CN117976193A - Test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine syndrome type by metabonomics - Google Patents
Test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine syndrome type by metabonomics Download PDFInfo
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Abstract
The invention provides a test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine symptoms by metabonomics, which is characterized by comprising the following steps: 1. the test contents are as follows: 1) Serum sampling, 2) serum sample preparation, 1H NMR spectrum acquisition; 3) Pretreatment: introducing FID signals of the serum samples into MestReNova software; assignment of the spectrum peak according to HMDB; the integrated data is imported into SIMCA-P + for PCA, PLS-DA and OPLS-DA; 2. results and analysis 4), performing pattern recognition multivariate analysis on the normalized data; 5) Volcanic map of OPLS-DA and screening for differential metabolites; 6) Secondary listing of metabolite differences in serum for each group and normal healthy control group; 7) Metabolic pathway analysis 8) comparing different traditional Chinese medicine diagnosis serum metabolic differences 9) inter-group differential metabolism and possible pathway analysis; the method is scientific and effective.
Description
Technical Field
The invention relates to a test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine symptoms by metabonomics.
Background
Hepatolenticular degeneration (hepatolenticular degeneration, HLD), also known as Wilson's Disease (WD), is a copper metabolic disorder caused by ATP7B gene mutation and loss of function due to autosomal recessive inheritance. Excessive copper ions cannot be transported out from cells, and a large amount of copper is deposited on organs such as liver, brain, cornea and kidney for a long time, so that clinical liver and kidney function damage, mental and intelligent disorders, dyskinesia and the like are caused. The incidence rate of the world population is 1/3 ten thousand-1/10 ten thousand, european and American countries are rare, and the incidence rate of Asia, especially eastern Asia and southeast Asia regions is higher, wherein the incidence rate of China is obviously higher than that of other countries.
How to make the curative effect of traditional Chinese medicine widely accepted in the industry at home and abroad, and the traditional Chinese medicine theory system and the unique diagnosis and treatment method thereof are scientifically verified, become the key problems of the development of traditional Chinese medicine and move to the world. Therefore, finding a marker which accords with the objectification evaluation of the traditional Chinese medicine is a key measure. The symptoms of traditional Chinese medicine include syndrome and syndrome, the nature of the disease and the manifestation of the disease. The syndrome research is taken as the research premise and basis of syndrome standardization, and is also one of the key problems of the current research of traditional Chinese medicine. The syndrome of traditional Chinese medicine is a pathophysiological state generated by the disturbance of the mutual relationship between the systems of the internal and external environments of the organism under the action of pathogenic factors, and is a comprehensive diagnosis concept. It is suggested that the study on the syndromes should not be limited to a specific organ and organ, but should be returned to the concept of Chinese medicine. Metabonomics mainly researches the change of endogenous metabolites and the organic relation between the change of the endogenous metabolites and physiological and pathological phenotypes after the organism is disturbed, and clarifies the change rule of the endogenous small molecular metabolites by combining a bioinformatics method to obtain key biomarkers and characterize the overall functional state of the organism. Because metabolism (metabolites) is at the end of the regulation of biochemical activities of biological systems, and contains direct and comprehensive biomarker information reflecting physiological phenotypes, metabonomics is increasingly becoming a very powerful analytical tool for the study of functional changes in living systems as a whole. As a "top-down" strategy, metabolomics is capable of reflecting the function of organisms through the end products of the metabolic network. This property is consistent with the core idea of the traditional Chinese medicine "holistic concept". The metabonomics discovers the differential metabolic spectrum and the interference path of different symptoms of the traditional Chinese medicine through systematic analysis of metabolites, is helpful for clarifying the substance basis and the action mechanism of different symptoms of the traditional Chinese medicine, provides an objective substance basis for scientific elucidation of the theory of the traditional Chinese medicine, and provides a new idea for standardization of symptoms. The method is characterized in that a metabonomics method is used for searching for a marked metabolite (spectrum) among different symptoms of the traditional Chinese medicine and related metabolic pathways of the metabolite, so that the change trend of related substances among different symptoms is clear, and a modern technological platform is built for the research of the symptoms of the traditional Chinese medicine and the objective research of the theory of the traditional Chinese medicine. The syndrome of traditional Chinese medicine is the result of the integral change of the organism, the change of the metabolite reveals the dynamic process and regulation law of the integral vital activity, and the syndrome of traditional Chinese medicine is revealed from the aspect of what happens to the organism. The metabonomics characterizes the metabolic profile and the biological markers of the traditional Chinese medicine symptoms, and provides a new target for objective diagnosis of the traditional Chinese medicine symptoms and accurate evaluation of clinical curative effects of the prescription.
Disclosure of Invention
The invention aims to provide a test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine symptoms by metabonomics.
In order to achieve the above object, the present invention adopts the following technical scheme:
The invention provides a test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine symptoms by metabonomics, which comprises the following steps:
1. Test content
1) Serum samples, and group numbers were individually as follows:
2) Preparing a serum sample, and then performing 1H NMR spectrum acquisition;
3) Preprocessing the acquired data:
Firstly, importing a free induction decay signal FID signal of each serum sample into MestReNova software; spectral peak assignment was performed according to HMDB; then importing the integrated data into SIMCA-P software to perform principal component analysis, partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis;
2. Results and analysis
4) Principal component analysis PCA
After the sectional integration, carrying out normalization processing on the data of each integration interval;
Performing pattern recognition multivariate analysis on the normalized data by using SIMCA-P+ software, wherein in principal component analysis PCA, a data scale conversion mode of self-adaptive conversion UV and Pareto Par is used; performing partial least square method PLS using data scale conversion mode of Paretopar and self-adaptive conversion UV; to find the correlation between the NMR data X-variable and Y-variable sample grouping information; r 2 X and Q 2 obtained after cross-validation of the quality of the model by partial least squares discriminant analysis PLS-DA respectively represent an interpretable variable of the model and a predictability measure of the model, the effectiveness of the model is judged, and metabolites with significant changes are judged through Pelson correlation coefficient significant difference detection;
41 Plotting 2D and 3D PCA score based on the 1H CPMG NMR spectra of the 6 groups of obtained serum;
42 Comparing serum metabolism difference of each group of diagnosis diseases with that of a normal health physical examination group, and drawing PCA of 1 H CPMG NMR spectrum, partial least squares discriminant analysis PLS-DA and orthogonal partial least squares discriminant analysis OPLS-DA score map;
5) Performing discriminant analysis on OPLS-DA and screening volcanic diagrams of differential metabolites by using an orthogonal partial least square method;
