Application of PVRIG as target spot in preparation of products for diagnosing type 1 diabetes and predicting disease progression

Abstract

The invention provides application of PVRIG as a target spot in preparation of products for diagnosing type 1 diabetes and predicting disease progression. The invention specifically relates to. The expression of the PVRIG on the surfaces of NK cells, CD16 positive NK cells and CD16 negative NK cells in a melatony patient and a non-melatony patient of the type 1 diabetes mellitus has differential expression, and the expression of the PVRIG on the surfaces of NK cells, CD16 positive NK cells and CD16 negative NK cells in a patient with faster pancreatic islet function failure and a patient with slower pancreatic islet function failure of the type 1 diabetes mellitus has differential expression, and the expression of the PVRIG on the surfaces of NK cells, CD16 positive NK cells and CD16 negative NK cells in a patient with slower pancreatic islet function failure of the type 1 diabetes mellitus has differential expression. Therefore, the PVRIG is closely related to the type 1 diabetes mellitus and can be used as a new target of the type 1 diabetes mellitus, and a new way is provided for diagnosis and disease progression prediction of the type 1 diabetes mellitus.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to the application of PVRIG as a target in the preparation of products for the diagnosis and prediction of disease progression of type 1 diabetes. Background Art

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

[0003] PVRIG is an inhibitory immune checkpoint receptor belonging to the poliovirus receptor (PVR) family and was discovered in 2016. PVRIG is expressed on the surface of T cells and NK cells. By binding to the ligand PVRL2, it transmits immunosuppressive signals and competitively antagonizes CD226 to exert immunosuppressive functions. Currently, PVRIG has been applied to tumor immunotherapy, but it has not been reported in autoimmune diseases and metabolic diseases (such as T1D). Summary of the Invention

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

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

[0006] In a specific embodiment, the type 1 diabetes diagnosis product is used to diagnose the disease development stage of diabetic patients, and the disease development stage includes the honeymoon period or the non-honeymoon period.

[0007] In a specific embodiment, when the expression level of PVRIG on the surface of NK cells or NK cell subsets in type 1 diabetes patients is 80% - 120% of the standard expression level, the probability of diagnosing that the diabetic patients are in the honeymoon period is high, and the standard expression level is the expression level of PVRIG on the surface of NK cells or NK cell subsets of healthy controls.

[0008] In a specific embodiment, when the expression level of PVRIG on the surface of NK cells or NK cell subsets in patients with type 1 diabetes is low relative to the standard expression level and lower than 80% of the standard expression level, the probability of diagnosing that the diabetic patient is in the non-honeymoon period is high.

[0009] In a specific embodiment, 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 a specific embodiment, 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 failure.

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

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

[0013] First, the present invention for the first time discovers that there are differential expressions of the PVRIG expression levels on the surfaces of NK cells, CD16-positive NK cells, and CD16-negative NK cells in the honeymoon period and non-honeymoon period of patients with type 1 diabetes. Thus, based on the detected PVRIG expression levels on the surfaces of NK cells, CD16-positive NK cells, or CD16-negative NK cells, it can be used to diagnose the stage of disease development of patients with type 1 diabetes, providing a basis for individualized treatment.

[0014] Second, the present invention for the first time discovers that the PVRIG expression levels on the surfaces of NK cells, CD16-positive NK cells, and CD16-negative NK cells are negatively correlated with the rate of pancreatic islet function failure. The PVRIG expression level in the group with slower pancreatic islet function failure is significantly higher than that in the group with faster pancreatic islet function failure. Thus, based on the relationship between the detected PVRIG expression levels on the surfaces of NK cells, CD16-positive NK cells, or CD16-negative NK cells and the rate of pancreatic islet function failure, it can be used to predict diabetes progression, providing a basis for individualized treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 For the NK cells, CN16 of the healthy control group (HC), honeymoon period patient group (PR), and non-honeymoon period patient group (NPR) - NK cells and CN16 + Expression of PVRIG on the surface of NK cells, where Figure 1 A in shows the expression of PVRIG on the surface of NK cells in the healthy control group (HC), honeymoon period patient group (PR), and non-honeymoon period patient group (NPR), Figure 1 B in shows the CN16 of the healthy control group (HC), honeymoon period patient group (PR), and non-honeymoon period patient group (NPR)- Expression of PVRIG on the surface of NK cells Figure 1 In which, C is CN16 of the healthy control group (HC), honeymoon period patient group (PR), and non-honeymoon period patient group (NPR) + Expression of PVRIG on the surface of NK cells

