Peripheral blood protein markers for early diagnosis of Alzheimer's disease, their application and auxiliary diagnosis system

Abstract

The invention provides a peripheral blood protein marker for early diagnosis of Alzheimer's disease. The protein marker comprises protein P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG) or P00736 (C1R), and application of the protein marker for early diagnosis of Alzheimer's disease in a diagnostic kit. Finally, an early diagnosis model for Alzheimer's disease is established. The overall prediction area under the curve of the model is AUC=0.758, the sensitivity is 78%, and the specificity is 75%.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese invention patent application with application number 202210245944.X filed on March 14, 2022, the contents of which are incorporated into this application by reference. Technical Field

[0003] The present invention belongs to the field of biological detection technology, and specifically relates to a peripheral blood protein marker for diagnosing early stage of Alzheimer's disease (mild cognitive impairment, MCI) based on blood, its application and medical auxiliary diagnosis system. Background Art

[0004] (1) Prior Art

[0005] Alzheimer's disease (AD), commonly known as senile dementia, is a neurodegenerative disease characterized by decreased memory, cognition and daily living ability. It has a high prevalence, high disability rate and heavy disease burden among the elderly. Epidemiological surveys show that the prevalence of the elderly population over 60 years old is 3-5%. The prevalence doubles with every 5 years of age. It can reach 20% for those over 80 years old and 50% for those over 90 years old. my country is an aging society. According to data released by the National Bureau of Statistics, by the end of 2020, the population aged 60 and above in my country will reach 260 million, accounting for 18.7% of the total population. Among them, there are 10 million patients with dementia, and what is even more serious is that my country also has more than 30 million patients with mild cognitive impairment (MCI) in the early stage of dementia (2019, The Lancet) [Longfei J, Meina Q, et al. Dementia in China: epidemiology, clinical management, and research advances. Lancet Neurology. 2019, 19 (1): p81-92], and the medical and nursing burden is extremely heavy. Currently, there is no drug that can delay or reverse the disease process, so early diagnosis and intervention of Alzheimer's disease is crucial.

[0006] Alzheimer's disease can be roughly divided into the asymptomatic stage, mild cognitive impairment (MCI) and dementia stage. Patients may have mild cognitive impairment for about 10 years before obvious clinical symptoms appear. Although the daily living ability of patients at this stage is not affected, the pathological changes of dementia have already existed and developed in the brain until obvious clinical symptoms appear. Therefore, MCI is considered the best stage for early diagnosis and intervention.

