A method for accurately evaluating the toxic effects of bisphenol exposure based on the binding activity of metabolic transformation products to estrogen receptors

Abstract

The present application discloses a method for accurately evaluating the exposure toxicity effects of bisphenols based on the binding activity of metabolic transformation products with estrogen receptors. The present invention reveals that BPs are mainly present in the form of multiple MTPs after entering the body. The comprehensive use of the test results of the binding activity of the parent BPs and their MTPs with estrogen receptors to judge the metabolic interference effect can significantly improve the prediction accuracy. The present invention proposes for the first time a method for calculating the relative toxicity reduction index based on the binding activity of BPs and their MTPs with estrogen receptors and the relative content of BPs and their MTPs. In the future, accurate and rapid evaluation of the exposure toxicity effects of new pollutants such as BPs can be achieved through in vivo metabolic transformation research and in vitro computational simulation.

Description

Technical Field

[0001] The present invention belongs to the field of environmental exposure risk assessment of chemicals, and relates to a method for accurately evaluating the toxic effects of exposure to bisphenols (BPs) based on the binding activity of metabolic transformation products with estrogen receptors. Background Art

[0002] Bisphenol A (BPA), as a synthetic material for polycarbonate and epoxy resin, is widely used in the manufacture of plastic bottles, sippy cups for young children, and coatings on the inside of food and beverage cans. However, in recent years, more and more toxicological and environmental epidemiological studies have shown that exposure to BPA is an important risk factor for childhood ADHD, autism, diabetes, hypertension and other diseases, which has led to its use in products being gradually restricted. Instead, some manufacturers produce "BPA-free products" by developing halogen-modified, alkyl-modified and benzene-modified bisphenol analogues (BPs), such as bisphenol AF (BPAF), bisphenol B (BPB) and bisphenol AP (BPAP). These BPs often only undergo simple computational toxicology or animal toxicology safety assessments before being promoted to market applications. Among them, molecular docking technology based on computer simulation is widely used in the rapid toxicity assessment of BPs. Rigid docking, flexible docking and other algorithms are used to simulate the binding mode of ligand and target, and the docking results are evaluated through a specific scoring function to select the best ligand-target binding conformation, thereby calculating the binding energy and affinity of BPs to specific receptors, and realizing the determination of the strength of biological pathway activation.

[0003] A large number of studies have shown that the competitive binding of BPs to endogenous estrogen receptor (ER) proteins is one of the important molecular initiating events of BPs-induced toxicity. However, previous studies have mainly focused on the binding activity of parent BPs to ER, ignoring the binding of metabolic transformation products (MTPs) produced during BPs metabolism to ER, which has led to large deviations in in vivo and in vitro toxicity assessment results. BPA and its analogs contain a group of phenolic compounds that can be converted into active metabolites through phase I metabolism. These active metabolites usually have stronger protein receptor binding activity and can covalently bind to DNA, proteins and endogenous substances, resulting in genotoxicity and hepatotoxicity. Due to their phenolic structure, BPs can also be directly converted into water-soluble conjugates through phase II metabolism to reduce toxicity and promote excretion. Population studies have shown that the MTP level of BPA in serum is usually higher than that of parent BPA. Therefore, after BPs enter the body, they may have been mostly converted into metabolic transformation products before binding to ER. If only the parent compound is considered when evaluating the exposure toxicity effect of BPs in molecular docking, the uncertainty of the toxicity results of exposure assessment will increase. Therefore, it is necessary to study the metabolic transformation of BPs in vivo and explore the binding activity of their MTP and ER to more accurately reveal the potential exposure toxicity of BPs. Summary of the invention

[0004] Current studies usually evaluate the potential exposure toxic effects of BPs based on the binding activity of parent BPs with ER, ignoring the study of the binding of MTPs produced during BPs metabolism with ER, which leads to large deviations in the in vivo and in vitro toxicity assessment results. To this end, the purpose of the present invention is to provide a method for accurately evaluating the exposure toxic effects of bisphenols (BPs) based on the binding activity of metabolic transformation products with estrogen receptors, and to provide a feasible technical method for the rapid evaluation of the exposure toxic effects of new BPs.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A method for evaluating the toxic effects of exposure to bisphenols, comprising the following steps:

[0007] (1) Animal exposure, serum acquisition and data collection: Experimental animals were gavaged with bisphenols at an exposure (dosing) concentration of 50-400 mg / kg / day for more than 15 days. After the exposure, the serum of the experimental animals was obtained, purified and analyzed. The samples were analyzed and measured using an ultra-high performance liquid chromatography tandem quadrupole / orbitrap high-resolution mass spectrometer, and several fragment ion spectra were collected.

