Use of a phosphatidylglycerol

Abstract

The application belongs to the technical field of biology and particularly relates to application of phosphatidylglycerol. The phosphatidylglycerol PG (20:4_22:6) is used as a biomarker, and through analysis on metabolites of follicular fluid, the application realizes diagnosis of decreased ovarian reserve (DOR) with high sensitivity, high specificity and high accuracy.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, specifically to the application of phosphatidylglycerol. Background Technology

[0002] Diminished ovarian reserve (DOR) refers to a woman's ovarian reserve being lower than the normal level for her age, meaning a decrease in the number of ovarian follicles, but still some ovarian responsiveness. Clinically, patients often present with reproductive dysfunction such as scanty menstruation, irregular menstruation, and infertility. Typical biomarker changes can be observed in medical tests: decreased levels of anti-Müllerian hormone (AMH) and reduced antral follicle count (AFC).

[0003] Although DOR has a serious impact on the fertility of women of childbearing age, the pathogenesis of DOR is still unclear. Some scholars believe that it is related to factors such as genetics, immune abnormalities, iatrogenic damage (such as radiotherapy and chemotherapy) and psychological stress. Its pathogenesis may involve factors such as oocyte mitochondrial dysfunction, abnormal apoptosis of follicular granulosa cells, local oxidative stress imbalance in the ovary and abnormal angiogenesis, but the specific pathways of action have not yet been elucidated.

[0004] In assisted reproductive technology (ART), patients with degenerative retinopathy (DOR) can benefit from specific treatments, such as higher doses of ovulation-inducing drugs and growth hormone therapy. Therefore, accurate identification of DOR patients is crucial. Clinically, DOR is primarily diagnosed using a combination of AMH and AFC methods, but international consensus on diagnostic criteria has not yet been reached. Existing diagnostic indicators for DOR have some shortcomings in terms of sensitivity, specificity, and accuracy. Therefore, developing a highly sensitive, specific, and accurate biomarker to improve the accurate diagnosis of DOR is of great significance to both the industry and society. Summary of the Invention

[0005] To overcome the aforementioned technical problems, this invention provides an application of phosphatidylglycerol. This invention uses phosphatidylglycerol PG (20:4-22:6) as a biomarker, and through the analysis of follicular fluid metabolites, achieves high sensitivity, high specificity, and high accuracy in diagnosing dopamine reversion (DOR).

[0006] This invention provides a method for detecting metabolites in isolated follicular fluid, comprising the following steps: detecting the concentration X of PG (20:4-22:6) in the isolated follicular fluid of a test subject, thereby determining whether the test subject is a patient with diminished ovarian reserve (DOR), wherein:

[0007] The value of X is 0.001 * R * c * F * V / m.

[0008] R: The ratio of the peak area of ​​the analyte to the peak area of ​​the internal standard;

[0009] c: Internal standard concentration (μmol / L);

[0010] F: Internal standard correction factor;

[0011] V: Sample extract (μL);

[0012] m: Sample size (μL).

[0013] In this invention, when the concentration X of PG (20:4-22:6) in the follicular fluid of the test subject is less than 4.323 × 10⁻⁶, the condition is defined as follows: -8 A concentration of mol / L indicates that the subject of the test is a patient with diminished ovarian reserve.

[0014] In this invention, the detection method is a non-diagnostic and non-therapeutic detection method.

[0015] In this invention, the internal standard is phosphatidylglycerol PG (15:0 / 18:1-d7).

[0016] This invention provides the application of phosphatidylglycerol as a biomarker in the preparation of products for diagnosing and / or providing early warning of diminished ovarian reserve, wherein the phosphatidylglycerol is PG (20:4-22:6).

[0017] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0018] The reagents and raw materials used in this invention are all commercially available.

[0019] The positive and progressive effects of this invention are as follows: This invention uses phosphatidylglycerol PG (20:4-22:6) as a biomarker and analyzes follicular fluid metabolites to achieve a diagnosis of diminished ovarian reserve (DOR) with high sensitivity, high specificity and high accuracy. Attached Figure Description

[0020] Figure 1 The results of metabolite changes in the follicular fluid lipid group of DOR patients and control groups are ranked. Red and green dots represent the top ten metabolites that increased and decreased, respectively.