The difference of metabolites between the experimental group phlegm-blood stasis inter-group/damp-heat inter-group/liver-kidney yang-deficiency group/liver-kidney yin-deficiency group/liver-qi Yu Jiezu and the normal healthy control group, which is highlighted in the five tables, is observed under three critical values of |r| >0.602, VIP >1 and p <0.05, according to the orthorhombic least square discriminant analysis OPLS-DA coefficients and VIP, r and p value lists obtained by NMR data of metabolites in serum of the inter-group/damp-heat inter-group/spleen-kidney yang-deficiency group/liver-kidney yin-deficiency group/liver-qi Yu Jiezu and the normal healthy control group;
6) Secondary listing of metabolite differences in serum for each group and normal healthy control group;
7) Metabolic pathway analysis
On the basis of the differential metabolites, analyzing the possible metabolic pathway changes, importing data into MetaboAnalyst for analysis, and plotting and observing the pathway of metabolite differences between the experimental group phlegm-blood stasis interconnecting group and the normal healthy control group, the pathway of metabolite differences between the experimental group damp-heat internal accumulation group and the normal healthy control group, the pathway of metabolite differences between the experimental group spleen-kidney yang deficiency group and the normal healthy control group, the pathway of metabolite differences between the experimental group liver-kidney yin deficiency group and the normal healthy control group, and the pathway of metabolite differences between the experimental group liver qi Yu Jiezu and the normal healthy control group;
8) Comparing serum metabolic differences of different traditional Chinese medicine diagnoses
81 According to the final screening result, comparing the difference between every two groups to 10 groups, screening out 4 groups to be stronger respectively:
1 | Group a vs. group d | Phlegm and blood stasis intercombination group | Liver-kidney yin deficiency group | (TY vs.GS) |
2 | Group a vs. group e | Phlegm and blood stasis intercombination group | Liver qi Yu Jiezu | (TY vs.GQ) |
3 | Group B vs. group e | Internal dampness-heat accumulation group | Liver qi Yu Jiezu | (SR vs.GQ) |
4 | Group C vs. group d | Spleen-kidney yang deficiency group | Liver-kidney yin deficiency group | (PS vs.GS) |
Drawing a PCA score graph of 1 H NMR spectra of serum obtained on the basis of a phlegm-stasis interconnection group, a liver-kidney yin deficiency group TY-GS, a phlegm-stasis interconnection group, a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group, a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group and a liver-kidney yin deficiency group PS-GS and a normal healthy control group ZC, and observing through a PCA result, wherein a small part of aliasing exists between the two groups in three data scale conversion modes Ctr, UV, par;
82 Partial least squares method-discriminant analysis PLS-DA
Performing partial least squares-discriminant analysis PLS-DA on the normalized data by using SIMCA-P + software to find out the correlation between the NMR data X variable and Y variable and the grouping information; the partial least square method-discriminant analysis PLS-DA uses a data scale conversion mode of UV and Par; PLS-DA checks the quality of the model by a 5-fold cross-validation method;
Drawing a PLS-DA score chart of 1 H NMR spectrum of serum obtained based on a phlegm-blood stasis interconnection group, a liver-kidney yin deficiency group TY-GS, a phlegm-blood stasis interconnection group, a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group, a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group, a liver-kidney yin deficiency group PS-GS and a normal healthy control group ZC;
83 Orthogonal partial least squares-discriminant analysis (OPLS-DA)
In order to further find the difference between groups, carrying out orthogonal partial least squares-discriminant analysis (OPLS-DA) on the plasma sample data, and using a data scale conversion mode of UV; the quality of the model is checked by a 5-fold cross validation method by OPLS-DA; and judging the effectiveness of the model by using R 2 X and Q 2 obtained after cross verification; after that, the arrangement sequence of the classification variable y is changed for a plurality of times by the arrangement experiment, including n=200 times, so as to obtain corresponding different random Q 2 values, and further test the effectiveness of the model, wherein R 2 X and Q 2 respectively represent variables interpretable by the model and predictability of the model;
Drawing an OPLS-DA score graph, a displacement test graph n=200 and a volcanic graph of 1 HNMR spectra of serum obtained based on a phlegm-stasis interconnection group and a liver-kidney yin deficiency group TY-GS, a phlegm-stasis interconnection group and a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group and a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group and a liver-kidney yin deficiency group PS-GS:
analyzing corresponding correlation coefficients of each metabolite through analysis of OPLS-DA, further inducing the metabolites with statistical significance, multiplying loading values of each variable with square root values of standard deviation of the loading values in a correlation coefficient graph, performing retrospective conversion of data, and comparing the retrospective conversion with a corresponding correlation coefficient critical value table to obtain the metabolites causing inter-group differences; screening correlation coefficients, and listing differential metabolites between the experimental group and the control group;
comprehensively considering the VIP value, the p value and the correlation coefficient |r| based on the volcanic diagram, taking a critical value, summarizing statistically significant metabolites, and simultaneously listing;
9) Differential metabolism and possible pathway analysis between groups
91 Differential metabolites
Observing differences of metabolites between an experimental group phlegm stasis interconnecting group and a liver-kidney yin deficiency group, between an experimental group phlegm stasis interconnecting group and a liver-qi stagnation group, between an experimental group damp-heat internal accumulation group and a liver-qi stagnation group, and between an experimental group spleen-kidney yang deficiency group and a liver-kidney yin deficiency group under two critical values of |r| >0.497, VIP >1 or P <0.05, and listing at the same time;
92 On the basis of the differential metabolites, analyzing the metabolic pathway changes possibly caused, importing the data into MetaboAnalyst analysis, and simultaneously plotting the metabolic pathway analysis;
Analyzing metabolite differential pathways of the experimental group phlegm-blood stasis intercommunicating group and the liver-kidney yin deficiency group through a graph; the experimental group phlegm-blood stasis interconnecting group and the metabolite difference path of liver qi Yu Jiezu; the pathway of the differences between the metabolic products of the experimental group in damp-heat and the liver qi Yu Jiezu; the way of metabolite differences between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group.
As a further improvement of the invention: the preparation of the serum sample in the step 2) specifically comprises the following steps:
400ul of serum samples were taken and added to 200 ul of phosphate buffer in heavy water containing 0.9% NaCl in mass ratio, the above solution was centrifuged in 10000g centrifuge at 4℃for 10min, and 550 ul of supernatant was taken to 5mm nuclear magnetic tube for NMR detection.
As a further improvement of the invention: the 0.9% NaCl according to the mass volume ratio is that 0.9 g of sodium chloride is weighed and dissolved in distilled water to be diluted to 100 ml; the phosphate buffer concentration is 90mM/L, pH 7.4.
As a further improvement of the invention: the step 2) 1H NMR acquisition of serum samples:
Carrying out data sampling on a 600MHz high-field nuclear magnetic resonance spectrometer equipped with an ultralow temperature probe, wherein the proton resonance frequency is 600.13MHz, the detection temperature is 298K, a NOESYPR-CPMG pulse sequence is adopted for carrying out data sampling, the echo time is set to be 70ms, and the relaxation time is set to be 2.0s; NOESYPR the sequence delay-90 ° -t1-90 ° -tm-90 ° -acquisition can suppress the water peak, wherein the delay time t1 is set to 2 μs, the delay time tm is 120ms, the number of signal accumulation times is 64, the number of sampling points is 32K, the spectral width is 10K, the sampling time is 2.73s, and the relaxation delay is 4s.
As a further improvement of the invention: wherein the experimental temperature is 25 ℃, and the specific parameters are as follows: the 90 pulse width, fixed interval t1 is 2 mus and the mixing time tm is 120ms.
