CN111735963A - Use of specific lectins for the identification of diabetic/non-diabetic nephropathy based on the carbohydrate chains of sialoglycoproteins - Google Patents
Use of specific lectins for the identification of diabetic/non-diabetic nephropathy based on the carbohydrate chains of sialoglycoproteins Download PDFInfo
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Abstract
The present invention provides the use of specific lectins for the identification of diabetic/non-diabetic renal diseases based on sialoglycoprotein sugar chains. The specific lectins are divided into an up-regulated group and a down-regulated group; wherein: the up-regulation group is at least one of NPA, HHL and PHA-E + L, PTL-II; down-regulation to at least one of MPL, SBA, LTL, AAL; if the expressions of glycoprotein sugar chain structures identified by the up-regulation group and the down-regulation group are respectively obviously up-regulated and obviously down-regulated, the nephropathy is particularly Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD); in addition, the following detection models were constructed:
Description
Technical Field
The invention relates to a marker for identifying diabetic nephropathy and non-diabetic nephropathy based on a glycoprotein carbohydrate chain, and a related product and application thereof.
Background
With the development of socioeconomic and the improvement of living standard of people, the incidence rate of diabetes mellitus is continuously increased, and the number of people suffering from diabetes mellitus may break through 6.93 hundred million by 2045 years. Diabetic Nephropathy (DN) occurs in up to 20-40% of diabetic patients. Studies have shown that diabetic patients are not necessarily diabetic nephropathy when they develop renal disease. Many studies have found that some patients with diabetes complicated with kidney disease have different sensitivities to different treatment regimens compared with diabetic nephropathy patients, and in order to distinguish them, the disease is called non-diabetic renal disease (NDRD), i.e. patients with primary kidney disease have diabetes but do not cause secondary diabetic kidney damage. Since Diabetic Nephropathy (DN) is not the same type of disease as non-diabetic renal disease (NDRD), and its pathological features, clinical manifestations, treatment response, disease progression rate and prognosis are different, it is important to make accurate differential diagnosis (there are few cases where DN and NDRD coexist, but they are rare, and the treatment scheme has no significant difference compared with DN and NDRD alone, so this study is not considered temporarily). The gold standard for identifying DN and NDRD is kidney biopsy puncture, but the gold standard cannot be developed conventionally due to the limitations of being invasive, high in cost, high in technical condition requirement and the like, so that a simple, easy, stable and reliable identification method is urgently needed in clinical work.
Glycosylation is an important post-translational modification, and more than half of the proteins found in mammals are glycoproteins. Glycosylation plays an important role in glycoprotein function, on one hand, glycosylation can promote protein folding and ensure correct conformation, and on the other hand, glycosylation mediates protein-protein interaction, so that the protein has complete biological functions. At the cellular level, glycosylation affects the recognition between cells and plays an important role in the processes of tumorigenesis, infiltration, and metastasis. Numerous studies have shown that changes in protein glycosylation occur during the development of the disease, and that disease-related aberrant glycosylation can reflect the progression of the disease.
With the development of medical technology, the development trend of in vitro detection is to find a nondestructive detection method. Saliva is one of body fluids of a human body, and in recent years, the saliva serving as a clinical sample is widely applied to drug level monitoring, disease condition monitoring and curative effect evaluation of various diseases such as AIDS, autoimmune diseases, alcoholic liver cirrhosis, cystic fibrosis, diabetes, cardiovascular diseases, caries and the like. And new technology and new method based on saliva detection will gradually become a direction for the development of noninvasive clinical diagnosis in the future.
Disclosure of Invention
The present invention aims to provide a scheme for identifying diabetic nephropathy/non-diabetic nephropathy based on sialoglycoprotein sugar chains.
The following application scheme is specifically obtained:
in a first aspect, use of specific lectins to construct a test tool for identifying diabetic/non-diabetic renal disease based on sialoglycoprotein sugar chains, said specific lectins being classified into an up-regulated group and a down-regulated group; wherein:
the up-regulation group is at least one of NPA, HHL and PHA-E + L, PTL-II;
down-regulation to at least one of MPL, SBA, LTL, AAL;
the identification basis is as follows: for a tested saliva sample of a patient with nephropathy, if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
Here, the specific form in which the test tool gives "authentication basis" is not limited, for example: the above conditions are described in the attached product specifications; if software is involved, the basis can also be embodied by a corresponding algorithm.
Furthermore, the test tool is a lectin chip, a kit or an intelligent terminal taking a lectin detection result as input.
