Method for evaluating health of sows in perinatal period based on metagenome sequence, synthetic microbial community and application

Abstract

The invention provides a method for evaluating the health of sows in the perinatal period based on metagenome sequences, a synthetic microbial community and application, and relates to the technical field of animal nutrition and biologication.The method comprises the steps of sample collection, index detection and judgment, microbial correlation analysis, screening platform construction and nutrient screening. According to the method, the problems that traditional diagnosis depends on highly invasive vein blood sampling and feedback lags behind are solved by excavating the relation between pregnant sow excrement microorganisms and serum insulin resistance and inflammatory factors. And early and accurate early warning of the metabolism health state of the sow can be realized by using fecal microorganisms. The constructed SynCom synthetic flora is not a general formula, but is a metabolic synergistic bacterium which is accurately locked based on big data and has the abundance significantly reduced in the perinatal period. Through directional supplement of the deleted members, excessive expansion of proinflammatory flora such as Prevotelasp.and the like can be effectively antagonized, and insulin resistance induced by inflammation is relieved from the source.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of animal nutrition and biotechnology, and in particular to a method for evaluating sow perinatal health based on metagenomic sequences, a synthetic microbial community and applications. BACKGROUND

[0002] The sow perinatal period (from the 107th day of pregnancy to the 7th day after delivery) is a critical transition period for its physiological metabolism. During this period, the body is under extremely high metabolic load and needs to complete the rapid switching from catabolism to lactation anabolism. This process is often accompanied by physiological insulin resistance (IR), which is manifested as a significant decrease in insulin sensitivity in peripheral target tissues such as skeletal muscle, fat, liver, etc., thereby leading to a decrease in glucose utilization efficiency, inducing compensatory hyperinsulinemia, and forming systemic metabolic disorders. Modern molecular immunology research has confirmed that chronic systemic low-grade inflammation is the core mechanism driving the occurrence and development of insulin resistance at this stage. The synergistic effect of insulin resistance and inflammation can cause multiple hazards to sows and offspring: for sows, glucose utilization disorders cause negative energy balance, and the body is forced to mobilize fat and protein reserves, producing a large amount of oxidative stress products, while inflammatory factors inhibit the hypothalamic appetite center, leading to a sharp decrease in feed intake during the perinatal period and early lactation, and insufficient energy supply to the uterine muscle layer causes contraction weakness, prolonging labor, increasing the risk of dystocia and stillbirth; for piglets, the adverse metabolic and inflammatory environment can interfere with placental nutrient transport, weaken the ability of the mammary gland to uptake nutrients, cause a decrease in colostrum and normal milk immunoglobulin content, and a disturbance in nutrient ratios, directly leading to low birth weight and low activity of piglets, and the lack of immune protection during lactation causes growth retardation, significantly increasing the mortality rate before weaning.

[0003] Although the hazards of sow perinatal insulin resistance have been clearly identified, there are still many core technical bottlenecks in the fields of health management and precise nutrition research, which restrict the improvement of breeding efficiency. The first problem is the lack of precise diagnostic methods and the lag, and the clinical evaluation of sow metabolic health highly depends on fasting blood glucose, serum insulin detection and insulin resistance index (HOMA-IR) calculation. Such indicators not only need to be obtained through invasive venous sampling, which can easily cause stress reactions in sows, but also only appear obvious changes after pathological conditions, which cannot realize early warning and large-scale screening. Secondly, the intervention means lack of pertinence and scientific support, and existing microecological preparations are mostly single probiotics or cross-species universal formulas, which cannot be designed in combination with the unique succession rules of sow perinatal intestinal flora, making it difficult to achieve precise adaptation and efficient intervention. Finally, there are obvious shortcomings in the research and development evaluation system. Traditional functional nutrients screening relies on large-scale live animal experiments, which need to span the entire pregnancy period, are greatly affected by individual differences in sows, and lead to high research and development costs and slow technical iteration.

