CN105784829A - Method for establishing finger-print molecular diagnostic model of saliva protein of kidney-deficiency syndrome of type-2 diabetes - Google Patents

Abstract

The invention discloses a method for establishing a finger-print molecular diagnostic model of saliva protein of kidney-deficiency syndrome of type-2 diabetes. The method comprises the following steps: collecting a sample; pre-treating the sample; performing nanometer magnetic bead activation; sampling a saliva sample; performing nanometer magnetic bead elution; collecting data; analyzing data; and establishing a diagnostic model. According to the method, a liquid chip is combined with an MALDI technique to be used for detecting finger-print of saliva protein of a patient suffered from kidney-deficiency syndrome of type-2 diabetes, screening out 9 differential protein peaks (P is less than 0.001) with significant statistical significances from the saliva protein and selecting two differential protein peaks at the mass-to-charge ratio of 5744.34 to 2410.88 for establishing the model; the recognition rate is 91.7%; the predictive ability is 95.6%. The model established according to the method provided by the invention can be used for correctly distinguishing the patient suffered from kidney-deficiency syndrome of type-2 diabetes from the patient suffered from different syndromes of TCM; a new approach is created for clinic disease diagnosis and traditional Chinese medicine distinguishing objectiveness; a new thought for researching the micro-distinguishing objectiveness of traditional Chinese medicine is explored.

Description

Establishment method of molecular diagnostic model of salivary protein fingerprint of type 2 diabetes mellitus with kidney deficiency syndrome

technical field

The invention belongs to the technical field of protein spectrum application, and in particular relates to a method for establishing a molecular diagnosis model of salivary protein fingerprint of type 2 diabetes mellitus syndrome.

Background technique

Type 2 diabetes (Type2diabetesmellitus, T2DM) is a common clinical metabolic disorder in the elderly. The biochemical examination mainly shows the increase of fasting blood glucose and (or) postprandial blood glucose. Typical symptoms may be accompanied by dry mouth, polydipsia, polyhydric In the late stage of the disease, patients often develop atherosclerotic lesions in the heart, brain, kidney and other tissues and organs, resulting in narrowing of the vascular lumen, which leads to a series of heart, brain, kidney, etc. Clinical manifestations of insufficiency of blood supply to tissues and organs such as the kidney, metabolic disorders or even failure. In critically ill patients, due to serious abnormalities in the metabolism of carbohydrates, fats, proteins and other substances, or improper use of insulin, the blood sugar soars sharply in a short period of time, causing the patient to appear in a critical state, such as diabetic ketoacidosis coma (DKA), Hyperglycemic hyperosmolar coma, if the rescue is not timely, the patient's life is in danger. In addition, the disability rate of type 2 diabetes is extremely high. Some elderly patients have serious adverse consequences such as cataracts, glaucoma, blindness, uremia, and even gangrene and amputation of limbs due to repeated or persistent increases in blood sugar, which seriously affect patients. Quality of life, increasing social and economic burden. At present, with the improvement of people's living standards, the extension of the average life expectancy, the aging of the population and the westernization of people's eating habits, the total number of type 2 diabetes patients in my country has ranked first in the world and is in a stage of rapid growth. At the same time, the age of patients with type 2 diabetes in my country is getting younger and younger, and the situation is very grim.

Relatively insufficient insulin secretion (insulin deficiency) and/or reduced biological effect of insulin (insulin resistance) is an important reason for persistently high blood sugar in patients with type 2 diabetes and is the main mechanism of type 2 diabetes. In order to better prevent and treat type 2 diabetes and reduce or prevent the occurrence of complications of type 2 diabetes, more in-depth research has been carried out on its pathogenesis. For example, insulin secretion or dysfunction in patients is not only related to environmental factors such as obesity and too little exercise, but also related to genetic factors such as genes, among which the gene nuclear transcription factor 7-like 2 (transcription factor 7-like2, TCF7L2) is the cause of type 2 Diabetes is the most important genetic gene. In addition to directly affecting the growth, differentiation and function of β cells to interfere with insulin secretion, it can also indirectly cause defects in islet function, thereby causing abnormal blood sugar. With the advancement of science, people's research on type 2 diabetes has gradually shifted from gene to gene expression protein research. For example, it was found that compared with normal people, type 2 diabetes patients had significantly lower salivary prohormone convertase (Prohormone convertase, PC) content The phenomenon, this enzyme is an endoprotease that can process the precursor of neuroendocrine hormones, and can act on pancreatic beta cells to remove part of the peptide from the secreted proinsulin to form insulin, and the reduction of its content can Insufficient production of insulin results in elevated blood sugar.

