Method for detecting early pregnancy of cattle by using metabonomics technology

Abstract

According to the method for detecting the early pregnancy of the cattle by using the metabonomics technology, tiny metabolic changes in bovine serum can be detected through the metabonomics technology, the pregnancy state can be accurately judged even in the early pregnancy stage, and the detection sensitivity is improved. The differential metabolites screened out through multivariate statistical analysis and a machine learning algorithm have high specificity, pregnant cattle and non-pregnant cattle can be effectively distinguished, and misdiagnosis and missed diagnosis are reduced. The sample collection and pretreatment processes are relatively simple, complex equipment and professional skills are not needed, and the method is suitable for rapid detection of large-scale farms. Detection can be carried out 18-25 days after cattle hybridization, the pregnancy state can be judged earlier than that of a traditional method, and more timely information is provided for breeding management of cattle herds.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, in particular to a method for detecting early pregnancy in cattle using metabolomics technology. Background Art

[0002] Early pregnancy diagnosis in cattle is crucial for improving reproductive efficiency and reducing feeding costs. Accurately and promptly determining whether a cow is pregnant allows for rational feeding and management, avoiding ineffective feeding of non-pregnant cows. It also allows for the timely detection of pregnancy abnormalities and the implementation of appropriate measures to ensure normal fetal development.

[0003] Currently, common methods for diagnosing early pregnancy in cattle include rectal examinations, ultrasounds, and hormone testing. Rectal examinations require experienced technicians and are not very accurate in early pregnancy. While ultrasounds can visually visualize the fetus, they are expensive and require specialized personnel, making them unsuitable for rapid testing in large-scale farms. Hormone testing also suffers from time constraints and limited sensitivity and specificity.

[0004] Metabolomics is a technique that qualitatively and quantitatively analyzes all small molecule metabolites within an organism or cell. It can reveal metabolic changes in an organism under specific physiological or pathological conditions. In recent years, metabolomics has been widely used in animal disease diagnosis and physiological status assessment. However, there is currently no effective method for accurately detecting early pregnancy in cattle using metabolomics. Therefore, we have developed a method for detecting early pregnancy in cattle using metabolomics. Summary of the Invention

