Methylated biomarker for metabolic diseases and application of methylated biomarker

Abstract

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

Description

[0001] This case is a divisional application of Chinese Patent Application No. 202411874114.9. The filing date of the original application is December 19, 2024, and the invention title is: Methylation Biomarkers for Metabolic Diseases and Their Applications. Technical Field

[0002] The present invention belongs to the technical field of medical diagnosis, and specifically relates to methylation biomarkers for metabolic diseases and metabolic abnormalities and their applications. Background Art

[0003] Disclosing the information of this background art is intended to enhance the understanding of the overall background of the present invention, and it is not necessarily to be regarded as an admission or an implication in any form that this information constitutes the prior art that has been well-known to those of ordinary skill in the art.

[0004] 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, and metabolic syndrome. These diseases not only have a high incidence but also great harm, and have been listed by the World Health Organization as non-communicable diseases that require global priority prevention and control. It is worth noting that the population suffering from metabolic diseases is tending to be younger, especially among children and adolescents, and the incidence of metabolic diseases and metabolic abnormalities in them has increased sharply. Research shows 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, and metabolic syndrome, and may affect the health of the next generation through intergenerational transmission. Therefore, the early identification and prevention and control of metabolic diseases in children and adolescents are particularly important.

[0005] Recent studies have shown that adult metabolic diseases can originate from adverse environmental exposures during the embryonic or developmental period. These adverse environmental factors will have a long-term impact on embryonic and fetal development through epigenetic mechanisms. For example, metabolic diseases such as obesity, diabetes, and polycystic ovary syndrome can be inherited to their offspring, thus increasing the risk of metabolic diseases in the next generation. However, currently, the means for predicting and screening metabolic diseases or metabolic abnormalities in children and adolescents are not yet mature, and there is a lack of effective biomarkers in clinical practice to early identify high-risk populations. The lack of such detection means not only limits the early prevention and control management of metabolic diseases but also brings huge challenges and burdens to future public health and medical resources.

[0006] Studies have shown that the occurrence of developmental origin metabolic diseases is mainly mediated by epigenetic mechanisms, among which DNA methylation is one of the most important epigenetic mechanisms. DNA methylation profoundly affects an individual's response to the external environment by regulating gene expression and is involved in the pathophysiological development process of metabolic diseases. In recent years, methylation biomarkers have shown important clinical application value in the risk screening, early diagnosis, and efficacy evaluation of various diseases. For example, the methylation levels at specific gene loci have been applied in fields such as early cancer screening and cardiovascular disease risk prediction. 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 this important gap and achieve early intervention and personalized management of metabolic diseases. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a set of DNA methylation biomarkers or their combinations for risk screening, early diagnosis, prediction, or prevention and treatment of metabolic diseases or metabolic abnormalities, and to promote the long-term metabolic health management of individuals.

[0008] To achieve the above purpose, the present invention adopts the following technical solutions.

[0009] A kit for predicting, screening, or diagnosing metabolic diseases or metabolic abnormalities in a subject, the kit comprising reagents for detecting the methylation degree of at least one of the following sites in a test sample: the site at 119708470-119708810 on chromosome 1 (chr1: 119708470-119708810), the site at 49653374-49653573 on chromosome 3 (chr3: 49653374-49653573), the site at 125029184-125029370 on chromosome 3 (chr3: 125029184-125029370), the site at 75987317-75987513 on chromosome 7 (chr7: 75987317-75987513), the site at 6145842-6146049 on chromosome 10 (chr10: 6145842-6146049), the site at 47942423-47942577 on chromosome 17 (chr17: 47942423-47942577), and the site at 46778792-46779015 on chromosome 19 (chr19: 46778792-46779015); The metabolic diseases include diabetes, impaired fasting glucose, impaired glucose tolerance, obesity, hyperlipidemia, fatty liver, polycystic ovary syndrome, and metabolic syndrome; The metabolic abnormalities include abnormal pancreatic islet function, insulin resistance, overweight, abnormal blood glucose regulation, and dyslipidemia.

[0010] Preferably, the kit contains reagents for detecting the methylation degree at least at one of chr3: 125029184-125029370 and chr7: 75987317-75987513 in a sample to be tested.

[0011] The above sites are sites of the human reference genome hg38 version.

[0012] The source of the sample to be tested is not limited and can be selected from one or more of blood, saliva, urine, urine exfoliated cells, urinary sediment, feces, semen, follicular fluid, sperm, egg, pre-implantation embryo, amniotic fluid, and organoids for convenient sampling and specific screening purposes; 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 pre-implantation embryo refers to an embryo developed in vitro to different stages within 14 days after fertilization, or a cell sample taken from the above pre-implantation embryo.

