WO2023039954A1 - Alzheimer's disease biomarker asparagine and application thereof - Google Patents

Abstract

An Alzheimer's disease biomarker and an application thereof. The Alzheimer's disease biomarker is asparagine. It is first detected that the level of asparagine in an Alzheimer's disease blood sample is significantly lower than that of a normal blood sample, and asparagine in blood is used as an Alzheimer's disease biomarker. By detecting the level of asparagine in blood, the early diagnosis of Alzheimer's disease can be assisted, thereby facilitating non-invasive and rapid detection, and the characteristics of timeliness, convenience, high specificity and high sensitivity are provided.

Description

A kind of Alzheimer's disease biomarker asparagine and its application technical field

The application belongs to the field of biotechnology, and relates to an Alzheimer's disease biomarker asparagine and its application.

Background technique

Alzheimer's disease (Alzheimer disease, AD), also known as senile dementia, is a progressive degenerative disease of the central nervous system that occurs in old age, characterized by progressive memory impairment and cognitive function decline and daily life. It is characterized by the loss of life ability, accompanied by neuropsychiatric symptoms such as personality changes, which seriously affects social and life functions. Since the pathogenesis of Alzheimer's disease is not yet fully understood, and its early symptoms are relatively secretive, Alzheimer's disease patients are easily missed. or misdiagnosis.

At present, early screening techniques for AD include positron emission tomography (PET) and detection of Aβ molecular levels in cerebrospinal fluid. Surgical infection, and the reliability of the above diagnostic techniques for the early diagnosis of AD is not stable, so it is difficult to be used for early screening of AD.

Therefore, the development of new markers for the early diagnosis of AD is one of the important directions for the diagnosis and treatment of AD in the future.

CN112858684A discloses a neurodegenerative disease marker Chromogranin B and its application, the markers include phosphorylated Chromogranin B protein, phosphorylation of nine Chromogranin B protein peptides as markers of neurodegenerative diseases, for AD Judgment and evaluation of symptoms of neurodegenerative diseases such as neurodegenerative diseases such as AD and PD. The pre-processing process of the kit is simple, the sample consumption is small, and the accuracy is high. It has important clinical guiding significance for the auxiliary diagnosis of AD and PD related indicators.

In recent years, with the development of high-throughput omics technologies such as genomics, transcriptomics, proteomics and metabolomics, the development of new biomarkers has been accelerated, especially metabolomics, through magnetic resonance spectroscopy and mass spectrometry, etc. The technology monitors the dynamic changes of metabolite profiles, and shows great advantages in screening disease-related biomarkers. Blood contains proteins, peptides, nucleic acids, lipids and other metabolites, and blood samples are easy to obtain and less invasive. It has great application potential in screening biomarkers.

In conclusion, the screening of new AD biomarkers based on blood metabolites will help to expand the judgment basis for the early diagnosis of AD, and can be combined with other marker detection to improve the accuracy of AD diagnosis and contribute to the diagnosis of AD. Early warning, pathological typing, and predictive evaluation of developmental stages, etc.

Contents of the invention

This application provides a biomarker of Alzheimer's disease, asparagine, and its application. This application conducts qualitative and quantitative analysis of metabolites in human blood based on high-resolution non-targeted metabolomics analysis technology. Paragine, as a marker of Alzheimer's disease, can assist in the early diagnosis of Alzheimer's disease by detecting the level of asparagine in blood, and has the characteristics of timeliness, convenience, high specificity and high sensitivity.

In the first aspect, a biomarker of Alzheimer's disease, said biomarker of Alzheimer's disease is asparagine (L-Asparagine).

Asparagine is an amino acid with a molecular formula of C ₂₇ H ₁₈ C _l3 N ₃ O and a molecular weight of 506.8103. It can be used as a drug for lowering blood pressure, dilating bronchi (asthma), resisting peptic ulcer and gastric dysfunction, etc. It is also used in microbial culture, sewage treatment of acrylonitrile, etc.