For lectin chips, more comprehensive sampling of lectin chips could be used in practice, but for the identification of diabetes/non-diabetes, only the specific lectins mentioned above are of interest. The preparation and detection of lectin chips are conventional methods, and the steps generally comprise saliva collection, salivary protein treatment and fluorescence labeling, chip detection and data analysis.
In a second aspect, a kit for identifying diabetic/non-diabetic nephropathy based on a sialoglycoprotein sugar chain, said kit being provided with a lectin; the lectin is the specific lectin described above; the instructions for use of the kit give the following identification: for a tested saliva sample of a patient with nephropathy, if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
The expression level of the specific lectin in the glycoprotein sugar chain of the control group can also be directly given in the instruction manual of the kit. The control group herein may be taken from diagnosed non-diabetic Nephropathy (NDRD), or from diagnosed Diabetic Nephropathy (DN), or both control groups may be established.
In a third aspect, an intelligent terminal includes a processor and a program memory, and when a program stored in the program memory is loaded by the processor, the following steps are implemented:
obtaining a lectin test result of a saliva sample to be tested of a patient with renal disease, wherein the lectin test result represents the expression level of glycoprotein sugar chains corresponding to the specific lectin;
acquiring the glycoprotein sugar chain expression level and identification basis (which can be recorded in an intelligent terminal in advance, or can be acquired from the outside, such as through networking and the like) of a corresponding control group; the identification basis is as follows: if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD);
and comparing the expression level of the glycoprotein sugar chain of the tested saliva sample with that of the control group, and outputting an identification conclusion according to the identification basis.
Accordingly, a computer-readable storage medium storing a computer program characterized in that: the computer program, when loaded by a processor, performs the steps set out above.
In a fourth aspect, the application also combines a binary Logistic stepwise regression analysis to utilize 37 kinds of lectins to construct a diagnosis model of diabetic nephropathy/non-diabetic nephropathy, and finally, the differentiation capability of the diagnosis model is evaluated through ROC curve analysis (receiver operating characterization curve).
An intelligent terminal comprising a processor and a program memory, wherein when a program stored in the program memory is loaded by the processor, the following steps are implemented:
obtaining a lectin test result of a saliva sample to be tested of a renal disease patient, wherein the lectin test result represents the expression level of glycoprotein sugar chains corresponding to lectins WGA and LTL;
calculating detection values of the following models based on the lectin test result;
outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is less than or equal to 0.443, the Diabetic Nephropathy (DN) is judged; otherwise, if the detected value is greater than 0.443, the disease is non-diabetic Nephropathy (NDRD).
Accordingly, a computer readable storage medium stores a computer program which, when loaded by a processor, performs the steps listed above.
The above-mentioned "prompt/output discrimination result" may be a result of directly outputting whether the detected value belongs to Diabetic Nephropathy (DN) or non-diabetic Nephropathy (NDRD), or may be a result of only providing a detection value, a reference value and a discrimination criterion, or both.
The specific form of the intelligent terminal can be a special self-service terminal device, and can also be a mobile phone, a tablet personal computer and the like which are commonly used by common users.
In a fifth aspect, a system for identifying diabetic/non-diabetic nephropathy based on sialoglycoprotein sugar chains, comprising:
A. a device for obtaining the expression level of a specific glycoprotein sugar chain structure of a saliva sample, which is specifically recognized by lectins WGA and LTL, respectively;
B. the intelligent terminal (calculating the detection value of the model) is described above.
In the system, the device for acquiring the expression level of the specific glycoprotein sugar chain structure of the saliva sample and the intelligent terminal for identifying the diabetic/non-diabetic nephropathy based on the salivary glycoprotein sugar chain can be integrated medical diagnosis comprehensive devices, can also be separated devices, and even do not have any signal connection (for example, lectin test results of the saliva sample can be automatically taken and sent by medical staff, patients and the like).
The device for obtaining the expression level of the sugar chain structure of the specific glycoprotein in the saliva sample comprises a lectin chip, an incubation box and a biochip scanning system, wherein at least lectin WGA and LTL are arranged on the lectin chip.
The specific operations of the application schemes of the above aspects can be referred and combined mutually.
The application has the following beneficial effects:
the method can rapidly and accurately identify whether the specific type of the renal disease patient to be tested belongs to diabetic nephropathy or non-diabetic nephropathy by detecting the expression level difference of the sugar chain structure of the specific glycoprotein in the saliva of the patient to be tested.