[0013] By comparing the methylation levels of the above sites in the sample to be tested and comparing with the same samples from normal subjects without metabolic diseases or metabolic abnormalities, if there are significant differences or exceed the threshold level, it indicates that the subject has a metabolic disease or metabolic abnormality or a high risk.

[0014] The reagent is a reagent used in the following methods for detecting methylation levels: fluorescence quantitative PCR, methylation-specific PCR, methylation immunoprecipitation PCR, digital PCR, DNA methylation chip, whole-genome methylation sequencing, targeted DNA methylation sequencing, pyrosequencing, bisulfite conversion sequencing, reduced representation bisulfite sequencing, methylation enrichment sequencing, and DNA methylation mass spectrometry, or one or more of them.

[0015] The present invention has the following advantages: The methylation biomarker provided by the present invention is highly correlated with metabolic abnormalities and can be used for the diagnosis, prediction, screening, prevention, and treatment of metabolic diseases or metabolic abnormalities. It has good accuracy and discrimination ability, is convenient to detect and has good stability, and can identify metabolically abnormal populations at an early age in children, which has important clinical significance for the early diagnosis and treatment of metabolic diseases and has great practical application value. Description of the Drawings

[0016] Figure 1 Random blood glucose of control group and model group mice at 8 weeks old (a) and 32 weeks old (b); Figure 2 Glucose tolerance results of control group and model group mice at 8 weeks old (a) and 32 weeks old (b); Figure 3 HOMA-β results of control group and model group mice at 8 weeks old (a) and 32 weeks old (b); Figure 4 Levels of differentially methylated regions in mice with metabolic diseases, where (a)-(g) are DMR1-7 respectively; Figure 5 Levels of peripheral blood DNA methylation markers in children with metabolic abnormalities and control children, where (a)-(g) are DMR1-7 respectively; Figure 6 Discrimination efficiency of methylation biomarkers for children with metabolic abnormalities. Specific implementation manners

[0017] 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.

[0018] Example 1 Discovery of DNA methylation biomarkers in animal models 1. Construction of an animal model of developmental origin metabolic disease (1) Wild-type C57BL / 6J female mice (purchased from Beijing Vital River Laboratories Co., Ltd.) were mated overnight with males when in proestrus or estrus by vaginal cytology examination. The morning when a mating plug was detected was considered E0.5. The pregnant female mice were randomly divided into two groups: model group: 250 μg of dihydrotestosterone was dissolved in a mixture of 10 μL of benzyl benzoate and 90 μL of corn oil, and was subcutaneously injected into the interscapular region of pregnant mice every day from E16.5 to E18.5; control group: the same pregnant mice were subcutaneously injected with a mixture of 10 μL of benzyl benzoate and 90 μL of corn oil every day.

[0019] (2) The offspring mice were weaned on the 21st day after birth. The random blood glucose levels of the offspring mice at 8 weeks old and 32 weeks old were detected at 9 am.

[0020] (3) After fasting for 16 h, the 8-week-old and 32-week-old offspring mice were subjected to a GTT test. Blood glucose was measured before intraperitoneal injection of 2 g / kg glucose and at 15, 30, 60, and 120 min after administration to detect changes in glucose tolerance.

[0021] (4) After fasting for 16 h, the fasting blood glucose of the 8-week-old and 32-week-old offspring mice was detected, and blood was collected from the tail vein. The fasting insulin level was measured by chemiluminescence immunoassay, and HOMA-β was calculated.

[0022] For the modeled mice, the glycometabolic phenotypes were detected at different ages. From Figure 1 the monitoring of blood glucose levels, it can be seen that the blood glucose levels of the mice in the model group were significantly decreased at 8 weeks old in the early stage, and the blood glucose levels at 32 weeks old were significantly higher than those in the control group with the increase of age, showing symptoms of hyperglycemia.

[0023] Glucose tolerance tests were performed on the modeled mice at different ages. By Figure 2 It can be seen that the glucose tolerance of the modeled mice decreased at the early age of 8 weeks. As the age increased to 32 weeks, the glucose tolerance was significantly higher than that of the control group, showing symptoms of glucose tolerance impairment.

[0024] HOMA-β values of the modeled mice were detected at different ages to evaluate islet function. By Figure 3 It can be seen that the HOMA-β value of the modeled mice increased significantly at the early age of 8 weeks. As the age increased to 32 weeks, the HOMA-β value was significantly lower than that of the control group, showing abnormal and disordered islet function.

[0025] The above results indicate that various metabolic abnormalities occurred in this animal model at the young age, and gradually progressed to the manifestation of diabetes in the long term with age growth, indicating the successful construction of a metabolic disease model.

