CN112986425B - PM 2.5 Detection marker for influence on lipid metabolism and application - Google Patents
PM 2.5 Detection marker for influence on lipid metabolism and application Download PDFInfo
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Abstract
The invention provides a PM 2.5 Detection marker for influence on lipid metabolism, PM 2.5 The detection markers are DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2. The serum lipidomic analysis is carried out by adopting a liquid chromatography-tandem mass spectrometry method, and the method combines the advantages of non-targeted high coverage, full-scanning accurate molecular weight qualitative and targeted multi-reaction monitoring (MRM) accurate quantification, is simple, convenient, sensitive and accurate, and can be used for researching toxicology differential metabolites.
Description
Technical Field
The invention relates to the field of biological medicine, mainly to diglyceride DG through PM 2.5 Differential lipid detection after exposure and found that the ratio of DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2 lipidomic differential metabolites could be used as PM 2.5 Exposure to markers that cause alterations in body lipid metabolism and their use in lipid metabolism studies.
Background
Air pollution is considered a global public health problem, and it is reported that about 90% of the world population lives in areas where air quality exceeds WHO guidelines. Particulate Matter (PM) is any particle suspended in air. Classified into inhalable Particles (PM) according to particle size of the particles 10 ) Fine particulate matter (PM 2.5 ) And ultrafine Particulate Matter (PM) 0.1 ). Wherein PM 2.5 (particles less than 2.5 microns in diameter) can penetrate deeply into the lungs, stimulating and corroding the alveolar walls, thereby compromising lung function. And environmental fine Particulate Matter (PM) 2.5 ) Air pollution is associated with an increased risk of various causes of death and is a currently known hazardous air pollutant that threatens public health.
Metabonomics is an emerging field involving the analysis of low molecular weight molecules or metabolites in biological samples, taking into account thatMetabolic characterization is considered a key element between genes and phenotypes, and metabolomics has begun to be applied to identify candidate biomarkers and to better understand the underlying mechanisms associated with air pollution. It combines the most advanced analytical instruments and pattern recognition techniques, and the emerging technologies of metabonomics include mass spectrometry, 1 H-NMR spectroscopy and liquid chromatography-mass spectrometry can monitor and discover metabolic changes in subjects associated with disease conditions.
Lipidomics are an important branch of metabonomics, and lipid metabolic disorders are known to be important biological features in the progression of a variety of diseases. Metabonomics and lipidomics studies have the unique advantage of identifying tissue/mechanism specific biomarkers, predicting therapeutic and clinical outcome, and enhancing our understanding of the pathophysiological basis of disease states. However, currently, PM is concerned 2.5 Exposure-related lipidomics studies remain less. Thus, for PM 2.5 The research in the field of exposed lipidomics remains to be explored further.
Metabonomics analysis methods are divided into targeted metabonomics and non-targeted metabonomics: the non-targeted metabolomics is based on limited background knowledge, and the system identification analysis is carried out on the whole organism metabolome to obtain metabolite data and discover differential metabolites.
Targeted metabolomics is more targeted than non-targeted metabolomics, places interest on several or classes of metabolites associated with biological events, and has significantly improved reproducibility and sensitivity.
Non-patent documents Exposure to fine airborne particulate matter induces macrophage infiltration, unfoldedprotein response, and lipid deposition in white adipose tissue disclose PM 2.5 Effects on lipid metabolism but PM is not presently disclosed 2.5 A marker for detecting an effect on lipid metabolism.
Disclosure of Invention
In a first aspect of the invention, there is provided a PM 2.5 A marker for detecting an effect on lipid metabolism, said marker being a diglyceride product, preferably said diglyceride product comprising DG16:0_16:0, DG16:0_18:2 or DG18:0_18:2.
Preferably, PM 2.5 The basis for diagnosing the influence of lipid metabolism is the ratio of FC values of the respective diglyceride products.
Preferably, the ratio of FC values of the diglyceride products is (DG16:0:0)/(DG16:0:18:2), (DG16:0:18:2)/(DG18:0:18:2) or (DG16:0:0)/(DG18:0:18:2), wherein the ratio of FC values is slightly affected when 0-1, the ratio of FC values is slightly affected when 1-2, and the ratio of FC values is slightly affected when more than 2.
