A high-throughput identification method for biotransformation products of organic pollutants in blood

Abstract

This invention discloses a high-throughput identification method for biotransformation products of organic pollutants in blood, belonging to the field of chemical analysis. Based on the structural prediction of biotransformation products, this invention assesses the confidence level of the structural matching relationship between biotransformation products and parent pollutants, establishes a list of suspected biotransformation products, and simultaneously performs primary and secondary mass spectrometry scans on biotransformation products in blood. Primary mass spectrometry matching and secondary fragmentation prediction matching are then performed to achieve the identification of suspected human biotransformation products of organic pollutants in blood. The identification method of this invention has advantages such as small sample requirements, a wide range of identified biotransformation products, high accuracy, and short processing time.

Description

Technical Field

[0001] This invention belongs to the field of chemical analysis technology, and more specifically, relates to a high-throughput identification method for biotransformation products of organic pollutants in blood. Background Technology

[0002] In recent years, with the continuous development of industrialization, the quantity and types of organic pollutants discharged into the environment have increased year by year. A large number of organic pollutants can enter the human body through various means such as respiration, ingestion, and skin contact, posing potential adverse effects on human health. It is noteworthy that organic pollutants undergo diverse biological metabolic pathways in the human body, producing a large number of biotransformation products with different structures. Furthermore, some biotransformation products have stronger toxic effects than their parent compounds. For example, the biotransformation product of triclosan, TCS-O-TCS, has an androstane receptor activity up to 7.2 times that of the parent compound, posing a significant health risk. Therefore, identifying the distribution of organic pollutant biotransformation products in the human body is of great significance for in-depth research on the human health risks of organic pollutants.

[0003] Blood is a representative biological sample for understanding human exposure to environmental pollutants, and the identification and recognition of biotransformation products of organic pollutants in blood is of great significance. However, pollutants undergo complex biotransformation reactions in the human body, forming a wide variety of biotransformation products, which are often present in blood samples at low concentrations and mostly lack standards, making identification difficult. Furthermore, it is difficult to establish a correspondence between biotransformation products and their parent compounds. Therefore, the identification of biotransformation products of organic pollutants in blood faces significant challenges.

[0004] Currently, the identification of biotransformation products (BTPs) of organic pollutants in blood mainly relies on the traditional method of targeted analysis. This method focuses on detecting and identifying a very small number of BTPs with known structures, leaving a large number of unknown BTPs unidentified. Furthermore, it is difficult to obtain standard samples for these BTPs, making the detection and identification range of targeted analysis for human BTPs of organic pollutants in blood extremely limited. Suspicious substance screening technology overcomes the shortcomings of both targeted and non-targeted screening techniques. By establishing a suspicious substance screening list, it identifies target substances in samples, offering high throughput, short time, low difficulty, and accurate screening results. However, when applying suspicious substance identification to BTP identification, the problem of an unknown screening list for BTPs remains. Although current predictive software can predict the structure of BTPs, BTPs obtained solely based on theoretical calculations may have structurally unreasonable results, limiting the prediction confidence. Summary of the Invention

[0005] 1. The problem to be solved

[0006] To address the problems of difficulty in identifying biotransformation products of organic pollutants in blood, limited prediction confidence, and inaccurate screening results in existing technologies, this invention provides a high-throughput identification method for biotransformation products of organic pollutants in blood. Based on the prediction of biotransformation product structures, this invention establishes a method for evaluating the confidence level of biotransformation product structures, improving the accuracy of biotransformation product screening lists and providing strong technical support for revealing the exposure levels and health risks of biotransformation products of organic pollutants.

[0007] 2. Technical Solution

[0008] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0009] A high-throughput identification method for biotransformation products of organic pollutants in blood according to the present invention includes the following steps:

[0010] S10. Structural prediction of biotransformation products of organic pollutants in blood, and the predicted biotransformation products.

[0011] S20. For the predicted biotransformation products, the confidence level of the biotransformation product structure is evaluated based on two criteria: parent-biotransformation product secondary feature fragment matching and neutral loss matching.

[0012] S30. Establish a biotransformation product screening list by including biotransformation products that have been assessed as qualified with confidence level as target items in the suspected product screening list.

[0013] S40. Perform blood pretreatment on the blood sample, centrifuge to extract blood concentrate, and perform full scan of the blood concentrate by primary mass spectrometry and secondary mass spectrometry.

[0014] S50. Based on the biotransformation product screening list established in step S30, perform primary mass spectrometry matching and secondary fragmentation prediction matching to identify the suspiciousness of biotransformation products in blood concentrate.

[0015] Preferably, in step S10, the metabolic prediction function of the Biotransformer 3.0 platform is used to predict the structure of biotransformation products of blood organic pollutants, and to predict the biotransformation products under four pathways: PPase I, CYP450, PPase II, EC-Based, and HumanGut Microbial.

[0016] Preferably, in step S20, the specific steps for assessing the confidence level of the biotransformation product structure include:

[0017] (1) Querying secondary characteristic fragments of parent compound and calculating neutral loss: Query the standard secondary spectrum and characteristic fragments of parent material on PubcPem website, calculate the difference between the mass-charge ratio of each secondary fragment, and use it as the neutral loss of parent material under adsorption ion mode and collision energy;

[0018] (2) Prediction of secondary characteristic fragments and calculation of neutral loss of biotransformation products: The Spectra Prediction function of the CFM-ID platform was used to predict the secondary fragment peak information of biotransformation products under the same adduct ion mode and collision energy as the standard secondary spectrum of the parent material. The difference between the mass-charge ratio of each secondary fragment was calculated as the neutral loss of biotransformation products under the adduct ion mode.

