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Article

Mechanisms Underlying the Impact of Feed-to-Gain Ratio Differences on Nutrient Metabolism in Simmental and Simmental × Hereford Crossbred Cattle Fed a Low-Energy Diet

by
Yi Wu
1,
Hao Chen
1,
Danling Zhang
1,
Lina Wang
1,
Qi Liu
1,
Chunjie Wang
2,
Aorigele Chen
1 and
Hairong Wang
1,*
1
College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
2
College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China
*
Author to whom correspondence should be addressed.
Animals 2026, 16(13), 2068; https://doi.org/10.3390/ani16132068 (registering DOI)
Submission received: 18 May 2026 / Revised: 30 June 2026 / Accepted: 2 July 2026 / Published: 4 July 2026
(This article belongs to the Section Animal Nutrition)
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Simple Summary

Improving feed efficiency is a key goal in sustainable beef production. By examining hormonal signals, blood metabolites, and gut microbes, this study investigated the differences in feed efficiency between Hereford × Simmental crossbred cattle and purebred Simmental cattle by analyzing hormone signals, blood metabolites, and gut microbes. We found that the level of Hypoglycin B was related to a reduced T3, which could affect growth performance. Metabolomics analysis identified Hypoglycin B, lysoPC, and DHAP as key discriminative metabolites. Pathway enrichment analysis revealed that these metabolites were primarily mapped to glycine, serine, and threonine metabolism; alanine, aspartate, and glutamate metabolism; and pantothenate and CoA biosynthesis. In our study, the relative abundances of Succinivibrio and Clostridium_sensu_stricto_6 were significantly greater in the H × S group than in the S × S group; it may have stronger energy extraction abilities and matches their lower F/G. Romboutsia was positively correlated with Hypoglycin B and negatively correlated with LysoPC (20:2 (11Z,14Z)/0:0), which might be related to changes in blood metabolites.

Abstract

To systematically investigate the metabolic and microbial differences in feed efficiency between Hereford × Simmental (H × S) crossbred and purebred Simmental cattle (S × S), 20 steers (initial weight = 268.80 ± 30.88 kg; initial age = 273.00 ± 14.09 days) were used, which were housed in mixed pens and fed a low-energy diet (0.75 Mcal Neg/kg DM). This study revealed that the level of Hypoglycin B was related to a reduced T3, which could affect growth performance. Metabolomics analysis identified Hypoglycin B, lysoPC, and DHAP as key discriminative metabolites. Pathway enrichment analysis revealed that these metabolites were primarily mapped to glycine, serine, and threonine metabolism; alanine, aspartate, and glutamate metabolism; and pantothenate and CoA biosynthesis. In our study, the relative abundances of Succinivibrio and Clostridium_sensu_stricto_6 were significantly greater in the H × S group than in the S × S group; it may have stronger energy extraction abilities and matches their lower F/G. Romboutsia was positively correlated with Hypoglycin B and negatively correlated with LysoPC (20:2 (11Z,14Z)/0:0), which might be related to changes in blood metabolites. Nevertheless, these explanations remain correlational and require functional validation through targeted interventions.