In a second aspect of the invention, there is provided a method of screening for the treatment or prophylaxis of PM 2.5 A method of treating a drug affecting lipid metabolism comprising the steps of:
a) Placing biological sample in PM 2.5 Culturing in the presence of drug candidates at an exposure dose;
b) Placing biological sample in PM 2.5 Culturing in the absence of drug candidates at the exposure dose;
c) Detecting PM in a biological sample of steps a) and b) 2.5 A value of a detection marker for an effect on lipid metabolism;
or c) determining lipid metabolism levels and animal mortality changes in the biological sample in both step a) and step b), whereby the results indicate whether the candidate drug can be used for the treatment or prevention of PM 2.5 Effects on lipid metabolism.
Preferably, the biological sample is an animal model, and more preferably, the animal model is selected from the group consisting of rat, mouse, rabbit, and monkey.
In a third aspect of the invention, there is provided a detection marker for use in the preparation of diagnostic PM 2.5 Use of a product affecting lipid metabolism, wherein said marker is a diglyceride product, preferably said diglyceride product comprises DG16:0_16:0, DG16:0_18:2 or DG18:0_18:2.
In a fourth aspect of the invention, there is provided a method of treating or preventing PM 2.5 Drugs affecting lipid metabolism targeting individual diglyceride products or combinations of diglyceride products, preferablyThe diglyceride product comprises DG16:0_16:0, DG16:0_18:2 or DG18:0_18:2.
In a fifth aspect of the invention, there is provided a PM 2.5 A method for detecting an effect on lipid metabolism, said method comprising detecting PM 2.5 Preferably, the detection marker for the influence of lipid metabolism is a diglyceride product, and more preferably, the diglyceride product comprises DG16:0:16, DG16:0:18:2 or DG18:0:18:2.
Preferably, the detection method comprises the following steps:
(1) Obtaining a lipid sample;
(2) Detecting the lipid sample using liquid chromatography-tandem mass spectrometry (LC-MS/MS);
(3) Calculating the ratio of FC values of the respective diglyceride products from the results obtained in the step (2).
Preferably, in the step (3), the ratio of the FC values is (DG16:0:0)/(DG16:0:18:2), (DG16:0:18:2)/(DG18:0:18:2) or (DG16:0:0)/(DG18:0:18:2), wherein the ratio of the FC values is slightly affected when 0 to 1, the ratio of the FC values is moderately affected when 1 to 2, and the ratio of the FC values is slightly affected when more than 2.
Preferably, in the step (1), the lipid sample is a serum lipid sample of a human or animal.
Preferably, in the step (2), the parameters of the high performance liquid chromatography include: mobile phase a consisted of tetrahydrofuran/methanol/water (30:20:50, v/v) and 10mmol/l ammonium formate. Mobile phase B consisted of tetrahydrofuran/methanol/water (75:20:5, v/v) and 10mmol/l ammonium formate; the flow rate is 0.6mL/min, and the sample injection amount is 2 μl; the column temperature is 55 ℃; the mobile phase gradient is 0.0min to 100% mobile phase A and 0% mobile phase B; 80% mobile phase A, 20% mobile phase B in 1.0 min; 3.0min,60% mobile phase A, 40% mobile phase B; 45% mobile phase A and 55% mobile phase B in 3.5 min; 7.5min 25% mobile phase A, 75% mobile phase B; 0% mobile phase A, 100% mobile phase B in 9.0 min; 11.0min:0% mobile phase a, 100% mobile phase B;11.5min 100% mobile phase A, 0% mobile phase B;13.0min:0% mobile phase A, 100% mobile phase B, or mobile phase gradient 0.0min:100% mobile phase A, 0% mobile phase B; 80% mobile phase A, 20% mobile phase B in 1.0 min; 3.0min,60% mobile phase A, 40% mobile phase B; 45% mobile phase A and 55% mobile phase B in 3.5 min; 7.5min 25% mobile phase A, 75% mobile phase B; 0% mobile phase A, 100% mobile phase B in 9.0 min; 11.0min:0% mobile phase a, 100% mobile phase B;11.5min 98% mobile phase A, 2% mobile phase B.
Preferably, in the step (2), the mass spectrum parameters include: adopting an electrospray ionization scanning detection mode and a multi-reaction monitoring (MRM) mass spectrum scanning mode; the Ion spray voltage was 5.5kv, the Ion source temperature was 500 ℃, the atomizing gas pressure (Ion source gas 1) was 50, and the assist gas pressure (Ion source gas 2) was 50.
