CN114705866A

CN114705866A - Blood-based forgetting type mild cognitive impairment early diagnosis peripheral blood protein marker, application and medical auxiliary diagnosis system thereof

Info

Publication number: CN114705866A
Application number: CN202210245944.XA
Authority: CN
Inventors: 许桦; 肖世富; 王涛
Original assignee: Shanghai Mental Health Center Shanghai Psychological Counselling Training Center
Current assignee: Shanghai Mental Health Center Shanghai Psychological Counselling Training Center
Priority date: 2022-03-14
Filing date: 2022-03-14
Publication date: 2022-07-05
Also published as: CN116754772A

Abstract

The invention provides a peripheral blood protein marker for amnesic MCI diagnosis, which comprises protein P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG) or P00736(C1R), and application of the protein marker for amnesic MCI diagnosis in preparation of an MCI diagnosis kit, and finally establishes a amnesic MCI diagnosis model, wherein the model predicts the AUC of MCI as a whole to be 0.758, has the sensitivity of 78% and has the specificity of 75%.

Description

Blood-based forgetting type mild cognitive impairment early diagnosis peripheral blood protein marker, application and medical auxiliary diagnosis system thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to a blood-based peripheral blood protein marker for early diagnosis of amnesic mild cognitive impairment, application and a medical auxiliary diagnosis system thereof.

Background

(1) Description of the Prior Art

Alzheimer's Disease (AD) is a neurodegenerative disease characterized primarily by decreased memory, cognition and daily living abilities, with high prevalence, disability rate and heavy disease burden among the elderly population. Epidemiological investigation shows that the prevalence rate of the old people over 60 years old is 3-5%, and the prevalence rate is increased by 1 time for every 5 years old, and can reach 20% above 80 years old and 50% above 90 years old. China is an aging society, and by the end of 2020 according to data published by the national statistical bureau, the population of 60 years old and above reaches 2.6 hundred million people, accounting for 18.7% of the total population in China. Conservative estimates, currently, in our country, dementia patients reach 1000 million, and in the patients with Mild Cognitive Impairment (MCI) 3000 million [ Longfei J, Meina Q, et al. Dementia in China: epidemic, clinical management, and research advances. Lancet neurology.2019,19(1): p81-92], the medical and nursing burden is extremely heavy and on a continuing rising trend. In this respect, there is no drug currently available that can slow or reverse the progression of the disease, and therefore early diagnosis and intervention of alzheimer's disease is of great importance.

Alzheimer's disease is roughly divided into an asymptomatic phase, a mild cognitive impairment phase (MCI), and a dementia phase. Patients may have a period of mild cognitive impairment of around 10 years before apparent clinical symptoms appear. Patients at this stage, while daily living ability is not affected, pathological changes of dementia are already present in the brain and gradually progress. Among them, amnestic mci (acmi) is considered to have the closest relationship with alzheimer dementia, and is the best stage for early diagnosis and intervention.