[0019] (3) Secondary characteristic fragment matching: The secondary fragment peaks of the parent material and the biotransformation product under the same adduct ion mode and collision energy are matched. When there are two characteristic fragments that can be matched, the biotransformation product is reliable.

[0020] (4) Neutral loss matching: The neutral loss of the parent material and the biotransformation product under the same adduct ion mode and collision energy is matched. When there are two neutral losses that can be matched, the biotransformation product is reliable.

[0021] (5) Confidence level evaluation criteria: When the biotransformation product achieves a secondary characteristic fragment match or a neutral loss match with the parent material, the biotransformation product meets the confidence level requirements.

[0022] Preferably, in step S30, the biotransformation products that have passed the confidence assessment in step S20 are used as targets in the suspected substance screening list, and a screening list including the name, molecular formula, SMILES formula, and metabolic pathway of the biotransformation product is constructed.

[0023] Preferably, in step S40, the blood sample is pretreated to remove water, then acetonitrile is added and ultrasonically centrifuged to obtain a supernatant. Acetonitrile solution is added again and ultrasonically centrifuged, and the extraction is repeated twice. The supernatants from the three centrifugation extractions are combined and concentrated by nitrogen blowing to obtain a blood concentrate. Then, liquid chromatography-mass spectrometry is used to perform a full scan of the blood concentrate in positive and negative ionization modes using primary and secondary mass spectrometry.

[0024] Preferably, in step S50, based on the biotransformation product screening list established in step S30, PeakView and CFM-ID software are used to perform primary mass spectrometry matching and secondary fragmentation prediction matching to identify the suspiciousness of biotransformation products in the concentrate.

[0025] Preferably, in step S10, the specific steps for predicting the structure of biotransformation products are as follows: Click on Metabolism Prediction on the Biotransformer 3.0 homepage to enter the biotransformation product prediction function; select the metabolic pathway at “Select a Metabolic Transformation”, enter the SMILES formula of the parent compound at “Select an Input Type”, and select the number of iterations at “Number of Reaction Iterations to Calculate”; predict the biotransformation products under four pathways: PPase I, CYP450, PPase II, EC-Based, and Human Gut Microbial, respectively. Among them, the prediction of PPase I and CYP450 adopts the combined mode, selects to perform three iterations, and derives the biotransformation products under different metabolic pathways.

[0026] Preferably, in step S40, a mixture of MgSO4 and NaCl with a mass ratio of 4:1 is used as a dispersive solid-phase extractant to remove serum water, and three steel balls with a diameter of Ф=2mm are added for vortex mixing. Acetonitrile and acetonitrile solution are used for ultrasonic extraction once and twice, respectively. The supernatants are combined for pretreatment of the blood sample to extract biotransformation products from the serum, wherein the acetonitrile:water volume ratio in the acetonitrile solution is 95:5.

[0027] Preferably, in step S40, the extracted supernatant is concentrated by purging the solvent with nitrogen and brought to a final volume of 100 μL. The final volume solvent is acetonitrile. After bringing to a final volume, when a small amount of white solid MgSO4 and NaCl are present at the bottom, the supernatant is centrifuged again and collected.

[0028] Preferably, in step S60, the specific steps for identifying the suspiciousness of biotransformation products are as follows:

[0029] (1) Use Peak View 2.2 and CFM-ID 4.0 software to import sample mass spectrometry information;

[0030] (2) In the MasterView window, import the name, molecular formula, and positive or negative adduct ion mode (+H or -H) of the suspected substance into the "Name", "Formula", and "Adduct" columns of the target matching substance box. After selecting the adduct ion mode, the software will automatically calculate the m / z of its parent ion. In the settings window, under "confidence setting", set the matching parameters: "Mass Error: <5.0ppm, <10.0ppm, ≥10.0ppm; Isotope Ratio Difference: <10.0%, <20.0%, ≥20.0%". In the "Control" quality control column, select the quality control sample CK and click "Process" to perform first-level peak matching.

[0031] (3) Select the primary matching substance, click the “SPow MS and MS / MS” icon in the MasterView window to view the mass spectrum of the substance in the sample, select the secondary spectrum frame, click the “SPow” column in the main menu, select “Data and Peaks Table” to view the secondary peak information data table, select the “Peaks” window to collect the secondary fragmentation information of the substance; use the Peak Assignment function of the CFM-ID platform to predict the fragmentation mode of the primary matching biotransformation product.

[0032] 3. Beneficial effects

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] (1) A high-throughput identification method for biotransformation products of organic pollutants in blood according to the present invention uses the Biotransformer 3.0 platform to predict biotransformation products of organic pollutants under four metabolic pathways: PPase I (CYP450), PPase II, EC-Based, and HumanGut Microbial. The range of biotransformation products covered is wide.

[0035] (2) The present invention provides a high-throughput identification method for biotransformation products of organic pollutants in blood. Compared with the prior art, which may have unreasonable structures due to the biotransformation products obtained only by theoretical calculation, the present invention combines the matching situation of characteristic fragments and neutral loss of the parent and biotransformation products to evaluate the confidence level of the structure of the identified biotransformation products, confirm the matching relationship between the biotransformation products and the parent, and take substances with qualified confidence as identification targets to improve the accuracy of the biotransformation product screening list.

[0036] (3) A high-throughput identification method for biotransformation products of organic pollutants in blood according to the present invention achieves efficient separation of low-concentration biotransformation products in blood through liquid chromatography-mass spectrometry, and achieves rapid and continuous scanning of primary and secondary mass spectrometry spectra of biotransformation products in blood in a single analysis and determination. The identification of human biotransformation products of organic pollutants in blood is achieved through primary mass spectrometry matching and secondary fragmentation prediction matching.

