Application of sugar chain marker combination in preparation of product for identifying drug-induced liver injury and autoimmune hepatitis

Abstract

The invention discloses an application of a sugar chain marker combination in preparation of a product for identifying drug-induced liver injury and autoimmune hepatitis. The marker combination comprises a combination of more than one of the following N-sugar chain markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb; wherein the NG1A2F-1 and the NG1A2F-2 are in the form of an isomeride. According to the invention, non-invasive serum detection is adopted, so that the risk of invasive examination such as liver biopsy is avoided, and the kit has high detection rate and good clinical application value. According to the invention, early differential diagnosis of DILI and AIH can be realized, a new technical means is provided for accurate diagnosis and glycomics research of liver diseases, and the kit has important clinical significance and application potential.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to the application of a glycan biomarker combination in the preparation of a product for differentiating drug-induced liver injury and autoimmune hepatitis. Background Art

[0002] Drug-induced liver injury (DILI) refers to acute or chronic liver injury directly or through idiosyncratic hepatotoxicity caused by various prescription drugs, over-the-counter drugs, biological agents, traditional Chinese medicines, health products and their metabolites. The pathogenesis of DILI is complex and involves multiple factors such as genetic polymorphisms of drug-metabolizing enzymes, abnormal immune responses and mitochondrial dysfunction.

[0003] Autoimmune hepatitis (AIH) is a chronic progressive liver disease characterized by hepatocyte injury, and is more common in middle-aged women. Its clinical features include elevated serum transaminases, hypergammaglobulinemia, positive autoantibodies and moderate to severe interface hepatitis in histopathology. The prevalence of AIH varies in different regions.

[0004] Current research has found that there are similarities in the pathogenesis and clinical features between DILI and AIH. Both are caused by immune-mediated liver injury, and there is overlap in clinical manifestations and laboratory test results, resulting in difficult differential diagnosis. Currently, the diagnosis of DILI mainly relies on the exclusion method, combined with the medication history, laboratory tests (such as ALT, AST, ALP, TBil, etc.) and RUCAM scale assessment, while the diagnosis of AIH relies on autoantibodies (such as ANA, SMA) and histopathological examination. However, these methods lack specificity, and some patients may simultaneously exhibit the characteristics of both DILI and AIH, further increasing the complexity of diagnosis.

[0005] Glycoproteins are a class of conjugated proteins formed by post-translational modification of proteins (i.e., glycosylation). Glycosylation is the process of transferring sugars to specific amino acid residues on proteins to form glycosidic bonds under the action of glycosyltransferases. Most glycoproteins are secretory proteins and are widely present in cell membranes, extracellular matrices, plasma and mucus. The N-glycans on proteins regulate the structure, stability and activity of proteins through processing and modification, thus playing an important role in maintaining the biological functions of the body. Therefore, changes in glycans are closely related to the occurrence and development of various diseases, including abnormal biological behaviors such as inflammation, invasion and metastasis of tumor cells.

[0006] In recent years, studies have found that abnormal changes in protein N-glycans are closely related to various diseases. For example, in cancers, inflammatory diseases, and metabolic diseases, abnormal glycan structures and quantities of N-glycoproteins have been widely reported. These abnormal glycans can not only serve as markers for disease diagnosis but may also be involved in the pathophysiological processes of diseases. Therefore, studying glycan changes helps to reveal the molecular mechanisms of diseases and provides a theoretical basis for the development of new diagnostic markers.

[0007] Currently, the diagnosis of DILI and AIH mainly relies on traditional laboratory tests and pathological methods, but these methods have the following limitations: (1) Insufficient specificity: The clinical manifestations and laboratory test results of DILI and AIH overlap, leading to a relatively high misdiagnosis rate. (2) Invasive examinations: Liver biopsy is an important means to distinguish DILI and AIH, but its invasiveness limits its clinical application. (3) Lack of specific markers: The sensitivity and specificity of existing markers (such as ALT, AST, autoantibodies) are relatively low, making it difficult to meet clinical needs. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the present invention provides the application of a glycan marker combination in the preparation of a product for differentiating drug-induced liver injury and autoimmune hepatitis. By applying glycomics methods to detect the changes in N-glycans in the sera of DILI and AIH patients and establishing a GAD model to distinguish DILI and AIH, it has the advantages of being non-invasive and having a high detection rate. It can not only significantly improve the treatment effect and survival rate of patients but also provide a new technical means for glycomics research and precision medicine for liver diseases.

