CN109666726B - Liver injury biomarker containing biological small molecules and genes, method and application - Google Patents
Liver injury biomarker containing biological small molecules and genes, method and application Download PDFInfo
- Publication number
- CN109666726B CN109666726B CN201811510620.4A CN201811510620A CN109666726B CN 109666726 B CN109666726 B CN 109666726B CN 201811510620 A CN201811510620 A CN 201811510620A CN 109666726 B CN109666726 B CN 109666726B
- Authority
- CN
- China
- Prior art keywords
- liver injury
- drug
- induced liver
- biomarker
- genes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6813—Hybridisation assays
- C12Q1/6834—Enzymatic or biochemical coupling of nucleic acids to a solid phase
- C12Q1/6837—Enzymatic or biochemical coupling of nucleic acids to a solid phase using probe arrays or probe chips
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N2030/022—Column chromatography characterised by the kind of separation mechanism
- G01N2030/027—Liquid chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/08—Hepato-biliairy disorders other than hepatitis
- G01N2800/085—Liver diseases, e.g. portal hypertension, fibrosis, cirrhosis, bilirubin
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Organic Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Wood Science & Technology (AREA)
- Engineering & Computer Science (AREA)
- Analytical Chemistry (AREA)
- Zoology (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Physics & Mathematics (AREA)
- Genetics & Genomics (AREA)
- General Health & Medical Sciences (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a biomarker for early discovering and early warning drug-induced liver injury, wherein the biomarker is a biological small molecule or a differential gene, and the biological small molecule comprises: ethylene acetyl glycine, 2-methyl-3 pentanoic acid, 3-indolebutyric acid, LPC (20:2), and LPC (22: 6). The invention also discloses a method for early discovering and early warning drug-induced liver injury, which is used for quantitatively detecting the concentration of the biological micromolecules or the differential genes in the sample. The invention also discloses application of the biomarker in scientific research and preparation of a drug-induced liver injury diagnostic kit or diagnostic equipment. The change of the biomarker occurs before the drug-induced liver injury, and the biomarker has an early warning effect; is beneficial to reducing the morbidity and the fatality rate of the drug-induced liver injury, is beneficial to improving the current situation of drug-induced liver injury prevention and control in China, and effectively relieves the double burdens of the organism and the economy of a patient.
Description
The application is a divisional application with the application number of 2018103903041, and the application date of a parent application is 2018-04-27.
Technical Field
The invention relates to the technical field of liver injury diagnosis, and relates to a liver injury biomarker containing biological small molecules and genes, a method and application.
Background
The domestic epidemiological survey shows that the Chinese herbal medicine preparation is one of the top three common medicines causing the drug-induced liver injury. Tripterygium glycosides are currently important traditional Chinese medicine preparations clinically used for treating immune diseases such as rheumatoid arthritis, psoriasis and the like, have obvious clinical curative effect, are also one of the traditional Chinese medicine preparations with more toxic effects, and are the most serious of acute liver injury. At present, the clinical diagnosis indexes and defects are as follows: at present, no unified and accepted diagnosis standard for the drug-induced liver injury exists in clinic, the diagnosis is only judged according to traditional indexes such as alanine Aminotransferase (ALT), aspartate Aminotransferase (AST), alkaline phosphatase (ALP) and the like, the specificity is poor, the severity of the liver injury is difficult to be completely reflected, and the type and the induction factor of the liver injury cannot be identified. In addition, histopathology examination is invasive and cannot be used for timely and accurate early warning before liver injury occurs. The hysteresis of the traditional index makes the traditional index unsuitable to be used as the standard of early warning and curative effect evaluation, and effective prevention and prognosis judgment cannot be realized.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.
It is yet another object of the present invention to provide biomarkers for early detection and warning of drug-induced liver injury.
It is yet another object of the present invention to provide a method for early detection and warning of drug-induced liver injury.
The invention also aims to provide application of the biomarker in scientific research and preparation of a drug-induced liver injury diagnostic kit or diagnostic equipment.
Therefore, the technical scheme provided by the invention is as follows:
a biomarker for early discovery and early warning of drug-induced liver injury, wherein the biomarker is a small biological molecule or a differential gene, and the small biological molecule comprises: ethylene acetyl glycine, 2-methyl-3 pentanoic acid, 3-indolebutyric acid, LPC (20:2), and LPC (22: 6).
Preferably, in the biomarker for early detection and early warning of drug-induced liver injury, the difference gene comprises: alcohol dehydrogenase ADHs genes and dopa decarboxylase DDC genes NM-001270852.1/NM-001270853.1/NM-012545.4, the alcohol dehydrogenase ADHs genes include Adh4 NM-017270.2, Adh6 NM-001012084.1 and Adh7 NM-134329.1.
Preferably, in the biomarker for early discovering and early warning of the drug-induced liver injury, the drug-induced liver injury is the liver injury caused by tripterygium glycosides.
The method for early discovering and early warning the drug-induced liver injury comprises the steps of detecting the concentration of a biomarker in a sample, wherein the biomarker is a small biological molecule or a differential gene, the small biological molecule comprises ethylene acetyl glycine, 2-methyl-3-pentanoic acid, 3-indolebutyric acid, LPC (20:2) and LPC (22:6), when the concentration of the small biological molecule is detected, quantitative detection is carried out by using a liquid chromatography tandem mass spectrometry method, and if the ethylene acetyl glycine is increased by more than 4 times than the normal concentration, the 2-methyl-3-pentanoic acid is increased by more than 1.5 times than the normal concentration, the 3-indolebutyric acid is decreased by less than 0.65 time than the normal concentration, the LPC (20:2) is decreased by less than 0.7 time than the normal concentration, and the LPC (22:6) is decreased by less than 0.7 time than the normal concentration, the sample is judged to have the drug-induced liver injury.
Preferably, in the method, when the vinyl acetyl glycine in the sample is increased by more than 4.07 times than the normal concentration, the 2-methyl-3-pentanoic acid is increased by more than 1.55 times than the normal concentration, the 3-indolebutyric acid is decreased by less than 0.67 times than the normal concentration, the LPC (20:2) is decreased by less than 0.71 times than the normal concentration, and the LPC (22:6) is decreased by less than 0.73 times than the normal concentration, the sample is judged to have the drug-induced liver injury.
Preferably, in the method, the differential gene includes an alcohol dehydrogenase ADHs gene and dopa decarboxylase DDC genes NM _001270852.1/NM _001270853.1/NM _012545.4, the alcohol dehydrogenase ADHs gene includes Adh4NM _017270.2, Adh6NM _001012084.1 and Adh7NM _134329.1, and when the expression level of the differential gene in the sample is detected, if the expression level of Adh4 gene is 4.2 times or more of the normal value, the expression level of DDC gene is 1.2 times or more of the normal value, the expression level of Adh6 gene is 47 times or more of the normal value, and the expression level of Adh7 gene is 0.8 times or more of the normal value, the sample is judged to have drug-induced liver damage.
Preferably, in the method, the drug-induced liver injury is liver injury caused by tripterygium glycosides.
Preferably, in the method, when the concentration of the biological small molecules is detected, the sample is animal serum.
Preferably, in the method, when the expression level of the sample difference gene is detected, the sample is animal liver tissue.
The biomarker is applied to scientific research and preparation of a drug-induced liver injury diagnostic kit or diagnostic equipment.
Preferably, in the application, the pharmaceutical liver injury diagnosis kit or diagnosis equipment is applied to diagnosis of liver injury caused by tripterygium glycosides.