[0016] Figure 2 For NK cells and CN16 of 20 patients in the honeymoon period stage (PR) and the stage after the end of their honeymoon period (PR-post) - NK cells and CN16 + Expression of PVRIG on the surface of NK cells, where Figure 2 In A, it is the expression of PVRIG on the surface of NK cells of patients in the honeymoon period stage (PR) and the stage after the end of their honeymoon period (PR-post) Figure 2 In B, it is CN16 of patients in the honeymoon period stage (PR) and the stage after the end of their honeymoon period (PR-post) - Expression of PVRIG on the surface of NK cells Figure 2 In C, it is CN16 of patients in the honeymoon period stage (PR) and the stage after the end of their honeymoon period (PR-post) + Expression of PVRIG on the surface of NK cells

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

[0018] Figure 4 For NK cells and CN16 at baseline of patients in the rapid decline group (rapid) and the slow decline group (slow) - NK cells and CN16 + Expression of PVRIG on the surface of NK cells, where Figure 4 In A, it is the expression of PVRIG on the surface of NK cells at baseline of patients in the rapid decline group and the slow decline group Figure 4 In B, it is CN16 at baseline of patients in the rapid decline group and the slow decline group - Expression of PVRIG on the surface of NK cells Figure 4 In C, it is CN16 at baseline of patients in the rapid decline group and the slow decline group + Expression of PVRIG on the surface of NK cells

[0019] Figure 5 It is a forest plot showing the correlation between baseline immunological characteristics and the risk of severe loss of islet function events based on the Cox proportional hazards model

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

[0021] Figure 7 Receiver operating characteristic curve (ROC curve) of the Logistic Regression model in 30 random samplings. Specific implementation mode

[0022] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0023] The materials, reagents, etc. used in the following embodiments can be obtained from commercial channels without special instructions. Specifically: The C-peptide detection kit (Advia Centaur System) was purchased from Siemens AG, Germany, the lymphocyte separation solution (Histopaque-1077) was purchased from Sigma-Aldrich, the Dulbecco's phosphate buffered solution (DPBS) was purchased from Gibco, and the flow cytometry detection antibodies such as Anti-Human CD3 Antibody, Anti-Human CD56 (NCAM-1) Antibody, and Anti-Human CD16 Antibody were all purchased from BD Biosciences. The flow cytometry detection antibody of Anti-human CD112R (PVRIG) Antibody and the cell viability dye (Zombie Aqua Fixable Viability Kit) were both purchased from BioLegend.

[0024] The specific operation steps for separating peripheral blood mononuclear cells (PBMCs) by density gradient centrifugation, identifying immune cell subsets by a full-spectrum flow cytometer, and detecting the expression of the PVRIG surface molecule by a full-spectrum flow cytometer in the present invention are as follows:

[0025] Density gradient centrifugation to isolate PBMCs: Human peripheral blood samples were collected in sodium heparin or EDTA anticoagulation tubes and processed within 2 hours. The specific operation included: whole blood was diluted with Dulbecco's phosphate buffered saline (DPBS, Gibco, USA) in a 1:1 ratio and carefully layered on top of 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 and without brakes at the lowest deceleration gear. After centrifugation, the mononuclear cell layer (PBMC) at the interface between plasma and separation medium 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 appropriate buffer for subsequent experiments. Cell count and activity were assessed by trypan blue staining on a hemacytometer or an automated cell counter.

[0026] Identification of immune cell subsets by full-spectrum flow cytometry: To analyze different NK cell subsets in peripheral blood, flow cytometry was used to perform surface labeling and detection of NK cells and their functional phenotypes in PBMC. The specific operation was as follows: 1-2×106PBMC were divided into flow tubes, washed with DPBS containing 2% fetal bovine serum (FBS), and then incubated with active dye (ZombieAqua, BioLegend) at room temperature in the dark for 15 minutes, followed by washing. A fluorescently labeled anti-human monoclonal antibody mixture 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, 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 detection on the machine. Data were collected using a BD LSRFortessa high-parameter flow cytometer and analyzed using FlowJo 10.8.1 software. Fluorescent labeling of flow cytometry antibodies was used to identify cell populations through appropriate gating strategies, and NK cells, CN16 - NK cells and CN16 + The proportion and distribution of NK cells and other groups.

