Rumen biomarker for predicting high intramuscular fat content of cattle and application of rumen biomarker
By analyzing the composition of rumen microorganisms, using filostrum succinate and Lactobacillus pane as biomarkers, combined with metagenomic sequencing technology, the prediction problem of high intramuscular fat content of bovine was solved, and the quality of beef and the breeding benefits were improved.
Patent Information
- Application Number
- CN202510325124.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-07-25
AI Technical Summary
The prior art is difficult to effectively predict the high intramuscular fat content of beef, which affects the quality assessment and market value of beef.
The rumen biomarkers produced by filostrum succinate and Lactobacillus paneus were used to analyze the composition of rumen microbial organisms through metagenomic sequencing technology, and the relative abundance was detected using 16SrRNA primers to construct a prediction model.
It achieves a trauma-free and simple prediction of high muscle fat content in beef, improves beef quality and breeding benefits, and breaks through the limitations of traditional methods.
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Figure CN120366482A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of microorganisms, and particularly to a rumen biomarker for predicting the high intramuscular fat content of cattle and its application. Background Art
[0002] The intramuscular fat content of beef is a key factor determining the formation of its marbling. Research shows that the increase in intramuscular fat content is positively correlated with the quality evaluation of marbling, that is, the higher the intramuscular fat content, the better the marbling rating. In addition, the increase in intramuscular fat content is also associated with the improvement of beef tenderness, juiciness and flavor, which in turn leads to an increase in the market price of beef.
[0003] Hirai et al. found that Japanese Wagyu beef with an intramuscular fat content (IMF) of about 40% exhibits a strong and persistent sweetness, as well as roasted / baked flavors represented by butter and roasted nuts. Luan et al. found that supplementing vitamin E (700 IU / head / day) in the diet of Yanbian beef cattle can significantly improve the beef marbling score and at the same time improve the meat quality parameters, especially tenderness. Therefore, the intramuscular fat content is a decisive factor in evaluating the marbling grade of beef, which is of great significance for evaluating meat flavor, not only directly affecting the market value of beef, but also affecting the economic benefits of the beef cattle breeding industry, and is a key indicator for measuring the quality and economic value of beef.
[0004] There are obvious individual differences in the intramuscular fat content of beef, which are mainly affected by factors such as breed, age, gender, feeding management, etc. Recent studies have found that ruminants with high and low intramuscular fat have different rumen microbiota characteristics, and regulating the rumen microbiota characteristics may improve intramuscular fat deposition in beef cattle. Kim et al. analyzed the rumen microbiota of 14 Hanwoo beef cattle using 16S rRNA gene sequencing and found that RFP12, Verrucomicrobia, Treponema hyodysenteriae, Porphyromonadaceae and Paludibacter were abundant in the high marbling group, while Olsenella was abundant in the low marbling group, indicating that there are differences in the rumen microbial structure, species and metabolic characteristics of Hanwoo beef cattle with different intramuscular fat contents, and they may play a role in the formation of beef marbling. This result is similar to the experimental results of Xiong et al. in yak rumen microbiota and Sha et al. in Tibetan sheep rumen microbiota.
