Biomarkers related to residual feed intake in growing chickens and their application

Abstract

The invention discloses a biomarker related to the residual feed intake of growing chickens and its application, belonging to the technical field of poultry breeding. The biomarker includes one or more of 13(S)-HOTrE, 20-carboxy-LTB4, Traumatic acid, 15-deoxy-delta-12,14-PGJ2, (LysoPC(20:2(11Z,14Z))), Phosphorycholine, Malic acid, D-4'-Phosphopantothenate, Sphingosine and Spermidine. The invented biomarker can realize the identification of residual feed intake, has high sensitivity and strong specificity, and is expected to become a new means of residual feed intake identification.

Description

Technical Field

[0001] The invention relates to the technical field of poultry breeding, and in particular to a biomarker related to the residual feed intake of growing chickens and an application thereof. Background Art

[0002] Residual feed intake (RFI) is an important indicator for evaluating feed efficiency. RFI refers to the difference between the actual feed intake of livestock and poultry and the expected feed intake for maintenance and growth obtained through regression, and has moderate heritability. Feed costs account for 60-70% of breeding costs. With the continuous increase in feed raw material prices, feed costs are also gradually increasing. Breeding livestock and poultry with low RFI is conducive to improving feed utilization and reducing feeding costs.

[0003] The selection of broiler residual feed intake can reduce body fat content, increase muscle content, reduce feed intake without affecting livestock and poultry growth, and improve feed efficiency. However, the calculation method of residual feed intake makes it difficult to measure in actual production. It not only requires more manpower and material resources, but also limits the selection of individuals or populations in breeding work to a certain extent.

[0004] Changes in individual metabolic characteristics can reflect the response of the animal body to environmental stimuli or changes, linking metabolites with biological phenotypes, and providing the possibility of discovering new biomarkers. Blood is an important carrier of metabolites. Changes in blood metabolic characteristics can partially reflect changes in animal feed efficiency. Blood biochemical indicators may contain markers indicating feed efficiency. Therefore, understanding the relationship between animal blood biochemical indicators and residual feed intake is of great significance for the selection and breeding of feed efficiency. However, there are currently no reports on biomarkers related to residual feed intake in growing chickens. Summary of the invention

[0005] In view of the above-mentioned prior art, the purpose of the present invention is to provide a biomarker related to the residual feed intake of growing chickens and its application. The biomarker of the present invention can realize the identification of residual feed intake, has high sensitivity and strong specificity, and is expected to become a new means of residual feed intake identification.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a biomarker related to the residual feed intake of growing chickens, including: one or more of 13-(S)-hydroxyoctadecatrienoic acid (13(S)-HOTrE), 20-carboxyleukotriene B4 (20-carboxy-LTB4), traumatic acid, 15-deoxy-Δ12,14-prostaglandin J2 (15-deoxy-delta-12,14-PGJ2), lysophosphatidylcholine (20:2(11Z,14Z)) (LysoPC (20:2(11Z,14Z))), phosphocholine (Phosphorycholine), sphingosine (Sphingosine), malic acid (Malic acid), D-4'-phosphopantothenate (D-4'-Phosphopantothenate) and spermidine (Spermidine).

[0008] Preferably, the biomarkers include: an up-regulated marker combination and a down-regulated marker combination;

[0009] The up-regulated marker combination consists of 13(S)-HOTrE, 20-carboxy-LTB4, Traumatic acid and 15-deoxy-delta-12,14-PGJ2;

[0010] The down-regulated marker combination consists of LysoPC (20:2 (11Z, 14Z)), Phosphorycholine, Malicacid, D-4'-Phosphopantothenate, Sphingosine and Spermidine.

[0011] The second aspect of the present invention provides the use of the above-mentioned biomarker in the preparation of a reagent or a kit for identifying the remaining feed intake of growing chickens.

[0012] In the above use, the kit includes a reagent combination for detecting the biomarker.

[0013] Preferably, the reagent combination includes a substance for detecting the biomarker by mass spectrometry.

