Plasma time-course metabolic markers for characterizing the physiological state of sows in early pregnancy and application thereof

Abstract

This invention discloses plasma time-series metabolic markers for characterizing the physiological state of sows in early pregnancy and their applications, belonging to the field of biotechnology. This invention screens out time-series metabolic markers that stably reflect the physiological state of early pregnancy: 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-meththioadenosine, and 4-methylpentanal. These markers are correlated with progesterone levels, with their levels continuously increasing from day 12 to day 21 of gestation, indicating that they can sensitively reflect changes in the endocrine and physiological states of the mother during pregnancy. Using these markers to identify the timing of early pregnancy and characterize the physiological state of early pregnancy in sows has advantages such as strong time-series specificity, high diagnostic accuracy, and good operational feasibility, making it suitable for early pregnancy monitoring and production management in large-scale pig farms.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a plasma time-series metabolic marker for characterizing the physiological state of early pregnancy in sows and its application. Background Technology

[0002] Embryo implantation refers to the process by which the blastocyst and the maternal endometrium gradually establish morphological and functional connections and form a maternal-fetal interface in early pregnancy. In pigs, the implantation period is relatively long, mainly occurring between days 10 and 24 of gestation. Days 12-18 (E12-E18) are the critical window for maternal pregnancy recognition and embryo implantation—the embryo releases estrogen and progesterone on day 12 as pregnancy recognition signals, enters the critical stage of implantation localization on day 15, and implantation is basically completed by around day 18.

[0003] The embryonic morphological development after entering the uterus is closely coupled with the aforementioned signal release process. The embryo enters the uterus from the fallopian tube at approximately the four-cell stage; and develops into a blastocyst around day 5 of gestation. However, at this time, the blastocyst has not adhered to the endometrium and remains in a free state. On days 6-7 of gestation, the blastocyst sheds its zona pellucida. At this stage, the embryo undergoes significant morphological changes, rapidly transforming from a spherical shape (2-6 mm in diameter) to a tubular shape (100-150 mm); subsequently, on days 10-12 of gestation, it develops into a filamentous shape (100-150 mm). During this period, the embryonic body rapidly differentiates, releasing large amounts of estrogen as a signal for maternal pregnancy recognition. On day 15 of gestation, it further elongates to 800-1000 mm, and the embryo begins to secrete a second wave of estrogen. Subsequently, under the joint regulation of hormones and related adhesion molecules, the embryo gradually establishes a stable adhesion relationship with the endometrial epithelial cells, completing the implantation process around day 18 of gestation. This shows that the morphological elongation of the embryo and the timing of estrogen secretion are highly synchronized in time, together constituting the core event of the dialogue between the mother and the embryo in early pregnancy.

[0004] In pigs, the synthesis of estrogen in the blastocyst is highly synchronized with the initiation of blastocyst elongation. Pregnancy recognition in pigs is initiated by the secretion of estrogen (primarily 17β-estradiol) by the embryo on days 11-12 of gestation. The maternal pregnancy recognition process occurs synchronously with the rapid morphological changes of the embryo during the pre-implantation phase (days 10-12), from a spherical shape to a tubular shape, and further development into a ribbon-like shape. Successful establishment of pregnancy also requires a second peak in 17β-estradiol secretion by the embryo on days 15-30 of gestation. Estrogen exerts both systemic effects and local regulatory functions in the mother, directly promoting progesterone secretion from the corpus luteum, which is crucial for maintaining pregnancy.

[0005] Progesterone, secreted by the corpus luteum, adrenal cortex, and placenta, is essential for embryo implantation, regulation of uterine development, uterine secretion, and mammary gland development. In sows, blood progesterone levels are low before estrus and mating, rise rapidly three days post-mating, peaking on day 12 of gestation, and then slowly decline to 10-20 ng / mL from day 14 to day 30, remaining relatively stable throughout pregnancy until farrowing. Overall, the coordinated fluctuations of estrogen and progesterone within the E12-E18 window constitute the core endocrine characteristic of the maternal transition from a non-pregnant to a pregnant state. The coordination of hormone levels during this period directly affects the successful completion of pregnancy recognition. Insufficient embryonic signals or asynchronous development among different embryos can interfere with this process. Especially in multiparous pigs, pregnancy establishment depends not only on the quality of individual embryos but also on the synchronicity of the entire embryonic population and the uterine capacity. Therefore, the pregnancy recognition stage is expected to be considered an important window for early pregnancy research and biomarker screening in pigs.

