Application of reagent for detecting excrement metabolites in preparation of cerebral infarction screening kit

Abstract

The invention discloses application of a reagent for detecting excrement metabolites in preparation of a cerebral infarction screening kit, and belongs to the field of biological medicine. The invention relates to the field of organic fertilizer, in particular to an organic fertilizer which is characterized in that the organic fertilizer is prepared from excrement metabolites, and the excrement metabolites are Ganoderma acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2: 0 / PGF1alpha) and 4-[(E)-5, 6-Dihydro-2, 3 '-bipyridin-3 (4H)-ylidenem]-3-methyloxpheol. It is found for the first time that by detecting the six metabolites in excrement, the risk that a subject suffers from cerebral infarction can be effectively prompted, effective screening of cerebral infarction is achieved, and an effective basis is provided for a patient to take related treatment measures or make decisions.

Description

Technical Field

[0001] The invention belongs to the field of biomedicine, and specifically relates to the application of a reagent for detecting fecal metabolites in the preparation of a cerebral infarction screening kit. Background Art

[0002] Cerebral infarction, also known as ischemic stroke, refers to a clinical syndrome of corresponding neurological deficits caused by cerebral blood circulation disorders due to various reasons, leading to ischemic and hypoxic necrosis of local brain tissue. According to statistics from the World Health Organization, cerebral infarction is the second leading cause of death in the world and the second most common cause of disability-adjusted life years in the world. With the increasing aging of the population, acute cerebral infarction in my country has become the main cause of disability among adults and has also become an important public health safety event. In clinical work, the diagnosis of cerebral infarction mainly relies on the judgment of clinical physicians on symptoms and signs and imaging examinations, but many diseases such as migraine and epileptic seizures may have similar clinical manifestations to cerebral infarction, and objective imaging examinations also have certain limitations. In order to enable patients with cerebral infarction to receive timely and effective treatment and reduce the mortality and disability rates of cerebral infarction, it is necessary to find effective biomarkers that can assist in the early diagnosis of cerebral infarction.

[0003] As the most important functional component in the intestine, the intestinal flora has an important impact on human health and disease. In recent years, the role of the intestinal flora in the gut-brain axis has gradually been recognized, and studies have shown that there is a potential link between the metabolites of the intestinal flora and cerebrovascular diseases. Metabolomics is at the terminal of transcription, gene and protein expression, and can directly and accurately reflect the current pathophysiological state of the organism. It is widely used in disease screening, disease diagnosis, drug development, nutrition, toxicology, sports medicine and other fields, especially to provide reliable theoretical basis and means for clinical disease screening and diagnosis. Studying metabolites that show significant differences in feces and finding fecal markers are of great significance for the early screening and diagnosis of cerebral infarction. Summary of the invention

[0004] The purpose of the present invention is to evaluate the correlation between fecal metabolites and cerebral infarction. By collecting fecal samples from healthy controls and patients with cerebral infarction, comprehensively analyzing the metabolomics of the samples, screening metabolites with significant differences in content between the two groups, and further analyzing the diagnostic efficacy of the differential metabolites, the present invention provides the use of reagents for detecting metabolites in the preparation of cerebral infarction screening reagents or kits.

[0005] The present invention provides the use of a reagent for detecting metabolites in preparing a cerebral infarction screening reagent or a kit, wherein the metabolites are Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol;

[0006] Among them, the structure of Ganoderic acid theta is

[0007]

[0008] The structure of Fructose-lysine is The structure of Pentaethylene glycol is

[0009]

[0010] The structure of 2-Chlorooctadecanoic acid is

[0011]

[0012] The structure of PA(2:0 / PGF1alpha) is

[0013]

[0014] The structure of 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol is

[0015]

[0016] Furthermore, the reagents for detecting metabolites include reagents for chromatography, spectroscopy, mass spectrometry or chemical analysis.

[0017] Furthermore, the mass spectrometry is chromatography-mass spectrometry.

[0018] Furthermore, the mass spectrometry is liquid chromatography-mass spectrometry.

[0019] Furthermore, the reagent for detecting metabolites is a reagent for ultra-high performance liquid chromatography tandem Fourier transform mass spectrometry detection.