[0014] The third aspect of the present invention provides the use of the above-mentioned biomarkers as targets in the breeding of chicken breeds with low residual feed intake.

[0015] A fourth aspect of the present invention provides a method for breeding chicken breeds with low residual feed intake, the method comprising the step of detecting biomarkers in chicken serum by mass spectrometry.

[0016] Preferably, the biomarkers include: an up-regulated marker combination and a down-regulated marker combination;

[0017] The up-regulated marker combination consists of 13(S)-HOTrE, 20-carboxy-LTB4, Traumatic acid and 15-deoxy-delta-12,14-PGJ2;

[0018] The down-regulated marker combination consists of LysoPC (20:2 (11Z, 14Z)), Phosphorycholine, Sphingosine, Spermidine, Malic acid, and D-4'-Phosphopantothenate.

[0019] Beneficial effects of the present invention:

[0020] The present invention discloses for the first time a combination of plasma markers for the residual feed intake of growing chickens and its application. The present invention finds that: 13(S)-HOTrE, 20-carboxy-LTB4, Traumatic Acid, 15-deoxy-delta-12,14-PGJ2, LysoPC (20:2 (11Z, 14Z)), Phosphocholine, Sphingosine, Malic acid, D-4'-Phosphopantothenate and Spermidine have significant differences in plasma content in high and low residual feed intake groups, are significantly correlated with residual feed intake, and participate in the regulation of cell apoptosis, protein metabolism, lipid metabolism, energy metabolism and immune response, and are potential biomarkers for identifying residual feed intake. After combining the up-regulated and down-regulated differential metabolites respectively, the sensitivity and specificity of the identification of residual feed intake are significantly improved. The identification of residual feed intake can be realized at the molecular level through metabolomics technology, which has high sensitivity and strong specificity, and is expected to become a new means for the identification of residual feed intake. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 :Total ion chromatogram of quality control sample; (A) ESI(+) total ion current; (B) ESI(-) total ion current.

[0022] Figure 2 : PCA score plot; (A) PCA score plot in positive ion mode; (B) PCA score plot in negative ion mode.

[0023] Figure 3 : OPLS-DA score graph; (A) positive ion OPLS-DA score graph; (B) negative ion OPLS-DA score graph.

[0024] Figure 4: OPLS-DA permutation test diagram; (A) positive ion OPLS-DA permutation test; (B) negative ion OPLS-DA permutation test.

[0025] Figure 5 : Volcano plot of differential metabolites; in the figure, red points represent significantly down-regulated differential metabolites, blue points represent significantly up-regulated differential metabolites, gray points represent non-significantly different metabolites, and the size of the scatter points represents the VIP value of the OPLS-DA model.

[0026] Figure 6 : Box plot of distribution of up-regulated differential metabolites.

[0027] Figure 7 : Box plot of distribution of down-regulated differential metabolites.

[0028] Figure 8 : ROC curves of up-regulated metabolites.

[0029] Fig. 9 : ROC curves of upregulated metabolite combinations.

[0030] Fig.10 : ROC curves of down-regulated metabolites.

[0031] Fig.11 : ROC curves of the down-regulated metabolite combinations. DETAILED DESCRIPTION

[0032] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0033] Terminology Note:

[0034] Growing chickens: refers to chickens aged 7-20 weeks.

[0035] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with specific embodiments.

[0036] The test materials used in the examples of the present invention that are not specifically described are all conventional test materials in the art and can be purchased through commercial channels. The breed of chicken used in the present invention is AA+ broiler chicken.

[0037] Example 1: Screening and identification of biomarkers related to residual feed intake in growing chickens

[0038] (1) Determination of residual feed intake and sample collection

[0039] 125 hens of 15 weeks of age and similar body weight were selected to measure feed intake at 15-20 weeks of age. The residual feed intake of each hen in RFI was estimated using the regression procedure of IBM SPSSStatistics 23 software using the following model:

[0040] RFI=ADFI-(a+b×MBW+c×ADG).

