Methods for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics

Abstract

This invention discloses a method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics. The method includes: inducing a rat bleeding model by gavage administration of warfarin, followed by intravenous injection of vitamin K1 via the tail vein; analyzing rat serum using 1H NMR to obtain the 1H NMR spectra of metabolites; screening the 1H NMR spectra of metabolites using an orthogonal partial least squares discriminant analysis model to identify potential biomarkers; performing metabolic pathway enrichment analysis on the potential biomarkers to identify relevant important metabolic pathways; and connecting the potential biomarkers within the important metabolic pathways to obtain a comprehensive metabolic network. This method provides a more comprehensive and unbiased explanation of the procoagulant mechanism of vitamin K1 at a holistic level, offering new insights for future research on the procoagulant mechanism of vitamin K1.

Description

Technical Field

[0001] This invention relates to the field of bioanalytical technology, and more specifically, to a method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics. Background Technology

[0002] Vitamin K1 is a fat-soluble vitamin containing a menadione structure, belonging to the vitamin K series of compounds. It is an essential nutrient for maintaining human health and affects the normal functioning of human physiological functions. Blood clotting is one of its main physiological functions, and vitamin K1 is an important cofactor in the synthesis of active clotting factors II, VII, IX, and X. Intracellularly, vitamin K is reduced to its bioactive hydroquinone form (VitaminKH2) by vitamin K epoxidase (VKOR) located in the endoplasmic reticulum membrane. VKH2, acting as a cofactor of γ-glutamyl carboxylase (GGCX), carboxylates glutamate (Glu) in vitamin K-dependent clotting factors to γ-carboxyglutamate (Gla). Simultaneously, VitaminKH2 is converted to vitamin K 2,3-epoxide. Subsequently, VKOR reduces vitamin K epoxide back to vitamin K, thus completing the catalytic cycle. This is the currently accepted hypothesis regarding the cyclic process of vitamin K synthesis of vitamin K-dependent clotting factors in vivo and the mechanism by which vitamin K exerts its clotting function. Coumarin-based warfarin anticoagulants exert their anticoagulant effect by blocking the activity of vitamin K cyclooxidase (VKOR), causing impaired synthesis of vitamin K-dependent clotting factors. However, high concentrations of vitamin K can reverse the anticoagulant effect of warfarin, and the specific mechanism of action remains unclear.

[0003] Metabolomics, following genomics, transcriptomics, and proteomics, is a rapidly emerging omics technology that qualitatively and quantitatively analyzes all low-molecular-weight (molecular weight less than 1000 Da) endogenous metabolites in an organism or cell. It provides a comprehensive and unbiased metabolic profile. Metabolomics emphasizes studying the subject as a whole, investigating metabolic networks or drug mechanisms by measuring changes in endogenous metabolites after stimulation or perturbation, or their changes over time. Zhu Jie et al. used metabolomics to explore the mechanism of action of tryptophan in treating ulcerative colitis in mice, finding that tryptophan may exert its anti-ulcerative colitis effect by affecting purine metabolism, arachidonic acid metabolism, and tryptophan metabolism. Using metabolomics to study the relationship between drugs and diseases, by observing changes in metabolic pathways, can systematically elucidate the relationship between drugs and diseases and supplement the explanation of drug mechanisms of action.

[0004] Liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance spectroscopy (NMR) are the three main analytical techniques used in metabolomics research. 1H-NMR-based metabolomics has advantages such as being unbiased and simple to process, and its research methods have become a powerful tool for studying the correlation between metabolic changes and drugs and diseases.

[0005] Therefore, it is necessary to design a research method based on 1H-NMR metabolomics to establish the coagulation effect of vitamin K1, so as to explain the mechanism of action of vitamin K1 in promoting coagulation more comprehensively and unbiasedly at the overall level, and provide new ideas for subsequent research on the mechanism of action of vitamin K1 in promoting coagulation. Summary of the Invention

[0006] To address the aforementioned issues, the purpose of this invention is to provide a method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics. This method provides a more comprehensive and unbiased explanation of the mechanism by which vitamin K1 exerts its procoagulant effect, offering new insights for subsequent research on the procoagulant mechanism of vitamin K1.

