CN117487937B - Application of miRNA marker combination in preparation of age prediction product - Google Patents
Application of miRNA marker combination in preparation of age prediction product Download PDFInfo
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Abstract
The invention provides an application of miRNA marker combination in preparation of a product for predicting age, and belongs to the technical field of genetic engineering diagnosis. According to the invention, miRNA extraction and fluorescence quantitative PCR methods are developed for tooth samples, the relative expression amounts of miR-22-3p and miR-29a-3p are respectively obtained through the methods, and the corresponding relation between the relative expression amounts of miR-22-3p and miR-29a-3p and the predicted age is analyzed through a kNN model, so that the predicted age of the samples is obtained. The application method has the characteristic of high detection sensitivity, can realize age prediction by only 0.05ng of tooth miRNA, and can predict the ages of tooth samples of people with ages of 20-70, and the error is within 4.5 years.
Description
Technical Field
The invention belongs to the technical field of genetic engineering diagnosis, and particularly relates to application of miRNA marker combination in preparation of a predicted age product.
Background
Forensic age inference is of great significance in various fields such as judicial judgment, individual identification, international immigration, child support, competitive sports and the like. In criminal cases, forensic age inferences made on individuals of unknown age will directly affect the qualitative sentency of the criminal penalty and help characterize the biological characteristics of individuals of unknown identity. The correct choice of age inference indicators, methods and reference samples will directly affect the accuracy and reliability of age inference. DNA methylation is widely studied as one of the biomarkers associated with aging. In recent years, with the development of DNA methylation detection technology, age-related methylation sites have been studied more, and the accuracy and sensitivity of individual age estimation have been further improved. However, the amount of DNA and the complex sulfite conversion process make the method difficult to apply in trace or degraded samples.
The tooth has important significance in the age estimation, is little influenced by internal and external factors because the tooth has strong resistance and long preservation time, is easy to leave at the crime scene, and has important significance for the qualification of the case. The main methods of tooth-age inference currently include morphological, imaging, and histological and embryological methods. However, these methods have some limitations in terms of accuracy and application range. For tooth age inference there are measurements based on pulp, length and width of the tooth, but with larger errors, further studies are to be done to determine if they are within acceptable ranges. Also based on measurements of crown index, pulp and tooth body area, gender may have an effect on the results. Furthermore, measurements based on pulp and tooth volume improve spatial resolution and accuracy.
Disclosure of Invention
The invention provides application of miRNA marker combination in preparation of age prediction products, and the detection method has the advantages of small required sample size, high accuracy and short time consumption, and can be used for carrying out age prediction on tooth samples of people with ages of 20-70, and the error is within 4.5 years.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides application of miRNA marker combination in preparation of a predicted age product, wherein the miRNA marker combination is miR-22-3p and miR-29a-3p in teeth.
The invention provides a method for predicting age by using the miRNA marker combination, which comprises the following steps: (1) extracting sample tooth miRNA, and reversely transcribing to obtain cDNA; (2) Performing PCR amplification on the obtained cDNA to obtain Ct value, and calculating delta Ct by taking U6 as an internal reference Sample of The method comprises the steps of carrying out a first treatment on the surface of the (3) Taking a sample of 20-30 ages as a control group, and calculating delta Ct according to the methods of the steps (1) and (2) Control ；
(4) According to the relative expression level of gene=2 - (. DELTA.Ct sample-DELTA.Ct control) Calculating the relative expression quantity of miRNA;
(5) And carrying out data fitting analysis on the relative expression quantity of the miRNA by using a kNN model to obtain the predicted age.
Preferably, the products include reagents, chips, test strips and kits.
Preferably, in extracting the miRNA, the teeth are crushed using a dental grinder, and the pulp is extracted for subsequent experiments.
Preferably, the step of treating the dental pulp comprises: adding 0.5-1.5 mL of lysate and 8-12 mu L of proteinase k into dental pulp, and heating and vibrating for 1.5-2.5 h.
Preferably, when reverse transcription is carried out, the miRNA concentration is 480-520 mug/L.
Preferably, the PCR reaction system is: 20 mu L of total volume, 10 mu L of 2 times miRcute enhanced miRNA premix, 0.4 mu L of forward primer, 0.4 mu L of reverse primer, 1 mu L of cDNA template solution and 8.2 mu L of double-distilled water.
Preferably, the PCR reaction conditions are as follows: 95℃15min,94℃20s,64℃30s 5 cycles, 72℃34s,94℃20s 40 cycles, 60℃34s.
Preferably, the kNN model is constructed based on Orange software, the relative expression quantity and actual age of miR-22-3p and miR-29a-3p genes in a training set.