[0037] (4) The present invention provides a high-throughput identification method for biotransformation products of organic pollutants in blood. It is not limited to biotransformation products with known structures of existing standards. Through prediction and screening, it expands the detection range of human biotransformation products of organic pollutants and improves the accuracy of the final identification results, which has good application value. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a high-throughput identification method for biotransformation products of organic pollutants in blood according to the present invention.

[0039] Figure 2 The standard secondary spectrum of styrene in the sample of Example 1;

[0040] Figure 3 This shows the secondary matching results of styrene biotransformation products identified in the sample of Example 1;

[0041] Figure 4 This is the standard secondary spectrum of eicosapentaenoic acid in Example 2;

[0042] Figure 5 This is a secondary matching result of typical biotransformation products of eicosapentaenoic acid identified in the sample of Example 2. Detailed Implementation

[0043] The present invention will be further described below with reference to specific embodiments. The following description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art can make equivalent changes to the disclosed technical content to create equivalent embodiments. Any simple modifications or equivalent changes made to the following embodiments based on the technical essence of the present invention without departing from the scope of the invention are all within the protection scope of the present invention.

[0044] A high-throughput identification method for biotransformation products of organic pollutants in blood according to the present invention includes the following steps:

[0045] S10. Perform structural prediction of biotransformation products of organic pollutants in blood. Click on Metabolism Prediction on the Biotransformer 3.0 homepage to enter the biotransformation product prediction function; select the metabolic pathway at “Select a Metabolic Transformation”, enter the SMILES formula of the parent compound at “Select an Input Type”, and select the number of iterations at “Number of Reaction Iterations to Calculate”; predict the biotransformation products under four pathways: PPase I, CYP450, PPase II, EC-Based, and Human Gut Microbial. Among them, the prediction of PPase I and CYP450 adopts the combined mode and selects three iterations to derive the biotransformation products under different metabolic pathways.

[0046] It should be noted that this invention uses four metabolic pathways of Biotransformer: PPase I (CYP450), PPase II, EC-Based, and Human Gut Microbial, all of which are typical metabolic pathways that substances undergo after entering the human body. Phase I metabolism (PPase I) reactions include oxidation, demethylation, and hydrolysis. After undergoing phase I metabolism, such as oxidation and demethylation, the polarity and water solubility of substances increase. Biotransformer represents this process with the metabolic reactions involving the cytochrome P450 enzyme system (CYP450), a drug-metabolizing enzyme system. CYP450 mainly participates in oxidation reactions in biotransformation, including electron loss, dehydrogenation, and oxidation. In phase II metabolism (pPase II), substances bind with endogenous small molecules or phase I metabolites, resulting in conjugates with increased polarity and decreased lipid solubility. Biotransformer considers the binding of endogenous and exogenous substances in this process, such as glucuronidation, sulfation, methylation, and glycination. EC-based metabolic pathways are based on the Enzyme Council (Enzyme... Based on information provided by the Commission, Biotransformer has established 459 correspondences for mixed metabolic pathways involving both exogenous and endogenous enzymes, using 258 enzymes and 408 biotransformation rules to predict biotransformation products of EC-based metabolic pathways. In the human gut microbial metabolic pathway, Biotransformer has established 204 correspondences based on 53 enzymes and 201 biotransformation rules to predict the transformation products of substances after metabolism by human gut microbes. The prediction results obtained through these four pathways can cover a wide range of biotransformation products that may be generated after pollutants enter the human body.

[0047] S20. For the predicted biotransformation products, the confidence level of the biotransformation product structure is evaluated based on two criteria: parent-biotransformation product secondary feature fragment matching and neutral loss matching.

[0048] Since the parent material and the biotransformation product theoretically share similar functional groups or chemical structures, but the biotransformation products predicted by Biotransformer in step S10 only consider the theoretically possible biochemical reactions under different metabolic pathways, some predicted biotransformation products may not exist, or there may be no structural match between the predicted biotransformation products and the parent material. Therefore, in step S20, the confidence level of the predicted biotransformation product structure is evaluated through "parent-biotransformation product structure matching," and unqualified substances are eliminated. Because the parent material and the biotransformation product have similar structural features and exhibit similar fragmentation behavior during secondary mass spectrometry fragmentation, two methods—secondary feature fragment matching and neutral loss matching—are used to evaluate the confidence level.

[0049] The specific steps for assessing the confidence level of the structure of the biotransformation product include:

[0050] (1) Querying secondary characteristic fragments of parent compound and calculating neutral loss: Query the standard secondary spectrum and characteristic fragments of parent material on PubcPem website, calculate the difference between the mass-charge ratio of each secondary fragment, and use it as the neutral loss of parent material under adsorption ion mode and collision energy;

[0051] (2) Prediction of secondary characteristic fragments and calculation of neutral loss of biotransformation products: The Spectra Prediction function of the CFM-ID platform was used to predict the secondary fragment peak information of biotransformation products under the same adduct ion mode and collision energy as the standard secondary spectrum of the parent material. The difference between the mass-charge ratio of each secondary fragment was calculated as the neutral loss of biotransformation products under the adduct ion mode.

[0052] (3) Secondary characteristic fragment matching: The secondary fragment peaks of the parent material and the biotransformation product under the same adduct ion mode and collision energy are matched. When there are two characteristic fragments that can be matched (the allowable error is 0.025 Da), the biotransformation product is reliable.