[0007] To achieve these objectives of the present invention, the present invention provides a method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics, comprising:

[0008] A hemorrhage model was induced in rats by administering warfarin, and the rats were treated with vitamin K1. A control group was established, and rat serum was collected for proton NMR spectroscopy to obtain the proton NMR information of the metabolites.

[0009] By establishing an orthogonal partial least squares discriminant analysis model, the 1H NMR spectrum information of metabolites was screened to obtain potential biomarkers; the screening criteria were that the variable importance projection value was greater than 1 and the p-value was less than 0.05.

[0010] Metabolic pathway enrichment of potential biomarkers was performed to identify the relevant metabolic pathways for vitamin K1 to exert its coagulation function.

[0011] By connecting potential biomarkers in important metabolic pathways through metabolic pathways, a comprehensive metabolic network can be obtained.

[0012] In the above scheme, serum metabolites from different groups were first analyzed using nuclear magnetic resonance spectroscopy to obtain 1H NMR spectra containing metabolite information. The spectra were then identified using the chemical shifts of standard compounds in the Chenomx NMR Suite 8.6 Evaluate software, as well as data from the Human Metabolome Database (HUMDB), the Biological Magnetic Resonance Data Bank (BMRB), and literature. An orthogonal partial least squares discriminant analysis model was established to screen data based on the conditions of variable importance projection values ​​greater than 1 and p-values ​​less than 0.05, thus obtaining potential biomarkers. The integrated values ​​of the potential biomarkers were exported in "txt" file format and subjected to an independent samples t-test using SPSS with an α = 0.05 significance level to obtain statistically significant metabolites. Metabolic pathway enrichment was performed based on these differentially differentiated metabolites to identify important metabolic pathways related to warfarin-induced hemorrhage in rats and the development of metabolic disorders in rats after vitamin K1 treatment. Finally, these important metabolic pathways were connected to obtain a comprehensive metabolic network.

[0013] Preferably, the rat serum is obtained by the following method:

[0014] Rats were randomly divided into a blank control group, a model group, and a vitamin K1 treatment group based on their weight. The model group and the vitamin K1 treatment group were administered warfarin sodium solution by gavage at a dose of 5 mg / kg. 1 Standard dosage administration, once daily at a fixed time by gavage for 3 consecutive days. The control group received an equal volume of physiological saline. After successful establishment of the rat hemorrhage model, the rats were kept on a fasting diet but allowed free access to water after the last warfarin treatment. The vitamin K1 treatment group received 360ug / 100g. -1 Both the control group and the model group were given the same dose of physiological saline, and samples were collected after 6 hours.

[0015] Preferably, the serum sample pretreatment method is as follows:

[0016] Add twice the volume of methanol to the serum and vortex to mix. Let stand for 20 min, then centrifuge at 13000 rpm for 15 min at 4 °C. Take the supernatant and dry it with nitrogen. Before NMR analysis, add 500 μL of PBS buffer to reconstitute the supernatant. Centrifuge at 12000 rpm for 10 min at 4 °C. Take 450 μL of the supernatant into a new centrifuge tube, add 50 μL of D2O containing 2 mg / mL TSP, vortex for 30 s to mix, and place in a 5 mm NMR tube until analysis.

[0017] Preferably, the conditions for the above-mentioned nuclear magnetic resonance spectroscopy analysis are as follows:

[0018] The proton frequency was 600.15 MHz, the temperature was 295.7 K, and the Nuclear Overhauser Effect Spectroscopy (NOESY, RD-90°-t1-90°-tm-90°-acquire) sequence was used. The number of scans was 32, the spectral width was 12500.0 Hz, and the pulse width was 10.97 μs.

[0019] Preferably, before performing multivariate statistical principal component analysis, the NMR spectrum is calibrated in MestReNova 6.1 software with a TSP chemical shift of 0 ppm, the baseline and phase are adjusted, the spectrum with chemical shifts of 0.5 ppm to 9.0 m is segmented with an equal width of 0.001 ppm, the water peak region of 4.5 ppm to 5.6 ppm and the methanol peak region of 3.34 ppm to 3.38 ppm are removed, and the peak area is normalized.

