Method for establishing high-throughput traditional chinese medicine and metabolite database lutmet-tcm and application thereof

Abstract

The application discloses a method for establishing a traditional Chinese medicine metabolite database, comprising the following steps: 1) establishing an LC-MS traditional Chinese medicine standard substance high-resolution mass spectrometry database containing retention time, first-order mass spectrometry information and second-order mass spectrometry information based on traditional Chinese medicine standard substances and a retention time correction internal standard group; 2) obtaining I and II phase metabolite samples of the traditional Chinese medicine standard substances through in-vitro incubation experiments, and extracting mass spectrometry information of the metabolites; 3) based on the traditional Chinese medicine standard substance database in step 1) and according to metabolic rules, a reaction type library, a reaction site library and in-vitro metabolism of the standard substances, metabolite information is summarized to form a metabolite database; and 4) combining the traditional Chinese medicine standard substance database and the metabolite database to obtain the traditional Chinese medicine metabolite database LutMet-TCM. The database provided by the application can meet the search requirements of traditional Chinese medicine compound prototype component, blood component and metabolite analysis and identification.

Description

Technical Field

[0001] This invention belongs to the field of database establishment technology, and relates to a method and application for establishing a high-throughput database of traditional Chinese medicine and its metabolites, LutMet-TCM. Background Technology

[0002] The identification of metabolites from traditional Chinese medicine (TCM) typically includes inferential and confirmatory identification. Preliminary inferential identification utilizes one or more molecular properties of the metabolite, while confirmatory identification compares these properties with reliable standards. Precise mass measurements and mass spectra are commonly used for metabolite identification. Currently, the main technological platforms for researching TCM metabolites are nuclear magnetic resonance (NMR) and mass spectrometry (MS) and their coupled techniques. MS offers advantages such as high selectivity and low detection limits, and it is highly compatible with chromatographic separation techniques such as gas chromatography, liquid chromatography, and capillary electrophoresis, making it an ideal tool for metabolite identification. Furthermore, powerful bioinformatics tools can be used to automate data analysis, making metabolite identification more convenient and feasible.

[0003] Currently, commonly used databases for the study of natural products of traditional Chinese medicine (TCM) include TCM@Taiwan, CEMTDD, CHDD, ETCM, TM-MC, TCMID, and YaTCM. Each database contains a large amount of compound data, providing assistance in the analysis of TCM herbs, prescriptions, and their components. However, the structural information and mass spectra of natural products in these databases are incomplete and not all information is publicly available, which limits the research on natural products. Commonly used databases for metabolic pathways include KEGG, BioCyc, and Reactome. These databases have a wide range of functions, covering genome sequencing, genome metabolism, and metabolic pathway analysis. However, databases specifically addressing the annotation of TCM metabolism are scarce. Currently, research data on TCM metabolites still largely comes from literature reports, and the lack of TCM metabolism databases slows down the progress of TCM metabolism research. Summary of the Invention

[0004] To address the shortcomings of existing technologies and the limitations of current TCM metabolism databases, this invention provides a method for establishing a TCM metabolite database. Through a combination of various programming languages, manual calibration, and verification using standard databases, a self-built database has been developed that features a wide variety of natural products, detailed metabolite information, and the ability to quickly, efficiently, and accurately analyze TCM metabolites. This database can serve as a TCM metabolism service for identifying the original components of TCM compound formulas, analyzing blood-entry components, and identifying other metabolites.

[0005] This invention provides a method for establishing a database of metabolites of traditional Chinese medicine, comprising:

[0006] Step 1) Establishing an LC-MS high-resolution mass spectrometry database of traditional Chinese medicine standards, containing retention time, primary mass spectrometry information, and secondary mass spectrometry information, based on the traditional Chinese medicine standards and retention time correction internal standard group;

[0007] Step 2): In vitro incubation experiment to obtain phase I and II metabolite samples of traditional Chinese medicine standard, and extract mass spectrometry information of metabolites;

[0008] Step 3) Based on the LC-MS high-resolution mass spectrometry database of Chinese medicine standards in Step 1) and according to the metabolic rules, reaction type library, reaction site library and scoring annotation to the in vitro metabolic process of the standards, the information of metabolites is summarized to form a metabolite database.