However, the diagnosis of preclinical AD is very difficult. In more than 10 years, the discovery of biomarkers makes it possible to accurately diagnose AD at an early stage. The united states national institute of aging and the alzheimer's disease association (NIA-AA) recommended fluorodeoxyglucose-positron emission tomography (FDG-PET), amyloid-positron emission tomography (Α β -PET), and detection of Α β 42, T-tau, P-tau in cerebrospinal fluid as biomarkers for early diagnosis of AD in 2011. However, these examinations are expensive (FDG-PET, Abeta-PET) or invasive (cerebrospinal fluid examination), and are mainly used in the field of research and cannot be popularized. In recent years, the research finds that the change of components in the blood of the dementia patients has correlation with the pathological changes of the brain thereof, and can be used as a biomarker for early diagnosis. In addition, peripheral blood has the characteristics of easy acquisition, repeatable detection and the like, and becomes a hotspot of international leading-edge research in the current AD field. Potential biomarkers in peripheral blood mainly include MicroRNA, plasma proteins, etc. in blood, but research in this field is still in an exploratory stage. Results from MicroRNA studies in Blood were not consistent and no large sample data was available [ Miller, J.and J.Kauwe, differentiating Clinical disruption A scoring Using Blood RNA levels. genes,2020.11: p.706 ]; the plasma protein is limited by the sensitivity and stability of the detection method due to low content and various types, and the result is not consistent enough. For proteomics, Kiddle et al (2014) summarized 21 study analyses, 163 Candidate proteins were obtained, 94 of which were verified again after screening, and the results of the study differed from those of the previous one [ Kiddle, s.j., et al, cancer blood protein markers of Alzheimer's Disease on set and progression: a systematic review and reproduction study. journal of Alzheimer's Disease Jad,2014.38(3): p.515., Japanese birch, Shokukui, Alzheimer's Disease peripheral blood protein biomarker research progress. In 2015, another investigator screened a group of proteins (including S100-A9 protein, CD226 antigen, body-transplant inflammatory factor 1, endothelial cell selective adhesion molecule, leukocyte differentiation antigen CD84, diagnosis sensitivity of MCI 96.7%, specificity 80.0%, accuracy 92.5% [ ZHAO X, Lejnine S, Spond J, et al. A candate plasma protein ligand peptide' S Disease [ J ]. J Alzheimer Disease Jad,2015,43(2):549-, unprecedented interest was elicited [ Karikari, T., et al, Blood phosphorylated tau181as a biobased maker for Alzheimer's disease: a diagnostic performance and prediction modifying therapy using data from a four reactive patients, the Lancet Neurology,2020.19: p.422-433 ], but its diagnostic value is mainly reflected in AD clinical stage patients. Other studies in recent 2 years have included plasma GFAP Protein, p-tau217, p-tau231, etc., which, in general, are more than 90% accurate for clinical diagnosis of dementia, but the diagnostic value of MCI stage in early stages of dementia remains to be further confirmed [ Oeckl, P., et al, Global fibrous Acidic Protein in Serum incorporated in Alzheimer's Disease and minerals with Cognitive impact.J. Alzheimer's Disease, 2019.67(2): p.481-488; chatterjee, P., et al, Diagnostic and Diagnostic plasma biomakers for a preliminary Alzheimer's disease Alzheimer's definition, 2021; ashton, N.J., et al., Plasma p-tau231: a new biobased marker for inducing Alzheimer's disease pathology acta neuropathohol 2021.141(5): p.709-724; thijssen, E.H., et al, Plasma phosphorylated tau217 and phosphorylated tau181as biomarkers in Alzheimer's disease and front conditional logical bar generation a retroactive diagnostic performance test Lancet neuron, 2021.20(9): p.739-752 ].

In general, biomarkers that can be used for early diagnosis of AD are still lacking, and proteins in peripheral blood completely have the potential as biomarkers for early diagnosis of dementia, but the content of the proteins in peripheral blood is low, the variety of the proteins is large, and the detection technology is extremely high. Therefore, there is a need to find reliable and sensitive biomarkers for early diagnosis of AD by effective techniques.

(2) Problems or disadvantages of the prior art

With the advancement of proteomics technology, differential proteins in blood can be searched from a large scale level and become potential biomarkers. The proteomics technologies currently used for high-throughput peripheral blood detection and screening mainly include the following categories:

(a) immunocapture (immunocapture) -based detection methods: the method realizes qualitative and quantitative detection of protein by combining specific binding of antigen and antibody as principle and combining with a plurality of specific technologies, such as: protein liquid phase chip (luminanex xMAP), electrochemiluminescence technology msd (mesoscale discovery), etc. [ betula, schofu, alzheimer's disease peripheral blood protein biomarker research progress. the journal of chinese clinicians: electronic edition, 2017(10): p.1821-1824 ]. A large amount of target protein can be detected in a small amount of blood sample. The high-flux characteristic of the method can meet the requirement of clinical research, but the method is based on the specific binding of antigen and antibody, is not suitable for detection without hypothesis targets, is greatly influenced and limited by the selected antibody, and has unstable result.