[0007] However, early diagnosis of Alzheimer's disease is very difficult. In the past 10 years, the National Institute on Aging and the Alzheimer's Association (NIA-AA) have introduced biomarker testing, which enables early diagnosis of AD patients before obvious clinical symptoms appear. In 2011, NIA-AA recommended the use of fluorodeoxyglucose-positron emission tomography (FDG-PET), amyloid protein-positron emission tomography (Aβ-PET), and Aβ42, T-tau, and P-tau in cerebrospinal fluid as biomarkers for early diagnosis of AD. However, these tests are either expensive (FDG-PET, Aβ-PET) or invasive (cerebrospinal fluid examination). They are still mainly used in research fields and cannot be promoted. Therefore, biomarkers that are easy to obtain and easy to detect have become the focus of everyone's research. In recent years, studies have found that changes in the composition of some substances in the blood of patients with dementia are correlated with pathological changes in their brains, which can reflect pathological changes in the brain. At the same time, blood also has the advantages of easy acquisition and repeatable testing. Therefore, blood testing may become an effective way to diagnose dementia in the early stage. However, the components in the blood are complex and low in content, and are easily affected by various factors. Therefore, it is necessary to find sensitive and reliable biomarkers for early diagnosis related to dementia. Current studies have found potential biomarkers for dementia in peripheral blood, mainly including MicroRNA, plasma proteins and other indicators in the blood, but they are still in the exploratory stage. The research results of MicroRNA in the blood are not consistent enough, and there is still a lack of research evidence from large sample data [Miller, J. and J. Kauwe, Predicting Clinical Dementia Rating Using Blood RNA Levels. Genes, 2020.11: p.706.]; and plasma proteins are limited by the sensitivity and stability of the detection method due to their low content and wide variety, and the results are not consistent enough. In terms of proteomics research, Kiddle et al. (2014) summarized 21 research analyses and obtained 163 candidate proteins. After screening, 94 of them were verified again. The research results were not consistent with the previous ones [Kiddle, SJ, et al., Candidate blood proteome markers of Alzheimer's disease onset and progression: a systematic review and replication study. Journal of Alzheimers Disease Jad, 2014. 38(3): p.515., Xu Hua, Xiao Shifu, Progress in research on peripheral blood protein biomarkers of Alzheimer's disease. Chinese Journal of Clinical Physicians: Electronic Edition, 2017(10): p.1821-1824.].In 2015, other researchers used the same detection method as Kiddle et al. to screen a group of proteins (including S100-A9 protein, CD226 antigen, somatic transplant inflammatory factor 1, endothelial cell selective adhesion molecule, and leukocyte differentiation antigen CD84), with a sensitivity of 96.7%, a specificity of 80.0%, and an accuracy of 92.5% for diagnosing MCI [Zhao X, Lejnine S, Spond J, et al. A candidate plasma protein classifier to identify Alzheimer's disease [J]. J Alzheimers Disease Jad, 2015, 43 (2): 549-563]. However, this result was not replicated and verified by subsequent studies. It was not until 2020 that Karikari et al. published the results of a large sample study in Lancet Neurology, and the diagnostic value of proteins in peripheral blood as biomarkers for the diagnosis of dementia was recognized. The study verified the reliability and stability of Tau-181 protein in diagnosing dementia in more than 1,000 patients. The results showed that Tau-181 has good sensitivity and stability in diagnosing patients in the dementia stage [Karikari, T., et al., Blood phosphorylated tau181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. The Lancet Neurology, 2020.19: p.422-433.].Research in the past two years also includes the diagnostic value of plasma GFAP protein, p-tau217, p-tau231, etc. Overall, the accuracy of these proteins in the diagnosis of clinical dementia can reach more than 90%, but the diagnostic value of these proteins in the early MCI stage of dementia is still not stable enough and needs further confirmation [Oeckl, P., et al., Glial Fibrillary Acidic Protein in Serum is Increased in Alzheimer's Disease and Correlates with Cognitive Impairment. J Alzheimers Dis, 2019. 67(2): p. 481-488.; Chatterjee, P., et al., Diagnostic and prognostic plasma biomarkers for preclinical Alzheimer's disease. Alzheimers Dement, 2021.; Ashton, NJ, et al., Plasma p-tau231: a new biomarker for incipient Alzheimer's disease pathology. Acta Neuropathol, 2021.141(5):p.709-724; Thijssen, EH, et al., Plasma phosphorylated tau 217 and phosphorylated tau 181 as biomarkers in Alzheimer's disease and frontotemporal lobar degeneration: a retrospective diagnostic performance study. Lancet Neurol, 2021.20(9):p.739-752.].

[0008] In summary, the early diagnosis of dementia is an important social need. At present, there is still a lack of easy-to-test and easy-to-promote early diagnosis methods for AD. Peripheral blood is easy to obtain and can be tested repeatedly, which is fully capable of being used as an early diagnosis tool for dementia. However, the protein content in the blood is low and there are many types, which requires high sensitivity and stability of the detection technology. Therefore, it is necessary to find reliable and sensitive AD early diagnosis biomarkers through effective technology.

[0009] (2) Problems or shortcomings of existing technologies

[0010] With the advancement of proteomics technology, we can find differential proteins in the blood on a large scale and find potential biomarkers by screening differential proteins. Currently, proteomics technologies used for high-throughput peripheral blood testing and screening mainly include the following categories:

[0011] (a) Detection methods based on immunocapture: This method is based on the specific binding of antigen and antibody, and combines a variety of specific technologies to achieve qualitative and quantitative detection of proteins, such as protein liquid phase chip (Lumicaninex xMAP), electrochemiluminescence technology MSD (mesoscale discovery), etc. [Xu Hua, Xiao Shifu, Research Progress of Peripheral Blood Protein Biomarkers for Alzheimer's Disease. Chinese Journal of Clinical Physicians: Electronic Edition, 2017(10): p.1821-1824.]. A large number of target proteins can be detected in a small amount of blood sample. Its high throughput characteristics can meet the needs of clinical research. However, this method is based on the specific binding of antigen and antibody, and is not suitable for detection of hypothesis-free targets. At the same time, it is greatly affected and restricted by the selected antibodies, and the results are not stable enough.