[0005] The purpose of the present invention is to address the problems raised by the existing background technology. In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solution: a method for detecting early pregnancy in cattle using metabolomics technology, comprising the following steps: Step 1: Sample collection: Step 11. Blood samples were collected from cows 18-22 days after mating. 10 mL of blood was collected from the jugular vein of the cow using a sterile vacuum blood collection tube. Step 12: Centrifugation: Let the collected blood sample stand at room temperature for 30-60 minutes. After the blood coagulates, centrifuge it at 3000 rpm for 15 minutes and carefully aspirate the upper serum. Step 13: Storage: The separated serum was divided into sterile centrifuge tubes and stored in a -80°C refrigerator for later use; Step 2: Sample pretreatment: Step 21. Add pre-chilled methanol-water mixture: Remove the serum sample from the -80°C freezer and thaw at room temperature; add 200 μL of serum sample to a sterile centrifuge tube, then add 800 μL of pre-chilled methanol-water (4:1 volume ratio) mixture, and vortex for 30 seconds to thoroughly mix the serum and the mixture; Step 22: Protein precipitation: Place the mixed sample in a -20°C refrigerator for 30 minutes to allow the protein to fully precipitate. Step 23: Centrifuge and obtain the supernatant: Centrifuge at 13,000 rpm at 4°C for 15 minutes and transfer the supernatant to a new sterile centrifuge tube. Step 24, drying: Place the supernatant in a vacuum concentrator and dry at 37°C until completely dry; Step 25. Redissolution: Add 200 μL of a 1:1 acetonitrile-water mixture to the dried sample. Vortex for 30 seconds and sonicate for 10 minutes to fully resolubilize the sample. Centrifuge at 13,000 rpm for 15 minutes at 4°C and collect the supernatant for subsequent metabolomics analysis. Step 3: Metabolomics analysis Step 31, liquid chromatography conditions: Chromatographic column: ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm); Mobile phase: Mobile phase A is 0.1% formic acid in water, and mobile phase B is 0.1% formic acid in acetonitrile. Gradient elution program: 0-8 min, 4% mobile phase B; 8-12 min, 4%-98% mobile phase B; 12-14 min, 98% mobile phase B; 14-20 min, 98%-4% mobile phase B; 14.1-18 min, 5% mobile phase B; flow rate: 0.3 mL / min; column temperature: 40°C; injection volume: 5 μL; Step 32, mass spectrometry conditions: ion source: electrospray ionization (ESI), simultaneous acquisition of positive and negative ion modes; Capillary voltage: 3.0 kV in positive ion mode, 2.5 kV in negative ion mode; Cone voltage: 30 V in both positive and negative ion modes; Source temperature: 120°C; Desolvation temperature: 400°C; Desolvation gas flow rate: 800 L / h; Scan range: m / z 50–1200; Step 4: Data processing and analysis: Data preprocessing: Progenesis QI software was used to perform peak alignment, peak extraction, and normalization on the LC-MS data to remove noise and baseline drift. Multivariate statistical analysis: Principal component analysis (PCA) was used to perform a preliminary analysis of all samples to observe the overall distribution of the samples. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were then used to screen for metabolites with significant differences between the pregnant and non-pregnant groups. Step 5: Build a diagnostic model Screening characteristic metabolites: Based on the differential metabolites screened in step 4, the random forest algorithm was used to screen characteristic metabolites for the diagnosis of early pregnancy in cattle; The screened characteristic metabolite data were used to establish a diagnostic model for early pregnancy in cattle using the support vector machine (SVM) algorithm. Step 6: Sample testing and judgment Sample processing: The serum samples of the cattle to be tested were processed according to the method in step 2; Metabolomics analysis: Analyze the treated serum sample according to the liquid chromatography-mass spectrometry conditions in step 3 to obtain metabolite data; Data input and judgment: The obtained metabolite data is subjected to the same data preprocessing as in step 4, and then the screened characteristic metabolite data is extracted and input into the established SVM diagnostic model; based on the output of the model, it is determined whether the cow to be tested is pregnant. If the model predicts pregnancy, the cow to be tested is determined to be pregnant; if the prediction result is not pregnant, the cow to be tested is determined to be non-pregnant.

[0006] As a preferred technical solution of the present invention, step 7, model validation, is also included: collecting 50-100 new bovine serum samples for training and test sets, covering both pregnant and non-pregnant samples; processing and analyzing these samples according to steps 2-3 to obtain metabolite data, and performing data preprocessing according to step 4 to extract characteristic metabolite data; inputting the characteristic metabolite data into the established SVM diagnostic model for prediction, and recording the prediction results.

[0007] As a preferred technical solution of the present invention, step 8, model optimization is also included: optimization of the model on the external validation set, re-optimization of the characteristic metabolites screened in steps 4 and 5; adjusting the VIP value standard to 0.8-1.2 and the P value standard to 0.05-0.1.

[0008] As a preferred technical solution of the present invention, in step 5, during the process of establishing the diagnostic model, the algorithm is adjusted: the number of input layer nodes is set to the number of characteristic metabolites, the hidden layer is set to 2-3 layers, the number of nodes in each layer is 1.5 times, 1 times, and 0.5 times the number of characteristic metabolites, respectively, the number of output layer nodes is 1, indicating pregnancy or non-pregnancy; the number of iterations is set to 100-500 times.

[0009] As a preferred technical solution of the present invention, step 5, establishing a diagnostic model also includes an MLP model, and the weight of the MLP model is 0.4.

[0010] As a preferred technical solution of the present invention, the method further includes step 9, database updating, collecting new cow serum sample data every 3-6 months, including samples from different seasons and different breeding environments; processing and analyzing the new data according to steps 2-4 to extract characteristic metabolite data; adding the new data to the database, and using the new data to regularly update and optimize the diagnostic model.