[0041] Where a is the intercept, b and c are the partial regression coefficients, ADG is the average daily gain, MBW is the metabolic body weight, and ADFI is the average daily feed intake.

[0042] By the two-tail method, the eight chickens with the highest residual feed intake were selected as the high residual feed intake group (HRFI group), and the eight chickens with the lowest residual feed intake were selected as the low residual feed intake group (LRFI group). Blood was collected from the subwing vein, anticoagulated with EDTA, centrifuged at 3,000r / min for 10min, and the upper plasma was separated and stored in a -80℃ refrigerator for long-term storage.

[0043] (2) Plasma metabolome sequencing

[0044] ①Sample pretreatment

[0045] Take 200 μL of each plasma sample in an EP tube, add 80 μL of methanol and 400 μL of methyl tert-butyl ether (LMTBE), vortex mix for 30 seconds, and then ultrasonically extract for 30 minutes (5°C, 40KHz). The sample was placed at -20°C for 30 minutes and then centrifuged at high speed (13000rmp, 4°C) for 15 minutes. 350 μL of supernatant was removed and dried in a vacuum concentrator, and 100 μL of the reconstituted solution (V 异丙醇 :V 乙腈 =1:1, volume ratio). After vortex mixing for 30 seconds, ultrasonication was performed at 40KHz in an ice water bath for 5 minutes. After high-speed centrifugation (13000rmp, 4℃) for 5 minutes, 80μL of supernatant was transferred to a vial with an inner insert for analysis. At the same time, all sample metabolites were mixed with equal volumes to prepare quality control samples (Quality control, QC). During the instrument analysis, a QC sample was inserted into every 6 samples and tested in the same batch as the samples to examine the repeatability of the entire analysis process.

[0046] ②LC-MS / MS analysis

[0047] The instrument platform for this LC-MS analysis was Thermo Fisher's ultra-high performance liquid chromatography tandem Fourier transform mass spectrometry UHPLC-Q Exactive HF-X system for liquid chromatography-mass spectrometry detection.

[0048] Chromatographic conditions: 2 μL of sample was separated by BEH C18 column ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA) and then entered mass spectrometry detection. Mobile phase A was 10 mM ammonium acetate-5% acetonitrile aqueous solution (the mobile phase A contained 0.1% formic acid, volume fraction), and mobile phase B was 2 mM ammonium acetate-47.5% acetonitrile-47.5% isopropanol-10% water (the mobile phase A contained 0.02% formic acid, volume fraction). The injection volume was 2 μL, and the column temperature was 40°C. Separation gradient: 0-3.5min, mobile phase A decreased from linear 100% to 75.5%, mobile phase B increased from linear 0% to 24.5%, flow rate 0.40mL / min; 3.5-5min, mobile phase A decreased from linear 75.5% to 35%, mobile phase B increased from linear 24.5% to 65%, flow rate 0.40mL / min; 5-5.5min, mobile phase A decreased from linear 35% to 0%, mobile phase B increased from linear 65% to 100%, flow rate 0.40mL / min; 5.5-7.4min, mobile phase Phase A was maintained at 0%, mobile phase B was maintained at 100%, and the flow rate was 0.60 mL / min; from 7.4 to 7.6 min, mobile phase A increased linearly from 0% to 48.5%, and mobile phase B decreased linearly from 100% to 51.5%, and the flow rate was 0.60 mL / min; from 7.6 to 7.8 min, mobile phase A increased linearly from 48.5% to 100%, and mobile phase B decreased linearly from 51.5% to 0%, and the flow rate was 0.50 mL / min; from 7.8 to 10 min, mobile phase A was linearly maintained at 100%, and mobile phase B was linearly maintained at 0%. The flow rate was 0.40 mL / min.