[0008] The experimental animals include rodents or non-rodents;

[0009] The rodents are preferably rats and mice;

[0010] The non-rodent animal is preferably a rabbit, dog, monkey, or others;

[0011] The bisphenol substances include bisphenol A and its substitutes;

[0012] The bisphenol A substitutes include bisphenol AF (BPAF), bisphenol B (BPB) and bisphenol AP (BPAP);

[0013] The method of obtaining serum from experimental animals is that after the exposure, the experimental animals are fasted for more than 12 hours, anesthetized, and subjected to abdominal incision to collect blood from the abdominal aorta, which is then centrifuged to obtain serum;

[0014] (2) Screening of potential metabolic transformation products: Based on the possible phase I and phase II metabolic transformation reactions of bisphenols, a list of suspected MTPs of bisphenols was constructed; Compound Discoverer software was used to perform an automated screening workflow on the fragment ion spectra collected in step (1), including: peak extraction (Select Spectra), peak alignment (AlignRetention Times), expected compound screening (Find Expected Compounds), expected compound combination (GroupExpected Compounds), compound identification and annotation, and fragment ion search scoring (FISh Scoring); the parent ions with high fragment ion scores were matched with the list of suspected MTPs to screen out the molecular formulas of potential metabolic transformation products of bisphenols;

[0015] There are 7 phase I metabolic transformation reactions, including oxidation, reduction, hydrolysis, reductive defluorination, oxidative defluorination, etc.; there are 17 phase II metabolic transformation reactions, including acetylation, methylation, glucuronide conjugation, sulfation, glutathione conjugation, glycine binding, cysteine ​​coupling, etc.; the maximum number of reactions for phase I and phase II metabolic reactions is set to 2, respectively, and the total number of reactions is 3. Through the combination of metabolic reactions, a list of suspected MTPs of bisphenols (including thousands of potential metabolic transformation products) is constructed;

[0016] (3) Structural analysis: Use metabolic simulation software to calculate the energy required for reactions at different sites of bisphenols, thereby identifying potential targets of metabolic reactions, and preliminarily deduce their structural formula based on the molecular formula obtained in step (2). Subsequently, input the deduced structural formula into the Compound Discoverer software, and use the fragment ion search algorithm to automatically simulate the characteristic mass spectrum fragmentation pattern of the compound, calculate the similarity between the fragment ions actually collected and the theoretically generated (calculated by the software) fragment ions, and determine the structural formula of the metabolic transformation product with a matching degree >20%. In addition, perform semi-quantitative analysis based on the peak area intensity of the metabolic transformation product to determine the relative content (RC) of MTPs.

[0017] The metabolic simulation software includes ADMET Predictor™ and BioTransformer 3.0;

[0018] (4) Calculation of binding energy between bisphenols and their metabolites and ERs: Draw a 3D model of bisphenols and their metabolites determined in step (3), obtain a model of the protein receptor, perform molecular docking between the ligand and the receptor, calculate the Gasteiger charge of the ligand and the receptor, set a grid box to cover the receptor binding pocket, perform docking calculation using the Lamarckian algorithm, and calculate the binding energy between bisphenols and their metabolites and the protein receptor;

[0019] The 3D model of bisphenols and metabolic transformation products is preferably drawn using Chem3D 20.0;

[0020] The molecular docking of the ligand and the receptor is preferably performed using Autodock 4.2;

[0021] (5) Calculation of Relative Toxicity Reduction Index (RTRI): Based on the binding energy (BE) of MTPs and their parent compounds with ERα, calculate the rate of change of the binding energy of MTPs relative to that of the parent BPs, ∆BE (Formula 1). Finally, sum the product of RC and ∆BE of all MTPs of a single bisphenol substance (Formula 2) to obtain the RTRI value. The larger the RTRI value, the weaker the receptor activation ability of BPs and their MTPs, the greater the degree of toxicity reduction, and the smaller the potential exposure toxicity effect.