1. Introduction

Since the 1960s, China has introduced high-quality foreign beef cattle breeds, which are currently raised all over the country. By crossbreeding these breeds with our local yellow cattle, beef production has clearly improved [1]. Among the beef cattle breeds in China, those most commonly crossbred with our yellow cattle are Simmental, Charolais, Limousin, and Angus, with Simmental crossbreeds being the main type [2]. However, overall, the supply of high-quality breeding stock still does not meet the upgraded needs of industry. Therefore, the lack of high-quality specialized beef breeds is among the main bottlenecks limiting the improvement of China’s beef industry. This directly affects the quality structure of our beef products, since much of the beef comes from dual-purpose cattle, local yellow cattle, and culled dairy cows, resulting in insufficient production and uneven meat quality, which makes it difficult to meet consumers’ demand for high-quality, healthy beef [3]. Against this backdrop, optimizing beef cattle performance through crossbreeding has become a necessary choice for the efficient and healthy development of China’s beef industry.
In beef cattle production, feed costs account for approximately 60–75% of total operating expenses, making feed efficiency a critical determinant of both farm profitability and production system sustainability [4]. F/G is a core economic indicator for measuring beef cattle production performance. It directly reflects the efficiency of feed conversion and is closely related to farming costs and economic benefits. It not only affects the growth rate and slaughter performance of animals but also indirectly regulates intramuscular fat deposition and meat quality by changing how the body distributes energy [5]. The hindgut microbiota plays a key role in further breaking down feed residue and producing volatile fatty acids (VFAs), and its compositional differences are closely related to feed efficiency [6]. Research has shown that in high-feed-efficiency groups (low F/G), hindgut microbes present unique metabolic traits, such as increased expression of genes involved in carbohydrate metabolism and VFA production [7]. From a metabolic perspective, improvements in F/G are often accompanied by changes in the serum metabolite profile, including the reprogramming of metabolites related to amino acids, fatty acids, and energy metabolism [8]. Integrative analysis of the microbiome and metabolome further revealed that certain bacterial groups (such as Lactobacillus and Blautia) can influence the body’s energy metabolism and fat deposition by regulating the production of key metabolites such as propionate and butyrate [9]. Previous work by Omondi et al. revealed that feed efficiency in ruminants is intimately linked to differences in rumen microbial community composition [10,11]. However, research on how gut microbes affect host physiological and metabolic functions is still limited, although this topic is receiving increasing attention [12].
Therefore, integrating metabolomic and microbiomic data facilitates the construction of a multi-omics association map significantly correlated with feed efficiency variations from a systems biology perspective, thereby providing candidate biomarkers and potential intervention targets for optimizing beef cattle production performance.

2. Materials and Methods

This study was conducted in Chifeng, Inner Mongolia, China, and consent was obtained from the legal owners for the use of animals and private land. The sampling was approved by Inner Mongolia Agricultural University, Hohhot, China (protocol number 2020079; 21 November 2020), and the researchers confirmed that they followed the relevant requirements of the ARRIVE guidelines for in vivo animal research.

2.1. Animal Management and Sample Collection

In total, 20 steers (initial weight = 268.80 ± 30.88 kg; initial age = 273.00 ± 14.09 days) were used in this study, comprising 10 purebred Simmental (S × S) and 10 Simmental × Hereford crossbreds (H × S). All the experimental cattle were raised in mixed pens at the Shengquan Ecological Animal Husbandry Co., Ltd. farm in Chifeng, Inner Mongolia, and the feeding plan followed the same strategy (see Table 1). Before the formal experiment, there was a 15-day prefeeding period, followed by a 90-day trial period. They were fed a total mixed ration twice a day (08:00 and 18:00) and had free access to food and water. At the conclusion of the experiment on Day 90, blood and fecal samples were collected from the experimental cattle after they had fasted. The sampler wore disposable long-arm gloves and manually collected fresh fecal samples from the rectum of each animal. The serum, plasma, and fecal samples were aliquoted, immediately placed in liquid nitrogen for transport to the laboratory, and subsequently stored at −80 °C until further analysis [13]. The present design provides a terminal snapshot of endocrine, metabolomic, and microbial states rather than longitudinal characterization of adaptation dynamics.

2.2. Microbial Community Diversity Analysis of Rectal Contents

For microbiome analysis, fresh rectal contents were collected on Day 90 (n = 10), and microbial DNA was extracted according to the instructions of the E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The quality and concentration of the DNA were determined using 1.0% agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA), and the samples were stored at −80 °C until further use [14]. Sequencing was carried out by Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems, Foster City, CA, USA) and paired-end sequenced on the Illumina NextSeq 2000 platform (Illumina, San Diego, CA, USA). The raw sequence data have been deposited in the SRA database and are publicly available under accession number PRJNA1465087 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1465087) (accessed on 13 May 2026) as of the publication of this paper. The specific procedures followed the method described by He [15]. For the classification of ASVs and their descriptions, please refer to reference [16].