The invention at least comprises the following beneficial effects:
based on interdisciplinary characteristics of metabonomics and lncRNA gene chip technology, the obtained metabolite biomarker and gene biomarker have the following advantages: 1. biomarkers were validated efficiently by the lncRNA → mRNA → metabolic pathway of metabolic end products. 2. The change of the biomarker occurs before the drug-induced liver injury, and compared with the hysteresis of the traditional index, the change of the biomarker has the function of early warning. 3. The method is favorable for reducing the morbidity and the fatality rate of the drug-induced liver injury, provides important basis for screening, prevention intervention, individualized prevention and treatment and accurate medication of clinical drug-induced liver injury high risk groups, is favorable for improving the current situation of prevention and control of the drug-induced liver injury in China, and effectively relieves the double burdens of organisms and economy of patients.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIGS. 1A, 1B, 1C, 1D, 1E and 1F are respectively the change of glutamic-oxalacetic transaminase (AST), glutamic-pyruvic transaminase (ALT), Total Bilirubin (TBIL), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP) and Triglyceride (TG) of liver enzyme indexes of rats in a control group and corresponding rats in a low dose and high dose group respectively in six-week modeling of a liver injury model of a tripterygium glycosides rat;
fig. 2 is a graph of respective processing results of liver tissue HE staining pathological sections of a tripterygium glycosides rat liver injury model in one embodiment of the present invention, including a 2A high dose one-week group, a 2B high dose two-week group, a 2C high dose three-week group, a 2D high dose four-week group, a 2E high dose five-week group, a 2F high dose six-week group, a 2G low dose one-week group, a 2H low dose two-week group, a 2I low dose three-week group, a 2J low dose four-week group, a 2K low dose five-week group, a 2L low dose six-week group, a 2M high dose control group, and a 2N low dose control group;
FIGS. 3A and 3B are total ion flow diagrams for chromatography in positive and negative ion modes, respectively, in accordance with an embodiment of the present invention;
FIGS. 4A and 4B are the OPLS-DA score plots for the pre-dose control group and the first to fifth week high dose group administered in the positive ion test mode (4A) and the negative ion test mode (4B), respectively (●: pre-dose control group; ■: one week group administered; tangle-solidup: two week group administered;
three week group of administration; solid: the four week group was administered;
five week group administration);
FIG. 5A is the OPLS-DA score chart of the control group and the high dose group under the positive ion mode (● represents the control group, and a-solidup represents the high dose group), and 5B is the result of 100 replacement verifications of the PLS-DA model under the positive ion mode of the control group and the high dose group;
FIG. 6A is the OPLS-DA score plot of the control group and the high dose group under the negative ion mode ((● represents the control group, a-solidup represents the high dose group), and 6B is the result of the PLS-DA model under the negative ion mode of the control group and the high dose group after 100 times of replacement test;
FIGS. 7A and 7B are ROC curves for 13 potential biomarkers of drug-induced liver injury, 7A is a ROC curve for 4 up-regulated metabolites in a drug-induced liver injury model, and 7B is a ROC curve for 9 down-regulated metabolites in a drug-induced liver injury model;
FIGS. 8A and 8B show the content change of metabolites in 2, which are up-regulated in serum of rats in the drug-induced liver injury model, FIG. 8A shows the content change of 2-methyl-3-pentanoic acid, and FIG. 8B shows the content change of acetoacetylglycine;
FIGS. 9A, 9B and 9C show the content change of 3 down-regulated metabolites in the serum of rats in the drug-induced liver injury model, wherein 9A shows the content change of LPC (22:6), 9B shows the content change of LPC (20:2) and 9C shows the content change of 3-indolebutyric acid;
FIG. 10 is a visualization of the results of pharmacological liver injury-related metabolic pathway analysis;
FIG. 11 is a volcanic plot of differentially expressed genes in another embodiment of the invention (gray spots: genes with P > 0.05; green spots: genes with FC <2, P ≦ 0.05; red spots: significantly upregulated differential genes with FC ≧ 2, P ≦ 0.05; blue spots: significantly downregulated differential genes with FC ≦ 2, P ≦ 0.05);
FIG. 12 is a graph of differential gene stratification between a pre-dose control group (BK) and a high dose fourth week group (H4) according to another embodiment of the present invention.
FIG. 13 shows 20 biological pathways involved in up-regulating differentially expressed genes in another embodiment of the invention;
FIG. 14 is a drawing of another embodiment of the invention showing the downregulation of 20 biological pathways associated with differentially expressed genes;
FIG. 15 is a schematic correlation diagram of a small biological molecule and a differential gene in the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
Based on the interdisciplinary characteristics of metabonomics and lncRNA gene chip technology, the obtained metabolite biomarker and gene biomarker have the following advantages: 1. the biomarker was validated efficiently by the metabolic pathway lncRNA → mRNA → metabolic end products. 2. The change of the biomarker occurs before the drug-induced liver injury, and compared with the hysteresis of the traditional index, the change of the biomarker has the function of early warning. 3. The method is favorable for reducing the morbidity and the fatality rate of the drug-induced liver injury, provides important basis for screening, prevention intervention, individualized prevention and treatment and accurate medication of clinical drug-induced liver injury high risk groups, is favorable for improving the current situation of prevention and control of the drug-induced liver injury in China, and effectively relieves the double burdens of organisms and economy of patients.
The invention provides a biomarker for early discovering and early warning of drug-induced liver injury, wherein the biomarker is a biological small molecule or a differential gene, and the biological small molecule comprises: ethylene acetyl glycine, 2-methyl-3 pentanoic acid, 3-indolebutyric acid, LPC (20:2), and LPC (22: 6).
In one embodiment of the present invention, preferably, the differential gene includes: alcohol dehydrogenase ADHs genes and dopa decarboxylase DDC genes NM-001270852.1/NM-001270853.1/NM-012545.4, the alcohol dehydrogenase ADHs genes include Adh4 NM-017270.2, Adh6 NM-001012084.1 and Adh7 NM-134329.1.
In one embodiment of the present invention, preferably, the drug-induced liver injury is liver injury caused by tripterygium glycosides.
The invention provides a method for early discovering and early warning drug-induced liver injury, which detects the concentration of a sample biomarker, the biomarker is a biological small molecule or a differential gene, the biological small molecule comprises ethylene acetyl glycine, 2-methyl-3 pentanoic acid, 3-indolebutyric acid, LPC (20:2) and LPC (22:6), when the concentration of the biological micromolecules is detected, quantitative detection is carried out by using a liquid chromatography tandem mass spectrometry, and if the ethylene acetyl glycine is increased by more than 4 times than the normal concentration, the 2-methyl-3 pentanoic acid is increased by more than 1.5 times than the normal concentration, the 3-indolebutyric acid is reduced by less than 0.65 time than the normal concentration, the LPC (20:2) is reduced by less than 0.7 time than the normal concentration, and the LPC (22:6) is reduced by less than 0.7 time than the normal concentration, the sample is judged to have drug-induced liver injury.
In the above embodiment, preferably, when the sample contains ethylene acetyl glycine at a concentration 4.07 times or more higher than the normal concentration, 2-methyl-3-pentanoic acid at a concentration 1.55 times or more higher than the normal concentration, 3-indolebutyric acid at a concentration 0.67 times or less lower than the normal concentration, LPC (20:2) at a concentration 0.71 times or less lower than the normal concentration, and LPC (22:6) at a concentration 0.73 times or less lower than the normal concentration, it is determined that the sample has a drug-induced liver injury.
In one embodiment of the present invention, preferably, the differential genes include an alcohol dehydrogenase ADHs gene and dopa decarboxylase DDC genes NM _001270852.1/NM _001270853.1/NM _012545.4, the alcohol dehydrogenase ADHs genes include Adh4NM _017270.2, Adh6NM _001012084.1 and Adh7NM _134329.1, and when the expression level of the differential genes in a sample is detected, if the expression level of Adh4 gene is 4.2 times or more of the normal value, the expression level of DDC gene is 1.2 times or more of the normal value, the expression level of Adh6 gene is 47 times or more of the normal value, and the expression level of Adh7 gene is 0.8 times or more of the normal value, the sample is determined to have drug liver damage.
In one embodiment of the present invention, preferably, the drug-induced liver injury is liver injury caused by tripterygium glycosides.
In the above protocol, preferably, when the concentration of the biomolecular substance is detected, the sample is animal serum.
In the above-mentioned aspect, preferably, when the expression amount of the sample-specific gene is detected, the sample is an animal liver tissue.
