A method of screening for immune-related metabolites in a calf based on non-targeted metabolomics

Abstract

The present application relates to the field of biomedical technology, and particularly relates to a method for screening immune-related metabolites of a calf based on non-targeted metabolomics. The method comprises the following steps: S1. collecting peripheral blood of the calf at different time periods from birth to establishment of the immune system; S2. performing non-targeted metabolomics analysis on the peripheral blood samples at the different time periods to obtain differential metabolites at the different time periods; S3. annotating, clustering, performing enrichment analysis and performing overall change analysis of metabolic pathways on the differential metabolites at the different time periods screened out through a KEGG database to obtain key metabolites of the calf during establishment of the immune system and response mechanisms. The present application studies the expression change patterns of metabolic products of the calf before and after colostrum, understands the establishment process and response mechanisms of the immune system of the calf, and provides evidence and theoretical basis for when the passive immune system of the calf ends and how the immune system of the calf is regulated at the molecular level.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a method for screening immune-related metabolites in calves based on non-targeted metabolomics. Background Technology

[0002] Metabolites form the basis of an organism's phenotype, helping to understand biological processes and their mechanisms more intuitively and effectively. Based on qualitative and quantitative analysis of metabolites, metabolomics can be used to elucidate metabolic pathways or networks, study the metabolic basis of macroscopic phenotypic phenomena in different organisms, investigate the response mechanisms of metabolites to physical, chemical, or pathogenic stimuli such as diseases and drugs, assess the breeding value of economic traits in animals, and evaluate the safety of food and drugs.

[0003] Untargeted metabolomics is a commonly used metabolomics research method. The main research idea is to compare experimental groups and control groups, detect metabolites in the samples, obtain quantitative information, find statistically significant differential metabolites between different groups, and explain the relationship between the discovered metabolites and biological processes or biological states.

[0004] Currently, when calves are not fed colostrum, it is difficult to find lymph nodes in their bodies from birth or even several days after birth. However, after colostrum administration, maternal cells can be detected in the circulatory system within 4 hours, reach a peak after 24 hours, and are almost undetectable after 36 hours. Within two to three weeks after colostrum administration, labeled lymphocytes can be detected in secondary lymphocytes, and the number of lymphocytes and lymph nodes increases. This may be due to the combined effects of maternal antibodies, maternal cells, maternal cytokines, and growth factors, along with stimulation from external bacterial invasion and proliferation, which promote the increase in the number of lymphocytes and lymph nodes. Therefore, timely and adequate administration of qualified colostrum to calves after birth is one of the reasons for the successful establishment of their immune system, which may affect the proliferation of specific cells and the stable expression of specific genes. In view of this, this invention provides a method for screening immune-related metabolites in calves based on non-targeted metabolomics. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method for screening immune-related metabolites in calves based on non-targeted metabolomics. The aim is to understand the establishment process and response mechanism of the calf's immune system by studying the expression changes of its metabolites before and after colostrum, providing a basis and theoretical foundation for understanding when the passive immune system terminates and how the autoimmune system is regulated at the molecular level.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for screening immune-related metabolites in calves based on non-targeted metabolomics includes the following steps: S1. Collect peripheral blood samples from different time periods, from calf birth to the establishment of the immune system, to obtain peripheral blood samples at different time periods; S2. Perform non-targeted metabolomics analysis on peripheral blood samples from different time periods to obtain differential metabolites at different time periods; S3. Using the KEGG database, the differential metabolites selected from different time periods were annotated, clustered, enriched, and subjected to overall metabolic pathway changes to obtain key metabolites in the establishment process and response mechanism of the calf's immune system.

[0007] Based on the above technical solution, the present invention can be further improved as follows.

[0008] Furthermore, the peripheral blood at different time periods includes peripheral blood within 2 hours of calf birth, peripheral blood from 5 to 8 days after birth, and peripheral blood from 12 days after birth.

[0009] Furthermore, the breed of the calf includes at least one of the Murrah buffalo and the Nili-Raffi buffalo.

[0010] Further, in step S3, differentially expressed metabolites from different time periods are first annotated using the KEGG database (Kanehisa et al., 2000). Then, using the KEGG annotation information of the differentially expressed metabolites identified according to screening criteria, five significantly enriched KEGG metabolic pathways are selected, and cluster analysis is performed on all differentially expressed metabolites in these pathways. Finally, based on the differentially expressed metabolite results, KEGG pathway enrichment analysis is performed. The Rich Factor is the ratio of the number of differentially expressed metabolites in the corresponding pathway to the total number of metabolites annotated in that pathway; a larger Rich Factor indicates a greater degree of enrichment. The P-value is the hypergeometric test p-value, and the formula for calculating the hypergeometric distribution is shown below: ; Where N represents the number of KEGG-annotated metabolites among all metabolites, n represents the number of differentially expressed metabolites in N, M represents the number of metabolites in a specific KEGG pathway in N, and m represents the number of differentially expressed metabolites in a specific KEGG pathway in M. A p-value closer to 0 indicates more significant enrichment. Finally, the overall metabolic pathway change analysis is specifically performed using the Differential Abundance Score (DA Score), a pathway-based method for analyzing metabolic changes. The DA Score captures the overall changes in all metabolites within a specific pathway, and its calculation formula is as follows: .

[0011] Furthermore, non-targeted metabolomics analysis was performed on peripheral blood samples from different time periods to obtain differentially expressed metabolites, including the following specific steps: S21 Add internal standard extraction solution to peripheral blood samples from different time periods, centrifuge, and obtain supernatant; S22 The supernatant is subjected to chromatography-mass spectrometry to obtain raw data; S23 performs preprocessing on the original data, including filtering and identification, to obtain preprocessed data; S24 performs multivariate and univariate statistical analysis on the preprocessed data to obtain differential metabolites at different time periods.

[0012] Furthermore, the internal standard extract in step S21 is an acetonitrile / methanol solution with a mass content of 15%~25%.