Compared with the prior art, the invention has the following beneficial effects:
the application method of the invention develops miRNA extraction and fluorescence quantitative PCR methods aiming at tooth samples to obtain PCR analysis data, and then adopts a kNN model to analyze the predicted age based on the PCR data of two miRNAs (miR-22-3 p and miR-29a-3 p), and the method has the characteristic of high detection sensitivity, can realize age prediction by only 0.05ng of tooth miRNA, and can predict the ages of tooth samples of people with ages of 20-70, and the error is within 4.5 years.
The method has high analysis efficiency, the completion time of miRNA extraction and fluorescence quantitative PCR is only 3 hours, 96 samples can be analyzed at most in one detection, and the detection experiment time is shortened. The application method has low cost, and the detection cost of each sample is within 100 yuan.
Detailed Description
The invention provides application of miRNA marker combination in preparation of a predicted age product, wherein the miRNA marker combination is miR-22-3p and miR-29a-3p in teeth. The miR-22-3p and miR-29a-3p genes are extracted from dental pulp.
The invention provides a method for predicting age by using the miRNA marker combination, which comprises the following steps: (1) extracting sample tooth miRNA, and reversely transcribing to obtain cDNA; (2) Performing PCR amplification on the obtained cDNA, calculating to obtain a Ct value, and calculating to obtain a delta Ct by taking U6 as an internal reference Sample of The method comprises the steps of carrying out a first treatment on the surface of the (3) Taking a sample of 20-30 ages as a control group, and calculating delta Ct according to the methods of the steps (1) and (2) Control The method comprises the steps of carrying out a first treatment on the surface of the (4) According to the relative expression level of gene=2 - (. DELTA.Ct sample-DELTA.Ct control) Calculating the relative expression quantity of miRNA; (5) And carrying out data fitting analysis on the relative expression quantity of the miRNA by using a kNN model to obtain the predicted age. The invention shortens the detection time by optimizing miRNA extraction, reverse transcription and PCR amplification and model prediction.
In the present invention, in order to avoid contamination of body fluids such as blood adhering to teeth, it is necessary to crush teeth by a dental grinder and take dental pulp for subsequent experiments when extracting miRNA. The treatment steps of the invention for dental pulp comprise: adding 0.5-1.5 mL of lysate and 8-12 mu L of proteinase k into dental pulp, and heating and vibrating for 1.5-3.5 h. The lysate and proteinase K can destroy the original structure of dental pulp, so that miRNA is fully exposed, and the subsequent extraction is convenient. The lysate and proteinase k according to the invention are not specifically described and are commercially available as is well known in the art.
In the invention, when reverse transcription is carried out, the miRNA concentration is set to 480-520 mug/L, and preferably 500 mug/L. The reverse transcription system for reverse transcription of the invention comprises: 10 [ mu ] L2X miRNA fluorescence quantitative reaction buffer (2X miRNA RT Reaction Buffer), 2 [ mu ] L miRNA fluorescence quantitative enzyme mixed solution (miRNA RT Enzyme Mix), 2 [ mu ] L total RNA template and 6 [ mu ] L ribonuclease double-distilled water; the reverse transcription reaction conditions were: 42 ℃ for 60min,95 ℃ for 5min and 4 ℃ for 5min. The reverse transcription product obtained by the invention is packaged and then stored at the temperature of minus 20 ℃.
In the invention, aiming at miR-22-3p, a forward primer used for PCR amplification is gcgctgccagttgaagaactgt (SEQ ID No. 1), and a reverse primer is derived from a miRcute enhanced miRNA fluorescence quantitative detection kit (SYBR Green, purchased from Tiangen Biochemical technology (Beijing)) Co., ltd.; for miR-29a-3p, a forward primer used for PCR amplification is gcgcgcaccatctgaaatcggt (SEQ ID No. 2), and a reverse primer is derived from a miRcute enhanced miRNA fluorescence quantitative detection kit (SYBR Green, purchased from Tiangen Biochemical technology (Beijing)) Co., ltd.; for internal reference U6, the forward primer used for PCR amplification was a has-U6 fluorescent quantitative PCR primer (purchased from Tiangen Biochemical technology (Beijing) Co., ltd., CD 201-0145), and the reverse primer was derived from a miRcute enhanced miRNA fluorescent quantitative detection kit (SYBR Green, purchased from Tiangen Biochemical technology (Beijing) Co., ltd.). The internal reference U6 is purchased from Tiangen Biochemical technology (Beijing) limited company.