[0004] To address the aforementioned problems, existing technologies have not yet formed a complete solution, and all methods have significant shortcomings, urgently requiring innovative technological breakthroughs. At the diagnostic level, there are currently no non-invasive, early warning molecular diagnostic models, which cannot meet the precise monitoring needs of large-scale farming scenarios. At the intervention level, general-purpose microecological preparations are difficult to target and regulate the gut microbiota of peripartum sows, failing to fundamentally alleviate the synergistic effect of inflammation and insulin resistance. At the R&D evaluation level, commercially available in vitro screening platforms mostly focus on basic gas production or general digestive function testing, lacking the ability to simulate the complex regulatory logic between microbiota, metabolites, inflammation, and insulin resistance, thus failing to provide accurate and efficient evaluation support for the development of functional nutrients. These technological shortcomings make it difficult to effectively manage peripartum insulin resistance in sows, thereby affecting farming efficiency and piglet survival rates. Against this backdrop, developing an integrated technical solution encompassing precise diagnosis, targeted intervention, and efficient R&D evaluation has become an urgent need in the field of animal nutrition, leading to the development of this invention. Summary of the Invention

[0005] In view of this, the present invention provides a method for assessing the peripartum health of sows based on metagenomic sequences, a synthetic microbial community and its application, which solves the technical problem that the lack of an integrated technical solution for accurate diagnosis, targeted intervention and efficient research and evaluation in the prior art makes it difficult to effectively control the peripartum insulin resistance of sows, thereby affecting breeding efficiency and piglet survival rate.

[0006] According to one aspect of the present invention, the present invention provides a method for assessing the peripartum health of sows based on metagenomic sequences, comprising the following steps: (1) Sample collection: Fresh fecal and blood samples were collected from sows during gestation and peripartum periods for subsequent testing and analysis; (2) Indicator detection and judgment: The fasting glucose content and insulin content in the blood sample are detected, and the fasting glucose content and insulin content are used as the basis for judging the changes in insulin sensitivity of sows; (3) Microbial correlation analysis: Metagenomic sequencing technology was used to analyze the composition and structure of fecal microorganisms in sows at different physiological stages, analyze the correlation between sow insulin sensitivity and fecal microbial species, and identify fecal microbial species that are strongly correlated with the decrease in insulin sensitivity in sows during the peripartum period. (4) Construction of rapid screening platform: Based on the above correlation analysis results, a rapid screening platform for nutrients to improve insulin sensitivity in sows during the peripartum period was established; (5) Nutrient screening: Using fecal microorganisms from peripartum sows as the inoculation source, in vitro nutritional intervention experiments were conducted through the aforementioned nutrient rapid screening platform to identify the types of nutrients that can target and promote the growth of microorganisms that are highly correlated with the insulin sensitivity of sows, thereby achieving targeted nutrient screening.

[0007] Optionally, the gestational period specifically refers to the second trimester, which is from day 60 to 85 of gestation, and the perinatal period is from day 107 of gestation to day 7 postpartum.

[0008] Optionally, the blood sample is anterior vena cava blood sample, and the number of samples for each physiological stage and each type of sample is not less than 50.

[0009] Optionally, the insulin resistance index can be calculated by detecting fasting glucose and insulin levels in serum samples to quantify the insulin sensitivity of sows; The insulin resistance index is calculated as: fasting blood glucose mmol / L × fasting insulin μU / mL / 22.5.

[0010] Optionally, the insulin resistance index is positively correlated with the abundance of a certain strain of Prevotella, and is significantly negatively correlated with the abundance of Lactobacillus reuteri, Lactobacillus johnsonii, and Lactobacillus amyloliquefaciens.

[0011] Optionally, the rapid nutrient screening platform constructs an in vitro fermentation evaluation system by simulating the physiological environment of the sow's intestines, and completes nutrient screening and effect evaluation by monitoring the changes in the abundance of target microorganisms in the fermentation broth.

[0012] Optionally, the nutrients include, but are not limited to, pectin, glucan, glucomannan, arabinoxylan, or arabinogalactan, and their targeted promotion effect on target microorganisms and their potential to improve insulin sensitivity in sows can be verified through the screening platform.

[0013] According to one aspect of the present invention, the present invention provides a synthetic microbial community obtained by screening based on any of the methods described above, the synthetic microbial community being composed of metabolic synergists, specifically including Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus amyloliquefaciens.

[0014] Optionally, the biomass ratio of Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus amyloliquefaciens is from 1:1:1 to 3:2:1.