Traditional Chinese medicine classifies type 2 diabetes as "diabetes", and has a deep understanding and understanding of it, and has established its own schools and treatment theories, such as kidney deficiency theory, spleen deficiency theory, yin deficiency and dryness theory, and spleen theory Governance, treatment from the liver, etc. Traditional Chinese medicine treatment of type 2 diabetes can indeed improve the body's sensitivity to insulin, reduce the side effects and dosage of western medicine, and delay or even effectively prevent the occurrence and development of diabetic complications. However, in real life, the diagnosis and treatment of type 2 diabetes with traditional Chinese medicine has not been well valued and widely used. The main reason is that there are too many methods of syndrome differentiation and typing of type 2 diabetes, and the lack of reliable objective quantification Indicators, which to a considerable extent affect the feasibility and specific operability of traditional Chinese medicine treatment of type 2 diabetes. Therefore, in order to improve the development of TCM theory of type 2 diabetes, it is necessary to start the modern research and development of TCM syndrome differentiation and treatment theory of type 2 diabetes. With the rapid development of modern medical technology, it has become an inevitable trend to use integrated traditional Chinese and Western medicine to study the theory of traditional Chinese medicine. This research starts from the idea of combining macroscopic syndrome differentiation and microscopic syndrome differentiation, and uses salivary proteomics technology to explore type 2 The microscopic material changes of different syndromes of diabetes, try to carry out research on the combination of "disease" and "syndrome" of type 2 diabetes, and provide molecular material basis for the standardization and objectification of clinical syndrome differentiation and classification of type 2 diabetes, with a view to promoting Diagnosis and treatment of type 2 diabetes with traditional Chinese medicine.

With the completion of the Human Genome Project (humangenomicproject, HGP), people have gradually shifted their research focus to proteome research. The term proteome was proposed by Williams and Wilkins in 1994. It refers to all the proteins encoded by the genome, that is, all the proteins of a certain species, individual, organ, tissue and even cells, emphasizing the whole of all the proteins corresponding to the genome. In 2001, when Nature and Science announced the draft of the human genome, they respectively published the comments and prospects of Abbott and Fields. Subsequently, the status of proteomics has been elevated to an unprecedented height, and it is considered to be the commanding height and decisive point of the frontier research strategy of functional genomics. Proteomics is an emerging scientific research technology, which is different from the traditional single gene or single protein research model, but analyzes the composition, expression, modification, structure and function of all proteins in the body or cells at the overall level and interaction, as well as the interaction between protein and nucleic acid, have a comprehensive and essential understanding of the complex activities of life, and conduct dynamic research on proteins from the overall level.

Protein is the executor of life activities and the product of gene expression. Its expression, modification, function and mutual interaction are all affected by genetic and environmental factors, so the appearance of disease in the body will inevitably lead to changes in protein content or form. These The changed protein molecules can provide the basis for the material basis of clinical disease diagnosis. Proteomics mainly uses modern advanced detection technology to detect the characteristics of proteins expressed by all genes in the body ranging from a single cell to the entire body, and compares and analyzes the differentially expressed proteins in physiological or pathological states, drug use and non-medication states To find out the proteins with different expression in different states, so as to provide the basis for molecular diagnostic markers for research on the material basis of life, early detection and diagnosis of diseases, etc.

In recent years, with the development and innovation of modern science and biochemical trace detection and analysis technology, saliva, as a body fluid that is easy to collect clinically and has no traumatic operation, has gradually entered the perspective of people's research activities, and has an inestimable scientific research value. potential and clinical application prospects. First of all, compared with serum samples, the collection of saliva samples is safer, non-invasive, painless for patients during the collection process, easy to accept, and there is no risk of blood-borne disease transmission; compared with urine samples, saliva samples It has the advantages of real-time sampling and more convenience. In addition, saliva testing requires a small sample size, low cost, and easy storage and transportation. Most importantly, the components of saliva samples are highly similar to the components of body fluids such as blood and urine, and have strong sensitivity and specificity for disease diagnosis.

Saliva is an important body fluid that is common in the human body. It contains a large number of blood components such as hormones, proteins, enzymes, antibodies, complements, cytokines, and various microorganisms. These blood components are transported passively across the cell membrane along the concentration gradient or against the concentration gradient It is secreted in saliva through active transport and other pathways, and is in a dynamic process with the influence of pathophysiological changes in the body. Study the relationship between changes in saliva pH, electrolytes, biochemical indicators, microorganisms, immune indicators, proteins and other components and diseases, and found that changes in saliva components can be used as reference indicators for disease diagnosis, curative effect judgment, and drug monitoring. has important potential application value. According to the current literature, saliva can not only be used as a diagnostic marker for infectious diseases such as AIDS and hepatitis B, oral cancer and breast cancer, but also for diabetes, arthritis, heart disease and kidney disease. Diagnosis of various systemic diseases has become the preferred specimen for replacing serological examination by researchers in western countries.