In the invention, the PCR reaction system is as follows: 20 mu L of total volume, 10 mu L of 2X miRcute enhanced miRNA premix (SYBR & ROX), 0.4 mu L of forward primer, 0.4 mu L of reverse primer, 1 mu L of cDNA template solution and 8.2 mu L of double distilled water; the PCR reaction conditions were: 95℃15min,94℃20s,64℃30s 5 cycles, 72℃34s,94℃20s 40 cycles, 60℃34s. The PCR is fluorescence quantitative PCR, and Ct values of miR-22-3p, miR-29a-3p and U6 of the sample can be calculated after the PCR test is finished.
In the invention, the relative gene expression amounts of miR-22-3p and miR-29a-3p, which are calculated by using the above formula and are obtained through the Ct values of miR-22-3p, miR-29a-3p and U6 measured by fluorescence quantitative PCR, are subjected to data fitting analysis by using a kNN model. The kNN model is constructed based on Orange software, the relative expression quantity and the actual age of miR-22-3p and miR-29a-3p genes in a training set. The training set provided by the invention is 20 healthy male tooth samples in North China for determining the age. The parameters of the data fitting training by utilizing the kNN model are the neighbor number (Number of neighbors): 5, a step of; metric (Metric): euclidean metric (Euclidean); weight (Weight): unified (uniformity). The invention adopts Orange software evaluation analysis for data and model analysis.
In the invention, the age prediction analysis by using the constructed kNN model comprises the following steps: (1) Entering an Orange software operation page, and introducing test data (the relative gene expression amounts of miR-22-3p and miR-29a-3 p) at the file (1); (2) And (3) double-clicking the kNN model, namely popping up a parameter adjustment page, wherein the parameter is set as the neighbor number (Number of neighbors): 5, a step of; metric (Metric): euclidean metric (Euclidean); weight (Weight): unification (uniformity); (3) Selecting to test the test data (Test on test data), namely predicting the test set data by using the trained model; (4) And (5) carrying out double-click prediction (predictors) to obtain an actual result of the test set prediction, and obtaining the predicted age.
In the present invention, all components are commercially available products well known to those skilled in the art unless specified otherwise.
The technical solutions of the present invention will be clearly and completely described in the following in connection with the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
1. Extraction of tooth miR-22-3p and miR-29a-3p genes
After collecting the tooth materials, the preparation work of miR-22-3p and miR-29a-3p genes can be carried out. If the storage is needed, the product should be placed in a cool and ventilated place.
(1) The teeth were crushed using a dental mill, and the pulp was taken and placed in a ribonuclease-free tube.
(2) To the tube, 1mL of lysate (from Tiangen Biochemical technology (Beijing) Co.) and 10. Mu.L of proteinase k (from Biyun Tian Biotechnology) were added and shaken in a water bath with shaking for 3 hours.
(3) And standing at room temperature for 5min to completely separate the nucleic acid protein complex.
(4) Centrifuging at 12000rpm (13400 Xg) for 10min, taking supernatant, and transferring into a new centrifuge tube without ribonuclease.
(5) 200. Mu.L of chloroform was added thereto, the tube was covered with a cap, vigorously shaken for 15s, and left at room temperature for 5 minutes.
(6) Centrifuge at 12000rpm (13400 Xg) for 15min at room temperature, and divide the sample into three layers: a yellow organic phase, an intermediate layer and a colorless aqueous phase, the aqueous phase was transferred to a new tube.
(7) Measuring the volume of the transfer liquid, slowly adding 1/3 of the volume of the transfer liquid into the absolute ethyl alcohol, and uniformly mixing; transferring the obtained solution and the precipitate into an adsorption column miRspin, standing for 2min at room temperature, centrifuging at 12000rpm (13400 Xg) at room temperature for 30, centrifuging, discarding the adsorption column miRspin, and retaining the effluent.
(8) Measuring the volume of the effluent, slowly adding 2/3 of the volume of absolute ethyl alcohol into the effluent, and uniformly mixing; transferring the obtained solution and the precipitate into an adsorption column miralute, standing for 2min at room temperature, centrifuging at 12000rpm (13400 Xg) for 30s at room temperature, centrifuging, discarding the effluent, and reserving the adsorption column miralute.
(9) 500 mu L of deproteinized solution MRD is added into an adsorption column mirilute, the mixture is kept stand at room temperature for 2min, and the mixture is centrifuged at 12000rpm (to 13400 Xg) for 30s at room temperature, and the waste liquid is discarded.
(10) 500. Mu.L of rinse solution RW is added into an adsorption column mirilute, the mixture is kept stand at room temperature for 2min, the mixture is centrifuged at 12000rpm (13400 Xg) for 30s at room temperature, and the waste liquid is discarded and the steps are repeated.
(11) The column mirilute was placed in a 2ml collection tube and centrifuged at 12000rpm (13400 Xg) for 1min at room temperature to remove residual liquid.
(12) Transferring the adsorption column mirilute into a new 1.5ml centrifuge tube without ribonuclease, adding 25 mu L of double distilled water without ribonuclease, standing for 2min at room temperature, and centrifuging at 12000rpm (13400 Xg) for 2min to obtain a solution.
2. Reverse transcription reaction
(1) The concentration of the obtained solution was measured by using a spectrophotometer, and the concentrations of miR-22-3p and miR-29a-3p were set at 500. Mu.g/L.
(2) And (3) configuring a 20 [ mu ] L reverse transcription reaction system: 10 [ mu ] L2X miRNA fluorescence quantitative reaction buffer (purchased from Tiangen Biochemical technology (Beijing) Co., ltd.), 2 [ mu ] L miRNA fluorescence quantitative enzyme mixed solution (purchased from Tiangen Biochemical technology (Beijing) Co., ltd.), 2 [ mu ] L total miR-22-3p or miR-29a-3p gene solution, and 6 [ mu ] L ribonuclease double-distilled water.
(3) The reverse transcription reaction was performed under the following conditions: 42 ℃ 60min,95 ℃ 5min,4 ℃ 5min.
(4) Packaging the reverse transcription product, and storing at-20deg.C.
3. PCR reaction
(1) Appropriate amount of cDNA was taken and diluted 10-fold with sterile deionized water.
(2) And (3) configuring a 20 [ mu ] L PCR reaction system: 10 [ mu ] L2 XmiRcute enhanced miRNA premix (SYBR & ROX), 0.4 [ mu ] L forward primer, 0.4 [ mu ] L reverse primer, 1 [ mu ] L cDNA template solution and 8.2 [ mu ] L double distilled water; the forward primer involved in the process is shown in Table 1, and the reverse primer is a kit miRcute enhanced miRNA fluorescence quantitative detection kit (SYBR Green) (purchased from Tiangen Biochemical technology (Beijing)) Co.
(3) The experiment was completed using a fluorescent quantitative PCR instrument, and the PCR reaction experiment was performed under the following conditions:
15min at 95 ℃, 20s at 94 ℃, 30s 5 cycles at 64 ℃, 34s at 72 ℃, 20s 40 cycles at 94 ℃,
60℃ 34s。
(4) And after the experiment is finished, calculating the Ct values of miR-22-3p and miR-29a-3p of each sample.
(5) The Ct value of internal reference U6 (purchased from Tiangen Biochemical technologies (Beijing) Co., ltd.) was calculated using the above method.
TABLE 1 PCR primers
| Gene | Forward primer |
| miR-22-3p | gcgctgccagttgaagaactgt（SEQ ID No.1） |
| miR-29a-3p | gcgcgcaccatctgaaatcggt（SEQ ID No.2） |
| U6 | Has-U6 fluorescent quantitative PCR primer (purchased from Tiangen Biochemical technology (Beijing) Co., ltd., CD 201-0145) |
4. Analysis of PCR results
Obtaining Ct of each group by fluorescence quantitative PCR, wherein U6 is used as an internal reference; delta Ct Sample of =Ct miR-22-3p or miR-29a-3p -Ct U6 The relative expression amounts of genes among different groups were calculated according to the following formula: relative expression level of gene=2 - (. DELTA.Ct sample-DELTA.Ct control) 。
Wherein DeltaCt of miR-22-3p and miR-29a-3p Control Obtaining: selecting teeth samples of healthy men in North China of 20 years old, 21 years old, 24 years old, 25 years old, 26 years old, 27 years old and 28 years old, obtaining Ct values of the teeth samples by adopting the method, and obtaining a formula delta Ct=Ct based on the Ct value of the U6 internal reference miR-22-3p or miR-29a-3p -Ct U6 Calculating delta Ct of 7 samples, wherein the average value of the delta Ct values of 7 samples is delta Ct Control The value may be used as it is in calculating the relative expression amount. The results are shown in Table 2 below.
TABLE 2 DeltaCt of miR-22-3p and miR-29a-3p Control Value of
| miRNA | △Ct Control |
| miR-22-3p | 0.678909421 |
| miR-29a-3p | 6.020330407 |
Example 2 age prediction
(1) 20 healthy male tooth samples in North China with determined ages are selected, actual ages are counted, and Ct values corresponding to miR-22-3p, miR-29a-3p and U6 in each sample are calculated by using the method of example 1, and the results are shown in Table 3. The data from this table are combined with the ΔCt in example 1 Control And calculating the relative expression quantity of miR-22-3p and miR-29a-3p in each sample according to the value and the formula.
TABLE 3 Ct values for 20 samples
(2) Based on the relative expression amounts of miR-22-3p and miR-29a-3p obtained in the step (1), an Orange software evaluation Support Vector Machine (SVM) algorithm, an iterative algorithm (AdaBoost), a Random Forest (Random Forest) algorithm, a k-neighbor (kNN) algorithm, a decision Tree (Tree) model and a gradient lifting (Gradient Boosting) algorithm are applied. Age parameter adjustment was performed using 20-fold cross-validation, with all models ultimately validated using a subset of validation (blind). To evaluate the different statistical models, the residuals were compared using Mean Absolute Error (MAE) analysis. The evaluation results are shown in Table 4. As can be seen from the results of Table 4, the kNN model has the best effect, the error is 4.47 years old, R 2 The value reached 0.899.
Table 4 results of evaluation of fitting data to different machine learning models
(3) 10 healthy men in North China with determined ages are selected, and a kNN model constructed by the training set is used as an evaluation model for predicting the ages. The relative expression levels of miR-22-3p and miR-29a-3p of the 10 samples were obtained according to the procedure described in example 1, and were input into the kNN model database, and data fitting parameters using kNN were set as the number of neighbors (Number of neighbors): 5, metric (Metric): euclidean metric (Euclidean), weight (Weight): unified (uniformity), the predicted age can be obtained, and the results are shown in table 5. As shown in the results of Table 5, the sample age predicted by the method is similar to the actual age, the average error is 4.28, and the average error is similar to the error obtained by the kNN model, which indicates that the prediction method has good stability and sensitivity and can be used for age prediction.
Table 5 10 sample age prediction statistics
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (3)
1. A method for predicting age by using miRNA marker combination is characterized in that,
the miRNA markers are combined into miR-22-3p and miR-29a-3p in teeth;
the method for predicting age by using miRNA marker combination comprises the following steps:
(1) Crushing teeth, taking dental pulp, extracting miRNA in the dental pulp, and performing reverse transcription to obtain cDNA;
(2) Performing PCR amplification on the obtained cDNA to obtain Ct value, and using U6 as internal reference and adopting formula delta Ct Sample of =Ct miR -22-3p or miR-29a-3p -Ct U6 Calculating delta Ct Sample of ；
(3) Taking a sample of 20-30 ages as a control group, and calculating delta Ct according to the methods of the steps (1) and (2) Control ；
(4) According to the relative expression level of gene=2 - (. DELTA.Ct sample-DELTA.Ct control) Calculating the relative expression quantity of miR-22-3p or miR-29a-3p;
(5) Based on the relative expression quantity of miR-22-3p and miR-29a-3p, carrying out data analysis by using a kNN model to obtain a predicted age;
the step of treating dental pulp in the step (1) comprises the following steps: adding 0.5-1.5 mL of lysate and 8-12 mu L of proteinase k into dental pulp, and heating and vibrating for 1.5-3.5 h;
when reverse transcription is carried out in the step (1), the miRNA concentration is determined to be 480-520 mug/L;
the specific steps in the step (5) comprise: inputting the calculated miR-22-3p and miR-29a-3p relative expression quantity in the step (4) into a kNN model database, and setting data fitting parameters by using kNN as neighbor numbers: 5, measuring: euclidean metric, weight: unified, the predicted age can be obtained;
the kNN model is constructed based on the Orange software, the miR-22-3p gene relative expression quantity and the actual age of the miR-29a-3p gene in the training set.
2. The method of claim 1, wherein the PCR reaction system is: 20 mu L of total volume, 10 mu L of 2 times miRcute enhanced miRNA premix, 0.4 mu L of forward primer, 0.4 mu L of reverse primer, 1 mu L of cDNA template solution and 8.2 mu L of double-distilled water.
3. The method of claim 1, wherein the PCR reaction conditions are: 95℃15min,94℃20s,64℃30s 5 cycles, 72℃34s,94℃20s 40 cycles, 60℃34s.
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| CN111172261A (en) * | 2020-03-16 | 2020-05-19 | 北京市理化分析测试中心 | Age prediction method based on blood trace miRNA |
| CN111356774A (en) * | 2017-06-26 | 2020-06-30 | 维也纳自然资源与生命科学大学 | Novel biomarkers for detecting senescent cells |
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| CN111356774A (en) * | 2017-06-26 | 2020-06-30 | 维也纳自然资源与生命科学大学 | Novel biomarkers for detecting senescent cells |
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