The biomarker is applied to scientific research (such as establishment and evaluation of a drug liver injury animal model, screening and evaluation of drug or compound hepatotoxicity and the like) and preparation of a drug liver injury diagnostic kit or diagnostic equipment.
In one embodiment of the present invention, preferably, in the application, the pharmaceutical liver injury diagnosis kit or diagnosis device is applied to diagnosis of liver injury caused by tripterygium glycosides.
In order that those skilled in the art will better understand the present invention, the following examples are now provided for illustration:
example 1
Serum metabonomics research of tripterygium glycosides induced liver injury rat model
2 materials and methods
2.1 instruments and materials
Dai' an
A series of ultra high performance liquid chromatographs (ultra high performance liquid chromatography, UHPLC, California, USA), Q-active quadrupole-orbitrap high-resolution tandem mass spectrometers (Thermo Scientific, Waltham, MA, USA), and other equipment and reagents commonly used in the art. Tripterygium glycosides tablets (batch No. 150302; specification: 10mg x 50 tablets/bottle, Fujian Hui Natural biopharmaceutical industry Co., Ltd.);
experimental animals: 114 Wistar rats with the age of 6-8 weeks, the weight of 160-180 g, the number of licenses: SCXK (Jing) 2012 and 0001.
2.2 sample Collection and preparation
2.2.1 rat liver injury model establishment and sample Collection
The liver injury model of Tripterygium glycosides rat can be established successfully by continuous gavage for 42 days at a dosage of 18.9 mg/kg. The rat administration was divided into three groups: a normal control group, a low dose group (9.5mg/kg, converted to 1.5mg/kg according to the adult clinical maximum dose), and a high dose group (18.9 mg/kg). And is specifically divided into: low dose first to sixth week groups (L1-L6); high dose first to sixth week groups (H1-H6); the administration of the drug was performed in a blank group (BK0) and week-to-week control groups (BK1 to BK6), and the total number of rats was 6 per group, 114.
Weekly corresponding control and high and low dose groups of rats were sacrificed on days 7, 14, 21, 28, 35 and 42 of the dosing period, respectively. Each rat was bled 4mL from the inferior vena cava and centrifuged at 3500rpm for 10min at 4 ℃. Dividing the separated serum into three parts, storing the first part at 4 ℃ for serum biochemical index detection, storing the second part at-80 ℃ for serum metabolome detection, and storing the second part at-80 ℃ for later use. Completely separating liver, weighing, dividing liver tissue into three parts, fixing the first part with 10% neutral formaldehyde solution to make liver tissue pathological section, freezing the second part with liquid nitrogen for differential gene expression analysis, and freezing the rest at-80 deg.
2.2.2 preparation of samples
The frozen serum samples at-80 ℃ were thawed at 4 ℃ and vortexed at 2500rpm for 3 min. Precisely sucking 40 μ L of serum sample, adding 300 μ L of glacial acetonitrile, vortexing at 2500rpm for 5min to mix thoroughly, centrifuging at 4 deg.C at 10000rpm for 10min, and precipitating protein. The supernatant was aspirated at 200. mu.L, and concentrated by centrifugation at 36 ℃ for 1h to dryness. Before sample loading detection, adding 200 μ L of 2% acetonitrile-containing water solution into the centrifuged and concentrated sample for redissolution, performing vortex mixing at 2500rpm for 3min to completely dissolve the sample, centrifuging at 4 ℃ at 10000rpm for 10min, collecting supernatant, filtering with 96-well plate, and performing UPLC-MS/MS analysis.
2.3 sample analysis test conditions
2.3.1 chromatographic conditions
A chromatographic column: waters ACQUITY UPLC HST 3(1.8 μm, 2.1X 100mm), and protective column ACQUITY UPLC HST 3VANGUARD (1.8 μm). Column temperature: at 30 ℃. Flow rate: 0.25 mL/min. Sample introduction volume: 5 μ L. Mobile phase: a is an aqueous solution containing 0.1% formic acid; b is acetonitrile; linear gradient elution, using initial mobile phase equilibrium chromatographic column for 8min before sample injection, and the specific gradient elution conditions are as follows:
TABLE 1-1 gradient elution conditions
Mobile phase A: water, 0.1% formic acid; mobile phase A: 0.1% formic acid in water;
mobile phase B: acetonitrile; mobile phase B: acetonitrile.
2.3.2 Mass Spectrometry conditions
The ion source is a heating ESI source and adopts a positive and negative ion detection mode for detection. The various gas circuits used were nitrogen.
The positive ion detection mode mass spectrometry parameters were as follows:
the parameters of the negative ion detection mode mass spectrum are as follows:
2.4 statistical treatment of enzymology indices
Statistical analysis of serum biochemical results was performed using SPSS16.0 software for weekly control, low dose and high dose groups of rats, and one-way ANOVA was used for comparisons between groups, with P <0.05 indicating that the differences were statistically significant.
2.5 multivariate statistical analysis data processing
2.5.1 data preprocessing
And respectively adopting LC-MS/MS methods under positive and negative ion detection modes to analyze serum samples of rats in a normal control group and rats in a liver injury group. And aiming at the obtained LC-MS/MS original Data, converting the Data file in the original raw format into an mzXML format through a Mass Matrix MS Data FileConversion software. Then leading the data into open source data processing software XCMS for peak identification, peak filtration and peak alignment to obtain a two-dimensional data array comprising mass-to-charge ratio (m/z), retention time and peak area [21-22] . 2.5.2 multivariate statistical analysis
The two-dimensional data array was input into multivariate statistical analysis software SIMCA-P13.0 (VersionAB,
sweden), before data analysis, mean-centering and pareto-scaling treatments were performed to eliminate the effect of noise and false peaks on data analysis. A pattern recognition model is established by using orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (partial least squares-discriminant analysis,PLS-DA) was performed on the established model for cross-validation analysis.
2.5.3 screening for differential metabolites
All differential variables contributing to the packet (VIP >1.0) were picked by VIP (variable import on project) values and S-plot load maps of the variables in the OPLS-DA model. And performing reliability verification through a confidence interval with Jack-knit, and deleting two groups of variables with seriously overlapped data by using an original variable profile. The differences between the groups of the screened variables are guaranteed to have statistical significance by carrying out t-test (P <0.05) on the difference variables between the groups. Further calculating a second-level partial correlation coefficient between the difference variables by using Pearson co-correlation analysis; and for the variable with the correlation coefficient r larger than 0.8, removing isotope ions, additive ions or fragment ions by combining the CAMERA analysis result and the extracted ion flow chromatogram or the accurate mass number and retention time of the ions, and obtaining accurate molecular ions.
2.6 structural identification of potential biomarkers
Through high-resolution MS and MS/MS spectrum analysis, isotope abundance ratio and mass spectrum cracking characteristics, combined with network database retrieval: the structural identification of the differential metabolites found was carried out with HMDB (http:// www.hmdb.ca /), Massbank (http:// www.massbank.jp /), METLIN (http:// METLIN. script. edu /) and KEGG (http:// www.kegg.jp /). And finally determining the structure of the differential metabolite through the retention time and MS/MS spectrum comparison analysis with the standard.
3 results and discussion
3.1 enzymological index analysis
The progression of liver injury in rats is measured by measuring the common clinical liver function indices of glutamic-oxaloacetic transaminase (AST), glutamic-pyruvic transaminase (ALT), Total Bilirubin (TBIL), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP) and Triglyceride (TG). FIGS. 1A-1F show the change in liver enzyme index of weekly control rats and corresponding low dose and high dose model rats during the six week molding process. As shown in the figure, the AST index increased in both the low and high dose groups from the first week to the fourth week compared to the control group; the elevated AST index in the first and fourth weeks of the high dose group was statistically significant; the AST index was significantly increased from week two to week four in the low dose group; both dose groups showed no significant change at week five and week six. ALT index also showed a trend of elevation in the low and high dose groups in the first and second weeks, and the elevation of ALT in both the low and high dose groups in the first week was statistically significant; the ALT change plateaued from week three to week six and tended to be reduced compared to the control group, which may be related to the animal's self-tolerance. The TBIL index was significantly elevated in the third week high dose group and the ALP index was significantly elevated in the first week high dose group. The GGT and TG indicators did not change significantly from week one to week six. The change of liver enzyme indexes AST and ALT in serum is basically consistent with the literature report.