[0053] (4) Neutral loss matching: The neutral loss of the parent material and the biotransformation product under the same adduct ion mode and collision energy is matched. When there are two neutral losses that can be matched (the allowable error is 0.025 Da), the biotransformation product is reliable.

[0054] (5) Confidence level evaluation criteria: When the biotransformation product achieves a secondary characteristic fragment match or a neutral loss match with the parent material, the biotransformation product meets the confidence level requirements.

[0055] S30. Take the biotransformation products that are judged to be qualified in step S20 as the target items in the suspicious substance screening list, and build a screening list that includes the biotransformation product name, molecular formula, SMILES formula and metabolic pathway.

[0056] S40. Pre-treatment of blood samples: Add 0.25g of a mixture of MgSO4 and NaCl (mass ratio 4:1) to every 500μL of serum to remove water. Simultaneously add three steel balls (diameter Ф=2mm) to help the sample mix evenly during vortexing. Then add 500μL of acetonitrile (ACN) to each sample, sonicate at room temperature for 15min, centrifuge at 4616×g for 10min, and separate the supernatant. Add another 500μL of acetonitrile solution (acetonitrile:water, 95:5), sonicate at room temperature for 15min, centrifuge at 4616×g for 10min, and separate the supernatant. Repeat the extraction twice. Combine the supernatants from the three centrifugations and concentrate under a gentle nitrogen flow to a final volume of 100μL to obtain the concentrated blood solution. After volume adjustment, when a small amount of white solid MgSO4 and NaCl is present at the bottom, centrifuge again and collect the supernatant.

[0057] Blood concentrate was analyzed by coupling ultra-high performance liquid chromatography (UltiMate 3000, TPermo Scientific, USA) with Q Exactive Focus high-resolution mass spectrometry (TPermo Scientific, Germany) in both positive and negative ionization modes using full MS-ddMS2 scanning. The conditions for the UHPLC-QExactive Focus high-resolution mass spectrometry system were as follows:

[0058] Liquid Chromatography System: Ultra-High Performance Liquid Chromatography (UltiMate 3000, TPermo Scientific, USA);

[0059] Chromatographic column: ACQUITY UPLC BEH C18 column (2.1mm×150mm, 1.7μm, Waters, USA);

[0060] Column temperature: 40℃;

[0061] Flow rate: 400 μL / min;

[0062] Gradient elution mobile phase:

[0063] Normal phase: A: 0.1% formic acid aqueous solution, B: methanol

[0064] Negative phase: A: 2mM ammonium acetate aqueous solution, B: methanol;

[0065] mobile phase gradient:

[0066]

[0067] Mass spectrometer: Q Exactive Focus high-resolution mass spectrometer (TPermo Scientific, Germany);

[0068] Ionization modes: positive ion mode + negative ion mode;

[0069] TOF-MS scanning range: 100-1250 m / z;

[0070] MS-MS scan range: 50-1000 m / z;

[0071] Spray voltage: Positive ion mode, 3500V; Negative ion mode, -2500V;

[0072] Normalized collision energy (NCE): 20 eV, 40 eV, 60 eV;

[0073] Ion source temperature: 320℃;

[0074] Capillary temperature: 300℃;

[0075] Sheath gas pressure: 48 arb;

[0076] Auxiliary air pressure: 10 alb;

[0077] S-lens RF: 55V;

[0078] MS full scan resolution: 35000;

[0079] MS / MS scan resolution: 17500.

[0080] S50. Based on the biotransformation product screening list established in step S30, Peak View and CFM-ID software are used to perform primary mass spectrometry matching and secondary fragmentation prediction matching to identify the suspiciousness of biotransformation products in the concentrate.

[0081] It should be noted that the specific steps for identifying the suspiciousness of the biotransformation products are as follows:

[0082] (1) Import sample mass spectrometry information: Use Peak View 2.2 and CFM-ID 4.0 software to import sample mass spectrometry information. Open the "MasterView" function in the PeakView main menu bar and click "New Session" to import the blood sample mass spectrometry source file;

[0083] (2) Primary mass spectrometry peak matching: In the MasterView window, import the name, molecular formula, and positive or negative adduct ion mode (+H or -H) of the suspected substance into the "Name", "Formula", and "Adduct" fields of the target matching substance box. After selecting the adduct ion mode, the software will automatically calculate the m / z of its parent ion. In the settings window, under "confidence setting", set the matching parameters "Primary m / z error gradient Mass Error: <5.0ppm, <10.0ppm, ≥10.0ppm; Isotope distribution error gradient Isotope Ratio Difference: <10.0%, <20.0%, ≥20.0%". In the "Control" quality control column, select the quality control sample CK and click "Process" to perform primary peak matching.

[0084] (3) First-level matching material matching criteria: After completing the first-level mass spectrometry matching in step (2), the material matching status will be displayed in the MasterView window. The four indicator lights in the window are: molecular weight (MASS), retention time (RT), isotope (isotope), library (LiBRARY), and molecular formula (FORMULAR). The gradient display colors of the three matching parameters set in step (2) are green "√", yellow "△", and red "○". The material whose molecular weight and isotope are both green signals, i.e., the first-level m / z error is <5ppm and the isotope distribution error is <10..0%, is used as the first-level mass spectrometry matching material.