[0012] (b) Detection methods based on aptamers: This type of method is based on the specific binding of aptamers and target proteins. Aptamers are single-stranded oligonucleotides that can effectively recognize and bind to target proteins. This binding has the characteristics of high affinity and high specificity. This method also has the characteristics of high throughput, and its results have better stability. It is also the most commonly used method at present, but its results may still fluctuate due to the influence of the selected aptamer [Gold, L., et al., Aptamer-based multiplexed proteomic technology for biomarker discovery. Plos One, 2010.5(12): p.e15004.], thereby reducing stability.

[0013] (c) Mass spectrometry (MS) detection method: Mass spectrometry detection is more expensive than the first two methods. Its characteristics are that it is free from the influence of antibodies on the results and has the best stability and accuracy. In recent years, with the continuous advancement of mass spectrometry detection technology, its sensitivity has been continuously improved. It can not only perform non-target (non-hypothesis) detection of proteins in the blood, but also directly perform quantitative detection of candidate proteins. Combined with radionuclide labeling and other technologies, it can achieve accurate quantification of low-abundance proteins in the blood [Xu Hua, Xiao Shifu, Research progress of peripheral blood protein biomarkers for Alzheimer's disease. Chinese Journal of Clinical Physicians: Electronic Edition, 2017(10): p.1821-1824.] The research results are stable and reliable, and it is the gold standard for low-abundance protein detection. Summary of the invention

[0014] The main purpose of the present invention is to provide a stable, reliable and simple detection technology that can be used to diagnose the early stage of Alzheimer's disease (MCI) in order to solve the problem of difficulty in diagnosing early stage Alzheimer's disease in the prior art.

[0015] To achieve the above objectives, the present invention provides, on the one hand, a peripheral blood protein marker for early diagnosis of Alzheimer's disease, wherein the protein marker comprises protein P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG) or P00736 (C1R).

[0016] The second aspect of the present invention provides the use of the above-mentioned reagent for detecting peripheral blood protein markers for early diagnosis of Alzheimer's disease in the preparation of an early diagnosis kit for Alzheimer's disease.

[0017] Preferably, the kit can measure the content of P02753 (RBP4) protein, P22352 (GPX3) protein, P23560 (BDNF) protein, P02765 (AHSG) protein, or P00736 (C1R) protein in plasma.

[0018] Preferably, the kit can measure the content of any peptide segment of P02753 (RBP4) or P22352 (GPX3) or P23560 (BDNF) or P02765 (AHSG) or P00736 (C1R).

[0019] The third aspect of the present invention provides a medical auxiliary diagnosis system for the early diagnosis of Alzheimer's disease, wherein the diagnostic system includes an MCI diagnostic model, and the MCI diagnostic model includes proteins P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), P00736 (C1R), age, and gender.

[0020] The blood-based peripheral blood protein markers for early diagnosis of Alzheimer's disease, application and medical auxiliary diagnosis system provided by the present invention have the following beneficial effects:

[0021] 1. Use non-targeted proteomics technology (DIA) to conduct open exploration of differential proteins between groups, reduce human bias, and have better objectivity.

[0022] 2. We use the IPA (Ingenuity pathway analysis) bioanalysis software to analyze the functional pathways and related diseases to which the proteins belong, screen them layer by layer, and select proteins that are related to neurological diseases and dementia; then we gradually verify and finally determine the biomarkers for early diagnosis of dementia, which have good stability and reliability.

[0023] 3. Using a highly accurate mass spectrometer combined with isotope standard peptides, low-abundance proteins in blood samples were accurately quantified, and the test results were stable and reliable. On this basis, a stable prediction model was established, and finally a set of prediction patterns consisting of five proteins, P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), and P00736 (C1R), as well as gender and age factors, were obtained. The overall model predicted the area under the curve (AUC) of MCI = 0.758, with a sensitivity of 78% and a specificity of 75%, which has good stability and good sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a diagram showing the results of pathway analysis of differentially expressed proteins between the MCI and NC groups of the present invention.

[0025] Figure 2 It is the PRM experimental process and detection schematic diagram of the present invention.

[0026] Figure 3a-1 to Figure 3e-2 The graphs show the concentrations of five proteins, namely P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), and P00736 (C1R), and the corresponding results for diagnosing MCI.

[0027] Figure 4 This is a diagram showing the results of diagnosing MCI using the prediction model of the present invention. DETAILED DESCRIPTION

[0028] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further described below in conjunction with specific embodiments.