As a further improvement of the invention: the step 3) is specifically as follows:
Firstly, FID signals of serum samples are imported into MestRenova software, spectrograms are subjected to Fourier transformation, phase adjustment, baseline correction and calibration treatment, and all spectrograms are multiplied by an exponential window function with a broadening factor of 1Hz when subjected to Fourier transformation so as to improve the signal to noise ratio;
Serum nuclear magnetic resonance spectra were scaled with a bimodal scale of lactic acid at 1.332ppm, with an integration interval of 8.5-0.5ppm and an integration interval of 0.002ppm, with the removal of the two segments of the regions 4.667-5.179ppm and 5.50-6.00ppm containing residual water and urea peaks and their effects, and with spectral peak assignment according to HMDB;
and then importing the integrated data into SIMCA-P software to perform principal component analysis, partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis.
As a further improvement of the invention: and 6) comprehensively considering the VIP value, the p value and the related coefficient |r| based on the volcanic diagram, taking VIP >1, p <0.05 and |r| >0.497 as critical values, and simultaneously meeting the metabolites of two of the three conditions to obtain the metabolites with statistical significance.
As a further improvement of the invention: the step 7):
The way of metabolite differences between the experimental group of sputum stasis-interconnecting group and the normal healthy control group: synthesis of ketone bodies and
Degradation; arginine and proline metabolism; butyrate metabolism; metabolism of pyruvate; glycolysis or glycogen generation; the pathway of metabolite differences between the experimental group with the accumulation of damp-heat and the normal healthy control group: glyoxylic acid and dicarboxylic acid metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; valine, leucine and isoleucine degradation; butyrate metabolism;
the way of metabolite differences between the experimental group of spleen-kidney yang deficiency and the normal healthy control group: metabolism of pyruvate; glycolysis or glycogen generation; cysteine and methionine metabolism; synthesizing and degrading ketone bodies;
The way of metabolite differences between the liver-kidney yin deficiency group of the experimental group and the normal healthy control group: alanine, aspartic acid and glutamic acid metabolism; butyrate metabolism; arginine biosynthesis; synthesizing and degrading ketone bodies; arginine and proline metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; glutathione metabolism;
the pathway of metabolite differences between experimental group liver qi Yu Jiezu and normal healthy control group: alanine, aspartic acid and glutamic acid metabolism; glyoxylic acid and dicarboxylic acid metabolism; glycine, serine and threonine metabolism; arginine biosynthesis; histidine metabolism; the citric acid cycle TCA cycle; butyrate metabolism; metabolism of pyruvate.
As a further improvement of the invention: step 8), based on volcanic diagram, comprehensively considering VIP value, p value and related coefficient |r|, taking VIP >1, p <0.05, |r| >0.497 as critical values, and simultaneously meeting the metabolites of two of the three conditions, and calculating the metabolites with statistical significance.
As a further improvement of the invention: said step 92):
Metabolite differential pathways in the experimental group of phlegm-blood stasis intercommunicating group and liver-kidney yin deficiency group: synthesizing and degrading ketone bodies; arginine and proline metabolism; butyrate metabolism; metabolism of pyruvate; glycolysis or glycogen generation;
the experimental group phlegm-blood stasis interconnecting group and the metabolite differential pathway of liver qi Yu Jiezu: glyoxylic acid and dicarboxylic acid metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; valine, leucine and isoleucine degradation; butyrate metabolism;
The pathway of the differences between the metabolic products of the experimental group in damp-heat and liver qi Yu Jiezu: metabolism of pyruvate; glycolysis or glycogen generation; cysteine and methionine metabolism; synthesizing and degrading ketone bodies;
The way of metabolite difference between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group: butyrate metabolism; synthesizing and degrading ketone bodies; metabolism of D-glutamine and D-glutamate; biosynthesis of arginine.
The nmr spectrometer is divided into a high-field nmr spectrometer and a low-field nmr spectrometer, and first, the sensitivity of the high-field nmr spectrometer is higher than that of the low-field nmr spectrometer, and if the sample concentration is low, the signal-to-noise ratio of the spectrogram measured by the low-field nmr spectrometer is low, and the signal-to-noise ratio of the high-field nmr spectrometer is improved. Second, the peak separation measured by the high-field nmr is more open than that measured by the low-field nmr, and the spectrogram analysis is easier. The whole method is as follows: principal component analysis (PRINCIPAL COMPONET ANALYSIS, PCA), adaptive scaling (unit VARIANCE SCALING, UV) and Pareto (Pareto scaling, par). In addition, the symbol "/" in "the phlegm-blood stasis intercommunicating group/the damp-heat internal accumulation group/the spleen-kidney yang deficiency group/the liver-kidney yin deficiency group/the liver-qi Yu Jiezu" indicates "or"; the SIMCA-P + software is identical to the SIMCA-P software.
Drawings
Fig. 1 is: 2D and 3D PCA score plots of 1 H CPMG NMR spectra of serum were obtained based on group 6.
Fig. 2 is: PCA (FIG. 2-1), PLS-DA (FIG. 2-2), OPLS-DA score plots (FIG. 2-3) of 1 H CPMG NMR spectra of serum were obtained based on the sputum stasis-intermodal group and the normal healthy control group.
Fig. 3 is: PCA (FIG. 3-1), PLS-DA (FIG. 3-2), OPLS-DA score plots (FIG. 3-3) of 1 H CPMG NMR spectra of serum were obtained based on the damp-heat accumulation group and the normal healthy control group.
Fig. 4 is: PCA (FIG. 4-1), PLS-DA (FIG. 4-2), OPLS-DA score plots (FIG. 4-3) of 1 H CPMG NMR spectra of serum were obtained based on spleen and kidney yang deficiency group versus normal healthy control group.
Fig. 5 is: PCA (FIG. 5-1), PLS-DA (FIG. 5-2), OPLS-DA score plots (FIG. 5-3) of 1 H CPMG NMR spectra of serum were obtained based on liver-kidney yin deficiency group and normal healthy control group.
Fig. 6 is: PCA (FIG. 6-1), PLS-DA (FIG. 6-2), OPLS-DA score plots (FIG. 6-3) of 1 H CPMG NMR spectra of serum were obtained based on liver qi Yu Jiezu versus normal healthy controls.
Fig. 7 is: volcanic pattern of OPLS-DA and screening for differential metabolites (FIG. 7-1 phlegm stasis inter-group vs. normal healthy control group; FIG. 7-2 damp-heat in-accumulation group vs. normal healthy control group; FIG. 7-3 spleen-kidney yang deficiency group vs. normal healthy control group; FIG. 7-4 liver-kidney yin deficiency group vs. normal healthy control group; FIG. 7-5 liver-qi stagnation group vs. normal healthy control group).
Fig. 8 is: the data were imported MetaboAnalyst for analysis and their metabolic change pathway analysis patterns.
