Combined metabolic marker for judging insulin resistance risk of high-fat diet child rats in perinatal period of mother rats under bisphenol S exposure and detection kit of combined metabolic marker

Abstract

The invention discloses a combined metabolic marker for judging insulin resistance risk of high-fat diet child rats under bisphenol S exposure in the perinatal period of female rats and a detection kit thereof, and relates to combined use of metabolites ergothioneine and 3-hydroxybutyryl carnitine as markers in white adipose tissues of child rats, which is used for judging insulin resistance risk of high-fat diet child rats under the action of BPS exposure in the perinatal period of female rats. The insulin resistance risk of the child rats. According to the method, the insulin resistance risk effect is judged by detecting the relative concentration of metabolites in a white adipose tissue sample of a child mouse, calculating a combined marker variable P based on a binary logistic regression equation and determining an intercept point value. The kit has the characteristics of high sensitivity, high detection efficiency, low cost and good repeatability, and has a good application prospect.

Description

Technical Field

[0001] The present invention relates to the fields of analytical chemistry, biochemistry and environmental toxicology, and in particular to a combined metabolic marker and a detection kit thereof for judging the insulin resistance risk of offspring of maternal rats exposed to bisphenol S (BPS) during the perinatal period and fed a high-fat diet. Background Art

[0002] In view of the increasingly stringent production and use restrictions of bisphenol A (BPA), the structural analog BPS has been widely used in the food industry, packaging materials and other fields as its main substitute. Compared with BPA, although BPS has a longer half-life, is more difficult to degrade and has stronger skin penetration, BPS was considered relatively "safe" in the past because its estrogenic effect and anti-androgenic effect are weaker than BPA, which are 32±28% and 25% of BPA respectively. In recent years, the potential insulin resistance risk of BPS has gradually been discovered. BPS can significantly promote in vitro fat differentiation, enhance lipid accumulation in cells, affect the physiological function of insulin and thus interfere with the glucose metabolism of organisms. However, there is still a lack of understanding of its key effects in early life, especially the intergenerational transmission effects under exposure. The risk and effect of insulin resistance in offspring exposed to BPS by mothers need to be further evaluated.

[0003] In previous work, we found that when maternal mice were exposed to BPS during the perinatal period, the weight of offspring on a high-fat diet increased significantly, and their glucose tolerance and insulin tolerance were significantly reduced. Pathological examination also confirmed the enlargement of white fat cells in offspring. These results suggest that the impact of maternal exposure to BPS during the perinatal period on offspring on a high-fat diet cannot be ignored. Considering the cumbersome and time-consuming nature of glucose tolerance, insulin tolerance and pathological examinations, it is of great practical significance to develop an accurate and stable method to determine the risk effect of insulin resistance in offspring on a high-fat diet when maternal mice are exposed to BPS during the perinatal period.

[0004] Metabolomics can reflect the endogenous metabolic disturbances of living systems in response to pathophysiological stimuli or genetic modifications. Metabolomics research uses chromatography-mass spectrometry, nuclear magnetic resonance and other technologies to conduct high-throughput qualitative and quantitative analysis of metabolites in cells, tissues and body fluids. Most of the research objects are small molecules with a molecular weight between 50-1500Da, such as glucose, amino acids, nucleotides, bile acids and lipids. Using metabolomics research methods to compare the endogenous metabolome disorders of the body under different exposure conditions will help to discover metabolic markers related to the effects of environmental hazards, thereby assisting in the assessment of exposure risks. In the metabolomics research of urban pollution exposures such as secondhand smoke, polycyclic aromatic hydrocarbons and benzene, related metabolite markers (such as cotinine, 1-hydroxyphenanthrene and carnitine C12:2, etc.) have been successfully used to assess the risk of pollutant exposure.

