Asparagine as a biomarker for Alzheimer's disease and its use

Abstract

This application relates to an Alzheimer's disease biomarker and uses thereof, wherein the Alzheimer's disease biomarker is asparagine. [Solution] It has been detected for the first time that asparagine levels in blood samples of Alzheimer's disease are significantly lower than those in normal blood samples. By detecting asparagine levels in blood as a biomarker for Alzheimer's disease, it is possible to assist in the early diagnosis of Alzheimer's disease, contribute to non-invasive and rapid detection, and have the characteristics of being timely, convenient, highly specific, and highly sensitive.

Description

Technical Field

[0001] This application belongs to the field of biotechnology and relates to asparagine as an Alzheimer's disease biomarker and its use.

Background Art

[0002] Alzheimer's disease (AD), also known as dementia, is a degenerative disease of the central nervous system that occurs in old age and is characterized by progressive development, with symptoms such as gradual memory impairment, decline in cognitive function, and loss of daily living ability. It is accompanied by neuropsychiatric symptoms such as personality changes, which have a significant impact on society and living functions. The pathogenesis of Alzheimer's disease has not been fully clarified, and its early symptoms are relatively hidden, so patients with Alzheimer's disease are easily overlooked or misdiagnosed.

[0003] At present, early screening technologies for AD include positron emission tomography (PET) and cerebrospinal fluid Aβ molecule level detection. The former requires injecting a certain dose of radioactive substance into the subject, and the latter has a large operation injury, is prone to surgical infection, and the reliability of the above diagnostic technologies for early diagnosis of AD is also unstable, so it is difficult to use for early screening of AD.

[0004] Therefore, the development of new markers for early diagnosis of AD is one of the important directions for future AD diagnosis and treatment.

[0005] CN112858684A discloses a neurodegenerative disease marker Chromogranin B and its use. The marker contains phosphorylated Chromogranin B protein, and those in which 9 peptide fragments of Chromogranin B protein are phosphorylated are used as markers for neurodegenerative diseases, and are used for the judgment and evaluation of symptoms of neurodegenerative diseases such as AD and PD. The pre-treatment process of the constructed reagent kit sample is simple, the sample consumption is small, the accuracy is high, and it has important clinical guiding significance for the diagnosis assistance of AD and PD related indicators.

[0006] In recent years, with the development of high-throughput omics technologies such as genomics, transcriptomics, proteomics, and metabolomics, research and development of novel biomarkers has accelerated. In particular, metabolomics, by monitoring the dynamic changes in metabolite graphs using techniques such as magnetic resonance spectroscopy and mass spectrometry, has shown a significant advantage in screening disease-related biomarkers. Blood contains proteins, polypeptides, nucleic acids, lipids, and other metabolites, and blood samples are readily available and have low invasiveness, giving it enormous application potential for biomarker screening.

[0007] In summary, screening for AD biomarkers based on novel blood metabolites will expand the criteria for early diagnosis of AD, and when combined with the detection of other markers, it can improve the accuracy of AD diagnosis, contributing to early warning of the disease, pathological classification, and prediction and evaluation of its developmental stage. [Overview of the project] [Problems that the invention aims to solve]

[0008] This application provides asparagine as an Alzheimer's disease biomarker and its use. Based on high-resolution non-targeted metabolomics analysis techniques, this application qualitatively and quantitatively analyzes metabolites in human blood and, for the first time, identifies asparagine in blood as an Alzheimer's disease marker. By detecting asparagine levels in the blood, it can assist in the early diagnosis of Alzheimer's disease, and is characterized by being timely, convenient, highly specific, and highly sensitive. [Means for solving the problem]

[0009] In Embodiment 1, It is asparagine (L-Asparagine). Alzheimer's disease biomarkers.

[0010] Asparagine is an amino acid, and its molecular formula is C 27 H 18 C l3It is N3O with a molecular weight of 506.8103 and can be used as a medicine for lowering blood pressure, dilating bronchodilation (anti-asthma), treating peptic ulcers and gastric dysfunction, and is also used in microbial culture and acrylonitrile wastewater treatment.