[0022] Formula 1

[0023] Formula 2

[0024] Among them, △BE is the change rate of the binding energy of MTPs relative to the parent BPs; BE BPs and BE MTPs are the binding energies of BPs and their MTPs; RC i The sum is △BE i are the relative content of the i-th MTPs and its binding energy change rate, respectively.

[0025] Compared with the prior art, the present invention has the following advantages and effects:

[0026] 1. The present invention reveals that BPs are mainly present in the form of various MTPs after entering the body. Comprehensive use of the test results of the binding activity of maternal BPs and their MTPs with estrogen receptors to determine the metabolic interference effect can significantly improve the prediction accuracy.

[0027] 2. This invention proposes for the first time a method for calculating the relative toxicity reduction index based on the binding activity of BPs and their MTPs with estrogen receptors and the relative content of BPs and their MTPs. In the future, accurate and rapid evaluation of the exposure toxic effects of new pollutants such as BPs can be achieved through in vivo metabolic transformation studies and in vitro computational simulations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a diagram of the metabolic transformation pathway of BPAF in vivo.

[0029] Figure 2 It is a diagram of the metabolic conversion pathway of BPAP in the body.

[0030] Figure 3 This is a diagram of the metabolic transformation pathway of BPB in vivo.

[0031] Figure 4 This is the relative concentration distribution diagram of different metabolic transformation products of BPAF, BPB and BPAP in serum.

[0032] Figure 5 Binding pockets for estrogen receptor α (ERα) (A) and ERβ (B).

[0033] Figure 6 Binding of parent BPs (red) and their phase I (green) and phase II (blue) metabolites to ERα and ERβ.

[0034] Figure 7 The molecular forces between typical phase I MTPs and ERα amino acid residues (A); the change in the minimum binding energy of MTPs to the receptor compared with the parent BPs (B).

[0035] Figure 8 The differences in metabolic profiles between the control group and the different BPs exposure groups were shown based on partial least squares discriminant analysis.

[0036] Fig. 9 is the permutation test result of partial least squares discriminant analysis.

[0037] Fig.10 The metabolites that changed significantly after exposure to different doses of BPs were analyzed based on the volcano plot.

[0038] Fig.11 is the number of metabolites that cause interference with different doses of BPs exposure.

[0039] Fig.12 This is a correlation analysis diagram of the relative toxicity reduction index (RTRI) of BPs and the number of affected metabolites. DETAILED DESCRIPTION

[0040] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0041] Example

[0042] A method for accurately evaluating the exposure toxicity effects of bisphenols based on the binding activity of their metabolic transformation products to estrogen receptors, comprising the following steps:

[0043] 1. Animal exposure

[0044] Given that dietary intake is the main route of exposure to BPs in the human population, this study used oral administration for exposure, with an exposure interval of 15 days. Rats were randomly divided into a control group and low, medium, and high exposure groups, with 5 females and 5 males in each group, weighing 180 to 220 g. The low, medium, and high exposure groups were exposed to 50, 200, and 400 mg / kg / day of BPs, respectively, while the control group was only given an equal amount of corn oil. After the end of the exposure, the rats were fasted for 12 hours, anesthetized with isoflurane, positioned dorsally, and laparotomy was performed to collect blood from the abdominal aorta. 2 ml of blood was collected using lithium heparin anticoagulation tubes and centrifuged at 3500 rpm for 5 minutes to obtain serum.

[0045] 2. Study on metabolic transformation of BPs

[0046] 2.1 Serum purification and enrichment

[0047] Take 400 μL of blood supernatant and add 1.2 mL of methanol precooled at -80°C. Vortex the sample for 30 seconds, place it at -80°C for 60 min, and centrifuge it at 14,000 rpm for 15 minutes. Take 800 μL of supernatant, vacuum concentrate to dryness, add 200 μL of 1% formic acid methanol solution to reconstitute, centrifuge it at 14,000 rpm for 15 minutes, and take 120 μL of supernatant for analysis.

