CN116429926A - Non-targeted screening method for antibiotics in soil - Google Patents
Non-targeted screening method for antibiotics in soil Download PDFInfo
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- CN116429926A CN116429926A CN202310251228.7A CN202310251228A CN116429926A CN 116429926 A CN116429926 A CN 116429926A CN 202310251228 A CN202310251228 A CN 202310251228A CN 116429926 A CN116429926 A CN 116429926A
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Abstract
A non-targeted screening method for antibiotics in soil belongs to the technical field of soil detection. The method applies a metabonomics method to non-targeted screening of antibiotics in soil, and changes the process of searching for a biomarker in the traditional metabonomics into the process of searching for a marker compound corresponding to residual antibiotics in the soil in the research. The method has high feasibility and expands application fields, and lays a solid foundation for exploring non-targeted screening of more pollutants in water and gas environment media. The metabonomics method can complete the non-targeted screening of antibiotics in soil comprehensively, accurately and rapidly.
Description
Technical Field
The invention relates to a non-targeted screening method for antibiotics in soil, and belongs to the technical field of soil detection.
Background
Antibiotics have been widely used in medicine, animal husbandry, and aquaculture as drugs and growth promoters. The annual consumption of antibiotics worldwide is reported to be 10-20 ten thousand tons, but more than 30% of antibiotics are excreted into environmental media such as soil in the form of raw drugs or metabolites. Up to now, more than 30 antibiotics including tetracyclines, quinolones, sulfonamides and macrolides have been detected in the surrounding soil, with total residual concentrations up to mg/kg levels far exceeding the ecotoxicological threshold (100 μg/kg) of antibiotics in soil proposed by the national institutional international coordination of veterinary drug registration technology (SCVICH). Antibiotics accumulated in the soil can destroy microbial colonies, induce the generation of drug-resistant bacteria, and affect the diversity and functions of biological communities by combining with other ions. More seriously, some plant-derived foods (such as lettuce, cucumber, carrot, potato, pea, wheat, barley and corn) can absorb antibiotics from the soil, accumulate further as an important migration mode through the food chain, and finally pose a hazard to human health. In order to effectively monitor the change of antibiotics in soil and evaluate the ecological risk thereof, it is urgent to develop a feasible method for screening and analyzing antibiotics in soil.
Solid phase extraction and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) are the most commonly used methods for detecting antibiotics in soil, and satisfactory detection results are obtained by continuously optimizing pretreatment technology and analysis conditions. Since the source of antibiotics in the soil is related to the application of fertilizers and irrigation water contaminated with antibiotics, there are certain differences in the type and concentration of antibiotics in the soil in different regions. With the development of the modern chemical industry, more and more novel antibiotics can enter the soil, and uncertainty of antibiotic pollution is greatly increased. Essentially, existing methods for detection of antibiotics in soil are mostly targeted assays, i.e. screening in databases established by a given antibiotic. These methods have good detection performance for antibiotics in the database, but cannot identify antibiotics outside the database, increasing the potential exposure risk of antibiotics. Therefore, development of non-targeted detection technology is an important point in achieving comprehensive monitoring and risk assessment of antibiotics in soil.
Disclosure of Invention
Metabonomics methods for non-targeted screening of contaminants are an emerging analytical technique that presents great advantages when analyzing large amounts of data with complex features such as small sample size, large amounts of interferents, high noise, etc. More complex soil substrates and pollution conditions become a major obstacle in establishing metabonomics screening methods.
In order to solve the problems in the prior art, the invention converts the process of tracking residual pollutants into the process of tracking corresponding 'marker compounds' through a metabonomics analysis strategy; the technical scheme adopted is as follows: a method for non-targeted screening of antibiotics in soil, comprising the steps of:
s1, collecting soil samples
Collecting surface soil samples which are not subjected to fertilization and irrigation, and removing foreign matters; the soil sample is obtained after the sample is air-dried, ground and sieved by 50-100 meshes;
s2, extracting and purifying
a) Accurately weighing a soil sample, placing the soil sample into a polypropylene pipe, adding ciprofloxacin-d 8 methanol solution as an internal standard of recovery rate, and uniformly mixing by vortex;
b) Acetonitrile and Na 2 EDTA-Mclvaine buffer solution is poured into a centrifuge tube according to the volume ratio of 1:1, and is subjected to shaking and ultrasonic centrifugation; repeatedly extracting, mixing the supernatant, concentrating with nitrogen blower, and diluting with pure water to original volume; adjusting the pH value of the solution to 8.0 to obtain a sample solution;
c) Sequentially adding methanol and pure water to activate the HLB column, transferring the sample solution into the column, continuously adding water to wash the HLB column, and eluting with methanol and acetonitrile at a volume ratio of 1:1 after pumping the pump; drying the eluted liquid nitrogen gas, dissolving in methanol-0.1% formic acid water solution, and filtering for later use; methanol-0.1% formic acid aqueous solution with the volume ratio of methanol being 40%;