Drawings
FIG. 1 is a scattergram analysis of the differential lectin results among the lectin chip analysis results of 38 saliva samples (diabetic nephropathy DN, n-19; non-diabetic nephropathy NDRD, n-19). In the figure, the ordinate represents the normalized fluorescence intensity NFI corresponding to the lectin on the lectin chip, the horizontal line in the figure represents the comparison between the two groups shown at the two ends, P is P-Value obtained according to one-way variance analysis, wherein P is less than 0.05, which represents significant difference; p <0.01, indicating that the difference was very significant; p <0.001, indicating very significant difference; DN: diabetic nephropathy patients, NDRD: patients with non-diabetic nephropathy.
FIG. 2 shows the results of ROC curve analysis of 8 different lectins selected from the lectin chip; AUC is area under line.
FIG. 3 is a DN/NDRD discrimination model constructed based on lectin chip results and its diagnostic ability was evaluated by ROC curve analysis.
Detailed Description
The following describes the relevant experiments and conclusions of the present application to disclose the scientificity and feasibility of the technical solution of the present application. It should be understood that the development process and effort of the applicant is more than this.
Screening of saliva protein differential sugar chain structure of diabetic nephropathy and non-diabetic nephropathy patients
The research method comprises the following steps:
1.1 saliva sample Collection and pretreatment
Saliva samples from Diabetic Nephropathy (DN) and non-diabetic Nephropathy (NDRD) patients used in this experiment were strictly approved by the general hospital of the liberation force (Human Research Ethics Committees (HRECs)). All volunteers donated saliva samples, along with clinicians assisting in sampling guidance, were informed, consented and highly coordinated to the study work, completing collection of saliva samples under uniform sampling requirements. The concrete requirements are as follows: the organs of the sample donor except kidney disease should not have chronic diseases such as inflammation and tumor, and the donor needs to be sure not to eat within 3 hours before collecting saliva and not to take medicines within 24 hours when sampling, then gargle three times with clean sterile physiological saline (0.9% NaCl) to ensure the oral hygiene of the donor and no food residues, the tongue tip of the donor is propped against the upper jaw, and the saliva sample naturally secreted under the tongue is collected into a 2mL centrifuge tube, and 10 μ L Protease Inhibitor (Protease Inhibitor Cocktail, Sigma-Aldrich, U.S. A) is immediately added into the ice bath for temporary storage. A total of 38 saliva samples were collected under clinician guidance: 19 diabetic nephropathy patients (DN, n ═ 19) and 19 non-diabetic nephropathy patients (NDRD, n ═ 19) are listed, and specific sample information is shown in Table 1.
Collecting saliva within 12 hours, subpackaging saliva into centrifuge tubes according to 1mL, adding 1 XPBS to complement to 1mL if the quantity is less than 1mL, centrifuging for 10,000g × 15min, carefully sucking supernatant, measuring the concentration by a micro nucleic acid protein analyzer (Nano-drop), adding protease inhibitor according to the quantity of 1mg saliva protein to 10 μ L protease inhibitor, mixing uniformly, and subpackaging at-80 ℃ for storage.
TABLE 1 lectin chip for distinguishing diabetic nephropathy from non-diabetic nephropathy and diagnosis model construction saliva sample information
1.2 fluorescence labeling and quantification of saliva proteins of patients
The protein concentration of the saliva sample was measured using a Nanophotometer, and 100. mu.g of saliva protein was taken together with an equal volume of 0.1mol/L Na2CO3/NaHCO3pH9.3 buffer mixed, add 5 u L fluorescent solution at room temperature and light incubated for 3 hours, during which time the sample is strictly protected from light and kept shaking. After the reaction was completed, 5. mu.L of 4M hydroxylamine solution was added to the sample and reacted on ice for 15 minutes. Separating the fluorescence labeling protein by using a Sephadex G-25 desalting column. And (3) collecting the fluorescent sample by using a new 1.5mL centrifuge tube, quantifying, protecting the fluorescence-labeled sample from light to prevent the quenching of the fluorescent group, and storing at-20 ℃.
1.3 lectin chip comparative analysis of the glycoprotein carbohydrate chain profile in patients with diabetic and non-diabetic nephropathy
The present application employs a lectin chip composed of 37 kinds of lectins, which can recognize and bind common N-sugar chain and O-sugar chain structures. The specific preparation process of the chip is described in Yannan Qin et al.
