Glycerophospholipid markers of wild and farmed bighead carp and their applications

Abstract

The present application relates to the technical field of bighead carp identification or confirmation, and specifically to glycerophospholipid markers and applications of wild and farmed bighead carp. The markers include at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4). The markers can accurately distinguish between wild and farmed bighead carp, provide scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing in the Yangtze River Basin, reduce overfishing of wild resources, and provide a technical foundation for better management and protection of bighead carp resources.

Description

Technical Field

[0001] The present application relates to the technical field of bighead carp identification or confirmation, and specifically to glycerophospholipid markers and applications of wild and farmed bighead carp. Background Art

[0002] Bighead carp (Aristichthys nobilis) is an important aquaculture species in my country, mainly distributed in the Yangtze River Basin. Due to water pollution, overfishing and other factors, the fish biodiversity in the Yangtze River Basin is constantly decreasing. Therefore, my country has fully implemented the "Ten-Year Fishing Ban in the Yangtze River" decision since 2020, cracking down on illegal fishing in the Yangtze River Basin and illegal sales of Yangtze River catches in the market. However, it is currently difficult to distinguish between wild and farmed aquatic products in the Yangtze River Basin in China, and thus it is difficult to meet the requirements of the "Yangtze River Fishing Ban" policy. Therefore, developing identification or confirmation technology for wild and farmed bighead carp, exploring markers for wild and farmed bighead carp, and promoting their application are reliable means to support the "Yangtze River Fishing Ban" policy. Summary of the invention

[0003] In order to solve the problem that the biological traits of wild and farmed bighead carp are similar but the identification technology is lacking, this application uses ultra-high throughput liquid chromatography-mass spectrometry for the first time to collect muscle samples from different wild and farmed bighead carp populations, conduct metabolomics analysis, explore the common differences and changes in the metabolites of wild and farmed bighead carp, and screen out the markers of glycerophospholipids: phosphatidylcholine (15:0 / 22:6) (PC (15:0 / 22:6), HMDB ID: HMDB0007958), glycerol 3-phosphate (Glycerol3-phosphate, PubChem CID: 439162), phosphatidylcholine (18:1 / 18:2) (PC (18:1 / 18:2), PubChem CID: 53478723), phosphatidylcholine (17:1 / 18:2) (PC (17:1 / 18:2), PubChem CID: 138271945), phosphatidylcholine (15:0 / 18:2) (PC (15:0 / 18:2), PubChem CID: 24778665), phosphatidylglycerol (22:4 / 22:4) (PG (22:4 / 22:4), PubChem CID: 138306632), acylphosphatidylcholine (P-16:0) (LPC (P-16:0), PubChem CID: 10917802), acylphosphatidylethanolamine (18:3) (LPE (18:3), PubChem CID: 53480927), acylphosphatidylcholine (20:4) (LPC (20:4), PubChem CID: 53480469), phosphatidylcholine (16:0 / 18:2) (PC (16:0 / 18:2), PubChem CID: 5287971), phosphatidylcholine (16:0 / 20:4) (PC (16:0 / 20:4), PubChem CID: 138183174). The results of liquid chromatography-mass spectrometry were used for statistical and logistic regression analysis to obtain a logistic regression analysis model. The source of the test samples was determined according to the logistic regression model, which can accurately distinguish between wild and farmed species with good sensitivity and specificity. In addition, the markers and uses provided in this application can provide scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing in the Yangtze River Basin, reduce overfishing of wild resources, and provide a technical basis for better management and protection of bighead carp resources.

[0004] To this end, the embodiments of the present application disclose at least the following technical solutions:

[0005] In a first aspect, the embodiments disclose a marker comprising at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0006] In an embodiment of the first aspect, the marker comprises at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0007] In an embodiment of the first aspect, the marker comprises at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2).

[0008] In an embodiment of the first aspect, the marker comprises at least one of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0).

[0009] In an embodiment of the first aspect, the marker comprises at least one of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0010] In the second aspect, the embodiments disclose markers for distinguishing wild bighead carp from farmed bighead carp, which contain at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0011] In an embodiment of the second aspect, the markers include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0012] In a third aspect, the embodiments disclose a marker for distinguishing wild bighead carp from farmed bighead carp, which contains at least one of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2).

[0013] In an embodiment of the third aspect, the markers comprise phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2).

[0014] In a fourth aspect, the embodiments disclose a marker for distinguishing wild bighead carp from farmed bighead carp, which contains at least one of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0).

[0015] In an embodiment of the fourth aspect, the markers comprise phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0).

[0016] In the fifth aspect, the embodiments disclose a marker for distinguishing wild bighead carp from farmed bighead carp, which contains at least one of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0017] In an embodiment of the fifth aspect, the markers comprise acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0018] In a sixth aspect, the embodiments disclose a reagent composition comprising a reagent for detecting the marker described in any one of the first to fifth aspects.

[0019] In an embodiment of the sixth aspect, the reagent comprises a reagent for extracting metabolites from a test sample. In some embodiments, the reagent comprises steel beads and a methanol-water solution (4:1, v / v).

[0020] In the seventh aspect, the embodiments disclose the markers described in any one of the first to fifth aspects and / or the use of the markers described in the first to fifth aspects for detecting the markers in distinguishing wild and farmed bighead carp.

[0021] In an eighth aspect, an embodiment discloses a method for distinguishing wild bighead carp from farmed bighead carp.

[0022] In an embodiment of the eighth aspect, the method includes a method for testing the content of each marker in the marker described in the second aspect in the test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, the individual markers include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (16:0 / 20:4).

[0023] In an embodiment of the eighth aspect, the method further includes: obtaining a first logistic regression model and a first threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the first logistic regression model to obtain a first wild probability; determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the size of the first wild probability and the first threshold. In some embodiments, the first logistic regression model is: X=7.325×A-39.462×B-74.266×C-47.563×D-0.669×E-26.894×F+51.610×G+43.993×H+3.788×I+21.002×J-13.328×K+435.356; first wild probability=1 / (1+e －X); wherein A, B, C, D, E, F, G, H, I, J, and K are respectively the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), and the first wild probability represents the probability that the test sample is a wild bighead carp. In some embodiments, if the first wild probability is greater than the first threshold value, the test sample is a wild bighead carp. In some embodiments, if the first wild probability is less than the first threshold value, the test sample is a farmed bighead carp.

[0024] In an embodiment of the eighth aspect, the step of determining the first threshold comprises: according to the collected muscle sample, phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), The standardized value of choline (16:0 / 20:4) is used as a training set; a binary logistic regression model is trained with the training set to obtain a plurality of first wild probability values; the sensitivity (true positive rate) and 1-specificity (false positive rate) of each first wild probability value are calculated respectively, and the ROC curve is drawn with 1-specificity as the abscissa and sensitivity as the ordinate; the Youden index corresponding to each first wild probability is calculated (Youden index = sensitivity + specificity - 1), and the maximum Youden index is obtained, and the first wild probability corresponding to the maximum Youden index is the first threshold value. In some embodiments, the first threshold value is 0.50.

[0025] In a ninth aspect, an embodiment discloses a method for distinguishing wild bighead carp from farmed bighead carp.

[0026] In an embodiment of the ninth aspect, the method includes a method for testing the content of each marker in the marker described in the third aspect in the test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, each marker includes phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2).

[0027] In an embodiment of the ninth aspect, the method further includes: obtaining a second logistic regression model and a second threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the second logistic regression model to obtain a second wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the magnitude of the second wild probability and the second threshold. In some embodiments, the second logistic regression model is: X=8.058×A-17.834×B-11.751×C+192.892; second wild probability=1 / (1+e －X ); wherein A, B, C, D, and E are the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2), respectively, and the second wild probability represents the probability that the test sample is a wild bighead carp. In some embodiments, if the second wild probability is greater than the second threshold value, the test sample is a wild bighead carp. In some embodiments, if the second wild probability is less than the second threshold value, the test sample is a farmed bighead carp.

[0028] In the embodiment of the ninth aspect, the second threshold determination step includes: according to the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the second wild probability values; respectively calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the second wild probability values, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each second wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the second wild probability corresponding to the maximum Youden index is the second threshold. In some embodiments, the second threshold is 0.264.

[0029] In a tenth aspect, an embodiment discloses a method for distinguishing wild bighead carp from farmed bighead carp.

[0030] In an embodiment of the tenth aspect, the method includes a method for testing the content of each marker in the marker described in the fourth aspect in a test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, each marker includes phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0).

[0031] In an embodiment of the tenth aspect, the method further includes: obtaining a third logistic regression model and a third threshold value; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the third logistic regression model to obtain a third wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the third wild probability and the third threshold value. In some embodiments, the third logistic regression model is: X=-783.064×A-65.945×B-247.869×C+951.348×D-2333.109; third wild probability=1 / (1+e －X ); wherein A, B, and C are the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0), respectively, and the third wild probability represents the probability that the test sample is a wild bighead carp. In some embodiments, if the third wild probability is greater than the third threshold value, the test sample is a wild bighead carp. In some embodiments, if the third wild probability is less than the third threshold value, the test sample is a farmed bighead carp.

[0032] In an embodiment of the tenth aspect, the step of determining the third threshold value includes: using the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the third wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the third wild probability values, and drawing an ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each third wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the third wild probability corresponding to the maximum Youden index is the third threshold value. In some embodiments, the third threshold value is 0.50.

[0033] In an eleventh aspect, the embodiment discloses a method for distinguishing wild bighead carp from farmed bighead carp.

[0034] In an embodiment of the eleventh aspect, the method includes a method for testing the content of each marker in the marker described in the fifth aspect in the test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, the individual markers include acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0035] In an embodiment of the eleventh aspect, the method further includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the fourth wild probability and the fourth threshold. In some embodiments, the fourth logistic regression model is: X = -8.140 × A + 14.542 × B - 7.169 × C + 1.332 × D - 23.886; fourth wild probability = 1 / (1 + e －X ); wherein A, B, C, D, E, F, and G are the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively, and the fourth wild probability represents the probability that the test sample is a wild bighead carp. In some embodiments, if the fourth wild probability is greater than the fourth threshold value, the test sample is a wild bighead carp. In some embodiments, if the fourth wild probability is less than the fourth threshold value, the test sample is a farmed bighead carp.

[0036] In the embodiment of the eleventh aspect, the step of determining the fourth threshold value includes: using the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the fourth wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the fourth wild probability values, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each fourth wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the fourth wild probability corresponding to the maximum Youden index is the fourth threshold value. In some embodiments, the fourth threshold value is 0.196.

[0037] In an embodiment of any one of the eighth to eleventh aspects, the standardized value is a value obtained by normalizing the chromatographic peak area detected by each marker and performing a logarithmic transformation with a base of 10.

[0038] In an embodiment of any one of the eighth to eleventh aspects, the method further comprises a liquid chromatography-mass spectrometry method.

[0039] In an embodiment of any one of the eighth to eleventh aspects, in the liquid chromatography detection step, a SHIMADZU-LC30 ultra high performance liquid chromatography system (UHPLC) is used for detection, and an ACQUITY HSST3 (2.1×100mm, 1.8μm) (Waters, Milford, MA, USA) chromatographic column, column temperature 40℃, chromatographic mobile phase A is 0.1% formic acid aqueous solution, chromatographic mobile phase B is 100% acetonitrile solution, elution mode is gradient elution, elution time is 15 minutes, flow rate is 0.3mL / min, specific injection volume is 4μL. The specific procedure of chromatographic gradient elution is as follows: 0→2min, 0% B; 2-6min, B changes linearly from 0% to 48%; 6→10min, B changes linearly from 48% to 100%; 10→12min, B is maintained at 100%; 12→12.1min, B changes linearly from 100% to 0%, 12.1→15min, B is maintained at 0%.

[0040] In an embodiment of any one of the eighth to eleventh aspects, in the mass spectrometry detection step, electrospray ionization (ESI) is used for positive ion (+) and negative ion (-) mode detection. After UPLC separation, mass spectrometry analysis is performed using a QEPlus mass spectrometer (ThermoScientific), and ionization is performed using a HESI source, and the ionization conditions are as follows: spray voltage is 3.8 kV (+) and 3.2 kV (-); capillary temperature is 320°C; probe heater temperature is 350°C; sheath gas is 30 arbitrary units; auxiliary gas flow is 5 arbitrary units; S-Lens radio frequency level is 50 arbitrary units.

[0041] In an embodiment of any one of the eighth to eleventh aspects, in the mass spectrum detection step, the mass spectrum acquisition time is 15 minutes. The parent ion scanning range is 75-1050 m / z; the primary mass spectrum resolution is 70,000 @ m / z200; the number of target ions is 3e6; the maximum injection time of the primary mass spectrum is 100 ms. The secondary mass spectrometry analysis is collected according to the following method: after each full scan, the secondary mass spectra of the 10 highest intensity parent ions are triggered to collect, the secondary mass spectrum resolution is 17,500 @ m / z200; the number of target ions is 1e5, and the maximum injection time of the secondary mass spectrum is 50 ms.

[0042] In an embodiment of any one of the eighth to eleventh aspects, the results obtained by the liquid chromatography-mass spectrometry are quality controlled. The quality control step comprises: preparing a quality control (QC) sample by mixing aliquots of the supernatant from all samples; evaluating the repeatability of the data and the stability of the instrument based on the overlapping base peak ion chromatogram (BPC) of the QC sample; determining the residue using the BPC of the blank sample; and evaluating the reliability and stability of the instrument analysis using principal component analysis (PCA) of all samples.

[0043] In an embodiment of any one of the eighth to eleventh aspects, the raw data obtained by liquid chromatography-mass spectrometry is subjected to peak alignment, retention time correction and peak area extraction using MSDIAL software. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The confusion matrix test results of the metabolic marker composition provided in the examples (phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (16:0 / 20:4)) for distinguishing wild bighead carp from farmed bighead carp.

[0045] Figure 2 The confusion matrix test results of the metabolic marker composition (phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2)) provided in the examples for distinguishing wild bighead carp from farmed bighead carp.

[0046] Figure 3 The confusion matrix test results of the metabolic marker composition provided in the examples (phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0)) for distinguishing wild bighead carp from farmed bighead carp.

[0047] Figure 4 The confusion matrix test results of the metabolic marker composition provided in the examples (acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (16:0 / 20:4)) for distinguishing wild bighead carp from farmed bighead carp. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. The reagents not described separately in detail in the present application are all conventional reagents and can be obtained from commercial channels; the methods not described in detail are all conventional experimental methods and can be obtained from the prior art.

[0049] Mining of glycerophospholipid markers for distinguishing wild and farmed bighead carp

[0050] 1. Collection of muscle samples from bighead carp

[0051] The wild bighead carp populations involved in the examples come from the Minjiang River, Dongting Lake and Poyang Lake in the Yangtze River Basin. The farmed bighead carp populations involved in the examples come from farms in Hubei Province, Hunan Province and Jiangsu Province. The samples were taken from the upper back muscles of bighead carp and immediately frozen in liquid nitrogen after sampling, and transferred to a -80℃ refrigerator for storage until the subsequent metabolite extraction.

[0052] 2. Sample metabolite extraction

[0053] Thaw the sample at 4℃, weigh 50mg of the sample, add 2 medium-sized steel balls, add 200μL pre-cooled methanol-water solution (4:1, v / v), homogenize and break in a tissue disruptor, then add 800μL pre-cooled 80% methanol-water solution, vortex mix, ultrasonicate in an ice bath for 20min, stand overnight at -20℃, centrifuge at 16000g4℃ for 20min, take the supernatant, and evaporate the supernatant in a high-speed vacuum concentrator centrifuge. For mass spectrometry detection, add 100μL methanol-water solution (1:1, v / v) to re-dissolve, centrifuge at 20000g4℃ for 15min, and take the supernatant for mass spectrometry injection analysis.

[0054] 3. UHPLC-MS / MS Detection and Analysis

[0055] UHPLC-MS / MS analysis Metabolomics analysis was performed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2 LC-30AD, Shimadzu Corporation, Japan) combined with Q-Exactive Plus (Thermo Scientific, San Jose, USA). The samples were placed in an autosampler at 4 °C throughout the analysis process, and the samples were analyzed using a SHIMADZU-LC30 ultra-high performance liquid chromatography system (UHPLC) using ACQUITY HSST3 (2.1 × 100mm, 1.8μm) (Waters, Milford, MA, USA) chromatographic column, column temperature 40 ° C, chromatographic mobile phase A is 0.1% formic acid aqueous solution, chromatographic mobile phase B is 100% acetonitrile solution, elution mode is gradient elution, elution time is 15 minutes, flow rate is 0.3mL / min, specific injection volume is 4μL. The specific procedure of chromatographic gradient elution is as follows: 0→2min, 0% B; 2→6min, B changes linearly from 0% to 48%; 6→10min, B changes linearly from 48% to 100%; 10→12min, B is maintained at 100%; 12→12.1min, B changes linearly from 100% to 0%, 12.1→15min, B is maintained at 0%.

[0056] Each sample was detected by electrospray ionization (ESI) in positive (+) and negative (-) modes. After UPLC separation, the samples were analyzed by mass spectrometry using a QEPlus mass spectrometer (Thermo Scientific), using a HESI source for ionization. The ionization conditions were as follows: spray voltage of 3.8 kV (+) and 3.2 kV (-); capillary temperature of 320 °C; probe heater temperature of 350 °C; sheath gas of 30 arbitrary units; auxiliary gas flow of 5 arbitrary units; S-Lens radio frequency level of 50 arbitrary units.

[0057] The mass spectrometry acquisition settings were as follows: the mass spectrometry acquisition time was 15 min. The parent ion scan range was 75-1050 m / z; the primary mass spectrometry resolution was 70,000 @ m / z 200; the number of target ions was 3e6; and the maximum primary injection time was 100 ms. The secondary mass spectrometry analysis was acquired according to the following method: the secondary mass spectra of the 10 highest intensity parent ions were triggered after each full scan, the secondary mass spectrometry resolution was 17,500 @ m / z 200; the number of target ions was 1e5, and the maximum secondary injection time was 50 ms.

[0058] 4. Quality Control

[0059] Quality control (QC) samples were prepared by pooling aliquots of supernatant from all samples. Data repeatability and instrument stability were evaluated based on the overlapping base peak ion chromatograms (BPCs) of the QC samples. Residues were determined using the BPCs of blank samples. The reliability and stability of the instrument analysis were evaluated using principal component analysis (PCA) of all samples.

[0060] 5. Processing and analysis of metabolomics data

[0061] The raw data were aligned, retained and corrected using MSDIAL software, and the peak areas were extracted. The metabolite structures were identified by accurate mass matching (mass deviation mass tolerance <10ppm) and secondary spectrum matching (mass deviation mass tolerance <0.01Da), and public databases such as HMDB, MassBank, GNPS, and the self-built Baipu metabolite standard library (BP-DB) were searched. For the extracted data, the ion peaks with missing values ​​>50% within the group were deleted and did not participate in the subsequent statistical analysis; the total peak area of ​​the positive and negative ion data was normalized, the positive and negative ion peaks were integrated, and Python software was used for pattern recognition. The data was preprocessed by Unit variance scaling (UV) before subsequent data analysis. In this study, the raw data files (.wiff format) were first imported into Progenesis QI software for data preprocessing procedures. This included reviewing the steps of alignment, peak selection, and peak detection, and performing data normalization at the same time. The HMDB and LIPID MAPS public databases were used in the annotation of metabolites. Finally, a data matrix containing m / z, retention time (RT), peak intensity, sample name, and metabolite name is generated.

[0062] 6. Screening of significantly different metabolites

[0063] The data matrix was imported into SIMCA 14.1 software for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection latent variable structure discriminant analysis (OPLS-DA). After the OPLS-DA model was constructed, the model was validated and evaluated for overfitting by 200 permutation tests. Then, based on the differentially changed metabolites (DMs) with P value < 0.05, Fold change > 1.5 or Fold change < 0.667, and VIP value > 1.0 obtained from the OPLS-DA model, the significantly different metabolites between wild and farmed bighead carp were obtained.

[0064] 7. Statistical analysis methods

[0065] GraphPad Prism 8.0 software was used to perform statistical analysis on the biochemical analysis data. Student's t-test was used to calculate the differences in data means between different groups. SPSS was used to construct and analyze the binary logistic regression model, and the input data were the standardized values ​​of each metabolite, that is, the logarithmic transformation value (log10) with a base of 10 after peak area normalization, which was used to reduce the skewed distribution of the overall values ​​and make the data close to a normal distribution. The experimental results are presented in the form of mean plus or minus standard deviation (SD), where the asterisk (*) indicates that the difference between the experimental group and the control group is statistically significant. Specifically, *P<0.05 indicates a significant difference, and **P<0.01 indicates an extremely significant difference.

[0066] 8. Results

[0067] The constructed OPLS-DA model was used to compare the two groups of bighead carp samples from wild and farmed populations. Metabolites with OPLS-DA VIP values ​​greater than 1.0 obtained by variance analysis were considered to be potential markers that help class discrimination. The combination of difference fold (FC) ≥1.5 or ≤0.667 and P<0.05 was used to screen significantly different metabolites based on P value and VIP value. The study of significantly different metabolites in a single fish species is easily affected by the inherent characteristics and errors of that specific species, while the analysis of significantly different metabolites shared by different fish species can avoid the errors of a single fish species and improve the accuracy and reliability of the results.

[0068] Therefore, the embodiment obtained the significant difference metabolites shared by these four fish species under wild and culture conditions by performing VEEN intersection processing on the wild and cultured difference metabolites of bighead carp. Subsequently, cluster analysis was performed for these shared significant difference metabolites, and it was found that they contained a variety of glycerophospholipid substances, namely: phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (16:0 / 20:4). The reference mass-to-charge ratio, retention time (min), ionization mode, and subcategory of the above-mentioned glycerophospholipid substances are shown in Table 1. The P value, difference fold, Log2 (difference fold), and OPLS-DAVIP of the significantly different metabolites between wild and farmed bighead carp are shown in Table 2.

[0069] Table 1 Information on markers with significant differences between wild and farmed bighead carp

[0070] Markers Reference mass-to-charge ratio Retention time (min) Ionization mode Category (Major Category) Phosphatidylcholine (15:0 / 22:6) 850.5575 9.128 [MH]- Glycerophospholipids Glycerol 3-phosphate 171.00642 0.864 [MH]- Glycerophospholipids Phosphatidylcholine (18:1 / 18:2) 784.58508 11.17 [M+H]+ Glycerophospholipids Phosphatidylcholine (17:1 / 18:2) 828.5744 9.1 [MH]- Glycerophospholipids Phosphatidylcholine (15:0 / 18:2) 802.55878 9.097 [MH]- Glycerophospholipids Phosphatidylglycerol (22:4 / 22:4) 873.565 10.206 [2M-H]- Glycerophospholipids Acylphosphatidylcholine (P-16:0) 480.34479 10.441 [M+H]+ Glycerophospholipids Acylphosphatidylethanolamine (18:3) 476.27719 9.217 [M+H]+ Glycerophospholipids Acylphosphatidylcholine (20:4) 544.34033 9.726 [M+H]+ Glycerophospholipids Phosphatidylcholine (16:0 / 18:2) 758.5694 10.687 [M+H]+ Glycerophospholipids Phosphatidylcholine (16:0 / 20:4) 782.57001 10.726 [M+H]+ Glycerophospholipids

[0071] Table 2 Differences in markers common to wild and cultured bighead carp

[0072]

[0073] Metabolic marker composition for distinguishing wild bighead carp from farmed bighead carp and its use

[0074] The main body of the current data discrimination model is to build a model through multiple indicators for comprehensive judgment. Among them, logistic regression, as a simple and intuitive model, has many advantages. First, it is easy to understand and explain, does not require complex mathematical derivation or calculation, and has high computational efficiency. Secondly, logistic regression can provide highly interpretable result output, can be classified by setting thresholds, and the coefficients and intercepts of the model can provide the degree and direction of the contribution of the variable to the target variable.

[0075] 1. Selection of metabolic marker composition

[0076] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild bighead carp and farmed bighead carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small determination error, and high data integrity are selected as markers for distinguishing wild bighead carp and farmed bighead carp. The markers for distinguishing wild bighead carp and farmed bighead carp provided in the embodiment include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0077] 2. How to distinguish wild bighead carp from farmed bighead carp

[0078] The embodiment also discloses a method for distinguishing wild bighead carp from farmed bighead carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple standardized values ​​of the content of multiple markers in a test sample; inputting the multiple standardized values ​​into the first logistic regression model to obtain a first wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the magnitude of the first wild probability and the first threshold. Among them, multiple markers include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0079] In some embodiments, the step of obtaining a first logistic regression model includes:

[0080] 1) Obtain training samples

[0081] Embodiment The muscle samples of 36 wild bighead carps and 36 cultured bighead carps were subjected to the same liquid phase detection as above, and the chromatographic peak areas of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) in each sample were obtained. These chromatographic peak areas were standardized to obtain standardized values. The standardization step comprises: respectively normalizing the chromatographic peak areas of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4). After that, a logarithmic transformation with base 10 was performed to obtain the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively. The standardized values ​​of these muscle samples, phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), were used as training samples.

[0082] 2) Training

[0083] The standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) were used as independent variables, and the probability of the sample being wild was used as the dependent variable for training a binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: performing composition analysis on the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; performing significance test on the independent variable, including degrees of freedom, significance and score; performing the Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training the binary logistic regression model to obtain the first logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0084] In some steps, the embodiment performs a single degree of freedom test on the independent variables of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) in the muscle sample content of wild bighead carp and farmed bighead carp, including giving degrees of freedom, significance and score detection. The results are shown in Table 3. The overall statistical score of the content of these four markers as independent variables is high, and the significance is less than 0.05, indicating that it will have a significant impact on the corresponding variables in general.

[0085] Table 3 Significance test and score of independent variables in the logistic regression model of markers for wild and farmed bighead carp

[0086]

[0087]

[0088] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of the markers of wild bighead carp and farmed bighead carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=1.000>0.05, that is, the 0 hypothesis is accepted. The results are shown in Table 4. The model fits the real data well and can truly and reliably reflect the reliable relationship between the original variables.

[0089] Table 4 Hosmer-Lemmeshaw test for logistic regression models of markers for wild and farmed bighead carp

[0090] step Chi-square Degrees of Freedom Significance 1 0.001 6 1.000

[0091] 3) Obtain the first logistic regression model

[0092] In some steps, the embodiment performs binary logistic regression analysis on the logistic regression model of markers of wild bighead carp and cultured bighead carp. Table 5 shows the effect size Exp(B) of the regression model and the significance of each marker.

[0093] Table 5 Analysis of binary logistic regression model in marker model of wild bighead carp and farmed bighead carp

[0094]

[0095] Embodiment According to the standardized values ​​of the above 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples, the first logistic regression model is as follows:

[0096] X=7.325×A-39.462×B-74.266×C-47.563×D-0.669×E-26.894×F+51.610×G+43.993×H+3.788×I+21.002×J-13.328×K+435.356;

[0097] First wild probability = 1 / (1+e －X );

[0098] Among them, A, B, C, D, E, F, G, H, I, J, and K are the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively. The first wild probability represents the probability that the test sample is wild bighead carp.

[0099] The steps provided in the embodiment are to calculate the first threshold value by ROC curve. The steps provided in some embodiments include: using 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples as phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16 ...choline (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2 The standardized value of fatty acylcholine (16:0 / 20:4) is used as a training set; a binary logistic regression model is trained with the training set to obtain multiple first wild probability values ​​as mentioned above; the sensitivity (true positive rate) and 1-specificity (false positive rate) of each first wild probability value are calculated respectively, and the ROC curve is drawn with 1-specificity as the abscissa and sensitivity as the ordinate; the Youden index corresponding to each predicted wild probability is calculated (Youden index = sensitivity + specificity - 1), and the maximum Youden index is obtained, and the first predicted wild probability corresponding to the maximum Youden index is the first threshold.

[0100] In some embodiments, if the first wild probability is greater than the first threshold, the test sample is determined to be wild bighead carp. If the first wild probability is less than the first threshold, the test sample is determined to be farmed bighead carp.

[0101] In some embodiments, the first threshold value obtained by using the above test set and calculating the ROC curve is 0.50. That is, when the first wild probability is greater than 0.50, the test sample is determined to be wild bighead carp; when the first wild probability is less than 0.50, the test sample is determined to be farmed bighead carp.

[0102] 3. Identification application and testing

[0103] In some test cases, 36 muscle samples (including 18 wild and 18 farmed) were selected, including phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) as markers, and their standardized values ​​were input into the above-mentioned first logistic regression model to obtain the first wild probability; according to the size of the first wild probability and the first threshold (0.50), it was determined whether the test sample was wild bighead carp or farmed bighead carp.

[0104] The above training set was used to construct a binary logistic regression model with standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), and the standardized values ​​of phosphatidylcholine (15:0 / 22:6) were used to construct a binary logistic regression model, and the standardized values ​​of phosphatidylcholine (15:0 / 22:6) were used to construct a binary logistic regression model. The first logistic regression model was constructed by combining the standardized values ​​of phosphatidylcholine (15:0 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), and the 36 muscle samples were tested respectively. The discrimination results were subjected to ROC curve test. The results are shown in Table 6. As can be seen from Table 6, the first logistic regression model constructed by combining the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) has higher AUC value, sensitivity, and specificity.

[0105] Table 6 ROC test results of each marker and its combination for distinguishing wild and farmed bighead carp

[0106]

[0107]

[0108] The discrimination results were input into the SPSS20.0 analysis software for confusion matrix test. The confusion matrix, also known as the error matrix, is a standard format for expressing accuracy evaluation, expressed in the form of a matrix with n rows and n columns. Specific evaluation indicators include overall accuracy, mapping accuracy, user accuracy, etc. These accuracy indicators reflect the accuracy of image classification from different aspects. Each column of the confusion matrix represents a predicted category, and the total number of each column represents the number of data predicted to be in that category; each row represents the true category of the data, and the total number of data in each row represents the number of data instances in that category. The results are as follows: Figure 1 As shown in the figure, only one of the 36 bighead carp samples was misjudged, and the overall accuracy of the interactive validation was as high as 97.22%.

[0109] This shows that phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) can be used as markers to identify wild and farmed bighead carp in the Yangtze River Basin. Its excellent ROC curve results, good correct diagnosis rate, and good complementarity of markers all indicate that the four glycerophospholipids have the potential to identify wild and farmed bighead carp.

[0110] Metabolic marker composition for distinguishing wild bighead carp from farmed bighead carp and its use

[0111] 1. Selection of metabolic marker composition

[0112] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild bighead carp and farmed bighead carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as markers for distinguishing wild bighead carp from farmed bighead carp. The markers for distinguishing wild bighead carp from farmed bighead carp provided in the embodiment include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2).

[0113] The embodiment also provides a kit for detecting these markers, which includes reagents for detecting these markers, such as reagents required for liquid chromatography-mass spectrometry.

[0114] 2. How to distinguish wild bighead carp from farmed bighead carp

[0115] The embodiment also discloses a method for distinguishing wild bighead carp from farmed bighead carp. The method includes: obtaining a second logistic regression model and a second threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the second logistic regression model to obtain a second wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp according to the magnitude of the second wild probability and the second threshold. Among them, the multiple markers include phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2).

[0116] In some embodiments, the step of obtaining a second logistic regression model comprises:

[0117] 1) Obtain training samples

[0118] Embodiment The muscle samples of 36 wild bighead carps and the muscle samples of 36 cultured bighead carps were detected by the same liquid phase as above, and the chromatographic peak areas of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) in each sample were obtained. These chromatographic peak areas were standardized to obtain standardized values. The step of the standardized treatment includes: after the chromatographic peak areas of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) were normalized, the logarithmic conversion with a base of 10 was performed, and the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) can be obtained respectively. The standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) of these muscle samples were used as training samples.

[0119] 2) Training

[0120] The standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train the binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training of the binary logistic regression model, and obtaining a second logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0121] In some steps, the embodiment performs a single degree of freedom test on the muscle sample content of the independent variables phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2), including giving degrees of freedom, significance, and score detection. The results are shown in Table 7. The overall statistical score of the content of these four markers as independent variables is high, and the significance is less than 0.001, indicating that the independent variable will have a very significant impact on the corresponding variable in general.

[0122] Table 7 Significance test and score of independent variables in the logistic regression model of markers of wild and farmed bighead carp

[0123]

[0124] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of the markers of wild bighead carp and farmed bighead carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=0.988>0.05, that is, the 0 hypothesis is accepted. The results are shown in Table 8. The constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0125] Table 8 Hosmer-Lemmeshaw test of logistic regression model for wild and farmed bighead carp

[0126] step Chi-square Degrees of Freedom Significance 1 1.298 7 0.988

[0127] 3) Obtain the second logistic regression model

[0128] In some steps, the embodiment performs binary logistic regression analysis on markers for distinguishing wild bighead carp from farmed bighead carp. Table 9 shows the effect size Exp(B) of the regression model and the significance of each marker.

[0129] Table 9 Binary logistic regression analysis of wild and farmed bighead carp

[0130]

[0131] Embodiment According to the standardized values ​​of the above 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples, the first logistic regression model is as follows:

[0132] X=8.058×A-17.834×B-11.751×C+192.892;

[0133] Second wild probability = 1 / (1+e －X );

[0134] A, B, and C are the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2), respectively, and the second wild probability represents the probability that the test sample is a wild bighead carp.

[0135] The steps provided in the embodiment are calculated by ROC curve to obtain the second threshold value. The steps provided in some embodiments include: using the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) of 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples as training sets; training a binary logistic regression model with the training set to obtain multiple second wild probability values ​​as described above; respectively calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each second wild probability value, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the first predicted wild probability corresponding to the maximum Youden index is the second threshold value.

[0136] In some embodiments, if the second wild probability is greater than the second threshold, the test sample is determined to be wild bighead carp. If the second wild probability is less than the second threshold, the test sample is determined to be farmed bighead carp.

[0137] In some embodiments, the second threshold value obtained by using the above test set and calculating the ROC curve is 0.264. That is, when the second wild probability is greater than 0.264, the test sample is determined to be wild bighead carp; when the second wild probability is less than 0.264, the test sample is determined to be farmed bighead carp.

[0138] 3. Identification application and testing

[0139] In some test cases, 36 muscle samples (including 18 wild and 18 farmed) of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) were selected as markers, and their standardized values ​​were input into the above-mentioned second logistic regression model to obtain the second wild probability; based on the size of the second wild probability and the second threshold (0.264), it was determined whether the test sample was wild bighead carp or farmed bighead carp.

[0140] The standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) in the above training set were used to construct a binary logistic regression model, and the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) were combined to construct a second logistic regression model. The 36 muscle samples were tested respectively, and the discrimination results were subjected to ROC curve test. The results are shown in Table 10. As can be seen from Table 10, the second logistic regression model constructed by combining the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) for discriminating the test samples has a higher AUC value, sensitivity, and specificity.

[0141] Table 10 ROC test results of each marker and its combination for distinguishing wild and farmed bighead carp

[0142] Markers AUC Sensitivity Specificity Phosphatidylcholine (15:0 / 22:6) 0.895 0.778 0.944 Glycerol 3-phosphate 0.880 0.778 0.889 Phosphatidylcholine (18:1 / 18:2) 0.873 0.944 0.667 Composition 0.985 0.944 0.944

[0143] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test. The results are as follows: Figure 2 As shown in the figure, only 2 samples among the 36 bighead carp samples were misjudged, and the overall accuracy of the interactive validation was as high as 94.44%.

[0144] This shows that phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2) can be used as markers to identify wild and farmed bighead carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate, and good complementarity of the markers all indicate that the four glycerophospholipids have the potential to identify wild and farmed bighead carp.

[0145] Metabolic marker composition for distinguishing wild bighead carp from farmed bighead carp and its use

[0146] 1. Selection of metabolic marker composition

[0147] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild bighead carp and farmed bighead carp, and a binary logistic regression model is trained according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as markers for distinguishing wild bighead carp from farmed bighead carp. The markers for distinguishing wild bighead carp from farmed bighead carp provided in the embodiment include phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0).

[0148] The embodiment also provides a kit for detecting these markers, which includes reagents for detecting these markers, such as reagents required for liquid chromatography-mass spectrometry.

[0149] 2. How to distinguish wild bighead carp from farmed bighead carp

[0150] The embodiment also discloses a method for distinguishing wild bighead carp from farmed bighead carp. The method comprises: obtaining a third logistic regression model and a third threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the third logistic regression model to obtain a third wild probability; determining whether the test sample is the wild bighead carp or the farmed bighead carp according to the size of the third wild probability and the third threshold. Among them, the multiple markers include phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0).

[0151] In some embodiments, the step of obtaining the third logistic regression model comprises:

[0152] 1) Obtain training samples

[0153] Embodiment The muscle samples of 36 wild bighead carps and 36 farmed bighead carps were subjected to the same liquid phase detection as above to obtain the chromatographic peak areas of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) in each sample. These chromatographic peak areas were standardized to obtain standardized values. The step of standardization includes: normalizing the chromatographic peak areas of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0), and then performing logarithmic transformation with a base of 10 to obtain the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0). The standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) of these muscle samples are used as training samples.

[0154] 2) Training

[0155] The standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train the binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training of the binary logistic regression model, and obtaining a third logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0156] In some steps, the embodiment uses muscle samples of independent variables phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) to perform a single degree of freedom test, including giving degrees of freedom, significance, and score detection. As shown in Table 11, the overall statistical score of the independent variable is high, and the significance is less than 0.0001, indicating that the independent variable will have a very significant impact on the corresponding variable in general.

[0157] Table 11 Significance test and score of independent variables in the logistic regression model of markers of wild and farmed bighead carp

[0158]

[0159] In order to further analyze the influence of constant and independent variable on dependent variable in multiple degrees of freedom, the embodiment also performs Hosmer-Lemmeshow test on the logistic regression model. Under the condition of multiple degrees of freedom, the significance of binary logistic regression model is P=1.000>0.05, that is, the zero hypothesis is accepted. As shown in Table 12, the constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0160] Table 12 Hosmer-Lemmeshaw test of logistic regression model for wild and farmed bighead carp

[0161] step Chi-square Degrees of Freedom Significance 1 0.001 4 1.000

[0162] 3) Obtain the third logistic regression model

[0163] In some steps, the embodiment performs binary logistic regression analysis on the logistic regression model of markers of wild bighead carp and cultured bighead carp. Table 13 shows the effect size Exp(B) of the regression model and the significance of each marker.

[0164] Table 13 Binary logistic regression analysis of wild and farmed bighead carp

[0165]

[0166] Embodiment According to the standardized values ​​of the above 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples, the third logistic regression model is as follows:

[0167] X=-783.064×A-65.945×B-247.869×C+951.348×D-2333.109;

[0168] The third wild probability = 1 / (1+e －X );

[0169] A, B, C, and D are the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0), respectively, and the third wild probability represents the probability that the test sample is wild bighead carp.

[0170] The steps provided in the embodiment are calculated by ROC curve to obtain the third threshold value. The steps provided in some embodiments include: using the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) of 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples as training sets; training a binary logistic regression model with the training set to obtain multiple third wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each third wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the third predicted wild probability corresponding to the maximum Youden index is the third threshold value.

[0171] In some embodiments, if the third wild probability is greater than the third threshold, the test sample is determined to be wild bighead carp. If the third wild probability is less than the third threshold, the test sample is determined to be farmed bighead carp.

[0172] In some embodiments, the third threshold value obtained by using the above test set and calculating the ROC curve is 0.50. That is, when the third wild probability is greater than 0.50, the test sample is determined to be wild bighead carp; when the third wild probability is less than 0.50, the test sample is determined to be farmed bighead carp.

[0173] 3. Identification application and testing

[0174] In some test cases, phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) of 36 muscle samples (including 18 wild and 18 farmed) were selected as markers, and their standardized values ​​were input into the above-mentioned third logistic regression model to obtain the third wild probability; based on the size of the third wild probability and the third threshold value (0.50), it was determined whether the test sample was wild bighead carp or farmed bighead carp.

[0175] The standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) in the above training set were used to construct binary logistic regression models separately, and the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) were combined to construct a third logistic regression model. The 36 muscle samples were tested separately, and the discrimination results were subjected to ROC curve test. The results are shown in Table 14. As shown in Table 14, the third logistic regression model constructed by combining the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) had higher AUC value, sensitivity, and specificity.

[0176] Table 14 ROC test results of each marker and its combination for distinguishing wild and farmed bighead carp

[0177] Markers AUC Sensitivity Specificity Phosphatidylcholine (17:1 / 18:2) 0.867 0.944 0.833 Phosphatidylcholine (15:0 / 18:2) 0.836 0.722 0.833 Phosphatidylglycerol (22:4 / 22:4) 0.830 0.778 0.778 Acylphosphatidylcholine (P-16:0) 0.827 0.833 0.889 Composition 0.994 1.000 0.944

[0178] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test. The results are as follows: Figure 3 As shown in the figure, only one of the 36 bighead carp samples was misjudged, and the overall accuracy of the interactive validation was as high as 97.22%.

[0179] This shows that the logistic regression model constructed with phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0) as markers and their standardized values ​​can be used to distinguish wild and farmed bighead carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate, and good complementarity of the markers indicate that the five glycerophospholipids have the potential to identify wild and farmed bighead carp.

[0180] Metabolic marker composition for distinguishing wild bighead carp from farmed bighead carp and its use

[0181] 1. Selection of metabolic marker composition

[0182] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild bighead carp and farmed bighead carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as markers for distinguishing wild bighead carp from farmed bighead carp. The markers for distinguishing wild bighead carp from farmed bighead carp provided in the embodiment include acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0183] The embodiment also provides a kit for detecting these markers, which includes reagents for detecting these markers, such as reagents required for liquid chromatography-mass spectrometry.

[0184] 2. How to distinguish wild bighead carp from farmed bighead carp

[0185] The embodiment also discloses a method for distinguishing wild bighead carp from farmed bighead carp. The method comprises: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple standardized values ​​of the content of multiple markers in the test sample; inputting the multiple standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; determining whether the test sample is the wild bighead carp or the farmed bighead carp according to the size of the fourth wild probability and the fourth threshold. Among them, the multiple markers include acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4).

[0186] In some embodiments, the step of obtaining the fourth logistic regression model comprises:

[0187] 1) Obtain training samples

[0188] Example 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples were subjected to the same liquid phase detection as above to obtain the chromatographic peak areas of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) in each sample. These chromatographic peak areas were standardized to obtain standardized values. The standardization step includes: normalizing the chromatographic peak areas of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively, and performing a logarithmic transformation with a base of 10 to obtain the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4). The standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) of these muscle samples are used as training samples.

[0189] 2) Training

[0190] The standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train the binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training of the binary logistic regression model, and obtaining the fourth logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0191] In some steps, the embodiment performs a single degree of freedom test on the muscle sample content of the independent variables acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), including giving degrees of freedom, significance, and score detection. The results are shown in Table 15, and the overall statistical scores of these independent variables are high, with a significance of less than 0.0001, indicating that the independent variables will have a very significant impact on the corresponding variables in general.

[0192] Table 15 Significance test and score of independent variables in the joint marker model of wild bighead carp and farmed bighead carp

[0193]

[0194] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of the markers of wild bighead carp and farmed bighead carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=0.995>0.05, that is, the 0 hypothesis is accepted. As shown in Table 16, the constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0195] Table 16 Hosmer-Lemmeshaw test for binary logistic regression model of wild and farmed bighead carp

[0196]

[0197]

[0198] 3) Obtain the fourth logistic regression model

[0199] In some steps, the embodiment performs binary logistic regression analysis on markers for distinguishing wild bighead carp from farmed bighead carp. Table 17 shows the effect size Exp(B) of the regression model and the significance of each marker.

[0200] Table 17 Binary logistic regression analysis of wild and farmed bighead carp

[0201]

[0202] Embodiment According to the standardized values ​​of the above 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples, the fourth logistic regression model is as follows:

[0203] X=-8.140×A+14.542×B-7.169×C+1.332×D-23.886;

[0204] Fourth wild probability = 1 / (1+e －X );

[0205] Among them, A, B, C, and D are the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively, and the fourth wild probability represents the probability that the test sample is wild bighead carp.

[0206] The steps provided in the embodiment are calculated by ROC curve to obtain the fourth threshold value. The steps provided in some embodiments include: using the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) of 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples as training sets; training a binary logistic regression model with the training set to obtain multiple fourth wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each fourth wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the fourth predicted wild probability corresponding to the maximum Youden index is the fourth threshold value.

[0207] In some embodiments, if the fourth wild probability is greater than the fourth threshold, the test sample is determined to be wild bighead carp. If the fourth wild probability is less than the fourth threshold, the test sample is determined to be farmed bighead carp.

[0208] In some embodiments, the fourth threshold value obtained by using the above test set and calculating the ROC curve is 0.196. That is, when the fourth wild probability is greater than 0.196, the test sample is determined to be wild bighead carp; when the fourth wild probability is less than 0.196, the test sample is determined to be farmed bighead carp.

[0209] 3. Identification application and testing

[0210] In some test cases, 36 muscle samples (including 18 wild and 18 farmed) including acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) were selected as markers, and their standardized values ​​were input into the fourth logistic regression model to obtain the fourth wild probability; based on the size of the fourth wild probability and the fourth threshold (0.196), it was determined whether the test sample was wild bighead carp or farmed bighead carp.

[0211] The standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) in the above training set were used to construct binary logistic regression models separately, and the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) were combined to construct a fourth logistic regression model. The 36 muscle samples were tested separately, and the discrimination results were subjected to ROC curve test. The results are shown in Table 18. As can be seen from Table 18, the fourth logistic regression model constructed by combining the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) for discriminating the test samples has a higher AUC value, sensitivity, and specificity.

[0212] Table 18 ROC test results of each marker and its combination for distinguishing wild and farmed bighead carp

[0213]

[0214]

[0215] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test, such as Figure 4 As shown in the figure, 4 out of 36 bighead carp samples were misjudged, and the overall correct rate of interactive validation was 88.89%.

[0216] This shows that the stability of using acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4) as markers and regression models to distinguish wild and farmed bighead carp is good. The excellent ROC curve results, good correct diagnosis rate, and good complementarity of markers indicate that the five glycerophospholipids have the potential to distinguish wild and farmed bighead carp.

[0217] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. Use of biomarkers and / or reagents for detecting the biomarkers in distinguishing wild and farmed bighead carp, the distinction comprising: Testing the content of biomarkers in the test samples, the biomarkers are phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), phosphatidylcholine (16:0 / 20:4); obtaining a first logistic regression model and a first threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers in the test samples; Inputting the plurality of the standardized values ​​into the first logistic regression model to obtain a first wild probability; Determine whether the test sample is the wild bighead carp or the farmed bighead carp according to the first wild probability and the first threshold; If the first wild probability is greater than the first threshold, the test sample is a wild bighead carp; as well as If the first wild probability is less than the first threshold, the test sample is farmed bighead carp; Among them, the first logistic regression model is: X = 7.325 × A-39.462 × B-74.266 × C-47.563 × D-0.669 × E-26.894 × F + 51.610 × G + 43.993 × H + 3.788 × I + 21.002 × J-13.328 × K + 435.356; the first wild probability = 1 / (1+e －X ), wherein A, B, C, D, E, F, G, H, I, J, and K are the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, phosphatidylcholine (18:1 / 18:2), phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), acylphosphatidylcholine (P-16:0), acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively, and the first wild probability represents the probability that the test sample is wild bighead carp; 2. Use of biomarkers and / or reagents for detecting said biomarkers in distinguishing wild from farmed bighead carp, said distinction comprising: The step of testing the content of biomarkers in the test sample, wherein the biomarkers are phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2); Obtain a second logistic regression model and a second threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers in the test samples; Inputting the plurality of the standardized values ​​into the second logistic regression model to obtain a second wild probability; Determine whether the test sample is the wild bighead carp or the farmed bighead carp according to the second wild probability and the second threshold; If the second wild probability is greater than the second threshold, the test sample is a wild bighead carp; and If the second wild probability is less than the second threshold, the test sample is farmed bighead carp; Among them, the second logistic regression model is: X = 8.058 × A-17.834 × B-11.751 × C + 192.892; the second wild probability = 1 / (1+e －X ); wherein A, B, and C are the standardized values ​​of phosphatidylcholine (15:0 / 22:6), glycerol 3-phosphate, and phosphatidylcholine (18:1 / 18:2), respectively, and the second wild probability represents the probability that the test sample is a wild bighead carp.

3. Use of biomarkers and / or reagents for detecting said biomarkers in distinguishing wild from farmed bighead carp, said distinction comprising: Testing the content of biomarkers in the test samples, wherein the biomarkers are phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0); Obtaining a third logistic regression model and a third threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers in the test samples; Inputting the plurality of standardized values ​​into the third logistic regression model to obtain a third wild probability; Determine whether the test sample is the wild bighead carp or the farmed bighead carp according to the third wild probability and the third threshold; If the third wild probability is greater than the third threshold, the test sample is a wild bighead carp; and If the third wild probability is less than the third threshold, the test sample is farmed bighead carp; Among them, the third logistic regression model is: X = -783.064 × A - 65.945 × B - 247.869 × C + 951.348 × D - 2333.109; the third wild probability = 1 / (1+e －X ); wherein A, B, C, and D are the standardized values ​​of phosphatidylcholine (17:1 / 18:2), phosphatidylcholine (15:0 / 18:2), phosphatidylglycerol (22:4 / 22:4), and acylphosphatidylcholine (P-16:0), respectively, and the third wild probability represents the probability that the test sample is a wild bighead carp.

4. Use of biomarkers and / or reagents for detecting said biomarkers in distinguishing wild from farmed bighead carp, said distinction comprising: Testing the content of biomarkers in the test samples, wherein the biomarkers are acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4); obtaining a fourth logistic regression model and a fourth threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers in the test samples; Inputting the plurality of standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; Determine whether the test sample is the wild bighead carp or the farmed bighead carp according to the fourth wild probability and the fourth threshold value; If the fourth wild probability is greater than the fourth threshold, the test sample is a wild bighead carp; as well as If the fourth wild probability is less than the fourth threshold value, the test sample is farmed bighead carp; Among them, the fourth logistic regression model is: X = -8.140 × A + 14.542 × B - 7.169 × C + 1.332 × D - 23.886, the fourth wild probability = 1 / (1 + e －X ), wherein A, B, C, and D are the standardized values ​​of acylphosphatidylethanolamine (18:3), acylphosphatidylcholine (20:4), phosphatidylcholine (16:0 / 18:2), and phosphatidylcholine (16:0 / 20:4), respectively, and the fourth wild probability represents the probability that the test sample is a wild bighead carp.

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