CN111763742A - Methylation marker, method for determining age of individual and application - Google Patents
Methylation marker, method for determining age of individual and application Download PDFInfo
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Abstract
The invention relates to the field of gene detection, in particular to a methylation marker, a method for determining the age of an individual and application thereof. The methylation marker is selected from: at least one of chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888 and chr6: 11044894. The method for determining the age of an individual comprises the following steps: measuring the methylation level of the above methylation marker in the genomic DNA of the biological sample; determining an age of the biological sample based on a statistical predictive model analysis. The methylation level prediction method provided by the invention can be used for rapidly, efficiently and accurately determining the age of an individual.
Description
Technical Field
The invention relates to the field of gene detection, in particular to a method for determining the age of an individual by measuring the methylation level of a methylation marker and application thereof.
Background
Methylation, which refers to the process of catalytic transfer of a methyl group from an active methyl compound (e.g., S-adenosylmethionine) to another compound, is one of the earliest gene epigenetic modifications and is also one of the important research contents in epigenetics. In recent years, with intensive research on DNA methylation by scientists, DNA methylation has been found to be significantly correlated with age. For example, in 2014, Renata Zbiec-Piekarska et al modeled 7 CpG sites in the ELOVL2 gene in 303 blood samples aged 2-75 years. The fitted correlation coefficient R is 92.7%, and the prediction error is 6.85 years; in 2015, Renata Zbiec-Pi' ekarska et al further performed sequencing analysis on 41 methylation sites of seven genes, ELOVL2, C1orf132, TRIM59, KLF14, FHL2, SLC6a4 and F5, in the same blood sample, and selected five sites with the highest correlation for modeling, and the fitted correlation coefficient R was 97.1% and the standard deviation was 4.457 years. However, age-related methylation detection needs further improvement.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. To this end, it is an object of the present invention to propose a method for age identification by means of a methylation marker indicative of age and by means of the methylation level of a methylation marker indicative of age.
The inventor of the invention finds out in the research process that: there are tens of thousands of age-related methylation sites that have been reported, and these methylation sites are not age-related. The inventor selects 7 sites on the ELOVL2 gene with higher degree of correlation with human age according to the correlation between human age and human DNA methylation, and the sites comprise: the relative degree of the 7 sites with the age of the individual is higher, and the 7 sites can be used as methylation markers to confirm the age of the biological sample or the individual. Further, the health status of an individual is reflected by the identification of the age of biological samples from different parts of the same individual.
By using software simulation, the methylation degree of the sample is judged by using the sites, and compared with other sites for judgment, the correlation between the methylation degree of the sample and the sites is stronger, and the obtained result is more reliable. Of course, when these sites are used to judge the degree of methylation, one site thereof may be used, or two or more sites thereof may be used. Preferably, three sites, four sites, five sites, six sites and all seven sites are used for judging the methylation degree of the individual, and the obtained judgment result is more reliable. The combined use of these sites can be used to determine the age of a biological sample to reflect the health status of an individual.
Specifically, the invention provides the following technical scheme:
according to a first aspect of the invention, there is provided a methylation marker. The methylation marker is selected from at least one of the following: chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888 and chr6: 11044894.
In at least some embodiments of the invention, the methylation markers are selected from at least two of chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6: 11044894.
In at least some embodiments of the invention, at least four of chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6:11044894 are selected.
In at least some embodiments of the present invention, all of chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6:11044894 are selected.
According to a second aspect of the invention, there is provided a primer pair for specifically amplifying a methylation marker according to any one of the embodiments of the first aspect of the invention.
In some embodiments of the invention, the primer pair is SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6 or SEQ ID NO 3 and SEQ ID NO 4.
According to a third aspect of the present invention there is provided a probe immobilised on a chip or free in solution, the probe being capable of specifically recognising a methylation marker as described in any one of the embodiments of the first aspect of the present invention.
According to a fourth aspect of the invention, there is provided a kit comprising a primer pair according to any one of the embodiments of the second aspect of the invention and/or a probe according to the third aspect of the invention.
According to a fifth aspect of the present invention, there is provided a method of determining the age of a biological sample, comprising: measuring the methylation level of a methylation marker in genomic DNA of the biological sample, the methylation marker being a methylation marker according to the first aspect of the invention; determining an age of the biological sample based on a statistical predictive model analysis.
According to an embodiment of the present invention, the method for determining the age of a biological sample described above may further comprise the following technical features:
in some embodiments of the invention, the biological sample is selected from blood of a biological individual. In addition, saliva, epidermis, kidney, liver, brain, or the like may be used for analysis to determine the age of the biological sample.
In some embodiments of the invention, the measuring the methylation level of the methylation marker in the genomic DNA of the biological sample comprises: treating genomic DNA from the biological sample with bisulfite conversion to convert unmethylated cytosines in the methylation markers to uracils to obtain conversion products; and constructing a sequencing library based on the transformation product, sequencing, and obtaining the methylation level of the methylation marker in the genome DNA of the biological sample.
In some embodiments of the invention, the sequencing comprises high throughput sequencing of the sequencing library using the sequencing platform BGISEQ-500.
In some embodiments of the invention, constructing a sequencing library based on the transformation products comprises: carrying out PCR amplification on the transformation product to obtain a target fragment; performing end repair on the target fragment, and connecting a joint to obtain a connection product; subjecting the ligation products to a circularization treatment to obtain the sequencing library.
In some embodiments of the invention, the size of the target fragment is 200-260 bp. The target fragment is in the range and is suitable for library construction and sequencing by utilizing a sequencing platform.
In some embodiments of the invention, the transformation product is PCR amplified using primer pairs SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6 or primer pairs SEQ ID NO 3 and SEQ ID NO 4.
In some embodiments of the invention, the statistical predictive model is selected from at least one of a multiple linear regression model or a support vector machine model.
In some embodiments of the present invention, the multiple linear regression model is at least one of a binary linear regression model or a quaternary linear regression model.
In some embodiments of the invention, the multiple linear regression model is constructed with the methylation rate of the site as an independent variable and the age as a dependent variable.
According to a sixth aspect of the present invention, there is provided a method of determining the age of an individual, comprising: collecting a tissue sample from the individual; extracting genomic DNA of the individual from the collected tissue sample; measuring the methylation level of a methylation marker on the genomic DNA of the individual, said methylation marker being a methylation marker according to any one of the embodiments of the first aspect of the invention; determining the age of the individual based on statistical predictive model analysis.
According to a seventh aspect of the invention, there is provided a method of determining the health status of an individual, comprising: collecting a tissue sample from the individual; extracting genomic DNA of the individual from the collected tissue sample; measuring the methylation level of a methylation marker on the genomic DNA of the individual, said methylation marker being a methylation marker according to any one of the embodiments of the first aspect of the invention; determining an age of the individual based on a statistical predictive model analysis as a biological age of the individual; comparing the biological age of the individual to the age of life of the individual.
The term "life age" refers to an age estimated based on the year, month and day of birth of an individual. "biological age" also known as medical age, is usually expressed in terms of the degree of development of the body. In the process of growth and development of individuals, due to the influence of factors such as heredity, nutrition, diseases and physical exercise, the biological age is often inconsistent with the living age of the individuals, and even the biological age is different by years. The difference between the biological age of an individual and the life age of the individual often reflects the health status of the individual, for example, people who insist on exercise for a long time mostly show that the biological age is smaller than the life age; whereas long irregular lives and work may show a biological age greater than the life age. The determination of the health status of individuals can be aided by analyzing the methylation levels of methylation markers on the genomic DNA of individuals, using a model to determine the biological age of the individuals, and then comparing the living ages of the individuals. Such a state of health may be manifested as, for example, "sub-healthy", "unhealthy", "exhibiting aging above age" or "younger than age", and the like.
According to an embodiment of the present invention, the method for determining the health status of an individual as described above may further include the following technical features:
in some embodiments of the invention, the biological age of the individual is greater than the age of life of the individual, indicating accelerated aging of the individual.
In some embodiments of the invention, the method of determining the health status of an individual as described above further comprises: collecting a first tissue sample and a second tissue sample from the individual, determining a biological age of the first tissue sample and a biological age of the second tissue sample, respectively; comparing the biological age of the first tissue sample to the biological age of the second tissue sample to determine the health status of the first and second tissue samples.
Herein, "biological age of first tissue sample" and "biological age of second tissue sample" refer to the analysis of methylation levels of tissue samples from different sites of the same individual, i.e., the biological age of the individual is determined from the first tissue sample and the biological age of the individual is determined from the second tissue sample, and are counted as "biological age of first tissue sample" and "biological age of second tissue sample". The biological age of the first tissue sample is then compared to the biological age of the second tissue to reflect the health status of the first tissue sample and the second tissue sample. It should be noted that, in general terms, tissues in different parts have different aging laws. When comparing the health status of different tissue samples, it is necessary to judge according to the biological age of the individual to which the tissue sample corresponds.
In some embodiments of the invention, a biological age of the first tissue sample being greater than a biological age of the second tissue sample indicates that the first tissue sample is unhealthy.
According to an eighth aspect of the present invention, a system for determining the health status of an individual, comprises: a sample collection unit for collecting a tissue sample from the individual; a DNA extraction unit connected to the sample collection unit for extracting genomic DNA of the individual from the collected tissue sample; a methylation assay unit, said methylation assay unit being linked to said methylation assay unit, said methylation assay unit being configured to measure the methylation level of a methylation marker on the genomic DNA of said individual, said methylation marker being a methylation marker according to any one of the embodiments of the first aspect of the invention; a statistical analysis unit connected to the methylation determination unit, the statistical analysis unit determining an age of the individual as a biological age of the individual based on a statistical prediction model analysis; a comparison unit connected with the statistical analysis unit, the comparison unit being used for comparing the biological age of the individual with the life age of the individual.
In some embodiments of the invention, in the above system, the biological age of the individual is greater than the age of life of the individual, indicating accelerated aging of the individual.
The beneficial effects obtained by the invention are as follows: the method is realized by reserving methylated base C in DNA, converting unmethylated base C into T base, performing PCR amplification on a converted product, constructing a library, and performing high-throughput sequencing, is mature, is not interfered by various factors such as nutritional factors, environmental factors, anthropogenic differences and the like, is simple to operate, is efficient and accurate, requires short time, and reduces the range of age errors. According to the technical scheme of the invention, the minimum average absolute deviation of 20-65 years old is 1.97 after the data are processed, so that the prediction error is greatly reduced, and the accuracy is improved.
Drawings
FIG. 1 is a graph of methylation level versus age for a target region of a Crick chain provided in accordance with an embodiment of the present invention, wherein the abscissa represents age group and the ordinate represents methylation rate.
FIG. 2 is a graph of Watson chain target region methylation level versus age provided in accordance with an embodiment of the present invention, wherein the abscissa represents age group and the ordinate represents methylation rate.
FIG. 3 is a graph of a single point linear regression of a Crick chain provided in accordance with an embodiment of the present invention, wherein the abscissa represents methylation rate and the ordinate represents age.
Fig. 4 is a comparison of predicted age and life age of a Crick chain provided in accordance with an embodiment of the present invention, wherein the abscissa represents true age value and the ordinate represents predicted age value.
Fig. 5 is a comparison of predicted age and life age of a screened Crick chain provided in accordance with an embodiment of the present invention, wherein the abscissa represents true age value and the ordinate represents predicted age value.
FIG. 6 is a result of Watson chain single point linear regression provided in accordance with an embodiment of the present invention, wherein the abscissa represents methylation rate and the ordinate represents age.
Fig. 7 is a comparison of a predicted age and a life age of a Watson chain provided according to an embodiment of the present invention, wherein the abscissa represents a true age value and the ordinate represents a predicted age value.
Fig. 8 is a comparison result of predicted age and life age of the filtered Watson chain, wherein the abscissa represents a true age value and the ordinate represents a predicted age value, according to an embodiment of the present invention.
Fig. 9 is a comparison of predicted age and life age of integrated 3 loci provided according to an embodiment of the invention, wherein the abscissa represents true age value and the ordinate represents predicted age value.
Fig. 10 is a comparison of predicted age and real age of a Crick chain support vector machine model provided according to an embodiment of the present invention, wherein the abscissa represents the real age value and the ordinate represents the predicted age value.
Fig. 11 is a comparison of the predicted age and the real age of the Watson chain support vector machine model provided according to an embodiment of the present invention, wherein the abscissa represents the real age value and the ordinate represents the predicted age value.
Fig. 12 is a comparison of predicted age and real age of an integrated 7-site support vector machine model provided according to an embodiment of the present invention, wherein the abscissa represents the real age value and the ordinate represents the predicted age value.
Wherein the reference numerals in FIGS. 1 to 12 are referred to, the reference numerals 1 to 7 represent the positions 1 to 7, and the positions 1 to 7 are chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888 and chr6:11044894, respectively.
Fig. 13 is a schematic diagram of a system for determining a health status of an individual provided in accordance with an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The technical scheme provided by the invention is mainly designed aiming at the identification of human age, and the invention selects genes with higher relativity and sites thereof from the relevant sites recorded in the reference, namely 7 adjacent sites on the ELOVL2 gene of chromosome 6 of human body: chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888 and chr6:11044894 (methylation occurs in exon regions of genes, the sites are located on ELOVL2 gene and on chromosome 6); each site given is given with reference to GRCh38version28 in the NCBI database.
Based on the sites, primer sequences are respectively designed on the flanks of the sites by using PrimerPrimier5 and Oligo7 software, the annealing temperature of each primer is about 50 ℃, primer dimer or other nonspecific products caused by mismatch cannot be generated, and the length of the amplified product is between 200 and 260 bp. And each pair of primers is compared by Blast to ensure the specificity of the sequence. And carrying out PCR amplification test on each pair of primers, carrying out agarose gel electrophoresis detection and repeatedly optimizing until a clear single amplification band is obtained. In at least some embodiments of the invention, the primer pair sequences are SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6. These primer pairs can be used for PE100 sequencing. In at least some embodiments of the invention, the primer pair sequences are SEQ ID NO 3 and SEQ ID NO 4, with which the SE200 sequencing can be performed. Experiments prove that the primers are used for amplification, and agarose gel electrophoresis detection is carried out on the amplification product, so that a clear and single amplification band can be obtained by amplification. The primer may be used at a concentration of 20. mu.M, and the annealing temperature may be 50 ℃.
Based on the above primers, the present invention provides a kit comprising the above primer pair, and further comprising a substance for amplification, for example, Taq DNA polymerase, buffer, dNTP mix and the like.
According to another aspect of the invention, there is provided a method of determining the age of a biological sample, comprising: measuring the methylation level of a methylation marker in the genomic DNA of the biological sample, the methylation marker being the aforementioned methylation marker. Determining an age of the biological sample based on a statistical predictive model analysis. The biological sample includes, but is not limited to, blood.
Treating genomic DNA from the biological sample with bisulfite conversion in order to convert unmethylated cytosines in the methylation markers to uracils while measuring the methylation level of methylation markers in the genomic DNA of the biological sample, obtaining conversion products; and then constructing a sequencing library and sequencing on the transformation products to obtain the methylation level of the methylation marker in the genome DNA of the biological sample.
In the transformation treatment, DNA in a Blood sample was extracted using a DNA Blood mini Kit (QIAGEN) Kit, and the extracted DNA was C-T converted by the EZ DNA methylation-Gold Kit (ZYMO) Kit, and C in the DNA which had been methylated was retained.
When constructing the sequencing library, the following method can be adopted to construct the sequencing library: amplifying an amplification product to obtain a target fragment, then carrying out end repairing on the target fragment, connecting different adapters, connecting different samples with different adapters for identifying the samples, carrying out Pre-PCR on the Adapter-PCR product, purifying the obtained product, carrying out cyclization and Make DNB, then loading DNB on a chip, finally installing the chip on BGISEQ-500RS, and sequencing by using an SE200 or PE100 platform. Wherein the size of the target fragment is 200-260bp, and the size of the target fragment meets the requirement of sequencing.
The data obtained from the experiment were processed to obtain average methylation rates of Crick chain, Watson chain, and Crick + Watson. Through separate calculation, stability and relevance of each site can be determined, and an age prediction model can be established conveniently later. 44 groups of data from 20-65 years old were selected and first analyzed using a boxplot of R versus methylation and age. Then, two modes are selected to establish a model, namely a multiple linear Regression model and a support vector machine (SVR) model. An age prediction model is built by adopting an svm () function of an e1071 packet in R, firstly, parameters 'Cost is 2, gamma is 0.1 and epsilon is 0.1' are used for building an SVR model, and the SVR model is a linear and nonlinear data classification method which uses nonlinear mapping to map original data to a high-dimensional space and searches an optimal separation hyperplane in the space. In the case of linear divisibility, there is such a hyperplane that separates the classes in space and the hyperplane is the largest distance from the classes, i.e., the largest edge hyperplane, by selecting the parameters to obtain a non-linear mapping from the input space to the output space. And then, carrying out cluster analysis on the data by using a summary function of the SVM by using the data obtained by the obtained model to obtain an absolute value of the total age value under the model, dividing the absolute value by the data amount (44 groups) to obtain a model separation parameter of the SVM, and finally carrying out analysis based on the SVR model on the age to be predicted again by using the separation parameter to obtain the predicted age. And (4) comparing the predicted age with the real age according to the obtained age deviation value predicted by the model, and screening the sites with better linear correlation for modeling again. Research shows that all methylation rates of the Watton chain and the Crick chain at seven sites are commonly used, and the obtained result has high stability and small deviation value.
And adopting an lm () function in the R as a multiple linear regression model, drawing a linear relation of the predicted age value, and judging whether the error is too large. Wherein the multiple linear regression model is relative to the single linear regression model. Unary linear regression is a method that accounts for the variation of dependent variables with one of the major influencing factors as the independent variable. In the process of studying different problems, the variation of the dependent variable is often influenced by several important factors, and in this case, it is necessary to use two or more influencing factors as independent variables to explain the variation of the dependent variable, which is multiple regression, also called multiple regression. When a plurality of independent variables and dependent variables present linear relations, the regression analysis is multivariate regression.
For example, assuming that y is the dependent variable x1, x2 … xk is the independent variable, and there is a linear relationship between the independent variable and the dependent variable, the multiple linear regression model is:
y＝b0+b1x1+…+bkxk+e
wherein, b0Is a constant term, b1, b2 … bk are regression coefficients, 1 is x1，x2…xkAt the time of fixation, x1The effect of each increment of one unit on y, i.e. 1 partial regression coefficient on y; in the same way 2 is x1，x2…xkAt the time of fixation, x2The effect of each increment of one unit on y, i.e., 2 partial regression coefficients on y, and so on. If two independent variables x1，x2When the same dependent variable y is linearly related, the two-dimensional linear regression model can be described as follows:
y＝b0+b1x1+b2x2+e
therefore, the relationship between the ages of different individuals and the methylation rates of methylation markers on genomic DNA can be obtained by using a multiple linear regression model, so that the age of an unknown sample can be determined by measuring the methylation rates of methylation markers on the genomic DNA of the unknown sample. Such age determination can be applied to various fields, for example, to the field of forensic medicine, and to age determination of unknown samples to analyze age information of criminal suspects or victims, thereby helping case detection. The age of an individual can also be analyzed by determining the age of a sample to obtain the biological age of the individual and comparing it to the age of the individual.
According to yet another aspect of the present invention, there is provided a system for determining the health status of an individual, as shown in fig. 13, comprising: the DNA analysis device comprises a sample collection unit, a DNA extraction unit, a methylation determination unit, a statistical analysis unit and a comparison unit, wherein the DNA extraction unit is connected with the sample collection unit, the methylation determination unit is connected with the methylation determination unit, the statistical analysis unit is connected with the methylation determination unit, and the comparison unit is connected with the statistical analysis unit. Wherein the sample collection unit is for collecting a tissue sample from the individual; the DNA extraction unit is used for extracting the genome DNA of the individual from the collected tissue sample; the methylation determination unit is used for measuring the methylation level of a methylation marker on the genome DNA of the individual, wherein the methylation marker is the methylation marker provided by the invention; the statistical analysis unit determines an age of the individual as a biological age of the individual based on a statistical prediction model analysis; the comparison unit is used for comparing the biological age of the individual with the life age of the individual. The system can be used to confirm the health condition of an individual by comparing the measured biological age of the individual with the life age of the individual. For example, if the biological age of the individual is greater than the life age of the individual, it is used to indicate that the individual is accelerating aging.
The scheme of the invention will be explained with reference to the examples. It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples, where specific techniques or conditions are not indicated, are to be construed according to the techniques or conditions described in the literature in the art or according to the product specifications. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products commercially available.
Example 1
7 CpG sites on ELOVL2 gene of chromosome 6 are selected and modeled in 44 blood samples of 20-65 years old.
1. Site determination
The invention screens and analyzes relevance according to genes related to age recorded in literature, thereby screening and determining the EVOVL2 gene and 7 sites on the gene, which are respectively: the gene is used as a methylation marker for determining the age of a biological sample, and the gene is used as a methylation marker for chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888 and chr6: 11044894.
2. Sample collection
Blood samples of different age groups are collected at 5-year intervals, and 5-10 parts of each blood sample is collected. Of these, 2/3 blood samples were used for modeling and 1/3 blood samples were used for validation. Wherein 4 parts are collected at 20-25 years old, 10 parts are collected at 25-30 years old, 6 parts are collected at 30-35 years old, 3 parts are collected at 35-40 years old, 6 parts are collected at 40-45 years old, 4 parts are collected at 45-50 years old, 5 parts are collected at 50-55 years old, 1 part is collected at 55-60 years old, 4 parts is collected at 60-65 years old, 2 parts is collected at 65-70 years old, and 45 blood samples are collected together.
3. DNA extraction
The genomic DNA of the above Blood samples was extracted using a DNA extraction Kit (QIAGEN DNA Blood mini Kit), respectively, comprising the following steps:
1) mu.L of the blood sample, 400. mu.L of Buffer AL and 40. mu.L of Protease (or Protease K) were added to 2ml centrifuge tubes, and vortexed to mix well.
2) Incubate at 56 deg.C and 900rpm for 30min in an incubator, then take out for a short time and centrifuge, the liquid in the centrifuge tube should be dark green, and there is no blood clot at the bottom of the tube.
3) Add 400. mu.L of absolute ethanol, invert gently several times, centrifuge briefly. The mixture (not more than 750ul) in the centrifuge tube was added to the extraction column, centrifuged at 8000rpm for 30s at room temperature, and the waste liquid was discarded. Repeating for several times until the mixed solution is completely added.
4) Add 500. mu.L Buffer AW1, centrifuge at 8000rpm for 30s at room temperature, and discard the waste. 500. mu.L of BufferAW2 was added, and the mixture was centrifuged at 8000rpm for 30 seconds at room temperature, and the waste liquid was discarded.
5) Placing the extraction column on a collecting tube, centrifuging at 12000rpm for 2min, removing the excessive waste liquid, and air-drying for 3min to volatilize the residual alcohol in the tube.
6) Placing the extraction column on a new 1.5mL centrifuge tube, adding 200 μ L Buffer AE, and incubating at room temperature for 10 min; centrifuging at 12000rpm for 2min at room temperature to obtain DNA solution, quantifying with Qubit3.0, and storing at-20 deg.C.
4. C-T conversion
1) To the C-T Conversion Reagent (CT Conversion Reagent, ZYMO) was added the following reagents: h20, 900 μ L; M-Dilution, 300. mu.L; m-dispensing Buffer, 50. mu.L. Vortex and mix well for 10min at room temperature.
2) Add 130. mu. L C-T Conversion Reagent to 20. mu.L of DNA sample (if less than 20. mu.L, H2Complete with O). And (3) mixing uniformly and packaging into 2 PCR tubes. Placing in a PCR instrument at 98 deg.C for 10min, 64 deg.C for 2.5hours, 4 deg.C for no more than 20 hours.
3) To Zymo-SpinTMAdding 600 mu L M-Binding Buffer into IC Column, placing in a collecting tube, adding C-T conversion product into a test tube, mixing, and rotating at maximum speed (>10000g) Centrifuging for 30s, and discarding the waste liquid.
4) 100 mu L M-Wash Buffer was added, the mixture was centrifuged at maximum speed (>10000g) for 30s, and the waste solution was discarded.
5) Adding 200 mu L M-depletion Buffer, standing at room temperature for 15-20min, centrifuging at maximum rotation speed (>10000g) for 30s, and discarding the waste liquid.
6) 200 mu L M-Wash Buffer was added, the mixture was centrifuged at maximum speed (>10000g) for 30s, and the waste solution was discarded.
7) The column was placed in a new 1.5ml centrifuge tube, 10. mu. L M-Elution Bufer was added, the mixture was left to stand for 5min, and centrifuged at maximum speed (>10000g) for 30s to recover DNA. Storage at-20 ℃.
5. Primer screening
The primers used in the invention are synthesized by Shanghai Producer, all the primers are designed by using primer primier5 and oligo7 software, 2-8 pairs of primers are designed for each site to be selected, a PCR amplification-agarose gel electrophoresis method is used for primary screening, DNA samples used in PCR are samples collected by the second part, TaKaRa Epi Taq HS (5U/. mu.l) used in the primary screening is used as hot start Taq enzyme, a reaction system is shown in Table 2, and temperature cycle conditions are shown in Table 3. 3 pairs of primers were selected from the agarose gel electrophoresis assay (as shown in Table 1).
Wherein SEQ ID NO 1 and SEQ ID NO 2 in Table 1 are upstream and downstream primers as a pair of primers; SEQ ID NO:3 and SEQ ID NO 4 as a pair of primers; SEQ ID NO 5 and SEQ ID NO 6 as a pair of primers. The non-reaction complementation between each pair of primers means that the specificity of each pair of primers is high, and the complementary pairing between the primers does not occur.
Wherein, the sample which is transformed by C-T in the previous step can be amplified into Watson chains by utilizing SEQ ID NO. 1 and SEQ ID NO. 2; the Crick chain with 7 sites can be amplified by utilizing SEQ ID NO. 5 and SEQ ID NO. 6; and (4) establishing a library of the amplified Watson chain and Crick chain, and then using the library for subsequent PE100 platform sequencing.
Meanwhile, through experimental verification, products amplified by the C-T transformed sample by using the primers of SEQ ID NO. 3 and SEQ ID NO. 4 can be used for SE200 sequencing after library construction (not specifically listed).
The following shows only how the in-machine sequencing and validation process was performed with the PE100 platform using SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6 as primers.
TABLE 1 primer sequences
TABLE 2 primer screening reaction System
TABLE 3 primer selection amplification temperature conditions
6. Agarose gel electrophoresis
The amplification products were subjected to agarose gel electrophoresis detection according to the following procedure, thereby screening out primers that were capable of amplifying a single and clear amplification product:
1) a 2% agarose gel was prepared, following agarose gel volume: adding the corresponding DNA staining solution with the ratio of 30: 1;
2) adding 6 Xloading dye (loadingDye) into the PCR product at a ratio of 1:5, loading 20. mu.L, and loading 10. mu.L with TAKARA DL2000 as DNA Marker;
3) setting the voltage to be 140V, and performing electrophoresis for 30 min;
4) and (3) shooting an electrophoresis gel image by using a gel imager, and screening primers capable of amplifying a single and clear band.
7. Building warehouse
1) And amplifying the DNA after C-T conversion according to an amplification system shown in the table 2 and an amplification temperature condition shown in the table 3, adding 2-fold volume of magnetic beads into the obtained PCR product for purification, and measuring the concentration by using the qubit 3.0.
2) And (3) repairing the tail end: 50ng of the sample was taken and water was added to 40. mu.L, and the sample and the reagent were mixed according to Table 4, and the end was repaired under the temperature conditions shown in Table 5.
TABLE 4 end repair System
TABLE 5 end repair temperature conditions
3) Connecting Adapter:
TABLE 6 Adapter connection scheme
TABLE 7 Adapter connection temperature conditions
According to the mixing system of table 6 and the temperature conditions of table 7, the end-repaired product was mixed with reagents and Adapter-ligated in a PCR instrument, one sample for each Adapter. Add 1 volume of magnetic beads to the Adapter product and purify by bead method using 24. mu. L H2And O is redissolved, and 22 mu L of redissolved product is taken for the next step.
4)Pre-PCR
The 22ul solution from the previous step was subjected to Pre-PCR according to the system shown in Table 8 and the amplification temperature conditions shown in Table 9.
TABLE 8 Pre-PCR System
TABLE 9 Pre-PCR temperature conditions
Addition of 2 volumes of magnetic beads to the Pre-PCR product was performed by the magnetic bead method using 32. mu. L H2And O is redissolved, and 30ul of the solution is sucked into a new 1.5ml centrifuge tube, and 1 mu L of the solution is taken and the concentration of the solution is measured by using the Qubit 3.0.
8. And (3) loading:
1) taking 140ng of the Pre-PCR product, adding water to 48 mu L, preheating according to a table 10, carrying out DNA cyclization according to a cyclization system in a table 11 and a cyclization temperature condition in a table 12, taking 20 mu L of the cyclization product, carrying out Make DNB reaction according to a table 13, immediately placing the reaction product on an ice box when the temperature of the reaction product in the table 13-2 is reduced to 4 ℃, adding 20 mu L of LDNB termination buffer solution, slowly dripping and mixing by using a wide-mouth sucker, taking 2 mu L of the reaction product, and detecting the concentration by using a ssDNA detection kit and a qubit 3.0;
2) adding 32 mu L of DNB loading buffer solution II and 1 mu L of DNB enzyme Mix II into the mixed solution in the step 1), and slowly dripping and mixing for a plurality of times by using a wide-mouthed suction head; cleaning a Load instrument, installing a chip and a chip holder, putting a Load reagent and a sample, and starting to Load the sample on the chip;
3) the chip with the loaded sample is placed on BGISEQ-500RS, the sequencing reagent is placed and preloaded, and sequencing is started by using the PE100 sequencing platform.
TABLE 10 preheat temperature conditions
TABLE 11 cyclization system
TABLE 12 cyclization
TABLE 13 Make DNB System and temperature conditions
9. Analyzing data and establishing model
(1) Results of the experiment
And processing the offline data obtained by the experiment, and extracting all the site information of the offline data by taking a human gene database with the version of 28 in an NCBI database as a guide file. The depth of the Crick and Watson chains at each site is then obtained. Screening the information of the required 7 sites, and calculating the obtained information to obtain the methylation rates of a Crick chain, a Watson chain and Crick + Watson. 44 groups of data from 20-65 years old were selected and box plots of methylation rates of Crick and Watson chains versus age were first plotted in R, as shown in FIG. 1 and FIG. 2 below. Wherein the Crick strand is a DNA strand in which transcription does not occur, i.e., a non-template strand, and the Watson strand is a DNA strand transcribed into mRNA. The numbers 1 to 7 in FIGS. 1 and 2 correspond to the methylation of the following sites in different age groups, respectively: chr6:11044867 (corresponding to reference numeral 1), chr6:11044873 (corresponding to reference numeral 2), chr6:11044875 (corresponding to reference numeral 3), chr6:11044877 (corresponding to reference numeral 3), chr6:11044880 (corresponding to reference numeral 4), chr6:11044888 (corresponding to reference numeral 5), and chr6:11044894 (corresponding to reference numeral 6). The abscissa of each reference numeral represents the age group, respectively, and the abscissa reference numerals a, b, c, d, e, f and g represent [20,25), [25,30), [30,35), [35,40), [40,45), [45,50, [50,55), respectively, wherein "[" represents the inclusion number and ")" represents the exclusion number, for example, [20,25) represents the age group between 20 and 25, including 20, but not including 25. The ordinate represents the methylation ratio, respectively.
As is clear from FIGS. 1 and 2, the 7 sites selected in the present invention have correlation with age, and the methylation rate is positively correlated with age as a whole.
(2) Modeling
Two ways are used to build the model: multiple linear regression model, support vector machine regression model.
1) Multiple linear regression: using lm () function in R
Taking a Crick chain as an example, a regression equation and a linear graph (shown in fig. 3 and obtained by ggplot 2) of 7 sites of the Crick chain are obtained, and then the quality of a multiple linear regression model is analyzed according to a comparison result (shown in fig. 4) of a predicted age value and a real age value obtained by the regression equation.
Wherein the abscissa in fig. 3 represents methylation rate, the ordinate represents age, and the middle oblique line represents a regression line generated based on each sample. The abscissa in fig. 4 represents the true age value, the ordinate represents the predicted age value, and the upper left corner represents the MAD value. The MAD value represents the mean absolute deviation, i.e., the ratio of the absolute deviation value to the number of samples, where the absolute deviation value refers to the absolute value of the difference between the true age value and the predicted age value. The smaller the MAD value, the smaller the difference between the predicted age value and the true age value.
Combining the results of FIG. 1, FIG. 3 and FIG. 4, the comprehensive analysis shows that site 1(chr6:11044867) and site 4(chr6:11044880) have better regression effect, after site 1 and site 4 are screened out, a linear equation of two elements is obtained through lm () function, and the predicted age obtained through the equation is compared with the life age again (as shown in FIG. 5). Where the abscissa in fig. 5 represents the true age value and the ordinate represents the predicted age value.
The same analysis was performed on Watson chains using the same method as above. The results shown in fig. 6 and 7 were obtained.
And (3) combining the results of the images 2, 6 and 7 to analyze that the site 1 and the site 2 have better regression effect. Then, a linear equation of two is obtained for the selected site 1 and site 2lm () functions, and the predicted age obtained through the equation is compared with the life age again, thereby obtaining fig. 8.
Results of Watson + Crick multivariate Linear model
Integrating the site 1 and the site 4 of the Crick chain and the site 1 and the site 2 of the Watson chain, establishing a multiple linear regression model of 3 sites to obtain a quaternary one-time regression equation, and comparing a predicted age value obtained according to the regression equation with a real age value (figure 9).
Formula：y＝33x1-2x2+46x3+49x4
(wherein X1, X2 is the methylation rate of Crick chain, X3, X4 is the methylation rate of Watson chain, X1: Chr6: X11044867, X2: Chr6: X11044877, X3: Chr6: X11044867, X4: Chr6: X11044873).
2) Support vector machine model: svm () function using e1071 packet in R
Analyzing a Crick chain result of the model by a support vector machine;
obtaining a support vector machine model of 7 sites of a Crick chain, and comparing a predicted age value obtained by analyzing the model with a real age value (figure 10);
a support vector machine analyzes a model Watson chain result;
the same treatment was performed on the Watson chain and the Crick chain to obtain FIG. 11;
watson + Crick support vector machine model results;
the support vector machine model was obtained by integrating 7 sites obtained by multiple linear regression, and then the process was followed to obtain fig. 12.
Through multivariate linear regression and a support vector machine model, the method can be analyzed, and the minimum average absolute deviation of the age of 20-55 years can be predicted to be 1.97 through the methylation rate, so that the prediction error is greatly reduced, and the accuracy is improved.
According to the embodiment, after C-T conversion is carried out on the whole genome, primers are designed based on the 7 sites, library construction and sequencing are carried out after PCR amplification, methylation rates are counted through a multiple linear regression model and a support vector machine regression model, the age of the biological sample is further determined, the experiment operation is simple, and the data analysis model is reliable.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; may be mechanically coupled, may be electrically coupled or may be in communication with each other; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
SEQUENCE LISTING
<110> Shenzhen Huada medical science Co Ltd
<120> methylation marker, and method and application for determining age of individual
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Claims (10)
1. A methylation marker, wherein the methylation marker is selected from at least one of:
chr6:11044867，chr6:11044873，chr6:11044875，chr6:11044877，chr6:11044880,chr6:11044888，chr6:11044894；
optionally, at least two selected from chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6: 11044894;
optionally, at least four selected from chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6: 11044894;
optionally, all selected from chr6:11044867, chr6:11044873, chr6:11044875, chr6:11044877, chr6:11044880, chr6:11044888, chr6: 11044894.
2. A primer pair for specifically amplifying the methylation marker of claim 1;
optionally, the primer pair is SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6 or SEQ ID NO 3 and SEQ ID NO 4.
3. A probe immobilized on a chip or free in a solution, wherein the probe is capable of specifically recognizing the methylation marker of claim 1.
4. A kit comprising the primer set according to claim 2 and/or the probe according to claim 3.
5. A method of determining the age of a biological sample, comprising:
measuring the methylation level of a methylation marker in genomic DNA of the biological sample, the methylation marker being the methylation marker of claim 1;
determining an age of the biological sample based on a statistical predictive model analysis.
6. The method of claim 5, wherein the biological sample is selected from the group consisting of blood of a biological individual;
optionally, the measuring the methylation level of the methylation marker in the genomic DNA of the biological sample comprises:
treating genomic DNA from the biological sample with bisulfite to convert unmethylated cytosines in the methylation marker to uracil to obtain a conversion product;
constructing a sequencing library based on the transformation product, sequencing, and obtaining the methylation level of the methylation marker in the genome DNA of the biological sample;
optionally, the sequencing is high throughput sequencing of the sequencing library using the sequencing platform BGISEQ-500;
optionally, constructing a sequencing library based on the transformation products comprises:
carrying out PCR amplification on the conversion product to obtain a target fragment;
performing end repair on the target fragment, and connecting a joint to obtain a connection product;
circularizing said ligation products to obtain said sequencing library;
optionally, the size of the target fragment is 200-260 bp;
optionally, the transformation product is subjected to PCR amplification using primer pairs SEQ ID NO 1, SEQ ID NO 2, SEQ ID NO 5 and SEQ ID NO 6 or primer pairs SEQ ID NO 3 and SEQ ID NO 4.
7. The method of claim 5, wherein the statistical prediction model is selected from at least one of a multiple linear regression model or a support vector machine model;
optionally, the multiple linear regression model is a binary linear regression model or a quaternary linear regression model;
optionally, the methylation rate of the locus is used as an independent variable, and the age is used as a dependent variable, so as to construct the multiple linear regression model.
8. A method of determining the age of an individual, comprising:
collecting a tissue sample from the individual;
extracting genomic DNA of the individual from the collected tissue sample;
measuring the methylation level of a methylation marker on the genomic DNA of the individual, the methylation marker being the methylation marker of claim 1;
determining the age of the individual based on statistical predictive model analysis.
9. A method of determining the health status of an individual, comprising:
collecting a tissue sample from the individual;
extracting genomic DNA of the individual from the collected tissue sample;
measuring the methylation level of a methylation marker on the genomic DNA of the individual, the methylation marker being the methylation marker of claim 1;
determining an age of the individual based on a statistical predictive model analysis as a biological age of the individual;
comparing the biological age of the individual to the age of life of the individual;
optionally, the biological age of the individual is greater than the age of life of the individual, indicating accelerated aging of the individual;
optionally, further comprising:
collecting a first tissue sample and a second tissue sample from the individual, determining a biological age of the first tissue sample and a biological age of the second tissue sample, respectively;
comparing the biological age of the first tissue sample to the biological age of the second tissue sample in order to determine the health status of the first and second tissue samples;
optionally, the biological age of the first tissue sample is greater than the biological age of the second tissue sample to indicate that the first tissue sample is unhealthy.
10. A system for determining the health status of an individual, comprising:
a sample collection unit for collecting a tissue sample from the individual;
a DNA extraction unit connected to the sample collection unit for extracting genomic DNA of the individual from the collected tissue sample;
a methylation assay unit linked to the methylation assay unit for measuring the methylation level of a methylation marker on genomic DNA of the individual, the methylation marker being the methylation marker of claim 1;
a statistical analysis unit connected to the methylation determination unit, the statistical analysis unit determining an age of the individual as a biological age of the individual based on a statistical prediction model analysis;
a comparison unit connected to the statistical analysis unit, the comparison unit for comparing the biological age of the individual with the life age of the individual;
optionally, the biological age of the individual is greater than the age of life of the individual, indicating accelerated aging of the individual.
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