Aconitine poisoning biomarker as well as screening method and application thereof

Abstract

The invention belongs to the technical field of network toxicology and metabonomics detection, and provides an aconitine poisoning biomarker as well as a screening method and application thereof, the aconitine poisoning biomarker is phenylalanine, tyrosine and beta-phenylethylamine, specifically, the ratio of phenylalanine to tyrosine is increased, and the ratio of tyrosine to beta-phenylethylamine is decreased. According to the invention, the relative change of increase of phenylalanine / tyrosine ratio and decrease of tyrosine / beta-phenylethylamine ratio is simultaneously used as a judgment standard of related evidence of poisoning caused by taking the aconitum traditional Chinese medicine before life, so that the influence caused by individual difference is greatly reduced; the method disclosed by the invention is suitable for most organic toxic drugs with multi-path and multi-target toxic effects, particularly has a great development prospect in research on poisoning of natural drugs and toxicants, and can be used for rapidly and accurately determining poisoning biomarkers with lower cost through network toxicology and molecular docking technologies; the reference and the application value can be well provided for the prenatal poisoning judgment of the natural medicine poison.

Description

Technical Field

[0001] This invention belongs to the field of network toxicology and metabolomics detection technology, specifically relating to an aconitine poisoning biomarker, its screening method, and its application. Background Technology

[0002] Aconitum species have a long history of medicinal use in my country, with approximately 200 species widely distributed. Commonly used medicinal materials include Aconitum carmichaelii (Sichuan Aconitum), Aconitum kusnezoffii (Northern Aconitum), Aconitum kusnezoffii (Yellow-flowered Aconitum), Aconitum kusnezoffii (Snow-striped Aconitum), and Aconitum kusnezoffii var. kansui. Aconitum species are highly valued for their unique pharmacological effects and significant pharmacological activities, particularly in dispelling wind and dampness, reducing inflammation, and relieving pain. The main active ingredients are diterpenoid diester-type aconitine alkaloids, including aconitine, neoaconitine, and hypoaconitine. Because these active ingredients are also toxic, with aconitine being the most potent, and because the therapeutic and toxic doses are close, the therapeutic window is narrow. Poisoning and even death often occur due to improper processing.

[0003] Furthermore, the analysis of poisoning caused by aconite-related traditional Chinese medicines is limited to the observation of suspected poisoning signs during autopsy and the detection of aconite alkaloids. Routine pathological examinations for aconite alkaloid poisoning do not reveal any specific signs. Due to the instability of diterpenoid diester aconite alkaloids, they are easily decomposed after adding water or heating, and metabolized rapidly. Moreover, the decomposition products and metabolites are identical, meaning that the original drug cannot be found in most cases of aconite-related traditional Chinese medicine poisoning, posing a significant challenge to forensic workers. Consequently, various suicide, homicide, poisoning, and death cases arising from this are difficult to solve quickly. For a long time, the analysis of aconite-related traditional Chinese medicine poisoning and the identification of the cause of death have been a difficult issue in forensic identification. At the same time, the research on specific poisoning markers of aconite-related traditional Chinese medicines is a key and challenging issue in the field of forensic toxicology.

[0004] Network toxicology is an emerging interdisciplinary field that has yielded significant results in research on multi-component and multi-target drugs. It aims to combine drugs, diseases, and targets through network modeling to describe the toxicological properties of target poisons. By integrating network visualization analysis with toxicity prediction, it seeks to understand the toxic effects and mechanisms of poisoning within the body. The development of metabolomics technology plays an increasingly important role in forensic toxicology biomarker research. The search for poisoning biomarkers based on metabolomics technology as markers of pre-mortem poisoning has become a hot topic in forensic toxicology identification. Summary of the Invention

[0005] This invention addresses the challenges in forensic identification of poisoning and cause of death associated with existing aconitine-based traditional Chinese medicines by providing a biomarker for aconitine poisoning, its screening method, and its application.

[0006] The present invention is achieved by the following technical solution: an aconitine poisoning biomarker, wherein the aconitine poisoning biomarker is phenylalanine, tyrosine and β-phenylethylamine, specifically, the ratio of phenylalanine / tyrosine increases and the ratio of tyrosine / β-phenylethylamine decreases.

