A metabolite and its use in the diagnosis of sle

Abstract

The application discloses a metabolite and application thereof in diagnosing SLE, and the metabolite is Benzoyl-DL-arginine-naphthylamid. The application firstly finds that the metabolite as a biomarker presents significant difference between SLE patients and healthy people, and has a higher AUC value, which indicates that the metabolite marker has higher diagnostic value in SLE, and has wide application prospect in clinic.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a metabolite and its application in the diagnosis of SLE, and more specifically, to the application of the metabolite Benzoyl-DL-arginine-naphthylamid in the diagnosis of SLE. Background Technology

[0002] Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown etiology with highly heterogeneous clinical manifestations, characterized by the production of large amounts of autoantibodies, immune complex deposition, and multi-organ system involvement. SLE commonly affects women of childbearing age, and the disease often presents as a chronic course with alternating exacerbations and remissions. Without timely intervention, it can lead to serious complications such as renal failure and neuropsychiatric lupus. Therefore, studying the pathophysiological processes of SLE and establishing a comprehensive system for its prevention, diagnosis, treatment, and prognosis is of great significance.

[0003] Metabolomics is an emerging omics technology and an important component of systems biology. It primarily studies small molecule compounds (less than 1000 molecules in mass) in the body fluids, cells, and tissues of biological organisms. The main analytical platforms include high-resolution, high-sensitivity, and high-throughput modern instruments such as chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR). By qualitatively or quantitatively studying changes in the types, quantities, and concentrations of perturbed metabolites in the body, it reveals alterations in metabolic pathways. Metabolomics is at the terminal stage of transcription, gene, and protein expression, and can directly and accurately reflect the current pathophysiological state of an organism. It has wide applications in disease diagnosis, drug development, nutrition, toxicology, and sports medicine, especially providing reliable theoretical basis and methods for clinical disease diagnosis. Studying metabolites that show significant differences in blood and searching for diagnostic biomarkers is of great significance for achieving rapid and accurate diagnosis of SLE. Summary of the Invention

[0004] In order to evaluate the correlation between metabolites and SLE, this invention collects samples from healthy controls and SLE patients, comprehensively analyzes the metabolomics of the samples, screens metabolites with significantly different levels in the two groups, and further analyzes the diagnostic efficacy of the differentially expressed metabolites, thereby discovering biomarkers suitable for the diagnosis and treatment of SLE.

[0005] Specifically, the present invention provides the following technical solution: The first aspect of this invention provides the application of a reagent for detecting the content of metabolites in a sample in the preparation of products for diagnosing systemic lupus erythematosus.

[0006] Furthermore, the metabolite is Benzoyl-DL-arginine-naphthylamid.

[0007] This invention, through extensive and in-depth research, employed metabolomics studies on blood samples from patients with systemic lupus erythematosus (SLE) and healthy controls to screen for the significantly different metabolite Benzoyl-DL-arginine-naphthylamid. Further bioinformatics analysis confirmed its ability to accurately diagnose SLE. This invention was thus completed.

[0008] In this invention, metabolites are defined as having statistical significance (i.e., p-value less than 0.05 and / or q-value less than 0.10, as indicated by Welch's T-test). (test) or Wilcoxon's rank test The levels determined by the sum test vary.

[0009] Furthermore, the PubChem CID of the metabolite Benzoyl-DL-arginine-naphthylamid is 172636206, and its molecular formula is C2. 23 H 25 N5O2, structural formula as follows Figure 4 As shown.

[0010] Furthermore, the reagents include reagents for detection by chromatography, spectroscopy, mass spectrometry, nuclear magnetic resonance, and chemical analysis.

[0011] Furthermore, the mass spectrometry method is chromatography. Mass spectrometry.

[0012] As an alternative implementation method, the chromatographic technique is gas chromatography, and the combined technique is called gas chromatography. Mass spectrometry (GC / MS, GCMS or GC) MS). As those skilled in the art will know, in this technique, a gas chromatograph is used to separate different compounds. The separated compound stream is then fed into a mass spectrometer for ionization, mass analysis, and detection as described above.

[0013] As another preferred embodiment, the chromatographic technique is liquid chromatography, and the combined technique is called liquid chromatography. Mass spectrometry (LC / MS, LCMS or LC) MS). As those skilled in the art will know, this technique uses liquid mobile phase chromatography to separate compounds. Typically, the liquid phase is a mixture of water and an organic solvent. The separated compounds are then fed into a mass spectrometer for ionization, mass analysis, and detection as described above.