In a sixth aspect of the invention, there is provided a method of detecting a marker in evaluating PM 2.5 Use of a marker for the detection of an effect on lipid metabolism, said marker being a diglyceride product, preferably said diglyceride product comprising DG16:0_16:0, DG16:0_18:2 or DG18:0_18:2.
In a seventh aspect of the invention, there is provided the use of a detection marker for reducing blood lipid, reducing visceral fat, inhibiting weight gain, said detection marker being a diglyceride product, preferably said diglyceride product comprising DG16:0_16:0, DG16:0_18:2 or DG18:0_18:2.
The term FC value as used herein refers to the fold difference in metabolite concentration.
The lipids such as DG16:0/16:0, DG16:0/18:2 and DG18:0 in the invention are diglycerides, wherein the numbers represent the number of carbon and hydrogen bonds, for example, 16:0 in DG16:0/16:0 represents 16 carbons and 0 hydrogen bonds; in 17:1LPA, LPA is lysophosphatidic acid, 17:1 represents 17 carbons, 1 hydrogen bond.
The method combines the advantages of non-targeted high coverage, full-scan accurate molecular weight characterization and targeted multi-reaction monitoring (MRM) accurate quantification. Has the characteristics of simplicity, convenience, sensitivity and accuracy, and can be used for researching toxicology differential metabolites.
The foregoing is merely illustrative of some aspects of the present invention and is not, nor should it be construed as limiting the invention in any respect.
All patents and publications mentioned in this specification are incorporated herein by reference in their entirety. It will be appreciated by those skilled in the art that certain changes may be made thereto without departing from the spirit or scope of the invention.
The following examples further illustrate the invention in detail and are not to be construed as limiting the scope of the invention or the particular methods described herein.
Drawings
Embodiments of the present invention are described in detail below with reference to the attached drawing figures, wherein:
fig. 1: PM (particulate matter) 2.5 Effects on APOE mouse serum lipid metabolites DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2, wherein FIG. 1A is PM 2.5 The effect on DG16:0_16:0, FIG. 1B is PM 2.5 Impact on DG16:0_18:2, FIG. 1C is PM 2.5 Impact on DG18:0_18:2, control is Control group, PM 2.5 Is PM 2.5 Treatment group, 2-mole-exposure represents exposure PM 2.5 In the environment for 2 months, 1-mole-recovery is under PM exposure 2.5 After the environment reached 2 months and after a recovery period of one month, the observations were made, representative of P compared to the control group under 2-montah-exposure conditions<0.05; # represents P compared with the control group under 1-mole-recovery condition<0.05；
Fig. 2: APOE mouse serum lipid profile, wherein Control is Control group, PM 2.5 Is PM 2.5 Treatment group, 2-mole-exposure represents exposure PM 2.5 In the environment for 2 months, 1-mole-recovery is under PM exposure 2.5 After the environment reaches 2 months, the environment is observed after a recovery period of one month;
fig. 3: PM (particulate matter) 2.5 Birth queue analysis of effects on neonatal lipid metabolites DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2.
Fig. 4: birth queue lipid profile, wherein Control is Control, PM 2.5 Is PM 2.5 Treatment groups.
Detailed Description
The invention will be further described with reference to specific embodiments, and advantages and features of the invention will become apparent from the description. These examples are merely exemplary and do not limit the scope of the invention in any way. It will be understood by those skilled in the art that various changes and substitutions of details and forms of the technical solution of the present invention may be made without departing from the spirit and scope of the present invention, but these changes and substitutions fall within the scope of the present invention.
In each of the following examples, the devices and materials were obtained from several companies as indicated below:
mice deficient in ApoE (ApoE -/- ) Purchased from beijing life river laboratory animal technology limited (beijing, china);
the large-volume air particle sampler is purchased from Wuhan Sihong, china, and has the goods number: TH-1000CII;
the low volume continuous PM sampler was purchased from APM Engineering co.ltd; cargo number: PMS-104;
quartz fiber filters were purchased from Pall, model: 8X 10in;47mm;
polytetrafluoroethylene (PTFE) filters were purchased from Tisch, model: 47mm;
ultra performance liquid chromatography and triple quadrupole mass spectrometry (UPLC-QqQ-MS/MS) were purchased from ABI, cat: ABI6500;
kineex C18 column was purchased from Phenomenex, model number: 4.6 mm. Times.100 mm,2.6 μm.