[0005] Metagenomic sequencing technology has been widely used in studying the structural characteristics of gastrointestinal flora and its impact on intramuscular fat deposition. Yang et al. used metagenomic sequencing to analyze the effect of dietary niacin supplementation on the rumen flora of Xiangzhong black cattle and found that adding niacin to the feed could increase microorganisms positively correlated with intramuscular fat deposition in beef, such as Ligilactobacillus ruminis, Anaerovibrio lipolyticus, and Mitsuokella multacida, thus increasing the intramuscular fat content in beef. Zheng et al. used rectal fecal shotgun metagenomic sequencing and targeted metabolomics to reveal the differences in the intestinal microbiome and muscle gene expression between Angus cattle and Simmental cattle. It was found that the intestinal microorganisms of Angus cattle had higher fatty acid biosynthesis ability, which was consistent with the higher fatty acid content in their longissimus dorsi muscle. Moreover, it was found that intestinal bacteria such as B. uniformis, B. vulgatus, and R. inulinivorans were positively correlated with muscle metabolism-related genes (ATP2A1, MSTN, ACTN3, MYLPF, MYL1, and TNNT3). In-depth analysis of the genetic potential of microbial communities through metagenomics helps to understand the interactions between hosts and microorganisms and how these interactions affect animal health and production efficiency. Chai et al. detailed the dynamic changes in the rumen microbiome and resistome of goat kids from birth to 84 days; Xue et al. revealed the individualized characteristics of the rumen resistome of dairy cows by studying the rumen metagenomic resistome of dairy cows, and different resistance types were closely related to the lactation traits of dairy cows (such as milk protein production), indicating that metagenomics plays an important role in explaining the interaction between microorganisms and hosts. Summary of the Invention
[0006] The object of the present invention is to solve the disadvantages existing in the prior art and propose a kind of.
[0007] One object of the present invention is to propose a rumen biomarker for predicting high intramuscular fat content in cattle, including: at least one of Fibrobacter succinogenes and Limosilactobacillus panis.
[0008] Preferably, the breed of cattle is Pingliang Red Cattle.
[0009] Another object of the present invention is to propose an application of a reagent for detecting the above rumen biomarker in preparing a kit for predicting high intramuscular fat content in cattle or in breeding.
[0010] Preferably, the above reagent includes 16S rRNA primers for detecting or amplifying the above rumen biomarker.
[0011] A third object of the present invention is to provide an application of the above-mentioned rumen biomarker in the preparation of a model for predicting intramuscular fat content in cattle.
[0012] A fourth object of the present invention is to provide a method for predicting intramuscular fat content in cattle, comprising the following steps:
[0013] S1. Collect bovine rumen fluid samples, and extract nucleic acid samples from the bovine rumen fluid samples;
[0014] S2. Determine the relative abundance information of the above-mentioned rumen biomarker in the nucleic acid sample obtained in S1;
[0015] S3. Compare the relative abundance information obtained in S2 with a reference data set or a reference value.
[0016] In the present invention, the reference data set refers to the relative abundance information of each biomarker obtained by operating on cattle with known high intramuscular fat content and known low intramuscular fat content, which is used as a reference for the relative abundance of each biomarker.
[0017] In the present invention, the reference value refers to the reference value or normal value of cattle with high intramuscular fat content. Those skilled in the art know that when the sample size is large enough, the range of normal values (absolute values) of each biomarker in the sample can be obtained by using well-known detection and calculation methods in the art. When detecting the level of a biomarker by a detection method, the absolute value of the biomarker level in the sample can be directly compared with the reference value to predict the intramuscular fat level of the cattle.
[0018] Preferably, the specific operation of S2 is as follows: constructing a DNA library using the nucleic acid sample obtained in S1, sequencing the DNA library to obtain sequencing results, and comparing the sequencing results with a reference gene set to determine the relative abundance information of the rumen biomarker as described above.
[0019] More preferably, the reference gene set includes performing metagenomic sequencing on samples from several cattle with high intramuscular fat content and several cattle with low intramuscular fat content to obtain a non-redundant gene set, and then combining the non-redundant gene set with the rumen microbial gene set to obtain the reference gene set.
[0020] Preferably, in S3, when comparing, an increase in Fibrobacter succinogenes or / and Lactobacillus panis indicates a high intramuscular fat content in the cattle.
[0021] Beneficial effects:
[0022] The rumen is the place where ruminants digest and absorb agricultural and sideline products such as straw. Microbial digestion plays an important role in the overall digestion and absorption process. Therefore, the present invention measures microbial to predict the intramuscular fat content level of cattle. The method is simple and non-invasive, which can not only intervene in advance to increase the intramuscular fat content, but also conduct screening or elimination operations in advance to facilitate the breeding of cattle and improve the breeding efficiency of cattle. At the same time, sequencing technology is used to compare the composition and species of rumen microorganisms of different individual beef cattle in the form of relative abundance, breaking through the limitations of qPCR, and long-term data can be accumulated for updating. Description of the Drawings
[0023] Figure 1 They are the α-diversity and β-diversity of rumen microbial species. Among them, (a) is the box plot of the species dilution curve. The abscissa represents the number of samples, the ordinate represents the number of detected species, and the color of the box represents the grouping; (b) is the box plot of species α-diversity. Each box plot represents a diversity index. The abscissa and different boxes represent the grouping, and the ordinate is the index value. The method and result of the hypothesis test are marked in the upper left corner of each box plot. p < 0.05 indicates that there are significant differences in the α-index among groups; (c) is PLS-DA at the genus level, and (d) is PLS-DA at the species level.
[0024] Figure 2 They are the comparison diagrams of differential bacterial genera of rumen microorganisms in the HIMF group and the LIMF group. Among them, (a) is the stacked bar chart of species abundance at the genus level, and (b) is the stacked bar chart of species abundance at the species level.
[0025] Figure 3 They are the analysis diagrams of the functional differences of rumen microorganisms in the HIMF group and the LIMF group. Among them, (a) is the box plot of the CAzy functional difference analysis, (b) is the Venn diagram of the CAzy functional analysis difference, (c) is the box plot of the CAzy functional difference at the level 2 hierarchy, and (d) is the bar chart of the CAzy functional difference STAMP analysis.
[0026] Figure 4 They are the stacked diagrams of the functional abundances of rumen microorganisms in the HIMF group and the LIMF group. Among them, (a) is at the EggNOG level, and (b) is at the COG level.
[0027] Figure 5 They are the KEGG differential function analysis diagrams. Among them, (a) is the box plot of the KEGG functional β-diversity analysis, (b) is the bar chart of the functional gene statistics, (c) is the enrichment diagram of the functional KEGG Pathway, and (d) is the bar chart of the functional difference STAMP analysis results.
[0028] Figure 6Analysis of potential biomarkers for high muscle fat in Pingliang Red Cattle. Among them, (a) is the LEfSe circular phylogenetic tree diagram, (b) is the heat map of the correlation analysis between rumen microorganisms and intramuscular fat content, and (c) is the ROC curve, and the area under the curve is the AUC value. Detailed implementation mode
[0029] The present invention will be further explained below in conjunction with specific embodiments.
[0030] Example 1 Animal experiment and sample processing
[0031] Select 18 Pingliang Red Cattle steers in the same abattoir, allowing them to feed and drink freely. One month before sampling, use antimicrobial agents to exclude infectious diseases and other force majeures. Slaughter them uniformly at 36 months of age. After slaughter, collect 800 ml of rumen fluid from each cow, filter it through four layers of coarse gauze, and store it at -80 °C for metagenomic sequencing; and take 500 g of the tenderloin for intramuscular fat content and marbling analysis.
[0032] Ocular muscle marbling: Usually, the cross-section of the ocular muscle at the 12th - 13th intercostal space is used as a representative for visual comparison scoring with a standard card. Using the 5-point beef marbling standard in China, the marbling grade is divided into 1 - 5 levels. The larger the number, the more obvious the marbling and the higher the intramuscular fat content.
[0033] The intramuscular fat content is determined by the Soxhlet extraction method, and the steps are as follows: Weigh about 1.5 g of meat sample, record the weight as m1, cut it into pieces and place it in an oven at 37 °C for 48 h. After drying, grind it into powder, wrap the powder with a filter paper folded into a fat bag, and dry it at 37 °C twice and weigh it until the weight remains unchanged, then record the weight as m2. Use petroleum ether with a boiling range of 30 - 60 to determine the crude fat content through an automatic fat analyzer. The parameters are adjusted as follows: extraction temperature 65 °C, extraction time 360 min, pre-drying time 10 min, reflux time 15 min. After the extraction is completed, take out the fat bag and dry it to a constant weight, and record the weight as m3.
[0034] The calculation formula for IMF content is as follows:
[0035] IMF content = (m2 - m3) ÷ m1 × 100%.
[0036] The intramuscular fat content and marbling scores of 18 Pingliang Red Cattle are shown in Table 1:
[0037] Table 1 Intramuscular fat content and marbling scores of Pingliang Red Cattle
[0038]
[0039] Example 2 Rumen metagenomic sequencing
[0040] The DNA of the rumen fluid samples of Pingliang Red Cattle was extracted using the MagPure Stool DNA KF Kit B (MAGEN, Guangzhou, China). After detecting its concentration and integrity by a microplate reader and agarose gel electrophoresis, the library was constructed by BGI Genomics Technology Services Co., Ltd. using the BGIOptimal DNA Library Prep Kit (BGI-Shenzhen, China), and the metagenomic high-throughput sequencing was performed on the DNBSEQ-T7 sequencing platform.
[0041] The SOAPnuke (V2.2.1) software was used to filter the original sequencing data, removing the Reads containing 0.1% undetermined bases (N bases); removing the Reads containing sequencing adapter sequences (with 15 bases or longer regions aligned to the adapter sequences); removing the Reads containing more than 50% low-quality bases (bases with Q < 20); using Bowtie2 (2.4.4) for data alignment to remove the sequences aligned to the host genome (https: / / www.ncbi.nlm.nih.gov / search / all / ?term=ARS-UCD1.2), and using the Samtools (1.2) software for data processing to obtain CleanData. After quality control, the assembly software MEGAHIT was used to perform de-novo assembly on the samples, filtering out the assembled sequences with a length less than 300 bp.
[0042] First, MetaGeneMark was used to predict metagenomic genes, and then the CD-HIT software was used to remove redundancy from the gene prediction results of each sample. According to the sequence similarity (setting the identity threshold to 95% and the coverage threshold to 90%), they were classified into one of the categories or became the representative sequences of a new cluster, completing the clustering process. Finally, the Salmon software was used for quantification, and the obtained TPM value was the normalized gene abundance value.
[0043] The TPM quantification formula is shown as follows:
[0044]
[0045] The Kraken2 software was used for species annotation, and at the same time, Bracken was used to estimate the species-level abundance of metagenomic samples using the Bayesian algorithm and the Kraken classification results. When selecting the database, the Nt (202011) database was used for rumen fluid samples.
[0046] Calculate the alpha diversity of species and functions using R packages, including the Chao1 index, Shannon index, and Simpson index. At the same time, measure the differences between samples or groups, that is, beta diversity, by calculating the Euclidean distance, Bray-Curtis distance, and Jensen-Shannon divergence.
[0047] Use the BLASTP function of the software Diamond to perform functional annotation on non-redundant genes. This includes CAZy (EggNOG, COG) and KEGG, etc. In addition, use the ROC curve to analyze biomarkers at the model prediction level, and finally use Spearman correlation analysis for biomarkers.
[0048] (1) For the metagenomic sequencing of rumen content samples in this study, on average, each sample generated 10.44 Gb of raw sequence data (10.22 - 10.62 Gb), and the average GC content of all samples was 50.04%. After removing low-quality sequences, a total of 180.76 Gb of high-quality sequence data was obtained from 18 samples, with an average sequencing depth of 10.04 Gb / sample (10.02 - 10.07 Gb).
[0049] At the phylum level, a total of 70 phyla were identified in the rumen microbiome. Among them, the top 5 phyla with the highest abundances were Bacteroidetes (45.72% ± 6.13%), Firmicutes (21.78% ± 4.06%), Proteobacteria (15.21% ± 1.94%), Actinobacteria (6.31% ± 0.59%), and Fibrobacteres (3.54% ± 2.00%). In addition, Euryarchaeota (0.89% ± 0.22%) was the only archaeal phylum detected in this study.
[0050] (2) Alpha diversity and Beta diversity
[0051] The species accumulation curve of rumen microorganisms in Pingliang Red Cattle is as shown in Figure 1 (a). The difference in the genus-level bacterial community diversity between the HIMF group and the LIMF group is as shown in Figure 1 (b). Compared with the LIMF group, the Chao1 index of the HIMF group was higher than that of the LIMF group, the Simpson index was lower than that of the LIMF group, and the Shannon index was lower than that of the LIMF group, but the differences were not significant (p > 0.05). The PLS-DA at the genus level and species level are shown in Figure 1 (c) and Figure 1(d) shows that there are significant differences between the two groups of HIMF and LIMF data at the genus and species levels.
[0052] (3) Relative abundance analysis at the genus and species levels
[0053] The stacked bar chart of the top 30 bacterial genera and species with relative abundances of rumen microorganisms in Pingliang Red Cattle is as shown in Figure 2 (a). Streptomyces is significantly higher in the HIMF group than in the LIMF group (p < 0.05), and Bacillus is significantly lower in the HIMF group than in the LIMF group (p < 0.05).
[0054] The stacked bar chart of the top 30 bacterial species with relative abundances is as shown in Figure 2 (b). Prevotella_intermedia in the HIMF group is significantly higher than in the LIMF group (p < 0.05).
[0055] (4) Relative abundance analysis related to CAzy enzymes
[0056] The results of the differential analysis of carbohydrate-active enzymes based on the CAzy database are as shown in Figure 3 (a). It is found that there are significant differences in the enzymes with carbohydrate degradation functions in the rumen microorganisms between the HIMF group and the LIMF group (p < 0.05), indicating that the differences in the carbohydrate degradation functions of the rumen microbiota between the HIMF group and the LIMF group may be caused by differences in enzyme abundances.
[0057] Figure 3 (b) shows that there are 2 unique CAzy enzymes in the HIMF group.
[0058] The box plot of the differences in carbohydrate function enzymes at the level2 level ( Figure 3 (c)) shows that the relative abundances of glycoside hydrolases (GH55, GH117, GH133, GH148, GH177), carbohydrate esterases (CE15, CE20), polysaccharide lyases (PL35), carbohydrate-binding modules (CBM92), and glycosyltransferase classes (GT64, GT101) in the HIMF group are significantly higher than those in the LIMF group (p < 0.05).
[0059] The bar chart of the STAMP analysis of CAZy functional differences ( Figure 3 (d)) shows the CAZy families with significantly different abundances between the HIMF and LIMF groups.
[0060] (5) Stacked bar chart of rumen microbial functional abundances
[0061] The obtained genes were predicted for EggNOG functional classification using the Eggnog evolutionary genealogy of genes software, and the results are as Figure 4 shown in (a), and it was found that amino acid transport and metabolism and lipid transport and metabolism functions were significantly enriched in the TOP30.
[0062] COG analysis showed that the stacked plot of the top 30 functions in relative abundance was as Figure 4 shown in (b), and the top five functional classifications were: translation, ribosome structure and biogenesis, general function prediction, transcription, cell wall / membrane / envelope biogenesis, and amino acid transport and metabolism.
[0063] (6) KEGG differential function analysis
[0064] Based on the KEGG database, functional differences in the microbial genes of the two groups of samples were analyzed at the KO level as Figure 5 shown in (a), and it was found that the functions within the HIMF group were clustered, and the rumen microbiota in the LIMF group was slightly more dispersed, with significant differences between the two groups (p < 0.05).
[0065] The KEGG functional classification results were as Figure 5 shown in (b), indicating that the functions of the rumen microbiota of Pingliang Red Cattle were mainly distributed in lysine biosynthesis, cysteine and methionine metabolism, streptomycin biosynthesis, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, fructose and mannose metabolism, methane metabolism, glycosaminoglycan degradation, other glycan degradation, biotin metabolism, porphyrin metabolism, d-amino acid metabolism, limonene and pinene degradation, terpenoid backbone biosynthesis pathways, etc.
[0066] Figure 5 (c) showed that: compared with the HIMF group, the aminoacyl-tRNA biosynthesis activity in the LIMF group was higher or the metabolic functions were richer.
[0067] STAMP analysis (as Figure 5 shown in (d)) also showed that the geraniol degradation pathway, photosynthesis pathway, MAPK signaling pathway, and biosynthesis of antibiotics in the vancomycin group in the LIMF group had higher biological activities.
[0068] (7) Potential biomarker analysis
[0069] Analysis of potential biomarkers for high muscle fat in Pingliang Red Cattle was as Figure 6 shown.
[0070] Figure 6 (a) showed that Limosilactobacillus_panis was significantly enriched in the HIMF group.
[0071] Analysis of the correlation between strains and IMF% (as Figure 6 (shown in (b)) found that Fibrobacter_succinogenes, Limosilactobacillus_panis, and Streptomyces were positively correlated with intramuscular fat content.
[0072] ROC analysis, as Figure 6 (shown in (c)), showed that Fibrobacter_succinogenes (AUC = 0.753) and Limosilactobacillus_panis (AUC = 0.765) exhibited high prediction accuracy.
[0073] In summary, the present invention uses metagenomic sequencing technology to analyze the species and structural characteristics of rumen microorganisms in the high intramuscular fat group of Pingliang Red Cattle. It was found that there were certain differences in α-diversity and β-diversity of rumen microorganisms in the high and low intramuscular fat groups of Pingliang Red Cattle (p > 0.05), and significant differences were observed in the expression abundance at the genus and species levels, the abundance related to CAzy enzymes, and functional analysis (p < 0.05). This indicates that the rumen microbial flora of Pingliang Red Cattle has a certain effect on intramuscular fat deposition in beef, and Limosilactobacillus_panis and Fibrobacter_succinogenes in the rumen microorganisms can be used as potential biomarkers for high intramuscular fat in Pingliang Red Cattle.
[0074] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.
Claims
1. A rumen biomarker for predicting the intramuscular fat content of cattle, characterized in that, Comprising: At least one of Fibrobacter succinogenes and Limosilactobacillus panis.
2. The rumen biomarker for predicting the intramuscular fat content in beef cattle according to any one of claims 1, wherein The breed of cattle is Pingliang Red Cattle.
3. Use of a reagent for detecting the rumen biomarker according to claim 1 or 2 in the preparation of a kit for predicting intramuscular fat content in cattle or in breeding.
4. The application according to claim 3, characterized in that The reagent comprises 16S rRNA primers for detecting or amplifying the rumen biomarker.
5. Use of the rumen biomarker according to claim 1 or 2 in the preparation of a model for predicting intramuscular fat content in cattle.
6. A method for predicting the intramuscular fat content of cattle, characterized in that, Comprising the following steps: S1. Collect a bovine rumen fluid sample and extract a nucleic acid sample from the bovine rumen fluid sample; S2. Determine the relative abundance information of the rumen biomarker according to claim 1 or 2 in the nucleic acid sample obtained in S1; S3. Compare the relative abundance information obtained in S2 with a reference data set or a reference value.
7. The method according to claim 6, characterized in that The specific operation of S2 is as follows: construct a DNA library using the nucleic acid sample obtained in S1, sequence the DNA library to obtain a sequencing result, and compare the sequencing result with a reference gene set to determine the relative abundance information of the rumen biomarker according to claim 1 or 2.
8. The method according to claim 6, wherein In S3, when comparing, an increase in Fibrobacter succinogenes or / and Limosilactobacillus panis indicates that the cattle has a high intramuscular fat content.