[0029] The present invention adopts mass spectrometry proteomics detection technology. First, plasma proteins in the peripheral blood of patients with aMCI (amnestic mild cognitive impairment) in the early stage of Alzheimer's disease and normal cognitive elderly people (NC) are detected and compared. The differential proteins obtained are screened layer by layer and verified step by step. Then, the obtained targeted proteins are accurately quantified and verified by combining the isotope labeling method, so as to find biomarkers that can be used for early diagnosis of AD. The whole research process is divided into four stages: the first stage uses data independent acquisition (DIA) to conduct non-targeted and open exploration of differential proteins in the peripheral blood of aMCI and normal elderly people (NC); the second stage uses bioinformatics technology to gradually screen the differential proteins between the groups, and through layer-by-layer screening of molecular pathways, functional mechanisms, etc., irrelevant proteins are eliminated and the range of candidate proteins is established. The screening strategy included: (1) proteins with statistical differences between groups reaching Student's t Test P < 0.05 and a difference multiple of 1.5 or above; (2) IPA (Ingenuity pathway analysis) bioanalysis software to analyze the functional pathways and related diseases to which the proteins belong, and screen them layer by layer to select proteins related to the pathogenesis of neurological diseases and dementia; in the third stage, the candidate proteins were preliminarily verified in 38 elderly people, proteins with poor detection and prediction effects were eliminated, and further screening was performed to obtain 20 candidate biomarkers; in the fourth stage, the diagnostic efficacy of the candidate biomarkers was verified in 155 elderly people. Parallel reaction monitoring (PRM) mass spectrometry technology was used in combination with isotope-labeled synthetic peptides to accurately quantify the 20 target proteins. The test was conducted in 155 elderly people, and a group of proteins (RBP4, GPX3, BDNF, AHSG, C1R) was finally obtained. Combined with age and gender, the sensitivity of early diagnosis of aMCI reached 78% and the specificity reached 75%.

[0030] like Figure 1 As shown in the figure, IPA software was used to analyze the differential proteins obtained by comparing the aMCI and NC groups, and their functional pathways. There were 17 pathways with statistical differences with a -log value of P value above 1.3 (equivalent to p < 0.05 level), and they were ranked from large to small according to the significance of statistical differences: acute response signaling pathway, LXR / RXR activation, coagulation system, NADH repair, role of tissue factor in cancer, FXR / RXR activation, GP6 signaling pathway, IL-8 signaling, choline biosynthesis III, extrinsic prothrombin activation pathway, early-onset diabetes of youth (MODY) signaling, glycolysis I, gluconeogenesis I, intrinsic prothrombin activation pathway, phospholipase, interaction of agrin at the neuromuscular junction, etc.

[0031] like Figure 2 As shown in the figure, targeted peptides of more than 110 target proteins were selected by SpectroDive8 software, and preliminary experiments were carried out based on the selected targeted peptides to analyze the detection effects of the target peptides. Proteins with poor signal intensity and unsatisfactory detection effects were eliminated, and proteins with relatively ideal detection effects were retained for formal PRM quantitative detection and verification.

[0032] As shown in Figure 3, the model is composed of five proteins, P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), and P00736 (C1R), as well as gender and age factors. The overall prediction AUC of the model for MCI is 0.758, with a sensitivity of 78% and a specificity of 75%.

[0033] 1.DIA mass spectrometry detection

[0034] In this example, the number of cases tested by DIA was: 6 cases in the aMCI group and 6 cases in the NC group. The average age of the aMCI group was 68.17±6.369 years old, and the average age of the NC group was 70.50±10.585 years old. There was no statistical difference in age between the two groups (F=1.775, P=0.412>0.05). There was no statistical difference in gender composition between the aMCI group and the NC group (X 2 =0.670, P=0.733>0.05). The test sample was human peripheral blood plasma. Sample collection and processing were completed in strict accordance with unified specifications. The blood collection conditions were: the subjects fasted for 12 hours, and elbow venous blood was collected in the morning of the next day and placed in a red anticoagulant tube. Blood was centrifuged within 2 hours after collection to separate plasma and blood cells.

[0035] Table 1. Reagents and instrument specifications required for DIA mass spectrometry experiments

[0036]

[0037]

[0038] The DIA mass spectrometry detection process includes sample pretreatment, reversed phase liquid chromatography separation (RPLC), DDA detection and library construction, DIA individual sample detection, etc. The detected proteins are subjected to spectral peak information extraction, correction and integration using Spectronaut Pulsar, a professional analysis software for DIA mass spectrometry detection.