[0020] Furthermore, the metabolites are fecal metabolites.

[0021] The present invention also provides a kit for screening cerebral infarction, the kit comprising reagents for detecting metabolites in a sample, wherein the metabolites are Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol.

[0022] Furthermore, the sample is feces.

[0023] Furthermore, the kit also includes instructions for use of the kit.

[0024] Furthermore, the kit also includes reagents for processing samples.

[0025] The present invention also provides a cerebral infarction prediction system, characterized in that it comprises the following modules:

[0026] 1. Data input module

[0027] Used to input characteristic data of patients with cerebral infarction, wherein the characteristic data are the expression levels of metabolites Ganoderic acidtheta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol;

[0028] 2. Model building module

[0029] A model for predicting patients with cerebral infarction is constructed using the characteristic data of the input module; the model is Y=1 / (1+EXP(-0.065*GAT-4.173*PG-0.463*ED-1.075*FL+8.998*CA-1.307*PA(2:0 / PGF1alpha)-16.584)), wherein GAT represents the expression level of Ganoderic acid theta, PG represents the expression level of Pentaethyleneglycol, ED represents the expression level of 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, FL represents the expression level of Fructose-lysine, CA represents the expression level of 2-Chlorooctadecanoic acid, PA(2:0 / PGF1alpha) represents the expression level of PA(2:0 / PGF1alpha), EXP represents the exponential power of e, and Y represents the probability of cerebral infarction;

[0030] 3. Prediction Module

[0031] Input the characteristic data of the patient to be predicted into the model constructed in step 2 and output the prediction result. If the Y value is greater than 0.5, the patient can be judged as a cerebral infarction patient.

[0032] Furthermore, the metabolites are fecal metabolites.

[0033] The present invention also provides a computer-readable storage medium on which the cerebral infarction prediction system as described above is stored.

[0034] The present invention has achieved the following beneficial effects:

[0035] The present invention discovers for the first time that by detecting these six metabolites in feces, it is possible to effectively indicate the risk of cerebral infarction in subjects, achieve effective screening of cerebral infarction, and provide an effective basis for patients to take relevant treatment measures or make decisions.

[0036] The method of the present invention for screening cerebral infarction by detecting the above six fecal metabolites has high accuracy and specificity, broad development prospects, and can provide important basis for risk assessment, clinical screening and diagnosis of cerebral infarction and the search for potential drug targets.

[0037] The kit of the present invention is particularly suitable for screening cerebral infarction lacking typical symptoms. Patients with cerebral infarction lacking typical symptoms are easily misdiagnosed or missed due to unclear or atypical symptoms. When investigating the causes of patients with abnormal neurological function, the kit of the present invention can be used for preliminary screening, and the preliminary screening results can provide direction and guidance for further diagnosis by clinicians. The present invention provides a new screening marker for cerebral infarction and a new screening kit for cerebral infarction, which can realize effective screening of cerebral infarction and have good application prospects.

[0038] Obviously, according to the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, other various forms of modification, replacement or change may be made.

[0039] The above contents of the present invention are further described in detail below through specific implementation methods in the form of embodiments. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples. All technologies realized based on the above contents of the present invention belong to the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 OPLS-DA statistical analysis of fecal metabolites in patients with cerebral infarction and healthy controls.

[0041] Figure 2 Figure 3 Graphs showing the levels of Ganoderic acid theta (A), Fructose-lysine (B), Pentaethylene glycol (C), ED (4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol; D), 2-Chlorooctadecanoic acid (E), and PA (2:0 / PGF1alpha) (F) in patients with cerebral infarction and healthy controls.

[0042] Figure 3The receiver operating characteristic (ROC) curve results of the cerebral infarction diagnostic model constructed based on the levels of Ganoderic acid theta, Fructose-lysine, Pentaethyleneglycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol in the training set (A) and the test set (B). DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods for which specific conditions are not specified in the examples are usually based on conventional conditions.

