阅读说明：本技术 用于评估猪肉肌内脂肪含量的生物标志物及其应用 (Biomarker for evaluating intramuscular fat content of pork and application thereof ) 是由 马云龙 詹慧雯 赵书红 李新云 谢胜松 付亮亮 赵云霞 舒航 张凯丽 彭夏 于 2020-09-30 设计创作，主要内容包括：本发明公开了生物标志物在评估猪肉肌内脂肪含量中的应用,利用代谢组学分析挖掘出一组有效的生物标志物,包括8种甘油三酯类物质TG(18:1/18:4/18:4)、TG(14:0/20:1/22:1)、TG(18:1/18:1/20:0)、TG(12:0/16:0/18:3)、TG(18:0/20:1/20:1)、TG(16:0/16:1/20:1)、TG(18:1/18:1/22:0)、TG(18:1/18:3/18:3)。通过生物标志物对猪肉的肌内脂肪含量进行简便、快速的评估,可辅助开展猪肉品质分类,解决肉质性状难于度量的问题,进而推动肉质性状遗传评估工作的开展。(The invention discloses application of biomarkers in evaluating intramuscular fat content of pork, and a group of effective biomarkers comprising 8 triglyceride substances TG (18:1/18:4/18:4), TG (14:0/20:1/22:1), TG (18:1/18:1/20:0), TG (12:0/16:0/18:3), TG (18:0/20:1/20:1), TG (16:0/16:1/20:1), TG (18:1/18:1/22:0) and TG (18:1/18:3/18:3) are excavated by utilizing metabonomic analysis. The intramuscular fat content of the pork is simply and quickly evaluated through the biomarker, the pork quality classification can be assisted to be developed, the problem that the meat quality is difficult to measure is solved, and the development of the meat quality genetic evaluation work is further promoted.)

1. The application of the biomarker in the evaluation of the content of intramuscular fat of pork is characterized in that the biomarker comprises one or more of triglyceride TG (18:1/18:4/18:4), TG (14:0/20:1/22:1), TG (18:1/18:1/20:0), TG (12:0/16:0/18:3), TG (18:0/20:1/20:1), TG (16:0/16:1/20:1), TG (18:1/18:1/22:0) and TG (18:1/18:3/18: 3).

2. The method for evaluating the intramuscular fat content of pork by using the biomarker of claim 1, wherein the biomarker in the muscle tissue is quantitatively detected by using liquid chromatography tandem mass spectrometry, and the biomarker is ranked according to the content of the biomarker and grouped according to the high level and the low level, and the sample with high content of the biomarker has high intramuscular fat content.

3. The method for evaluating the intramuscular fat content of pork by using the biomarkers according to claim 2, wherein any biomarker is selected for quantitative analysis, the content of the markers is ranked and grouped into high and low levels, the grouping result is tested by using a univariate analysis method, and the sample with high content of the biomarker has high intramuscular fat content.

4. The method for evaluating the intramuscular fat content of pork by using the biomarkers as claimed in claim 2, wherein a plurality of biomarkers are selected for quantitative analysis, the markers are sorted and grouped according to the mean value of the combination of the biomarkers, the grouping result is tested by using a multivariate analysis method, and the high mean value of the combination of the biomarkers indicates the high intramuscular fat content.

Technical Field

The invention belongs to the technical field of pork quality character molecular marker identification, and particularly relates to a biomarker for evaluating intramuscular fat content of pork and application of the biomarker in evaluating pork quality.

Background

Pork is an important animal food in daily life of people, China is the country with the largest pork consumption at present, and the live pig yield and the pork import quantity are all in the front of the whole world. With the continuous improvement of living standard and consumption standard of people, pork quality is also gradually concerned. The method not only comprises the basic safety and sanitation problems and needs to meet the standards of green food, but also has higher requirements on the taste and the tenderness of meat, and how to improve the pork quality becomes an important research direction of the whole industrial chain from cultivation to slaughtering processing.

The meat quality is a comprehensive character, and detection indexes commonly used for evaluating the pork quality comprise PH, meat color, muscle fat content, drip loss, marbling, tenderness, flavor substances and the like. Wherein, the intramuscular fat content (IMF) is a key factor influencing the pork quality, and the intramuscular fat is also the material basis of tender and juicy meat and delicious taste. At present, intramuscular fat content has also been listed as an important selection character in the breeding work of new-century pigs in many developed pig-raising countries. Intramuscular fat refers to fat deposited in muscle, consisting of fat in intramuscular adipose tissue and muscle fibers, and is mainly present on the extramuscular membrane, fascial membrane and endomuscular membrane. The intramuscular fat of the pig is an important index of pork quality, and has obvious influence on tenderness, water retention capacity, shearing force, flavor and juiciness of meat. Therefore, the improvement of pork quality by changing the content of intramuscular fat is an effective way.