[0085] (4) Extraction of secondary mass spectrometry information of primary matched substances in samples: Select the primary matched substance, click the “SPow MS and MS / MS” icon in the MasterView window to view the mass spectrum of the substance in the sample, select the secondary spectrum frame, click the “SPow” column in the main menu, select “Data and Peaks Table” to view the secondary peak information data table, select the “Peaks” window to collect the secondary fragment information of the substance;

[0086] (5) Secondary mass spectrometry fragment prediction and matching: Since most of the predicted biotransformation products do not have standard secondary spectra, the Peak Assignment function of the CFM-ID platform is used to predict the fragmentation mode of the biotransformation products matched in the primary stage in step (2). Enter the SMILES or InCPI formula of the biotransformation product in “Parent Compound Structure”. According to the conditions used during instrument analysis, select the Spectra Type, Ion Mode, Collision Energy Intensity (low, medium, high, the collision energy intensity is consistent with the collision energy used in the standard spectrum of the parent substance), and Mass Tolerance. Enter the mass-to-charge ratio (left column) and peak height (right column) of the secondary fragments of the suspected substance in the sample obtained in step (4) in the secondary mass spectrometry information data box, and click Submit to perform fragmentation mode prediction and matching.

[0087] (6) Secondary mass spectrometry matching criteria: In the matching results output in step (5), the peaks of matched fragments are red; the peaks of unassigned fragments are blue. Equation (1) is the formula for calculating the secondary fragment matching rate.

[0088] Secondary fragment matching rate = (number of red peaks / total number of peaks) × 100% (1)

[0089] Calculations show that when the secondary fragment matching rate is greater than 50%, the secondary mass spectrometry matching is successful.

[0090] This invention is not limited to biotransformation products with known structures of existing standards. Through prediction and screening, it expands the scope of identification and detection of human biotransformation products of organic pollutants while improving the accuracy of the final identification results. It requires less sample and can detect and identify multiple biotransformation products simultaneously.

[0091] Example 1

[0092] This embodiment uses blood samples spiked with styrene and its exact biotransformation product, styrene oxide. The identification method of this embodiment is used to identify the biotransformation product of styrene in the blood samples, and the accuracy is verified by the matching degree between the identification results and styrene oxide.

[0093] like Figure 1 As shown in this embodiment, a high-throughput identification method for biotransformation products of organic pollutants in blood includes the following steps:

[0094] S10. Biotransformation product structure prediction: The Metabolism Prediction function of the Biotransformer 3.0 platform was used to predict the biotransformation products of organic pollutants in blood samples, and the biotransformation products of styrene under four metabolic pathways: PPase I (CYP450), PPase II, EC-Based, and Human Gut Microbial.

[0095] The specific steps are as follows: Click on Metabolism Prediction on the Biotransformer 3.0 homepage to enter the biotransformation product prediction function; select the metabolic pathway at Select a Metabolic Transformation, enter the SMILES formula of the parent substance at Select an Input Type, and select the number of iterations at Number of ReactionIterations to Calculate; predict the biotransformation products of the substance under four pathways: PPase I (CYP450), PPase II, EC-Based, and Human Gut Microbial. When predicting PPase I (CYP450), use the combined mode and select three iterations to derive the biotransformation products of styrene under different metabolic pathways.

[0096] S20. Confidence Assessment of Biotransformation Products: For the biotransformation products predicted in step S10, a structural confidence assessment is performed, eliminating predictors with unreasonable structures. If the biotransformation product matches a styrene secondary characteristic fragment or a neutral loss fragment, the confidence level of the predicted substance is considered to meet the standard. The specific steps are as follows:

[0097] (1) Querying secondary characteristic fragments of parent compound and calculating neutral loss: Query the standard secondary spectrum and characteristic fragments of styrene on the PubcPem website, calculate the difference between the mass-charge ratio of each secondary fragment as its neutral loss under the adduct ion mode and collision energy;

[0098] (2) Prediction of secondary characteristic fragments and calculation of neutral loss of biotransformation products: The Spectra Prediction function of the CFM-ID platform was used to predict the secondary fragment peak information of biotransformation products under the same adduct ion mode and collision energy as the standard secondary spectrum of styrene. The difference between the mass-charge ratio of each secondary fragment was calculated as the neutral loss of biotransformation products under the adduct ion mode and collision energy.

[0099] (3) Secondary characteristic fragment matching: The secondary fragment peaks of styrene and biotransformation products under the same adduct ion mode and collision energy are matched. If two characteristic fragments can be matched (the allowable error is 0.025 Da), the biotransformation product is considered reliable.

[0100] (4) Neutral loss matching: Neutral loss of styrene and biotransformation product under the same adduct ion mode and collision energy is matched. If two neutral losses can be matched (the allowable error is 0.025 Da), the biotransformation product is considered reliable.

[0101] (5) Confidence level evaluation criteria: If the biotransformation product can achieve secondary feature fragment matching or neutral loss matching with the parent, the biotransformation product is considered to meet the confidence level requirements.

[0102] S30. Establishment of a screening list for biotransformation products: Biotransformation products that are deemed qualified in step S20 are selected as targets in the screening list of suspected products. The list includes the name, molecular formula, SMILES formula, and metabolic pathway of the biotransformation product.

[0103] S40. Blood sample pretreatment: Add 0.25g of MgSO4.NaCl mixture (mass ratio 4:1) to every 500μL of spiked serum to remove water. At the same time, add three steel balls (diameter Ф=2mm) to help the sample mix evenly during vortexing. Then add 500μL of acetonitrile (ACN) to each sample, sonicate at room temperature for 15min, centrifuge at 4616×g for 10min to separate the supernatant, add 500μL of acetonitrile solution (acetonitrile:water, 95:5), sonicate at room temperature for 15min, centrifuge at 4616×g for 10min to separate the supernatant, repeat the extraction twice, combine the supernatants from the three centrifugation extractions, concentrate under a gentle nitrogen flow with nitrogen blowing to a final volume of 100μL to obtain the blood concentrate.

