Metabolites for diagnosing systemic lupus erythematosus

Abstract

This invention discloses metabolites for diagnosing systemic lupus erythematosus (SLE). For the first time, this invention discovered and verified a significant difference in the expression levels of Cysteine-S-sulfate serum metabolites in samples from SLE patients and healthy individuals, thus proposing Cysteine-S-sulfate as a biomarker for SLE diagnosis. Based on Cysteine-S-sulfate, corresponding auxiliary early diagnostic reagents and kits have been developed, possessing broad scientific research value and clinical applications, providing significant convenience for early screening, clinical diagnosis, and intervention treatment.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically, this invention relates to a metabolite for diagnosing systemic lupus erythematosus. Background Technology

[0002] Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the production of large amounts of autoantibodies, triggering a systemic inflammatory response that leads to damage to multiple organs and even death. SLE symptoms fluctuate in severity, and currently there is no cure; patients require lifelong medication. Therefore, accurate clinical diagnosis for early detection and standardized treatment are crucial for alleviating symptoms and controlling the disease.

[0003] However, SLE often affects multiple tissues and organs throughout the body, leading to high heterogeneity in clinical presentation and making clinical diagnosis difficult. The current clinical classification and diagnostic criteria for SLE (2019 EULAR / ACR SLE classification and diagnostic criteria) includes one entry criterion, three immunological domains, and seven clinical phenotypic domains. Patients require multiple examinations for diagnosis, including laboratory tests (such as complete blood count, urinalysis, liver function tests, complement tests, antinuclear antibody tests, and cerebrospinal fluid examination), imaging examinations (such as ultrasound examination for pericardial effusion and pulmonary hypertension; CT scan for interstitial lung disease), physical examination (including head, chest, abdomen, skin, neurological status, musculoskeletal examination, etc.), and special examinations (such as renal biopsy and lumbar puncture). However, this diagnostic standard has many shortcomings. In practical application, it requires a high level of clinical experience, is difficult to perform, time-consuming, and involves many invasive procedures that can cause significant pain and discomfort to patients.

[0004] Metabolomics primarily studies small-molecule metabolites that serve as substrates and products of various metabolic pathways, thereby obtaining information beyond genomics and proteomics. Among metabolites from various sources, serum metabolites are relatively stable and easily quantified; therefore, non-invasive diagnostic monitoring of diseases using serum metabolites is highly feasible. However, current research on SLE largely focuses on genomics and proteomics; therefore, it is necessary to discover new, more valuable diagnostic biomarkers for SLE from a metabolomics perspective. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the purpose of this invention is to provide a diagnostic metabolite for SLE with high diagnostic efficacy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of this invention provides the use of a reagent for detecting the level of Cysteine-S-sulfate in a sample in the preparation of products for diagnosing systemic lupus erythematosus.

[0007] Furthermore, the reagent detects the level of Cysteine-S-sulfate in a sample using one or more of the following methods: nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, and chromatography-mass spectrometry.

[0008] Furthermore, the reagent was used to detect the level of Cysteine-S-sulfate in the sample by chromatography-mass spectrometry.

[0009] Furthermore, the reagent can detect the level of Cysteine-S-sulfate in a sample using either targeted or non-targeted methods.

[0010] Furthermore, the samples include blood, serum, plasma, and urine.

[0011] Furthermore, the sample is serum.

[0012] In this invention, the Cysteine-S-sulfate has the CAS number 1637-71-4, the PubChem chemical code number 24892471, the molecular formula C3H7NO5S2, and the structural formula as follows: Figure 1 As shown.

[0013] In this invention, the sample or test sample used to detect the metabolite biomarker refers to a composition obtained from or derived from a subject (e.g., an individual of interest) and comprising cellular entities and / or other molecular entities characterized and / or identified based on physical, biochemical, chemical and / or physiological features.

[0014] Furthermore, the subject refers to any individual of interest, preferably a living organism suffering from or suspected of having systemic lupus erythematosus, including humans, other mammals, preferably primates, and particularly preferably humans.

[0015] Furthermore, the sample is a biological sample. Biologically derived samples (i.e., biological samples) typically contain a variety of metabolites. Preferred experimental samples for use in the methods of the present invention are those derived from bodily fluids, preferably from blood, plasma, serum, feces, lymph, sweat, saliva, tears, semen, vaginal fluid, urine, or cerebrospinal fluid, or from samples derived from cells, tissues, or organs, for example, through vivisection. This also includes samples containing subcellular compartments or organelles (such as mitochondria, the Golgi network, or peroxisomes). In addition, biological samples also include gaseous samples, such as volatiles from an organism. Biological samples are obtained from subjects as specifically described elsewhere herein. Techniques for obtaining the different types of biological samples described above are well known in the art. For example, blood samples are obtained through blood collection, urine samples through urine collection, and fecal samples through fecal collection.