[0049] Mass spectrometry conditions: The sample was ionized by electrospray, and the mass spectrometry signals were collected in positive and negative ion scanning modes. Mass scanning range: 70-1050 m / z. Ion spray voltage, positive ion voltage 3000 V, negative ion voltage -3000 V, S-Lens voltage 50, capillary temperature 325 ° C, sheath gas flow rate 50 arb, auxiliary gas flow rate 13 arb, ion source heating temperature 425 ° C, 20-60 V cyclic collision energy, MS 1 Resolution 60000, MS 2 Resolution 7500.

[0050] (3) Data preprocessing and database search

[0051] After the computer was completed, the LC-MS raw data was imported into the metabolomics processing software Progenesis QI (Waters Corporation, USA) for baseline filtering, peak identification, integration, retention time correction, peak alignment and identification, etc., and finally a data matrix containing information such as retention time, mass-to-charge ratio and peak intensity was obtained. The data matrix used the 80% rule to remove missing values, that is, retaining variables with more than 80% non-zero values ​​in at least one group of samples, and then filling in the vacant values ​​(filling the vacant values ​​with the minimum value in the original matrix). In order to reduce the errors caused by sample preparation and instrument instability, the response intensity of the sample mass spectrum peak was normalized by the sum normalization method to obtain the normalized data matrix. At the same time, the variables with the relative standard deviation (RSD) of the QC sample>30% were deleted, and log10 logarithmization was performed to obtain the final data matrix for subsequent analysis. The data matrix was searched for characteristic peaks, the mass spectrum information was matched with the database, the mass error was set to be less than 10ppm, and the metabolites were identified based on the secondary mass spectrometry. The main databases for metabolite identification are HMDB (http: / / www.hmdb.ca / ) and Metlin (https: / / metlin.scripps.edu / ) as well as self-built databases.

[0052] (4) Metabolite identification and screening of key differential metabolites

[0053] The R software package ropls (Version 1.6.2) was used for principal component analysis (PCA). PCA is an unsupervised multivariate statistical analysis method that recombines the original variables into several new independent comprehensive variables (i.e., principal components). All factors are ranked by importance, and usually the small factors at the back are ignored. By reducing the dimension, the data is simplified, which can generally reflect the overall differences between the samples in each group and the variability between the samples in the group. Orthogonal least squares discriminant analysis (OPLS-DA) was also performed. OPLS-DA combines the two methods of orthogonal signal correction (OSC) and PLS-DA to better distinguish the differences between groups and improve the validity and analytical ability of the model. The stability of the model was evaluated by seven-fold cross validation. The response permutation testing was used to randomly arrange the variables of the previously defined classification Y matrix 200 times by fixing the X matrix. After each arrangement and combination, a new PLS or OPLS model was constructed, and the corresponding model cumulative R was calculated. 2 Y and Q 2 Then combine the original classified Y matrix, the Y matrix of n different permutations and R 2 Y, Q 2Linear regression was performed, and the intercept value of the regression line and the y-axis was used as a criterion for measuring whether the model was overfitting, to evaluate the accuracy of the PLS and OPLS models, and Student's t test and difference fold analysis were performed. The variable weight value (VIP) obtained by the OPLS-DA model > 1 and P < 0.05 of the student's t test were used as the screening criteria for significantly different metabolites.

[0054] (5) KEGG analysis and metabolic pathway analysis of differential lipid metabolites in NSCLC

[0055] The KEGG database (https: / / www.kegg.jp / kegg / pathway.html) was used to annotate the screened differential metabolites, and the Python package scipy.stats was used for pathway enrichment analysis. The metabolic pathways were further screened by Fisher's exact test to obtain the biological pathways most relevant to residual feed intake.

[0056] (6) “Gene-enzyme-reaction-metabolite” network analysis of significantly differential metabolites

[0057] The fold changes and P values ​​of all differential lipid metabolites in NSCLC were input into the MetScape plug-in of CytoScape (http: / / metscape.ncibi.org / ) to obtain the overall network of "gene-enzyme-biochemical reaction-metabolite" and all sub-networks involved in the differential metabolites.

