Plasma biomarker related to total litter size character of sow and application of plasma biomarker

Abstract

The invention discloses a plasma biomarker related to the character of the total litter size of sows and application of the plasma biomarker, and a group of effective organic acid biomarkers including 1-deoxyvaleric acid, arginine succinic acid, cinnamic acid, iminodiacetic acid and risedronic acid are analyzed and excavated by utilizing metabonomics. The total litter size of the sow is simply, conveniently and rapidly predicted through the biomarker, the work of selecting and remaining the total litter size of the sow in advance can be assisted, the problem that accurate breeding through a genome is difficult due to low heritability of the total litter size is solved, and then improvement of the character of the total litter size of the sow is promoted.

Description

Technical Field

[0001] The invention belongs to the technical field of pig biomarker screening, and in particular relates to plasma metabolite biomarkers related to the total number of piglets born (TNB) trait of Large White sows and applications thereof. Background Art

[0002] As one of the important industries in animal husbandry, pig farming provides a guarantee for human food needs. The reproductive performance of sows is related to the efficiency of the pig industry, among which the total number of piglets born by sows is an important indicator to measure the reproductive performance of sows. There are two main ways to improve the total number of piglets born by sows. One is to improve the total number of piglets born by optimizing nutrition and management and other production methods, and the other is to improve the total number of piglets of the next generation of pigs through breeding. The former can often only be observed and changed at the macro level, and the biological mechanism of the trait cannot be understood, and thus it cannot be changed at the microscopic molecular level; the latter is due to the complex genetic mechanism of the total number of piglets trait of sows, and the heritability is only about 0.1 (Zhao et al, 2022), and at present, the analysis of the total number of piglets of sows at home and abroad is not in-depth and comprehensive enough. Therefore, even if breeding is carried out through more advanced genomic selection methods, the improvement of the total number of piglets trait is relatively slow. In this regard, further exploring biomarkers related to the total litter size phenotype through studying metabolites, the molecular substances closest to the phenotype, will not only help us understand the deep biological mechanisms of the trait, but also may find substances that directly affect the trait, thereby more efficiently improving complex traits such as total litter size.

[0003] At present, the metabolome detection technology for biological samples has become relatively mature, and liquid chromatography tandem mass spectrometry (LC-MS / MS) can accurately detect metabolites qualitatively and quantitatively. Many disease biomarkers for medical diagnosis have been discovered through whole metabolome association analysis (MWAS) (Lee et al, 2022), but biomarkers for the total litter size trait of sows have not yet been reported, and research and exploration are urgently needed. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects and shortcomings of the prior art and to explore plasma biomarkers related to the total litter size trait of sows. The biomarkers can be used as molecular phenotypes to provide detection targets for improving the total litter size in the sow production process.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] A method for screening plasma markers related to the total litter size trait of sows, the method comprising the following steps:

[0007] (1) Plasma tissues were collected from 627 Large White sows, and plasma metabolites were quantitatively and qualitatively detected by combining non-targeted detection and broad targeted detection, obtaining abundance data for 1,827 metabolites;

[0008] (2) Collect the total litter size records and related fixed effect data of 627 Large White sows at the time of sampling;

[0009] (3) (Quality control was performed on the missing values ​​of the total litter size phenotypic data and metabolome data, and the data of 1827 metabolites from 554 pigs were retained;

[0010] (4) The least absolute shrinkage and selection operator (LASSO) was used to regress and fit the total litter size and metabolome data. The optimal λ in the LASSO model was obtained by averaging the optimal λ after three ten-fold cross validations and put into the final LASSO model. Twenty metabolites with model coefficients not equal to 0 were obtained as characteristic metabolites of the total litter size trait.

[0011] (5) By performing ROC curve analysis on characteristic metabolites of different correlations and substance categories, plasma biomarkers that were negatively correlated with the total litter size trait of sows were screened out, including 5 organic acid metabolites, whose English names and molecular formulas are detailed in Table 1.