[0016] Furthermore, the products include reagent kits, chips, test strips, high-throughput sequencing systems, equipment, and devices.

[0017] In this invention, the product may comprise a solid substrate such as a chip, a glass slide, an array, etc., having reagents capable of detecting and / or quantifying one or more serum metabolites or other sample-derived 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 the detection and quantification of metabolite markers in blood samples, in any quantity or in any combination thereof.

[0018] Furthermore, the product also includes reagents for processing samples.

[0019] Furthermore, the samples described above are pretreated before being used for detection in this invention. This pretreatment may include processes necessary to release or separate compounds, or to remove excess substances or waste. Suitable techniques include centrifugation, extraction, fractionation, purification, and / or enrichment of compounds. Additionally, other pretreatments are performed to provide compounds in forms or concentrations suitable for compound analysis. For example, if gas chromatography-coupled mass spectrometry is used in the methods of this invention, the compounds will need to be derivatized prior to the gas chromatography. Suitable and necessary pretreatments depend on the tools used in the methods of this invention and are well known to those skilled in the art. Samples pretreated as described above are also included in the term "sample" as used herein.

[0020] Furthermore, the product also includes an instruction manual that clearly explains how to use the product to assess whether a subject has SLE or is at risk of developing SLE.

[0021] A second aspect of the present invention provides a product for diagnosing systemic lupus erythematosus.

[0022] Furthermore, the product includes reagents for detecting the level of Cysteine-S-sulfate in a sample.

[0023] A third aspect of this invention provides the application of Cysteine-S-sulfate in constructing a diagnostic model for systemic lupus erythematosus.

[0024] The fourth aspect of this invention provides a method for constructing a diagnostic model for systemic lupus erythematosus.

[0025] Furthermore, the method includes obtaining level data of Cysteine-S-sulfate in the sample and inputting the data into a machine learning algorithm to construct a diagnostic model.

[0026] Furthermore, the method includes the following steps: dividing the Cysteine-S-sulfate level data into a training set and a validation set, extracting the Cysteine-S-sulfate expression level data from the training set and inputting it into a machine learning algorithm to construct a prediction model, and validating the model through the validation set to evaluate its performance.

[0027] Furthermore, the diagnostic model obtains classification results using the following criteria: when the level of Cysteine-S-sulfate is higher than the optimal cutoff value, the subject is classified as having systemic lupus erythematosus or at risk of developing systemic lupus erythematosus; if the level of Cysteine-S-sulfate is lower than the optimal cutoff value, the subject is classified as not having systemic lupus erythematosus.

[0028] In this invention, the term "optimal cutoff value" refers to a value that is statistically relevant to a particular outcome when compared with the analysis results. In a preferred embodiment, the optimal cutoff value is determined based on the statistical conclusions of studies comparing SLE patients and healthy individuals. Some such studies are shown in the Examples section of this document, but studies from the literature and the experience of users of the methods described herein can also be used to generate or adjust the optimal cutoff value. The optimal cutoff value can also be determined by considering the patient's genetic background, clinical characteristics, work environment, and other relevant factors. In some embodiments of the invention, the optimal cutoff value is selected from a peak intensity of 59184.

[0029] Furthermore, the samples include blood, serum, plasma, and urine.

[0030] Furthermore, the sample is serum.

[0031] In some embodiments of the present invention, the methods for constructing the diagnostic model are known to those skilled in the art and can be implemented and realized in different ways, linking metabolite expression levels with a certain probability or risk. Preferably, the measured concentrations of a biomarker and one or more other biomarkers are mathematically combined, and the combined value is associated with the fundamental question of whether or not one has a disease or the risk of having that disease. The measured biomarker values ​​can be combined using any suitable existing mathematical method, and a predictive model can be constructed using machine learning algorithms.

[0032] Furthermore, the machine learning algorithm includes algorithmic models developed using various development tools.

[0033] Furthermore, the development tools include TensorFlow, ScikitLearn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, VertexAI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, AmazonML, MLJAR, and Spell.