[0011] This invention provides a method for qualitatively and quantitatively analyzing blood metabolites based on high-resolution non-targeted metabolomics analysis technology. It detects that asparagine levels in blood samples from Alzheimer's disease patients are significantly lower than in normal blood samples. By using asparagine in the blood as an Alzheimer's disease biomarker and detecting asparagine levels in the blood, it is possible to assist in the early diagnosis of Alzheimer's disease.

[0012] This application provides for the use of asparagine as a biomarker to aid in the early diagnosis of Alzheimer's disease.

[0013] In Embodiment 2, the present application provides the use of the Alzheimer's disease biomarker described in Embodiment 1 in the construction of an early diagnostic model for Alzheimer's disease and / or in the manufacture of an early diagnostic device for Alzheimer's disease.

[0014] In Embodiment 3, the present invention relates to an early diagnostic model for Alzheimer's disease, The input variables of the early diagnosis model for Alzheimer's disease include the peak intensity value of the asparagine mass spectrometry described in Embodiment 1. This provides an early diagnostic model for Alzheimer's disease.

[0015] Preferably, the output variable of the early diagnosis model for Alzheimer's disease includes the expression difference multiplier, and the formula for calculating the expression difference multiplier is as shown in equation (1).

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[0016] Preferably, the criterion for determining a positive result for Alzheimer's disease is the aforementioned difference in expression ratio ≤ 0.64.

[0017] In the present application, by sufficiently comparing and analyzing the peak intensity values of mass spectrometry of asparagine in normal blood samples and AD blood samples and performing a reasonable design, an early diagnosis model for Alzheimer's disease is constructed. The model uses the peak intensity value of mass spectrometry of asparagine as an input variable and the expression difference multiple as an output variable, can quickly output the results, and fully represents samples with abnormal asparagine levels, assisting in the early diagnosis of Alzheimer's disease.

[0018] In aspect 4, the present application a sample preparation unit that prepares a sample to be measured into a sample solution to be measured that can be used for separation by a liquid chromatograph, a detection unit that separates the sample solution to be measured with the liquid chromatograph, processes the separated sample with a mass spectrometer, and measures the peak intensity value of mass spectrometry of asparagine described in aspect 1 in the sample; an analysis unit that inputs the detected peak intensity value of mass spectrometry of asparagine into the early diagnosis model for Alzheimer's disease described in aspect 3 for analysis; and an evaluation unit that outputs the expression difference multiple corresponding to the sample and determines whether it is positive for Alzheimer's disease. provided is an early diagnosis device for Alzheimer's disease.

[0019] In the early diagnosis device for Alzheimer's disease of the present application, the units effectively cooperate with each other, are simple and efficient, can quickly complete the processing and detection of samples, can obtain the expression difference multiple, and perform an evaluation of positive for Alzheimer's disease with a reasonably designed judgment criterion, which has important significance for the early diagnosis of Alzheimer's disease.

[0020] Preferably, the sample to be measured includes a blood sample.

[0021] Preferably, the method for preparing the sample solution to be measured includes putting the sample to be measured into an aqueous acetonitrile solution, centrifuging to collect the supernatant, and obtaining the sample solution to be measured.

[0022] Preferably, the method for preparing the sample solution to be measured is as follows: (1) Put the sample to be measured into a precooled methanol / acetonitrile / aqueous solution, mix it, and perform ultrasonic treatment for 25 - 35 min (for example, it may also be 26 min, 27 min, 28 min, 29 min or 32 min), let it stand at -20~-15 °C (for example, it may also be -19 °C, -18 °C, -16 °C or -17 °C) for 5 - 15 min (for example, it may also be 6 min, 7 min, 8 min, 9 min, 10 min, 12 min or 14 min), centrifuge at 0~4 °C (for example, it may also be 1 °C, 2 °C or 3 °C), 12000 - 16000×g (for example, it may also be 12200×g, 12400×g, 12600×g, 12800×g, 13200×g, 12600×g, 15000×g or 15800×g) for 15 - 25 min (for example, it may also be 16 min, 17 min, 18 min, 19 min, 20 min, 21 min, 22 min, 23 min or 24 min), take the supernatant and perform vacuum drying to obtain a pretreated sample; (2) Put the pretreated sample into 80 - 120 μL of acetonitrile aqueous solution for redissolution, vortex it, centrifuge at 0~4 °C at 12000 - 16000×g (for example, it may also be 12200×g, 12400×g, 12600×g, 12800×g, 13200×g, 12600×g, 15000×g or 15800×g) for 10 - 20 min (for example, it may also be 11 min, 12 min, 13 min, 14 min, 15 min, 16 min, 17 min, 18 min or 19 min), take the supernatant to obtain the sample solution to be measured.