The content of intramuscular fat of pork is between 2 and 3 percent, which is a relatively ideal standard, and when the content of intramuscular fat is lower than 2 percent, the pork has poor texture and mouthfeel; and when the content is more than 3%, the flavor of the meat is not significantly improved. At present, the muscle fat content of most commercial pigs is lower than the ideal standard, so that the strengthening of the regulation and control of the muscle fat is more and more emphasized.

How to rapidly and accurately measure the intramuscular fat content of pork becomes an urgent problem to be solved. At present, the main method for measuring the content of intramuscular fat is a Soxhlet extraction method, which not only has complex steps and long detection time, but also has harsh experimental conditions and is easy to make mistakes in the measuring process. As a character which is difficult to measure, the meat quality character is often difficult to develop in the genetic evaluation of breeding pigs. The efficient pig intramuscular fat content assessment method can greatly reduce the cost of breeding assessment and solve the problem that the traits are difficult to measure on the basis of accurately assessing pork quality, avoiding determination errors and shortening detection time, so that the accuracy of individual selection is improved, and effective technical support and decision guidance are provided for the actual breeding work and the pork industrial chain of the pig raising industry. Therefore, the rapid and accurate evaluation of the intramuscular fat content performance of the pigs by utilizing the efficient biomarkers analyzed and mined by the metabolome has important theoretical significance and economic value.

Disclosure of Invention

The method utilizes the metabonomics technology, and is based on means such as an UPLC-MS/MS detection platform, a self-built database, multivariate statistical analysis and the like, the metabolites in the longissimus muscle tissue of the back of the pig are separated and identified, the metabolite difference of the intramuscular fat content is analyzed, and important characteristics and specific biomarkers related to the intramuscular fat character are searched, so that the efficient and accurate evaluation of the intramuscular fat content of the pig is realized, and powerful technical support is provided for the actual breeding work and decision.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention adopts the following method to screen the differential metabolite between the intramuscular fat content tissue sample and the low intramuscular fat content tissue sample:

(1) separating and qualitatively quantifying total metabolites of the muscle tissue by using liquid chromatography-tandem mass spectrometry (LC-MS/MS);

(2) performing qualitative and quantitative determination on the metabolites by liquid chromatography tandem mass spectrometry by using partial least squares (PLS-DA), performing discriminant analysis by combining Orthogonal Signal Correction (OSC), removing irrelevant differences according to a metabolite profile (metabolite profile), and screening different metabolites;

(3) identifying metabolites obtained by analysis by using an orthogonal partial least squares (OPLS-DA) method, selecting metabolites with Variable Importance Projection (VIP) values larger than or equal to 1, and selecting metabolites with fold change values (fold change) larger than or equal to 2 or smaller than or equal to 0.5 in univariate analysis as metabolite biomarker candidates;

(4) confirming the up-regulation or down-regulation type of differential expression of the differential metabolite biomarker candidate through the load value of the orthogonal partial least square discriminant analysis;

(5) the KEGG (Kyoto Encyclopedia of Genes and genomes) database is used for annotating the metabolic pathways and the reactions of different metabolites, and 8 biomarkers for the intramuscular fat content assessment of pigs are finally determined through the channel enrichment analysis.

By analyzing metabolites in muscle tissue of individuals with high and low differential levels of intramuscular fat content using LC-MS/MS, 446 metabolites have been detected; further, 171 effective metabolite biomarkers were screened by orthogonal partial least squares discriminant analysis (OPLS-DA), Variable Importance Projection (VIP) values, fold change values (fold change), and the like; finally, through Log 2FC processing of fold difference, ROC curve and KEGG channel enrichment analysis, finally determining that 8 triglyceride biomarkers are used for pig intramuscular fat content assessment, wherein the method comprises the following steps: TG (18:1/18:4/18:4), TG (14:0/20:1/22:1), TG (18:1/18:1/20:0), TG (12:0/16:0/18:3), TG (18:0/20:1/20:1), TG (16:0/16:1/20:1), TG (18:1/18:1/22:0) and TG (18:1/18:3/18: 3).

