Microecological marker for evaluating the potential of fiber digestion in pig feed and application thereof

Abstract

The present application relates to the field of biotechnology, in particular to a kind of microecological marker for evaluating the potential of pig feed fiber digestion and its application.The microecological marker includes microorganism and metabolite marker;The microorganism marker is Prevotella symbiotica ( Prevotella communis ) And / or ruminococcus flavefaciens ( Xylanibacter ruminicola ) ; The metabolite marker is methyl helianthoneate F glucoside (Methyl Helianthoneate F Glucoside).The present application also provides a method for evaluating the potential of pig feed fiber digestion, and ruminococcus flavefaciens is combined with methyl helianthoneate F glucoside, and the AUC value of its prediction of the potential of pig feed fiber digestion can reach 0.917, which is significantly better than single marker.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a microecological biomarker for assessing the digestibility potential of pig dietary fiber and its application. Background Technology

[0002] Currently, the pig farming industry is highly dependent on corn-soybean meal diets. Developing and utilizing abundant unconventional fiber feed resources such as soybean hulls, wheat bran, and corn stalks is an important way to reduce feeding costs. However, as monogastric animals, pigs have difficulty secreting fiber-degrading enzymes (such as cellulase and hemicellulase), resulting in generally low utilization rates of dietary fiber, especially with high-fiber diets.

[0003] Studies have shown that the gut microbiota (mainly the cecum and colon) of pigs is a key executor of fiber degradation. Significant differences in gut microbiota structure exist among individuals, leading to vast variations in their ability to digest and utilize the same fiber diet. Therefore, identifying core microorganisms and metabolite markers related to pigs' efficient degradation and utilization of crude fiber could provide microecological targets for early breeding of pig breeds with "efficient fiber utilization," and allow for targeted enrichment of these beneficial bacteria through nutritional regulation, thereby achieving precise improvement in fiber feed utilization.

[0004] While existing research reports on the relationship between pig gut microbiota and fiber digestion exist, most remain at the level of describing differences in microbial community structure, failing to identify biomarkers with high predictive accuracy that can be directly applied to production practices. In particular, there is a lack of schemes that can combine microorganisms with key metabolites as biomarkers. Furthermore, whether the combined use of specific metabolites, as products or signaling molecules of fiber degradation, with functional microbiota can improve predictive accuracy is not reported in existing technologies. Therefore, there is an urgent need in this field for a tool and method that can effectively assess the potential of pigs for dietary fiber digestion to guide precision feeding and breed selection in actual production. Summary of the Invention

[0005] In view of the deficiencies and shortcomings of the prior art, the purpose of this invention is to provide a microecological marker for assessing the digestibility potential of pig dietary fiber and its application, aiming to provide a reference for judging the utilization potential of crude fiber feed by pigs.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of this invention provides the application of reagents for detecting the abundance or concentration of microecological markers in the preparation of products predicting the digestibility potential of pig dietary fiber, wherein the microecological markers include microbial and metabolite markers; the microbial markers are Prevotella commensal bacteria (…). Prevotella communis) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methylhelianthoneate F glucoside.

[0007] In the aforementioned applications, the reagents for detecting the microbial markers include those used in any of the following methods: 16S rRNA gene sequencing, metagenomic sequencing, and quantitative real-time PCR.

[0008] In the aforementioned application, the reagents used to detect the metabolite markers include those used in the following method: liquid chromatography-mass spectrometry.

[0009] A second aspect of this invention provides a kit for evaluating the digestibility potential of pig dietary fiber, comprising specific primers or probes for detecting the abundance of a microbial biomarker, and standards for detecting the concentration of a metabolite biomarker; wherein the microbial biomarker is a Prevotella symbiotic bacterium (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methylhelianthoneate F glucoside.

[0010] A third aspect of the present invention provides a method for evaluating the fiber digestibility potential of pig diets, comprising the following steps: The abundance or concentration of microecological markers in fecal samples from pigs was detected, including microbial and metabolite markers; the microbial markers were Prevotella symbiotic bacteria (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methyl helianthoneate F glucoside. If the detection results are input into the prediction model, and the predicted probability of the microecological marker is higher than 0.5, then the pig's fiber digestion potential is determined to be better than the average level of the population.

[0011] A fourth aspect of the present invention provides a method for raising pigs, comprising the following steps: The abundance or concentration of microecological markers in fecal samples from pigs was detected, including microbial and metabolite markers; the microbial markers were Prevotella symbiotic bacteria (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methyl helianthoneate F glucoside. The detection results are input into the prediction model. If the prediction probability of the microecological marker is higher than 0.5, it indicates that the pig has good digestibility potential for dietary fiber; otherwise, it indicates that its digestibility potential is relatively limited. For pigs or herds predicted to have relatively limited digestive potential, it is recommended to add exogenous fiber-degrading enzyme preparations or microecological preparations to their diet, or reduce the dietary fiber level; for pigs or herds predicted to have good digestive potential, the proportion of fiber feed in the diet should be appropriately increased.

