TWI688770B - Method for detecting diabetic nephropathy - Google Patents

Abstract

The present invention provides a method for detecting diabetic nephropathy, which includes step (a) detecting the expression amount of apoprotein A4 adsorbed by concanavalin A (Con A) in a plasma sample of an individual to be tested; and (b) Compare the expression amount of Concanavalin A adsorbed apolipoprotein A4 in the plasma sample of the tested individual with the Concanavalin A adsorbed apolipoprotein A4 reference substance; wherein the Concanavalin A adsorbed by the tested individual When the expression level of apolipoprotein A4 is higher than that of concanavalin A adsorbed to apolipoprotein A4 control substance, the individual to be tested has the risk of suffering from diabetic nephropathy.

Description

Method for detecting diabetic nephropathy

The invention relates to the field of diabetic nephropathy, in particular to the field of detecting diabetic nephropathy.

End-stage renal disease (ESRD) is an important issue in global public health. Diabetic Nephropathy (DN) is the main cause of end-stage renal disease. Diabetic nephropathy refers to a condition in which kidney dysfunction occurs in type 1 or type 2 diabetic patients. Generally speaking, patients with type 1 diabetes have a higher probability to develop renal function lesions at the same time. However, studies in recent years have shown that after entering obvious proteinuria, the course of type 2 diabetes and type 1 diabetic nephropathy are not significantly different. Diabetic nephropathy can be roughly divided into three stages: initial stage (microalbuminuria), clinical diabetic nephropathy stage (obvious proteinuria), and end stage renal disease.

About 30-50% of the diabetes (Diabetes Mellitus, DM) population in Taiwan develops into diabetic kidney failure each year. About 40%-50% of current dialysis patients in China are caused by diabetes. Among the many causes of end-stage kidney disease in the United States and Japan, more than 40% are caused by diabetes. The global diabetes population reached 382 million in 2013, and it is estimated to reach 592 million by 2035, which is equivalent to 10.1% of the global adult population aged 20 to 79 years.

Early diagnosis of kidney disease and appropriate medical intervention can effectively prevent or delay the progression of the disease to the end stage of kidney disease. The clinical features of diabetic nephropathy include histopathological changes, especially "mesangial expansion". Kidney pathology is currently the mainstream method of slicing needles, and it is relatively safe, but it is an invasive examination after all, and it still has risks. The "microproteinuria period" is often used to confirm the onset of diabetic nephropathy, but the symptoms of proteinuria are often detected in other diseases, such as high blood pressure, inflammation, or heart disease. And only 30-45% of patients with microproteinuria will develop to proteinuria within ten years. Moreover, even if kidney pathological changes were observed, many patients still showed normal proteinuria. Therefore, an accurate and non-invasive detection method is needed to confirm and observe the course of diabetic nephropathy.

If the attack rate of diabetic nephropathy can be effectively reduced, it can not only improve the quality of life of patients, but also save a lot of medical resources for the country. Therefore, how to detect and prevent these chronic diseases early to avoid or delay the occurrence of kidney failure is an urgent matter in the prevention and treatment of kidney disease; this can not only reduce the impact of the disease on the family and personal career, but also reduce the overall social economy The burden of cost.

The amount of protein expression in the circulatory system can reflect the physiological state. In the study of protein proteomics of urine in diabetic nephropathy, several proteins that can be used as biomarkers have been found, but since the kidney function has been damaged at this time, the changes in the expression of these urinary proteins may be directly from the kidney disease itself. On the other hand, plasma has a large amount of albumin and αβγ globulin, and some proteins with potential as biomarkers may also be expressed at very low concentrations. Therefore, the use of affinity protein detection test can greatly improve the efficiency of searching biomarkers.

The purpose of the present invention is to develop a suitable blood biomarker for diabetic nephropathy using protein body technology, and the biomarker has been verified and confirmed to be an effective and sensitive early detection of diabetic nephropathy (DN) Blood biomarkers. In order to achieve the above-mentioned problems and objectives, the present invention uses LC-MS to detect the protein of concanavalin A (Con A) adsorbed protein in the blood of patients in normal group, diabetic group and diabetic nephropathy group. The performance level is verified by Western blot method and confirmed by Con A-based ELISA, and the method for early detection of diabetic nephropathy of the present invention is obtained.

The invention provides a detection kit for detecting diabetic nephropathy, comprising: a reagent for detecting the expression amount of concanavalin A (Con A) adsorbed apolipoprotein A-IV (ApoA4) in a blood sample And a reference substance, wherein the reference substance is from a sample not suffering from diabetic nephropathy.

The performance reagent that detects the adsorption of Con A to ApoA4 is an immunoassay detection reagent.