[0015] According to one aspect of the present invention, the present invention provides an application of the synthetic microbial community as described above in evaluating the peripartum health of sows.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) Precise and non-invasive diagnosis: This invention solves the problem of traditional diagnosis relying on highly invasive venous blood sampling and having a delayed feedback by exploring the relationship between fecal microorganisms of pregnant sows at days 85 and 110 and serum insulin resistance and inflammatory factors. Early and accurate warning of the metabolic health status of sows can be achieved by utilizing fecal microorganisms.

[0017] (2) The intervention strategy is scientific and targeted: The SynCom synthetic microbiota constructed in this invention is not a general formula, but a metabolically potentiating bacterium that has experienced a significant decrease in abundance during the perinatal period, precisely identified based on big data. By selectively replenishing these missing members through nutrient-targeted regulation, it is possible to effectively counteract the excessive expansion of pro-inflammatory bacteria such as Prevotella sp., thereby alleviating inflammation-induced insulin resistance from the source.

[0018] (3) High R&D efficiency and low cost: The supporting in vitro rapid screening platform can highly simulate the real physicochemical environment of sows during the peripartum period (39℃, pH 6.85). Compared with time-consuming and laborious animal live experiments, this platform can complete the preliminary determination of substrate regulation ability within 36-48 hours, which greatly shortens the R&D cycle of nutrients that improve insulin sensitivity in sows.

[0019] (4) Strong applicability: The nutrients such as arabinoxylan and arabinogalactan demonstrated by this invention exhibit excellent probiotic growth-promoting activity in in vitro systems, providing solid data support for the industrial production of high-quality, functionally defined feed additives for sows. 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 description of the embodiments or the prior art 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 This is a PCoA analysis diagram of microbial β-diversity differences; Figure 2 This is a Veen diagram showing the variation in the gut microbiota composition of sows during pregnancy; Figure 3 It is a bar chart of the relative abundance of microbial communities; Figure 4 This is a heatmap of the abundance of fecal microorganisms at the species level on days 85 and 110 of gestation in sows. Figure 5 This is a Wilcoxon rank-sum test plot; Figure 6This is a LEfSe analysis chart; Figure 7 This is a Spearman correlation analysis plot; Figure 8 This is a diagram showing the β-diversity of microorganisms involved in the in vitro fermentation of five nutrients. Figure 9 This is a differential criterion plot of LEfSe from in vitro fermentation microorganisms of five nutrients; Figure 10 This is a topology diagram of an in vitro nutrient screening platform for improving insulin sensitivity in sows. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0023] Example 1: Screening and construction of synthetic communities of insulin-sensitive microorganisms in sows based on metagenomics I. Experimental Objective By analyzing the differences and correlations in fasting blood glucose, blood insulin, and fecal microbial metagenomics of 53 sows at different physiological stages and from different samples, the types of differentially expressed microorganisms associated with decreased insulin sensitivity in sows during the peripartum period were identified, such as... Figure 10 As shown.

[0024] II. Materials and Methods 1. Sample collection Fecal samples: Fecal samples were collected from 53 sows at gestation days 85 and 110. Approximately 5 g of fresh feces were collected from each sow, aliquoted into 10 mL sterile centrifuge tubes, and immediately stored in liquid nitrogen for analysis of the microbial composition of the feces. Blood samples: On days 85 and 110 of gestation, approximately 5 mL of blood was collected from the anterior vena cava of 53 sows. After standing for 30 min, the blood was centrifuged at 3,500 rpm (3,000 × g) for 15 min at 4°C. The supernatant serum was collected and stored at -80°C for further analysis. 2. Sample Analysis (1) Fecal microbial metagenomics: DNA was extracted from samples using the FastPure Stool DNA Isolation Kit (MJYH, Shanghai, China). After DNA extraction, DNA concentration and purity were measured, and DNA integrity was checked using 1% agarose gel electrophoresis. DNA fragmentation was performed using a Covaris M220 (Genesys Pharma, China), and fragments of approximately 350 bp were selected for constructing PE libraries. Library construction was performed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, USA). Metagenomic sequencing was performed using the Illumina NovaSeq™ X Plus (Illumina, USA) sequencing platform, completed by Shanghai MajorBio Technology Co., Ltd., and subsequent analyses were performed on the MajorBio cloud platform (https: / / cloud.majorbio.com).