s3, separation analysis
All compounds were isolated by Accumore RP-MS chromatography;
mobile phases A and B are respectively 0.1% formic acid-water and 0.1% formic acid-methanol solution, and the flow rate is 0.2-0.5mL/min;
elution procedure: 0 to 3.0min,10 to 15 percent of B;3.0 to 6.0min,15 percent of B to 60 percent of B;6.0 to 7.0min,60 to 95 percent of B;7.0 to 9.0min,95 percent of B;9.0 to 10.0min,95 percent of B to 10 percent of B;10.0 to 12.0min,10 percent of B;
electrospray ion source positive mode (ESI) is equipped with a quadrupole/electrostatic field orbitrap LC-MS/MS system + ) Analyzing the compound in the detection library and ciprofloxacin-d 8;
s4, metabonomics data processing
a) Multivariate analysis
Metabonomics non-targeted screening requires using RAW format output files under an HPLC-MS/MS full scanning mode, converting the files into mzXML format files through Proteowizard software, and uploading the mzXML format files to a Workflow4 Metabolic platform for analysis;
identifying all chromatographic peaks through peak detection, peak alignment and retention time calibration, and then normalizing, centering, scaling and data conversion are carried out on the data to obtain peak intensity values, so as to form a data matrix taking variables and sample names as horizontal coordinates and vertical coordinates respectively; each variable contains a set of information, noted mxtox, where M and T represent mass-to-charge ratio and retention time, respectively; after a data matrix is imported through SIMCA 14.1 software, principal component analysis and orthogonal partial least squares discriminant analysis are carried out, and variables meeting the conditions are selected as candidate variables of a 'marker compound' according to the importance of substitution test, S-plot and variable projection in an orthogonal partial least squares discriminant analysis model depending on a specific rule;
the specific rules chosen are:
r in orthorhombic partial least square discriminant analysis model 2 Y and Q 2 For evaluating the interpretation level of the model along the Y-axis and the prediction level of the model, respectively; the farther from the origin the points on the X-axis and Y-axis represent the greater the contribution of the variable to the inter-lineup difference, the higher the confidence level; the absolute value of the confidence coefficient is higher than 0.9 and is used as a candidate variable for screening the marker compound;
the VIP value reflects the loading weight of the variable and is used for feature selection; each variable has a corresponding VIP value that is positively correlated with the importance of the variable; VIP >1.5 is a threshold for selecting "marker compound" candidate variables;
b) Univariate analysis
Performing univariate analysis by adopting paired t-test and multiple change of concentration, wherein the paired t-test is used for judging whether a marked concentration difference exists between a specific concentration group and another concentration group of the screened marker compound, and the p value of the paired t-test is smaller than 0.05;
a variable with concentration multiple difference larger than 2 among groups is selected as a candidate variable of 'marker compound';
s5, establishing a mass spectrum database
Establishing an antibiotic detection library, wherein antibiotics in the detection library comprise oxaquinic acid, cinnoxacin, norfloxacin, enoxacin, ciprofloxacin, lomefloxacin, danofloxacin, enrofloxacin, ofloxacin, marbofloxacin, sparfloxacin, difloxacin, bamboo peach mycin, erythromycin, spiramycin, toxamycin, tilmicosin, tylosin, tetracycline, terramycin, sulfapyridine, sulfamethoxazole, sulfathiazole, sulfamethazine, sulfamethoxazole, benzoyl sulfadiazine, sulfamethoxypyridazine, sulfachloropyridazine, trimethoprim, sulfaquinoxaline, sulfabenzene pyrazole and sulfanifedine;
analyzing the 36 antibiotics to obtain information such as accurate molecular weight, fragment ions, retention time and the like, performing simulated fragmentation on the 36 antibiotics by Xcalibur software, and determining that the deviation of the mass number of the actual fragment ions in the secondary mass spectrogram and the theoretical accurate mass number is lower than 5 multiplied by 10 -6 And the ions with the abundance intensity row five at the front are taken as characteristic fragment ions. The acquired retention time, accurate mass number and characteristic fragment ion information are input into Trace Finder software, and 36 antibiotic screening databases are established.
And (3) comparing the mass-to-charge ratio of the 'marker compound' found in the step (S4) and the relative abundance information of ions with the information in the antibiotic screening database to achieve the purpose of matching the 'marker compound'.
SignCompound "compare with information in antibiotic screening database: in Full MS/dd-MS 2 In the mode, screening analysis is carried out on actual soil sample data through Trace Finder software, and the set parameters are as follows: the deviation of the accurate mass number of the parent ion is 5 multiplied by 10 -6 Peak threshold intensity 1×10 4 Signal to noise ratio 5, sub-ion accurate mass deviation 5 x 10 -6 And searching in a database, wherein the number of the matched minimum fragments is 1, and the compounds meeting all the conditions are subjected to preliminary screening.
In the step S5, the "marker compound" which is not matched in the antibiotic screening database is entered into the compound database for screening; the compound databases were SciFinder, pubChem, chemSpider, metlin and Massbank.
Performing compound recovery rate calibration in a soil sample, and performing recovery rate calculation by taking ciprofloxacin-d 8 as a recovery rate internal standard;
preparing a series of standard curve solutions of ciprofloxacin-d 8 in a blank soil extracting solution, and calculating the recovery rate of the ciprofloxacin-d 8 in a soil sample; the recovery of ciprofloxacin-d 8 in each soil sample was converted to 100% recovery by multiplying the recovery by a calibration factor, and the peak intensities of ciprofloxacin-d 8 and the peak intensities of all variables were calibrated accordingly.
Na 2 EDTA-Mclvaine buffer was prepared as follows: with Na 2 HPO 4 、Na 2 EDTA and citric acid are dissolved in pure water to prepare Na with 0.1mol/L 2 EDTA-Mclvaine buffer, and then HCl or NaOH solution is used for adjusting the pH to 4.0.
It is well known that it is difficult to directly determine the safe concentration threshold of antibiotics in the soil due to the mandatory official documents lacking the maximum residual limit of antibiotics in the soil. SCVICH suggests 100 μg/kg as a safety threshold for antibiotics in soil, but considering that antibiotics in soil may harm human health through the enrichment effect of food chains, we refer to the maximum residual limit of most antibiotics in animal-derived foods proposed in national standard GB31650-2019 and the european union regulatory committee 37/2010 guidelines not less than 10 μg/kg, and select this concentration as the test concentration for developing a non-targeted screening method for metabonomics of antibiotics in soil.
The invention has the beneficial effects that: the research applies a metabonomics method originally aiming at exploring the change of micromolecular metabolites in biology to non-targeted screening of antibiotics in soil, and changes the process of searching for a biomarker in traditional metabonomics into the process of searching for a marker compound corresponding to residual antibiotics in soil in the research. The method has high feasibility and expands application fields, and lays a solid foundation for exploring non-targeted screening of more pollutants in water and gas environment media. The metabonomics method can complete the non-targeted screening of antibiotics in soil comprehensively, accurately and rapidly.
Drawings
Fig. 1 is a total ion flow diagram of a standard additive soil sample set output by the W4M platform.
Fig. 2 is a PCA plot of a standard additive soil sample group.
FIG. 3 is an OPLS-DA graph of a standard additive soil sample group.
FIG. 4 is an S-plot of a standard additive soil sample set.
FIG. 5 is a displacement test chart of a standard additive soil sample set.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
(1) An antibiotic detection library was established, wherein the antibiotics contained 36 antibiotics, and detailed information about the 36 antibiotics is shown in table 1.
TABLE 1 basic information of 36 antibiotics
TABLE 1 basic information of 36 antibiotics
Methanol and acetonitrile (HPLC grade, merck company, germany); formic acid (HPLC grade, shanghai's national institute of Electrical and electronics Engineers, china); ethylene diamine tetraacetic acid disodium salt (Na) 2 EDTA), sodium hydrogen phosphate (Na) 2 HPO 4 ) Citric acid (national pharmaceutical group chemical agent limited, china); ultrapure water (Milli-Q ultrapure water system, merck corporation, germany); ciprofloxacin-d 8 hydrochloride solution (100. Mu.g/mL, methanol, first Standard, USA). Standard of 36 antibiotics (purity>98.3%) were purchased from First standard company (united states), sigma company (united states), TRC company (canada) and dr.
(2) Solution preparation
Respectively preparing antibiotic methanol solutions with the concentration of 100 mug/mL, respectively taking 1mL of each solution, mixing, and then further using methanol to fix the volume to 100mL to obtain 36 antibiotic mixed solutions with the concentration of 1 mug/mL. 100ng/mL of a methanol solution of ciprofloxacin-d 8 was obtained after dilution of 100. Mu.g/mL of the methanol solution. Na is mixed with 2 HPO 4 (5.5g)、Na 2 EDTA (37.2 g) and citric acid (12.9 g) were dissolved in 1L pure water to prepare Na 2 EDTA-Mclvaine buffer (0.1 mol/L), and then 0.1mol/L HCl or NaOH solution was used to adjust the pH of the buffer to 4.0.
(3) Sample collection
And collecting a forest surface soil sample which is not fertilized and irrigated, and removing foreign matters such as stones, weeds and the like. The samples were air dried, ground, sieved (100 mesh) and stored in a refrigerator at 4 ℃.
(4) Sample extraction and purification
a) 2.0, 5.0 and 10.0g of soil samples were added to different 50mL polypropylene centrifuge tubes, followed by 20, 50 and 100. Mu.L of mixed solutions containing 36 antibiotics (1. Mu.g/mL), respectively. 0.5mL ciprofloxacin-d 8 methanol solution (100 ng/mL) was added as an internal standard for recovery, and the solution was vortexed for 1min.
b) 15mL of 50% (V/V) acetonitrile/Na 2 EDTA-Mclvaine buffer (0.1 mol/L) was poured into the centrifuge tube, and after 20min shaking and 10min sonication, centrifuged at 4500r/min for 5min. After repeating the extraction 3 times, the supernatant was mixed, concentrated to 9mL with a nitrogen blower, and diluted to 15mL with pure water. The pH of the solution was adjusted to 8.0 using a 0.1mol/L NaOH solution.
c) The HLB column was activated by adding 6mL of methanol and 6mL of purified water sequentially, then the sample solution was transferred to the column, the HLB column was rinsed by continuing to add 10mL of water, and after being pumped down, eluted with 10mL of methanol-acetonitrile (1:1, V/V). After the eluting liquid nitrogen gas is blown dry, the eluting liquid nitrogen gas is dissolved in 1mL of 40% (V/V) methanol-0.1% formic acid/water solution, and the eluting liquid nitrogen gas is filtered for later use.
(5) Sample grouping and naming
Samples from the 20, 50 and 100ng/mL groups were designated sample 1-1 to sample 1-9, sample 2-1 to sample 2-9 and sample 3-1 to sample 3-9, respectively. mu.L of the solution was pipetted from each sample and mixed well as Quality Control (QC) samples. And repeating the sample injection for 3 times before starting the sample injection and after completing the sample injection of each concentration group to obtain 12 QC sample points, namely QC-1 to QC-12, which are used for supervising the stability of the HPLC-MS/MS.
(6) Separation analysis method
Electrospray ion Source Positive mode (ESI) was equipped with a quadrupole/Electrostatic field orbitrap LC-MS/MS System (Q exact Plus, thermo Inc., USA) + ) 36 antibiotics and ciprofloxacin-d 8 were analyzed.
All antibiotics were isolated by Accumore RP-MS chromatography (100X 2.1mm,2.6 μm particle size, thermo company, USA). The sample loading was 10. Mu.L. Mobile phases a and B were 0.1% (V/V) formic acid-water and 0.1% (V/V) formic acid-methanol solution, respectively, at a flow rate of 0.3mL/min. Elution procedure: 0 to 3.0min,10 to 15 percent of B;3.0 to 6.0min,15 percent of B to 60 percent of B;6.0 to 7.0min,60 to 95 percent of B;7.0 to 9.0min,95 percent of B;9.0 to 10.0min,95 percent of B to 10 percent of B;10.0 to 12.0min,10 percent of B. The column temperature was maintained at 40 ℃.
The Q actual Plus parameters were set as follows: the capillary and heating temperature are 320 ℃; the sheath gas and the auxiliary gas are N 2 The flow rates were 40 and respectively10arb; the spray voltage and the lens voltage are 3200V and 50V, respectively; scanning mode: full scan/data dependent secondary scan (Full MS/dd-MS) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the MS parameters: full scan resolution 70000, automatic gain control target (AGC target) 1×10 6 Maximum residence time (maximum IT) of 100ms, m/z scanning range of 100-1000; secondary mass spectrometry parameter setting: resolution 17500,AGC target 2 ×10 5 The maximum dwell time is 50ms.
(7) Metabonomics data processing
Metabonomics non-targeted screening requires the use of RAW format output files in HPLC-MS/MS full scan mode, which are converted to mzXML format files by ProteoWizard software, which are uploaded to the Workflow4 metablomics (W4M) platform (https:// Workflow4 metablomics. All chromatographic peaks are identified through peak detection, peak alignment and retention time calibration, and then normalization, centering, scaling and data conversion are carried out on the data to obtain peak intensity values, so that a data matrix with variables and sample names as horizontal and vertical coordinates is formed. Each variable contains a set of information, noted mxtox, where M and T represent mass-to-charge ratio and retention time (in seconds), respectively. All variables have corresponding chromatographic peaks, some of which belong to the "marker compound" and others are derived from the interferents, and the process of finding the "marker compound" is actually a process of finding a satisfactory variable.
Principal Component Analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), which are the most commonly used multivariate analysis methods in metabonomics studies, were performed after the data matrix was imported by SIMCA 14.1 software. The substitution test, S-plot and variable projection importance (VIP) in the OPLS-DA model depend on specific rules to pick eligible variables as "marker compound" candidate variables that should have significantly lower peak intensities in the 20ng/mL concentration group and significantly higher peak intensities in the 100ng/mL concentration group.
In order to effectively narrow the searching range of the variables, the overlapping variables in the two concentration groups are selected for univariate analysis such as paired t-test and concentration multiple change, and the effectiveness of the marker compound can be further verified. Matching "marker compounds" is achieved by comparing the found "marker compound" information (e.g., mass to charge ratio and relative abundance of ions) with information in some authoritative compound databases (e.g., sciFinder, pubChem, chemSpider, metlin and Massbank). In this study, for convenience, we considered table 1 as a small database of 36 antibiotics, with information on their mass-to-charge ratios, retention times, adduct structures, etc., to achieve the goal of rapid matching of the identity of the "marker compound".
(8) Data preprocessing
The total ion flow diagram of 36 antibiotics at three concentration gradients is shown in figure 1. Because of the complexity of the soil matrix, there may be significant differences in recovery of each antibiotic in different soil samples. As can be seen from fig. 1, there is no obvious positive correlation between concentration levels and peak intensities, and therefore, calibration of antibiotic recovery is required. Based on the results of the previous study, ciprofloxacin-d 8 was selected as the recovery internal standard (parent ion m/z 340.19132; fragment ions m/z 296.20156, 253.15933 and 239.14367; retention time 5.05 minutes) for recovery calculations. The method comprises the following steps: a series of standard curve solutions (5, 10, 25, 50 and 100 ng/mL) of ciprofloxacin-d 8 were prepared in the blank soil extract for calculation of recovery of ciprofloxacin-d 8 in the soil samples. As shown in Table 2, the recovery rates for the 20, 50 and 100ng/mL groups were 72.7% to 84.6%, 66.6% to 78.9% and 61.5% to 76.4%, respectively. The recovery of ciprofloxacin-d 8 in each soil sample was converted to 100% recovery by multiplying the recovery by a calibration factor, and the peak intensities of ciprofloxacin-d 8, as well as the peak intensities of all variables, were calibrated accordingly. Null variables with variable peak intensities greater than 30% relative standard deviation in QC group or any concentration group were deleted. Finally, 4491 variables were obtained for further analysis.
Table 2 recovery (%) of ciprofloxacin-d 8 in soil sample group (n=9)
Example 2 multivariate analysis
(1) Principal component analysis results
PCA can classify samples and further eliminate extreme samples in cases where the sample class is unknown. Thus, PCA can be used to evaluate data quality and identify outliers. In fig. 2, all samples were trusted within a 95% confidence interval, with no extreme data and outliers occurring. The QC group samples had good aggregation, demonstrating the stability of HPLC-MS/MS and the reliability of the obtained data. Each concentration group of samples is clustered together but remote from the other groups of samples, meaning that there are important variable differences between the concentration groups, providing the opportunity to find "marker compounds".
(2) OPLS-DA results
OPLS-DA has advantages over PCA in terms of packet design and acquisition of detailed inter-group information. Fig. 3 depicts the separation of two camps, wherein the Y-axis left side camps represent a particular concentration set and the Y-axis right side camps represent the remaining two concentration sets. The significant separation between the two camps means that there are significantly different variables. R in OPLS-DA model 2 Y and Q 2 For evaluating the interpretation level of the model along the Y-axis and the prediction level of the model, respectively. When both parameter values are close to 1, we consider that the OPLS-DA model has good reliability and predictive power. In FIG. 3, R 2 Y and Q 2 Values are not less than 0.986, demonstrating the robustness of the OPLS-DA model in this study. The S-plot is made up of data points representing all variables. The points on the X-axis and Y-axis that are further from the origin represent the greater contribution of the variable to the inter-lineup difference, the higher the confidence level. Thus, the most diverse variable between camps should be found at both ends of the S-plot. An absolute value of confidence above 0.9 is considered a threshold for screening the variable. The overlap of the variable in the first quadrant of fig. 4a (which should have a significantly low concentration in the 20ng/mL concentration group) and the variable in the third quadrant of fig. 4b (which should have a significantly high concentration in the 100ng/mL concentration group) was chosen as a candidate "marker compound".
By Q 2 The significance test (p value) of Y is used as an evaluation index, and 200 iterative displacement tests are adopted to carry out the over-simulation of the OPLS-DA modelAnd (5) performing a total evaluation. P is p<0.05 corresponds to Q in the permutation test chart 2 Regression line intercept was less than 0.05, indicating that the OPLS-DA model was not easily overfitted. As shown in FIG. 5, all Q 2 The regression line intercept was significantly below 0.05, indicating that none of the OPLS-DA models were overfitted. The VIP value reflects the loading weight of the variable for feature selection. Each variable has a corresponding VIP value that is positively correlated with the importance of the variable. Typically, VIP>1.5 is considered to be a threshold for selecting a variable with a characteristic candidate "marker compound". In this study, we found all "marker compounds" representing 36 antibiotics, as shown in table 3. In addition, the same metabonomics analysis method is adopted to measure the residual concentration of 36 antibiotics in the air clay soil extract, and the result shows that the residual concentration is negligible<0.1 ng/mL) eliminates the interference of the 36 antibiotics inherent in the soil matrix with the search for "marker compounds".
Table 3 information on 36 "marker compounds" screened in soil sample group
Table 3 information on 36 "marker compounds" screened in soil sample group
a Two sets of VIP values, 100 and 20ng/mL, respectively; b two sets of coordinate values of 100 and 20ng/mL, respectively; c mass error (ppm) = (W4M platform extraction molecular weight-HPLC-MS/MS extraction molecular weight) ×10 6 HPLC-MS/MS extraction of molecular weight.
Example 3 univariate analysis
The paired t-test and fold change in concentration are univariate analysis methods commonly used in metabonomics studies to determine whether a significant concentration difference exists between a screened "marker compound" and another concentration group, and to select as "marker compound" a variable having a fold difference of greater than 2 between groups. In the study, p values (significance levels) calculated by paired t-tests are all smaller than 0.05, so that the screened marker compounds are proved to have obvious inter-group differences, and meanwhile, the change multiples of the concentration of the marker compounds are all larger than 2, so that the effectiveness of the screened marker compounds is further supported.
The detection limit of 36 antibiotics was determined according to the method of the U.S. environmental protection agency 8061A. Firstly, 2.0g of a blank soil sample was weighed and subjected to pretreatment to obtain 1mL of a blank soil sample extract. mu.L of methanol solution (1. Mu.g/mL) containing 36 antibiotics was diluted with 1mL of the blank soil sample extract to obtain 20ng/mL of a solution thereof. The same method as above was sampled to obtain 7 parallel samples, and the same metabonomics analysis was performed to obtain peak intensities of 36 antibiotics. The average peak intensity in 7 parallel samples for each antibiotic corresponds to the 20ng/mL concentration level for that antibiotic. Thus, we can calculate the formula: peak intensity x 20/average peak intensity, the concentration of each antibiotic (unit: ng/mL) in either sample was calculated and used for standard deviation calculation of parallel samples. Finally, the detection limit of 36 antibiotics was calculated to be 0.6 to 2.6. Mu.g/kg, as shown in Table 3.
Example 4 practicality test
1) Sample collection
Collecting surface soil sample, removing foreign matters such as stone, air drying, grinding and sieving (100 mesh) after air drying, and preserving at 4deg.C.
2) Extraction and purification
2.50g of soil sample is accurately weighed, placed in a 15mL polypropylene tube, and 0.5mL ciprofloxacin-d 8 methanol solution (100 ng/mL) is added as an internal standard of recovery rate, and vortex mixed uniformly. 15mL of 50% (V/V) acetonitrile/Na 2 EDTA-Mclvaine buffer (0.1 mol/L) was poured into the centrifuge tube, shaken for 20min, sonicated for 10min, and centrifuged at 4500r/min for 5min. After repeating the extraction 3 times, the supernatant was mixed, concentrated to 9mL with a nitrogen blower, and diluted to 15mL with pure water. The pH of the solution was adjusted to 8.0 using a 0.1mol/L NaOH solution. 6mL of methanol and 6mL of pure water were sequentially added to activate the HLB column, and then the sample solution was transferred toThe column was rinsed with additional 10mL of water, and after pumping, eluted with 10mL of methanol-acetonitrile (1:1, V/V). After the eluting liquid nitrogen gas is blown dry, the eluting liquid nitrogen gas is dissolved in 1mL of 40% (V/V) methanol-0.1% formic acid/water solution, and the eluting liquid nitrogen gas is filtered for later use.
3) Analysis conditions
Electrospray ion Source Positive mode (ESI) was equipped with a quadrupole/Electrostatic field orbitrap LC-MS/MS System (Q exact Plus, thermo Inc., USA) + ) The antibiotics and ciprofloxacin-d 8 were analyzed. All compounds were isolated by Accumore RP-MS chromatography (100X 2.1mm,2.6 μm particle size, thermo company, USA). The sample loading was 10. Mu.L. Mobile phases a and B were 0.1% (V/V) formic acid-water and 0.1% (V/V) formic acid-methanol solution, respectively, at a flow rate of 0.3mL/min. Elution procedure: 0 to 3.0min,10 to 15 percent of B;3.0 to 6.0min,15 percent of B to 60 percent of B;6.0 to 7.0min,60 to 95 percent of B;7.0 to 9.0min,95 percent of B;9.0 to 10.0min,95 percent of B to 10 percent of B;10.0 to 12.0min,10 percent of B. The column temperature was maintained at 40 ℃. The Q actual Plus parameters were set as follows: the capillary and heating temperature are 320 ℃; the sheath gas and the auxiliary gas are N 2 The flow rates were 40 and 10arb, respectively; the spray voltage and the lens voltage are 3200V and 50V, respectively; scanning mode: full scan/data dependent secondary scan (Full MS/dd-MS) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the MS parameters: full scan resolution 70000, automatic gain control target (AGC target) 1×10 6 Maximum residence time (maximum IT) of 100ms, m/z scanning range of 100-1000; secondary mass spectrometry parameter setting: resolution 17500,AGC target 2 ×10 5 The maximum dwell time is 50ms.
4) Establishing a mass spectrum database
Under the optimized mass spectrum condition, 36 antibiotics are analyzed to obtain information such as accurate molecular weight, fragment ions, retention time and the like, the 36 antibiotics are simulated and cracked by Xcalibur software, and the deviation of the mass number of the actual fragment ions in the secondary mass spectrogram and the theoretical accurate mass number is lower than 5 multiplied by 10 -6 And the ions with the abundance intensity row five at the front are taken as characteristic fragment ions. And inputting the acquired information such as the retention time, the accurate mass number, the characteristic fragment ions and the like into Trace Finder software, and establishing a 36-kind antibiotic screening database. In Full MS/dd-MS 2 In the mode, screening analysis is carried out on actual soil sample data through Trace Finder software, and the set parameters are as follows: the deviation of the accurate mass number of the parent ion is 5 multiplied by 10 -6 Peak threshold intensity 1×10 4 Signal to noise ratio 5, sub-ion accurate mass deviation 5 x 10 -6 And searching in a database, wherein the number of the matched minimum fragments is 1, and the compounds meeting all the conditions are subjected to preliminary screening.
With the above method we selected greenhouse soil in the forward region of Dalian city, the village and river city and Jin Puxin region as the subject of study, and from table 4 it was seen that 5 antibiotics of oxytetracycline, aureomycin, norfloxacin, ciprofloxacin and enrofloxacin were detected at each of the 6 sampling points, which were attributed to their wide use as pharmaceuticals and feed additives in animal husbandry and their strong adsorption capacity to soil particles. The Jin Puxin area is more contaminated than the other two areas. The total concentration of antibiotics at each sampling point is more than 120 mug/kg, exceeds the safety concentration limit value (100 mug/kg) proposed by SCVICH, and the investigation of the sources of antibiotics in soil should be carried out as soon as possible, and measures are taken to cut off the pollution sources. The results of greenhouse soil screening demonstrate the utility of our metabonomics method in achieving non-targeted screening of antibiotics in soil, but as we introduced earlier, the method only considers the case where the antibiotic concentration is not less than 10 μg/kg, which may result in some low concentration antibiotics not being detected. Thus, the actual situation of antibiotic contamination of the soil of Dalian city may be more serious than what we expect, and much attention should be paid to solve the potential crisis.
TABLE 4 screening results of antibiotic concentration in greenhouse soil for 3 administrative regions in Dalian City (μg/kg)
To achieve comprehensive risk assessment of antibiotics in soil, a better solution is to combine the advantages of targeted and non-targeted screening methods, such as by means of reducing the detection limit of contaminants and expanding the detection range of contaminants.
Although the invention has been described with reference to the above embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the invention.
Claims (5)
1. A method for non-targeted screening of antibiotics in soil, comprising the steps of:
s1, collecting soil samples
Collecting surface soil samples which are not subjected to fertilization and irrigation, and removing foreign matters; the soil sample is obtained after the sample is air-dried, ground and sieved by 50-100 meshes;
s2, extracting and purifying
a) Accurately weighing a soil sample, placing the soil sample into a polypropylene pipe, adding ciprofloxacin-d 8 methanol solution as an internal standard of recovery rate, and uniformly mixing by vortex;
b) Acetonitrile and Na 2 EDTA-Mclvaine buffer solution is poured into a centrifuge tube according to the volume ratio of 1:1, and is subjected to shaking and ultrasonic centrifugation; repeatedly extracting, mixing the supernatant, concentrating with nitrogen blower, and diluting with pure water to original volume; adjusting the pH value of the solution to 8.0 to obtain a sample solution;
c) Sequentially adding methanol and pure water to activate the HLB column, transferring the sample solution into the column, continuously adding water to wash the HLB column, and eluting with methanol and acetonitrile at a volume ratio of 1:1 after pumping the pump; drying the eluted liquid nitrogen gas, dissolving in methanol-0.1% formic acid water solution, and filtering for later use; methanol-0.1% formic acid aqueous solution with the volume ratio of methanol being 40%;
s3, separation analysis
All compounds were isolated by Accumore RP-MS chromatography;
mobile phases A and B are respectively 0.1% formic acid-water and 0.1% formic acid-methanol solution, and the flow rate is 0.2-0.5mL/min;
elution procedure: 0 to 3.0min,10 to 15 percent of B;3.0 to 6.0min,15 percent of B to 60 percent of B;6.0 to 7.0min,60 to 95 percent of B;7.0 to 9.0min,95 percent of B;9.0 to 10.0min,95 percent of B to 10 percent of B;10.0 to 12.0min,10 percent of B;
adopting a quadrupole/electrostatic field orbit trap LC-MS/MS system, and preparing an electrospray ion source positive mode to analyze the compound and ciprofloxacin-d 8 in a detection library;
s4, metabonomics data processing
a) Multivariate analysis
Metabonomics non-targeted screening requires using RAW format output files under an HPLC-MS/MS full scanning mode, converting the files into mzXML format files through Proteowizard software, and uploading the mzXML format files to a Workflow4 Metabolic platform for analysis;