2.3. Plasma Metabolomic Profiling by LC–MS/MS

After fasting, blood was collected from each animal in anticoagulated blood collection tubes and allowed to stand for 30 min; the samples were subsequently centrifuged (80–2, Jiangsu Kangjie Medical Devices Co., Ltd., Taizhou, China) at 1076.7 rcf for 10 min to separate the plasma, which was then divided into centrifuge tubes and stored at −80 °C for LC–MS/MS analysis. The metabolites from the samples were extracted following the methods of Chen et al. [13]. The resulting supernatant was transferred to sample vials for instrumental analysis. To monitor the stability of the analytical system, equal aliquots from all the samples were pooled to create a quality control (QC) sample. The QC material was processed and injected in the same manner as the study samples at intervals of every 5–15 injections. Chromatographic separation was carried out on a Thermo UHPLC-Q Exactive HF-X system fitted with an ACQUITY HSS T3 column (100 × 2.1 mm, 1.8 μm; Waters, Milford, MA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), following the procedures described by Yang et al. [16] and Chen et al. [13].

2.4. Determination of Blood Indices

All serum biochemical indices were measured in triplicate using bovine-specific ELISA kits (Ruixin Biotechnology, Quanzhou, China) according to the manufacturer’s protocols [17]. For each sample, if the coefficient of variation (CV) among the three replicates exceeded 10%, the replicate with the greatest deviation from the mean was excluded, and the remaining two replicates was used for statistical analysis. We measured the concentrations of FFAs, glucagon (GC), growth hormone (GH), gonadotropin-releasing hormone (GnRH), insulin (INS), leptin (LEP), T3, thyroxine (T4), TG, TRH, TSH, low-density lipoprotein (LDL), high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL).

2.5. Data Statistics and Analyses

Statistical analyses were performed using SPSS 24.0. Serum biochemical parameters, F/G, initial body weight, final body weight, and ADG were analyzed using independent-samples ANOVA, and statistical significance was determined by independent-samples ANOVA. The results of this study are presented as the means ± standard deviations (SD), and p < 0.05 was considered statistically significant. F/G = DMI/ADG. On the 45th day of the experiment, the DMI value was measured according to Chen’s method over three consecutive days [18]. To avoid the confounding effects of heat stress on feed intake, DMI measurements were conducted prior to the onset of heat stress. Specifically, individual feed intake was recorded continuously for 3 days during the mid-experimental period (Day 45), when ambient temperature remained within the thermoneutral zone (daily mean temperature < 25 °C and THI < 72). The average DMI over this 3-day window was used as the representative feed intake value for 5 cattle throughout the entire experimental period, as preliminary observations indicated that within-animal variation in DMI during the pre-heat-stress phase was minimal (coefficient of variation < 5%). Owing to space limitations, we measured the DMI of 5 cattle, which is somewhat limiting.