The internal standard sample Avanti is
Mass Spec Standard from Avanti Polar Lipids, inc., model 330707;
distilled water was purchased from dronesian, cat No.: bj9231;
isopropyl alcohol was purchased from Fisher, cat: a451-4;
methanol was purchased from Merck, cat: 1.06035.1000;
ammonium formate was purchased from Sigma, cat: 70221-25G-F;
HPLC chloroform was purchased from Merck, cat: 1.02444.4000;
HPLC tetrahydrofuran was purchased from Merck, cat: 1.08101.4008;
brown sample bottles were purchased from Waters, cat: 186000848;
sample caps were purchased from Waters, cat: 186000305;
150ul of the insert with spring was purchased from Waters, waters cat: wat094171
10mL glass centrifuge tube, round bottom purchased from Shanghai Annotation laboratory technologies Co., ltd., product number: ABBO-2016010401;
SIMCA version 14.1 is available from Umetrics, sweden;
LipidSearch software was purchased from Thermo Fisher Scientific;
IBM SPSS version 25.0;
r software, version: 3.6.0;
analytical software model: ABI6500 version 1.6.
Example 1 PM 2.5 Effects on lipid metabolism in mice
1. Construction of ApoE knockout (silencing) mouse hyperlipidemia model
ApoE knockout (ApoE-knockout, apoE-/-, apoE-knockout) mice are the most widely used and more studied ones of genetically engineered mice. Among all animal models of hyperlipidemia, apoE knockout mice are the most typical animals conforming to human mixed hyperlipidemia.
Mice deficient in ApoE (ApoE -/- ) Obtained from Beijing Living river laboratory animal technologies Co., ltd (Beijing, china). All animal experiments were approved by the laboratory animal welfare committee of the university of capital medical science. Male mice of 8 weeks of age were fed a high fat diet (consisting of 21% fat and 0.15% cholesterol) randomized into control and PM 2.5 Treatment groups. PM (particulate matter) 2.5 ApoE of the treatment group -/- Mice received intratracheal instillation of PM every 3 days under anesthesia (2, 2-tribromoethanol, 10mg/kg bw) 2.5 For up to two months, whereas the control group eluted saline intratracheal instillation with a corresponding volume of blank filter. 13 mice per group, were instilled with PM in the trachea 2.5 2 pieces ofAfter month recovery was 1 month. Upon exposure to PM 2.5 Immediately after 2 months or 1 month recovery, the animals were euthanized.
2.PM 2.5 Sample collection and extraction
A large volume air particle sampler and two small volume continuous PM samplers are employed. The sampling site is located on the roof of the campus building in Beijing one of China, and is 6m away from the ground. The sampling point is located on the roof of a campus building in Beijing, china and is 6m away from the ground. To collect particulate matter for biological studies, month 4 to 5 of 2016, PM was passed through a bulk sampler using a quartz fiber filter (8X 10in, pall, USA) at a constant flow rate of 1.05m3/min 2.5 PM is gathered to cutting point 2.5 . In terms of chemical and physical characterization, PM 2.5 PM with a constant flow rate of 16.67L/min by a small volume sampler on quartz fiber filters (47 mm, pall, U.S.) and Polytetrafluoroethylene (PTFE) filters 2.5 Is the cutting point. The mini-filter was equilibrated for 48 hours (relative humidity 30%,25 ℃) and weighed before and after sampling. The sampled filters were stored at-80 ℃ in a dark environment until further processing.
The dried sample is diluted and mixed by sterile normal saline after being irradiated by ultraviolet rays for 2 hours, and PM is suspended by ultrasound for 30 minutes 2.5 And (5) standby. PM (particulate matter) 2.5 Based on physiological parameters of mice and WHO air quality guidelines (WHO): the respiration rate of 25g mice was 163 times per minute and the respiration rate was 0.15ml per breath. Average PM of 24 hours according to temporary target-1 (IT-1) 2.5 (75. Mu.g/m) 3 By WHO of PM, the amount recommended) the daily exposure was 2.6 micrograms. PM to be exposed daily after 100 times of uncertainty is applied 2.5 The concentration was 10mg/kg bw.