[0007] This invention also provides a method for screening aconitine poisoning biomarkers. The method utilizes network toxicology and non-targeted metabolomics techniques to screen for aconitine poisoning biomarkers. By analyzing the structure of aconitine and constructing a topological network analysis, GO and KEGG enrichment analyses are performed on the analysis results to screen the core signaling pathways of aconitine. Combined with the results of non-targeted metabolomics analysis, phenylalanine (L-Phe), tyrosine (L-Tyr), and β-phenylethylamine (β-PEA) are identified as aconitine poisoning biomarkers. Plasma samples from aconitine-poisoned rats are pretreated using organic protein precipitation and then detected by high-performance liquid chromatography-tandem mass spectrometry to obtain the relative changes in the phenylalanine / tyrosine ratio and the tyrosine / β-phenylethylamine ratio.

[0008] Furthermore, the specific steps are as follows: (1) Prediction of aconitine toxicity-related target genes: The 2D structure of aconitine was downloaded from Pubchem in SDF format and imported into the structure analysis databases Swisstargetprediction and Pharmapper to obtain aconitine-related target genes. Simultaneously, the toxic effects of aconitine were imported into the disease databases GeenCards and CTD to obtain the corresponding toxic target genes. The relevant target genes of aconitine and the relevant toxic target genes were imported into the UniProt database respectively. After merging and deduplication through the Venny platform, the toxicity-related target genes of aconitine were obtained. (2) The aconitine toxicity-related target genes obtained in step (1) were imported into the STRING bioinformatics database to construct a PPI network. The PPI network was then imported into Cytoscape software to construct a topological network analysis diagram of the aconitine toxicity-related target genes. Core target genes of aconitine toxicity were screened, and the affinity of the core target genes to aconitine was verified by molecular docking simulation. At the same time, the core target genes of aconitine toxicity were imported into the David bioinformatics database for GO and KEGG enrichment analysis to obtain the biological processes BP, cellular components CC, molecular functions MF and related pathways related to the target genes, and pathways with p < 0.05 were screened. (3) Constructing a rat model of aconitine poisoning for non-targeted metabolomics analysis of aconitine: 100 μL of plasma from rats poisoned by aconitine was collected, 10 μL of 1 μg / mL internal standard 5-HICA solution was added, followed by 200 μL of a mixture of acetonitrile and methanol in a volume ratio of 3:1. The mixture was vortexed for 1 min, centrifuged at 4℃ and 12000 rpm for 10 min, and the supernatant was collected. After filtration through a 0.22 μm organic filter membrane, 5 μL of the supernatant was injected and analyzed by LC-MS / MS to obtain the content and ratio of phenylalanine, tyrosine and β-phenylethylamine in the plasma of rats poisoned by aconitine. The results were statistically analyzed and combined with metaanalyst simulation to screen out metabolic pathways with p < 0.05 and impact > 1, namely phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and riboflavin metabolism. (4) After comprehensively analyzing and screening the results of the first two steps, identify pathways with overlap and high reliability, and detect the core metabolites and their upstream and downstream substances in the pathways; quantify using the established standard curves of phenylalanine, tyrosine and β-phenylethylamine; convert the quantitative results into the ratio of phenylalanine / tyrosine and the ratio of tyrosine / β-phenylethylamine.

[0009] This invention also provides the application of the aconitine poisoning biomarker in the preparation of reagents for detecting poisoning by Aconitum spp.

[0010] Further, the specific application method is as follows: detect the amount of three aconitine poisoning biomarkers, phenylalanine, tyrosine and β-phenylethylamine, in the sample to be tested, compare the ratio of phenylalanine / tyrosine in the sample to be tested and the ratio of tyrosine / β-phenylethylamine in the blank group.

[0011] In the initial stage of this invention, network toxicology combined with molecular docking technology was used to screen the core pathways of aconitine toxicity. Simultaneously, non-targeted metabolomics was used to detect aconitine-intoxicated rat plasma, identifying 15 differentially expressed metabolites, including phenylalanine, tyrosine, and riboflavin. Further screening and statistical analysis revealed three pathways with p < 0.05 and impact > 1: phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, and riboflavin metabolism. From these three pathways, β-phenylethylamine, phenylalanine, and tyrosine were screened. After further screening using network toxicology results, including 8 metabolites such as p-hydroxyphenylpyruvic acid, phenylalanine, tyrosine, and β-phenylethylamine in the phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis pathways were identified as biomarkers of aconitine poisoning. The three selected aconitine poisoning biomarkers were then applied to two cases of aconitine poisoning. Furthermore, an increase in the phenylalanine / tyrosine ratio and a decrease in the tyrosine / β-phenylethylamine ratio were used as the diagnostic criteria, thus providing supporting evidence for poisoning caused by the ingestion of aconitine-related traditional Chinese medicines before death.

