A methylation biomarker for metabolic diseases and its application

Abstract

The present invention belongs to the field of medical diagnosis technology, and provides a methylation biomarker for metabolic diseases and its application: detecting the methylation level of at least one of chr1:119708470-119708810, chr3:49653374-49653573, chr3:125029184-125029370, chr7:75987317-75987513, chr10:6145842-6146049, chr17:47942423-47942577, chr19:46778792-46779015 from the sample to be tested. The high methylation level of the biomarker provided by the present invention is highly correlated with metabolic abnormalities, has good accuracy and distinguishing ability, can identify people with metabolic abnormalities in early childhood, and has important clinical significance for early diagnosis and treatment of related diseases.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical diagnosis, and specifically relates to a methylation biomarker for metabolic diseases and metabolic abnormalities and applications thereof. Background Art

[0002] The disclosure of this background information is intended to enhance understanding of the general background of the invention and should not necessarily be taken as an acknowledgment or any form of suggestion that this information constitutes the prior art already known to a person skilled in the art.

[0003] In modern society, metabolic diseases have become one of the main chronic diseases threatening human health, covering various types such as diabetes, obesity, fatty liver, metabolic syndrome, etc. These diseases not only have a high incidence rate, but also have great harm. They have been listed as non-communicable diseases with global priority prevention and control by the World Health Organization. It is worth noting that the incidence of metabolic diseases is becoming younger, especially in children and adolescents, whose incidence of metabolic diseases and metabolic abnormalities has risen sharply. Studies have shown that the occurrence of these childhood metabolic abnormalities will have a serious impact on their health in adulthood, leading to the early onset and aggravation of various metabolic diseases such as diabetes, hypertension, metabolic syndrome, and may affect the health of the next generation through intergenerational transmission. Therefore, early identification and prevention and control of metabolic diseases in children and adolescents are particularly important.

[0004] Studies in recent years have shown that metabolic diseases in adulthood can be caused by adverse environmental exposure during the embryonic or developmental period. These adverse environmental factors can have long-term effects on embryonic and fetal development through epigenetic mechanisms. For example, metabolic diseases such as obesity, diabetes, and polycystic ovary syndrome can be inherited by their offspring, thereby increasing the risk of metabolic diseases in future generations. However, the current prediction and screening methods for metabolic diseases or metabolic abnormalities in children and adolescents are not yet mature, and there is still a lack of effective biomarkers in the clinic to identify high-risk groups early. The lack of such detection methods not only limits the early prevention and control of metabolic diseases, but also brings huge challenges and burdens to future public health and medical resources.

[0005] Studies have shown that the occurrence of developmental metabolic diseases is mainly mediated by epigenetic mechanisms, among which DNA methylation is one of the most important epigenetic mechanisms. DNA methylation profoundly affects the individual's response to the external environment by regulating gene expression, and is involved in the pathophysiological development of metabolic diseases. In recent years, methylation biomarkers have shown important clinical application value in risk screening, early diagnosis and efficacy evaluation of various diseases. For example, the methylation level of specific gene loci has been applied to early screening of tumors and risk prediction of cardiovascular diseases. However, the research on specific methylation biomarkers for metabolic diseases or metabolic abnormalities in children and adolescents is still in its infancy, and there is an urgent need to develop more accurate and efficient methylation biomarkers to fill the gap in this important field, so as to achieve early intervention and personalized management of metabolic diseases. Summary of the invention

[0006] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a group of DNA methylation biomarkers or a combination thereof to perform risk screening, early diagnosis, prediction or prevention and treatment of metabolic diseases or metabolic abnormalities, and promote long-term metabolic health management of individuals.

[0007] To achieve the above purpose, the present invention adopts the following technical solution.