[0039] 1.1 Experimental procedures

[0040] 1.1.1 Sample preparation

[0041] The protein extraction required for the test is required to be free of environmental protein, PEG, Triton, glycerol and other contaminants. The preparation requirements for the peptides are that the non-specific reduction alkylation rate is less than 30%, the non-specific enzyme cleavage rate is less than 5%, and there is no obvious non-specific modification. The pre-treatment process is as follows:

[0042] (1) Plasma was diluted 100-fold and protein was quantitatively analyzed using the BCA method;

[0043] (2) According to the measured protein concentration, take 100 μg of protein and dilute it to 100 μl with 100 mM TEAB;

[0044] (3) Add 1uL of 1M reducing agent DTT to a final concentration of 10mM and incubate at 37°C for 1 hour;

[0045] (4) Add 2uL 1M iodoacetamide to a final concentration of 20mM and incubate at room temperature in the dark for 1 hour;

[0046] (5) Precipitation was performed with 5 times the volume of acetone as the sample and allowed to stand at -20°C overnight (12 hours);

[0047] (6) Centrifuge at 12,000 g for 20 min at 4°C;

[0048] (7) Remove the supernatant, add 1 mL of -20 °C ethanol and acetone (ratio 1:1) to the protein precipitate, mix, shake and wash for 5 minutes, and repeat twice;

[0049] (8) Centrifuge at 12,000 g for 20 min at 4°C;

[0050] (9) Remove the supernatant and place the protein precipitate to dry naturally at room temperature until the precipitate becomes transparent;

[0051] (10) Add 100 μl of 50 mM ammonium bicarbonate to the precipitate, and then add 2.5 μg of sequencing-grade trypsin at a ratio of 1:40 (w:w), and incubate at 37°C for overnight enzymatic digestion.

[0052] 1.1.2 HPLC separation

[0053] The samples were separated by high pH reverse phase separation. Equal amounts of peptides from all samples were taken, mixed and freeze-dried, and buffer A (buffer A: 20 mM ammonium formate aqueous solution, ammonia adjusted to pH 10.0) was added for fractionation. The high performance liquid chromatograph Ultimate 3000 system was connected to the XBridge C18coLumicann reverse phase column, and a linear gradient was used for high pH separation. Within 30 minutes, the concentration of mobile phase B (20 mM ammonium formate was added to 80% acetonitrile, and the pH value of ammonia was adjusted to pH 10.0) was adjusted from 5% to 45%, and the column was balanced under the initial conditions for 15 minutes. The column maintained a flow rate of 1 ml per minute and the temperature was maintained at 30°C. A total of 12 fractions were collected under these conditions.

[0054] 1.1.3 Mass spectrometry data-dependent acquisition and library construction

[0055] Each fraction was suspended with 30 μl of solvent C (0.1% formic acid in water), and then corrected by adding iRT peptides. After separation by nano-LC, the samples were analyzed by online electrospray tandem mass spectrometry. The experiment was performed on the Easy-nLC1200 system, which was connected to a Q Exatives Plus mass spectrometer equipped with an electrospray ion source. After 3 μl of peptide sample was loaded onto the trapping column at a flow rate of 10 μl per minute, it was separated in the analytical column by a linear gradient, and 5% solution D (D: 0.1% formic acid in acetonitrile) was adjusted to 40% D within 150 minutes. The column was equilibrated under the initial conditions for 10 minutes, the flow rate was controlled at 300 nL per minute, and the voltage of the electrospray ion source was set to 2 kV. The mass spectrometer operated in data-dependent acquisition mode, automatically switching between primary mass spectrometry (MS) and secondary mass spectrometry (MS / MS). Full scan spectra were obtained at 70K mass resolution, with a mass-to-charge ratio (m / z) range of 350-1550, and automatic gain control (AGC) was used to control the number of ions entering the ion trap, with a target setting of 3e6, followed by subsequent high-energy collision dissociation MS / MS scans at a resolution of 17.5K. Parameter settings: isolation window was set to 1.6Da, AGC target was set to 1e5, MS / MS Fixedfirst mass was set to 200, microscan was set to 1, and dynamic exclusion time was 30 seconds.

[0056] The raw data were searched using the Swiss-Prot homo sapiens (2017-3-27) database. Trypsin was set as the digestive enzyme during the search, and the specific parameters were set as follows: (1) The error range allowed for the fragment ion mass during the search was 0.050 Dalton; (2) The error range allowed for the parent ion mass during the search was 10.0 PPM; (3) The allowed fixed modification: carbamidomethyl (C); (4) The allowed variable modification: asparagine and glutamine deamidation, methionine oxidation, and protein N-terminal acetylation. The above search information was used to establish a spectral database and a comparison standard for subsequent DIA detection.