[0013] Furthermore, the chromatographic conditions for the chromatography-mass spectrometry (GC-MS) detection in step S22 are as follows: Column: Waters ACQUITY Premier HSS T3 Column 1.8 µm, 2.1 mm × 100 mm; Mobile phase A: 0.1% formic acid / water solution (v / v), Mobile phase B: 0.1% formic acid / acetonitrile solution (v / v); Instrument column temperature: 40°C; Flow rate: 0.4 mL / min; Injection volume: 4 μL. In step S22, the mass spectrometry conditions for the chromatography-mass spectrometry (GC-MS) detection are as follows: the sample is ionized by electrospray ionization, and mass spectrometry signals are acquired using positive and negative ion scanning modes, respectively.

[0014] Furthermore, the preprocessing of the raw data in step S23, which involves filtering and identifying the raw data, specifically includes the following steps: importing the raw data into metabolomics processing software for baseline filtering, peak identification, integration, retention time correction, and peak alignment to obtain a data matrix containing retention time, mass-to-charge ratio, and peak intensity information; then performing feature peak search and identification to obtain the preprocessed data.

[0015] Furthermore, the specific methods of multivariate statistical analysis described in step S24 include principal component analysis (PCA) and orthogonal partial least squares analysis (OPLS-DA).

[0016] Furthermore, the specific methods of univariate statistical analysis described in step S24 include fold change analysis and hypothesis testing p-value analysis.

[0017] Furthermore, the key metabolites include key metabolites during the postnatal development of the Mora buffalo, key metabolites during the transition of Mora buffalo developmental stages, and key metabolites characterizing differences between buffalo breeds. Key metabolites in the postnatal development of the Mora buffalo include xanthoaphin, liothyronine, 12-ketolithocholic acid, penitrem F, and various phosphatidylcholine (PCs). Key metabolites involved in the developmental transition of the Mora buffalo include prostaglandin F1α alcohol, immunomodulatory phytosaponins (Medicagenic acid), sulfated steroid hormones (Estradiol-17β-sulfate), and glucocorticoids (Hydrocortisone). The key metabolites characterizing differences between buffalo breeds include derivatives of opioid receptor antagonists (7-Benzylidenenaltrexone), mushroom toxins (Phalloidin), immunosuppressant drugs (Pimecrolimus), nucleotide precursors (Uracil), oligopeptides, and sulfur-containing amino acids.

[0018] The beneficial effects of this invention are: This invention reveals that a series of key metabolites play a central role in the development of the calf's immune system. Phosphatidylcholine (PCs), a cornerstone of cell membrane construction, shows significant upregulation, a direct marker of rapid lymphocyte proliferation; while triiodothyronine provides essential energy support for this process by increasing metabolic rate. As development progresses, the appearance of various small peptides and prostaglandin F1α-ol indicates a shift in immune regulation from rapid construction to refined signal transduction and inflammation modulation; simultaneously, the decrease in hydrocortisone signifies the relief of immunosuppression caused by birth stress, creating conditions for the full development of the autoimmune system. Furthermore, changes in exogenous metabolites such as Xanthoaphin and Penitrem F suggest that oxidative stress management and exogenous stimuli may play a role in "training" the immune system. Crucially, breed comparisons revealed fundamental differences: 7-Benzylidenenaltrexone and Phalloidin are persistent metabolic markers in Nili-Raffi buffalo, suggesting their unique genetic background; and in the later stages of immune establishment, upregulation of Pimecrolimus in Nili-Raffi buffalo strongly suggests that the breed tends to establish an inhibitory immune microenvironment to prevent overactivation, while Uracil levels reflect active lymphocyte proliferation and nucleic acid synthesis metabolism. These key metabolites collectively map the dynamic establishment of the calf's immune system at the molecular level. They also depict the dynamic metabolic profile of the calf's immune system establishment under colostrum stimulation and reveal the different immune metabolic strategies employed by buffalo calves from different genetic backgrounds. Attached Figure Description

[0019] Figure 1 PCA score plots of mass spectrometry data for each group of samples and quality control samples; Figure 2 Venn diagram showing the number of metabolites differing between groups; Figure 3 This is a bar chart showing the difference multiples; where A. ML_2 vs. ML_1, B. ML_3 vs. ML_1, C. ML_3 vs. ML_2, D. NL_1 vs. ML_1, E. NL_2 vs. ML_2, and F. NL_3 vs. ML_3. Figure 4 This is a dynamic distribution diagram of the differences in metabolite content; where A. ML_2 vs. ML_1, B. ML_3 vs. ML_1, C. ML_3 vs. ML_2, D. NL_1 vs. ML_1, E. NL_2 vs. ML_2, and F. NL_3 vs. ML_3. Figure 5This is a heatmap of differential metabolite clusters; where A. ML_2 vs. ML_1, B. ML_3 vs. ML_1, C. ML_3 vs. ML_2, D. NL_1 vs. ML_1, E. NL_2 vs. ML_2, and F. NL_3 vs. ML_3. Figure 6 Heatmap of differential metabolite clustering for pathways; Figure 7 This is a differential metabolite pathway enrichment map; where A. ML_2 vs. ML_1, B. ML_3 vs. ML_1, C. ML_3 vs. ML_2, D. NL_1 vs. ML_1, E. NL_2 vs. ML_2, and F. NL_3 vs. ML_3. Figure 8 The graph shows the differential abundance scores; where A. ML_2 vs. ML_1, B. ML_3 vs. ML_1, C. ML_3 vs. ML_2, D. NL_1 vs. ML_1, E. NL_2 vs. ML_2, and F. NL_3 vs. ML_3. Detailed Implementation

[0020] The principles and features of this invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they should be performed according to the techniques or conditions described in the literature in this field, or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0021] Example The metabolomics analysis in this embodiment includes two main parts: metabolomics experiments and data analysis. The data analysis mainly includes screening for differentially expressed metabolites and elucidating metabolic pathways. Based on the metabolomics data obtained from experimental design, sample collection and processing, metabolite extraction, and metabolite detection and analysis, metabolite identification and quality control analysis of sample data can be performed, and some differentially expressed metabolites can be screened out, thereby enabling relevant functional prediction and analysis of the metabolites in the sample.