[0009] The present invention is achieved through the following technical solutions:

[0010] The application of a glycan marker combination in the preparation of a product for differentiating drug-induced liver injury and autoimmune hepatitis, wherein the glycan marker combination comprises a combination of one or more of the following N-glycan markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers.

[0011] Preferably, the product comprises a reagent or kit for detecting the relative content of the glycan marker combination in a sample.

[0012] Preferably, the sample is blood, serum, or plasma of a subject.

[0013] Preferably, the GAD model is used to identify drug-induced liver injury and autoimmune hepatitis. The GAD model uses the relative content of the sugar chain marker combination as the input variable, and the GAD value is calculated by the following equation:

[0014] GAD value = (-3.678 × relative content of NGA2FB) + (2.383 × relative content of NA3Fb) + (3.007 × relative content of NA4) - (5.264 × relative content of NA4Fb) - 2.986;

[0015] When the GAD value ≥ 2.932, it indicates that the subject has drug-induced liver injury. When the GAD value < 2.932, it indicates that the subject has autoimmune hepatitis.

[0016] A kit for identifying drug-induced liver injury and autoimmune hepatitis, comprising a reagent for detecting the relative content of the sugar chain marker combination in a sample; the sugar chain marker combination includes a combination of one or more of the following N-glycan markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers.

[0017] Preferably, the reagent includes:

[0018] Reagent A: NH4HCO3 with a concentration of 2 - 10 mM is added to an SDS solution with a mass concentration of 2 - 3%;

[0019] Reagent B1: A glycoside exohydrolase solution with a concentration of 0.05 - 5 U / μL;

[0020] Reagent B2: A glycoside endohydrolase with a concentration of 0.5 - 2 mM is added to hydrogen peroxide with a neutral pH;

[0021] Reagent C: ddH2O;

[0022] Reagent D: A mixed solution formed by mixing a fluorescent labeling solution with a concentration of 2 - 20 mM and a 1 M DMSO solution.

[0023] Preferably, the fluorescent labeling solution is trisodium 8-aminopyrene-1,3,6-trisulfonate.

[0024] Preferably, Reagent B1 and Reagent B2 are premixed before use.

[0025] Preferably, the volume ratio of Reagent A, Reagent B1, Reagent B2, Reagent C, and Reagent D is (3 - 5):5:5:50:(3 - 5).

[0026] A computer device for differentiating drug-induced liver injury and autoimmune hepatitis, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the following steps:

[0027] Step 1) Obtain the relative content data of the glycan biomarker combination in the subject sample; the glycan biomarker combination includes a combination of one or more of the following N-glycan biomarkers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers;

[0028] Step 2) Input the data into the GAD model to calculate the GAD value; the GAD model uses the relative content of the glycan biomarker combination as the input variable, and the GAD model calculates the GAD value using the following equation:

[0029] GAD value = (-3.678 × relative content of NGA2FB) + (2.383 × relative content of NA3Fb) + (3.007 × relative content of NA4) - (5.264 × relative content of NA4Fb) - 2.986;

[0030] When the GAD value ≥ 2.932, it indicates that the subject has drug-induced liver injury, and when the GAD value < 2.932, it indicates that the subject has autoimmune hepatitis;

[0031] Step 3) Differentiate whether the subject has drug-induced liver injury or autoimmune hepatitis according to the GAD value.

[0032] The beneficial effects of the present invention are as follows:

[0033] (1) By quantifying each peak of the glycan map, the present invention finds that P2 (NGA2FB), P9 (NA3Fb), P10 (NA4), and P11 (NA4Fb) are significantly different between DILI and AIH patients (P < 0.05). The GAD model established based on these glycan biomarkers has an area under the ROC curve (AUC) of more than 0.889, indicating that the model has excellent discrimination ability. When the set cutoff value is 2.932, the detection rate of the model for DILI > 84.21%, and the detection rate for AIH > 80.88%. This performance is significantly better than traditional diagnostic methods (such as ALT, AST, and autoantibody detection), which can effectively reduce the misdiagnosis rate and improve the diagnostic accuracy.

[0034] (2) The present invention detects glycan markers based on serum samples, without relying on invasive examination methods such as liver biopsy, significantly reducing the pain and risk of patients and improving patient compliance. This method is easy to operate and suitable for large-scale clinical applications, especially for early screening and dynamic monitoring.