[0008] A kit for predicting, screening or diagnosing a metabolic disease or metabolic abnormality in a subject, the kit comprising detecting from a sample to be tested: 119708470-119708810 site of chromosome 1 (chr1: 119708470-119708810), 49653374-49653573 site of chromosome 3 (chr3: 49653374-49653573), 125029184-125029370 site of chromosome 3 (chr3: 125029184-125029370), 75987317-75987513 site of chromosome 7 (chr7: 75987317-75987513), a reagent for the methylation degree of at least one of the 6145842-6146049 site on chromosome 10 (chr10:6145842-6146049), the 47942423-47942577 site on chromosome 17 (chr17: 47942423-47942577), and the 46778792-46779015 site on chromosome 19 (chr19: 46778792-46779015);

[0009] The metabolic diseases include diabetes, impaired fasting glucose, impaired glucose tolerance, obesity, hyperlipidemia, fatty liver, polycystic ovary syndrome and metabolic syndrome;

[0010] The metabolic abnormalities include abnormal islet function, insulin resistance, overweight, abnormal blood sugar regulation, and dyslipidemia.

[0011] Preferably, the kit comprises a reagent for detecting the methylation degree of at least one of chr3: 125029184-125029370 and chr7: 75987317-75987513 from a sample to be tested.

[0012] The above sites are the sites of the hg38 version of the human reference genome.

[0013] The source of the sample to be tested is not limited. For the convenience of sampling and specific screening purposes, it can be selected from one or more of blood, saliva, urine, urine exfoliated cells, urine sediment, feces, semen, follicular fluid, sperm, egg, preimplantation embryo, amniotic fluid, and organoids; the blood is any one of whole blood, plasma, and serum; preferably, the source of the sample to be tested is selected from blood or sperm. The preimplantation embryo refers to an embryo that has developed to different stages in vitro within 14 days after fertilization, or a cell sample taken from the above-mentioned preimplantation embryo.

[0014] By comparing the methylation levels of the above-mentioned sites in the sample to be tested and comparing them with the same samples from normal subjects without metabolic diseases or metabolic abnormalities, if there is a significant difference or exceeds the threshold level, it indicates that the subject has a metabolic disease or metabolic abnormality or is at a higher risk.

[0015] The reagents are reagents used in the following methods for detecting methylation levels: one or more of fluorescent quantitative PCR, methylation-specific PCR, methylation immunoprecipitation PCR, digital PCR, DNA methylation chip, whole genome methylation sequencing, targeted DNA methylation sequencing, pyrophosphate sequencing, bisulfite conversion sequencing, simplified bisulfite sequencing, methylation enrichment sequencing and DNA methylation mass spectrometry.

[0016] The present invention has the following advantages:

[0017] The methylation biomarkers provided by the present invention are highly correlated with metabolic abnormalities, can be used for the diagnosis, prediction, screening or prevention of metabolic diseases or metabolic abnormalities, have good accuracy and discrimination ability, are easy to detect and have good stability, and can identify people with metabolic abnormalities in early childhood. They are of great clinical significance for the early diagnosis and treatment of metabolic diseases and have great practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Random blood glucose levels of mice in the control group and model group at 8 weeks of age (a) and 32 weeks of age (b);

[0019] Figure 2The glucose tolerance results of mice in the control group and model group at 8 weeks of age (a) and 32 weeks of age (b);

[0020] Figure 3 The HOMA-β results of mice in the control group and model group at 8 weeks of age (a) and 32 weeks of age (b);

[0021] Figure 4 The differentially methylated region levels of mice with metabolic diseases, where (a)-(g) are DMR1-7, respectively;

[0022] Figure 5 The levels of DNA methylation markers in peripheral blood of children with metabolic abnormalities and control children, where (a)-(g) are DMR1-7, respectively;

[0023] Figure 6 To investigate the discriminatory efficacy of methylation biomarkers in identifying children with metabolic abnormalities. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with embodiments and drawings, but the present invention is not limited by the following embodiments.

[0025] Example 1 Discovery of DNA methylation markers in animal models

[0026] 1. Construction of animal models of developmental metabolic diseases

[0027] (1) Wild-type C57BL / 6J female mice (purchased from Beijing Weitong Lihua Company) were in proestrus or estrus by vaginal cytology and mated with males overnight. The morning of the detection of post-mating plugs was considered E0.5. Pregnant female mice were randomly divided into two groups: modeling group: 250μg of dihydrotestosterone was dissolved in a mixture of 10μL benzyl benzoate and 90μL corn oil, and injected subcutaneously in the interscapular area of ​​pregnant mice every day from E16.5 to E18.5; control group: the same pregnant mice were subcutaneously injected with a mixture of 10μL benzyl benzoate and 90μL corn oil every day.

