Alzheimer's disease biomarker based on brain metabolites and application thereof

Abstract

The invention relates to an Alzheimer's disease biomarker based on brain metabolites and application thereof. The biomarker comprises any one or a combination of at least two of palmitic acid, DHA (docosahexaenoic acid), gallic acid, 11Z, 14Z and 17Z eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid. The metabolite is used as the Alzheimer's disease biomarker, early diagnosis of the Alzheimer's disease is assisted by detecting the level of the metabolite, rapid detection is facilitated, and the method has the advantages of being timely, convenient to use and high in specificity and sensitivity.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology and relates to a biomarker for Alzheimer's disease based on brain metabolites and its application. Background Art

[0002] Alzheimer's disease (AD), also known as senile dementia, is a progressive neurodegenerative disease of the central nervous system that occurs in old age. It is characterized by progressive memory impairment, cognitive decline, and loss of daily living ability, accompanied by neuropsychiatric symptoms such as personality changes, seriously affecting social and life functions, and has become a major public health problem affecting the world. Dementia usually occurs after the age of 65. However, with the development of society, factors such as the accelerating pace of life, high work pressure, irregular diet and work and rest have led to an increasingly younger population of AD patients. Many elderly people in their 50s or even 40s are affected. Since the pathogenesis of Alzheimer's disease has not been fully elucidated and its early symptoms are relatively hidden, AD patients are easily misdiagnosed or missed. Therefore, finding biomarkers with high sensitivity and high accuracy is of great significance for the diagnosis and drug intervention of Alzheimer's disease. At present, the diagnosis of AD mainly relies on memory scales, PET, and the detection of the levels of pathological indicators such as Aβ and phosphorylated tau in cerebrospinal fluid and blood. However, the detection results of these diagnostic indicators in clinical practice are still controversial, and there is still a lack of effective detection evidence for the early symptoms of AD.

[0003] In recent years, with the development of high-throughput omics technologies such as genomics, transcriptomics, proteomics, and metabolomics, the research and development of new biomarkers have been accelerated. In particular, metabolomics, through technologies such as magnetic resonance spectroscopy and mass spectrometry, monitors the dynamic changes in metabolite profiles, showing great advantages in screening disease-related biomarkers and having broad application prospects in elucidating the molecular pathogenic mechanisms of AD and the pathophysiological changes caused by it. Cerebrospinal fluid (CSF) can directly reflect the pathological changes of the brain tissue. The core CSF markers known to be related to AD include Aβ42, total tau protein (t-tau), and phosphorylated tau protein (p-tau). These core CSF markers have high diagnostic accuracy, and the sensitivity and specificity in the MCI stage can reach 85-90%; they can not only be used as diagnostic markers for the AD dementia stage but also be used to predict the outcome of MCI. Studies have also found new CSF markers related to the progression of AD, mainly including molecules related to the Aβ metabolic pathway and synaptic markers. For example, high concentrations of neurogranin in CSF can predict the conversion of MCI to AD and are related to rapid memory impairment during follow-up. In addition, studies have also found that an increase in the level of D-serine in CSF may also be one of the markers for early AD diagnosis. Therefore, screening for early AD diagnosis biomarkers based on metabolites in cerebrospinal fluid is expected to improve the accuracy of AD diagnosis and contribute to early warning of the disease, pathological typing, and prediction and evaluation of the development stage, etc. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide an Alzheimer's disease biomarker based on brain metabolites and its application.

[0005] To achieve the purpose of this invention, the following technical solutions are adopted:

[0006] In the first aspect, the present invention provides an Alzheimer's disease biomarker based on brain metabolites, and the biomarker includes any one or a combination of at least two of palmitic acid, DHA, gallic acid, 11Z,14Z,17Z-eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid, or arachidonic acid.