The method for evaluating the intramuscular fat content of pork by using the biomarkers comprises the following steps:

quantitative detection is carried out on the biomarkers in the muscle tissue by using liquid chromatography tandem mass spectrometry, sequencing and high-low level grouping are carried out according to the content of the biomarkers, and the intramuscular fat content of a sample with high content of the biomarkers is high.

Further, one or more biomarkers are selected for quantitative analysis, the mean values of the markers or the marker combinations are sorted and grouped at high and low levels, the grouping results are tested by using a univariate or multivariate analysis method, and the sample with high content of the biomarkers has high intramuscular fat content.

Compared with the prior art, the invention has the following advantages and beneficial effects:

the method screens out the biomarker group for evaluating the intramuscular fat content performance of the pork by utilizing metabonomics analysis, can efficiently and accurately detect the muscle tissue of the pig by the screened biomarkers, is beneficial to optimizing and improving the classification flow of pork products, and can promote a breeding field to evaluate the breeding value of pork quality traits by solving the problem that the pork quality is difficult to measure, simultaneously avoid the loss caused by carcass measurement, and greatly reduce the cost of breeding evaluation.

Efficient biomarker detection can quickly evaluate intramuscular fat content performance of pork, improves accuracy of individual selection, provides an effective scheme for improving efficiency of pork quality detection in the market, provides effective technical support and decision guidance for actual breeding work and pork industrial chain of the pig industry, and has important production and economic benefits.

Drawings

FIG. 1 is a graph of OPLS-DA scores for a high fat content muscle group versus a low fat content muscle group;

FIG. 2 is a graph of OPLS-DA s-plot for a high fat content muscle group and a low fat content muscle group;

figure 3 is a graph of the performance characteristics of subjects using different metabolite indices to assess intramuscular fat levels.

Detailed Description

Example 1 screening for differential metabolites between high and low fat content muscle tissue samples

First, Soxhlet extraction of sample

1. And (3) unfreezing the longissimus muscle sample of the back of the pig in an oven at 60 ℃ for 18-24 hours, cutting fascia, mincing by a meat mincer, and putting the minced meat in a dish for weighing, wherein the weight of the dish is m1, and the total weight of the dish and the meat paste is m 2. The temperature of the oven is adjusted to 100 ℃, the baking is carried out for 2 hours, sample turning is carried out once every hour after 2 hours, and the drying is continued to a constant weight state.

2. The sample dried to constant weight is subjected to powdering, sieving and packaging, and the following processes are sequentially performed: and taking out the sample from the oven, placing the sample into a dryer for cooling for 30 minutes, weighing the sample again after taking out the sample, recording the constant weight m3 of the plate and the minced meat, placing the sample into a crusher for crushing and sieving, filling the sample powder into three filter paper bags, and weighing the filter paper bags, wherein the weight of each filter paper bag is m 4. The oven temperature was adjusted to 100 ℃ and all samples were baked again for 5.5 hours.

3. Performing Soxhlet extraction, and sequentially performing the following processes: the sample was removed from the oven and allowed to cool for 30 minutes, the filter paper pack was weighed, and the filter paper pack constant weight m5 was recorded. The ether was poured into a soxhlet extractor and the filter paper pack was placed into the soxhlet extractor and extracted overnight at 65 ℃ for 12 hours.

4. The sample was taken out of the soxhlet extractor, after 15 minutes of volatilization in a fume hood, the sample was put into an oven to bake at 100 ℃ for 2.5 hours, taken out, cooled for 30 minutes, weighed, and the constant weight m6 of the filter paper bag after extraction was recorded.

5. Based on the above steps, the method calculates the intramuscular fat content of the sample as the average of three filter paper pack repetitions:

second, pretreatment of the metabolome of the sample

Muscle tissue samples stored at-80 deg.C (10 samples, 50mg tissue samples per sample) were taken, placed in liquid nitrogen for 2 minutes, thawed on ice for 5 minutes after being taken out, and vortexed and mixed uniformly. Repeating the steps for three times; centrifugation was carried out at 12000r/min at 4 ℃ for 10 minutes. 300. mu.L of the supernatant was taken and added to the corresponding numbered centrifuge tube, and 1mL of a lipid extract (containing methanol and methyl t-butyl ether) was added thereto. Vortex for 2 min, sonicate for 5 min, add 500uL of water. Vortex for 1min, centrifuge at 12000r/min at 4 ℃ for 10 min. After the centrifugation, 500. mu.L of the supernatant was aspirated into the numbered centrifuge tubes and concentrated. Redissolved using 100 μ L mobile phase B acetonitrile (0.04% acetic acid) for LC-MS/MS analysis.