[0021] Figure 2 Statistical results of metabolites in the lipid profile of follicular fluid from DOR patients and controls that showed at least a 1.75-fold change before and after.

[0022] Figure 3 The concentrations of different PG metabolites in the follicular fluid of DOR patients and control groups.

[0023] Figure 4The figure shows the receiver operating characteristic (ROC) curve of the concentration of PG metabolites in follicular fluid versus patients with DOR. Detailed Implementation

[0024] The present invention is further illustrated below by way of embodiments, but the invention is not limited to the scope of the embodiments described herein. Experimental methods in the following embodiments that do not specify specific conditions were performed according to conventional methods and conditions, or as selected according to the product instructions.

[0025] Example 1: This invention uses ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS / MS) to detect follicular fluid metabolites and quantify specific lipids. The specific operation steps are as follows:

[0026] 1.1 Study Subjects and Samples: This study collected follicular fluid samples from patients undergoing assisted reproductive technology at Sir Run Run Shaw Hospital, affiliated with Zhejiang University School of Medicine. The samples included follicular fluid from 100 clinically diagnosed patients with normal ovarian response (CT, control) and 58 patients with diminished ovarian reserve (DOR). The inclusion criteria for DOR patients were based on the 2011 ESHRE (European Society for Human Reproduction and Embryology) Bologna Criteria for poor ovarian response: AMH (Anti-Müllerian Hormone) <1.1 ng / ml or AFC (Antral Follicle Count) <5. The inclusion criteria for CT (control) patients were: 2.0 ng / ml ≤ AMH < 6.0 ng / ml, and 10 ≤ AFC < 20. Patients with polycystic ovary syndrome, hyperprolactinemia, Cushing's syndrome, congenital adrenal hyperplasia, or other abnormal ovarian response diseases were excluded. Each patient was informed of the purpose of the sample and signed an informed consent form. The study was approved by the Ethics Committee of Sir Run Run Shaw Hospital, affiliated with Zhejiang University School of Medicine.

[0027] 1.2 Collection of follicular fluid: The samples collected in this invention are all intended for in vitro fertilization (IVF). in vitroFollicular fluid from mature follicles (follicle diameter ≥18 mm) in patients undergoing IVF (In Vitro Fertilization). Patients receive controlled ovarian hyperstimulation (IVF). During this period, ovarian response is monitored via transvaginal ultrasound, including follicle size, serum E2, P, LH, and FSH levels. Medication dosages are adjusted according to individual patient responses. When at least 1-2 dominant follicles in the dominant follicular group have a diameter ≥18 mm, and considering the patient's estrogen levels, a single injection of 5000-10000 units of HCG or HCG + Dupilumab is administered to induce ovulation. Oocyte retrieval is performed approximately 36 hours later under transvaginal ultrasound guidance. During the procedure, a single-lumen tube is used to aspirate the follicles. The collected follicular fluid is collected after the cumulus coronae complex is removed at a constant temperature. The remaining follicular fluid is then collected, centrifuged at 5000 rpm, aliquoted, and frozen at -80°C for later use.

[0028] 1.3 Determination of follicular fluid metabolites:

[0029] 1.3.1 Sample Pretreatment and Internal Standard Addition: After thawing, vortex for 10 s to mix, and transfer 50 μL of each sample; add 1 mL of lipid extraction buffer containing internal standard (methyl tert-butyl ether: methanol = 3:1, V / V) and vortex for 15 min; add 200 μL of water, vortex for 1 min, and centrifuge at 12000 r / min for 10 min at 4 ℃; after centrifugation, transfer 200 μL of the supernatant to the corresponding numbered centrifuge tube and concentrate to dryness; add 200 μL of lipid reconstitution solution (acetonitrile: isopropanol = 1:1, V / V), vortex for 3 min, centrifuge at 12000 r / min for 3 min, and transfer the supernatant for UPLC-MS analysis.

[0030] 1.3.2 Detection and Analysis:

[0031] The data acquisition instrument system mainly includes ultra-high performance liquid chromatography (UPLC) and tandem mass spectrometry (MS).