[0034] Furthermore, the algorithm model includes generalized linear model, principal component analysis, logistic regression analysis, LASSO regression analysis, nearest neighbor analysis, support vector machine, neural network model, and random forest model.

[0035] The fifth aspect of this invention provides a systemic lupus erythematosus diagnostic system.

[0036] Furthermore, the system includes a data classification unit, which is used to substitute Cysteine-S-sulfate level data into a diagnostic model constructed according to the method of the fourth aspect of the present invention to obtain a classification result of whether the sample has systemic lupus erythematosus or whether there is a risk of having systemic lupus erythematosus.

[0037] Furthermore, the system also includes a data acquisition unit, which is used to acquire level data of Cysteine-S-sulfate in the sample.

[0038] Furthermore, the system also includes an output unit for outputting classification results.

[0039] Furthermore, the samples include blood, serum, plasma, and urine.

[0040] Furthermore, the sample is serum.

[0041] The sixth aspect of this invention provides a diagnostic device for systemic lupus erythematosus.

[0042] Furthermore, the diagnostic device includes a memory and a processor.

[0043] Furthermore, the memory is used to store program instructions.

[0044] Furthermore, the processor is used to execute program instructions, which, when executed, are used to perform the following operations: obtain level data of Cysteine-S-sulfate in the sample, input the level data into a diagnostic model constructed based on the method described in the fourth aspect of the present invention, and obtain a classification result of whether the sample has systemic lupus erythematosus or whether there is a risk of having systemic lupus erythematosus.

[0045] To provide interaction with the user, the device may be a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the computer. Other types of devices may also be used to provide interaction with the user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including voice input, speech input, or tactile input).

[0046] Furthermore, the samples are selected from blood, serum, and plasma.

[0047] Furthermore, the sample is serum.

[0048] A seventh aspect of the present invention provides a computer-readable storage medium.

[0049] Furthermore, the computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: acquiring level data of Cysteine-S-sulfate in a sample, inputting the level data into a diagnostic model constructed based on the method described in the fourth aspect of the present invention, and obtaining a classification result of whether the sample suffers from systemic lupus erythematosus or whether there is a risk of suffering from systemic lupus erythematosus.

[0050] Any combination of one or more computer-readable media can be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments, more specific examples of computer-readable storage media include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CDROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0051] Furthermore, the samples include blood, serum, plasma, and urine.

[0052] Furthermore, the sample is serum.

[0053] Advantages and beneficial effects of the present invention: This invention is the first to discover and verify significant differences in the expression levels of Cysteine-S-sulfate metabolites in samples from SLE patients and healthy individuals, thus proposing Cysteine-S-sulfate as a biomarker for SLE diagnosis. The development of corresponding auxiliary early diagnostic reagents and kits based on Cysteine-S-sulfate has broad scientific research value and clinical applications, providing significant convenience for early screening, clinical diagnosis, and intervention treatment. Attached Figure Description

[0054] Figure 1 The structural formula for Cysteine-S-sulfate; Figure 2 Differential expression plot of Cysteine-S-sulfate in the training set (concentration is the peak intensity); Figure 3 To validate the differential expression plot of Cysteine-S-sulfate in the set (concentration is the peak intensity); Figure 4 ROC curves for Cysteine-S-sulfate on the training and validation sets. Detailed Implementation

[0055] The following will clearly and completely describe the concept and technical effects of this application in conjunction with embodiments, so as to fully understand the purpose, features and effects of this application. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are all within the scope of protection of this application.

[0056] The embodiments of this application are described in detail below. These embodiments are exemplary and are only used to explain this application, and should not be construed as limiting this application. Unless otherwise specified in the following embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0057] Example 1: Application of Cysteine-S-sulfate in the diagnosis of systemic lupus erythematosus I. Experimental Subjects The training program collected 199 patients with systemic lupus erythematosus (SLE group) from Beijing Hospital, and at the same time selected 42 age-matched healthy volunteers (Healthy control, HC group); In the validation set, 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.

[0058] The sample type is peripheral venous serum.

[0059] II. Experimental Methods Serum metabolomics analyses of both the training and validation sets were performed at LipidsTech International, 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.