[0023] Preferably, the volume ratio of methanol, acetonitrile and water in the methanol / acetonitrile / aqueous 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.

[0024] Preferably, the volume ratio of acetonitrile to water in the acetonitrile aqueous solution is (1-2):1, and includes, but is 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.

[0025] Preferably, the liquid chromatograph includes an ultrafast liquid chromatograph.

[0026] Preferably, the ultrafast liquid chromatograph includes an Agilent 1290 Infinity LC ultrafast liquid chromatograph.

[0027] Preferably, the mass spectrometer includes a tandem time-of-flight mass spectrometer.

[0028] Preferably, the tandem time-of-flight mass spectrometer includes an AB Triple TOF 6600 mass spectrometer.

[0029] Preferably, the data processing is The method includes obtaining primary and secondary spectra from the separated sample using a tandem time-of-flight mass spectrometer, converting the primary and secondary spectra to mzXML format, performing peak alignment, retention time correction, peak area extraction, and structural identification, and measuring the peak intensity value of the asparagine mass spectrometry in the sample as described in Embodiment 1.

[0030] As a preferred technical proposal, the early diagnostic device for Alzheimer's disease is: A sample preparation unit that prepares samples awaiting measurement into a sample solution that can be used for separation by liquid chromatography, A detection unit that separates the sample solution awaiting measurement using the liquid chromatograph, obtains primary and secondary spectra from the separated sample using a tandem time-of-flight mass spectrometer, converts the primary and secondary spectra to mzXML format, performs peak alignment, retention time correction, peak area extraction, and structural identification, and measures the peak intensity value of the mass spectrometry of asparagine in the sample as described in Embodiment 1, An analysis unit that inputs the peak intensity value of the detected asparagine mass spectrometry into the early diagnosis model for Alzheimer's disease described in Embodiment 3 and performs analysis, The system includes an evaluation unit that outputs an expression difference ratio corresponding to a sample and determines whether or not it is positive for Alzheimer's disease.

[0031] In this application, it is expected that detecting asparagine levels in a blood sample and combining it with other detection results as a diagnostic basis can assist in the early diagnosis of Alzheimer's disease and improve the accuracy of the diagnosis of Alzheimer's disease. However, it cannot be used alone as a diagnostic indicator that can diagnose Alzheimer's disease with 100% certainty.

[0032] In this application, KEGG pathway analysis reveals that L-asparagine belongs to the biosynthesis of amino acids pathway.

[0033] In Embodiment 5, the present application provides the use of the Alzheimer's disease biomarker described in Embodiment 1 in screening for drugs that treat and / or prevent Alzheimer's disease.

[0034] In other words, drugs that treat and / or prevent Alzheimer's disease are screened using the Alzheimer's disease biomarker according to Embodiment 1 as a target. [Effects of the Invention]

[0035] Compared to conventional technology, this invention offers the following beneficial effects.

[0036] This invention is the first to detect that asparagine levels in blood samples from Alzheimer's disease patients are significantly lower than in normal blood samples. By using asparagine in the blood as an Alzheimer's disease biomarker and detecting asparagine levels in the blood, it can assist in the early diagnosis of Alzheimer's disease. This contributes to non-invasive and rapid detection and is characterized by its timeliness, convenience, high specificity, and high sensitivity. [Brief explanation of the drawing]