[0009] Step 4) Merge the database of Chinese medicine standard products and the database of metabolites to obtain the database of Chinese medicine metabolites LutMet-TCM.

[0010] The database provided by this invention can meet the retrieval needs for the original components, blood-entering components, and metabolites of traditional Chinese medicine compound preparations.

[0011] Step 1) Establishing a database of standard Chinese medicines includes:

[0012] ① Common and important metabolic pathways and metabolites were screened through HMDB (Hypermetallurgical Data Base) and literature review. These pathways and metabolites included carbohydrate metabolism, lipid metabolism, glutamine metabolism, energy metabolism, and amino acid metabolism. The HMDB primarily selected these pathways. A preliminary list of standards to be purchased was determined, and over a thousand traditional Chinese medicine (TCM) standards were procured. After eliminating the possibility of peak indistinguishability due to isomers in each group, 15 TCM standards were grouped together for mass spectrometry analysis. A Python script was used for rapid and efficient peak identification, and peak extraction yielded primary and secondary mass spectra, isotope peaks, different addition ion peaks, retention times, and other mass spectrometric information for the TCM standards. A multi-component internal standard was used to correct and reduce retention time shifts caused by different batches of mixed standards. This internal standard had advantages such as good linearity, uniform peak distribution across the elution gradient, and no interference between components, effectively reflecting the instrument's condition at different injection times.

[0013] The standard products in this invention are common Chinese medicine ingredient standard products that are currently available on the market, and the database contains more than 3,000 standard products.

[0014] ② The information extracted from the script is manually checked to further clarify the mass spectrometry information of the standard, including (1) checking the retention time of different addition ions; (2) checking the secondary fragments of different addition ions.

[0015] ③ For compounds with unknown retention times and mass spectrometry information, a natural product structure library is constructed, and the prediction function of QI software is used to predict the retention times and mass spectrometry information of these compounds, thus enriching and supplementing the database of traditional Chinese medicine standards. The training set is derived from information on standards already used in the instrument, including retention times and logP values, and a model is trained using this information.

[0016] In step 2), the physiological environment of human metabolism is simulated by an in vitro incubation system. Phase I and II metabolic reactions are triggered by adding enzyme sources and phase I and II cofactors. Oxidation reactions occur under the catalysis of CYP-450 enzyme system, monooxygenase, and peroxidase, involving the loss of electrons, oxidation, and dehydrogenation. Reduction reactions refer to the metabolism of compounds containing carbonyl, nitro, and azo groups to generate corresponding hydroxyl and amino compounds. Hydrolysis reactions can occur directly with esterases to generate corresponding alcohols and acids. Conjugation reactions, with the participation of cofactors, add highly polar functional groups to the hydroxyl groups of compounds. Metabolic samples are subjected to organic reagent precipitation of proteins, high-speed centrifugation, and the supernatant is collected before being analyzed.

[0017] The annotation process for metabolites in step 3) includes:

[0018] ① A script written in Python can be used to quickly and efficiently identify metabolite peaks, extract peaks, and determine retention times;

[0019] ② In the early stage, a library of reaction types (oxidation, reduction, hydrolysis, and conjugation reactions) of different categories of natural products was compiled, and the metabolic reactions of compounds were classified according to different reaction types;

[0020] ③ By reviewing a large number of literatures, summarize the information and patterns of metabolic reaction sites of a class of compounds (for example, flavonoids are prone to phase II metabolism at positions 6 and 7), and then match the reaction site information of metabolites according to the reaction site library.

[0021] ④ Based on the above information on metabolites, output the metabolic annotation process of Chinese medicine components (reaction type, metabolite structure, molecular formula, precise molecular weight, smiles, retention time, etc.).

[0022] Based on the database established by the above methods, this invention also proposes the application of a high-throughput database of traditional Chinese medicine and its metabolites in the analysis and identification of the original components, blood-entering components and metabolites of traditional Chinese medicine compound prescriptions.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] The traditional Chinese medicine (TCM) metabolism database established in this invention contains 3,600 TCM standards and 15,000 TCM metabolites, along with their retention times, primary mass spectrometry (PMS) data, and secondary mass spectrometry (MS) data. It is continuously being updated, with a mass-to-charge ratio ranging from 85 to 1200, covering various types of natural TCM products and their metabolites, including flavonoids, coumarins, terpenes, organic acids, phenylpropanoids, alkaloids, lignins, and cardiac glycosides.