[0025] (2) The concentrations of TNF-α, IL-10 and insulin (INS) in sow serum were detected using an ELISA kit (Nanjing Jiancheng Biotechnology Institute, Nanjing, China). Serum GLU concentration was determined using a fully automated biochemical analyzer (Jiangsu Zecheng Biotechnology Co., Ltd.).

[0026] III. Test Results 3.1. The differences in serum inflammatory factors and glucose metabolism-related indicators in gestational sows at 85 and 110 days of gestation are shown in Table 1. The results showed that there was no significant difference in serum glucose levels between gestational sows at 110 days and 85 days. Serum INS, HOMA-IR, and the inflammatory factor TNF-α were significantly increased in gestational sows at 110 days of gestation. P < 0.05), the anti-inflammatory factor IL-10 decreased significantly ( P < 0.05).

[0027] Table 1 Serum inflammatory factors and glucose metabolism-related indicators ( n = 53) 3.2. Differences in fecal microbiota between pregnant sows at 85 days and 110 days of gestation. 3.2.1 Differences in β-diversity To clarify the overall differences in the gut microbiota of sows at different stages of pregnancy, this study performed PCoA analysis based on metagenomic data, such as... Figure 1 As shown, a clear separation trend was observed in samples d85 and d110. This indicates that the species composition of the sow's gut microbiota undergoes a fundamental change as pregnancy progresses into the perinatal period.

[0028] 3.2.2 Veen Diagram Analysis To further analyze the variation in gut microbiota composition during gestation in sows, this study performed Venn diagram analysis on the fecal metagenomics of day 85 and day 110, as follows: Figure 2 As shown, there are 25,556 common microorganisms between d85 and d110, accounting for 95.57% of the total composition. d85 has 634 specific microorganisms (2.37%), which represent health indicators for sows during metabolic homeostasis. d110 has 551 microorganisms (2.06%), which show a high degree of spatiotemporal consistency with insulin resistance and increased inflammation levels observed in sows after entering the peripartum period.

[0029] 3.2.3 Relative Abundance Community Bar Chart Species-level tagging of fecal metagenomics at d85 and d110 revealed that while the gut microbiota remained relatively stable during the peripartum period, significant structural succession of core functional strains occurred, such as... Figure 3 As shown, Lactobacillus johnsonii , Limosilactobacillus reuteri Significant decrease, while Prevotella _sp. and other parameters increased significantly. 3.2.4 Relative Abundance of Communities Abundance heatmap analysis of fecal microorganisms at the species level in sows on days 85 and 110 of gestation, such as... Figure 4 As shown, the microbial community exhibits highly regular and significantly characteristic abundance changes after entering the perinatal period. The results indicate that d110... Prevotella _sp.、 Treponema _sp. and Sodaliphilus The abundance of species represented by *Lactobacillus* sp. increased significantly, indicating that these bacterial communities accumulate in large quantities as pregnancy progresses; at the same time, many probiotic groups with potential metabolic regulatory functions, especially members of the *Lactobacillus* genus, such as... Lactobacillus johnsonii , Limosilactobacillus reuteri , Lactobacillus _sp. and Lactobacillus amylovorus Its abundance decreased dramatically in the d110 stage.

[0030] 3.2.5 Microbial Differences Analysis of the proportion of fecal microorganisms at d85 and d110 stages using the Wilcoxon rank-sum test showed highly significant changes in the core microbiota, such as... Figure 5 As shown. The results indicate that, Prevotella _sp.、 Treponema _sp.、 Candidatus Cryptobacteroides _sp. andSodaliphilus The relative abundance of _sp. at d 110 was significantly increased. Conversely, Lactobacillus johnsonii and Limosilactobacillus reuteri It decreased significantly in the d110 stage.

[0031] 3.2.6 LEfSe Analysis Species showing significant differences between the d85 and d110 stages were identified using linear discriminant analysis of effect size (LEfSe), such as... Figure 6 As shown, the results indicated a highly significant difference in biomarkers between the two groups at an LDA score greater than 3.0. The characteristic microbiota significantly enriched in the gut of d85 mainly included... Lactobacillus johnsonii , Clostridium _sp.、 Lactobacillus sp. , Limosilactobacillus reuteri , Lactobacillus amylovorus , Terrisporobacter _sp. and Akkermansia The presence of *Lactobacillus* species such as *_sp.* reflects the dominance of beneficial bacteria at this stage. In the d110 stage, a fundamental shift in the dominant species occurred, manifested as... Prevotella _sp.、 Treponema _sp.、 Sodaliphilus _sp. and Candidatus Onthomorpha _sp.、 Candidatus Cryptobacteroides _sp.、 Candidatus Limimorpha caballi Significant upregulation in strains such as [list of strains].