[0020] Preferably, the specific steps for obtaining the potential differential metabolites are as follows:

[0021] The obtained nuclear magnetic resonance spectroscopy data were imported into SIMCA-P14.1, and principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis were performed on the blank group, model group, and treatment group. Principal component analysis is an unsupervised learning method that can realistically reflect the clustering of samples. Figure 5 It can be seen that the model group and the blank group were well separated, with no overlap or intersection, indicating that there is a significant difference in the metabolic patterns of the two groups; the treatment group and the model group were poorly separated, with overlap, indicating that after vitamin K1 treatment, the metabolic pattern of the treatment group reverted to the normal group pattern.

[0022] Potential differential metabolites among the groups were screened, and variables with a variable importance projection value greater than 1 and a p value less than 0.05 were selected as differential metabolites. The variable importance projection value is the importance of the independent variable X in the model in explaining the dependent variable Y. Variables with an importance projection value greater than 1 were selected. The p value is the value obtained by using the t test on the variables in two different groups. A p value less than 0.05 indicates that there is a significant difference between the two groups.

[0023] Among them, the fitting ability indices of the orthogonal partial least squares discriminant analysis model are as follows:

[0024] Model group VS blank group: R 2 X = 0.608, R 2 Y = 0.975, Q 2 =0.874;

[0025] Treatment group VS model group: R 2 X = 0.523, R 2 Y = 0.966, Q 2 =0.821.

[0026] Preferably, the specific steps for enriching metabolic pathways of differential metabolites are as follows: export the integral values ​​of potential biomarkers in "txt" file format, perform statistical analysis using SPSS with an independent samples t-test, and use α = 0.05 as the significance level to obtain metabolites with statistical differences.

[0027] Based on these different metabolites, metabolic pathways were enriched to identify important metabolic pathways related to the development of hemorrhage in rats caused by warfarin and metabolic disorders in rats treated with vitamin K1; these important metabolic pathways were then connected to obtain a comprehensive metabolic network.

[0028] The present invention has at least the following beneficial effects:

[0029] 1. This invention uses nuclear magnetic resonance spectroscopy to analyze serum metabolites of rats in the blank group, model group and treatment group to obtain potential biomarkers related to vitamin K1 treatment of warfarin-induced bleeding.

[0030] 2. This invention uses methanol to pretreat rat serum to precipitate proteins, thereby reducing the influence of macromolecules when used for nuclear magnetic resonance spectroscopy analysis and enhancing the sensitivity of small molecule metabolite detection.

[0031] 3. This invention screens differential metabolites and constructs a metabolic network based on nuclear magnetic resonance spectroscopy combined with metabolomics technology, and obtains the metabolic pathway of vitamin K1 in promoting blood clotting. It elucidates the mechanism of action of vitamin K1 in promoting blood clotting in a holistic, comprehensive and unbiased manner, and provides new ideas for subsequent research on the mechanism of action of vitamin K1 in promoting blood clotting.

[0032] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0033] Figure 1 This is a graph showing the PT and APTT biochemical indicators of rats in different groups after drug administration in an embodiment of the present invention;

[0034] Figure 2 These are images of the lungs of rats in different groups after drug administration in this embodiment of the invention;

[0035] Figure 3 This is a spectrum obtained from hydrogen nuclear magnetic resonance spectroscopy analysis in an embodiment of the present invention;

[0036] Figure 4 This is an embodiment of the invention based on nuclear magnetic resonance spectroscopy analysis. 1 H- 1 HCOSY diagram;

[0037] Figure 5 PCA diagrams of serum metabolites in rats from different groups after drug administration are shown in the embodiments of the present invention.