[0011] As a preferred technical solution of the present invention, the step 5, establishing the diagnostic model, further includes encrypting the model transmission: encrypting the transmitted data using the SSL / TLS encryption protocol.

[0012] As a preferred technical solution of the present invention, step 4, data processing and analysis; using Progenesis QI software, the stability of the LC-MS system and the retention time range of the chromatographic peak are 0.1-0.5 minutes.

[0013] As a preferred technical solution of the present invention, in the step of establishing a diagnostic model, step 4, multivariate statistical analysis of data processing and analysis: setting the variable importance projection value greater than 1 and the P value less than 0.05 as the standard for screening differential metabolites.

[0014] As the preferred technical solution of the present invention, step 5, establishing a diagnostic model: dividing the sample data into a training set and a test set in a ratio of 7:3, using the training set data to train the SVM model, and optimizing the model parameters through grid search and cross-validation methods.

[0015] Compared with the existing technology, the present invention has the following beneficial effects: the present invention can detect tiny metabolic changes in bovine serum through metabolomics technology, can accurately judge the pregnancy status even in the early stages of pregnancy, and improves the sensitivity of detection.

[0016] The differential metabolites screened out by the present invention through multivariate statistical analysis and machine learning algorithms have strong specificity and can effectively distinguish pregnant cows from non-pregnant cows, reducing the occurrence of misdiagnosis and missed diagnosis.

[0017] The sample collection and pre-treatment process of the present invention is relatively simple, does not require complex equipment and professional skills, and is suitable for rapid detection in large-scale farms.