[0037] [Figure 1] This chart shows asparagine levels in blood samples from AD model mice and wild-type mice. [Figure 2] This figure shows histidine levels in cerebral cortex samples from AD model mice and wild-type mice. [Figure 3] This figure shows glycine levels in cerebral cortex samples from AD model mice and wild-type mice. [Figure 4] This figure shows the levels of dihydroxyacetone phosphate in cerebral cortical samples from AD model mice and wild-type mice. [Figure 5] This figure shows the levels of D-erythrose-4-phosphate in cerebral cortical samples from AD model mice and wild-type mice. [Figure 6] This figure shows the levels of phosphoenolpyruvate in cerebral cortical samples from AD model mice and wild-type mice. [Figure 7] Figure showing 3-phosphoglycerate levels in cerebral cortical samples from AD model mice and wild-type mice. [Modes for carrying out the invention]

[0038] To further describe the technical means used in this application and their effects, the application will be described below with reference to examples and drawings. It should be understood that the specific embodiments described herein are for interpretation purposes only and do not limit the application.

[0039] Unless specific techniques or conditions are described in the examples, the procedures shall be carried out in accordance with the techniques or conditions described in the literature within this art, or in accordance with the product instructions. Unless the manufacturer of any reagents or equipment used is specified, they are all standard products purchased through legitimate channels. [Examples]

[0040] Example 1 This example involved qualitative and quantitative analysis of metabolites in blood samples from 9-month-old AD model mice (APP / PS1 transgenic mice, provided by Nanjing University Model Animal Research Center) and wild-type (WT) mice.

[0041] Blood samples were taken from 10 AD model mice and 10 wild-type mice cultured under the same conditions. The wild-type mouse samples were numbered sequentially from SWT-1-1 to SWT-1-10, and the AD model mouse samples were numbered sequentially from STG-1-1 to STG-1-10. Asparagine levels in the samples were detected using ultrafast liquid chromatography-tandem time-of-flight mass spectrometry. The specific method included the following steps:

[0042] Step (1) The blood sample was placed in a pre-cooled methanol / acetonitrile / aqueous solution (volume ratio 2:2:1), mixed by vortexing, treated with low-temperature ultrasound for 30 minutes, allowed to stand at -20°C for 10 minutes, centrifuged at 4°C and 14000×g for 20 minutes, the supernatant was taken and vacuum-dried to obtain the pre-treated sample.

[0043] Step (2) The pre-treated sample was redissolved in 100 μL of acetonitrile aqueous solution (volume ratio was acetonitrile:water = 1:1), vortexed, centrifuged at 4°C and 14000 × g for 15 min, the supernatant was taken and injected as a sample for analysis, and separated using an Agilent 1290 Infinity LC ultrahigh performance liquid chromatography system (UHPLC) HILIC column, with a column temperature of 25°C, a flow rate of 0.5 mL / min, and a sample injection volume of 2 μL. The mobile phase composition consisted of phase A, which was an aqueous solution of ammonium acetate and aqueous ammonia (the final concentrations of both ammonium acetate and aqueous ammonia were 25 mM), and phase B, which was acetonitrile. The gradient elution program was as follows. Between 0 and 0.5 min, the B phase was 95%. Between 0.5 and 7 min, the B phase changed linearly from 95% to 65%. Between 7 and 8 min, the B phase changed linearly from 65% to 40%. Between 8 and 9 min, the B phase remained at 40%. Between 9 and 9.1 min, the B phase changed linearly from 40% to 95%. Between 9.1 and 12 min, the B phase remained at 95%. Throughout the entire analysis, the sample was placed in an autosampler at 4°C.

[0044] Step (3) An AB Triple TOF 6600 mass spectrometer was used to collect primary and secondary spectra of the sample after separation using the ultrafast liquid chromatography system in Step (2). The ESI source conditions were: Ion Source Gas1 (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30, source temperature: 600℃, Ion Supply Voltage Floating (ISVF) ±5500V (two modes: positive and negative), TOF MS scan m / z range: 60~1000Da, product ion scan m / z range: 25~1000Da, TOF MS scan accumulation time: 0.20 s / spectra, product ion scan accumulation time: 0.The spectrometry interval was 0.5 s / spectra, and secondary mass spectrometry was performed using information-dependent acquisition (IDA) in high-sensitivity mode, with Declustering potential (DP): ±60 V (two modes: positive and negative), Collision Energy: 35 ± 15 eV. The IDA settings were: Exclude isotopes within 4 Da, Candidate ions to monitor per cycle: 10. After converting the collected raw data in Wiff format to mzXML format using ProteoWizard, peak alignment, retention time correction, and peak area extraction were performed using XCMS software. Metabolite structure identification was performed on the data extracted by XCMS, and asparagine levels in the sample were analyzed. Variation piety analysis (Fold Change Analysis, FC Analysis), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and T-test (Student's) were performed using the R language tool (R package (ropls)). A t-test was performed, and as shown in Figure 1 and Table 1, the asparagine levels in the blood samples of AD model mice were significantly lower than those of wild-type mice. This indicates that asparagine in the blood can be used as a biomarker for Alzheimer's disease, and that detecting asparagine levels in the blood can aid in the early diagnosis of Alzheimer's disease.