Fig. 9 is: based on the phlegm-blood stasis and liver-kidney yin deficiency group (TY-GS), the phlegm-blood stasis and liver-qi Yu Jiezu (TY-GQ), the damp-heat internal accumulation group and liver-qi Yu Jiezu (SR-GQ), the spleen-kidney yang deficiency group and liver-kidney yin deficiency group (PS-GS) and the normal healthy control group (ZC), obtaining a PCA score map of a 1H NMR spectrum of serum; the specific table is as follows;
FIG. 9-1 | Group a vs. group d | Phlegm and blood stasis intercombination group | Liver-kidney yin deficiency group | (TY vs.GS) |
FIG. 9-2 | Group a vs. group e | Phlegm and blood stasis intercombination group | Liver qi Yu Jiezu | (TY vs.GQ) |
FIG. 9-3 | Group B vs. group e | Internal dampness-heat accumulation group | Liver qi Yu Jiezu | (SR vs.GQ) |
FIG. 9-4 | Group C vs. group d | Spleen-kidney yang deficiency group | Liver-kidney yin deficiency group | (PS vs.GS) |
Fig. 10 is: PLS-DA score maps of 1H NMR spectra of serum were obtained based on the phlegm-stasis and liver-kidney yin deficiency group (TY-GS), the phlegm-stasis and liver-qi Yu Jiezu (TY-GQ), the damp-heat internal accumulation group and liver-qi Yu Jiezu (SR-GQ), the spleen-kidney yang deficiency group and liver-kidney yin deficiency group (PS-GS) and the normal healthy control group (ZC);
FIG. 10-1 (TY vs. GS vs. ZC); FIG. 10-2 (TY vs. GS); FIG. 10-3 (TY vs. GQ vs. ZC); FIGS. 10-4 (TY vs. GQ); FIGS. 10-5 (SR vs. GQ vs. ZC); FIGS. 10-6 (SR vs. GQ); FIGS. 10-7 (PS vs. GS vs. ZC); FIGS. 10-8 (PS vs. GS).
Fig. 11 is: OPLS-DA score map, displacement test map (n=200) and volcanic map of 1H NMR spectrum of serum were obtained based on the phlegm-stasis and liver-kidney yin deficiency group (TY-GS), the phlegm-stasis and liver-qi Yu Jiezu (TY-GQ), the damp-heat internal accumulation group and liver-qi Yu Jiezu (SR-GQ), the spleen-kidney yang deficiency group and liver-kidney yin deficiency group (PS-GS), and marked circles in the colored volcanic map represent metabolites with statistically significant differences;
FIG. 11-1 (TY-GS UV); FIG. 11-2 (TY-GQ UV); FIG. 11-3 (SR-GQ UV); FIGS. 11-4 (PS-GS UV).
Fig. 12 is: based on the differential metabolites, the possible metabolic pathway changes are analyzed, and the data are imported MetaboAnalyst for analysis, which is a metabolic pathway analysis chart.
Fig. 13 is: 6 sets of sample PCA label references.
Detailed Description
The following describes in more detail the test method for providing evidence of small molecular metabolic markers for hepatolenticular degeneration in traditional Chinese medicine by using the metabonomics provided by the invention through specific examples:
example 1
Experimental part: NMR metabonomics study of serum of 5 patients diagnosed by Chinese medicine differentiation
Sample description:
serum sampling analysis (serum)
400Ul of serum samples were taken and added to 200 ul of phosphate buffer (90 mM/L, pH 7.4) in heavy water containing 0.9% NaCl, the above solution was centrifuged in a 10000g centrifuge at 4℃for 10min, and 550 ul of supernatant was taken to a 5mM nuclear magnetic tube for NMR detection.
1 H NMR spectrum acquisition:
1 H NMR experiments on serum samples were performed on a 600MHz high field nuclear magnetic resonance spectrometer (Bruker Corporation, karlsruhe, germany) equipped with an ultra-low temperature probe. Wherein the proton resonance frequency is 600.13MHz and the detection temperature is 298K. Data sampling was performed using NOESYPR-CPMG pulse sequence with an echo time set to 70ms and a relaxation time set to 2.0s. NOESYPR sequences (delay-90-t 1-90°-tm -90-acquisition) can suppress the water peaks, wherein the delay time t 1 is set to 2 mu s, the delay time t m is 120ms, the signal accumulation times are 64 times, the sampling point number is 32K, the spectrum width is 10K, the sampling time is 2.73s, and the relaxation delay is 4s.
Data preprocessing:
first, FID signals of each serum sample are introduced into MestReNova (V12.0, mestrelab Research s.l.) software, and fourier transform, phase adjustment, baseline correction, scaling and other treatments are performed on the spectrograms, and all spectrograms are multiplied by an exponential window function with a broadening factor of 1Hz when fourier transform is performed, so as to improve the signal-to-noise ratio.
The serum nuclear magnetic resonance spectrum was scaled with a bimodal scale of lactic acid at 1.332ppm, with an integration interval of 8.5-0.5ppm and an integration interval of 0.002ppm, the regions containing residual water and urea peaks and their effects (two segments of 4.667-5.179ppm and 5.50-6.00 ppm) were removed, and the spectral peak assignment was performed according to HMDB (Human Metabolome Database (hmdb. Ca)).
The integrated data is then imported into SIMCA-P software (version 14.0, umetrics AB,Sweden) to perform principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis.
2. Results and analysis
For systematic study of changes in serum metabolism caused by disease, multivariate statistical analysis methods were used for further study.
2.1PCA
After the segment integration, the data of each integration interval is normalized. Pattern recognition multivariate analysis was performed on the normalized data using SIMCA-p+ software (V14.0, umetrics AB, umea, sweden). In Principal Component Analysis (PCA), data scale conversion methods of adaptive conversion (UV) and pareto (Par) are used. The partial least squares method (PLS) uses data scaling methods of pareto (Par) and adaptive scaling (UV) to find correlations between NMR data (X variable) and other variables (Y variable, packet information). PLS-DA judges the quality of the model by using R 2 X and Q 2 (representing model interpretable variables and model predictability, respectively) obtained after cross-validation. Metabolites with significant changes were determined by Pearson correlation coefficient significance difference detection (Pearson' sproduct-moment correlation coefficient). The 6 sets of sample PCAs (reference numerals) are shown in FIG. 13. The 2D and 3D PCA score plots of 1 H CPMG NMR spectra of serum obtained based on 6 groups are shown in fig. 1.
2.2 Comparing the serum metabolic differences between each group of diagnosed disease and normal healthy physical examination group:
2.21
Wherein, PCA score graphs of 1 H CPMG NMR spectra of serum obtained based on the sputum stasis-associated group and the normal healthy control group are shown in FIG. 2-1; PLS-DA score graphs of 1 H CPMG NMR spectra of serum obtained based on the sputum and blood stasis interconnection group and normal healthy control group are shown in FIG. 2-2; the OPLS-DA score of 1 HCPMG NMR spectra of serum obtained based on the sputum stasis-associated group and the normal healthy control group is shown in FIGS. 2-3.
2.22
PCA of 1 H CPMG NMR spectrum of serum obtained based on damp-heat accumulation group and normal healthy control group is shown in FIG. 3-1, PLS-DA is shown in FIG. 3-2, and OPLS-DA score is shown in FIG. 3-3.