[0038] Figure 6 PLS-DA diagrams of serum metabolites in rats from different groups after drug administration in this embodiment of the invention;

[0039] Figure 7 This is an OPLS-DA diagram of serum metabolites from different groups of rats after drug administration in an embodiment of the present invention;

[0040] Figure 8 This is a 200-permutation test plot of PLS-DA analysis of serum metabolites in rats from different groups after drug administration in an embodiment of the present invention.

[0041] Figure 9 This is a 200-permutation test plot of OPLS-DA analysis of serum metabolites in rats from different groups after drug administration in an embodiment of the present invention.

[0042] Figure 10 This is a visualization heatmap of differentially metabolites in the serum of rats in different groups after drug administration, as described in this embodiment of the invention.

[0043] Figure 11 This is a metabolic pathway map enriched online on the Metaboanalyst platform according to an embodiment of the present invention;

[0044] Figure 12 The comprehensive metabolic network diagram obtained in the embodiments of the present invention. Detailed Implementation

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments, so that those skilled in the art can implement it based on the description.

[0046] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0047] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0048] Example

[0049] A type based on 1 The research method of using HNMR metabolomics technology to study vitamin K1 treatment in warfarin-induced hemorrhage rats includes:

[0050] Step 1: Twenty-four 7-week-old male SD rats were selected. After acclimatization for 7 days, the rats were randomly divided into three groups (n=8 per group): a control group, a model group, and a vitamin K1 treatment group. The model group and the vitamin K1 treatment group were administered warfarin sodium solution by gavage at a dose of 5 mg / kg. -1 Standard dosage administration, once daily at a fixed time by gavage. The control group received an equal volume of normal saline. Administration continued for 3 days. After the last warfarin-induced modeling, the patient was kept on a fasting but not allowed to drink water. Four hours later, the vitamin K1 treatment group received a tail vein injection of 36 μg / kg of vitamin K1 solution. -1 The blank control group and the model group were given the same dose of physiological saline. Six hours later, they were anesthetized with urethane, and serum, plasma and lung samples were collected.

[0051] Step 2: Add twice the volume of methanol to the serum, vortex to mix, let stand for 20 min, centrifuge at 13000 rpm for 15 min at 4℃, collect the supernatant, dry the supernatant with nitrogen gas, and reconstitute with 500 μL of PBS buffer before NMR analysis. Centrifuge at 12000 rpm for 10 min at 4℃, collect 450 μL of the supernatant in a new centrifuge tube, add 50 μL of D2O containing 2 mg / mL TSP, vortex for 30 s to mix, and place in a 5 mm NMR tube until analysis. The collected rat serum from the three groups was analyzed using 1H NMR spectroscopy to obtain NMR spectral information containing metabolites. The conditions for NMR analysis were as follows: proton frequency of 600.15 MHz, temperature of 295.7 K, using Nuclear Overhauser Effect Spectroscopy (NOESY, RD-90°-t1-90°-tm-90°-acquire) sequence, 32 scans, spectral width of 12500.0 Hz, and pulse width of 10.97 μs.

[0052] Step 3: The 1H NMR data obtained in Step 2 are used to identify the spectra based on the chemical shifts of standard compounds in the Chenomx NMR Suite 8.6 Evaluate software, as well as data from the Human Metabolome Database (HUMDB), the Biological Magnetic Resonance Data Bank (BMRB), and literature. The results are then analyzed using two-dimensional NMR spectra. 1 H- 1 HCOSY was used for verification.

[0053] Step 4: The 1H NMR data obtained in Step 2 were calibrated using MestReNova 6.1 software with a TSP chemical shift of 0 ppm. The baseline and phase were adjusted, and the spectrum with chemical shifts of 0.5 ppm to 9.0 m was divided into equal widths of 0.001 ppm. The water peak region of 4.5 ppm to 5.6 ppm and the methanol peak region of 3.34 ppm to 3.38 ppm were removed, and the peak areas were normalized.

[0054] Step 5: Classify the data processed in Step 4 into different groups, and use SIMCA-14.1 software to perform principal component analysis on each group to determine whether there are differences in metabolic patterns among the different groups, thereby verifying the reliability of the data acquisition.