Third, metabolome LC-MS/MS analysis

The separation and identification of metabolites is achieved using a data acquisition instrument system, including ultra-high performance liquid chromatography (UPLC) and tandem mass spectrometry (MS/MS).

Setting liquid phase conditions: 1) a chromatographic column: waters acquisition UPLC HSS T3C 181.8 μm, 2.1mm 100 mm; 2) mobile phase: phase A is ultrapure water (0.04% ethanol), phase B is acetonitrile (0.04% acetic acid); 3) elution gradient: 0min of water/acetonitrile (95:5V/V), 11.0min of 5:95V/V, 12.0min of 5:95V/V, 12.1min of 95:5V/V and 14.0min of 95: 5V/V; 4) the flow rate was 0.4ml/min, the column temperature was 40 ℃ and the amount of sample was 5. mu.l.

Setting mass spectrum conditions: electrospray ion source (ESI) set at 550 ℃, mass spectrometer voltage 5000v (positive), -4500v (negative), -4500v (negative), ion source gas i (gs i)55psi, gas ii (gs ii)60psi, curtain gas (CUR) 25psi, collisional-induced ionization (CAD), set at high parameters. In a triple quadrupole (Qtrap), each ion pair is scan detected according to an optimized Declustering Potential (DP) and Collision Energy (CE).

Fourth, pretreatment of metabolome detection data

Based on a self-built target standard database (MWDB), qualitatively analyzing metabolites according to retention time RT (Retention time), primary and secondary ion pair information and secondary spectrum data of a detected substance; carrying out quantitative analysis on the metabolite by utilizing a triple quadrupole mass spectrometry (MRM) multi-reaction detection mode to obtain the submerged machine data of different samples;

converting the sample test results into a calculable statistical value by sequentially performing the following processes:

1. extraction of chromatographic peaks: extraction of all metabolites using software analysis 1.6.3 processing mass spectrometry data

Performing area integration under the peaks on the ion chromatographic peaks respectively;

2. correction of chromatographic peaks: the sample lower organic mass spectrum file was processed using software MultiQuant, and the chromatographic peaks in different samples of the same metabolite were subjected to integral correction.

3. Sample quality control analysis: the repeatability of the samples under the same treatment method was examined. During the analysis of the instrument, 1 quality control sample is inserted into each 10 detection analysis samples to detect the repeatability of the analysis process. The high stability of the instrument is guaranteed so as to guarantee the repeatability and reliability of the data.

Statistical analysis of five or more variables

And performing model analysis on two groups of samples with high intramuscular fat content and low intramuscular fat content by adopting a partial least squares discriminant analysis (PLS-DA) supervised pattern recognition multivariate statistical analysis method, wherein PLS-DA can maximize interval components and is beneficial to searching for differential metabolites. Orthogonal partial least squares discriminant analysis (OPLS-DA) combines Orthogonal Signal Correction (OSC), and can decompose X matrix information into two types of information, Y-related and non-related, and remove orthogonal signals, which are differential information not related to model classification, to screen differential variables. OPLS-DA score map As shown in FIG. 1, after filtering out noise signals irrelevant to classification, two groups of samples are completely divided into two clusters, and the two clusters are respectively in the 1 st principal component (T [1 ]]) The positive side and the negative side of the sample, namely the two groups of samples with different levels of intramuscular fat content have obvious metabolic difference, and the separation of metabolic profiles can be well realized. The model performs 200 random permutation and combination experiments on data, n is 200, and parameters of the OPLS-DA model are evaluated: r²Y＝0.999，Q²0.826, model interpretation rate (R)²Y) and model prediction rate (Q)²) The values are all close to 1, which shows that the fitting accuracy of the OPLS-DA model is high, and the difference between samples with different levels of intramuscular fat content can be well explained.

Sixthly, screening of differential metabolites

The metabolites obtained by discriminant analysis by the orthogonal partial least square method are more reliable because orthogonal signals irrelevant to classification are filtered. Metabolites with a variable projection importance in projection (VIP) value greater than or equal to 1 were selected as differential metabolite candidate markers, and OPLS-DA s-plot plotted from the VIP results, as shown in FIG. 2. Based on the differential metabolites found, the invention further screens effective biomarkers with more representativeness and higher sensitivity by combining univariate analysis, thereby further simplifying the detection procedure on the basis of ensuring the detection accuracy. Selecting metabolites with fold difference values (fold change) of more than or equal to 2 or less than or equal to 0.5, finding 171 kinds of candidate markers of the differential metabolites in total, meanwhile, in order to reduce the number of the differential metabolites, comparing the fold difference change of each differential metabolite in two groups of samples with high and low intramuscular fat levels, taking Log2 as the base, sorting the calculation results, selecting the differential metabolites with | Log 2FC | of more than 2.2 and 30 substances in total as biological candidate markers, detecting the retention time and the ionic current intensity of the metabolites based on a local metabolic database, wherein the detection results of the differential metabolites are shown in Table 1.