[0057] 1.1.4 Mass spectrometry data independent acquisition and data analysis

[0058] The DIA assay was performed as follows: 9 μl of each sample was added with iRT peptides and run on an Easy-nLC 1200 system connected to a Q Exactives Plus mass spectrometer equipped with an online electrospray ionization source. 3 μl of peptide sample was loaded onto the trapping column at a flow rate of 10 μl / min and then separated in the analytical column with a linear gradient, with the concentration of D solvent adjusted from 5% to 40% over 150 min. The column was equilibrated for 10 min under initial conditions, with a flow rate of 300 nL / min and an electrospray ionization source voltage set to 2 kV. The mass spectrometer was operated in data-independent acquisition mode, automatically switching between primary and secondary mass spectrometry acquisition. Full scan spectra were acquired at 70K mass resolution, with a mass-to-charge ratio range of 350–1550 and an AGC target set to 3e6, followed by subsequent high-energy collisional dissociation MS / MS scans at 17.5K resolution with an AGC target of 3e6. 39 windows were used to fragment and scan all peptides in this range, and the window coverage is as follows (Table 2).

[0059] Table 2. DIA scanning window coverage

[0060]

[0061] Spectronaut Pulsar software was used to optimize data acquisition and analysis, mainly including: setting cycle time (obtaining optimal extraction of elution peaks), adjusting parent ion windows according to chromatographic peak width, and adjusting cycle time. 6-8 data points were extracted from each peak for analysis, and the peak information was extracted, corrected, and integrated. The DIA mass spectrometry data was analyzed with default parameter settings (BGS Factory Settings-default). The protein qualitative standard was the parent ion threshold (precursor sorthreshold) set to 1.0% FDR (false discovery rate). At the same time, the software was used for preliminary analysis of differential proteins among the three groups. The setting standard of differential proteins was pairwise comparison among the three groups: Student's t Test analyzed proteins with P < 0.05 and a difference of 1.5 or more.

[0062] 2. Analysis of differentially expressed proteins between groups and screening of candidate biomarkers

[0063] Since PRM targeted mass spectrometry has certain limitations on the number of target proteins, in order to ensure the accuracy of low-abundance protein quantification in subsequent studies, it is necessary to analyze hundreds of proteins obtained by DIA detection, gradually screen them, and establish candidate biomarkers for subsequent verification and diagnostic analysis. Differential proteins are imported into IPA (Ingenuity pathway analysis) bioanalysis software to analyze the functional pathways and related diseases to which the proteins belong. Figure 1 ), screen layer by layer, select proteins related to neurological diseases and dementia pathogenesis; identify the target proteins most likely to be associated with AD and enter the next step of verification;

[0064] 3. PRM quantitative analysis (preliminary verification)

[0065] The candidate proteins were obtained and preliminarily verified in 38 elderly people, and proteins with poor detection results were eliminated for further screening.

[0066] 3.1 Experimental materials, reagents and equipment

[0067] The collected biological samples were subjected to protein extraction, reduction alkylation and enzymatic hydrolysis using the Biognosys Pretreatment Kit Pro (Biognosys AG). Then, an appropriate amount of enzymatically hydrolyzed peptides were taken from each sample and mixed with PQ500 peptide labeling reagent in a certain proportion. The prepared samples were first collected using the Unscheduled PRM method to collect target peptides to optimize the retention time window. After the method was optimized, formal experiments could be carried out. The collected data were used for identification and quantitative analysis of target peptides using SpectroDive (Biognosys AG) software.

[0068] Table 3. Reagents and instrument specifications required for PRM detection

[0069]

[0070]

[0071] Experimental procedures

[0072] 3.2 PRM Preliminary Experiment

[0073] The selection criteria for targeted peptides are: ① unique peptides; ② the peptide sequence length is greater than 7 amino acids and less than 20 amino acids; ③ the peptide is completely cleaved, there are no missed cleavage sites, and the peptide sequence does not contain proline; ④ the peptide can contain fixed modifications but not variable modifications.