3. Serum lipid sample preparation
The internal standard samples Avanti comprise 17:1LPA (lysophosphatidic acid), 17:1LPC (lysophosphatidylcholine), 17:0-14:1PA (phosphatidic acid), 17:0-14:1PC (phosphatidylcholine), 17:0-14:1PE (phosphatidylethanolamine), 17:0-14:1PG (phosphatidylglycerol), 17:0-14:1PI (phosphatidylinositol), 17:0-14:1PS (phosphatidylserine), 19:0 Cholesterol Ester (CE), sphingosine (D17:1), C17 ceramide (d18:1/17:0), lyso SM (D17:1), 17:0SM (d18:1/17:0), cholesterol (D7), cardiolipin mixture I, deuterated TG mixture I and deuterated DG mixture I. The preparation method of the serum lipid sample of the ApoE gene knockout mouse comprises the following specific steps:
1. taking 80u1 samples (serum) into a 4ml glass centrifuge tube;
2. adding 10ul internal standard (-20deg.C for preservation);
3. adding 300ul of methanol (Merck liquid chromatography level), and mixing by vortex for 60s;
4. adding 500ul chloroform, and mixing by vortex for 60s;
5. adding 250ul of water (distilled water of the Chen-Chen type), and mixing by vortex for 60s;
6. centrifuging at normal temperature for 3000r and 10 min;
7. collecting the lower chloroform layer into a 2ml EP tube;
8. adding 600ul chloroform into the glass centrifuge tube, and repeating the steps 6 and 7;
9. drying with nitrogen, and storing in-80deg.C refrigerator;
10. and (3) re-dissolving: taking 100ul of isopropanol/methanol (50:50) complex solution, and re-suspending;
11. the supernatant was vortexed for 15s and after centrifugation at 4000rpm for 15min at 4℃the supernatant was aspirated into a brown flask for LC-MS/MS analysis.
4. Blood lipid analysis
Targeted lipidomic analysis was performed using ultra high performance liquid chromatography and triple quadrupole mass spectrometry (UPLC-QqQ-MS/MS). Chromatographic separation was performed using a kineex C18 column, the column temperature being maintained at 55 ℃.
Mobile phase a consisted of tetrahydrofuran/methanol/water (30:20:50, v/v) and 10mmol/l ammonium formate. Mobile phase B consisted of tetrahydrofuran/methanol/water (75:20:5, v/v) and 10mmol/l ammonium formate. The flow rate and the sample injection amount were 0.6ml/min and 2. Mu.l, respectively. The gradient procedure was as follows:
0 minutes, 100% mobile phase a;0% mobile phase B.1min,80% mobile phase A;20% mobile phase B.3.0 minutes, 60% mobile phase a;40% mobile phase B.3.5 minutes, 45% mobile phase a;55% mobile phase B.7.5 minutes, 25% mobile phase a;75% mobile phase B.9.0 minutes, 0% mobile phase A;100% mobile phase B.11 minutes, 0% mobile phase a;100% mobile phase B.11.5 minutes, 100% mobile phase a;0% mobile phase B.13.0 minutes, 0% mobile phase a;100% mobile phase B.
Wherein, mass spectrum detection conditions are: adopting an electrospray ionization scanning detection mode and a multi-reaction monitoring (MRM) mass spectrum scanning mode; the Ion spray voltage was 5.5kv, the Ion source temperature was 500 ℃, the atomizing gas pressure (Ion source gas 1) was 50, and the assist gas pressure (Ion source gas 2) was 50.
The high energy collision decomposition MS 2 mode, which depends on negative data, is used for data acquisition. We used LipidSearch software (us Thermo Fisher Scientific) for peak selection and alignment. The data were then quantized and log transformed by comparison with an internal standard, which was then further imported into SIMCA (version 14.1; umetrics, sweden) for multivariate statistical analysis. Statistical differences between the evaluation groups were examined using Student's t. The differential lipids are thought to pass the thresholds VIP >1, p <0.05 and FC >1.5 or < 0.67.
5. Results
PM 2.5 Effects on serum lipid metabolites of APOE mice as shown in fig. 1, differential lipid heatmaps of APOE knockout mice as shown in fig. 2, diglyceride series products, DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2, were statistically different, and FC values were 0.45, 0.23, 0.16, pm, respectively 2.5 The contents of the three lipids in the treatment group are reduced compared with the control group. Wherein the ratio of each FC (dg16:0:0)/(dg16:0:18:2) =1.957, (dg16:0:18:2)/(dg18:0:18:2) =1.438 is between 1 and 2, PM 2.5 A moderate effect on the lipid; (dg16:0_16:0)/(dg18:0_18:2) = 2.813)>2，PM 2.5 The effect is more remarkable.