identifying all chromatographic peaks through peak detection, peak alignment and retention time calibration, and then normalizing, centering, scaling and data conversion are carried out on the data to obtain peak intensity values, so as to form a data matrix taking variables and sample names as horizontal coordinates and vertical coordinates respectively; each variable contains a set of information, noted mxtox, where M and T represent mass-to-charge ratio and retention time, respectively; after a data matrix is imported through SIMCA 14.1 software, principal component analysis and orthogonal partial least squares discriminant analysis are carried out, and variables meeting the conditions are selected as candidate variables of a 'marker compound' according to the importance of substitution test, S-plot and variable projection in an orthogonal partial least squares discriminant analysis model depending on a specific rule;
the specific rules chosen are:
r in orthorhombic partial least square discriminant analysis model 2 Y and Q 2 For evaluating the interpretation level of the model along the Y-axis and the prediction level of the model, respectively; the farther from the origin the points on the X-axis and Y-axis represent the greater the contribution of the variable to the inter-lineup difference, the higher the confidence level; screening candidate variables of 'marker compounds' with confidence absolute value higher than 0.9;
the VIP value reflects the loading weight of the variable and is used for feature selection; each variable has a corresponding VIP value that is positively correlated with the importance of the variable; VIP >1.5 is a threshold for selecting "marker compound" candidate variables;
b) Univariate analysis
Performing univariate analysis by adopting paired t-test and multiple change of concentration, wherein the paired t-test is used for judging whether a marked concentration difference exists between a specific concentration group and another concentration group of the screened marker compound, and the p value of the paired t-test is smaller than 0.05;
a variable with concentration multiple difference larger than 2 among groups is selected as a candidate variable of 'marker compound'; s5, establishing a mass spectrum database
Establishing an antibiotic detection library, wherein antibiotics in the detection library comprise oxaquinic acid, cinnoxacin, norfloxacin, enoxacin, ciprofloxacin, lomefloxacin, danofloxacin, enrofloxacin, ofloxacin, marbofloxacin, sparfloxacin, difloxacin, bamboo peach mycin, erythromycin, spiramycin, toxamycin, tilmicosin, tylosin, tetracycline, terramycin, sulfapyridine, sulfamethoxazole, sulfathiazole, sulfamethazine, sulfamethoxazole, benzoyl sulfadiazine, sulfamethoxypyridazine, sulfachloropyridazine, trimethoprim, sulfaquinoxaline, sulfabenzene pyrazole and sulfanifedine;
analyzing the 36 antibiotics to obtain information such as accurate molecular weight, fragment ions, retention time and the like, performing simulated fragmentation on the 36 antibiotics by Xcalibur software, and determining that the deviation of the mass number of the actual fragment ions in the secondary mass spectrogram and the theoretical accurate mass number is lower than 5 multiplied by 10 -6 And the ions with the abundance intensity row five at the front are taken as characteristic fragment ions. The acquired retention time, accurate mass number and characteristic fragment ion information are input into Trace Finder software, and 36 antibiotic screening databases are established.
And (3) comparing the mass-to-charge ratio of the 'marker compound' found in the step (S4) and the relative abundance information of ions with the information in the antibiotic screening database to achieve the purpose of matching the 'marker compound'.
2. A method for non-targeted screening of antibiotics in soil according to claim 1, wherein: the "marker compound" is compared to information in the antibiotic screening database: at Full MS/dd-MS 2 In the mode, screening analysis is carried out on actual soil sample data through Trace Finder software, and the set parameters are as follows: the deviation of the accurate mass number of the parent ion is 5 multiplied by 10 -6 Peak threshold intensity 1×10 4 Signal to noise ratio 5, sub-ion accurate mass deviation 5 x 10 -6 And searching in a database, wherein the number of the matched minimum fragments is 1, and the compounds meeting all the conditions are subjected to preliminary screening.
3. A method for non-targeted screening of antibiotics in soil according to claim 1, wherein: in the step S5, the "marker compound" which is not matched in the antibiotic screening database is entered into the compound database for screening; the compound databases were SciFinder, pubChem, chemSpider, metlin and Massbank.
4. A method for non-targeted screening of antibiotics in soil according to claim 1, wherein: performing compound recovery rate calibration in a soil sample, and performing recovery rate calculation by taking ciprofloxacin-d 8 as a recovery rate internal standard;
preparing a series of standard curve solutions of ciprofloxacin-d 8 in a blank soil extracting solution, and calculating the recovery rate of the ciprofloxacin-d 8 in a soil sample; the recovery of ciprofloxacin-d 8 in each soil sample was converted to 100% recovery by multiplying the recovery by a calibration factor, and the peak intensities of ciprofloxacin-d 8 and the peak intensities of all variables were calibrated accordingly.
5. A method for non-targeted screening of antibiotics in soil according to claim 1, wherein: na (Na) 2 EDTA-Mclvaine buffer was prepared as follows: with Na 2 HPO 4 、Na 2 EDTA and citric acid are dissolved in pure water to prepare Na with 0.1mol/L 2 EDTA-Mclvaine buffer, and then HCl or NaOH solution is used for adjusting the pH to 4.0.
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