[0012] Compared with existing technologies, this invention provides a method for screening biomarkers of aconitine poisoning. It uses the relative changes in the phenylalanine / tyrosine ratio and the tyrosine / β-phenylethylamine ratio as criteria for judging evidence of poisoning from pre-mortem ingestion of aconitine-based traditional Chinese medicines, greatly reducing the impact of individual differences. Compared with existing technologies, the method described in this invention is applicable to most organic poisons with multi-pathway and multi-target toxic effects, and has great development potential, especially in the study of poisoning by natural drug toxins. By using network toxicology and molecular docking technology, it can quickly and accurately identify poisoning biomarkers at a lower cost, providing valuable reference and application for determining pre-mortem poisoning by natural drug toxins. Attached Figure Description

[0013] Figure 1 The structures of aconitine are shown in 2D(A) and 3D(B). Figure 2 This is a network diagram of protein-protein interactions (PPIs) of aconitine. Figure 3 A topological network diagram of the target genes of aconitine; Figure 4 The GO enrichment analysis diagram for aconitine; Figure 5 KEGG enrichment analysis diagram of aconitine; Figure 6 The total plasma ion current (TIC) plot is shown. Figure 7 This is a multiple reaction monitoring (MRM) graph of β-phenylethylamine in plasma; Figure 8 This is a multiple reaction monitoring (MRM) graph of phenylalanine in plasma; Figure 9 This is a multiple reaction monitoring (MRM) graph of tyrosine in plasma. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and all materials publicly cited herein and cited by them are incorporated herein by reference.

[0016] Equivalent technologies of the specific embodiments described herein that are readily apparent to those skilled in the art through routine experimentation are included in this application.

[0017] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment; unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent stores.

[0018] I. Network toxicology and molecular docking procedures are as follows: 1. Target gene prediction of aconitine: Figure 1 To obtain the 2D image of aconitine downloaded from PubChem in SDF format, the 2D and 3D images of aconitine were imported into the SwissTargetPrediction and PharmaMapper databases, respectively, to obtain the structural prediction target genes of aconitine. The target genes were then searched in the CTD database using "aconitine" as the keyword. These target genes were imported into the UniProt database to obtain the official gene symbol format. Finally, the relevant target genes of aconitine were obtained by merging and deduplicating the data using the Venny platform.

[0019] 2. Prediction of aconitine-related toxicity target genes: Search for relevant toxicity target genes in the GeenCard and CTD databases using the keywords "cardiotoxicity" and "neurotoxicity". Import the obtained target genes into the UniProt database to obtain the official gene symbol format. Import the relevant target genes of aconitine and related toxicity target genes into the Venny platform to screen and obtain the toxicity-related target genes of aconitine.

[0020] 3. PPI Network Construction: The screened aconitine toxicity-related target genes were imported into the STRING database to construct the PPI network. Figure 2 To investigate how proteins collaborate and interact, a protein-protein interaction (PPI) network of aconitine was obtained. The PPI network was saved in TSV format and imported into the NetworkAnalyst module of Cytoscape 3.9.1 software. The network was then adjusted and plotted based on degree values, centered on the core target gene. Figure 3 This study aims to analyze the "topological position" and "influence" of target genes in a multi-level regulatory network, thereby constructing a topological network analysis diagram of aconitine target genes.

[0021] 4. GO and KEGG Enrichment Analysis: The target genes obtained in step 2 were imported into the David database for GO and KEGG enrichment analysis to obtain the target gene-related BP (Biological Process), CC (Cellular Component), MF (Molecule Function), and related pathways. The top 10 BP, CC, and MF values ​​with p < 0.05 were imported into the MicroBioinformatics platform and plotted. Figure 4 A three-in-one bar chart of GO enrichment analysis of aconitine; the top 20 pathways with p<0.05 were imported into the MicroBio platform and plotted. Figure 5 The visualization results of pathway enrichment are presented using color to represent "reliability", the horizontal axis to represent "enrichment intensity", and the bubble size to represent "number of genes". This allows for the rapid screening of the core biological pathways most likely involved by differentially expressed molecules, providing direction for subsequent mechanism studies (such as network analysis and experimental verification). This results in the construction of a bubble chart for the KEGG enrichment analysis of aconitine.