[0028] (2) The offspring mice were weaned on the 21st day after birth. The random blood glucose levels of the 8-week-old and 32-week-old offspring mice were measured at 9:00 a.m.

[0029] (3) GTT test was performed on 8-week-old and 32-week-old offspring mice after fasting for 16 h. Blood glucose was measured before intraperitoneal injection of 2 g / kg glucose and 15, 30, 60, and 120 min after administration to detect changes in glucose tolerance.

[0030] (4) After 8-week-old and 32-week-old offspring mice were fasted for 16 hours, fasting blood glucose was tested and blood was collected from the tail vein. Fasting insulin levels were measured using chemiluminescent immunoassay and HOMA-β was calculated.

[0031] Glucose metabolism phenotypes of model mice were tested at different ages. Figure 1 Monitoring of blood glucose levels showed that the blood glucose levels of mice in the model group were significantly reduced at the early stage of 8 weeks of age. As they aged to 32 weeks of age, their blood glucose levels were significantly higher than those of the control group, and symptoms of hyperglycemia appeared.

[0032] Glucose tolerance test was performed on model mice at different ages. Figure 2 It can be seen that the glucose tolerance of the mice in the modeling group was reduced at the early age of 8 weeks. As they aged to 32 weeks, their glucose tolerance was significantly higher than that of the control group, and symptoms of impaired glucose tolerance appeared.

[0033] The HOMA-β values ​​of model mice were measured at different ages to evaluate pancreatic islet function. Figure 3 It can be seen that the HOMA-β value of the model group mice at the early age of 8 weeks was significantly increased, and as they aged to 32 weeks, the HOMA-β value was significantly lower than that of the control group, indicating abnormal and disordered pancreatic islet function.

[0034] The above results show that the animal model has multiple metabolic abnormalities in youth, and gradually progresses to the appearance of diabetes in the long term with age, indicating that the metabolic disease model has been successfully constructed.

[0035] 2. Whole-genome methylation detection of mouse sperm

[0036] Compared with whole blood sources, gametes, especially sperm, have higher biomarker discovery efficiency due to their high cell purity and lack of mixed cell type influence. The epididymal tail sperm of the control group and model group mice were isolated and purified at the early 8 weeks of age, and upstreamed in a culture medium at 37°C. The mature sperm from the upstream was aspirated, centrifuged, washed, and then frozen.

[0037] The sperm of the control group and model group mice were placed in a lysis buffer containing proteinase K and incubated at 55°C for 5 hours. Then, genomic DNA was extracted using the TIANamp genomic DNA kit (Tiangen Company). The concentration and integrity of the DNA were evaluated using a Nessler spectrophotometer and agarose gel electrophoresis, respectively. Then, a DNA library for enzymatic methylation sequencing was prepared as follows: the genomic DNA was broken into 100bp-300bp by sonication and then purified using the MiniElute PCR purification kit (QIAGEN). The purified DNA was bisulfite converted using the EpiArt DNA enzymatic methylation kit (Vazyme). The fragmented DNA was end-repaired, and then the genomic fragments were ligated to the methylation sequencing adapter. Finally, the converted DNA fragments were PCR amplified and sequenced using the Illumina HiSeqTM 2500.

[0038] 3. Analysis of differential methylation in sperm of model mice

[0039] The bisulfite sequencing reads of the sperm DNA samples of the above-mentioned modeling mice were used to remove adapters and low-quality bases using fastp software. The obtained reads were aligned to the mouse reference genome (Ensembl_release110) using BSMAP software, and methylated cytosine was detected using the correction algorithm described by Lister R. et al. The methylation level was calculated based on the percentage of methylated cytosine in the whole genome, each chromosome, and different regions of the genome. In order to determine the differentially methylated regions (DMRs) between the two samples, the minimum read coverage of the methylation status of a single base was set to 4, the number of GCs in each window was ≥ 5, the absolute value of the methylation ratio difference was ≥ 0.1, and P≤ 0.01. The DMRs between the two groups of samples are shown in Table 1.