[0005] Given the crucial regulatory role of metabolites in organisms, the present invention adopts a metabolomics research method based on ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) to detect metabolites in the white adipose tissue of offspring mice. Through bioinformatics analysis, metabolomic characteristic perturbations under exposure are screened, aiming to discriminate the insulin resistance risk of offspring mice on a high-fat diet under perinatal BPS exposure of the mother mouse. After multiple optimizations, the present invention determines the combined use of the metabolic markers ergothioneine and 3-hydroxybutyrylcarnitine in white adipose tissue, and proposes its new use for discriminating the insulin resistance risk of offspring mice on a high-fat diet under perinatal BPS exposure of the mother mouse. Since there are many influencing factors for a single metabolite and metabolites have multiple "identities" in living organisms, screening combined metabolic markers composed of a few metabolites and calculating the "discrimination probability" P value (Probability) using a discrimination formula helps to improve discrimination sensitivity and specificity. There is currently no report on combining these two metabolites for discriminating the insulin resistance risk of offspring mice caused by perinatal BPS exposure of the mother mouse. Summary of the Invention

[0006] Aiming at the above problems existing in the prior art, the purpose of the design of the present invention is to provide a technical solution of a combined metabolic marker and its detection kit for judging the insulin resistance of offspring mice on a high-fat diet promoted by perinatal BPS exposure of the mother mouse based on white adipose tissue samples, which is specifically realized through the following technical solutions:

[0007] In the first aspect of the present invention, a combined marker for judging the insulin resistance of offspring mice on a high-fat diet promoted by perinatal BPS exposure of the mother mouse is provided based on the white adipose samples of offspring mice. The combined marker includes the following two metabolites: ergothioneine and 3-hydroxybutyrylcarnitine.

[0008] In the second aspect of the present invention, the use of the combined metabolic marker for judging the insulin resistance risk of offspring mice on a high-fat diet promoted by perinatal BPS exposure of the mother mouse is provided.

[0009] In the third aspect of the present invention, a method for judging the insulin resistance of offspring mice on a high-fat diet promoted by perinatal BPS exposure of the mother mouse is provided, which is judged using the combined marker P:

[0010] P = 1 / [1 + e -(-159770.680a-1206.173b+27.122)

[0011] where a is the relative content of ergothioneine in white adipose tissue, and b is the relative content of 3-hydroxybutyrylcarnitine. If P > 0.5, it is judged that there is an insulin resistance risk for offspring mice on a high-fat diet under perinatal BPS exposure of the mother mouse.

[0012] In the fourth aspect of the present invention, a kit for detecting the combined metabolic marker is provided, including: ​

[0013] Standards: ergothioneine and 3-hydroxybutyrylcarnitine. The standards are respectively used for the qualitative analysis of metabolites in corresponding white adipose tissues. Both are based on the accurate mass of the mass spectrum, the secondary mass spectrum fragments and the retention time as the qualitative basis.

[0014] Pretreatment extraction solution: A methanol solution containing 0.50 μg / ml Tryptophan-d5 and 0.25 μg / ml Carnitine C2-d3 as internal standards. Tryptophan-d5 is used to correct ergothioneine and Carnitine C2-d3 is used to correct 3-hydroxybutyrylcarnitine under the positive ion detection mode.

[0015] Eluent: Mobile phase A is ultrapure water (containing 0.1% formic acid), and mobile phase B is acetonitrile (containing 0.1% formic acid).

[0016] The fifth aspect of the present invention provides a method for calculating combined biomarker variables in a detection kit, including the following steps:

[0017] (1) Pretreatment of white adipose tissue samples of offspring mice: Ultra-pure water is added to the white adipose tissue samples from offspring mice for grinding, and then methanol containing internal standards and acetonitrile solution are added in sequence, and then metabolites are extracted.

[0018] (2) Separation and identification of the sample metabolites extracted in step (1) based on ultra-high performance liquid chromatography-mass spectrometry.

[0019] (3) Auxiliary qualitative confirmation of the detected ions with ergothioneine and Carnitine C2-d3 standards provided in the kit:

[0020] (a) Perform ultra-high performance liquid chromatography-mass spectrometry analysis on ergothioneine and Carnitine C2-d3 standards in the kit to determine the chromatographic retention time, measured mass-to-charge ratio, and secondary mass spectrum characteristic ions of the standards.