[0018] The present invention can perform detection 18-25 days after the cattle are bred, and can judge the pregnancy status earlier than the traditional method, thus providing more timely information for the breeding management of the cattle herd. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The test data diagram provided by the present invention; Figure 2 The test data diagram provided by the present invention; Figure 3 This is the experimental data diagram provided by the present invention. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0021] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0022] Example 1: A method for detecting early pregnancy in cattle using metabolomics technology, comprising the following steps: Step 1: Sample collection: Step 11. Blood samples were collected from cows 18-22 days after mating. 10 mL of blood was collected from the jugular vein of the cow using a sterile vacuum blood collection tube. Step 12: Centrifugation: Let the collected blood sample stand at room temperature for 30-60 minutes. After the blood coagulates, centrifuge it at 3000 rpm for 15 minutes and carefully aspirate the upper serum. Step 13: Storage: The separated serum was divided into sterile centrifuge tubes and stored in a -80°C refrigerator for later use; Step 2: Sample pretreatment: Step 21. Add pre-chilled methanol-water mixture: Remove the serum sample from the -80°C freezer and thaw at room temperature; add 200 μL of serum sample to a sterile centrifuge tube, then add 800 μL of pre-chilled methanol-water (4:1 volume ratio) mixture, and vortex for 30 seconds to thoroughly mix the serum and the mixture; Step 22: Protein precipitation: Place the mixed sample in a -20°C refrigerator for 30 minutes to allow the protein to fully precipitate. Step 23: Centrifuge and obtain the supernatant: Centrifuge at 13,000 rpm at 4°C for 15 minutes and transfer the supernatant to a new sterile centrifuge tube. Step 24, drying: Place the supernatant in a vacuum concentrator and dry at 37°C until completely dry; Step 25. Redissolution: Add 200 μL of a 1:1 acetonitrile-water mixture to the dried sample. Vortex for 30 seconds and sonicate for 10 minutes to fully resolubilize the sample. Centrifuge at 13,000 rpm for 15 minutes at 4°C and collect the supernatant for subsequent metabolomics analysis. Step 3: Metabolomics analysis Step 31, liquid chromatography conditions: Chromatographic column: ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm); Mobile phase: Mobile phase A is 0.1% formic acid in water, and mobile phase B is 0.1% formic acid in acetonitrile. Gradient elution program: 0-8 min, 4% mobile phase B; 8-12 min, 4%-98% mobile phase B; 12-14 min, 98% mobile phase B; 14-20 min, 98%-4% mobile phase B; 14.1-18 min, 5% mobile phase B; flow rate: 0.3 mL / min; column temperature: 40°C; injection volume: 5 μL; Step 32, mass spectrometry conditions: ion source: electrospray ionization (ESI), simultaneous acquisition of positive and negative ion modes; Capillary voltage: 3.0 kV in positive ion mode, 2.5 kV in negative ion mode; Cone voltage: 30 V in both positive and negative ion modes; Source temperature: 120°C; Desolvation temperature: 400°C; Desolvation gas flow rate: 800 L / h; Scan range: m / z 50–1200; Step 4: Data processing and analysis: Data preprocessing: Progenesis QI software was used to perform peak alignment, peak extraction, and normalization on the LC-MS data to remove noise and baseline drift. Multivariate statistical analysis: Principal component analysis (PCA) was used to perform a preliminary analysis of all samples to observe the overall distribution of the samples. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were then used to screen for metabolites with significant differences between the pregnant and non-pregnant groups. Step 5: Build a diagnostic model Screening characteristic metabolites: Based on the differential metabolites screened in step 4, the random forest algorithm was used to screen characteristic metabolites for the diagnosis of early pregnancy in cattle; The screened characteristic metabolite data were used to establish a diagnostic model for early pregnancy in cattle using the support vector machine (SVM) algorithm. Step 6: Sample testing and judgment Sample processing: The serum samples of the cattle to be tested were processed according to the method in step 2; Metabolomics analysis: Analyze the treated serum sample according to the liquid chromatography-mass spectrometry conditions in step 3 to obtain metabolite data; Data input and judgment: The obtained metabolite data is subjected to the same data preprocessing as in step 4, and then the screened characteristic metabolite data is extracted and input into the established SVM diagnostic model; based on the output of the model, it is determined whether the cow to be tested is pregnant. If the model predicts pregnancy, the cow to be tested is determined to be pregnant; if the prediction result is not pregnant, the cow to be tested is determined to be non-pregnant.

[0023] Step 7, Model Validation: Collect 50-100 new bovine serum samples for training and test sets, covering both pregnant and non-pregnant samples; process and analyze these samples according to Steps 2-3 to obtain metabolite data; perform data preprocessing according to Step 4 to extract characteristic metabolite data; input the characteristic metabolite data into the established SVM diagnostic model for prediction, and record the prediction results.

[0024] Step 8. Model optimization: Optimize the model on the external validation set and re-optimize the characteristic metabolites screened in steps 4 and 5; adjust the VIP value standard to 0.8-1.2 and the P value standard to 0.05-0.1.

[0025] Step 5. Adjust the algorithm during the process of establishing the diagnostic model: set the number of input layer nodes to the number of characteristic metabolites, set the hidden layer to 2-3 layers, and the number of nodes in each layer to 1.5 times, 1 times, and 0.5 times the number of characteristic metabolites, respectively. The number of output layer nodes is 1, indicating pregnancy or non-pregnancy; the number of iterations is set to 100-500.

[0026] Step 5: Establishing a diagnostic model also includes an MLP model, and the weight of the MLP model is 0.4.

[0027] Step 9: Database update. Collect new cow serum sample data every 3-6 months, including samples from different seasons and breeding environments. Process and analyze the new data according to steps 2-4 to extract characteristic metabolite data. Add the new data to the database and use the new data to regularly update and optimize the diagnostic model.

[0028] Step 5: Establishing the diagnostic model also includes model transmission encryption: encryption uses the SSL / TLS encryption protocol to encrypt the transmitted data.

[0029] Step 4: Data processing and analysis: Progenesis QI software was used to analyze the stability of the LC-MS system and the retention time range of the chromatographic peaks was 0.1–0.5 min.