[0058] (7) Confirmation of biomarkers related to residual feed intake in growing chickens

[0059] Based on the results of differential metabolite screening and metabolic pathway analysis, candidate biomarkers related to the residual feed intake of growing chickens were obtained. SPSS software was used to draw the ROC curve of a single indicator for the candidate plasma differential biomarkers screened in this study, and the sensitivity and specificity of the combined effect of the indicators were calculated, the diagnostic efficacy of the combined effect of multiple indicators was analyzed, and the biomarkers related to the residual feed intake of growing chickens were confirmed.

[0060] The results obtained by using the method of applying the plasma biomarker of the present invention in the diagnosis of residual feed intake are as follows:

[0061] (1) Metabolite profile

[0062] The total ion chromatogram of the quality control sample is shown in Figure 1 The total ion chromatograms of the quality control samples in positive and negative ion modes have good peak shapes and relatively uniform distribution, indicating that the system is stable and the test data are reliable.

[0063] In positive and negative ion modes, 4084 and 4251 mass peaks were extracted respectively after removing low-mass ions according to relative standard deviation (RSD)>0.3. Through primary and secondary mass spectrometry data, 325 and 283 metabolites were annotated respectively, mainly lipids, carbohydrates, vitamins, organic acids and peptides.

[0064] (2) UHPLC-Q Exactive HF-X / MS data analysis

[0065] ①Multivariate principal component analysis (PCA)

[0066] Principal component analysis can simplify data using mathematical methods, generally reflect the overall differences between groups of samples and the variability between samples within a group, find outlier samples, identify sample clusters with high similarity, etc. PCA modeling analysis was performed using the R software package ropls (Version 1.6.2). In positive and negative ion modes, the PCA score graph ( Figure 2 ) showed that all samples were within the 95% confidence interval, and there was a clear separation between the different residual feed intake groups in the positive ion mode, but no clear separation in the negative ion mode. 2 X (the explanation rate of the model to the X matrix) were 0.581 and 0.578, both greater than 0.5, and the quality control samples were clustered together in the two modes, indicating that the system used in the experiment had high stability and repeatability.

[0067] ② Orthogonal partial least squares discriminant analysis (OPLS-DA)

[0068] The R software package ropls (Version 1.6.2) was used to perform OPLS-DA modeling analysis on the plasma sample metabolome data. The quality of the model was tested by 7-fold cross validation. The score scatter plot was obtained by filtering out information irrelevant to the grouping through orthogonal rotation ( Figure 3 ), it can be seen that the difference between the two groups of samples is very significant, and the samples are within the 95% confidence interval. 2 X is 0.332 and 0.358 respectively, R 2 Y (the model's explanation rate for the Y matrix) is 0.956 and 0.992 respectively. 2 (the predictive ability of the model) are 0.641 and 0.445 respectively. Generally speaking, R 2 and Q 2 The closer to 1, the more reliable the model is. A value higher than 0.5 is better, and a value higher than 0.3 is acceptable. Therefore, this model is reliable. The sample points of the HRFI group and the LRFI group are obviously clustered into two categories, and the separation on the principal component coordinate axis is good, which indicates that there are obvious differences between the two groups of plasma samples.

[0069] ③ Replacement test

[0070] In order to verify the reliability of the OPLS-DA data model and prevent overfitting, we conducted 200 general selection random permutation tests on the model. 2 When the intercept of the regression line with the Y axis is less than 0.05, the model can be considered robust and reliable, and there is no overfitting. 2 The intercepts of the regression line and the Y axis are -0.2246 and -0.0273 respectively, indicating that the permutation test of the model passes, there is no overfitting phenomenon, and the model is reliable ( Figure 4 ).