Saliva has biological functions such as digestion of food, lubrication, defense protection, buffering, mechanical cleaning, antibacterial and endocrine, while proteins and polypeptides are the most important substances with biological functions in saliva components. More than 2300 saliva proteins and polypeptides have been found so far There are mainly α-amylase, albumin, cysteine protease, IgA, lysozyme, lactoferrin, mucin and other proteins, among which 98% of salivary proteins are statherin and transferrin. However, by classifying the functions of the discovered salivary proteins, it was found that proteins with unknown functions accounted for 28.7% (the largest proportion), proteins related to immune function accounted for 21%, and proteins related to protein replication and repair accounted for 1.6%. Proteins related to cell motility and secretion accounted for 4.8%, proteins related to transcription and ribosomes accounted for 2.3%, proteins related to cell reproduction and cell cycle accounted for 4.2%, and proteins related to signal transduction, metabolism, cytoskeleton and inner membrane Accounted for 9.7%, 5.2%, 7.1% and so on. It can be seen that the salivary proteins whose biological functions are still unknown still account for a considerable proportion, which needs further research and excavation, and is of great significance. Recent studies have confirmed that the application of high-throughput and high-precision proteomics technology has made it possible for salivary protein biomarkers to be used in early diagnosis and prevention of diseases, targeted biological protein therapy, and prognosis monitoring and judgment.

Therefore, providing a method for establishing a molecular diagnostic model of salivary protein fingerprints for type 2 diabetes with kidney deficiency syndrome that is simple, fast, requires less specimen, and has high sensitivity and specificity has become a technical problem that needs to be solved urgently in this technical field.

Contents of the invention

In view of this, the present invention aims to solve the problems of complex, low sensitivity, poor specificity, long detection time, and large amount of specimens in the existing type 2 diabetes kidney deficiency syndrome detection method, and provides a type 2 diabetes kidney deficiency syndrome saliva protein fingerprint molecular diagnosis Model building method.

In order to solve the above technical problems, the present invention discloses a method for establishing a molecular diagnostic model of salivary protein fingerprint of type 2 diabetes with kidney deficiency syndrome, comprising the following steps:

(1) Sample collection;

(2) Sample pretreatment: The collected saliva samples were centrifuged in a 4°C low-temperature centrifuge with a rotation speed of 3000r/min for 10min within 2h, and distributed into cryopreservation tubes (EP tubes) according to 50μl/tube, and placed in a -80°C refrigerator Freeze for later use;

(3) Activation of nano magnetic beads;

(4) Loading of saliva samples: ④ Take 5 μl of saliva samples, add 10 μl of U9 lysate, mix and incubate for 30 minutes, then add 185 μl of WashBuffer to dilute (the final sample volume of saliva is 2.5 μl); Add 100 μl of processed saliva sample to the tube (be careful not to generate air bubbles), incubate at room temperature for 30 minutes, incubate on a magnet for 1 minute, and remove the supernatant; ⑥Add 100 μl of WashBuffer to the PCR tube, elute for 5 minutes, and place in Incubate on a magnet for 1 min, remove the supernatant; ⑦Repeat step ⑥ once, repeat step ⑥ to remove impurities, elute cleaner, and obtain a purer target protein;

(5) Nano-magnetic beads elution: add 10 μl of ElutionBuffer to each PCR tube, elute for 5 minutes (not less than 5 minutes), place on a magnet and incubate for 1 minute, take 5 μl of the supernatant and transfer it to another PCR tube, and Add 5 μl of CHCA saturated solution and mix well, pipette 2 μl of the mixed solution and apply it to the Au/Steel chip, air-dry, then read the chip on the machine and collect data;

(6) Data collection;

(7) Data analysis;

(8) Establish a diagnostic model: use Biomarker Pattern Software 5.0.2 to calculate the discriminative value of the changes of multiple variables (m/z protein mass spectrum peaks) on the two samples by using the decision tree algorithm, and determine the best diagnostic model.

Further, the sample collection method described in step (1) is as follows: the type 2 diabetes mellitus kidney deficiency syndrome group and the normal control group rinse their mouths with water before going to bed the night before the collection, and then no longer eat any food and medicine, and collect the samples in the early morning of the next day. After getting up and gargling, the samples were taken on an empty stomach. The saliva collected within the first 5 minutes was swallowed naturally and then collected. The collected saliva was placed in a 50ml centrifuge tube with a stopper pre-cooled in an ice bath. A total of 2-3ml of saliva samples were collected for each clinical case. Each of the centrifuge tubes containing the saliva samples was placed in an ice box and stored at 4°C.

Further, the nano-magnetic beads activation method described in step (3) is: ① Take 50 μl of WCX nano-magnetic beads and add them to a 200 μl PCR tube, place them on a magnet and incubate for 1 min (note that the magnetic beads will not agglomerate due to too long incubation time) ), remove the supernatant; ②add 100μl of WashBuffer to elute for 5min, incubate on the magnet for 1min, and remove the supernatant; times, so that mass spectrometry detection is less likely to be interfered with.

Further, the data collection method in step (6) is: use a mass spectrometer to read the chip information, set the laser intensity to 190, the sensitivity to 5, the mass-to-charge ratio range of the collected data is 2000-25000m/z, and the signal collection position is 40 ~60, with an average of 20 collections per point, and a total of 100 collection points, using CiphergenProteinchipSoftware3.2.1 software to automatically collect data, the ordinate is the peak intensity (relative protein content), and the abscissa is the protein mass-to-charge ratio (m/z); Before data collection, the instrument was calibrated with a known All-in-one peptide standard chip, and the laser ion current was 0.5.