[0048] 2.2 Data Collection

[0049] Ultra-high performance liquid chromatography tandem quadrupole / orbitrap high-resolution mass spectrometer (Orbitrap Exploris240) was used for analysis and determination. The specific conditions were as follows: the chromatographic separation column was a Hypersil GOLD AQ C18 column (150 × 2.1 mm, 1.9 um, Thermo Fisher Scientific, Germany), the organic phase (B) was methanol, the aqueous phase (B) was deionized water, the column temperature was 35 °C, and the injection volume was 2 μL. The gradient elution program was as follows: 0-2 min 2% B, flow rate 0.25 mL / min; 2-8 min 2-98% B, flow rate 0.25 mL / min; 8.01-11 min 98% B, flow rate 0.40 mL / min; 11.01-14 min 2% B, flow rate 0.25 mL / min. Mass spectrometry detection was performed in electrospray ionization (ESI) mode, and the spray voltage in negative ion mode was –2.5 kV. The ESI source parameters were as follows: the sheath gas, auxiliary gas, and sweep gas were set to 45, 8, and 1 Arb, respectively. The ion transfer tube and vaporizer temperatures were 320 and 350 °C, respectively. The data acquisition process included full mass spectrometry scan, dynamic exclusion, target mass exclusion, peak detection, and fragment ion scan. In full scan mode, the resolution was 120,000 and the scan range was 70-1000 m / z. Fragment ion scan: isolation window: 2 m / z; collision energy type: standardized; HCD collision energy: 20%, 40%, 60%, 80%; resolution: 15,000; scan range mode: automatic; AGC target: standard; number of fragment ion spectra collected: 5.

[0050] 2.3 Suspected screening

[0051] Based on the possible phase I and phase II metabolic transformation reactions of BPs, a list of suspected MTPs of BPs was constructed. Among them, there are 7 phase I metabolic processes, including oxidation, reduction, hydrolysis, reductive defluorination, oxidative defluorination, etc.; there are 17 phase II metabolic processes, including acetylation, methylation, glucuronide conjugation, sulfation, glutathione conjugation, glycine binding, cysteine ​​coupling, etc. The maximum number of reactions for phase I and phase II metabolic reactions was set to 2, and the total number of reactions was set to 3. The list of potential MTPs was formed through the combination of metabolic reactions. Using Compound Discoverer software, an automated screening workflow for suspected MTPs was established, including peak extraction, peak alignment, expected compound screening, expected compound combination, compound identification and annotation, and fragment ion search scoring. By matching the list of suspected MTPs with high-resolution spectral information, the molecular formula list of potential MTPs of three BPs was screened out.

[0052] 2.4 Structural analysis

[0053] ADMET Predictor™ and BioTransformer 3.0 metabolic simulation software were used to calculate the energy required for reactions at different sites of BPs, thereby identifying potential targets for metabolic reactions, and preliminarily inferring the structural formula of the BPs metabolic transformation products based on their molecular formulas. Subsequently, the inferred structural formula was input into the compound annotation editor in the Compound Discoverer software, and the fragment ion search (FISh) algorithm was used to automatically simulate the characteristic mass spectrometry fragmentation pattern of the compound, thereby calculating the similarity between the actual collected fragment ions and the theoretically generated fragment ions, and finally determining the structure of the metabolic transformation product based on the matching degree (>20%). In addition, semi-quantitative analysis was performed based on the peak area intensity of MTPs to clarify the relative content of MTPs.

[0054] 3. Molecular docking simulation study of BPs and ERs

[0055] The activation effects of BPs and their MTPs on ERα and ERβ receptors were studied as follows: 3D models of ligand molecules BPs and their metabolic transformation products were drawn using Chem3D 20.0, and energy minimization was performed using the computational method of Molecular Mechanics 2 (Chen, T., Shu, X., Zhou, H., Beckford, FA, & Misir, M. (2023). Algorithm selection for protein-ligand docking: strategies and analysis onACE. Scientific reports, 13(1), 8219. https: / / doi.org / 10.1038 / s41598-023-35132-5.). ERα and ERβ are nuclear receptors, and their protein models can be obtained from the Protein Data Base protein database. After preparing the ligand and protein receptor, the molecular docking experiment of ligand and receptor was carried out using Autodock 4.2. The Gasteiger charges of ligand and receptor were calculated, the grid box was set to cover the receptor binding pocket, and the Lamarckian algorithm was used for docking calculation (Fuhrmann, J., Rurainski, A., Lenhof, HP, & Neumann, D. (2010). A new Lamarckian genetic algorithm for flexible ligand-receptor docking. Journal of computational chemistry, 31(9), 1911–1918. https: / / doi.org / 10.1002 / jcc.21478.), the (minimum) binding energy was calculated, and the ligand-receptor binding conformation with the highest frequency was screened. The LigPlus software was used to analyze the interaction between ligand and receptor in the best binding conformation, and the hydrogen bonds and hydrophobic forces between ligand and receptor were calculated. The DoGSiteScorer online tool was used to retrieve and predict potential binding pockets of receptor proteins, and the conformation of the ligand-receptor complex was visualized using PyMOL 2.5.0.