[0012] Table 1: Five plasma biomarkers associated with total litter size in Large White sows

[0013]

[0014] A method for predicting the total litter size of pigs based on biomarkers: using liquid chromatography tandem mass spectrometry to quantitatively detect biomarkers in the blood tissue of sows, the biomarkers include 1-deoxypentanoic acid, argininosuccinic acid, cinnamic acid, iminodiacetic acid and risedronic acid, and classifying the combination of biomarkers into high and low levels, and individuals with low levels of the biomarkers have high total litter size. Furthermore, the breed of the pig is Large White pig.

[0015] The present invention has the beneficial effects:

[0016] The present invention accurately screened out a plasma biomarker combination that can quickly evaluate the total litter size of Large White pigs through metabolomics detection and analysis. The provided plasma biomarker combination can efficiently and accurately screen out Large White sows with a higher total litter size (ROC-AUC is 0.856, and the classification accuracy is very high), which is convenient for the early selection of high-yielding sows, and plays an important role in accelerating the breeding process and increasing production benefits.

[0017] The abundances of the five organic acid biomarkers screened out by the present invention are all negatively correlated with the total litter size of sows. On the one hand, using a combination of biomarkers with the same correlation can directly calculate the mean of their abundance to judge the total litter size, which is easier to use; on the other hand, screening biomarkers of the same substance category facilitates targeted detection of a single type of substance, and the detection cost is lower. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 : The cross-validation diagram (above) and the LASSO selection path diagram (below) of three repetitions in the LASSO model training in the present invention.

[0019] Figure 2 : Receiver operating characteristic curves of the five negatively correlated organic acid metabolite combinations and the five amino acid metabolite combinations in Table 1 for assessing the total litter size trait in pigs. DETAILED DESCRIPTION

[0020] Example 1 Detection of plasma metabolites

[0021] (1) Sample collection

[0022] Blood samples were collected from 347 Canadian Large White pigs, 237 Danish Large White sows, and 43 French Large White sows at different gestation stages in Xinjiang, Inner Mongolia, and Anhui.

[0023] (2) Sample extraction

[0024] ①Take out the blood sample from the -80℃ freezer and thaw it on ice until there is no ice in the sample (all subsequent operations must be performed on ice);

[0025] ② After the sample is thawed, vortex for 10 seconds to mix, and transfer 50 μL of the sample to the corresponding numbered centrifuge tube;

[0026] ③ Add 300 μL of methanol extract containing 20% ​​acetonitrile, vortex for 3 min, and centrifuge at 12000 r / min for 10 min at 4°C;

[0027] ④ After centrifugation, transfer 200 μL of the supernatant to another corresponding numbered centrifuge tube and place it in a -20℃ refrigerator for 30 min;

[0028] ⑤ Centrifuge again at 12000r / min for 3min at 4℃, transfer 180μL of supernatant into the liner tube of the corresponding injection bottle for analysis on the analyzer.

[0029] (3) Non-targeted detection

[0030] The data acquisition instrument system mainly includes ultra-high performance liquid chromatography and quadrupole time-of-flight mass spectrometry (TripleTOF6600, AB SCIEX).

[0031] The liquid chromatography conditions mainly include: chromatographic column: ACQUITY HSS T3 (2.1×100 mm, 1.8 μm); mobile phase A: 0.1% formic acid; mobile phase B: acetonitrile solution containing 0.1% formic acid; column temperature: 40°C; flow rate: 0.4 ml / min; injection volume: 5 μL. The positive and negative ion mobile phase gradient conditions are detailed in Table 2, and the mass spectrometry conditions in the positive and negative ion modes are detailed in Table 3.

[0032] Table 2: Positive and negative ion mobile phase gradient conditions

[0033]

[0034] Table 3: Mass spectrometry conditions in positive and negative ion modes

[0035]

[0036] (4) Extensive targeted testing

[0037] The data acquisition instrument system mainly includes ultra-high performance liquid chromatography (ExionLC AD) and tandem mass spectrometry

[0038]

[0039] T3 method chromatographic acquisition conditions: chromatographic column: Waters ACQUITY UPLC HSS T3C18 (1.8μm, 2.1mm×100mm); mobile phase: phase A is 0.1% formic acid, phase B is acetonitrile solution containing 0.1% formic acid; elution gradient: 0min for phase A: phase B = 95:5 (V / V), 11.0min for A:B = 10:90 (V / V), 12.0min for phase A: phase B = 10:90 (V / V), 12.1min for phase A: phase B = 95:5 (V / V), 14.0min for phase A: phase B = 95:5 (V / V); flow rate 0.4ml / min; column temperature 40℃; injection volume 2μl.