Further, the data analysis method described in step (7) is as follows: firstly use ProteinchipSoftware3.2.1 to correct the total ionic strength and molecular weight of all raw data to make it uniform; for peaks located at 2000-25000m/z, use BiomarkerWizard software to filter noise, Set the initial noise filtering value to 5, the secondary signal-to-noise ratio to 2, and cluster with 10% as the minimum threshold. After the above data preprocessing, use the t test to compare the salivary protein in the type 2 diabetic kidney deficiency syndrome group and the normal control group Mass spectrometry data (completed by BiomarkerWizard software), find out the protein peaks with statistically significant expression differences between the two groups, P<0.05 means the difference is statistically significant.

Further, in step (7), a total of 79 differential protein peaks (P<0.05) were detected in the mass spectrum peak (m/z) range of 2000 to 25000, of which 9 were in the type 2 diabetic kidney deficiency group and normal The difference between the control group was extremely significant (P<0.001), and the nine differentially expressed protein peaks were specifically as follows:

Compared with prior art, the present invention can obtain and comprise following technical effect:

1) The present invention uses a liquid chip combined with MALDI technology to detect the saliva protein fingerprints of patients with type 2 diabetes and kidney deficiency syndrome, screen out meaningful specific biomarkers from saliva proteins, and establish a diagnostic model for the combination of related diseases and syndromes.

2) The present invention screens out 330 statistically significant (P<0.05) differential protein peaks, 9 of which have extremely significant differences between the type 2 diabetic kidney deficiency syndrome group and the normal control group (P<0.001), and select quality The charge ratio (m/z) was 5744.34, 2410.88 two differential protein peaks were modeled, the recognition rate was 91.7%, and the prediction ability was 95.6%. The results of clinical back substitution test showed that the sensitivity of the diagnostic model was 83.3%, and the specificity was 75.6%. It shows that the model has excellent diagnostic efficiency, high sensitivity and strong specificity for type 2 diabetes mellitus with kidney deficiency syndrome.

3) The model constructed by the present invention can correctly distinguish type 2 diabetes patients with kidney deficiency syndrome and patients with different TCM syndrome types, which has important clinical diagnostic significance, opens up a new way for clinical disease diagnosis and TCM dialectical objectification, and provides a new way for TCM The study of micro dialectics has opened up new ideas.

4) The technical solution of the present invention has shown good application prospects, and is worthy of further in-depth research and clinical promotion and transformation.

Of course, implementing any product of the present invention does not necessarily need to achieve all the technical effects described above at the same time.

Description of drawings

The accompanying drawings described here are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute improper limitations to the present invention. In the attached picture:

Fig. 1 is a typical protein peak diagram of the type 2 diabetic kidney deficiency syndrome group and the normal control group in the embodiment of the present invention.

Fig. 2 is the diagnosis model of the type 2 diabetic kidney deficiency syndrome group and the normal control group in the embodiment of the present invention.

detailed description

The implementation of the present invention will be described in detail below with reference to the drawings and examples, so as to fully understand and implement the implementation process of how to use technical means to solve technical problems and achieve technical effects in the present invention.

Example

1 Screening and diagnostic criteria of research subjects

1.1 Case collection time

October 2014 - February 2015.

1.2 Diagnostic criteria

1.2.12 Western medicine diagnostic criteria for type 2 diabetes

Referring to the current "Guidelines for Diabetes Diagnosis and Treatment" published by the World Health Organization (WHO) and the American Diabetes Association (ADA) in 2014, the diagnostic criteria for type 2 diabetes are as follows:

The patient has typical symptoms of "three more and one less":

1) Glycated hemoglobin (HbA1C) ≥ 6.5%;

2) Fasting blood glucose concentration (FPG) ≥ 7.0mmol/L;

3) Random blood glucose concentration (GLU) ≥ 11.1mmol/L;

4) Oral glucose tolerance test (OGTT) 2-hour blood glucose concentration ≥ 11.1mmol/L, that is, take 75 grams of anhydrous glucose dissolved in water as the sugar load, and perform this in strict accordance with the requirements of the World Health Organization (WHO) test.

The patient does not have clear typical symptoms of hyperglycemia:

5) The test should be repeated to confirm the results, that is, in addition to the above diagnostic criteria, another oral glucose tolerance test (OGTT) 2-hour blood glucose concentration ≥ 11.1mmol/L, or another test found that the fasting blood glucose (FPG) concentration ≥ 7.0mmol/L L.

The typical "three more and one less" symptoms of type 2 diabetes refer to excessive thirst, excessive drinking, frequent hunger, increased urination and weight loss.

The precise definition of fasting means that the patient prohibits calorie intake for at least 8 hours.