[0056] 4. Calculation of Relative Toxicity Reduction Index (RTRI)

[0057] The metabolic transformation of exogenous pollutants in the body usually proceeds in the direction of forming low-toxicity, high-hydrophilic MTPs to achieve toxicity reduction and excretion, thereby reducing the impact on the organism. Therefore, in this study, we proposed the concept of RTRI and defined it as the degree to which pollutants in the body reduce their binding activity to receptor proteins through metabolic transformation to achieve toxicity reduction. Subsequently, we determined the relative content (Relative Content, RC) of different MTPs of BPAF, BPAP and BPB based on the peak area intensity. Based on the binding energy (Binding Energy, BE) of MTPs and their parent compounds with ERα, the change rate ∆BE of the binding energy of MTPs relative to the parent BPs was calculated (Formula 1). Finally, the RC and ∆BE products of all MTPs of a single BP were summed (Formula 2) to obtain the RTRI value. The larger the RTRI value, the weaker the activation ability of BPs and their MTPs on receptors, the greater the degree of toxicity reduction, and the smaller the potential exposure toxicity effect.

[0058] Formula 1

[0059] Formula 2

[0060] Among them, △BE is the change rate of the binding energy of MTPs relative to the parent BPs; BE BPs and BE MTPs are the binding energies of BPs and their MTPs; RC i The sum is △BE i are the relative content of the i-th MTPs and its binding energy change rate, respectively.

[0061] 5. Evaluation of metabolic disruption effects of BPs exposure

[0062] 5.1 Serum sample cleanup

[0063] Take 100 μL serum supernatant and add 300 μL methanol precooled at -80°C. Vortex the sample for 30 seconds, place it at -80°C for 60 min, and centrifuge it at 14,000 rpm for 15 minutes. Take 200 μL supernatant, add 50 μL isotope internal standard and 50 μL vitamin C (to prevent metabolite oxidation), and vacuum concentrate to dryness. Then, add 200 μL of 1% formic acid-10% methanol aqueous solution to reconstitute, let it stand for 15 minutes, vortex it for 30 seconds, centrifuge it at 14,000 rpm for 15 minutes, and take 120 μL of supernatant for analysis.

[0064] 5.2 Non-targeted metabolomics analysis

[0065] Orbitrap Exploris 240 was used for analysis and determination, and the chromatographic and mass spectrometric analysis parameters were the same as those in 2.2 Data Acquisition. After data acquisition, a non-targeted metabolomics screening analysis process was established based on Compound Discoverer 3.3 SP2 software, including peak detection, peak alignment, peak extraction, peak integration, compound identification and annotation, missing value interpolation, quality control correction, background subtraction, and peak area normalization. The screened compounds were identified and annotated using databases such as ChemSpider, mzVault, Mass Lists, and mzCloud. The quality assurance and control (QC) measures during the non-targeted metabolomics analysis were as follows: ① Open process samples were prepared to exclude interference caused by material and reagent contamination; ② 10 µL of each serum sample was mixed to prepare QC samples, which were interspersed with the samples for determination to monitor the variability during instrument analysis.

[0066] 5.3 Targeted metabolomics analysis

[0067] A single-standard mixed standard solution was purchased and a standard curve was prepared. An ultra-high performance liquid chromatography-tandem quadrupole / orbitrap high-resolution mass spectrometer was used to construct a high-throughput detection method that can simultaneously detect 283 metabolites, including 103 amino acids, 83 carbohydrates, 72 nucleotides, and 25 energy metabolism metabolites. The chromatographic and mass spectrometric conditions were the same as those for non-targeted screening. The chromatographic peaks of each compound were extracted based on the accurate molecular mass of the parent ion, and the isotope internal standard method was used for quantitative analysis. Among them, the linear range of amino acid compounds was 125~4000 μg / L, the linear range of carbohydrates and energy metabolites was 62.5~2000 μg / L, and the linear range of nucleotides was 54.75~1750 μg / L.