[0055] Step 6: Classify the data processed in Step 4 into different groups and perform partial least squares discriminant analysis on each group to analyze the changes in metabolic patterns in rats with hemorrhage caused by vitamin K1 intervention.

[0056] Step 7: Classify the data processed in Step 4 into different groups, and perform orthogonal partial least squares discriminant analysis on each group. The model fitting ability indices for the orthogonal partial least squares discriminant analysis are as follows: Model group VS Blank group: R 2 X = 0.608, R 2 Y = 0.975, Q 2 =0.874; Treatment group VS model group: R 2 X = 0.523, R 2 Y = 0.966, Q 2 =0.821;

[0057] Chemical shifts with a variable importance projection value greater than 1 and p less than 0.05 were selected as differentially significant metabolites. The peak areas of the differentially significant metabolites were integrated and normalized, and exported in "txt" format. SPSS 20.0 software was used for statistical analysis. P < 0.05 was used to indicate a significant difference, and differentially significant metabolites were screened out.

[0058] Step 8: Upload the differential metabolites obtained in Step 7 to the MetaboAnalyst online analysis webpage for pathway enrichment, obtain the relevant important metabolic pathways, and finally connect the important related metabolites through metabolic pathways to obtain a comprehensive metabolic network diagram for analysis.

[0059] Data Analysis

[0060] 1. Twenty-four 7-week-old male SD rats were selected. After 7 days of acclimatization feeding, the rats were randomly divided into three groups (n=8 per group): a control group, a model group, and a vitamin K1 treatment group. The model group and the vitamin K1 treatment group were administered warfarin sodium solution by gavage at a dose of 5 mg / kg. -1 Standard dosage administration, once daily at a fixed time by gavage. The control group received an equal volume of normal saline. Administration continued for 3 days. After the last warfarin-induced modeling, the patient was kept on a fasting but not allowed to drink water. Four hours later, the vitamin K1 treatment group received a tail vein injection of 36 μg / kg of vitamin K1 solution. -1 Both the control group and the model group were given the same dose of normal saline. Six hours later, the patients were anesthetized with urethane, and lung, serum, and plasma samples were collected. Whole blood was collected using coagulation vessels treated with calcium citrate anticoagulation, and plasma was separated. Prothrombin time (PT) and activated partial thromboplastin time (APTT) were then measured using an automated coagulation analyzer. The results are as follows: Figure 1 .

[0061] from Figure 1 Data showed that the prothrombin time (PT) and activated partial thromboplastin time (APTT) were normal in the blank control group; compared with the blank control group, the prothrombin time (PT) and activated partial thromboplastin time (APTT) of the model group were significantly increased, indicating that the coagulation function of the rats in the model group was abnormal; compared with the model group, the prothrombin time (PT) and activated partial thromboplastin time (APTT) of the vitamin K1 treatment group were significantly reduced, and the coagulation function was restored to normal.

[0062] 2. Wash the rat's lungs with physiological saline to remove surface blood, blot dry with filter paper, and take photographs. Figure 2 .

[0063] from Figure 2 The data shows that, compared with the control group, warfarin caused significant lung hemorrhage in rats with obvious bleeding points; in the vitamin K1 treatment group, no lung hemorrhage was observed, indicating that the hemorrhage model was successfully established and that vitamin K1 played a procoagulant role.

[0064] 3. Using the method described in the examples, 1H NMR spectroscopy was performed on the serum of rats from different groups. The spectra were identified based on the chemical shifts of standard compounds in the Chenomx NMR Suite 8.6 Evaluate software, as well as data from the Human Metabolome Database (HUMDB), the Biological Magnetic Resonance Data Bank (BMRB), and literature. The results are as follows: Figure 3 In this study, C represents the control group, M represents the model group, and Z represents the treatment group.

[0065] 4. Using the method described in the embodiments, two-dimensional NMR spectra are obtained. 1 H- 1 HCOSY validated the metabolites in one-dimensional 1H NMR spectra, and the results are as follows: Figure 4 .

[0066] 5. Principal component analysis was performed on the serum 1H NMR spectra of rats from different groups using the methods described in the examples. The results are as follows: Figure 5 C represents the control group, M represents the model group, and Z represents the treatment group.