3. Results

As shown in Table 2, there were no significant differences in body weight at 0 d, body weight at 90 d, or ADG among the test cattle. The F/G of the cattle in the H × S group was significantly lower than the S × S group (p < 0.05).
As shown in Table 3, compared with those in the S × S group, the T3 and the FFA contents in the S × S group were significantly lower (p < 0.05). There were no significant differences in the other indicators between the two groups.
As is evident from Figure 1, none of the α diversity indices reached significance (p > 0.05), suggesting that the diversity of the gut microbiota did not substantially differ between the two groups of cattle. In contrast, the β diversity analysis (Figure 2) revealed that within the 95% confidence interval, the flora of the two groups of experimental cattle could be distinctly differentiated (p < 0.05). Notably, these findings suggest significant changes in the structure of the gut microbiota.
As shown in Figure 3, a phylum-level microbial community analysis of the gut microbiota was conducted. The sample means were calculated for each group. Among all the valid sequences from the two groups, five dominant phyla (Firmicutes, Bacteroidetes, Spirochaetes, Verrucomicrobiota, and Patescibacteria) were detected, whereas the relative abundance of each of the remaining phyla was less than 1%. In the hindgut of the experimental cattle (in Figure 4), the relative abundance of Verrucomicrobiota (p < 0.05) and Actinobacteriota (p < 0.05) in the S × S group was significantly greater than that in the H × S group.
As shown in Figure 5, 20 bacterial genera whose relative abundance was >1% were identified in rectal content samples from experimental cattle. The dominant genera included Ruminococcaceae UCG-005, the Rikenellaceae RC9 gut group, the Christensenellaceae R-7 group, Prevotellaceae UCG-003, and Bacteroides. As shown in Figure 6, the relative abundances of Anaerosporobacter, Succinivibrio, and Clostridium-sensu-stricto-6 in the H × S group were significantly greater than those in the S × S group (p < 0.05); in addition, the relative abundances of Akkermansia, Romboutsia, Paeniciostridium, DNF00809, and Family-XIII-UCG-001 in the S × S group were significantly greater than those in the H × S group (p < 0.05).
Score plots of the PLS-DA results are shown in Figure 7A,C, and the OPLS-DA results are shown in Figure 7B,D. The different metabolites identified in the two groups were verified. The R2Y values of POS and NEG were 0.9929 and 0.9426, respectively. Panels (A,B) and (C,D) represent the POS group and the NEG group, respectively. In Figure 7A,C, red and blue represent the comparison results of the S × S and H × S groups, respectively, with those of the control cattle (n = 10).
The various metabolites between the two groups are clearly shown in Figure 8, and the top 10 metabolites ranked by p value are labelled. Detailed information on these metabolites is provided in Table 4. Additionally, the concentrations of Hypoglycin B, DHAP (18:0), and GpCho decreased in the H × S group. The concentrations of LysoPC(20:2(11Z,14Z)/0:0), elaidic acid, and Pc(P-16:0/4:0) were greater in the H × S group.
As shown in Figure 9, the metabolites identified in plasma are involved in alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism; and pantothenate and CoA biosynthesis, which were significantly enriched in the H × S group and S × S group (Figure 9).
In the bovine gut, Verrucomicrobiota was entirely assigned to Akkermansia; thus, only the genus-level data were used for correlation analysis. Correlation analysis revealed that cattle serum T3 was significantly positively correlated with FFAs (r = 0.65; p < 0.01), that Hypoglycin B was significantly positively correlated with T3 levels (r = 0.61; p < 0.01), that LysoPC was negatively correlated with T3 and Hypoglycin B (r = 0.52; p < 0.05; r = 0.64; p < 0.01), and that LysoPC was significantly negatively correlated with T3 and Hypoglycin B (r = 0.52; p < 0.05; r = 0.64; p < 0.01). Romboutsia was positively correlated with Hypoglycin B (r = 0.49, p < 0.05) (Figure 10).