[0068] 5.4 Analysis of metabolic interference effects

[0069] Statistical analysis was performed using R language. The differences in serum metabolites between the control group and the BPs exposure group were presented using volcano plots and partial least squares discriminant analysis (PLS-DA) models. One-way ANOVA and Dunnett's multiple comparison test were used to evaluate the intergroup differences in serum metabolites after BPs exposure. Metabolites with fold change >1.2 and p-value <0.05 were selected for KEGG pathway enrichment analysis using the online tool MetaboAnalyst 6.0. Finally, the metabolic perturbation effects of various BPs exposures were evaluated based on the number of affected metabolites and metabolic pathways.

[0070] 6. Correlation analysis between RTRI and metabolic disruption effects

[0071] To verify whether the RTRI value can accurately predict the exposure toxicity effect of BPs, we used the metabolic interference effect measured by animal experiments as a toxicity evaluation index to evaluate the association between the RTRI value and the metabolic interference effect. The stronger the metabolic interference effect after BPs exposure, the more serum metabolites are affected, so the number of affected metabolites represents the intensity of the metabolic interference effect of BPs exposure. Using Pearson correlation analysis, the relationship between the RTRI and metabolic interference effect of the three BPs was preliminarily established to verify whether the relationship between the two is consistent. The higher the correlation coefficient, the better the consistency, indicating that RTRI can be used to predict the strength of the metabolic interference effect caused by BPs exposure, and vice versa.

[0072] 7. Research Results

[0073] 7.1 Identification of Metabolic Transformation Products of Bisphenols

[0074] In step 2.4, a total of 19 BPs metabolites were identified (Table 1), including 6 BPAF metabolites, 7 BPAP metabolites, and 6 BPB metabolites. The structural confidence of most metabolites can reach L2 level, with FISh coverage >20%, and at least two fragment ions matched in the database with a mass deviation of <5 ppm.

[0075]

[0076] The metabolic transformation products of the 19 BPs include oxidation products, glucosidase conjugates, glucuronide conjugates, and methylation products. Based on the phase I and phase II metabolic reaction rules, the in vivo metabolic transformation pathways of BPAF, BPAP, and BPB were drawn respectively ( Figures 1 to 3 ).

[0077] Subsequently, semi-quantitative analysis was performed based on the peak area intensity of BPs metabolic transformation products to clarify the relative content of metabolic transformation products. The results showed that maternal BPAF in serum accounted for 3.6% of the total metabolic transformation product concentration, while its phase I and II combined products accounted for 0.4% (BPAF_M1) and 96.0% ( Figure 4 ). In serum, parent BPB accounted for 0.5% of the total metabolic conversion product concentration, while its phase I and II conjugates accounted for 0.2% (BPB_M1) and 99.2%, respectively. In contrast, only parent BPAP (0.9%) and II conjugates (99.1%) were found in serum after BPAP exposure, and no phase I oxidation products were found. This shows that after BPs compounds enter the body, they can be rapidly metabolized and converted to form II conjugates, and only a small part of the serum is parent BPs. Therefore, if only the binding activity of parent BPs with ERs is evaluated while ignoring its metabolic conversion products, it may cause huge exposure toxicity assessment deviations.

[0078] 7.2 Evaluation of the binding activity of BPs and their transformation products to ER

[0079] Step 3: Use molecular docking simulation technology to predict the binding activity of different MTPs with ER.

[0080] The results showed that the parent BPs and their phase I MTPs mainly bind to ERα at pockets 2 and 10, and to ERβ at pockets 1 and 2, while phase II MTPs can bind to various pockets of ERs ( Figure 5 and Figure 6 ).

[0081] The minimum binding energies between most ligands and ERα and ERβ were lower than −5.0 kcal / mol, indicating that these MTPs had good binding activity with these two receptors (Tables 2 and 3). The hydrogen bonds and hydrophobic interactions formed by phase I MTPs with ERα and ERβ were similar to those of their parent compounds (Tables 2 and 3 and Figure 7 A).