[0021] (b) In the white adipose tissue samples of offspring mice, chromatographic peaks of ergothioneine with a theoretical mass-to-charge ratio of 230.0958 and 3-hydroxybutyrylcarnitine with a mass-to-charge ratio of 248.1492 are respectively extracted under a reasonable mass-to-charge ratio tolerance threshold.

[0022] (c) Check whether the secondary mass spectrum ions of the above two chromatographic peaks conform to the secondary mass spectrum bond-breaking rules of the corresponding metabolites, and at the same time combine the chromatographic retention behavior to confirm the target metabolites ergothioneine and 3-hydroxybutyrylcarnitine. Among them, carnitine metabolites can obtain secondary characteristic ions with m / z of 85.03 under the positive ion mode.

[0023] (4) For the target metabolites ergothioneine and 3-hydroxybutyrylcarnitine in the identified samples, their chromatographic peak intensities were first corrected by weighing the white adipose tissue samples of the offspring mice, and then compared with the internal standards Tryptophan-d5 and Carnitine C2-d3 respectively to obtain the relative concentrations of the above two metabolites.

[0024] The combined biomarker P was obtained by binary logistic regression calculation of ergothioneine and 3-hydroxybutyrylcarnitine.

[0025] The principle of the present invention is as follows:

[0026] (1) Using the metabolomics analysis technology of ultra-high performance liquid chromatography-quadrupole and orbitrap hybrid Fourier transform ultra-high resolution mass spectrometry, the white adipose tissue of the offspring mice exposed to BPS during the perinatal period of the mother mice and the control group samples were respectively subjected to metabolomics analysis to obtain the qualitative and quantitative analysis results of metabolites.

[0027] (2) Screening for differential metabolites according to the correlation between univariate non-parametric tests and the results of insulin resistance biological phenotypes, and combining the requirements for metabolite stability, potential biomarkers were discovered in the discovery set samples of the first batch of model establishment. The specific method is as follows:

[0028] i) Typical differential screening of metabolome: Based on the comparison between the control group and the maternal exposure group, in the metabolic profile of the offspring white adipose tissue, based on the statistical significance requirements of univariate (p < 0.05 and FDR < 0.1) and multivariate (VIP > 1), differential metabolites meeting the above requirements were initially screened, and then characteristic differential metabolites with a change multiple greater than 4 (ratio > 4 or < 0.25) in the comparison between the two groups were further obtained.

[0029] ii) Screening for differential metabolites related to biological phenotypes: In the metabolome results, metabolites highly correlated with the changes in the insulin resistance phenotypes of mice (glucose tolerance test (GTT), insulin tolerance test (ITT)) were screened (Spearman correlation coefficient |R| > 0.6 and p < 0.05).

[0030] iii) Analyzing the stability requirements: Based on the more stringent analytical quality requirements for potential markers, candidate differential metabolites were further screened with the condition of %RSD < 20% in the QC samples. iv) Obtaining the intersection of metabolites from the above three data screening methods, and finally preferably selecting the combination of ergothioneine and 3-hydroxybutyrylcarnitine.

[0031] The beneficial effects of the present invention:

[0032] In the present invention, two metabolites, ergothioneine and 3-hydroxybutyrylcarnitine, are screened from the white adipose tissue of offspring mice to form a combined biomarker for judging the risk of insulin resistance in offspring mice on a high-fat diet under perinatal BPS exposure. The detection kit involved in the present invention has the advantages of simplicity, rapidity and good repeatability for the detection of the above metabolite combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Structural formulas of two biomarkers ((A) ergothioneine; (B) 3-hydroxybutyrylcarnitine);

[0034] Figure 2 Correlation analysis of insulin resistance-related phenotypic indicators and significantly different metabolites;

[0035] Figure 3 Contents of biomarkers in each group of samples ((A) ergothioneine; (B) 3-hydroxybutyrylcarnitine, expressed as mean ± standard error); *: 0.01 < p < 0.05, **: 0.001 < p < 0.01,

[0036] ***: p < 0.001);

[0037] Figure 4 ROC curve analysis ((A) ROC curve diagram of the combined biomarker in Example 1; (B) ROC curve diagram of the combined biomarker in Example 2; (C) ROC curve diagram of the combined biomarker in Example 3). DETAILED DESCRIPTION OF THE INVENTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments for better understanding of the technical solution.

