCC is the concordance correlation coefficient; RMSEtraining and RMSEEXT are the rootmean-square error for the training set and validation set, respectively; straining and sEXT are standard errors for the training set and validation set, respectively; MAEtraining and MAEEXT are mean absolute error for the training set and validation set, respectively; p is the significant level. As shown in Table S8, the value of VIF is less than 10, indicating there is no serious multicollinearity among the variables. The R2training, Q2LOO, Q2BOOT, Q2EXT, and CCC are greater than 0.700, implying the model had a good goodnessof-fit, robustness, and external prediction performances according to the acceptable criteria (Q2 > 0.600, R2 > 0.700, CCC > 0.850).64 According to the Y-scrambling test criteria (R2YS < 0.3, Q2YS < 0.05); this model also has no accidental correlation.75 The plot of observed versus predicted log RP was shown in Figure S12. The applicability domain of the developed model was characterized using the Williams plot and Euclidean distance-

Figure 4. Percentage of the formed total hydrogen bonds, oxygen/ nitrogen hydrogen bonds, and halogen hydrogen bonds. The percentages were the ratio of conformations formed total hydrogen bonds, oxygen/nitrogen hydrogen bonds, and halogen hydrogen bonds to the total conformations (500 frames).

residues involved in forming hydrogen bonds were also analyzed. As shown in Table S6, 11 amino acid residues were observed. The most important amino acid residues are Lys 15, Lys 15′ and Thr 119′ (Figure S10). The halogen bonds analyzing results indicated that only eight compounds formed halogen bonds with hTTR and the halogen bonds occupancy ratio was low (Table S7). As shown in Figure 5, most of the high potency hTTR disruptors could form stable ionic pair interactions and/or hydrogen bonds. In addition to forming orientational noncovalent interactions, all the compounds have hydrophobic interactions with hTTR (Figure S11). The molecular modeling results indicated that ionic pair, hydrogen bonds, and hydrophobic interactions are the dominant interactions between the model compounds and hTTR. Thus, appropriate descriptors should be selected to characterize the critical interactions in the QSAR modeling. 7024

DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028

Article

Environmental Science & Technology based approach (Figure S13). As shown, all the chemicals in both the training set and the validation set were in the domain, indicating the training set had great representativeness. The log D term is a descriptor for the proteinophilic property.48,54 It can be used to characterize the hydrophobic interactions between the chemicals and hTTR as well as describe the distribution ability of ionizable compounds from water to protein. Its positive coefficient in the developed model indicated that a compound with high hydrophilicity and/or distribution ability may lead to a higher log RP value. The dipoleadj term was the chemical form adjusted dipole moment of the molecule. It was used to describe the polarity of a given compound.76 Its negative coefficient in the model implied that a compound with high polarity may result in a lower log RP value. The dipoleadj values of 3,5-dihalo-4-hydroxybenzoic acids and halo-salicylic acids are higher than that of 2,4,6-trihalophenols, 2,6-dihalo-4-nitrophenols, and 3,5-dihalo-4-hydroxybenzaldehydes except for 3,5-dichloro-4-hydroxybenzoic acid (Table S9), indicating the molecular polarity of the first two groups are higher than that of the last three groups. It is therefore conceivable why the 3,5-dihalo- 4-hydroxybenzoic acids and halo-salicylic acids exhibited low hTTR binding potency. Environmental Implications. The potential adverse effects caused by DBPs are international issues of common concern.77,78 However, little is known about whether the DBPs, especially the emerging DBPs could induce potential endocrine related detrimental effects and what is the underlying mechanism of endocrine disruptor action. To our knowledge, the data stated here are the first from a study of the disrupting effects of newly identified polar phenolic-DBPs on serum hormone transport protein. We have documented that the binding affinity of some phenolic-DBPs to hTTR was similar to that of T4. Thus, attention should be given to determine the possible adverse effects elicited by the action of serum hormone transport protein disrupting. In addition, this result also heightened interest in testing other mechanisms of endocrine disruption for phenolic-DBPs, such as activating/ inhibiting hormone receptors, inhibiting hormone synthesis, and metabolism-related enzymes, and so on. Lastly, a QSAR model was also developed here. The data gap for other phenolic-DBPs on their hTTR binding potency can be filled by the model.

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substituent group in para-position compared with 2,4,6-tribromophenol; correlation of Hammett substituent constants and observed log RP; views of model compounds in the binding site; hydrogen bonds occupancy ratio of amino acid residues; hydrophobic interaction of model compounds with hTTR; plot of predicted versus observed log RP values; applicability domains for the developed QSAR model (PDF)

AUTHOR INFORMATION

Corresponding Authors

*Tel.: +86 025-84315521. Fax: +86 025-84315827. E-mail: [email protected]. *E-mail: [email protected]. ORCID

Xianhai Yang: 0000-0002-9469-2946 Jingwen Chen: 0000-0002-5756-3336 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The study was supported by National Natural Science Foundation of China (No. 21507038, No. 41671489, No. 21507061) and Environmental Monitor Scientific Foundation of Jiangsu Province (No.1804).

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b00218. Selection of ANSA competitive fluorescence displacement assay; reagents purchase information; total number of atoms for simulated systems; selected molecular descriptors; criteria for classification of chemicals based on in vitro toxicity results; information on substituents and their Hammett constant; occupancy ratio of amino acid residues involved in forming hydrogen bonds; halogen bonds occupancy ratio; descriptions of the modeled descriptors and corresponding t, p, VIF values; values of the modeled descriptors, observed and predicted log RP; chemical structures of model compounds; plots of ANSA binding to hTTR; fluorescence spectra of DMSO; fluorescence spectra and fluorescence displacement curve of model compounds; different 7025

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DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028

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DOI: 10.1021/acs.est.9b00218 Environ. Sci. Technol. 2019, 53, 7019−7028