1.2.2 TCM syndrome differentiation standard for type 22 diabetes mellitus

On the basis of confirming the clinical diagnosis of Western medicine as type 2 diabetes, refer to the "Reference Standards for Syndrome Differentiation of Deficiency Syndrome of Traditional Chinese Medicine" formulated by the National Professional Committee for Deficiency Syndrome Research of Integrated Traditional Chinese and Western Medicine and the "Guiding Principles for Clinical Research of New Chinese Medicines" formulated and issued by the Ministry of Health of the People's Republic of China in 2002 Regarding the diagnosis suggestion of type 2 diabetes, the collected data of type 2 diabetes patients are divided into spleen deficiency syndrome, kidney deficiency syndrome and other syndromes:

The diagnostic criteria for kidney deficiency syndrome are:

1) Kidney Qi Deficiency Syndrome: sore back pain (except for traumatic ones), shin soreness, soft knees or heel pain for a long time, tinnitus, hearing loss or even deafness, hair loss, tooth shaking, mental fatigue, lack of breath, lazy speech, urination Incessant draining, frequent urination at night or even incontinence, decreased sexual function, menstruation or premature ejaculation, irregular menstruation or infertility in women, pale tongue with thin white coating, weak or soft pulse;

2) Kidney Yin Deficiency Syndrome: The patient has sore waist and knees, dull pain in the waist, dizziness, tinnitus, frequent insomnia, dreaminess, night sweats, weight loss, emaciation, burning fire in the afternoon, conscious hand, foot, upset and fever, dry mouth and throat, and dense stool Constipation, short red urine, red tongue, less or even no coating, and pulse condition;

3) Kidney Yang Deficiency Syndrome: The patient is usually afraid of cold, cold limbs, especially lower limbs, puffy feet and face, sunken presses, pale complexion, diarrhea at dawn, clear and loose stools, clear and long urine, nighttime Increased urine output, sore waist and knees, impotence or premature ejaculation in men, thin leucorrhea in women, menstrual disorders, infertility, pale and fat tongue with white and moist fur, deep and weak pulse.

1.2.3 Diagnostic criteria of normal control group

The screening standard for the normal control group is that the fasting plasma glucose concentration can fluctuate between 4.4mmol/L (80mg/dl) and 6.1mmol/L (110mg/dl), and the plasma glucose concentration level is 4.4mmol/L two hours after meals. L (80mg/dl) to 8.0mmol/L (144mg/dl), and no serious disease was found.

1.3 Inclusion criteria of cases

1) It meets the diagnostic criteria of type 2 diabetes, and the patient's age, gender, clinical manifestations, whether there are serious complications, tongue coating, pulse condition and other basic information should be collected.

2) In the normal control group, type 2 diabetes was ruled out by biochemical examination in the Physical Examination Department of the Second People's Hospital of Shenzhen City, Guangdong Province, and all physical and chemical indicators were normal, without other serious diseases.

1.4 Exclusion criteria for cases

On the day before the collection of saliva samples, the researchers of this project screened and excluded patients with type 2 diabetes mellitus. Patients with any of the following criteria and above were not included in the case and should be excluded:

1) Type 2 diabetes patients under 30 years old or over 70 years old;

2) Patients with incomplete information on gender, age, clinical manifestations, tongue coating, pulse condition, etc.;

3) Patients who are unwilling to provide saliva samples or who have insufficient collection of saliva samples;

4) Women diagnosed with type 2 diabetes who are pregnant or breastfeeding;

5) Patients diagnosed with type 1 diabetes and other special types of diabetes;

6) Patients diagnosed with type 2 diabetes, but complicated by diabetic ketoacidosis and hyperosmolar coma;

7) Patients with severe schizophrenia;

8) Patients suffering from inflammation and tumor diseases of local oral cavity and salivary glands.

1.5 Exclusion criteria for cases

After the collection of saliva samples was completed, the researchers of this project screened and eliminated the basic information of type 2 diabetes patients received that day, and any type 2 diabetes patients with any of the following criteria and above should be excluded:

1) Those who were included due to negligence during the case screening process;

2) Those whose medical records meet the above exclusion criteria;

3) Those who are not clear about the clinical manifestations and TCM syndrome differentiation of tongue and pulse;

4) Patients who did not strictly abide by the standard operation of saliva sampling;

5) Inadvertently contaminated persons appear during the transfer of saliva samples;

6) The amount of saliva sample collected is obviously insufficient.

2 Basic information of included cases

All the saliva samples collected in this study were from patients with clinically diagnosed type 2 diabetes mellitus in the Department of Endocrinology, Shenzhen Second People’s Hospital, Guangdong Province. The healthy people in the normal control group were volunteers who had physical examinations in the physical examination department of our hospital and were free from diabetes, hyperlipidemia, essential hypertension, coronary atherosclerotic heart disease, obesity and other diseases.

3 research groups

There were 12 cases in the type 2 diabetes group and 45 cases in the normal control group.