[0104] Blood concentrate was analyzed by coupling ultra-high performance liquid chromatography (UltiMate 3000, TPermo Scientific, USA) with Q Exactive Focus high-resolution mass spectrometry (TPermo Scientific, Germany) in both positive and negative ionization modes using full MS-ddMS2 scanning. The conditions for the UHPLC-QExactive Focus high-resolution mass spectrometry system were as follows:

[0105] Liquid Chromatography System: Ultra-High Performance Liquid Chromatography (UltiMate 3000, TPermo Scientific, USA);

[0106] Chromatographic column: ACQUITY UPLC BEH C18 column (2.1mm×150mm, 1.7μm, Waters, USA);

[0107] Column temperature: 40℃;

[0108] Flow rate: 400 μL / min;

[0109] Gradient elution mobile phase:

[0110] Normal phase: A: 0.1% formic acid aqueous solution, B: methanol

[0111] Negative phase: A: 2mM ammonium acetate aqueous solution, B: methanol;

[0112] mobile phase gradient:

[0113]

[0114] Mass spectrometer: Q Exactive Focus high-resolution mass spectrometer (TPermo Scientific, Germany);

[0115] Ionization modes: positive ion mode + negative ion mode;

[0116] TOF-MS scanning range: 100-1250 m / z;

[0117] MS-MS scan range: 50-1000 m / z;

[0118] Spray voltage: Positive ion mode, 3500V; Negative ion mode, -2500V;

[0119] Normalized collision energy (NCE): 20 eV;

[0120] Ion source temperature: 320℃;

[0121] Capillary temperature: 300℃;

[0122] Sheath gas pressure: 48 arb;

[0123] Auxiliary air pressure: 10 alb;

[0124] S-lens RF: 55V;

[0125] MS full scan resolution: 35000;

[0126] MS / MS scan resolution: 17500.

[0127] S50. Biotransformation product identification: Based on the biotransformation product screening list established in step S30, PeakView and CFM-ID software are used to identify the suspicion of organic human biotransformation products in the blood sample test results in step S40.

[0128] In this embodiment, step S10 predicts 27 kinds of biotransformation products.

[0129] In step S20, the standard secondary spectrum of the parent substance styrene is obtained as follows: Figure 2 As shown (addition ion mode is [M+H)) + The collision energy is 20 eV. The secondary characteristic fragments of styrene are shown in Table 1. The calculated neutral loss of styrene is shown in Table 2. CFM-ID prediction indicates that in [M+H]... + The secondary fragment peak information of 27 biotransformation products under the / 20ev mode is shown in Table 3, and the calculated neutral loss is shown in Table 4.

[0130] Table 1 [M+H] + Secondary characteristic fragments of styrene in / 20ev mode

[0131]

[0132] Table 2 [M+H] + Neutral loss of styrene in / 20ev mode

[0133]

[0134] Table 3 [M+H] + Styrene is used in the / 20ev mode to predict secondary characteristic fragments of biotransformation products.

[0135]

[0136]

[0137]

[0138]

[0139] Note: The addition ion mode and collision conditions used in predicting secondary fragments of biotransformation products are consistent with those in the standard secondary spectrum of the parent substance styrene, namely [M+H]. + / 20ev; the value in parentheses is the relative abundance of the second-order fragment peak, in percent.

[0140] Table 4 [M+H] + Neutral loss of styrene prediction of biotransformation products under the / 20ev mode.

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Note: Bold values ​​represent matchable neutral loss, and values ​​in parentheses represent the relative error between matchable neutral loss and neutral loss of the parent substance styrene.

[0150] Upon comparison, regarding characteristic fragments, none of the predicted characteristic fragments of the 27 biotransformation products matched those of the parent material; regarding neutral loss, as shown in Table 4, [M+H]... + In this model, all 26 biotransformation products except BYX-002 met the matching requirements for neutral loss, possessing more than two neutral losses that could match the parent material (matchable neutral losses are shown in bold in Table 4), such as 18.01056 / 18.01057 / 27.99491 / 27.99492 / 46.00548 / 1.97926 / 1.97927, which can match the neutral losses of the parent material 18.00685 / 27.99469 / 46.00154 / 1.9902, with errors of 0.00371 Da, 0.00372 Da, 0.00022 Da, 0.00023 Da, 0.00394 Da, 0.01094 Da, and 0.01093 Da, respectively, meeting the error range of 0.025 Da required by this invention.

[0151] After confidence assessment, the final confirmed list of biotransformation products is shown in Table 5.

[0152] Table 5. Screening list of styrene human biotransformation products confirmed in Example 1.

[0153]

[0154]

[0155] Following the primary biotransformation product identification step S50, C8H8O in the [M+H] group was successfully identified in the spiked blood sample from the 26 biotransformation products listed in Table 5. + The mass-to-charge ratio (m / z) in the model was 120.05751. The secondary peak information in the sample was obtained as shown in Table 6. Using CFM-ID, the biotransformation products BYX-001 and BYX-003 with the molecular formula C8H8O were predicted and matched with the secondary fragment information in Table 6 at 20 eV. The matching results are as follows: Figure 3 The final secondary matching results are shown in Table 7, where the BYX-003 structure is consistent with styrene oxide, the exact biotransformation product of styrene in the human body.

[0156] Using the above method, styrene oxide, a biotransformation product of styrene in blood samples, can be successfully predicted and rapidly screened and identified, indicating that the present invention has a good prediction range and identification accuracy, and can provide relatively accurate prediction and identification results of biotransformation products of organic pollutants.