[0027] Full spectrum flow cytometry was used to detect the expression of PVRIG surface molecules: using the fluorescent labeling of flow cytometry antibodies, appropriate gating strategies were used to detect the expression of PVRIG surface molecules in NK cells, CN16 - NK cells and CN16 + PVRIG marker in NK cells and other populations + Subpopulations, ultimately obtaining NK cells, CN16- NK cells and CN16 + Expression of PVRIG on the surface of NK cells

[0028] Example 1

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

[0030] 1.1 Sample selection

[0031] Newly diagnosed T1D patients (35 cases), honeymoon period patients (50 cases) and healthy controls (20 cases) who have been clearly diagnosed with T1D were recruited and followed up regularly for 2 years. The patients were reexamined every 3 months. The determination of the honeymoon period (PR) was based on one of the following two criteria: (1) C-peptide level ≥ 300 pmol / L under stimulation of the Mixed Meal Tolerance Test (MMTT), or (2) Insulin Dose Adjusted HbA1c (IDAA1c) ≤ 9, where IDAA1c = HbA1c (%) + 4 × daily insulin dose (units / kg body weight). When the C-peptide value was missing, the IDAA1c value was used to determine whether it was in the honeymoon period.

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

[0033] For all samples, after fasting for at least 10 hours, venous blood samples were collected on an empty stomach the next morning. Blood samples were collected again from honeymoon period patients after following up until the end of the honeymoon period. Peripheral blood was separated into PBMCs by density gradient centrifugation, and immune subsets were detected by flow cytometry. Cell populations were identified through appropriate gating strategies, and NK cells, CN16 - NK cells and CN16 + NK cells were obtained for subsequent detection of PVRIG expression levels and to obtain the proportions and distributions of each population.

[0034] 1.3 Detection of PVRIG expression levels

[0035] The expression of the PVRIG surface molecule on each immune subset cell identified in step 1.2 was detected using a full-spectrum flow cytometer. The detection results are shown in Figure 1 and Figure 2 as shown.

[0036] Figure 1 For NK cells and CN16 of the healthy control group (HC), honeymoon period patient group (PR) and non-honeymoon period patient group (NPR) - NK cells and CN16 +Expression of PVRIG on the surface of NK cells, where Figure 1 A in Figure 1 shows the expression of PVRIG on the surface of NK cells in the healthy control group (HC), honeymoon-phase patients group (PR), and non-honeymoon-phase patients group (NPR). - Expression of PVRIG on the surface of NK cells Figure 1 B in + shows the expression of PVRIG on the surface of NK cells in the healthy control group (HC), honeymoon-phase patients group (PR), and non-honeymoon-phase patients group (NPR). From Figure 1 it can be seen that the expression level of PVRIG in honeymoon-phase patients on NK cells, CN16 - NK cells and CN16 + on the surface of NK cells is significantly higher than that in non-honeymoon-phase patients and is close to the expression level of PVRIG in the healthy control group.

[0037] Figure 2 shows the expression of PVRIG on the surface of NK cells, CN16 - NK cells and CN16 + in the NK cells of 20 patients during the honeymoon phase (PR) and after the end of the honeymoon phase (PR-post), where Figure 2 A in Figure 2 shows the expression of PVRIG on the surface of NK cells in the honeymoon phase (PR) and after the end of the honeymoon phase (PR-post) of the patients. - Expression of PVRIG on the surface of NK cells Figure 2 B in + shows the expression of PVRIG on the surface of NK cells in the honeymoon phase (PR) and after the end of the honeymoon phase (PR-post) of the patients. From Figure 2 it can be seen that the expression level of PVRIG in patients in the honeymoon phase on NK cells, CN16 - NK cells and CN16 + on the surface of NK cells is significantly higher than that after the end of the honeymoon phase.