[0045] [Table 1]

[0046] Furthermore, through annotation and analysis of the KEGG pathway, it was discovered that asparagine belongs to the biosynthesis of amino acids pathway. Asparagine is one of the 20 most common amino acids, an amino acid with an amide group, and is produced from aspartic acid through transamination. It can also be converted to aspartic acid under the catalytic action of asparaginase. Asparagine is involved in the development of brain function, plays an important role in the body's ammonia cycle, regulates the body's metabolism, and enhances the body's immunity.

[0047] Example 2 In this example, qualitative and quantitative analysis of metabolites was performed on cerebral cortical samples from 9-month-old AD model mice and wild-type (WT) mice.

[0048] Cerebral cortex samples were collected from 10 AD model mice and 10 wild-type mice cultured under the same conditions. The wild-type cerebral cortex samples were numbered sequentially from CWT-1-1 to CWT-1-10, and the AD model mouse cerebral cortex samples were numbered sequentially from CTG-1-1 to CTG-1-10. Qualitative and quantitative analysis of metabolites was performed using an ultrafast liquid chromatography-tandem time-of-flight mass spectrometer. The specific method included the following steps.

[0049] Step (1) After killing the mouse by cutting its neck, the brain shell was cut with scissors to expose the brain, and the brain was cut in two parts along the middle section. The cerebellum, brainstem, thalamus, hypocortex, and hippocampus were removed respectively, leaving the cerebral cortex. Complete samples of both the left and right cerebral cortices were collected and placed in pre-cooled methanol / acetonitrile / aqueous solution (volume ratio 2:2:1), mixed by vortexing, treated with low-temperature ultrasound for 30 minutes, allowed to stand at -20°C for 10 minutes, centrifuged at 4°C and 14000×g for 20 minutes, the supernatant was taken and vacuum-dried to obtain the pre-treated sample.

[0050] Step (2) The pre-treated sample was redissolved in 100 μL of acetonitrile aqueous solution (volume ratio was acetonitrile:water = 1:1), vortexed, centrifuged at 14000 × g at 4°C for 15 min, the supernatant was taken and injected into the sample for analysis, and separated using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC) HILIC column under the same conditions as in Example 1.

[0051] Step (3) Using an AB Triple TOF 6600 mass spectrometer, primary and secondary spectra of the sample were collected from the sample separated in the ultrafast liquid chromatography system in Step (2), under the same conditions as in Example 1. The collected raw data in Wiff format was converted to mzXML format using ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. Metabolite structure identification was performed on the data obtained from the XCMS extraction, followed by unvariable statistical analysis, multidimensional statistical analysis, differential metabolite screening, differential metabolite correlation analysis, and KEGG pathway analysis. Here, the variable weight values ​​obtained by the OPLS-DA model (Variable Importance for the Projection (VIP) can be used to determine the strength and interpretability of the influence of each metabolite's expression mode on the classification of samples in each group, and to uncover biologically significant difference molecules. In this example, VIP values ​​and p-values ​​were comprehensively considered to screen for significant difference metabolites. As shown in Figures 2-7 and Table 2, the levels of histidine (L-Histidinol), glycine, dihydroxyacetone phosphate, and D-Erythrose 4-phosphate in the cerebral cortical tissue of AD model mice were significantly higher than in wild-type mice, while the levels of phosphoenolpyruvate and 3-phosphoglycerate were significantly lower than in wild-type mice. Furthermore, all of the above metabolites belong to the amino acid biosynthesis pathway. This means that the pathway is associated with Alzheimer's disease, and asparagine also belongs to the amino acid biosynthesis pathway. From another perspective, this suggests that changes in asparagine levels in blood metabolites may reflect abnormalities in the related metabolic pathway in the AD brain, which has significant implications for early clinical diagnosis.