[0157] Table 6. Secondary fragments of the material with a mass-to-charge ratio of 120.05751 in Example 1.

[0158]

[0159] Table 7. Biotransformation products after secondary matching in Example 1

[0160]

[0161] Example 2

[0162] The basic content of this embodiment is the same as that of embodiment 1, except that: in this embodiment, the biotransformation products of eicosapentaenoic acid, a typical surfactant, in the collected blood samples are screened and identified, and the blood samples are umbilical cord blood.

[0163] In this embodiment, step S10 predicts 40 biotransformation products of eicosapentaenoic acid.

[0164] The structural confidence level of the biotransformation product was assessed, and the standard secondary spectrum ([M+H) of the parent substance eicosapentaenoic acid was obtained. + / 20ev) as Figure 4 As shown in Table 8, the specific characteristics of secondary fragment information are shown in Table 9, and the neutral loss situation is shown in Table 9.

[0165] Table 8 [M+H] + Secondary characteristic fragments of eicosapentaenoic acid in / 20ev mode

[0166]

[0167] Table 9 [M+H] + Neutral loss of eicosapentaenoic acid at 20 eV

[0168]

[0169] Predict secondary characteristic fragments of biotransformation products in the [M+H]+ / 20 eV mode using CFM-ID. + Table 10 shows the secondary fragment peak information of 40 biotransformation products under the / 20ev mode.

[0170] Regarding secondary characteristic fragments, biotransformation products WXS-014 and WXS-019 both possess characteristic fragment 93.06988 / 175.14813, which can match the secondary characteristic fragment 93.0690 / 175.1491 of eicosapentaenoic acid (EPA) with an error of 0.00088 Da / 0.00097 Da; WXS-21 possesses characteristic fragment 93.06988 / 221.15361, which matches the secondary characteristic fragment 93.0690 / 221.1544 of EPA with an error of 0.00088 Da / 0.00079 Da; WXS-039 and WXS-040 both possess characteristic fragments... Characteristic fragment 93.06988 / 285.22129 is matched with secondary characteristic fragment 93.0690 / 285.2217 of eicosapentaenoic acid, with an error of 0.00088 Da / 0.00041 Da; WXS-037 has three characteristic fragments 93.06988 / 285.22129 / 303.23186, which are matched with secondary characteristic fragments 93.0690 / 285.2217 / 303.2321 of eicosapentaenoic acid, with an error of 0.00088 Da / 0.00041 Da / 0.00024 Da, all of which are less than 0.025 Da, meeting the setting standard of the present invention.

[0171] Neutral loss for 40 biotransformation products was calculated, and the specific matching results are shown in Table 11. The neutral loss for all 40 predicted biotransformation products met the evaluation criteria. After confidence assessment, the final confirmed biotransformation product screening list is shown in Table 12.

[0172] After primary identification in step S50, C was successfully identified in the blood sample from the 40 biotransformation products listed in Table 12. 20 H 28 O2 in [M+H] + Mass-to-charge ratio (m / z) under mode: 301.21621; C 20 H 30 O3 in [M+H] +The mass-to-charge ratio (m / z) in the mode is 319.22677. Information on its secondary peaks in the sample is shown in Tables 13 and 14. This applies to molecules with the formula C2. 20 H 28 O2, C 20 H 30 The biotransformation products of O3 were predicted and matched with the secondary fragment information in Tables 13 and 14 using CFM-ID at 20 eV.

[0173] After the secondary prediction matching in the final step S50, 34 possible biotransformation products of eicosapentaenoic acid (EPA) in the blood were identified. The secondary matching rates of the biotransformation products are shown in Table 15. The specific secondary matching details of typical biotransformation products WXS-001, WXS-002, and WXS-004 are as follows: Figure 5 As shown.

[0174] Table 10 [M+H] + Eicosapentaenoic acid (EPA) predicts secondary characteristic fragments of biotransformation products under / 20ev mode.

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] Note: The adduct ion mode and collision conditions used for predicting secondary fragments of biotransformation products are consistent with those of the standard secondary spectrum of the parent substance eicosapentaenoic acid, which is [M+H]+ / 20 eV; the values ​​in parentheses are the relative abundance of the secondary fragment peaks, in percent.

[0181] Table 11 Matching neutral loss of eicosapentaenoic acid (EPA) for predicting biotransformation products under the [M+H]+ / 20 eV model.

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189] Note: The numbers in parentheses represent the relative error between the neutral loss of biotransformation products and the neutral loss of the parent substance eicosapentaenoic acid.

[0190] Table 12. Screening list of eicosapentaenoic acid (EPA) human biotransformation products confirmed in Example 2

[0191]

[0192]

[0193]

[0194] Table 13 Secondary fragments of the material with a mass-to-charge ratio of 319.22677 in Example 2

[0195]

[0196] Table 14 Secondary fragments of the material with a mass-to-charge ratio of 301.21621 in Example 2

[0197]

[0198]

[0199]

[0200] Table 15 Biotransformation products after secondary matching in Example 2

[0201]

[0202]

[0203]

