Rumen microbial marker related to lactation performance of ruminant and application of rumen microbial marker

Abstract

The invention relates to the technical field of biomarkers, in particular to a biomarker related to the lactation performance of ruminants and application of the biomarker. The biomarker is one or more of prevotella brucella (Prevotella copri), prevotella brevis (Prevotella brevis) and prevotella ruminella (Prevotella ruminella), and the biomarker is one or more of the prevotella brucella (Prevotella copri), the prevotella copri (Prevotella copri), the prevotella brevis (Prevotella ruminella) and the prevotella ruminella (Prevotella ruminella). According to the invention, PCR detection is carried out on the abundance of the marker microorganisms, so that the breeding of ruminants with lactation phenotypes with high milk yield and high milk fat percentage can be assisted. In addition, the marker microorganism can also be applied to preparation of a kit for detecting the lactation performance of the ruminant, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of biomarkers, and in particular to a rumen microbial marker related to the lactation performance of ruminants and an application thereof. Background Art

[0002] The milk yield and fat content of ruminants are two key indicators for measuring lactation performance. They not only affect the output and nutritional value of dairy products, but also directly determine the market economic benefits of dairy products. However, in actual production, the fluctuation of milk yield and the frequent occurrence of fat inhibition in dairy animals have seriously restricted the stable development of the dairy industry. For the synthesis of milk fat and milk in ruminants, the rumen plays an indispensable role as the center of the host-feed metabolic network. Rumen microorganisms produce key metabolites such as volatile fatty acids through biofermentation, providing important carbon sources and precursors for the synthesis of milk, fat and other milk components, thus guiding a large number of related works on regulating the lactation performance of ruminants with rumen microorganisms as targets. However, the existing work mainly targets single lactation phenotypes such as milk yield and fat content of ruminants, while the milk yield of ruminants is usually inversely proportional to the content of key nutrients in milk such as fat content. Therefore, there is currently a lack of systematic understanding of the coordinated regulation between milk yield and fat content.

[0003] As the largest functional group in the rumen of ruminants, Prevotella performs multiple metabolic functions in the rumen ecosystem, especially in the decomposition of carbohydrates, protein metabolism, nitrogen cycle, and vitamin synthesis. It can efficiently decompose complex carbohydrates such as cellulose, hemicellulose, and starch in the diet to generate metabolic molecules such as volatile fatty acids and functional coenzymes, thereby providing energy for ruminants and regulating their physiological activities. Xue Mingyuan (Xue, Ming-Yuan, et al. "Multi-omics reveals that the rumen microbiome and its metabolome together with the hostmetabolome contribute to individualizeddairy cowperformance." Microbiome 8 (2020): 1-19.) and other studies have found that Prevotella in the rumen can affect the milk production and milk protein rate of dairy cows by regulating the host's volatile fatty acids (VFAs) and amino acid metabolism. In addition, Zhang Chenguang et al. (Zhang, Chenguang, et al. "An integrated microbiome-and metabolome-genome-wide association study reveals the role of heritable ruminal microbial carbohydrate metabolism in lactation performance in Holstein dairy cows." Microbiome 12 (2024): 232.) found that the relative abundance of Prevotella spp. in the rumen of dairy cows was significantly positively correlated with their lactation phenotype. Another study found that after feeding nano zinc particles to lactating dairy goats, the relative abundance of Prevotella in the rumen increased significantly and was significantly positively correlated with the breast milk phenotype of dairy goats (Xie, Shan, et al. "Zinc oxide nanoparticles improve lactation and metabolism in dairy goats by modulating the rumen microbiota." Frontiers in Microbiology 15 (2024): 1483680.).At present, preliminary studies have been conducted on the correlation between the genus Prevotella and the lactation performance of ruminants, but the precise identification of Prevotella species that regulate the lactation phenotype of ruminants is still lacking. Summary of the invention

[0004] The purpose of the present invention is to provide the application of Prevotella species in influencing the milk yield and milk fat rate of ruminants. The present invention points out that Prevotella brucei, Prevotella coelicolor, Prevotella brevis and Prevotella rumenii are microbial markers in the rumen of ruminants with high milk yield and high milk fat rate.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides a biomarker related to the lactation performance of ruminants, wherein the biomarker is one or more of Prevotella bryantii, Prevotella copri, Prevotella brevis and Prevotella ruminicola.