3.2 histopathological results analysis
The HE staining pathological section of rat liver injury caused by Tripterygium wilfordii multiglycoside can be seen in FIG. 2. According to the literature report, the liver injury caused by tripterygium glycosides is time-dependent and dose-dependent. As can be observed from fig. 2A to 2N, the liver tissues of the rats in the control group after six weeks of molding had no significant pathological changes, the liver lobules and cell structures were normal, and the liver cords were regularly arranged; the high dose group showed liver cell swelling from the first week to the fourth week, and most of the liver cells showed vesicular steatosis. In the high dose groups at week five and week six, there was a disorder of hepatic cord structure, infiltration of inflammatory cells in hepatic lobules and regions of the sink, and steatosis in hepatic cells. The liver cell structure of the low dose group from the first week to the fourth week is basically normal, and fat change appears in individual cells in the visual field; the hepatocytes that appeared fatty in the fifth and sixth weeks were slightly more abundant than in the first four weeks. In summary, the high dose group caused significant damage to rat livers over the six week molding time, whereas the low dose group had substantially normal rat livers with mild changes at the fifth and sixth weeks as dosing time increased.
3.3 evaluation of data reliability
In the positive and negative ion detection mode, LC-MS detection was performed on serum samples of high and low dose groups of rats with liver injury and control groups of rats, and total ion mobility chromatograms (TICs) are shown in fig. 3A and 3B. Many metabolites in the serum sample can be well separated, the retention time is within 30min, and the method is suitable for the detection and analysis requirements of large samples.
3.4 profiling of metabolites in serum
A total of 4019 variables were obtained in the positive ion detection mode and 2064 variables were obtained in the negative ion detection mode. And respectively importing the two-dimensional data arrays obtained in the positive and negative ion modes into SIMCA-P software for multivariate statistical analysis.
Pathological section and serological enzymology results are synthesized, the rats in the high-dose group are prompted to have obvious liver damage in six weeks of administration, and data of the first five weeks are selected for multivariate analysis. In order to more clearly observe the change tracks of the whole profiles of serum metabolites of rats with liver injury caused by tripterygium glycosides in five weeks before and after administration, samples of a control group and a high-dose group before administration are selected and are simultaneously subjected to OPLS-DA model analysis. The results of the OPLS-DA analysis obtained from LC- (+) ESI/MS spectra shown in fig. 4A and 4B indicate that 6 sample groups of the pre-administration control group and the first to fifth week high dose groups achieved more distinct clustering and grouping, wherein the samples administered in the fourth and fifth week groups were more divergent from the control group than the samples administered in the first to third week groups, indicating that the pathological lesions gradually worsened with the time of liver injury. The same statistical analysis was performed on the data arrays obtained from the LC- (-) ESI/MS (FIG. 4B) spectra, and the results showed that more obvious clustering and grouping could be obtained among six groups.
After the whole metabolic profile analysis is carried out on the serum samples of the rats of the control group and the high-dose group before the administration, the high-dose group is found to have obvious disturbance on the metabolic level after the first week of the administration, and the disturbance is consistent with the observation result of pathological tissues. Therefore, the LC-MS spectrum data of all samples in the high-dose group and all samples in the control group are selected for OPLS-DA multivariate statistical analysis in the later period, and the potential biomarkers which can be used for early discovery of the drug-induced liver injury can be searched beneficially.
3.5 Pattern recognition model building and verification
PLS-DA and OPLS-DA models are selected for supervised data analysis, and displacement verification (displacement test) is adopted to avoid overfitting of the PLS-DA models so as to prove the reliability of the models. And further screening the differential metabolites by combining a variable VIP value in an OPLS-DA model, a load graph with a jack-knit confidence interval and an original variable profile graph.
In order to find out the differential metabolites in the serum of rats in the control group and the high-dose group, the LC- (+/-) ESI/MS data of the serum sample is subjected to supervised data analysis by using an OPLS-DA model in the research. As shown in FIG. 5A, the OPLS-DA model obtained from data obtained from LC- (+) ESI/MS spectra contained 1 predicted component and 2 orthogonal components, with a variable [ R ] of 46.2% 2 (X)]Can be used to explain 74.5% difference between groups [ R ] 2 (Y)]The prediction capability is 52.2% through cross validation [ Q ] 2 Y]。R 2 (Y) and Q 2 The difference between Y is 0.223 (generally not more than 0.2-0.3), Q 2 The Y value is more than 50%, which indicates that the model prediction capability is good. In order to avoid model overfitting, the PLS-DA model with the same number of main components as the OPLS-DA model is subjected to displacement verification, and FIG. 5B shows R obtained by performing displacement verification on the PLS-DA model after 100 times of modeling 2 Y (Green circle) and R of the real model 2 The Y value together forms a regression line with intercept of 0.375 (theoretically less than 0.3-0.4), Q 2 Y (blue squares) and Q of the real model 2 The Y values together form a regression line intercept of-0.192 (ideally less than 0.05).
The OPLS-DA model (FIG. 6A) from the data obtained by LC- (-) ESI/MS spectroscopy contained 1 predicted component and 2 orthogonal components, with 61.5% variance [ R ] 2 (X)]Can be used to account for 64.5% difference between groups [ R 2 (Y)]The prediction capability is 49.4% [ Q ] after cross validation 2 Y]。R 2 (Y) and Q 2 The difference between Y is 0.151. Performing permutation verification on the PLS-DA model with the principal component number of 3, and obtaining R through 100 modeling permutation verification results in FIG. 6B 2 R of Y (green circle) and true model 2 The Y value together forms a regression line intercept of 0.388, Q 2 Y (blue square) and Q of real model 2 The Y values together form a regression line intercept of-0.227.
The verification results show that after multivariate statistical analysis is carried out on data results obtained by the serum samples in the positive and negative ion detection modes, the data model meets the relevant parameter standards, and potential biomarkers can be effectively screened.
3.6 screening of potential biomarkers
(1) This study used VIP list in combination with S-plot load mapping to pick all differential variables contributing to the packet. Firstly, preliminarily screening difference variables through an S-plot load diagram; and combining the VIP list, selecting a variable with a VIP value larger than 1, and considering that the contribution of the variable to the model grouping is higher than the average level when the VIP value is larger than 1 [31] And further verifying the reliability of the variables through a jack-knit confidence interval, and deleting the variables crossing zero values. Through the screening steps, 252 differential variables are obtained in the positive ion detection mode, and 170 differential variables are obtained in the negative ion detection mode.
(2) Carrying out independent sample t test on the detection intensity (peak area) of the difference variable in a control group and a high-dose group, and deleting the variable with the difference of no statistical significance (P >0.05) between the two groups; variables with heavily crossed data between groups were deleted in conjunction with the original metabolic profile. Through the screening, 152 differential variables were obtained in the positive ion detection mode and 129 differential variables were obtained in the negative ion detection mode.
(3) By manually extracting the difference variable chromatographic peaks in the original data chromatogram, the accuracy of the result is verified, unreliable difference metabolites are eliminated, 84 difference variables are obtained in a positive ion detection mode, and 38 difference variables are obtained in a negative ion detection mode.
(4) And further carrying out Pearson co-correlation analysis on the difference variables, and calculating the secondary partial correlation coefficient of each difference variable. And for variables with the correlation coefficient R larger than 0.8, judging whether the ions with the same retention time are from the same metabolites or not by combining the extracted ion current chromatogram or the accurate mass number and retention time of the ions and the information given by the CAMERA method in the R language, and removing the adduct ions, the isotope ions and the fragment ions of the same metabolites to obtain accurate molecular ions. Through the screening, 30 differential variables are finally obtained in the positive ion detection mode, 25 differential variables are obtained in the negative ion detection mode, and detailed information can be shown in tables 1-2.