[0022] 1. Metabolomics experiments.

[0023] 1.1 Experimental Design and Sample Collection: This study collected peripheral blood samples from calves at three time points (within 2 hours of birth, 5-8 days after birth, and 12 days after birth) for non-targeted metabolomics analysis. The samples included three Mora buffalo and one Nili-Raffi buffalo, all collected in 2025 from the Guangxi Zhuang Autonomous Region Buffalo Research Institute Breeding Farm. The samples were ML_1 (within 2 hours of birth for Mora buffalo), ML_2 (5-8 days after birth for Mora buffalo), ML_3 (12 days after birth for Mora buffalo), NL_1 (within 2 hours of birth for Nili-Raffi buffalo), NL_2 (5-8 days after birth for Nili-Raffi buffalo), and NL_3 (12 days after birth for Nili-Raffi buffalo), totaling 12 samples. These 12 samples were divided into 6 groups for metabolic studies, and 3618 metabolites were detected. To explore the differences in metabolites among different samples, the sample groups and corresponding information are shown in Table 1. Table 1 Sample Number Information 1.2 Metabolite Extraction Process: ① Remove the sample from the -80°C freezer and place it on ice to thaw until there is no ice in the sample (all subsequent operations shall be performed on ice); ② After thawing the sample, vortex for 10 s to mix, and transfer 50 μL of the sample to the corresponding numbered centrifuge tube; ③ Add 300 μL of 20% acetonitrile methanol internal standard extraction solution, vortex for 3 min, and centrifuge at 12000 r / min for 10 min at 4°C; ④ After centrifugation, transfer 200 μL of the supernatant to another corresponding numbered centrifuge tube and let it stand in a -20°C refrigerator for 30 min; ⑤ Centrifuge at 12000 r / min for 3 min at 4°C, and transfer 180 μL of the supernatant into the inner liner of the corresponding sample vial for analysis.

[0024] 1.3 Detection and analysis of metabolites to obtain metabolomics data: (1) Chromatographic and mass spectrometric acquisition conditions: ① T3 chromatographic conditions: The ultra-high performance liquid chromatograph used was model LC-30A, brand Shimadzu.

[0025] A. Chromatographic column: Waters ACQUITY Premier HSS T3 Column 1.8 µm, 2.1 mm × 100 mm; B. Mobile phase A: 0.1% formic acid / water; Mobile phase B: 0.1% formic acid / acetonitrile; C. Instrument column temperature: 40 °C; flow rate: 0.4 mL / min; injection volume: 4 μL.

[0026] Table 2. Mobile phase gradient conditions for T3 column ② Mass spectrometry conditions: The mass spectrometer is a TripleTOF 6600+, brand SCIEX. The mass spectrometry conditions for the AB TripleTOF 6600 are shown in Table 3 below: Table 3 AB TripleTOF 6600 mass spectrometry conditions 1.4 Data Preprocessing: Raw mass spectrometry data were converted to mzXML format using ProteoWizard. Peak extraction, alignment, and retention time correction were performed using XCMS. Peaks with a missing rate >50% in each sample group were filtered, and blank values ​​were filled using KNN. Peak areas were corrected using the SVR method. The corrected and screened peaks were then used for metabolite identification by searching laboratory-built databases, integrating public libraries, prediction libraries, and using the metDNA method. Finally, substances with a comprehensive score of 0.5 or higher and a QC sample CV value less than 0.3 were extracted and identified. Positive and negative patterns were then merged (retaining the substance with the highest qualitative grade and the smallest CV value), resulting in the all_sample_data.xlsx file. The number of identified metabolites is shown in Table 4.

[0027] Table 4. Statistics on the number of identified metabolites 1.5 Data Sample Quality Control Analysis.

[0028] (1) Total ion chromatogram: Quality control (QC) samples are prepared by mixing sample extracts and are used to analyze the repeatability of samples under the same processing method. During instrumental analysis, a QC sample is typically inserted for every 10 analyzed samples to monitor the repeatability of the analytical process. By overlaying and analyzing the total ion current (TIC) chromatograms of different QC samples, the repeatability of metabolite extraction and detection, i.e., technical repeatability, can be determined. The high stability of the instrument provides crucial assurance for the repeatability and reliability of the data. The total ion current curves for metabolite detection in this invention exhibit high overlap, meaning that retention times and peak intensities are consistent, indicating good signal stability when the mass spectrometer detects the same sample at different times. The high stability of the instrument provides crucial assurance for the repeatability and reliability of the data.

[0029] (2) Peak elution of the internal standard in the blank sample: Blank samples were interspersed throughout the experiment, and their peak patterns reflected the presence of any residual substances during the detection process. In this invention, no significant peaks were detected in the internal standards within the blank samples, indicating minimal residue and that cross-contamination between samples was within a controllable range.

[0030] 2. Metabolomics sample data analysis.

[0031] 2.1 Screening of differentially expressed metabolites.

[0032] Metabolomics data are characterized by high dimensionality and massive volume, thus requiring a combination of univariate and multivariate statistical analysis methods. Analysis should be conducted from multiple perspectives based on the data characteristics to accurately identify differentially expressed metabolites. Multivariate statistical analysis methods include Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). Univariate statistical analysis methods include hypothesis testing and fold change (FC) analysis.

[0033] (1) Principal component analysis (PCA).

[0034] Before performing differential analysis, principal component analysis is first conducted on the grouped samples for comparison. Multivariate statistical analysis can simplify and reduce the dimensionality of high-dimensional and complex data while preserving the original information to the greatest extent possible, allowing for the establishment of reliable mathematical models to summarize and generalize the metabolic profile characteristics of the research subjects. Principal Component Analysis (PCA) is an unsupervised pattern recognition method for multidimensional data statistical analysis. It transforms a set of potentially correlated variables into a set of linearly uncorrelated variables through orthogonal transformation; this transformed set of variables is called the principal components.