(b) Aptamer (aptamer) -based detection methods: such methods are based on the specific binding of an aptamer to a target protein. The aptamer is a single-stranded oligonucleotide, can effectively recognize and bind to a target protein, and the binding has the characteristics of high affinity and high specificity. This method is also characterized by high throughput, with better stability as a result, which is also the most widely used method, but the results may still fluctuate under the influence of the selected Aptamer [ Gold, l., et al, Aptamer-based multiplexed genomic technology for biomarker discovery. plos One,2010.5(12): p.e15004 ]. Thereby degrading stability.

(c) Mass Spectrometry (MS) -based detection method: the mass spectrometric detection method is more expensive than the former two methods, and has the characteristics of getting rid of the influence of the antibody on the result and having optimal stability and accuracy. In recent years, with the continuous progress of mass spectrometry detection technology, the sensitivity of the mass spectrometry detection technology is continuously improved, the protein in blood can be detected without targets (non-hypothesis), the candidate protein can be directly detected quantitatively, and the technology such as nuclide labeling is combined, so that the accurate quantification of the low-abundance protein in blood [ schoolbox, xiaoshifu, alzheimer peripheral blood protein biomarker research progress ] can be realized.

Disclosure of Invention

The main purpose of the present invention is to provide a simple and convenient detection technique which is stable and reliable and can be used for diagnosing early dementia (amnesic MCI), aiming at the problem of difficult early diagnosis of AD in the prior art.

In order to achieve the above objects, the present invention provides, in one aspect, a peripheral blood protein marker for amnestic MCI diagnosis, the protein marker comprising protein P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG) or P00736 (C1R).

The invention provides an application of the peripheral blood protein marker for amnesic MCI diagnosis in preparing an MCI diagnosis kit.

Preferably, the kit is capable of determining the content of P02753(RBP4) protein or P22352(GPX3) protein or P23560(BDNF) protein or P02765(AHSG) protein or P00736(C1R) protein in plasma.

Preferably, the kit contains any peptide fragment of P02753(RBP4) or P22352(GPX3) or P23560(BDNF) or P02765(AHSG) or P00736 (C1R).

The invention provides a forgetting type MCI medical auxiliary diagnosis system, wherein the diagnosis system comprises an MCI diagnosis model, and the MCI diagnosis model comprises proteins P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG), P00736(C1R), age and sex.

The peripheral blood protein marker for early diagnosis of amnesic mild cognitive impairment based on blood, the application and the medical auxiliary diagnosis system thereof have the following beneficial effects:

1. the non-targeting proteomics technology (DIA) is adopted to carry out open exploration on the differential protein among the groups, so that the artificial bias is reduced, and the objectivity is better.

2. Introducing IPA (International pathway analysis) biological analysis software to analyze the function path and related diseases of the protein, screening layer by layer, and selecting the protein related to nervous system diseases and dementia pathogenesis; and the stability and the accuracy of the research result are improved by gradually verifying.

3. The method adopts a mass spectrum analyzer with extremely high precision, combines isotope standard peptide to accurately quantify the target protein, and has stable and reliable detection result. On the basis of establishing a prediction model, a group of prediction modes consisting of five proteins P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG) and P00736(C1R) and factors of gender and age are finally obtained, the AUC of the MCI predicted by the model as a whole is 0.758, the sensitivity is 78% and the specificity is 75%.

Drawings

FIG. 1 is a graph showing the results of pathway analysis of aMCI and NC group differential proteins of the present invention.

FIG. 2 is a schematic diagram of the PRM experimental procedure and detection of the present invention.

FIG. 3 is a graph of the results of model prediction of aMCI in accordance with the present invention.

Detailed Description

In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described in the following combined with the specific embodiments.