[0039] Example 1

[0040] 1. Collection of offspring mouse white adipose tissue samples

[0041] Select the high-dose study group of the first batch of model establishment.

[0042] Pregnant C57BL / 6 mice were fed with normal diet under constant temperature [(23±1)°C] and constant humidity [(55±5)%] conditions, and followed a strict 12h / 12h light-dark cycle. To minimize BPS exposure outside of experimental treatments, we used polystyrene cages to house the animals. They were divided into a control group and a high-dose BPS group (5mg / kg / d) in the first batch of model establishment, and BPS exposure was carried out by subcutaneous injection. After the pregnant mice gave birth, BPS exposure continued according to the group during lactation until the mother mice were sacrificed after lactation ended. The offspring mice were adaptively fed for 1 week after lactation, and then fed with high-fat diet for 8 weeks. Finally, the white adipose tissue of the offspring mice was collected, quickly washed with physiological saline after sampling, treated with liquid nitrogen and finally stored in a -80°C refrigerator.

[0043] 2. Sample pretreatment

[0044] First, the white adipose tissue of the offspring mice stored at -80°C was taken out and accurately weighed (~15mg), then placed in a 2mL centrifuge tube (with magnetic beads pre-added), 200μL of ultrapure water was added, and tissue homogenization was performed 4 times using a high-throughput rapid grinder (65Hz, 60s). After grinding, 400μL of methanol extraction solution containing internal standard and 400μL of acetonitrile were added in sequence, vortexed for 30s, sonicated in an ice bath for 10min using an ultrasonic cleaner, left to stand at -20°C for 1h and then centrifuged at high speed (15,000g, 4°C, 15min). The supernatant was quantitatively collected and vacuum dried at low temperature in a freeze-drying centrifuge concentrator, and the obtained freeze-dried sample was stored in a -80°C refrigerator.

[0045] Before the freeze-dried samples of the metabolome were detected by ultra-high performance liquid chromatography-mass spectrometry, they were re-dissolved with a mixed solution of methanol / ultrapure water (1:1, v / v) and then injected.

[0046] 3. Data acquisition

[0047] (1) Ultra-high performance liquid chromatography conditions: An ultra-high performance liquid chromatography (UltiMateTM 3000, ThermoFisher, USA) system was used. The metabolites extracted were separated using an HSS C18 column (2.1×100mm, 1.8μm, Waters, USA) in positive ion mode. During the separation process, the column temperature was set to 50°C, and the injection chamber temperature was controlled at 8°C. In positive ion mode, mobile phase A was ultrapure water (containing 0.1% formic acid), and mobile phase B was acetonitrile (containing 0.1% formic acid). A gradient elution mode was used, and the gradient was as follows: First, keep 2% B for 0.5min, then linearly increase to 98% B in the range of 0.5 - 12min, then quickly drop to 2% B within 0.1min, and equilibrate for 8.5min until the next injection. The flow rate was 0.4mL / min.

[0048] (2) Mass spectrometry conditions: A quadrupole and orbitrap hybrid Fourier transform ultra-high resolution mass spectrometer (Q-Exactive, Thermo Fisher, USA) was used, and the mass spectrometry analysis was performed in the positive ion mode of the electrospray ionization source. The mass spectrometry parameters were set as follows: spray voltage 3.5 kV, capillary temperature 325 °C, nebulizer temperature 400 °C, sheath gas flow rate 50 arb, auxiliary gas flow rate 15 arb, normalized collision energy (NCE) was 15, 30, and 45 respectively, TopN (the highest abundance ions for fragmentation) was 10, the full scan MS resolution was 140000 respectively, the ddMS2 resolution was 35000 respectively, and the scan range was 70 - 1000 m / z. During the acquisition process, Xcalibur software (Thermo, USA) was used to record the total ion current chromatogram and MS spectra.