The present invention also provides a method for detecting diabetic nephropathy, comprising the steps of: (a) detecting the expression amount of concanavalin A (Con A) adsorbed apolipoprotein A4 in a blood sample of an individual to be tested; and (b) Compare the expression amount of Concanavalin A adsorbed apolipoprotein A4 in the blood sample of the tested individual with the Concanavalin A adsorbed apolipoprotein A4 reference substance; wherein the Concanavalin A adsorbed by the tested individual When the expression level of apolipoprotein A4 is higher than that of the concanavalin A adsorbed apolipoprotein A4 control, the individual to be tested has a risk of suffering from diabetic nephropathy.

The control strain is from an individual who does not suffer from diabetes and/or diabetic nephropathy.

When the expression amount of ApoA4 adsorbed by ConA in the sample is higher than that of the control, preferably the expression amount of ApoA4 adsorbed by Con A in the sample is at least 1.4 times that of the control.

The test kit provided by the present invention can be used in the method provided by the present invention. The method of use includes the steps of: (1) obtaining a blood sample of an individual to be tested; (2) using the reagent of the test kit to detect the The expression amount of Con A adsorbed ApoA4 in the specimen; and (3) Compare the expression amount of ApoA4 adsorbed by Con A in the specimen and the control. The amount of Con A adsorbed ApoA4 in this sample is higher than that of the control, then the individual is at risk of developing diabetic nephropathy.

The blood specimens of the present invention include whole blood specimens, plasma specimens, and serum specimens.

The method used in the present invention to detect the expression level of serum Con A adsorption ApoA4 is an immunoassay method, including Western blot method, ELISA, ELISA microfluidic chip, fluorescent immunoassay, radioimmunoassay, immunoprecipitation method, flow cytometry , Surface plasmon resonance method, rate turbidity measurement method, radioimmunoassay method, unidirectional radiation immunodiffusion method, precipitation method and/or agglutination method.

The immunoassay detection reagents used in the present invention include Western blot detection reagents, ELISA detection reagents, ELISA microfluidic wafer detection reagents, fluorescent immunoassay detection reagents, radioimmunoassay detection reagents, immunoprecipitation detection reagents, flow cytometry Cytometer detection reagents, surface plasmon resonance detection reagents, rate turbidity detection reagents, radioimmunoassay detection reagents, unidirectional radiation immunodiffusion detection reagents, precipitation detection reagents, and/or agglutination detection reagents.

experimental design

The experimental design outline of this embodiment is shown in FIG. 1.

In this example, the test subjects were divided into three groups: a control group (healthy person, Health Control, HC, 21 persons), a diabetes group (Diabetic Mellitus, DM, 62 persons), and a diabetic nephropathy (DN, 59 persons) . The statistical characteristics of experimenters in each group are shown in Table 1. According to Table 1, compared with the control group, patients in the DM group and the DN group have higher blood glucose concentrations. The BMI of the DN group was higher than that of the control group. In addition, compared with the control group and the DM group, the DN group had higher BUN and Cr performance.

a：p＜0.05（相較於對照組）；b：p＜0.05（相較於DM組） ；c：p＜0.05（相較於DN組） Table 1. Patient statistical characteristics

a: p<0.05 (compared to the control group); b: p<0.05 (compared to the DM group); c: p<0.05 (compared to the DN group)

6 samples of the same gender and age were selected from the three groups as the discovery cohort, and 14 samples of the same gender and age were selected from the three groups as the verification cohort, Select samples from three groups (HC: n=21; DM: n=62; DN: n=59) as validation groups (Validation cohort). ConA affinity chromatography

In this example, the Pierce™ Coomassie Plus (Bradford) Assay Kit was used to detect the protein concentration in the serum. Add 4 μL of serum in duplicate and BSA to a 96-well plate containing 200 μL of reaction reagent, and then incubate the plate at 37°C for 10 minutes. Then measure the absorbance of each sample at 595 nm. A linear standard curve was prepared using BSA standard concentration (0-1500 μg/mL). The protein concentration to be measured is calculated using the formula of the BSA linear standard curve. The steps of enriching serum-derived Con A-bound proteins obtained from serum are as follows. 100 μL of pooled serum was mixed with 300 μL of agarose-bound ConA at 4°C and left overnight. The ConA adsorbed serum was washed with 8 times of binding buffer (binding buffer, 25 mM Tris/1 mM MnCl ₂ /1 mM CaCl ₂ /500 mM NaCl; pH=7.6). Protein digestion