This application is based on high-resolution non-targeted metabolomics analysis technology for qualitative and quantitative analysis of blood metabolites. It is detected that the level of asparagine in Alzheimer's disease blood samples is significantly lower than that in normal blood samples, and asparagine in blood is used as The Alzheimer's disease biomarker can assist in the early diagnosis of Alzheimer's disease by detecting the level of asparagine in the blood.

This application provides the application of asparagine as a biomarker in assisting the early diagnosis of Alzheimer's disease.

In the second aspect, the present application provides the application of the Alzheimer's disease biomarker as described in the first aspect in constructing an early diagnosis model of Alzheimer's disease and/or preparing an early diagnosis device for Alzheimer's disease.

In a third aspect, the present application provides an early diagnosis model of Alzheimer's disease, the input variables of the early diagnosis model of Alzheimer's disease include the peak intensity value of the asparagine mass spectrum described in the first aspect.

Preferably, the output variables of the Alzheimer's disease early diagnosis model include differential expression multiples, and the calculation formula of the differential expression multiples is shown in equation (1):

Preferably, the criteria for judging positive for Alzheimer's disease is that the differential expression factor is ≤0.64.

In this application, a model for early diagnosis of Alzheimer's disease was constructed by fully comparing and analyzing the peak intensity values of asparagine mass spectrum in normal blood samples and AD blood samples, and carrying out rational design. The peak intensity of paragine mass spectrometry is the input variable, and the differential expression fold is the output variable, which can quickly output the results and fully characterize the samples with abnormal asparagine levels, thereby assisting the early diagnosis of Alzheimer's disease.

In a fourth aspect, the present application provides an early diagnosis device for Alzheimer's disease, which includes the following units:

Sample preparation unit: prepare the sample to be tested into a sample solution to be tested that can be used for separation by liquid chromatography;

Detection unit: using the liquid chromatograph to separate the sample solution to be tested, using a mass spectrometer to perform data processing on the separated sample, and measuring the peak intensity value of the asparagine mass spectrum described in the first aspect in the sample;

Analysis unit: input the detected peak intensity value of the mass spectrum of asparagine into the early diagnosis model of Alzheimer's disease described in the third aspect for analysis; and

Evaluation unit: output the differential expression multiple corresponding to the sample, and judge whether it is positive for Alzheimer's disease.

In the early diagnosis device for Alzheimer's disease of the present application, each unit cooperates effectively, is simple and efficient, and can quickly complete sample processing, detection and obtain differential expression multiples, and at the same time, use rationally designed judgment criteria to detect positive results for Alzheimer's disease. Evaluation is of great significance for the early diagnosis of Alzheimer's disease.

Preferably, the sample to be tested includes a blood sample.

Preferably, the preparation method of the sample solution to be tested includes adding the sample to be tested into an aqueous acetonitrile solution, centrifuging and collecting the supernatant to obtain the sample solution to be tested.

Preferably, the preparation method of the sample solution to be tested comprises the following steps:

(1) Take the sample to be tested and add it to pre-cooled methanol/acetonitrile/water solution, mix and sonicate for 25-35 minutes (for example, it can be 26min, 27min, 28min, 29min or 32min), and place it at -20～-15°C (for example, it can be -19°C, -18°C, -16°C or -17°C) for 5-15min (for example, 6min, 7min, 8min, 9min, 10min, 12min or 14min), at 0-4°C (for example, 1 ℃, 2℃ or 3℃), 12000～16000×g (such as 12200×g, 12400×g, 12600×g, 12800×g, 13200×g, 12600×g, 15000×g or 15800×g) Centrifuge for 15-25 minutes (for example, 16 minutes, 17 minutes, 18 minutes, 19 minutes, 20 minutes, 21 minutes, 22 minutes, 23 minutes or 24 minutes), take the supernatant and vacuum-dry to obtain the pretreated sample;

(2) Add the pretreated sample to 80-120 μL of acetonitrile aqueous solution to re-dissolve, vortex, and place at 0-4°C, 12000-16000×g (for example, 12200×g, 12400×g, 12600×g, 12800×g, ×g, 13200×g, 12600×g, 15000×g or 15800×g) centrifuge for 10-20min (for example, 11min, 12min, 13min, 14min, 15min, 16min, 17min, 18min or 19min), take the supernatant , to obtain the sample solution to be tested.