[0022] 5. Molecular docking: The 3D structure of aconitine obtained in step 1 was imported into Pymol software for dehydration and ligand removal, and the results were saved in PDB format. The core target gene obtained in step 3 was imported into the PDB database to obtain the corresponding protein structure, and then imported into Pymol software for dehydration and ligand removal, with the results saved in PDB format. The docking of aconitine with the core target gene was simulated in Autodock software to obtain the binding energy between aconitine and the target gene, and the binding sites were imported into Pymol software to create a docking visualization.

[0023] II. Using a rat model of aconitine poisoning, conventional non-targeted metabolomics experiments were conducted. Plasma samples were collected for analysis. After statistical analysis and database simulation, three pathways with p<0.05 and impact>1 were screened out: phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and riboflavin metabolism. Combined with aconitine KEGG pathway enrichment analysis, phenylalanine, tyrosine, and β-phenylethylamine in the phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis pathways were finally used as biomarkers of aconitine poisoning.

[0024] The non-targeted metabolomics procedure is as follows: 1. Animal Experiment: Healthy male SD rats (n=18) were randomly divided into 3 groups (6 rats per group): (1) aconitine 1-fold LD50 group (0.85 mg / kg); (2) aconitine 2-fold LD50 group (1.70 mg / kg); (3) blank control group (physiological saline). All animals were acclimatized under standard laboratory conditions for 7 days and fasted for 12 hours before the experiment. The administration regimen was as follows: the experimental group was given the corresponding dose of aconitine by gavage (the administration volume was uniformly 2 mL), and the control group was given an equal volume of physiological saline. After administration, the rats were placed alone in metabolic cages and allowed to eat freely. Urine was collected on an ice collector. After 24 hours, the collected urine was placed in a cryovial, an appropriate amount of sodium azide was added, and it was stored in a -80°C freezer. The rats were anesthetized with ether, and blood from the abdominal aorta was collected and placed in a heparin anticoagulant tube. After standing for 15 minutes, the tube was centrifuged at 3500 r / min for 15 minutes. The supernatant was collected in a cryovial and stored in a -80°C freezer.

[0025] 2. Sample pretreatment and determination: (1) Aconitine group: 100 μL of plasma from aconitine poisoning rat model was taken into a 1.5 mL EP tube, 10 μL of 1 μg / mL internal standard 5-hydroxyindole-2-carboxylic acid (5-HICA) solution was added, followed by 200 μL of acetonitrile:methanol (3:1) mixed solution. The mixture was vortexed for 1 min, centrifuged at 12000 rpm at 4℃ for 10 min, and the supernatant was collected. After filtration through a 0.22 μm organic filter membrane, 5 μL of the supernatant was injected and analyzed by LC-MS / MS.

[0026] (2) Blank group: 100 μL of plasma from the blank control group rat model was drawn into a 1.5 mL EP tube, and the rest of the operation was the same as (1).

[0027] The gradient elution modes are shown in Table 1, and the mass spectrometry conditions are shown in Table 2.

[0028] Table 1: Liquid Chromatography Conditions Table 2: Mass spectrometry parameters for phenylalanine, tyrosine, and β-phenylethylamine 2. Results Analysis: Phenylalanine, tyrosine, and β-phenylethylamine were separated and detected by LC-MS / MS, and total ion current (TIC) chromatograms and multiple reaction monitoring (MRM) chromatograms of each substance were obtained. Figure 6 This is a TIC chromatogram of plasma. The chromatographic behavior of each substance in the chromatogram is good, and each substance is well separated from the internal standard 5-HICA, with retention times of 2.93 min, 6.82 min, 7.00 min, and 7.01 min, respectively. Figure 7This is the MRM chromatogram of β-phenylethylamine in plasma. Characteristic qualitative (122.3 / 77.15) and quantitative ion pairs (122.3 / 105.25) appear at the same retention time of 7.00 min. Figure 8 The image shows the MRM plot of phenylalanine in plasma. Characteristic qualitative (166.3 / 103.25) and quantitative ion pairs (166.3 / 120.20) appear at the same retention time of 6.82 min. Figure 9 The MRM plot of tyrosine in plasma shows characteristic qualitative (182.15 / 136.30) and quantitative ion pairs (182.15 / 91.20) at the same retention time of 7.01 min.