[0025] The metabolites in this Traditional Chinese Medicine (TCM) metabolism database have diverse matrices, and the reaction systems are identical to those for animal-derived TCMs, exhibiting relatively stable reactions. This database has been validated with over 3000 standards. Manual verification and QI software prediction ensure that this database can perform highly accurate qualitative analysis of different sample matrices from both plant and animal sources. This database offers more comprehensive metabolite information and faster retrieval speed, assisting in TCM metabolic research and improving the efficiency of TCM metabolic analysis. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a technical roadmap for a method of establishing a database of Chinese herbal metabolites according to the present invention.

[0028] Figure 2 This is a mass spectrometry diagram of the Chinese medicine standards in the Chinese medicine metabolism database of this invention.

[0029] Figure 3 This is a schematic diagram of some metabolites in the herbal metabolism database of this invention.

[0030] Figure 4 This is a schematic diagram of some metabolic mass spectrometry information in the Chinese medicine metabolism database of this invention.

[0031] Figure 5 This is a schematic diagram of the metabolic annotation process output from the Chinese herbal medicine metabolism database of this invention. Detailed Implementation

[0032] The invention will be further described in detail below with reference to the specific embodiments and accompanying drawings. Except for the contents specifically mentioned below, the processes, conditions, and experimental methods for implementing the invention are all common knowledge and general knowledge in the art, and the invention does not have any particular limitations.

[0033] Example

[0034] Combination Figure 1 As shown, this embodiment describes the steps for establishing a database of metabolites of traditional Chinese medicine:

[0035] (I) Mass Spectrometry and Chromatographic Conditions

[0036] The ACQUITY UPLC I-Class plus system is equipped with a Q-Exactive quadrupole orbital mass spectrometer with an ESI source. In full scan mode, the mass spectrometry resolution is 120,000, and the AGC target is set to 3e. 6 Scanning range: 80-1200 m / z; resolution of secondary mass spectrometry: 30000; AGC target set: 1e 6 The isolation window was set to 1.8 m / z, the cycle count was set to 10, and the (N)CE / stepped (N)CE was set to 10, 20, and 40 eV. The column was an UPLC HSS T3 (100 mm × 2.1 mm, 1.8 μm).

[0037] Chromatographic conditions: Mobile phase A was 0.1% formic acid in water, mobile phase B was acetonitrile, the injection solution was 50% methanol in water, the flow rate was 0.35 mL / min, and the sample injection volume was 2 μL. The elution program was a 16-minute elution gradient.

[0038] (II) Instrumental testing of mixed standard samples of traditional Chinese medicine

[0039] We selected and purchased over a thousand kinds of Chinese herbal natural product standards (including flavonoids, coumarins, terpenes, organic acids, phenylpropanoids, alkaloids, lignins, cardiac glycosides, etc.). Each set of 15 non-isomer standards was mixed together for mass spectrometry analysis. Internal standard group H3 was interspersed in the accompanying samples to correct for retention time deviations caused by the mass spectrometry instrument and different batches of samples.

[0040] (III) Establishment of a database of standard Chinese medicines

[0041] A Python script was used to quickly and efficiently extract information such as peak identification, peak extraction, retention time, primary mass spectrometry, and secondary fragmentation from traditional Chinese medicine (TCM) standards, and to establish a TCM standards database.

[0042] (iv) Manually verify the information extracted from the script

[0043] The manual verification of mass spectrometry information, retention time, secondary fragments, isotope peaks, and addition ion mode extracted from the script is further performed.

[0044] (V) QI Prediction of Mass Spectrometry Information of Compounds

[0045] Structural information was crawled from natural product libraries on different websites using scripts to form a compound structure library. The compound structure library was imported into QI and the retention time and mass spectrometry information of the compounds were obtained by using the prediction function of QI software. This information was then supplemented and improved into the database of Chinese medicine standards.