4. Discussion

The FFA levels in the H × S group of cattle decreased significantly, indicating reduced fat breakdown. Moreover, T3 levels decreased significantly, whereas T4, TSH, and TRH levels did not significantly change. T3 plays a central role in regulating metabolic efficiency and energy expenditure [19]. Lower circulating T3, which correlates with a suppressed basal metabolic rate, reduces maintenance energy requirements. This sparing effect potentially increases the net energy available for productive functions, although this partitioning depends on the physiological state and nutrient supply [20]. In our study, correlation analysis revealed that bull serum T3 levels were significantly positively correlated with FFA levels (r = 0.65, p < 0.01). In healthy animals, changes in T3 and FFA tend to occur in the same direction [21]. The lower T3 aligns with reduced FFA, which may help shift energy towards muscle deposition and optimize growth performance. This finding is consistent with our trial results, where the F/G ratio in the H × S group was significantly greater than that in the S × S group.
The results of the metabolomic analysis in the present study found that serum Hypoglycin B levels were significantly lower in the H × S group than in the S × S group. Hypoglycin is a naturally occurring amino acid derivative found in the ackee fruit (Blighia sapida) and is known to inhibit fatty acid β-oxidation by interfering with the activity of acyl-CoA dehydrogenases, thereby blocking mitochondrial fatty acid oxidation and leading to hypoglycaemia and lipid accumulation [22]. The results of the correlation analysis in this study revealed that Hypoglycin B was significantly positively correlated with T3 levels (r = 0.61, p < 0.01). Therefore, the Hypoglycin B content in the H × S group may be related to a reduced T3, enabling efficient energy redistribution, which in turn is associated with growth performance. In addition, our data also revealed that in the H × S group, the intermediate metabolites involved in liver lipid synthesis—DHAP (18:0) and glycerophosphocholine (GpCho)—were both downregulated. GpCho is an important intermediate in membrane phospholipid metabolism and serves as an indicator of the regulation of lipid metabolic homeostasis [23]. As an intermediate in glycolysis, dihydroxyacetone phosphate (DHAP) is the precursor of the glycerol backbone in triglyceride biosynthesis; its decreased level likely reflects that fat formation in the H × S group is suppressed. Lysophosphatidylcholine (LysoPC) is involved in lipid signalling and the regulation of insulin sensitivity [24]. Correlation analysis revealed that LysoPC was negatively correlated with T3 and Hypoglycin B (r = 0.52, p < 0.05; r = 0.64, p < 0.01). Notably, in the H × S group, the expression of LysoPC (20:2 (11Z, 14Z)/0:0) increased significantly. In bull serum, LysoPC was significantly negatively correlated with T3 and Hypoglycin B (r = 0.52, p < 0.05; r = 0.64, p < 0.01). LysoPC, a key intermediate in glycerophospholipid metabolism, can activate the mitogen-activated protein kinase (MAPK) pathway and promote cell proliferation [25]. T3 is an important hormone for regulating protein deposition, and changes in nutritional levels can affect T3 production by regulating peripheral 5′-deiodinase activity, thereby influencing muscle growth efficiency [26,27]. So, LysoPC might be linked to lower fat mobilization. Additionally, KEGG enrichment analysis indicated that these differentially abundant metabolites are involved in glycine, serine, and threonine metabolism; alanine, aspartate, and glutamate metabolism; and pantothenate and CoA biosynthesis. The glycine, serine, and threonine metabolism pathway serves as a central hub for amino acid metabolism, one-carbon metabolism, and redox homeostasis [28]. Locasale et al. identified glycine and threonine as key metabolites that can be used to predict feed efficiency phenotypes. Pantothenate is a precursor of coenzyme A (CoA), an essential cofactor for fatty acid oxidation and the tricarboxylic acid (TCA) cycle [29]. In recent years, researchers have used combined metabolomics and microbiome analyses, linking changes in metabolites with microbial species, to offer new insights into how gut microbes regulate the body’s metabolism [30].
The gut microbiota coexists with the host and plays important roles in regulating digestion, gut development, nutrient absorption, and metabolism [31]. The gut microbiota is regarded as the ‘second genome’ of ruminants, significantly impacting various physiological functions and strongly influencing host metabolism and health [32,33,34]. In our study, the relative abundances of Succinivibrio and Clostridium_sensu_stricto_6 were significantly greater in the H × S group than in the S × S group. As important succinate-producing bacteria in the gut of ruminants, Succinivibrio can produce succinate and activate gluconeogenesis in the ruminant gut, where they participate in the production of propionate [35]. The increased proportions of the Succinivibrio and Clostridium_sensu_stricto_6 populations suggest that the H × S group of cattle may have stronger carbohydrate fermentation and energy extraction abilities, which matches their lower feed conversion ratio (F/G). Similarly, the high abundance of Clostridium_sensu_stricto might be related to its enhanced ability to produce butyrate. [36]. By serving as the favoured oxidative fuel of colonocytes, butyrate reinforces gut barrier function and lessens the energy drain imposed by inflammatory processes [36]. The gut microbiota has been shown to play a key role in regulating feed efficiency through multiple mechanisms, including the modulation of intestinal architecture, immune function, and host energy homeostasis [37]. In summary, the relative abundance of Akkermansia increased within the S × S group. Akkermansia is specialized for mucin degradation in the intestinal mucus layer; however, it concurrently stimulates the host to replenish mucin, an interplay that may support mucosal barrier preservation and metabolic well-being [38]. In this study, the relative abundance of Romboutsia in the H × S group was significantly lower than that in the S × S group. Romboutsia has been shown to improve lipid profiles and body metabolism [39]. Correlation analysis revealed that Romboutsia was positively correlated with Hypoglycin B (r = 0.49, p < 0.05) and negatively correlated with LysoPC (20: 2 (11Z, 14Z)/0: 0) (r = 0.48, p < 0.05), which might be related to changes in blood metabolites. A notable limitation of this study is the small sample size (n = 5) for DMI measurements, owing to farming environment constraints. This limits the statistical power to detect subtle associations and may affect the generalizability of our findings. Therefore, independent validation in larger cohorts with precisely recorded individual feed intake and hindgut fermentation parameters is essential to confirm the utility of these candidate markers.

5. Conclusions

Our study reveals variations in hindgut microbiota composition and metabolism in cattle. This study revealed that the level of Hypoglycin B was related to a reduced T3, which could affect growth performance. Metabolomics analysis identified Hypoglycin B, lysoPC, and DHAP as key discriminative metabolites. Pathway enrichment analysis revealed that these metabolites were primarily mapped to glycine, serine, and threonine metabolism; alanine, aspartate, and glutamate metabolism; and pantothenate and CoA biosynthesis. In our study, the relative abundances of Succinivibrio and Clostridium_sensu_stricto_6 were significantly greater in the H × S group than in the S × S group; it may have stronger energy extraction abilities and matches their lower F/G. Romboutsia was positively correlated with Hypoglycin B and negatively correlated with LysoPC (20:2 (11Z,14Z)/0:0), which might be related to changes in blood metabolites. Nevertheless, these explanations remain correlational and require functional validation through targeted interventions.

Author Contributions

Methodology, A.C.; conceptualization, Y.W. and H.C.; software, H.C.; formal analysis, Y.W.; validation, Y.W. and D.Z.; investigation, L.W. and Q.L.; data curation, C.W.; writing—original draft preparation, Y.W.; resources, A.C.; writing—review and editing, D.Z.; visualization, Q.L. and L.W.; project administration, H.W.; supervision, A.C.; funding acquisition, H.W. and C.W.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Basic Scientific Research Business Expenses Project of Universities Directly under the Inner Mongolia Autonomous Region (BR231520) and the National Natural Science Foundation of China (32160817).

Institutional Review Board Statement

Use Committee of Inner Mongolia Agricultural University, Hohhot, China (protocol number 2020079).

Informed Consent Statement

Written informed consent has been obtained from the owner of the animals involved in this study.

Data Availability Statement

The raw sequence data have been deposited in the SRA repository and are publicly available under the accession number PRJNA1465087 as of the date of publication (https://www.ncbi.nlm.nih.gov/sra/PRJNA1465087) (accessed on 13 May 2026).

Conflicts of Interest

The authors declare no conflicts of interest.

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Animals 16 02068 g001
Figure 1. Differences in the α diversity between the two groups.
Figure 1. Differences in the α diversity between the two groups.
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Animals 16 02068 g002
Figure 2. Differences in the β diversity between the two groups.
Figure 2. Differences in the β diversity between the two groups.
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Animals 16 02068 g003
Figure 3. Bar chart of the relative abundance of species at the phylum level in the two groups.
Figure 3. Bar chart of the relative abundance of species at the phylum level in the two groups.
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Animals 16 02068 g004
Figure 4. Phylum-level differences between the two groups. * indicates a significant difference between the two groups and ** indicates a highly significant difference between the two groups.
Figure 4. Phylum-level differences between the two groups. * indicates a significant difference between the two groups and ** indicates a highly significant difference between the two groups.
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Animals 16 02068 g005
Figure 5. Bar chart of the relative abundance of species at the genus level in the two groups.
Figure 5. Bar chart of the relative abundance of species at the genus level in the two groups.
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Animals 16 02068 g006
Figure 6. Genus-level differences between the two groups. * indicates a significant difference between the two groups and ** indicates a highly significant difference between the two groups.
Figure 6. Genus-level differences between the two groups. * indicates a significant difference between the two groups and ** indicates a highly significant difference between the two groups.
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Animals 16 02068 g007
Figure 7. Corresponding validation plots of orthogonal projections to PLS-DA models (A,C) and permutation tests of the OPLS-DA models (B,D).
Figure 7. Corresponding validation plots of orthogonal projections to PLS-DA models (A,C) and permutation tests of the OPLS-DA models (B,D).
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Animals 16 02068 g008
Figure 8. Volcano plot showing the differentially abundant metabolites of the H × S group vs the S × S group in POS (A) and NEG (B) ion modes. Red and blue represent upregulation and downregulation, respectively, whereas grey indicates no significant difference. p-Values of each metabolite from the differential analysis are shown on the y-axis.
Figure 8. Volcano plot showing the differentially abundant metabolites of the H × S group vs the S × S group in POS (A) and NEG (B) ion modes. Red and blue represent upregulation and downregulation, respectively, whereas grey indicates no significant difference. p-Values of each metabolite from the differential analysis are shown on the y-axis.
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Animals 16 02068 g009
Figure 9. Enrichment analysis of differentially abundant metabolites between the two groups of cattle. The x-axis shows the pathway impact values from the topology analysis; the y-axis shows the p-values of the metabolic pathways from the enrichment analysis.
Figure 9. Enrichment analysis of differentially abundant metabolites between the two groups of cattle. The x-axis shows the pathway impact values from the topology analysis; the y-axis shows the p-values of the metabolic pathways from the enrichment analysis.
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Animals 16 02068 g010
Figure 10. Correlation analysis between several differential hormones, blood metabolites and gut microbes.
Figure 10. Correlation analysis between several differential hormones, blood metabolites and gut microbes.
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Table 1. Composition and nutrient levels (dry matter basis) of the experimental diets.
Table 1. Composition and nutrient levels (dry matter basis) of the experimental diets.
ComponentsProportion (%)
DM46.9
ADF30.3
NDF43.8
CP14.2
Lignin4.9
Fat2.4
Ash9.6
Ca0.77
P0.43
Mg0.40
K1.42
NEg, Mcal/kg0.75
ME, Mcal/kg2.37
Ingredients(%)
Corn stover16.67
Oat hay20.83
Corn silage25.00
Corn17.80
Wheat bran8.40
Soybean meal9.50
CaHPO40.30
NaCl0.50
Premix 11.00
Total100.00
1 The premix contained (per kg premix) 370,000 IU vitamin A, 100,000 IU vitamin, D3, 5000 IU vitamin E, 3000 mg of Zn, 2500 mg Fe, 1680 mg of Mn, 780 mg of Cu, 150 g of Ca, 30 mg of I, 30 mg of Se, 26 g of P, and 20 mg of Co.
Table 2. Production performance of the cattle in the two groups.
Table 2. Production performance of the cattle in the two groups.
ItemH × SS × Sp-Value
Body Weight at 0 d (kg, n = 10)271.95 ± 30.65265.65 ± 32.420.661
Body Weight at 90 d (kg, n = 10)323.95 ± 40.44313.15 ± 38.230.547
ADG (kg, n = 10)0.58 ± 0.250.53 ± 0.090.553
DMI (kg/d, n = 5)9.02 ± 0.339.38 ± 0.170.062
F/G (n = 5)15.77 ± 1.8418.82 ± 1.420.046
Table 3. Serum metabolites of the cattle in the two groups.
Table 3. Serum metabolites of the cattle in the two groups.
ItemH × SS × Sp-Value
FFA (µmol/L)245.4003 ± 15.20543261.2284 ± 23.001880.014
GC (ng/L)57.7769 ± 8.9976957.0716 ± 5.953710.772
GH (µg/L)30.1999 ± 7.3418331.4343 ± 6.406310.574
HDL (µmol/L)618.3208 ± 247.35008580.4158 ± 255.483130.636
GnRH (ng/L)30.1429 ± 3.9811131.5688 ± 3.562770.240
INS (mIU/L)21.5711 ± 2.0440222.128 ± 2.695090.466
LDL (µmol/L)861.2502 ± 72.96546875.9297 ± 70.979750.523
LEP (ng/L)2590.0001 ± 225.523752588.2112 ± 216.558510.980
T3 (pmol/L)44.6276 ± 5.1273954.7997 ± 9.275220.000
T4 (pmol/L)337.0282 ± 79.7434376.1366 ± 91.726170.158
TG (µmol/L)413.5283 ± 22.09324418.3621 ± 21.002750.483
TRH (pg/mL)39.3845 ± 8.5447538.6603 ± 3.813460.731
TSH (µIU/L)436.8627 ± 62.12588413.1569 ± 32.351640.138
VLDL (µg/mL)29.318 ± 6.3815729.0771 ± 3.251960.881
Table 4. Plasma metabolites of the cattle in the two groups.
Table 4. Plasma metabolites of the cattle in the two groups.
MetaboliteVIPFC (H × S/S × S)p-ValuePositive/NegativeM/Z
Vidarabine5.30370.7957<0.00001neg312.0963
Hypoglycin B5.22040.8439<0.00001neg269.1153
3-Dehydroshikimate4.39620.84590.0007106pos204.063
Benzyldimethyltetradecylammonium4.30421.24850.007932pos332.3313
5-(3′,4′-Dihydroxyphenyl)-Gamma-Valerolactone 4′-Sulfate3.16390.91430.003176neg269.0135
Pi(Pgj2/20:1(11Z))3.05781.12280.04422pos975.5891
Pc(P-16:0/4:0)2.94581.08210.0008536pos572.3716
3-Methoxytyrosine2.94421.08740.0008606neg210.0771
Bis(2-Ethylhexyl) Adipate2.89830.94370.0000488pos371.3155
Uridine2.78171.07520.003498neg279.0397
N-(3-Aminopropyl)-N-Methylcarbamic Acid Tert-Butyl Ester2.76911.1090.0404pos189.1596
(10E,12E,14E)-16-Hydroxy-9-oxooctadeca-10,12,14-trienoylcarnitine2.70521.0740.008055neg450.2883
2′-Deoxyuridine2.70231.0620.0003545neg227.0676
Glycolithocholic Acid2.66331.06950.01417neg432.3136
Gamma-Cehc Glucuronide2.60320.95540.0001321neg421.1524
Tributyl Phosphate2.53510.89150.04576pos267.1719
Elaidic Acid2.38711.03970.0004399neg327.255
2-Hydroxyphenylacetic Acid2.36830.94820.001878neg151.0397
Ser Glu2.35650.96530.001994pos235.0923
1-Pyrroline2.32231.0930.006717pos70.0655
Mg(Pgf1Alpha/0:0/0:0)2.30590.91420.02325pos448.3266
Gpcho(20:1/18:3)2.30170.9590.03207pos832.5836
Demethoxyfumitremorgin C2.25750.94130.03776neg370.1521
Lovastatin Acid2.23431.05640.02775neg421.2612
Lysopc(20:2(11Z,14Z)/0:0)2.19271.03150.0002144neg592.3648
N-Palmitoyl Tyrosine2.15321.04330.0487neg464.3035
Dhap(18:0)2.12740.95870.007211pos500.275
4-Ethylphenol2.12661.04350.006544neg121.0654
Tetramethylchromanol Glucoside2.06420.97250.0006524neg427.1992
11,17-Dihydroxy-3,20-Dioxopregn-4-en-21-yl Acetate2.02930.95320.01609pos446.2541
Kitaguni2.02591.05040.01169neg241.0446
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Wu, Y.; Chen, H.; Zhang, D.; Wang, L.; Liu, Q.; Wang, C.; Chen, A.; Wang, H. Mechanisms Underlying the Impact of Feed-to-Gain Ratio Differences on Nutrient Metabolism in Simmental and Simmental × Hereford Crossbred Cattle Fed a Low-Energy Diet. Animals 2026, 16, 2068. https://doi.org/10.3390/ani16132068

AMA Style

Wu Y, Chen H, Zhang D, Wang L, Liu Q, Wang C, Chen A, Wang H. Mechanisms Underlying the Impact of Feed-to-Gain Ratio Differences on Nutrient Metabolism in Simmental and Simmental × Hereford Crossbred Cattle Fed a Low-Energy Diet. Animals. 2026; 16(13):2068. https://doi.org/10.3390/ani16132068

Chicago/Turabian Style

Wu, Yi, Hao Chen, Danling Zhang, Lina Wang, Qi Liu, Chunjie Wang, Aorigele Chen, and Hairong Wang. 2026. "Mechanisms Underlying the Impact of Feed-to-Gain Ratio Differences on Nutrient Metabolism in Simmental and Simmental × Hereford Crossbred Cattle Fed a Low-Energy Diet" Animals 16, no. 13: 2068. https://doi.org/10.3390/ani16132068

APA Style

Wu, Y., Chen, H., Zhang, D., Wang, L., Liu, Q., Wang, C., Chen, A., & Wang, H. (2026). Mechanisms Underlying the Impact of Feed-to-Gain Ratio Differences on Nutrient Metabolism in Simmental and Simmental × Hereford Crossbred Cattle Fed a Low-Energy Diet. Animals, 16(13), 2068. https://doi.org/10.3390/ani16132068

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