2.23
The PCA score chart of 1 H CPMG NMR spectrum of serum obtained based on spleen and kidney yang deficiency group and normal healthy control group is shown in figure 4-1, PLS-DA score chart is shown in figure 4-2, OPLS-DA score chart is shown in figure 4-3.
2.24
The PCA score chart of 1 H CPMG NMR spectrum of serum obtained based on liver-kidney yin deficiency group and normal healthy control group is shown in FIG. 5-1, PLS-DA score chart is shown in FIG. 5-2, and OPLS-DA score chart is shown in FIG. 5-3.
2.25
PCA score plots for 1 H CPMG NMR spectra of serum obtained based on liver qi Yu Jiezu and normal healthy controls are shown in FIG. 6-1, PLS-DA score plots in FIG. 6-2, and OPLS-DA score plots in FIG. 6-3.
2.26 Volcanic diagrams of OPLS-DA and screening for differential metabolites
Referring to FIG. 7, the volcanic pattern of OPLS-DA and screening for differential metabolites is shown in FIG. 7-1 in comparison to the normal healthy control; FIG. 7-2 shows the accumulation of damp-heat in the group versus the normal healthy control group; fig. 7-3 spleen-kidney yang deficiency versus normal healthy control; fig. 7-4 liver-kidney yin deficiency group versus normal healthy control group; fig. 7-5 liver qi stagnation groups vs. normal healthy controls.
TABLE 1 OPLS-DA coefficients and VIP, r and p values from NMR data of metabolites in serum of the sputum-stasis-interconnecting group and the normal healthy control group
TABLE 2 OPLS-DA coefficients, and VIP, r and p values from NMR data of metabolites in serum of the damp-heat accumulation group and the normal healthy control group
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TABLE 3 OPLS-DA coefficients and VIP, r and p values from NMR data of metabolites in serum of spleen-kidney yang deficiency group and normal healthy control group
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TABLE 4 OPLS-DA coefficients and VIP, r and p values from serum metabolite NMR data of liver-kidney yin deficiency group and normal healthy control group
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TABLE 5 OPLS-DA coefficients, and VIP, r and p values, derived from NMR data of metabolites in liver qi Yu Jiezu and serum of normal healthy controls
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Metabolite differences between the experimental group phlegm stasis inter group/damp-heat internal accumulation group/spleen kidney yang deficiency group/liver-kidney yin deficiency group/liver qi Yu Jiezu and the normal healthy control group highlighted by text data in the five tables were observed at three critical values of |r| >0.602, VIP >1 and p < 0.05.
TABLE 6 metabolite differences in serum of the sputum stasis-interconnecting and normal healthy control groups
TABLE 7 metabolite differentiation in serum of the damp-heat accumulating group and the normal healthy control group
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TABLE 8 metabolite differentiation in serum of spleen-kidney yang deficiency group and normal healthy control group
TABLE 9 metabolite differences in serum from liver-kidney yin deficiency group and normal healthy control group
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TABLE 10 metabolite differences in liver qi Yu Jiezu and serum from normal healthy controls
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2.27 Metabolic pathway analysis
Based on the differential metabolites, the possible metabolic pathway changes are analyzed, and the data are imported MetaboAnalyst for analysis, and the metabolic pathway changes are analyzed as shown in FIG. 8:
A. The way of metabolite differences between the experimental group of sputum stasis-interconnecting group and the normal healthy control group: 1. synthesizing and degrading ketone bodies; 2. arginine and proline metabolism; 3. butyrate metabolism; 4. metabolism of pyruvate; 5. glycolysis or glycogen formation
B. The pathway of metabolite differences between the experimental group with the accumulation of damp-heat and the normal healthy control group: 1. glyoxylic acid and dicarboxylic acid metabolism; 2. citric acid cycle (TCA cycle); 3. metabolism of pyruvate; 4. glycolysis or glycogen generation; 5. valine, leucine and isoleucine degradation; 6. butyrate metabolism
C. The way of metabolite differences between the experimental group of spleen-kidney yang deficiency and the normal healthy control group: 1. metabolism of pyruvate; 2. glycolysis or glycogen generation; 3. cysteine and methionine metabolism; 4. synthesis and degradation of ketone bodies
D. The way of metabolite differences between the liver-kidney yin deficiency group of the experimental group and the normal healthy control group: 1. alanine, aspartic acid and glutamic acid metabolism; 2. butyrate metabolism; 3. arginine biosynthesis; 4. synthesizing and degrading ketone bodies; 5. arginine and proline metabolism; 6. the citric acid cycle is the TCA cycle; 7. metabolism of pyruvate; 8. glycolysis or glycogen generation; 9. glutathione metabolism
E. The pathway of metabolite differences between experimental group liver qi Yu Jiezu and normal healthy control group: 1. alanine, aspartic acid and glutamic acid metabolism; 2. glyoxylic acid and dicarboxylic acid metabolism; 3. glycine, serine and threonine metabolism; 4. arginine biosynthesis; 5. histidine metabolism; 6. the citric acid cycle is the TCA cycle; 7. butyrate metabolism; 8. metabolism of pyruvate.
2.3 Comparison of serum metabolic differences in different diagnostic applications of traditional Chinese medicine
According to the final screening result, comparing the difference between every two groups to 10 groups, screening out 4 groups with stronger difference is respectively as follows:
1 | Group a vs. group d | Phlegm and blood stasis intercombination group | Liver-kidney yin deficiency group | (TY vs.GS) |
2 | Group a vs. group e | Phlegm and blood stasis intercombination group | Liver qi Yu Jiezu | (TY vs.GQ) |
3 | Group B vs. group e | Internal dampness-heat accumulation group | Liver qi Yu Jiezu | (SR vs.GQ) |
4 | Group C vs. group d | Spleen-kidney yang deficiency group | Liver-kidney yin deficiency group | (PS vs.GS) |
2.31
1 | Group a vs. group d | Phlegm and blood stasis intercombination group | Liver-kidney yin deficiency group | (TY vs.GS) |
PCA score graphs of 1 H NMR spectra of serum obtained based on the sputum stasis-associated group and the liver-kidney yin deficiency group (TY-GS) and the normal healthy control group (ZC) are shown in FIG. 9-1;
2 | Group a vs. group e | Phlegm and blood stasis intercombination group | Liver qi Yu Jiezu | (TY vs.GQ) |
PCA score graphs of 1 H NMR spectra of serum obtained based on the sputum and blood stasis interconnection group and liver qi Yu Jiezu (TY-GQ) and normal healthy control group (ZC) are shown in FIG. 9-2;
3 | Group B vs. group e | Internal dampness-heat accumulation group | Liver qi Yu Jiezu | (SR vs.GQ) |
PCA score graphs of 1 H NMR spectra of serum obtained based on damp-heat accumulation group and liver qi Yu Jiezu (SR-GQ) and normal healthy control group (ZC) are shown in FIGS. 9-3;
4 | group C vs. group d | Spleen-kidney yang deficiency group | Liver-kidney yin deficiency group | (PS vs.GS) |
PCA score graphs of 1 H NMR spectra of serum based on liver-kidney yin deficiency group (PS-GS) and normal healthy control group (ZC) are shown in FIGS. 9-4.
