Use of pvrig as a target in the preparation of a product for diagnosing and predicting disease progression in type 1 diabetes

Abstract

The application provides application of PVRIG as a target point in preparation of a type 1 diabetes diagnosis and disease progression prediction product. Specifically, PVRIG has different expressions on the surfaces of NK cells, CD16 positive NK cells and CD16 negative NK cells in honeymoon patients and non-honeymoon patients of type 1 diabetes, and PVRIG has different expressions on the surfaces of NK cells, CD16 positive NK cells and CD16 negative NK cells in patients with faster islet function failure and patients with slower islet function failure of type 1 diabetes, so that PVRIG is closely related to type 1 diabetes, can be used as a new target point of type 1 diabetes, and provides a new way for diagnosis and disease progression prediction of type 1 diabetes.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically involving the application of PVRIG as a target in the preparation of products for the diagnosis and disease progression prediction of type 1 diabetes. Background Technology

[0002] Type 1 diabetes (T1D) is a common metabolic disease with a complex pathogenesis involving genetic, environmental factors, and the destruction of pancreatic β cells by the autoimmune system. Because the progression of type 1 diabetes is a relatively complex pathological process, it presents certain limitations for clinical diagnosis and treatment.

[0003] PVRIG is an inhibitory immune checkpoint receptor belonging to the poliovirus receptor (PVR) family, discovered in 2016. PVRIG is expressed on the surface of T cells and NK cells, and exerts its immunosuppressive function by binding to its ligand PVRL2, transmitting immunosuppressive signals, and competitively antagonizing CD226. Currently, PVRIG has been used in tumor immunotherapy, but its application in autoimmune diseases and metabolic diseases (such as T1D) has not yet been reported. Summary of the Invention

[0004] The purpose of this invention is to provide a new target, PVRIG (NK cells), for the diagnosis and prediction of disease progression in type 1 diabetes, thus offering a new approach to the diagnosis and prediction of disease progression in type 1 diabetes.

[0005] To achieve the above objectives, the present invention provides the application of reagents for detecting the expression levels of NK cells and / or NK cell subsets PVRIG in the preparation of products for the diagnosis and disease progression prediction of type 1 diabetes, wherein the NK cell subsets are CD16-positive NK cells or CD16-negative NK cells.

[0006] In one specific implementation, the type 1 diabetes diagnostic product is used to diagnose the disease development stage in a diabetic patient, the disease development stage including the honeymoon period or the non-honeymoon period.

[0007] In one specific implementation, when the PVRIG expression level on the surface of NK cells or NK cell subsets in a type 1 diabetic patient is 80% to 120% of the standard expression level, the probability of diagnosing the diabetic patient as being in a honeymoon period is high, and the standard expression level is the PVRIG expression level on the surface of NK cells or NK cell subsets in healthy controls.

[0008] In one specific implementation, when the PVRIG expression level on the surface of NK cells or NK cell subsets in a type 1 diabetic patient is low relative to the standard expression level and is less than 80% of the standard expression level, the probability of diagnosing the diabetic patient as being in a non-honeymoon period is high.

[0009] In one specific implementation, the type 1 diabetes disease progression prediction product is used to predict diabetes progression based on the rate of pancreatic islet function failure.

[0010] In one specific implementation, the expression level of PVRIG on the surface of NK cells or NK cell subsets in patients with type 1 diabetes is negatively correlated with the rate of pancreatic islet function decline.

[0011] In one specific implementation, the product is a reagent or a reagent kit.

[0012] The beneficial effects of the present invention include at least the following:

[0013] I. This invention is the first to discover that the expression levels of PVRIG on the surface of NK cells, CD16-positive NK cells, and CD16-negative NK cells differ between the honeymoon and non-honeymoon phases in patients with type 1 diabetes. Thus, the PVRIG expression levels on the surface of NK cells, CD16-positive NK cells, or CD16-negative NK cells can be used to diagnose the stage of disease progression in patients with type 1 diabetes, providing a basis for personalized treatment.