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] III. Experimental Results First, differentially expressed metabolites between SLE and HC were screened in the training set. The results showed that Cysteine-S-sulfate differed significantly between the two groups of subjects. Figure 2 Furthermore, validation using a validation set showed that the expression level of Cysteine-S-sulfate metabolite in the serum of SLE patients in the validation set was significantly higher than that in the HC group, and the difference was highly significant. Figure 3 Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. A diagnostic indicator was considered "highly accurate" when the area under the ROC curve (AUC) ≥ 0.9, "accurate" when 0.8 ≤ AUC < 0.9, and "moderately accurate" when 0.7 ≤ AUC < 0.8. ROC curve results are shown below. Figure 4 As shown, Cysteine-S-sulfate exhibits high diagnostic efficacy in both the training and validation sets, with AUCs of 0.958 and 0.961, sensitivities of 89.95% and 90.59%, and specificities of 88.10% and 100%, respectively.

[0067] The above experimental results indicate that Cysteine-S-sulfate can be used as a blood diagnostic marker to distinguish between SLE and healthy individuals, and can be used to prepare products for diagnosing systemic lupus erythematosus, such as kits and reagents.

[0068] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. Application of reagents for detecting Cysteine-S-sulfate levels in samples in the preparation of products for diagnosing systemic lupus erythematosus.

2. Use according to claim 1, characterized in that, The reagent detects the level of Cysteine-S-sulfate in a sample by one or more of the following methods: nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, and chromatography-mass spectrometry. Preferably, the reagent is used to detect the level of Cysteine-S-sulfate in the sample by chromatography-mass spectrometry.

3. Use according to claim 1, characterized in that, The samples include blood, serum, plasma, and urine; Preferably, the sample is serum; Preferably, the product includes reagent kits, chips, test strips, high-throughput sequencing systems, equipment, and devices; Preferably, the product further includes reagents for processing samples.

4. A product for diagnosing systemic lupus erythematosus, characterized by, The product includes reagents for detecting Cysteine-S-sulfate levels in samples.

5. Application of Cysteine-S-sulfate in constructing a diagnostic model for systemic lupus erythematosus.

6. A method of constructing a diagnostic model for systemic lupus erythematosus, characterized by, The method includes obtaining level data of Cysteine-S-sulfate in a sample and inputting the data into a machine learning algorithm to construct a diagnostic model. Preferably, the diagnostic model obtains classification results using the following criteria: when the level of Cysteine-S-sulfate is higher than the optimal cutoff value, the subject is classified as having systemic lupus erythematosus or at risk of developing systemic lupus erythematosus; if the level of Cysteine-S-sulfate is lower than the optimal cutoff value, the subject is classified as not having systemic lupus erythematosus. Preferably, the sample includes blood, serum, plasma, and urine; Preferably, the sample is serum.

7. The method of claim 6, wherein, The machine learning algorithms include algorithm models developed using various development tools; Preferably, the development tools include TensorFlow, ScikitLearn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, VertexAI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, AmazonML, MLJAR, and Spell. Preferably, the algorithm model includes generalized linear model, principal component analysis, logistic regression analysis, LASSO regression analysis, nearest neighbor analysis, support vector machine, neural network model, and random forest model.

8. A systemic lupus erythematosus diagnosis system characterized by comprising: The system includes a data classification unit, which is used to substitute Cysteine-S-sulfate level data into the diagnostic model constructed by the method according to any one of claims 6-7 to obtain a classification result of whether the sample has systemic lupus erythematosus or whether there is a risk of having systemic lupus erythematosus. Preferably, the system further includes a data acquisition unit, which is used to acquire level data of Cysteine-S-sulfate in the sample; Preferably, the system further includes an output unit for outputting classification results; Preferably, the sample includes blood, serum, plasma, and urine; Preferably, the sample is serum.

9. A systemic lupus erythematosus diagnosing apparatus characterized by comprising: The diagnostic device includes a memory and a processor; The memory is used to store program instructions; The processor is used to execute program instructions, which, when executed, are used to perform the following operations: obtain level data of Cysteine-S-sulfate in the sample, input the level data into a diagnostic model constructed based on the method of any one of claims 6-7, and obtain a classification result of whether the sample has systemic lupus erythematosus or whether there is a risk of having systemic lupus erythematosus; Preferably, the sample includes blood, serum, plasma, and urine; Preferably, the sample is serum.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: acquiring level data of Cysteine-S-sulfate in a sample, inputting the level data into a diagnostic model constructed based on the method described in any one of claims 6-7, and obtaining a classification result of whether the sample suffers from systemic lupus erythematosus or whether there is a risk of suffering from systemic lupus erythematosus. Preferably, the sample includes blood, serum, plasma, and urine; Preferably, the sample is serum.

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