[0007] The present invention qualitatively and quantitatively analyzes brain metabolites based on targeted metabolomics analysis technology, and uses an ultra-high performance liquid chromatography-triple quadrupole mass spectrometer (UHPLC-QTRAP MS) to detect metabolites in samples. This technology has high selectivity and high sensitivity, and uses specifically developed sample preparation and chromatographic separation methods to qualitatively and quantitatively analyze more than three hundred common metabolites. It is detected that the levels of palmitic acid, DHA (4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoic acid), gallic acid, 11Z,14Z,17Z-eicosatrienoic acid, glycodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid in Alzheimer's disease brain metabolites are significantly higher than those in normal brain metabolites. Taking them as Alzheimer's disease biomarkers can assist in the early diagnosis of Alzheimer's disease.

[0008] In a second aspect, the present invention provides an application of the Alzheimer's disease biomarker based on brain metabolites according to the first aspect in constructing an early diagnosis model for Alzheimer's disease and / or preparing an early diagnosis device for Alzheimer's disease.

[0009] In a third aspect, the present invention provides an early diagnosis model for Alzheimer's disease, and the input variables of the early diagnosis model for Alzheimer's disease include the mass spectrometry peak intensity values of the Alzheimer's disease biomarkers described in the first aspect;

[0010] The output variables of the early diagnosis model for Alzheimer's disease include the fold change in expression.

[0011] Preferably, the calculation formula for the fold change in expression is as follows:

[0012]

[0013] Preferably, the judgment criterion for positive Alzheimer's disease is:

[0014] The fold change in expression of palmitic acid ≥ 1.396, the fold change in expression of DHA ≥ 1.292, the fold change in expression of gallic acid ≥ 0.705, the fold change in expression of eicosatrienoic acid ≥ 1.512, the fold change in expression of glycodeoxycholic acid ≥ 0.482, the fold change in expression of palmitoleic acid ≥ 1.649, the fold change in expression of linoleic acid ≥ 1.565, the fold change in expression of erucic acid ≥ 0.751, the fold change in expression of petroselinic acid ≥ 1.261 or the fold change in expression of arachidonic acid ≥ 1.261.

[0015] In the present invention, an early diagnosis model for Alzheimer's disease is constructed. The model uses the mass spectrometry peak intensity values of Alzheimer's disease biomarkers as input variables and the fold change in expression as the output variable, which can quickly output results and fully characterize samples with abnormal levels of Alzheimer's disease biomarkers, thereby assisting in the early diagnosis of Alzheimer's disease.

[0016] Fourthly, the present invention provides an early diagnosis device for Alzheimer's disease, which comprises the following units:

[0017] A sample preparation unit for performing the following steps:

[0018] To prepare the sample to be tested into a sample solution to be tested that can be separated by a liquid chromatograph;

[0019] A detection unit for performing the following steps:

[0020] Separate the sample solution to be tested using the liquid chromatograph, detect the separated sample using a mass spectrometer, perform data processing, and determine the mass spectrometry peak intensity value of the Alzheimer's disease biomarker described in the first aspect in the sample;

[0021] An analysis unit for performing the following steps:

[0022] Input the detected peak intensity value of the Alzheimer's disease biomarker mass spectrum into the early diagnosis model for Alzheimer's disease described in the third aspect for data analysis, output the fold change in expression corresponding to the sample, and determine whether it is positive for Alzheimer's disease.

[0023] In the early diagnosis device for Alzheimer's disease of the present invention, the units cooperate effectively, are simple and efficient, can quickly complete sample processing, detection, and obtain the fold change in expression, and at the same time perform an Alzheimer's disease positive assessment with a reasonably designed judgment criterion, which is of great significance for the early diagnosis of Alzheimer's disease.

[0024] Preferably, the sample to be tested includes cerebrospinal fluid.

[0025] Preferably, the data processing includes:

[0026] Use MultiQuant software to perform peak extraction on the MRM raw data, calculate the ratio of the peak area of the Alzheimer's disease biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value.

[0027] Preferably, the device comprises the following units:

[0028] A sample preparation unit for performing the following steps:

[0029] To prepare the sample to be tested into a sample solution to be tested that can be separated by a liquid chromatograph;

[0030] A detection unit for performing the following steps:

[0031] Separate the sample solution to be measured using the liquid chromatograph, detect the separated sample using a mass spectrometer, perform peak extraction on the MRM raw data using MultiQuant software, and calculate the ratio of the peak area of the Alzheimer's biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value.