[0014] In a preferred embodiment, the mass spectrometry method is tandem mass spectrometry. The tandem mass spectrometry method is selected from ion trap mass spectrometry, quadrupole time-of-flight mass spectrometry, triple quadrupole mass spectrometry, quadrupole ion trap mass spectrometry, and ion mobility spectrometry. Quadrupole Ion Traps Time-of-flight mass spectrometry, quadrupole Orbital trap mass spectrometry, ion mobility spectrometer Quadrupole ion trap mass spectrometry, triple quadrupole Orbital trap mass spectrometry, quadrupole ion trap Orbital trap mass spectrometry, time-of-flight or ion trap Fourier transform mass spectrometry.

[0015] The terms "sample" and "sample" are used interchangeably in this invention. When used in this invention, it refers to a composition obtained from or derived from a subject (e.g., an individual of interest) that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical, and / or physiological characteristics. For example, the phrase "disease sample" or variations thereof refers to any sample obtained from a subject of interest that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (e.g., tumor tissue samples), primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture medium, tissue extracts such as homogenized tissue, tumor tissue, cell extracts, and combinations thereof.

[0016] In some embodiments, the sample is selected from blood, serum, or plasma.

[0017] In a specific implementation, the sample is serum.

[0018] Furthermore, when the levels of metabolites in the subject's sample increased significantly, the subject had systemic lupus erythematosus or was at risk of developing systemic lupus erythematosus.

[0019] After separation and analysis using appropriate mass spectrometry, the metabolites identified in the subject's sample can be used to detect SLE in the subject. Typically, this step involves comparing the level of the metabolite in the subject's sample with a reference value, wherein the level of the metabolite in the sample compared with the reference value indicates SLE in the subject.

[0020] As an alternative implementation, an increase in the level of the metabolite in the sample compared to the reference value indicates SLE in the subject.

[0021] A second aspect of the present invention provides a method for constructing a diagnostic model for systemic lupus erythematosus.

[0022] Furthermore, the construction method includes the following steps: 1) Collect the results of metabolite content detection in samples from patients with systemic lupus erythematosus and healthy controls, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid; 2) Construct a diagnostic model based on the information collected in step 1).

[0023] Furthermore, the methods for constructing diagnostic models include, but are not limited to, classification and logistic regression, k-nearest neighbor algorithm, Naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO, and convolutional neural network.

[0024] As used in this article, the term "healthy control" refers to a subject or subject group who is diagnosed by a physician as not having SLE based on qualitative or quantitative test results.

[0025] A third aspect of the present invention provides the application of metabolites in constructing computational models for predicting systemic lupus erythematosus or in systems / devices incorporating said computational models.

[0026] Furthermore, the metabolite is Benzoyl-DL-arginine-naphthylamid.

[0027] The computational model employs algorithms developed and obtained through the application of statistical methods. Suitable statistical methods include discriminant analysis (DA) (i.e., linear, quadratic, and regular DA), kernel methods (i.e., SVM), and nonparametric methods (i.e., k-means analysis). Nearest neighbor classifiers, PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest, boost / bag method), generalized linear models (i.e., logistic regression), principal component analysis (i.e., SIMCA), generalized superposition models, fuzzy logic-based methods, and methods based on neural networks and genetic algorithms. Those skilled in the art will have no problem selecting appropriate statistical methods to evaluate the metabolites of the present invention and thereby obtaining suitable mathematical algorithms. In one embodiment, the statistical method used to obtain the mathematical algorithm used in evaluating SLE is selected from DA (i.e., linear, quadratic, regular discriminant analysis), Kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest, boost method), or generalized linear models (i.e., logistic regression).

[0028] The area under the receiver operating characteristic (ROC) is an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of a diagnostic method is best described by its receiver operating characteristic (ROC). The ROC plot is a line graph derived from all sensitivity / specificity pairs that continuously change the decision threshold across the entire range of observed data.

[0029] The x-axis represents the false positive score, or 1-specificity [defined as (number of false positive results) / (number of true negative results + number of false positive results)]. It is an indicator of specificity and is calculated entirely from the unaffected subgroups. Because the true and false positive scores are calculated completely separately using test results from two different subgroups, the ROC plot is independent of the prevalence of the disease in the sample. Each point on the ROC plot represents a sensitivity / 1-specificity corresponding to a specific decision threshold. A test with perfect discrimination (the two outcome distributions do not overlap) has an ROC plot through the top left corner, where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). The theoretical plot for a test that does not discriminate (the outcome distributions are the same in both groups) is a 45° diagonal line from the bottom left to the top right corner. Most plots fall between these two extremes. (If the ROC curve falls entirely below the 45° diagonal, this can be easily corrected by reversing the "positive" criterion from "greater than" to "less than" or vice versa.) Qualitatively, the closer the curve is to the top left corner, the higher the overall accuracy of the test.