Tables 1-2 potential biomarkers for control and high dose groups of liver injury in positive and negative ion mode
a.Fold change was calculated by the ratio of mean value of control group to high dose group.
3.7 structural identification of potential biomarkers
For structural identification of potential biomarkers, [ M + H ] of potential biomarkers is first determined] + Or [ M-H] - Molecular ions, which are preliminarily conjectured with molecular compositions of possible biomarkers by combining the accurate mass number of the molecular ions with an isotopic abundance ratio method; the possible structures of the possible biomarkers are deduced by combining the high-resolution MS and MS/MS spectrums of the metabolites, the mass spectrum cracking characteristics of the metabolite structures and the search of the online metabolite database (HMBD, Metlin, KEGG and the like).
The analysis of the other possible biomarkers found in this study using the methods and ideas described above identified the structures of 13 potential biomarkers, including: picolinic acid, 2-methyl-3-pentanoic acid, histidinol, acetoacetylglycine, glutamic acid, 7-methylguanine, 5-hydroxytryptamine, 3-indolebutyric acid, gentisic acid, and four lysophosphatidylcholines LPC (18:2), LPC (20:3), LPC (20:2) and LPC (22:6), for specific information see tables 1-3. The remaining biomarkers with undetermined structure were either not able to obtain the desired MS/MS profile, mainly due to their low serum content, or not enough information was available in the metabolite database to confirm their structure.
3.8 diagnostic sensitivity and specificity evaluation of potential biomarkers
The diagnostic performance of the identified 13 potential biomarkers was evaluated in this study using receiver operating characteristics curve (ROC). The area under the ROC curve (AUC) may reflect the magnitude of the diagnostic index's ability to distinguish between positive and negative diagnoses. A greater AUC value indicates greater diagnostic accuracy, generally considered less diagnostic value when 0.5< AUC < 0.7; at 0.7< AUC <0.9, diagnostic value is moderate; when AUC is more than or equal to 0.9, the diagnostic value is higher.
ROC curve analysis was performed using SPSS16.0 software, fig. 7A is a ROC curve of 4 potential biomarkers that were upregulated in liver injured rat serum, where AUC of acetoacetylglycine and 2-methyl-3-pentanoic acid were 0.840 and 0.727, respectively, indicating that they have certain diagnostic value; FIG. 7B is a ROC curve of 9 potential biomarkers of down-regulation in serum of rats with liver damage, wherein the AUC of 3-indolebutyric acid, LPC (20:2) and LPC (22:6) are 0.828, 0.735 and 0.715 respectively, which shows that the diagnosis of the potential biomarkers of the drug-induced liver damage has certain accuracy.
The 5 potential markers were further analyzed for changes in rat serum in the normal control group and the low dose group. From the pathological results, the low dose group showed no significant pathological changes in the first to fourth weeks, while the fifth week showed a small amount of cell lipogenesis, thus comparing the ALT marker, which is more sensitive in association with liver damage, with the 5 potential markers present in the low dose groups in the first to fifth weeks. FIGS. 8A and 8B are graphs showing the changes in the levels of two metabolites that were upregulated in the serum of low dose rats. Compared with a control group, the content of the acetoacetyl glycine and the 2-methyl-3-pentanoic acid in the low-dose group in the first to fifth weeks is obviously increased, while the ALT index is not changed obviously; the result shows that the acetoacetylglycine and the 2-methyl-3-pentanoic acid are more sensitive than ALT indexes when the liver injury occurs, can give an early warning before the liver parenchyma injury occurs, and can be potential biomarkers found in the early stage of the drug liver injury.
FIGS. 9A, 9B, and 9C are graphs showing the change in serum levels of three metabolites that were down-regulated in the low dose group rat model. Compared with a control group, the content of 3-indolebutyric acid, LPC (20:2) and LPC (22:6) in the fourth and fifth weeks of low dose shows obvious reduction, and ALT index is not changed remarkably, which prompts that the combination of the three metabolites can more effectively warn liver injury and can become a potential biomarker discovered in the early stage of drug-induced liver injury.
3.9 analysis of metabolic pathways associated with drug-induced hepatic injury
In order to further analyze the metabolic pathways possibly involved in the mechanism of the occurrence and development of the drug-induced liver injury, 13 discovered potential biomarkers are used for carrying out relevant metabolic pathway analysis. MetabioAnalyst 3.0(www.metaboanalyst.ca) is an online website dedicated to provide comprehensive data analysis for metabolomics, in which a pathway analysis module (pathway analysis module) is based on Kyoto Encyclopedia of Genes and Genes (KEGG) metabolic pathway database, and can integrate the results of enrichment analysis (enrichment analysis) and pathway topology analysis (pathway topology analysis), determine the metabolic pathway most relevant to the research results, and visualize it for visual analysis.
Inputting English names of 13 identified potential drug-induced liver injury biomarkers into a metabolic pathway analysis module, matching with standard compound information in a database such as KEGG and the like, selecting a Rattus norvegicus (Rat) pathway database to be analyzed for searching, and finally finding out that 13 metabolic pathways are possibly related to the occurrence mechanism of drug-induced liver injury and relate to glutamate metabolism, nitrogen metabolism, lipid metabolism, histidine metabolism, methyl butyrate metabolism, alanine, aspartic acid and glutamic acid metabolism, glutathione metabolism, glycerophospholipid metabolism, tryptophan metabolism, tyrosine metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis and porphyrin and chlorophyll metabolism, and the results can be shown in tables 1-4.
The results of visualization of metabolic pathways are shown in fig. 10, where the smaller the P value, the darker the node color; the larger the metabolic Pathway Impact value (PI), the larger the radius of the node, and the greater the correlation between the Pathway represented by the node and the drug-induced liver injury. Of the 13 relevant metabolic pathways found, there were a total of 4 pathways with an impact value PI >0.1, namely glutamate metabolism, alanine, aspartic acid and glutamic acid metabolism, lipid metabolism, tryptophan metabolism, indicating that these 4 pathways are closely related to the occurrence of drug-induced liver injury.
TABLE 1-4 Metabolic pathway analysis of potential biomarkers for drug-induced liver injury
a.The raw p was original p value derived from enrichiment analysis.
4 small knot
The study adopts a metabonomics method based on LC-MS/MS to analyze the serum of the tripterygium glycosides induced liver injury model rat and a normal control rat. Establishing an OPLS-DA pattern recognition model through multivariate statistical analysis; 55 different metabolites were obtained from the sera of rats in the high dose liver injury group and rats in the normal control group by a series of screens such as VIP list, Jack-knit confidence interval, etc. Analysis of the structure of the potential biomarkers by high resolution MS spectroscopy and MS/MS spectroscopy identified the structure of 13 distinct metabolites, 4 of which: 2-methyl-3-pentanoic acid, L histidinol, acetoacetylglycine and glutamic acid are obviously upregulated in the serum of rats with liver injury; 9 metabolites: down-regulation of picolinic acid, 7-methylguanine, 5-hydroxytryptamine, 3-indolebutyric acid, gentisic acid and four lysophosphatidylcholines LPC (18:2), LPC (20:3), LPC (20:2) and LPC (22:6) occurred. The ROC curve is adopted to analyze the diagnosis sensitivity and specificity of the differential metabolite, and the 5 metabolites of the ethylene acetyl glycine, the 2-methyl-3 pentanoic acid, the 3-indolebutyric acid, the LPC (20:2) and the LPC (22:6) are found to have certain diagnosis value as potential biomarkers for early detection of the drug-induced liver injury. Further, the identified potential biomarkers are subjected to related metabolic pathway analysis, and the fact that 4 pathways of glutamate metabolism, alanine metabolism, aspartic acid metabolism, glutamic acid metabolism, lipid metabolism and tryptophan metabolism in rats with the drug-induced liver injury are obviously disordered is found, which indicates that the metabolic pathways are obviously related to the occurrence of the drug-induced liver injury.