[0026] 2. Whole-genome methylation detection of mouse sperm Compared with whole blood sources, gametes, especially sperm, have higher biomarker discovery efficiency due to their high cell purity and no influence of mixed cell types. At the early age of 8 weeks in the above-mentioned modeled mice, the cauda epididymidis sperm of the control group and the modeled group were isolated and purified, and upstream in a culture medium at 37°C. The mature sperm in the upstream were aspirated, centrifuged and washed, and then frozen.

[0027] The sperm of the control group and the modeled group 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). The concentration and integrity of DNA were evaluated using a NanoDrop spectrophotometer and agarose gel electrophoresis, respectively. Then, a DNA library for enzymatic methylation sequencing was prepared as follows: Genomic DNA was fragmented into 100 bp - 300 bp by sonication, and then purified using the MiniElute PCR Purification Kit (QIAGEN). The purified DNA was subjected to bisulfite conversion using the EpiArt DNA Enzymatic Methylation Kit (Vazyme). The fragmented DNA was end-repaired, and then the genomic fragments were ligated to methylation sequencing adapters. Finally, the converted DNA fragments were PCR amplified and sequenced using the Illumina HiSeqTM 2500.

[0028] 3. Differential methylation analysis of sperm in modeled mice For the bisulfite sequencing reads of the sperm DNA samples of the above-mentioned model mice, the fastp software was used to remove adapters and low-quality bases. The obtained reads were aligned to the mouse reference genome (Ensembl_release110) using the BSMAP software, and the methylation cytosines were detected using the correction algorithm described by Lister R. et al. The methylation level was calculated based on the percentage of methylated cytosines in the whole genome, each chromosome, and different regions of the genome. To determine the differentially methylated regions (DMRs) between the two samples, the minimum read coverage of the single-base methylation status 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.

[0029] Table 1 DMR locations in model mice and corresponding DMR locations in humans By performing a comparative analysis of the DMRs of the two groups of samples, it can be seen from Figure 4 that DMR1 - DMR7 in the sperm DNA samples of the model group were all significantly higher than those in the control group. The above data indicate that these 7 differentially methylated DNA regions can be used as methylation biomarkers to distinguish the metabolic abnormality or metabolic disease group and predict the risk of future metabolic diseases.

[0030] Example 2 Validation of biomarkers in individuals with metabolic abnormalities To verify whether the DMRs found in Example 1 can be used for the diagnosis and prediction of metabolic diseases and individuals with metabolic abnormalities, female subjects with polycystic ovary syndrome and hyperandrogenemia and their offspring were selected for clinical validation: 1. Enrollment and sample collection of subjects According to the approval regulations of the Ethics Committee of Shandong University, subject samples were collected from the birth cohort of the Affiliated Reproductive Hospital of Shandong University, and all mothers participating in the study had given written informed consent. This study included women with pre-pregnancy basal hormone level detection and their offspring children at birth. Maternal hormone levels were measured on the 3rd day of the menstrual cycle by chemiluminescence assay, and maternal hyperandrogenism (total serum testosterone > 48.1 ng / dL) was defined according to the instructions of the detection kit. These women also met the clinical diagnosis of polycystic ovary syndrome. The offspring of these women born through assisted reproductive technology were followed up for a long time, and children who had completed growth and development assessments and provided blood samples to measure metabolic biochemical parameters were included in this study. The control group was the offspring children born to mothers without hyperandrogenism and other chronic diseases. A total of 20 offspring children born to mothers with hyperandrogenism and 20 age-matched control offspring children were included in the final analysis, and their clinical information and various metabolic indicators are compared in Table 2. Peripheral blood samples of the above-mentioned subjects were collected with anticoagulant tubes and frozen in a -80°C refrigerator for storage.

[0031] Table 2 Clinical information and metabolic indicators of the subjects Comparative analysis of multiple metabolic indicators of the two groups of offspring children through Table 2 shows that compared with the control group, the offspring children in the hyperandrogenism group had a significantly increased body mass index, indicating a tendency to be overweight; the fasting insulin and HOMA-IR indices were significantly increased, indicating insulin resistance; the HOMA-β index was significantly increased, indicating abnormal pancreatic islet β-cell function. All these indicators indicated impaired blood glucose regulation in the offspring children of the hyperandrogenism group. In addition, the average levels of total serum cholesterol and triglycerides in the offspring children of the hyperandrogenism group were also higher than those in the control group, indicating a risk of dyslipidemia. The abnormalities of multiple metabolic indicators such as weight gain, decreased insulin sensitivity, and disorders of glucose and lipid metabolism all showed that the risk of metabolic diseases such as obesity and diabetes in the offspring children of the hyperandrogenism group in the long term was significantly increased.