[0032] 3.2.7 Spearman Correlation Analysis Spearman correlation analysis of the abundance of core gut microbiota in sows with host inflammatory factors and glucose metabolism indicators revealed a significant functional coupling between microbial community succession and the physiological health status of sows in late pregnancy. Results are as follows: Figure 7 As shown, significantly enriched in the d110 stage Prevotella _sp. showed a highly significant negative correlation with the anti-inflammatory factor IL-10, and a highly significant positive correlation with insulin (INS) and the insulin resistance index (HOMA-IR). Treponema _sp.、 Caudoviricetes *Lactobacillus* sp. and various *Candidatus* species also generally showed a positive correlation with INS, HOMA-IR, and the pro-inflammatory factor TNF-α, revealing the potential risk role of this type of flora in inducing peripartum insulin resistance and systemic inflammation in sows. In stark contrast, *Lactobacillus* members, particularly those dominant at day 85, showed a significantly lower risk. Limosilactobacillus reuteri , Lactobacillus sp .and Lactobacillus amylovorusIt showed a highly significant positive correlation with IL-10 and a highly significant negative correlation with INS and HOMA-IR, suggesting that Lactobacillus plays a core synergistic role in maintaining host glucose metabolism homeostasis and anti-inflammatory balance.

[0033] IV. Results Analysis This study systematically analyzed the physiological indicators and gut metagenomic data of 53 sows at days 85 and 110, revealing the microecological evolution mechanism of decreased insulin sensitivity in sows during the peripartum period, and providing a core basis for the construction of nutrient screening platforms and synthetic microbial communities.

[0034] 1. Succession of perinatal metabolic characteristics and gut microbiota structure As sows transition from late gestation to the peripartum period, their physiological state undergoes a significant shift. PCoA analysis revealed highly significant community segregation (R=0.65). P The correlation coefficient (= 0.001) and the Venn plot together demonstrate that perinatal stress drives the remodeling of the gut microbiota. Although the core flora exhibits high stability (95.57%), the rise and fall of key minority strains are the decisive factors in altering metabolic state.

[0035] 2. Screening and identification of core functional strains By combining the Wilcoxon rank-sum test and LEfSe analysis (LDA > 3.0), this study identified the key bacterial communities driving metabolic fluctuations: Healthy synergistic bacteria (d85 marker): Lactobacillus johnsoni i Limosilactobacillus reuteri and Lactobacillus amylovorus Lactobacillus species, represented by *Lactobacillus*. These strains were significantly enriched at day 85 and are a key force in maintaining metabolic homeostasis during gestation in sows.

[0036] Stress-prone bacteria (d110 marker): Prevotella _sp.、 Treponema _sp.、 Sodaliphilus Represented by *Candidatus* sp. and various other microorganisms. They proliferate significantly during the perinatal period, constituting a precise predictive indicator for assessing metabolic disorders.

[0037] 3. Correlation between microorganisms and insulin sensitivity Spearman correlation analysis further clarified the causal logic between the above-mentioned microbial community and host phenotype: Lactobacillus species showed a highly significant positive correlation with the anti-inflammatory factor IL-10 and a highly significant negative correlation with INS and HOMA-IR. This demonstrates that Lactobacillus plays an irreplaceable positive role in improving insulin sensitivity in sows by maintaining immune homeostasis.

[0038] Prevotella _sp., on the other hand, showed completely opposite association characteristics, with its upregulation directly pointing to increased systemic inflammation and exacerbated insulin resistance.

[0039] 4. Constructing synthetic microbial communities SynCom's key microbes are dominant in late gestation (d 85) of sows, but significantly shrink during the peripartum period (d 110) when insulin sensitivity declines.

[0040] Lactobacillus johnsonii ( Lactobacillus johnsonii The abundance of d110 decreased significantly from about 4.5% in d85 to about 1% in d110. It is one of the most significant biomarkers in the d85 stage (LDA > 4) and shows a clear negative correlation with HOMA-IR.