[0040] The mass spectrometry acquisition conditions of the T3 method were as follows: electrospray ion source (ESI) temperature 500°C, mass spectrometer voltage 5500V (positive), -4500V (negative), ion source gas I (GS I) 50psi, gas II (GS II) 50psi, curtain gas (CUR) 25psi, collision induced ionization (CAD) parameter set to high. In the triple quadrupole (Qtrap), each ion pair was scanned and detected according to the optimized declustering voltage (DP) and collision energy (CE).

[0041] (5) Qualitative and quantitative analysis of metabolites

[0042] All sample extracts were mixed in equal amounts to form QC samples, and non-targeted detection was performed on the LC-QTOF-MS / MS platform. Accurate identification was performed based on the self-built standard database MWDB (including secondary spectra, retention time RT), Maiwei integrated DB-all public database (including Metlin, HMDB, KEGG and other databases), AI prediction library and MetDNA, and multiple ion pairs and retention time RT of identified metabolites were extracted. Combined with the self-built target database, an exclusive new library was formed. Finally, in all samples, MRM precise quantification was performed on the metabolites in the new library based on the Q-Trap instrument platform. After obtaining the liquid mass spectrometry data of different samples, the sub-peak area of ​​the extracted ion chromatographic peaks of all metabolites was integrated separately, and the chromatographic peaks of the same metabolite in different specimens were integrated and corrected. Finally, 1,827 plasma metabolites were quantitatively detected in 627 large white sows.

[0043] Example 2 Screening and verification of plasma metabolites associated with total litter size in large white sows

[0044] (1) Phenotypic statistics

[0045] The present invention collected the farrowing data of 627 sows, of which 554 individuals had the total farrowing number records of the parity at the time of sampling. The specific descriptive statistical results are shown in Table 4.

[0046] Table 4: Descriptive statistics of the total litter size of Large White pigs

[0047]

[0048] As shown in Table 4, the coefficient of variation of the total litter size phenotype is greater than 30%, indicating that this trait has great analytical value.

[0049] (2) Screening of characteristic metabolites of total litter size in Large White pigs

[0050] The experimental pig group used for the whole metabolome association analysis in this embodiment is Large White pigs (n=554), and the metabolite data are plasma metabolites detected by TM broad-based targeted technology (n=1827). Based on the glmnet package in R software, LASSO regression modeling was performed on the total litter size of Large White pigs (response variable) and plasma metabolome data (independent variable). In order to train the model to the best parameters as much as possible, the present invention uses ten-fold cross validation to pre-train the LASSO model parameters. At the same time, in order to ensure the accuracy and stability of the model, the present invention repeats the ten-fold cross validation 3 times, and finally takes the average of the best λ obtained from each repeated training as the final optimal λ of the model. The cross validation diagram and selection path diagram of the three repetitions are shown in Figure 1The parameters of the final LASSO model and the results of screening important biomarkers of total litter size in Large White pigs are shown in Table 5. The material classification information of the 20 important characteristic metabolites related to the total litter size trait of Large White pigs and their specific influence coefficients on the trait in the model are shown in Table 6.

[0051] Table 5: LASSO final model parameters and characteristic metabolite screening results

[0052]

[0053] Table 5 Description: DEV% indicates the percentage of variance of the response variable explained by the model, and 76.67% indicates that the model has a high goodness of fit.

[0054] Table 6: Material classification information and influence coefficients of important markers related to the total litter size trait of Large White pigs

[0055]

[0056]

[0057] Notes on Table 6: If the Chinese name column is “-”, it means that the substance does not have an accurate Chinese name yet. The positive or negative value of the influence coefficient represents the correlation with the property.