From the PCA results, it was found that there was a small portion of aliasing between both groups in the three data scaling modes (Ctr, UV, par).
In order to further study the statistical difference characteristics among groups, a supervised multivariate statistical analysis method, namely partial least squares discriminant analysis (PLS-DA), is adopted to perform data discriminant analysis, and modeling analysis is performed on the data again, wherein the analysis results are as follows.
2.32PLS-DA
The correlation between NMR data (X variable) and other variables (Y variable, packet information) was found by Partial Least Squares (PLS) of normalized data using SIMCA-P + software (V14.0, umetrics AB, umea, sweden). Partial least squares-discriminant analysis (PLS-DA) uses a data scale conversion scheme of UV, par. The quality of the model was checked by PLS-DA using a 5-fold cross-validation method.
Wherein PLS-DA score charts of 1 H NMR spectra of serum obtained based on the phlegm-stasis and liver-kidney yin deficiency group (TY-GS), the phlegm-stasis and liver-qi Yu Jiezu (TY-GQ), the damp-heat internal accumulation group and liver-qi Yu Jiezu (SR-GQ), the spleen-kidney yang deficiency group and liver-kidney yin deficiency group (PS-GS) and the normal healthy control group (ZC) respectively are shown in FIG. 10-1, FIG. 10-2, FIG. 10-3, FIG. 10-4, FIG. 10-5, FIG. 10-6, FIG. 10-7 to FIG. 10-8.
2.4OPLS-DA
To further find the inter-group differences, orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed on the plasma sample data using a data scale conversion scheme of UV. The quality of the model was checked by 5-fold cross validation with OPLS-DA. And evaluating the effectiveness of the model by using R 2 X and Q 2 (representing the interpretable variables of the model and the predictability of the model respectively) obtained after cross validation. After that, the arrangement order of the classification variables y is changed by the arrangement experiment randomly many times (n=200) to obtain corresponding different random Q 2 values, and the validity of the model is further checked. The method can reduce the influence of noise or systematic errors, and maximally highlight the difference between different groups in the model.
OPLS-DA score map, displacement test map (n=200) and volcanic map of 1H NMR spectrum of serum were obtained based on the phlegm-stasis and liver-kidney yin deficiency group (TY-GS), the phlegm-stasis and liver-qi Yu Jiezu (TY-GQ), the damp-heat internal accumulation group and liver-qi Yu Jiezu (SR-GQ), the spleen-kidney yang deficiency group and liver-kidney yin deficiency group (PS-GS), see FIGS. 11-1, 11-2, 11-3 and 11-4, and marked circles in the colored volcanic map represent metabolites with statistically significant differences.
The distinction between the two groups is evident compared to the PCA score plot, with each sample point within each group being relatively more concentrated. Indicating that the differences in metabolites in serum samples between the two groups are significant. And the model interpretation capability is strong and the prediction capability is good from the analysis of two performance indexes, namely the description model interpretation capability R 2 and the prediction capability Q 2.
The metabolites of statistical interest were further generalized by analysis of OPLS-DA, and by analysis of the corresponding correlation coefficients for each metabolite. In the correlation coefficient graph, the loading value of each variable is multiplied by the square root value of the standard deviation of the variable, and then the retrospective conversion of the data is carried out. And then comparing with a corresponding correlation coefficient critical value table to obtain metabolites causing group-to-group differences. The differential metabolites between the experimental and control groups, as screened for correlation coefficients, are shown in tables 15-18 below.
Volcanic plot, the size of the experimental point represents VIP value, and the large and small points respectively represent VIP value in: greater than 1, and others. The abscissa represents the ratio of the metabolite concentrations of the two groups, the coordinate value is 0, the metabolite concentrations of the two groups are equal (the ratio is 1), and the coordinate value 1 represents the metabolite concentration 2 times that of the control group. The ordinate is the p-value between the two groups. In the figure, the black dotted line corresponds to p=0.05, and the p value above the lateral direction is smaller than 0.05. The color of the dot represents the absolute value |r| of the correlation coefficient, and the more red the color represents the absolute value of the correlation coefficient, the higher the correlation, and conversely the more blue the correlation, the less the correlation.
Based on volcanic diagram, comprehensively considering VIP value, p value and correlation coefficient |r|, taking VIP >1, p <0.05 and |r| >0.497 as critical values, and simultaneously meeting the metabolites of two of three conditions, and calculating the metabolites with statistical significance. As shown in tables 11-15.
TABLE 11 relationship between OPLS-DA factor and VIP, r and p values from NMR data of metabolites in serum of the sputum-stasis-interconnecting group and the liver-kidney yin deficiency group
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a Correlation coefficients, signs represent positive and negative correlations of concentration, respectively. The correlation coefficient |r| >0.497 is used as a statistical significance critical value based on the discrimination significance. "-" means that the correlation coefficient |r| <0.497; b Importance of variables in prediction; c p represents the p value, and p <0.05 represents the difference as determined by t-test, and is statistically significant.
TABLE 12 OPLS-DA coefficients and VIP, r and p values from NMR data of metabolites in the sputum and blood stasis interconnecting group and liver qi Yu Jiezu serum
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TABLE 13 OPLS-DA coefficients, and VIP, r and p values from NMR data of metabolites in serum of the damp-heat accumulation group and liver qi Yu Jiezu
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TABLE 14 relationship between OPLS-DA factor and VIP, r and p values based on NMR data of metabolites in serum of spleen-kidney yang deficiency group and liver-kidney yin deficiency group
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2.5 Differential metabolism between groups and possible pathway analysis
2.5.1 Differential metabolites
Table 15|r| >0.497, VIP >1 or P <0.05 differences in metabolites between the experimental group of phlegm-stasis-interconnecting group and the liver-kidney yin deficiency group
Table 16|r| >0.497, VIP >1 or P <0.05, the differences in metabolites between the experimental group of phlegm-stasis and liver-qi stagnation groups
Table 17|r| >0.497, VIP >1 or p <0.05 differences in metabolites between the groups of the experimental group with damp-heat accumulation and liver-qi stagnation
Table 18|r| >0.497, VIP >1 or p <0.05 differences in metabolites between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group
2.5 Metabolic pathway analysis
Based on the differential metabolites, the possible metabolic pathway changes are analyzed, and the data are imported MetaboAnalyst for analysis, wherein the metabolic pathway changes are analyzed as follows:
Metabolite differential pathway in the experimental group of phlegm-blood stasis intercommunicating group and liver-kidney yin deficiency group: 1. synthesizing and degrading ketone bodies; 2. arginine and proline metabolism; 3. butyrate metabolism; 4. metabolism of pyruvate; 5. glycolysis/glycogen production B experimental group phlegm stasis interconnecting group and metabolite differential pathway of liver qi Yu Jiezu: 1. glyoxylic acid and dicarboxylic acid metabolism; 2. the citric acid cycle is the TCA cycle; 3. metabolism of pyruvate; 4. glycolysis or glycogen generation; 5. valine, leucine and isoleucine degradation; 6. butyrate metabolism
The pathway of differences between the metabolic products of the group accumulated in damp-heat and the liver qi Yu Jiezu in the C experimental group: 1. metabolism of pyruvate; 2. glycolysis or glycogen generation; 3. cysteine and methionine metabolism; 4. synthesis and degradation of ketone bodies
D. the way of metabolite difference between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group: 1. butyrate metabolism; 2. synthesizing and degrading ketone bodies; metabolism of D-glutamine and D-glutamic acid; 4. biosynthesis of arginine.