The invention adopts a mass spectrometry proteomics detection technology, firstly detects and compares plasma proteins in aMCI and peripheral blood of normal cognitive old people (NC), obtains differential proteins through layer-by-layer screening and gradual verification, and then combines an isotope labeling method to accurately quantify and verify the obtained target protein, thereby searching for biomarkers for AD early diagnosis. The whole research process is divided into four stages: in the first stage, Data Independent Acquisition (DIA) is adopted to perform non-targeting and open exploration on differential protein of aMCI and normal old people (NC) peripheral blood; and in the second stage, the differential proteins among groups are gradually screened through a biological information technology, irrelevant proteins are removed through layer-by-layer screening of molecular pathways, functional mechanisms and the like, and the range of candidate proteins is determined. The screening strategy comprises the following steps: (1) statistical differences among groups were achieved: student's t Test tests the protein with P less than 0.05 and difference multiple of 1.5 and above; (2) IPA (Ingenity pathway analysis) biological analysis software, which analyzes the function pathway and related diseases of the protein, screens layer by layer, and selects the protein which is related to the pathogenesis of nervous system diseases and dementia; in the third stage, the obtained candidate proteins are subjected to preliminary verification in 38 elderly people, proteins with poor detection and prediction effects are removed, and further screening is carried out to obtain 20 candidate biomarkers; in the fourth stage, the diagnostic efficacy of the candidate biomarkers was verified in 155 elderly populations. The 20 targeted proteins are accurately quantified by adopting a Parallel Reaction Monitoring (PRM) mass spectrum technology and combining isotope labeling synthetic peptide fragments. A group of proteins (RBP4, GPX3, BDNF, AHSG and C1R) are finally obtained by verification in 155 old people, the sensitivity of the early diagnosis of aMCI reaches 78% and the specificity reaches 75% by combining age and gender.

As shown in fig. 1, IPA software analyzed the differential proteins obtained from the acci versus NC groups, and their functional pathways. Taking a total of 17 pathways with statistical differences in the-log value of the P value above 1.3 (equivalent to a P < 0.05 level), the significance of the statistical differences is ranked from large to small: 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 in the young (MODY) signaling, glycolysis I, gluconeogenesis I, intrinsic prothrombin activation pathway, phospholipase, interaction of agrin at the neuromuscular junction, etc.

As shown in FIG. 2, targeted peptide fragments of 110 target proteins are selected by SpectroDive8 software, pre-experiments are carried out according to the selected targeted peptide fragments, the detection effect of the target peptide fragments is analyzed, proteins with poor signal intensity and unsatisfactory detection effect are removed, proteins with relatively ideal detection effect are reserved, and formal PRM quantitative detection and verification are carried out.

As shown in fig. 3, the model consists of five proteins, P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG), P00736(C1R), and sex and age factors, and the overall predicted MCI of the model has AUC 0.758, sensitivity 78%, and specificity 75%.

DIA Mass Spectrometry

In this embodiment, the number of instances for DIA detection is: 6 cases of aMCI group and 6 cases of NC group. a isThe mean age of the MCI group was 68.17 ± 6.369 years, the mean age of the NC group was 70.50 ± 10.585 years, and there were no statistical differences between the ages of the two groups (F ═ 1.775, P ═ 0.412 > 0.05). No statistical differences were found in gender constitution between aMCI and NC groups (X)²0.670, P > 0.733.05). The detection sample is human peripheral blood plasma. Sample collection and processing is done strictly according to a uniform specification. The blood collection conditions were: the subjects fasted for 12 hours, and in the next morning, the elbow venous blood was taken and placed in a red anticoagulation tube. Blood centrifugation was completed within 2 hours after collection, and plasma and blood cells were separated.

TABLE 1.DIA Mass Spectrometry required reagents and Instrument specifications

The DIA mass spectrometry detection process comprises sample pretreatment, reverse liquid chromatography (RPLC), DDA detection and library building, DIA individual sample detection and the like. And extracting, correcting and integrating the spectrum peak information of the protein obtained by detection by adopting professional analysis software Spectronaut Pulsar for DIA mass spectrum detection.