[0032] An analysis unit for performing the following steps:

[0033] Input the detected mass spectrometry peak intensity value of the Alzheimer's biomarker into the Alzheimer's early diagnosis model described in the third aspect for data analysis, output the differential expression multiple corresponding to the sample, and determine whether it is positive for Alzheimer's disease.

[0034] In a fifth aspect, the present invention provides an application of the Alzheimer's biomarker based on brain metabolites described in the first aspect as a target in screening drugs for treating or preventing Alzheimer's disease.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention has for the first time detected that the levels of 10 metabolites in the brain metabolites of Alzheimer's disease are significantly higher than those of normal samples. Using its mass spectrometry peak intensity value as a detection index for auxiliary judgment of the symptoms of Alzheimer's disease, it has the characteristics of high detection accuracy, convenience, speed, and safety and non-invasiveness, and has important clinical guiding significance for auxiliary diagnosis of AD-related indicators. Using it as an Alzheimer's biomarker, and providing an Alzheimer's early diagnosis model and device, early diagnosis of Alzheimer's disease can be assisted by detecting the levels of specific metabolites, which helps for rapid detection and has the characteristics of timeliness, convenience, high specificity, and high sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a mass spectrometry peak intensity diagram of 11Z,14Z,17Z-eicosatrienoic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control group mice (WT).

[0038] Figure 2 It is a mass spectrometry peak intensity diagram of 4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control group mice (WT).

[0039] Figure 3 It is a mass spectrometry peak intensity diagram of arachidonic acid in cerebrospinal fluid samples of AD model mice (AD) and wild-type control group mice (WT).

[0040] Figure 4 It is the mass spectrometry peak intensity map of linoleic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0041] Figure 5 It is the mass spectrometry peak intensity map of palmitic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0042] Figure 6 It is the mass spectrometry peak intensity map of erucic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0043] Figure 7 It is the mass spectrometry peak intensity map of palmitoleic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0044] Figure 8 It is the mass spectrometry peak intensity map of petroselinic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0045] Figure 9 It is the mass spectrometry peak intensity map of glycodeoxycholic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT).

[0046] Figure 10 It is the mass spectrometry peak intensity map of gallic acid in the cerebrospinal fluid samples of AD model mice (AD) and wild-type control mice (WT). Specific implementation manners

[0047] Experimental instruments and reagents

[0048] AB 5500 / 6500Q-trap mass spectrometer (AB SCIEX)

[0049] Agilent 1290Infinity LC ultra-high pressure liquid chromatograph (Agilent)

[0050] Low-temperature high-speed centrifuge (Eppendorf5430R)

[0051] Chromatographic column: Waters, ACQUITY UPLC BEH Amide 1.7μm, 2.1mm×100mmcolumn

[0052] Waters, ACQUITYUPLC BEH C181.7μm, 2.1mm×100mm column

[0053] Acetonitrile (Merck, 1499230-935)

[0054] Ammonium acetate (Sigma, 70221)

[0055] Methanol (Fisher, A456-4)

[0056] Ammonia water (Sigma, 221228)

[0057] Ammonium formate (Sigma, 70221)

[0058] Formic acid (Sigma, 00940)

[0059] Isotope standard (Cambridge Isotope Laboratories)

[0060] Example 1

[0061] Sample extraction method

[0062] Collect cerebrospinal fluid samples from diseased individuals and normal healthy control donors. Take an appropriate amount of the sample and add pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v). Vortex mix, ultrasonicate at low temperature for 30 min, stand at -20 °C for 10 min, centrifuge at 14000 g at 4 °C for 20 min. Take the supernatant and dry it under vacuum. When performing mass spectrometry analysis, dissolve it in 100 μL of acetonitrile aqueous solution (acetonitrile:water = 1:1, v / v), vortex, centrifuge at 14000 g at 4 °C for 15 min, and take the supernatant for injection analysis.

[0063] Using the above technical method, we performed qualitative and quantitative analysis of metabolites on the cerebral cortex samples of 9-month-old male AD model mice (10 mice) and their littermate wild-type male healthy mice (WT) control group (9 mice).