[0044] Ganoderic acid theta, Chinese full name: Ganoderma lucidum theta, CAS number: 294674-15-0, structure:

[0045] Fructose-lysine, Chinese full name: fructose-lysine, CAS number: 21291-40-7, structure:

[0046]

[0047] Pentaethylene glycol, Chinese full name: Pentaethylene glycol, CAS number: 4792-15-8, structure:

[0048]

[0049] 2-Chlorooctadecanoic acid, Chinese full name: 2-chlorooctadecanoic acid, CAS number: 56279-49-3, structure:

[0050] PA (2:0 / PGF1alpha), Chinese full name: phosphatidic acid (2:0 / prostaglandin F1α), structure:

[0051]

[0052] 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, Chinese full name: 4-[(E)-5,6-dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, structure:

[0053] Example 1: Cerebral infarction screening kit

[0054] The kit contains reagents for detecting the expression levels of the following six metabolites in the sample: Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and

[0055] 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol.

[0056] The kit also includes instructions for use of the kit.

[0057] Example 2: Screening and efficacy determination of cerebral infarction-related metabolites

[0058] 1. Sample Collection

[0059] 1. Fecal samples were collected from 60 patients with cerebral infarction (Stroke) and 60 healthy controls (HCs).

[0060] 2. Inclusion criteria for cerebral infarction group:

[0061] (1) Have signed the informed consent form;

[0062] (2) Meet the diagnostic criteria for acute cerebral infarction in the "Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke in China (2023 Edition)";

[0063] (3) Aged 18-70 years old;

[0064] (4) Body mass index (BMI), blood routine, blood sugar, glycosylated hemoglobin, and blood lipids were all within normal range.

[0065] 3. Exclusion criteria for cerebral infarction group:

[0066] (1) Combination with other diseases, including neurological diseases, mental diseases, cardiovascular diseases, digestive system diseases, respiratory system diseases, metabolic diseases, blood system diseases and tumors;

[0067] (2) Taking the following medications within 3 months: antibiotics, probiotics, laxatives, contraceptives, immunosuppressants, gastrointestinal mucosal protective agents, traditional Chinese medicine, antiplatelet drugs, statins, antipsychotic drugs, folic acid, and metformin;

[0068] (3) Patients undergoing intravenous thrombolysis and intravascular intervention;

[0069] (4) Pregnant or lactating women.

[0070] 4. Inclusion criteria for healthy control group:

[0071] (1) Have signed the informed consent form;

[0072] (2) Aged 18-70 years old;

[0073] (3) BMI, blood routine, blood sugar, glycosylated hemoglobin, and blood lipids were all within normal range.

[0074] 5. Exclusion criteria for healthy control group:

[0075] (1) Combination with other diseases, including neurological diseases, mental diseases, cardiovascular diseases, digestive system diseases, respiratory system diseases, metabolic diseases, blood system diseases and tumors;

[0076] (2) Taking the following medications within 3 months: antibiotics, probiotics, laxatives, contraceptives, immunosuppressants, gastrointestinal mucosal protective agents, traditional Chinese medicine, antiplatelet drugs, statins, antipsychotic drugs, folic acid, and metformin;

[0077] (3) Pregnant or lactating women.

[0078] 2. Non-targeted metabolomics testing

[0079] 1. Stool sample preparation:

[0080] (1) Accurately weigh 50 mg of sample into a 2 mL centrifuge tube and add a 6 mm diameter grinding bead;

[0081] (2) Add 400 μL of extract (methanol: water = 4:1 (v:v)) containing 0.02 mg / mL of internal standard (L-2-chlorophenylalanine);

[0082] (3) Grind in a frozen tissue grinder for 6 min (-10°C, 50 Hz);

[0083] (4) low-temperature ultrasonic extraction for 30 min (5°C, 40 KHz);

[0084] (5) Place the sample at -20°C for 30 minutes;

[0085] (6) Centrifuge for 15 min (13,000 g, 4°C), transfer the supernatant to a vial with an inner cannula and analyze on an analyzer;

[0086] (7) In addition, 20 μL of supernatant was taken from each sample and mixed to serve as a quality control sample.