[0012] Beneficial effects: This invention provides a microecological biomarker for assessing the digestibility potential of pig dietary fiber, the microecological biomarker including microbial and metabolite biomarkers; the microbial biomarker is a Prevotella symbiotic bacterium (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methyl helianthoneate F glucoside. Detecting the abundance or concentration of this microecological marker in pig feces can provide a reference for assessing the pigs' potential for utilizing crude fiber feed, thereby helping farmers adopt targeted and differentiated feeding management strategies and reduce feeding costs. Attached Figure Description

[0013] Figure 1 The figure shows the effects of high-fiber diets (HCF) and low-fiber diets (LCF) on the body weight, fiber digestibility, enzyme activity, and gut microbiota structure of Rongchang pigs in this embodiment of the invention.

[0014] Figure 2 This is a diagram showing the key bacterial species and the minimum combined bacterial community covering the entire pathway in each process of cellulose degradation, as selected by the random forest model in this embodiment of the invention.

[0015] Figure 3 This is a diagram showing the key bacterial species and the minimum combined bacterial community covering the entire pathway in each process of hemicellulose degradation, as selected by the random forest model in this embodiment of the invention.

[0016] Figure 4 This is a graph showing the screening of metabolites in this invention, and the results of screening microorganisms and metabolite combinations related to pig fiber digestion using "logistic regression + leave-one-out cross-validation". It includes differential metabolite volcano plots, and ROC curve comparison graphs of bacteria, metabolites, and "bacteria + metabolite" combinations. Detailed Implementation

[0017] This invention provides a microecological biomarker for assessing the digestibility potential of pig dietary fiber and its application. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0018] Example 1 Experimental Design and Sample Collection 1. Laboratory animals and diets Twenty-four healthy Rongchang pigs of similar weight (approximately 234 days old) were randomly divided into two groups: a low-fiber diet group (LCF, n=12) and a high-fiber diet group (HCF, n=12). Both diets were isonitrogenous and isoenergetic. The high-fiber diet, with soybean hulls as the main source of fiber (13.92%) and a small amount of rice bran added, had a significantly higher crude fiber content (8.49%) than the low-fiber diet group (3.39%).

[0019] 2. Feeding Management and Sample Collection Experimental pigs were housed individually with free access to feed and water. The pre-feeding period was 7 days, and the trial period was 23 days. During the last 3 days of the trial period, fresh fecal samples were collected from each pig. A portion was immediately used to determine fecal cellulose and hemicellulase activity, while the remainder was flash-frozen in liquid nitrogen and stored at -80°C for metagenomics and metabolomics analysis. Feed intake was recorded, and body weight was measured at the end of the experiment.

[0020] Example 2 Phenotypic determination 1. Measurement indicators: Growth performance: Record body weight and calculate average daily weight gain.

[0021] Apparent fiber digestibility: The contents of neutral detergent fiber, acid detergent fiber and acid detergent lignin were determined by the endogenous indicator method (acid-insoluble ash), and the apparent digestibility of cellulose and hemicellulose was calculated.

[0022] Fecal enzyme activity index: Determination of fecal cellulose and hemicellulase activity.

[0023] 2. Results: like Figure 1 As shown, compared with the LCF group, the HCF group did not show significant changes in body weight and daily weight gain, but the apparent digestibility of both cellulose and hemicellulose was significantly increased. P <0.05%, fecal cellulose and hemicellulase activity were significantly increased ( P <0.05), and the composition of fecal microorganisms changed significantly ( P<0.05). This result indicates that high-fiber diets significantly alter the intestinal microecological environment and fiber digestibility in pigs, and the differences in digestibility among individuals are closely related to the microecological structure.

[0024] Example 3 Metagenomic sequencing and metabolomics analysis 1. Metagenomic sequencing and analysis Total DNA was extracted from fecal samples of pigs in the LCF and HCF groups. Libraries were constructed, and metagenomic sequencing was performed using the Illumina NovaSeq 6000 platform. The raw metagenomic sequencing data underwent low-quality sequence removal and host sequence filtering to obtain high-quality sequences. After data quality control, SPAdes (v4.0.0) was used to assemble the high-quality sequences from each sample individually. Based on the assembled contigs, species annotation was performed using Kraken2 (v2.1.3). Subsequently, Prodigal (v2.6.3) was used to predict ORFs from the assembled contigs, and CD-HIT (v4.8.1) was used to cluster the ORFs, ultimately obtaining a non-redundant microbial gene set. Representative sequences from the non-redundant gene set were translated into protein sequences using EMBOSS (v6.6.0) for subsequent alignment with the eggNOG 5.0 database, obtaining species and EC enzyme data based on the contig level.