[0014] Second, this invention is the first to discover that the expression level of PVRIG on the surface of NK cells, CD16-positive NK cells, and CD16-negative NK cells is negatively correlated with the rate of pancreatic islet function decline. The PVRIG expression level in the group with slower pancreatic islet function decline is significantly higher than that in the group with faster pancreatic islet function decline. Thus, the progression of diabetes can be predicted based on the relationship between the detected PVRIG expression level on the surface of NK cells, CD16-positive NK cells, or CD16-negative NK cells and the rate of pancreatic islet function decline, providing a basis for personalized treatment. Attached Figure Description

[0015] Figure 1 NK cells and CD16 in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR) - NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 1 In the figure, A represents the expression of PVRIG on the surface of NK cells in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR). Figure 1 In this context, B represents the CD16 levels in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR).- Expression of PVRIG on the surface of NK cells Figure 1 In this context, C represents the CD16 levels in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR). + Expression of PVRIG on the surface of NK cells;

[0016] Figure 2 NK cells and CD16 were analyzed in 20 patients during the honeymoon phase (PR) and the post-honeymoon phase (PR-post). - NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 2 In the figure, A represents the expression of PVRIG on the surface of NK cells during the honeymoon phase (PR) and the post-honeymoon phase (PR-post). Figure 2 B in the text represents the CD16 levels during the patient's honeymoon phase (PR) and the post-PR phase (PR-post). - Expression of PVRIG on the surface of NK cells Figure 2 In the diagram, C represents the CD16 levels during the patient's honeymoon phase (PR) and the post-PR phase (PR-post). + Expression of PVRIG on the surface of NK cells;

[0017] Figure 3 This is a graph showing the relationship between CP-AUC concentration and follow-up time in patients in the rapid and slow CP-AUC groups of Example 2.

[0018] Figure 4 NK cells and CD16 levels at baseline in patients in the rapid decline group (rapid) and the slow decline group (slow) - NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 4 A represents the expression of PVRIG on the surface of NK cells at baseline in patients in the rapid decline group and the slow decline group. Figure 4 B represents the baseline CD16 levels in patients in the rapid decline group and the slow decline group. - Expression of PVRIG on the surface of NK cells Figure 4 C represents the baseline CD16 levels in patients in the rapid decline group and the slow decline group. + Expression of PVRIG on the surface of NK cells;

[0019] Figure 5 Forest plot showing the correlation between baseline immunological characteristics and the risk of severe pancreatic islet dysfunction events based on the Cox proportional hazards model;

[0020] Figure 6 A statistical graph showing the average area under the receiver operating characteristic curve (AUC) and its 95% confidence interval for each machine learning model in 30 random samplings;

[0021] Figure 7 The receiver operating characteristic (ROC) curves for the logistic regression model in 30 random samplings. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] Unless otherwise specified, all materials and reagents used in the following examples are commercially available. Specifically: the C-peptide assay kit (Advia Centaur System) was purchased from Siemens, Germany; lymphocyte separation medium (Histopaque-1077) was purchased from Sigma-Aldrich; Dulbecco phosphate-buffered saline (DPBS) was purchased from Gibco; flow cytometry antibodies including Anti-Human CD3 Antibody, Anti-Human CD56 (NCAM-1) Antibody, and Anti-Human CD16 Antibody were purchased from BD Biosciences; and flow cytometry antibody for Anti-human CD112R (PVRIG) Antibody and cell viability dye (Zombie Aqua Fixable Viability Kit) were purchased from BioLegend.

[0024] The specific operational steps for separating peripheral blood mononuclear cells (PBMCs) using density gradient centrifugation, identifying immune cell subsets using full-spectrum flow cytometry, and detecting the expression of PVRIG surface molecules using full-spectrum flow cytometry in this invention are as follows:

[0025] Density gradient centrifugation was used to separate PBMCs: Human peripheral blood samples were collected via heparin or EDTA anticoagulant tubes and processed within 2 hours. The procedure involved diluting whole blood 1:1 with Dulbecco phosphate-buffered saline (DPBS, Gibco, USA) and carefully separating it onto an equal volume of lymphocyte separation medium (Histopaque-1077, Sigma-Aldrich, USA). The samples were centrifuged at 800×g for 25 minutes at room temperature on the lowest deceleration setting without brakes. The mononuclear cell layer (PBMCs) at the plasma-separation medium interface was carefully aspirated and transferred to a new centrifuge tube, and washed twice with DPBS (300×g, 10 minutes). The final cell pellet was resuspended in an appropriate buffer for subsequent experiments. Cell counting and viability were assessed using a hemocytometer or automated cell counter via trypan blue staining.