[0038] Combined with Figure 1 and Figure 2 it can be 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 the present invention, in the NK cells and CN16 of type 1 diabetes patients- NK cells and CN16 + When the PVRIG expression level on the surface of NK cells is 80% - 120% of the standard expression level, it indicates that the PVRIG expression levels of the two are close; when the NK cells and CN16 of type 1 diabetic patients - NK cells and CN16 + When the PVRIG expression level on the surface of NK cells is low expression relative to the standard expression level and lower than 80% of the standard expression level, it indicates that the PVRIG expression level is significantly lower than the standard expression level.

[0040] In the present invention, the standard expression level is that of NK cells and CN16 of healthy controls - NK cells and CN16 + The PVRIG expression level on the surface of NK cells.

[0041] Example 2

[0042] The expression levels of PVRIG on the surfaces of NK cells and NK cell subsets predict the rate of islet function failure in T1D patients

[0043] 2.1 Case selection

[0044] Newly diagnosed T1D patients (67 cases) who had been clearly diagnosed with T1D were recruited and followed up regularly for a total of 2 years, with the patients having a review every 3 months. The patients were classified based on the rate of islet function failure. Please refer to Figure 3 , during the first 18 months of follow-up, 35 patients whose CP-AUC decreased to less than 50% of the baseline level were classified into the "rapid decline" group, while the remaining 32 patients whose CP-AUC did not decrease below 50% of the baseline were classified into the "slow decline" group.

[0045] Figure 3 For the 67 newly diagnosed T1D patients recruited during the 2-year follow-up, the specific details of the division into "rapid decline" and "slow decline" groups according to the rate of islet function failure Figure 3 shows the changing trends of CP-AUC over time for the two groups of patients. Specifically: Attached Figure 3 the significance analysis in is for the comparison between groups in each follow-up month (i.e., the differences in data between different groups in the same follow-up month); Attached Figure 3 the final "****" is the total difference in the follow-up changing trends of the overall data of the "rapid decline" and "slow decline" groups.

[0046] 2.2 Identify the immune subset cells of the above cases

[0047] Peripheral blood samples were collected from all study subjects at baseline enrollment. PBMCs were separated from the peripheral blood samples by density gradient centrifugation, and immune subsets were detected by flow cytometry. Cell populations were identified through an appropriate gating strategy, and NK cells, CN16 were identified. - NK cells and CN16 + NK cells, and the proportions and distributions of each population were obtained.

[0048] 2.3 Detection of PVRIG expression level

[0049] The expression of the PVRIG surface molecule on the immune subset cells identified in step 2.2 was detected using a full-spectrum flow cytometer. The detection results are shown in Figure 4 as follows.

[0050] Figure 4 For the expression of PVRIG on the NK cells, CN16 - NK cells and CN16 + NK cells at baseline in patients in the rapid decline group (rapid) and the slow decline group (slow), where Figure 4 A shows the expression of PVRIG on the NK cells at baseline in patients in the rapid decline group and the slow decline group, Figure 4 B shows the expression of PVRIG on the CN16 - NK cells at baseline in patients in the rapid decline group and the slow decline group, Figure 4 C shows the expression of PVRIG on the CN16 + NK cells at baseline in patients in the rapid decline group and the slow decline group. It can be seen from Figure 4 that the expression level of PVRIG on the NK cells, CN16 - NK cells and CN16 + NK cells in the group with slower pancreatic islet function failure is significantly higher than that in the group with faster pancreatic islet function failure. It shows that PVRIG + cells have a relatively high proportion in NK cells, CN16 - NK cells and CN16 + NK cells and are protective factors for the pancreatic islet function of T1D patients.

[0051] 2.4 Cox regression analysis

[0052] After collecting and organizing the clinical follow-up data of patients within 2 years and the immunological characteristic data at baseline, through the Cox proportional hazards regression model, it was analyzed whether the immunological characteristics related to PVRIG could predict the risk of the occurrence of severe pancreatic islet function loss events (C-peptide < 100 pmol / L). The results are shown in Table 1 and Figure 5 .

[0053] Table 1

[0054]

[0055] Figure 5 It shows the correlation between baseline immunological characteristics and the risk of severe loss of islet function 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).

[0056] From Table 1 and Figure 5 it can be seen that NK cells and CN16 - NK cells and CN16 + The expression of PVRIG on the surface of NK cells can all independently serve as protective factors and are significantly associated with the risk of severe loss of islet function during follow-up.