[0067] Principal component analysis (PCA) is an unsupervised learning method that accurately reflects the clustering of samples. Figure 5 It can be seen that the model group and the blank group were well separated, with no overlap or intersection, indicating that there is a significant difference in the metabolic patterns of the two groups; the treatment group and the model group were poorly separated, with overlap, indicating that after vitamin K1 treatment, the metabolic pattern of the treatment group reverted to the normal group pattern.

[0068] 6. Partial least squares discriminant analysis was performed on the proton NMR spectra of different groups of rats using the methods described in the examples. The results are as follows: Figure 6 Where C represents the control group, M represents the model group, and Z represents the treatment group. The percentage of X and Y matrix information that the PLS-DA classification model can explain, Q... 2 This is obtained through cross-validation and used to evaluate the predictive ability of the PLS-DA model, Q. 2 The closer the value is to 1, the better the model's predictive ability.

[0069] from Figure 6 It can be seen that the blank group, model group, and treatment group were well separated, indicating that the metabolic patterns of the model group and treatment group changed. The model group was mainly concentrated on the right side, while the control group and treatment group were concentrated on the right side, indicating that the metabolic pattern of rats after warfarin-induced hemorrhage was significantly changed from that of the control group. After vitamin K1 treatment, the metabolic pattern of the treatment group reverted to that of the control group.

[0070] 7. Perform 200 permutation tests on the partial least squares discriminant analysis model. The results are as follows: Figure 8 .

[0071] from Figure 8 It can be seen that the partial least squares discriminant analysis model does not overfit, and the results are reliable.

[0072] 8. The method described in the examples was used to perform orthogonal partial least squares discriminant analysis on the hydrogen NMR spectra of different groups of rats. The results are as follows: Figure 7 C represents the control group, M represents the model group, and Z represents the treatment group.

[0073] 9. From Figure 9It can be seen that the orthogonal partial least squares discriminant analysis model did not overfit, and the results are reliable. After administration of warfarin and vitamin K1, there were significant changes in the serum metabolites of rats.

[0074] 10. The differentially metabolites in the serum of rats from different groups were enriched using the methods described in the examples. The differentially metabolites screened are shown in Table 1, and the related metabolic pathways are as follows: Figure 11 .

[0075] Table 1: Differences in metabolites and their content in different groups: Compared with the blank group: *<0.05, **<0.01; Compared with the model group: #<0.05, ##<0.01.

[0076]

[0077] As shown in Table 1, compared with the control group, the model group had significantly increased levels of 10 metabolites: L-leucine, L-valine, L-threonine, L-lysine, L-tryptophan, L-phenylalanine, lactic acid, phosphocreatine, creatine, and creatinine; and significantly decreased levels of 2 metabolites: 3-hydroxybutyric acid and phosphocholine. Compared with the model group, the treatment group had significantly increased levels of 4 metabolites: pyruvate, taurine, phosphocholine, and trimethylamine oxide; and significantly decreased levels of 5 metabolites: L-threonine, L-lysine, lactic acid, creatinine, and creatine.

[0078] Combination Figure 10 It can be seen that the model group clustered together, while the treatment group and the control group clustered together, indicating that the differential metabolite change trends in the treatment group and the control group were consistent, while the trend in the model group was different from that in the treatment group and the control group.

[0079] Combination Figure 11 It can be seen that the metabolic pathways with the largest associated impact values ​​in the model group are: the biosynthetic metabolism of L-phenylalanine, tyrosine and L-tryptophan has an impact value of 0.5, the L-phenylalanine metabolism has an impact value of 0.357, and the L-tryptophan metabolism has an impact value of 0.143; the metabolic pathways with the largest associated impact values ​​in the vitamin K1 treatment group are: the taurine and hypotaurine metabolism has an impact value of 0.429, the pyruvate metabolism has an impact value of 0.207, and the glycolytic gluconeogenesis metabolism has an impact value of 0.1.