[0007] Preferably, the lactation performance is milk yield and / or milk fat percentage.

[0008] Preferably, the ruminant is a dairy cow, a dairy goat or a dairy sheep.

[0009] Preferably, the lactation performance of ruminants is characterized by detecting the microbial abundance of the marker in the rumen.

[0010] The present invention also provides the use of the biomarker in preparing a kit for detecting the lactation performance of ruminants.

[0011] The present invention also provides a method for improving the lactation performance of ruminants, by increasing the abundance of Prevotella species in the rumen of ruminants to improve the milk production and milk fat rate of ruminants, wherein the Prevotella species is one or more of Prevotella brucei, Prevotella coelicolor, Prevotella brevis or Prevotella rumenica.

[0012] Preferably, the ruminant is a dairy cow, a dairy goat or a dairy sheep.

[0013] The present invention also provides the use of the biomarker in ruminant breeding, wherein the biomarker can assist in the selection of ruminants with a lactation phenotype of high milk yield and high milk fat percentage.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The present invention is based on the lactation phenotype data of dairy goats, screens out 10 dairy goats with high milk production and high milk fat rate (HH) and 10 dairy goats with low milk production and low milk fat rate (LL), and accurately identifies the markers of microorganisms in the rumen of dairy goats with high milk production and high milk fat rate (HH) through LEfSe and ROC analysis, and finds that at least one of the Prevotella species, such as Prevotella brasiliensis, Prevotella corymbe, Prevotella brevis and Prevotella rumensis, may be the key microbial marker for dairy goats with high milk production and high milk fat rate.

[0016] The present invention provides microbial markers in the rumen of ruminants with a high milk yield and high milk fat rate lactation phenotype. By performing 16s rDNA quantitative PCR detection on the abundance of the above-mentioned marker microorganisms, it is possible to assist in the selection of ruminants with a high milk yield and high milk fat rate lactation phenotype. In addition, the above-mentioned marker microorganisms can also be used in the preparation of a kit for detecting the lactation performance of ruminants, which has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0018] Figure 1 This is a diagram showing the screening basis for the lactation and rumen fermentation parameter phenotypes and rumen microbial markers of dairy goats with high milk yield and high milk fat content in Example 1 of the present invention.

[0019] Figure 2 This is a ROC analysis verification diagram in Example 1 of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0021] Example 1

[0022] Screening of microbial markers in the rumen of dairy goats with high milk yield and high milk fat content

[0023] 1.1 Selection of dairy goat groups with high milk yield and high milk fat percentage

[0024] The experiment selected 177 healthy Saanen goats in the middle of lactation (parity = 2.20 ± 0.48, DIM = 121.50 ± 8.05, mean ± SD) raised in a commercial dairy goat farm. The goats were fed in a single pen in a well-ventilated pen, milked twice a day at 5:00 and 17:00, and fed after each milking. The milk production data of dairy goats were collected daily in the first and fourth weeks of September 2022, and milk samples were collected at a volume ratio of 3:2 before milking twice on the last day of milk production data collection. The milk components (milk protein, milk fat, lactose, etc.) of dairy goats were determined using a milk composition analyzer.

[0025] Previous studies have shown that the milk production and milk fat rate of dairy animals are usually inversely proportional (Wu, Xuehui, et al. "Serummetabolome profiling revealed potential biomarkers for milk protein yield indairy cows." Journal ofproteomics 184(2018):54-61.), so the high and low thresholds for milk production and milk fat rate of goats were set according to the mean value ± 0.5 × SD. The high and low thresholds for milk production were 2.58 kg and 2.14 kg, respectively, and the high and low thresholds for milk fat rate were 3.93% and 3.33%, respectively. Ten goats with high milk production and high milk fat rate (HH) and ten goats with low milk production and low milk fat rate (LL) were screened out, and the average milk production of goats (kg / d) × milk fat rate (%) was used as the milk fat yield of dairy goats.