Claims (10)
1. The test method for providing small molecule metabolic marker evidence for hepatolenticular degeneration traditional Chinese medicine symptoms by metabonomics is characterized by comprising the following steps:
1. Test content
1) Serum samples, and group numbers were individually as follows:
2) Preparing a serum sample, and then performing 1H NMR spectrum acquisition;
3) Preprocessing the acquired data:
Firstly, importing a free induction decay signal FID signal of each serum sample into MestReNova software; spectral peak assignment was performed according to HMDB; then, the integrated data is imported into SIMCA-P + software to carry out principal component analysis, partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis;
2. Results and analysis
4) Principal component analysis PCA
After the sectional integration, carrying out normalization processing on the data of each integration interval;
Performing pattern recognition multivariate analysis on the normalized data by using SIMCA-P + software, and using a data scale conversion mode of self-adaptive conversion UV and Pareto Par in principal component analysis PCA; performing partial least square method PLS using data scale conversion mode of Paretopar and self-adaptive conversion UV; to find the correlation between the NMR data X-variable and Y-variable sample grouping information; r 2 X and Q 2 obtained after cross-validation of the quality of the model by partial least squares discriminant analysis PLS-DA respectively represent an interpretable variable of the model and a predictability measure of the model, the effectiveness of the model is judged, and metabolites with significant changes are judged through Pelson correlation coefficient significant difference detection;
41 Plotting 2D and 3D PCA score based on the 1H CPMG NMR spectra of the 6 groups of obtained serum;
42 Comparing serum metabolism difference of each group of diagnosis diseases with that of a normal health physical examination group, and drawing PCA of 1 H CPMG NMR spectrum, partial least squares discriminant analysis PLS-DA and orthogonal partial least squares discriminant analysis OPLS-DA score map;
5) Performing discriminant analysis on OPLS-DA and screening volcanic diagrams of differential metabolites by using an orthogonal partial least square method;
The difference of metabolites between the experimental group phlegm-blood stasis inter-group/damp-heat inter-group/liver-kidney yang-deficiency group/liver-kidney yin-deficiency group/liver-qi Yu Jiezu and the normal healthy control group, which is highlighted in the five tables, is observed under three critical values of |r| >0.602, VIP >1 and p <0.05, according to the orthorhombic least square discriminant analysis OPLS-DA coefficients and VIP, r and p value lists obtained by NMR data of metabolites in serum of the inter-group/damp-heat inter-group/spleen-kidney yang-deficiency group/liver-kidney yin-deficiency group/liver-qi Yu Jiezu and the normal healthy control group;
6) Secondary listing of metabolite differences in serum for each group and normal healthy control group;
7) Metabolic pathway analysis
On the basis of the differential metabolites, analyzing the possible metabolic pathway changes, importing data into MetaboAnalyst for analysis, and plotting and observing the pathway of metabolite differences between the experimental group phlegm-blood stasis interconnecting group and the normal healthy control group, the pathway of metabolite differences between the experimental group damp-heat internal accumulation group and the normal healthy control group, the pathway of metabolite differences between the experimental group spleen-kidney yang deficiency group and the normal healthy control group, the pathway of metabolite differences between the experimental group liver-kidney yin deficiency group and the normal healthy control group, and the pathway of metabolite differences between the experimental group liver qi Yu Jiezu and the normal healthy control group;
8) Comparing serum metabolic differences of different traditional Chinese medicine diagnoses
81 According to the final screening result, comparing the difference between every two groups to 10 groups, screening out 4 groups to be stronger respectively:
Drawing a PCA score graph of 1 H NMR spectra of serum obtained on the basis of a phlegm-stasis interconnection group, a liver-kidney yin deficiency group TY-GS, a phlegm-stasis interconnection group, a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group, a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group and a liver-kidney yin deficiency group PS-GS and a normal healthy control group ZC, and observing through a PCA result, wherein a small part of aliasing exists between the two groups in three data scale conversion modes Ctr, UV, par;
82 Partial least squares method-discriminant analysis PLS-DA
Performing partial least squares-discriminant analysis PLS-DA on the normalized data by using SIMCA-P + software to find out the correlation between the NMR data X variable and Y variable and the grouping information; the partial least square method-discriminant analysis PLS-DA uses a data scale conversion mode of UV and Par; PLS-DA checks the quality of the model by a 5-fold cross-validation method;
Drawing a PLS-DA score chart of 1 H NMR spectrum of serum obtained based on a phlegm-blood stasis interconnection group, a liver-kidney yin deficiency group TY-GS, a phlegm-blood stasis interconnection group, a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group, a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group, a liver-kidney yin deficiency group PS-GS and a normal healthy control group ZC;
83 Orthogonal partial least squares-discriminant analysis (OPLS-DA)
In order to further find the difference between groups, carrying out orthogonal partial least squares-discriminant analysis (OPLS-DA) on the plasma sample data, and using a data scale conversion mode of UV; the quality of the model is checked by a 5-fold cross validation method by OPLS-DA; and judging the effectiveness of the model by using R 2 X and Q 2 obtained after cross verification; after that, the arrangement sequence of the classification variable y is changed for a plurality of times by the arrangement experiment, including n=200 times, so as to obtain corresponding different random Q 2 values, and further test the effectiveness of the model, wherein R 2 X and Q 2 respectively represent variables interpretable by the model and predictability of the model;
Drawing an OPLS-DA score graph, a displacement test graph n=200 and a volcanic graph of 1 HNMR spectra of serum obtained based on a phlegm-stasis interconnection group and a liver-kidney yin deficiency group TY-GS, a phlegm-stasis interconnection group and a liver-qi Yu Jiezu TY-GQ, a damp-heat internal accumulation group and a liver-qi Yu Jiezu SR-GQ, a spleen-kidney yang deficiency group and a liver-kidney yin deficiency group PS-GS:
analyzing corresponding correlation coefficients of each metabolite through analysis of OPLS-DA, further inducing the metabolites with statistical significance, multiplying loading values of each variable with square root values of standard deviation of the loading values in a correlation coefficient graph, performing retrospective conversion of data, and comparing the retrospective conversion with a corresponding correlation coefficient critical value table to obtain the metabolites causing inter-group differences; screening correlation coefficients, and listing differential metabolites between the experimental group and the control group;
comprehensively considering the VIP value, the p value and the correlation coefficient |r| based on the volcanic diagram, taking a critical value, summarizing statistically significant metabolites, and simultaneously listing;
9) Differential metabolism and possible pathway analysis between groups
91 Differential metabolites
Observing differences of metabolites between an experimental group phlegm stasis interconnecting group and a liver-kidney yin deficiency group, between an experimental group phlegm stasis interconnecting group and a liver-qi stagnation group, between an experimental group damp-heat internal accumulation group and a liver-qi stagnation group, and between an experimental group spleen-kidney yang deficiency group and a liver-kidney yin deficiency group under two critical values of |r| >0.497, VIP >1 or P <0.05, and listing at the same time;
92 On the basis of the differential metabolites, analyzing the metabolic pathway changes possibly caused, importing the data into MetaboAnalyst analysis, and simultaneously plotting the metabolic pathway analysis;
Analyzing metabolite differential pathways of the experimental group phlegm-blood stasis intercommunicating group and the liver-kidney yin deficiency group through a graph; the experimental group phlegm-blood stasis interconnecting group and the metabolite difference path of liver qi Yu Jiezu; the pathway of the differences between the metabolic products of the experimental group in damp-heat and the liver qi Yu Jiezu; the way of metabolite differences between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group.