[0064] Chromatography-mass spectrometry analysis

[0065] (1) Chromatographic conditions

[0066] The samples were separated using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) system with HILIC and C18 columns. The column temperature of the HILIC column was 35 °C, the flow rate was 0.3 mL / min, the injection volume was 2 μL, and the mobile phase composition was A: water + 100 mM ammonium acetate + 1.2% ammonia water, B: acetonitrile. The gradient elution program was as follows: 0 - 1.0 min, 85% B; 1.0 - 3.0 min, B linearly changed from 85% to 80%; 3.0 - 4.0 min, 80% B; 4.0 - 6.0 min, B linearly changed from 80% to 70%; 6.0 - 10.0 min, B linearly changed from 70% to 50%; 10 - 12.5 min, B was maintained at 50%; 12.5 - 12.6 min, B linearly changed from 50% to 85%; 12.6 - 18 min, B was maintained at 85%. The column temperature of the C18 column was 40 °C, the flow rate was 0.4 mL / min, the injection volume was 2 μL, and the mobile phase composition was A: water + 50 mM ammonium formate + 0.4% formic acid, B: methanol. The gradient elution program was as follows: 0 - 5 min, B linearly changed from 5% to 60%; 5 - 11 min, B linearly changed from 60% to 100%; 11 - 13 min, B was maintained at 100%; 13 - 13.1 min, B linearly changed from 100% to 5%; 13.1 - 16 min, B was maintained at 5%. During the whole analysis process, the samples were placed in a 4 °C autosampler. To avoid the influence caused by the signal fluctuation of the instrument detection, the samples were analyzed continuously in a random order. QC samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.

[0067] (2) Mass spectrometry conditions

[0068] Mass spectrometry analysis was performed using an AB 6500 QTRAP mass spectrometer (AB SCIEX). The ESI source conditions were as follows: sheath gas temperature, 350 °C; dry gas temperature, 350 °C; sheath gas flow, 11 L / min; dry gas flow, 10 L / min; capillary voltage, 4000 V or -3500 V in positive or negative modes, respectively; nozzle voltage, 500 V; and nebulizer pressure, 30 psi. The MRM mode was used for monitoring.

[0069] (3) Data analysis process

[0070] Peak extraction was performed on the MRM raw data using MultiQuant or Analyst software to obtain the ratio of the peak area of each substance to the peak area of the internal standard, and the content was calculated according to the standard curve.

[0071] It can be seen from Figures 1 - 10 that the results of the between-group difference comparison analysis showed that the levels of several metabolites, including Elaidic acid, 4Z,7Z,10Z,13Z,16Z,19Z-Docosahexaenoic Acid (DHA), Gallic acid, 11Z,14Z,17Z-Eicosatrienoic Acid, Glycodeoxycholic acid (GDCA), Palmitoleic Acid, Linoleic acid, Erucic acid, Petroselinic acid, and Arachidonic acid in the AD group were significantly different from those in the control group, indicating that these metabolites play an important role in differentiating this disease model.

[0072] Further classification analysis found that Elaidic acid, 4Z,7Z,10Z,13Z,16Z,19Z-Docosahexaenoic Acid (DHA), 11Z,14Z,17Z-Eicosatrienoic Acid, Palmitoleic Acid, Linoleic acid, Erucic acid, Petroselinic acid, and Arachidonic acid belong to fatty acids, while Gallic acid belongs to benzenes and Glycodeoxycholic acid (GDCA) belongs to bile acids. These results suggest that changes in the levels of brain tissue metabolites may reflect abnormalities in related metabolic pathways in the AD brain, which is of great significance for early clinical diagnosis. Since it is not convenient to take brain tissue from patients for detection clinically, relevant indicators are mostly detected by taking cerebrospinal fluid in practical applications.

[0073] The applicant declares that the present invention uses the above embodiments to illustrate a biomarker for Alzheimer's disease based on brain metabolites and its application, but the present invention is not limited to the above embodiments, that is, it does not mean that the present invention must rely on the above embodiments to be implemented. Those skilled in the art should understand that any improvement to the present invention, the equivalent replacement of each raw material of the product of the present invention, the addition of auxiliary components, the selection of specific methods, etc., all fall within the protection scope and the disclosure scope of the present invention.