[0025] 2. Metabolomics analysis Untargeted detection of metabolites in fecal samples was performed using an ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-Q Exactive HF-X) system. Peak signals were extracted from raw mass spectrometry data using Progenesis QI software to generate a three-dimensional matrix containing retention time, mass-to-charge ratio, and peak intensity. High-frequency missing variables were removed using an 80% rule, and remaining missing values ​​were filled with the minimum within-group value and then normalized. Variables with a relative standard deviation (RSD) > 30% in the quality control samples were removed. Finally, the data were logarithmic. 10 Transformation was performed to improve normality. Compound identification was based on matching against HMDB, Metlin, and a self-built database of standards. The reliability of the model was assessed using partial least squares discriminant analysis (PLS-DA) combined with substitution tests, and differentially expressed metabolites were screened based on the criteria of VIP>1 and t-test P<0.05.

[0026] Example 4 Screening of key functional microbiota and metabolites 1. Screening of key microbial strains for cellulose / hemicellulose degradation EC enzymes associated with the three major enzyme systems of cellulose degradation (endoglucanase, exoglucanase, and β-glucosidase) and the three major enzyme systems of hemicellulose degradation (debranching enzyme, endoxylanase, and exoxylanase) were annotated from metagenomic data.

[0027] A random forest model was used to screen bacterial species that made the highest contribution to the taxonomic classification of high and low groups, based on the number of EC enzymes.

[0028] like Figure 2 As shown, the minimal microbial community covering the entire cellulose degradation pathway is: *Segastella feces* (…). Segatella copri ), Rumen xylanobacterium ( Xylanibacter ruminicola ), white rumen cocci ( Ruminococcus albus ).

[0029] like Figure 3 As shown, the minimal microbial community covering the entire hemicellulose degradation pathway is: *Segastella feces* (…). Segatella copri ), Rumen xylanobacterium ( Xylanibacter ruminicola ), Prevotella commensal bacteria ( Prevotella communis ).

[0030] 2. Screening of key metabolites Differential metabolites were screened between the high-fiber and low-fiber diet groups. Volcano plots were constructed using fold change and p-value (see [link to volcano plot]). Figure 4 ).

[0031] Results: Several significantly differentially expressed metabolites were identified, among which methylhelianthoneate F Glucoside was significantly enriched in the HCF group (P<0.05, FC>2). This substance belongs to the glucosinolate class of compounds and is a glycoside form of secondary metabolites released after plant cell wall disruption.

[0032] Example 5 Construction and evaluation of marker combinations 1. Method: Using high-fiber and low-fiber groups as binary dependent variables, and the key bacteria screened in Example 4 ( X. ruminicola , P. communis The abundance or concentration of [various metabolites] and key metabolites (Methyl Helianthoneate F Glucoside) were used as independent variables. A predictive model was constructed using logistic regression combined with leave-one-out cross-validation (LOOCV), and the predictive accuracy of the model was evaluated by the area under the receiver operating characteristic (AUC).

[0033] 2. Results: likeFigure 4 As shown in Table 1: Table 1

[0034] The results showed that when using microbial biomarkers alone for prediction, the optimal biomarker was... Xylanibacter ruminicola (AUC = 0.674). When used alone as a metabolite biomarker, Methyl Helianthoneate FGlucoside showed significantly better predictive power (AUC = 0.903) than any single microbial biomarker. However, Xylanibacter ruminicola The prediction result (AUC = 0.910) after combination with Methyl Helianthoneate F Glucoside was not optimal, reflecting the unpredictable nature of combined predictions using microbial and metabolite biomarkers. The optimal combination when using microbial and metabolite biomarkers is... Prevotella communis + Methylhelianthoneate F Glucoside (AUC = 0.917) was significantly higher than that of the single marker, indicating that the combination has the highest predictive accuracy and discrimination ability. This suggests that the combination of markers can serve as an effective reference indicator for assessing the dietary fiber digestibility potential of pigs.

[0035] Example 6 Building and validating the prediction model Based on the optimal biomarker combination obtained in Example 5 through logistic regression combined with leave-one-out cross-validation (LOOCV): Prevotella commensal bacteria ( Prevotella communis In addition to methylhelianthoneate F Glucoside, this invention further constructs a mathematical classification model for predicting the dietary fiber digestibility potential of individual pigs.