[0052] [Table 2]

[0053] In summary, this invention is the first to detect that asparagine levels in blood samples from Alzheimer's disease patients are significantly lower than in normal blood samples. By using asparagine in the blood as an Alzheimer's disease biomarker and detecting asparagine levels in the blood, it can assist in the early diagnosis of Alzheimer's disease, contributing to non-invasive and rapid detection, and possessing the characteristics of timely, convenient, highly specific, and highly sensitive.

[0054] While the present application has described the detailed method with respect to the above-described embodiments, the applicant declares that the application is not limited to the above-described method, that is, it does not mean that the application must be implemented in accordance with the above-described method. Those skilled in the art should understand that any improvements to the present application, equivalent substitutions and additions of auxiliary components to each of the raw materials of the product of the present application, and selection of specific forms are all included within the scope of protection and disclosure of the present application. Furthermore, the present invention includes the following embodiments. [Aspect 1] It is asparagine. Alzheimer's disease biomarkers. [Aspect 2] Use of the Alzheimer's disease biomarker described in Embodiment 1 in the construction of an early diagnostic model for Alzheimer's disease and / or in the manufacture of an early diagnostic device for Alzheimer's disease. [Aspect 3] The input variables for the early diagnosis model of Alzheimer's disease include the peak intensity value of asparagine mass spectrometry as described in Embodiment 1. An early diagnostic model for Alzheimer's disease. [Aspect 4] The output variable of the early diagnosis model for Alzheimer's disease includes the expression difference multiplier, and the formula for calculating the expression difference multiplier is as shown in equation (1).

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Claims

1. A sample preparation unit used to prepare a sample awaiting measurement into a sample solution usable for separation by liquid chromatography, comprising: (1) placing the sample awaiting measurement into a pre-cooled methanol / acetonitrile / aqueous solution, mixing, performing ultrasonic testing for 25-35 min, allowing to stand at -20 to -15°C for 5-15 min, centrifugation at 12000-16000 × g at 0-4°C for 15-25 min, taking the supernatant and vacuum drying to obtain a pre-treated sample; and (2) redissolving the pre-treated sample in 80-120 μL of acetonitrile aqueous solution, vortexing, centrifugation at 12000-16000 × g at 0-4°C for 10-20 min, taking the supernatant and obtaining the sample awaiting measurement solution; A detection unit that separates the sample solution awaiting measurement using the liquid chromatograph, processes the separated sample data using a mass spectrometer, and measures the peak intensity value of asparagine in the sample by mass spectrometry, An analysis unit that inputs the peak intensity values ​​of the detected asparagine mass spectrometry into an early diagnosis model for Alzheimer's disease and performs analysis, The system includes an evaluation unit that outputs the expression difference ratio corresponding to the sample and determines whether or not it is positive for Alzheimer's disease, The aforementioned sample awaiting measurement is derived from a mouse. The input variable for the early diagnosis model of Alzheimer's disease is the peak intensity value of asparagine mass spectrometry, and the output variable is the expression difference ratio. The formula for calculating the expression difference ratio is shown in equation (1). [Math 1] An early diagnostic device for Alzheimer's disease.

2. If the sample awaiting measurement is derived from a mouse, the criterion for determining positivity for Alzheimer's disease is the expression difference ratio ≤ 0.

64. The apparatus according to claim 1.

3. The aforementioned sample awaiting measurement includes a blood sample. The apparatus according to claim 1.

4. The aforementioned data processing is, This method involves obtaining primary and secondary spectra from a sample separated using a tandem time-of-flight mass spectrometer, converting the primary and secondary spectra to mzXML format, performing peak alignment, retention time correction, peak area extraction, and structural identification, and measuring the peak intensity value of the asparagine in the sample by mass spectrometry. The apparatus according to claim 1.

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