[0026] Full-spectrum flow cytometry for the identification of immune cell subsets: To analyze different NK cell subsets in peripheral blood, flow cytometry was used to label and detect the surface of NK cells and their functional phenotypes in PBMCs. The specific procedure is as follows: 1~2×10 6 PBMCs were aliquoted into flow cytometry tubes, washed with DPBS containing 2% fetal bovine serum (FBS), and then incubated with reactive dye (Zombie Aqua, BioLegend) at room temperature in the dark for 15 minutes, followed by washing. A mixture of fluorescently labeled anti-human monoclonal antibodies was added and incubated at 4°C in the dark for 30 minutes. The antibodies used included Anti-Human CD3 Antibody, Anti-Human CD56 (NCAM-1) Antibody, and Anti-Human CD16 Antibody from BD Biosciences, and Anti-Human PVRIG Antibody from BioLegend. After staining, the cells were washed again and resuspended in FACS buffer for flow cytometry analysis. Data were acquired using a BD LSR Fortessa high-parameter flow cytometer and analyzed using FlowJo 10.8.1 software. Cell populations were identified using appropriate gating strategies based on the fluorescent labeling of the flow cytometry antibodies, and NK cells and CD16 cells were obtained. - NK cells and CD16 + The proportion and distribution of populations such as NK cells.

[0027] Full-spectrum flow cytometry was used to detect the expression of PVRIG surface molecules: using fluorescent labeling of flow cytometry antibodies, and employing appropriate gating strategies, expression of PVRIG molecules was detected in NK cells and CD16 cells. - NK cells and CD16 + PVRIG markers in NK cell populations and other populations +Subpopulations, ultimately yielding NK cells and CD16 - NK cells and CD16 + Expression of PVRIG on the surface of NK cells.

[0028] Example 1

[0029] Correlation between PVRIG expression levels on the surface of NK cells and NK cell subsets and the stage of T1D in patients.

[0030] 1.1 Sample Selection

[0031] Newly diagnosed T1D patients (35 cases), honeymoon patients (50 cases), and healthy controls (20 cases) were recruited and followed up regularly for 2 years, with patients re-examined every 3 months. The honeymoon period (PR) was determined based on one of the following two criteria: (1) C-peptide level ≥ 300 pmol / L under mixed meal tolerance test (MMTT) stimulation, or (2) insulin dose-adjusted HbA1c (IDAA1c) ≤ 9, where IDAA1c = HbA1c (%) + 4 × daily insulin dose (units / kg body weight). When C-peptide value was missing, IDAA1c value was used to determine whether the patient was in the honeymoon period.

[0032] 1.2 Identification of immune subset cells in the above samples

[0033] All samples were collected from fasting patients for at least 10 hours, with venous blood samples collected the following morning. Patients in the honeymoon period were followed up until the end of the honeymoon period, at which point blood samples were collected again. Peripheral blood was used to separate PBMCs using density gradient centrifugation, and immune subsets were detected by flow cytometry. Cell populations were identified using appropriate gating strategies, and NK cells and CD16 cells were identified. - NK cells and CD16 + NK cells were collected to detect PVRIG expression levels and obtain the proportion and distribution of each population.

[0034] 1.3 Detection of PVRIG expression levels

[0035] The expression of PVRIG surface molecules in each immune subset of cells identified in step 1.2 was detected using full-spectrum flow cytometry. Detailed results can be found in [link to relevant documentation]. Figure 1 and Figure 2 As shown.

[0036] Figure 1 NK cells and CD16 in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR)- NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 1 In the figure, A represents the expression of PVRIG on the surface of NK cells in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR). Figure 1 In this context, B represents the CD16 levels in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR). - Expression of PVRIG on the surface of NK cells Figure 1 In this context, C represents the CD16 levels in the healthy control group (HC), the honeymoon period patient group (PR), and the non-honeymoon period patient group (NPR). + Expression of PVRIG on the surface of NK cells. Figure 1 It can be seen that PVRIG in honeymoon patients affects NK cells and CD16. - NK cells and CD16 + The expression level on the surface of NK cells was significantly higher than that of patients in the non-honeymoon period, and was close to that of PVRIG in the healthy control group.