[0035] (3) The glycan marker combination provided by the present invention can achieve differential diagnosis of DILI and AIH at the early stage of the disease, helping patients receive targeted treatment as early as possible and avoiding the deterioration of the condition. For DILI patients, early diagnosis can promptly stop the causative drug and take liver protection treatment; for AIH patients, early diagnosis can initiate immunosuppressive treatment as early as possible, thus significantly improving the overall cure rate and survival rate of patients.

[0036] (4) The present invention first reveals the differential expression of specific glycans (such as NGA2FB, NA3Fb, NA4, NA4Fb) in DILI and AIH, providing a new direction for studying the pathological mechanisms of the two diseases. The discovery of glycan markers not only helps to understand the role of glycosylation modification in liver injury, but also provides an important reference for the application of glycomics in disease diagnosis and treatment.

[0037] (5) The detection method of the present invention is not only applicable to the differential diagnosis of DILI and AIH, but also can be extended to the diagnosis and typing of other liver diseases (such as viral hepatitis, alcoholic liver disease). This method has important application potential in the fields of disease marker screening, disease diagnosis and glycomics research, laying a foundation for the development of new diagnostic tools and treatment targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Serum glycan maps of subjects with autoimmune hepatitis (A) and drug-induced liver injury (B);

[0039] Figure 2 Training set ROC curve of the GAD model established based on the glycan marker combination for differentiating drug-induced liver injury and autoimmune hepatitis;

[0040] Figure 3 Validation set ROC curve of the GAD model established based on the glycan marker combination for differentiating drug-induced liver injury and autoimmune hepatitis. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0042] Unless otherwise specified, the technical means used in the following embodiments are all conventional means well known to those skilled in the art. The experimental methods without specific conditions indicated are all conventional methods in the art.

[0043] Materials, reagents, etc. used in the following examples can be obtained from commercial sources unless otherwise specified.

[0044] Example 1

[0045] 1. Test samples

[0046] In this example, serum samples from 99 subjects with autoimmune hepatitis (AIH) and 340 subjects with drug-induced liver injury (DILI) were collected. All samples were from Beijing You'an Hospital, Capital Medical University; the above DILI and AIH samples were all examined according to the recommendations in the clinical practice guidelines, and the test results met the clinical diagnostic criteria. The following experiments have been filed and approved by the ethics committee.

[0047] To clarify the differential value of glycan markers for DILI and AIH, the research subjects were randomly divided into a training set and a validation set, with 352 cases in the training set and 87 cases in the validation set.

[0048] 2. Instrument and equipment

[0049] Capillary electrophoresis analyzer, automatic biochemical analyzer, PCR, centrifuge.

[0050] 3. Detection reagents

[0051] Reagent A: NH4HCO3 with a concentration of 2 - 10 mM was added to an SDS solution with a mass concentration of 2 - 3%.

[0052] Reagent B1: Glycoside exohydrolase solution with a concentration of 0.05 - 5 U / μL.

[0053] Reagent B2: Glycoside endohydrolase with a concentration of 0.5 - 2 mM was added to neutral hydrogen peroxide.

[0054] Reagent C: ddH2O

[0055] Reagent D: A mixed solution prepared by mixing a fluorescent labeling solution (sodium 8-aminopyrene-1,3,6-trisulfonate) with a concentration of 2 - 20 mM and 1 M DMSO solution.

[0056] 4. Glycan map detection

[0057] (1) Release of N-glycan chains

[0058] 5 - 10 μL of reagent A was added to 3 - 10 μL of the sample, heated at 90 - 100 °C for 10 - 20 min, cooled to 4 °C, 5 - 10 μL of reagent B1 and reagent B2 premixed in a 1:1 volume ratio was added, reacted at 35 - 40 °C for 2 - 3 h, and 50 μL of reagent C was added.

[0059] (2) Labeling of N-glycan chains

[0060] Take 5 - 10 μL of the sample solution in step (1), dry it at 70 - 80 °C for 35 - 40 min, then add 3 μL of reagent D, place it at 80 - 95 °C for 1 - 2 h, and finally add 50 μL of reagent C.

[0061] (3) Detection of N - oligosaccharide chains

[0062] Take 5 - 10 μL of the oligosaccharide chain sample prepared in step (2) and place it in a 96 - well plate dedicated to ABI, and detect it by an ABI3500 sequencer to obtain a glycan map.