[0030] In the step of establishing the diagnostic model, step 4, multivariate statistical analysis of data processing and analysis: set the variable importance projection value greater than 1 and the P value less than 0.05 as the criteria for screening differential metabolites.

[0031] Step 5: Establish a diagnostic model: Divide the sample data into a training set and a test set in a ratio of 7:3, use the training set data to train the SVM model, and optimize the model parameters through grid search and cross-validation methods.

[0032] Example 2: A method for detecting early pregnancy in cattle using metabolomics technology. Sample collection and pretreatment: Sample collection: Fifty healthy cows were selected, and blood samples were collected 21 days after mating. The collected blood samples were placed in centrifuge tubes containing sodium heparin anticoagulant and centrifuged at 3500 rpm for 12 minutes to separate the serum. The serum was transferred to a new centrifuge tube and stored at -80°C until needed.

[0033] Sample preparation: Serum samples were removed from a -80°C freezer and thawed at room temperature. 150 μL of serum sample was placed in a centrifuge tube and 4 volumes (600 μL) of pre-chilled methanol-water mixture (methanol:water = 4:1, v / v) were added. The sample was vortexed for 1.5 minutes to thoroughly mix. The tube was then allowed to stand at -20°C for 45 minutes to precipitate proteins. The tube was then centrifuged at 13,000 rpm for 18 minutes. The supernatant was transferred to a new centrifuge tube and dried with nitrogen. Finally, 150 μL of reconstitution solution (acetonitrile:water = 1:1, v / v) was added. The sample was vortexed for 1.5 minutes to fully dissolve the sample. The tube was then centrifuged at 13,000 rpm for 12 minutes. The supernatant was collected for analysis.

[0034] Metabolomics analysis: The treated serum samples were analyzed by LC-MS technology under the following conditions: liquid chromatography conditions: the chromatographic column was a C18 reverse-phase column (2.1 mm × 100 mm, 1.8 μm); the mobile phase A was 0.1% formic acid in water, and the mobile phase B was 0.1% formic acid in acetonitrile; the gradient elution program was: 0-2 min, 5% B; 2-18 min, 5%-95% B; 18-20 min, 95% B; 20-22 min, 95%-5% B; 22-30 min, 5% B; the flow rate was 0.3 mL / min; the column temperature was 40°C; and the injection volume was 5 μL.

[0035] Mass spectrometry conditions: electrospray ionization (ESI) source with simultaneous acquisition in positive and negative ion modes; scan range: m / z 50–1000; capillary voltage: 3.5 kV; cone voltage: 40 V; ion source temperature: 120°C; desolvation temperature: 350°C; desolvation gas flow rate: 800 L / h; and cone gas flow rate: 50 L / h.

[0036] Data processing and analysis The raw data collected by LC-MS were imported into MarkerLynx XS software for peak identification, peak alignment, and integration preprocessing. This generated a dataset containing metabolite peak area information for each sample. PCA and PLS-DA were then used to analyze the dataset and identify metabolites that were significantly different between the pregnant and non-pregnant groups (P < 0.05). A total of 20 differentially expressed metabolites were identified.

[0037] Building a diagnostic model Based on the 20 differential metabolites identified, a support vector machine (SVM) algorithm was used to develop a diagnostic model for early pregnancy in cattle. The 50 sample data were divided into a training set (35 samples) and a validation set (15 samples) in a 7:3 ratio. The model was trained using the training set data and validated using the validation set data. After multiple adjustments to the model parameters, the resulting model achieved an accuracy of 93.3% on the validation set.

[0038] Sample testing and judgment Ten cows were selected for testing. Metabolite data were obtained using the aforementioned sample collection, pretreatment, and analysis methods. This data was then input into the established diagnostic model for diagnosis. The results showed that the model accurately determined the pregnancy status of nine cows, with an accuracy rate of 90%.