Preferably, the volume ratio of methanol, acetonitrile and water in the methanol/acetonitrile/water solution is (1-2):(1-2):1 including but not limited to 1.2:2:1, 1.2:1:1, 2 :2:1, 1.4:1.5:1, 1.6:1.2:1, 1.8:2:1, 1.9:1.8:1 or 1.1:1.4:1.

Preferably, the volume ratio of acetonitrile and water in the aqueous acetonitrile solution is (1-2):1, including but not limited to 1.1:1, 1.2:1, 1.3:1, 1.5:1, 1.6:1, 1.7:1 , 1.8:1 or 1.9:1.

Preferably, the liquid chromatograph includes an ultra-high performance liquid chromatograph.

Preferably, the ultra-high performance liquid chromatograph comprises Agilent 1290 Infinity LC ultra-high performance liquid chromatograph.

Preferably, the mass spectrometer comprises a tandem time-of-flight mass spectrometer.

Preferably, the tandem time-of-flight mass spectrometer includes AB Triple TOF 6600 mass spectrometer.

Preferably, the data processing includes:

Use the tandem time-of-flight mass spectrometer to collect the first-order spectrum and second-order spectrum of the separated sample, and convert the first-order spectrum and second-order spectrum into mzXML format, and then perform peak alignment and retention time For calibration, peak area extraction and structure identification, determine the peak intensity value of the asparagine mass spectrum described in the first aspect in the sample.

As a preferred technical solution, the Alzheimer's disease early diagnosis device includes the following units:

Sample preparation unit: prepare the sample to be tested into a sample solution to be tested that can be used for separation by liquid chromatography;

Detection unit: use the liquid chromatograph to separate the sample solution to be tested, use a tandem time-of-flight mass spectrometer to collect the first-order spectrum and second-order spectrum of the separated sample, and collect the first-order spectrum The figure and the secondary spectrum are converted into mzXML format, and then peak alignment, retention time correction, peak area extraction and structural identification are performed to determine the peak intensity value of the asparagine mass spectrum described in the first aspect in the sample;

Analysis unit: input the detected peak intensity value of the mass spectrum of asparagine into the early diagnosis model of Alzheimer's disease described in the third aspect for analysis; and

Evaluation unit: output the differential expression multiple corresponding to the sample, and judge whether it is positive for Alzheimer's disease.

In this application, the detection of asparagine levels in blood samples can be used as a diagnostic basis, combined with other detection results, to assist in the early diagnosis of Alzheimer's disease, and it is expected to improve the accuracy of Alzheimer's disease diagnosis. However, it cannot be used alone as a diagnostic indicator for 100% diagnosis of Alzheimer's disease.

In the present application, it is found that L-Asparagine belongs to the biosynthesis pathway (Biosynthesis of amino acids pathway) of amino acids through KEGG pathway analysis.

In a fifth aspect, the present application provides the application of the Alzheimer's disease biomarker described in the first aspect in screening drugs for treating and/or preventing Alzheimer's disease.

That is, the Alzheimer's disease biomarker described in the first aspect is used as a target to screen a drug for treating and/or preventing Alzheimer's disease.

Compared with the prior art, the present application has the following beneficial effects:

This application detects for the first time that the level of asparagine in blood samples of Alzheimer's disease is significantly lower than that of normal blood samples, and asparagine in blood is used as a biomarker of Alzheimer's disease. By detecting the level of asparagine in blood, it can assist Early diagnosis of Alzheimer's disease is helpful for non-invasive rapid detection, and has the characteristics of timeliness, convenience, high specificity and high sensitivity.

Description of drawings

Figure 1 is a diagram of asparagine levels in the blood samples of AD model mice and wild-type mice;

Figure 2 is a diagram of histidine levels in the cerebral cortex samples of AD model mice and wild-type mice;

Figure 3 is a diagram of glycine levels in the cerebral cortex samples of AD model mice and wild-type mice;

Figure 4 is a graph showing the levels of dihydroxyacetone phosphate in the cerebral cortex samples of AD model mice and wild-type mice;

Figure 5 is a graph showing the level of D-erythrose-4-phosphate in the cerebral cortex samples of AD model mice and wild-type mice;

Figure 6 is a graph showing the levels of phosphoenolpyruvate in the cerebral cortex samples of AD model mice and wild-type mice;

Fig. 7 is a graph showing the levels of 3-phosphoglycerate in the cerebral cortex samples of AD model mice and wild-type mice.