[0006] Nevertheless, relying solely on the aforementioned hormonal indicators for early pregnancy determination still has significant limitations. Regarding physiological interference, there are significant differences in basal endogenous hormone levels among individuals, and hormone fluctuations before pregnancy identification primarily reflect rhythmic changes in the estrous cycle rather than pregnancy-specific signals. In terms of accuracy, hormone testing lacks sensitivity and specificity. Regarding operational feasibility, plasma hormone testing requires specialized equipment and cumbersome procedures, limiting its large-scale application in production lines. These three aspects indicate that relying solely on traditional hormonal indicators is insufficient for accurately determining the physiological state of sows in the very early stages of pregnancy, necessitating the discovery of novel molecular indicators that can more early and stably reflect changes in the body's metabolic state during pregnancy establishment.

[0007] Metabolomics is a technical system for the systematic analysis of small-molecule metabolites in biological systems, mainly including detection methods such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Among them, broad-targeted metabolomics technology can achieve high coverage of metabolite detection while ensuring quantitative accuracy, and has advantages in systematically elucidating complex biological processes. Currently, this technology has been widely used in research related to pregnancy in humans and livestock such as cattle, sheep, and pigs to analyze the characteristics of maternal metabolic changes during pregnancy. Existing studies have shown that lipid metabolism, amino acid metabolism, energy metabolism, and bile acid metabolism pathways may all change to varying degrees during pregnancy, suggesting that the maternal metabolic network has obvious dynamic remodeling characteristics during the establishment of pregnancy.

[0008] In contrast, research on sow metabolism in early pregnancy remains relatively limited. Current studies mainly focus on uterine fluid, endometrium, and local embryonic tissues, with limited research on changes in maternal peripheral plasma metabolism. Furthermore, existing studies are largely based on comparative analyses between single time points in pregnancy and non-pregnancy, lacking continuous dynamic monitoring of the critical time windows for pregnancy identification and embryo implantation, making it difficult to systematically reflect the dynamic changes in maternal metabolism during pregnancy establishment. Therefore, there is still a lack of systematic research on the dynamic changes in maternal plasma metabolism in early pregnancy in sows, and a lack of relevant metabolic indicators that can stably reflect changes in the physiological state of sows in early pregnancy. Summary of the Invention

[0009] The purpose of this invention is to provide plasma time-series metabolic markers for characterizing the physiological state of sows in early pregnancy and their applications, thereby addressing the problems existing in the prior art. This invention screens out time-series metabolic markers 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-methylthioadenosine, and 4-methylpentanal that can stably reflect the physiological state of early pregnancy. These markers are correlated with progesterone levels, with their levels continuously increasing from day 12 to day 21 of gestation, indicating that they can sensitively reflect changes in the endocrine state of the mother during pregnancy. Using these markers to identify the timing of early pregnancy and characterize the physiological state of early pregnancy in sows has the advantages of strong time-series specificity, high diagnostic accuracy, and good operational feasibility, making it suitable for early pregnancy monitoring and production management in large-scale pig farms.

[0010] To achieve the above objectives, the present invention provides the following solution: This invention provides a plasma time-series metabolic marker for characterizing the physiological state of sows in early pregnancy. The plasma time-series metabolic marker is 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal in sow plasma.

[0011] The longer the gestation period of a sow, the higher the content of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal in her plasma.

[0012] The present invention also provides the use of products that detect the above-mentioned plasma time-series metabolic markers in the preparation of products for identifying the early gestation period of sows.

[0013] The present invention also provides the use of products that detect the above-mentioned plasma time-series metabolic markers in the preparation of products for characterizing the physiological state of early pregnancy in sows.

[0014] The present invention also provides a product for identifying the early gestation period of sows, the product comprising reagents or instruments for detecting the aforementioned plasma time-series metabolic markers.

[0015] Furthermore, the longer the gestation period of the sow, the higher the content of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal in the plasma.

[0016] The present invention also provides a product for characterizing the physiological state of sows in early pregnancy, the product comprising reagents or instruments for detecting the aforementioned plasma time-series metabolic markers.

[0017] The present invention discloses the following technical effects: This invention focuses on the critical window period of early pregnancy in sows, E12-E18. Through extensive targeted metabolomics analysis of peripheral blood plasma from sows during early pregnancy, it systematically reveals the dynamic evolution of metabolism during the establishment of pregnancy and screens out three time-series metabolic markers that can stably reflect the physiological state of early pregnancy: 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-meththioadenosine, and 4-methylpentanal. 1-Stearyl-2-palmitoyl-sn-glycerol-3-phosphate choline was correlated with human chorionic gonadotropin (hCG) levels, with levels continuously increasing from day 12 to day 21 of pregnancy. 5-Deoxy-5-methylthionine was correlated with progesterone (P4) levels, with levels continuously increasing from day 12 to day 21 of pregnancy. 4-Methylpentanal showed a significant positive correlation with progesterone (P4) levels, with levels continuously increasing from day 12 to day 21 of pregnancy, exhibiting a trend highly consistent with that of P4. These findings indicate that these three biomarkers can sensitively reflect changes in the maternal endocrine state during pregnancy.