TABLE 1 differential metabolites between high and low fat content muscle groups

Seventhly, ROC curve analysis and identification test of biomarker substance

On the basis of the scheme, the biomarker capable of effectively evaluating the intramuscular fat content of pork is further screened aiming at the 30 screened differential metabolite candidate markers, so that the detection program is further optimized on the basis of ensuring the detection accuracy. The method utilizes a Receiver Operating Characteristic curve (ROC) to evaluate the performance prediction accuracy of the differential metabolite, and the ROC curve analysis takes (1-specificity) as an abscissa and sensitivity as an ordinate, combines the sensitivity and specificity of the test, is a scientific and reliable evaluation detection means, and can indicate the effect of evaluating the intramuscular fat content by the biomarker found by the method. The larger the area under the curve (AUC), the better the prediction effect, and when the AUC is close to 0.5, no prediction significance is achieved; AUC < 0.7, which means lower prediction accuracy; AUC is 0.7-0.9, indicating moderate accuracy; when AUC is greater than 0.9, the prediction accuracy is higher. As shown in FIG. 3, 8 triglyceride metabolites (TG (18:1/18:4/18:4), TG (14:0/20:1/22:1), TG (18:1/18:1/20:0), TG (12:0/16:0/18:3), TG (18:0/20:1/20:1), TG (16:0/16:1/20:1), TG (18:1/18:1/22:0) and TG (18:1/18:3/18:3)) in different intramuscular fat content groups were subject to ROC analysis, and AUC was 1.000 and AUC was 0.771 and 0.727 respectively after ROC analysis of acyl carnitine and free acyl carnitine. Wherein the use of triglyceride metabolite biomarkers exhibits a sensitivity of 100%, a specificity of 100% and an AUC value of 1.000 when assessing intramuscular fat content levels. Therefore, comparison of 8 triglyceride metabolites can be used to accurately judge the intramuscular fat level.

The invention also utilizes the KEGG database to perform the enrichment analysis of the metabolic pathways, annotates the metabolic pathways and reactions of the found differential metabolites and verifies the association between the enriched related pathways and the characters. The affected metabolic pathways in different intramuscular fat levels of pigs are annotated, and it is known that the affected glyceride metabolism, cholesterol metabolism, thermogenesis, insulin resistance, regulation of fat digestion and decomposition, and the pathway of digestion and absorption of vitamins have correlation with each other, and the metabolism is closely related to the variation of intramuscular fat content of pork. And from this 8 biomarkers for intramuscular fat content assessment in pigs were finalized.

According to the invention, the content of the intramuscular fat of the pigs can be effectively evaluated by utilizing metabolome analysis, the pigs with high intramuscular fat and the pigs with low intramuscular fat are separated differently, and the method has high sensitivity and specificity. The 8 detected differential metabolites are 8 up-regulated metabolites, and are shown to be significantly higher in the muscle of the high intramuscular fat pig than in the low intramuscular fat pig. The 8 differential metabolites are triglyceride compounds. In the actual work of pork quality classification and pig genetic evaluation, the levels of intramuscular fat content of pigs can be indicated quickly and efficiently only by carrying out metabonomics detection and OPLS-DA analysis on one or more of the 8 metabolites in the table 2.

Intramuscular fat content assessment using a single biomarker, as follows: after the selected biomarkers are quantitatively detected, sorting and grouping the biomarkers according to the content and the level of the biomarkers, and detecting grouping results by using a fold difference value (fold change) in univariate analysis, wherein the sample with high biomarkers has high intramuscular fat content; the intramuscular fat content of the biomarkers of various combination forms is evaluated by the following method: after the selected biomarker combination is quantitatively detected, the content of the selected marker is subjected to scale standardization, the mean value of the marker combination of each sample individual is calculated, the mean value is sorted and grouped according to the high and low levels, the grouping result can be tested by multivariate PLS-DA and OPLS-DA analysis, and if the mean value of the biomarker combination is high, the intramuscular fat content of the corresponding sample is high.

TABLE 2 intramuscular fat content biomarker identified finally by ROC Curve analysis

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and is also applicable to other kinds of breeding pigs. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.