[0071] ④ Screening of metabolites with differences in high and low residual feed intake

[0072] Under the condition that the VIP value of the first principal component of the OPLS-DA model was >1 and P<0.05, 75 differential metabolites were annotated and identified. In the positive and negative ion modes, there were 38 and 37 differential metabolites, respectively ( Figure 5 ), red dots represent significantly down-regulated differential metabolites, blue dots represent significantly up-regulated differential metabolites, gray dots represent non-significantly different metabolites, and the size of the scatter points represents the VIP value of the OPLS-DA model. It mainly includes lipids, organic acids and phenylpropiotones, which account for 52.54%, 18.64% and 13.56% of the total, respectively.

[0073] ⑤ Cluster analysis of differential metabolites

[0074] Cluster analysis of differential metabolites was performed, see Figure 6 . The eight samples in each group were clustered together and showed relatively consistent abundance. Compared with the low residual feed intake group, 37 differential metabolites were low in the high residual feed intake group, including lipids, organic acids and their derivatives, nitrogen-containing compounds, etc.; 38 differential metabolites were high in the high residual feed intake group, including lipids, phenylpropionones, organic acids and their derivatives, etc.

[0075] ⑥ KEGG analysis and metabolic pathway enrichment analysis of differential metabolites in residual feed intake

[0076] KEGG pathway enrichment analysis was performed on the significantly differential metabolites, and a total of 19 metabolic pathways were enriched, of which 7 were significantly enriched. The down-regulated metabolites were lysophosphatidylcholine (20:2(11Z,14Z)), phosphocholine, sphingosine, spermidine, malic acid, and The up-regulated metabolites 13-(S)-hydroxyoctadecatrienoic acid (13(S)-HOTrE), 15-deoxy-Δ12,14-prostaglandin J2 (15-deoxy-delta-12,14-PGJ2), 20-carboxyleukotriene B4 (20-carboxy-LTB4) and traumatic acid were significantly enriched in β-alanine metabolism, glycerophospholipid metabolism, cell apoptosis, programmed cell necrosis and citric acid cycle (TCA cycle) pathways (Table 1).

[0077] The feed efficiency of low residual feed intake was higher, so it was mainly discussed. "Down-regulated expression" and "up-regulated expression" refer to the low residual feed intake group relative to the high residual feed intake group.

[0078] Table 1 KEGG enrichment analysis

[0079]

[0080] The abundance of metabolites related to the up- and down-regulated significantly enriched pathways between the high and low residual feed intake groups was as follows Figure 6 and Figure 7 shown.

[0081] ⑦ “Gene-enzyme-reaction-metabolite” network analysis of significantly different lipid metabolites

[0082] The fold change and P value of all significantly different metabolites were input into the MetScape plug-in of CytoScape (http: / / metscape.ncibi.org / ) to obtain the overall network of "gene-enzyme-biochemical reaction-metabolite" and all sub-networks in which the significantly different metabolites participated.

[0083] ⑧Confirmation of biomarkers for residual feed intake

[0084] Based on the results of differential metabolite screening and metabolic pathway analysis, candidate biomarkers related to the residual feed intake of growing chickens were obtained. The ROC curves of single and multiple indicators of the candidate plasma differential biomarkers screened in this study were drawn using IBM SPSS Statistics 23 software, and the sensitivity and specificity were calculated. The diagnostic efficacy of the combined action of multiple indicators was analyzed to confirm the biomarkers related to the residual feed intake of growing chickens. The sensitivity, specificity and AUC of the up-regulated biomarkers, down-regulated biomarkers and biomarker combinations are shown in Tables 2 and 3, and the ROC curves are shown in Tables 2 and 3. Figure 8 , Fig. 9 , Fig.10 , Fig.11 shown.

[0085] SPSS software was used to draw the ROC curve of the differential metabolites in the two groups. The ROC curve is a curve that reflects the relationship between sensitivity and specificity. The horizontal axis X-axis is 1-specificity, also known as the false positive rate (false alarm rate). The closer the X-axis is to zero, the higher the accuracy; the vertical axis Y-axis is called sensitivity, also known as the true positive rate (sensitivity). The larger the Y-axis, the better the accuracy. Sensitivity refers to the proportion of actual down-regulated metabolites that the screening method can correctly judge as down-regulated. Specificity refers to the proportion of actual up-regulated metabolites that the screening method can correctly judge as down-regulated.