[0032] The liquid phase conditions mainly include:

[0033] 1) Chromatographic column: Thermo Accucore™ C30 column, id 2.1 x 100 mm, 2.6 μm;

[0034] 2) Mobile phase: Phase A: Acetonitrile / water (60 / 40, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate); Phase B: Acetonitrile / isopropanol (10 / 90, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate).

[0035] 3) Mobile phase gradient: 0 min A / B (80:20, V / V), 2 min (70:30, V / V), 4 min (40:60, V / V), 9 min (15:85, V / V), 14 min (10:90, V / V), 15.5 min (5:95, V / V), 17.3 min (5:95, V / V), 17.5 min (80:20, V / V), 20 min (80:20, V / V);

[0036] 4) Flow rate 0.35 ml / min; column temperature 45℃; injection volume 2 μl.

[0037] The mass spectrometry conditions mainly included: an electrospray ionization (ESI) source temperature of 500℃, a mass spectrometry voltage of 5500V in positive ion mode, a mass spectrometry voltage of -4500V in negative ion mode, ion source gas 1 (GS1) at 45 psi, gas 2 (GS2) at 55 psi, and curtain gas (CUR) at 35 psi. In the triple quadrupole, each ion pair was scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0038] 1.3.3 Lipid quantification:

[0039] Mass spectrometry data were processed using Analyst 1.6.3 software, and qualitative analysis of sample lipids was performed based on local lipid database information. The chromatographic peaks detected for each substance in different samples were then corrected to ensure accurate quantification. The integrated peak area values ​​of the extracted metabolites were substituted into the formula to calculate the actual concentration X = 0.001 * R * c * F * V / m, where:

[0040] R: The ratio of the peak area of ​​the analyte to the peak area of ​​the internal standard;

[0041] c: Internal standard concentration (μmol / L);

[0042] F: Internal standard correction factor (F=1+0.02*(total carbon number-36)+0.05*total double bond number);

[0043] V: Sample extract (μL);

[0044] m: Sample size (μL).

[0045] Using the method described above, the concentration of specific lipids in the follicular fluid of a subject can be detected and calculated. The resulting X value is associated with the risk that the subject has DOR. Depending on the specific application scenario, an appropriate X value can be selected as the threshold for a subject to have DOR.

[0046] Specifically, ROC analysis was performed using a single lipid concentration as the independent variable to obtain the area under the ROC curve (AUC) and determine the model's predictive efficiency. The optimal cutoff value X (the concentration of a specific lipid) for the metabolite was obtained by calculating the Youden index, thus determining the threshold for a subject to suffer from DOR.

[0047] 1.4 Results Analysis and Statistics:

[0048] Follicular fluid metabolomics data Figure 1 , Figure 2 and Figure 4 Use the Metware cloud platform (https: / / cloud.metware.cn / # / tools / tool-list) for graphing and statistical analysis; Figure 3 GraphPad Prism 8 software was used for plotting and statistical analysis.

[0049] 1.5 Discussion of Results:

[0050] 1.5.1 Lipidometral analysis of follicular fluid:

[0051] First, the ten metabolites with the largest changes in DOR in both the lipid group patients and the control group were selected, and the results are as follows: Figure 1 As shown, the target metabolites are represented by green dots and black text, while metabolites for which there is no significant difference in changes between DOR patients and the control group are represented by gray dots and gray text.

[0052] according to Figure 1 It can be seen that among the DOR patients and control group in the lipid group, PG (20:4_22:6) ranked in the top ten and the change was the most significant.

[0053] The results of this analysis indicate that PG(20:4_22:6) has the potential to be used as a diagnostic marker for DOR. In addition, the analysis also found that the changes in PG(12:0_22:5) and PG(22:0_20:5) in the lipid profile of follicular fluid were relatively weak, and obviously they could not be used as biomarkers for the diagnosis of DOR.

[0054] Furthermore, metabolites in the lipid group that showed at least a 1.75-fold change in DOR between patients and the control group were selected, and the experimental results are as follows: Figure 2 As shown.

[0055] according to Figure 2It was found that 16 metabolites in the lipid group showed a significant 1.75-fold change compared with the control group, all of which were downregulated. Among them, the decrease in PG (20:4-22:6) was significantly higher than that of other lipid metabolites, such as HexCer (d18:1 / 23:0) and DG (16:0-22:5).

