A rumen microbial marker associated with lactation performance of ruminants and use thereof

Abstract

The present application relates to the technical field of biomarkers, and particularly relates to a biomarker related to lactation performance of ruminants and application thereof. The marker is one or more of Prevotella bryantii, Prevotella copri, Prevotella brevis and Prevotella ruminicola. The present application can assist in breeding ruminants with high milk yield and high milk fat percentage lactation phenotype by PCR detection of the abundance of the above-mentioned marker microorganisms. In addition, the above-mentioned marker microorganisms can also be applied in the preparation of a kit for detecting the lactation performance of ruminants, and have broad application prospects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomarkers, and particularly relates to a rumen microbial marker related to lactation performance of ruminants and application thereof. BACKGROUND

[0002] Milk yield and milk fat percentage are two key indicators for measuring lactation performance of ruminants, which not only affect the yield 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 phenomenon of milk fat inhibition of dairy animals often occur, which seriously limits the stable development of dairy industry. For the synthesis of milk fat and milk of ruminants, the rumen plays an indispensable role as the center of the host-feeding metabolic network. Rumen microorganisms generate key metabolites such as volatile fatty acids through biological fermentation, which provide important carbon sources and precursor substances for the synthesis of milk fat and other milk components, so a large number of related works on regulating the lactation performance of ruminants by targeting rumen microorganisms have been carried out. However, the existing works mainly focus on single lactation phenotype such as milk yield and milk fat percentage of ruminants, and the milk yield of ruminants is usually inversely proportional to the content of key nutritional elements in milk such as milk fat percentage, so the current lack of systematic understanding of the synergistic regulation between milk yield and milk fat percentage.

[0003] Prevotella is the largest functional group in the rumen of ruminants, which performs multiple metabolic functions in the rumen ecosystem, especially in the decomposition of carbohydrates, protein metabolism, nitrogen cycling, 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 et al. (Xue, Ming-Yuan, et al. "Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance." Microbiome 8 (2020): 1-19.) found that Prevotella in the rumen can regulate the metabolism of volatile fatty acids (VFAs) and amino acids in the host, thereby affecting milk yield and milk protein rate in dairy cows. In addition, Zhang 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. Studies have also found that feeding nano-zinc particles to lactating dairy goats significantly increased the relative abundance of Prevotella in the rumen and was significantly positively correlated with the lactation 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, the correlation between Prevotella and the lactation performance of ruminants has been preliminarily studied, but the accurate identification of Prevotella species regulating the lactation phenotype of ruminants is still relatively lacking. SUMMARY

[0004] The application aims to provide the application of Prevotella species in affecting the milk yield and milk fat percentage of ruminants.

[0005] In order to achieve the above application purposes, the application provides the following technical solutions.

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

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

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

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

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

[0011] The application also provides a method for improving the lactation performance of ruminants, which increases the milk yield and the milk fat percentage of ruminants by increasing the abundance of Prevotella species in the rumen of ruminants, and the Prevotella species is one or more of Prevotella bryantii, Prevotella copri, Prevotella brevis and Prevotella ruminicola.

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

[0013] The application also provides the application of the biomarker in ruminant breeding, which can assist in breeding ruminants with high milk yield and high milk fat percentage lactation phenotype.

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

[0015] The present application is based on lactation phenotype data of dairy goats, 10 high-yield milk and high-fat milk (HH) and 10 low-yield milk and low-fat milk (LL) dairy goats are screened out, through LEfSe and ROC analysis, the markers of microorganisms in the rumen of high-yield milk and high-fat milk (HH) dairy goats are accurately identified, and at least one of the Prevotella species such as Prevotella bovis, Prevotella corporis, Prevotella brevis and Prevotella ruminicola may be the key microbial marker of high-yield milk and high-fat milk dairy goats.