Con A adsorbed protein obtained with 3 mg of serum was subjected to in-solution digestion using the In-Solution Tryptic Digestion and Guanidination Kit. In the reduction step, 15 μL of digestion buffer, 1.5 μL of reduction buffer, and 3 μg/μL of sample are mixed to form a mixed solution. Adjust the volume of the mixed solution to 27 μL with ultrapure water and incubate at 95°C for 5 minutes. 3 μL of alkylation buffer containing iodoacetamide was added to the mixed solution, and incubated at room temperature in the dark for 20 minutes. In the digestion step, 1 μL of activated trypsin was added to the mixed solution (final mixed solution volume was 32 μL), and then incubated at 37°C for 3 hours. Then add 1 μL of activated trypsin and incubate at 30°C overnight. LC-MS/MS

The peptide mixture formed by the breakdown of trypsin was analyzed by LC-MS/MS. The peptide mixture was separated by chromatography column (25 cm x 75 μm id BEH130 C18, 130 Å pore size, 1.7-μm particles); gradient: buffer B (0.1% formic acid in acetonitrile) increased from 5% to 40 within 30 minutes %; flow rate: 300 nl/min; column temperature: 35°C; Buffer A: 0.1% formic acid in water. An Orbitrap analyzer was used to obtain a full-scan MS spectrum (m/z 350-1600) with an AGC target value of 10 ⁶ . Orbitrap system resolution is set to r = 60,000 (system resolution value is provided by m/z 400). The top 10 strongest ions are sequentially segregated to a target value of 7,000 and fragmented in a linear ion trap using CID. Dynamic exclusion time is 90 seconds

Use PEAKS 7 to analyze MS/MS mass spectrometry data. Search in the protein database and use de novo sequence to identify the protein with mass spectrometry results. Quantify proteins with different signal intensities using non-labeled quantification Expectation-maximization-based algorithm is used for deconvolution operation, feature detection, and refinement calculation. Use PEAKS 7.0's PEAKS Q function to quantitatively analyze the database search results, using the following parameters: mass error tolerance (mass error tolerance): 20 ppm; retention time (retention time shift tolerance): 6 minutes; error detection rate ( false detection rate): <1%. Immunoblot

The amount of ApoA4 protein bound by serum-derived Con A was detected with 25 μg of serum-derived Con A-bound protein by Western blotting, using anti-ApoA4 monoclonal antibody. ELISA

Anti-ApoA4 monoclonal antibody was used to detect the protein expression of Con A adsorbed to ApoA4. All samples were duplicated.

In other embodiments, immunoprecipitation analysis, radioimmunoassay (RIA), or immuno-chromatographic assay may also be used. data analysis

Student's t- test was used to analyze the significance of the difference between the amount of Con A adsorbed ApoA4 protein, the amount of protein captured by Con A obtained by ApoA4 in serum, and the dot density measurement method.

result Candidate biomarker exploration ( Discovery )

From the three groups (control, DM, DN), 6 samples of the same gender and age were selected as the exploration group. Run LC-MS to identify each Con A-adsorbed protein. As shown in Table 2, 32 Con A adsorbed proteins in the three groups showed different performance levels. Comparing the DN and DM groups, 25 Con A adsorbed proteins showed higher performance in the DN group, and 7 Con A adsorbed proteins showed lower performance. Among these proteins, ApoA4 has gradually increased performance in the control group, DM group, and DN group (gradual increase of 2 times), which is also consistent with the development of DN. The results show that ApoA4 can be used as a serum biomarker for the progression of DN.

驗證（ Verification ） Table 2. a: multiples increase or decrease; b: not applicable, not applicable

Verification (Verification)

From the three groups, 14 samples of the same gender and age were selected as the verification group. Western blot method was used to quantify Con A adsorption ApoA4 in the samples of the verification group to verify the analysis results of MS. The results are shown in Figure 2A. Compared with the control group, the DM group had a significant increase in ApoA4 performance (p=0.0243), and the DN group also had a significant increase in serum ApoA4 performance (p=0.0008). Compared with the control group, the performance of ApoA4 was even higher (p=0.0001). The Western blot semi-quantitative ROC curve in Figure 2B shows that since AUCs are significantly higher than 0.5, ApoA4 can be used to distinguish the control group, DM group, and DN group. The AUC results are shown in Table 3.

確認（ Validation ） Table 3. Comparison Table of AUC, Sensitivity, and Specificity of Western Ink Dot Method

Confirmation (Validation)

Con A-based ELISA confirmed that Con A adsorbed ApoA4 protein by Con A-based ELISA in the discovery group (Concover Cohort) and verification group (Verification cohort). Performance in each group. The results are shown in Figure 3A. Compared with the control group, the expression level of serum ApoA4 in the DM group increased significantly (p<0.0001), and the expression level of DM ApoA4 was 1.79 times that of the control group. Compared with the DM group, the serum ApoA4 expression was also significantly increased by 1.46 times (p=0.0161). The DN was even higher than the control group ApoA4 performance (p=0.0001). The DN ApoA4 performance was the control group 2.61 times. The ROC curve of Figure 3B shows that ApoA4 can be used to distinguish the control group, DM group, and DN group. The results of AUC (Area Under the Curve of ROC) are shown in Table 4.