The chip was removed, placed in a 37 ℃ vacuum desiccator for vacuum drying for 30min, then washed in 1 XPBST for 5min × 2 times, and then washed with 1 XPBS for 5min × 2 times. And (5) spin-drying by using a small glass slide centrifugal machine after cleaning. Then adding 120 mu L of lectin chip sealing liquid into each sample application area of the chip, placing the chip in an incubation box for sealing, and reacting for 1h at 37 ℃ in a constant-temperature incubation box to seal active groups on the surface of the chip so as to reduce the signal value of the back of the slide during fluorescence scanning. After the sealing is finished, washing the chip by using 1 XPBST for 5min multiplied by 2 times and 1 XPBS for 5min multiplied by 2 times, adding fluorescence labeled salivary protein into each spot area of the chip after drying, mixing the fluorescence labeled salivary protein with 1.5 times of incubation buffer solution, incubating the chip at the constant temperature of 37 ℃ for 3h, washing the chip by using 1 XPBST for 5min multiplied by 2 times and 1 XPBS for 5min multiplied by 2 times after the reaction is finished, and reading data by using a laser confocal chip scanner after drying. The scanning parameters are set as: excitation wavelength 532nm, PMT power 70% and excitation intensity 100%.
1.4 lectin chip data analysis
The quantification process of the lectin chip fluorescence signal was performed by GenePix Pro (4000B) software, and data obtained by data extraction for each array included: the net difference FI (fluorescence intensity) obtained by subtracting the background signal from the probe signal, the standard deviation SD (Standard development) of the background, and the like. In the analysis process, firstly, judging the validity of the point data according to the FI/SD of each point, taking the point with the FI/SD being more than or equal to 1.5 as valid data, calculating the standard normalized fluorescence signal value NFI (normalized fluorescence intensity) of each probe, namely dividing the mean FI value of each probe by the sum of the FI values of 37 detection probes, and expressing the sum as follows by using a formula: NFIx＝Median FIx/(Median FI1+Median FI2+Median FI3+…+Median FI37) From this, NFI for 37 lectin probes per array were obtained and used for statistical analysis.
The statistical analysis here was mainly based on the ratio analysis of DN and NDRD group data using GraphPad Prism 6.0, combined with t-test screening for lectins with significant differences in NFI (ratio of either lectin to greater than 1.50 or less than 0.67, and p to less than 0.05 between groups).
The research results are as follows:
2.1 lectin chip comparative analysis of sugar chains of salivary glycoproteins from patients with DN and NDRD
The lectin chip was used to comparatively analyze glycoprotein sugar chain profiles of saliva samples of 19 DN patients and 19 NDRD patients. The comparison result is shown in fig. 1, and a series of statistical data can visually reflect the significance and the dispersion degree of the difference compared among the groups on the graph. According to the results of the difference analysis among the groups: compared with other glycoprotein sugar chain structures in saliva, the lectin PHA-E + L of the lectin NPA and HHL of High-Mannose and Man alpha 1-6Man, the lectin PHA-E + L of the lectin GlcNAc, bi-antisense N-glucans, tri-and tetra-antisense complex-type N-glucan, the lectin PTL-II of Gal, blood group H and T-antisense in DN patients can be specifically recognized, and the binding strength of the lectin PHA-E + L of Gal, blood group H and T-antisense in NDRD patients is obviously higher. While the binding strength of the lectin MPL recognizing Gal beta 1-3GalNAc, the lectin SBA recognizing alpha-or beta-linked terminal GalNAc, (GalNAc) n, GalNAc alpha 1-3Gal, blood-group A, the lectin LTL recognizing Fuc alpha 1-3Gal beta 1-4GlcNAc and the lectin AAL recognizing GlcNFuc alpha 1-3/6 in DN patients was significantly lower than that in NDRD patients.
From this, the following clinical applications can be derived: for a patient with unknown specific type of nephropathy, detecting the salivary glycoprotein sugar chain structure, if the glycoprotein sugar chain structure specifically recognized by the lectins NPA, HHL and PHA-E + L, PTL-II is obviously up-regulated, and the glycoprotein sugar chain structure specifically recognized by the lectins MPL, SBA, LTL and AAL is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
Second, construction of DN diagnosis model by 37 kinds of agglutinin
Accurate discrimination between DN and NDRD patients was achieved by analysis of chip data for glycoprotein carbohydrate chain profiles in combination with lectins as follows. ROC curve analysis was first performed on the 8 lectins with significant differences to assess the diagnostic and identification ability of the samples. In general, the method was considered to be discriminatory when the area under the line (AUC) of the ROC curve reached 0.80. The patients with DN and NDRD were distinguished by using the above 8 lectins as indicators, and the results of ROC analysis are shown in FIG. 2, in which the AUC of the lectin NPA is 0.831, the specificity is 73.68%, the sensitivity is 84.21%, and the AUC of the remaining 7 lectins is not 0.8. Indicating that a single lectin has certain disadvantages for distinguishing between DN and NDRD patients.