[0039] Test Example 1: Method Accuracy Verification Test Purpose of the trial To validate the accuracy of a metabolomics-based method for detecting early pregnancy in cattle and compare it with traditional rectal examination.

[0040] Test materials Experimental cattle: 100 healthy cows aged 2-5 years old and of similar weight were selected, and they were fed and managed uniformly and artificially inseminated using the same breeding plan.

[0041] Main reagents and instruments: methanol, acetonitrile, and formic acid reagents are all chromatographic grade; centrifuge, liquid chromatography-mass spectrometry (LC-MS), and nitrogen blow dryer.

[0042] Test methods Sample collection On day 21 after mating, blood samples were collected from 100 cows. The blood was placed in a centrifuge tube containing sodium heparin anticoagulant and centrifuged at 3500 rpm for 12 minutes. The serum was separated, transferred to a new centrifuge tube, and stored at -80°C.

[0043] Metabolomics testing Sample pretreatment: Thawed serum was taken out from the −80°C freezer, 150 μL of serum was added to 600 μL of pre-chilled methanol-water (4:1, v / v) mixed solution, vortexed for 1.5 minutes, allowed to stand at −20°C for 45 minutes, centrifuged at 13,000 r / min for 18 minutes, the supernatant was nitrogen-dried, 150 μL of acetonitrile-water (1:1, v / v) reconstitution solution was added, vortexed for 1.5 minutes, centrifuged at 13,000 r / min for 12 minutes, and the supernatant was used for analysis.

[0044] LC-MS analysis: Analyses were performed according to the established liquid chromatography and mass spectrometry conditions. The liquid chromatography was performed on a C18 reversed-phase column (2.1 mm × 100 mm, 1.8 μm). Mobile phase A consisted of 0.1% formic acid in water and mobile phase B consisted of 0.1% formic acid in acetonitrile. The gradient elution program was: 0–2 min, 5% B; 2–18 min, 5%–95% B; 18–20 min, 95% B; 20–22 min, 95%–5% B; 22–30 min, 5% B. The flow rate was 0.3 mL / min, the column temperature was 40°C, and the injection volume was 5 μL. Mass spectrometry: electrospray ionization (ESI) source, simultaneous acquisition in positive and negative ion modes, scan range m / z 50-1000, capillary voltage 3.5 kV, cone voltage 40 V, source temperature 120°C, desolvation temperature 350°C, desolvation gas flow rate 800 L / h, cone gas flow rate 50 L / h.

[0045] Data processing and model judgment: The LC-MS data were imported into the software for preprocessing, PCA and PLS-DA were used to screen differential metabolites, support vector machine was used to establish a diagnostic model, and the sample data were input into the model to judge the pregnancy status.

[0046] Traditional rectal examination On day 35 after breeding, 100 cows were examined rectally by experienced veterinarians to determine pregnancy status.

[0047] Test results

[0048] Comparing the results of the two methods, 41 pigs were found to be pregnant by the metabolomics method and also confirmed by rectal examination; 53 pigs were found to be non-pregnant by the metabolomics method and also confirmed by rectal examination. The accuracy of the metabolomics method was calculated to be 94% ((41 + 53) ÷ 100), highly consistent with the results of the traditional rectal examination method, demonstrating the high accuracy of this metabolomics method.

[0049] Test Example 2: Method Sensitivity and Specificity Verification Test Purpose of the trial To validate the sensitivity and specificity of a metabolomics-based method for detecting early pregnancy in cattle.

[0050] Test materials Same as Test Example 1.

[0051] Test methods A total of 200 cows with known pregnancy status (100 pregnant cows and 100 non-pregnant cows) were selected. Blood samples were collected on days 18, 21, and 25 after mating. The blood samples were processed and analyzed according to the above-mentioned metabolomics detection method to determine pregnancy status.