Table 4. AUC, sensitivity, and specificity comparison table of ELISA

Referring to FIG. 4A, serum Con A adsorption ApoA4 expression was positively correlated with BUN (r=0.316, p=0.0001); referring to FIG. 4B, serum Con A adsorption ApoA4 expression was positively correlated with Cr (r=0.192, p=0.0001). Related. Referring to FIGS. 4C and 4D, the expression level of serum Con A adsorption ApoA4 was inversely correlated with the expression level of serum protein (r=-0.212, p=0.0205) and the expression level of serum albumin (r=-0.191, p=0.032). These results also show that the expression of ApoA4 in serum Con A is correlated with renal function.

Therefore, the present invention provides a Con A adsorbed ApoA4 as a dynamic blood biomarker for detecting the progression of diabetic nephropathy.

After the above detailed description, it has been fully shown that the present invention has progress in implementation, and is a new invention that has not been seen before, which fully meets the requirements of the invention patent, and the application is submitted according to law. However, the above is only the preferred embodiment of the present invention, and it should not be used to limit the scope of the implementation of the creation, that is, the equal changes and modifications made according to the patent scope of the invention, should all fall within the scope of the invention patent .

HC: Healthy

DM: Diabetes

DN: diabetic nephropathy

Figure 1 is the experimental design outline of the present invention. Figure 2 shows the performance of Con A adsorbed ApoA4 protein in the three groups in the verification group; Figure 2A: Western blot method result diagram; Figure 2B: Western blot semi-quantitative ROC curve diagram. Fig. 3 is an ELISA result chart confirming that Con A adsorbs ApoA4 protein in the group; Fig. 3A: Con A adsorbs ApoA4 protein in ELISA OD values in three groups; Fig. 3B: ROC curve graph. Figure 4 is a correlation diagram of the expression level of ApoA4 adsorption by serum Con A and various proteins; Figure 4A: the expression level of ApoA4 adsorption by serum Con A is positively correlated with BUN (r=0.316, p=0.0001); Figure 4B: ApoA4 adsorption by serum Con A Performance was positively correlated with Cr (r=0.192, p=0.0001); Figure 4C: Serum Con A adsorption ApoA4 performance was inversely correlated with serum protein performance (r=-0.212, p=0.0205); Figure 4D: Serum The expression of Con A adsorbed ApoA4 was inversely correlated with the expression of serum albumin (r=-0.191, p=0.032).

HC: Healthy

DM: Diabetes

DN: diabetic nephropathy

Claims (8)

A detection kit for detecting diabetic nephropathy, comprising: a reagent for detecting the expression amount of Concanavalin A (Con A) adsorbed Apolipoprotein A-IV (ApoA4) in blood samples; And a reference substance, wherein the reference substance is from a sample not suffering from diabetes and/or diabetic nephropathy. The kit as described in item 1 of the patent application scope, wherein the reagent is an immunoassay detection reagent. A method for detecting diabetic nephropathy, comprising the steps of: (a) detecting the expression amount of concanavalin A (Con A) adsorbed apolipoprotein A4 in a blood sample of an individual to be tested; and (b) comparison The expression amount of Concanavalin A adsorbed to apolipoprotein A4 in the blood sample of the subject to be tested and the amount of Concanavalin A adsorbed to apolipoprotein A4 by a reference product, which is derived from a person without diabetes and/or Or an individual with diabetic nephropathy; wherein the expression amount of concanavalin A adsorbed apolipoprotein A4 in the blood sample of the individual to be tested is more than 2.6 times the blood control of an individual without diabetes; or When the expression amount of concanavalin A adsorbed apolipoprotein A4 in the blood sample of the test subject is more than 1.4 times that of the blood control of a diabetic individual without diabetic nephropathy, the test subject has suffered The risk of diabetic nephropathy. The method as described in item 3 of the patent application scope, wherein the individual to be tested is a patient with diabetes. The method as described in item 3 of the patent application scope, wherein the method for detecting the expression amount of concanavalin A adsorbed to apolipoprotein A4 in the blood sample of the subject is an immunoassay detection method. The method as described in item 3 of the patent application scope, wherein the blood sample is a whole blood sample, a serum sample, and a plasma sample. The method as described in item 3 of the patent application scope, wherein the blood sample is a plasma sample. The method as described in item 7 of the patent application scope, wherein the immunoassay detection method includes Western blot method, ELISA, ELISA microfluidic chip, fluorescent immunoassay, radioimmunoassay, immunoprecipitation method, flow cytometry, surface Plasma resonance method, rate turbidity measurement method, radioimmunoassay method, unidirectional radiation immunodiffusion method, precipitation method and/or agglutination method.

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