[0026] Gpower (v3.1) was used to calculate the statistical power of 10 dairy goats in each group. The statistical power of milk production and milk fat percentage were both >99%, and the grouping had statistical power.

[0027] Rumen digesta from HH and LL sheep were collected by oral gastric tube before morning feeding for volatile fatty acid determination. The concentrations of various volatile fatty acids in rumen digesta samples were determined by gas chromatography.

[0028] Briefly, rumen digesta were thawed at 4°C and centrifuged at 16,000 r / min for 10 min. Subsequently, 2 mL of supernatant was mixed with 400 μL of 25% metaphosphoric acid and vortexed thoroughly. The mixture was allowed to stand at 4°C for 4 h and then centrifuged again at 16,000 r / min for 10 min. 200 μL of supernatant was mixed with 200 μL of cinnamic acid (10 g / L) and vortexed, then filtered through a 0.45 μm membrane filter for analysis. The gas chromatography parameters were set as follows: the inlet temperature was maintained at 250°C; the column temperature was initially set to 45°C, then increased to 150°C at a rate of 20°C / min and maintained for 5 minutes. For the results of the comparison of the differences in dairy goat screening, lactation phenotype and rumen fermentation parameters between the HH and LL groups, see. Figure 1 AJ. Figure 1 A is a schematic diagram and screening results of selecting HH and LL dairy goats after determining high and low thresholds based on the mean values ​​of milk production and milk fat content ± 0.5SD; Figure 1 BE were milk yield, milk fat percentage, milk fat yield and total solid content in milk of HH and LL groups of dairy goats, respectively; Figure 1 FJ are total volatile fatty acids (VFAs), acetic acid, propionic acid, butyric acid and ethyl / propylene ratio (A / Pratio) in the rumen of dairy goats in the HH and LL groups, respectively.

[0029] The results showed that the milk yield, milk fat percentage, milk fat yield and total solids in milk of HH goats were significantly higher than those of LL group (P<0.05). Similarly, the concentrations of total volatile fatty acids, acetic acid, propionic acid, butyric acid and valeric acid in rumen digesta of HH goats were also significantly higher than those of LL group (P<0.05).

[0030] 1.2 Metagenomic Sequencing

[0031] DNA from rumen digesta samples of HH and LL dairy goats was extracted using the bead beating method. The concentration and purity of the extracted DNA were assessed using a TBS-380 fluorometer and a NanoDrop 2000 UV-Vis spectrophotometer, respectively. The quality of the DNA extracts was assessed on a 1% agarose gel. DNA samples were stored at -80°C. The DNA extracts were fragmented into fragments of approximately 400 bp using a Covaris M220 ultrasonic disruptor. Paired-end libraries were constructed using the NEXTFLEX Rapid DNA-Seq Kit. Sequencing was performed on the Illumina NovaSeq 6000 platform using the NovaSeq 6000S4 kit v1.5 (300 cycles) to generate 150 bp paired-end reads according to the manufacturer's protocol.

[0032] The raw data of metagenomic sequencing were filtered out after low-quality sequences were removed and host sequences were filtered to obtain high-quality sequences.

[0033] The high-quality sequences of each sample were assembled separately using metaSPAdes (v3.15.5), and the contig sequences with a length of less than 500 bp were deleted using a custom script after assembly. In order to improve sequence utilization and identify rare genes of rumen microorganisms in dairy goats, the high-quality sequences of all samples were aligned with the corresponding assembled contigs using Bowtie 2 (v2.5.1) to obtain the unassembled sequences of each sample. The unassembled sequences of all samples were collected and assembled together using MEGAHIT (v1.2.9).

[0034] Prodigal (v2.6.3) was used to predict ORFs of the assembled contigs. After removing ORFs less than 100 bp in length, CD-HIT (v4.8.1) was used to cluster the remaining ORFs, and finally a non-redundant microbial gene set was obtained.

[0035] EMBOSS Transeq (v6.6.0.0) was used to translate the representative sequences in the non-redundant gene set into protein sequences for subsequent species annotation. SOAPaligner / Soap2 (v2.21) was used to align the non-redundant genes with the high-quality sequences of each sample at 95% identity to calculate gene abundance. DIAMOND (v 2.1.8.162) was used to align the non-redundant protein sequences with the NCBI-NR (version: October 2022) database to obtain microbial taxonomy information under the condition of e cutoff value ≤1e-5.