Example 2
Liver differential gene expression profile research of tripterygium glycosides induced liver injury rat model
2 materials and methods
2.1 instruments and materials are all common in the field
2.2 Collection of samples
The sample used in this study was rat liver tissue from a liver injury model induced by Tripterygium wilfordii polyglycoside, and in this example, the rat liver tissue from example 1 was used. And selecting samples of rats in a control group and rats in a drug-induced liver injury high-dose group which are administered for the first week, the second week and the fourth week before administration for carrying out differential gene expression analysis by combining liver enzyme indexes and pathological results. 3 samples were randomly selected from 6 samples per week for gene chip detection. The number of the samples extracted is: controls BK1, BK5, BK 6; high dose first week group H1-3, H1-5, H1-6; high dose second week groups H2-4, H2-5, H2-6; high dose first week group H4-2, H4-5, H4-6. All samples were stored in liquid nitrogen prior to extraction and detection.
2.3 extraction and quality detection of liver tissue RNA
Total RNA from liver tissues was quantified using a NanoDrop ND2000, and RNA integrity and purity was checked by Agilent model 2100 bioanalyzer and 1% agarose electrophoresis. An RNA Integrity Number (RIN) greater than 7 and 28S/18S >0.7 indicates good RNA Integrity; the absorbance values of the RNA at 260nm and 280nm are measured, and the OD260/280 value is about 2.0, which indicates that the purity of the extracted RNA is higher and can be used for gene chip analysis. And after the RNA quality is qualified, marking the sample, hybridizing the chip and eluting the reference chip standard process. The extraction of total RNA of liver and the detection of gene chip expression profile are all completed by Shanghai Ouyi company.
2.4 Gene chip assay
Total liver RNA was reverse-transcribed into double-stranded cDNA, and cRNA labeled with biotin Cyanine-3-CTP (Cy3) was further synthesized. The chip used in this study was an Agilent Rat LncRNA Array. The labeled cRNA was hybridized to the chip, and the chip was scanned with an Agilent Scanner G2505C after elution to obtain the original probe signal. Raw images were processed and raw data were extracted using Feature Extraction software (version10.7.1.1, Agilent Technologies) and subjected to quantile normalization using Genespring software (version 13.1, Agilent Technologies).
2.5 statistical processing and bioinformatics analysis
And (3) carrying out statistical treatment on the normalized data, wherein the comparison among groups adopts an independent sample t test, and the difference with P less than or equal to 0.05 has statistical significance. Differential expression gene screening is carried out by using P value and Fold Change (FC) value of t test, and the screening standard is that the up-regulation or down-regulation Fold change FC is more than or equal to 2.0 and P is less than 0.05. Further carrying out unsupervised hierarchical clustering on the differential expression genes, and displaying the expression modes of the differential genes among different samples by utilizing a heat map form. KEGG enrichment analysis was performed on the differential genes to determine the biological pathways that are primarily affected by the differential genes.
2.6 fluorescent quantitative PCR assay of differentially expressed Gene mRNA
And performing PCR quantitative detection analysis on the mainly influenced differential genes on the biological pathway to obtain an accurate quantitative result. The method comprises the following specific steps:
RNA extraction: total RNA was extracted, concentration and OD260/OD280 were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA), and RNA integrity was checked by agarose gel electrophoresis.
Reverse transcription: the RNA to be tested was reverse transcribed into cDNA using HiScript II Q RT Supermix for qPCR (+ gDNA wrapper) (Vazyme, R223-01). Reverse transcription system: the first step is as follows: total RNA, 0.5. mu.g; 4 XgDNA wiper Mix, 2 ul; (ii) a Nucleic-free H 2 O to 8. mu.l, reaction procedure: 42 ℃ for 2 min. The second step is that: 5 XHiScript II Q RT Supermix II was added a 2 μ l, reaction procedure:
10min at 25 ℃, 30min at 50 ℃ and 5min at 85 ℃. The total reaction volume was 10 ul. After the reverse transcription is finished, 90 μ l of nucleic-free H is added 2 O is stored in a refrigerator at-20 ℃ for later use.
Designing a primer: the primers were designed using Roche LCPDS2 software and synthesized by Beijing Optimalaceae New Biotechnology, Inc.
Fluorescent quantitative PCR: by using
Green PCR Kit (Qiagen, Germany) in
Reactions were performed on a model 480 II fluorescent quantitative PCR instrument (Roche, Swiss). The method comprises the following steps:
Green PCR Master Mix，5μl；10μM Forward primer，0.2μl；10μM Reverse primer，0.2μl；cDNA，1μl；Nuclease-free H 2 o, 3.6. mu.l. PCR procedure: 5min at 95 ℃; 10s at 95 ℃, 30s at 60 ℃ and 40 cycles. Detecting product specificity by using a melting curve after circulation is finished: the temperature was slowly raised from 60 ℃ to 97 ℃ and fluorescence signals were collected 5 times per ℃ C.
Calculating the expression amount: and obtaining an amplification curve and a melting curve, and calculating the expression amount and the relative expression amount by adopting a 2-delta Ct method (a delta Ct (equal to Ct) target gene-Ct reference gene, a delta Ct (equal to delta Ct) experimental group sample-a delta Ct control group sample).
3 results and discussion
3.1 concentration and quality detection of Total RNA extracted from liver tissue
Table 2-1 shows the results of the total RNA concentration and quality measurements. The extraction result of the total RNA of 12 samples is good, the absorbance OD260/280 values of the total RNA are all between (2.0 +/-0.2), 28S/18S is greater than 1.5, and RIN is greater than or equal to 7, which indicates that the integrity and the purity of the RNA all meet the detection requirements of the chip.
TABLE 2-1 Total RNA concentration and quality test results
3.3 screening of differentially expressed genes
The analysis result of the gene chip is screened by using the difference times of the difference significance P value and the standardized signal value obtained by the t test, and the standard is that FC is more than or equal to 2.0 and P is less than or equal to 0.05. The expression of 1803 mRNA in the H4 group and the BK group is different after screening, wherein the expression is up-regulated 759 and down-regulated 1044. FIG. 11 is a volcano plot of differentially expressed genes, with the X-axis being the log base 2 of the fold difference and the Y-axis being the negative log base 10 of the P-value. FIG. 12 is a hierarchical cluster plot of differentially expressed genes for the pre-dose control group (BK) and the high dose fourth week group (H4).
3.4 analysis of pathways related to differentially expressed genes
Relative Pathway (Pathway) analysis is carried out on the differential gene by using a KEGG database, and the significance of the enrichment of the differential gene in each Pathway entry is calculated by using a statistical test method, wherein the significance enrichment of the differential gene in the Pathway is represented by P < 0.05.
The Pathway function analysis was performed on 1803 genes differentially expressed by enrichment analysis of KEGG database, and fig. 13 and 14 are the relevant biological pathways ranked 20 for up-and down-regulated expressed gene enrichment analysis. Enrichment results show that the up-regulated differentially expressed genes significantly participate in 17 biological pathways, wherein 3 pathways involved in amino acid metabolism are involved, including arginine biosynthesis, amino acid biosynthesis and alanine, aspartic acid and glutamate metabolism; the down-regulation of differentially expressed genes is significantly involved in 8 biological pathways, of which 1 amino acid metabolic pathway, namely tyrosine metabolism, is involved. Specific pathway information is shown in tables 2-2 and 2-3. Among them, tyrosine metabolism pathway, alanine, aspartic acid and glutamate metabolism pathway.
TABLE 2-2 17 biological pathways with significant enrichment of up-regulated differential genes
Table 2-2.20 biological pathways of related up-regulated genes
a.The description of biological pathway
b.The number of target genes in this pathway
c.Significant P values of Enrichment in pathway
d.The P values after calibration by Benjamini-Hochberg method
Tables 2-3 Down-Regulation of 8 biological pathways with significant enrichment of differential genes
a.The description of biological pathway
b.The number of target genes in this pathway
c.Significant P values of Enrichment in pathway
d.The P values after calibration by Benjamini-Hochberg method
Through the research result of prophase metabonomics, the metabolic pathway of Tyrosine metabolism is determined, and the gene of the Tyrosine metabolic pathway can be detected as an evidence.