[0029] Table 3 shows the contents and ratios of phenylalanine, tyrosine, and β-phenylethylamine in the plasma of aconitine-poisoned rats and the control group (saline group). In the 2LD50 group, the contents of phenylalanine and tyrosine in the plasma of aconitine-poisoned rats were 264.89 ng / mL and 163.29 ng / mL, respectively. In the LD50 group, the contents of phenylalanine and tyrosine in the plasma of aconitine-poisoned rats were 293.38 ng / mL and 241.27 ng / mL, respectively. In the control saline group, the contents of phenylalanine and tyrosine in the plasma of rats were 519.68 ng / mL and 538.93 ng / mL, respectively. Comparing the 2LD50 and LD50 experimental groups with the control group, it was found that the levels of phenylalanine and tyrosine in the plasma of aconitine-poisoned rats were decreased, and the decrease was greater in the 2LD50 group than in the LD50 experimental group. The phenylalanine / tyrosine ratio in the 2LD50 group was 1.62, in the LD50 group it was 1.22, and in the control group it was 0.96. This shows that the higher the toxic dose administered by gavage, the higher this ratio is compared to the control group. Since phenylalanine is also an upstream substance in the tyrosine metabolic pathway and can synthesize tyrosine, and phenylalanine is an essential amino acid for humans, an increase in the phenylalanine / tyrosine ratio can be used as a criterion for judging aconitine poisoning. Meanwhile, it was found that there was no significant difference in β-phenylethylamine levels between the 2LD50 and LD50 experimental groups and the control group. The tyrosine / β-phenylethylamine ratio was 6772.28 in the 2LD50 group, 9262.50 in the LD50 group, and 12389.18 in the control group. Comparing the 2LD50 and LD50 experimental groups with the control group, it was found that the tyrosine / β-phenylethylamine ratio was reduced in the plasma of aconitine-poisoned rats, and the reduction was greater in the 2LD50 group than in the LD50 experimental group. This shows that the higher the dose administered by gavage, the lower the ratio is compared with the control group. Therefore, a decrease in the tyrosine / β-phenylethylamine ratio can also be used as a criterion for judging aconitine poisoning.

[0030] Changes in the phenylalanine / tyrosine and tyrosine / β-phenylethylamine ratios can reflect changes in the levels of endogenous metabolites after poisoning to some extent. Therefore, using an increase in the phenylalanine / tyrosine ratio and a decrease in the tyrosine / β-phenylethylamine ratio as criteria for judging relevant evidence of poisoning from aconite-derived Chinese herbal medicines before death reduces the impact of individual differences.

[0031] Table 3: Determination of endogenous metabolite content in plasma of aconitine-poisoned rats Table 4 shows the blood samples from two deceased patients with aconitine poisoning to validate the aconitine poisoning biomarkers screened in animal experiments. In Sample 1, Mr. Zuo took seven doses of traditional Chinese medicine prepared by a barefoot doctor in 2018. That same evening, he developed symptoms such as fever and vomiting and died despite resuscitation efforts. Tests revealed high levels of aconitine-related components in Mr. Zuo's heart blood, liver, and stomach contents. In Sample 2, Mr. Xu died at home in 2024 after taking traditional Chinese medicine prepared by an unlicensed doctor at a health shop. Aconitine was detected in the medicine Mr. Xu consumed. Blank blood samples were obtained from six randomly selected healthy adult males.

[0032] Table 4 shows the levels and ratios of phenylalanine, tyrosine, and β-phenylethylamine in the blood of the two aconitine poisoning cases and the blank sample (healthy individuals). In Sample 1 (Left), the levels of phenylalanine and tyrosine in the blood were 6220.60 ng / mL and 3881.79 ng / mL, respectively. In Sample 2 (Xu), the levels of phenylalanine and tyrosine in the blood were 5304.02 ng / mL and 5437.13 ng / mL, respectively. In the blank sample, the levels of phenylalanine and tyrosine in the blood were 637.11 ng / mL and 807.10 ng / mL, respectively. The concentrations of aconitine in blood were measured in ng / mL. Comparing the two aconitine poisoning samples with a control sample, the phenylalanine / tyrosine ratio in sample 1 (Left) was 1.60, in sample 2 (Xu) it was 0.98, and in the control sample it was 0.79. This showed that the phenylalanine / tyrosine ratios in both samples were higher than in the control sample. The tyrosine / β-phenylethylamine ratio in sample 1 (Left) was 607, in sample 2 (Xu) it was 8972, and in the control sample it was 15924.32. This showed that the tyrosine / β-phenylethylamine ratios in both samples were lower than in the control sample. This conclusion is consistent with the trends in the phenylalanine / tyrosine and tyrosine / β-phenylethylamine ratios shown in Table 3 of the aconitine poisoning rat plasma results compared to the control group.