1.1 Experimental procedures

1.1.1 sample preparation

The protein extraction required by detection is free from pollution of environmental protein, PEG, Triton, glycerol and the like, and the preparation of the peptide fragment is required to be that the nonspecific reductive alkylation is less than 30%, the nonspecific enzyme digestion rate is less than 5%, and no obvious nonspecific modification exists. The pretreatment process comprises the following steps:

(1) plasma was diluted 100-fold and protein quantification was performed by BCA method;

(2) according to the measured protein concentration, 100ug of protein is respectively taken and diluted to 100ul by 100mM TEAB;

(3) 1M reducing agent DTT 1uL was added to a final concentration of 10mM and incubated at 37 ℃ for 1 hour;

(4) adding 2uL of 1M iodoacetamide to a final concentration of 20mM, and incubating for 1 hour at room temperature in the absence of light;

(5) the precipitate was treated with 5 volumes of acetone to the sample and allowed to stand overnight (12 hours) at-20 ℃;

(6) centrifuging at 12000g for 20 min at 4 ℃;

(7) removing supernatant, adding 1mL of ethanol and acetone (ratio of 1:1) at-20 ℃ into the protein precipitate, mixing, shaking and washing for 5 minutes, and repeating for 2 times;

(8) centrifuging at 12000g for 20 min at 4 ℃;

(9) removing supernatant, and naturally drying the protein precipitate at room temperature until the precipitate becomes transparent;

(10) 100ul of 50mM ammonium bicarbonate was added to the pellet, and 2.5ug of sequencing grade pancreatin was added at a ratio of 1:40 (mass ratio w: w) and subjected to enzymolysis overnight at 37 ℃.

1.1.2 high Performance liquid chromatography separation

And (3) separating the sample by adopting a high pH reverse phase separation method. An equal amount of the peptide fragments was collected from all samples, mixed and lyophilized, and buffer A (buffer A:20mM ammonium formate aqueous solution, ammonia adjusted to pH10.0) was added for fractionation. The high performance liquid chromatograph Ultimate3000 system is connected with an Xbridge C18 coclumican reversed-phase column, and linear gradient is adopted for high pH separation. The concentration of mobile phase B (20 mM ammonium formate in 80% acetonitrile and PH of ammonia to PH10.0) was adjusted from 5% to 45% over 30 minutes, the column was equilibrated for 15 minutes at initial conditions, the column maintained a flow rate of 1ml per minute, the temperature was kept stable at 30 ℃, and a total of 12 fractions were collected.

1.1.3 dependent acquisition and banking of Mass Spectrometry data

Each fraction was suspended in 30. mu.l of solvent C (0.1% formic acid in water), corrected by addition of iRT peptide, separated by nano-liquid phase (nano-LC) and analyzed by tandem mass spectrometry by online electrospray. The experiment was carried out on an Easy-nLC 1200system connected to a Q Exatives Plus mass spectrometer equipped with an electrospray ion source. Mu.l of a peptide fragment sample was applied to the trap column at a flow rate of 10. mu.l per minute, and then separated in a linear gradient in the analytical column, and 5% solution D (D: 0.1% acetonitrile formate) was adjusted to 40% D in 150 minutes. The column was equilibrated at initial conditions for 10 minutes, flow was controlled at 300nL per minute, and the voltage of the electrospray ion source was set at 2 kV. The mass spectrometer operates in a data dependent acquisition mode, automatically switching between primary Mass Spectrometry (MS) and secondary mass spectrometry (MS/MS). A full scan spectrum was obtained at 70K mass resolution with a mass to charge ratio (m/z) range of 350-. Setting parameters: the isolation window (isolation window) is set to 1.6Da, the AGC target is set to 1e5, the MS/MS Fixed first mass is set to 200, the microscan is set to 1, and the dynamic exclusion time is 30 seconds.

Searching the original data by a Swiss-Prot homo sapiens (2017-3-27) database, setting trypsin (trypsin) as a digestive enzyme during searching, and setting specific parameters as: (1) the allowable error range of the fragment ion mass in searching is 0.050 daltons; (2) the allowable error range of the mass of the parent ions in the searching process is 10.0 PPM; (3) allowed fixed modifications: urea methylation (Carbamidomethyl, C); (4) allowed variable modifications: asparagine (asparaginine) and glutamine (glutamine) deamidation, methionine (methionine) oxidation, protein N-terminal acetylation. And establishing a spectrogram database through the database searching information, and establishing a comparison standard for subsequent DIA detection.