[0046] (vi) In vitro incubation experiment

[0047] The in vitro incubation system simulates the physiological environment of human metabolism. By adding enzyme sources and phase I and II cofactors, it triggers phase I and II metabolic reactions (mainly oxidation, reduction, hydrolysis, and conjugation reactions). Oxidation reactions occur under the catalysis of enzymes such as the CYP-450 enzyme system, monooxygenases, and peroxidases, involving the loss of electrons, oxidation, and dehydrogenation. Reduction reactions refer to the metabolism of compounds containing carbonyl, nitro, and azo groups to produce corresponding hydroxyl and amino compounds. Hydrolysis reactions do not require the catalysis of other cofactors and can directly hydrolyze with esterases to produce the corresponding alcohols and acids. Conjugation reactions involve adding highly polar functional groups such as glucuronic acid or sulfonic acid to the hydroxyl group of a compound; this reaction requires the participation of cofactors. Metabolic samples are centrifuged at high speed to precipitate proteins before being analyzed.

[0048] (vii) Extraction of information about metabolites

[0049] A script written in Python can be used to quickly and efficiently extract information such as peak identification, peak extraction, retention time, primary mass spectrometry, and secondary fragmentation of metabolites from traditional Chinese medicine.

[0050] (viii) The annotation process of traditional Chinese medicine metabolism

[0051] The information on metabolites is classified and summarized by the database of Chinese medicine reaction types and metabolic reaction sites, and the information of metabolites (reaction type, metabolite structure, molecular formula, precise molecular weight, smiles, retention time, etc.) is output.

[0052] Table 1. List of some standard samples used in this invention (100 samples)

[0053]

[0054]

[0055]

[0056]

[0057] (vii) Integration and improvement of self-built databases and information

[0058] The database of traditional Chinese medicine (TCM) standards and the database of TCM metabolite annotations were merged and named LutMet-TCM. LutMet-TCM is expanded using samples from different animal and plant matrices. This embodiment meticulously divides the database, classifies and organizes the structures of compounds, and provides more comprehensive information on TCM metabolites.

[0059] (a) Database testing

[0060] To compare the advantages of the Chinese herbal medicine metabolism database with other databases, this embodiment prepared sample data for the identification of two types of Chinese herbal medicine components. These two sample data were searched in the non-targeted database and the Chinese herbal medicine metabolism database, respectively. The qualitative ability of the two databases was compared, and the number of metabolites in the high-target area was used to characterize the qualitative ability of the database. The percentage increase in the number of identified components in the Chinese herbal medicine metabolism database compared with the non-targeted database was used to represent the improvement rate of qualitative ability, as shown in Table 3.

[0061] Table 2. List of search results for the analysis of two traditional Chinese medicine components in different databases.

[0062]

[0063] As shown in Table 2, the analysis of the search results of the two traditional Chinese medicine components in two different databases shows that when the score of the ethanol extract of Gastrodia elata was greater than 65, the non-targeted database identified 0 products, while the traditional Chinese medicine metabolism database identified 9 metabolites. When the score of the extract of medicinal plants was greater than 57, the non-targeted database identified 51 metabolites, while the traditional Chinese medicine metabolism database identified 86 metabolites. The qualitative ability of the traditional Chinese medicine metabolism database was more than 70% higher than that of the non-targeted database.

[0064] The scope of protection of this invention is not limited to the above embodiments. Any variations and advantages that can be conceived by those skilled in the art without departing from the spirit and scope of the inventive concept are included in this invention and are protected by the appended claims.