[0032] 2. Determination of DNA methylation biomarker levels in the peripheral blood of the subjects (1)Extraction of genomic DNA from peripheral blood Genomic DNA from peripheral blood was extracted using the QIAGEN Blood Genomic DNA Mini Kit. 200 μL of plasma samples from the enrolled 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. The wash buffer was used to remove impurities, and purified DNA was obtained after elution.

[0033] (2)Methylation immunoprecipitation The size of DNA fragments broken by ultrasound was detected by agarose gel electrophoresis, and the range of DNA fragments was required to be 100bp - 600bp. The DNA samples were divided into two groups according to Input and IP. The Input group was stored at -20°C for later use. The IP group used magnetic beads and a specific antibody against methylated cytosine to immunoprecipitate the fragments enriched with methylated DNA in the genome. After incubation overnight at 4°C, the specific 5mc methylated DNA samples were eluted from the magnetic beads.

[0034] (3)Methylation quantitative PCR assay Configure a 25 µL PCR amplification system for real-time quantitative PCR detection: Table 3 PCR amplification system ; Perform real-time quantitative PCR detection according to the following procedure: Table 4 PCR detection procedure .

[0035] (4)Analysis of methylation biomarker levels Using the DNA sample of the Input group as a template, qPCR detection was performed with specific primers for the gene to be tested to obtain Ct(input). Using the DNA sample of the IP group as a template for detection to obtain Ct(IP). Calculate the methylation level according to the following formula: The level of differentially methylated region of the gene to be tested % = 2 [Ct (input)-3.32-Ct (IP)] ×100.

[0036] It can be seen from Figure 5 that compared with the children of the control group, the levels of 7 DNA methylation regions in the blood of the children of the high androgen group were all significantly increased, which was well verified in the children population with clinical metabolic abnormalities. The above data further indicate that the selected 7 DNA regions can be used as methylation biomarkers to screen or predict the risk of metabolic diseases or metabolic abnormalities.

[0037] 3. Diagnostic discrimination efficacy of DNA methylation biomarkers in people with metabolic abnormalities ROC curves were plotted based on the levels of DNA methylation biomarkers in the peripheral blood of children in each group. The AUC values for independently differentiating people with metabolic abnormalities are shown in Figure 6Among them, the AUC value of DMR1 is 0.6875, with a corresponding sensitivity of 0.5 and a specificity of 0.85; the AUC value of DMR2 is 0.74, with a corresponding sensitivity of 0.8 and a specificity of 0.65; the AUC value of DMR3 is 0.67, with a corresponding sensitivity of 0.8 and a specificity of 0.65; the AUC value of DMR4 is 0.6637, with a corresponding sensitivity of 0.7 and a specificity of 0.65; the AUC value of DMR5 is 0.8112, with a corresponding sensitivity of 0.7 and a specificity of 0.9; the AUC value of DMR6 is 0.7388, with a corresponding sensitivity of 0.9 and a specificity of 0.65; the AUC value of DMR7 is 0.855, with a corresponding sensitivity of 0.85 and a specificity of 0.8. Taking the two markers with the best diagnostic performance above for combination, the result shows that the AUC value of the DMR5 + DMR7 combination is 0.945, with a corresponding sensitivity of 1 and a specificity of 0.75. The performance results show that the above methylation markers all have good diagnostic discrimination efficacy for the metabolic abnormality group. The above results further verified in the population that the above DMR1-7 can be used as effective methylation biomarkers for metabolic abnormalities and long-term metabolic diseases.

[0038] In summary, the methylation biomarkers provided by the present invention can effectively distinguish the control group and 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. Moreover, they have high accuracy, high specificity, and high sensitivity, and are suitable for routine screening, early risk prediction, and disease management of large-scale populations.

[0039] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. Use of a reagent for detecting differentially methylated regions and methylation levels 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 differential methylation region is located at positions 46778792-46779015 on chromosome 19; The metabolic diseases are selected from diabetes, impaired fasting glucose, impaired glucose tolerance, obesity, hyperlipidemia, fatty liver, polycystic ovary syndrome, and metabolic syndrome; The metabolic abnormalities are selected from abnormal pancreatic islet function, insulin resistance, overweight, abnormal blood glucose regulation, and dyslipidemia; The positions are those of the human reference genome hg38 version.

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, urinary exfoliated cells, urinary sediment, feces, semen, follicular fluid, sperm, eggs, pre-implantation embryos, amniotic fluid, and organoids; 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 reagent is the reagent used in the following methods for detecting methylation levels: fluorescence quantitative PCR, methylation-specific PCR, methylation immunoprecipitation PCR, digital PCR, DNA methylation chip, whole-genome methylation sequencing, targeted DNA methylation sequencing, pyrosequencing, bisulfite conversion sequencing, reduced representation bisulfite sequencing, methylation enrichment sequencing, and DNA methylation mass spectrometry, or one or more of them.

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