[0035] The data processing principle of PCA is as follows: The original data is compressed into n principal components to describe the characteristics of the original dataset. PC1 represents the most prominent feature describing the multidimensional data matrix, PC2 represents the most significant feature describing the data matrix other than PC1, and so on, up to PCn. PCA uses the built-in statistical function `prcomp` in R software (www.r-project.org / ). Setting the `scale=True` parameter of the `prcomp` function indicates that the data undergoes unit variance scaling (UV) processing.

[0036] Principal component analysis (PCA) was performed on the samples (including quality control samples) to preliminarily understand the overall metabolite differences among the groups and the magnitude of variability within each group. PCA results showed metabolomic separation trends among the groups, indicating whether there were metabolomic differences within the sample groups (Chen et al., 2009). PCA scores are shown below. Figure 1 As shown; PC1 represents the first principal component, PC2 represents the second principal component, and the percentage represents the explanatory power of the principal component for the dataset; each point in the figure represents a sample, and samples in the same group are represented by the same color. Group indicates grouping.

[0037] (2) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

[0038] Based on the OPLS-DA model (biological replicates ≥ 3), the variable importance in projection (VIP) can be used to initially screen for metabolites that differ between different varieties or tissues. Furthermore, combining this with univariate analysis using the p-value / FDR (biological replicates ≥ 2) or FC value can further screen for differentially expressed metabolites.

[0039] ① Screening criteria for differentially expressed metabolites: A. Select metabolites with VIP > 1. The VIP value represents the influence of the inter-group difference of the corresponding metabolite on the classification of samples in the model. Generally, metabolites with VIP > 1 are considered to have significant differences.

[0040] B. Select metabolites with a p-value < 0.05 (Student's t-test). A significant difference in metabolites is considered statistically significant if there is a statistically significant difference between different groups.

[0041] like Figure 2 The results show that ML_2 compared to ML_1 identified 286 differentially expressed metabolites, ML_3 compared to ML_1 identified 329 differentially expressed metabolites, and ML_2 compared to ML_3 identified 46 differentially expressed metabolites. In the figure, each circle represents a comparison group; the numbers in the overlapping areas of the circles represent the number of differentially expressed metabolites common to both comparison groups, while the numbers in the non-overlapping areas represent the number of differentially expressed metabolites specific to each comparison group.

[0042] ② Bar chart of differential metabolites: After qualitative and quantitative analysis of the detected metabolites, the fold changes in the quantitative information of metabolites in each group were compared, taking into account the specific sample groupings. Figure 3The results of the top 20 metabolites with the highest fold changes in each group comparison showed that, during the development of Mora buffalo, comparisons between ML_2 and ML_1, and between ML_3 and ML_1, revealed significant upregulation of metabolites such as Xanthoaphin, 2,4,5-Trihydroxytoluene, Pentirem F, and 1-Pentadecanoyl-glycero-3-phosphoethanolamine, indicating continuous activation of antioxidant and membrane phospholipid metabolic pathways from birth to lactation adaptation. By the ML_3 vs. ML_2 stage, the metabolic profile shifted to an exogenous response, with differential expression of metabolites such as 2-N-Dodecyltetrahydrothiophene, Epiandrosterone, Linezolid, and Saxitoxin. The x-axis represents the log2FC of the differentially expressed metabolites (base 2), and the y-axis represents the differentially expressed metabolites. Red indicates upregulation of metabolites, and green indicates downregulation.

[0043] In breed comparisons, the Nili-Raffi buffalo exhibited unique metabolic characteristics. In NL1 vs. ML1 comparisons, Nili-Raffi buffalo showed significant differences in various glycerophospholipids (such as PC(22:4(7Z,10Z,13Z,16Z) / P-18:1(11Z))) and cyclic peptides (such as Westernamide). In NL2 vs. ML2 comparisons, this breed-specific metabolites such as 7-Benzylidenenaltrexone, Phaloidin, and Pristinamycin IB were upregulated. In NL3 vs. ML3 comparisons, all of the top 20 differentially expressed metabolites, including oligopeptides (such as Lys-Tyr-Val-Met-Leu, Tyr-Ala-Glu-Leu-Arg), Djenkolic acid, Inosine triphosphate, and Pristinamycin IB, were significantly lower in Nili-Raffi buffalo than in contemporary Mora buffalo, showing a comprehensive downward trend.

[0044] like Figure 3 This indicates that metabolic changes in Mora buffalo calves are primarily observed during their adaptive development after birth. In contrast, Nelly-Raffi buffalo exhibit a unique metabolic basis from birth, particularly in lipid composition and exogenous substance metabolism. By 12 days of age, compared to Mora buffalo at the same stage, Nelly-Raffi buffalo generally showed lower levels of a series of key oligopeptides, sulfur-containing amino acids, antibiotics, and nucleotide metabolites in their blood, revealing a fundamental difference in the metabolic developmental trajectories of the two breeds in the second week after birth.

[0045] (3) Dynamic distribution of differences in metabolite content.

[0046] To more clearly and intuitively illustrate the overall metabolic differences, fold change (FC) values ​​were calculated for metabolites in the comparison groups. After calculation, the metabolites were arranged from smallest to largest according to their FC values, and a dynamic distribution chart of metabolite content differences was plotted, as shown below. Figure 4 The top 10 metabolites that were upregulated and downregulated were labeled. Figure 4 The results show: ① Metabolite changes in ML2 compared to ML1: Significantly increased metabolites include various phosphatidylcholine derivatives (such as PC (22:4 / P-18:1), 1-Myristoyl-2-palmitoyl-sn-glycero-3-phosphocholine), the thyroid hormone liothyronine, the bile acid derivative 12-ketolithocholic acid, and xanthoaphin and penitrem F. Significantly decreased metabolites include estradiol-17β-sulfate, maculosin, and methadone. The significant upregulation of phosphatidylcholine indicates highly active cell membrane synthesis and repair activities, which is highly consistent with the physiological processes of rapid lymphocyte proliferation, immune cell activation, and lymph node development after colostrum ingestion. Increased liothyronine may provide energy for the rapid division and function of immune cells by regulating basal metabolic rate. Upregulation of 12-ketolithocholic acid may be related to the digestion and absorption of the high fat content in colostrum and the activation of liver function. The decrease in Estradiol-17β-sulfate may reflect the shift in the calf's endocrine system from a fetal to a neonatal pattern after colostrum intake.