[0057] 2.5 Evaluate the predictive performance of the expression of PVRIG in each cell subset of NK cells as a predictive indicator

[0058] Using machine learning classification model algorithms, multiple prediction models for predicting the rate of islet function failure in T1D patients are constructed. The prediction models can be Cox proportional hazards regression models or machine learning classification models such as K-Nearest Neighbor (KNN), Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes (Gaussian NB), Decision Tree, Random Forest, and Ensemble Gradient Boosting Models (XGBoost, Light GBM, CatBoost).

[0059] Please refer to Figure 6 , Figure 6 which shows the area under the curve (AUC) of the average receiver operating characteristic curve and its 95% confidence interval for each machine learning classification model in 30 random samplings. The area under the receiver operating characteristic curve (AUC) reflects the discrimination efficacy of each model in distinguishing the rate of decline of islet function through PVRIG-related immunological characteristics at the patient's baseline. The discrimination efficacy is best shown by the Logistic Regression model, reaching an average area under the receiver operating characteristic curve (AUC) = 0.70.

[0060] Please refer to Figure 7 , Figure 7 which shows the receiver operating characteristic curve (ROC curve) of the best-performing Logistic Regression model in 30 random samplings, with an average area under the receiver operating characteristic curve of 0.70 and a 95% confidence interval of 0.67 - 0.73.

[0061] From Figure 6 and Figure 7 It can be seen that the baseline PVRIG has good discrimination efficacy in the expression of each cell subset of NK, and can predict the speed of islet function failure in T1D patients at the early stage of the disease. This result suggests that the expression level of PVRIG can be used to predict the speed of islet function failure in T1D patients and provide a basis for individualized treatment.

[0062] It should be noted that the speed of islet function failure can reflect the disease progression of newly diagnosed T1D. The faster the islet function fails, the faster the islets are damaged, indicating that the disease progression of T1D is faster.

[0063] Combined with the experimental results of Example 1 and Example 2, it can be known that there are differential expressions in the expression of PVRIG on NK cells, CN16 - NK cells and CN16 + on the surface of NK cells between honeymoon period patients and non-honeymoon period patients with type 1 diabetes, and there are also differential expressions in the expression of PVRIG on NK cells, CN16 - NK cells and CN16 + on the surface of NK cells between patients with faster islet function failure and patients with slower islet function failure in type 1 diabetes. Specifically, PVRIG is significantly lowly expressed in non-honeymoon period patients, and the expression of PVRIG is negatively correlated with the speed of islet function failure, indicating that PVRIG is closely related to type 1 diabetes and can be used as a new target for type 1 diabetes, providing a new way for the diagnosis and disease progression prediction of type 1 diabetes.

[0064] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions and substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. Use of a reagent for detecting the expression level of PVRIG in NK cells and / or NK cell subsets in the preparation of a product for diagnosing type 1 diabetes and predicting disease progression, characterized in that, The NK cell subset is CD16-positive NK cells or CD16-negative NK cells.

2. The application according to claim 1, wherein The type 1 diabetes diagnosis product is used to diagnose the disease development stage of diabetic patients, and the disease development stage includes a honeymoon period or a non-honeymoon period.

3. The application according to claim 2, wherein When the expression level of PVRIG on the surface of NK cells or NK cell subsets in type 1 diabetic patients is 80% - 120% of the standard expression level, the probability of diagnosing that the diabetic patient is in the honeymoon period is high, and the standard expression level is the expression level of PVRIG on the surface of NK cells or NK cell subsets of healthy controls.

4. The application according to claim 3, wherein When the expression level of PVRIG on the surface of NK cells or NK cell subsets in type 1 diabetic patients is low expression relative to the standard expression level and lower than 80% of the standard expression level, the probability of diagnosing that the diabetic patient is in the non-honeymoon period is high.

5. The application according to claim 1, wherein The type 1 diabetes disease progression prediction product is used to predict diabetes progression based on the rate of pancreatic islet function failure.

6. The application according to claim 5, characterized in that, The expression level of PVRIG on the surface of NK cells or NK cell subsets in type 1 diabetic patients is negatively correlated with the rate of pancreatic islet function failure.

7. The application according to any one of claims 1 to 6, characterized in that, The product is a reagent or a kit.

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