Claims

1. A method for establishing a high-throughput database of traditional Chinese medicine and its metabolites, LutMet-TCM, characterized in that, Includes the following steps: Step 1: Establish an LC-MS high-resolution mass spectrometry database of traditional Chinese medicine (TCM) standards, including retention time, primary mass spectrometry information, and secondary mass spectrometry information, based on TCM standards and retention time correction internal standards. The establishment of this LC-MS high-resolution mass spectrometry database includes the following sub-steps: Step 1.1: Metabolic pathways and metabolites were screened through HMDB and literature review to preliminarily determine the list of standards to be purchased. Traditional Chinese medicine (TCM) standards were procured. After eliminating the possibility of indistinguishable chromatographic peaks due to isomers in each group, 15 TCM standards were grouped together for mass spectrometry analysis. A Python script was used for peak identification, and primary and secondary mass spectra, isotope peaks, different addition ion peaks, and retention times were extracted. A multi-component internal standard was used to correct and reduce retention time shifts caused by mixing different batches of standards. Common and important metabolic pathways and metabolites included carbohydrate metabolism, lipid metabolism, glutamine metabolism, energy metabolism, and amino acid metabolism. These metabolic pathways were primarily selected from HMDB. Step 1.2: Manually verify the information extracted from the script to further clarify the mass spectrometry information of the standard; Step 1.3: For compounds with unknown retention times and mass spectrometry information, a natural product structure library is constructed, and the training set and QI software prediction function are used to predict the compounds with unknown retention times and mass spectrometry information, thereby enriching and supplementing the LC-MS high-resolution mass spectrometry database of traditional Chinese medicine standards. Step 2: Obtain phase I and II metabolite samples of traditional Chinese medicine standards through in vitro incubation, and extract mass spectrometry information of the metabolites; In step 2, an in vitro incubation system simulates the physiological environment of human metabolism. Phase I and II metabolic reactions are triggered by adding enzyme sources and phase I and II cofactors. Oxidation reactions occur under the catalysis of the CYP-450 enzyme system, monooxygenases, and peroxidases, involving the loss of electrons, oxidation, and dehydrogenation. Reduction reactions refer to the metabolism of compounds containing carbonyl, nitro, and azo groups to generate corresponding hydroxyl and amino compounds. Hydrolysis reactions can directly occur with esterases to generate corresponding alcohols and acids. Conjugation reactions, with the participation of cofactors, add highly polar functional groups to the hydroxyl groups of compounds. Metabolic samples are subjected to organic reagent precipitation of proteins, high-speed centrifugation, and the supernatant is collected before being analyzed. Step 3: Based on the LC-MS high-resolution mass spectrometry database of traditional Chinese medicine standards and according to the metabolic rules, reaction type library, reaction site library and scoring annotation of the in vitro metabolic process of the standards, the information of metabolites is summarized to form a metabolite database. The annotation process for the metabolites includes: Step 3.1: Use a Python script to quickly and efficiently identify, extract, and retain metabolite peaks. Step 3.2: In the early stage, summarize the reaction type library of different categories of natural products and classify the metabolic reactions of compounds according to different reaction types; Step 3.3: By reviewing a large number of literatures, summarize the information and patterns of compound metabolic reaction sites, and then match the reaction site information of metabolites with the reaction site database. Step 3.4: Output the metabolic annotation process of traditional Chinese medicine components by integrating the above metabolite information; Step 4: Merge the LC-MS high-resolution mass spectrometry database of Chinese medicine standards with the metabolite database to obtain the Chinese medicine metabolite database LutMet-TCM.

2. The method for establishing the high-throughput database of traditional Chinese medicine and its metabolites, LutMet-TCM, as described in claim 1, is characterized in that... Step 1.2 includes (1) checking the retention times of different adducts; and (2) checking the secondary fragments of different adducts.

3. The method for establishing the high-throughput database of traditional Chinese medicine and its metabolites, LutMet-TCM, as described in claim 1, is characterized in that... In step 1.3, the training set is derived from information on the standard samples already tested, including retention time and logP value. The model is trained using the information from the standard samples.

4. The method for establishing the high-throughput database of traditional Chinese medicine and its metabolites, LutMet-TCM, as described in claim 1, is characterized in that... The reaction type library includes: oxidation, reduction, hydrolysis, and binding reactions; the metabolic annotation process includes: reaction type, metabolite structure, molecular formula, precise molecular weight, SMILES, and retention time.

5. A high-throughput database of traditional Chinese medicine and its metabolites established by the method described in any one of claims 1-4.

6. The application of a high-throughput database of traditional Chinese medicine and its metabolites established by any one of claims 1-4 in the analysis and identification of the original components, blood-entering components and metabolites of traditional Chinese medicine compound prescriptions.

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