Example 2 PM 2.5 Influence on lipid metabolism in human body
1. Birth queue and data collection for pregnant women
In birth queues, we recruited 203 pregnant women from the affiliated Beijing gynaecology and obstetrics hospitals of the university of capital medical science in Beijing, china, from 2 months 2017 to 10 months 2018. Inclusion criteria: a. age 20-45 years old; b. no smoking history or smoking cessation for more than 3 years; c. living in Beijing city for over 1 year before pregnancy; d. and (5) a single tire. We excluded participants who had a history of hypertension, diabetes and other systemic diseases known prior to pregnancy; long-term drug treatment; fetal chromosomal abnormalities and stillbirth. Subjects were divided into a hyperlipidemia group and a control group according to the concentration of serum Triglycerides (TG) of pregnant women. Cord blood serum was collected and stored at-80 ℃. Gestational age is calculated from the date of birth minus the Last Menstrual Period (LMP). If it is 2500 g at birth after 37 weeks gestation, it is defined as term Low Birth Weight (LBW), and if it is 37 weeks gestation, it is defined as premature birth (PTB). Participants signed project informed consent. The ethical committee of the hospital in gynaecology and obstetrics, beijing affiliated to the university of capital medical science approved the study.
2.PM 2.5 Contamination exposure assessment
PM during study 2.5 The ground measurement data comes from the chinese environmental monitoring center (CNEMC). On the basis of our previous work, we updated the estimates using MAIAC AOD products downloaded from the NASA climate simulation center (ftp:// maiac@dataportal.nccs.nasa.gov/DataRelease /). The study predicts PM of participants at 1km spatial resolution based on ground monitoring data, satellite remote sensing, land utilization information (percent urban coverage and greening) and meteorological information (temperature, barometric pressure, wind speed and relative humidity) 2.5 Exposure amount. Model development uses a random forest model (machine learning algorithm) that is robust to overfitting and noise and exhibits high predictive power. The 10-fold cross-validation (CV) results showed that in this study, PM 2.5 CVR2 of daily estimated concentration [ Root Mean Square Error (RMSE)]85%. Gestation specific average exposure was calculated from daily concentrations and defined as gestation stage 1 (1-90 days), stage 2 (91-180 days), stage 3 (181 days to childbirth) and whole gestation period.
3. Preparation of lipidomic specimens
The internal standard is purchased from Avanti [ (15:0-18:1 (d 7) PC (lecithin), 15:0-18:1 (d 7) PE (phosphatidylethanolamine), 15:0-18:1 (d 7) PS (phosphatidylserine), 15:0-18:1 (d 7) PG (phosphatidylglycerol), 15:0-18:1 (d 7) PI (phosphatidylinositol), 15:0-18:1 (d 7) PA (phosphatidic acid), 18:1 (d 7) LPC (lysophosphatidylcholine), 18:1 (d 7) LPE (lysophosphatidylethanolamine), 18:1 (d 7) Cholesterol Ester (CE), 18:1 (d 7) MG (monoglyceride), 15:0-18:1 (d 7) DG (diglyceride), 15:0-18:1 (d 7) 15:0TG (triglyceride), d18:1-18:1 (d 9) SM (sphingomyelin), cholesterol (d 7) ].
The preparation experimental steps of the serum lipid sample of the pregnant woman are as follows:
1. taking 80u1 samples (serum) into a 10ml glass centrifuge tube;
2. adding 10ul internal standard (-20deg.C for preservation);
3. 600ul of methanol (Merck liquid chromatography level) is added, and vortex mixing is carried out for 60s;
4. 1000ul of chloroform is added, and vortex mixing is carried out for 60s;
5. adding 500ul of water (distilled water of the Chen-Chen type), and mixing by vortex for 60s;
6. centrifuging at normal temperature for 3000r and 10 min;
7. collecting the lower chloroform layer into a 2ml EP tube;
8. adding 600ul chloroform into the glass centrifuge tube, and repeating the steps 6 and 7;
9. drying with nitrogen, and storing in-80deg.C refrigerator;
10. and (3) re-dissolving: taking 100ul of isopropanol/methanol (50:50) complex solution, and re-suspending;
11. the supernatant was vortexed for 15s and after centrifugation at 4000rpm for 15min at 4℃the supernatant was aspirated into a brown flask for LC-MS/MS analysis.