[0063] (4) Data analysis and processing

[0064] Compare and analyze the N - glycome data of DILI and AIH patients: Divide the peak height value of each peak by the sum of the heights of all peaks, and quantitatively calculate the relative content of each peak, that is, the peak quantification of the N - glycome map. Compare and analyze the 11 N - oligosaccharide chain peaks of the quantified DILI group and AIH group N - glycome maps.

[0065] The N - glycome map is as Figure 1 shown, and it consists of P1 (NGA2F, non - galactosylated α - 1,6 core fucosylated biantennary N - glycan), P2 (NGA2FB, non - galactosylated α - 1,6 core fucosylated bisected N - glycan), P3 (NG1A2F - 1, single - branched galactosylated α - 1,6 core fucosylated biantennary N - glycan), P4 (NG1A2F - 2, single - branched galactosylated α - 1,6 core fucosylated biantennary N - glycan), P5 (NA2, galactosylated biantennary N - glycan), P6 (NA2F, galactosylated α - 1,6 core fucosylated biantennary N - glycan), P7 (NA2FB, galactosylated α - 1,6 core fucosylated bisected N - glycan), P8 (NA3, galactosylated triantennary N - glycan), P9 (NA3Fb, galactosylated α - 1,3 - branched fucosylated triantennary N - glycan), P10 (NA4, galactosylated tetraantennary N - glycan), P11 (NA4Fb, galactosylated α - 1,3 core fucosylated tetraantennary N - glycan), where NG1A2F - 1 and NG1A2F - 2 are isomers.

[0066] It can be seen from Figure 1 that P1, P2, P3, P4, P6, and P7 in the AIH map are all significantly increased; while P8, P9, P10, and P11 in the DILI map show an increasing trend. There are obvious differences between the two diseases in the glycome map.

[0067] (4 - 1) Training of the GAD model

[0068] Quantify the peaks of the glycan map ( Figure 1 ), and compare and analyze the relative contents of each peak of DILI patients (272 cases) and AIH patients (80 cases). The results are shown in Table 1 below.

[0069] Table 1 Logistic regression analysis of 11 N-glycan influencing factors

[0070]

[0071] As can be seen from Table 1, the peak values (relative contents) of P2 (NGA2FB), P9 (NA3Fb), P10 (NA4), and P11 (NA4Fb) are statistically significant (P < 0.05). A GAD model is established by logistic regression and calculated using the following equation:

[0072] GAD value = (-3.678 × relative content of NGA2FB) + (2.383 × relative content of NA3Fb) + (3.007 × relative content of NA4) - (5.264 × relative content of NA4Fb) - 2.986.

[0073] Distinguish DILI and AIH through the established GAD model. As Figure 2 shown, when the cutoff value of the GAD model established in this example is 2.932, the AUC value under the training ROC curve reaches 0.906. The detection rates of this model for DILI and AIH are 88.6% and 83.09% respectively. When the GAD value ≥ 2.932, it indicates that the subject has DILI, and when the GAD value < 2.932, it indicates that the subject has AIH.

[0074] (4-2) Verification of the GAD model

[0075] Quantify the peaks of the glycan map ( Figure 1 ), compare and analyze the relative contents of each peak of DILI patients (68 cases) and AIH patients (19 cases), and verify using the above GAD model. The results are shown in Table 2 and Figure 3 shown.

[0076] Table 2 Verification results of the GAD model

[0077] Disease type Quantity DILI detection rate AIH detection rate Drug-induced liver injury 68 84.21％(57 / 68) - Autoimmune hepatitis 19 - 80.88％(15 / 19)

[0078] As can be seen from Table 2 and Figure 3 known, when the cutoff value of the GAD model established in this example is 2.932, the area AUC value under the verification ROC curve is 0.889. The detection rate of this model for DILI is 84.21%, and the detection rate for AIH is 80.88% at the same time.

[0079] The experimental results of this embodiment show that the above-mentioned glycan biomarker combination can be used as a biomarker for the auxiliary diagnosis of DILI and AIH.