[0082]

[0083]

[0084] Subsequently, this application explored the changes in the minimum binding energy of MTPs compared to their parent BPs and receptors ( Figure 7 B). The results showed that the binding energies of BPAF_M1 and BPAF_M4 in ERs were lower than those of BPAF_M0, while other MTPs showed higher binding energies. BPAF_M1 is the product of BPAF oxidation, while BPAF_M4 is derived from the methylation of BPAF_M1. Similar phenomena were observed for BPAP and BPB. Therefore, the MTPs formed by oxidation can enhance their binding activity with ERs, while methylation reduces the binding activity, although the latter has less effect on the reduction of binding activity than glucuronic acid complexation. It is worth noting that compared with the parent BPAP, the minimum binding energy of its MTPs with ERs increased by 21.0−52.2%, while this trend was significantly weaker in BPAF and BPB. Therefore, BPAP exhibits a more significant metabolic detoxification effect than BPAF and BPB.

[0085] 7.3 Evaluation of metabolic disruption effects of BPs exposure

[0086] In step 5, in the non-targeted metabolomics screening, a total of 434 metabolites were identified in the positive ion mode and 498 metabolites were identified in the negative ion mode. In the targeted quantification, a total of 94 metabolites were detected. Subsequently, all identified metabolites were combined for analysis to evaluate the metabolic interference effects caused by BPs exposure and to find differential metabolites. Partial least squares discriminant analysis (PLS-DA) showed that there were significant differences in the metabolic profiles between the control group and the different BPs exposure groups ( Figure 8 The intercepts of goodness of fit (R2) and goodness of prediction (Q2) indicate that the PLS-DA model is reliable and does not overfit ( Fig. 9 ).

[0087] Subsequently, one-way analysis of variance and Dunnett's multiple comparison test were used to evaluate the inter-group differences in serum metabolites after BPs exposure, and a volcano plot was used to display the metabolites that changed significantly after exposure to different doses of BPs. Among them, metabolites that simultaneously satisfied the fold change >1.2 and p-value <0.05 were considered to be significantly changed. The results showed that the number of disturbed metabolites was positively correlated with the exposure dose of BPs. Compared with the control group, low, medium, and high concentrations of BPAF exposure can lead to significant changes in the concentrations of 258 (138 downregulated and 120 upregulated), 303 (170 downregulated and 133 upregulated), and 405 (192 downregulated and 213 upregulated) metabolites, respectively ( Fig.10 ). In addition, compared with the control group, low, medium and high concentrations of BPAP exposure can cause significant changes in the concentrations of 15, 94 and 189 metabolites, respectively, and low, medium and high concentrations of BPB exposure can cause significant changes in the concentrations of 137, 174 and 277 metabolites, respectively. Different concentrations of BPAF, BPB and BPAP exposure caused a cumulative significant change in 554, 391 and 236 serum metabolites, respectively ( Fig.11 ). The number of metabolites affected by low-concentration BPAF exposure was significantly higher than that caused by low-concentration BPB and BPAP. In summary, BPs exposure interfered with serum metabolites in a dose-dependent manner, and the degree of metabolic interference effect was BPAF>BPB>BPAP.

[0088] 7.4 Association of RTRI with metabolic disruption effects

[0089] Since ERα is the major estrogen receptor subtype in animals, this application focuses on the binding analysis of ligands to ERα.

[0090] As shown in Table 2, the lowest binding energies of the parent BPAF, BPB, BPAP with ERα are -6.1, -7.6, -8.0 respectively. Therefore, the binding activity order of BPs with ERα is: BPAP > BPB > BPAF. However, the evaluation of the metabolic interference effect obtained exactly the opposite results, and the order is: BPAP < BPB < BPAF. Thus, it is difficult to accurately predict the exposure toxicity effect solely relying on the binding activity test of the parent BPs with the receptor. Therefore, this application further explores that comprehensively using the binding activity test results of the parent BPs and their MTPs with the ERα receptor to predict the metabolic interference effect can improve the accuracy.

[0091] As shown in Table 4, the RTRIs of BPAF, BPB, BPAP are 0.1256, 0.1495, 0.2746 respectively, which indicates that the degree of toxicity reduction of BPAP in vivo through metabolic transformation is the largest, followed by BPB and BPAF. It should be noted that the RTRI ratio of BPAF, BPB, BPAP is 1:1.2:2.2, while the intensity of the metabolic interference effect is 2.3:1.4:1. Pearson correlation analysis shows that there is a significant negative correlation between the RTRI of BPs and the number of affected metabolites, and the correlation coefficient is -0.926 ( Fig.12 ). Thus, it can be seen that the toxicity reduction degree of BPs is significantly negatively correlated with their metabolic interference effect. The larger the RTRI value of BPs, the weaker the metabolic interference effect. Therefore, comprehensively using the binding activity test results of the parent BPs and their MTPs with the ERα receptor to distinguish the metabolic interference effect can significantly improve the prediction accuracy.