[0086] Youden index, also known as the accuracy index, is an index used to judge the authenticity of a screening test when it is assumed that the harmfulness of false negatives (missed diagnosis rate) and false positives (misdiagnosis rate) are of equal significance. Youden index = sensitivity + specificity - 1. The larger the Youden index, the greater the authenticity and the better the diagnostic method.

[0087] The results showed that the AUC values ​​of the four up-regulated differential metabolites were >0.875 (range: 0.875–0.984), the sensitivity was >87.5% (range: 87.5–100%), and the specificity was >87.5% (range: 87.5–100.0%) (results are shown in Table 2 and Figure 8 ). Subsequent combined analysis found that the AUC of the combination of the four up-regulated metabolites was 1.000, with a sensitivity of 100.0% and a specificity of 100.0% (see Fig. 9 ).

[0088] Table 2 Expression and diagnostic significance of upregulated metabolite combinations

[0089]

[0090] The ROC results of the six down-regulated differential metabolites are shown in Table 3 and Fig.10, the AUC values ​​of the six down-regulated differential metabolites were >0.766 (range: 0.766–0.922), the sensitivity was >62.5% (range: 62.5–100%), and the specificity was >50.0% (range: 50.0–87.5%) (results are shown in Table 3 and Fig.10 ). Subsequent combined analysis found that the AUC of the combination of 6 down-regulated metabolites was 1.000, the sensitivity was 100.0%, and the specificity was 100.0% (results are shown in Table 3 and Fig.11 ).

[0091] Table 3 Expression and diagnostic significance of down-regulated metabolite combinations

[0092]

[0093] It was found that a single biomarker did not have good discrimination performance in terms of high and low residual feed intake, and the up- and down-regulated differential metabolite combinations were confirmed as biomarkers for distinguishing high and low residual feed intake. Based on the comparison of the sensitivity and specificity of the two metabolite combinations in the plasma of the high and low residual feed intake groups, it can be seen that the two combinations have good feasibility and can be used for auxiliary identification or detection of residual feed intake in growing chickens. When used, they can be distinguished based on the amount of the two marker combinations in the metabolomics results.

[0094] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A biomarker related to the residual feed intake of growing chickens, characterized in that: The biomarkers include: an up-regulated marker combination and a down-regulated marker combination; The up-regulated marker combination consists of 13-(S)-hydroxyoctadecatrienoic acid (13(S)-HOTrE), 20-carboxyleukotriene B4 (20-carboxy-LTB4), traumatic acid and 15-deoxy-Δ12,14-prostaglandin J2 (15-deoxy-delta-12,14-PGJ2); The down-regulated marker combination consists of lysophosphatidylcholine (20:2(11Z,14Z)), phosphocholine, malic acid, D-4'-phosphopantothenate, sphingosine and spermidine.

2. Use of the biomarker according to claim 1 in the preparation of a reagent or a kit for identifying the residual feed intake of growing chickens, characterized in that: The kit comprises a reagent combination for detecting the biomarker of claim 1; the reagent combination comprises a substance for detecting the biomarker of claim 1 by mass spectrometry.

3. A method for breeding chicken breeds with low residual feed intake, characterized in that: The method comprises the steps of detecting biomarkers in chicken serum by mass spectrometry; The biomarkers include: an up-regulated marker combination and a down-regulated marker combination; The up-regulated marker combination consists of 13-(S)-hydroxyoctadecatrienoic acid (13(S)-HOTrE), 20-carboxyleukotriene B4 (20-carboxy-LTB4), traumatic acid and 15-deoxy-Δ12,14-prostaglandin J2 (15-deoxy-delta-12,14-PGJ2); The down-regulated marker combination consists of lysophosphatidylcholine (20:2(11Z,14Z)), phosphocholine, malic acid, D-4'-phosphopantothenate, sphingosine and spermidine.

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