[0074] According to the selected peptide information, a preliminary experiment was conducted, and two prepared peptide samples were used for detection. Take an equal amount of peptides from the sample, mix and freeze-dry, redissolve in buffer A (buffer A: 20mM ammonium formate aqueous solution, ammonia adjusted to pH 10.0), and connect the reverse column with the Ultimate3000 system for high pH reverse separation. The separation uses a linear gradient, and the gradient increases from 5% B to 45% B within 30 minutes (B: 80% acetonitrile with 20mM ammonium formate, ammonia adjusted to pH 10.0). The column was balanced for 15 minutes under the initial conditions, the column maintained a flow rate of 1 ml per minute, and the temperature was maintained at 30°C. Under this condition, 6 fractions were collected for analysis.

[0075] After adding 30uL of solvent C (C solvent is 0.1% formic acid aqueous solution; D solvent is 0.1% formic acid acetonitrile solution) to each fraction to make a suspension, iRT peptide was added, reverse liquid phase separation was used, and tandem mass spectrometry detection and analysis were performed using an online electrospray ion source. The Easy-nLC 1000system was used to complete the experiment. At the same time, the system was connected to the Orbitrap Fusion Tribrid mass spectrometry detection instrument equipped with an electrospray ion source. 10uL of peptide sample was loaded onto the capture column at a rate of 10uL per minute, and then separated in the analytical column with a linear gradient, increasing the concentration of 3% D to 32% D within 120 minutes. The column was balanced under initial conditions for 10 minutes, controlling the flow rate of 300nL per minute, and the voltage of the electrospray ion source was 2 kilovolts.

[0076] The mass spectrometer first worked in data-dependent acquisition mode, and the instrument automatically switched between primary mass spectrometry (MS) and secondary mass spectrometry (MS / MS) acquisition. Scanning was performed at 60K mass resolution, and the mass-to-charge ratio (m / z) range of the full scan spectrum was between 350-1550, followed by subsequent high-energy collision (HCD) secondary mass spectrometry (MS / MS) scanning at 15K resolution. Specific parameter settings: isolation window was set to 1.6Da, AGC target was set to 400000, MS / MS Fixedfirst mass was set to 110, Microscan was set to 1, and dynamic exclusion time was 45 seconds.

[0077] The preliminary experimental data were imported into Spectronaut Pulsar, and the library was searched through the embedded Mascot Distiller version 2.6. The database was set to Swissprot homo sapiens (2017-3-27), and trypsin was set as the enzymatic enzyme. The specific parameters used for the library search were: the mass error range of the fragment ion was allowed to be 0.050Da; the mass error range of the parent ion was allowed to be 10.0PPM; the fixed modification was allowed to be Carbamidomethyl (C); the variable modification was allowed to be Asparagine and Glutamine deamidation, Methionine oxidation, and protein N-terminal acetylation. The library search results were imported into SpectroDive 8 software for analysis, and proteins that could not be detected or whose target peptide signal intensity was not ideal were removed. The parent ion and analysis method were set according to the preliminary experimental results, and then the formal PRM detection was carried out.

[0078] 3.3PRM formal testing and data collection

[0079] Take 9uL of sample, add iRT peptide, pass through the online electrospray ion source, and detect on the same mass spectrometer, setting the mass spectrometry detection gradient to 120 minutes. 2uL of peptide sample is loaded onto the capture column at a flow rate of 10uL per minute, and separated in the analytical column with a linear gradient: the concentration of solvent D is increased from 3% to 32% within 120 minutes. The analytical column is equilibrated under initial conditions for 10 minutes, the flow rate is controlled at 300nL per minute, and the electrospray ion source voltage is 2 kilovolts. The mass spectrometer first works in data-dependent acquisition mode, and the instrument automatically switches between MS (primary mass spectrometry) and MS / MS (secondary mass spectrometry) acquisition. Scan at 60K mass resolution, and obtain a full scan spectrum with a mass-to-charge ratio range of 350-1550, followed by subsequent high-energy collision MS / MS scans at 15K resolution, and the mass spectrometer selects the PRM module. Each sample is tested once again based on the initial test to increase the accuracy of protein quantification. The collected data were imported into SpectroDive 8 for analysis, and the signal intensity of the samples was corrected to eliminate the human errors caused by sample preparation and instrument detection. The best 6 fragment ion (daughter ion) signals were collected for each peptide, and the sum of the peak areas of the fragment ion spectrum represented the peptide signal intensity. 3-5 peptides were selected for each protein, and the average signal intensity of the peptides represented the expression level of the protein.

[0080] 4. Isotope-labeled synthetic peptides are combined with PRM technology for precise quantification and verification (expanded sample verification).