In addition, it is also noted that: the ratio of lectin WGA, which recognizes Multivalent Sia and (GlcNAc) n, between DN and NDRD groups (DN/NDRD) was 1.46, although not more than 1.5, the statistical analysis showed significant statistical differences in the two groups of data (p < 0.01).
The present application builds a diagnostic model by using a binary stepwise Logistic regression method in combination with 37 lectins to improve discrimination.
A DN/NDRD identification model is constructed by combining DN and NDRD patient saliva protein agglutinin chip data through a binary stepwise Logistic regression method by using SPSS software, and is used for distinguishing DN from NDRD patients. The final optimized model formula is as follows:
the AUC of model DN/NDRD is 0.9, cut-off value is 0.443 (DN patient is detected to be less than or equal to 0.443, otherwise NDRD patient is detected to be more than 0.443), the sensitivity is 94.74 percent, and the specificity is 89.47 percent; the ROC curves of 17 out of 19 DN patients and 18 out of 19 NDRDs can be accurately identified as shown in fig. 3.
The model only relates to two kinds of agglutinin, and can identify diabetic nephropathy/non-diabetic nephropathy more simply, conveniently and accurately at low cost.
Claims (9)
1. Use of specific lectins for the construction of a test tool for the identification of diabetic/non-diabetic nephropathy based on sialoglycoprotein sugar chains, characterized in that: the specific lectins are divided into an up-regulated group and a down-regulated group; wherein:
the up-regulation group is at least one of NPA, HHL and PHA-E + L, PTL-II;
down-regulation to at least one of MPL, SBA, LTL, AAL;
the identification basis is as follows: for a tested saliva sample of a patient with nephropathy, if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
2. Use according to claim 1, characterized in that: the test tool is a lectin chip, a kit or an intelligent terminal taking a lectin detection result as input.
3. A kit for identifying diabetic/non-diabetic nephropathy based on a sialoglycoprotein sugar chain, said kit being provided with a lectin; the method is characterized in that: the lectin is the specific lectin described in claim 1; the instructions for use of the kit give the following identification: for a tested saliva sample of a patient with nephropathy, if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
4. An intelligent terminal comprising a processor and a program memory, characterized in that: the program stored in the program memory realizes the following steps when being loaded by the processor:
obtaining a lectin test result of a saliva sample to be tested of a renal disease patient, the lectin test result representing a glycoprotein sugar chain expression level corresponding to a specific lectin described in claim 1;
acquiring glycoprotein sugar chain expression level and identification basis of a corresponding control group; the identification basis is as follows: if the expression of the glycoprotein sugar chain structure identified by the up-regulation group is obviously up-regulated, and the expression of the glycoprotein sugar chain structure identified by the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD);
and comparing the expression level of the glycoprotein sugar chain of the tested saliva sample with that of the control group, and outputting an identification conclusion according to the identification basis.
5. A computer-readable storage medium storing a computer program, characterized in that: which when loaded by a processor carries out the steps as listed in claim 4.
6. An intelligent terminal comprising a processor and a program memory, characterized in that: the program stored in the program memory realizes the following steps when being loaded by the processor:
obtaining a lectin test result of a saliva sample to be tested of a renal disease patient, wherein the lectin test result represents the expression level of glycoprotein sugar chains corresponding to lectins WGA and LTL;
calculating detection values of the following models based on the lectin test result;
outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is less than or equal to 0.443, the Diabetic Nephropathy (DN) is judged; otherwise, if the detected value is greater than 0.443, the disease is non-diabetic Nephropathy (NDRD).
7. A computer-readable storage medium storing a computer program, characterized in that: which when loaded by a processor carries out the steps as set out in claim 6.
8. A system for identifying diabetic/non-diabetic nephropathy based on sialoglycoprotein sugar chains, comprising:
A. a device for obtaining the expression level of a specific glycoprotein sugar chain structure of a saliva sample, which is specifically recognized by lectins WGA and LTL, respectively;
B. an intelligent terminal as claimed in claim 6.
9. The system for identifying diabetic/non-diabetic nephropathy based on sialoglycoprotein sugar chains according to claim 8, wherein: the equipment for acquiring the expression level of the sugar chain structure of the specific glycoprotein in the saliva sample comprises a lectin chip, an incubation box and a biochip scanning system, wherein at least lectin WGA and LTL are arranged on the lectin chip.
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