[0049] 4. Data preprocessing

[0050] Based on the accurate mass number m / z of metabolites, secondary mass spectrometry characteristic fragmentation information (MS / MS), and retention time (tR), the raw data was compared with the standard sample database to achieve accurate identification of metabolites.

[0051] The accurately identified metabolites were imported into TraceFinder (Thermo, USA) for metabolite quantification. Subsequently, combined with the mass weighing of the white adipose tissue samples of the offspring mice and the content of internal standards, the chromatographic peak intensity was corrected.

[0052] 5. Result analysis

[0053] The structural formula of the metabolite is as Figure 1 shown. The correlation analysis between insulin resistance-related phenotypic indicators and significantly different metabolites is as Figure 2 shown, and the content of biomarkers in each group of samples is as Figure 3 shown.

[0054] Using the data statistical software SPSS, further binary logistic regression calculation was performed on ergothioneine and 3-hydroxybutyrylcarnitine to obtain the combined biomarker P. The regression equation is as follows:

[0055] P = 1 / [1 + e -(-159770.680a-1206.173b+27.122)

[0056] where a is the relative content of ergothioneine in white adipose tissue, and b is the relative content of 3-hydroxybutyrylcarnitine in white adipose tissue.

[0057] The samples of the control group (C) and the high-dose BPS group (5 mg / kg / d) in modeling batch 1 were included in the discriminant comparison. Based on the discriminant variable P obtained from this biomarker, the area under the ROC curve (AUC value) for discrimination was 1( Figure 4 ​A), at a cut-off value of 0.5, both the sensitivity and specificity are 100% (Table 1). The above results indicate that this biomarker has good discrimination potential and can be used to discriminate the risk of insulin resistance in offspring mice on a high-fat diet under maternal BPS exposure.

[0058] Example 2

[0059] 1. Collection of white adipose tissue samples from offspring mice:

[0060] This example uses the low-dose study group of Modeling Batch 1.

[0061] Collect white adipose samples from offspring mice on a high-fat diet under low-dose (0.05 mg / kg / d) BPS exposure during the perinatal period of female mice in Modeling Batch 1. The collection method is the same as in Example 1.

[0062] 2. Sample pretreatment: The same as in Example 1.

[0063] 3. Data collection: The same as in Example 1.

[0064] 4. Data preprocessing: The same as in Example 1.

[0065] 5. Based on the comparison between the control group (C) and the low-dose BPS group (0.05 mg / kg / d) of Modeling Batch 1, substitute the corresponding biomarker content information in Example 2 into the binary logistic equation constructed in Example 1. Example 2 is used to verify the feasibility of the combined use of the above two biomarkers to further confirm its discrimination effect.

[0066] Include the samples of the control group and the low-dose BPS group (0.05 mg / kg / d) of Modeling Batch 1 in the discrimination comparison. Based on the judgment variable P obtained from this biomarker, the area under the ROC curve (AUC value) for discrimination is 1( Figure 4 B), at a cut-off value of 0.5, both the sensitivity and specificity are 100% (Table 1). The above results indicate that this biomarker has good discrimination potential and can be used to discriminate the risk of insulin resistance under BPS exposure.

[0067] Example 3

[0068] 1. Collection of white adipose tissue samples from offspring mice:

[0069] This example uses the high-dose study group of another batch of modeling (i.e., Modeling Batch 2) at different times.

[0070] Collect white adipose samples from offspring mice on a high-fat diet under high-dose (5 mg / kg / d) BPS exposure during the perinatal period of female mice in Modeling Batch 2. The collection method is the same as in Example 1.

[0071] 2. Sample pretreatment: The same as in Example 1.

[0072] 3. Data collection: The same as in Example 1.

[0073] 4. Data preprocessing: The same as in Example 1.

[0074] 5. Based on the comparison between the control group (C) and the high-dose BPS group (5 mg / kg / d) in the second batch of model establishment, substitute the corresponding marker content information in Example 3 into the binary logic equation constructed in Example 1. Example 3 is used to verify the feasibility of the combined use of the above two markers to further confirm its discrimination effect.