[0058] (3) ROC curve analysis and identification of biomarkers

[0059] On the basis of the above scheme, the 20 characteristic metabolites screened were further screened for biomarkers that can effectively indicate the total litter size of pigs, so as to further optimize the detection procedure on the basis of ensuring the accuracy of the detection. First, a high-yield group (samples in the top 10% of the total litter size) and a low-yield group (samples in the bottom 10% of the total litter size) were constructed, and the training set and the test set were split in a ratio of 7:3, and the training and testing of the logistic regression model were performed through 5-fold cross validation. According to the 20 characteristic metabolites screened by LASSO, according to different correlations and the number of metabolites of different categories, the present invention selected the metabolite combinations of amino acids and their metabolites (5 kinds) and the metabolite combinations of organic acids and their derivatives (5 kinds) that are negatively correlated with the total litter size in Table 6 for subsequent analysis. The receiver operating characteristic (ROC) curve is used to evaluate the performance prediction accuracy of the characteristic metabolite combination. The ROC curve analysis uses the false positive rate (1-specificity) as the horizontal axis and the true positive rate (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 effectiveness of the biomarker in indicating the total litter size. The larger the area under the curve (AUC), the better the prediction effect. When AUC = 0.5, the model has no distinguishing ability and the performance is the same as random guessing; AUC < 0.7, indicating a low prediction accuracy; AUC > 0.7, the model has good distinguishing ability; AUC > 0.8, the model performs well; AUC > 0.9, the model performs excellently. Figure 2 As shown, the AUC of the combination of characteristic metabolites of the five organic acids screened by the present invention for distinguishing high and low total litter size after ROC analysis is 0.856. Therefore, the total litter size can be distinguished with high accuracy through this biomarker combination; while the AUC of the combination of amino acids and their metabolites for distinguishing high and low total litter size after ROC analysis is 0.783, which is relatively low and is not selected in the present invention.

[0060] Main references:

[0061] Lee,A.M.,Hu,J.,Xu,Y.,Abraham,A.G.,Xiao,R.,Coresh,J.,Rebholz,C.,Chen,J.,Rhee,E.P.,Feldman,H.I.,Ramachandran,V.S.,Kimmel,P.L.,Warady,B.A.,Furth,S.L.,Denburg,M.R.,&CKD Biomarkers Consortium(2022).Using Machine Learning toIdentify Metabolomic Signatures of Pediatric Chronic Kidney DiseaseEtiology.Journal of the American Society of Nephrology:JASN,33(2),375–386.Zhao,Y.X.,Gao,G.X.,Zhou,Y.,Guo,C.X.,Li,B.,El-Ashram,S.,&Li,Z.L.(2022).Genome-wide association studies uncover genes associated with litter traitsin the pig.Animal:an international journal of animal bioscience,16(12),100672.

Claims

1. The use of plasma biomarkers in assessing the total litter size trait of sows, characterized in that: The plasma biomarkers include 1-deoxyvaleric acid, argininosuccinic acid, cinnamic acid, iminodiacetic acid and risedronic acid.

2. The use of plasma biomarkers in the preparation of a sow total litter size prediction kit, characterized in that: The plasma biomarkers include 1-deoxyvaleric acid, argininosuccinic acid, cinnamic acid, iminodiacetic acid and risedronic acid.

3. The use according to claim 1 or 2, characterized in that: The abundance of the plasma biomarker was negatively correlated with the total litter size trait in sows.

4. The use according to claim 1 or 2, characterized in that: The breed of the pig is Large White pig.

5. A method for predicting the total litter size of sows based on biomarkers, characterized in that: The method comprises using liquid chromatography tandem mass spectrometry to quantitatively detect biomarkers in the blood tissue of sows, wherein the biomarkers include 1-deoxypentanoic acid, argininosuccinic acid, cinnamic acid, iminodiacetic acid and risedronic acid, and classifying the biomarker combination into high and low levels, wherein individuals with low levels of the biomarkers have a high total litter size.

6. The method according to claim 5, characterized in that The breed of the pig is Large White pig.

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