1.1.4 independent acquisition and data analysis of Mass Spectrometry data

The DIA detection procedure is as follows: samples of 9ul each were taken, iRT peptide was added, and the system was connected to a Q exact Plus mass spectrometer equipped with an online electrospray ion source, and run on an Easy-nLC 1200 system. Mu.l of a sample of the peptide fragment were applied to a trapping column at a flow rate of 10. mu.l per minute and subsequently separated in a linear gradient in an analytical column, the concentration of the D solvent being adjusted from 5% to 40% within 150 minutes. The column was equilibrated at initial conditions for 10 minutes, flow was controlled at 300nL per minute, and the voltage of the electrospray ion source was set at 2 kV. The mass spectrometer operates in a data independent acquisition mode and automatically switches between primary mass spectrum acquisition and secondary mass spectrum acquisition. The full scan spectra were acquired at 70K mass resolution, with mass to charge ratio ranging from 350-. All peptides within this range were cleaved and scanned using 39 windows, which are covered below (table 2).

TABLE 2 DIA scanning Window coverage

The method adopts Spectronaut Pulsar software to optimize data acquisition and analysis, and mainly comprises the following steps: setting cycle time (obtaining optimum extraction of elution peak), adjusting parent ion window according to chromatographic peak width, and adjusting cycle time. Extracting 6-8 data points from each peak for analysis, extracting, correcting and integrating peak information, analyzing DIA mass spectrum data with a default parameter setting (BGS Factory Settings-default), and setting a precursor threshold (1.0% FDR (plasma discovery rate) as a protein qualitative standard. Meanwhile, the software is adopted to carry out preliminary analysis on the difference proteins among the three groups, and the setting standard of the difference proteins is pairwise comparison among the three groups: student's t Test analyzes protein with P less than 0.05 and difference multiple of 1.5 and above.

2. Inter-group differential protein analysis and candidate biomarker screening

Since PRM-targeted mass spectrometry has a certain limit on the number of target proteins, to ensure the accuracy of low-abundance protein quantification in subsequent studies, hundreds of proteins obtained by DIA detection need to be analyzed, screened step by step, and candidate biomarkers are established for subsequent validation and diagnostic analysis. Introducing different proteins into IPA (insulin pathway analysis) biological analysis software, analyzing the function pathway and related diseases of the proteins (figure 1), and screening layer by layer to select proteins related to nervous system diseases and dementia pathogenesis; identifying the target protein most likely to be associated with AD proceeds to the next step of validation.

PRM quantitative analysis (preliminary verification)

The obtained candidate proteins are preliminarily verified in 38 old people, proteins with poor detection effects are removed, and further screening is carried out.

3.1 Experimental materials, reagents and devices

The collected biological samples are subjected to protein extraction, reductive alkylation and enzymolysis by adopting a Biognosys pretreatment Kit (Sample pretreatment Kit Pro, Biognosys AG), and then a proper amount of peptide fragments subjected to enzymolysis are taken from each Sample and are doped into a PQ500 standard peptide reagent according to a certain proportion. The prepared sample firstly adopts an unsched PRM method to collect a target peptide fragment so as to optimize a retention time window, and a formal experiment can be carried out after the method is optimized. The collected data are subjected to identification and quantitative analysis of the target peptide fragment by SpectroDive (Biognosys AG) software.

TABLE 3 reagents and instrumentation specifications for PRM detection

Experimental procedure

3.2 PRM Pre-experiments

The selection criteria of the targeting peptide fragment are as follows: characteristic peptide fragment (unique peptide); the length of the peptide fragment sequence is more than 7 amino acids and less than 20 amino acids; thirdly, the peptide segment is completely cut by enzyme without cutting missing sites, and the peptide segment sequence does not contain proline; peptide fragments may comprise fixed modifications, but no variable modifications.

And (3) carrying out a preliminary experiment according to the selected peptide fragment information, and detecting by using 2 prepared peptide fragment samples. An equal amount of peptide fragments was sampled, mixed and lyophilized, redissolved in buffer A (buffer A:20mM ammonium formate aqueous solution, ammonia adjusted to pH10.0), and then connected to a reverse column using the Ultimate3000 system for high pH reverse separation using a linear gradient from 5% B to 45% B within 30 minutes (B: 20mM ammonium formate added to 80% acetonitrile, ammonia adjusted to pH 10.0). The column was equilibrated at initial conditions for 15 minutes, the column maintained a flow rate of 1ml per minute, the temperature was maintained constant at 30 ℃, and 6 fractions were collected for analysis under these conditions.