2. The test method for providing evidence of small molecule metabolic markers for traditional Chinese medicine syndrome of hepatolenticular degeneration using metabonomics according to claim 1, wherein: the preparation of the serum sample in the step 2) specifically comprises the following steps:
400ul of serum samples were taken and added to 200 ul of phosphate buffer in heavy water containing 0.9% NaCl in mass ratio, the above solution was centrifuged in 10000g centrifuge at 4℃for 10min, and 550 ul of supernatant was taken to 5mm nuclear magnetic tube for NMR detection.
3. The test method for providing evidence of small molecule metabolic markers for hepatolenticular degeneration of traditional Chinese medicine according to claim 2, wherein: the 0.9% NaCl according to the mass volume ratio is that 0.9 g of sodium chloride is weighed and dissolved in distilled water to be diluted to 100 ml; the phosphate buffer concentration is 90mM/L, pH 7.4.
4. The method of claim 1, wherein the step 2) 1H NMR collection of serum samples is:
Carrying out data sampling on a 600MHz high-field nuclear magnetic resonance spectrometer equipped with an ultralow temperature probe, wherein the proton resonance frequency is 600.13MHz, the detection temperature is 298K, a NOESYPR-CPMG pulse sequence is adopted for carrying out data sampling, the echo time is set to be 70ms, and the relaxation time is set to be 2.0s; NOESYPR the sequence delay-90 ° -t1-90 ° -tm-90 ° -acquisition can suppress the water peak, wherein the delay time t1 is set to 2 μs, the delay time tm is 120ms, the number of signal accumulation times is 64, the number of sampling points is 32K, the spectral width is 10K, the sampling time is 2.73s, and the relaxation delay is 4s.
5. The method for providing evidence of small molecule metabolic markers for hepatolenticular degeneration according to claim 4, wherein the experimental temperature is 25 ℃, and the specific parameters are: the 90 pulse width, fixed interval t1 is 2 mus and the mixing time tm is 120ms.
6. The method for testing the evidence of small molecule metabolic markers provided by metabolomics in traditional Chinese medicine for hepatolenticular degeneration according to claim 1, wherein the step 3) is specifically:
Firstly, FID signals of serum samples are imported into MestRenova software, spectrograms are subjected to Fourier transformation, phase adjustment, baseline correction and calibration treatment, and all spectrograms are multiplied by an exponential window function with a broadening factor of 1Hz when subjected to Fourier transformation so as to improve the signal to noise ratio;
Serum nuclear magnetic resonance spectra were scaled with a bimodal scale of lactic acid at 1.332ppm, with an integration interval of 8.5-0.5ppm and an integration interval of 0.002ppm, with the removal of the two segments of the regions 4.667-5.179ppm and 5.50-6.00ppm containing residual water and urea peaks and their effects, and with spectral peak assignment according to HMDB;
and then importing the integrated data into SIMCA-P software to perform principal component analysis, partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis.
7. The method for testing the evidence of the small molecular metabolic markers provided by the metabonomics of the traditional Chinese medicine syndrome type of hepatolenticular degeneration according to claim 1, wherein the step 6) is characterized in that based on volcanic diagram, the VIP value, the p value and the correlation coefficient |r| are comprehensively considered, and the metabolites meeting two of three conditions simultaneously are calculated as the metabolites with statistical significance by taking VIP >1, p <0.05 and |r| >0.497 as critical values.
8. The method of claim 1, wherein the step 7) is:
The way of metabolite differences between the experimental group of sputum stasis-interconnecting group and the normal healthy control group: synthesizing and degrading ketone bodies; arginine and proline metabolism; butyrate metabolism; metabolism of pyruvate; glycolysis or glycogen generation; the pathway of metabolite differences between the experimental group with the accumulation of damp-heat and the normal healthy control group: glyoxylic acid and dicarboxylic acid metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; valine, leucine and isoleucine degradation; butyrate metabolism;
the way of metabolite differences between the experimental group of spleen-kidney yang deficiency and the normal healthy control group: metabolism of pyruvate; glycolysis or glycogen generation; cysteine and methionine metabolism; synthesizing and degrading ketone bodies;
The way of metabolite differences between the liver-kidney yin deficiency group of the experimental group and the normal healthy control group: alanine, aspartic acid and glutamic acid metabolism; butyrate metabolism; arginine biosynthesis; synthesizing and degrading ketone bodies; arginine and proline metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; glutathione metabolism;
the pathway of metabolite differences between experimental group liver qi Yu Jiezu and normal healthy control group: alanine, aspartic acid and glutamic acid metabolism; glyoxylic acid and dicarboxylic acid metabolism; glycine, serine and threonine metabolism; arginine biosynthesis; histidine metabolism; the citric acid cycle TCA cycle; butyrate metabolism; metabolism of pyruvate.
9. The method for testing the evidence of the small molecular metabolic markers provided by the metabonomics of the traditional Chinese medicine syndrome type of hepatolenticular degeneration according to claim 1, wherein in the step 8), based on volcanic diagrams, the VIP value, the p value and the correlation coefficient |r| are comprehensively considered, and the metabolites which meet two of three conditions simultaneously are considered to be statistically significant are obtained by taking VIP >1, p <0.05 and |r| >0.497 as critical values.
10. The method of claim 1, wherein the step 92) is performed by:
Metabolite differential pathways in the experimental group of phlegm-blood stasis intercommunicating group and liver-kidney yin deficiency group: synthesizing and degrading ketone bodies; arginine and proline metabolism; butyrate metabolism; metabolism of pyruvate; glycolysis or glycogen generation;
the experimental group phlegm-blood stasis interconnecting group and the metabolite differential pathway of liver qi Yu Jiezu: glyoxylic acid and dicarboxylic acid metabolism; the citric acid cycle TCA cycle; metabolism of pyruvate; glycolysis or glycogen generation; valine, leucine and isoleucine degradation; butyrate metabolism;
The pathway of the differences between the metabolic products of the experimental group in damp-heat and liver qi Yu Jiezu: metabolism of pyruvate; glycolysis or glycogen generation; cysteine and methionine metabolism; synthesizing and degrading ketone bodies;
The way of metabolite difference between the spleen-kidney yang deficiency group and the liver-kidney yin deficiency group of the experimental group: butyrate metabolism; synthesizing and degrading ketone bodies; metabolism of D-glutamine and D-glutamate; biosynthesis of arginine.
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