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. 2024 Oct 14;14:24005. doi: 10.1038/s41598-024-74988-z

Effect of different genetic backgrounds on rumen microbiota and serum metabolic phenotypes in beef cattle

Yaxing Zhao 1,#, Hao Chen 2,#, Pengfei Zhao 2,#, Chunhua Zhang 1, Yi Wu 2, Xiaorui Li 2, Mingke Huangfu 2, Zhimeng Chen 2, Chunjie Wang 3, Bo Liu 2, Huasai Simujide 2, Aorigele Chen 2,✉, Haizhou Sun 1,✉
PMCID: PMC11473713  PMID: 39402126

Abstract

Species with different genetic backgrounds exhibit distinct metabolic traits. Nine beef cattle were selected for the experiment to study changes in serum metabolic phenotypes, rumen microbiota diversity, and composition in beef cattle from different genetic backgrounds. Three groups were Chinese Simmental (S group), Simmental×Chinese Holstein (SH group), and Simmental × Mongolian (SM group) cattle. We used ELISA to detect serum biochemical indicators. The Short-chain fatty acids (SCFAs) in the rumen were examined, and a significant difference was observed in the acetic acid content of the three experimental groups (p < 0.01). The propionic acid content in the rumen of the S group was significantly higher than that of the SH and SM groups (p < 0.05). The A/P ratios of both the S and SM groups were significantly higher than that of the SH group (p < 0.05). We analyzed rumen microbiota composition and diversity in each group of cattle using 16 S rRNA sequencing and found that their composition was generally similar in the three groups of crossbred fattening cattle; however, the f_Bacteroidales_RF16_group and g_norank_f_Bacteroidales_RF16_group were significantly enriched in the SH group, whereas Treponema and Spirochaetia were significantly enriched in the SM group. Spirochaetia was significantly enriched in the SM group. Differences in rumen bacterial enrichment indicated that starch, protein, and cellulolytic abilities differed among the S, SH, and SM groups. The results of Spearman correlation analysis confirmed the correlation between rumen genera and serum biochemical indices. Overall, differences in rumen microflora play an important role in influencing the serum metabolic phenotype.

Keywords: Serum biochemical indicators, Rumen SCFAs, Rumen microbiota, 16S rRNA sequencing, Simmental cattle

Subject terms: Genetics, Microbiology

Introduction

Beef is widely consumed because of its taste, high protein content, and essential trace elements1. According to previous research, progeny from different host genetics often have high production potential regarding performance and product quality2–4. In recent years, several researchers have compared Simmental cattle with other breeds or crosses to study their potential for beef cattle production. A study found that Simmental cattle provide higher yields of low-fat beef than Xuanhan yellow cattle and yaks; however their meat is slightly less red and tender5. Using Charolais cattle as a sire to cross Seaford and Simmental cattle showed that Simmental and Charolais crosses exhibited excellent growth performance6. Growth performance and meat quality indicators of animals tend to correlate, to some extent, with blood biochemical indicators7. A study comparing the serum biochemical indices of six different cattle breeds in early summer found significant differences in the serum total protein (TP), globulin (GLB), blood urea nitrogen (BUN), creatinine and calcium levels8. High-concentrate rations fed to Yilin and Angus cattle showed an increase in serum aspartate transaminase (AST) activity and a decrease in alkaline phosphatase (ALP) activity9. These findings suggest that different breeds of beef cattle exhibit different levels of substance metabolism even under the same environmental and feeding conditions. Therefore, the selection of suitable crossbreed cattle for beef production is a high quality option.

The rumen is a unique digestive organ in ruminants that plays an important role in digestion and absorption of feed. Therefore, every feeding management program for ruminants essentially regulates rumen function10. The rumen ferments structural carbohydrates into short-chain fatty acids (SCFAs), which provide 70–80% of an animal’s energy for vital activities11. The results of numerous previous studies have shown that rumen SCFA content and microbiota composition are closely related to the living environment and nutritional status of the animal, and are also important indicators of health status12–14. Firmicutes and Bacteroidota are usually the main bacterial phyla found in the rumen15. Moreover, rumen microbiota are susceptible to several factors such as nutrition16, stress17 (cold stress, heat stress and social stress) and additives (plant additives and monensin)18. However, recent studies have shown a correlation between the genetic background and microbiota composition in the gastrointestinal tract of ruminants19,20.One study observed that approximately 50% of microbial taxa in the rumen are influenced by genetic factors21.