[0018] Based on quantitative validation using targeted metabolomics, the levels of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, and 4-methylpentanal showed statistically significant differences at four time points: days 12, 15, 18, and 21 of gestation in sows. ROC curve evaluation showed that, using E12 as a reference, the AUCs of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, and 4-methylpentanal were all above 0.7 when distinguishing between E15, E18, and E21, with 4-methylpentanal reaching AUCs of 0.813, 0.977, and 1.000, respectively, indicating that the discriminative ability of these three biomarkers gradually improves as gestation progresses. In actual breeding, based on their levels, combined with a pre-established standard curve and a gestation time discrimination model, the early gestation stage of sows can be objectively and accurately determined. Compared with traditional hormone detection methods (such as hCG, E2, P4), this method has the advantages of strong time specificity, high diagnostic accuracy, and good operational feasibility, making it suitable for early pregnancy monitoring and production management in large-scale pig farms.

[0019] This invention provides a scientific basis and experimental foundation for a deeper understanding of the metabolic regulation mechanism established during sow pregnancy and the subsequent development and application of related biomarkers. It also provides practical detection indicators and decision-making basis for the accurate identification of early pregnancy time in sows, the assessment of reproductive performance, and the precision management of large-scale pig farms. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The content and variation patterns of three reproductive hormones in the plasma of sows during early pregnancy; (A) Curve of change in plasma hCG content during early pregnancy; (B) Curve of change in plasma E2 content during early pregnancy; (C) Curve of change in plasma P4 content during early pregnancy; Figure 2 A diagram showing the composition of metabolite categories; Figure 3 TIC diagram of QC sample under HILIC negative ion mode; Figure 4 TIC diagram of QC sample under T3 negative ion mode; Figure 5 TIC plot of QC sample under T3 positive ion mode; Figure 6 Correlation analysis chart for QC samples; Figure 7 For K-means cluster analysis of metabolites; (A) Subclass 1; (B) Subclass 2; (C) Subclass 3; (D) Subclass 4; Figure 8 KEGG pathway enrichment analysis for strongly related metabolites; (A) KEGG pathway diagram of hCG-related metabolites; (B) KEGG pathway diagram of E2-related metabolites; (C) KEGG pathway diagram of P4-related metabolites; Figure 9 This is a schematic diagram illustrating the principle of MRM detection. Figure 10The content of candidate metabolic markers was determined; (A) 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine content; (B) (20S)-20-Hydroxypregn-4-en-3-one content; (C) LPC(14:0 / 0:0) content; (D) 4-Methylpentanal content; (E) Homocysteine ​​content; (F) 5'-Deoxy-5'-(Methylthio)Adenosine content; (G) Cortisol content; Figure 11 The time-series association analyses of candidate metabolic markers and reproductive hormones were conducted as follows: (A) Association analysis of 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine with hCG; (B) Association analysis of (20S)-20-Hydroxypregn-4-en-3-one with hCG; (C) Association analysis of LPC(14:0 / 0:0) with E2; (D) Association analysis of 4-Methylpentanal with P4; (E) Association analysis of Homocysteine ​​with P4; (F) Association analysis of 5'-Deoxy-5'-(Methylthio)Adenosine with P4; (G) Association analysis of Cortisol with P4. Figure 12 ROC curve evaluation for candidate metabolic biomarkers; (A) ROC curve of candidate metabolic biomarkers distinguishing between E12 and E15; (B) ROC curve of candidate metabolic biomarkers distinguishing between E12 and E18; (C) ROC curve of candidate metabolic biomarkers distinguishing between E12 and E21. Detailed Implementation

[0022] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0023] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0024] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0025] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0026] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0027] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment; unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent stores.

[0028] Example 1: Method for collecting plasma samples and dynamically detecting reproductive hormones in sows during early pregnancy This embodiment constructs a dynamic change profile of reproductive hormones in early pregnancy in sows, providing basic data for subsequent screening of metabolic biomarkers.

[0029] 1. Experimental animals and sample collection The experiment was conducted at a large-scale pig farm in Guangxi from August to October 2024. Six Landrace sows of uniform growth age, with 2-3 parities and good health were selected as experimental subjects. Blood samples were collected from the anterior vena cava at seven sampling points during the early stages of gestation: day 3 (E3), day 6 (E6), day 9 (E9), day 12 (E12), day 15 (E15), day 18 (E18), and day 21 (E21). After anticoagulation, the blood samples were centrifuged at 3000×g for 10 min at 4°C to obtain plasma, which was then stored at -80°C for later use.