Adding 30uL of solvent C (the solvent C is 0.1% formic acid aqueous solution; the solvent D is 0.1% formic acid acetonitrile solution) into each fraction to prepare suspension, adding iRT peptide, separating by reverse liquid phase, and performing tandem mass spectrometry detection and analysis by using an online electrospray ion source. The experiment was completed using an Easy-nLC 1000system, and the system was connected to an Orbitrap Fusion triple mass spectrometer equipped with an electrospray ion source. 10uL of peptide fragment samples were loaded onto the trap column at a rate of 10uL per minute, followed by separation in a linear gradient in the analytical column, increasing the 3% D concentration to 32% D concentration over 120 minutes, the column equilibrated at the initial conditions for 10 minutes, controlling the flow rate at 300nL per minute, and the electrospray ion source at a voltage of 2 kV.

The mass spectrometer is first operated in a data dependent acquisition mode with the instrument automatically switched between primary Mass Spectrometry (MS) and secondary mass spectrometry (MS/MS) acquisition. Scanning at the mass resolution of 60K, the mass-to-charge ratio (m/z) range of the full-scan spectrum is 350-1550, and then the subsequent high-energy collision (HCD) secondary mass spectrum (MS/MS) scanning is completed under the condition of 15K resolution. Setting specific parameters: the isolation window (isolation window) is set to 1.6Da, the AGC target is set to 400000, the MS/MS Fixed first mass is set to 110, the Microcan is set to 1, and the dynamic exclusion time is 45 seconds.

The pre-experimental data was imported into Spectronaut Pulsar, and the library was searched by means of an embedded Mascot Distiller version 2.6, where the database was set to Swissprot homo sapiens (2017-3-27) and trypsin (trypsin) was set to zymolase. Concrete parameters adopted for searching the library are as follows: the mass error range of the fragment ions is allowed to be 0.050 Da; allowing the mass error range of the parent ions to be 10.0 PPM; allows the immobilization modification to Carbammidomethyl (C); allowing variable modifications to Asparagine and Glutamine deamidation, Methionine oxidation, N-terminal acetylation of proteins. And (3) importing the library searching result into SpectroDive8 software for analysis, removing proteins which cannot be detected or have unsatisfactory target peptide segment signal intensity, setting a parent ion and an analysis method according to a pre-experiment result, and then carrying out formal PRM detection.

3.3PRM formal detection and data acquisition

And (3) adding iRT peptide into a 9uL sample, detecting on the same mass spectrometer after an online electrospray ion source, and setting a mass spectrum detection gradient to be 120 minutes. A 2uL peptide fragment sample was loaded onto a trap column at a flow rate of 10uL per minute and separated in an analytical column with a linear gradient: the concentration of solvent D was increased from 3% to 32% in 120 minutes. The column was allowed to equilibrate for 10 minutes at initial conditions, the flow rate was controlled at 300nL per minute, and the electrospray ion source voltage was 2 kV. The mass spectrometer is first operated in a data dependent acquisition mode, with the instrument automatically switching between MS (primary mass spectrum) and MS/MS (secondary mass spectrum) acquisition. Scanning at the mass resolution of 60K to obtain the mass-to-charge ratio range of a full-scan spectrogram of 350-1550, then performing subsequent high-energy collision MS/MS scanning at the resolution of 15K, and selecting a PRM module by mass spectrometry. Each sample was tested in duplicate on the basis of the initial test to increase the accuracy of protein quantification. The collected data are imported into SpectroDive8 for analysis, and the signal intensity of the sample is corrected to eliminate human errors caused by sample preparation and instrument detection. The best 6 fragment ion (daughter ion) signals were collected for each fragment, and the sum of the fragment ion spectral peak areas (peak areas) represents the signal intensity of the fragment. 3-5 peptide fragments are selected for each protein, and the average signal intensity of the peptide fragments represents the expression amount of the protein.