4 Experimental methods

4.1 Main instruments and reagents

Weak cation exchange (WCX) nano-magnetic beads, WashBuffer, ElutionBuffer, U9 lysate and MALDI-TIF-MS (Protein Fingerprint Spectrometer Type I) are all products of Huzhou Saierdi Biomedical Technology Co., Ltd.; PBSⅡ-c type The protein chip reader is a product of Ciphergen Company in the United States; the high-speed desktop low-temperature centrifuge (Eppendorf Company); -80°C refrigerator (Harris Company); dH ₂ O (HPLC grade), and CHCA are products of Sigma Company.

4.2 Sample collection

The night before the sampling, the patients were instructed to rinse their mouths with water three times before going to bed (after rinsing their mouths, no food and medicines would be added), and the next morning they woke up and rinsed their mouths before collecting samples on an empty stomach. The patient swallowed the saliva naturally within the first 5 minutes, and then put the sterile saliva tube cotton ball into the mouth. After the saliva accumulated to a certain amount, spit the cotton ball back into the 50ml pre-cooled centrifuge tube with stopper that had been pre-cooled in an ice bath. A total of 2-3ml of saliva samples were collected from each clinical case, and each centrifuge tube containing the saliva samples was placed in an ice box and stored at low temperature.

4.3 Sample processing

All the collected saliva was centrifuged in a 4°C low-temperature centrifuge at a speed of 3000r/min for 10min within 2h, and distributed into cryopreservation tubes (EP tubes) according to 50μl/tube, and stored in a -80°C refrigerator for future use. The specimens were taken out during the experiment and thawed at room temperature.

4.4 Activation of Nano Magnetic Beads

① Take 50 μl of WCX Magnetic Beads nano-magnetic beads and add them to a 200 μl PCR tube, incubate on the magnet for 1 min (be careful not to agglomerate the magnetic beads due to too long incubation time), remove the supernatant; ② add 100 μl of WashBuffer to elute for 5 min, Place on a magnet and incubate for 1 min, remove the supernatant; ③ add 100 μl of WashBuffer to the PCR tube to elute for 5 min, incubate on the magnet for 1 min, remove the supernatant, that is, repeat step ② once. The purpose of repetition is to elute more cleanly and remove impurities. It is best to elute twice, so that the possibility of mass spectrometry detection will be less likely to be interfered.

4.5 Elution and loading of saliva samples

①Take 5 μl of each saliva sample, add 10 μl of U9 lysate, mix and incubate for 30 minutes, then add 185 μl of WashBuffer to dilute (the final sample volume of saliva is 2.5 μl); ②Add 100 μl to the PCR tube containing activated magnetic beads for processing (be careful not to generate bubbles), incubate at room temperature for 30 minutes, incubate on a magnet for 1 minute, remove the supernatant; ③ add 100 μl of WashBuffer to the PCR tube, elute for 5 minutes, and incubate on a magnet for 1 minute, remove Supernatant; ④Repeat step ③ once to remove impurities, elute cleaner, and obtain a purer target protein.

4.6 Nanometer magnetic bead elution

Add 10 μl of ElutionBuffer to each PCR tube, elute for 5 minutes (not less than 5 minutes), place on a magnet and incubate for 1 minute, take 5 μl of the supernatant and transfer it to another PCR tube, add 5 μl of CHCA saturated solution and mix well , pipette 2 μl of the mixed solution and apply it to the Au/Steel chip, air-dry, and then read the chip on the computer to collect data.

4.7 Data collection

Use a mass spectrometer to read the chip information, set the laser intensity to 190, the sensitivity to 5, the mass-to-charge ratio range of the collected data is 2000-25000m/z, the signal collection position is 40-60, and the average collection point is 20 times, and the total collection point is 100 times. CiphergenProteinchipSoftware3.2.1 software is used to automatically collect data, the ordinate is the peak intensity (relative protein content), and the abscissa is the protein mass-to-charge ratio (m/z). Before data collection for each experiment, the instrument was calibrated with a known All-in-one peptide standard chip, and the laser ion current was 0.5.

4.8 Bioinformatics analysis

All raw data were corrected for total ionic strength and molecular weight with ProteinchipSoftware3.2.1 to make them uniform; for peaks at 2000-25000m/z, noise was filtered with BiomarkerWizard software. Set the initial noise filtering value to 5, the secondary signal-to-noise ratio to 2, and cluster with 10% as the minimum threshold. After the above data preprocessing, use the t test to compare the salivary protein in the type 2 diabetic kidney deficiency syndrome group and the normal control group Mass spectrometry data (completed by BiomarkerWizard software), find out the protein peaks with statistically significant expression differences between the two groups. P<0.05 means the difference is statistically significant.

4.9 Building a diagnostic model

Using BiomarkerPatternSoftware5.0.2, the decision tree algorithm is used to calculate the discriminative value of the changes of multiple variables (m/z protein mass spectrum peaks) on the two samples, and determine the best screening model, that is, the diagnostic model. SPSS software was used to compare the baseline values of each group, age comparison was performed by one-way analysis of variance, all data were expressed by ±s, and gender composition comparison was performed by X ² test.