The Inner Mongolian Autonomous Region of China has a well-developed breeding base for Holstein dairy cattle. Fattening of Holstein bulls is also important22. Mongolian cattle are an ancient local breed in China with excellent adaptability to the ecological environment of Inner Mongolia23. Simmental cattle are representative introduced breeds and usually exhibit excellent production performance24 However, little research has been conducted on fattening cattle using these three-breed combinations. Therefore, the objective of this study was to elucidate the microbial composition of the gastrointestinal tract and the serum metabolic status of fattening cattle of three breed combinations. This study aimed to provide useful information for improving the health status and production performance of crossbred cattle.

Materials and methods

Ethics approval and consent to participate

All protocols and procedures involving animals were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China) and were approved by the Animal Welfare Committee of China Inner Mongolia Agricultural University. the animals were reared in the same environment, were fed the same diet and were humanely sacrificed. The study is reported in accordance with ARRIVE guidelines. The experiment has been approved by the farm owner.

Animals and experimental procedure

The study was conducted at the Tianmuzhen fattening cattle farm in Zalaid Banner, Xing’an League, Inner Mongolia Autonomous Region. Nine healthy fattening cattle with similar body condition and food intake were selected for the experiment. Three Chinese Simmental (S), three Simmental × Chinese Holstein (SH), and three Simmental × Mongolian (SM) cattle (24 months)were used. The test cattle were fed TMR during fattening, with free access to food and water for 180 d. The composition and nutrient levels of the diet are shown in Table 1. After fattening, the cattle were slaughtered at the Tianmuzhen slaughterhouse according the method of Cafferky[2]. At slaughter, 10 mL blood was collected in disposable vacuum blood collection tubes. Thereafter, the blood samples were immediately centrifuged at 4 ℃ for 10 min at 3,500 r/min, and serum samples were transferred to 1.5 mL centrifuge tubes for storage in liquid nitrogen and brought back to the laboratory for determination of serum biochemical parameters. Rumen contents were collected immediately after the slaughter of each group, and rumen fluid was obtained by filtration using three layers of medical gauze. The rumen fluid was divided into 5 mL lyophilized tubes, dated and numbered, immersed in liquid nitrogen, and subsequently transferred to a -80 °C refrigerator for microbial 16 S rRNA sequencing and rumen SCFAs analysis.

Table 1.

Composition and nutrient levels of the diet of beef cattle.

Ingredient, % of DM Nutrient composition2
Dry rice straw 26.50 OM, % of DM 92.69
Corn silage 23.50 EE, % of DM 3.49
Corn 24.87 CP, % of DM 13.61
Wheat bran 11.10 ADF, % of DM 20.70
Soybean meal 12.23 NDF, % of DM 41.56
CaHPO3 0.30 NEm,3 MJ/kg DM 9.67
NaCl 0.50 Ca, % of DM 0.68
Premix1 1.0 P, % of DM 0.35
Total 100
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1Premix contained (per kg of premix): 420,000 IU of vitamin A, 110,000 IU of vitamin D3, 4,200 IU of vitamin E, 2620 mg of Fe, 155 g of Ca, 30 g of P, 820 mg of Cu, 1,800 mg of Mn, 3,500 mg of Zn, 30 mg of I, 35 mg of Se, and 22 mg of Co.

2OM, organic matter; EE, ether extract; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber; NEm, net energy for maintenance; Ca, calcium; P, phosphorus.

3Calculated.

Determination of serum biochemical indicators

Serum biochemical indices: Free amino acid (FAA), BUN, Crea (Cr), TP, albumin (ALB), phosphocreatine kinase (CPK), alanine aminotransferase (ALT), AST, and uric acid (UA), were measured using an ELISA kit (Quanzhou Ruixin Biotechnology Co., Ltd.).

