Serum metabonomics-based longevity biomarker and application thereof

Abstract

The invention discloses a longevity biomarker based on serum metabonomics and application of the longevity biomarker, and belongs to the technical field of biomedicine. The invention discloses a long-life biomarker based on serum metabonomics. The long-life biomarker is mandelic acid, gluteomide, fenmetrizine, hydroxyheptanoic acid, norfloxacin, N-acetyl serotonin, 3, 4-methylenedioxy amphetamine, palmitoleic acid, cinchonidine, gulose, 5-amino valeraldehyde, 12, 12-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3, 5-dihydroxy-3 The feed additive is prepared from the following raw materials: 3, 13-dihydroxy-9Z-octadecenoic acid, goose carnosine, (3Z)-plant pigment bile, light blue bacteria and L-selenium methyl selenocysteine. The invention provides a new marker for diagnosing the body health aging and predicting the life of the human body, can be used for preparing a product for predicting the longevity possibility, and has an application prospect in the field of anti-aging health.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and more particularly to a longevity biomarker based on serum metabolomics and application thereof. Background Art

[0002] Benefiting from the improvement of living standards, medical technology and living environment, the average life expectancy of the global population has been greatly extended. According to statistics, by 2050, the global elderly population (>60 years old) is expected to reach 2.1 billion. However, longer life does not mean healthy aging. The elderly often suffer from one or more geriatric diseases, which poses a significant burden on the economy and healthcare system. Therefore, discovering the key markers of healthy aging in long-lived elderly people and evaluating the serum metabolite characteristics of healthy aging in the elderly population can play a role in health risk warning and help save public health costs.

[0003] Centenarians are the representative group of healthy aging. They can live to over 90 years old and rarely suffer from aging-related diseases. Currently, research on healthy aging focuses on longevity genes, transcription factors, and epigenetic factors, but the information obtained so far is still very limited.

[0004] Metabolomics mainly obtains information on the dynamic changes of metabolites in organisms over time and pathophysiological processes by detecting changes in small molecule metabolites (MWK<1000), including sugars, lipids, amino acids, vitamins, etc. Metabolites, as the final product of cellular physiological activities, can truly and sensitively reflect the functional state of cells. Metabolomics has changed the traditional idea of ​​single marker detection and has unique advantages in using a group of metabolite groups as "pattern markers" for diagnosis. Although metabolomics started late, it has shown strong advantages compared with traditional diagnostic methods and research methods. With the increase of age, characteristic changes in endogenous small molecule metabolites will inevitably occur. Metabolomics, which relies on advanced separation, analysis and calculation methods, has the ability and advantage to distinguish characteristic metabolites under different age conditions from a holistic perspective. Using metabolomics to study the metabolites that are different from ordinary elderly people but no different from young people in centenarians can explore this complex physiological process from a holistic perspective.

[0005] Metabolites in serum play an important role in human health and disease. Therefore, developing a serum metabolic marker with high specificity and good sensitivity is of great significance for evaluating the healthy aging characteristics of the elderly population. However, there is currently a lack of effective predictive serum metabolic markers for long-lived elderly people in clinical practice.

[0006] Therefore, providing a longevity biomarker based on serum metabolomics and its application is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0007] In view of this, the present invention provides a longevity biomarker based on serum metabolomics and application thereof.

[0008] The present invention identifies the changes in the relative abundance of serum metabolites associated with centenarians, and provides a method for distinguishing centenarians from ordinary elderly people through the relative abundance of serum metabolic markers.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] A longevity biomarker based on serum metabolomics, wherein the longevity biomarker is mandelic acid, glutethimide, phenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetylserotonin, 3,4-methylenedioxyamphetamine, palmitoleic acid, cinchonidine, gulose, 5-aminovaleraldehyde, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome choline, cerulenin and L-selenomethylselenocysteine.

[0011] Furthermore, the longevity biomarker based on serum metabolomics is used in the preparation of longevity prediction preparations.