[0047] ② Changes in metabolites in ML3 compared to ML1: Significantly elevated metabolites: Xanthoaphin, liothyronine, 12-Ketolithochonic acid, and Penitreme F were continuously upregulated compared to ML2, with the emergence of new upregulated metabolites such as Medicagenic acid (a saponin with immunomodulatory activity). Significantly decreased metabolites: The decrease in hydrocortisone (a glucocorticoid) was particularly noteworthy, while estradiol-17β-sulfate was continuously downregulated. The continued upregulation of key metabolites (such as Xanthoaphin and liothyronine) indicates that the metabolic and immune activation induced by colostrum is maintained up to 12 days after birth. The decrease in hydrocortisone may indicate that immunosuppression caused by birth stress is being relieved, which is beneficial to the further development and response of the calf's own immune system. The presence of Medicagenic acid may indicate that plant-derived metabolites (possibly from feed or transmitted through the mother) are beginning to participate in the regulation of the immune microenvironment.

[0048] ③ Changes in metabolites of ML3 compared to ML2: Significantly increased metabolites include small peptides (such as Tyr-Ser-Phe-Val-Phe, Met-Asn-Ser-Phe-Val, Leu-Thr-Gly-Lys-Thr), Prostaglandin F1α Alcohol (a prostaglandin derivative involved in inflammation and immune regulation), and Medicagenic acid. Significantly decreased metabolites include other small peptides (such as Arg-Ile-Val-Leu-Val, Arg-Phe-Val-Asp) and Atorvastatin (inhibits cholesterol synthesis). The metabolite profile has shifted from being dominated by phospholipids and hormones to showing a greater presence of small peptides and specific immunomodulatory molecules. This shift may indicate a change in the focus of metabolic support from "cellular construction" to "immune signaling and fine-tuning." The active presence of small peptides may be a product of protein digestion and absorption or a biologically active signaling molecule directly involved in immune regulation. This increased complexity of the metabolic network reflects a transition in the calf's immune system from relying on maternal passive immunity to establishing its own active immune response capabilities.

[0049] ④ Differences between NL_1 and ML_1, and between NL_2 and ML_2: Significant features: 7-Benzylidenenaltrexone, Phalloidin, and specific phospholipids were repeatedly found in metabolites that were significantly upregulated in the Nili-Raffi buffalo. This indicates innate breed differences in basal metabolism and colostrum response between the two buffalo breeds. These differences may stem from variations in the basal levels or activation thresholds of metabolic pathways due to different genetic backgrounds.

[0050] ⑤ Differences in the later stages of immune system development (NL3 vs. ML3): Significant features: At 12 days of age, Nili-Raffi buffaloes, compared to Mora buffaloes, showed a sustained upregulation of 7-Benzylidenenaltrexone and Phalloidin, with newly observed upregulation of Pimecrolimus (an immunosuppressant) and Uracil (involved in nucleotide synthesis). The upregulation of Pimecrolimus may suggest that Nili-Raffi buffaloes employ a different immunomodulatory strategy at this stage, potentially favoring a more "inhibitory" or "regulatory" immune microenvironment to prevent excessive immune activation. The upregulation of Uracil may reflect higher levels of lymphocyte proliferation and nucleic acid synthesis metabolism. Figure 4 The horizontal axis represents the cumulative number of substances arranged from smallest to largest by the difference factor, and the vertical axis represents the logarithm of the difference factor to the base 2. Each point represents a substance, with green points representing the top 10 substances that were downgraded and red points representing the top 10 substances that were upgraded.

[0051] 2.2 Cluster Analysis Cluster analysis is a multivariate statistical analysis method for classification. Metabolite content data were processed using unit variance scaling (UV), and heatmaps were generated using the ComplexHeatmap package in R software. The results are as follows. Figure 5 ; Figure 5 The horizontal axis represents the sample name, the vertical axis represents differential metabolite information, and "Group" indicates grouping. Different colors represent the different values ​​obtained after standardization of relative content (red represents high content, green represents low content). Specifically, `heatmap_class` is a heatmap categorized by substance, where `Class` represents the primary category of the substance; `heatmap_col-row_cluster` performs cluster analysis on both metabolites and samples, with the cluster lines on the left representing metabolite clusters and the cluster lines at the top representing sample clusters; `heatmap_row_cluster` performs cluster analysis only on metabolites, with the cluster lines on the left representing metabolite clusters.

[0052] 2.3 Functional annotation and enrichment analysis of differentially metabolized KEGG.

[0053] The KEGG (Kyoto Encyclopedia of Genes and Genomes) database helps researchers study genes, expression information, and metabolite content as a holistic network. As a leading public database on pathways, KEGG provides integrated pathway queries, including the metabolism of carbohydrates, nucleotides, amino acids, and the biodegradation of organic matter. It not only provides all possible metabolic pathways but also offers comprehensive annotations of the enzymes catalyzing each step of the reaction, including amino acid sequences, links to PDB libraries, and more, making it invaluable for biological research. A powerful tool for in vivo metabolic analysis and metabolic network research.