3.5 fluorescent quantitative PCR assay of differentially expressed Gene mRNA
In combination with the results of the studies obtained in metabolomics, the expression of genes differentially expressed on the tyrosine metabolic pathway: performing PCR quantitative detection and analysis on alcohol dehydrogenase ADHs (ADH6, ADH4 and ADH7) and dopa decarboxylase DDC genes to obtain accurate quantitative results of the genes of the health group and the drug liver injury model group. Tables 2-5 show the specific names of the genes and the primer information.
Specific names and primer information of genes in tables 2 to 5
The relationship between the specific expression level and the relative expression level and the correlation fold is shown in Table 2.
TABLE 2 differential mRNA fluorescent quantitation PCR results
4 small knot
The research adopts gene chip technology combined with bioinformatics analysis to perform preliminary research on differential gene expression profiles of liver tissues of a tripterygium glycosides induced liver injury rat model. 1803 differential expression genes are detected by taking FC (fiber channel) of more than or equal to 2.0 and P of less than or equal to 0.05 as screening standards aiming at the differential expression genes between a blank control group rat and a drug liver injury group rat, wherein 759 of the differential expression genes are up-regulated and 1044 of the differential expression genes are down-regulated. Pathway (Pathway) functional analysis based on KEGG database was performed on differentially expressed genes. The enrichment result shows that: the up-regulated differentially expressed genes mainly participate in 17 biological pathways, wherein 3 pathways related to amino acid metabolism are involved, including arginine biosynthesis, amino acid biosynthesis and alanine, aspartic acid and glutamate metabolism; the down-regulation of differentially expressed genes is mainly involved in 8 biological pathways, of which 1 amino acid metabolic pathway, namely tyrosine metabolism, is involved. Further aiming at the genes concerned by the tyrosine metabolic pathway, the expression quantity in the corresponding group is obtained, mRNA fluorescent quantitative PCR verification is carried out, and a basis is provided for verifying the reliability of the biomarker.
Combining examples 1 and 2, it can be seen from fig. 15 that the biomarkers found in example 1 identified the structures of 13 potential biomarkers in total, including: picolinic acid, 2-methyl-3-pentanoic acid, histidinol, acetoacetylglycine, glutamic acid, 7-methylguanine, 5-hydroxytryptamine, 3-indolebutyric acid, gentisic acid and four lysophosphatidylcholines LPC (18:2), LPC (20:3), LPC (20:2) and LPC (22:6) involved in glutamate metabolism, nitrogen metabolism, lipid metabolism, histidine metabolism, methyl butyrate metabolism, alanine, aspartic acid and glutamic acid metabolism, glutathione metabolism, glycerophospholipid metabolism, tryptophan metabolism, tyrosine metabolism, arginine and proline metabolism, most of which are involved in amino acid metabolism; in addition, the analysis result of the metabolic pathway of the gene lncRNA shows that the metabolic pathway of amino acid is related, and the quantitative verification of the genes related to the metabolic pathway of tyrosine (ADH4 and the like) is carried out, so that the difference is confirmed; this indicates that the 13 potential biomarkers mentioned above are indeed associated with the relevant differential genes (ADH4, etc.).
Example 3
A high-sensitivity and high-throughput liquid chromatography tandem mass spectrometry quantitative animal serum biomolecule kit and preparation thereof, and a gene chip technology detection animal gene marker kit and preparation thereof, which are composed of a biomolecule standard curve with specific concentration, a quality control sample, a sample precipitation reagent, an internal standard solution and a mobile phase regulating solution; and extracting RNA and detecting the expression profile of the gene chip.
The method comprises the following steps:
(1) preparing biological small molecule (ethylene acetyl glycine, 2-methyl-3 pentanoic acid, 3-indolebutyric acid, LPC (20:2) and LPC (22:6)) stock solution, marker mother solution, a standard curve sample and a quality control sample; (2) preparing a sample precipitation reagent; (3) and (3) preparing liquid formic acid by using a mobile phase.
The method for quantitatively detecting the biological small molecules of the serum comprises the following steps:
(1) pretreatment of a serum sample: melting serum at 4 deg.C, mixing by vortex, precipitating protein with acetonitrile, adding internal standard solution, mixing by vortex, centrifuging at high speed, and collecting supernatant.
(2) Liquid phase separation:
a. by C 18 、C 8 Or cyano bond and silica gel as stationary phase;
b. mobile phase: acetonitrile; methanol; ammonium acetate; ammonium formate; isocratic or gradient elution; the flow rate is controlled. The mobile phases used are: acetonitrile; methanol; ammonium acetate; ammonium formate; reversed phase chromatography isocratic or gradient separation.
(3) Mass spectrometry: an electrospray ion source; an atmospheric pressure chemical ionization source; positive ion mode.
Quantitatively detecting the concentration of a biomarker in the serum of the liver tissue of a sample by using a liquid chromatography-tandem mass spectrometry method, and judging that the sample has drug-induced liver injury if the concentration of the biomolecule in the serum is increased by more than 4 times than the normal concentration, the concentration of 2-methyl-3-pentanoic acid is increased by more than 1.5 times than the normal concentration, the concentration of 3-indolebutyric acid is reduced by less than 0.65 time than the normal concentration, the concentration of LPC (20:2) is reduced by less than 0.7 time than the normal concentration and the concentration of LPC (22:6) is reduced by less than 0.7 time than the normal concentration when the concentration of the biomolecule in the serum is detected
Example 4
The kit for detecting animal gene marker by gene chip technology and its preparation method comprise the following steps:
(1) extraction of RNA: total RNA was quantified using a NanoDrop ND2000, and RNA integrity and purity was checked by Agilent model 2100 bioanalyzer and 1% agarose electrophoresis. (2) Preparing difference genes (ADH6, ADH4, ADH7 and DDC) and reference gene primer sequences, PCR curves and dissolution curves; (3) measuring the absorbance values of the RNA at 260nm and 280 nm; (4) gene chip analysis
The method for detecting the differential gene of the serum comprises the following steps:
(1) detection and analysis of gene chip: the chip Agilent Rat LncRNA Array used. The labeled cRNA was hybridized to the chip and after elution the chip was scanned with an Agilent Scanner G2505C to obtain the original probe signal. Raw images were processed and raw data were extracted using Feature Extraction software (version10.7.1.1, Agilent Technologies) and subjected to quantile normalization using Genespring software (version 13.1, Agilent Technologies).
(2) mRNA detection and data processing: RT-PCR combines cDNA synthesis using RNA as a template with PCR, providing a rapid and sensitive method for analyzing gene expression. When the expression level of the differential gene in the serum of the liver tissue of the sample is detected, if the expression level of the Adh4 gene is more than 4.2 times of the normal value, the expression level of the DDC gene is more than 1.2 times of the normal value, the expression level of the Adh6 gene is more than 47 times of the normal value, and the expression level of the Adh7 gene is more than 0.8 times of the normal value, the sample is judged to have the drug-induced liver injury.
Example 5
The biomarker is applied to scientific research (such as establishment and evaluation of drug-induced liver injury animal models, screening and evaluation of drug or compound hepatotoxicity and the like), preparation of drug-induced liver injury diagnostic kits or diagnostic equipment, and application of the drug-induced liver injury diagnostic kits or diagnostic equipment to diagnosis of liver injury caused by tripterygium glycosides
The number of modules and the scale of the process described herein are intended to simplify the description of the invention. Applications, modifications and variations of the biomarkers, methods and applications for early detection and early warning of liver damage of the present invention will be apparent to those skilled in the art.
While embodiments of the invention have been described above, it is not intended to be limited to the details shown, described and illustrated herein, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed, and to such extent that such modifications are readily available to those skilled in the art, and it is not intended to be limited to the details shown and described herein without departing from the general concept as defined by the appended claims and their equivalents.