5 results

5.1 Age and gender distribution

Among the 12 patients with type 2 diabetes mellitus with kidney deficiency syndrome, there were 8 male patients and 4 female patients; among the 45 healthy people in the normal control group, there were 27 male patients and 18 female patients. (Table 1)

Table 12 type diabetes group and normal control group distribution

Table 1 shows: After statistical analysis, there was no significant difference in gender and age distribution in each group (P>0.05).

5.22 Type Diabetes Kidney Deficiency Syndrome Comparison Results with Normal Control Group

After standardizing the original protein fingerprints of 57 saliva samples (including 12 patients with type 2 diabetes mellitus and 45 cases of normal control group), they were analyzed with Biomarker Wizard software. 79 differentially expressed protein peaks were detected (P<0.05), 9 of which had extremely significant differences between the type 2 diabetic kidney deficiency syndrome group and the normal control group (P<0.001), and the 9 differentially expressed protein peaks were specifically shown in Table 2:

Table 2 Difference peak results of normal control group and type 2 diabetes kidney deficiency group

Figure 1 is a representative protein spectrum of the type 2 diabetic kidney deficiency syndrome group and the normal control group, in which the ordinate is the peak intensity (relative protein content), and the abscissa is the protein mass-to-charge ratio (m/z).

Using BiomarkerPatternSoftware5.0.2, the decision tree algorithm was used to calculate the discriminative value of multiple variables (m/z protein mass spectrum peaks) on the two samples, and determine the best screening model (Figure 2), and finally selected m/z as 5744.34 and 2410.88 The diagnostic decision tree model composed of two m/z differential protein peaks, the sensitivity and specificity of the model were 91.7% (11/12), and the specificity was 95.6% (43/45) (see Table 3).

As shown in Figure 2, when one of the following conditions is met: ①m/z2410.88≤4.53 and m/z5744.34≤0.598, ②m/z5744.34≤1.14 and m/z2410.88>4.53 indicates type 2 Diabetic kidney deficiency syndrome group; when one of the following conditions is met: ①m/z2410.88≤4.53 and m/z5744.34>0.598 and m/z5744.34≤1.14, ②m/z5744.34>1.14 indicates the normal group. M: Relative intensity of mass spectrum peaks.

Diagnosis efficiency of the diagnostic model built in Table 3 (results of clinical back-substitution test)

group Number of cases correct diagnosis (example) Error diagnosis (example) Correct rate(%) Type 2 diabetic kidney deficiency syndrome group 12 11 1 91.7 normal control group 45 43 2 95.6

In order to verify the diagnostic model composed of the two differential protein peaks of 5744.34 and 2410.88, the established diagnostic model was verified by the cross method. Among the 12 patients with type 2 diabetes and kidney deficiency syndrome, 10 cases were correctly classified, and 2 cases were classified correctly. Misclassification, the sensitivity was 83.3% (10/12); 45 routine healthy control samples, 34 cases were correctly classified, 11 cases were classified incorrectly, and the specificity was 75.6% (34/45) (see Table 4).

Diagnosis efficiency of the diagnostic model built in Table 4 (cross-validation results)

group Number of cases correct diagnosis (example) Error diagnosis (example) Correct rate (%) 10 --> Type 2 Diabetic Kidney Deficiency Syndrome Group 12 10 2 83.3 normal control group 45 34 11 75.6

The invention uses a liquid chip combined with MALDI technology to detect the saliva protein fingerprints of type 2 diabetes patients with kidney deficiency syndrome, screens out meaningful specific biomarkers from the saliva protein, and establishes a diagnostic model for the combination of related diseases and syndromes. Screened 330 statistically significant (P<0.05) differential protein peaks, 9 of which had extremely significant differences between the type 2 diabetic kidney deficiency syndrome group and the normal control group (P<0.001), and selected the mass-to-charge ratio (m /z) was used to model the two differential protein peaks of 5744.34 and 2410.88, with a recognition rate of 91.7% and a predictive ability of 95.6%. The results of clinical back substitution test showed that the sensitivity of the diagnostic model was 83.3%, and the specificity was 75.6%. It shows that the model has excellent diagnostic efficiency, high sensitivity and strong specificity for type 2 diabetes mellitus with kidney deficiency syndrome. The model constructed by the invention can correctly distinguish patients with type 2 diabetes mellitus with kidney deficiency syndrome and patients with different TCM syndrome types, which has important clinical diagnostic significance, opens up a new way for clinical disease diagnosis and TCM dialectical objectification, and provides a new way for TCM microcosmic dialectics. Research has opened up new ideas. The invention shows good application prospects, and is worthy of further in-depth research and clinical promotion and transformation.

For example, certain terms are used in the description and claims to refer to specific components or methods. Those skilled in the art should understand that different regions may use different terms to refer to the same component. The description and claims do not use the difference in name as a way to distinguish components. As mentioned throughout the specification and claims, "comprising" is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. The following descriptions in the specification are preferred implementation modes for implementing the present invention, but the descriptions are for the purpose of illustrating the general principle of the present invention, and are not intended to limit the scope of the present invention. The scope of protection of the present invention should be defined by the appended claims.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a good or system comprising a set of elements includes not only those elements but also includes items not expressly listed. other elements of the product, or elements inherent in the commodity or system. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or system comprising said element.

The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the forms disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various Various other combinations, modifications, and environments can be made within the scope of the inventive concept described herein, by the above teachings or by skill or knowledge in the relevant field. However, changes and changes made by those skilled in the art do not depart from the spirit and scope of the present invention, and should all be within the protection scope of the appended claims of the present invention.

Claims (6)

1. a type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up, it is characterised in that comprise the following steps:

(1) sample collection；

(2) sample pretreatment: the saliva sample of collection is built in the centrifugal 10min of 4 DEG C of refrigerated centrifuges of rotating speed 3000r/min at 2h, and is sub-packed in cryopreservation tube according to 50 μ l/ pipes, is placed in-80 DEG C of refrigerator freezings and saves backup；

(3) nanometer magnetic bead activation；

(4) saliva sample loading: 4. take 5 μ l saliva samples, adds the U9 lysate of 10 μ l, after 30min is hatched in mixing, adds the WashBuffer dilution of 185 μ l；5. in the PCR pipe containing activated magnetic beads, add the 100 μ l saliva sample handled well, incubated at room 30min, be placed on Magnet and hatch 1min, remove supernatant；6. in pipe, add the WashBuffer eluting 5min of 100 μ l again, then PCR pipe is placed on Magnet and hatches 1min, remove supernatant；7. step is repeated 6. once；

(5) nanometer magnetic bead eluting: add the ElutionBuffer of 10 μ l in each PCR pipe, eluting 5min, it is positioned on Magnet and hatches 1min, take 5 μ l supernatant and move in another PCR pipe, and the CHCA saturated solution adding 5 μ l fully mixes, draw 2 μ l mixed solutions and be loaded onto on Au/Steel chip, air-dry, then go up machine-readable coring sheet, collect data；

(6) data acquisition；

(7) data analysis；

(8) diagnostic cast is set up: adopt decision Tree algorithms to calculate the differentiation to two samples of multiple variable change and be worth, it is determined that diagnostic cast.

2. type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up as claimed in claim 1, it is characterized in that, the described sample collection method of step (1) is: type 2 diabetes mellitus nephrasthenia syndrome group and Normal group are gargled at the evening before that day clear water just before going to bed of drawing materials, no longer take food afterwards any food and medicine, in second day early morning get up gargle after draw materials on an empty stomach, saliva in front 5min starts after naturally swallowing to collect, the saliva collected is placed in the 50ml tool plug centrifuge tube of ice bath pre-cooling, each clinical case gathers saliva sample 2-3 milliliter altogether, each described centrifuge tube equipped with saliva sample is placed in ice chest, 4 DEG C of preservations.

3. type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up as claimed in claim 2, it is characterized in that, the described nanometer magnetic bead activation method of step (3) is: 1. takes WCX nanometer magnetic bead 50 μ l and joins the PCR pipe of 200 μ l, it is placed on Magnet and hatches 1min, remove supernatant；2. add the WashBuffer eluting 5min of 100 μ l, Magnet is hatched 1min, remove supernatant；3. step is repeated 2. once.

4. type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up as claimed in claim 3, it is characterized in that, step (6) described collecting method is: before data acquisition, rectify an instrument with polypeptide standard chips, and lasing ion stream is 0.5；Adopting mass spectrograph to read chip information, arranging laser intensity is 190, and sensitivity is 5, and the mass charge ratio range collecting data is 2000～25000m/z, signal collection position 40～60, collects 20 times for average every, and collecting total point is 100 times.

5. type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up as claimed in claim 4, it is characterized in that, step (7) described data analysing method is: all initial datas first do total ionic strength and molecular weight calibration so that it is reach homogeneous；It is pointed to 2000～25000m/z peak value, filtering noise, arranging initial noise filtering value is 5, secondary signal to noise ratio is 2, cluster with 10% for minimum threshold, after above-mentioned data prediction, adopt t inspection to compare type 2 diabetes mellitus nephrasthenia syndrome group and Normal group sialoprotein matter mass spectrometric data, finding out the protein peak that between 2 groups, differential expression is statistically significant, P < 0.05 is that difference is statistically significant.

6. type 2 diabetes mellitus nephrasthenia syndrome sialoprotein finger printing molecule diagnostic model method for building up as claimed in claim 5, it is characterized in that, in step (7), it it is 79, differential protein peak P < 0.05 altogether being detected within the scope of 2000～25000m/z at mass spectra peak, wherein there are 9 difference P < 0.001 in type 2 diabetes mellitus nephrasthenia syndrome group and Normal group, having extremely notable meaning, described 9 differential expression protein peaks are specific as follows: