Construction method of model for evaluating net energy level of daily ration of Dingan black pigs based on metabonomics

Abstract

The invention discloses a model for evaluating the net energy level of Dingan black pig daily ration based on metabonomics and a construction method of the model, and eight plasma metabolism markers related to the net energy level of daily ration and a change rule of the plasma metabolism markers are found by comparing the difference of plasma metabolomics of daily ration with different net energy levels. A dynamic prediction model between the plasma biomarker and the daily ration net energy level is established, and then the net energy level of the daily ration fed by the Dingan black pigs in the actual production process is predicted. According to the invention, metabonomics is utilized to predict the net energy level of the fed daily ration, so that not only can errors caused by body type evaluation and pig herd stress caused by repeated weight weighing be avoided, but also effective energy value errors caused by heat consumption increase differences of energy feed raw materials or protein feed raw materials can be reduced; application of unconventional feed raw materials such as palm meal and coconut meal in Hainan is facilitated, and the breeding cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of feed nutritional level prediction, and particularly relates to a method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics. Background Art

[0002] In large-scale pig production, feed costs account for about 60% to 70% of pork production, of which energy feed costs account for the largest proportion. Carbohydrates, lipids, proteins and amino acids are the basic sources of energy in animal feed. More than 70% of the nutrients in the feed consumed by animals are mainly absorbed and utilized as energy substances. The energy obtained from the feed is mainly stored in the animal body in the form of protein, fat and glycogen, thereby maintaining the animal's growth, production (hair, milk, egg production) and reproductive performance.

[0003] Metabolomics was first proposed by Professor Nicholson in 1999. It is a new type of omics technology after genomics and proteomics. Unlike other omics, metabolomics studies biological systems by observing the changes in metabolites or their changes over time after the biological system (cells, tissues or organisms) is stimulated or disturbed (such as diet, specific gene mutations or environmental changes). The research substances are mainly endogenous small molecules with a relative molecular mass of less than 1000, which have the characteristics of high throughput and high sensitivity. According to different research purposes, metabolomics is divided into targeted metabolomics and non-targeted metabolomics. The primary function of targeted metabolic analysis is to quantify specific metabolites and study one or several metabolic pathways, while the role of non-targeted metabolic analysis is to maximize the detection of cells, tissues, and organs to analyze more metabolites from samples in order to collect more material information. Traditional nutritional metabolism mainly focuses on blood biochemical indicators, product concentrations and hormone levels, and it is difficult to establish a research model for complex systems of biological small molecules (such as peptides, amino acids and their derivatives, lipids and their metabolites). Therefore, the use of metabolomics technology to evaluate individual eating habits and nutritional levels and to explore changes in metabolic pathways in the body has been widely used in nutrition research. At present, the energy system of pigs is mainly divided into the digestive system, metabolic energy system and net energy system. The net energy system not only takes into account all energy losses in the metabolic process, but also effectively avoids the evaluation error of the effective energy value of high-protein, high-fiber, high-starch and high-fat feeds in the digestive energy and metabolic energy systems, and can more accurately predict the energy level of feeds.

[0004] In actual pig production, energy feed accounts for the largest proportion of feed costs. Too low a dietary energy level will reduce the growth performance of the pig herd, while too high a dietary energy level will lead to energy waste and excessive fat deposition, which will affect the carcass quality. Traditional evaluation methods mainly use body weight or average daily weight gain to judge whether the energy level of the diet meets the requirements of pigs, which has the disadvantages of long cycle and low timeliness.

[0005] At present, the research on energy requirement of pigs and the formulation of feeding standards are mainly aimed at Durbin × Chang × Daren hybrid pigs. The direct method and factorial method are usually used to establish the energy requirement estimation model: 1) Direct method, that is, to establish a regression equation between metabolic energy intake and body weight of growing and fattening pigs, and estimate the metabolic energy intake required at a specific body weight through the obtained reference curve; 2) Factorial method, that is, to divide metabolic energy into maintenance metabolic energy (MEm) and growth metabolic energy (MEg), establish a regression equation between daily growth metabolic energy and daily weight gain (ADG), and estimate the metabolic energy requirement based on the obtained reference curve according to daily weight gain. Although the traditional animal nutrition method is accurate in establishing the nutritional requirement model of digestible energy and metabolic energy, it has problems such as large workload, long feeding cycle and high cost.

[0006] Most of the research on feeding standards for local pig breeds in my country is still blank. There is no dynamic prediction research on the energy requirements and effective energy value of daily diets for Hainan local pigs, which seriously affects the standardization and large-scale breeding of local pig breeds. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics. Metabolomics technology can identify changes in endogenous small molecules in the body with high throughput and high sensitivity, and the net energy system removes the heat consumption of feed in the body on the basis of metabolic energy, which can more accurately evaluate the effective energy value of feed raw materials, especially agricultural products and related by-products, and predict growth performance.

[0008] The model for evaluating the net energy level of Ding'an black pig diet based on metabolomics of the present invention is a dynamic regression prediction model for Ding'an black pig plasma metabolite markers and net energy level of diet, which is specifically as follows: NE=2743.25+60.91X 1 -72.82X 2 -21.91X 3 +52.21X 4 -65.08X 5 +8.64X 6 -3.82X 7 +1.51X 8 (R 2 =0.962), Where NE is the net energy level, kcal / kg, X 1 is the relative content of linoleic acid in pig plasma, X 2 is the relative content of 9,10,13-trihydroxyoctadecenoic acid (9,10,13-TriHOME) in porcine plasma, X 3is the relative content of 27-deoxy-5b-cyprinol in porcine plasma, X 4 is the relative content of 7a-hydroxy-cholestene-3-one in pig plasma, X 5 is the relative content of 3a,7a,12a-trihydroxy-5b-cholestan-26-al in porcine plasma, X 6 is the relative content of 3alpha,7alpha,12alpha-trihydroxy-5beta-cholestanate in porcine plasma. 7 is the relative content of cholesterol glucuronide in pig plasma, X 8 It is the relative content of dehydroisoandrosterone 3-glucuronide in porcine plasma.

[0009] The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics of the present invention comprises the following steps: S1. Collect samples; S2. Sample pretreatment; S3.LC-MS / MS analysis; S4. Detection data preprocessing; S5. Identification of metabolites; S6. Statistical analysis for multiple group comparisons.

[0010] The S1 sample collection refers to the blood collected on the 14th and 28th days in each of the five net energy level treatment groups of Ding'an black pigs for non-targeted metabolomics determination.

[0011] The S2 sample pretreatment refers to placing the collected blood in a blood collection tube and centrifuging it, taking the plasma and placing it in a centrifuge tube for low-temperature ultrasonic extraction and centrifugation, then removing the supernatant, re-dissolving, extracting, and centrifuging, and finally taking the supernatant for analysis. 20 µL of supernatant is taken from each sample and mixed as a quality control sample.

[0012] The S3 LC-MS / MS analysis refers to the separation and identification of supernatant samples using ultra-high performance liquid chromatography and tandem mass spectrometry.

[0013] The S4 test data preprocessing refers to using the relative standard deviation of the quality control samples to remove metabolites with missing values ​​exceeding 20% ​​in all groups, filling the missing values ​​with the minimum value, and finally performing total normalization and logarithmic transformation.

[0014] The specific steps of identifying the S5 metabolites are as follows: the raw data are imported into the metabolomics processing software Progenesis QI v3.0 for baseline filtering, peak identification, integration, retention time correction, and peak alignment, and finally a data matrix containing retention time, mass-to-charge ratio, and peak intensity information is obtained; then the software is used to perform characteristic peak library search and identification, and the MS and MS / MS mass spectrum information is matched with the metabolic database, and the MS mass error is set to less than 10 ppm, and the metabolites are identified according to the secondary mass spectrum matching score.

[0015] The S6 multi-group comparative statistical analysis refers to the use of partial least squares discriminant analysis (PLS-DA) supervised pattern recognition multivariate statistical analysis method to perform model analysis on plasma samples of diets with different net energy levels. There is a certain metabolic spectrum separation between different dietary levels, and the obtained differential metabolites are mainly enriched in linoleic acid metabolism, α-linolenic acid metabolism, primary bile acid biosynthesis and bile secretion pathways.

[0016] Among the differential metabolites, those enriched in the linoleic acid metabolic pathway include linoleic acid and 9,10,13-trihydroxyoctadecenoic acid; those enriched in the biosynthesis metabolic pathway of primary bile acids include 27-deoxy-5b-ichthyocholic acid, 7a-hydroxy-cholesten-3-one, 3a,7a,12a-trihydroxy-5b-cholestane-26-al and 3α,7α,12α-trihydroxy-5β-cholestate; and those enriched in the bile secretion metabolic pathway include cholesterol glucuronide and dehydroisoandrosterone 3-glucuronide.

[0017] The beneficial effects of the present invention are: 1. The present invention compares the differences in plasma metabolomes of diets with different net energy levels, discovers 8 biomarkers related to the net energy level of the diet, namely plasma metabolic markers and their changing patterns, and establishes a dynamic prediction model between plasma biomarkers and the net energy level of the diet. Without the need for long-term growth performance comparison, the net energy level of the diet fed to Ding'an black pigs in the actual production process can be predicted, and then it can be determined whether its energy requirement level is met.

[0018] 2. Using metabolomics to predict the net energy level of the diet can not only avoid the errors caused by body shape assessment and the stress of pigs caused by repeated weight weighing, but also reduce the error of effective energy value caused by the difference in heat increase consumption of energy feed raw materials or protein feed raw materials. It is more conducive to the application of unconventional feed raw materials such as palm meal and coconut meal in Hainan, and reduces breeding costs.

[0019] 3. As a method to quickly predict the net energy level of the daily diet of Ding'an black pigs, it can reduce the tediousness of group weight monitoring in traditional methods, and more efficiently and conveniently predict the net energy level of the daily diet fed to Ding'an black pigs in actual production, so as to timely avoid insufficient energy supply or waste of energy raw materials, provide new technical support for the large-scale breeding and precise nutrition of Ding'an black pigs, reduce the waste of manpower, material resources and costs in the actual production process, and improve breeding production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This study was the effect of dietary net energy level on plasma metabolites in Ding'an black pigs on the 14th day of growth. Figure 1 -A is the PLS-DA score graph of plasma samples on day 14; Figure 1 -B is the metabolic differential classification diagram of plasma samples on day 14; Figure 1 -C is the rank sum test bar graph of plasma samples on day 14; Figure 1 -D is the KEGG enrichment analysis diagram of plasma samples on day 14.

[0021] Figure 2 This study was the effect of dietary net energy level on plasma metabolites in Ding'an black pigs on the 28th day of growth. Figure 2 -A is the PLS-DA score graph of plasma samples on day 28; Figure 2 -B is the metabolic differential classification diagram of plasma samples on day 28; Figure 2 -C is the rank sum test bar graph of plasma samples on day 28; Figure 2 -D is the KEGG enrichment analysis diagram of plasma samples on day 28.

[0022] Figure 3 This is the ROC curve analysis chart on the 14th day. Figure 3 -A is the ROC curve analysis diagram between the 2250 kcal / kg and 2475 kcal / kg control groups; Figure 3 -B is the ROC curve analysis between the 2325 kcal / kg and 2475 kcal / kg control groups; Figure 3 -C is the ROC curve analysis diagram between the 2400 kcal / kg and 2475 kcal / kg control groups; Figure 3 -D is the ROC curve analysis between the 2550 kcal / kg and 2475 kcal / kg control groups.

[0023] Figure 4 This is the ROC curve analysis chart on the 28th day. Figure 4 -A is the ROC curve analysis diagram between the 2250 kcal / kg and 2475 kcal / kg control groups; Figure 4-B is the ROC curve analysis between the 2325 kcal / kg and 2475 kcal / kg control groups; Figure 4 -C is the ROC curve analysis diagram between the 2400 kcal / kg and 2475 kcal / kg control groups; Figure 4 -D is the ROC curve analysis between the 2550 kcal / kg and 2475 kcal / kg control groups. DETAILED DESCRIPTION

[0024] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0025] The present invention first adopts a net energy system to configure the feeding diet. A suitable feed configuration scheme is the basis for systematically solving the large-scale production of livestock and poultry. Metabolomics technology has the advantages of collecting and measuring sample metabolites at any time, and can monitor the dynamic changes of endogenous small molecules (such as lipid metabolites and amino acid derivatives) that are ignored by biochemical indicators. Therefore, the present invention uses metabolomics technology to explore plasma biomarkers related to the net energy level of the diet, and establishes a dynamic regression prediction model of Ding'an black pig plasma metabolite markers and the net energy level of the diet.

[0026] Example

[0027] The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics of the present invention comprises the following steps: S1. Sample collection: 600 Ding'an black pigs with an average weight of 38.5±1.11 kg were selected, and five net energy level treatment groups of 2250 kcal / kg, 2325 kcal / kg, 2400 kcal / kg, 2475 kcal / kg and 2550 kcal / kg were set according to the NRC (2012) feeding standards. Each treatment had 6 replicates, each replicate had 20 pigs, and the entire experimental period was 28 days. Blood was collected on the 14th and 28th days for non-targeted metabolomics determination. The experimental diet formula and nutrient level are shown in Table 1 below.

[0028] Table 1 Experimental diet formula

[0029] ①The premix provided the following per kg of diets: niacinamide, 125.00 mg; pantothenic acid, 100 mg; vitamin A, 2.5 KIU; vitamin B1, 10 mg; vitamin B2, 30 mg; vitamin B6, 4 mg; vitamin D3, 9 KIU; vitamin E, 90 mg; vitamin K3, 7.5 mg; copper, 400 mg; selenium, 1 mg; iron, 400 mg; manganese, 200 mg; ②Nutrient levels are calculated values.

[0030] S2. Sample pretreatment: On the 14th and 28th days, 10 mL of porcine anterior vena cava blood was randomly collected in a sodium heparin anticoagulation blood collection tube, and centrifuged at 3000g and 4℃ for 10 min to obtain plasma. 100 μL of plasma sample was accurately transferred to a 1.5 mL centrifuge tube, and 400 μL of extraction solution was added. The extract contained methanol: acetonitrile = 1:1 (v:v). After vortex mixing for 30 seconds, low-temperature ultrasonic extraction was performed at 5℃ and 40KHz for 30 minutes. The sample was placed at -20℃ for 30 minutes and then centrifuged at 13000g and 4℃ for 15 minutes. The supernatant was transferred and dried with nitrogen gas. 120 μL of reconstitution solution was added for reconstitution. The reconstitution solution contained acetonitrile: water = 1:1. After vortex mixing for 30 seconds, low-temperature ultrasonic extraction was performed at 5℃ and 40KHz for 5 minutes. The sample was centrifuged at 13000g and 4℃ for 10 minutes. The supernatant was transferred to an injection vial with an inner cannula for analysis. At the same time, 20 μL of supernatant was taken from each sample and mixed as quality control samples (QC).

[0031] S3.LC-MS / MS analysis: separation and identification of metabolites using data acquisition instrument systems including ultra-performance liquid chromatography (UPLC) and tandem mass spectrometry (MS / MS).

[0032] Set the liquid phase conditions: 1) Chromatographic column: ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 µm; Waters, Milford, USA); 2) Mobile phase: Mobile phase A is 95% water + 5% acetonitrile (containing 0.1% formic acid), mobile phase B is 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), injection volume is 3 μL, column temperature is 40°C; The mass spectrometry conditions were set as follows: scan type: 70-1050 m / z, sheath gas flowrate: 60 arb, auxiliary gas flow rate: 20 arb, heater temp: 350°C, capillary temp: 320°C, spray voltage: 3400 V (+), -3000 V (-), S-Lens RF Level: 70, normalized collision energy: 20%, 40%, 60%, resolution (Full MS): 60000, resolution (MS 2): 15000.

[0033] S4. Preprocessing of detection data: Use the relative standard deviation (RSD) of QC samples as a measure to filter out deviation values ​​(RSD>0.3 is eliminated), remove metabolites with more than 20% missing values ​​in each group or in all groups, fill the missing values ​​with the minimum value, and finally perform total normalization and logarithmic transformation (Log10).

[0034] S5. Identification of metabolites: The raw data were imported into the metabolomics processing software Progenesis QI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, peak alignment, etc., and finally a data matrix containing information such as retention time, mass-to-charge ratio and peak intensity was obtained. The software was then used to search the library for characteristic peaks, match the MS and MS / MS mass spectrometry information with the metabolic database, and set the MS mass error to less than 10 ppm. At the same time, metabolites were identified based on the secondary mass spectrometry matching score. The main databases are mainstream public databases such as http: / / www.hmdb.ca / and https: / / metlin.scripps.edu / , as well as self-built data.

[0035] S6. Multi-group comparative statistical analysis. The partial least squares discriminant analysis (PLS-DA) supervised pattern recognition multivariate statistical analysis method was used to conduct model analysis on plasma samples of diets with different net energy levels. PLS-DA can maximize the interval components and is conducive to finding differential metabolites. The PLS-DA score is shown in the figure below. Figure 1 -A (14th day) and 2-A (28th day), there was a certain separation of metabolic profiles between different dietary levels. On the 14th day, there were 319 Kruskal-Wallis differential metabolites in the five treatment groups (VIP>1), and the HMDB superclass compound classification showed that they were mainly lipids and lipid molecules (116), organic acids and their derivatives (60), etc. ( Figure 1 -B), such as Linoleic acid, DG(13:0 / PGD1 / 0:0), Coroloside, Equol 7-O-glucuronide ( Figure 1 -C), these differential metabolites were mainly enriched in linoleic acid metabolism, 5-hydroxytryptamine synapses, primary bile acid biosynthesis and bile secretion metabolism pathways ( Figure 1 -D). On the 28th day, there were 307 Kruskal-Wallis differential metabolites among the five treatment groups (VIP>1), and the HMDB superclass classification showed that they were mainly lipids and lipid molecules (103), organic acids and their derivatives (57), etc. ( Figure 2 -B), such as Linoleic acid, Estrone glucuronide, DG(15:0 / 18:3(6Z,9Z,12Z) / 0:0), 4-Ketoretinoic acidglucuronide( Figure 2 -C), these differential metabolites are mainly enriched in linoleic acid metabolism, α-linolenic acid metabolism, primary bile acid biosynthesis, bile secretion and other pathways ( Figure 2 -D).

[0036] The above results showed that linoleic acid metabolism, primary bile acid biosynthesis, and bile secretion were the main differential metabolic pathways in this study. Among them, linoleic acid and 9,10,13-trihydroxyoctadecenoic acid (9,10,13-TriHOME) are mainly enriched in the linoleic acid metabolic pathway, 27-deoxy-5b-cyprinol, 7a-hydroxy-cholestene-3-one, 3a,7a,12a-trihydroxy-5b-cholestan-26-al and 3alpha,7alpha,12alpha-trihydroxy-5beta-cholestanate are mainly enriched in the biosynthesis metabolic pathway of primary bile acids, and cholesterol glucuronide (Cholesterol glucuronide) and dehydroisoandrosterone 3-glucuronide are mainly enriched in the bile secretion metabolic pathway. The above 8 metabolites can be used as potential biomarkers for the evaluation of dietary net energy level. According to the ROC curve ( Figure 3 , Figure 4 ), linoleic acid, 9,10,13-trihydroxyoctadecenoic acid, 27-deoxy-5b-ichthyolic acid, 7a-hydroxy-cholesten-3-one, 3a,7a,12a-trihydroxy-5b-cholestane-26-al, 3α,7α,12α-trihydroxy-5β-cholestate, cholesterol glucuronide and dehydroisoandrosterone 3-glucuronide can accurately evaluate the net energy level of the diet, and the best prediction model is NE=2743.25+60.91X 1 -72.82X 2 -21.91X 3 +52.21X 4 -65.08X 5 +8.64X 6 -3.82X 7 +1.51X 8 (R 2 =0.962), Where NE is the net energy level, kcal / kg, X 1 is the relative content of linoleic acid in pig plasma, X 2 is the relative content of 9,10,13-trihydroxyoctadecenoic acid in pig plasma, X 3 is the relative content of 27-deoxy-5b-ichthyolic acid in pig plasma, X4 is the relative content of 7a-hydroxy-cholesten-3-one in pig plasma, X 5 is the relative content of 3a,7a,12a-trihydroxy-5b-cholestane-26-al in pig plasma, X 6 is the relative content of 3α,7α,12α-trihydroxy-5β-cholesterol in porcine plasma, X 7 is the relative content of cholesterol glucuronide in pig plasma in vitro, X 8 It is the relative content of dehydroepiandrosterone 3-glucuronide in pig plasma in vitro.

[0037] ROC curve analysis and identification test of biomarkers: For the 8 potential differential metabolites screened, the receiver operating characteristic (ROC) curve was used to evaluate the performance prediction accuracy of the differential metabolites. The ROC curve analysis uses (1-specificity) as the horizontal axis and sensitivity as the vertical axis. It combines the sensitivity and specificity of the test and is a scientific and reliable evaluation and detection method that can indicate the effect of the biomarkers found by the present invention in evaluating the net energy level of the diet. The larger the area under the curve (AUC), the better the prediction effect. When the AUC is close to 0.5, there is no prediction significance; AUC < 0.7 indicates a low prediction accuracy; AUC is between 0.7-0.9, indicating medium accuracy; AUC > 0.9 indicates a high prediction accuracy.

[0038] Figure 3 and Figure 4 The ROC curve results of the treatment groups on days 14 and 28 were compared with the 2475 kcal / kg control group, which is the net energy level recommended by NRC for growing pigs. Figure 3 -A is the ROC curve analysis diagram between the 2250 kcal / kg and 2475 kcal / kg control groups; Figure 3 -B is the ROC curve analysis between the 2325 kcal / kg and 2475 kcal / kg control groups; Figure 3 -C is the ROC curve analysis diagram between the 2400 kcal / kg and 2475 kcal / kg control groups; Figure 3 -D is the ROC curve analysis between the 2550 kcal / kg and 2475 kcal / kg control groups; Figure 4 -A is the ROC curve analysis diagram between the 2250 kcal / kg and 2475 kcal / kg control groups; Figure 4 -B is the ROC curve analysis diagram between the 2325 kcal / kg and 2475 kcal / kg control groups; Figure 4-C is the ROC curve analysis diagram between the 2400 kcal / kg and 2475 kcal / kg control groups; Figure 4 -D is the ROC curve analysis between the 2550 kcal / kg and 2475 kcal / kg control groups.

[0039] like Figure 3 and Figure 4 As shown in the figure, compared with the 2475 kcal / kg treatment group, the relative contents of the eight biomarkers showed a better predictive effect on the increase or decrease of the net energy level of the diet.

[0040] Through the dynamic prediction model between plasma biomarkers and dietary net energy levels, the relative level of each biomarker can be obtained after collecting blood for metabolomics measurement. After substituting the plasma markers and dietary net energy level into the dynamic prediction model, the net energy level of the feeding diet can be obtained, which can be compared with the net energy level recommended by the feeding standards. If it is too high or too low, the diet formula can be adjusted at any time.

[0041] In the present invention, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0042] Although the above embodiments have been shown and described, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. Changes, modifications, substitutions and variations of the above embodiments by those of ordinary skill in the art are all within the scope of protection of the present invention.