[0054] (1) Metabolite function annotation: Metabolites interact within organisms, forming different pathways. The KEGG database (Kanehisa et al., 2000) was used to annotate metabolites, displaying only metabolic pathways containing differentially expressed metabolites; pathways without differentially expressed metabolites were not shown. The annotation status of the selected significantly differentially expressed metabolites in the KEGG database was statistically analyzed. Some results are shown in Tables 5 and 6: Table 5. Annotation of Differential Metabolite KEGG Note: Index: Substance ID; Compounds: Substance name; Type: Metabolite up / down regulation type; cpd_ID: Metabolite ID in the KEGG database; kegg_map: KEGG database pathway number.

[0055] Table 6. Statistical table of differentially expressed metabolites in each pathway of KEGG. Note: ko_ID: the ko number of the pathway in the KEGG database; Sig_compound: the number of differentially significant metabolites annotated by KEGG in this pathway; compound: the number of metabolites belonging to this pathway among the detected metabolites; Sig_compound_all: the number of differentially significant metabolites annotated by KEGG; compound_all: the number of metabolites annotated by KEGG among all detected metabolites.

[0056] (2) Cluster analysis of differential metabolites in the KEGG pathway.

[0057] Using KEGG annotation information of differentially metabolites identified according to screening criteria, five significantly enriched KEGG metabolic pathways were selected. Cluster analysis was performed on all differentially metabolites in these pathways to better study the variation of substance content in different groups in potentially important metabolic pathways. If a pathway had fewer than five differentially metabolites, that pathway was not displayed. The results are as follows: Figure 6 . Figure 6 The horizontal axis represents the sample name, and the vertical axis represents the differential metabolites. Different colors are used to fill in different values ​​obtained after standardization of relative content (red represents high content, green represents low content). The annotation bar above the heatmap corresponds to the sample group (Group). The tree diagram on the left side of the heatmap represents the hierarchical clustering results of differential metabolites. The annotation bar on the right side of the clustering tree corresponds to the primary classification of substances (Class). Different colors represent different substance categories.

[0058] (3) KEGG enrichment analysis of differential metabolites.

[0059] Based on the differential metabolite results, KEGG pathway enrichment analysis was performed. The Rich Factor is the ratio of the number of differentially expressed metabolites in the corresponding pathway to the total number of metabolites annotated in that pathway; a higher Rich Factor indicates a greater degree of enrichment. The P-value is the hypergeometric test p-value, and the formula for calculating the hypergeometric distribution is shown below: ; Where N represents the number of KEGG-annotated metabolites among all metabolites, n represents the number of differentially expressed metabolites in N, M represents the number of metabolites in a specific KEGG pathway in N, and m represents the number of differentially expressed metabolites in a specific KEGG pathway in M. The closer the p-value is to 0, the more significant the enrichment. Figure 7 The size of the midpoint represents the number of differentially significant metabolites enriched in the corresponding pathway. The top 20 pathways by P-value are displayed in ascending order: ① ML_2 vs. ML_1: Compared to the 2-hour postnatal period, the peripheral blood metabolome of 5-8 day old Mora calves showed significant enrichment in pathways such as lysosome, prolactin signaling pathway, and endocrine resistance (rich factor mostly between 0.4 and 0.6), with 7-10 differentially expressed metabolites involved in each pathway. This indicates that phagocytic activity of immune cells, hormone-immune cross-regulation, and cell proliferation signals were collectively activated. Meanwhile, the amino sugar and nucleotide sugar metabolism pathway spanned almost the entire graph, with the highest number of enriched metabolites, suggesting that glycogen synthesis and glycoprotein modification provided sufficient energy and membrane structure materials for the rapidly proliferating lymphocytes.

[0060] ② ML_3 vs. ML_1: By 12 days, compared with the first 2 hours after birth, pathways such as cancer pathways, choline metabolism in cancer, and mineral absorption showed the highest enrichment, with each pathway contributing 14-18 differential metabolites, indicating a continued amplification of the immune-proliferative network. Meanwhile, immune-related pathways such as thyroid hormone signaling pathway, lysosome, and tuberculosis remained significantly enriched, with the number of metabolites remaining at 6-10, indicating that the immune memory and antigen clearance mechanisms triggered by colostrum were still operating at a high intensity.

[0061] ③ ML_3 vs. ML_2: Compared to 5-8 days, pathways such as steroid biosynthesis, tuberculosis, and parathyroid hormone synthesis, secretion, and action were significantly enriched at 12 days, with each pathway containing 4-6 differential metabolites, suggesting that immune regulation has shifted from early activation to homeostasis. Meanwhile, metabolic support pathways such as mineral absorption and choline metabolism in cancer remained enriched, indicating that the continued proliferation of lymphocytes does not reduce their demand for membrane lipids and ions.

[0062] ④ NL_1 vs. ML_1: Compared to Mora, Nili-Raffi calves showed significant enrichment in pathways such as AMPK signaling, adipocytokine signaling, and amino sugar and nucleotide sugar metabolism within 2 hours of birth. The AMPK and amino sugar pathways contained 57 and 15 differential metabolites, respectively, the most of any two breeds, suggesting the greatest difference in basal energy perception and sugar metabolism patterns between the two breeds. Stress-apoptotic pathways such as alcoholic liver disease and amyotrophic lateral sclerosis were also significantly enriched, with 29-43 enriched metabolites, indicating that Nili-Raffi calves had higher levels of oxidative stress immediately after birth.

[0063] ⑤ NL_2 vs ML_2: Comparing the two breeds at 5-8 days, the enrichment pattern was almost identical to that of NL_1 compared to ML_1. AMPK signaling pathway, adipocytokine signaling pathway, and amino sugar and nucleotide sugar metabolism remained the most significant, with 57, 43, and 15 different metabolites, respectively. This indicates that the differences in energy-immune cross-regulation between breeds remained prominent after colostrum intake. At the same time, the enrichment of metabolites in pathways such as alcoholism and amoebiasis remained as high as 29-43, suggesting that Nili-Raffi calves still experienced more significant metabolic stress during the colostrum-induced immune window.