4. Lipid component analysis
The two mobile phases were subjected to targeted lipidomic analysis using liquid chromatography/tandem triple quadrupole mass spectrometry: phase A, tetrahydrofuran: methanol: water (30:20:50, v/v) and 10mM/L ammonium formate; phase B, tetrahydrofuran: methanol: water (75:20:5, v/v) and 10mM/L ammonium formate. By using
C18 LC column (100X 4.6mm,2.6 μm, phenomenex, USA) was maintained at 55deg.C, flow rate 0.6mL/min, and sample injection amount 2. Mu.L was used for chromatographic separation. The gradient procedure is: 0min,100% mobile phase a;0% mobile phase B.1min,80% mobile phase a;20% mobile phase B.3.0min,60% mobile phase A;40% mobile phase B.3.5min,45% mobile phase A;55% mobile phase B.7.5min,25% mobile phase A;75% mobile phase B.9.0min,0% mobile phase A;100% mobile phase B.11min,0% mobile phase a;100% mobile phase B.11.5min,98% mobile phase A;2% mobile phase B. The mobile phase analysis software (ABI 6500version 1.6, usa) performs peak extraction and alignment.
Wherein, mass spectrum detection conditions are: adopting an electrospray ionization scanning detection mode and a multi-reaction monitoring (MRM) mass spectrum scanning mode; the Ion spray voltage was 5.5kv, the Ion source temperature was 500 ℃, the atomizing gas pressure (Ion source gas 1) was 50, and the assist gas pressure (Ion source gas 2) was 50.
Lipid species and fat factor concentrations were logarithmically converted to an approximately normal distribution. According to the prior literature ("Effects of prenatal exposure to air particulate matter on the risk ofpreterm birth and roles ofmaternal and cord blood LINE-1methylation:A birth cohort study in Guangzhou,China[J)]Environment International ",2019,133 (2019): 105177" study of exposure of atmospheric fine particulate matter PM2.5 during pregnancy and influence on premature labor ". J.sinensis epidemiology 2016,37 (4): 572-577". Effect ofairborne particulate matter of 2.5.5. Mu. m or less on preterm birth: A national birth cohort study in China ". Environment International,2018,121 (PT.2): 1128-1136.) based on their association with PM 2.5 The following variables are considered potential confounding factors for potential correlation of exposure or poor labor outcome: age of mother; antenatal Body Mass Index (BMI); carrying out pregnancy; performing embryo times; a conception season; complications of pregnancy (hypertension of pregnancy, gestational diabetes, etc.); a delivery mode; sex of neonate; ambient air temperature; relative humidity. We use a multivariate linear regression model to evaluate PM 2.5 The exposure is correlated with umbilical cord blood fat factor or lipidome. We tested the correction effect of maternal hyperlipidemia based on maternal serum triglyceride concentrations in a moderation analysis regulatory analysis model. These analyses were tailored to the potential confounding factors described above.
5. Clinical sample experimental results
All statistical analysesBoth were performed using IBM SPSS statistics version 25.0 and R3.6.0 software. p value<0.05 is statistically significant. By analyzing umbilical cord blood of high-fat pregnant women, diglyceride products were found: dg16:0_16:0, dg16:0_18:2 and Dg18:0_18:2 all have statistical differences, PM 2.5 The effect on neonatal lipid metabolites is shown in figure 3 and the birth queue lipid profile is shown in figure 4. At the same time, early pregnancy PM 2.5 The median of the concentration is defined as PM 2.5 The FC values of dg16:0_16:0, dg16:0_18:2 and dg18:0_18:2 were calculated as 0.98, 0.91, 0.46, respectively, and the FC ratios of the three (dg16:0:0)/(dg16:0_18:2) =1.08, (dg16:0:18:2)/(dg18:0_18:2) =1.95, pm 2.5 Are all between 1 and 2 PM 2.5 The two are moderately influenced; (dg16:0_16:0)/(dg18:0_18:2) =2.11, pm 2.5 Is a weight impact.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the simple modifications belong to the protection scope of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described further.