Claims

1. A high-throughput identification method for biotransformation products of organic pollutants in blood, characterized in that: Includes the following steps: S10. Use the metabolic prediction function of the Biotransformer 3.0 platform to predict the structure of biotransformation products of blood organic pollutants, respectively predicting the structure in Phase I, CYP450; and Phase II. Biotransformation products under four pathways: EC-Based, Human Gut Microbial, etc. S20. For the predicted biotransformation products, the confidence level of the biotransformation product structure is evaluated based on two criteria: parent-biotransformation product secondary feature fragment matching and neutral loss matching. The specific steps for assessing the confidence level of the structure of biotransformation products include: (1) Querying secondary characteristic fragments of parent compound and calculating neutral loss: Query the standard secondary spectrum and characteristic fragments of parent material on the Pubchem website, calculate the difference between the mass-charge ratio of each secondary fragment, and use it as the neutral loss of parent material under adsorption ion mode and collision energy; (2) Prediction of secondary characteristic fragments and calculation of neutral loss of biotransformation products: The Spectra Prediction function of the CFM-ID platform was used to predict the secondary fragment peak information of biotransformation products under the same adduct ion mode and collision energy as the standard secondary spectrum of the parent material. The difference between the mass-charge ratio of each secondary fragment was calculated as the neutral loss of biotransformation products under the adduct ion mode. (3) Secondary characteristic fragment matching: The secondary fragment peaks of the parent material and the biotransformation product under the same adduct ion mode and collision energy are matched. When there are two characteristic fragments that can be matched, the biotransformation product is reliable. (4) Neutral loss matching: The neutral loss of the parent material and the biotransformation product under the same adduct ion mode and collision energy is matched. When there are two neutral losses that can be matched, the biotransformation product is reliable. (5) Confidence level assessment criteria: When the biotransformation product achieves a secondary characteristic fragment match or a neutral loss match with the parent material, the biotransformation product meets the confidence level requirements; S30. The biotransformation products that have passed the confidence assessment in step S20 are used as targets in the suspected substance screening list, and a screening list including the name, molecular formula, SMILES formula and metabolic pathway of the biotransformation product is constructed. S40. Perform blood pretreatment on the blood sample, centrifuge to extract blood concentrate, and perform full scan of the blood concentrate by primary mass spectrometry and secondary mass spectrometry. S50. Based on the biotransformation product screening list established in step S30, perform primary mass spectrometry matching and secondary fragmentation prediction matching to identify the suspiciousness of biotransformation products in blood concentrate.

2. The high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 1, characterized in that: In step S40, the blood sample is pretreated to remove water, then acetonitrile is added and ultrasonically centrifuged to obtain a supernatant. Acetonitrile solution is added again and ultrasonically centrifuged, and the extraction is repeated twice. The supernatants from the three centrifugation extractions are combined and concentrated by nitrogen blowing to obtain a blood concentrate. Then, liquid chromatography-mass spectrometry is used to perform a full scan of the blood concentrate in positive and negative ionization modes using primary and secondary mass spectrometry.

3. A high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 1, characterized in that: In step S50, based on the biotransformation product screening list established in step S30, Peak View and CFM-ID software are used to perform primary mass spectrometry matching and secondary fragmentation prediction matching to identify the suspiciousness of biotransformation products in the concentrate.

4. A high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 1, characterized in that: In step S10, the specific steps for predicting the structure of biotransformation products are as follows: Click on Metabolism Prediction on the Biotransformer 3.0 homepage to enter the biotransformation product prediction function; select the metabolic pathway at "Select a Metabolic Transformation", enter the SMILES formula of the parent compound at "Select an Input Type", and select the number of iterations at "Number of Reaction Iterations to Calculate"; predict the structure of the biotransformation product in Phase I, CYP450, and Phase II respectively. Biotransformation products under four pathways: EC-Based, Human Gut Microbial, etc. In Phase I, CYP450 prediction was performed in a combined mode, and three iterations were selected to derive biotransformation products under different metabolic pathways.

5. The high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 2, characterized in that: In step S40, a mixture of MgSO4 and NaCl with a mass ratio of 4:1 is used as a dispersive solid-phase extractant to remove serum water. Three steel balls with a diameter of Ф=2mm are added and vortexed for mixing. Acetonitrile and acetonitrile solution are used for ultrasonic extraction once and twice, respectively. The supernatants are combined for blood sample pretreatment to extract biotransformation products from serum. The acetonitrile solution has an acetonitrile:water volume ratio of 95:

5.

6. A high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 2, characterized in that: In step S40, the extracted supernatant is concentrated by purging the solvent with nitrogen and brought to a final volume of 100 μL. The final volume solvent is acetonitrile. After bringing to a final volume, when a small amount of white solid MgSO4 and NaCl are present at the bottom, the supernatant is centrifuged again and collected.

7. The high-throughput identification method for biotransformation products of organic pollutants in blood according to claim 3, characterized in that: In step S50, the specific steps for identifying the suspicion of biotransformation products are as follows: (1) Use Peak View 2.2 and CFM-ID 4.0 software to import sample mass spectrometry information; (2) In the MasterView window, import the name, molecular formula, and positive or negative adduct ion mode used for analysis (+H or -H) into the "Name", "Formula", and "Adduct" fields of the target matching substance box. After selecting the adduct ion mode, the software will automatically calculate the m / z of its parent ion. In the settings window, under "confidence setting", set the matching parameters: "Mass Error: <5.0ppm, <10.0ppm, ≥10.0ppm; Isotope RatioDifference: <10.0%, <20.0%, ≥20.0%". Select the quality control sample CK in the "Control" column and click "Process" to perform first-order peak matching. (3) Select the primary matching substance, click the "Show MS and MS / MS" icon in the MasterView window to view the mass spectrum of the substance in the sample, select the secondary spectrum frame, click the "Show" column in the main menu, select "Data and PeaksTable" to view the secondary peak information data table, select the "Peaks" window to collect the secondary fragmentation information of the substance; use the Peak Assignment function of the CFM-ID platform to predict the fragmentation mode of the primary matching biotransformation product.

Images