[0040] Table 1 Model mouse DMR locations and corresponding human DMR locations

[0041]

[0042] By comparing the DMR of the two groups of samples, Figure 4 It can be seen that DMR1-DMR7 in the sperm DNA samples of the modeling group were significantly higher than those of the control group. The above data show that these 7 differentially methylated DNA regions can be used as methylation biomarkers to distinguish metabolic abnormalities or metabolic disease groups and predict the risk of long-term metabolic diseases.

[0043] Example 2 Validation of biomarkers in people with metabolic abnormalities

[0044] In order to verify whether the DMR found in Example 1 can be used for the diagnosis and prediction of metabolic diseases and metabolic abnormalities, the offspring born to female subjects with polycystic ovary syndrome and hyperandrogenism were selected for clinical verification:

[0045] 1. Enrollment of subjects and sample collection

[0046] According to the approval of the Ethics Committee of Shandong University, the samples of the subjects were collected from the offspring cohort of the Reproductive Hospital affiliated to Shandong University, and all mothers participating in the study had obtained written informed consent. This study included women with pre-pregnancy basal hormone level testing and their offspring. Maternal hormone levels were measured by chemiluminescence assay on the third day of the menstrual cycle, and maternal hyperandrogenism (serum total testosterone>48.1ng / dL) was defined according to the instructions of the test kit. These women also met the clinical diagnosis of polycystic ovary syndrome. The offspring born of these women through assisted reproductive technology were followed up for a long time, and the children who completed the growth and development assessment and provided blood samples for the measurement of metabolic biochemical parameters were included in this study. The control group was the offspring born to mothers without hyperandrogenism and other chronic diseases. A total of 20 offspring born to mothers with hyperandrogenism and 20 offspring of the age-matched control group were included in the final analysis. Their clinical information and metabolic indicators are shown in Table 2. Peripheral blood samples were collected from the subjects in the above groups in anticoagulant tubes and stored in a refrigerator at -80℃.

[0047] Table 2 Clinical information and metabolic indicators of subjects

[0048]

[0049] Table 2 compares and analyzes multiple metabolic indicators of the two groups of offspring. It can be seen that compared with the control group, the offspring of the Kaohsiung group had a significantly higher body mass index, indicating that they were overweight; the fasting insulin and HOMA-IR index were significantly increased, indicating that they had insulin resistance; the HOMA-β index was significantly increased, indicating that they had abnormal pancreatic β-cell function. These indicators all indicate that the blood sugar regulation of the offspring of the Kaohsiung group was impaired. In addition, the average serum total cholesterol and triglyceride levels of the offspring of the Kaohsiung group were also higher than those of the control group, indicating that they were at risk of dyslipidemia. The abnormalities of multiple metabolic indicators such as weight gain, decreased insulin sensitivity, and glucose and lipid metabolism disorders indicate that the offspring of the Kaohsiung group have a significantly increased risk of metabolic diseases such as obesity and diabetes in the long term.

[0050] 2. Determination of DNA methylation biomarker levels in peripheral blood of subjects

[0051] (1) Extraction of genomic DNA from peripheral blood

[0052] Peripheral blood genomic DNA was extracted using the QIAGEN Blood Genomic DNA Mini Kit. 200 µL of plasma samples from children were added to the lysis buffer containing proteinase K and incubated at 56°C for 10 minutes. Ethanol was added to the lysate and loaded onto the QIAamp spin column. Washing buffer was used to remove impurities, and purified DNA was obtained after elution.

[0053] (2) Methylation immunoprecipitation

[0054] Agarose gel electrophoresis was used to detect the size of ultrasonically broken DNA fragments, and the DNA fragment range was required to be 100bp-600bp. The DNA samples were divided into two groups according to Input and IP. The Input group was placed at -20℃ for standby use. The IP group used magnetic beads and specific antibodies for methylated cytosine to immunoprecipitate fragments of methylated DNA enriched in the genome. After overnight incubation at 4℃, the specific 5mc methylated DNA samples were eluted from the magnetic beads.

[0055] (3) Methylation quantitative PCR assay

[0056] Configure 25 µL PCR amplification system for real-time quantitative PCR detection:

[0057] Table 3 PCR amplification system

[0058] ;

[0059] Perform real-time quantitative PCR assays according to the following procedure:

[0060] Table 4 PCR detection procedure

[0061] .