[0081] The 20 candidate biomarkers obtained from the above screening were precisely quantified and validated with expanded samples by combining isotope-labeled synthetic peptide PRM technology. The validation subjects were 155 elderly people, whose general information is shown in Table 4.

[0082] Table 4. Comparison of general data between NC and aMCI groups

[0083]

[0084] Note: NC = normal cognition; aMCI = amnestic mild cognitive impairment

[0085] Orbitrap Fusion using EASY-nanoLC1200 with high precision TM Lumos TM Tribrid TM Mass spectrometer (Thermo Fisher Scientific, MA, USA) was used for quantification. First, representative peptides of the target protein were selected and mass spectrometry signals were collected to obtain quantitative information of the target protein. At the same time, standard isotope standard peptides were synthesized and standard curves were drawn to achieve absolute quantification of the protein.

[0086] First, the parent ion information of the target peptide was selectively detected in the primary mass spectrometer (Q1); the parent ion was fragmented; and finally, the information of all fragments within the selected parent ion window was detected in the secondary mass spectrometer using a high-resolution, high-mass precision analyzer. The experiment included two groups of samples. During sample preparation, PQ500 (Ki-3019-96, Biognosys AG, Switzerland) isotope-labeled peptides were added to all samples to be tested for identification and quantitative analysis of the target peptides. All samples were collected in a random loading manner. The collected PRM data were normalized and quantitatively analyzed using SpectronDive 9.10 (Biognosys AG, Switzerland) software, and the p value was calculated using the Students ttest (unpaired, two-tailed) algorithm. The difference fold was calculated using the average value of each group. Sample pretreatment was performed according to the instructions of the Biognosys Sample Preparation Kit Pro (Ki-3013, Biognosys AG, Switzerland). The prepared peptides were subjected to PRM analysis using a LC-MS / MS system equipped with an online nanospray ion source.

[0087] 5. Data Analysis and Modeling

[0088] 5.1 Statistical analysis was performed on the quantitative results of the target proteins obtained. First, the efficacy analysis of the diagnosis of MCI of a single protein was performed. The results obtained were screened according to the standards of P < 0.05 and AUC > 0.58. Finally, the five proteins P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), and P00736 (C1R) obtained had good diagnostic value. The AUCs for predicting MCI were 0.61, 0.62, 0.60, 0.60, and 0.58, respectively. The concentration and AUC of the target protein are shown in Figure 3.

[0089] 5.2 In order to further improve the diagnostic value, the obtained protein and other data were subsequently modeled. The purpose of modeling is to find an optimal model to more effectively distinguish aMCI from healthy people. The ratio of training set and test set was adjusted to 75%:25%; all data were interpolated with missing values ​​by Bagging Tree. First, peptides were screened using data with missing values ​​filled in, and t-tests were performed on each peptide. Secondly, proteins with poor efficacy were eliminated, that is, only peptides with the smallest pvalue were retained to represent the protein content. The above screened proteins were trained on the model using the Bootstrap 1000 resampling method. 75% of all samples were taken as training set samples, and the remaining 25% were used as prediction set samples. The model was established using the logistic regression method. The model is composed of five proteins, P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), and P00736 (C1R), as well as gender and age factors. The overall prediction of MCI by the model has an AUC of 0.758, a sensitivity of 78%, and a specificity of 75% (see Figure 4 shown).

[0090] Fitting formula:

[0091] In addition to the above-mentioned technical solutions, mass spectrometry can also be used to detect the five proteins P02753 (RBP4), P22352 (GPX3), P23560 (BDNF), P02765 (AHSG), P00736 (C1R) and their corresponding peptides, as well as antigen-antibody binding technology and aptamer binding technology to detect these five proteins.

[0092] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. Use of a reagent for detecting the content of peripheral blood protein markers RBP4, GPX3, BDNF, AHSG and C1R protein in the preparation of a kit for early diagnosis of Alzheimer's disease.

2. The use according to claim 1, characterized in that The kit includes reagents for determining the contents of RBP4, GPX3, BDNF, AHSG and C1R protein in plasma.

3. The use according to claim 1 or 2, characterized in that The kit includes reagents for measuring peptide segments of RBP4, GPX3, BDNF, AHSG and C1R proteins.

4. An early medical auxiliary diagnosis system for Alzheimer's disease, characterized in that: The diagnostic system comprises an early diagnosis model for Alzheimer's disease, which includes RBP4, GPX3, BDNF, AHSG and C1R proteins, age and gender.

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