[0036] 1.3 Identification of rumen microbial markers of high milk yield and high milk fat percentage phenotype dairy goats based on LEfSe analysis

[0037] LEfSe analysis, i.e., analysis of species with significant differences between groups (also known as biomarker analysis), uses linear discriminant analysis (LDA) to estimate the effect of the abundance of each component (species) on the differential effect of the rumen microbiota between each group. This analysis found species with significant differences in abundance in the rumen microbiota between HH and LL sheep, namely, Prevotella bryantii, Prevotella copri, Pevotella brevis, and Prevotella ruminicola, which are microbial markers in the rumen of HH sheep.

[0038] Specific results such as Figure 1 As shown in KO, Figure 1 K is the Beta diversity analysis of the rumen microbiome of dairy goats in the HH and LL groups; Figure 1L is the difference test of the relative abundance of bacteria and archaea in the rumen of dairy goats in the HH and LL groups at the domain level using the Wilcoxon rank-sum test; 1M is the test of the difference of microorganisms at the genus taxonomy level using the Wilcoxon rank-sum test at the microbial genus level; Figure 1 N is the screening of microbial markers in the rumen of HH and LL dairy goats using LefSe analysis at the threshold of LDA>2, P<0.05; Figure 1 O showed that the screened rumen microbial markers of HH dairy goats were significantly positively correlated with key lactation phenotypes (|R|>0.5, P<0.05).

[0039] 1.4 Verification of the accuracy of microbial markers in the rumen of HH dairy goats based on ROC analysis

[0040] Receiver operating characteristic (ROC) curve analysis is mainly used in the field of microbiology to determine the accuracy of screened microbial markers. The area under the ROC curve (AUC) is usually between 1.0 and 0.5; when AUC>0.5, the closer the AUC is to 1, the better the diagnostic effect; when AUC is 0.5-0.7, it has a lower accuracy, when AUC is 0.7-0.9, it has a certain accuracy, and when AUC is above 0.9, it has a higher accuracy; when AUC=0.5, it means that the diagnostic method is completely ineffective and has no diagnostic value.

[0041] By ROC analysis, we determined the AUC values ​​corresponding to Prevotella bryantii, Prevotella copri, Pevotella brevis, and Prevotella ruminicola identified as microbial markers in the rumen of HH dairy goats.

[0042] Specific results such as Figure 2 As shown in AD, Prevotella bryantii, Prevotella copri, Pevotella brevis and Prevotella ruminicola can distinguish HH and LL dairy goats with AUC=0.88, AUC=0.84, AUC=0.85 and AUC=0.84, respectively, which fully demonstrates that the above microbial markers can be used to predict milk production and milk fat percentage of ruminants.

[0043] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A biomarker related to the lactation performance of ruminants, characterized in that: The marker is one or more of Prevotella bryantii, Prevotella copri, Prevotella brevis and Prevotella ruminicola.

2. The biomarker according to claim 1, characterized in that The lactation performance is milk yield and / or milk fat percentage.

3. The biomarker according to claim 1, characterized in that The ruminant is a dairy cow, a dairy goat or a dairy sheep.

4. The marker according to claim 1, characterized in that The lactation performance of ruminants is characterized by detecting the microbial abundance of the markers in the rumen.

5. Use of the biomarker according to any one of claims 1 to 4 in the preparation of a kit for detecting the lactation performance of ruminants.

6. A method for improving the lactation performance of ruminants, characterized in that: The milk production and milk fat rate of ruminants are improved by increasing the abundance of Prevotella species in the rumen of ruminants, wherein the Prevotella species are one or more of Prevotella brucei, Prevotella coelicolor, Prevotella breve or Prevotella rumenica.

7. The method according to claim 6, characterized in that The ruminant is a dairy cow, a dairy goat or a dairy sheep.

8. Use of the biomarker according to any one of claims 1 to 4 in ruminant breeding, characterized in that: The biomarker can assist in breeding ruminants with a lactation phenotype of high milk yield and high milk fat percentage.

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