[0030] The fourth aspect of this invention provides a product for diagnosing systemic lupus erythematosus.

[0031] Furthermore, the product includes a reagent for detecting metabolites in a sample, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid.

[0032] Furthermore, the product includes a reagent kit.

[0033] Furthermore, the kit also includes reagents for processing samples.

[0034] Furthermore, the kit also includes instructions for using the kit to assess whether a subject has or is susceptible to systemic lupus erythematosus.

[0035] The most reliable results are likely obtained when samples are processed in a laboratory setting. For example, samples can be obtained from subjects in a physician's office and then sent to a hospital or commercial medical laboratory for further testing. However, in many cases, it may be desirable to provide immediate results in a clinician's office or allow subjects to perform the test at home. In some cases, the need for portable, pre-packaged, disposable tests that can be used by subjects without assistance or guidance is more important than high accuracy. In many cases, especially with physician follow-up, preliminary testing, or even tests with reduced sensitivity and / or specificity, may suffice. Therefore, assays provided as kits can involve detecting and measuring relatively small amounts of metabolites, reducing the complexity and cost of the assay.

[0036] Any form of sample assay capable of detecting sample metabolites, as described herein, can be used. Typically, the assay quantifies metabolites in a sample to a certain extent, such as whether their concentration or amount is above or below a predetermined threshold. Such kits may take the form of test strips, dipsticks, cassettes, cartridges, chip-based or bead-based arrays, multiwell plates, or a series of containers. One or more reagents are provided to detect the presence and / or concentration and / or amount of selected sample metabolites. A subject's sample may be dispensed directly into the assay or indirectly from stored or previously obtained samples. The presence or absence of metabolites above or below a predetermined threshold can be indicated, for example, by colorimetric, fluorescent, electrochemiluminescent, or other outputs (e.g., as in enzyme immunoassays (EIA), such as enzyme-linked immunosorbent assays (ELISA).

[0037] In one embodiment, the kit may comprise a solid substrate such as a chip, slide, array, etc., having reagents capable of detecting and / or quantifying one or more sample metabolites immobilized at predetermined locations on the substrate. As an illustrative example, reagents immobilized at discrete predetermined locations may be provided to the chip for detecting and quantifying the presence and / or concentration and / or amount of biomarkers in a sample. As described above, elevated levels of the metabolites are found in samples from subjects with SLE. The chip may be configured to provide detectable output (e.g., a color change) only when the concentration of the metabolite exceeds a threshold, which is selected or distinguishes between the concentration and / or amount of metabolites in control subjects and the concentration and / or amount in patients with or susceptible to SLE. In one embodiment, the threshold may be a peak area of ​​39.32.

[0038] The fifth aspect of this invention provides a system / device for diagnosing systemic lupus erythematosus.

[0039] Furthermore, the system / device includes: (1) Data acquisition module, used to acquire characteristic value data of metabolites in the sample of the subject to be tested, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid; (2) Diagnostic prediction module, used to analyze and process the feature value data obtained by the data acquisition module to obtain the diagnostic prediction result of the subject; (3) Result output module, used to output the subject diagnosis prediction results obtained by the diagnosis prediction module.

[0040] A sixth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon.

[0041] Furthermore, when the computer program is executed by the processor, it performs the following operations: The characteristic value data of metabolites in the sample of the subject to be tested are obtained, and the subject is determined to have systemic lupus erythematosus by comparing the characteristic value data of metabolites with a threshold; the metabolite is Benzoyl-DL-arginine-naphthylamid.