[0056] Combination Figure 1 and Figure 2 The analysis results further indicate that, compared with other lipid types, such as HexCer (d18:1 / 23:0) and DG (16:0_22:5), PG (20:4_22:6) has greater potential as a biomarker for diagnosing DOR.

[0057] 1.5.2 Assess the concentrations of specific PG metabolites in the follicular fluid of DOR patients and controls in the lipid group:

[0058] Based on the lipid quantification method described above, specific PG metabolites in follicular fluid were selected as evaluation targets, and their concentrations were measured. These specific PG metabolites included PG (22:0–20:5), PG (12:0–22:5), or PG (20:4–22:6). The results are as follows: Figure 3 As shown.

[0059] according to Figure 3 It was found that the average relative intensity of PG (20:4-22:6) in the follicular fluid metabolites was 3572.65 in the control group and 352.41 in the DOR group (P<0.001); the average relative intensity of PG (12:0-22:5) in the control group was 1.50×10⁻⁶. 5 The value in the DOR group is 1.49 × 10⁻⁶. 5 P>0.05; the mean relative intensity of PG(22:0_20:5) in the control group was 2.63×10. 4 The value in the DOR group is 2.63 × 10⁻⁶. 4 P>0.05. Therefore, compared with PG(12:0_22:5) and PG(22:0_20:5), the metabolite concentration of PG(20:4_22:6) was significantly downregulated in DOR patients.

[0060] Therefore, PG(20:4_22:6) can be used as a biomarker to diagnose DOR.

[0061] 1.5.3 ROC curves were used to evaluate the performance of models classifying CT and DOR using specific PG metabolites:

[0062] Furthermore, ROC curves were plotted using the relative intensity of specific PGs as the test variable and whether DOR was present as the state variable. The ordinate represented sensitivity, the abscissa 1-specificity, and the AUC (area under the curve). The ROC curves were used to evaluate the performance of the model using metabolites to classify CT and DOR. Specifically, ROC analysis was performed using the concentration of specific PG metabolites as the independent variable, and the area under the ROC curve (AUC) was obtained. The results are as follows: Figure 4 As shown.

[0063] according to Figure 4 It can be seen that the AUC of PG(20:4_22:6) is 0.959 (0.019-0.998, P<0.001); the AUC of PG(12:0_22:5) is 0.554 (0.450-0.638, P>0.05); and the AUC of PG(22:0_20:5) is 0.565 (0.468-0.662, P>0.05).

[0064] The above experimental results indicate that not all PGs can serve as biomarkers for diagnosing and warning of DOR patients, but PG(20:4_22:6) can effectively diagnose and warn of DOR.

[0065] 1.5.4 Establish a model for PG(20:4_22:6) to diagnose DOR:

[0066] PG(20:4_22:6), PG(12:0_22:5), and PG(22:0_20:5) are respectively as follows: Figure 4 As shown, the AUC of any single lipid is lower than that of PG(20:4_22:6) (0.959). Therefore, PG(20:4_22:6) has a significantly better diagnostic or early warning potential for DOR (e.g., in terms of accuracy and specificity) than other lipid molecules.

[0067] Therefore, when PG (20:4_22:6) was chosen as a biomarker for diagnosing or predicting DOR, phosphatidylglycerol PG (15:0 / 18:1-d7) was selected as the internal standard, and its F-value was calculated to be 0.99. Then, the Youden index was calculated using ROC curves, yielding an optimal cutoff value of 4.323 × 10⁻⁶ for PG (20:4_22:6). -8 mol / L, meaning if the measured concentration X of PG (20:4_22:6) in the sample is less than 4.323 × 10⁻⁶ mol / L. -8 A concentration of mol / L may indicate that the subject has DOR.

[0068] Example 2 Evaluation of the detection effect of phosphatidylglycerol PG (20:4-22:6).

[0069] In this embodiment, 82 women who underwent assisted reproductive treatment at Sir Run Run Shaw Hospital affiliated with Zhejiang University School of Medicine were diagnosed as DOR-positive patients and DOR-negative patients according to the Bologna criteria. Then, the AMH diagnostic method, AFC diagnostic method and the diagnostic method of Example 1 (PG diagnostic method) were used to diagnose whether they had DOR.