[0064] ⑥ NL_3 vs. ML_3: Twelve days later, breed comparisons showed a high degree of overlap with previous data. The AMPK signaling pathway, with 57 differentially expressed metabolites, far surpassed the previous pathway, with a Rich Factor of approximately 1.0, further widening the brand differences. Amino sugar and nucleotide sugar metabolism remained significantly enriched with 15 metabolites, indicating continued divergence in sugar utilization strategies between the two buffalo breeds. While pathways such as alcoholic liver disease and amyotrophic lateral sclerosis remained significant, the number of enriched metabolites decreased slightly to 29, suggesting that as the immune system matures, the differences in oxidative stress between breeds have narrowed slightly, but differences in energy-metabolism regulation remain dominant. Results are as follows. Figure 7 The horizontal axis represents the Rich Factor for each pathway, and the vertical axis represents the pathway name (sorted by P-value). The color of the point reflects the P-value, with redder points indicating more significant enrichment. The size of the point represents the number of differentially enriched metabolites.

[0065] (4) Analysis of overall changes in metabolic pathways.

[0066] The Differential Abundance Score (DA Score) is a pathway-based method for analyzing metabolic changes. It captures the overall changes in all metabolites within a specific pathway, and its calculation formula is as follows: ; The top 20 channels by P-value ranking are displayed in ascending order, such as... Figure 8 As shown; Figure 8 The vertical axis represents the differentially expressed pathway names (sorted by P-value), and the horizontal axis represents the differential abundance score (DA Score). The DA Score reflects the overall change in the expression of all metabolites in the pathway. A score of 1 indicates an upregulation trend for all identified metabolites in the pathway, while a score of -1 indicates a downregulation trend. The length of the line segment represents the absolute value of the DA Score, and the size of the dots at the endpoints of the line segment represents the number of differentially expressed metabolites in the pathway. Dots located to the left of the central axis and with a longer line segment indicate a stronger tendency for downregulation of the overall expression of the pathway, while dots located to the right of the central axis and with a longer line segment indicate a stronger tendency for upregulation. Larger dots indicate a greater number of metabolites. The color of the line segment and dots reflects the P-value; the closer to red, the smaller the P-value, and the closer to purple, the larger the P-value.

[0067] 3. Key metabolites.

[0068] 3.1 Key metabolites in the postnatal development of Murrah buffalo calves: (ML_2 vs. ML_1 and ML_3 vs. ML_1): These metabolites are continuously activated after colostrum intake, representing a core change from passive immunity to the establishment of an autoimmune system. Xanthoaphin: A flavonoid pigment that may have antioxidant activity; its upregulation indicates the body is continuously in a state of needing to cope with oxidative stress after birth. Liothyronine: A thyroid hormone. It increases the basal metabolic rate, providing energy for the rapid proliferation and activation of immune cells, and is a key molecule connecting metabolism and immunity. 12-Ketolithocholic acid: A bile acid derivative. It reflects the digestion and absorption of high fat in colostrum and the activation of liver function, and is a marker of the link between nutritional metabolism and immune establishment. Penitrem F: A neurotoxic mycotoxin whose origin is unclear, but its continuous upregulation may act as an exogenous stimulant, continuously stimulating and "training" the calf's immune system. Various phosphatidylcholine (PCs) such as PC (22:4 / P-18:1) and 1-Myristoyl-2-palmitoyl-sn-glycero-3-phosphocholine. As major components of the cell membrane, their significant upregulation directly confirms the vigorous demand for cell membrane synthesis and repair during rapid lymphocyte proliferation, lymph node development, and immune cell activation.

[0069] 3.2 Key metabolites during developmental transitions (ML_3 vs. ML_2): The presence of these metabolites marks a new stage in the development of the immune system, shifting from rapid construction to fine-tuning. Small peptides: These may be protein digestion products or biologically active signaling molecules. Their active appearance signifies a shift in metabolic support focus from "cellular construction" to "immune signaling and fine-tuning." Prostaglandin F1α Alcohol: A prostaglandin derivative directly involved in inflammation and immune regulation. Its upregulation indicates an enhanced active, fine-tuned immune regulatory mechanism. Medicagenic acid: A plant saponin with immunomodulatory activity. Its presence may indicate that plant-derived metabolites are beginning to participate in shaping the calf's immune microenvironment. Estradiol-17β-sulfate: A sulfated steroid hormone; its decrease reflects the transition of the calf's endocrine system from a fetal to a neonatal model, a marker of physiological stage transition. Hydrocortisone: Significantly decreased in ML3. As a glucocorticoid, its decrease signifies the relief of immunosuppression caused by birth stress, creating favorable conditions for the full development and response of the calf's own immune system.

[0070] 3.2 Key metabolites characterizing differences among buffalo breeds: These metabolites reveal fundamental differences between the Nili-Rafi buffalo and the Mora buffalo in their basal metabolism and immune response strategies.

[0071] ① The marker of the upward movement of Nili-Raffi buffalo: 7-Benzylidenenaltrexone: A derivative of an opioid receptor antagonist, its physiological function is unclear in this context, but its sustained upregulation at three time points is one of the most prominent metabolic characteristics of the Nili-Raffi buffalo, suggesting breed-specific neuro-immune regulation or exogenous substance metabolic pathways. Phalloidin: A mushroom toxin. Its persistently high levels also point to a possible unique history of exogenous substance exposure or metabolic detoxification pathway in the Nili-Raffi buffalo.

[0072] ② Key differential metabolites in the later stages of immune system development (NL3 vs. ML3): Pimecrolimus: An immunosuppressant. Its upregulation in Nili-Raffi buffalo is one of the most crucial findings, strongly suggesting that this breed tends to establish a more "inhibitory" or "regulatory" immune microenvironment in the second week after birth to prevent excessive immune activation, which may be a unique immune homeostasis strategy. Uracil: A nucleotide precursor. Its upregulation in Nili-Raffi buffalo, combined with the presence of inosine triphosphate in the differential metabolite list, reflects potentially more active lymphocyte proliferation and nucleic acid synthesis in this breed. Commonly downregulated oligopeptides and sulfur-containing amino acids: such as Lys-Tyr-Val-Met-Leu, Tyr-Ala-Glu-Leu-Arg, and Djenkolic acid. These substances were generally lower in Nili-Raffi buffalo in NL3 versus ML3, revealing significant differences between the two breeds in protein metabolism, small peptide utilization, or sulfur-containing amino acid metabolic pathways, which may be the metabolic basis for their different immune manifestations.