[0041] Lactobacillus reuteri ( Limosilactobacillus reuteri The abundance of this bacterium decreased significantly from approximately 2.5% on day 85 to below 0.5% on day 110. This bacterium showed a highly significant positive correlation with IL-10 (an anti-inflammatory factor) and a highly significant negative correlation with INS (insulin) and HOMA-IR, making it a core bacterium for regulating glucose metabolism.

[0042] Lactobacillus amyloliquefaciens ( Lactobacillus amylovorus The heatmap showed a severe loss in the d110 stage, which was identified by LEfSe analysis as a key marker of d85 and was significantly negatively correlated with HOMA-IR.

[0043] Based on the above correlation analysis between metagenomics and physiological indicators, the SynCom component ratio determined in this embodiment ( Lactobacillus johnsonii : Limosilactobacillus reuteri : Lactobacillus amylovorus The ratios of 1:1:1 to 3:2:1 are derived from a dual logical deduction based on the reduction of d85 microbial abundance and the weighting of functional enhancement. Natural abundance restoration (upper limit ratio 3:2:1): Refer to healthy d 85 stage Lactobacillus johnsonii (Approximately 4.5%) and Limosilactobacillus reuteri (Approximately 2.5%) The ratio is approximately 1.8:1. This is in combination with slightly lower abundance. Lactobacillus amylovorus It also has a significant distribution at this stage, but the abundance is slightly lower. A 3:2:1 upper limit is set to restore the original micro-ecological structure to the greatest extent.

[0044] Functional enhancement weights (lower limit ratio 1:1:1): based on correlation analysis. Lactobacillus johnsoniiIt showed a highly significant positive correlation with the anti-inflammatory factor IL-10 and a highly significant negative correlation with HOMA-IR and TNF-α. By proportionally strengthening the core functional bacteria, the aim was to compensate for the metabolic function deficiency caused by the reduced abundance of Lactobacillus in the d110 stage.

[0045] Example 2: In vitro fermentation screening of nutrients that improve insulin sensitivity in sows during the peripartum period I. Experimental Objective This experiment aims to screen nutrients that improve insulin sensitivity in sows during the peripartum period through in vitro fermentation.

[0046] II. Materials and Methods 2.1. Materials The nutrients involved in Example 2 are: pectin, β-glucan, glucomannan, arabinoxylan, and arabinogalactan.

[0047] 2.2. In vitro fermentation method 2.2.1 Preparation of microbial inoculum To simulate the intestinal environment of sows during the peripartum period, fresh feces were collected from healthy sows at day 110 of gestation.

[0048] Collection and mixing: In an anaerobic workbench, feces: physiological saline: glycerol were mixed in a ratio of 1:5:0.6. After thorough processing using a pulverizer, the mixture was dispensed into 50 mL centrifuge tubes and rapidly frozen in liquid nitrogen. Finally, it was stored at -80°C for later use. Pre-inoculation treatment: Before use, thaw the bacterial culture in a 39°C water bath and centrifuge at 800 r / min for 3 min, and collect the supernatant as the inoculation solution.

[0049] 2.2.2 Pre-digestion of experimental substrates Gastric digestion: Weigh 7.5-10 g of the substrate into an Erlenmeyer flask and add 250 mL of phosphate buffer A (0.1 mol / L, pH=6.0) to prepare a suspension. Adjust the pH to 3.5 with 0.1 mol / L hydrochloric acid, then add 15 mL of pepsin solution (0.025 g / mL). Stir in a 39℃ water bath for 120 min.

[0050] Small intestinal digestion: After gastric digestion, add 50 mL of 0.6 mol / L sodium hydroxide and 100 mL of phosphate buffer B (0.2 mol / L, pH=6.8) to adjust the pH to 6.8. Add 15 mL of trypsin solution (0.1 g / mL) to each bottle and incubate in a water bath for 4 h.

[0051] Collection of pre-digestion residue: After digestion, filter the residue using a 300-mesh filter bag and rinse the container with distilled water to ensure complete transfer of the material. After drying the filter bag at 65℃ for 24-36 h, collect the dry material and store it at -20℃ for later use.