Detailed ways

In order to further illustrate the technical means and effects adopted by the present application, the present application will be further described below in conjunction with the embodiments and accompanying drawings. It can be understood that the specific implementation manners described here are only used to explain the present application, but not to limit the present application.

If no specific technique or condition is indicated in the examples, it shall be carried out according to the technique or condition described in the literature in this field, or according to the product specification. The reagents or instruments used were not indicated by the manufacturer, and they were all conventional products commercially available through regular channels.

Example 1

In this example, qualitative and quantitative analysis of metabolites was performed on the blood samples of 9-month-old AD model mice (APP/PS1 transgenic mice, provided by the Model Animal Research Institute of Nanjing University) and wild-type (WT) mice.

The blood of 10 AD model mice and 10 wild-type mice cultured under the same conditions were collected respectively, and the samples of wild-type mice were numbered SWT-1-1～SWT-1-10 in turn, and the AD model mice were numbered SWT-1-1～SWT-1-10. The samples are sequentially numbered STG-1-1~STG-1-10, and the level of asparagine in the samples is detected by ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry. The specific methods include:

(1) Take a blood sample and add pre-cooled methanol/acetonitrile/water solution (volume ratio: 2:2:1), vortex to mix, cryogenic ultrasound for 30min, place at -20°C for 10min, centrifuge at 4°C, 14000×g 20min, take the supernatant and carry out vacuum drying to obtain the pretreated sample;

(2) Add the pretreated sample to 100 μL of acetonitrile aqueous solution (volume ratio: acetonitrile: water = 1:1) to redissolve, vortex, centrifuge at 4°C, 14000×g for 15 min, and take the supernatant for analysis. Agilent 1290 Infinity LC Ultra High Performance Liquid Chromatography System (UHPLC) HILIC column was used for separation, the column temperature was 25°C; the flow rate was 0.5mL/min; the injection volume was 2μL; the mobile phase composition included: Phase A: a mixture of ammonium acetate and ammonia water Aqueous solution (the final concentration of ammonium acetate and ammonia water are both 25mM), phase B: acetonitrile; the gradient elution program is as follows: 0 ~ 0.5min, 95% phase B; 0.5 ~ 7min, phase B changes linearly from 95% to 65%; 7 ～8min, phase B changes linearly from 65% to 40%; 8～9min, phase B maintains at 40%; 9～9.1min, phase B changes linearly from 40% to 95%; 9.1～12min, phase B maintains at 95% %; The sample was placed in an autosampler at 4°C throughout the analysis process;

(3) AB Triple TOF 6600 mass spectrometer is used to collect the first-order and second-order spectra of the samples separated by ultra-high performance liquid chromatography in step (2). The ESI source conditions are as follows: Ion Source Gas1 (Gas1): 60 , Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30, source temperature: 600℃, IonSapary Voltage Floating (ISVF) ±5500V (positive and negative two modes); TOF MS scan m/z range: 60- 1000Da, product ion scan m/z range: 25-1000Da, TOF MS scan accumulation time 0.20s/spectra, product ion scan accumulation time 0.05s/spectra; the secondary mass spectrum is obtained by information dependent acquisition (IDA) and high sensitivity Mode, Declustering potential (DP): ±60V (both positive and negative modes), Collision Energy: 35±15eV, IDA settings are as follows: Exclude isotopes within 4Da, Candidate ions to monitor per cycle: 10, the collected original Wiff format The data was converted into mzXML format by ProteoWizard, and then the XCMS software was used for peak alignment, retention time correction and peak area extraction, and the metabolite structure identification was carried out on the data extracted by XCMS, and the level of asparagine in the sample was analyzed, and the R language tool was used (R package (ropls)) for Fold Change Analysis (FC Analysis), Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and T Test (Student's t-test), The results are shown in Figure 1 and Table 1. The level of asparagine in blood samples of AD model mice was significantly lower than that of wild-type mice, indicating that asparagine in blood can be used as a biomarker of Alzheimer's disease. The level of asparagine can assist in the early diagnosis of Alzheimer's disease.