Claims

1. A method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics, characterized in that: The following steps are involved: S1. Collect samples; S2. Sample pretreatment; S3.LC-MS / MS analysis; S4. Detection data preprocessing; S5. Identification of metabolites; S6. Statistical analysis for multiple group comparisons; The model is a dynamic regression prediction model for specifying Anhei pig plasma metabolic markers and dietary net energy levels, as follows: NE=2743.25+60.91X1-72.82X2-21.91X3+52.21X4-65.08X5+8.64X6-3.82X7+1.51X8(R 2 =0.962), Wherein, NE is the net energy level, kcal / kg, X1 is the relative content of linoleic acid in porcine plasma, X2 is the relative content of 9,10,13-trihydroxyoctadecenoic acid in porcine plasma, X3 is the relative content of 27-deoxy-5b-ichthyolic acid in porcine plasma, X4 is the relative content of 7a-hydroxy-cholesten-3-one in porcine plasma, X5 is the relative content of 3a,7a,12a-trihydroxy-5b-cholestane-26-al in porcine plasma, X6 is the relative content of 3α,7α,12α-trihydroxy-5β-cholestate in porcine plasma, X7 is the relative content of cholesterol glucuronide in porcine plasma, and X8 is the relative content of dehydroisoandrosterone 3-glucuronide in porcine plasma.

2. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 1, characterized in that: The S1 sample collection refers to the blood collected on the 14th and 28th days in each of the five net energy level treatment groups of Ding'an black pigs for non-targeted metabolomics determination.

3. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 1, characterized in that: The S2 sample pretreatment refers to placing the collected blood in a blood collection tube and centrifuging it, taking the plasma and placing it in a centrifuge tube for low-temperature ultrasonic extraction and centrifugation, then removing the supernatant, re-dissolving, extracting, and centrifuging it, and finally taking the supernatant for analysis. At the same time, 20 µL of supernatant is taken from each sample and mixed as a quality control sample.

4. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 3, characterized in that: The S3 LC-MS / MS analysis refers to the separation and identification of supernatant samples using ultra-high performance liquid chromatography and tandem mass spectrometry.

5. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 3, characterized in that: The S4 test data preprocessing refers to using the relative standard deviation of the quality control samples to remove metabolites with missing values ​​exceeding 20% ​​in all groups, filling the missing values ​​with the minimum value, and finally performing total normalization and logarithmic transformation.

6. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 1, characterized in that: The specific steps for identifying the S5 metabolites are as follows: the raw data are imported into the metabolomics processing software Progenesis QI v3.0 for baseline filtering, peak identification, integration, retention time correction, and peak alignment, and finally a data matrix containing retention time, mass-to-charge ratio, and peak intensity information is obtained; the software is then used to perform characteristic peak library search and identification, and the MS and MS / MS mass spectrometry information is matched with the metabolic database, the MS mass error is set to less than 10 ppm, and the metabolites are identified based on the secondary mass spectrometry matching score.

7. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 1, characterized in that: The S6 multi-group comparative statistical analysis refers to the use of partial least squares discriminant analysis (PLS-DA) supervised pattern recognition multivariate statistical analysis method to perform model analysis on plasma samples of diets with different net energy levels. There is a certain metabolic spectrum separation between different dietary levels, and the obtained differential metabolites are mainly enriched in linoleic acid metabolism, α-linolenic acid metabolism, primary bile acid biosynthesis and bile secretion pathways.

8. The method for constructing a model for evaluating the net energy level of Ding'an black pig diet based on metabolomics according to claim 7, characterized in that: Among the differential metabolites, those enriched in the linoleic acid metabolic pathway include linoleic acid and 9,10,13-trihydroxyoctadecenoic acid; those enriched in the biosynthesis metabolic pathway of primary bile acids include 27-deoxy-5b-ichthyocholic acid, 7a-hydroxy-cholesten-3-one, 3a,7a,12a-trihydroxy-5b-cholestane-26-al and 3α,7α,12α-trihydroxy-5β-cholestate; and those enriched in the bile secretion metabolic pathway include cholesterol glucuronide and dehydroisoandrosterone 3-glucuronide.

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