Determination of SCFAs in rumen fluid

Rumen SCFAs underwent liquid chromatography-electrospray ionization mass spectrometry/mass spectrometry (LC-ESI-MS/MS ) (UHPLC-Qtrap) for qualitative and quantitative determination of target compounds in samples with the following parameters: chromatographic conditions: ExionLC AD system, Waters BEH C18 (150*2.1 mm,1.7 μm) liquid chromatographic column, column temperature 40 °C, injection volume of mobile phase A (0.1% formic acid-water solution), mobile phase B (0.1% formic acid-acetonitrile). The mass spectrometry conditions were as follows: AB SCIEX QTRAP 6500 + with negative mode detection, curtain gas of 35, collision gas of medium, ion spray Voltage of -4500 V, temperature of 450 °C, ion source gas1 of 40, and ion source gas2 (GS2) of 40.

Automatic identification and integration of each ion fragment was performed using the AB Sciex quantitative software OS with default parameters, aided by manual inspection. A linear regression standard curve was plotted using the mass spectral peak area of the analyte as the vertical coordinate, and the concentration of the analyte as the horizontal coordinate. The concentration of the sample to be measured was calculated by substituting the peak area of the target into the linear equation of the standard curve. The actual concentration of the target in the sample was calculated based on the sampling volume.

Rumen fluid 16 S rRNA sequencing

Rumen fluid samples from the three groups were divided into six replicates per group for a total of 18 samples. There were 2 replicates per animals.The 16 S rRNA microbial sequencing platform was the Illumina MiSeq PE300 (Majorbio BioPham Technology, Shanghai, China).

Total DNA was extracted from rumen fluid samples using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek). The concentration and purity of the DNA were examined using a NanoDrop 2000 spectrophotometer, and DNA integrity was examined using 1% agarose gel electrophoresis at 5 V/cm for 20 min. The lysozyme and proteinase K were 20 u/ug and 2 mg/mL. To obtain DNA sequences for high-throughput sequencing, primers were designed with reference to Tian25, using primer 338 F (5 ‘-ACTCCTACGGGAGGCAG-CAG-3’) and 806R (5’-GGACTACHVGGGT- WTCTAAT − 3’), the V3–V4 region of the rumen fluid bacterial 16S rRNA gene was amplified using PCR. Mixed samples were purified with PCR products using the AxyPrep DNA Gel Extraction Kit, and the PCR products were quantified using the Quantus™ Fluorometer.

High-throughput sequencing was performed using an Illumina MiSeq sequencing platform. Libraries were built using the NEXTFLEX Rapid DNA-Seq Kit and sequenced on an Illumina MiSeq PE300 platform. The raw sequences were quality controlled using fastp (https://github.com/OpenGene/fastp, version 0.20.0) software and sequencing was performed using FLASH (http://www.cbcb.umd.edu/software/flash, version 1.2.7) for splicing. Sequences were clustered based on 97% similarity using UPARSE software (http://drive5.com/uparse/, version 7.1). OTUs were generated and analyzed for Shannon, Simpson, Chao1, and ACE indices in the alpha diversity of rumen bacteria using Mothur software (https://www.mothur.org/w./Download_mothur) at 97% sequence similarity. Using QIIME software(www.drive5.com), bacterial OTU representative sequences were compared with the Silva database, the community composition of each sample at the phylum (phylum) and genus (genus)26 levels was counted, and phylotaxonomic histograms were plotted27. In this study, we used the Illumina MiSeq sequencing platform to perform high-throughput sequencing of the V3–V4 high-variance region of the 16 S rRNA gene. Double-end sequence data were obtained after sequencing. First, we distinguished samples according to the barcode and primer sequences at the first and last ends to obtain valid sequences and correct sequence orientation. The optimized raw data were quality controlled using Trimmomatic and FLASH software.

Running LEfSe software consists of the following three steps: Step 1: We need to convert a table of common abundance information for species, genes, etc., into a format that LEfSe recognizes. Step 2: 1: The biomarker was detected by nonparametric factor Kruskal-Wallis rank sum test in two or more groups of samples.2: Based on the significantly different species genes in the previous step, Wilcoxon rank sum test was performed between the two groups to detect the difference between the groups.3: Linear discriminant analysis (LDA) was used to evaluate the influence of species with significant differences (LDA score), and finally obtain the biomarker.Step 3: Draw various pictures based on the data from Step 2.

Correlation analysis of rumen microbiota and metabolic phenotypes

In this study, correlation analysis was conducted between metabolic phenotypes and rumen microorganisms in order to discover the potential relationship between serum metabolites and rumen microorganisms, and biomarkers of serum metabolites were finally selected.Because there is no negative correlation according to the test results, the negative correlation is not considered. The calculation results in a threshold value of 0.6, which is the standard for identifying highly correlated microbiome feature-metabolite pairs.

Statistical analyses

Study data are expressed as the mean ± Standard Deviation (SD). Statistical analyses were performed using SPSS software (version 26.0; SPSS, Chicago, Illinois, USA). One-way ANOVA (Duncan’s test) was used for comparisons between the three groups, with p < 0.05 as the significance threshold. Correlation analysis between gut microbiota and metabolic phenotypes was performed using Spearman’s correlation analysis. There are random effect animal ID was accounted for in these models.

Results

Effect of different genetic backgrounds on serum biochemical indices

The effect of different genetic backgrounds on serum biochemical indices in Fig. 1. This shows that the serum levels of Cr and ALB and the activity of AST in the SM group were significantly higher than those in the S and SH groups (p < 0.05). The activity of CPK in the serum of the SM group was significantly higher than that in the S group (p < 0.05); however, it was not significantly different from that in the SH group (p > 0.05). Serum levels of FAA, UN, TP, UA, and ALT were not significantly different (p > 0.05).

Fig. 1.

Fig. 1

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Effect of different genetic backgrounds on serum biochemical indices albumin (ALB); alanine aminotransferase (ALT); aspartate transaminase (AST); blood urea nitrogen (BUN); phosphocreatine kinase (CPK); Creatine (Cr); Free amino acid (FAA); total protein (TP); uric acid (UA); *(P < 0.05),***(P < 0.01).

Effect of different genetic backgrounds on rumen SCFAs

Effect of different genetic backgrounds on rumen SCFAs in Fig. 2. This shows that there was a significant difference in the acetic acid content in the rumen of the three experimental groups, and the S group > the SM group > the SH group (p < 0.01). The propionic acid content in the rumen of the S group was significantly higher than that in the SH and SM groups (p < 0.05). The total SCFA content was not significantly different between the S and SM groups; however, it was significantly higher than that in the SH group (p < 0.01). No significant differences were observed in the content of butyric acid, isobutyric acid, valeric acid, isovaleric acid, caproic acid, and 4-methylvaleric acid in the rumens of the three experimental groups (p > 0.05).

Fig. 2.

Fig. 2

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Effect of different genetic backgrounds on rumen SCFAs Short-chain fatty acids (SCFAs); *(P < 0.05),***(P < 0.01).

Effect of different genetic backgrounds on rumen microbiota composition and diversity

In total, 815,649 valid sequences were generated from 18 samples tested in this experiment. The total number of valid sequences was 341,902,762 bases. The average valid sequence of each sample was 45,314, and the sequence length was mainly distributed between 401 and 450 bp (Fig. 3. A). The average length of the valid sequences was 419 bp, which was used for subsequent analysis of the samples. To understand the number of strains and genera in the sequencing results, the original valid sequences were clustered using UPARSE software, and the sequence similarity level was divided into OTUs with 97% as the threshold after the minimum sample number was drawn flat. The Silva database (Release138 http://www.arb-silva.de) was used to obtain the species taxonomic information corresponding to each OTU for bioinformatic statistical analysis. A total of 1997 OTUs were detected in this experiment, which were classified into 17 phyla, 32 classes, 73 orders, 120 families, 244 genera, and 504 species. The number of OTUs shared by the three test groups was 1396, and the number of OTUs specific to the three groups was 114 for the S group, 43 for the SH group, and 134 for the SM group (Fig. 3. B).

Fig. 3.

Fig. 3

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Effect of different genetic backgrounds on rumen microbiota composition and diversity.

All three groups of ruminal fluid samples had > 98.5% community coverage, indicating that the accuracy and reproducibility of this assay met the requirements (Fig. 3C). The results showed no significant changes in the Shannon, Simpson, and Chao1 indices of the three test groups of cattle (p > 0.05), and the rumen microbiota Ace index of the S group was significantly higher than that of the SH group (p < 0.05) (Fig. 3C).

Five dominant phyla with relative abundances > 1% were detected in rumen microbiota of the three test groups of cattle, with Bacteroidota and Firmicutes dominating the rumen. The other three dominant phyla were Patescibacteria, Fibrobacterota, and Spirochaetota (Fig. 3.D). Other phyla with < 1% relative abundance were categorized as Others. Twenty-eight superior genera with a relative abundance > 1% were identified. These included Prevotella, Succiniclasticum, Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG-001, norank_f_F082, and NK4A214_group (Fig. 3.E). Genera with an abundance < 1% were classified as Others .

No different phyla were detected in the S and SH groups; four different phyla were detected in the S and SM groups. Spirochaetota was significantly higher in the rumen of the SM group than in that of the S group (p < 0.05), and Verrucomicrobiota, Proteobacteriota, and WPS-2 were significantly higher in the rumen of the S group than in that of the SM group (p < 0.05) (Fig. 3 F). No significant differences were observed for any other phyla at the phylum level. At the genus level, NK4A214_group, Christensenellaceae_R-7_group, norank_f_UCG-010, Papillibacter, Lachnospiraceae_FCS020_group, norank_f_Ruminococcaceae, Lachnoclostridium, norank_f_Marinifilaceae, and unclassified_c_Clostridia were significantly less abundant than in the S group ( p < 0. 05), and Bacteroidales_BS11_gut_group were significantly less abundant than in the S group (p < 0.01), whereas norank_f_Bacteroidales_RF16_group, Prevotellaceae_UCG-003, Prevotellaceae_UCG-004, and norank_f_Bacteroidales_RF16_group were less abundant than in the S group (p < 0.01). norank_f_norank_o_Clostriidia_vadinBB60_group was significantly more abundant than in the S group (p < 0.05), and unclassified_f_Lachnospiraceae was significantly more abundant than in the S group (p < 0.01) (Fig. 3G). In contrast to the S group, the relative abundances of Succiniclasticum, norank_f_UCG-010, norank_f_Bacteroidales_BS11_gut_group, norank_f_norank_o_WCHB1-41, Lachnospiraceae_FCS020_group, and unclassified_f_Lachnospiraceae_FCS020_group in the rumen of the SM group, norank_f_Marinifilaceae, norank_f_norank_o_Rickettsiales, norank_f_norank_o_norank_c_norank_p_WPS-2, and Pyramidobacter were significantly lower than those in the S group (p < 0.05). Papillibacter, norank_f_vadinBE97, and Ruminiclostridium were significantly less abundant than in the S group (p < 0.01), and Bifidobacterium and Asteroleplasma were significantly less abundant than in the S group (p < 0.01). Asteroleplasma were significantly higher than that in the S group (p < 0.05), and the relative abundance of Treponema was significantly higher than that in the S group (p < 0.01) (Fig. 3H).

The dominant microorganisms in the rumen of the three different cross combinations of S groups were investigated. In the S group, Oscillatoriaceae, g_norank_f_UCG-010, f_UCG-010, Papillibacter, norank_f_Bacteroidales_BS11_gut_group, f_Bacteroidales_BS11_gut_group, and Lachnospiraceae_FCS020_group were significantly enriched compared with the other groups. In the SM group, Treponema, Spirochaetales, Spirochaetia, Spirochaetota, and g_unclassified_f_Lachnospiraceae were significantly enriched compared to those in the other groups. In the SH group, f_Bacteroidales_RF16_group and g-_norank_f_Bacteroidales_RF16_group were significantly enriched compared with the other groups (Fig. 3. I).

Correlation analysis of rumen microbiota and metabolic phenotypes

Correlation analyses were conducted using Spearman’s rank correlation coefficient analysis. Significant correlations (P < 0.05) are indicated by *, and extremely significant correlations (P < 0.01) are indicated by **. Red represents positive correlation coefficients, deep red represents the strong positive correlation coefficients, blue represents negative correlation coefficients, and deep blue represents strong negative correlation coefficients. The bar on the right with numbers shows the values of the correlation coefficients [ρ]. The greatest to lowest ranked relative abundances of the microbiome taxa based on all the samples are shown in the longitudinal direction from left to right; the metabolic phenotypes values are shown in the transverse direction.

The Spearman method was selected for the analysis, and a correlation coefficient of 0.6 was used as the threshold value for screening. The correlation results were obtained, as shown in Fig. 4. In the S group, Prevotella significantly and negatively correlated with ALB and UA levels (p < 0.05). The norank_f_Bactorcidales_RF16_group showed a significant positive correlation with ALB levels (p < 0.01) (Fig. 4 A). In the SH group, norank_f_Bactorcidales_RF16_group showed a significant negative correlation with AST levels (p < 0.05). prevotellaceae_UCG-001 was significantly and negatively correlated with ALB (p < 0.01) and UA (p < 0.05) levels. norank_f _p-251-o5 showed a significant positive correlation with AST levels (p < 0.01). norank_f-F082 showed a significant negative correlation with BUN levels (p < 0.01) (Fig. 4. B). In the SM group, Prevotella significantly and positively correlated with AST levels (p < 0.05). rikenellaceae_RC9_gut_group showed a significant negative correlation with AST levels (p < 0.05). Ruminococcus showed a significant negative correlation with BUN (p < 0.05) and CPK (p < 0.01). NK4A214 showed a significant negative correlation with AST, BUN (p < 0.05), and CPK (p < 0.01). norank_f_Bactorcidales_RF16_group showed a significant positive correlation with FAA (p < 0.01). norank_f_ Muribaculaceae showed a significant negative correlation with AST (p < 0.05). christensenellaceae_R-7_group showed a significant negative correlation with CPK (p < 0.05). norank_f_norank_o_Clostridia_UCG-014 showed a significant negative correlation with ALT levels (p < 0.05). Fibrobacter showed a significant positive correlation with AST levels (p < 0.05) (Fig. 4 C).

Fig. 4.

Fig. 4

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Correlation patterns based on the beef cattle showing associations between rumen microbiota and metabolic phenotypes. (A) Correlation analysis of rumen microbiota and metabolic phenotypes in S group. (B) Correlation analysis of rumen microbiota and metabolic phenotypes in SH group. (C) Correlation analysis of rumen microbiota and metabolic phenotypes in SM group.

Discussion

Serum ALB is a water-soluble anionic globular protein synthesized only by the liver and is one of the major components of the total blood protein. Reduced serum ALB levels indicated weight loss and decrease muscle mass, which are indirect markers of malnutrition28. In the present study, serum ALB levels which was within normal limits, were significantly higher in the SM group than in the S and SH groups, which may represent better uptake and transport of various substances produced in the body by the SM group. AST is a transaminase capable of catalyzing the transamination of glutamate and oxaloacetate to produce α-ketoglutarate and aspartate. Damage to hepatocytes when animals are subjected to high levels of external stress, including cold and heat stress, inflammatory responses, and changes in diet, leads to marked increases in serum AST activity29. In this study, although the serum AST activity of the SM group was significantly higher than that of the S and SH groups, the serum AST activity of all three groups was higher than normal (mean = 157.90 U/L30). This result indicated that the liver cells of all three fattening cows were damaged, which may be related to the metabolic imbalance caused by the winter cold stress conditions in the three groups during the late fattening period31. Cr is produced at a relatively stable level in the body and is not easily influenced by diet; therefore, it can indicate muscle quantity and quality32. In this study, serum Cr levels in the SM group were significantly higher than those in the S and SH groups. Phosphocreatine is an energy storage substance in the muscle tissue. CPK content in skeletal and other muscles can catalyze the interconversion between creatine and phosphocreatine33. In this study, we found that serum CPK level in the SM group was significantly higher than that in the S group, whereas the serum CPK level in the SH group was not significantly different from that in the S and SM groups. Therefore, it was speculated that the SM group has a greater amount of muscle tissue and activity than the S and SH groups.

SCFAs are important fermentation products that link animal health to rumen microbiota34. Acetic acid can be produced in the rumen by the fermentation of various rumen bacteria, with Wood-Ljungdahl as the main pathway35. Acetic acid is absorbed through the rumen wall into the bloodstream and, except for a small fraction in the liver, is converted to acetoacetic acid. Most of SCFAs are involved in lipid synthesis and metabolism via the tricarboxylic acid cycle as metabolic substrates. The results showed a significant difference in the concentration of acetic acid in the rumen of crossbred fattening cattle, with the S, SM, and SH groups in order of concentration. Propionic acid is absorbed by the body through the rumen wall and isomerized to glucose in the liver to provide energy36. The results showed that ruminal propionic acid concentration in the S group was significantly higher than that in the SH and SM groups. Acetic acid, propionic acid, and butyric acid are the main components of SCFAs, accounting for > 90% of total SCFAs. Some SCFAs are absorbed into the blood through the rumen wall and have specific physiological functions, in addition to their nutritional effects on organisms. It has been shown that the use of butyrate to culture enteric dorsal root ganglia, which modulates the enteric nervous system by increasing the proportion of cholinergic neurons, increases intestinal motility, whereas propionic acid decreases it37,38. Studies have also shown that a small fraction of acetate can cross the blood-brain barrier and activate hypothalamic neurons to produce satiety in mice when acetate is administered39. The A/P ratio is usually a direct indicator of dietary structure and nutrient levels. In the present study, the diets were fixed during the experiment so that the A/P ratio could be used as an indicator of the rumen’s ability to digest and utilize nutrients in beef cattle. The results showed that the A/P ratio of the S and SM groups was significantly higher than that of the SH group, and the rumen fermentation model of the other two groups was more inclined toward acetic acid fermentation than that of the SH group.

The ruminal microbiota acts as the first link between feed and ruminant organisms, and feed nutrition and concentrate to forage ratio are the main factors influencing the composition of rumen microbiota40. However, as research has progressed, studies have also demonstrated interactions between host and ruminal microbiota41. In experiments with mice, sex was found to be a factor influencing gut microbiota42. In cattle, breed was found to have a greater effect on rumen microbiota composition than sex, and the differences in findings may be due to species differences43. Therefore, the differences in rumen microbiota observed in this study may be due to genotypic differences. The sequencing results of this study showed that approximately 70% of OTU sequences were common to the three groups, indicating that the major bacterial composition of the rumen was similar among crossbred feedlot cows despite their different genetic backgrounds. The Ace index of the S group was significantly higher than that of the SH group, indicating that more bacterial strains were found in the S group.

In this study, Bacteroidota and Firmicutes were the dominant phyla, with the highest relative abundances in the rumen of all three test groups. Moreover, the sum of the relative abundances of the two phyla in the rumen of each test group exceeded 90%. The ratio of Firmicutes/Bacteroidota was closely related to the digestive capacity of the rumen of beef cattle. In previous studies, Bacteroidota strains were found to have high levels of oligosaccharide-degrading enzymes and branched-chain amylase genes, whereas cellulase and hemicellulase were found in Firmicutes strains44. To further understand the composition of rumen microbiota in different genetic backgrounds, their OTUs of were annotated at the genus level. The results of this study showed that the three dominant rumen genera in crossbred feedlot cattle were Prevotella, Succiniclasticum, Rikenellaceae_RC9_gut_group. Prevotella is the most widespread and abundant genus of bacteria in the rumen, classified as Bacteroidota, and previous studies reported that Prevotella possesses high hemicellulose degradation ability and plays an important role in the degradation of non-structural carbohydrates and proteins in plants45. Succiniclasticum is the most abundant genus of bacteria from plants that are gram-negative, anaerobic, classified as Firmicutes, and isolated from the rumen of cows fed silage as their main roughage, which is part of the microbiome of plants and quantitatively ferments succinic acid to propionic acid46. Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG-001 were classified as Bacteroidota, whereas NK4A214 was classified as Firmicutes47.

Sometimes, differences between groups of a phylum or genus are statistically significant; however, in reality, their relative abundance is so low that they are unlikely to be biologically responsible for epistatic differences48. Therefore, standard tests of statistical significance were combined with other tests encoding biological consistency and effect correlation using LEfSe analysis to identify the traits most likely to explain the differences between groups. The results of this study showed that f_Bacteroidales_RF16_group and g_norank_f_Bacteroidales_RF16_group were enriched in the rumen of the SH group. In addition, the relative abundance of g_norank_f_Bacteroidales_RF16_group was significantly higher in the rumen of the SH group than that in the S group. g_norank_f_Bacteroidales_RF16_group was classified as Bacteroidota, suggesting that Bacteroides rumen bacteria can produce acetic acid via the pyruvate-acetyl coenzyme A pathway or propionic acid via the succinic acid pathway to provide SCFAs to the organism49. Although Bacteroidota such as Prevotella, g_norank_f_Bacteroidales_RF16_group, and Prevotellaceae_UCG-003 were abundantly enriched in the rumen of the SH group, there were more dominant genera in the rumen of the S group. The results of LEfSe analysis showed that six taxonomic units were identified in the rumen of the SM group, which can be summarized as Treponema, Spirochaetia. Treponema and Spirochaetia can both be classified as Spirochaetota, whose main fermentation substrates are carbohydrates, and some of Treponema can use amino acids as their own energy sources50. While Lachnospiraceae belongs to Firmicutes, members of this family possess fibrinolytic and cellulolytic enzyme activities51. In the present study, both Spirochaetota and Treponema were the dominant microbiota, with significantly higher relative abundances in the rumen of the SM group than in the S group.

The correlation between serum metabolites and rumen microflora has been confirmed in previous studies on dairy cattle52; however, it has been less reported in beef cattle. In this study, Spearman correlation analysis was performed between rumen microbiota and serum biochemical indices in different combinations of Simmental crossbred fattening cows, and the results showed that there was a correlation between the relative abundance of rumen genera and serum indices in the three test groups, and different degrees of correlation were exhibited in the different groups. Prevotella negatively correlated with ALB in the S and SH groups and positively correlated with AST in the SM group. Prevotella was the genus with the highest relative abundance and belongs to Bacteroidota, an important phylum for the production of propionic acid from dietary fiber degradation53. Propionic acid plays an important role in host susceptibility to bacterial and fungal infections54. Although Prevotella did not show significant differences in the rumens of the three experimental groups of cattle in this study, its high relative abundance can be explained to some extent by different serological indicators. Succiniclasticum was the second most abundant genus in relative order, and its abundance was significantly higher in the rumen of the SM group than in the S group; however, no correlation with blood biochemical parameters was found in these two groups. NK4A214, Succiniclasticum, norank_f_Bactorcidales_RF16_group, and Christensenellaceae_R7-group were significantly different between the test groups. These genera belong to Firmicutes and can produce highly active cellulases and hemicellulases in the rumen, where they can ferment nutrients in the feed to produce SCFAs, such as acetic acid and butyric acid55. Butyrate is a key nutrient for the induction of tight junction protein expression and can influence the inflammatory response by regulating the release of inflammatory cytokines56. In this study, these genera were less correlated with serum biochemical indices in the S group; however, they negatively correlated with some protein metabolism-related indices in the SH and SM groups. Therefore, it is speculated that the differences in serum biochemical indices in the three groups of cattle may be related to differences in the abundance of Firmicutes and Bacteroidota, resulting in different levels of SCFA production.

Conclusions

Rumen fermentation patterns of the S and SM groups of were more oriented toward acetic acid fermentation than those of the SH group. That starch, protein, and cellulose catabolism differed among the S, SH, and SM groups. In the three experimental fattening groups, there was a correlation between rumen bacterial species and serum biochemical parameters, and the SM group showed a better ability to metabolize nutrients.

Acknowledgements

We thank tutors Aorigele Chen for their support and guidance in this work.

Author contributions

Conceptualization, C.W. and A.C.; methodology, A.C.; validation, C.Z.; formal analysis, P.Z. and Y.Z.; investigation, Y.W.; resources, X.L. and Z.C.; data curation, M.H.; writing—original draft prepa-ration, P.Z. and Y.Z.; writing—review and editing, H.C. P.Z. and Y.Z.; visualization, B.L.; supervision, Hu.S. and Ha.S.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by High-level Talent Start-up Research Project of Inner Mongolia Agriculture University (Project No. NDYB 2022-2), Youth Fund Project of College of Animal Science, Inner Mongolia Agricultural University (Project No. QF 2022-07), and National Natural Science Foundation of China (project no. 31660677) and National Key Research and Development Program (2022YFD1301102).

Data availability

The datasets presented in this study can be found in online repositories. The name of the repository/repositories and accession number(s) can be found below: NCBI; PRJNA1052410.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Yaxing Zhao, Hao Chen and Pengfei Zhao.

Contributor Information

Aorigele Chen, Email: aori6009@163.com.

Haizhou Sun, Email: sunhaizhou@china.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The name of the repository/repositories and accession number(s) can be found below: NCBI; PRJNA1052410.

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