[0012] Furthermore, the longevity biomarkers are used as quality control samples for gas chromatography-mass spectrometry.

[0013] Furthermore, the method for screening longevity biomarkers based on serum metabolomics comprises the following steps:

[0014] (1) The collected serum samples were used as analytical samples;

[0015] (2) Non-targeted metabolomics analysis was performed on each analyzed sample using GC-MS coupling technology to obtain the raw metabolic data of each serum sample;

[0016] (3) performing data analysis on the raw metabolic data obtained in step (2) to screen for differential metabolites;

[0017] (4) The AUC value was 0.9343 obtained through ROC curve analysis, confirming the longevity biomarker.

[0018] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a longevity biomarker based on serum metabolomics and its application, by analyzing the serum metabolome of centenarians, their immediate family members, ordinary elderly people, and young people, analyzing differential metabolites, and mining potential predictive biomarkers. The present invention provides new markers for diagnosing healthy aging of the body and predicting human life span, and can be used to prepare products that predict the possibility of longevity, and has application prospects in the field of anti-aging health. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0020] Figure 1 The accompanying drawing is an experimental flow chart of the present invention for screening and validating longevity biomarkers based on serum metabolomics;

[0021] Figure 2 The accompanying drawing is a Venn diagram of the intersection of the present invention's screening of candidate metabolites potentially associated with longevity, differential metabolites between centenarians and ordinary elderly people, and differential metabolites between the immediate family members of centenarians and ordinary elderly people;

[0022] Figure 3 The accompanying drawing is a bee colony diagram of 16 differential metabolites screened by the present invention;

[0023] Figure 4 The attached figure is a heat map of 16 differential metabolites screened by the present invention;

[0024] Figure 5 The accompanying figure is a ROC curve diagram of the 16 differential metabolite test sets screened by the present invention.

[0025] Figure 6 The attached figure is a ROC curve diagram of the validation set of 16 differential metabolites screened by the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example 1

[0028] Experimental flow chart for screening and validating longevity biomarkers based on serum metabolomics, see Figure 1 .

[0029] (I) Sample collection and information collection

[0030] Serum samples were collected from healthy people in Guangdong Province, China, with an age range from 20 to over 100 years old. They were divided into four groups: centenarians (CE group, ≥100 years old, 101 people), lineal relative of centenarians (CE-L group, 65-89 years old, 51 people), the Elderly (Elderly group, 65-89 years old, 64 people), and the Young (Young group, 20-50 years old, 62 people). The direct relatives of the centenarians included in the study were of the same age group as the ordinary elderly, and the questionnaire survey showed that there was no person over 90 years old among their parents or siblings.

[0031] Serum sample collection: Medical staff collected fasting blood samples from the subjects by venipuncture, left them at room temperature for 30 minutes, and then transported them to the laboratory for centrifugation (3500rpm, 10min) at 4°C. The separated serum samples were stored in a -80°C ultra-low temperature refrigerator for future use.

[0032] (II) Serum non-target metabolome test

[0033] 1) Metabolite extraction: (1) Thaw the experimental samples at 4°C, vortex the samples for 1 min after thawing, and mix them evenly; (2) Accurately transfer 100 μL of serum sample into a 2 mL centrifuge tube; (3) Add 400 μL of methanol (stored at -20°C) and vortex for 1 min; (4) Centrifuge at 12,000 rpm for 10 min at 4°C, take all the supernatant, transfer it to a new 2 mL centrifuge tube, and concentrate to dryness; (5) Accurately add 150 μL of 2-chloro-L-phenylalanine (4 ppm) solution prepared in 80% methanol aqueous solution (stored at 4°C) to re-dissolve the sample, take the re-dissolved solution and filter it through a 0.22 μm membrane, add the filtrate to the detection bottle for LC-MS detection; (6) Transfer 20 μL of the filtrate from each sample and mix them as quality control samples.

[0034] 2) LC-MS detection:

[0035] (1) Chromatographic conditions: ThermoVanquish ultra-high performance liquid chromatography system, using ACQUITY HSST3 (2.1x150mm, 1.8μm) column, flow rate of 0.25mL / min, column temperature of 40℃, injection volume of 2μL. Positive ion mode, mobile phase of 0.1% formic acid acetonitrile (B2) and 0.1% formic acid aqueous solution (A2), gradient elution program: 0~1 min, 2% B2; 1~9 min, 2%~50% B2; 9~12 min, 50%~98% B2; 12~13.5 min, 98% B2; 13.5~14 min, 98%~2% B2; 14~20 min, 2% B2. In negative ion mode, the mobile phase was acetonitrile (B3) and 5 mM ammonium formate aqueous solution (A3), and the gradient elution program was: 0-1 min, 2% B3; 1-9 min, 2%-50% B3; 9-12 min, 50%-98% B3; 12-13.5 min, 98% B3; 13.5-14 min 98%-2% B3; 14-17 min, 2% B3.

[0036] (2) Mass spectrometry conditions: ThermoOrbitrapExploris120 mass spectrometer (Thermo Fisher Scientific, USA), electrospray ion source (ESI), data acquisition in positive and negative ion modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 30 arb, and the auxiliary gas was 10 arb. The capillary temperature was 325 °C, the primary full scan was performed at a resolution of 60000, the primary ion scan range was m / z100-1000, and HCD was used for secondary fragmentation, the collision energy was 30%, the secondary resolution was 15000, the first 4 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information. Quality control samples (QC) were prepared by mixing equal volumes of extracts of all samples. The volume of each QC was the same as that of the sample, and it was processed and tested in the same way as the analytical sample. During the instrument analysis, one QC sample was inserted into every 10 samples to investigate the stability of the entire detection process.

[0037] 3) Metabolomics data processing: After the computer is completed, the original mass spectrometry file is converted into the mzXML file format through the MSConvert tool in the Proteowizard software package (v3.0.8789). The RXCMS software package is used for peak detection, peak filtering, and peak alignment processing, and finally a quantitative list of substances is obtained. The parameters are bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdif-0.01, method=”centWave”. The public databases HMDB, massbank, LipidMaps, mzcloud, KEGG and self-built material libraries are used for substance identification, and the parameters are set to ppm<30ppm. The LOESS signal correction method based on QC samples is used to achieve data correction and eliminate systematic errors. Substances with RSD>30% in Q samples are filtered out during data quality control.

[0038] (III) Analysis of differential metabolites

[0039] First, according to the original LC-MS data analysis, 12,809 and 12,137 metabolites were captured in positive and negative ion modes, respectively. 808 secondary metabolites were further confirmed and annotated using databases such as HMDB, massbank, LiqidMaps, and mzcloud using accurate molecular weight and MS / MS fragmentation patterns.

[0040] First, in order to screen out the differential metabolites between centenarians and ordinary elderly people, differential metabolite analysis was performed, and 322 differential metabolites were obtained (p < 0.0001). Further, the serum differential metabolites of centenarians' immediate relatives and ordinary elderly people were compared, and 213 metabolites were obtained (p < 0.0001).

[0041] In order to obtain metabolites that are common to centenarians and their immediate family members and that are different from those of the general elderly group, 322 differential metabolites and 213 metabolites were analyzed to obtain a set of both, with a total of 187 metabolites ( Figure 2 ).

[0042] Afterwards, in order to exclude the differences between metabolites caused by age, an age-related analysis was performed on these 187 metabolites (Table 1), of which 109 metabolites were correlated with age, and 78 metabolites were not correlated with age. The 78 metabolites not correlated with age were used for subsequent longevity marker screening.

[0043] Table 1 Quantitative statistics of the correlation between 187 candidate metabolites and age

[0044] No correlation with age (p>0.05) There is a correlation with age (p<0.05) Number of metabolites 78 109

[0045] Note: Correlation was calculated using the Spearman's rank correlation method.