[0042] The implementation of the methods and / or systems of this invention may include performing or completing selected tasks manually, automatically, or in combination thereof. Furthermore, the actual instruments and equipment according to the embodiments of the methods and / or systems of this invention can implement multiple selected tasks via hardware, software, firmware, or a combination thereof using an operating system. Moreover, this application can employ one or more computer-usable storage media (including but not limited to disk storage, CDs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0043] Advantages and benefits of the present invention: The present invention is the first to discover that the metabolite Benzoyl-DL-arginine-naphthylamid can be used as a biomarker for the early diagnosis of SLE. It shows strong differences between patient samples and healthy samples and has high detection sensitivity, providing a strong basis for the early diagnosis of SLE. Attached Figure Description

[0044] Figure 1 Differential expression plot of Benzoyl-DL-arginine-naphthylamid in the training set (concentration is the peak intensity); Figure 2 To validate the differential expression plot of benzoyl-DL-arginine-naphthylamid in the set (concentration is the peak intensity); Figure 3 ROC curves of Benzoyl-DL-arginine-naphthylamid on the training and validation sets; Figure 4 The structural formula is Benzoyl-DL-arginine-naphthylamid. Detailed Implementation

[0045] The present invention will be further described below with reference to embodiments. The following description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make equivalent modifications to the disclosed technical content to create equivalent embodiments. Any simple modifications or equivalent changes made to the following embodiments based on the technical essence of the present invention without departing from the scope of the invention are all within the protection scope of the present invention.

[0046] Unless otherwise specified in the following examples, the conditions should be performed under standard conditions or conditions recommended by the manufacturer. Reagents or instruments whose manufacturers are not specified are all commercially available products.

[0047] Example of a metabolite for diagnosing SLE: Benzoyl-DL-arginine-naphthylamid I. Patient Information: Training set: 199 patients with systemic lupus erythematosus (SLE group) were collected from Beijing Hospital, and 42 age-matched healthy volunteers (Healthy control, HC group) were selected at the same time; Validation set: In addition, 85 patients with systemic lupus erythematosus (SLE group) were collected from Beijing Hospital, and 17 age-matched healthy volunteers (Healthy control, HC group) were selected at the same time; All SLE patients met the clinical diagnostic criteria. This study was approved by the Ethics Committee of Beijing Hospital, and all patients signed informed consent forms.

[0048] The sample type is peripheral venous serum.

[0049] II. Experimental Methods Serum metabolomics analyses of both the screening and validation sets were performed at LipidsTech International Co., Ltd., and the specific steps are as follows: 1. Sample pretreatment Add 400 µL of extraction buffer (methanol) containing mixed isotope internal standards to 100 µL of the sample to be tested, mix well, centrifuge at 12000 rpm for 10 minutes at 4 °C, transfer the supernatant to a new 1.5 ml centrifuge tube, dry using a centrifuge concentrator, reconstitute with 120 µL of 5% acetonitrile, and then perform liquid chromatography-mass spectrometry (LC-MS) analysis.

[0050] 2. LC-MS analysis The instrument used was an ultra-high pressure liquid chromatograph (Agilent 1290 II, Agilent Technologies, Germany) tandem high resolution mass spectrometer (5600 Triple TOF Plus, AB Sciex, Singapore).

[0051] Chromatographic conditions: Chromatographic separation was performed using an ACQUITY UPLC HSS T3 reversed-phase column (1.8 μm, 3.0 × 100 mm, Waters, Dublin, Ireland). Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was acetonitrile solution containing 0.1% formic acid. The gradient elution program was set as follows: 0 min–11 min, 2% B–98% B; 11.0 min–12.0 min, 98% B; 12.0 min–12.1 min, 98% B–2% B; 12.1 min–15.0 min, 2% B. The column temperature was controlled at 40°C, the injection volume was 2 μL, and the flow rate was 0.3 mL / min.

[0052] Mass spectrometry conditions: Mass spectrometry was performed using a high-resolution tandem time-of-flight mass spectrometer (AB Sciex Triple TOF5600 Plus system) equipped with an electrospray ionization (ESI) source. Data acquisition was conducted in both positive ion (ESI+) and negative ion (ESI-) modes. Ion source parameters: Ion source temperature 450°C; Curtain gas pressure 35 psi; Nebulizer gas (Gas 1) and auxiliary heating gas (Gas 2) pressures were both 50 psi. Voltage settings: Ion spray voltage 5500 V for positive ion mode, and [other voltage settings] for negative ion mode. 4500 V. Scan mode: Information-dependent acquisition (IDA) mode, full scan using TOF-MS, scan range m / z 60. 700, cumulative time 100ms; the secondary mass spectrometer (Product Ion Scan) fragments ions whose intensity exceeds the set threshold in the primary scan, with the collision energy (CE) set to (±) 35±15 eV and the cumulative time 50ms.

[0053] 3. Data Processing Raw mass spectrometry data were acquired and processed using AnalystTF 1.7.1 software (AB Sciex, Concord, ON, Canada). The acquired raw data were then imported into MarkerView 1.3 software (AB Sciex, Concord, ON, Canada) to extract peak areas, mass-to-charge ratios, and retention times from the primary mass spectra, generating a two-dimensional data array and filtering isotope peaks.