Moreover, any combination of the various embodiments of the invention can be made without departing from the spirit of the invention, which should also be considered as disclosed herein.
Claims (7)
1. PM (particulate matter) 2.5 A method for detecting an effect on lipid metabolism, comprising detecting PM 2.5 The FC value of a detection marker for the influence of lipid metabolism is a diglyceride product, the diglyceride product is a combination of at least two of DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2, and the ratio of the FC values of the diglyceride products is as followsThe value is (DG16:0:0)/(DG16:0:18:2), (DG16:0:18:2)/(DG18:0:18:2) or (DG16:0:0)/(DG18:0:18:2), wherein the ratio of the FC values is slightly affected when 0-1, the ratio of the FC values is moderately affected when 1-2, and the ratio of the FC values is slightly affected when more than 2.
2. The method according to claim 1, wherein the method comprises:
(1) Obtaining a lipid sample;
(2) Detecting the lipid sample by liquid chromatography-tandem mass spectrometry;
(3) Calculating the ratio of FC values of the respective diglyceride products from the results obtained in the step (2).
3. The method according to claim 2, wherein in the step (1), the lipid sample is a serum lipid sample of a human or animal.
4. The method according to claim 3, wherein in the step (2), the parameters of the liquid chromatography include: mobile phase a consisted of tetrahydrofuran/methanol/water (30:20:50, v/v) and 10mmol/l ammonium formate, mobile phase B consisted of tetrahydrofuran/methanol/water (75:20:5, v/v) and 10mmol/l ammonium formate; the flow rate is 0.6mL/min, and the sample injection amount is 2 μl; the column temperature is 55 ℃; the mobile phase gradient is 0.0min to 100% mobile phase A and 0% mobile phase B; 80% mobile phase A, 20% mobile phase B in 1.0 min; 3.0min,60% mobile phase A, 40% mobile phase B; 45% mobile phase A and 55% mobile phase B in 3.5 min; 7.5min 25% mobile phase A, 75% mobile phase B; 0% mobile phase A, 100% mobile phase B in 9.0 min; 11.0min:0% mobile phase a, 100% mobile phase B;11.5min 100% mobile phase A, 0% mobile phase B;13.0min:0% mobile phase A, 100% mobile phase B, or mobile phase gradient 0.0min:100% mobile phase A, 0% mobile phase B; 80% mobile phase A, 20% mobile phase B in 1.0 min; 3.0min,60% mobile phase A, 40% mobile phase B; 45% mobile phase A and 55% mobile phase B in 3.5 min; 7.5min 25% mobile phase A, 75% mobile phase B; 0% mobile phase A, 100% mobile phase B in 9.0 min; 11.0min:0% mobile phase a, 100% mobile phase B;11.5min 98% mobile phase A, 2% mobile phase B.
5. The method of any one of claims 2-4, wherein the mass spectrometry parameters comprise: adopting an electrospray ionization scanning detection mode and a mass spectrum scanning mode of multi-reaction monitoring; the Ion spray voltage was 5.5kv, the Ion source temperature was 500 ℃, and Ion source gas 1 was 50,Ion source gas 2 and 50.
6. Preparation and evaluation of PM by detection markers 2.5 The application of the detection marker in the products affecting lipid metabolism is characterized in that the detection marker is a diglyceride product, the diglyceride product is a combination of at least two of DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2, the ratio of FC values of the diglyceride product is (DG16:0:0)/(DG16:0_18:2), (DG16:0:18:2)/(DG18:0_18:2) or (DG16:0:0)/(DG18:0_18:2), wherein the ratio of FC values is slightly affected when 0-1, the ratio of FC values is moderately affected when 1-2, and the ratio of FC values is slightly affected when more than 2.
7. Detection of markers in the evaluation of PM 2.5 The application of the detection marker in the lipid metabolism influence is characterized in that the detection marker is a diglyceride product, the diglyceride product is a combination of at least two of DG16:0_16:0, DG16:0_18:2 and DG18:0_18:2, the ratio of FC values of the diglyceride product is (DG16:0:0)/(DG16:0_18:2), (DG16:0_18:2)/(DG18:0_18:2) or (DG16:0:16:0)/(18:0_18:2), wherein the ratio of the FC values is slightly influenced when the ratio of the FC values is 1-2, and the ratio of the FC values is more than 2 and is slightly influenced when the ratio of the FC values is more than 2.
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