[0046] Screening "rejuvenation" metabolites as longevity biomarkers. This type of metabolite refers to metabolites that are not different between centenarians and young people, but are different between ordinary elderly people and young people. This type of metabolite can distinguish centenarians from ordinary elderly people and also shows the rejuvenation of centenarians. Therefore, 16 metabolites that are not different between centenarians and young people, but are different between ordinary elderly people and young people were further screened from the 78 metabolites ( Figure 3 and Figure 4 ). Compared with the average elderly, the abundance of 15 metabolites was down-regulated, namely, mandelic acid, glutethimide, phenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetyl serotonin, 3,4-methylenedioxyamphetamine, palmitoleic acid, cinchonidine, gulose, 5-aminovaleraldehyde, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome bile, and cerulenin, and the abundance of one metabolite was up-regulated, which was L-selenomethylselenocysteine. The changing trends of the above metabolites indicate the health status of the body.

[0047] Therefore, the 16 differential metabolites finally screened (Table 2) are the differential metabolites shared by centenarians and their immediate family members and ordinary elderly people, and are not affected by age. At the same time, there is no difference between centenarians and young people, but there are differences between the elderly and young people. In order to evaluate the recognition potential of the 16 differential metabolites for distinguishing centenarians from ordinary elderly people, a test was conducted and an ROC curve was constructed. The combined diagnosis AUC value was 0.9343 ( Figure 5 ).

[0048] Table 2: Differential metabolites of “rejuvenation”

[0049] Serial number Chinese name English name 1 Mandelic acid Mandelicacid 2 Grumet Glutethimide 3 Phenmetrazine Phenmetrazine 4 Hydroxyheptanoic acid 6-Hydroxyhexanoic acid 5 Norfloxacin Norfloxacin 6 N-acetyl serotonin N-Acetylserotonin 7 3,4-Methylenedioxyamphetamine 3,4-Methylenedioxyamphetamine 8 Palmitoleic acid Palmitoleicacid 9 Cinchonidine Cinchonidine 10 Guluo Sugar L-Gulose 11 5-Aminopentanal 5-Aminopentanal 12 12,13-Dihydroxy-9Z-octadecenoic acid 12,13-DHOME 13 Anserine Anserine 14 (3Z)-phytochrome bile (3Z)-Phytochromobilin 15 Cerulean Cerulenin 16 L-Selenomethylselenocysteine Se-Methylselenocysteine

[0050] Longevity biomarker validation

[0051] In order to verify the reliability and stability of the longevity biomarkers screened above, 44 samples of centenarians and 28 samples of ordinary elderly people were collected as validation sets. The AUC value of the ROC curve of 16 metabolites was 0.9602 ( Figure 6 ), indicating that the 16 metabolite pairs can effectively distinguish centenarians from ordinary elderly people, have a good discriminant effect on longevity, and can be used as longevity biomarkers.

[0052] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A longevity biomarker based on serum metabolomics, characterized in that: The longevity biomarkers are mandelic acid, glutethimide, phenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetyl serotonin, 3,4-methylenedioxyamphetamine, palmitoleic acid, cinchonidine, gulose, 5-aminovaleraldehyde, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome choline, cerulenin and L-selenomethylselenocysteine.

2. Use of a serum metabolomics-based longevity biomarker as described in claim 1 in the preparation of a longevity prediction preparation.

3. The use according to claim 2, characterized in that: The longevity biomarkers are used as quality control samples for gas chromatography-mass spectrometry.

4. The method for screening longevity biomarkers based on serum metabolomics according to claim 1, characterized in that: The following steps are involved: (1) The collected serum samples were used as analytical samples; (2) Non-targeted metabolomics analysis was performed on each analyzed sample using GC-MS coupling technology to obtain the raw metabolic data of each serum sample; (3) performing data analysis on the raw metabolic data obtained in step (2) to screen for differential metabolites; (4) The AUC value was 0.9343 obtained through ROC curve analysis, confirming the longevity biomarker.

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