Table 1

the	Metabolites	VIP	Differential expression fold	p-value	significant
AD model mice vs wild-type mice	Asparagine	1.02	0.64	0.035	*

Note: * is p<0.05.

In addition, through further KEGG pathway annotation and analysis, it was found that asparagine belongs to the biosynthesis pathway of amino acids (Biosynthesis of amino acids pathway). Asparagine is one of the 20 most common amino acids, and it is an amino acid with an amide group. Aspartic acid is produced by transamination, and can be converted into aspartic acid under the catalysis of asparaginase, which is related to the development of brain function and plays an important role in the body's ammonia cycle. The role of immunity.

Example 2

In this example, the qualitative and quantitative analysis of metabolites was performed on the cerebral cortex samples of 9-month-old AD model mice and wild-type (WT) mice.

The cerebral cortex samples of 10 AD model mice and 10 wild-type mice cultured under the same conditions were collected respectively. The mouse cerebral cortex samples were sequentially numbered CTG-1-1~CTG-1-10, and the metabolites were qualitatively and quantitatively analyzed by ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry, and the specific methods included:

(1) After the mouse was killed by neck dissection, the skull was cut open with surgical scissors to expose the brain, and the brain was cut into two halves along the midbrain seam, and the cerebellum, brainstem, thalamus, hypocortex and hippocampus were removed, leaving the cerebral cortex , collect the left and right sides of the complete cerebral cortex as a sample, add pre-cooled methanol/acetonitrile/water solution (volume ratio 2:2:1), vortex mix, cryogenic ultrasound for 30min, place at -20°C for 10min, and store at 4°C , Centrifuge at 14000×g for 20min, take the supernatant and carry out vacuum drying to obtain the pretreated sample;

(2) Add the pretreated sample to 100 μL of acetonitrile aqueous solution (volume ratio: acetonitrile: water = 1:1) to redissolve, vortex, centrifuge at 4°C, 14000×g for 15 min, and take the supernatant for analysis. Adopt Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC) HILIC chromatographic column to separate, and condition is identical with embodiment 1;

(3) AB Triple TOF 6600 mass spectrometer is used to collect the first-order and second-order spectrograms of the samples separated by the ultra-high performance liquid chromatography in step (2), the conditions are the same as in Example 1, and the collected Wiff format The original data were converted into mzXML format by ProteoWizard, and then XCMS software was used for peak alignment, retention time correction and peak area extraction, and the metabolite structure identification was performed on the data extracted by XCMS, followed by univariate statistical analysis, multidimensional statistical analysis, difference Metabolite screening, differential metabolite correlation analysis and KEGG pathway analysis, among which, the variable weight value (Variable Importance for the Projection, VIP) obtained by the OPLS-DA model can be used to measure the expression pattern of each metabolite and classify each group of samples Distinguish the influence strength and explanatory ability, and mine the differential molecules with biological significance. In this embodiment, the VIP value and p-value are comprehensively considered to screen the significant differential metabolites. The results are shown in Figure 2-Figure 7 and Table 2, AD The levels of histidine (L-Histidinol), glycine (Glycine), dihydroxyacetone phosphate (Dihydroxyacetone phosphate) and D-erythrose-4-phosphate (D-Erythrose 4-phosphate) in the cerebral cortex of model mice were significantly The level of phosphoenolpyruvate (Phosphoenolpyruvate) and 3-phosphoglycerate (3-Phospho-D-glycerate) was significantly lower than that of wild-type mice, and the above metabolites belong to the biosynthesis of amino acids Pathway (Biosynthesis of amino acids pathway), indicating that there is a correlation between the biosynthesis pathway of amino acids (Biosynthesis of amino acids pathway) and Alzheimer's disease, and asparagine also belongs to the biosynthesis pathway of amino acids, which shows that blood Changes in the level of asparagine in metabolites may reflect the abnormality of related metabolic pathways in AD brain, which is of great significance for early clinical diagnosis.