[0087] 2. Non-targeted metabolomics detection:

[0088] (1) The instrument platform used was the Thermo Fisher Scientific UHPLC-Q Exactive HF-X system.

[0089] (2) Chromatographic conditions: The chromatographic column was ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA); the mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), the mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), the injection volume was 2 μL, and the column temperature was 40°C.

[0090] (3) Mass spectrometry conditions: positive and negative ion source voltages were both 3.5 kV, scanning range was 70-1050 m / z, capillary heating temperature was 325 °C, sheath gas flow rate was 50 arb, and resolution was 60,000.

[0091] The above-mentioned instruments and methods were used to perform non-targeted metabolomics detection to obtain the expression level of each metabolite in abundance.

[0092] 3. Metabolite Identification

[0093] The raw data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, peak alignment, etc., and finally a data matrix containing information such as retention time, mass-to-charge ratio and peak intensity was obtained. The software was then used to search the library for characteristic peaks, match the MS and MS / MS mass spectrometry information with the metabolic database, and set the MS mass error to less than 10 ppm. At the same time, metabolites were identified based on the secondary mass spectrometry matching score. The databases are mainstream public databases such as http: / / www.hmdb.ca / and https: / / metlin.scripps.edu / .

[0094] 3. Data Processing

[0095] 1. Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) analysis

[0096] In order to obtain the metabolite information showing significant differences between the cerebral infarction group and the healthy control group, the supervised multidimensional statistical method, namely OPLS-DA, was used to perform statistical analysis on the two groups of samples.

[0097] The VIP (Variable Importance in the Projection) value of the OPLS-DA model > 1 was used, combined with the p value of the t-test < 0.05 to find differentially expressed metabolites.

[0098] 2. Use training set data to screen biomarker combinations

[0099] A generalized linear model was used for co-occurrence analysis to further identify important differential metabolites in feces. The subjects were then divided into a training set (healthy controls, n = 40; patients with cerebral infarction, n = 40) and a test set (healthy controls, n = 20; patients with cerebral infarction, n = 20). The metabolites in the training set were input into the random forest classifier, and the classifier was cross-validated 5 times. The metabolites screened by the random forest model were used to calculate the risk of cerebral infarction for each individual, and the ROC curve was drawn. The AUC was calculated to evaluate the performance of the discriminant model and select the combination with the best discriminant performance. The importance index of each metabolite was output in the model. The higher the importance index, the higher the importance of the marker in discriminating between cerebral infarction and non-cerebral infarction.

[0100] 3. Use validation set data to verify the biomarker combination obtained by screening

[0101] The model was verified using a random independent population. If the probability of disease was ≥ 0.5, it was predicted that the individual had a risk of cerebral infarction or had cerebral infarction.

[0102] IV. Results

[0103] 1. Differences in fecal metabolites between patients with cerebral infarction and healthy subjects

[0104] OPLS-DA analysis suggested that the differential metabolites between the cerebral infarction group and the healthy control group could effectively distinguish ( Figure 1). Compared with the control group, the cerebral infarction group showed a significant increase in 96 metabolites and a significant decrease in 106 metabolites. Among these changed metabolites, generalized linear model analysis suggested that 12 metabolites (4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, Pentaethylene glycol, Fructose-lysine, Calystegine A6, Trans-4-Aminocyclohexanecarboxylic acid, Dihydrocitrinone, Cyclopentanone, Muricatin C, Estradiol benzoate, Ganoderic acid theta (GAT), PA (2:0 / PGF1alpha) and 2-Chlorooctadecanoic acid) were important metabolites involved in the occurrence of cerebral infarction.

[0105] 2. Best biomarker combination for screening / diagnosis of cerebral infarction

[0106] The random forest algorithm analysis of the above 12 metabolites in the training set samples and after correction for age, BMI and gender showed that six differential metabolites, including Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA(2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, may be independent risk factors for stroke. Figure 2 The levels of these six differential metabolites in patients with cerebral infarction and healthy control group.