[0037] Figure 2 NK cells and CD16 were analyzed in 20 patients during the honeymoon phase (PR) and the post-honeymoon phase (PR-post). - NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 2 In the figure, A represents the expression of PVRIG on the surface of NK cells during the honeymoon phase (PR) and the post-honeymoon phase (PR-post). Figure 2 B in the text represents the CD16 levels during the patient's honeymoon phase (PR) and the post-PR phase (PR-post). - Expression of PVRIG on the surface of NK cells Figure 2 In the diagram, C represents the CD16 levels during the patient's honeymoon phase (PR) and the post-PR phase (PR-post). + The expression of PVRIG on the surface of NK cells, from Figure 2 It can be seen that PVRIG in patients in the honeymoon phase affects NK cells and CD16. - NK cells and CD16 + The expression level on the surface of NK cells was significantly higher than that in the phase after the end of their honeymoon period.

[0038] Combination Figure 1 and Figure 2 It is known that the expression level of PVRIG is closely related to the disease stage of T1D patients and can be used as a biomarker for diagnosing the disease stage.

[0039] In this invention, NK cells and CD16 cells of patients with type 1 diabetes... - NK cells and CD16 + When the expression level of PVRIG on the surface of NK cells is 80%~120% of the standard expression level, it indicates that the expression levels of PVRIG in both patients are similar; when the expression levels of NK cells and CD16 in patients with type 1 diabetes are similar... - NK cells and CD16 + When the expression level of PVRIG on the surface of NK cells is low relative to the standard expression level and is less than 80% of the standard expression level, it indicates that the PVRIG expression level is significantly lower than the standard expression level.

[0040] In this invention, the standard expression levels are NK cells and CD16 in healthy controls. - NK cells and CD16 + PVRIG expression level on the surface of NK cells.

[0041] Example 2

[0042] PVRIG expression levels on the surface of NK cells and NK cell subsets predict the rate of pancreatic islet function failure in patients with type 1 diabetes.

[0043] 2.1 Case Selection

[0044] We recruited 67 newly diagnosed type 1 diabetes mellitus (T1D) patients and followed them regularly for a total of 2 years, with follow-up every 3 months. Patients were categorized based on the rate of pancreatic islet function decline; please refer to [link to relevant documentation]. Figure 3 During the first 18 months of follow-up, 35 patients whose CP-AUC decreased to 50% below baseline were classified as the "rapid decline" group, while the remaining 32 patients whose CP-AUC did not decrease to 50% below baseline were classified as the "slow decline" group.

[0045] Figure 3 This section details the specific division of 67 newly diagnosed T1D patients into "rapid decline" and "slow decline" groups based on the rate of pancreatic function decline during a 2-year follow-up period. Figure 3 The text presents the trends of CP-AUC over time in two groups of patients, specifically: Appendix Figure 3 The significance analysis in the data refers to the comparisons between groups within each follow-up month (i.e., the differences in data between different groups within the same follow-up month); Appendix Figure 3 The final "****" represents the total difference in the follow-up trend of the overall data between the "rapid decline" and "slow decline" groups.

[0046] 2.2 Identification of immune subsets in the above cases

[0047] Peripheral blood samples were collected from all participants at baseline enrollment. Peripheral blood cells (PBMCs) were isolated using density gradient centrifugation, and immune subsets were detected by flow cytometry. Cell populations were identified using appropriate gating strategies, and NK cells and CD16 cells were identified. - NK cells and CD16 + NK cells were collected, and the proportion and distribution of each population were obtained.

[0048] 2.3 Detection of PVRIG expression levels

[0049] The expression of PVRIG surface molecules in each immune subset of cells identified in step 2.2 was detected using full-spectrum flow cytometry. Detailed results can be found in [link to relevant documentation]. Figure 4 As shown.

[0050] Figure 4 NK cells and CD16 levels at baseline in patients in the rapid decline group (rapid) and the slow decline group (slow) - NK cells and CD16 + The expression of PVRIG on the surface of NK cells, among which, Figure 4 A represents the expression of PVRIG on the surface of NK cells at baseline in patients in the rapid decline group and the slow decline group. Figure 4 B represents the baseline CD16 levels in patients in the rapid decline group and the slow decline group. - Expression of PVRIG on the surface of NK cells Figure 4 C represents the baseline CD16 levels in patients in the rapid decline group and the slow decline group. + The expression of PVRIG on the surface of NK cells, from Figure 4 It can be seen that NK cells and CD16 in the group with slower pancreatic islet function failure - NK cells and CD16 + The expression level of PVRIG on the surface of NK cells was significantly higher than that in the group with faster pancreatic islet function decline. This indicates that PVRIG... + Cells in NK cells, CD16 - NK cells and CD16 + A higher proportion of NK cells is a protective factor for pancreatic islet function in patients with T1D.