[0030] 2. Reproductive hormone testing and data processing The expression levels of key reproductive hormones, including human chorionic gonadotropin (hCG), estradiol (E2), and progesterone (P4), were detected using enzyme-linked immunosorbent assay (ELISA). All experimental data were first compiled using Excel spreadsheets, followed by one-way ANOVA using SPSS 27.0 software. When the ANOVA results showed significant differences, Tukey's multiple comparison method was used for inter-group comparisons. Hormone concentration change curves were plotted using GraphPad Prism 10 software. Results are expressed as mean ± standard error, with P < 0.05 considered statistically significant and P ≥ 0.05 indicating no significant difference.

[0031] 3. Results Analysis like Figure 1 As shown, the results indicate that hCG reaches a relatively high level at E3, E12, E18 and E21, and decreases significantly at E6 and E15 (P<0.05); while E2 and P4 show a gradual upward trend, especially after E15, the concentration increase is significant (P<0.05).

[0032] Example 2: Screening of candidate metabolic biomarkers based on broadly targeted metabolomics and hormone association analysis 1. Sample and metabolomics detection Using plasma samples from seven time points in Example 1 as the research object, extensively targeted metabolomics (UPLC-MS / MS) was employed to perform qualitative and quantitative analysis of plasma metabolites, obtaining systematic metabolite profile information. The results are as follows: Figures 2-5 As shown, a total of 2206 metabolites were identified, of which 841 were detected in positive ion mode and 1365 in negative ion mode. The metabolites were mainly divided into three categories: amino acids and their metabolites, organic acids and their derivatives, and benzene compounds, accounting for 18.96%, 14.33%, and 12.38%, respectively. The correlation analysis of QC samples is shown in the figure below. Figure 6 As shown in the figure, the results indicate that the r-values ​​of all plasma QC samples were greater than 0.90, indicating that the experimental platform was operating stably, the above test results were reliable, and the data quality was high.

[0033] Further cluster analysis revealed significant dynamic changes in different metabolites during early pregnancy in sows. Based on differences in expression trends, all metabolites were clustered into four subclasses (Subclass 1-4), each exhibiting specific temporal change patterns at different stages of pregnancy. Figure 7The results showed that the plasma metabolic profile in early pregnancy in sows was not a static process, but rather exhibited distinct phased metabolic remodeling characteristics accompanying the progress of pregnancy. Different metabolites showed differentiated temporal changes during embryonic development, embryo implantation, and maternal physiological adaptation, suggesting the existence of a complex and highly coordinated metabolic regulatory network in early pregnancy.

[0034] 2. Correlation analysis Using changes in hCG, E2, and P4 concentrations as reference indicators, Pearson correlation analysis was performed on the abundance of metabolites and reproductive hormone levels. The screening criteria were |r| > 0.70 and P < 0.05, thus identifying metabolites significantly correlated with hormonal changes in early pregnancy. This invention analyzed the level changes of three reproductive hormones (hCG, E2, and P4) and metabolites (positive ions: 841, negative ions: 1365) in sow plasma using Pearson analysis, finding that changes in the concentrations of the three reproductive hormones in early pregnancy in sows were significantly correlated with multiple metabolites (|r| > 0.70, P < 0.05).

[0035] As shown in Table 1, the analysis revealed 92 metabolites significantly correlated with hCG, 210 metabolites significantly correlated with E2, and 167 metabolites significantly correlated with P4. Specifically, correlation analysis between hCG and positive ion metabolites showed 21 negative correlations (r < -0.70, P < 0.05, same below), while the negative ion metabolite showed 46 positive correlations (r > 0.70, P < 0.05, same below) and 25 negative correlations. Correlation analysis between E2 and positive ion metabolites showed 44 positive correlations and 15 negative correlations, while the negative ion metabolite showed 136 positive correlations and 15 negative correlations. Correlation analysis between P4 and positive ion metabolites showed 63 positive correlations and 50 negative correlations, while the negative ion metabolite showed 37 positive correlations and 17 negative correlations.

[0036] Table 1. Pearson correlation analysis between hormones and metabolites 3. Metabolic pathway enrichment analysis To further explore the metabolic changes involved in the relevant metabolites and their potential biological significance, this invention uploaded the screened relevant metabolite data to the MetaboAnalyst platform for KEGG metabolic pathway enrichment analysis. For example... Figure 8As shown, the results indicate that these metabolites were annotated to multiple related metabolic pathways. Generally, metabolic pathways with a pathway impact > 0.2 and a p-value < 0.05 in the t-test are considered the most relevant. KEGG pathway enrichment analysis revealed that metabolites significantly associated with hCG were enriched in 15 pathways, with the two pathways showing the most significant changes being steroid hormone biosynthesis and linoleic acid metabolism. Metabolites significantly associated with E2 were mainly enriched in 19 metabolic pathways, including two significant pathways (p < 0.05): alpha-linolenic acid metabolism and glycerophospholipid metabolism. Metabolites significantly associated with P4 were enriched in 21 metabolic pathways, with three pathways showing significant correlation (P<0.05): cysteine ​​and methionine metabolism, glycerophospholipid metabolism, and steroid hormone biosynthesis. These results suggest that these metabolites collectively participate in the regulation of key biological events during early pregnancy in pigs, including embryo implantation, fetal development, and maternal metabolism.

[0037] 4. Screening of candidate metabolic biomarkers Based on the above KEGG metabolic pathway enrichment results, metabolites enriched in metabolic pathways that simultaneously satisfy pathway impact>0.2 and P<0.05 were selected as candidate metabolic biomarkers for the potential dynamic changes in the physiological state of sows in early pregnancy. Among them, the metabolites related to hCG that are enriched in the steroid hormone biosynthesis pathway mainly include estradiol 17-(β-D-glucuronide), Δ4-pregn-20α-ol-3-one ((20S)-20-Hydroxypregn-4-en-3-one), 11-dehydrocorticosterone, and 5β-dihydroaldosterone; the linoleic acid metabolic pathway contains only 1-stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine. E2-related metabolites are mainly enriched in glycerophospholipid metabolism, including lysophosphatidylcholine 14:0 (LPC(14:0 / 0:0)), lysophosphatidylcholine 20:2 (LPC(20:2 / 0:0)), and lysophosphatidylserine_LPS(20:0) (LPS(20:0)); α-linolenic acid metabolism includes stearidonic acid. Metabolites associated with P4 that are enriched in the glycerophospholipid metabolic pathway include LPC (0:0 / 22:5) and 1-(1Z-octadecenyl)-sn-glycero-3-phosphocholine; those in the cysteine ​​and methionine metabolic pathways include 5'-deoxy-5'-(Methylthio) adenosine and homocysteine; and those in the steroid hormone biosynthesis pathways mainly include 4-methylpentanal, cortisol, and estrone. Based on these results, a total of 16 candidate metabolic biomarkers were screened (Table 2).

[0038] Table 2. Identification information and correlation analysis results of 16 candidate metabolites. Example 3 Targeted Validation Based on Plasma Early Pregnancy Metabolic Markers To validate the candidate differential biomarkers obtained from extensive targeted metabolomics screening in the previous stage, this invention further utilizes targeted metabolomics technology to quantitatively validate core metabolites related to early pregnancy in sows. Plasma samples were recollected from 24 sows at four time points: days 12, 15, 18, and 21 of early pregnancy. The expression levels of 16 candidate biomarkers were systematically analyzed, and receiver operating characteristic (ROC) curve analysis was used to screen out characteristic metabolic biomarkers of early pregnancy in sows with excellent diagnostic efficacy. After feasibility assessment, 11 key metabolites among the 16 candidate metabolic biomarkers were deemed suitable for the validation stage.

[0039] 1. Experimental animals and sample collection Based on previous experiments, in order to verify the accuracy of the candidate metabolic biomarkers, this experiment collected samples again, selecting 24 sows of similar growth age, parity, and good health as experimental animals. All sows were kept under the same feeding and management conditions.

[0040] 2. Test Procedure The detection of target metabolites in the samples was performed using a UPLC-MS / MS platform. After obtaining the raw mass spectrometry information, qualitative confirmation was performed using the MWDB database established based on standards, combined with the chromatographic-mass spectrometric characteristics of the target compounds, and qualitative annotation of the target compounds was performed. Quantitative analysis was performed in MRM mode, by integrating the chromatographic peak areas of the target ion pairs and combining them with a standard curve to achieve absolute quantification. The specific procedure is as follows: 2.1 Processing Flow (1) T3 derivatization method: Remove the sample from the -80℃ freezer and thaw it on ice. Transfer 50 μL of sample to a 1.5 mL centrifuge tube, add 250 μL of methanol extraction buffer (containing 20% ​​acetonitrile), vortex for 3 minutes, and centrifuge for 10 minutes (4℃, 12000 r / min). Transfer 250 μL of the supernatant to a new centrifuge tube and incubate at -20℃ for 30 minutes. Centrifuge again at 4℃, 12000 r / min for 10 minutes, and collect 180 μL of the supernatant as the sample solution. Store at -20℃ for analysis and T3 derivatization. Transfer 30 μL of the above extraction buffer, add 10 μL of mixed derivatizing reagent, vortex for 10 seconds, and derivatize at 30℃ for 1 hour. After derivatization, add 260 μL of pure acetonitrile pre-chilled at -20℃ for 30 minutes to terminate the reaction, vortex for 1 minute, and store at -20℃ for later analysis.

[0041] (2) Lipid extraction method: First, thaw the sample and vortex for 10 seconds to mix. Transfer 100 μL of the sample to the corresponding centrifuge tube. Add 1 mL of lipid extraction solution prepared with methyl tert-butyl ether and methanol at a volume ratio of 3:1, and vortex for 15 min. Then add 200 μL of ultrapure water, vortex for 1 min, and incubate at 4°C for 10 min. Centrifuge the sample at 12000 r / min for 3 min at 4°C, collect 600 μL of the supernatant to a 1.5 mL centrifuge tube, and concentrate to dryness. Add 100 μL of a reconstitution solution prepared with acetonitrile and isopropanol in equal proportions, vortex for 3 min to redissolve the sample, and centrifuge again for 3 min (centrifugation conditions as above). Finally, transfer 50 μL of the supernatant to a glass-lined tube for LC-MS analysis.

[0042] 2.2 Liquid Chromatography-Mass Spectrometry Conditions This invention uses an ultra-high performance liquid chromatography-tandem mass spectrometry system for data acquisition. The liquid chromatography system used is ExionLC™ AD (SCIEX), and the mass spectrometry system is QTRAP® 6500+ (SCIEX).

[0043] 2.2.1. Chromatographic Detection Conditions (1) The liquid chromatography conditions used for the T3 derivatization system are as follows: the column is an ACQUITY HSS T3 column (2.1 mm × 100 mm, 1.8 μm); the mobile phase A is water and the mobile phase B is acetonitrile; the injection volume is 2 μL; the column temperature is set to 40℃ (see Table 3).

[0044] Table 3 Mobile phase gradient (2) Liquid chromatography conditions for the T3 system: An ACQUITY HSS T3 column (inner diameter 2.1 mm × 100 mm, particle size 1.8 μm) was used; mobile phase A was water and mobile phase B was acetonitrile; the injection volume was 2 μL; the column temperature was set to 40℃ (see Table 4 for specific parameters).

[0045] Table 4. Mobile phase gradient (3) Liquid phase conditions for lipid system: The chromatographic column was Acclaim C30 3um 2.1*100 mm; the mobile phase A was acetonitrile / water (60 / 40); the mobile phase B was acetonitrile / isopropanol (10 / 90); the injection volume and column temperature were 2 μL and 40℃, respectively (see Table 5).

[0046] Table 5. Mobile phase gradient 2.2.2. Mass Spectrometry Conditions Mass spectrometry analysis was performed using ESI positive and negative ion modes. The ion spray voltage was set to 5500 V and -4500 V, respectively; the curtain gas was 35 psi; the ion source temperature was 500℃; ion source gas 1 and gas 2 were set to 50 psi and 60 psi, respectively; the collision gas was set to Medium; the scanning mode was MRM; and the inlet potential and collision cell outlet potential were both set to ±10 V.

[0047] 2.3 Qualitative and quantitative analysis of candidate metabolic biomarkers To achieve accurate qualitative and quantitative analysis of candidate metabolic biomarkers, a Multi-Reaction Monitoring (MWDB) database was first established based on standards, and qualitative annotation was performed on the mass spectrometry data. Subsequently, quantitative detection was conducted using triple quadrupole mass spectrometry in multiple reaction monitoring (MRM) mode. Figure 9 This mode effectively reduces interference from impurity ions by sequentially screening precursor ions and characteristic fragment ions, thereby improving detection sensitivity and quantitative stability. After data acquisition, the chromatographic peaks of each target analyte are integrated, and quantitative analysis is completed using either internal or external standard methods. Simultaneously, based on the characteristic ion information of candidate metabolic biomarkers, the corresponding MRM parameters are optimized and set; specific parameters are shown in Table 6.

[0048] Table 6. Multiple Response Monitoring (MRM) Parameters Note: RT is the retention time; DP is the declustering voltage; CE is the collision energy. 2.4 Constructing the Standard Curve Standard working solutions of the target metabolites were prepared using a serial dilution method, with concentrations of 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, and 5000 ng / mL. Mass spectrometry was performed on the standard working solutions at each concentration, and the peak intensity data of the quantitative ions were recorded. A standard curve was constructed with standard concentration as the x-axis and peak area as the y-axis.

[0049] 2.5 Determination of the content of target substances in samples The standard curve is constructed using standard solutions of different concentration gradients. A linear regression is performed with the standard concentration as the independent variable and the peak area response value as the dependent variable to obtain the regression equation and related parameters for each target metabolite. After the sample is tested, the obtained peak area is substituted into the corresponding equation to calculate the concentration. Then, based on the volume correction relationship during sample processing, the actual content of each metabolite in the original sample is calculated. The content of each target substance in the liquid sample is calculated using the following formula: The content of each substance in the sample (ng / mL) = C*(V1 / V4) / (V2 / V3); The meanings of the letters in the above formula are as follows: C: Concentration value (ng / mL) obtained by substituting the ratio of the integrated peak area in the sample into the standard curve; V1: Extraction volume (mL); V2: Volume of concentrated solution (mL); V3: Volume of the reconstituted solution (mL); V4: Sample volume (mL).

[0050] The obtained metabolite content data were compared using one-way ANOVA to compare differences between different time points in pregnancy. When the overall difference reached a significant level, Tukey's multiple comparison method was used for further comparison. GraphPad Prism 10 software was used to plot hormone concentration change curves. Results are expressed as mean ± standard error, with P < 0.05 as the standard for statistical significance and P ≥ 0.05 indicating no significant difference.

[0051] 3. Standard curves for candidate metabolic biomarkers In the early stages of this invention, a broad-based metabolomics and hormone association analysis was conducted. Sixteen candidate biomarkers screened through KEGG pathway enrichment analysis were validated. Quantitative analysis revealed that seven candidate metabolic biomarkers were stably detected: 4-Methylpentanal, Homocysteine, 5'-Deoxy-5'-(Methylthio)Adenosine, (20S)-20-Hydroxypregn-4-en-3-one, LPC(14:0 / 0:0), 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine, and Cortisol. As shown in Table 7, mass spectrometry peak intensity data for each concentration of standard solution were obtained. A linear regression equation was established, and the correlation coefficient (r) was calculated. Standard curves for different substances were then plotted based on the linear regression equation.

[0052] Table 7 Standard curves for 7 metabolites 4. Determination of the content of candidate metabolic markers To verify the candidate metabolic biomarkers screened in the previous analysis based on the association between hormones and broadly targeted metabolomics, this invention substitutes the peak area of ​​each target metabolite in the sample into the corresponding standard curve equation to calculate its concentration value, and further combines the volume conversion formula in the sample pretreatment process to obtain the absolute content of 7 metabolites in the sample.

[0053] 4.1 Determination of the levels of candidate metabolic markers related to hCG First, using the standard curve formula, 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine and (20S)-20-Hydroxypregn-4-en-3-one were precisely quantified. For example... Figure 10 As shown in Figure AB, the results indicated that 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine showed a continuous increasing trend from E12 to E21, reaching its highest level at E21; while (20S)-20-Hydroxypregn-4-en-3-one exhibited a pattern of first increasing and then decreasing, reaching a relatively high value at E15, followed by a gradual decline at E18 and E21. These results suggest that these two candidate metabolites exhibit significant temporal variation characteristics in early pregnancy in sows.

[0054] 4.2 Determination of the content of candidate metabolic markers related to E2 By combining the standard curve formula of LPC (14:0 / 0:0), the target substance can be accurately quantified. For example... Figure 10 As shown in Figure C, the results indicate that LPC (14:0 / 0:0) exhibits phased fluctuations in early pregnancy in sows. Its content increases from E12 to E15, decreases slightly at E18, and then increases significantly again at E21, reaching its highest level.

[0055] 4.3 Determination of the content of candidate metabolic markers related to P4 Based on standard curve formulas for 4-Methylpentanal, Homocysteine, 5'-Deoxy-5'-(Methylthio)Adenosine, and Cortisol, analysis revealed that P4-related candidate metabolites exhibited different dynamic change patterns in early pregnancy in sows. Figure 10 As shown in the DG results, 4-Methylpentanal continued to increase from E12 to E21, exhibiting a pattern consistent with the trend of P4; Homocysteine ​​showed an overall decreasing trend, opposite to P4; 5'-Deoxy-5'-(Methylthio)Adenosine gradually increased from E12 to E18 and maintained a high level at E21; Cortisol showed phased fluctuations, reaching its lowest level on day 15 of pregnancy, and remaining at around 10 ng / mL in the other three phases.

[0056] 5. Time-series association analysis of candidate metabolic biomarkers and reproductive hormones Based on the measured levels of seven candidate metabolic markers, Pearson correlation analysis was further performed between these seven target substances and three reproductive hormones (hCG, E2, and P4) to further explore their correlations. The results of the further correlation analysis are as follows: Figure 11 As shown, the results indicate that only 4-Methylpentanal is significantly correlated with P4 reproductive hormone levels, while 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine and 5'-Deoxy-5'-(Methylthio) Adenosine show some correlation trends, but do not reach a significant level.

[0057] 6. ROC curve evaluation of candidate metabolic biomarkers ROC curve analysis is used to assess the accuracy of metabolites as biomarkers and can identify metabolites that are crucial for distinguishing between groups. ROC curves are plotted to screen metabolites with significant differences and assess their classification and recognition capabilities. A better metabolite recognition ability is indicated by an area under the ROC curve (AUC) greater than 0.8, and the closer the AUC value is to 1, the better the recognition performance. Based on these results, to evaluate the reliability of the three metabolic biomarkers, ROC curves will be constructed and AUC values ​​calculated using targeted metabolomics data. ROC models will be constructed with E12 as the control group and E15, E18, and E21 respectively.

[0058] The results were obtained by evaluating the ROC curves of three key candidate metabolites. Figure 12 As shown, 4-Methylpentanal exhibited the most outstanding ROC performance. With E12 as the control group, its AUCs at E15, E18, and E21 were 0.813, 0.977, and 1.000, respectively, indicating that its discriminative ability continuously increased as pregnancy progressed, reaching almost complete differentiation at E21. In contrast, while 1-Stearoyl-2-palmitoyl-sn-glycero-3-phosphocholine and 5'-Deoxy-5'-(Methylthio) Adenosine also showed some diagnostic potential, their overall AUCs were lower than those of 4-Methylpentanal.

[0059] In summary, this invention, through extensive targeted metabolomics analysis of plasma samples from sows during the critical window of early pregnancy (E12-E21), combined with screening for correlations between temporal changes in reproductive hormones and metabolites, ultimately identified and validated 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-meththioadenosine, and 4-methylpentanal as temporal metabolic biomarkers characterizing the duration of early pregnancy in sows. This invention constructed content variation spectra of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-meththioadenosine, and 4-methylpentanal at four time points: E12, E15, E18, and E21. ROC curve analysis showed that, using E12 as a reference, 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-meththionine, and 4-methylpentanal all had AUC values ​​higher than 0.7 when distinguishing between E15, E18, and E21. 4-Methylpentanal was particularly outstanding, with AUCs reaching 0.813, 0.977, and 1.000, respectively, and its discriminative ability showed a significant increasing trend as pregnancy progressed. The AUCs of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine were 0.766, 0.907, and 0.860, respectively, showing a slight decrease at E21 compared to E18. The AUCs of 5-deoxy-5-meththionine were 0.711, 0.817, and 0.817, showing an overall stable trend. Overall, the AUC of the three markers remained stable above 0.7 at all time points, demonstrating good discriminative power.

[0060] In practical applications, 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-meththioadenosine, and 4-methylpentanal can serve as stable and quantifiable plasma metabolic indicators for the auxiliary identification of early pregnancy in sows. The specific procedure is as follows: peripheral blood from the anterior vena cava of the sow is collected, and after plasma separation, the three markers are quantitatively detected using LC-MS / MS or other detection methods. The obtained concentrations are then substituted into a standard curve to obtain absolute concentration values. These concentration values ​​can be directly used to assess the early pregnancy stage of the sow—higher concentrations indicate a longer gestation period (E12 < E15 < E18 < E21). Combined with the temporal variation patterns established in this invention, an objective judgment can be made regarding the early pregnancy stage of the sow. Compared with traditional methods that rely on reproductive hormones (hCG, E2, P4), this method has stronger time specificity, higher resolution accuracy, and good potential for operational standardization. It is suitable for early pregnancy monitoring, reproductive efficiency assessment, and precision feeding management in large-scale pig farms, and has clear practical value and prospects for industrialization.

[0061] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A plasma time-series metabolic biomarker characterizing the physiological state of sows in early pregnancy, characterized in that, The plasma time-series metabolic markers are 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphocholine, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal from sow plasma. The longer the gestation period of a sow, the higher the content of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal in her plasma.

2. The use of the product containing the plasma time-series metabolic markers of claim 1 in the preparation of a product for identifying the early gestation period of sows.

3. The use of the product containing the plasma time-series metabolic markers of claim 1 in the preparation of products for characterizing the physiological state of early pregnancy in sows.

4. A product for determining the early gestation period of sows, characterized in that, The product contains reagents or instruments for detecting the plasma time-series metabolic markers of claim 1.

5. The product according to claim 4, characterized in that, The longer the gestation period of a sow, the higher the content of 1-stearoyl-2-palmitoyl-sn-glycerol-3-phosphate choline, 5-deoxy-5-methylthioadenosine, or 4-methylpentanal in her plasma.

6. A product characterizing the physiological state of a sow in early pregnancy, characterized in that, The product contains reagents or instruments for detecting the plasma time-series metabolic markers of claim 1.

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