[0016] The present application provides microbial markers in the rumen of lactating ruminants with high milk yield and high milk fat percentage, and the abundance of the above-mentioned marker microorganisms can be detected by 16s rDNA quantitative PCR to assist the breeding of ruminants with high milk yield and high milk fat percentage. In addition, the above-mentioned marker microorganisms can also be applied to prepare a kit for detecting the lactation performance of ruminants, which has a broad application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0018] Figure 1 The figure is for screening of lactation and rumen fermentation parameter phenotype and rumen microbial marker of high-yield milk and high-fat milk dairy goats in Example 1 of the present application.

[0019] Figure 2 The figure is for ROC analysis verification in Example 1 of the present application. DETAILED DESCRIPTION

[0020] The technical solutions provided by the present application will be described in detail below in combination with the embodiments, but they should not be understood as limiting the scope of protection of the present application.

[0021] Example 1

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

[0023] 1.1 Screening of high-yield milk and high-fat milk dairy goat population

[0024] A total of 177 healthy Sannen dairy goats (parity = 2.20 ± 0.48, DIM = 121.50 ± 8.05, mean ± SD) at mid-lactation were selected from a commercial dairy goat farm. The goats were housed in the same barn with good ventilation and were fed individually. The goats were milked twice daily at 5:00 and 17:00, and were fed after each milking. Milk yield data were collected daily during the first and fourth weeks of September 2022, and milk samples were collected at a volume ratio of 3:2 before the last two milkings on the last day of milk yield data collection. The milk components (milk protein, milk fat, lactose, etc.) were determined using a milk component analyzer.

[0025] Previous studies have shown that milk yield and milk fat percentage are usually inversely related in dairy animals (Wu, Xuehui, et al. "Serum metabolome profiling revealed potential biomarkers for milk protein yield in dairy cows." Journal of proteomics 184 (2018): 54-61.). Therefore, the high and low thresholds for milk yield and milk fat percentage were set at the mean ± 0.5 x SD, respectively. The high and low thresholds for milk yield were 2.58 kg and 2.14 kg, respectively, and the high and low thresholds for milk fat percentage were 3.93% and 3.33%, respectively. Ten goats with high milk yield and high milk fat percentage (HH) and ten goats with low milk yield and low milk fat percentage (LL) were selected. The average milk yield kg / d x milk fat percentage % of the goats was used as the milk fat yield of the dairy goats.

[0026] The statistical power of each group of 10 dairy goats was calculated using Gpower (v3.1), and the statistical power of milk yield and milk fat percentage was >99%, indicating that the grouping was statistically significant.

[0027] Rumen digesta from the HH and LL goats were collected using an oral stomach tube before morning feeding for volatile fatty acid determination. Gas chromatography was used to determine the concentrations of various volatile fatty acids in the rumen digesta samples.

[0028] Briefly, rumen digest was 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 left 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, and then filtered through a 0.45 μm membrane filter for analysis. The parameters of gas chromatography were set as follows: the injection port temperature was maintained at 250°C; the column temperature was initially set at 45°C, and then increased to 150°C at a rate of 20°C / min, and maintained for 5 min. The results of comparison of screening, lactation phenotype and rumen fermentation parameters of HH and LL dairy goats are shown in Tables 1-8. Figure 1 A-J. Figure 1 A is a schematic diagram of screening HH and LL dairy goats after determining high and low thresholds of average values ± 0.5 SD of milk yield and milk fat percentage, and the screening results; Figure 1 B-E are milk yield, milk fat percentage, milk fat yield and total solid content in milk of HH and LL dairy goats, respectively; Figure 1 F-J are total volatile fatty acids (VFAs), acetic acid, propionic acid, butyric acid and A / Pratio in the rumen of HH and LL dairy goats, respectively.

[0029] The results showed that the milk yield, milk fat percentage, milk fat yield and total solid content in milk of HH dairy 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 pentanoic acid in the rumen digest of HH dairy goats were also significantly higher than those of LL group (P < 0.05).

[0030] 1.2 Metagenomic sequencing

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

[0032] After removing low-quality sequences and filtering host sequences, high-quality sequences were obtained from the raw data of metagenomic sequencing.

[0033] High-quality sequences from each sample were assembled individually using metaSPAdes (v3.15.5). After assembly, contiguous sequences shorter than 500 bp were removed using a custom script. To improve sequence utilization and identify rare genes in goat rumen microbes, high-quality sequences from all samples were aligned with corresponding assembly contigs using Bowtie 2 (v2.5.1) to obtain unassembled sequences for each sample. The unassembled sequences from all samples were collected and co-assembled using MEGAHIT (v1.2.9).

[0034] ORF prediction was performed on the assembled contigs using Prodigal (v2.6.3). After removing ORFs shorter than 100 bp, the remaining ORFs were clustered using CD-HIT (v4.8.1) to obtain a non-redundant microbial gene set.

[0035] Representative sequences from the non-redundant gene set were translated into protein sequences using EMBOSS Transeq (v6.6.0.0) for subsequent species annotation. Non-redundant genes were aligned with high-quality sequences from each sample at 95% identity using SOAPaligner / Soap2 (v2.21) to calculate gene abundance. Non-redundant protein sequences were aligned with the NCBI-NR (version: October 2022) database using DIAMOND (v 2.1.8.162) at an e-cutoff value ≤ 1e-5 to obtain microbial taxonomic information.

[0036] 1.3 Identification of Rumen Microbial Markers for High Milk Yield and High Milk Fat Percentage Phenotypes in Dairy Goats Based on LEfSe Analysis

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

[0038] Specific results are as follows Figure 1 As shown in KO, Figure 1 K represents the Beta diversity analysis of the rumen microbiome of dairy goats in the HH and LL groups; Figure 1L is the relative abundance of bacteria and archaea in the rumen of HH and LL dairy goats at the domain level, and the difference is tested by Wilcoxon rank-sum test; 1M is the difference in microbial genus level, and the difference is tested by Wilcoxon rank-sum test at the genus classification level; Figure 1 N is the microbial marker in the rumen of HH and LL dairy goats, respectively, screened by LefSe analysis under the threshold of LDA>2, P<0.05; Figure 1 O shows that the microbial markers screened from the rumen of HH dairy goats are significantly positively correlated with the key lactation phenotype (|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 microorganisms to determine the accuracy of the screened microbial markers. The area under the ROC curve (AUC) is usually between 1.0 and 0.5; in the case of AUC>0.5, the closer the AUC is to 1, the better the diagnostic effect; AUC in 0.5-0.7 has low accuracy, AUC in 0.7-0.9 has certain accuracy, AUC above 0.9 has high accuracy; AUC=0.5 means that the diagnostic method does not work at all and has no diagnostic value.

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

[0042] The specific results are shown in Figure 2 A-D, 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 indicates that the above microbial markers can be used to predict the milk yield and milk fat rate of ruminants.

[0043] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, several improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as falling within the protection scope of the present application.

Claims

1. Prevotella brevis ( Prevotella brevis The application of the detection reagent of ) in the preparation of a kit for detecting the lactation performance of ruminants, characterized in that, The lactation performance is defined as milk yield and milk fat percentage; the detection reagent is used to detect Prevotella shortness of breath (…). Prevotella brevis The abundance of microorganisms in the rumen was used to characterize the lactation performance of ruminants, wherein the ruminants were dairy goats.

2. A method for improving the lactation performance of ruminants, characterized in that, By increasing the amount of Prevotella shortness of breath in the rumen of ruminants ( Prevotella brevis The abundance of ) is used to increase milk production and milk fat percentage in ruminants; the ruminants are dairy goats.

3. Prevotella brevis ( Prevotella brevis The application of the detection reagent of ) in ruminant breeding is characterized by, The detection reagent can assist in the selection of ruminants with high milk yield and high milk fat percentage lactation phenotype; the ruminant is a dairy goat.

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