[0052] 2.2.3 Preparation of in vitro fermentation culture medium Solution A (mineral mixture): Dissolve the following chemical reagents in 100 mL of ultrapure water: 0.66 g calcium chloride (CaCl2·2H2O); 0.5 g manganese chloride (MnCl2·4H2O); 0.05 g cobalt chloride (CoCl2·6H2O); 0.4 g ferric chloride (FeCl3·6H2O). Solution B (nitrogen-containing buffer): Dissolve the following chemical reagents in 500 mL of ultrapure water: 2.0 g ammonium bicarbonate (NH4HCO3); 17.5 g sodium bicarbonate (NaHCO3); 0.841 g L-cysteine ​​(L-cys); Solution C (phosphate buffer): Dissolve the following chemical reagents in 500 mL of ultrapure water: 2.85 g disodium hydrogen phosphate (Na2HPO4); 3.1 g potassium dihydrogen phosphate (KH2PO4); 0.3 g magnesium sulfate heptahydrate (MgSO4·7H2O); Solution 4 (reducing solution): Dissolve the following reagents in 100 mL of ultrapure water: 0.625 g sodium sulfide nonahydrate (Na2S·9H2O); 0.16 g sodium hydroxide (NaOH).

[0053] Preparation of working culture medium: Mix 800 mL of ultrapure water, 0.2 mL of solution A, 400 mL of solution B, 400 mL of solution C, and 80 mL of reducing solution. Adjust the pH to 6.85 using 6 mol / L hydrochloric acid or 10 mol / L sodium hydroxide.

[0054] Sterilization: Place the prepared culture medium in an autoclave for high-pressure sterilization to ensure no interference from other microorganisms.

[0055] 2.2.4 Fermentation process Weighing and Adding Liquid: Accurately weigh 0.3 g of the predigested substrate and place it into a sterile fermentation flask. Ensure that the particle size of each sample group is consistent to eliminate the influence of physical morphology on fermentation kinetics. Add 25 mL of sterile fermentation culture medium to each flask and preheat in an incubator for 10-15 min to ensure that the temperature inside the flask reaches a constant 39°C.

[0056] Anaerobic inoculation: Add 5 mL of inoculum to each bottle in an anaerobic incubator. Immediately after inoculation, stopper and cap the bottle to seal it, then place it in a 39°C incubator to begin fermentation.

[0057] 2.3. Sample Analysis Six technical replicates were set up for each nutrient. After 36 h of fermentation, the pH of the fermentation broth was measured, and 2 mL of the fermentation broth was stored in cryovials. Microbial community analysis was performed by metagenomic sequencing; the analytical methods are detailed in Example 1.

[0058] III. Test Results 3.1. β-diversity of in vitro fermentation microorganisms for five nutrients PCoA analysis of in vitro fermentation microbial communities for five nutrients revealed significant differences in the ability of different substrates to reshape microbial β-diversity, such as... Figure 8 As shown, arabinoxylan had the most dramatic effect on the microbial community structure, exhibiting the highest R value and the clearest intergroup separation (R = 0.646, P = 0.003). Arabinogalactan (R = 0.606, P = 0.004) and glucomannan (R = 0.581, P = 0.005) also showed highly significant community-regulating effects, significantly altering the spatial distribution of microorganisms in the fermentation system.

[0059] In contrast, the regulatory effect of β-glucan was relatively moderate but still significant (R = 0.374, P = 0.01). However, the pectin-treated group and the control group showed high overlap on the PCoA axis and failed to induce significant changes in the microbial community structure (R = 0.131, P = 0.139). Overall, arabinoxylan, arabinogalactan, and glucomannan exhibited stronger microbial community regulatory activity in in vitro fermentation systems and are advantageous trophic nutrients for improving the intestinal health of peripartum sows.

[0060] 3.2. β-diversity of in vitro fermentation microorganisms for five nutrients Based on LEfSe discriminant analysis, it was found that the five tested nutrients exhibited significant species specificity in regulating the in vitro fermentation microbial community, and showed different performances in enriching the SynCom core members identified in this invention. The results are as follows... Figure 9 The results showed that glucomannan, arabinoxylan, and arabinogalactan exhibited the best precise regulatory effects, and these three types of nutrients could significantly enrich (LDA > 3.0) all three core synergistic strains in SynCom, namely... Lactobacillus johnsonii , Limosilactobacillus reuteri andLactobacillus amylovorus .

[0061] Selective regulation of β-glucan: β-glucan can significantly upregulate the levels of β-glucan in SynCom. Lactobacillus johnsonii and Limosilactobacillus reuteri However, they failed to show any interest in Lactobacillus amylovorus Significant enrichment effect. Limitations of pectin: The pectin-treated group failed to effectively induce the three core metabolic synergists in SynCom, and the main biomarkers for significant enrichment were... Lactobacillus delbrueckii Non-target strains. Characteristics of the control group (Con): In the control group without the above nutrient intervention, the enriched biomarkers were mainly concentrated in... Prevotella _sp. and Treponema Species such as *Lactobacillus* sp. were included. In vivo association analysis showed that these species were significantly positively correlated with HOMA-IR and inflammatory factor levels in sows during the peripartum period. In summary, the rapid screening platform established using this invention demonstrates that arabinoxylan, arabinogalactan, and glucomannan possess significant potential to improve insulin sensitivity in sows during the peripartum period by precisely enriching the core *Lactobacillus* members targeted in this invention.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing peripartum health of sows based on metagenomic sequences, characterized in that, Includes the following steps: (1) Sample collection: Fresh fecal and blood samples were collected from sows during gestation and peripartum periods for subsequent testing and analysis; (2) Indicator detection and judgment: The fasting glucose content and insulin content in the blood sample are detected, and the fasting glucose content and insulin content are used as the basis for judging the changes in insulin sensitivity of sows; (3) Microbial correlation analysis: Metagenomic sequencing technology was used to analyze the composition and structure of fecal microorganisms in sows at different physiological stages, analyze the correlation between sow insulin sensitivity and fecal microbial species, and identify fecal microbial species that are strongly correlated with the decrease in insulin sensitivity in sows during the peripartum period. (4) Screening platform construction: Based on the above correlation analysis results, a rapid screening platform for nutrients to improve insulin sensitivity in sows during the peripartum period was established; (5) Nutrient screening: Using fecal microorganisms from peripartum sows as the inoculation source, in vitro nutritional intervention experiments were conducted through the aforementioned nutrient rapid screening platform to identify the types of nutrients that can target and promote the growth of microorganisms that are highly correlated with the insulin sensitivity of sows, thereby achieving targeted nutrient screening.

2. The method according to claim 1, characterized in that, The term "pregnancy period" specifically refers to the second trimester, which is from day 60 to day 85 of pregnancy. The term "perinatal period" refers to the period from day 107 of pregnancy to day 7 postpartum.

3. The method according to claim 1, characterized in that, The blood samples are anterior vena cava blood samples, and the number of samples for each physiological stage and each type of sample is no less than 50.

4. The method according to claim 1, characterized in that, The insulin resistance index is calculated by detecting fasting glucose and insulin levels in serum samples to quantify insulin sensitivity in sows. The insulin resistance index is calculated as: fasting blood glucose mmol / L × fasting insulin μU / mL / 22.

5.

5. The method according to claim 4, characterized in that, The insulin resistance index was positively correlated with the abundance of a certain strain of Prevotella, and was significantly negatively correlated with the abundance of Lactobacillus reuteri, Lactobacillus johnsonii, and Lactobacillus amyloliquefaciens.

6. The method according to claim 1, characterized in that, The rapid nutrient screening platform constructs an in vitro fermentation evaluation system by simulating the physiological environment of the sow's intestines. It completes nutrient screening and effect evaluation by monitoring changes in the abundance of target microorganisms in the fermentation broth.

7. The method according to claim 6, characterized in that, The nutrients include, but are not limited to, pectin, glucan, glucomannan, arabinoxylan, or arabinogalactan, and their targeted promotion effects on target microorganisms and their potential to improve insulin sensitivity in sows can be verified through the screening platform.

8. A synthetic microbial community obtained by screening based on the method described in any one of claims 1-7, characterized in that, The synthetic microbial community is composed of metabolic synergists, specifically including Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus amyloliquefaciens.

9. The synthetic microbial community according to claim 8, characterized in that, The biomass ratio of Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus amyloliquefaciens is from 1:1:1 to 3:2:

1.

10. An application of the synthetic microbial community as described in claim 8 or 9 in evaluating the peripartum health of sows.

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