[0070] The diagnostic criteria for the diagnostic method (PG diagnostic method) in Example 1 are as follows: the optimal cutoff value for PG (20:4-22:6) is 4.323 × 10⁻⁶, obtained by calculating the Youden index. -8 mol / L, AUC is 0.959, that is, when the concentration of PG (20:4_22:6) in the sample is less than 4.323×10 mol / L, -8 A concentration of mol / L indicates that the subject has DOR and is counted as positive; otherwise, it is counted as negative.

[0071] The diagnostic results of DOR using the AMH diagnostic method are shown in Table 1 below:

[0072] Table 1:

[0073] The diagnostic results of DOR using the AFC diagnostic method are shown in Table 2 below:

[0074] Table 2:

[0075] The diagnostic results of DOR using the PG diagnostic method are shown in Table 3 below:

[0076] Table 3:

[0077] Based on the data in Tables 1-3 above, the consistency rate between the PG diagnostic method and the clinical diagnostic results was calculated using sensitivity, specificity, and accuracy.

[0078] Sensitivity: Sensitivity is the ability to diagnose DOR-positive patients. It is calculated as: Sensitivity = number of true positives / (number of true positives + number of false negatives). The higher the value, the more effective the test result in detecting DOR.

[0079] Sensitivity of PG diagnosis: 31 / (31+1)=96.9%.

[0080] Sensitivity of AMH diagnosis: 26 / (26+6) = 81.3%.

[0081] Sensitivity of AFC diagnosis: 27 / (27+5)=84.4%.

[0082] Therefore, compared with the AMH and AFC diagnostic methods, the PG diagnostic method has higher sensitivity for the diagnosis of DOR patients.

[0083] Specificity: Specificity measures the ability to correctly identify DOR-negative patients. The higher the value, the lower the probability of misdiagnosing negative patients. It is calculated as: Specificity = number of true negatives / (number of true negatives + number of false positives).

[0084] Specificity of PG diagnosis: 47 / (47+3)=94.0%.

[0085] The specificity of the AMH diagnostic method is 47 / (47+3)=94.0%.

[0086] The specificity of the AFC diagnostic method is 38 / (38+12)=76.0%.

[0087] Therefore, compared with the AFC diagnostic method, the PG diagnostic method has higher specificity for the diagnosis of DOR patients.

[0088] Accuracy: Accuracy measures the proportion of samples correctly classified by the diagnostic model overall. It is calculated as: Accuracy = (Number of true positives + Number of true negatives) / Number of samples.

[0089] Accuracy of PG diagnosis: (31+47) / 82=95.1%.

[0090] The accuracy of the AMH diagnostic method is (26+47) / 82=89.0%.

[0091] The accuracy of the AFC diagnostic method is: (27+38) / 82=79.3%.

[0092] Therefore, compared with the AMH and AFC diagnostic methods, the PG diagnostic method has higher accuracy in diagnosing DOR patients.

Claims

1. The application of phosphatidylglycerol as a biomarker in the preparation of products for diagnosing ovarian reserve deficiency, wherein the phosphatidylglycerol is PG (20:4-22:6).

2. The application as described in claim 1, characterized in that, The diagnosis of diminished ovarian reserve is achieved by detecting metabolites in isolated follicular fluid. The detection method includes the following steps: detecting the concentration X of PG (20:4-22:6) in the follicular fluid of the test subject to determine whether the test subject is a patient with diminished ovarian reserve (DOR), wherein: The ; R: The ratio of the peak area of ​​the analyte to the peak area of ​​the internal standard; c: Internal standard concentration; F: Internal standard correction factor; V: Volume of sample extract; m: The sample size.

3. The application as described in claim 2, characterized in that, When the concentration X of PG (20:4_22:6) in the follicular fluid of the test subject is less than 4.323×10 -8 A concentration of mol / L indicates that the subject of the test is a patient with diminished ovarian reserve.

4. The application as described in claim 2, characterized in that, The internal standard mentioned is phosphatidylglycerol PG (15:0 / 18:1-d7).

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