4. The isotope labeling synthetic peptide fragment is combined with PRM technology to carry out accurate quantification and verification (sample expansion verification).

And (3) combining isotope labeling synthetic peptide fragment PRM technology to carry out accurate quantification and sample expansion verification on the 20 candidate biomarkers obtained by screening. The verification subjects were 155 elderly persons, and the general data thereof are shown in Table 4.

TABLE 4 comparison of general data for NC and aMCI groups

Note: NC is normal cognitive function; amnestic mild cognitive impairment

Orbitrap Fusion using series EASY-nanolC1200 with high precision^TMLumos^TMTribrid^TMQuantitation was performed by mass spectrometry (Thermo Fisher Scientific, MA, USA). Firstly, selecting a representative peptide fragment of a target protein, and carrying out mass spectrum signal acquisition on the peptide fragmentAnd acquiring quantitative information of the target protein, and simultaneously, synthesizing isotope standard peptide fragments and drawing a standard curve to realize absolute quantification of the protein.

Firstly, selectively detecting parent ion information of a target peptide fragment in a primary mass spectrum (Q1); fragmenting the parent ions; and finally, detecting the information of all fragments in the selected parent ion window in the secondary mass spectrum by using a high-resolution and high-mass precision analyzer. The experiment contained 2 sets of samples. During sample preparation, PQ500(Ki-3019-96, Biognosys AG, Switzerland) isotopically labeled peptide fragments were added to all samples to be tested for identification and quantitative analysis of the peptide fragments of interest. All samples were collected in a random loading manner. The PRM data collected were normalized and quantified by the SpectronDive 9.10(Biognosys AG, Switzerland) software, and p-values were calculated by Students ttest (unpaired, two-tailed) algorithm, and fold-of-difference calculation was performed using the average for each group. Sample pretreatment was performed according to the Biognosys Sample Preparation Kit Pro Kit (Ki-3013, Biognosys AG, Switzerland) instructions. The prepared peptide fragments were subjected to PRM analysis via an LC-MS/MS system equipped with an on-line nano-ion source.

5 data analysis and modelling

The purpose of modeling is to find an optimal model to effectively distinguish between aMCI and healthy people. Adjusting the proportion of the training set to the test set to 75%: 25 percent; and carrying out Bagging Tree missing value interpolation on all the data. Peptide screening was first performed using the data filled with missing values and t-test was performed for each peptide. And secondly, rejecting the protein with poor efficiency, namely only reserving peptide with minimum pvalue to represent the content of the protein. The proteins selected above were model-trained using the Bootstrap 1000-time resampling method. 75% of all samples were taken as training set samples, the remaining 25% being prediction set samples. And (4) carrying out model establishment by adopting a logistic regression method. The model consists of five proteins, namely P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG) and P00736(C1R), and factors of gender and age, and the overall predicted MCI of the model is equal to 0.758, the sensitivity is 78% and the specificity is 75% (see fig. 3).

Fitting formula:

in addition to the above-mentioned technical schemes, the five proteins P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG), P00736(C1R) and their peptide fragments can be detected by mass spectrometry, and the 5 proteins can be detected by antigen-antibody binding technology and aptamer binding technology.

In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Claims

1. A peripheral blood protein marker for amnesic MCI diagnosis, wherein the protein marker comprises protein P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG) or P00736 (C1R).

2. Use of the peripheral blood protein marker for amnesic MCI diagnosis according to claim 1 in the preparation of MCI diagnostic kit.

3. The use according to claim 2, wherein the kit is for determining the content of P02753(RBP4) protein or P22352(GPX3) protein or P23560(BDNF) protein or P02765(AHSG) protein or P00736(C1R) protein in plasma.

4. The use according to claim 3, wherein the kit comprises any peptide fragment of P02753(RBP4) or P22352(GPX3) or P23560(BDNF) or P02765(AHSG) or P00736 (C1R).

5. The forgetting-type MCI medical auxiliary diagnosis system is characterized by comprising an MCI diagnosis model, wherein the MCI diagnosis model comprises proteins P02753(RBP4), P22352(GPX3), P23560(BDNF), P02765(AHSG), P00736(C1R), age and gender.