[0092]

[0093] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A method for evaluating the toxic effects of exposure to bisphenols, characterized in that The following steps are involved: (1) Animal exposure, serum acquisition and data collection: Experimental animals were gavaged with bisphenols at an exposure concentration of 50-400 mg / kg / day for more than 15 days. After the exposure, the serum of the experimental animals was obtained, purified and analyzed. The samples were analyzed and measured using an ultra-high performance liquid chromatography tandem quadrupole / orbitrap high-resolution mass spectrometer, and several fragment ion spectra were collected. The bisphenol substances include bisphenol A and its substitutes; (2) Screening of potential metabolic transformation products: Based on the possible phase I and phase II metabolic transformation reactions of bisphenols, a list of suspected MTPs of bisphenols was constructed; Compound Discoverer software was used to perform an automated screening workflow on the fragment ion spectra collected in step (1), including: peak extraction, peak alignment, expected compound screening, expected compound combination, compound identification and annotation, and fragment ion search scoring; the parent ions with high fragment ion scores were matched with the list of suspected MTPs to screen out the molecular formulas of potential metabolic transformation products of bisphenols; (3) Structural analysis: Use metabolic simulation software to calculate the energy required for reactions at different sites of bisphenols, thereby identifying potential targets of metabolic reactions, and preliminarily deduce their structural formula based on the molecular formula obtained in step (2); then, input the deduced structural formula into the Compound Discoverer software, and use the fragment ion search algorithm to automatically simulate the characteristic mass spectrum fragmentation pattern of the compound, calculate the similarity between the fragment ions actually collected and the theoretically generated fragment ions, and determine the structural formula of the metabolic transformation product with a matching degree of >20%; in addition, perform semi-quantitative analysis based on the peak area intensity of the metabolic transformation product to determine the relative content RC of MTPs; (4) Calculation of binding energy between bisphenols and their metabolites and ERs: Draw a 3D model of bisphenols and their metabolites determined in step (3), obtain a model of the protein receptor, perform molecular docking between the ligand and the receptor, calculate the Gasteiger charge of the ligand and the receptor, set a grid box to cover the receptor binding pocket, perform docking calculation using the Lamarckian algorithm, and calculate the binding energy between bisphenols and their metabolites and the protein receptor; (5) Calculation of relative toxicity reduction index: Based on the binding energy of MTPs and their parent compounds with ERα, the change rate of the binding energy of MTPs relative to the parent BPs, ∆BE, is calculated; finally, the product of RC and ∆BE of all MTPs of a single bisphenol is summed to obtain the RTRI value; Formula 1 Formula 2 Among them, △BE is the change rate of the binding energy of MTPs relative to the parent BPs; BE BPs and BE MTPs are the binding energies of BPs and their MTPs; RC i The sum is △BE i are the relative content of the i-th MTPs and its binding energy change rate; The larger the RTRI value, the weaker the ability of BPs and their MTPs to activate receptors, the greater the degree of toxicity reduction, and the smaller the potential exposure toxicity effect.

2. The method according to claim 1, characterized in that: The bisphenol A substitutes described in step (1) include bisphenol AF, bisphenol B and bisphenol AP.

3. The method according to claim 1, characterized in that: The metabolic simulation software described in step (3) includes ADMET Predictor™ and BioTransformer 3.

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4. The method according to claim 1, characterized in that: The 3D model of bisphenols and metabolic transformation products described in step (4) is drawn using Chem3D 20.

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5. The method according to claim 1, characterized in that: The molecular docking of the ligand and the receptor described in step (4) is performed using Autodock 4.

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6. The method according to claim 1, characterized in that: The experimental animals described in step (1) include rodents or non-rodents.

7. The method according to claim 6, characterized in that: The rodents are rats and mice.

8. The method according to claim 6, characterized in that: The non-rodent animals are rabbits, dogs and monkeys.

9. The method according to claim 1, characterized in that: The step (1) of obtaining serum from experimental animals is that after the exposure, the experimental animals are fasted for more than 12 hours, anesthetized, and subjected to abdominal incision to collect blood from the abdominal aorta, which is then centrifuged to obtain serum.

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