[0074] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0075] In addition, it should be noted that, in the case of no contradiction, the various specific technical features described in the above specific embodiments can be combined in any appropriate manner. In order to avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

Claims

1. A biomarker for Alzheimer's disease based on brain metabolites, characterized in that, The biomarker includes any one or a combination of at least two of palmitic acid, DHA, gallic acid, 11Z,14Z,17Z-eicosatrienoic acid, glycochenodeoxycholic acid, palmitoleic acid, linoleic acid, erucic acid, petroselinic acid or arachidonic acid.

2. Use of the biomarker for Alzheimer's disease based on brain metabolites according to claim 1 in constructing an early diagnosis model for Alzheimer's disease and / or preparing an early diagnosis device for Alzheimer's disease.

3. An early diagnosis model for Alzheimer's disease, characterized in that, The input variable of the early diagnosis model for Alzheimer's disease includes the mass spectrometry peak intensity value of the Alzheimer's disease biomarker described in claim 1; The output variable of the early diagnosis model for Alzheimer's disease includes the fold change in expression.

4. The early diagnosis model for Alzheimer's disease according to claim 3, characterized in that, The calculation formula for the fold change in expression is as follows:

5. The early diagnosis model for Alzheimer's disease according to claim 3 or 4, characterized in that, The judgment criterion for positive Alzheimer's disease is: The fold change in expression of palmitic acid ≥ 1.396, the fold change in expression of DHA ≥ 1.292, the fold change in expression of gallic acid ≥ 0.705, the fold change in expression of eicosatrienoic acid ≥ 1.512, the fold change in expression of glycochenodeoxycholic acid ≥ 0.482, the fold change in expression of palmitoleic acid ≥ 1.649, the fold change in expression of linoleic acid ≥ 1.565, the fold change in expression of erucic acid ≥ 0.751, the fold change in expression of petroselinic acid ≥ 1.261 or the fold change in expression of arachidonic acid ≥ 1.

261.

6. An early diagnosis device for Alzheimer's disease, characterized in that, The device includes the following units: A sample preparation unit for performing the following steps: To prepare the sample to be tested into a sample solution to be tested that can be separated by a liquid chromatograph; A detection unit for performing the following steps: To separate the sample solution to be tested using the liquid chromatograph, detect the separated sample using a mass spectrometer, perform data processing, and determine the mass spectrometry peak intensity value of the Alzheimer's disease biomarker described in claim 1 in the sample; An analysis unit for performing the following steps: Input the detected peak intensity value of the Alzheimer's disease biomarker mass spectrometry into the early diagnosis model for Alzheimer's disease described in any one of claims 3-5 for data analysis, output the fold change in expression corresponding to the sample, and determine whether it is positive for Alzheimer's disease.

7. The device according to claim 6, characterized in that, The sample to be tested includes cerebrospinal fluid.

8. The device according to claim 6 or 7, characterized in that, The data processing includes: Using MultiQuant software to perform peak extraction on the MRM raw data, and calculating the ratio of the peak area of the Alzheimer's disease biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value.

9. The device according to any one of claims 6 - 8, characterized in that, The device includes the following units: A sample preparation unit for performing the following steps: To prepare the sample to be tested into a sample solution to be tested that can be separated by a liquid chromatograph; A detection unit for performing the following steps: To separate the sample solution to be tested using the liquid chromatograph, detect the separated sample using a mass spectrometer, use MultiQuant software to perform peak extraction on the MRM raw data, and calculate the ratio of the peak area of the Alzheimer's disease biomarker to the peak area of the internal standard as the mass spectrometry peak intensity value; An analysis unit for performing the following steps: Input the detected peak intensity value of the Alzheimer's disease biomarker mass spectrometry into the early diagnosis model for Alzheimer's disease described in any one of claims 3-5 for data analysis, output the fold change in expression corresponding to the sample, and determine whether it is positive for Alzheimer's disease.

10. Use of the biomarker for Alzheimer's disease based on brain metabolites according to claim 1 as a target in screening for drugs for treating or preventing Alzheimer's disease.

Images