[0062] (4) Analysis of methylation marker levels

[0063] The DNA samples of the Input group were used as templates, and the specific primers of the gene to be tested were used for qPCR detection to obtain Ct (input). The DNA samples of the IP group were used as templates for detection to obtain Ct (IP). The methylation level was calculated according to the following formula:

[0064] The differential methylation region level of the gene to be tested is % = 2 [Ct (input)-3.32-Ct (IP)] ×100.

[0065] Depend on Figure 5 It can be seen that compared with the offspring of the control group, the levels of the seven DNA methylation regions in the blood of the offspring of the Kaohsiung group were significantly increased, which was well verified in the clinical metabolic abnormality children population. The above data further show that the selected seven DNA regions can be used as methylation biomarkers to screen or predict the risk of metabolic diseases or metabolic abnormalities.

[0066] 3. The diagnostic efficacy of DNA methylation markers in people with metabolic abnormalities

[0067] ROC curves were drawn based on the DNA methylation marker levels in the peripheral blood of children in each group of subjects. The AUC values ​​for independently distinguishing people with metabolic abnormalities were shown in Figure 6Among them, the AUC value of DMR1 was 0.6875, with a corresponding sensitivity of 0.5 and a specificity of 0.85; the AUC value of DMR2 was 0.74, with a corresponding sensitivity of 0.8 and a specificity of 0.65; the AUC value of DMR3 was 0.67, with a corresponding sensitivity of 0.8 and a specificity of 0.65; the AUC value of DMR4 was 0.6637, with a corresponding sensitivity of 0.7 and a specificity of 0.65; the AUC value of DMR5 was 0.8112, with a corresponding sensitivity of 0.7 and a specificity of 0.9; the AUC value of DMR6 was 0.7388, with a corresponding sensitivity of 0.9 and a specificity of 0.65; and the AUC value of DMR7 was 0.855, with a corresponding sensitivity of 0.85 and a specificity of 0.8. The two markers with the best diagnostic performance were combined, and the results showed that the AUC value of the DMR5+DMR7 combination was 0.945, with a corresponding sensitivity of 1 and a specificity of 0.75. The performance results showed that the above methylation markers all had good diagnostic differentiation efficiency for metabolic abnormalities. The above results further verified that the above DMR1-7 can be used as effective methylation biomarkers for metabolic abnormalities and long-term metabolic diseases in the population.

[0068] In summary, the methylation biomarkers provided by the present invention can effectively distinguish the control group from the metabolic abnormality group, and have extremely high practical application value in the early diagnosis, prediction, screening or prevention and treatment of metabolic diseases and metabolic abnormalities. They have high accuracy, high specificity and high sensitivity, and are suitable for routine screening, early risk prediction and disease management of large populations.

[0069] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. Use of a reagent for detecting the methylation degree of a differentially methylated region in a sample to be tested in the preparation of a kit for diagnosing metabolic abnormalities in offspring of patients with hyperandrogenism, characterized in that: The differentially methylated region is the 75987317-75987513 site of chromosome 7 of the human reference genome hg38 version; The metabolic abnormality is selected from the group consisting of pancreatic islet dysfunction, insulin resistance, overweight, abnormal blood sugar regulation and dyslipidemia.

2. The kit according to claim 1, characterized in that The source of the sample to be tested is selected from one or more of blood, saliva, urine, urine exfoliated cells, urine sediment, feces, semen, follicular fluid, sperm, and egg; the blood is any one of whole blood, plasma, and serum.

3. The kit according to claim 1, characterized in that The source of the sample to be tested is selected from blood or sperm.

4. The kit according to claim 1, characterized in that The reagents are reagents used in the following methods for detecting methylation levels: one or more of fluorescent quantitative PCR, methylation-specific PCR, methylation immunoprecipitation PCR, digital PCR, DNA methylation chip, whole genome methylation sequencing, targeted DNA methylation sequencing, pyrophosphate sequencing, bisulfite conversion sequencing, simplified bisulfite sequencing and DNA methylation mass spectrometry.

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