[0073] This invention reveals that a series of key metabolites play a central role in the development of the calf's immune system. Phosphatidylcholine (PCs), a cornerstone of cell membrane construction, shows significant upregulation, a direct marker of rapid lymphocyte proliferation; while triiodothyronine provides essential energy support for this process by increasing metabolic rate. As development progresses, the appearance of various small peptides and prostaglandin F1α-ol indicates a shift in immune regulation from rapid construction to refined signal transduction and inflammation modulation; simultaneously, the decrease in hydrocortisone signifies the relief of immunosuppression caused by birth stress, creating conditions for the full development of the autoimmune system. Furthermore, changes in exogenous metabolites such as Xanthoaphin and Penitrem F suggest that oxidative stress management and exogenous stimuli may play a role in "training" the immune system. Crucially, breed comparisons revealed fundamental differences: 7-Benzylidenenaltrexone and Phalloidin are persistent metabolic markers in Nili-Raffi buffalo, suggesting their unique genetic background; and in the later stages of immune establishment, upregulation of Pimecrolimus in Nili-Raffi buffalo strongly suggests that the breed tends to establish an inhibitory immune microenvironment to prevent overactivation, while Uracil levels reflect active lymphocyte proliferation and nucleic acid synthesis metabolism. These key metabolites collectively map the dynamic establishment of the calf's immune system at the molecular level. They also depict the dynamic metabolic profile of the calf's immune system establishment under colostrum stimulation and reveal the different immune metabolic strategies employed by buffalo calves from different genetic backgrounds.

[0074] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for screening immune-related metabolites in calves based on non-targeted metabolomics, characterized in that, Includes the following steps: S1. Collect peripheral blood samples from different time periods, from calf birth to the establishment of the immune system, to obtain peripheral blood samples at different time periods; S2. Perform non-targeted metabolomics analysis on peripheral blood samples from different time periods to obtain differential metabolites at different time periods; S3. Using the KEGG database, the differential metabolites selected from different time periods were annotated, clustered, enriched, and subjected to overall metabolic pathway changes to obtain key metabolites in the establishment process and response mechanism of the calf's immune system.

2. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 1, characterized in that, The peripheral blood from different time periods includes peripheral blood from calves within 2 hours of birth, peripheral blood from 5 to 8 days after birth, and peripheral blood from 12 days after birth.

3. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 1, characterized in that, The breed of calves includes at least one of the Murrah buffalo and the Nili-Raffi buffalo.

4. A method for screening immune-related metabolites in calves based on non-targeted metabolomics according to any one of claims 1 to 3, characterized in that, Untargeted metabolomics analysis was performed on peripheral blood samples from different time periods to obtain differentially expressed metabolites. The specific steps included the following: S21. Add internal standard extraction solution to peripheral blood samples from different time periods, centrifuge, and obtain supernatant; S22. Perform chromatography-mass spectrometry on the supernatant to obtain raw data; S23. Perform preprocessing on the original data, including filtering and identification, to obtain preprocessed data; S24. Perform multivariate statistical analysis and univariate statistical analysis on the preprocessed data to obtain differential metabolites at different time periods.

5. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 4, characterized in that, The internal standard extract in step S21 is an acetonitrile / methanol solution with a mass content of 15%~25%.

6. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 4, characterized in that, The chromatographic conditions for the chromatography-mass spectrometry (GC-MS) detection in step S22 were as follows: Column: Waters ACQUITY Premier HSS T3 Column 1.8 µm, 2.1 mm × 100 mm; Mobile phase A: 0.1% formic acid / water solution (v / v), Mobile phase B: 0.1% formic acid / acetonitrile solution (v / v); Column temperature: 40°C; Flow rate: 0.4 mL / min; Injection volume: 4 μL. In step S22, the mass spectrometry conditions for the chromatography-mass spectrometry (GC-MS) detection are as follows: the sample is ionized by electrospray ionization, and mass spectrometry signals are acquired using positive and negative ion scanning modes, respectively.

7. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 4, characterized in that, The preprocessing of the raw data in step S23, which involves filtering and identifying the raw data, specifically includes the following steps: importing the raw data into metabolomics processing software for baseline filtering, peak identification, integration, retention time correction, and peak alignment to obtain a data matrix containing retention time, mass-to-charge ratio, and peak intensity information; then performing feature peak search and identification to obtain the preprocessed data.

8. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 4, characterized in that, The specific methods of multivariate statistical analysis described in step S24 include principal component analysis and orthogonal partial least squares analysis.

9. The method for screening immune-related metabolites in calves based on non-targeted metabolomics according to claim 4, characterized in that, The specific methods of univariate statistical analysis described in step S24 include fold analysis and hypothesis testing with p-value analysis.

10. A method for screening immune-related metabolites in calves based on non-targeted metabolomics according to any one of claims 1 to 9, characterized in that, The key metabolites include key metabolites during the postnatal development of the Mora buffalo, key metabolites during the transition of Mora buffalo developmental stages, and key metabolites characterizing differences between buffalo breeds. Key metabolites produced during the postnatal development of the Mora buffalo include flavin acid, thyroid hormones, bile acid derivatives, neurotoxic mycotoxins, and various phosphatidylcholine compounds. The key metabolites that the Mora buffalo undergo during its developmental stages include prostaglandin derivatives, immunomodulatory plant saponins, sulfated steroid hormones, and glucocorticoids. The key metabolites characterizing differences between buffalo breeds include derivatives of opioid receptor antagonists, mushroom toxins, immunosuppressant drugs, nucleotide precursors, oligopeptides, and sulfur-containing amino acids.

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