[0054] PeakView 2.2 (AB Sciex, Concord, ON, Canada) software was used to extract secondary mass spectrometry (MS / MS) data, which were then compared with metabolite databases (AB Sciex, Concord, ON, Canada), HMDB, and METLIN databases to annotate ion information.

[0055] Relative quantification was performed using the isotope internal standard method. A mixture of isotopically labeled internal standards (IS) (Cambridge Isotope Laboratories) was added to the sample for metabolite quantification. The peak area of ​​endogenous metabolites was normalized to the area of ​​their corresponding isotopically labeled structural analogs for quantification. For endogenous metabolites without labeled structural analogs, an automated algorithm selected the optimal internal standard for quantification using the normalized minimum coefficient of variation (COVs) rule.

[0056] III. Results The screening results in the training set are as follows Figure 1 As shown, the results indicated that the expression level of the benzoyl-DL-arginine-naphthylamid metabolite was significantly increased in SLE patients. Furthermore, validation on the validation set showed that its expression trend was the same as that of the training set. Figure 2 ).

[0057] The diagnostic efficacy of the metabolites was further evaluated using receiver operating characteristic (ROC) curves, and the results are as follows: Figure 3 As shown, Benzoyl-DL-arginine-naphthylamid exhibits high diagnostic efficacy in both the training and validation sets, with AUCs of 0.786 and 0.9, sensitivities of 75.88% and 83.53%, and specificities of 73.81% and 82.35%, respectively.

[0058] In conclusion, Benzoyl-DL-arginine-naphthylamid can serve as an early diagnostic biomarker for SLE.

[0059] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. The application of a reagent for detecting the content of metabolites in a sample in the preparation of products for diagnosing systemic lupus erythematosus, characterized in that, The metabolite is Benzoyl-DL-arginine-naphthylamid.

2. The application according to claim 1, characterized in that, The reagents include those used for detection by chromatography, spectroscopy, mass spectrometry, nuclear magnetic resonance, and chemical analysis. Preferably, the mass spectrometry method is chromatography. Mass spectrometry.

3. The application according to claim 1, characterized in that, The samples were selected from blood, serum, and plasma; Preferably, the sample is serum.

4. The application according to claim 1, characterized in that, When the levels of metabolites in a subject's sample are significantly elevated, the subject has systemic lupus erythematosus or is at risk of developing systemic lupus erythematosus.

5. A method for constructing a diagnostic model for systemic lupus erythematosus, characterized in that, The construction method includes the following steps: 1) Collect the results of metabolite content detection in samples from patients with systemic lupus erythematosus and healthy controls, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid; 2) Construct a diagnostic model based on the information collected in step 1).

6. The construction method according to claim 5, characterized in that, The methods for constructing diagnostic models include, but are not limited to, classification and logistic regression, k-nearest neighbor algorithm, Naive Bayes, support vector machine, decision tree, random forest, regression tree, gradient boosting decision tree, xgboost, lightweight gradient boosting machine, gradient boosting machine, LASSO, and convolutional neural network.

7. The application of metabolites in constructing computational models for predicting systemic lupus erythematosus or in systems / devices incorporating said computational models, characterized in that, The metabolite is Benzoyl-DL-arginine-naphthylamid.

8. A product for diagnosing systemic lupus erythematosus, characterized in that, The product includes a reagent for detecting metabolites in a sample, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid; Preferably, the product includes a reagent kit; Preferably, the kit further includes reagents for processing samples; Preferably, the kit also includes instructions for using the kit to assess whether a subject has or is susceptible to systemic lupus erythematosus.

9. A system / device for diagnosing systemic lupus erythematosus, characterized in that, The system / device includes: (1) Data acquisition module, used to acquire characteristic value data of metabolites in the sample of the subject to be tested, wherein the metabolite is Benzoyl-DL-arginine-naphthylamid; (2) Diagnostic prediction module, used to analyze and process the feature value data obtained by the data acquisition module to obtain the diagnostic prediction result of the subject; (3) Result output module, used to output the subject diagnosis prediction results obtained by the diagnosis prediction module.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the following operations: The characteristic value data of metabolites in the sample of the subject to be tested are obtained, and the subject is determined to have systemic lupus erythematosus by comparing the characteristic value data of metabolites with a threshold; the metabolite is Benzoyl-DL-arginine-naphthylamid.

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