[0107] The above six metabolites were used as joint markers to establish a prediction model Y = 1 / (1+EXP(-0.065*GAT-4.173*PG-0.463*ED-1.075*FL+8.998*CA-1.307*PA(2:0 / PGF1alpha)-16.584))(Note: GAT represents the expression level of Ganoderic acid theta, PG represents the expression level of Pentaethylene glycol, ED represents the expression level of 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, FL represents the expression level of Fructose-lysine, CA represents the expression level of 2-Chlorooctadecanoic acid, PA(2:0 / PGF1alpha), EXP represents the exponential power of e, and Y represents the probability of cerebral infarction. If the Y value is greater than 0.5, the patient can be judged as having cerebral infarction. If the Y value is less than 0.5, the patient is not judged as having cerebral infarction.

[0108] Figure 3 The area under the ROC curve of the model in the training set was 0.989, and the area under the ROC curve of the model in the test set was 0.973, both between 1 and 0.9, indicating that the receiver operating characteristic results of the model constructed with the levels of the six metabolites in the training set, Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, were good. It was confirmed that the combination of these six metabolites can be used as a potential biomarker to distinguish patients with cerebral infarction from healthy people, and can achieve effective screening of cerebral infarction, providing an effective basis for patients to take relevant treatment measures or make decisions.

[0109] The above results indicate that the method of the present invention for screening / diagnosing cerebral infarction by detecting the above six fecal metabolites has high accuracy and specificity, and has broad development prospects. It can provide an important basis for the risk assessment of cerebral infarction, clinical screening / diagnosis, and the search for potential drug targets.

Claims

1. Application of reagents for detecting metabolites in the preparation of cerebral infarction screening reagents or kits, Features: The metabolites are Ganoderic acid theta, Fructose-lysine, Pentaethylene glycol, 2-Chlorooctadecanoic acid, PA (2:0 / PGF1alpha) and 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol; Among them, the structure of Ganoderic acid theta is The structure of Fructose-lysine is The structure of Pentaethylene glycol is The structure of 2-Chlorooctadecanoic acid is The structure of PA(2:0 / PGF1alpha) is The structure of 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol is 2. The use according to claim 1, It is characterized in that The reagents for detecting metabolites include reagents for chromatography, spectroscopy, mass spectrometry or chemical analysis.

3. The use according to claim 2, It is characterized in that The mass spectrometry is chromatography-mass spectrometry.

4. The use according to claim 3, It is characterized in that The mass spectrometry is liquid chromatography-mass spectrometry.

5. The use according to any one of claims 1 to 4, It is characterized in that The metabolites are fecal metabolites.

6. A kit for screening cerebral infarction, It is characterized in that The kit comprises a reagent for detecting metabolites in a sample, and the metabolites are the metabolites described in claim 1.

7. The kit according to claim 6, It is characterized in that The sample is feces.

8. A cerebral infarction prediction system, It is characterized in that It contains the following modules:

1. Data input module Used to input characteristic data of patients with cerebral infarction, wherein the characteristic data is the expression level of the metabolite described in claim 1; 2. Model building module A model for predicting patients with cerebral infarction is constructed using the characteristic data of the input module; the model is Y=1 / (1+EXP(-0.065*GAT-4.173*PG-0.463*ED-1.075*FL+8.998*CA-1.307*PA(2:0 / PGF1alpha)-16.584)), wherein GAT represents the expression level of Ganoderic acid theta, PG represents the expression level of Pentaethyleneglycol, ED represents the expression level of 4-[(E)-5,6-Dihydro-2,3'-bipyridin-3(4H)-ylidenemethyl]-3-methoxyphenol, FL represents the expression level of Fructose-lysine, CA represents the expression level of 2-Chlorooctadecanoic acid, PA(2:0 / PGF1alpha) represents the expression level of PA(2:0 / PGF1alpha), EXP represents the exponential power of e, and Y represents the probability of cerebral infarction; 3. Prediction Module Input the characteristic data of the patient to be predicted into the model constructed in step 2 and output the prediction result. If the Y value is greater than 0.5, the patient can be judged as a cerebral infarction patient.

9. The cerebral infarction prediction system according to claim 8, It is characterized in that The metabolites are fecal metabolites.

10. A computer-readable storage medium storing the cerebral infarction prediction system according to any one of claims 8 or 9.

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