[0051] 2.4 Cox Regression Analysis

[0052] After collecting and organizing the patients' clinical follow-up data over 2 years and baseline immunological characteristics, a Cox proportional hazards regression model was used to analyze whether PVRIG-related immunological characteristics could predict the risk of severe pancreatic islet function loss events (C-peptide <100 pmol / L). The results are detailed in Table 1 and [Table data would be inserted here]. Figure 5 .

[0053]

[0054] Figure 5 The correlation between baseline immunological characteristics and the risk of severe pancreatic islet dysfunction events was demonstrated in the Cox proportional hazards model (where a hazard ratio <1 indicates that the corresponding characteristic is a protective factor, and a p-value <0.05 indicates that the characteristic is significantly associated with the event risk).

[0055] From Table 1 and Figure 5 It can be seen that the patient's NK cells and CD16 levels at baseline - NK cells and CD16 + NK cell surface PVRIG expression can be independently used as a protective factor and is significantly associated with the risk of severe pancreatic islet function loss during follow-up.

[0056] 2.5 Evaluate the predictive performance of PVRIG expression in different NK cell subsets as a predictive indicator.

[0057] Using machine learning classification model algorithms, multiple predictive models for predicting the rate of pancreatic islet function failure in patients with type 1 diabetes (T1D) are constructed. These predictive models can be Cox proportional hazards regression models or machine learning classification models such as K-nearest neighbors (KNN), logistic regression, support vector machine (SVM), Gaussian NB, decision tree, random forest, and ensemble gradient boosting models (XGBoost, Light GBM, CatBoost).

[0058] Please see Figure 6 , Figure 6 The mean area under the receiver operating characteristic (AUC) and its 95% confidence interval are shown for each machine learning classification model across 30 random samples. The AUC reflects the discriminative power of each model in distinguishing the rate of decline in pancreatic function based on baseline PVRIG-related immunological features. The logistic regression model showed the best discriminative power, achieving an AUC of 0.70.

[0059] Please see Figure 7 , Figure 7 The receiver operating characteristic (ROC) curves of the best-performing logistic regression model from 30 random samples are shown. The mean area under the ROC curve is 0.70, and the 95% confidence interval is 0.67–0.73.

[0060] from Figure 6 and Figure 7 It can be seen that baseline PVRIG expression levels in different NK cell subsets have good discriminative power and can predict the rate of pancreatic islet function failure in T1D patients in the early stages of the disease. This result suggests that PVRIG expression levels can be used to predict the rate of pancreatic islet function failure in T1D patients and provide a basis for personalized treatment.

[0061] It should be noted that the rate of pancreatic islet function failure can reflect the disease progression of newly diagnosed type 1 diabetes mellitus (T1D). The faster the pancreatic islet function fails, the faster the islets are destroyed, and the faster the T1D disease progresses.

[0062] Combining the experimental results of Examples 1 and 2, it can be seen that PVRIG significantly increases NK cells and CD16 levels in both honeymoon and non-honeymoon phase patients with type 1 diabetes. - NK cells and CD16 + Differential expression was observed on the surface of NK cells, and in PVRIG, NK cells and CD16 were found in patients with type 1 diabetes whose pancreatic islet function deteriorated more rapidly than those whose pancreatic islet function deteriorated more slowly. - NK cells and CD16 + Differential expression was observed on the surface of NK cells. Specifically, PVRIG was significantly underexpressed in non-honeymoon patients. PVRIG expression was negatively correlated with the rate of pancreatic islet function failure, indicating that PVRIG is closely related to type 1 diabetes and can serve as a new target for type 1 diabetes, providing a new approach for the diagnosis and prediction of disease progression of type 1 diabetes.

[0063] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions and substitutions can be made without departing from the inventive concept, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. The application of a reagent for detecting the expression levels of NK cells and / or NK cell subsets PVRIG in the preparation of a product for predicting the progression of type 1 diabetes, characterized in that, The NK cell subsets are CD16-positive NK cells or CD16-negative NK cells. The type 1 diabetes disease progression prediction product is used to predict diabetes progression based on the rate of pancreatic islet function failure.

2. The application according to claim 1, characterized in that, The expression level of PVRIG on the surface of NK cells or NK cell subsets in patients with type 1 diabetes is negatively correlated with the rate of pancreatic islet function decline.

3. The application according to claim 1 or 2, characterized in that, The product is a reagent or reagent kit.

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