[0033] Table 4: Content of endogenous metabolites in blank blood samples and case blood samples Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biomarker for aconitine poisoning, characterized in that: The aconitine poisoning biomarkers are phenylalanine, tyrosine, and β-phenylethylamine, specifically an increase in the phenylalanine / tyrosine ratio and a decrease in the tyrosine / β-phenylethylamine ratio.

2. A method for screening aconitine poisoning biomarkers, characterized in that: Aconitine poisoning biomarkers were screened using network toxicology and non-targeted metabolomics. The structure of aconitine was analyzed, and a topological network analysis was constructed. GO and KEGG enrichment analyses were performed on the results to screen the core signaling pathways of aconitine. Combined with the results of non-targeted metabolomics analysis, phenylalanine, tyrosine, and β-phenylethylamine were identified as aconitine poisoning biomarkers. Plasma samples from rats poisoned with aconitine were pretreated using organic protein precipitation and then detected by high-performance liquid chromatography-tandem mass spectrometry to obtain the relative changes in the phenylalanine / tyrosine ratio and the tyrosine / β-phenylethylamine ratio.

3. The method for screening aconitine poisoning biomarkers according to claim 2, characterized in that: The steps are as follows: (1) Prediction of aconitine toxicity-related target genes: The 2D structure of aconitine was downloaded from Pubchem in SDF format and imported into the structure analysis databases Swisstargetprediction and Pharmapper to obtain aconitine-related target genes. Simultaneously, the toxic effects of aconitine were imported into the disease databases GeenCards and CTD to obtain the corresponding toxic target genes. The relevant target genes of aconitine and the relevant toxic target genes were imported into the UniProt database respectively. After merging and deduplication through the Venny platform, the toxicity-related target genes of aconitine were obtained. (2) The aconitine toxicity-related target genes obtained in step (1) were imported into the STRING bioinformatics database to construct a PPI network. The PPI network was then imported into Cytoscape software to construct a topological network analysis diagram of the aconitine toxicity-related target genes. Core target genes of aconitine toxicity were screened, and the affinity of the core target genes to aconitine was verified by molecular docking simulation. At the same time, the core target genes of aconitine toxicity were imported into the David bioinformatics database for GO and KEGG enrichment analysis to obtain the biological processes BP, cellular components CC, molecular functions MF and related pathways related to the target genes, and pathways with p < 0.05 were screened. (3) Constructing a rat model of aconitine poisoning for non-targeted metabolomics analysis of aconitine: 100 μL of plasma from rats poisoned by aconitine was collected, 10 μL of 1 μg / mL internal standard 5-HICA solution was added, followed by 200 μL of a mixture of acetonitrile and methanol with a volume ratio of 3:

1. The mixture was vortexed for 1 min, centrifuged at 4℃ and 12000 rpm for 10 min, and the supernatant was collected. After filtration through a 0.22 μm organic filter membrane, 5 μL of the supernatant was injected and analyzed by LC-MS / MS to obtain the content and ratio of phenylalanine, tyrosine and β-phenylethylamine in the plasma of rats poisoned by aconitine. After statistical analysis of the results and combined with MetaAnilyst simulation, metabolic pathways with p < 0.05 and impact > 1 were selected, namely phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and riboflavin metabolism. (4) After comprehensively analyzing and screening the results of the first two steps, identify pathways with overlap and high reliability, and detect the core metabolites and their upstream and downstream substances in the pathways; quantify using the established standard curves of phenylalanine, tyrosine and β-phenylethylamine; convert the quantitative results into the ratio of phenylalanine / tyrosine and the ratio of tyrosine / β-phenylethylamine.

4. The application of the aconitine poisoning biomarker of claim 1 in the preparation of reagents for detecting poisoning by Aconitum spp.

5. The application according to claim 4, characterized in that: The specific application method is as follows: detect the amounts of three aconitine poisoning biomarkers, phenylalanine, tyrosine, and β-phenylethylamine, in the sample to be tested, compare the ratio of phenylalanine / tyrosine in the sample to be tested with the blank group, and compare the ratio of tyrosine / β-phenylethylamine.

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