[0075] The samples of the control group and the high-dose BPS group (5 mg / kg / d) in the second batch of model establishment were included in the discrimination comparison. Based on the judgment variable P obtained from this marker, the area under the ROC curve (AUC value) for discrimination was 1( Figure 4 C). At a cut-off value of 0.5, both the sensitivity and specificity were 100% (Table 1). The above results indicate that this marker has good discrimination potential and can be used for the discrimination of the risk of insulin resistance under BPS exposure.

[0076] Table 1. ROC curve analysis results of the combined marker

[0077]

Claims

1. A combined marker, characterized in that: The combined marker is a marker in white adipose tissue, composed of 3-hydroxybutyrylcarnitine It is composed of ((R)-3-hydroxy-butyrylcarnitine) and ergothioneine (L-Ergothioneine).

2. Use of the combined marker described in claim 1 in the assessment and identification of the risk of insulin resistance in offspring of maternal rats exposed to BPS during the perinatal period and fed a high-fat diet.

3. Use of the combined marker of claim 1 in distinguishing the difference between offspring of maternal mice exposed to BPS during the perinatal period and those of maternal mice not exposed to BPS and fed a high-fat diet.

4. A kit for determining whether there is a risk of insulin resistance in offspring of maternal rats exposed to BPS during the perinatal period, characterized in that The kit comprises a combined marker, which comprises: L-Ergothioneine and (R)-3-hydroxy-butyrylcarnitine.

5. A kit for determining whether there is a risk of insulin resistance in offspring of maternal rats exposed to BPS during the perinatal period according to claim 4, characterized in that: The kit also includes: Extraction solution: used for pre-treating white adipose tissue samples from offspring mice, the extraction solution is a methanol solution of two types of internal standards in positive ion analysis mode; Eluent: Mobile phase A is ultrapure water (containing 0.1% formic acid), and mobile phase B is acetonitrile (containing 0.1% formic acid).

6. A kit for determining whether there is a risk of insulin resistance in offspring of maternal rats exposed to BPS during the perinatal period according to claim 5, characterized in that The extracts are 0.50ug / ml of Tryptophan-d5 for calibration of L-Ergothioneine and 0.50ug / ml of 3-hydroxybutyrylcarnitine for calibration of ((R)-3-hydroxy-butyrylcarnitine) Carnitine C2-d3 at 0.25ug / ml.

7. A method for calculating the combined marker variable in a white adipose tissue sample of a mouse, characterized in that: The markers include ergothioneine and 3-hydroxybutyrylcarnitine, and the method includes the following steps: 1) Treating white adipose tissue samples from offspring mice with an extract containing an internal standard, precipitating proteins, further extracting metabolites, and then freeze-drying the samples. After reconstitution, ultra-performance liquid chromatography-mass spectrometry analysis was performed; 2) on the chromatogram that ultra performance liquid chromatography-mass spectrometry obtains, extract the chromatographic peak intensity of thioneine and 3-hydroxybutyrylcarnitine; 3) The detected ions are qualitatively confirmed with the standard products of two substances, ergothioneine and 3-hydroxybutyrylcarnitine, in the kit: the two substances standard solutions are subjected to ultra-high performance liquid chromatography-mass spectrometry analysis to determine the chromatographic retention time of the two standard ions, the measured mass-to-charge ratio of the two ions, and the secondary mass spectrometry characteristic ions, and compared with the two substances measured in the test sample; 4) Compare the chromatographic peak intensities of the two substances in each test sample after qualitative analysis with those of the internal standard in the extract to obtain the relative concentrations of the two substances; 5) The relative concentration values ​​of the above two metabolites were used to calculate the combined marker variable P based on the binary logistic regression equation.

8. A method for calculating the combined marker variable in the white adipose tissue sample of offspring mice as claimed in claim 7, characterized in that Step 2), the extraction parameters of thioneine are: positive ion mode, mass-to-charge ratio is the ion of 230.0958 ± 10ppm; The extraction parameters of 3-hydroxybutyrylcarnitine are: positive ion mode, mass-to-charge ratio is the ion of 248.1492 ± 10ppm.

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