[0080] The present invention detects the changes of N-glycans in the sera of patients with drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) by means of glycomics, screens out significantly different glycan biomarkers such as P2 (NGA2FB), P9 (NA3Fb), P10 (NA4), P11 (NA4Fb), etc., and establishes a GAD model based on this. The area under the ROC curve (AUC) of this model reaches above 0.889, the cutoff value is 2.932, the detection rate for DILI > 84.21%, and the detection rate for AIH > 80.88%, which is significantly superior to the traditional diagnostic methods. The present invention realizes the efficient differential diagnosis of DILI and AIH through non-invasive serum detection, avoids the risks of invasive examinations such as liver biopsy, and improves patient compliance. In addition, this method can achieve accurate diagnosis in the early stage of the disease, help patients receive targeted treatment as early as possible, thereby improving the cure rate and survival rate. The present invention not only provides a new technical means for the differential diagnosis of DILI and AIH, but also opens up a new direction for the application of glycomics in disease biomarker screening and pathological mechanism research, and has important clinical value and social significance.

[0081] The embodiments described above are only a part of the embodiments of the present invention, rather than all of the embodiments. The detailed description of the embodiments of the present invention is not intended to limit the scope of the present invention claimed, but merely represents the selected embodiments of the present invention. The protection scope of the present invention shall be subject to the scope claimed in the claims. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

Claims

1. Use of a sugar chain marker combination in the preparation of a product for identifying drug-induced liver injury and autoimmune hepatitis, characterized in that: The sugar chain marker combination includes a combination of more than one of the following N-sugar chain markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers.

2. The use according to claim 1, characterized in that: The product includes a reagent or a kit for detecting the relative content of the sugar chain marker combination in a sample.

3. The use according to claim 2, characterized in that: The sample is blood, serum or plasma of the subject.

4. The use according to claim 1, characterized in that: The identification of drug-induced liver injury and autoimmune hepatitis adopts the GAD model, the GAD model uses the relative content of the sugar chain marker combination as an input variable, and the GAD model calculates the GAD value using the following equation: GAD value = (-3.678 × relative content of NGA2FB) + (2.383 × relative content of NA3Fb) + (3.007 × relative content of NA4) - (5.264 × relative content of NA4Fb) - 2.986; When the GAD value is ≥2.932, it indicates that the subject suffers from drug-induced liver injury, and when the GAD value is <2.932, it indicates that the subject suffers from autoimmune hepatitis.

5. A kit for distinguishing drug-induced liver injury and autoimmune hepatitis, characterized in that: Comprising a reagent for detecting the relative content of the sugar chain marker combination in a sample; the sugar chain marker combination comprises a combination of more than one of the following N-sugar chain markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers.

6. A kit for distinguishing drug-induced liver injury and autoimmune hepatitis according to claim 5, characterized in that: The reagents include: Reagent A: Add 2-10mM NH4HCO3 to 2-3% SDS solution; Reagent B1: 0.05-5 U / μL exoglycosidase solution; Reagent B2: Add 0.5-2 mM endoglycosidase to hydrogen peroxide at a neutral pH; Reagent C: ddH2O; Reagent D: A mixture of 2-20 mM fluorescent labeling solution and 1 M DMSO solution.

7. A kit for distinguishing drug-induced liver injury and autoimmune hepatitis according to claim 6, characterized in that: The fluorescent labeling liquid is 8-aminopyrene-1,3,6-trisulfonic acid trisodium salt.

8. A kit for distinguishing drug-induced liver injury and autoimmune hepatitis according to claim 6, characterized in that: The reagent B1 and the reagent B2 are premixed before use.

9. A kit for distinguishing drug-induced liver injury and autoimmune hepatitis according to claim 6, characterized in that: The volume ratio of the reagent A, reagent B1, reagent B2, reagent C and reagent D is (3-5):5:5:50:(3-5).

10. A computer device for distinguishing drug-induced liver injury and autoimmune hepatitis, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: The processor performs the following steps: Step 1) obtaining relative content data of a sugar chain marker combination in a subject sample; the sugar chain marker combination includes a combination of more than one of the following N-sugar chain markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers; Step 2) inputting the data into the GAD model to calculate the GAD value; the GAD model uses the relative content of the sugar chain marker combination as an input variable, and the GAD model uses the following equation to calculate the GAD value: GAD value = (-3.678 × relative content of NGA2FB) + (2.383 × relative content of NA3Fb) + (3.007 × relative content of NA4) - (5.264 × relative content of NA4Fb) - 2.986; When the GAD value is ≥2.932, it indicates that the subject suffers from drug-induced liver injury, and when the GAD value is <2.932, it indicates that the subject suffers from autoimmune hepatitis; Step 3) identifying the subject as suffering from drug-induced liver injury or autoimmune hepatitis based on the GAD value.

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