Claims (1)
1. The application of the biomarker in the preparation of a drug-induced liver injury diagnostic kit is characterized in that the drug-induced liver injury diagnostic kit is applied to the diagnosis of liver injury caused by tripterygium glycosides, the biomarker is a small biological molecule, and the small biological molecule is: 2-methyl-3-pentanoic acid, 3-indolebutyric acid, LPC (20:2), or LPC (22: 6).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811510620.4A CN109666726B (en) | 2018-04-27 | 2018-04-27 | Liver injury biomarker containing biological small molecules and genes, method and application |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810390304.1A CN108588210B (en) | 2018-04-27 | 2018-04-27 | Hepatic injury biomarker, method and application comprising biological micromolecule and gene |
| CN201811510620.4A CN109666726B (en) | 2018-04-27 | 2018-04-27 | Liver injury biomarker containing biological small molecules and genes, method and application |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810390304.1A Division CN108588210B (en) | 2018-04-27 | 2018-04-27 | Hepatic injury biomarker, method and application comprising biological micromolecule and gene |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109666726A CN109666726A (en) | 2019-04-23 |
| CN109666726B true CN109666726B (en) | 2022-09-27 |
Family
ID=63610589
Family Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811510620.4A Active CN109666726B (en) | 2018-04-27 | 2018-04-27 | Liver injury biomarker containing biological small molecules and genes, method and application |
| CN201810390304.1A Active CN108588210B (en) | 2018-04-27 | 2018-04-27 | Hepatic injury biomarker, method and application comprising biological micromolecule and gene |
| CN201811509814.2A Active CN110129424B (en) | 2018-04-27 | 2018-04-27 | Biological small molecule and gene-containing liver injury biomarker, method and application |
Family Applications After (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810390304.1A Active CN108588210B (en) | 2018-04-27 | 2018-04-27 | Hepatic injury biomarker, method and application comprising biological micromolecule and gene |
| CN201811509814.2A Active CN110129424B (en) | 2018-04-27 | 2018-04-27 | Biological small molecule and gene-containing liver injury biomarker, method and application |
Country Status (1)
| Country | Link |
|---|---|
| CN (3) | CN109666726B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109164183A (en) * | 2018-09-29 | 2019-01-08 | 中国检验检疫科学研究院 | Hepar damnification relevant difference opposite sex endogenous marker and its screening technique and application |
| CN112083111A (en) * | 2019-06-12 | 2020-12-15 | 中国人民解放军总医院第五医学中心 | Non-invasive diagnosis marker for chronic drug-induced liver injury related cirrhosis and application thereof |
| CN112852943A (en) * | 2019-11-28 | 2021-05-28 | 上海益诺思生物技术股份有限公司 | Application of long-chain non-coding RNA in preparation of liver injury biomarker |
| CN112852944A (en) * | 2019-11-28 | 2021-05-28 | 上海益诺思生物技术股份有限公司 | Application of long-chain non-coding RNA in preparation of liver injury biomarker |
| CN112852942A (en) * | 2019-11-28 | 2021-05-28 | 上海益诺思生物技术股份有限公司 | Application of long-chain non-coding RNA in preparation of liver injury biomarker |
| CN112394178B (en) * | 2020-11-16 | 2022-08-05 | 首都医科大学附属北京朝阳医院 | Biomarker and kit for moxifloxacin-related liver injury and application of biomarker and kit |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104297442A (en) * | 2014-11-03 | 2015-01-21 | 天津中医药大学 | Application of endogenous small molecular substances to rapid detection of early cardiotoxicity |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5202296B2 (en) * | 2005-04-01 | 2013-06-05 | ユニバーシティ オブ フロリダ リサーチ ファウンデーション，インコーポレイティド | Biomarkers for liver injury |
| US20080213768A1 (en) * | 2006-08-17 | 2008-09-04 | Zhauhui Cai | Identification and use of biomarkers for non-invasive and early detection of liver injury |
| CN102803955B (en) * | 2009-05-21 | 2015-10-21 | 系统生物学研究所 | The new mark of hepatic injury |
| CN105861694A (en) * | 2016-05-11 | 2016-08-17 | 中南大学湘雅三医院 | Biomarker for predicating idiosyncratic liver injuries of child patients and application of biomarker |
-
2018
- 2018-04-27 CN CN201811510620.4A patent/CN109666726B/en active Active
- 2018-04-27 CN CN201810390304.1A patent/CN108588210B/en active Active
- 2018-04-27 CN CN201811509814.2A patent/CN110129424B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104297442A (en) * | 2014-11-03 | 2015-01-21 | 天津中医药大学 | Application of endogenous small molecular substances to rapid detection of early cardiotoxicity |
Non-Patent Citations (2)
| Title |
|---|
| Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity;Esperanza Gonzalez等;《Metabolomics》;20110714;第8卷(第6期);参见第997-1011页全文 * |
| 利培酮治疗精神分裂症患者血浆和尿液的代谢组学研究;蔡骅琳;《中国博士学位论文全文数据库 医药卫生科技辑》;20130215(第02期);E071-7，参见全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108588210B (en) | 2019-03-29 |
| CN109666726A (en) | 2019-04-23 |
| CN110129424B (en) | 2022-12-02 |
| CN108588210A (en) | 2018-09-28 |
| CN110129424A (en) | 2019-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109666726B (en) | Liver injury biomarker containing biological small molecules and genes, method and application | |
| Zhang et al. | Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach | |
| Shao et al. | Comprehensive analysis of individual variation in the urinary proteome revealed significant gender differences*[S] | |
| Poon | Opportunities and limitations of SELDI-TOF-MS in biomedical research: practical advices | |
| Xue et al. | A serum metabolomic investigation on hepatocellular carcinoma patients by chemical derivatization followed by gas chromatography/mass spectrometry | |
| US20070292869A1 (en) | Compositions and Methods for Analyzing Renal Cancer | |
| EP2284540A1 (en) | Method of diagnosing organ failure | |
| CN112881547B (en) | Screening method of early liver cancer diagnosis markers for liver cirrhosis and hepatitis people | |
| CN111562338B (en) | Application of transparent renal cell carcinoma metabolic marker in renal cell carcinoma early screening and diagnosis product | |
| CN108027361B (en) | Coronary heart disease biomarker and application thereof | |
| CN113960215A (en) | Marker for lung adenocarcinoma diagnosis and application thereof | |
| Hassan et al. | Metabolomics driven analysis of obesity-linked colorectal cancer patients via GC-MS and chemometrics: A pilot study | |
| Zhou et al. | Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure | |
| Bi et al. | LC/MS/MS-based liver metabolomics to identify chronic liver injury biomarkers following exposure to arsenic in rats | |
| Wessels et al. | Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities | |
| CN113406226B (en) | Method for detecting imatinib metabolite in plasma of GIST patient based on non-targeted metabonomics | |
| Zheng et al. | GC/MS-based urine metabolomics analysis of renal allograft recipients with acute rejection | |
| CN110568196B (en) | Metabolic marker related to low-grade glioma in urine and application thereof | |
| KR102627818B1 (en) | Acyl carnitines using metabolomics profiling for predicting oral cancer | |
| Liang et al. | High-throughput and multi-dimensional omics approach uncovers a huaxian capsule to ameliorate the dysregulated expression profiling of severe sepsis rats | |
| CN112147344B (en) | Metabolic marker of atherosclerotic cerebral infarction and application of metabolic marker in diagnosis and treatment | |
| CN110632231B (en) | Metabolic marker of glioblastoma in urine and use thereof in early diagnosis | |
| Zhizhong et al. | Urine metabolomics analysis of patients recovered of the Omicron variant of COVID-19 using ultrahigh-performance liquid chromatography with high-resolution mass spectrometry | |
| Pan et al. | LYZ and SST Prevent the Progression of Chronic Inflammation into Adenocarcinoma in the Stomach | |
| Tang et al. | Psoriasis Vulgaris of Blood Heat Syndrome in Plasma Based on Widely Targeted Techniques |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |