Biomarker, kit and method for predicting aging-related physiological age of mouse

Abstract

The invention provides a biomarker, a kit and a method for predicting the aging-related physiological age of a mouse, the biomarker comprises a plurality of CpG sites with different methylation levels, and the position information of the CpG sites is as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 109982829 and the like. The method can solve the problem of high cost of detecting the physiological age of the mouse through methylation level in the prior art, and is suitable for the field of animal age detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of animal age detection, and in particular to a biomarker, a kit and a method for predicting the physiological age related to mouse aging. BACKGROUND

[0002] In recent years, epigenetic studies have found that DNA methylation (DNAm) as an important epigenetic marker, its dynamic changes are highly correlated with the biological aging process. The methylation level of CpG site presents regular changes with age, making the "epigenetic clock" based on DNAm a core tool for quantifying physiological age. In human studies, the first representative epigenetic clock represented by Horvath estimates the physiological age by detecting the methylation level of specific CpG in the genome. The second generation clock PhenoAge integrates multi-dimensional methylation markers and serum markers, further improving the accuracy of physiological age prediction. Mice are the most important model organism, and the accurate prediction of their physiological age is of great significance to aging research and drug evaluation.

[0003] In 2018, a research team analyzed the methylation of different species through whole genome bisulfite sequencing (WGBS), and the research confirmed that the dynamic changes of DNA methylation (DNAm) (gain or loss of methylation level of specific CpG sites) are common molecular markers of mammals closely related to the aging process. These change patterns are highly conserved among species; in 2017, the research team constructed the first mouse multi-tissue epigenetic clock through reduced representation bisulfite sequencing (RRBS). However, the above-mentioned schemes have the problem of high cost, which is difficult to meet the needs of high-throughput screening and large-scale application. At present, whole genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) require high sequencing quantity (WGBS sequencing requires 90G sequencing data, and RRBS sequencing requires 10G sequencing data). Therefore, although these technologies improve the accuracy and applicability of epigenetic age prediction, the high sequencing cost and data processing requirements are still the bottleneck problem restricting their wide application. SUMMARY

[0004] The main purpose of the present application is to provide a biomarker, a kit and a method for predicting the physiological age related to mouse aging, to solve the problem of high cost in detecting the physiological age related to mouse aging by methylation level in the prior art.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a biomarker for predicting aging-related physiological age in mice is provided. The biomarker includes multiple CpG sites with different methylation levels, and the location information of the CpG sites is as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032. chr16: 87766091, chr17: 55971541, chr1: 109982829, chr1: 120371877, chr2: 166754974, chr3: 104805729, chr3: 30366752, chr4: 150889854, chr6: 52217280, chr7: 140923174, chr8: 123522992, and chr8: 75627175.

[0006] In a preferred embodiment, the weights of each CpG site are shown in Table 1 below:

[0007] Table 1

[0008] .

[0010] Furthermore, the aforementioned age-related physiological age in mice includes the mice's age in weeks.

[0011] Furthermore, the reference genome version of the aforementioned mice is mm10.

[0012] To achieve the above objectives, according to a second aspect of the present invention, a kit for predicting age-related physiological age in mice is provided, the kit comprising reagents capable of detecting the aforementioned biomarkers.

[0013] Furthermore, the kit includes primer pairs for amplifying the CpG sites, the primer pairs including a forward primer and a reverse primer; wherein the nucleotide sequences of the forward primer and the reverse primer corresponding to each CpG site are shown in Table 2 below:

[0014] Table 2

[0015] .

[0017] SEQ ID NO: 1: AAAGTGGTGGGGTTAGAATTTATTAGTTAG;

[0018] SEQ ID NO:2:ACTTTACATCTTTTACCCTTAAAACCACAT;

[0019] SEQ ID NO:3:TGTTTTGTGGAATTTTGTTGGAGTT;

[0020] SEQ ID NO:4:CTTAATTCCATTTCTCTCTCTACCCACCTA:

[0021] SEQ ID NO:5:AGTGTATYGGGTGGGAAGGGGAGA:

[0022] SEQ ID NO:6:ATTTCCAAAACCRCACAACRAACR;

[0023] SEQ ID NO:7:GGGGAATTTTTTTTGAGTAGTATGAGAAGG;

[0024] SEQ ID NO:8:ACTTACAAACCAAAATCTCAAACCCATTAA;

[0025] SEQ ID NO:9:AAGTTAAGGGTGGAGGGAAGGAGGG;

[0026] SEQ ID NO:10:CCCTCTCCCCAAATCCTCATAAACAC;

[0027] SEQ ID NO:11:GGTGGAGTTTYGGGTTAAAATGTATTTTA;

[0028] SEQ ID NO:12:TCTCCCRCCCTTCTAACAATCTAAT;

[0029] SEQ ID NO:13:ATTTTTGAGTTTAGTGTTYGGTTTTAGTAT;

[0030] SEQ ID NO:14:AAACTCTCCRAAACCTTACTTAACTCTA:

[0031] SEQ ID NO:15:TGGTTTYGAGATTATTTGAATTTGG;

[0032] SEQ ID NO:16:CAACRCRACCATTAAAATCCTCATA;

[0033] SEQ ID NO:17:GGAAAGGAAATTTTGTATTAGGTTTGTG;

[0034] SEQ ID NO:18:CAACTTACCATCCTAAAATCCTCCC;

[0035] SEQ ID NO:19:GGAAAGGAAATTTTGTATTAGGTTTGTG;

[0036] SEQ ID NO:20:CAACTTACCATCCTAAAATCCTCCC;

[0037] SEQ ID NO:21:TGGGAGATTTGATTGAGAGAGAAAGA;

[0038] SEQ ID NO:22:CCCACATCCACCACTTTCCAAAATC;

[0039] SEQ ID NO:23:TTTTATYGGAAGTTAAAGGGTGTAG;

[0040] SEQ ID NO:24:CCTAACACTTACCATACCTTCRTAAATATA;

[0041] SEQ ID NO:25:TGTTAGGAAGAGTTGAGTTTTTTTTGTTAT;

[0042] SEQ ID NO:26:TCAAAATCCCTTTCCATCTCTATACTTA;

[0043] SEQ ID NO:27:GGAGTYGTGGGTTTTGGTTTAGGT;

[0044] SEQ ID NO:28:CAAACTCTTCCTACTACAATTCCCRAT;

[0045] SEQ ID NO:29:TTATAGTYGGAYGYGAAGGGGTTTT;

[0046] SEQ ID NO:30:CCRAACCRACTTCTTACTCCTTTACA;

[0047] SEQ ID NO:31:TTTTAAGTTTTTTYGGGAYGAGAGG;

[0048] SEQ ID NO: 32: CACACRATCCAACACACRAAAAACAC;

[0049] SEQ ID NO: 33: GGTGGGTGGGTTTTTGTAGTATTTTTTATT;

[0050] SEQ ID NO: 34: ACTTCTTCTCCTCCAACTACCTCC;

[0051] SEQ ID NO: 35: TGATGGGAATGYGGATAGAT;

[0052] SEQ ID NO: 36: CATCCTCTACCTATCCACCRAAC.

[0053] In the above sequence, Y represents C or T, and R represents G or A.

[0054] To achieve the above objective, according to a third aspect of the present invention, a method for predicting the aging-related physiological age of mice is provided, the method comprising: S1) detecting the methylation level of a biomarker in the genomic DNA of a mouse, wherein the biomarker is the aforementioned biomarker; and S2) calculating the aging-related physiological age of the mouse based on the aforementioned methylation level.

[0055] Furthermore, S1 above includes using the above-described kit to detect the methylation level of the above-described biomarker.

[0056] Furthermore, the aforementioned age-related physiological age in mice includes the mouse's age in weeks, and the calculation of S2) includes: Mouse age in weeks = 960.62 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9 + β 10 X 10 +β 11 X 11 + β 12 X 12 + β 13 X 13 + β 14 X 14 + β 15 X 15 + β 16 X 16 + β 17 X 17 + β 18 X 18 , where β1 to β 18The methylation levels at the following CpG sites are: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 10 9982829, chr1:120371877, chr2:166754974, chr3:104805729, chr3:30366752, chr4:150889854, chr6:52217280, chr7:140923174, chr8:123522992, and chr8:75627175; X1 to X 18 The weights for the CpG sites mentioned above are shown in Table 3 below:

[0057] Table 3

[0058] .

[0060] Further, the methylation level in S1) above is obtained by measuring biological samples of the mice, including one or more of the brain, liver, lungs, heart or blood; optionally, the detection method for the methylation level includes multiplex PCR methylation sequencing technology.

[0061] By applying the technical solution of this invention and utilizing the biomarkers in this application, including 18 cross-tissue CpG sites that are highly correlated with mouse age, the amount of sequencing data can be significantly reduced while achieving accurate detection of the physiological age of mice. This facilitates the improvement of sequencing depth and accuracy, and has the advantages of low cost and high accuracy. Attached Figure Description

[0062] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0063] Figure 1 A graph showing the physiological age prediction results according to Embodiment 2 of the present invention is displayed. Detailed Implementation

[0064] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the embodiments.

[0065] As mentioned in the background section, while existing technologies can predict the physiological age of mice through methylation levels, the large amount of data required leads to high sequencing costs, limiting their practical application. In this application, the inventors attempt to develop a lower-cost prediction method and related biomarkers, and based on this, propose a series of protection schemes.

[0066] In a first typical embodiment of this application, a biomarker for predicting aging-related physiological age in mice is provided. The biomarker includes multiple CpG sites with different methylation levels. The location information of the CpG sites includes: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16929, chr17929, chr18929, chr19 ... r16: 87766091, chr17: 55971541, chr1: 109982829, chr1: 120371877, chr2: 166754974, chr3: 104805729, chr3: 30366752, chr4: 150889854, chr6: 52217280, chr7: 140923174, chr8: 123522992, and chr8: 75627175.

[0067] CpG sites are specific sequences in DNA where cytosine (C) and guanine (G) are adjacent, and are typically closely related to gene regulation and methylation status. This application discloses biomarkers associated with predicting aging-related physiological age in mice, including 18 specific CpG sites distributed across multiple chromosomes of the mouse genome. By focusing on these core aging-related sites, the amount of sequencing data required for DNA methylation sequencing can be significantly reduced from approximately 10 GB for traditional reduced genome methylation sequencing (RRBS) to only approximately 0.1 GB, achieving a hundredfold reduction in data volume and greatly lowering sequencing costs.

[0068] In a preferred embodiment, the weights of each CpG site are shown in Table 1.

[0069] In the technical solution and biomarkers of this application, the weights of CpG sites are further precisely quantified. These weights, as key parameters in the prediction model, directly affect the accuracy and reliability of mouse physiological age prediction. Specifically, the weights reflect the relative importance of each CpG site in age prediction. By performing precise multiplex PCR methylation sequencing on these sites and combining the assigned weights, high-precision prediction of mouse physiological age can be achieved. The determination of these weights is based on statistical analysis of a large amount of experimental data, ensuring the scientific validity and effectiveness of the prediction model and its results.

[0070] In a preferred embodiment, the aforementioned age-related physiological age of mice includes the age in weeks.

[0071] In this application, "mouse age in weeks" refers to the age of a mouse measured in weeks. Mouse age in weeks can be an integer or a fraction with a decimal point.

[0072] In a preferred embodiment, the reference genome version of the mouse is mm10.

[0073] The biomarkers described in this application, using mouse genome version mm10 as a reference, ensure accurate identification and localization of specific CpG sites, improving the precision of methylation level detection. This precise localization facilitates more accurate design of multiplex PCR primers, thereby achieving specific amplification of the target site in subsequent experiments, improving data quality and the performance of predictive models.

[0074] By combining the technical solutions, the biomarkers in this application are not limited to CpG sites in specific genome versions, a strategy that makes our method more adaptable and scalable. Through cross-version CpG site identification, we can utilize genomic information from different laboratories or databases without strictly matching specific genome versions, enhancing the compatibility and universality of our research. This flexibility applies not only to the mm10 version but also extends to other genome versions, further improving the model's stability and applicability, and making the prediction of mouse physiological age more accurate and reliable.

[0075] In a second typical embodiment of this application, a kit for predicting the aging-related physiological age of mice is provided, the kit comprising reagents capable of detecting the aforementioned biomarkers.

[0076] In a preferred embodiment, the kit includes primer pairs for amplifying the CpG sites; the primer pairs include forward primers and reverse primers, wherein the nucleotide sequences of the forward primers and reverse primers corresponding to each CpG site are shown in Table 2.

[0077] The aforementioned kit contains primer pairs for amplifying specific CpG sites, with the forward and reverse primers precisely designed to correspond to 18 key methylation sites on the mouse genome. In this kit, because the number of CpG sites to be detected is relatively small, optimized primer design not only improves the specificity of amplification but also significantly reduces primer dimer formation, ensuring a high proportion of effective data output and achieving a data utilization rate >80%, which is crucial for subsequent data analysis.

[0078] Furthermore, although the primer pairs listed herein are effective ways to achieve the objectives of this invention, given the diversity of the biological field, those skilled in the art can flexibly select and set other primers based on relevant solutions in the prior art, as long as these primers can also accurately and effectively amplify the target CpG site.

[0079] In addition to primer pairs, the kit may also contain other reagents for detecting the methylation level of CpG sites, such as methylation-sensitive restriction endonucleases, methylation-specific probes, etc. Those skilled in the art can flexibly select and set them according to relevant schemes and reagents in the prior art in order to achieve the best detection effect and cost-effectiveness.

[0080] In a third typical embodiment of this application, a method for predicting the aging-related physiological age of mice is provided. The method includes: S1) detecting the methylation level of a biomarker in the genomic DNA of a mouse, wherein the biomarker is the aforementioned biomarker; and S2) calculating the aging-related physiological age of the mouse based on the aforementioned methylation level.

[0081] The core of the aforementioned method for predicting age-related physiological age in mice lies in detecting the methylation levels of 18 specific CpG sites, which have been screened and confirmed to be highly correlated with the physiological age of mice. Targeted methylation sequencing of these sites significantly reduces the amount of sequencing data required, requiring only 0.1G of data, compared to the 10G of data required for RRBS sequencing, greatly reducing detection costs while improving sequencing depth and data accuracy.

[0082] In a preferred embodiment, S1) above includes detecting the methylation level of the biomarker using the above-described kit.

[0083] In a preferred embodiment, the aforementioned age-related physiological age of the mouse includes the mouse's age in weeks, and the calculation of S2) includes: the aforementioned mouse age in weeks = 960.62 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9 + β 10 X 10 + β11 X 11 + β 12 X 12 + β 13 X 13 + β 14 X 14 + β 15 X 15 + β 16 X 16 + β 17 X 17 + β 18 X 18 , where β1 to β 18 The methylation levels at the following CpG sites are: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 10 9982829, chr1:120371877, chr2:166754974, chr3:104805729, chr3:30366752, chr4:150889854, chr6:52217280, chr7:140923174, chr8:123522992, and chr8:75627175; X1 to X 18 The weights for the CpG sites mentioned above are shown in Table 3.

[0084] The calculation process described in S2) above uses an accurate linear model to predict the physiological age of mice. Specifically, the predicted age in weeks is based on the methylation levels of 18 specific CpG sites, with weights β1 to β18 corresponding to their contributions to age prediction. By multiplying the methylation level of each CpG site by its corresponding weight, summing the results, and adding the intercept 960.62, the predicted age in weeks can be obtained. This process effectively integrates information from multiple biomarkers, transforming it into a quantitative assessment of physiological age, and reducing the workload of complex model construction.

[0085] In a preferred embodiment, the methylation level in S1) above is obtained by measuring biological samples of the mice, including one or more of the brain, liver, lungs, heart or blood.

[0086] Applying the technical solution of this embodiment, in S1, the methylation level is obtained by measuring mouse biological samples. Specifically, the sample types involved include, but are not limited to, one or more of the following: brain, liver, lung, heart, or blood. These samples can all reflect the overall physiological state and accurate physiological age of the mouse. Selecting these biological samples as the detection targets not only ensures the accurate measurement of CpG site methylation levels but also improves the repeatability and reliability of the data. Furthermore, the aforementioned biomarkers are all cross-tissue CpG sites highly correlated with mouse age, allowing for flexible selection of the required biological sample types in actual testing, all of which can yield consistent and reliable results.

[0087] In a preferred embodiment, the method for detecting methylation levels includes multiplex PCR methylation sequencing technology.

[0088] Multiplex PCR (Polymerase Chain Reaction) methylation sequencing is a modern molecular biology technique that combines PCR amplification and high-throughput sequencing, particularly suitable for detecting the DNA methylation status of specific genomic regions. This technique allows for the simultaneous amplification of multiple target CpG islands or methylation sites in the same reaction system, followed by sequencing to determine the methylation levels of these sites.

[0089] Multiplex PCR methylation sequencing technology can detect multiple sites in a single reaction, greatly improving the efficiency of methylation analysis, and is especially suitable for high-throughput screening. Compared with whole-genome methylation sequencing (WGBS) or reduced-genomic methylation sequencing (RRBS), multiplex PCR only needs to amplify the region of interest, significantly reducing the total amount of data and cost required for sequencing. By selectively choosing the CpG sites to be detected, the number and type of target sites can be easily adjusted, achieving higher precision methylation analysis. It has advantages such as high efficiency, low cost, flexible detection sites, and high accuracy.

[0090] The existing technology, "Stubbs™, Bonder MJ, Stark AK, et al. Multi-tissue DNA methylation age predictor in mouse[J]. Genome biology, 2017, 18: 1-14.", screened 329 CpG sites based on RRBS methylation sequencing data to construct a mouse tissue methylation age prediction model. The correlation between predicted age and actual age was 0.977 in the training set and 0.839 in the validation set. However, using the method described in this application, we innovatively only need to screen a small number (18) of CpG sites that can predict the physiological age of mice across tissues. Using these 18 sites, the correlation performance between predicted age and actual age in the validation set reached 0.994, which is superior to the results of the aforementioned article in terms of both accuracy and cost.

[0091] The beneficial effects of this application will be explained in more detail below with reference to specific embodiments.

[0092] Example 1

[0093] The experimental procedure for detecting physiological age targets in mice using multiplex amplification primers is as follows:

[0094] I. Methylation Transformation

[0095] 1.1 Vortex the CT Conversion Reagent to mix well, then add 130 µL of CT Conversion Reagent and 20 µL of sample (30-200 ng genomic DNA, to be made up to the volume with nuclease-free water) to a nuclease-free PCR tube. If the DNA concentration is low, the maximum input volume can be increased to 40 µL, while the volume of CT Conversion Reagent remains at 130 µL.

[0096] 1.2 After vortexing or pipetting to mix, briefly centrifuge to collect the reaction solution to the bottom of the tube. Place the PCR tube in a PCR instrument and perform the following reaction: 95°C for 10 minutes; it can be stored at 20°C for up to 20 hours.

[0097] Note: The 20°C preservation step does not necessarily have to be 20 hours. You can proceed to the next step once the sample tube temperature reaches 20°C.

[0098] 1.3 Add 600 µL of binding buffer to the DNA columns (each DNA column is placed in a 2 mL collection tube), and transfer the transformed reaction product to the DNA columns. Gently invert 10-20 times to mix the reaction product with the binding buffer completely, and centrifuge at 12,000 rpm (13,400 × g) for 2 min.

[0099] Note: Simply invert the container to mix; do not mix vigorously. Flocculent material may appear during inversion (this is flocculation from the column membrane processing), which is normal and will not affect the experimental results. DNA Columns (in 2mL collection tubes) should be stored at 2–8°C and equilibrate to room temperature before use.

[0100] 1.4 Discard the filtrate, put the DNA Columns back into the 2 mL Collection Tube, add 100 µL of Wash Buffer (check that anhydrous ethanol has been added before use) along the tube wall to the DNA Columns, and centrifuge at 12,000 rpm (13,400 × g) for 1 min.

[0101] 1.5 Add 200 µL of desulfurization buffer along the tube wall to the adsorption column, allow the reaction to stand at room temperature (15 ~ 25℃) for 15 min, and centrifuge at 12,000 rpm (13,400 × g) for 1 min.

[0102] Note: The reaction time of the Desulphonation Buffer should not exceed 20 minutes.

[0103] 1.6 Add 200 µL Wash Buffer (check that anhydrous ethanol has been added before use) to the adsorption column along the tube wall and centrifuge at 12,000 rpm (13,400 × g) for 1 min.

[0104] 1.7 Repeat step 1.6, discard the filtrate, put the DNA Columns back into the 2 mL Collection Tube, and centrifuge the empty column at 12,000 rpm (13,400 × g) for 2 min.

[0105] 1.8 Transfer the DNA columns to new 1.5 mL nuclease-free centrifuge tubes and add 12 µL of elution buffer to the center of the adsorption column membrane. Incubate at room temperature for 1–2 min, then centrifuge at 12,000 rpm (13,400 × g) for 2 min.

[0106] 1.9 Discard the DNA columns and transfer the DNA product into a nuclease-free PCR tube for the next reaction. If not proceeding immediately, store at -85 to -65°C to prevent degradation.

[0107] II. Target Amplification

[0108] 2.1 Prepare the reaction system according to the system shown in Table 4 below (preparation on ice is recommended):

[0109] Table 4

[0110]

[0111] 2.2 After vortexing the prepared reaction mixture and briefly centrifuging, place it on a PCR instrument and proceed with the program shown in Table 5 below:

[0112] Table 5

[0113]

[0114] III. PCR Product Purification

[0115] 3.1 Add 30 μL of room temperature equilibrated DNA purification magnetic beads to a new 1.5 mL centrifuge tube. Add the product from the previous reaction to the corresponding centrifuge tube. Use a pipette to mix the magnetic beads and product. Incubate at room temperature for 5 minutes.

[0116] 3.2 Place the centrifuge tube from the previous step on a magnetic rack for 1 minute until the solution becomes clear. Then, carefully aspirate the supernatant with a pipette, discard the supernatant, and keep the magnetic beads.

[0117] 3.3 Add 200 μL of freshly prepared 80% ethanol to the centrifuge tube from the previous step, let stand for 30 seconds, then carefully aspirate the supernatant, discard the supernatant, and keep the magnetic beads.

[0118] 3.4 Repeat step 3.3 once.

[0119] 3.5 After instantaneous centrifugation with a handheld centrifuge, place the centrifuge tubes on a magnetic rack, remove any residual ethanol with a pipette, and allow them to air dry for 3 minutes.

[0120] Note: Residual ethanol can affect subsequent reactions, so it's essential to ensure all ethanol evaporates. However, care must also be taken to prevent the magnetic beads from drying out and cracking, as this will reduce the amount of nucleic acid recovered.

[0121] 3.6 Add 13 μL TE Buffer, mix the magnetic beads thoroughly, and let stand for 5 min.

[0122] 3.7 After short-term centrifugation using a handheld centrifuge, place the centrifuge tube on a magnetic rack and use a pipette to transfer 10.5 μL of supernatant into a new 200 μL PCR tube.

[0123] IV. Index PCR and Library Preparation

[0124] 4.1 Add the following reagents to the PCR tube from step 3.7 above: 2 μL Index Primer, 12.5 μL Multiplex PCR Mix.

[0125] Note: Choose one of the Index Primers from A1 to A48 for each sample. It is recommended to use different Index Primers for different samples that are planned to be sequenced in the same batch.

[0126] 4.2 After vortexing the prepared reaction mixture and briefly centrifuging, place it on a PCR instrument and proceed with the program shown in Table 6 below:

[0127] Table 6

[0128]

[0129] V. Library Purification

[0130] 5.1 Add 25 μL of room temperature equilibrated DNA purification magnetic beads to a new 1.5 mL centrifuge tube. Add the product from the previous reaction to the corresponding centrifuge tube. Use a pipette to mix the magnetic beads and product. Incubate at room temperature for 5 min.

[0131] 5.2 Place the centrifuge tube from the previous step on a magnetic rack for 1 minute until the solution becomes clear. Then, carefully aspirate the supernatant with a pipette, discard the supernatant, and keep the magnetic beads.

[0132] 5.3 Add 200 μL of freshly prepared 80% ethanol to the centrifuge tube from the previous step, let stand for 30 seconds, then carefully aspirate the supernatant, discard the supernatant, and keep the magnetic beads.

[0133] 5.4 Repeat step 5.3 once.

[0134] 5.5 After instantaneous centrifugation with a handheld centrifuge, place the centrifuge tubes on a magnetic rack, remove any residual ethanol with a pipette, and allow them to air dry for 3 minutes.

[0135] Note: Residual ethanol can affect subsequent reactions, so it's essential to ensure all ethanol evaporates. However, care must also be taken to prevent the magnetic beads from drying out and cracking, as this will reduce the amount of nucleic acid recovered.

[0136] 5.6 Add 33 μL TE Buffer, mix the magnetic beads thoroughly, and let stand for 5 min.

[0137] 5.7 After instantaneous centrifugation using a handheld centrifuge, place the centrifuge tube on a magnetic rack and use a pipette to transfer 30 μL of supernatant into a new 1.5 mL centrifuge tube.

[0138] Note: This is a library product and can be stored at -20℃.

[0139] VI. Library Quality Control & Quantification

[0140] 6.1 The library concentration was determined using a Qubit™ fluorometer. A normal library concentration is greater than 20 ng / µL, i.e., a library yield greater than 600 ng. Depending on laboratory conditions, library concentration can also be determined using qPCR-based absolute quantification methods.

[0141] 6.2 If needed, Fragment Analyzer (e.g., LabChip GX, GXII, GX Touch (PerkinElmer); Bioanalyzer, Tapestation (Agilent Technologies); FragmentAnalyzer (Advanced Analytical)) can be used to detect library length distribution.

[0142] VII. Sequencing

[0143] The library was subjected to high-throughput sequencing using the PE150 sequencing strategy (paired-end sequencing, 150 bp per read).

[0144] VIII. Data Analysis and Predictive Models:

[0145] A cross-tissue physiological age model was trained and validated based on methylation data and a linear regression model from 80 mouse brain, heart, lung, and liver tissue samples.

[0146] 1. For each tissue of each sample, calculate the methylation level (β value) of a single CpG site. The β value is calculated as follows: β = M / (M + U), where M is the number of methylated reads and U is the number of unmethylated reads.

[0147] 2. To construct a cross-organism prediction model, the key is to screen out methylation sites that are significantly associated with age and show consistent trends in all four tissues.

[0148] 3. Within each tissue, for all high-quality methylation sites, the Pearson correlation coefficient between their β values ​​and the actual age of the mice was calculated. A preliminary set of sites significantly associated with age in any tissue (e.g., p < 0.05) was identified. For the preliminarily selected sites, the direction (positive / negative correlation) and significance of their age-related correlation in four tissues (brain, heart, lung, and liver) were further analyzed. The screening criteria were defined as: significantly associated with age in at least three tissues (p < 0.05), and the correlation coefficients were consistent in direction (both positive or negative). This step aimed to capture core, cross-tissue conserved age-related methylation biomarkers.

[0149] 4. The standardized β values ​​of the finally selected cross-tissue consistent methylation sites were used as feature variables to construct a feature matrix with 80 rows and the number of feature sites as columns. Data from each sample across the four tissues were treated as independent observations to increase the sample size for model training and to explore the model's cross-tissue generalization ability. The corresponding response variable was the actual age of each mouse.

[0150] 5. Multiple linear regression was chosen as the basic prediction model because it is simple, highly interpretable, and exhibits good performance in handling such continuous prediction tasks. The basic form of the model is: Age = β0 + β1X1 + β2X2 + ... + β n X n Where Age is the predicted age of the mouse (in weeks), X1 to X n The β values ​​are the selected n cross-tissue consistent methylation sites, where β0 is the intercept, and β1 to β2 are the values ​​of the intercept. n These are the regression coefficients corresponding to each feature.

[0151] 6. Divide the integrated feature matrix (80 rows × n columns) and its corresponding mouse age data into a training set at a predetermined ratio (e.g., 70%, i.e., 56 mice). Use the training set data to estimate the coefficients of the linear regression model using the least squares method. To prevent overfitting, L2 regularization can be considered during model training.

[0152] 7. Model Simplification and Feature Importance Assessment Considering the potentially large number of features, stepwise regression or LASSO regression-based feature selection methods can be used to further simplify the model during training, retaining only the key methylation sites that contribute most to age prediction, thus improving the model's simplicity and robustness. Simultaneously, the relative importance of each methylation site to age prediction is assessed based on the absolute values ​​of the coefficients in the final model.

[0153] 8. Use the remaining 30% of the data (i.e., 24 mice) as the test set to evaluate the model's predictive performance.

[0154] 9. Use blood sample methylation data as a test set to evaluate the model's generalization performance.

[0155] The physiological age of the mice was finally calculated based on the following formula.

[0156] Mouse age = β0 + β1X1 + β2X2+ β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ β9X9+ β 10 X 10 + β 11 X 11 + β 12 X 12 + β 13 X 13 + β 14 X 14 + β 15 X 15 + β 16 X 16 + β 17 X 17 + β 18 X 18 .

[0157] in For the intercept, β1 to β 18The methylation levels at the following CpG sites are as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 1 09982829, chr1: 120371877, chr2: 166754974, chr3: 104805729, chr3: 30366752, chr4: 150889854, chr6: 52217280, chr7: 140923174, chr8: 123522992, chr8: 75627175; X1 to X 18 The weights of the corresponding CpG sites are shown in Table 3.

[0158] The specific calculation formula is as follows:

[0159] Mouse age = 960.62 + β1 × 1106.17 + β2 × 206.24 + β3 × 152.95 + β4× (-1483.1) + β5 × (-81.5) + β6 × 48.08 + β7 × (-248.17) + β8 × 143.68 +β9 × 160.26 + β10 × 34.23 + β11 × (-523.13) + β12 × (-502.59) + β13 × (-510.16) + β14 × (-138.99) + β15 × (-957.66) + β16 × (-116.17) + β17 × (-143.99) + β18 × 506.98.

[0160] Example 2

[0161] Using the methods and calculation formulas in Example 1 above, library construction, sequencing, and physiological age prediction were performed on 10 mice at approximately 36 weeks of age and 10 mice at approximately 84 weeks of age, respectively. The biological samples used were blood samples from the mice. The statistical results are shown in Table 7 below:

[0162] Table 7

[0163]

[0164] The correlation between mouse age predicted based on methylation levels and actual age was analyzed, with a correlation coefficient of 0.994. The results are as follows: Figure 1As shown. Similarly, accurate prediction results can be obtained by detecting the above-mentioned CpG sites in mouse brain, liver, lung, or heart samples and predicting physiological age.

[0165] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects: Existing whole-genome methylation sequencing (WGBS) and reduced-genomic methylation sequencing (RRBS) suffer from high sequencing data requirements (WGBS requires 90G of sequencing data, and RRBS requires 10G of sequencing data), resulting in high costs and making it difficult to meet the needs of high-throughput screening and large-scale applications. This technology screens 18 CpG sites highly correlated with mouse age and designs primers for multiplex PCR amplification targeting these sites. Through multiplex PCR methylation sequencing technology, bioinformatics analysis, and algorithmic models, it predicts the aging-related physiological age of mice. This method significantly reduces the amount of sequencing data (requiring only 0.1G of data, only one percent of the data required by RRBS), improves sequencing depth and accuracy, and features low cost and high accuracy. It can accurately predict the physiological age of mice, greatly promoting research in aging and drug evaluation.

[0166] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A biomarker for predicting aging-related physiological age in mice, characterized in that, The biomarkers include multiple CpG sites with different methylation levels. The location information of the CpG sites is as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 1 09982829, chr1:120371877, chr2:166754974, chr3:104805729, chr3:30366752, chr4:150889854, chr6:52217280, chr7:140923174, chr8:123522992, and chr8:75627175.

2. The biomarker according to claim 1, characterized in that, The weights of each CpG site are shown below: 。 3. The biomarker according to claim 1 or 2, characterized in that, The age-related physiological age of mice includes the age in weeks.

4. The biomarker according to claim 1 or 2, characterized in that, The reference genome version of the mouse is mm10.

5. A kit for predicting aging-related physiological age in mice, characterized in that, The kit includes reagents capable of detecting the biomarkers of any one of claims 1-4.

6. The reagent kit according to claim 5, characterized in that, The kit includes primer pairs for amplifying the CpG site, the primer pairs including a forward primer and a reverse primer; The nucleotide sequences of the forward and reverse primers corresponding to each CpG site are shown below: 。 7. A method for predicting aging-related physiological age in mice, characterized in that, The method includes: S1) Detect the methylation level of biomarkers in the genomic DNA of mice, wherein the biomarkers are any one of claims 1-4; S2) The aging-related physiological age of the mouse is calculated based on the methylation level.

8. The method according to claim 7, characterized in that, S1) includes detecting the methylation level of the biomarker using the kit described in claim 5 or 6.

9. The method according to claim 7, characterized in that, The mouse aging-related physiological age includes the mouse's age in weeks, and the calculation of S2) includes: The mouse age = 960.62 + β1X1 + β2X2+ β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ β9X9+β 10 X 10 + b 11 X 11 + b 12 X 12 + b 13 X 13 + b 14 X 14 + b 15 X 15 + b 16 X 16 + b 17 X 17 + b 18 X 18 , Wherein, β1 to β 18 The methylation levels at the following CpG sites are as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 109982829, chr1:120371877, chr2:166754974, chr3:104805729, chr3:30366752, chr4:150889854, chr6:52217280, chr7:140923174, chr8:123522992, and chr8:75627175; X1 to X 18 The weights corresponding to the CpG sites are as follows: 。 10. The method according to any one of claims 7-9, characterized in that, The methylation level in S1) is obtained by measuring biological samples of the mouse, including one or more of the brain, liver, lungs, heart, or blood. Optionally, the method for detecting the methylation level includes multiplex PCR methylation sequencing technology.

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