Table 2

Note: * is p<0.05, ** is p<0.01.

In summary, this application detects for the first time that the level of asparagine in blood samples of Alzheimer's disease is significantly lower than that of normal blood samples, and uses asparagine in blood as a biomarker of Alzheimer's disease to detect the level of asparagine in blood samples. Paragine levels can assist in the early diagnosis of Alzheimer's disease, contribute to non-invasive rapid detection, and have the characteristics of timeliness, convenience, high specificity and high sensitivity.

The applicant declares that the present application illustrates the detailed method of the present application through the above-mentioned examples, but the present application is not limited to the above-mentioned detailed method, that is, it does not mean that the application must rely on the above-mentioned detailed method to be implemented. Those skilled in the art should understand that any improvement to the present application, the equivalent replacement of each raw material of the product of the present application, the addition of auxiliary components, the selection of specific methods, etc., all fall within the scope of protection and disclosure of the present application.

Claims (10)

1. An Alzheimer's disease biomarker which is asparagine.
2. Application of the Alzheimer's disease biomarker according to claim 1 in constructing an early diagnosis model of Alzheimer's disease and/or preparing an early diagnosis device for Alzheimer's disease.
3. An early diagnosis model of Alzheimer's disease, whose input variables include the peak intensity value of the asparagine mass spectrum according to claim 1.
4. The early diagnosis model of Alzheimer's disease according to claim 3, wherein the output variable of the early diagnosis model of Alzheimer's disease includes differential expression multiples, and the calculation formula of the differential expression multiples is as shown in equation (1) Show:

   
5. The early diagnosis model of Alzheimer's disease according to claim 4, wherein the positive criterion for Alzheimer's disease is that the differential expression multiple is ≤0.64.
6. An early diagnosis device for Alzheimer's disease, which includes the following units:

   Sample preparation unit: prepare the sample to be tested into a sample solution to be tested that can be used for separation by liquid chromatography;

   Detection unit: using the liquid chromatograph to separate the sample solution to be tested, using a mass spectrometer to perform data processing on the separated sample, and measuring the peak intensity value of the asparagine mass spectrum according to claim 1 in the sample;

   Analysis unit: input the detected peak intensity value of the mass spectrum of asparagine into the Alzheimer's disease early diagnosis model described in any one of claims 3-5 for analysis; and

   Evaluation unit: output the differential expression multiple corresponding to the sample, and judge whether it is positive for Alzheimer's disease.
7. The device of claim 6, wherein the sample to be tested comprises a blood sample.
8. The device according to claim 6 or 7, wherein said data processing comprises:

   Use the tandem time-of-flight mass spectrometer to collect the first-order spectrum and second-order spectrum of the separated sample, and convert the first-order spectrum and second-order spectrum into mzXML format, and then perform peak alignment and retention time Correction, extraction of peak area and structure identification, determination of the peak intensity value of the asparagine mass spectrum according to claim 1 in the sample.
9. The device according to any one of claims 6-8, wherein the device comprises the following units:

   Sample preparation unit: prepare the sample to be tested into a sample solution to be tested that can be used for separation by liquid chromatography;

   Detection unit: use the liquid chromatograph to separate the sample solution to be tested, use a tandem time-of-flight mass spectrometer to collect the first-order spectrum and second-order spectrum of the separated sample, and collect the first-order spectrum The figure and the secondary spectrogram are converted into mzXML format, and then peak alignment, retention time correction, extraction of peak area and structural identification are carried out to determine the peak intensity value of the asparagine mass spectrum described in claim 1 in the sample;

   Analysis unit: input the detected peak intensity value of the mass spectrum of asparagine into the Alzheimer's disease early diagnosis model described in any one of claims 3-5 for analysis; and

   Evaluation unit: output the differential expression multiple corresponding to the sample, and judge whether it is positive for Alzheimer's disease.
10. Application of the Alzheimer's disease biomarker according to claim 1 in screening drugs for treating and/or preventing Alzheimer's disease.

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