[0052] Test results

[0053] The results showed that the sensitivity and specificity of the method gradually increased with the passage of time after mating. By day 25 after mating, the sensitivity reached 96% and the specificity reached 97%, indicating that the method has good sensitivity and specificity for early pregnancy detection in cattle and can effectively distinguish pregnant from non-pregnant cattle.

[0054] Test Example 3: Method stability test under different feeding environments Purpose of the trial To verify the stability of the method for detecting early pregnancy in cattle using metabolomics technology under different feeding environments.

[0055] Test materials Three cattle farms with different breeding environments were selected (Farm A: centralized breeding, balanced feed nutrition, clean environment; Farm B: semi-centralized breeding, relatively single feed, average environment; Farm C: free-range model, feed mainly based on natural pasture, more complex environment), and 50 healthy cows were selected from each farm for the experiment.

[0056] Test methods On day 21 after mating, blood samples were collected from cows at each of the three farms and processed and analyzed using the metabolomics assay described above to determine pregnancy status. A rectal examination was performed on day 35 after mating as a control.

[0057] Test results

[0058] The results showed that under different feeding environments, the accuracy of the metabolomics detection method was above 94%, which was highly consistent with the results of rectal examination, indicating that the method has good stability under different feeding environments and is not significantly affected by differences in feeding environments.

[0059] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above embodiments, the present invention is not limited to the above specific implementation methods. Therefore, any modification or replacement of the present invention; and all technical solutions and improvements thereof that do not depart from the spirit and scope of the invention are included in the scope of the claims of the present invention.

Claims

1. A method for detecting early pregnancy in cattle using metabolomics technology, characterized in that: The following steps are involved: Step 1: Sample collection: Step 11. Blood samples were collected from cows 18-22 days after mating. 10 mL of blood was collected from the jugular vein of the cow using a sterile vacuum blood collection tube. Step 12: Centrifugation: Let the collected blood sample stand at room temperature for 30-60 minutes. After the blood coagulates, centrifuge it at 3000 rpm for 15 minutes and carefully aspirate the upper serum. Step 13: Storage: The separated serum was divided into sterile centrifuge tubes and stored in a -80°C refrigerator for later use; Step 2: Sample pretreatment: Step 21. Add pre-chilled methanol-water mixture: Remove the serum sample from the -80°C freezer and thaw at room temperature; add 200 μL of serum sample to a sterile centrifuge tube, then add 800 μL of pre-chilled methanol-water (4:1 volume ratio) mixture, and vortex for 30 seconds to thoroughly mix the serum and the mixture; Step 22: Protein precipitation: Place the mixed sample in a -20°C refrigerator for 30 minutes to allow the protein to fully precipitate. Step 23: Centrifuge and obtain the supernatant: Centrifuge at 13,000 rpm at 4°C for 15 minutes and transfer the supernatant to a new sterile centrifuge tube. Step 24, drying: Place the supernatant in a vacuum concentrator and dry at 37°C until completely dry; Step 25. Redissolution: Add 200 μL of a 1:1 acetonitrile-water mixture to the dried sample. Vortex for 30 seconds and sonicate for 10 minutes to fully resolubilize the sample. Centrifuge at 13,000 rpm for 15 minutes at 4°C and collect the supernatant for subsequent metabolomics analysis. Step 3: Metabolomics analysis Step 31, liquid chromatography conditions: Chromatographic column: ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm); Mobile phase: Mobile phase A is 0.1% formic acid in water, and mobile phase B is 0.1% formic acid in acetonitrile. Gradient elution program: 0-8 min, 4% mobile phase B; 8-12 min, 4%-98% mobile phase B; 12-14 min, 98% mobile phase B; 14-20 min, 98%-4% mobile phase B; 14.1-18 min, 5% mobile phase B; flow rate: 0.3 mL / min; Column temperature: 40°C; injection volume: 5 μL; Step 32, mass spectrometry conditions: ion source: electrospray ionization (ESI), simultaneous acquisition of positive and negative ion modes; Capillary voltage: 3.0 kV in positive ion mode, 2.5 kV in negative ion mode; Cone voltage: 30 V in both positive and negative ion modes; Source temperature: 120°C; Desolvation temperature: 400°C; Desolvation gas flow rate: 800 L / h; Scan range: m / z 50–1200; Step 4: Data processing and analysis: Data preprocessing: Progenesis QI software was used to perform peak alignment, peak extraction, and normalization on the LC-MS data to remove noise and baseline drift. Multivariate statistical analysis: Principal component analysis (PCA) was used to perform a preliminary analysis of all samples to observe the overall distribution of the samples. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were then used to screen for metabolites with significant differences between the pregnant and non-pregnant groups. Step 5: Build a diagnostic model Screening characteristic metabolites: Based on the differential metabolites screened in step 4, the random forest algorithm was used to screen characteristic metabolites for the diagnosis of early pregnancy in cattle; The screened characteristic metabolite data were used to establish a diagnostic model for early pregnancy in cattle using the support vector machine (SVM) algorithm. Step 6: Sample testing and judgment Sample processing: The serum samples of the cattle to be tested were processed according to the method in step 2; Metabolomics analysis: Analyze the treated serum sample according to the liquid chromatography-mass spectrometry conditions in step 3 to obtain metabolite data; Data input and judgment: The obtained metabolite data is subjected to the same data preprocessing as in step 4, and then the screened characteristic metabolite data is extracted and input into the established SVM diagnostic model; based on the output of the model, it is determined whether the cow to be tested is pregnant. If the model predicts pregnancy, the cow to be tested is determined to be pregnant; if the prediction result is not pregnant, the cow to be tested is determined to be non-pregnant.

2. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: The model validation step also includes step 7: collecting 50-100 new bovine serum samples for training and test sets, covering both pregnant and non-pregnant samples; processing and analyzing these samples according to steps 2-3 to obtain metabolite data; and performing data preprocessing according to step 4 to extract characteristic metabolite data; The characteristic metabolite data were input into the established SVM diagnostic model for prediction, and the prediction results were recorded.

3. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: It also includes step 8, model optimization: optimization of the model on the external validation set, re-optimization of the characteristic metabolites screened in steps 4 and 5; adjusting the VIP value standard to 0.8 - 1.2 and the P value standard to 0.05 - 0.

1.

4. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: Step 5. Adjust the algorithm during the process of establishing the diagnostic model: set the number of input layer nodes to the number of characteristic metabolites, set the hidden layer to 2-3 layers, and the number of nodes in each layer to 1.5 times, 1 times, and 0.5 times the number of characteristic metabolites, respectively. The number of output layer nodes is 1, indicating pregnancy or non-pregnancy; the number of iterations is set to 100-500.

5. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: Step 5: Establishing a diagnostic model also includes an MLP model, and the weight of the MLP model is 0.

4.

6. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: The process also includes step 9, database update, which involves collecting new cow serum sample data every 3-6 months, including samples from different seasons and breeding environments; processing and analyzing the new data according to steps 2-4 to extract characteristic metabolite data; adding the new data to the database, and using the new data to regularly update and optimize the diagnostic model.

7. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: The step 5, establishing the diagnostic model, also includes model transmission encryption: encryption uses the SSL / TLS encryption protocol to encrypt the transmitted data.

8. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: Step 4: Data processing and analysis: Progenesis QI software was used to analyze the stability of the LC-MS system and the retention time range of the chromatographic peaks was 0.1–0.5 min.

9. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: In the step of establishing the diagnostic model, step 4, multivariate statistical analysis of data processing and analysis: setting the variable importance projection value greater than 1 and the P value less than 0.05 as the standard for screening differential metabolites.

10. The method for detecting early pregnancy in cattle using metabolomics technology according to claim 1, characterized in that: Step 5: Establish a diagnostic model: Divide the sample data into a training set and a test set in a ratio of 7:3, use the training set data to train the SVM model, and optimize the model parameters through grid search and cross-validation methods.

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