[0080] By connecting the important metabolic pathways obtained using the methods described in the examples, a comprehensive metabolic network is obtained, as shown in the following figures. Figure 12 In the figure, upward arrows indicate upregulation of metabolites, and downward arrows indicate downregulation. (Model group vs. control group) * P<0.05, ** P<0.01; Treatment group vs. model group, # P<0.05,## P<0.01.

[0081] from Figure 12 The analysis revealed that the biomarkers pyruvate and lactate affect the citric acid cycle by influencing oxaloacetate; L-lysine affects glutaryl-CoA, thus influencing pyruvate metabolism; R-3-hydroxybutyrate affects acetoacetate, thus affecting the pyruvate cycle; phosphoric acid choline, a biomarker in glycerophospholipid metabolism, affects choline, indirectly affecting glycine in the metabolism of glycine, serine, and L-threonine; glycine affects serine, thus indirectly affecting pyruvate, a biomarker in pyruvate metabolism; and L-threonine indirectly affects pyruvate, a biomarker in pyruvate metabolism, through glycine and serine, thereby affecting pyruvate metabolism and the citric acid cycle. In warfarin-induced hemorrhage rats, metabolic pathways involving serum pyruvate metabolism, glycine, serine, L-threonine metabolism, glycerophospholipid metabolism, and L-phenylalanine metabolism were disrupted, primarily affecting amino acid metabolism, energy metabolism, and lipid metabolism. In the vitamin K1 treatment group, lactate levels were reduced, thus improving the energy impairment caused by warfarin-induced coagulation dysfunction. L-threonine, phosphoric acid, L-lysine, creatinine, and creatine all showed significant reductions, indicating that the coagulation effect of vitamin K1 may be related to the regulation of glycine, serine, L-threonine, glycerophospholipid metabolism, and L-lysine degradation. Taurine has the function of stabilizing cell membranes and regulating blood stability; in the vitamin K1 treatment group, taurine levels were significantly increased, suggesting that taurine and hypotaurine metabolism may be related to the coagulation effect of vitamin K1.

[0082] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Further modifications can be readily implemented by those skilled in the art.

Claims

1. A method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics, characterized in that, include: After the rats are given intragastric administration of warfarin to cause a rat hemorrhage model, vitamin K1 injection is given for tail vein injection treatment, and the following steps are used 1 HNMR is used to analyze the serum of the rats to obtain metabolite nuclear magnetic hydrogen spectrum information; the serum sample processing conditions are as follows: two volumes of methanol are added to the serum, mixed, centrifuged at 4 DEG C, the supernatant is taken, dried by N2 blowing, PBS buffer solution is added for redissolution, centrifuged, the supernatant is taken, D2O containing 2 mg / mL TSP is added, mixed, and nuclear magnetic resonance determination is performed; By establishing an orthogonal partial least squares discriminant analysis model, the 1H NMR spectrum information of metabolites was screened to obtain potential biomarkers; The screening criteria included a variable importance projection value greater than 1 and a p-value less than 0.05, where p-value was the value obtained from a t-test between variables in two different groups. The steps to obtain the potential biomarkers were as follows: Rat serum metabolites after administration of warfarin and vitamin K1 were grouped, and principal component analysis, partial least squares (PLS) analysis, and orthogonal PLS discriminant analysis were performed on each group. Potential biomarkers among the groups were screened, and variables with differentially expressed metabolites having a variable importance projection value greater than 1 and a p-value less than 0.05 were selected as potential biomarkers. The orthogonal PLS discriminant analysis model fit indices were: Model group vs. Blank group: R0 2 X=0.608, R 2 Y=0.975, Q 2 =0.874; Treatment group VS model group: R 2 X=0.523, R 2 Y=0.966, Q 2 =0.821; Metabolic pathway enrichment analysis of potential biomarkers revealed important metabolic pathways related to the development of changes in metabolites in rats caused by warfarin-induced hemorrhage and vitamin K1 injection treatment. By connecting potential biomarkers in important metabolic pathways through metabolic pathways, a comprehensive metabolic network can be obtained.

2. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, The warfarin-induced rat hemorrhage model was obtained through the following methods: Male SD rats were selected and acclimatized. Based on weight, the animals were randomly divided into a control group, a model group, and a vitamin K1 treatment group. The model group and the vitamin K1 treatment group were administered warfarin sodium solution by gavage at a dose of 5 mg / kg. -1 Standard dosage was administered once daily by gavage at a fixed time. The control group received an equal volume of normal saline. Administration continued for 3 days. After the last warfarin-induced modeling, the patient was kept on a fasting but not allowed to drink water. Four hours later, the vitamin K1 treatment group received a tail vein injection of 36 μg / kg of vitamin K1 solution. -1 Both the control group and the model group were given the same dose of physiological saline, and serum was collected.

3. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, The conditions for the nuclear magnetic resonance spectroscopy analysis are as follows: The proton frequency was 600.15 MHz, the temperature was 295.7 K, and the Nuclear Overhauser Effect Spectroscopy sequence was used with 32 scans, a spectral width of 12500.0 Hz, and a pulse width of 10.97 μs.

4. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, The rat serum NMR spectroscopy metabolite attribution procedure was as follows: The spectra were identified based on the chemical shifts of standard compounds in the software Chenomx, as well as data from the Human Metabolome Database, the Biological Magnetic Resonance Data Bank website, and literature.

5. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, Prior to principal component analysis, the NMR spectra were calibrated using MestReNova software with a TSP chemical shift of 0 ppm. The baseline and phase were adjusted, and the spectra with chemical shifts of 0.5 ppm to 9.0 ppm were segmented with equal widths of 0.001 ppm. The water peak region of 4.5 ppm to 5.6 ppm and the methanol region of 3.34 ppm to 3.38 ppm were removed.

6. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, The specific steps for metabolic pathway enrichment of potential biomarkers are as follows: upload the potential biomarkers to the MetaboAnalyst analysis webpage for pathway enrichment.

7. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, By linking potential biomarkers in important metabolic pathways through these pathways, a comprehensive metabolic network is obtained. This network connects pyruvate, lactate, L-lysine, R-3-hydroxybutyrate, phosphocholine, L-threonine, and taurine through known biochemical reactions. Specifically, pyruvate and lactate affect the citric acid cycle by influencing oxaloacetate; L-lysine affects glutaryl-CoA, thereby affecting pyruvate metabolism; R-3-hydroxybutyrate affects acetoacetate, thus affecting the pyruvate cycle; phosphocholine affects choline, thereby indirectly affecting glycine in the metabolism of glycine, serine, and L-threonine; glycine affects serine, thus indirectly affecting pyruvate in pyruvate metabolism; and L-threonine indirectly affects pyruvate in pyruvate metabolism through glycine and serine, thereby influencing both pyruvate metabolism and the citric acid cycle.

8. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, Potential biomarkers include: pyruvate, taurine, phosphocholine, and trimethylamine oxide, which are significantly upregulated after treatment, and / or L-threonine, L-lysine, lactate, creatinine, and creatine, which are significantly downregulated after treatment; wherein, significant upregulation after treatment means an increase in levels compared with the model group and P<0.05 or P<0.01, and significant downregulation after treatment means a decrease in levels compared with the model group and P<0.05 or P<0.

01.

9. The method for studying the procoagulant mechanism of vitamin K1 based on nuclear magnetic resonance metabolomics as described in claim 1, characterized in that, The steps for screening metabolites' 1H NMR spectra to obtain potential biomarkers by establishing an orthogonal partial least squares discriminant analysis model include: setting up a blank group, a model group, and a vitamin K1 treatment group; constructing OPLS-DA models for the model group and the blank group, as well as OPLS-DA models for the treatment group and the model group; identifying pathologically relevant biomarkers caused by warfarin-induced bleeding by comparing the model group and the blank group; identifying biomarkers that can undergo regression after vitamin K1 treatment by comparing the treatment group and the model group; and identifying metabolites that simultaneously meet the criteria of significant difference in the model group and significant regression towards the blank group in the treatment group as potential biomarkers directly related to the procoagulant effect of vitamin K1.

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