[0036] 1. Model Building Using HCF as a positive event (coded as 1) and LCF as a negative event (coded as 0), a logistic regression model was fitted with the relative abundance of the bacterial community and the concentration of metabolites after log2(x+1) transformation as independent variables. The model form is as follows: log( = -17.589 + 2.637 × log2( X 1+1) +7.378 × log2( X 2+1) in: pThis represents the probability that the pigs being tested are predicted to have good digestibility potential for dietary fiber. X 1 is in feces P. communis The relative abundance; X 2 represents the concentration of Methyl Helianthoneate F Glucoside in feces.

[0037] 2. Threshold determination and model discrimination ability The optimal cutoff value is determined using the Yoden index. p =0.5, the confusion matrix is ​​as follows:

[0038] The model achieved an accuracy of 91.7% (sensitivity 91.7%, specificity 91.7%), indicating that it can accurately distinguish between individuals with high and low fiber digestive abilities. p ≥0.5 indicates that the pig has good digestibility of dietary fiber; if p A value <0.5 indicates that its digestion potential is relatively limited.

[0039] 3. Verification of the independence of independent variables To further verify the complementarity of the two joint markers, the values ​​after log2(x+1) transformation were analyzed. Prevotella communis Pearson correlation analysis was performed on the abundance and concentration of Methyl Helianthoneate F Glucoside. The results showed that the correlation coefficient between the two was [missing information]. r The correlation coefficient (AUC) is 0.188, indicating a very low correlation between the two variables, suggesting that they are independent and do not exhibit multicollinearity. Therefore, using them together can provide independent and complementary predictive information, which explains why the combined model (AUC = 0.917) outperforms the single metabolite model (AUC = 0.903).

[0040] In summary, this invention provides a combination of microecological markers highly correlated with the digestibility potential of pig dietary fiber, with the optimal combination being Prevotella commensal bacteria (…). Prevotella communis The combination of methyl helianthoneate F glucoside and methyl helianthoneate F glucoside yielded a LOOCV AUC of 0.917 and an accuracy of 91.7%. This combination can be used to develop kits or methods for assessing the digestibility of fiber diets (especially soybean hulls) in pigs, providing a reference for precision feeding and genetic breeding, and has extremely high industrial value.

[0041] It is understood that those skilled in the art can make equivalent substitutions or modifications to the technical solution and inventive concept of the present invention, and all such substitutions or modifications should fall within the protection scope of the appended claims.

Claims

1. The application of reagents for detecting the abundance or concentration of microecological markers in the preparation of products for assessing the digestibility potential of pig dietary fiber, characterized in that, The microecological markers include microbial and metabolite markers; The microbial marker is a comtrophic bacterium of the genus *Prevotella* (… Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola ); The metabolite marker is methyl helianthoneate F-glucoside.

2. The application according to claim 1, characterized in that, The reagents for detecting the microbial markers include those used in any of the following methods: 16S rRNA gene sequencing, metagenomic sequencing, and quantitative real-time PCR.

3. The application according to claim 1, characterized in that, The reagents used to detect the metabolite markers include those used in the following methods: liquid chromatography-mass spectrometry.

4. A kit for evaluating the digestibility potential of pig dietary fiber, characterized in that, It includes specific primers or probes for detecting the abundance of microbial biomarkers, and standards for detecting the concentration of metabolite biomarkers; The microbial marker is a comtrophic bacterium of the genus *Prevotella* (… Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola ); The metabolite marker is methyl helianthoneate F-glucoside.

5. A method for assessing the fiber digestibility potential of pig diets, characterized in that, Includes the following steps: The abundance or concentration of microecological markers in fecal samples from pigs was detected, including microbial and metabolite markers; the microbial markers were Prevotella symbiotic bacteria (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methyl helianthoneate F glucoside. If the detection results are input into the prediction model, and the predicted probability of the microecological marker is higher than 0.5, then the pig's fiber digestion potential is determined to be better than the average level of the population.

6. A method for raising pigs, characterized in that, Includes the following steps: The abundance or concentration of microecological markers in fecal samples from pigs was detected, including microbial and metabolite markers; the microbial markers were Prevotella symbiotic bacteria (…). Prevotella communis ) and / or rumen-degrading xylanobacterium ( Xylanibacter ruminicola The metabolite marker is methyl helianthoneate F glucoside. The detection results are input into the prediction model. If the prediction probability of the microecological marker is higher than 0.5, it indicates that the pig has good digestibility potential for dietary fiber; otherwise, it indicates that its digestibility potential is relatively limited. For pigs or herds predicted to have relatively limited digestive potential, it is recommended to add exogenous fiber-degrading enzyme preparations or microecological preparations to their diet, or reduce the dietary fiber level; for pigs or herds predicted to have good digestive potential, the proportion of fiber feed in the diet should be appropriately increased.

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